adding the directory of paper2

diff --git a/Paper2/Features.tex b/Paper2/Features.tex
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+
+The last stage of the proposed pipeline is naturally to take advantage
+of the produced core and pan genomes for biological studies. As
+this key stage is not directly related to the methodology for core
+and pan genomes discovery, we will only outline a few tasks that
+can be operated on the produced data.
+
+%\begin{figure}
+%\centering
+%\includegraphics[scale=0.215]{tree}
+%\caption{Part of a core genomes evolutionary tree (NCBI gene names)}
+%\label{coreTree}
+%\end{figure}
+
+Obtained results may be visualized by building a core genomes evolutionary tree. 
+% All core  genes generated  represent  an important information  in the  tree,
+% because they  provide ancestor information of two  or more
+% genomes. 
+Each  node in this  tree represents a chloroplast  genome or
+a predicted core. %, as depicted in Figure~\ref{coreTree}. In this figure, nodes labels are of the form \textit{(Genes number:Family name\_Scientific name\_Accession number)},while an edge is labeled with the number of gene loss when compared to its parents (a leaf  genome or  an intermediate core  genome). Such numbers can answer questions like: how many genes are different between two  species? Which functionality has been lost between an ancestor and its children ? For complete core treesbased either on NCBI names or on DOGMA ones, see supplementary data.
+
+A second application of such data is obviously to build accurate phylogenetic   
+trees, using tools like
+PHYML\cite{guindon2005phyml} or 
+RAxML{\cite{stamatakis2008raxml,stamatakis2005raxml}. 
+Consider a set of species, the last common core genome in the core tree 
+contains all the genes shared in common by these species. These genes may be
+multi aligned to serve as input of the phylogenetic tools mentioned above.
+An example of such a phylogenetic tree on core 58 (NCBI cores tree, see
+supplementary data) is provided in Appendix~\ref{philoTree}. Remark that, in
+order to constitute a relevant outgroup, we have simply blasted each gene 
+of this core on a chosen \emph{Cyanobacteria}.
+
+
+%,    BioNJ,    and
+%TNT\cite{goloboff2008tnt}}.    
+% In   this  work,   we   chose  to   use
+% RAxML\cite{stamatakis2008raxml,stamatakis2005raxml}   because   it  is
+% fast, accurate,  and can build large  trees when dealing  with a large
+% number of genomic sequences.
+% 
+% The procedure used to built a phylogenetic tree is as follows:
+% \begin{enumerate}
+% \item For each gene in a core gene, extract its sequence and store it in the database.
+% \item Use multiple alignment tools such as (****to be write after see christophe****) 
+% to align these sequences with each others.
+% \item Use an outer-group genome from cyanobacteria to calculate distances.
+% \item Submit the resulting aligned sequences to RAxML program to compute 
+% the distances and finally draw the phylogenetic tree.
+% \end{enumerate} 
+% 
+
+

diff --git a/Paper2/Mixed.tex b/Paper2/Mixed.tex
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+
+% 
+% \subsubsection{Processing annotated genomes from NCBI} 
+% 
+% The objective here is to generate sets  of genes from each  genome so that
+% genes are organized  without any duplication.  The input  is a list of
+% chloroplasts annotated genomes downloaded from the NCBI website. All genomes,
+% which consist in collections of
+% protein  coding sequences~\cite{parra2007cegma,RDOGMA},
+% are stored as \textit{fasta} files.   To be able to build the set of
+% core    genes,     we    need    to     preprocess    these    genomes
+% using  \textit{BioPython}  package \cite{chapman2000biopython}.   
+% % 
+% First of all, we starts by  converting  each  genome from  \textit{fasta}  file  to
+% GenVision \cite{geneVision}  format from DNASTAR. Each  genome is thus
+% converted in a list of genes, containing both gene names and occurrences. 
+% Gene
+% name duplication can be accumulated during the treatment of a genome.
+% % 
+% These  duplication  come   from  gene  fragments  (\emph{e.g.}   gene
+% fragments treated  with NCBI) and  from chloroplast DNA  sequences. To
+% ensure that  all the  duplication are removed,  each list of  gene is
+% translated  into a  set of  genes.  
+% % Note that  NCBI genome  annotation
+% produces genes except \textit{Ribosomal (rRNA)} genes.
+% 
+
+%\subsubsection{Processing genomes annotated by DOGMA}
+
+% Protein  coding genes are  identified in an  input genome
+%using sequence similarity of genes  in DOGMA database.  In addition in
+%comparison   with   NCBI    annotation   tool,   
+% It   can   detect
+% both \textit{transfer RNAs (tRNA)} and \textit{ribosomal RNAs (rRNA)}. 
+%  
+% The DOGMA  annotation process  is divided into  two tasks.   First, we
+% manually annotate chloroplast genomes using DOGMA web tool. The output
+% of this step is supposed to  be a collection of coding genes files for
+% each genome, organized in GeneVision file. The second task is to solve
+% the  gene   duplication  problem  and   therefore  we  have   used  two
+% methods. 
+
+\begin{figure}[H]
+\centering
+\includegraphics[scale=0.3]{gensim}
+\caption{Part of the implementation of the second method, compaire the common genes from NCBI and DOGMA.}
+\label{Meth2:gensim}
+\end{figure}
+
+\color{red}The second approach in this paper is an enhasement of the ICM\textit{Intersection Core Matrix} proposed in \cite{Alkindy2014} by considering gene names to find core genome. Based on gene names spelling, When they realizing simple homogenization of names provided by NCBI, they miss core genes which have slightly different name formats. \color{black} To enlarge the size of the core genome, to be as close as possible to the true natural one, we propose to integrate a similarity distance on gene names. Each similarity will be computed between a name from DOGMA, which operates as a reference here, and a name from NCBI as shown in figure~\ref{Meth2:gensim}. 
+
+The proposed distance  is the Levenshtein one, which is close to the Needleman-Wunsch, except that gap opening and extension penalties are equal. The same name is then set to sequences whose NCBI names are close according to this edit distance.
+
+The risk, by doing so, is to merge genes that are different but whose names are similar (for instance, ND4 and ND4L are two different mitochondrial genes but with similar names). The solution is thus to compare, in a second stage, the similarity of DNA sequences too (with a Needleman-Wunsch global alignment), and to simply ignore the gene if this similarity is below a given threshold. 
+
+By doing so, the second approach is designed, which takes the fundamental idea contained in the annotation-based approaches in the previous work. Remark that this approach is simply a deeper processing of the naming stage in the second approach in \cite{Alkindy2014}, the other stages being identical.
+
+The DNA similarity computation raises another problem in the case of DOGMA:
+contrary to what happens with gene features in NCBI, genes predicted by
+DOGMA may be fragmented in several parts. Such genes are signaled
+in the GeneVision file produced by DOGMA, as each fragment is in this file
+and with the same gene name. A gene whose name is present at least twice
+in the file is thus either a duplicated gene or a fragmented one.
+Obviously, fragmented genes must be defragmented before  the DNA similarity computation stage (remark that such a defragmentation has already been realized on NCBI website). As the orientation of each fragment is given in the GeneVision output, this defragmentation consists in concatenating all the possible permutations, and only keeping the permutation with the best similarity score to other sequences having the same gene name, if this score is larger than the given threshold.
+
+To put it in a nutshell, the genomes list of gene names are firstly updated in this third approach, following the process detailed in 
+Algorithm~\ref{Alg3:thirdM}, while Algorithm~\ref{Alg3:genechk} outlines  
+the \emph{geneChk} subroutine. These updated genomes are secondly sent to
+Algorithm~\cite{Alkindy2014}, which will produce the desired core genomes, see
+Figure~\ref{wholesystem} for an updated pipeline.
+
+%The first method, based  on gene name, translates each genome
+% %into a set  of genes without duplicates. 
+% The  second method avoid gene
+% duplication  through a  defragment  process. In  each iteration,  this
+% process  starts by taking  a gene  from gene  list, searches  for gene
+% duplication, if a duplication is found, it looks on the orientation of
+% the  fragment sequence.   If it  is positive  it appends  directly the
+% sequence to  gene files.  Otherwise reverse  complement operations are
+% applied  on the sequence,  which is  then also  append to  gene files.
+% Finally, a  check for missing start  and stop codons  is performed. At
+% the  end  of  the  annotation  process,  all  the  genomes  are  fully
+% annotated,  their   genes  are  defragmented,  and   gene  counts  are
+% available.
+% Remark that there is no gene duplication with gene annotations 
+% from DOGMA after applying gene de-fragmentation process. In  
+% fact, genome annotation with DOGMA can be the key difference when extracting core genes.
+% 
+% 
+% 
+% 
+% \subsubsection{Preprocessing}
+% 
+% In order  to extract  core genomes in  a suitable manner,  the genomic
+% data are preprocessed with two methods: on the one hand a method based
+% on gene  name and count,  and on  the other hand  a method based  on a
+% sequence quality control test.
+% 
+% In the first method, we extract  a list of genes from each chloroplast
+% genome.  Then we store this list of genes in the database under genome
+% nam and  genes counts can be  extracted by a  specific length command.
+% The \textit{Intersection  Core Matrix}, described  in next subsection,
+% is then  computed to  extract the core  genes.  The problem  with this
+% method can be stated as follows: how can we ensure that the gene which
+% is  predicted in  core genes  is the  same gene  in leaf  genomes? The
+% answer  to this problem  is that  if the  sequences of  any gene  in a
+% genome annotated  from DOGMA  and NCBI are  similar with respect  to a
+% given  threshold,  the method is operational when the sequences are not similar. The problem of attribution of a sequence to a gene in the core genome come to light.
+ 
+% The second method is based on  the underlying idea that it is possible to predict the the best annotated  genome by merging the annotated  genomes from NCBI
+% and DOGMA according to a quality test on genes names and sequences. To
+% obtain all  quality genes  of each genome,  we consider  the following
+% hypothesis: any gene  will appear in the predicted  genome if and only
+% if the  annotated genes  in NCBI and  DOGMA pass a  specific threshold
+% of  \textit{quality  control test}.   In  fact,  the Needle-man  Wunch
+% algorithm  is applied  to compare  both  sequences with  respect to  a
+% threshold. If  the alignment  score is above  the threshold,  then the
+% gene will be  retained in the predicted genome,  otherwise the gene is
+% ignored.   Once    the   prediction   of   all    genomes   is   done,
+% the \textit{Intersection Core Matrix} is computed on these new genomes
+% to extract core genes, as explained in Algorithm \ref{Alg3:thirdM}.
+% 
+
+\begin{algorithm}[H]
+\caption{Maximum similarity score between two sequences(geneChk)}
+\label{Alg3:genechk}
+\begin{algorithmic} 
+\REQUIRE $g1,g2 \leftarrow \text{NCBI gene sequence, DOGMA gene sequence}$
+\ENSURE $\text{Maximum similarity score}$
+\STATE $score1 \leftarrow needle(g1,g2)$
+\STATE $score2 \leftarrow needle(g1,Reverse(Complement(g2)))$
+\RETURN $max(score1,score2)$
+\end{algorithmic}
+\end{algorithm}
+
+
+\begin{algorithm}[H]
+\tiny
+\caption{Extract new genome based on gene quality test}\label{Alg3:thirdM}
+\begin{algorithmic} 
+\REQUIRE {$Gname \leftarrow \text{Genome Name}, Threshold \leftarrow 60, RNGenes \leftarrow \text{[ ]}, RDGenes \leftarrow \text{[ ]},PNGenes \leftarrow \text{[ ]}, PDGenes \leftarrow \text{[ ]}$}
+\ENSURE $geneList \leftarrow \text{Quality genes}$ 
+\FOR{$\text{gene in NCBI genes of Gname}$}
+    \IF {$\text{gene in RNGenes}$}
+        \STATE $dir(NCBI\_Genes) \leftarrow savePermutation(gene)$
+        \STATE $PNGenes \leftarrow gene$
+    \ELSE 
+       \STATE $RNGenes \leftarrow gene$
+    \ENDIF
+\ENDFOR
+\FOR{$\text{gene in Dogma genes of Gname}$}
+    \IF {$\text{gene in RDGenes}$}
+        \STATE $dir(Dogma\_Genes) \leftarrow savePermutation(gene)$
+        \STATE $PDGenes \leftarrow gene$
+    \ELSE 
+       \STATE $RDGenes \leftarrow gene$
+    \ENDIF
+\ENDFOR
+\STATE $geneList=\text{empty list}$
+\STATE $common=set(dir(NCBI\_Genes)) \cap set(dir(Dogma\_Genes))$
+\FOR{$\text{gene in common}$}
+    \STATE $\text{scores} \leftarrow \text{[ ]}$
+    \IF {$\text{gene NOT in PNGenes AND gene NOT in PDGenes}$}
+               \STATE \dots
+        \STATE $scores \leftarrow geneChk(g1,g2)$
+    \ELSIF {$\text{gene in PNGenes AND NOT gene in PDGenes}$}
+        \STATE $PGene \leftarrow loadPermutations('N',gene)$
+        \dots
+        \FOR {$\text{X in PGene}$}
+               \STATE \dots
+               \STATE $scores \leftarrow geneChk(g1,g2)$
+           \ENDFOR
+        \ELSIF {$\text{gene in PDGenes} AND \text{gene NOT in PNGenes}$} 
+           \STATE $PGene \leftarrow loadPermutations('D',gene)$
+           \STATE \dots
+           \FOR {$\text{X in PGene}$}
+               \STATE \dots
+               \STATE $scores \leftarrow geneChk(g1,g2)$
+           \ENDFOR
+    \ELSIF {$\text{gene in PDGenes} AND \text{gene in PNGenes}$} 
+           \FOR {$\text{X in loadPermutations('N',gene)}$}
+               \FOR {$\text{Y in loadPermutations('D',gene)}$}
+                       \STATE \dots    
+                       \STATE $scores \leftarrow geneChk(g1,g2)$
+               \ENDFOR
+           \ENDFOR
+\STATE $score \leftarrow max(scores)$
+\IF {$score > Threshold$}
+    \STATE $geneList \leftarrow gene$
+\ENDIF    
+\ENDIF             
+\ENDFOR
+\RETURN $geneList$
+\end{algorithmic}
+\end{algorithm}
+
+%\begin{algorithm}[H]
+%\tiny
+%\caption{Extract new genome based on gene quality test}\label{Alg3:thirdM}
+%\begin{algorithmic} 
+%\REQUIRE {$Gname \leftarrow \text{Genome Name}, Threshold \leftarrow 60, RNGenes \leftarrow \text{[ ]}, RDGenes \leftarrow \text{[ ]},PNGenes \leftarrow \text{[ ]}, PDGenes \leftarrow \text{[ ]}$}
+%\ENSURE $geneList \leftarrow \text{Quality genes}$ 
+%\FOR{$\text{gene in NCBI genes of Gname}$}
+%    \IF {$\text{gene in RNGenes}$}
+%        \STATE $dir(NCBI\_Genes) \leftarrow savePermutation(gene)$
+%        \STATE $PNGenes \leftarrow gene$
+%    \ELSE 
+%      \STATE $RNGenes \leftarrow gene$
+%    \ENDIF
+%\ENDFOR
+%\FOR{$\text{gene in Dogma genes of Gname}$}
+%    \IF {$\text{gene in RDGenes}$}
+%        \STATE $dir(Dogma\_Genes) \leftarrow savePermutation(gene)$
+%        \STATE $PDGenes \leftarrow gene$
+%    \ELSE 
+%      \STATE $RDGenes \leftarrow gene$
+%    \ENDIF
+%\ENDFOR
+%\STATE $geneList=\text{empty list}$
+%\STATE $common=set(dir(NCBI\_Genes)) \cap set(dir(Dogma\_Genes))$
+%\FOR{$\text{gene in common}$}
+%    \STATE $\text{scores} \leftarrow \text{[ ]}$
+%    \IF {$\text{gene NOT in PNGenes AND gene NOT in PDGenes}$}
+%        \STATE $g1 \leftarrow open(NCBI\_Genes(gene)).read()$         
+%        \STATE $g2 \leftarrow open(Dogma\_Genes(gene)).read()$
+%        \STATE $score \leftarrow geneChk(g1,g2)$
+%        \IF {$score > Threshold$}
+%            \STATE $geneList \leftarrow gene$
+%        \ENDIF
+%    \ELSIF {$\text{gene in PNGenes AND NOT gene in PDGenes}$}
+%        \STATE $PGene \leftarrow loadPermutations('N',gene)$
+%        \STATE $g2 \leftarrow open(Dogma\_Genes(gene)).read()$
+%        \FOR {$\text{X in PGene}$}
+%              \STATE $g1 \leftarrow open(NCBI\_Genes(X)).read()$      
+%              \STATE $scores \leftarrow geneChk(g1,g2)$
+%          \ENDFOR
+%          \STATE $score \leftarrow max(scores)$
+%          \IF {$score > Threshold$}
+%                  \STATE $geneList \leftarrow gene$
+%          \ENDIF
+%      \ELSIF {$\text{gene in PDGenes} AND \text{gene NOT in PNGenes}$} 
+%          \STATE $PGene \leftarrow loadPermutations('D',gene)$
+%          \STATE $g1 \leftarrow open(NCBI\_Genes(gene)).read()$
+%          \FOR {$\text{X in PGene}$}
+%              \STATE $g2 \leftarrow open(Dogma\_Genes(X)).read()$     
+%              \STATE $scores \leftarrow geneChk(g1,g2)$
+%              \STATE $score \leftarrow max(scores)$
+%          \ENDFOR
+%          \IF {$score > Threshold$}
+%              \STATE $geneList \leftarrow gene$
+%          \ENDIF
+%    \ELSIF {$\text{gene in PDGenes} AND \text{gene in PNGenes}$} 
+%          \FOR {$\text{X in loadPermutations('N',gene)}$}
+%              \FOR {$\text{Y in loadPermutations('D',gene)}$}
+%                      \STATE $g1 \leftarrow open(NCBI\_Genes(X)).read()$
+%                  \STATE $g2 \leftarrow open(Dogma\_Genes(Y)).read()$         
+%                      \STATE $scores \leftarrow geneChk(g1,g2)$
+%              \ENDFOR
+%          \STATE $score \leftarrow max(scores)$
+%          \IF {$score > Threshold$}
+%              \STATE $geneList \leftarrow gene$
+%          \ENDIF
+%          \ENDFOR
+%      \ENDIF              
+%\ENDFOR
+%\RETURN $geneList$
+%\end{algorithmic}
+%\end{algorithm}
+
+
+
+
+% 
+% \textbf{geneChk} is a subroutine used to find the best similarity score between 
+% two gene sequences after applying operations like \textit{reverse}, {\it complement}, 
+% and {\it reverse complement}. 
+
+
+  

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diff --git a/Paper2/abstract.tex b/Paper2/abstract.tex
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+\begin{abstract}
+\color{red}Investigating in the evolution of genomes become a hard task due to the amount of evolutionary techniques and the amount of genomes that raises every day. The important question to understand here is: how can we clusterize large amounts of chloroplast species?, and what are the common genes that play a role in the process of evolution among these species?. Clusterizing collection of species aims to find the common genes that share the same functionality properties. In other words, clustering helps us to find the core and pan genome among species that share a common properties, such us gene name, gene sequence, family, \dots, etc. According to other studies, finding such core and/or pan genome is not an easy task due to a large amount of computation, and requiring a rigorous methodology. \color{black}
+%Due to the recent evolution of sequencing techniques, the number of
+%available genomes is rising steadily, raising the problem to determine
+%what to do with such large sets of DNA data. An interesting question
+%is to understand what are the common functionality of a collection
+%of species or, conversely, to determine what is specific to a given
+%species when compared to other ones belonging in the same genus, family, etc.
+%Investigating such a problem means to find both core and pan genomes
+%of a collection of species, that is, genes in common to all the species
+%vs. genes present at least once in the set of genomes. However, to obtain
+%trustworthy core and pan genomes is not an easy task, leading to a large 
+%amount of computation, and requiring a rigorous methodology. Surprisingly,
+%as far as we know, this methodology in finding core and pan genomes has not really been
+%investigated in detail. This research work tries to fill this gap
+%by focusing only on chloroplastic genomes, whose reasonable sizes allow a deep study.
+%% DNA analysis  techniques have received  a lot of attention  these last
+%% years, because  they play an  important role in  understanding genomes
+%% evolution over time, and in phylogenetic and genetic analyses. 
+%% However systematic approaches to determine 
+%% %Various
+%%models  of  genomes  evolution  are  based  on  the  analysis  of  DNA
+%%sequences, SNPs,  mutations, and so on. 
+To achieve this goal, a collection of 99 chloroplasts are 
+considered in this article. Two methodologies will be 
+investigated, respectively based on sequence similarities and 
+%genes names taken 
+from annotation tools.
+The obtained results will finally be evaluated in terms of performances and 
+biological relevance.
+% Various  genes prediction methods will be
+% firstly compared, some of them being specific to chloroplastic genomes. 
+% Then clustering methods will be proposed and evaluated, in order
+% to group these coding sequences by orthologous genes.
+% % We  have recently investigated
+% the use of  core (\emph{i.e.}, common genes) and  pan genomes to infer
+% evolutionary  information  on  a  collection of  99~chloroplasts.   In
+% particular,  we  have  regarded  methods  to  build  a  genes  content
+% evolutionary  tree  using  distances  to core  genome.   However,  the
+% production of reliable  core and pan genomes is not  an easy task, due
+% to error  annotations. The aim of this methodology article is to 
+% % investigate various ways to .
+% We will  first compare different  approaches to
+% construct such a tree using fully annotated genomes provided by NCBI and
+% DOGMA, followed by a gene quality control among the common genes. Then
+% we  will explain  how, by  comparing  sequences from  DOGMA with  NCBI
+% contents, we  achieved to identify the  genes that play a  key role in
+% the dynamics of genomes evolution.
+
+\textbf{Keywords:} Core genome, Methodology, Pan genome, Genes prediction, Coding sequences clustering, Chloroplasts, Gene quality test. 
+\end{abstract}

diff --git a/Paper2/annotated.tex b/Paper2/annotated.tex
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+
+
+%\subsubsection{Using genes names provided by annotation tools}
+%
+%Instead of using the sequences predicted by annotation tools, we can
+%try to use the names associated to these sequences, when available.
+%The basic idea is thus to annotate all the sequences using a given
+%software, and to consider as core gene each sequence whose name can
+%be found in all the genomes.
+%Two annotation  techniques will be used in the remainder of this article,
+%namely DOGMA and NCBI. 
+%
+%
+%It is true that the NCBI annotations are of varying 
+%qualities, and sometimes such annotations are totally erroneous. As stated before, it is due to the
+%large variety of annotation tools that can been used during each
+%sequence submission process. However, we also considered it in this
+%article, as this database contains human-curated annotations. To say this
+%another way, DOGMA automatic annotations are good in average, while
+%NCBI contains very good human-based annotations together with very badly
+%annotated genomes. 
+%Let us finally remark that DOGMA also predict the locations of
+%\textit{ribosomal  RNA (rRNA)}, while they are not provided in 
+%gene features from NCBI. Thus core genomes constructed on NCBI
+%data will not contain rRNA.
+
+We now investigate core and pan genomes design
+using each of the two tools separately, which will constitute the second
+approach detailed in this article. From now on we will consider annotated
+genomes: either ``genes features'' downloaded from the NCBI, or the
+result of DOGMA.
+
+%\subsubsection{Names processing}
+%
+%As DOGMA is a deterministic annotation tool, when a given gene
+%is detected twice in two genomes, the same name will be attached
+%to the two coding sequences: DOGMA spells exactly in the same manner
+%the two gene names. So each genome is replaced by a list of gene 
+%names, and finding the core genes common to two genomes simply 
+%consists in intersecting the two lists of genes. The sole problem
+%we have detected using DOGMA on our 97 chloroplasts is the case
+%of the RPS12 gene: some genomes contain RPS12\_3end
+%or RPS12\_5end in the DOGMA result. We have manually 
+%considered that all these representatives belong to the same gene,
+%namely to RPS12.
+%
+%Dealing with NCBI names is more complicated, as various annotation
+%tools have been used together with human annotations, and because there is
+%no spelling rule for gene names. For instance, NAD6 mitochondrial gene is
+%sometimes written as ND6, while we can find RPOC1, RPOC1A, and RPOC1B in 
+%our chloroplasts. So if we simply consider NCBI data without 
+%treatment, intersecting two genomes provided as list of gene names often 
+%lead to duplication of misspelled genes. Automatic names homogenization is thus required
+%on NCBI annotations, the question being where to draw the line 
+%on correcting errors in the spelling of genes ? In this second approach,
+%we propose to automate only obvious modifications like putting all names
+%in capital letters and removing useless symbols as ``\_'', ``('', and ``)''.
+%Remark that such simple renaming process cannot tackle with the situations of NAD6 or 
+%RPOC1 evoked above. To go further in automatic corrections requires
+%to use edit distances like the Levenshtein one, however such an use will
+%raise false positives (different genes with close names will be homogenized). 
+%To solve this problem, a compromise that reduces the number of false positives, by considering the similarity between DNA sequences of genes having similar names, will be detailed in the third approach.
+%
+%At this stage, we now consider that each genome is mapped to a list of gene
+%names, where names have been homogenized in the NCBI case.
+
+
+
+%\subsubsection{Core genes extraction}
+%
+%% The goal of  this stage is to extract maximum core genes from sets of
+%% genes.  To find core genes, the following methodology is applied.
+%% 
+%
+%%\subsubsection{Intersection Core Matrix (\textit{ICM})}
+%
+%To extract  core genes, we  iteratively collect the maximum  number of
+%common  genes   between  genomes,  therefore   during  this  stage
+%an \textit{Intersection  Core Matrix}  (ICM) is built.   ICM is  a two
+%dimensional symmetric matrix where each row and each column correspond
+%to   one   genome.   Hence,   an   element   of   the  matrix   stores
+%the  \textit{Intersection Score}  (IS):  the cardinality  of the  core
+%genes   set  obtained   by  intersecting     the two genomes. 
+%%Maximum  cardinality results in selecting the  two genomes having
+%%the maximum score. 
+%Mathematically speaking, if we have $n$ genomes in
+%local database, the ICM is an $n \times n$ matrix whose elements
+%satisfy: 
+%\begin{equation}
+%score_{ij}=\vert g_i \cap g_j\vert
+%\label{Eq1}
+%\end{equation}
+%\noindent where $1 \leq i \leq n$, $1 \leq j \leq n$, and $g_i, g_j$ are 
+%genomes. The  generation of a new  core genome depends  obviously on the
+%value  of the  intersection scores  $score_{ij}$. More  precisely, the
+%idea is  to consider a  pair of genomes  such that their score  is the
+%largest element in the ICM. These two genomes are then removed from the matrix
+%and the  resulting new  core genome is  added for the  next iteration.
+%The ICM is then updated to take into account the new core genome: new IS
+%values are computed for it. This process is repeated until no new core
+%genome can be obtained.
+%
+%We  can observe  that  the ICM  is relatively  large  due to  the amount  of
+%species. As a consequence, the  computation of the intersection scores is
+%both  time and  memory consuming.  However,  since ICM  is obviously a  symmetric
+%matrix we can reduce the  computation overhead by considering only its
+%triangular  upper part.  The  time complexity  for this  process %after
+%%enhancement 
+%is thus $O(\frac{n.(n-1)}{2})$.  Algorithm~\ref{Alg1:ICM}
+%illustrates the construction  of the ICM matrix and  the extraction of
+%the  core  genomes, where  \textit{GenomeList}  represents the  database
+%storing all genomes  data. At each iteration, this algorithm computes the maximum
+%core genome with its two  parents (genomes).
+%
+%% ALGORITHM HAS BEEN REWRITTEN
+%
+%\begin{algorithm}[H]
+%\caption{Extract Maximum Intersection Score}
+%\label{Alg1:ICM}
+%\begin{algorithmic} 
+%\REQUIRE $L \leftarrow \text{genomes sets}$
+%\ENSURE $B1 \leftarrow \text{Max Core set}$ 
+%\FOR{$i \leftarrow 1:len(L)-1$}
+%        \STATE $score \leftarrow 0$
+%      \STATE $core1 \leftarrow set(GenomeList[L[i]])$
+%      \STATE $g1 \leftarrow L[i]$
+%      \FOR{$j \leftarrow i+1:len(L)$}
+%              \STATE $core2 \leftarrow set(GenomeList[L[j]])$
+%              \STATE $core \leftarrow core1 \cap core2$
+%              \IF{$len(core) > score$}
+%                  \STATE $score \leftarrow len(core)$
+%                \STATE $g2 \leftarrow L[j]$
+%                \ENDIF
+%      \ENDFOR
+%      \STATE $B1[score] \leftarrow (g1,g2)$
+%\ENDFOR
+%\RETURN $max(B1)$
+%\end{algorithmic}
+%\end{algorithm}
+%
+%For complete core trees based either on NCBI names or on DOGMA ones, (see \url{http://members.femto-st.fr/christophe-guyeux/}).
+%%\color{red} The second approach is dependent on gene names spelling. When realizing simple homogenization of names provided by NCBI, we miss core genes which have slightly different name formats. So that, good annotation tool is highly required. \color{black} 
+%
+%%\subsection{Features visualization}
+%%The last stage of the proposed pipeline is naturally to take advantage
+%%of the produced core and pan genomes for biological studies. As
+%%this key stage is not directly related to the methodology for core
+%%and pan genomes discovery, we will only outline a few tasks that
+%%can be operated on the produced data.
+%%
+%%\begin{figure}
+%%\centering
+%%\includegraphics[scale=0.215]{tree}
+%%\caption{Part of a core genomes evolutionary tree (NCBI gene names)}
+%%\label{coreTree}
+%%\end{figure}
+%%
+%%Obtained results may be visualized by building a core genomes evolutionary tree. 
+%%% All core  genes generated  represent  an important information  in the  tree,
+%%% because they  provide ancestor information of two  or more
+%%% genomes. 
+%%Each  node in this  tree represents a chloroplast  genome or
+%%a predicted core, as depicted in Figure~\ref{coreTree}. In this 
+%%figure, nodes labels are of the form 
+%%\textit{(Genes number:Family name\_Scientific name\_Accession number)},
+%%while an edge is labeled with the number of
+%%gene loss when compared to its parents (a leaf  genome or  an intermediate  
+%%core  genome). Such numbers can answer questions like:  
+%%how many genes are different between two  species? Which functionality has
+%%been lost between an ancestor and its children ? For complete core trees
+%%based either on NCBI names or on DOGMA ones, see supplementary data.
+%%
+%%
+%%
+%%A second application of such data is obviously to build accurate phylogenetic   
+%%trees, using tools like
+%%PHYML\cite{guindon2005phyml} or 
+%%RAxML{\cite{stamatakis2008raxml,stamatakis2005raxml}. 
+%%Consider a set of species, the last common core genome in the core tree 
+%%contains all the genes shared in common by these species. These genes may be
+%%multi aligned to serve as input of the phylogenetic tools mentioned above.
+%%An example of such a phylogenetic tree on core 58 (NCBI cores tree, see
+%%supplementary data) is provided in Appendix~\ref{philoTree}. Remark that, in
+%%order to constitute a relevant outgroup, we have simply blasted each gene 
+%%of this core on a chosen \emph{Cyanobacteria}.
+%%

diff --git a/Paper2/appendix.tex b/Paper2/appendix.tex
new file mode 100644 (file)
index 0000000..9674f1d
--- /dev/null
+++ b/Paper2/appendix.tex
@@ -0,0 +1,118 @@
+
+
+\subsection{Phylogenetic tree based on NCBI core 58}
+\label{philoTree}
+
+\begin{center}
+\includegraphics[scale=0.4]{phylo11}
+\end{center}
+
+
+\subsection{Example}
+
+\begin{table}[h]
+\begin{tabular}{cccccccc}
+\hline
+             &    &          & \multicolumn{3}{c}{Nb. of Genes} &           &            \\
+Genome       & Th & GAM      & NCBI     & DOGMA     & Common    & NCBI (\%) & DOGMA (\%) \\ \hline
+NC\_000925.1 & 60 & osneedle & 209      & 171       & 125       & 59.81     & 73.1       \\
+NC\_000927.1 & 60 & osneedle & 147      & 130       & 89        & 60.54     & 68.46      \\
+NC\_001319.1 & 60 & osneedle & 89       & 126       & 74        & 83.15     & 58.73      \\
+NC\_001568.1 & 60 & osneedle & 21       & 51        & 20        & 95.24     & 39.22      \\
+NC\_001603.2 & 60 & osneedle & 67       & 59        & 33        & 49.25     & 55.93      \\
+NC\_001666.2 & 60 & osneedle & 105      & 118       & 74        & 70.48     & 62.71      \\
+NC\_001713.1 & 60 & osneedle & 138      & 155       & 119       & 86.23     & 76.77      \\
+NC\_001840.1 & 60 & osneedle & 197      & 106       & 72        & 36.55     & 67.92      \\
+NC\_002186.1 & 60 & osneedle & 105      & 141       & 96        & 91.43     & 68.09      \\
+NC\_003386.1 & 60 & osneedle & 95       & 124       & 77        & 81.05     & 62.1       \\
+NC\_004543.1 & 60 & osneedle & 88       & 115       & 75        & 85.23     & 65.22      \\
+NC\_005086.1 & 60 & osneedle & 79       & 119       & 74        & 93.67     & 62.18      \\
+NC\_005087.1 & 60 & osneedle & 85       & 122       & 78        & 91.76     & 63.93      \\
+NC\_005353.1 & 60 & osneedle & 66       & 73        & 46        & 69.7      & 63.01      \\
+NC\_006050.1 & 60 & osneedle & 79       & 120       & 77        & 97.47     & 64.17      \\
+NC\_006137.1 & 60 & osneedle & 203      & 227       & 188       & 92.61     & 82.82      \\
+NC\_006290.1 & 60 & osneedle & 79       & 120       & 77        & 97.47     & 64.17      \\
+NC\_006861.1 & 60 & osneedle & 86       & 124       & 84        & 97.67     & 67.74      \\
+NC\_007288.1 & 60 & osneedle & 119      & 91        & 61        & 51.26     & 67.03      \\
+NC\_007578.1 & 60 & osneedle & 78       & 118       & 76        & 97.44     & 64.41      \\
+NC\_007898.3 & 60 & osneedle & 80       & 120       & 78        & 97.5      & 65         \\
+NC\_007957.1 & 60 & osneedle & 79       & 120       & 77        & 97.47     & 64.17      \\
+NC\_007977.1 & 60 & osneedle & 79       & 118       & 77        & 97.47     & 65.25      \\
+NC\_008097.1 & 60 & osneedle & 104      & 109       & 69        & 66.35     & 63.3       \\
+NC\_008099.1 & 60 & osneedle & 83       & 89        & 57        & 68.67     & 64.04      \\
+NC\_008114.1 & 60 & osneedle & 103      & 81        & 52        & 50.49     & 64.2       \\
+NC\_008289.1 & 60 & osneedle & 60       & 68        & 42        & 70        & 61.76      \\
+NC\_008325.1 & 60 & osneedle & 79       & 119       & 77        & 97.47     & 64.71      \\
+NC\_008336.1 & 60 & osneedle & 79       & 121       & 77        & 97.47     & 63.64      \\
+NC\_008359.1 & 60 & osneedle & 78       & 119       & 76        & 97.44     & 63.87      \\
+NC\_008372.1 & 60 & osneedle & 79       & 73        & 45        & 56.96     & 61.64      \\
+NC\_008407.1 & 60 & osneedle & 78       & 116       & 73        & 93.59     & 62.93      \\
+NC\_008456.1 & 60 & osneedle & 79       & 121       & 77        & 97.47     & 63.64      \\
+NC\_008457.1 & 60 & osneedle & 79       & 120       & 77        & 97.47     & 64.17      \\
+NC\_008535.1 & 60 & osneedle & 79       & 121       & 77        & 97.47     & 63.64      \\
+NC\_008588.1 & 60 & osneedle & 131      & 160       & 129       & 98.47     & 80.62      \\
+NC\_008796.1 & 60 & osneedle & 78       & 119       & 75        & 96.15     & 63.03      \\
+NC\_008822.1 & 60 & osneedle & 113      & 131       & 91        & 80.53     & 69.47      \\
+NC\_008829.1 & 60 & osneedle & 85       & 119       & 79        & 92.94     & 66.39      \\
+NC\_009143.1 & 60 & osneedle & 91       & 117       & 73        & 80.22     & 62.39      \\
+NC\_009598.1 & 60 & osneedle & 79       & 121       & 77        & 97.47     & 63.64      \\
+NC\_009599.1 & 60 & osneedle & 79       & 119       & 77        & 97.47     & 64.71      \\
+NC\_009600.1 & 60 & osneedle & 79       & 121       & 77        & 97.47     & 63.64      \\
+NC\_009601.1 & 60 & osneedle & 78       & 119       & 76        & 97.44     & 63.87      \\
+NC\_009618.1 & 60 & osneedle & 118      & 125       & 80        & 67.8      & 64         \\
+NC\_009765.1 & 60 & osneedle & 59       & 86        & 52        & 88.14     & 60.47      \\
+NC\_009808.1 & 60 & osneedle & 78       & 118       & 75        & 96.15     & 63.56      \\
+NC\_010361.1 & 60 & osneedle & 78       & 120       & 76        & 97.44     & 63.33      \\
+NC\_010433.1 & 60 & osneedle & 78       & 118       & 76        & 97.44     & 64.41      \\
+NC\_010442.1 & 60 & osneedle & 74       & 112       & 66        & 89.19     & 58.93      \\
+NC\_010772.1 & 60 & osneedle & 143      & 112       & 80        & 55.94     & 71.43      \\
+NC\_011031.1 & 60 & osneedle & 83       & 78        & 46        & 55.42     & 58.97      \\
+NC\_011600.1 & 60 & osneedle & 139      & 108       & 74        & 53.24     & 68.52      \\
+NC\_011942.1 & 60 & osneedle & 63       & 88        & 46        & 73.02     & 52.27      \\
+NC\_012097.1 & 60 & osneedle & 68       & 71        & 44        & 64.71     & 61.97      \\
+NC\_012099.1 & 60 & osneedle & 87       & 95        & 64        & 73.56     & 67.37      \\
+NC\_012568.1 & 60 & osneedle & 27       & 29        & 0         & 0         & 0          \\
+NC\_012903.1 & 60 & osneedle & 110      & 98        & 64        & 58.18     & 65.31      \\
+NC\_013707.2 & 60 & osneedle & 78       & 121       & 75        & 96.15     & 61.98      \\
+NC\_013823.1 & 60 & osneedle & 79       & 121       & 77        & 97.47     & 63.64      \\
+NC\_013991.2 & 60 & osneedle & 80       & 121       & 78        & 97.5      & 64.46      \\
+NC\_014267.1 & 60 & osneedle & 139      & 167       & 136       & 97.84     & 81.44      \\
+NC\_014287.1 & 60 & osneedle & 127      & 156       & 125       & 98.43     & 80.13      \\
+NC\_014346.1 & 60 & osneedle & 74       & 73        & 43        & 58.11     & 58.9       \\
+NC\_014348.1 & 60 & osneedle & 84       & 117       & 77        & 91.67     & 65.81      \\
+NC\_014570.1 & 60 & osneedle & 69       & 120       & 67        & 97.1      & 55.83      \\
+NC\_014674.1 & 60 & osneedle & 77       & 120       & 75        & 97.4      & 62.5       \\
+NC\_014675.1 & 60 & osneedle & 82       & 115       & 74        & 90.24     & 64.35      \\
+NC\_014676.2 & 60 & osneedle & 76       & 119       & 75        & 98.68     & 63.03      \\
+NC\_014699.1 & 60 & osneedle & 84       & 115       & 73        & 86.9      & 63.48      \\
+NC\_014808.1 & 60 & osneedle & 142      & 152       & 124       & 87.32     & 81.58      \\
+NC\_015403.1 & 60 & osneedle & 134      & 154       & 122       & 91.04     & 79.22      \\
+NC\_015645.1 & 60 & osneedle & 77       & 74        & 45        & 58.44     & 60.81      \\
+NC\_015830.1 & 60 & osneedle & 77       & 118       & 75        & 97.4      & 63.56      \\
+NC\_015899.1 & 60 & osneedle & 77       & 120       & 76        & 98.7      & 63.33      \\
+NC\_016058.1 & 60 & osneedle & 71       & 115       & 70        & 98.59     & 60.87      \\
+NC\_016063.1 & 60 & osneedle & 82       & 120       & 76        & 92.68     & 63.33      \\
+NC\_016065.1 & 60 & osneedle & 83       & 117       & 75        & 90.36     & 64.1       \\
+NC\_016068.1 & 60 & osneedle & 103      & 120       & 77        & 74.76     & 64.17      \\
+NC\_016069.1 & 60 & osneedle & 69       & 116       & 69        & 100       & 59.48      \\
+NC\_016433.2 & 60 & osneedle & 80       & 120       & 78        & 97.5      & 65         \\
+NC\_016468.1 & 60 & osneedle & 78       & 120       & 77        & 98.72     & 64.17      \\
+NC\_016670.1 & 60 & osneedle & 77       & 118       & 75        & 97.4      & 63.56      \\
+NC\_016727.1 & 60 & osneedle & 77       & 119       & 75        & 97.4      & 63.03      \\
+NC\_016731.1 & 60 & osneedle & 128      & 149       & 116       & 90.62     & 77.85      \\
+NC\_016732.1 & 60 & osneedle & 79       & 71        & 45        & 56.96     & 63.38      \\
+NC\_016733.1 & 60 & osneedle & 79       & 82        & 52        & 65.82     & 63.41      \\
+NC\_016734.1 & 60 & osneedle & 79       & 117       & 77        & 97.47     & 65.81      \\
+NC\_016735.1 & 60 & osneedle & 139      & 96        & 65        & 46.76     & 67.71      \\
+NC\_016736.1 & 60 & osneedle & 78       & 120       & 76        & 97.44     & 63.33      \\
+NC\_016753.1 & 60 & osneedle & 79       & 121       & 77        & 97.47     & 63.64      \\
+NC\_016986.1 & 60 & osneedle & 82       & 123       & 80        & 97.56     & 65.04      \\
+NC\_017006.1 & 60 & osneedle & 87       & 119       & 73        & 83.91     & 61.34      \\
+NC\_017609.1 & 60 & osneedle & 67       & 115       & 65        & 97.01     & 56.52      \\
+NC\_018357.1 & 60 & osneedle & 78       & 121       & 76        & 97.44     & 62.81      \\
+NC\_018523.1 & 60 & osneedle & 139      & 107       & 74        & 53.24     & 69.16      \\
+NC\_019601.1 & 60 & osneedle & 78       & 120       & 76        & 97.44     & 63.33      \\
+NC\_020014.1 & 60 & osneedle & 118      & 86        & 51        & 43.22     & 59.3       \\
+NC\_020018.1 & 60 & osneedle & 62       & 67        & 42        & 67.74     & 62.69      \\ \hline
+\end{tabular}
+\end{table}

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new file mode 100644 (file)
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+  volume={13},
+  pages={2005},
+  year={2002}
+}
+
+@article{10.1371/journal.pone.0052841,
+    author ={Blouin \emph{et al}},
+    %{Blouin, Yann AND Hauck, Yolande AND Soler,Charles AND Fabre, Michel AND Vong, Rithy ANDDehan, Céline AND Cazajous, Géraldine ANDMassoure, Pierre-Laurent AND Kraemer, PhilippeANDJenkins, Akinbowale AND Garnotel, EricAND Pourcel, Christine AND Vergnaud, Gilles}
+    journal = {PLoS ONE},
+    publisher = {Public Library of Science},
+    title = {Significance of the Identification in the Horn of Africa of an Exceptionally Deep Branching \textit{Mycobacterium tuberculosis} Clade},
+    year = {2012},
+    month = {12},
+    volume = {7},
+    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0052841},
+    pages = {e52841},
+    number = {12},
+    doi = {10.1371/journal.pone.0052841}
+}        
+
+
+
+@article{zafar2002coregenes,
+  title={CoreGenes: A computational tool for identifying and cataloging},
+  author={Zafar, Nikhat and Mazumder, Raja and Seto, Donald},
+  journal={BMC bioinformatics},
+  volume={3},
+  number={1},
+  pages={12},
+  year={2002},
+  publisher={BioMed Central Ltd}
+}
+
+@Article{17623808,
+AUTHOR = {Gomez-Valero, Laura and Rocha, Eduardo P C and Latorre, Amparo and Silva, Francisco J},
+TITLE = {Reconstructing the ancestor of Mycobacterium leprae: the dynamics of gene loss and genome reduction.},
+JOURNAL = {Genome Res},
+VOLUME = {17},
+YEAR = {2007},
+NUMBER = {8},
+PAGES = {1178-85},
+URL = {http://www.biomedsearch.com/nih/Reconstructing-ancestor-Mycobacterium-leprae-dynamics/17623808.html},
+PubMedID = {17623808},
+ISSN = {1088-9051}
+}
+
+@article{guindon2005phyml,
+  title={PHYML Online—a web server for fast maximum likelihood-based phylogenetic inference},
+  author={Guindon, Stephane and Lethiec, Franck and Duroux, Patrice and Gascuel, Olivier},
+  journal={Nucleic acids research},
+  volume={33},
+  number={suppl 2},
+  pages={W557--W559},
+  year={2005},
+  publisher={Oxford Univ Press}
+}
+
+@article{goloboff2008tnt,
+  title={TNT, a free program for phylogenetic analysis},
+  author={Goloboff, Pablo A and Farris, James S and Nixon, Kevin C},
+  journal={Cladistics},
+  volume={24},
+  number={5},
+  pages={774--786},
+  year={2008},
+  publisher={Wiley Online Library}
+}
+
+@article{stamatakis2008raxml,
+  title={The RAxML 7.0. 4 Manual},
+  author={Stamatakis, Alexandros},
+  journal={Department of Computer Science. Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen},
+  year={2008}
+}
+@article{stamatakis2005raxml,
+  title={RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees},
+  author={Stamatakis, Alexandros and Ludwig, Thomas and Meier, Harald},
+  journal={Bioinformatics},
+  volume={21},
+  number={4},
+  pages={456--463},
+  year={2005},
+  publisher={Oxford Univ Press}
+}
+
+
+@article{Bakke2009,
+    author = {Bakke \emph{et al}},
+    journal = {PLoS ONE},
+    publisher = {Public Library of Science},
+    title = {Evaluation of Three Automated Genome Annotations for \textit{Halorhabdus utahensis}},
+    year = {2009},
+    month = {07},
+    volume = {4},
+    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0006291},
+    pages = {e6291},
+    number = {7},
+    doi = {10.1371/journal.pone.0006291}
+}        
+
+@article{altschul1990basic,
+  title={Basic local alignment search tool},
+  author={Altschul, Stephen F and Gish, Warren and Miller, Webb and Myers, Eugene W and Lipman, David J},
+  journal={Journal of molecular biology},
+  volume={215},
+  number={3},
+  pages={403--410},
+  year={1990},
+  publisher={Elsevier}
+}
+
+@article{geneVision,
+  title={DNASTAR- GenVision Software for Genomic Visualizations},
+  author={DNASTAR},
+  url = {http://www.dnastar.com/products/genvision.php} 
+}
+
+@article{mcfadden2001primary,
+  title={Primary and secondary endosymbiosis and the origin of plastids},
+  author={McFadden, Geoffrey Ian},
+  journal={Journal of Phycology},
+  volume={37},
+  number={6},
+  pages={951--959},
+  year={2001},
+  publisher={Wiley Online Library}
+}
+
+@article{li2013complete,
+  title={Complete Chloroplast Genome Sequence of Holoparasite Cistanche deserticola (Orobanchaceae) Reveals Gene Loss and Horizontal Gene Transfer from Its Host Haloxylon ammodendron (Chenopodiaceae)},
+  author={Li \emph{et al}},
+  journal={PloS one},
+  volume={8},
+  number={3},
+  pages={e58747},
+  year={2013},
+  publisher={Public Library of Science}
+}

diff --git a/Paper2/classEquiv.tex b/Paper2/classEquiv.tex
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+\color{red}
+We still need to propose good methodology to predict good core genes that reflect natural biological relationships among species. Proposing new methods and compare them with previous ones can give us an indicator of which method can produce good functional genes. In this section, we will recall the definition with a fast revision of similarity based method (see \cite{Alkindy2014} for further details).
+This method, considers annotated genomes from NCBI and DOGMA and uses a distance-based similarity measure on genes' coding sequences. Such an approach requires annotated genomes, like the ones provided by the NCBI website.
+
+\subsubsection{Theoretical presentation}
+
+We start by fast revision of first method by the following preliminary definition~\cite{acgs13:onp,Alkindy2014}.\color{black}
+
+\begin{definition}
+\label{def1}
+Let $A=\{A,T,C,G\}$  be the nucleotides alphabet, and  $A^\ast$ be the
+set  of finite  words on  $A$  (\emph{i.e.}, of  DNA sequences).   Let
+$d:A^{\ast}\times   A^{\ast}\rightarrow[0,1]$   be   a   function called
+similarity measure   on
+$A^{\ast}$. Consider a given value $T\in[0,1]$ called a threshold. For
+all   $x,y\in  A^{\ast}$,   we   will  say   that  $x\sim_{d,T}y$   if
+$d(x,y)\leqslant T$.
+\end{definition}
+
+%\noindent $\sim_{d,T}$ is obviously an equivalence relation and when $d=1-\Delta$, where $\Delta$ is the similarity scoring function embedded into the emboss package , we will simply denote $\sim_{d,0.1}$ by $\sim$.
+
+Let be given a \emph{similarity} threshold $T$  and a \emph{similarity measure} $d$.
+The method begins by building  an undirected graph 
+between all the DNA~sequences $g$ of the set  of genomes as follows:
+there is  an edge between $g_{i}$ and $g_{j}$
+if  $g_i \sim_{d,T} g_j$ is established.
+This graph is further denoted as the ``similarity'' graph.
+We thus say that two coding sequences 
+$g_i$, $g_j$ are
+equivalent with respect to the relation $\mathcal{R}$ if both $g_i$ and 
+$g_j$  belong in the same 
+connected component (CC) of this similarity graph, \textit{i.e.}, if there is a path between $g_i$ 
+and $g_j$ in the graph. 
+
+It is not hard to see that this relation is an
+equivalence relation whereas $\sim$ is not.
+Any class for this relation   is  called  a ``gene'' 
+in this article,   where   its  representatives
+(DNA~sequences)  are the ``alleles''  of this  gene, such abuse of language
+being proposed to set our ideas down.  Thus  this first
+method   produces   for   each    genome   $G$,   which   is   a   set
+$\left\{g_{1}^G,...,g_{m_G}^G\right\}$    of   $m_{G}$    DNA   coding
+sequences, the  projection of each sequence according  to $\pi$, where
+$\pi$ maps each sequence into its gene (class) according to $\mathcal{R}$. In
+other     words,      a     genome     $G$      is     mapped     into
+$\left\{\pi(g_{1}^G),...,\pi(g_{m_G}^G)\right\}$.    Note    that    a
+projected genome has no duplicated gene since it is a set.
+
+Consequently, the core  genome (resp.,  the pan genome)  of two genomes
+$G_{1}$  and $G_{2}$  is defined  as  the intersection  (resp., as  the
+union) of their projected  genomes.  We finally consider the intersection
+of  all the  projected genomes,  which  is the  set of  all the  genes
+$\dot{x}$  such  that   each  genome  has  at  least   one  allele  in
+$\dot{x}$. This set will constitute the core genome of the whole species
+under consideration. The  pan genome is computed  similarly as the  union of all
+the projected  genomes. 
+
+% \begin{figure}
+% \begin{center}
+% \includegraphics[scale=0.5]{stats.png}
+% \end{center}
+% \caption{Size of core and pan genomes w.r.t. the similarity threshold, first approach with NCBI annotations and Needleman-Wunsch similarity measure.}\label{Fig:sim:core:pan}
+% \end{figure}
+% 
+
+
+% \begin{table}[h!]
+% \centering
+% \begin{tabular}{ccccccc}
+% \hline
+%           & \multicolumn{4}{c}{Method 1}                          & \multicolumn{2}{c}{Method 3}       \\ \hline
+%           & \multicolumn{2}{c}{NCBI}  & \multicolumn{2}{c}{DOGMA} & \multicolumn{2}{c}{NCBI and DOGMA} \\ \hline
+% Threshold & core       & pan          & core       & pan          & core            & pan              \\ \hline
+% 50        & 1          & 163          & 1          & 118          & \textbf{5}      & \textbf{245}     \\
+% 51        & 1          & 291          & 1          & 194          & -               & -                \\
+% 52        & 1          & 412          & 1          & 258          & -               & -                \\
+% 53        & 1          & 508          & 1          & 321          & -               & -                \\
+% 54        & 4          & 617          & 2          & 372          & -               & -                \\
+% 55        & \textbf{5} & \textbf{692} & 2          & 409          & -               & -                \\
+% 56        & \textbf{5} & \textbf{761} & \textbf{3} & \textbf{445} & -               & -                \\
+% 57        & 4          & 832          & \textbf{3} & \textbf{459} & -               & -                \\
+% 58        & 4          & 905          & 2          & 477          & -               & -                \\
+% 59        & 4          & 976          & 2          & 497          & -               & -                \\
+% 60        & 2          & 1032         & 2          & 519          & 4               & 242              \\
+% 61        & 2          & 1113         & 2          & 553          & -               & -                \\
+% 62        & 2          & 1186         & 2          & 580          & -               & -                \\
+% 63        & 2          & 1264         & 2          & 607          & -               & -                \\
+% 64        & 2          & 1352         & 2          & 644          & -               & -                \\
+% 65        & 1          & 1454         & 2          & 685          & -               & -                \\
+% 66        & 1          & 1544         & 1          & 756          & -               & -                \\
+% 67        & 0          & 1652         & 1          & 838          & -               & -                \\
+% 68        & 0          & 1775         & 1          & 912          & -               & -                \\
+% 69        & 0          & 1886         & 1          & 1007         & -               & -                \\
+% 70        & 0          & 2000         & 1          & 1116         & 3               & 242              \\
+% 80        & 0          & 3541         & 0          & 2730         & 1               & 242              \\
+% 90        & 0          & 5703         & 0          & 5181         & 0               & 241              \\ \hline
+% \end{tabular}
+% \caption{Size of core and pan genomes w.r.t. the similarity threshold, first and third approaches.}
+% \label{Fig:sim:core:pan}
+% \end{table}
+% 
+\begin{table}[h!]
+\centering
+\begin{tabular}{cccccccc}
+\hline
+          & \multicolumn{4}{c}{Method 1}                          & \multicolumn{2}{c}{Method 2}       \\ 
+\hline
+          & \multicolumn{2}{c}{NCBI}  & \multicolumn{2}{c}{DOGMA} & \multicolumn{2}{c}{NCBI and DOGMA} \\ \hline
+Threshold & core       & pan          & core       & pan          & core            & pan              \\ 
+\hline
+50        & 1          & 163          & 1          & 118          & \textbf{5}      & \textbf{245}     \\
+51        & 1          & 291          & 1          & 194          & -               & -                \\
+52        & 1          & 412          & 1          & 258          & -               & -                \\
+53        & 1          & 508          & 1          & 321          & -               & -                \\
+54        & 4          & 617          & 2          & 372          & -               & -                \\
+55        & \textbf{5} & \textbf{692} & 2          & 409          & -               & -                \\
+56        & \textbf{5} & \textbf{761} & \textbf{3} & \textbf{445} & -               & -                \\
+57        & 4          & 832          & \textbf{3} & \textbf{459} & -               & -                \\
+58        & 4          & 905          & 2          & 477          & -               & -                \\
+59        & 4          & 976          & 2          & 497          & -               & -                \\
+60        & 2          & 1032         & 2          & 519          & 4               & 242              \\
+61        & 2          & 1113         & 2          & 553          & -               & -                \\
+62        & 2          & 1186         & 2          & 580          & -               & -                \\
+63        & 2          & 1264         & 2          & 607          & -               & -                \\
+64        & 2          & 1352         & 2          & 644          & -               & -                \\
+65        & 1          & 1454         & 2          & 685          & -               & -                \\
+66        & 1          & 1544         & 1          & 756          & -               & -                \\
+67        & 0          & 1652         & 1          & 838          & -               & -                \\
+68        & 0          & 1775         & 1          & 912          & -               & -                \\
+69        & 0          & 1886         & 1          & 1007         & -               & -                \\
+70        & 0          & 2000         & 1          & 1116         & 3               & 242              \\
+80        & 0          & 3541         & 0          & 2730         & 1               & 242              \\
+90        & 0          & 5703         & 0          & 5181         & 0               &241              \\ 
+\hline
+\end{tabular}
+\caption{Size of core and pan genomes w.r.t. the similarity threshold, first and second 
+approache.}
+\label{Fig:sim:core:pan}
+\end{table}
+
+\subsubsection{Case study}% using NCBI annotations}
+
+Let us now consider the 99 chloroplastic genomes introduced earlier.
+We will use in this case study either the coding sequences 
+downloaded from NCBI website or the sequences predicted by DOGMA. 
+DOGMA, which stands for \textit{Dual  Organellar GenoMe Annotator}, has
+already been evoked in this article. This is a
+ tool  developed in 2004 at  University of Texas    for annotating plant
+chloroplast and  animal mitochondrial genomes.  This tool  
+translates  a  genome  in all  six  reading frames  and then
+queries  its own     amino     acid     sequence     database     using
+Blast  (blastx~\cite{altschul1990basic})  with  various ad hoc
+parameters. The choice of DOGMA is natural, as this annotation tool is
+reputed and specific to chloroplasts. 
+
+Each genome is thus constituted
+by a list of coding sequences. In this illustration study,
+we have evaluated the similarity between two sequences by
+using a global alignment. More precisely, the measure $d$ 
+introduced above is the similarity score provided after
+a Needleman-Wunch global alignment, as obtained by running
+the \emph{needle} command from the \emph{emboss} package
+released by EMBL~\cite{Rice2000}. Parameters of the \emph{needle}
+command are the default ones: 10.0 for gap open penalty and 0.5
+for  gap extension.
+
+The number of genes in the core genome and in the pan genome, 
+according to this first method using data and measure described above
+have been computed using the supercomputer facilities of the M\'esocentre
+de calcul de Franche-Comt\'e. Obtained results are
+represented in  Table~\ref{Fig:sim:core:pan} with respect to various
+threshold values on Needleman-Wunsch similarity scores. 
+Remark that when the threshold is large, 
+we obtain more connected components, but with small sizes (a large number
+of genes, with a few numbers of alleles for each of them). In other words,
+%the number 
+%of alleles of one gene is naturally small if the threshold is large.
+%Similarly, 
+when the threshold is large, the 
+pan genome is large too. %However due to the construction method of the
+%core genome,  this set of genes has few elements in such a  situation.  
+No matter the chosen annotation tool, this first approach  suffers from producing
+too small core genomes,  for any chosen similarity threshold, compared
+to   what  is   usually   expected  by   biologists.
+%  regarding   these
+%chloroplasts and their commonly accepted evolutionary scenario. 
+For NCBI, it is certainly due to a wrong determination of start and stop
+codons in some annotated genomes, due to a large variety of annotation 
+tools used during genomes submission on the NCBI server, some of them
+being old or deficient: such truncated 
+genes will not produce a large similarity score with their orthologous genes
+present in other genomes. The case of DOGMA is more 
+difficult to explain as, according to our experiments and to the
+state of the art, this gene prediction tool produces normally good
+results in average. The best explanation of such an under-performance
+is that a few genomes are very specific and far from the remainder ones, in terms
+of gene contents, which leads to a small number of genes in the global core
+genome. However this first approach cannot help us to determine
+which genomes must be removed from our set of data. To do so, we
+need to introduce a second approach based on gene names: from the
+problematic gene names, we will be able to trace back to the
+problematic genomes.
+
+% 
+% This first illustration emphasizes the importance to deal with
+% well-predicted coding sequences: gene prediction must be
+% achieved with the same tool on each genome, and this tool must
+% be well chosen among the state of the art ones. As we will use
+% good annotation tools, a natural idea is to take advantage to
+% the whole produced annotations (not only the predicted coding
+% sequence, but its name and location too for instance). The implementation
+% of this idea is the principle of the approaches for finding core
+% and pan genomes detailed below.
+% % We are  then left with the following  questions: how can
+% we improve the confidence put in the produced core? That is, how to obtain
+% such a core genome without considering coding sequences provided by the NCBI?

diff --git a/Paper2/conclusion.tex b/Paper2/conclusion.tex
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+In this research work, we studied two %three 
+methodologies for extracting core genes from a large set of chloroplasts genomes, and we developed 
+Python programs to evaluate them in practice. 
+%Extracted core genomes 
+%depend on both gene names and sequences.
+% Furthermore, that extract these core genes with the three methodologies. 
+
+We firstly considered to extract core genomes by the way of comparisons 
+(global alignment) of DNA sequences downloaded from NCBI database. 
+However this method failed to produce biologically 
+relevant core genomes, no matter the chosen similarity threshold, probably
+due to annotation errors. We then considered to use the DOGMA annotation tool 
+to enhance the genes prediction process. The second method consisted in extracting 
+gene names either from NCBI gene features or from DOGMA results. A first
+``intersection core matrix (ICM)'' where built, in which each coefficient 
+stored the intersection cardinality of the two genomes placed at the extremities
+of its row and column. New ICMs are 
+then constructed by selecting the maximum intersection score (IS)  in this matrix, 
+removing the two genomes having this score, and adding the corresponding 
+core genome in a new ICM construction. %Finally, in the third method, a genes quality test has been added before the ICMs computation, to ensure that the genes obtained in the NCBI annotation files are the same %(\emph{i.e.}, gene name and sequence) than the ones produced by DOGMA. 
+% A genes quality test has then been introduced  to construct new ICMs
+% on genomes 
+% only constituted by the genes that successfully passed 
+% a specific similarity threshold of 65\% on their sequences.
+% % , ICM 
+% % then will take place to extract the core genes.
+% 
+
+Core trees have finally been generated for each method, to investigate 
+the distribution of chloroplasts and core genomes. The tree from second 
+method based on DOGMA has revealed the best distribution of
+ chloroplasts regarding their evolutionary history. In particular, it appears to
+us that each endosymbiosis event is well branched in the DOGMA core tree.     
+
+In future work, we intend to deepen the methodology evaluation by considering
+new gene prediction tools and various similarity measures on both
+gene names and sequences. Additionally, we will investigate new clustering
+methods on the first approach, to improve the results quality in this promising way to 
+obtain core genes. Finally, the results produced with DOGMA will be 
+further investigated, biologically speaking: the genes content of each core
+will be studied while phylogenetic relations between all these species
+will be questioned.

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diff --git a/Paper2/discussion.tex b/Paper2/discussion.tex
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+
+
+
+
+%\subsection{Implementation}
+%\label{sec:implem}
+%All the  algorithms detailed in this article have  
+%been implemented using  Python~2.7 on a personal computer (Ubuntu~12.04 with 6~GiB memory,  quad-core Intel~i5 with  an operating  frequency of
+%2.5~GHz). %All programs can be downloaded at \begin{color}{red} \url{http://......} \end{color}. 
+%%genes  from large  amount of  chloroplast  genomes.
+%
+%%\begin{center}
+%\begin{table}[H]
+%\centering
+%\caption{Type of annotations and execution time}\label{Etime}
+%{%\scriptsize
+%%\begin{tabular}{p{2.3cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.25cm}}%p{0.5cm}p{0.25cm}p{0.5cm}p{0.2cm}}
+%\begin{tabular}{ccccccc}
+%\hline\hline
+% Method & \multicolumn{2}{c}{Annotation} & \multicolumn{2}{c}{Features} & \multicolumn{2}{c}{Exec. time (min.)} \\%& \multicolumn{2}{c}{Core genes} & \multicolumn{2}{c}{Bad genomes} \\
+%~ & N & D & Name & Seq & N & D \\%& N & D & N & D \\
+%\hline
+%First approach & $\surd$ & - & - & $\surd$ & 1.7 & -\\% & ? & - & 0 & -\\[0.5ex]
+%Second approach & $\surd$ & $\surd$ & $\surd$ & - & 4.98 & 1.52\\% & 28 & 10 & 1 & 0\\[0.5ex]
+%Third approach & $\surd$ & $\surd$ & $\surd$ & $\surd$ & \multicolumn{2}{c}{$\simeq$3 days + 1.29} \\%& \multicolumn{2}{c}{4} & \multicolumn{2}{c}{1}\\[1ex]
+%\hline
+%\end{tabular}
+%}
+%\end{table}
+%%\end{center} 
+%
+%%\vspace{-1cm}
+%
+%Table~\ref{Etime}  presents   the  annotation  type,
+%execution time,  and the  number of core  genes  for  each proposed  method. The following
+%notations have been used:  \textbf{N}  denotes NCBI and  \textbf{D} means  DOGMA,
+%while \textbf{Seq}  stands for sequence. The  two first {\it Annotation} columns
+%represent the algorithm used to annotate chloroplast genomes. The next two {\it
+%Features} columns mean  the kind  of gene feature used to extract core
+%genes: gene name, gene sequence, or  both of them. 
+%
+%It can be seen that
+%almost all methods need low execution time  to extract core genes
+%from the large set of chloroplast genomes. Only the third method requires
+%more than one day of computation (about 3-4 days) for sequence comparisons. However,
+%once the quality genomes are well constructed, it only takes 1.29~minutes to
+%extract the core genes. Such low execution times allow us  to use these
+%methods to extract all core genomes  on a personal computer. 
+%The lowest execution time (1.52~minutes)
+%is obtained with the second method using DOGMA annotations. 
+%
+%
+%The second important computational factor is the amount of memory necessary for each
+%methodology.   Table~\ref{mem}  shows  the  memory   usage  of  each
+%method. In this table, the values are  presented in megabyte
+%unit, while \textit{gV} means  geneVision~file~format. We can notice that
+%the quantity  of required memory  is relatively low  for all methods,
+%and is available  on any personal computer. The  different values also
+%show that the gene features  method based on DOGMA annotations has the
+%most   reasonable   memory   usage,   except  when   extracting   core
+%sequences. The third method gives the lowest values if we already have
+%the   ``quality   genomes'',   otherwise   it  will   consume   far   more
+%memory. Remark that the  amount of memory  used by the third method also
+%depends on the size of each genome.
+%
+%
+%\begin{table}[H]
+%\centering
+%\caption{Memory usages for each methodology (in MB)}\label{mem}
+%\tabcolsep=0.11cm
+%{\scriptsize
+%\begin{tabular}{p{2.5cm}@{\hskip 0.1mm}p{1.5cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}}
+%\hline\hline
+%Method& & Load Gen. & Conv. gV & Read gV & ICM & Core tree & Core Seq. \\
+%\hline
+%Gene prediction & NCBI & 108 & - & - & - & - & -\\
+%\multirow{2}{*}{Gene Features} & NCBI & 15.4 & 18.9 & 17.5 & 18 & 18 & 28.1\\
+%              & DOGMA& 15.3 & 15.3 & 16.8 & 17.8 & 17.9 & 31.2\\
+%Gene Quality  & ~ & 15.3 & $\le$3G & 16.1 & 17 & 17.1 & 24.4\\ 
+%\hline
+%\end{tabular}
+%}
+%\end{table}
+% 
+% 
+%\subsection{Results comparison}
+%
+%Method 2 has indicated to us that two genomes must be removed from the
+%set of chloroplasts, namely \textit{Epifagus virginiana} (NC\_001568.1)
+%and \textit{Cuscuta gronovii} (NC\_009765.1). The reason to
+%be of this update is that (1) these chloroplastic genomes are non functional ones,
+%and (2) considering them leads to a too small final core genome.
+%Additionally, we have been forced to remove \textit{NC\_012568.1  Micromonas pusilla} 
+%from the NCBI study, as its wrong annotations lead to an empty
+%final core genome.
+%
+%The number
+%of {\it  Core genes} in Table~\ref{Etime} represents the  amount of genes in  the last core
+%genome (the core genes shared by all the chloroplasts). 
+%%The main goal is to  find the maximum core genes that simulate
+%%biological background of chloroplasts. 
+%With  NCBI we obtained 28 genes for
+%96   genomes, while DOGMA approach produces 10   genes for  the whole 97  genomes.
+% However we will see that the distribution of  genomes 
+%in the NCBI core tree is less relevant, biologically speaking, than the one obtained
+%by using DOGMA naming process (see Section~\ref{sec:discuss}). 
+%
+%%\begin{sidewaystable}
+%\begin{table}
+%\centering
+%    \begin{tabular}{llllllllll}
+%    \hline
+%    Method         & \multicolumn{2}{l}{Connected Components} & \multicolumn{6}{l}{ICM-Genes' names} & ICM-Quality test \\ \hline
+%    Annotation     & NCBI                 & DOGMA & \multicolumn{3}{l}{NCBI} & \multicolumn{3}{l}{DOGMA} & NCBI and DOGMA   \\
+%    Nb. of genomes & 99                   & 99    & 99*              & 97  & 96  & 99    & 97  & 96  & 99               \\
+%    Core genome    & 5                    & 3     & 9                & 0   & 28  & 2     & 10  & 28  & 5                \\
+%    Pan genome     & 761                  & 445   & 766              & 764 & 737 & 297   & 297 & 297 & 245              \\ \hline
+%    \end{tabular}
+%\end{table}
+%
+%% Please remember to add \use{multirow} to your document preamble in order to suppor multirow cells
+%\begin{table}[h]
+%\begin{tabular}{ccccl}
+%\hline
+%Nb. of       & Methods                   & Type of & Size of & \multicolumn{1}{c}{Names of core genes}                                                                                                                                                              \\ 
+% genomes&&annotation&core genome&\\ \hline
+%\multirow{3}{*}{97}  & \multirow{2}{*}{Method 2} & NCBI               & 0                   & -                                                                                                                                                                                                    \\
+%\multicolumn{1}{l}{} & \multicolumn{1}{l}{}      & DOGMA              & 10                  & ATPI, PSAA, PSAB, PSBA,  \\
+%&&&&PSBE, PSBF, PSBL, RPL2,\\
+%&&&& TRNC-GCA, TRNH-GUG \\
+%\multicolumn{1}{l}{} & Method 3                  & Both    & 5                   & ATPI, ATPA, ATPH, PSBJ, PSBE                                                                                                                                                                         \\
+%\multirow{3}{*}{96}  & \multirow{2}{*}{Method 2} & NCBI               & 28                  & ATPA, ATPB, ATPE, ATPH,\\
+%&&&& ATPI, PETG, PSAA, PSAB,\\
+%&&&& PSAC, PSBA, PSBD, PSBE,\\
+%&&&& PSBF, PSBH, PSBJ, PSBK,\\
+%&&&& PSBN, RBCL, RPL14, RPL16,\\
+%&&&& RPL20, RPL36, RPS18, RPS3,\\
+%&&&& RPS4, RPS7, RPS8, RPS11                         \\
+%\multicolumn{1}{l}{} &  & DOGMA & 28                  & ATPA, ATPB, ATPI, ATPH, PETB, PETG, PSAA, PSAB, PSAC, PSBA, PSBD, PSBE, PSBF, PSBC, PSBJ, PSBI, PSBL, PSBT, RBCL, RPL2, RRN16, TRND-GUC, TRNFM-CAU, TRNH-GUG, TRNI-GAU, TRNN-GUU, TRNQ-UUG, TRNC-GCA \\
+%\multicolumn{1}{l}{} & \multicolumn{1}{l}{Method 3}      & Both     & 5                   & ATPI, ATPA, ATPH, PSBJ, PSBE                                                                                                                                                                         \\ \hline
+%\end{tabular}
+%\end{table}
+%%\end{sidewaystable}
+
+
+
+\subsection{Biological evaluation}\label{sec:discuss}
+It is well known that the first plants' endosymbiosis ended in a great diversification of 
+lineages comprising \textit{Red Algae}, \textit{Green Algae}, and \textit{Land Plants} (terrestrial).
+Several second endosymbioses occurred then: two involving a \textit{Red Algae} 
+and other heterotrophic eucaryotes and giving birth to both \textit{Brown Algae} 
+and \textit{Dinoflagellates} lineages; another involving a \textit{Green Algae} and 
+a heterotrophic eucaryote and giving birth to \textit{Euglens}~\cite{mcfadden2001primary}.
+
+The interesting point with the produced core trees (especially the one 
+obtained with DOGMA, see \url{http://members.femto-st.fr/christophe-guyeux/en/chloroplasts}) is 
+that organisms resulting from the first endosymbiosis are distributed in 
+each of the lineages found in the chloroplast genome structure 
+evolution. More precisely, all \textit{Red Algae} chloroplasts are grouped together in one lineage, while
+\textit{Green Algae} and \textit{Land Plants} chloroplasts are all in a second lineage. 
+Furthermore organisms resulting from the secondary endosymbioses are well localized in 
+the tree: both the chloroplasts of \textit{Brown Algae} and \textit{Dinoflagellates} 
+representatives are found exclusively in the lineage also comprising the 
+\textit{Red Algae} chloroplasts from which they evolved, while the \textit{Euglens} 
+chloroplasts are related to the \textit{Green Algae} chloroplasts from which they 
+evolved. 
+This makes sense in terms of biology, history of lineages, and 
+theories of chloroplasts origins (and so photosynthetic ability)  in 
+different Eucaryotic lineages~\cite{mcfadden2001primary}.
+
+Interestingly, the sole organisms under consideration that possess a 
+chloroplast (and so a chloroplastic genome) but that have lost the 
+photosynthetic ability (being parasitic plants) are found at the basis of 
+the tree, and not together with their phylogenetically related species. 
+This means that functional chloroplast genes are evolutionary constrained 
+when used in photosynthetic process, but loose rapidly their efficiency 
+when not used, as recently observed for a species of Angiosperms\cite{li2013complete}. 
+These species are \textit{Cuscuta-grovonii}, an Angiosperm (flowering plant) 
+at the base of the DOGMA Angiosperm-Conifers branch, and 
+\textit{Epipactis-virginiana}, also an Angiosperm, at the complete basis of this tree.
+
+Another interesting result is that \textit{Land Plants} that 
+represent a single sublineage originating from the large and diverse 
+lineage of \textit{Green Algae} in Eucaryotes history are present in two different 
+branches of the DOGMA tree, both associated with \textit{Green Algae}: one branch 
+comprising the basal grade of \textit{Land Plants} (mosses and ferns) and the second one
+containing the most internal lineages of \textit{Land Plants} (Conifers and flowering plants). 
+But independently of their split in two distinct branches of the DOGMA 
+tree, the \textit{Land Plants} always show a higher number of functional genes in 
+their chloroplasts than the \textit{Green Algae} from which they emerged, probably meaning that the
+terrestrial way of life necessitates more functional genes for an 
+optimal photosynthesis than the marine one. However, a more detailed 
+analysis of selected genes is necessary to better understand the reasons why
+such a distribution has been obtained.
+Remark finally that all these biologically interesting results are apparent
+only in the core tree based on DOGMA, while they are not so obvious in the NCBI one. 
+
+
+%\begin{figure}
+%\centering
+%\includegraphics[scale=0.37]{core}
+%\caption{Core}
+%\end{figure}
+%
+%
+%\begin{figure}
+%\centering
+%\includegraphics[scale=0.37]{pan}
+%\caption{Pan}
+%\end{figure}

diff --git a/Paper2/general.tex b/Paper2/general.tex
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+
+
+\begin{figure}[h]  
+  \centering
+    \includegraphics[width=0.75\textwidth]{Whole_system}
+\caption{A general overview of the annotation-based approach}\label{Fig1}
+\end{figure}
+
+%Figure~\ref{Fig1} presents a general overview of the entire proposed pipeline
+%for core and pan genomes production and exploitation, which consists   of    three stages:   \textit{Genomes    annotation},   \textit{Core   extraction}, and    \textit{Features    Visualization}.  
+% To  understand the  whole core extraction  process, we
+% describe briefly each  stage below. More details will  be given in the
+% coming subsections.   
+\color{red}In previous work \cite{Alkindy2014}, we proposed a pipeline for the extraction of core genome. In this work, the pipline is considered with quality test method in extracting core genes, for more details (see figure~\ref{Fig1}). As a starting point, an annotation uses a DNA sequences database % chosen  among   the  many  international  databases  storing %nucleotide sequences, 
+such as NCBI's GenBank~\cite{Sayers01012011}, the   European \textit{EMBL} database~\cite{apweiler1985swiss}, or the Japanese  \textit{DDBJ} one~\cite{sugawara2008ddbj}. 
+\color{black}
+Further more, It is possible to obtain annotated genomes (DNA coding sequences with gene
+names and locations) by interacting with these databases, either by directly downloading
+annotated genomes delivered by these websites, or by launching an 
+annotation tool on complete downloaded  genomes. 
+Obviously, this annotation stage must be of quality if we want
+to obtain acceptable core and pan genomes.
+% These  last years  the cost  of  sequencing genomes  has been  greatly
+% reduced,  and thus  more and  more genomes  are  sequenced.  Therefore
+% automatic annotation tools are required to deal with this continuously
+% increasing amount of genomics data. %Moreover, a reliable and accurate
+% %genome  annotation  process  is  needed  in order  to  provide  strong
+%indicators for the study of life\cite{Eisen2007}.
+%Various  cost-effective annotation  tools~\cite{Bakke2009} producing genomic  annotations at many levels of detail  have been designed recently, some reputed ones being: % NCBI~\cite{Sayers01012011}, DOGMA~\cite{RDOGMA},       cpBase~\cite{de2002comparative}, CpGAVAS~\cite{liu2012cpgavas},                   and CEGMA~\cite{parra2007cegma}. Such tools usually use one out of the three following methods  for finding  gene locations in large DNA sequences:  \textit{alignment-based},  \textit{composition based}, or a  combination of both~\cite{parra2007cegma}.   The alignment-based method  is used  when trying  to predict  a protein coding  sequence   by aligning a genomic DNA sequence with a cDNA  sequence  coding  an already known homologous  protein~\cite{parra2007cegma}.0 This approach is used for instance  in GeneWise~\cite{birney2004genewise}.  The alternative   method,   the    composition-based   one   (also   known as  \textit{ab initio})  is based  on  probabilistic  models of  genes structure~\cite{parra2000geneid}. % to  find genes  according  to  the  gene value  probability
+%(GeneID). 
+
+Using such annotated genomes, we will detail two general approaches for extracting the core genome, which is the third stage of the pipeline: the first one uses similarities computed on predicted coding sequences, while the second one uses all the information provided during the annotation stage.
+ 
+\color{red}instead of considering only gene sequences taken from NCBI or DOGMA, a quality test process is take place by working with gene names and sequences to produce quality genes. However, we will show that such a simple idea is not so easy to realize, and that it is not sufficient to only consider gene names provided by such tools while it gives good results in previous work \cite{Alkindy2014}. \color{black} 
+% 
+% 
+Annotation,  which  is the  first  stage,  is  an important  task  for extracting gene features. Indeed, to extract good gene feature, a good annotation tool  is obviously  required. 
+Indeed, such annotations can be used in various manners (based on gene names,  gene  sequences, protein  sequences, etc.) to extract the core and pan genomes.
+We will subsequently propose  methods that use gene  names and sequences for extracting core  genes and  producing  chloroplast evolutionary  tree.
+ 
+%\input{population_Table}
+The  final stage of our pipeline, only invoked in this article, is to take advantage
+of the information produced during the core and pan genomes search.
+This features visualization stage encompasses phylogenetic tree construction (see \cite{Alkindy2014} for more details)
+using core genes, genes content evolution illustrated by core trees, functionality 
+investigations, and so on.
+% 
+%    allows  to   visualize  genomes   and/or  gene   evolution  in chloroplast.    Therefore   we   use  representations   like   tables, phylogenetic  trees,  graphs,  etc.   to  organize  and  show  genomes relationships,  and  thus  achieve   the  goal  of  representing  gene
+% evolution.   In addition,  comparing these  representations  with ones issued from  another annotation tool dedicated to  large population of chloroplast genomes  give us biological perspectives to  the nature of chloroplasts evolution. %Notice that  a local database linked with each pipe stage is  used to store all the  information produced during the process.
+
+For illustration purposes, we have considered % GenBank-NCBI~\cite{Sayers01012011} as sequence
+%database:  
+99~genomes of chloroplasts downloaded from GenBank database~\cite{Sayers01012011}. These genomes
+lie in  the eleven type  of chloroplast families (see \cite{Alkindy2014} for more details).%as described in Table~\ref{Tab2}.
+Furthermore, two kinds of annotations will be considered in this document, namely the
+ones provided by NCBI on the one hand, and the ones by DOGMA on the other hand.
+%The
+%database in  our method must be  taken from any  confident data source
+%that stores annotated and/or unannotated chloroplast genomes. 
+% As stated in the previous section, we have
+% considered  GenBank-NCBI~\cite{Sayers01012011} as sequence
+% database.

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+\color{red}
+All different  algorithms have  been implemented using  Python on a personal computer running Ubuntu~12.04 with 6~GiB memory and
+a  quad-core Intel  core~i5~processor with  an operating  frequency of
+2.5~GHz. %All the programs can be downloaded at \url{http://......} .
+%genes  from large  amount of  chloroplast  genomes.
+
+\begin{center}
+\begin{table}[H]
+\caption{Type of annotation, execution time, and core genes.}\label{Etime}
+{\scriptsize
+\begin{tabular}{p{2cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.25cm}p{0.5cm}p{0.2cm}}
+\hline\hline
+ Method & \multicolumn{2}{c}{Annotation} & \multicolumn{2}{c}{Features} & \multicolumn{2}{c}{Exec. time (min.)} & \multicolumn{2}{c}{Core genes} & \multicolumn{2}{c}{Bad genomes} \\
+~ & N & D & Name & Seq & N & D & N & D & N & D \\
+\hline
+Gene prediction & $\surd$ & $\surd$ & - & $\surd$ & 1.7 & - & ? & - & 0 & -\\[0.5ex]
+%Gene Features & $\surd$ & $\surd$ & $\surd$ & - & 4.98 & 1.52 & 28 & 10 & 1 & 0\\[0.5ex]
+Gene Quality & $\surd$ & $\surd$ & $\surd$ & $\surd$ & \multicolumn{2}{c}{$\simeq$3 days + 1.29} & \multicolumn{2}{c}{4} & \multicolumn{2}{c}{1}\\[1ex]
+\hline
+\end{tabular}
+}
+\end{table}
+\end{center} 
+
+\vspace{-1cm}
+
+Table~\ref{Etime}  presents  for  each  method  the  annotation  type,
+execution time,  and the  number of core  genes. We use  the following
+notations:  \textbf{N}  denotes NCBI,  while  \textbf{D} means  DOGMA,
+and \textbf{Seq}  is for sequence. The first two {\it Annotation} columns
+represent the algorithm used to annotate chloroplast genomes. The next two ones {\it
+Features} columns mean  the kind  of gene feature used to extract core
+genes: gene name, gene sequence, or  both of them. It can be seen that
+almost all methods need low {\it Execution time} expended in minutes to extract core genes
+from the large set of chloroplast genomes. Only the gene quality method requires
+several days of computation (about 3-4 days) for sequence comparisons. However,
+once the quality genomes are well constructed, it only takes 1.29~minutes to
+extract core gene. Thanks to this low execution times that gave us a privilege to use these
+methods to extract core genes  on a personal computer rather than main
+frames or parallel computers. The lowest execution time: 1.52~minutes,
+is obtained with the second method using Dogma annotations. The number
+of {\it  Core genes} represents the  amount of genes in  the last core
+genome. The main goal is to  find the maximum core genes that simulate
+biological background of chloroplasts. With  NCBI we have 28 genes for
+96   genomes,   instead   of    10   genes   for   97   genomes   with
+Dogma. Unfortunately, the biological distribution of genomes with NCBI
+in core tree do not  reflect good biological perspective, whereas with
+DOGMA the  distribution of genomes is biologically  relevant. Some a few genomes maybe destroying core genes due to
+low  number  of  gene  intersection. More precisely, \textit{NC\_012568.1  Micromonas pusilla} is the only genome who destroyes the core genome with NCBI
+annotations for both gene features and gene quality methods.
+
+The second important factor is the amount of memory nessecary in each
+methodology.  Table   \ref{mem}  shows  the  memory   usage  of  each method. 
+In this table, the values are  presented in megabyte
+unit and \textit{gV} means  genevision~file~format. We can notice that
+the level  of memory which is  used is relatively low  for all methods
+and is available  on any personal computer. The  different values also
+show that the gene features  method based on Dogma annotations has the
+more   reasonable   memory   usage,   except  when   extracting   core
+sequences. The third method gives the lowest values if we already have
+the   quality   genomes,   otherwise   it  will   consume   far   more
+memory. Moreover, the  amount of memory, which is used by the third method also
+depends on the size of each genome.
+
+
+\begin{table}[H]
+\centering
+\caption{Memory usages in (MB) for each methodology}\label{mem}
+\tabcolsep=0.11cm
+{\scriptsize
+\begin{tabular}{p{2.5cm}@{\hskip 0.1mm}p{1.5cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}@{\hskip 0.1mm}p{1cm}}
+\hline\hline
+Method& & Load Gen. & Conv. gV & Read gV & ICM & Core tree & Core Seq. \\
+\hline
+Gene prediction & NCBI & 108 & - & - & - & - & -\\
+%\multirow{2}{*}{Gene Features} & NCBI & 15.4 & 18.9 & 17.5 & 18 & 18 & 28.1\\
+              %& DOGMA& 15.3 & 15.3 & 16.8 & 17.8 & 17.9 & 31.2\\
+Gene Quality  & ~ & 15.3 & $\le$3G & 16.1 & 17 & 17.1 & 24.4\\ 
+\hline
+\end{tabular}
+}
+\end{table}
+\color{black}
+%\begin{figure}[H]
+%  \centering \includegraphics[width=0.75\textwidth]{Whole_system} \caption{Overview
+%  of the pipeline, third approach}\label{wholesystem}
+%\end{figure}
+
+\begin{figure}[!ht]
+    \subfloat[Sizes of core genome\label{subfig-1:core}]{%
+      \includegraphics[width=0.5\textwidth]{coregenome}
+    }
+    \hfill
+    \subfloat[Sizes of pan genome\label{subfig-2:pan}]{%
+      \includegraphics[width=0.5\textwidth]{pangenome}
+    }
+    \caption{Sizes of Core and Pan genomes for first and second method.}
+    \label{fig:sizes of core and pan}
+  \end{figure}
+  
+
+\begin{figure}[!ht]
+    \subfloat[genes coverage of NCBI genomes\label{Cover:NCBI}]{%
+      \includegraphics[width=0.5\textwidth]{cover_ncbi}
+    }
+    \hfill
+    \subfloat[genes coverage of DOGMA genomes\label{cover:dogma}]{%
+      \includegraphics[width=0.5\textwidth]{cover_dogma}
+    }
+    \caption{Gene comparisons cover from NCBI and DOGMA, second method}
+    \label{fig:sizes of core and pan}
+  \end{figure}
+  
+\color{red}
+Figure~\ref{fig:sizes of core and pan} represent the sizes of core and pan genomes produced from the two methods. In figure~\ref{subfig-1:core} core genes are predicted, note that max core genes do not mean good genes. We are looking for genes that meet it's biological principles. The core genes produced from the first method specially from DOGMA can reflect its biological meaning, we will explain later in the section of disscusion the reason why. In figure~\ref{subfig-2:pan}, we can see that the values of pan genome from second method is still steady with different thresholds the second method, while in the first method pan genes increases when the threshold increased. 
+
+Furthermore, we calculate the correlation coeffecient formula for the second method and the results shows that the correlation for the annotation from DOGMA was $0.97$ while with NCBI was $0.69$.    
+
+\color{black}

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+\color{red}The idea behind the importance of identifying core genes is to understand the shared functionality of agiven set of species. 
+%Identifying  core genes  may be of importance  to understand shared functionality and specificity of a given set of species, or to construct their phylogeny using curated sequences.
+We introduced in previous work (see \cite{Alkindy2014}) two methods for discovering core and pan genes based on sequence similarity method and alignment based approache method. However, to
+determine both core and pan genomes of a large set of DNA sequences, we consider in this work compare the same clustering algorithm of sequence similarity method proposed in previous work with new method as an improvement of alignment based approach by considering sequence quality control test. More precisely,  we focus on
+the following  questions using a  collection of 99~chloroplasts as illustrative example: how
+can  we identify the  best core  genome (that is, an artificially designed set of
+coding sequences as close as possible to the real biological one) and  
+how to deduce scenarios regarding their genes loss.
+
+The existance of Chloroplasts is behind the fact that \color{black}chloroplasts found in Eucaryotes have 
+an endosymbiotic origin, meaning 
+that they come from the incorporation of a photosynthetic bacteria (Cyanobacteria) within an eucaryotic cell. They are fundamental key elements in 
+living organisms history, as they are organelles responsible for 
+photosynthesis. This latter is the main way to produce organic matters 
+from mineral ones using solar energy. Consequently photosynthetic 
+organisms are at the basis of most ecosystem trophic chains. Indeed
+photosynthesis in eucaryotes has allowed a great speciation in the lineage, 
+leading to a great biodiversity. From an ecological point of view, 
+photosynthetic organisms are at the origin of the presence of dioxygen 
+in the atmosphere (allowing extant life) and are the main source of mid
+to long term carbon storage, which is fundamental regarding current 
+climate changes. However, the chloroplasts evolutionary history is not totally
+well understood, at least large scale speaking, and their phylogeny requires
+to be further investigated.
+
+A key idea in phylogenetic classification is that a given DNA mutation shared 
+by at least two taxa has a larger probability to be inherited from a common 
+ancestor than to have occurred independently. Thus shared changes in genomes 
+allow to build relationships between species. In the case of chloroplasts, 
+an important category of genomes changes is the loss of functional genes, 
+either because they become ineffective or due to a transfer to the nucleus. 
+Thereby 
+%we hypothesize that 
+a small number of gene losses  among species indicates
+that these species are close to  each other and belong to a similar lineage,
+while a  large loss  means %that we  have an  evolutionary relationship
+%between species  from 
+%much more 
+distant lineages.
+%Phylogenetic relationships are mainly built by comparison of sets of coding and non-coding sequences.
+ Phylogenies of photosynthetic plants are important to assess the origin 
+ of chloroplasts and the modes of gene loss among lineages. 
+ These phylogenies are usually done using a few chloroplastic genes, 
+some of them being not conserved in all the taxa. 
+ %As phylogenetic relationships inferred from data matrices complete for each species included and with the same evolution history are better assumptions, 
+%we argue that
+This is why selecting core genes may be of interest for a new investigation 
+of photosynthetic plants phylogeny. 
+%To depict the links between species clearly, we here intend to built a phylogenetic tree showing the relationships based on the distances among gene sequences of a core genome. 
+However, the circumscription of the core chloroplast genomes for a given set of photosynthetic organisms needs bioinformatics investigations using sequence annotation and comparison tools, and various choices
+%of tools 
+are available. 
+
+\color{red}Our intention in this research work regarding the methodology in core and pan genomes determination is to investigate the impact of these choices. on the results. A general presentation of the approaches detailed in this document is provided in the next section. Then we will study in Section~\ref{sec:simil} the use of annotated genomes from NCBI website~\cite{Sayers01012011} with a coding  sequences clustering method based on the Needleman-Wunsch similarity scores~\cite{Rice2000}. %We will show that such an approach based on sequences similarity cannot lead to satisfactory results, biologically speaking. 
+%We will thus investigate name-sequence-based approaches in Section~\ref{sec:annot}, by using successively the gene names provided by NCBI and DOGMA~\cite{RDOGMA} annotations, where DOGMA is a recent annotation tool specific to chloroplasts. 
+While the second method will be proposed in Section~\ref{sec:mixed}, which intends to use gene name and sequence comparisons. \color{black}
+%Ways to take advantage of the produced core genomes are introduced in Section~\ref{sec:features},
+ Information regarding computation time and memory usage are provided in Section~\ref{sec:implem}. 
+Finally, a discussion based on biological aspects regarding the evolutionary history of the considered genomes
+will finalize our investigations, leading to our methodology proposal for core and pan genomes
+discovery of chloroplasts %(Section~\ref{sec:discuss}). 
+This research work ends by a conclusion section, in which our investigations will be summarized and intended future work will be planned. 
+
+
+% Other possible scientific questions to consider for introduction improvement:
+% Which bioinformatics tools are necessary for genes comparison in selected complete chloroplast genomes? Which bioinformatics tools are necessary to build a phylogeny of numerous genes and species, etc?
+% 

diff --git a/Paper2/llncs.cls b/Paper2/llncs.cls
new file mode 100644 (file)
index 0000000..6e1806d
--- /dev/null
+++ b/Paper2/llncs.cls
@@ -0,0 +1,1208 @@
+% LLNCS DOCUMENT CLASS -- version 2.18 (27-Sep-2013)\r
+% Springer Verlag LaTeX2e support for Lecture Notes in Computer Science\r
+%\r
+%%\r
+%% \CharacterTable\r
+%%  {Upper-case    \A\B\C\D\E\F\G\H\I\J\K\L\M\N\O\P\Q\R\S\T\U\V\W\X\Y\Z\r
+%%   Lower-case    \a\b\c\d\e\f\g\h\i\j\k\l\m\n\o\p\q\r\s\t\u\v\w\x\y\z\r
+%%   Digits        \0\1\2\3\4\5\6\7\8\9\r
+%%   Exclamation   \!     Double quote  \"     Hash (number) \#\r
+%%   Dollar        \$     Percent       \%     Ampersand     \&\r
+%%   Acute accent  \'     Left paren    \(     Right paren   \)\r
+%%   Asterisk      \*     Plus          \+     Comma         \,\r
+%%   Minus         \-     Point         \.     Solidus       \/\r
+%%   Colon         \:     Semicolon     \;     Less than     \<\r
+%%   Equals        \=     Greater than  \>     Question mark \?\r
+%%   Commercial at \@     Left bracket  \[     Backslash     \\\r
+%%   Right bracket \]     Circumflex    \^     Underscore    \_\r
+%%   Grave accent  \`     Left brace    \{     Vertical bar  \|\r
+%%   Right brace   \}     Tilde         \~}\r
+%%\r
+\NeedsTeXFormat{LaTeX2e}[1995/12/01]\r
+\ProvidesClass{llncs}[2013/09/27 v2.18\r
+^^J LaTeX document class for Lecture Notes in Computer Science]\r
+% Options\r
+\let\if@envcntreset\iffalse\r
+\DeclareOption{envcountreset}{\let\if@envcntreset\iftrue}\r
+\DeclareOption{citeauthoryear}{\let\citeauthoryear=Y}\r
+\DeclareOption{oribibl}{\let\oribibl=Y}\r
+\let\if@custvec\iftrue\r
+\DeclareOption{orivec}{\let\if@custvec\iffalse}\r
+\let\if@envcntsame\iffalse\r
+\DeclareOption{envcountsame}{\let\if@envcntsame\iftrue}\r
+\let\if@envcntsect\iffalse\r
+\DeclareOption{envcountsect}{\let\if@envcntsect\iftrue}\r
+\let\if@runhead\iffalse\r
+\DeclareOption{runningheads}{\let\if@runhead\iftrue}\r
+\r
+\let\if@openright\iftrue\r
+\let\if@openbib\iffalse\r
+\DeclareOption{openbib}{\let\if@openbib\iftrue}\r
+\r
+% languages\r
+\let\switcht@@therlang\relax\r
+\def\ds@deutsch{\def\switcht@@therlang{\switcht@deutsch}}\r
+\def\ds@francais{\def\switcht@@therlang{\switcht@francais}}\r
+\r
+\DeclareOption*{\PassOptionsToClass{\CurrentOption}{article}}\r
+\r
+\ProcessOptions\r
+\r
+\LoadClass[twoside]{article}\r
+\RequirePackage{multicol} % needed for the list of participants, index\r
+\RequirePackage{aliascnt}\r
+\r
+\setlength{\textwidth}{12.2cm}\r
+\setlength{\textheight}{19.3cm}\r
+\renewcommand\@pnumwidth{2em}\r
+\renewcommand\@tocrmarg{3.5em}\r
+%\r
+\def\@dottedtocline#1#2#3#4#5{%\r
+  \ifnum #1>\c@tocdepth \else\r
+    \vskip \z@ \@plus.2\p@\r
+    {\leftskip #2\relax \rightskip \@tocrmarg \advance\rightskip by 0pt plus 2cm\r
+               \parfillskip -\rightskip \pretolerance=10000\r
+     \parindent #2\relax\@afterindenttrue\r
+     \interlinepenalty\@M\r
+     \leavevmode\r
+     \@tempdima #3\relax\r
+     \advance\leftskip \@tempdima \null\nobreak\hskip -\leftskip\r
+     {#4}\nobreak\r
+     \leaders\hbox{$\m@th\r
+        \mkern \@dotsep mu\hbox{.}\mkern \@dotsep\r
+        mu$}\hfill\r
+     \nobreak\r
+     \hb@xt@\@pnumwidth{\hfil\normalfont \normalcolor #5}%\r
+     \par}%\r
+  \fi}\r
+%\r
+\def\switcht@albion{%\r
+\def\abstractname{Abstract.}\r
+\def\ackname{Acknowledgement.}\r
+\def\andname{and}\r
+\def\lastandname{\unskip, and}\r
+\def\appendixname{Appendix}\r
+\def\chaptername{Chapter}\r
+\def\claimname{Claim}\r
+\def\conjecturename{Conjecture}\r
+\def\contentsname{Table of Contents}\r
+\def\corollaryname{Corollary}\r
+\def\definitionname{Definition}\r
+\def\examplename{Example}\r
+\def\exercisename{Exercise}\r
+\def\figurename{Fig.}\r
+\def\keywordname{{\bf Keywords:}}\r
+\def\indexname{Index}\r
+\def\lemmaname{Lemma}\r
+\def\contriblistname{List of Contributors}\r
+\def\listfigurename{List of Figures}\r
+\def\listtablename{List of Tables}\r
+\def\mailname{{\it Correspondence to\/}:}\r
+\def\noteaddname{Note added in proof}\r
+\def\notename{Note}\r
+\def\partname{Part}\r
+\def\problemname{Problem}\r
+\def\proofname{Proof}\r
+\def\propertyname{Property}\r
+\def\propositionname{Proposition}\r
+\def\questionname{Question}\r
+\def\remarkname{Remark}\r
+\def\seename{see}\r
+\def\solutionname{Solution}\r
+\def\subclassname{{\it Subject Classifications\/}:}\r
+\def\tablename{Table}\r
+\def\theoremname{Theorem}}\r
+\switcht@albion\r
+% Names of theorem like environments are already defined\r
+% but must be translated if another language is chosen\r
+%\r
+% French section\r
+\def\switcht@francais{%\typeout{On parle francais.}%\r
+ \def\abstractname{R\'esum\'e.}%\r
+ \def\ackname{Remerciements.}%\r
+ \def\andname{et}%\r
+ \def\lastandname{ et}%\r
+ \def\appendixname{Appendice}\r
+ \def\chaptername{Chapitre}%\r
+ \def\claimname{Pr\'etention}%\r
+ \def\conjecturename{Hypoth\`ese}%\r
+ \def\contentsname{Table des mati\`eres}%\r
+ \def\corollaryname{Corollaire}%\r
+ \def\definitionname{D\'efinition}%\r
+ \def\examplename{Exemple}%\r
+ \def\exercisename{Exercice}%\r
+ \def\figurename{Fig.}%\r
+ \def\keywordname{{\bf Mots-cl\'e:}}\r
+ \def\indexname{Index}\r
+ \def\lemmaname{Lemme}%\r
+ \def\contriblistname{Liste des contributeurs}\r
+ \def\listfigurename{Liste des figures}%\r
+ \def\listtablename{Liste des tables}%\r
+ \def\mailname{{\it Correspondence to\/}:}\r
+ \def\noteaddname{Note ajout\'ee \`a l'\'epreuve}%\r
+ \def\notename{Remarque}%\r
+ \def\partname{Partie}%\r
+ \def\problemname{Probl\`eme}%\r
+ \def\proofname{Preuve}%\r
+ \def\propertyname{Caract\'eristique}%\r
+%\def\propositionname{Proposition}%\r
+ \def\questionname{Question}%\r
+ \def\remarkname{Remarque}%\r
+ \def\seename{voir}\r
+ \def\solutionname{Solution}%\r
+ \def\subclassname{{\it Subject Classifications\/}:}\r
+ \def\tablename{Tableau}%\r
+ \def\theoremname{Th\'eor\`eme}%\r
+}\r
+%\r
+% German section\r
+\def\switcht@deutsch{%\typeout{Man spricht deutsch.}%\r
+ \def\abstractname{Zusammenfassung.}%\r
+ \def\ackname{Danksagung.}%\r
+ \def\andname{und}%\r
+ \def\lastandname{ und}%\r
+ \def\appendixname{Anhang}%\r
+ \def\chaptername{Kapitel}%\r
+ \def\claimname{Behauptung}%\r
+ \def\conjecturename{Hypothese}%\r
+ \def\contentsname{Inhaltsverzeichnis}%\r
+ \def\corollaryname{Korollar}%\r
+%\def\definitionname{Definition}%\r
+ \def\examplename{Beispiel}%\r
+ \def\exercisename{\"Ubung}%\r
+ \def\figurename{Abb.}%\r
+ \def\keywordname{{\bf Schl\"usselw\"orter:}}\r
+ \def\indexname{Index}\r
+%\def\lemmaname{Lemma}%\r
+ \def\contriblistname{Mitarbeiter}\r
+ \def\listfigurename{Abbildungsverzeichnis}%\r
+ \def\listtablename{Tabellenverzeichnis}%\r
+ \def\mailname{{\it Correspondence to\/}:}\r
+ \def\noteaddname{Nachtrag}%\r
+ \def\notename{Anmerkung}%\r
+ \def\partname{Teil}%\r
+%\def\problemname{Problem}%\r
+ \def\proofname{Beweis}%\r
+ \def\propertyname{Eigenschaft}%\r
+%\def\propositionname{Proposition}%\r
+ \def\questionname{Frage}%\r
+ \def\remarkname{Anmerkung}%\r
+ \def\seename{siehe}\r
+ \def\solutionname{L\"osung}%\r
+ \def\subclassname{{\it Subject Classifications\/}:}\r
+ \def\tablename{Tabelle}%\r
+%\def\theoremname{Theorem}%\r
+}\r
+\r
+% Ragged bottom for the actual page\r
+\def\thisbottomragged{\def\@textbottom{\vskip\z@ plus.0001fil\r
+\global\let\@textbottom\relax}}\r
+\r
+\renewcommand\small{%\r
+   \@setfontsize\small\@ixpt{11}%\r
+   \abovedisplayskip 8.5\p@ \@plus3\p@ \@minus4\p@\r
+   \abovedisplayshortskip \z@ \@plus2\p@\r
+   \belowdisplayshortskip 4\p@ \@plus2\p@ \@minus2\p@\r
+   \def\@listi{\leftmargin\leftmargini\r
+               \parsep 0\p@ \@plus1\p@ \@minus\p@\r
+               \topsep 8\p@ \@plus2\p@ \@minus4\p@\r
+               \itemsep0\p@}%\r
+   \belowdisplayskip \abovedisplayskip\r
+}\r
+\r
+\frenchspacing\r
+\widowpenalty=10000\r
+\clubpenalty=10000\r
+\r
+\setlength\oddsidemargin   {63\p@}\r
+\setlength\evensidemargin  {63\p@}\r
+\setlength\marginparwidth  {90\p@}\r
+\r
+\setlength\headsep   {16\p@}\r
+\r
+\setlength\footnotesep{7.7\p@}\r
+\setlength\textfloatsep{8mm\@plus 2\p@ \@minus 4\p@}\r
+\setlength\intextsep   {8mm\@plus 2\p@ \@minus 2\p@}\r
+\r
+\setcounter{secnumdepth}{2}\r
+\r
+\newcounter {chapter}\r
+\renewcommand\thechapter      {\@arabic\c@chapter}\r
+\r
+\newif\if@mainmatter \@mainmattertrue\r
+\newcommand\frontmatter{\cleardoublepage\r
+            \@mainmatterfalse\pagenumbering{Roman}}\r
+\newcommand\mainmatter{\cleardoublepage\r
+       \@mainmattertrue\pagenumbering{arabic}}\r
+\newcommand\backmatter{\if@openright\cleardoublepage\else\clearpage\fi\r
+      \@mainmatterfalse}\r
+\r
+\renewcommand\part{\cleardoublepage\r
+                 \thispagestyle{empty}%\r
+                 \if@twocolumn\r
+                     \onecolumn\r
+                     \@tempswatrue\r
+                   \else\r
+                     \@tempswafalse\r
+                 \fi\r
+                 \null\vfil\r
+                 \secdef\@part\@spart}\r
+\r
+\def\@part[#1]#2{%\r
+    \ifnum \c@secnumdepth >-2\relax\r
+      \refstepcounter{part}%\r
+      \addcontentsline{toc}{part}{\thepart\hspace{1em}#1}%\r
+    \else\r
+      \addcontentsline{toc}{part}{#1}%\r
+    \fi\r
+    \markboth{}{}%\r
+    {\centering\r
+     \interlinepenalty \@M\r
+     \normalfont\r
+     \ifnum \c@secnumdepth >-2\relax\r
+       \huge\bfseries \partname~\thepart\r
+       \par\r
+       \vskip 20\p@\r
+     \fi\r
+     \Huge \bfseries #2\par}%\r
+    \@endpart}\r
+\def\@spart#1{%\r
+    {\centering\r
+     \interlinepenalty \@M\r
+     \normalfont\r
+     \Huge \bfseries #1\par}%\r
+    \@endpart}\r
+\def\@endpart{\vfil\newpage\r
+              \if@twoside\r
+                \null\r
+                \thispagestyle{empty}%\r
+                \newpage\r
+              \fi\r
+              \if@tempswa\r
+                \twocolumn\r
+              \fi}\r
+\r
+\newcommand\chapter{\clearpage\r
+                    \thispagestyle{empty}%\r
+                    \global\@topnum\z@\r
+                    \@afterindentfalse\r
+                    \secdef\@chapter\@schapter}\r
+\def\@chapter[#1]#2{\ifnum \c@secnumdepth >\m@ne\r
+                       \if@mainmatter\r
+                         \refstepcounter{chapter}%\r
+                         \typeout{\@chapapp\space\thechapter.}%\r
+                         \addcontentsline{toc}{chapter}%\r
+                                  {\protect\numberline{\thechapter}#1}%\r
+                       \else\r
+                         \addcontentsline{toc}{chapter}{#1}%\r
+                       \fi\r
+                    \else\r
+                      \addcontentsline{toc}{chapter}{#1}%\r
+                    \fi\r
+                    \chaptermark{#1}%\r
+                    \addtocontents{lof}{\protect\addvspace{10\p@}}%\r
+                    \addtocontents{lot}{\protect\addvspace{10\p@}}%\r
+                    \if@twocolumn\r
+                      \@topnewpage[\@makechapterhead{#2}]%\r
+                    \else\r
+                      \@makechapterhead{#2}%\r
+                      \@afterheading\r
+                    \fi}\r
+\def\@makechapterhead#1{%\r
+% \vspace*{50\p@}%\r
+  {\centering\r
+    \ifnum \c@secnumdepth >\m@ne\r
+      \if@mainmatter\r
+        \large\bfseries \@chapapp{} \thechapter\r
+        \par\nobreak\r
+        \vskip 20\p@\r
+      \fi\r
+    \fi\r
+    \interlinepenalty\@M\r
+    \Large \bfseries #1\par\nobreak\r
+    \vskip 40\p@\r
+  }}\r
+\def\@schapter#1{\if@twocolumn\r
+                   \@topnewpage[\@makeschapterhead{#1}]%\r
+                 \else\r
+                   \@makeschapterhead{#1}%\r
+                   \@afterheading\r
+                 \fi}\r
+\def\@makeschapterhead#1{%\r
+% \vspace*{50\p@}%\r
+  {\centering\r
+    \normalfont\r
+    \interlinepenalty\@M\r
+    \Large \bfseries  #1\par\nobreak\r
+    \vskip 40\p@\r
+  }}\r
+\r
+\renewcommand\section{\@startsection{section}{1}{\z@}%\r
+                       {-18\p@ \@plus -4\p@ \@minus -4\p@}%\r
+                       {12\p@ \@plus 4\p@ \@minus 4\p@}%\r
+                       {\normalfont\large\bfseries\boldmath\r
+                        \rightskip=\z@ \@plus 8em\pretolerance=10000 }}\r
+\renewcommand\subsection{\@startsection{subsection}{2}{\z@}%\r
+                       {-18\p@ \@plus -4\p@ \@minus -4\p@}%\r
+                       {8\p@ \@plus 4\p@ \@minus 4\p@}%\r
+                       {\normalfont\normalsize\bfseries\boldmath\r
+                        \rightskip=\z@ \@plus 8em\pretolerance=10000 }}\r
+\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}%\r
+                       {-18\p@ \@plus -4\p@ \@minus -4\p@}%\r
+                       {-0.5em \@plus -0.22em \@minus -0.1em}%\r
+                       {\normalfont\normalsize\bfseries\boldmath}}\r
+\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}%\r
+                       {-12\p@ \@plus -4\p@ \@minus -4\p@}%\r
+                       {-0.5em \@plus -0.22em \@minus -0.1em}%\r
+                       {\normalfont\normalsize\itshape}}\r
+\renewcommand\subparagraph[1]{\typeout{LLNCS warning: You should not use\r
+                  \string\subparagraph\space with this class}\vskip0.5cm\r
+You should not use \verb|\subparagraph| with this class.\vskip0.5cm}\r
+\r
+\DeclareMathSymbol{\Gamma}{\mathalpha}{letters}{"00}\r
+\DeclareMathSymbol{\Delta}{\mathalpha}{letters}{"01}\r
+\DeclareMathSymbol{\Theta}{\mathalpha}{letters}{"02}\r
+\DeclareMathSymbol{\Lambda}{\mathalpha}{letters}{"03}\r
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+\DeclareMathSymbol{\Phi}{\mathalpha}{letters}{"08}\r
+\DeclareMathSymbol{\Psi}{\mathalpha}{letters}{"09}\r
+\DeclareMathSymbol{\Omega}{\mathalpha}{letters}{"0A}\r
+\r
+\let\footnotesize\small\r
+\r
+\if@custvec\r
+\def\vec#1{\mathchoice{\mbox{\boldmath$\displaystyle#1$}}\r
+{\mbox{\boldmath$\textstyle#1$}}\r
+{\mbox{\boldmath$\scriptstyle#1$}}\r
+{\mbox{\boldmath$\scriptscriptstyle#1$}}}\r
+\fi\r
+\r
+\def\squareforqed{\hbox{\rlap{$\sqcap$}$\sqcup$}}\r
+\def\qed{\ifmmode\squareforqed\else{\unskip\nobreak\hfil\r
+\penalty50\hskip1em\null\nobreak\hfil\squareforqed\r
+\parfillskip=0pt\finalhyphendemerits=0\endgraf}\fi}\r
+\r
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+\halign{\hfil\r
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+{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr\gets\r
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+{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr\r
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+{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr<\cr\r
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+{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr\r
+<\cr\r
+\noalign{\vskip0.9pt}=\cr}}}}}\r
+\def\gid{\mathrel{\mathchoice {\vcenter{\offinterlineskip\halign{\hfil\r
+$\displaystyle##$\hfil\cr>\cr\noalign{\vskip1.2pt}=\cr}}}\r
+{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr>\cr\r
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+{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr\r
+>\cr\r
+\noalign{\vskip0.9pt}=\cr}}}}}\r
+\def\grole{\mathrel{\mathchoice {\vcenter{\offinterlineskip\r
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+{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr\r
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+{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr\r
+>\cr\noalign{\vskip-0.3pt}<\cr}}}}}\r
+\def\bbbr{{\rm I\!R}} %reelle Zahlen\r
+\def\bbbm{{\rm I\!M}}\r
+\def\bbbn{{\rm I\!N}} %natuerliche Zahlen\r
+\def\bbbf{{\rm I\!F}}\r
+\def\bbbh{{\rm I\!H}}\r
+\def\bbbk{{\rm I\!K}}\r
+\def\bbbp{{\rm I\!P}}\r
+\def\bbbone{{\mathchoice {\rm 1\mskip-4mu l} {\rm 1\mskip-4mu l}\r
+{\rm 1\mskip-4.5mu l} {\rm 1\mskip-5mu l}}}\r
+\def\bbbc{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm C$}\hbox{\hbox\r
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+{\setbox0=\hbox{$\scriptscriptstyle\rm C$}\hbox{\hbox\r
+to0pt{\kern0.4\wd0\vrule height0.9\ht0\hss}\box0}}}}\r
+\def\bbbq{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm\r
+Q$}\hbox{\raise\r
+0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.8\ht0\hss}\box0}}\r
+{\setbox0=\hbox{$\textstyle\rm Q$}\hbox{\raise\r
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+{\setbox0=\hbox{$\scriptstyle\rm Q$}\hbox{\raise\r
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+{\setbox0=\hbox{$\scriptscriptstyle\rm Q$}\hbox{\raise\r
+0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.7\ht0\hss}\box0}}}}\r
+\def\bbbt{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm\r
+T$}\hbox{\hbox to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}}\r
+{\setbox0=\hbox{$\textstyle\rm T$}\hbox{\hbox\r
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+{\setbox0=\hbox{$\scriptstyle\rm T$}\hbox{\hbox\r
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+{\setbox0=\hbox{$\scriptscriptstyle\rm T$}\hbox{\hbox\r
+to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}}}}\r
+\def\bbbs{{\mathchoice\r
+{\setbox0=\hbox{$\displaystyle     \rm S$}\hbox{\raise0.5\ht0\hbox\r
+to0pt{\kern0.35\wd0\vrule height0.45\ht0\hss}\hbox\r
+to0pt{\kern0.55\wd0\vrule height0.5\ht0\hss}\box0}}\r
+{\setbox0=\hbox{$\textstyle        \rm S$}\hbox{\raise0.5\ht0\hbox\r
+to0pt{\kern0.35\wd0\vrule height0.45\ht0\hss}\hbox\r
+to0pt{\kern0.55\wd0\vrule height0.5\ht0\hss}\box0}}\r
+{\setbox0=\hbox{$\scriptstyle      \rm S$}\hbox{\raise0.5\ht0\hbox\r
+to0pt{\kern0.35\wd0\vrule height0.45\ht0\hss}\raise0.05\ht0\hbox\r
+to0pt{\kern0.5\wd0\vrule height0.45\ht0\hss}\box0}}\r
+{\setbox0=\hbox{$\scriptscriptstyle\rm S$}\hbox{\raise0.5\ht0\hbox\r
+to0pt{\kern0.4\wd0\vrule height0.45\ht0\hss}\raise0.05\ht0\hbox\r
+to0pt{\kern0.55\wd0\vrule height0.45\ht0\hss}\box0}}}}\r
+\def\bbbz{{\mathchoice {\hbox{$\mathsf\textstyle Z\kern-0.4em Z$}}\r
+{\hbox{$\mathsf\textstyle Z\kern-0.4em Z$}}\r
+{\hbox{$\mathsf\scriptstyle Z\kern-0.3em Z$}}\r
+{\hbox{$\mathsf\scriptscriptstyle Z\kern-0.2em Z$}}}}\r
+\r
+\let\ts\,\r
+\r
+\setlength\leftmargini  {17\p@}\r
+\setlength\leftmargin    {\leftmargini}\r
+\setlength\leftmarginii  {\leftmargini}\r
+\setlength\leftmarginiii {\leftmargini}\r
+\setlength\leftmarginiv  {\leftmargini}\r
+\setlength  \labelsep  {.5em}\r
+\setlength  \labelwidth{\leftmargini}\r
+\addtolength\labelwidth{-\labelsep}\r
+\r
+\def\@listI{\leftmargin\leftmargini\r
+            \parsep 0\p@ \@plus1\p@ \@minus\p@\r
+            \topsep 8\p@ \@plus2\p@ \@minus4\p@\r
+            \itemsep0\p@}\r
+\let\@listi\@listI\r
+\@listi\r
+\def\@listii {\leftmargin\leftmarginii\r
+              \labelwidth\leftmarginii\r
+              \advance\labelwidth-\labelsep\r
+              \topsep    0\p@ \@plus2\p@ \@minus\p@}\r
+\def\@listiii{\leftmargin\leftmarginiii\r
+              \labelwidth\leftmarginiii\r
+              \advance\labelwidth-\labelsep\r
+              \topsep    0\p@ \@plus\p@\@minus\p@\r
+              \parsep    \z@\r
+              \partopsep \p@ \@plus\z@ \@minus\p@}\r
+\r
+\renewcommand\labelitemi{\normalfont\bfseries --}\r
+\renewcommand\labelitemii{$\m@th\bullet$}\r
+\r
+\setlength\arraycolsep{1.4\p@}\r
+\setlength\tabcolsep{1.4\p@}\r
+\r
+\def\tableofcontents{\chapter*{\contentsname\@mkboth{{\contentsname}}%\r
+                                                    {{\contentsname}}}\r
+ \def\authcount##1{\setcounter{auco}{##1}\setcounter{@auth}{1}}\r
+ \def\lastand{\ifnum\value{auco}=2\relax\r
+                 \unskip{} \andname\\r
+              \else\r
+                 \unskip \lastandname\\r
+              \fi}%\r
+ \def\and{\stepcounter{@auth}\relax\r
+          \ifnum\value{@auth}=\value{auco}%\r
+             \lastand\r
+          \else\r
+             \unskip,\r
+          \fi}%\r
+ \@starttoc{toc}\if@restonecol\twocolumn\fi}\r
+\r
+\def\l@part#1#2{\addpenalty{\@secpenalty}%\r
+   \addvspace{2em plus\p@}%  % space above part line\r
+   \begingroup\r
+     \parindent \z@\r
+     \rightskip \z@ plus 5em\r
+     \hrule\vskip5pt\r
+     \large               % same size as for a contribution heading\r
+     \bfseries\boldmath   % set line in boldface\r
+     \leavevmode          % TeX command to enter horizontal mode.\r
+     #1\par\r
+     \vskip5pt\r
+     \hrule\r
+     \vskip1pt\r
+     \nobreak             % Never break after part entry\r
+   \endgroup}\r
+\r
+\def\@dotsep{2}\r
+\r
+\let\phantomsection=\relax\r
+\r
+\def\hyperhrefextend{\ifx\hyper@anchor\@undefined\else\r
+{}\fi}\r
+\r
+\def\addnumcontentsmark#1#2#3{%\r
+\addtocontents{#1}{\protect\contentsline{#2}{\protect\numberline\r
+                     {\thechapter}#3}{\thepage}\hyperhrefextend}}%\r
+\def\addcontentsmark#1#2#3{%\r
+\addtocontents{#1}{\protect\contentsline{#2}{#3}{\thepage}\hyperhrefextend}}%\r
+\def\addcontentsmarkwop#1#2#3{%\r
+\addtocontents{#1}{\protect\contentsline{#2}{#3}{0}\hyperhrefextend}}%\r
+\r
+\def\@adcmk[#1]{\ifcase #1 \or\r
+\def\@gtempa{\addnumcontentsmark}%\r
+  \or    \def\@gtempa{\addcontentsmark}%\r
+  \or    \def\@gtempa{\addcontentsmarkwop}%\r
+  \fi\@gtempa{toc}{chapter}%\r
+}\r
+\def\addtocmark{%\r
+\phantomsection\r
+\@ifnextchar[{\@adcmk}{\@adcmk[3]}%\r
+}\r
+\r
+\def\l@chapter#1#2{\addpenalty{-\@highpenalty}\r
+ \vskip 1.0em plus 1pt \@tempdima 1.5em \begingroup\r
+ \parindent \z@ \rightskip \@tocrmarg\r
+ \advance\rightskip by 0pt plus 2cm\r
+ \parfillskip -\rightskip \pretolerance=10000\r
+ \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip\r
+ {\large\bfseries\boldmath#1}\ifx0#2\hfil\null\r
+ \else\r
+      \nobreak\r
+      \leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern\r
+      \@dotsep mu$}\hfill\r
+      \nobreak\hbox to\@pnumwidth{\hss #2}%\r
+ \fi\par\r
+ \penalty\@highpenalty \endgroup}\r
+\r
+\def\l@title#1#2{\addpenalty{-\@highpenalty}\r
+ \addvspace{8pt plus 1pt}\r
+ \@tempdima \z@\r
+ \begingroup\r
+ \parindent \z@ \rightskip \@tocrmarg\r
+ \advance\rightskip by 0pt plus 2cm\r
+ \parfillskip -\rightskip \pretolerance=10000\r
+ \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip\r
+ #1\nobreak\r
+ \leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern\r
+ \@dotsep mu$}\hfill\r
+ \nobreak\hbox to\@pnumwidth{\hss #2}\par\r
+ \penalty\@highpenalty \endgroup}\r
+\r
+\def\l@author#1#2{\addpenalty{\@highpenalty}\r
+ \@tempdima=15\p@ %\z@\r
+ \begingroup\r
+ \parindent \z@ \rightskip \@tocrmarg\r
+ \advance\rightskip by 0pt plus 2cm\r
+ \pretolerance=10000\r
+ \leavevmode \advance\leftskip\@tempdima %\hskip -\leftskip\r
+ \textit{#1}\par\r
+ \penalty\@highpenalty \endgroup}\r
+\r
+\setcounter{tocdepth}{0}\r
+\newdimen\tocchpnum\r
+\newdimen\tocsecnum\r
+\newdimen\tocsectotal\r
+\newdimen\tocsubsecnum\r
+\newdimen\tocsubsectotal\r
+\newdimen\tocsubsubsecnum\r
+\newdimen\tocsubsubsectotal\r
+\newdimen\tocparanum\r
+\newdimen\tocparatotal\r
+\newdimen\tocsubparanum\r
+\tocchpnum=\z@            % no chapter numbers\r
+\tocsecnum=15\p@          % section 88. plus 2.222pt\r
+\tocsubsecnum=23\p@       % subsection 88.8 plus 2.222pt\r
+\tocsubsubsecnum=27\p@    % subsubsection 88.8.8 plus 1.444pt\r
+\tocparanum=35\p@         % paragraph 88.8.8.8 plus 1.666pt\r
+\tocsubparanum=43\p@      % subparagraph 88.8.8.8.8 plus 1.888pt\r
+\def\calctocindent{%\r
+\tocsectotal=\tocchpnum\r
+\advance\tocsectotal by\tocsecnum\r
+\tocsubsectotal=\tocsectotal\r
+\advance\tocsubsectotal by\tocsubsecnum\r
+\tocsubsubsectotal=\tocsubsectotal\r
+\advance\tocsubsubsectotal by\tocsubsubsecnum\r
+\tocparatotal=\tocsubsubsectotal\r
+\advance\tocparatotal by\tocparanum}\r
+\calctocindent\r
+\r
+\def\l@section{\@dottedtocline{1}{\tocchpnum}{\tocsecnum}}\r
+\def\l@subsection{\@dottedtocline{2}{\tocsectotal}{\tocsubsecnum}}\r
+\def\l@subsubsection{\@dottedtocline{3}{\tocsubsectotal}{\tocsubsubsecnum}}\r
+\def\l@paragraph{\@dottedtocline{4}{\tocsubsubsectotal}{\tocparanum}}\r
+\def\l@subparagraph{\@dottedtocline{5}{\tocparatotal}{\tocsubparanum}}\r
+\r
+\def\listoffigures{\@restonecolfalse\if@twocolumn\@restonecoltrue\onecolumn\r
+ \fi\section*{\listfigurename\@mkboth{{\listfigurename}}{{\listfigurename}}}\r
+ \@starttoc{lof}\if@restonecol\twocolumn\fi}\r
+\def\l@figure{\@dottedtocline{1}{0em}{1.5em}}\r
+\r
+\def\listoftables{\@restonecolfalse\if@twocolumn\@restonecoltrue\onecolumn\r
+ \fi\section*{\listtablename\@mkboth{{\listtablename}}{{\listtablename}}}\r
+ \@starttoc{lot}\if@restonecol\twocolumn\fi}\r
+\let\l@table\l@figure\r
+\r
+\renewcommand\listoffigures{%\r
+    \section*{\listfigurename\r
+      \@mkboth{\listfigurename}{\listfigurename}}%\r
+    \@starttoc{lof}%\r
+    }\r
+\r
+\renewcommand\listoftables{%\r
+    \section*{\listtablename\r
+      \@mkboth{\listtablename}{\listtablename}}%\r
+    \@starttoc{lot}%\r
+    }\r
+\r
+\ifx\oribibl\undefined\r
+\ifx\citeauthoryear\undefined\r
+\renewenvironment{thebibliography}[1]\r
+     {\section*{\refname}\r
+      \def\@biblabel##1{##1.}\r
+      \small\r
+      \list{\@biblabel{\@arabic\c@enumiv}}%\r
+           {\settowidth\labelwidth{\@biblabel{#1}}%\r
+            \leftmargin\labelwidth\r
+            \advance\leftmargin\labelsep\r
+            \if@openbib\r
+              \advance\leftmargin\bibindent\r
+              \itemindent -\bibindent\r
+              \listparindent \itemindent\r
+              \parsep \z@\r
+            \fi\r
+            \usecounter{enumiv}%\r
+            \let\p@enumiv\@empty\r
+            \renewcommand\theenumiv{\@arabic\c@enumiv}}%\r
+      \if@openbib\r
+        \renewcommand\newblock{\par}%\r
+      \else\r
+        \renewcommand\newblock{\hskip .11em \@plus.33em \@minus.07em}%\r
+      \fi\r
+      \sloppy\clubpenalty4000\widowpenalty4000%\r
+      \sfcode`\.=\@m}\r
+     {\def\@noitemerr\r
+       {\@latex@warning{Empty `thebibliography' environment}}%\r
+      \endlist}\r
+\def\@lbibitem[#1]#2{\item[{[#1]}\hfill]\if@filesw\r
+     {\let\protect\noexpand\immediate\r
+     \write\@auxout{\string\bibcite{#2}{#1}}}\fi\ignorespaces}\r
+\newcount\@tempcntc\r
+\def\@citex[#1]#2{\if@filesw\immediate\write\@auxout{\string\citation{#2}}\fi\r
+  \@tempcnta\z@\@tempcntb\m@ne\def\@citea{}\@cite{\@for\@citeb:=#2\do\r
+    {\@ifundefined\r
+       {b@\@citeb}{\@citeo\@tempcntb\m@ne\@citea\def\@citea{,}{\bfseries\r
+        ?}\@warning\r
+       {Citation `\@citeb' on page \thepage \space undefined}}%\r
+    {\setbox\z@\hbox{\global\@tempcntc0\csname b@\@citeb\endcsname\relax}%\r
+     \ifnum\@tempcntc=\z@ \@citeo\@tempcntb\m@ne\r
+       \@citea\def\@citea{,}\hbox{\csname b@\@citeb\endcsname}%\r
+     \else\r
+      \advance\@tempcntb\@ne\r
+      \ifnum\@tempcntb=\@tempcntc\r
+      \else\advance\@tempcntb\m@ne\@citeo\r
+      \@tempcnta\@tempcntc\@tempcntb\@tempcntc\fi\fi}}\@citeo}{#1}}\r
+\def\@citeo{\ifnum\@tempcnta>\@tempcntb\else\r
+               \@citea\def\@citea{,\,\hskip\z@skip}%\r
+               \ifnum\@tempcnta=\@tempcntb\the\@tempcnta\else\r
+               {\advance\@tempcnta\@ne\ifnum\@tempcnta=\@tempcntb \else\r
+                \def\@citea{--}\fi\r
+      \advance\@tempcnta\m@ne\the\@tempcnta\@citea\the\@tempcntb}\fi\fi}\r
+\else\r
+\renewenvironment{thebibliography}[1]\r
+     {\section*{\refname}\r
+      \small\r
+      \list{}%\r
+           {\settowidth\labelwidth{}%\r
+            \leftmargin\parindent\r
+            \itemindent=-\parindent\r
+            \labelsep=\z@\r
+            \if@openbib\r
+              \advance\leftmargin\bibindent\r
+              \itemindent -\bibindent\r
+              \listparindent \itemindent\r
+              \parsep \z@\r
+            \fi\r
+            \usecounter{enumiv}%\r
+            \let\p@enumiv\@empty\r
+            \renewcommand\theenumiv{}}%\r
+      \if@openbib\r
+        \renewcommand\newblock{\par}%\r
+      \else\r
+        \renewcommand\newblock{\hskip .11em \@plus.33em \@minus.07em}%\r
+      \fi\r
+      \sloppy\clubpenalty4000\widowpenalty4000%\r
+      \sfcode`\.=\@m}\r
+     {\def\@noitemerr\r
+       {\@latex@warning{Empty `thebibliography' environment}}%\r
+      \endlist}\r
+      \def\@cite#1{#1}%\r
+      \def\@lbibitem[#1]#2{\item[]\if@filesw\r
+        {\def\protect##1{\string ##1\space}\immediate\r
+      \write\@auxout{\string\bibcite{#2}{#1}}}\fi\ignorespaces}\r
+   \fi\r
+\else\r
+\@cons\@openbib@code{\noexpand\small}\r
+\fi\r
+\r
+\def\idxquad{\hskip 10\p@}% space that divides entry from number\r
+\r
+\def\@idxitem{\par\hangindent 10\p@}\r
+\r
+\def\subitem{\par\setbox0=\hbox{--\enspace}% second order\r
+                \noindent\hangindent\wd0\box0}% index entry\r
+\r
+\def\subsubitem{\par\setbox0=\hbox{--\,--\enspace}% third\r
+                \noindent\hangindent\wd0\box0}% order index entry\r
+\r
+\def\indexspace{\par \vskip 10\p@ plus5\p@ minus3\p@\relax}\r
+\r
+\renewenvironment{theindex}\r
+               {\@mkboth{\indexname}{\indexname}%\r
+                \thispagestyle{empty}\parindent\z@\r
+                \parskip\z@ \@plus .3\p@\relax\r
+                \let\item\par\r
+                \def\,{\relax\ifmmode\mskip\thinmuskip\r
+                             \else\hskip0.2em\ignorespaces\fi}%\r
+                \normalfont\small\r
+                \begin{multicols}{2}[\@makeschapterhead{\indexname}]%\r
+                }\r
+                {\end{multicols}}\r
+\r
+\renewcommand\footnoterule{%\r
+  \kern-3\p@\r
+  \hrule\@width 2truecm\r
+  \kern2.6\p@}\r
+  \newdimen\fnindent\r
+  \fnindent1em\r
+\long\def\@makefntext#1{%\r
+    \parindent \fnindent%\r
+    \leftskip \fnindent%\r
+    \noindent\r
+    \llap{\hb@xt@1em{\hss\@makefnmark\ }}\ignorespaces#1}\r
+\r
+\long\def\@makecaption#1#2{%\r
+  \small\r
+  \vskip\abovecaptionskip\r
+  \sbox\@tempboxa{{\bfseries #1.} #2}%\r
+  \ifdim \wd\@tempboxa >\hsize\r
+    {\bfseries #1.} #2\par\r
+  \else\r
+    \global \@minipagefalse\r
+    \hb@xt@\hsize{\hfil\box\@tempboxa\hfil}%\r
+  \fi\r
+  \vskip\belowcaptionskip}\r
+\r
+\def\fps@figure{htbp}\r
+\def\fnum@figure{\figurename\thinspace\thefigure}\r
+\def \@floatboxreset {%\r
+        \reset@font\r
+        \small\r
+        \@setnobreak\r
+        \@setminipage\r
+}\r
+\def\fps@table{htbp}\r
+\def\fnum@table{\tablename~\thetable}\r
+\renewenvironment{table}\r
+               {\setlength\abovecaptionskip{0\p@}%\r
+                \setlength\belowcaptionskip{10\p@}%\r
+                \@float{table}}\r
+               {\end@float}\r
+\renewenvironment{table*}\r
+               {\setlength\abovecaptionskip{0\p@}%\r
+                \setlength\belowcaptionskip{10\p@}%\r
+                \@dblfloat{table}}\r
+               {\end@dblfloat}\r
+\r
+\long\def\@caption#1[#2]#3{\par\addcontentsline{\csname\r
+  ext@#1\endcsname}{#1}{\protect\numberline{\csname\r
+  the#1\endcsname}{\ignorespaces #2}}\begingroup\r
+    \@parboxrestore\r
+    \@makecaption{\csname fnum@#1\endcsname}{\ignorespaces #3}\par\r
+  \endgroup}\r
+\r
+% LaTeX does not provide a command to enter the authors institute\r
+% addresses. The \institute command is defined here.\r
+\r
+\newcounter{@inst}\r
+\newcounter{@auth}\r
+\newcounter{auco}\r
+\newdimen\instindent\r
+\newbox\authrun\r
+\newtoks\authorrunning\r
+\newtoks\tocauthor\r
+\newbox\titrun\r
+\newtoks\titlerunning\r
+\newtoks\toctitle\r
+\r
+\def\clearheadinfo{\gdef\@author{No Author Given}%\r
+                   \gdef\@title{No Title Given}%\r
+                   \gdef\@subtitle{}%\r
+                   \gdef\@institute{No Institute Given}%\r
+                   \gdef\@thanks{}%\r
+                   \global\titlerunning={}\global\authorrunning={}%\r
+                   \global\toctitle={}\global\tocauthor={}}\r
+\r
+\def\institute#1{\gdef\@institute{#1}}\r
+\r
+\def\institutename{\par\r
+ \begingroup\r
+ \parskip=\z@\r
+ \parindent=\z@\r
+ \setcounter{@inst}{1}%\r
+ \def\and{\par\stepcounter{@inst}%\r
+ \noindent$^{\the@inst}$\enspace\ignorespaces}%\r
+ \setbox0=\vbox{\def\thanks##1{}\@institute}%\r
+ \ifnum\c@@inst=1\relax\r
+   \gdef\fnnstart{0}%\r
+ \else\r
+   \xdef\fnnstart{\c@@inst}%\r
+   \setcounter{@inst}{1}%\r
+   \noindent$^{\the@inst}$\enspace\r
+ \fi\r
+ \ignorespaces\r
+ \@institute\par\r
+ \endgroup}\r
+\r
+\def\@fnsymbol#1{\ensuremath{\ifcase#1\or\star\or{\star\star}\or\r
+   {\star\star\star}\or \dagger\or \ddagger\or\r
+   \mathchar "278\or \mathchar "27B\or \|\or **\or \dagger\dagger\r
+   \or \ddagger\ddagger \else\@ctrerr\fi}}\r
+\r
+\def\inst#1{\unskip$^{#1}$}\r
+\def\fnmsep{\unskip$^,$}\r
+\def\email#1{{\tt#1}}\r
+\AtBeginDocument{\@ifundefined{url}{\def\url#1{#1}}{}%\r
+\@ifpackageloaded{babel}{%\r
+\@ifundefined{extrasenglish}{}{\addto\extrasenglish{\switcht@albion}}%\r
+\@ifundefined{extrasfrenchb}{}{\addto\extrasfrenchb{\switcht@francais}}%\r
+\@ifundefined{extrasgerman}{}{\addto\extrasgerman{\switcht@deutsch}}%\r
+\@ifundefined{extrasngerman}{}{\addto\extrasngerman{\switcht@deutsch}}%\r
+}{\switcht@@therlang}%\r
+\providecommand{\keywords}[1]{\par\addvspace\baselineskip\r
+\noindent\keywordname\enspace\ignorespaces#1}%\r
+}\r
+\def\homedir{\~{ }}\r
+\r
+\def\subtitle#1{\gdef\@subtitle{#1}}\r
+\clearheadinfo\r
+%\r
+%%% to avoid hyperref warnings\r
+\providecommand*{\toclevel@author}{999}\r
+%%% to make title-entry parent of section-entries\r
+\providecommand*{\toclevel@title}{0}\r
+%\r
+\renewcommand\maketitle{\newpage\r
+\phantomsection\r
+  \refstepcounter{chapter}%\r
+  \stepcounter{section}%\r
+  \setcounter{section}{0}%\r
+  \setcounter{subsection}{0}%\r
+  \setcounter{figure}{0}\r
+  \setcounter{table}{0}\r
+  \setcounter{equation}{0}\r
+  \setcounter{footnote}{0}%\r
+  \begingroup\r
+    \parindent=\z@\r
+    \renewcommand\thefootnote{\@fnsymbol\c@footnote}%\r
+    \if@twocolumn\r
+      \ifnum \col@number=\@ne\r
+        \@maketitle\r
+      \else\r
+        \twocolumn[\@maketitle]%\r
+      \fi\r
+    \else\r
+      \newpage\r
+      \global\@topnum\z@   % Prevents figures from going at top of page.\r
+      \@maketitle\r
+    \fi\r
+    \thispagestyle{empty}\@thanks\r
+%\r
+    \def\\{\unskip\ \ignorespaces}\def\inst##1{\unskip{}}%\r
+    \def\thanks##1{\unskip{}}\def\fnmsep{\unskip}%\r
+    \instindent=\hsize\r
+    \advance\instindent by-\headlineindent\r
+    \if!\the\toctitle!\addcontentsline{toc}{title}{\@title}\else\r
+       \addcontentsline{toc}{title}{\the\toctitle}\fi\r
+    \if@runhead\r
+       \if!\the\titlerunning!\else\r
+         \edef\@title{\the\titlerunning}%\r
+       \fi\r
+       \global\setbox\titrun=\hbox{\small\rm\unboldmath\ignorespaces\@title}%\r
+       \ifdim\wd\titrun>\instindent\r
+          \typeout{Title too long for running head. Please supply}%\r
+          \typeout{a shorter form with \string\titlerunning\space prior to\r
+                   \string\maketitle}%\r
+          \global\setbox\titrun=\hbox{\small\rm\r
+          Title Suppressed Due to Excessive Length}%\r
+       \fi\r
+       \xdef\@title{\copy\titrun}%\r
+    \fi\r
+%\r
+    \if!\the\tocauthor!\relax\r
+      {\def\and{\noexpand\protect\noexpand\and}%\r
+      \protected@xdef\toc@uthor{\@author}}%\r
+    \else\r
+      \def\\{\noexpand\protect\noexpand\newline}%\r
+      \protected@xdef\scratch{\the\tocauthor}%\r
+      \protected@xdef\toc@uthor{\scratch}%\r
+    \fi\r
+    \addtocontents{toc}{\noexpand\protect\noexpand\authcount{\the\c@auco}}%\r
+    \addcontentsline{toc}{author}{\toc@uthor}%\r
+    \if@runhead\r
+       \if!\the\authorrunning!\r
+         \value{@inst}=\value{@auth}%\r
+         \setcounter{@auth}{1}%\r
+       \else\r
+         \edef\@author{\the\authorrunning}%\r
+       \fi\r
+       \global\setbox\authrun=\hbox{\small\unboldmath\@author\unskip}%\r
+       \ifdim\wd\authrun>\instindent\r
+          \typeout{Names of authors too long for running head. Please supply}%\r
+          \typeout{a shorter form with \string\authorrunning\space prior to\r
+                   \string\maketitle}%\r
+          \global\setbox\authrun=\hbox{\small\rm\r
+          Authors Suppressed Due to Excessive Length}%\r
+       \fi\r
+       \xdef\@author{\copy\authrun}%\r
+       \markboth{\@author}{\@title}%\r
+     \fi\r
+  \endgroup\r
+  \setcounter{footnote}{\fnnstart}%\r
+  \clearheadinfo}\r
+%\r
+\def\@maketitle{\newpage\r
+ \markboth{}{}%\r
+ \def\lastand{\ifnum\value{@inst}=2\relax\r
+                 \unskip{} \andname\\r
+              \else\r
+                 \unskip \lastandname\\r
+              \fi}%\r
+ \def\and{\stepcounter{@auth}\relax\r
+          \ifnum\value{@auth}=\value{@inst}%\r
+             \lastand\r
+          \else\r
+             \unskip,\r
+          \fi}%\r
+ \begin{center}%\r
+ \let\newline\\\r
+ {\Large \bfseries\boldmath\r
+  \pretolerance=10000\r
+  \@title \par}\vskip .8cm\r
+\if!\@subtitle!\else {\large \bfseries\boldmath\r
+  \vskip -.65cm\r
+  \pretolerance=10000\r
+  \@subtitle \par}\vskip .8cm\fi\r
+ \setbox0=\vbox{\setcounter{@auth}{1}\def\and{\stepcounter{@auth}}%\r
+ \def\thanks##1{}\@author}%\r
+ \global\value{@inst}=\value{@auth}%\r
+ \global\value{auco}=\value{@auth}%\r
+ \setcounter{@auth}{1}%\r
+{\lineskip .5em\r
+\noindent\ignorespaces\r
+\@author\vskip.35cm}\r
+ {\small\institutename}\r
+ \end{center}%\r
+ }\r
+\r
+% definition of the "\spnewtheorem" command.\r
+%\r
+% Usage:\r
+%\r
+%     \spnewtheorem{env_nam}{caption}[within]{cap_font}{body_font}\r
+% or  \spnewtheorem{env_nam}[numbered_like]{caption}{cap_font}{body_font}\r
+% or  \spnewtheorem*{env_nam}{caption}{cap_font}{body_font}\r
+%\r
+% New is "cap_font" and "body_font". It stands for\r
+% fontdefinition of the caption and the text itself.\r
+%\r
+% "\spnewtheorem*" gives a theorem without number.\r
+%\r
+% A defined spnewthoerem environment is used as described\r
+% by Lamport.\r
+%\r
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\r
+\r
+\def\@thmcountersep{}\r
+\def\@thmcounterend{.}\r
+\r
+\def\spnewtheorem{\@ifstar{\@sthm}{\@Sthm}}\r
+\r
+% definition of \spnewtheorem with number\r
+\r
+\def\@spnthm#1#2{%\r
+  \@ifnextchar[{\@spxnthm{#1}{#2}}{\@spynthm{#1}{#2}}}\r
+\def\@Sthm#1{\@ifnextchar[{\@spothm{#1}}{\@spnthm{#1}}}\r
+\r
+\def\@spxnthm#1#2[#3]#4#5{\expandafter\@ifdefinable\csname #1\endcsname\r
+   {\@definecounter{#1}\@addtoreset{#1}{#3}%\r
+   \expandafter\xdef\csname the#1\endcsname{\expandafter\noexpand\r
+     \csname the#3\endcsname \noexpand\@thmcountersep \@thmcounter{#1}}%\r
+   \expandafter\xdef\csname #1name\endcsname{#2}%\r
+   \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#4}{#5}}%\r
+                              \global\@namedef{end#1}{\@endtheorem}}}\r
+\r
+\def\@spynthm#1#2#3#4{\expandafter\@ifdefinable\csname #1\endcsname\r
+   {\@definecounter{#1}%\r
+   \expandafter\xdef\csname the#1\endcsname{\@thmcounter{#1}}%\r
+   \expandafter\xdef\csname #1name\endcsname{#2}%\r
+   \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#3}{#4}}%\r
+                               \global\@namedef{end#1}{\@endtheorem}}}\r
+\r
+\def\@spothm#1[#2]#3#4#5{%\r
+  \@ifundefined{c@#2}{\@latexerr{No theorem environment `#2' defined}\@eha}%\r
+  {\expandafter\@ifdefinable\csname #1\endcsname\r
+  {\newaliascnt{#1}{#2}%\r
+  \expandafter\xdef\csname #1name\endcsname{#3}%\r
+  \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#4}{#5}}%\r
+  \global\@namedef{end#1}{\@endtheorem}}}}\r
+\r
+\def\@spthm#1#2#3#4{\topsep 7\p@ \@plus2\p@ \@minus4\p@\r
+\refstepcounter{#1}%\r
+\@ifnextchar[{\@spythm{#1}{#2}{#3}{#4}}{\@spxthm{#1}{#2}{#3}{#4}}}\r
+\r
+\def\@spxthm#1#2#3#4{\@spbegintheorem{#2}{\csname the#1\endcsname}{#3}{#4}%\r
+                    \ignorespaces}\r
+\r
+\def\@spythm#1#2#3#4[#5]{\@spopargbegintheorem{#2}{\csname\r
+       the#1\endcsname}{#5}{#3}{#4}\ignorespaces}\r
+\r
+\def\@spbegintheorem#1#2#3#4{\trivlist\r
+                 \item[\hskip\labelsep{#3#1\ #2\@thmcounterend}]#4}\r
+\r
+\def\@spopargbegintheorem#1#2#3#4#5{\trivlist\r
+      \item[\hskip\labelsep{#4#1\ #2}]{#4(#3)\@thmcounterend\ }#5}\r
+\r
+% definition of \spnewtheorem* without number\r
+\r
+\def\@sthm#1#2{\@Ynthm{#1}{#2}}\r
+\r
+\def\@Ynthm#1#2#3#4{\expandafter\@ifdefinable\csname #1\endcsname\r
+   {\global\@namedef{#1}{\@Thm{\csname #1name\endcsname}{#3}{#4}}%\r
+    \expandafter\xdef\csname #1name\endcsname{#2}%\r
+    \global\@namedef{end#1}{\@endtheorem}}}\r
+\r
+\def\@Thm#1#2#3{\topsep 7\p@ \@plus2\p@ \@minus4\p@\r
+\@ifnextchar[{\@Ythm{#1}{#2}{#3}}{\@Xthm{#1}{#2}{#3}}}\r
+\r
+\def\@Xthm#1#2#3{\@Begintheorem{#1}{#2}{#3}\ignorespaces}\r
+\r
+\def\@Ythm#1#2#3[#4]{\@Opargbegintheorem{#1}\r
+       {#4}{#2}{#3}\ignorespaces}\r
+\r
+\def\@Begintheorem#1#2#3{#3\trivlist\r
+                           \item[\hskip\labelsep{#2#1\@thmcounterend}]}\r
+\r
+\def\@Opargbegintheorem#1#2#3#4{#4\trivlist\r
+      \item[\hskip\labelsep{#3#1}]{#3(#2)\@thmcounterend\ }}\r
+\r
+\if@envcntsect\r
+   \def\@thmcountersep{.}\r
+   \spnewtheorem{theorem}{Theorem}[section]{\bfseries}{\itshape}\r
+\else\r
+   \spnewtheorem{theorem}{Theorem}{\bfseries}{\itshape}\r
+   \if@envcntreset\r
+      \@addtoreset{theorem}{section}\r
+   \else\r
+      \@addtoreset{theorem}{chapter}\r
+   \fi\r
+\fi\r
+\r
+%definition of divers theorem environments\r
+\spnewtheorem*{claim}{Claim}{\itshape}{\rmfamily}\r
+\spnewtheorem*{proof}{Proof}{\itshape}{\rmfamily}\r
+\if@envcntsame % alle Umgebungen wie Theorem.\r
+   \def\spn@wtheorem#1#2#3#4{\@spothm{#1}[theorem]{#2}{#3}{#4}}\r
+\else % alle Umgebungen mit eigenem Zaehler\r
+   \if@envcntsect % mit section numeriert\r
+      \def\spn@wtheorem#1#2#3#4{\@spxnthm{#1}{#2}[section]{#3}{#4}}\r
+   \else % nicht mit section numeriert\r
+      \if@envcntreset\r
+         \def\spn@wtheorem#1#2#3#4{\@spynthm{#1}{#2}{#3}{#4}\r
+                                   \@addtoreset{#1}{section}}\r
+      \else\r
+         \def\spn@wtheorem#1#2#3#4{\@spynthm{#1}{#2}{#3}{#4}\r
+                                   \@addtoreset{#1}{chapter}}%\r
+      \fi\r
+   \fi\r
+\fi\r
+\spn@wtheorem{case}{Case}{\itshape}{\rmfamily}\r
+\spn@wtheorem{conjecture}{Conjecture}{\itshape}{\rmfamily}\r
+\spn@wtheorem{corollary}{Corollary}{\bfseries}{\itshape}\r
+\spn@wtheorem{definition}{Definition}{\bfseries}{\itshape}\r
+\spn@wtheorem{example}{Example}{\itshape}{\rmfamily}\r
+\spn@wtheorem{exercise}{Exercise}{\itshape}{\rmfamily}\r
+\spn@wtheorem{lemma}{Lemma}{\bfseries}{\itshape}\r
+\spn@wtheorem{note}{Note}{\itshape}{\rmfamily}\r
+\spn@wtheorem{problem}{Problem}{\itshape}{\rmfamily}\r
+\spn@wtheorem{property}{Property}{\itshape}{\rmfamily}\r
+\spn@wtheorem{proposition}{Proposition}{\bfseries}{\itshape}\r
+\spn@wtheorem{question}{Question}{\itshape}{\rmfamily}\r
+\spn@wtheorem{solution}{Solution}{\itshape}{\rmfamily}\r
+\spn@wtheorem{remark}{Remark}{\itshape}{\rmfamily}\r
+\r
+\def\@takefromreset#1#2{%\r
+    \def\@tempa{#1}%\r
+    \let\@tempd\@elt\r
+    \def\@elt##1{%\r
+        \def\@tempb{##1}%\r
+        \ifx\@tempa\@tempb\else\r
+            \@addtoreset{##1}{#2}%\r
+        \fi}%\r
+    \expandafter\expandafter\let\expandafter\@tempc\csname cl@#2\endcsname\r
+    \expandafter\def\csname cl@#2\endcsname{}%\r
+    \@tempc\r
+    \let\@elt\@tempd}\r
+\r
+\def\theopargself{\def\@spopargbegintheorem##1##2##3##4##5{\trivlist\r
+      \item[\hskip\labelsep{##4##1\ ##2}]{##4##3\@thmcounterend\ }##5}\r
+                  \def\@Opargbegintheorem##1##2##3##4{##4\trivlist\r
+      \item[\hskip\labelsep{##3##1}]{##3##2\@thmcounterend\ }}\r
+      }\r
+\r
+\renewenvironment{abstract}{%\r
+      \list{}{\advance\topsep by0.35cm\relax\small\r
+      \leftmargin=1cm\r
+      \labelwidth=\z@\r
+      \listparindent=\z@\r
+      \itemindent\listparindent\r
+      \rightmargin\leftmargin}\item[\hskip\labelsep\r
+                                    \bfseries\abstractname]}\r
+    {\endlist}\r
+\r
+\newdimen\headlineindent             % dimension for space between\r
+\headlineindent=1.166cm              % number and text of headings.\r
+\r
+\def\ps@headings{\let\@mkboth\@gobbletwo\r
+   \let\@oddfoot\@empty\let\@evenfoot\@empty\r
+   \def\@evenhead{\normalfont\small\rlap{\thepage}\hspace{\headlineindent}%\r
+                  \leftmark\hfil}\r
+   \def\@oddhead{\normalfont\small\hfil\rightmark\hspace{\headlineindent}%\r
+                 \llap{\thepage}}\r
+   \def\chaptermark##1{}%\r
+   \def\sectionmark##1{}%\r
+   \def\subsectionmark##1{}}\r
+\r
+\def\ps@titlepage{\let\@mkboth\@gobbletwo\r
+   \let\@oddfoot\@empty\let\@evenfoot\@empty\r
+   \def\@evenhead{\normalfont\small\rlap{\thepage}\hspace{\headlineindent}%\r
+                  \hfil}\r
+   \def\@oddhead{\normalfont\small\hfil\hspace{\headlineindent}%\r
+                 \llap{\thepage}}\r
+   \def\chaptermark##1{}%\r
+   \def\sectionmark##1{}%\r
+   \def\subsectionmark##1{}}\r
+\r
+\if@runhead\ps@headings\else\r
+\ps@empty\fi\r
+\r
+\setlength\arraycolsep{1.4\p@}\r
+\setlength\tabcolsep{1.4\p@}\r
+\r
+\endinput\r
+%end of file llncs.cls\r

diff --git a/Paper2/main.aux b/Paper2/main.aux
new file mode 100644 (file)
index 0000000..5cf4e76
--- /dev/null
+++ b/Paper2/main.aux
@@ -0,0 +1,95 @@
+\relax 
+\citation{Alkindy2014}
+\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}}
+\newlabel{sec:intro}{{1}{1}}
+\citation{Sayers01012011}
+\citation{Rice2000}
+\citation{Alkindy2014}
+\citation{Sayers01012011}
+\citation{apweiler1985swiss}
+\citation{sugawara2008ddbj}
+\citation{Alkindy2014}
+\@writefile{toc}{\contentsline {section}{\numberline {2}An Overview}{3}}
+\newlabel{sec:general}{{2}{3}}
+\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces A general overview of the annotation-based approach\relax }}{3}}
+\providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
+\newlabel{Fig1}{{1}{3}}
+\citation{Alkindy2014}
+\citation{Sayers01012011}
+\citation{Alkindy2014}
+\citation{Alkindy2014}
+\citation{acgs13:onp,Alkindy2014}
+\@writefile{toc}{\contentsline {section}{\numberline {3}Core genes extraction}{4}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Similarity-based approach}{4}}
+\newlabel{sec:simil}{{3.1}{4}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.1}Theoretical presentation}{4}}
+\@writefile{thm}{\contentsline {definition}{{Definition}{1}{}}{4}}
+\newlabel{def1}{{1}{4}}
+\citation{altschul1990basic}
+\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Size of core and pan genomes w.r.t. the similarity threshold, first and second approache.\relax }}{5}}
+\newlabel{Fig:sim:core:pan}{{1}{5}}
+\citation{Rice2000}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.2}Case study}{6}}
+\citation{Alkindy2014}
+\citation{Alkindy2014}
+\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Annotation based approach}{7}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.2.1}Quality-test approach}{7}}
+\newlabel{sec:mixed}{{3.2.1}{7}}
+\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Part of the implementation of the second method, compaire the common genes from NCBI and DOGMA.\relax }}{7}}
+\newlabel{Meth2:gensim}{{2}{7}}
+\citation{Alkindy2014}
+\@writefile{loa}{\contentsline {algorithm}{\numberline {1}{\ignorespaces Maximum similarity score between two sequences(geneChk)\relax }}{8}}
+\newlabel{Alg3:genechk}{{1}{8}}
+\@writefile{loa}{\contentsline {algorithm}{\numberline {2}{\ignorespaces Extract new genome based on gene quality test\relax }}{9}}
+\newlabel{Alg3:thirdM}{{2}{9}}
+\@writefile{toc}{\contentsline {section}{\numberline {4}Implementation}{9}}
+\newlabel{sec:implem}{{4}{9}}
+\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Type of annotation, execution time, and core genes.\relax }}{9}}
+\newlabel{Etime}{{2}{9}}
+\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Memory usages in (MB) for each methodology\relax }}{10}}
+\newlabel{mem}{{3}{10}}
+\citation{mcfadden2001primary}
+\citation{mcfadden2001primary}
+\newlabel{subfig-1:core}{{3a}{11}}
+\newlabel{sub@subfig-1:core}{{(a)}{a}}
+\newlabel{subfig-2:pan}{{3b}{11}}
+\newlabel{sub@subfig-2:pan}{{(b)}{b}}
+\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Sizes of Core and Pan genomes for first and second method.\relax }}{11}}
+\@writefile{lof}{\contentsline {subfigure}{\numberline{(a)}{\ignorespaces {Sizes of core genome}}}{11}}
+\@writefile{lof}{\contentsline {subfigure}{\numberline{(b)}{\ignorespaces {Sizes of pan genome}}}{11}}
+\newlabel{fig:sizes of core and pan}{{3}{11}}
+\newlabel{Cover:NCBI}{{4a}{11}}
+\newlabel{sub@Cover:NCBI}{{(a)}{a}}
+\newlabel{cover:dogma}{{4b}{11}}
+\newlabel{sub@cover:dogma}{{(b)}{b}}
+\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Gene comparisons cover from NCBI and DOGMA, second method\relax }}{11}}
+\@writefile{lof}{\contentsline {subfigure}{\numberline{(a)}{\ignorespaces {genes coverage of NCBI genomes}}}{11}}
+\@writefile{lof}{\contentsline {subfigure}{\numberline{(b)}{\ignorespaces {genes coverage of DOGMA genomes}}}{11}}
+\newlabel{fig:sizes of core and pan}{{4}{11}}
+\@writefile{toc}{\contentsline {section}{\numberline {5}Discussion}{11}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}Biological evaluation}{11}}
+\newlabel{sec:discuss}{{5.1}{11}}
+\citation{li2013complete}
+\@writefile{toc}{\contentsline {section}{\numberline {6}Conclusion}{12}}
+\newlabel{sec:concl}{{6}{12}}
+\bibstyle{plain}
+\bibdata{biblio}
+\bibcite{acgs13:onp}{1}
+\bibcite{altschul1990basic}{2}
+\bibcite{apweiler1985swiss}{3}
+\bibcite{birney2004genewise}{4}
+\bibcite{de2002comparative}{5}
+\bibcite{Alkindy2014}{6}
+\bibcite{Bakke2009}{7}
+\bibcite{li2013complete}{8}
+\bibcite{Sayers01012011}{9}
+\bibcite{liu2012cpgavas}{10}
+\bibcite{guindon2005phyml}{11}
+\bibcite{mcfadden2001primary}{12}
+\bibcite{parra2000geneid}{13}
+\bibcite{parra2007cegma}{14}
+\bibcite{Rice2000}{15}
+\bibcite{RDOGMA}{16}
+\bibcite{stamatakis2008raxml}{17}
+\bibcite{stamatakis2005raxml}{18}
+\bibcite{sugawara2008ddbj}{19}

diff --git a/Paper2/main.bbl b/Paper2/main.bbl
new file mode 100644 (file)
index 0000000..7b1f670
--- /dev/null
+++ b/Paper2/main.bbl
@@ -0,0 +1,113 @@
+\begin{thebibliography}{10}
+
+\bibitem{acgs13:onp}
+Bassam Alkindy, Jean-Fran\c{c}ois Couchot, Christophe Guyeux, and Michel
+  Salomon.
+\newblock Finding the core-genes of chloroplast species.
+\newblock Journ\'ees SeqBio 2013, Montpellier, November 2013.
+
+\bibitem{altschul1990basic}
+Stephen~F Altschul, Warren Gish, Webb Miller, Eugene~W Myers, and David~J
+  Lipman.
+\newblock Basic local alignment search tool.
+\newblock {\em Journal of molecular biology}, 215(3):403--410, 1990.
+
+\bibitem{apweiler1985swiss}
+Rolf Apweiler, Claire O’Donovan, Maria~Jesus Martin, Wolfgang Fleischmann,
+  Henning Hermjakob, Steffen Moeller, Sergio Contrino, and Vivien Junker.
+\newblock Swiss-prot and its computer-annotated supplement trembl: How to
+  produce high quality automatic annotation.
+\newblock {\em EUR. J. BIOCHEM}, 147:9--15, 1985.
+
+\bibitem{birney2004genewise}
+Ewan Birney, Michele Clamp, and Richard Durbin.
+\newblock Genewise and genomewise.
+\newblock {\em Genome research}, 14(5):988--995, 2004.
+
+\bibitem{de2002comparative}
+Javier De~Las~Rivas, Juan~Jose Lozano, and Angel~R Ortiz.
+\newblock Comparative analysis of chloroplast genomes: functional annotation,
+  genome-based phylogeny, and deduced evolutionary patterns.
+\newblock {\em Genome research}, 12(4):567--583, 2002.
+
+\bibitem{Alkindy2014}
+Alkindy~B. \emph{et al}.
+\newblock Find core-genes for chloroplasts.
+\newblock 2014.
+
+\bibitem{Bakke2009}
+Bakke \emph{et al}.
+\newblock Evaluation of three automated genome annotations for
+  \textit{Halorhabdus utahensis}.
+\newblock {\em PLoS ONE}, 4(7):e6291, 07 2009.
+
+\bibitem{li2013complete}
+Li~\emph{et al}.
+\newblock Complete chloroplast genome sequence of holoparasite cistanche
+  deserticola (orobanchaceae) reveals gene loss and horizontal gene transfer
+  from its host haloxylon ammodendron (chenopodiaceae).
+\newblock {\em PloS one}, 8(3):e58747, 2013.
+
+\bibitem{Sayers01012011}
+Sayers \emph{et al}.
+\newblock Database resources of the national center for biotechnology
+  information.
+\newblock {\em Nucleic Acids Research}, 39(suppl 1):D38--D51, 2011.
+
+\bibitem{liu2012cpgavas}
+Zhang \emph{et al}.
+\newblock Cpgavas, an integrated web server for the annotation, visualization,
+  analysis, and genbank submission of completely sequenced chloroplast genome
+  sequences.
+
+\bibitem{guindon2005phyml}
+Stephane Guindon, Franck Lethiec, Patrice Duroux, and Olivier Gascuel.
+\newblock Phyml online—a web server for fast maximum likelihood-based
+  phylogenetic inference.
+\newblock {\em Nucleic acids research}, 33(suppl 2):W557--W559, 2005.
+
+\bibitem{mcfadden2001primary}
+Geoffrey~Ian McFadden.
+\newblock Primary and secondary endosymbiosis and the origin of plastids.
+\newblock {\em Journal of Phycology}, 37(6):951--959, 2001.
+
+\bibitem{parra2000geneid}
+Gen{\'\i}s Parra, Enrique Blanco, and Roderic Guig{\'o}.
+\newblock Geneid in drosophila.
+\newblock {\em Genome research}, 10(4):511--515, 2000.
+
+\bibitem{parra2007cegma}
+Genis Parra, Keith Bradnam, and Ian Korf.
+\newblock Cegma: a pipeline to accurately annotate core genes in eukaryotic
+  genomes.
+\newblock {\em Bioinformatics}, 23(9):1061--1067, 2007.
+
+\bibitem{Rice2000}
+P.~Rice, I.~Longden, and A.~Bleasby.
+\newblock Emboss: the european molecular biology open software suite.
+\newblock {\em Trends Genet}, 16(6):276--7, 2000.
+
+\bibitem{RDOGMA}
+Robert K.~Jansen Stacia K.~Wyman and Jeffrey~L. Boore.
+\newblock Automatic annotation of organellar genomes with dogma.
+\newblock {\em BIOINFORMATICS, oxford Press}, 20(172004):3252--3255, 2004.
+
+\bibitem{stamatakis2008raxml}
+Alexandros Stamatakis.
+\newblock The raxml 7.0. 4 manual.
+\newblock {\em Department of Computer Science.
+  Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen}, 2008.
+
+\bibitem{stamatakis2005raxml}
+Alexandros Stamatakis, Thomas Ludwig, and Harald Meier.
+\newblock Raxml-iii: a fast program for maximum likelihood-based inference of
+  large phylogenetic trees.
+\newblock {\em Bioinformatics}, 21(4):456--463, 2005.
+
+\bibitem{sugawara2008ddbj}
+Hideaki Sugawara, Osamu Ogasawara, Kousaku Okubo, Takashi Gojobori, and Yoshio
+  Tateno.
+\newblock Ddbj with new system and face.
+\newblock {\em Nucleic acids research}, 36(suppl 1):D22--D24, 2008.
+
+\end{thebibliography}

diff --git a/Paper2/main.blg b/Paper2/main.blg
new file mode 100644 (file)
index 0000000..315c5e6
--- /dev/null
+++ b/Paper2/main.blg
@@ -0,0 +1,59 @@
+This is BibTeX, Version 0.99c (TeX Live 2009/Debian)
+The top-level auxiliary file: main.aux
+The style file: plain.bst
+Database file #1: biblio.bib
+You're missing a field name---line 123 of file biblio.bib
+ :   
+ :   %Liu, Chang and Shi, Linchun and Zhu, Yingjie and Chen, Haimei and Zhang, Jianhui %and Lin, Xiaohan and Guan, Xiaojun},
+(Error may have been on previous line)
+I'm skipping whatever remains of this entry
+You're missing a field name---line 223 of file biblio.bib
+ :     
+ :     %{Blouin, Yann AND Hauck, Yolande AND Soler,Charles AND Fabre, Michel AND Vong, Rithy ANDDehan, Céline AND Cazajous, Géraldine ANDMassoure, Pierre-Laurent AND Kraemer, PhilippeANDJenkins, Akinbowale AND Garnotel, EricAND Pourcel, Christine AND Vergnaud, Gilles}
+(Error may have been on previous line)
+I'm skipping whatever remains of this entry
+Warning--empty journal in Alkindy2014
+Warning--empty journal in liu2012cpgavas
+Warning--empty year in liu2012cpgavas
+You've used 19 entries,
+            2118 wiz_defined-function locations,
+            627 strings with 7317 characters,
+and the built_in function-call counts, 6332 in all, are:
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+\documentclass{article}
+\usepackage{subfig}
+\usepackage{color}
+\usepackage{graphicx}
+\usepackage{url}
+\usepackage{cite}
+\usepackage{algorithm}
+\usepackage{algorithmic}
+\usepackage{pdflscape}
+\usepackage{authblk}
+\usepackage[T1]{fontenc}
+\usepackage{multirow,longtable}
+\usepackage{amsmath,mathtools}
+\usepackage{amssymb}
+\usepackage[standard]{ntheorem}
+\usepackage{stmaryrd}
+\usepackage[utf8]{inputenc}
+\usepackage{tikz}
+\usepackage{rotating}
+\usepackage{blindtext}
+
+% correct bad hyphenation here
+\hyphenation{op-tical net-works semi-conduc-tor}
+
+
+\title{Finding the Core-Genes of Chloroplasts based on Gene Similarity Approaches}
+\author[1,3]{Bassam Al Kindy} %\footnote{email: bassam.al-kindy@univ-fcomte.fr}
+\author[1,3]{Christophe Guyeux} 
+\author[1,3]{Jean-Fran\c{c}ois Couchot} 
+%\author[2,3]{Arnaud Mouly}
+\author[1,3]{Michel Salomon}
+\author[1,3]{Jacques M. Bahi}
+\affil[1]{FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science Department\\ Universit\'{e} de Franche-Comt\'{e}, France}
+%\affil[2]{Chrono-Environnement Lab., UMR 6249 CNRS}
+%{\small \it Authors in alphabetic order}
+%\affil[3]{Universit\'{e} de Franche-Comt\'{e}, France}
+\renewcommand\Authands{ and }
+
+\begin{document}
+
+\maketitle
+
+%IEEEtran, journal, \LaTeX, paper, template.
+\input{abstract}
+
+\section{Introduction}\label{sec:intro}
+\input{intro.tex}
+
+
+
+\section{An Overview}\label{sec:general}
+\input{general}
+
+\section{Core genes extraction}
+\subsection{Similarity-based approach}\label{sec:simil}
+% Main author : jfc
+\input{classEquiv}
+
+%\subsection{Annotation-based approach}\label{sec:annot}
+% Main author : bassam
+%\input{annotated}
+\subsection{Annotation based approach}
+\subsubsection{Quality-test approach}\label{sec:mixed}
+\input{Mixed}
+
+%\section{Features visualization}\label{sec:features}
+%\input{Features}
+
+\section{Implementation}\label{sec:implem}
+\input{implementation}
+%\section{Second Stage: to Find Closed Genomes}
+%\input{closedgenomes}
+
+\section{Discussion}
+\input{discussion.tex}
+\section{Conclusion}\label{sec:concl}
+\input{conclusion}
+
+\bigskip
+
+\textit{Computations have been performed on the supercomputer facilities of the M\'esocentre
+de calcul de Franche-Comt\'e.}
+
+
+\bibliographystyle{plain}
+
+\bibliography{biblio}
+
+%\section{Appendix}
+%\input{appendix}
+
+\end{document}

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+\contentsline {definition}{{Definition}{1}{}}{4}

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diff --git a/Paper2/population_Table.tex b/Paper2/population_Table.tex
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+  \begin{table}
+    \tiny
+    \caption[NCBI Genomes Families]{List of chloroplast genomes of photosynthetic Eucaryotes lineages from NCBI\label{Tab2}}
+    \begin{minipage}{0.50\textwidth}
+      \setlength{\tabcolsep}{4pt}
+      \begin{tabular}{|p{0.1cm}|p{0.2cm}|p{1.35cm}|p{2.65cm}|}
+      \hline
+      {{F.}}&{{\#}} & {{Acc. No}} & {{Scientific Name}} \\ 
+      \hline
+       %Entering First line
+      \parbox[t]{1mm}{\multirow{11}{*}{\rotatebox[origin=c]{90}{Brown Algae}}} &\multirow{11}{*}{11}  & NC\_001713.1 & \textit{Odontella sinensis} \\ 
+      & & NC\_008588.1 & \textit{Phaeodactylum tricornutum} \\ 
+      & & NC\_010772.1 & \textit{Heterosigma akashiwo} \\ 
+      & & NC\_011600.1 & \textit{Vaucheria litorea} \\ 
+      & & NC\_012903.1 & \textit{Aureoumbra lagunensis} \\ 
+      &  & NC\_014808.1 & \textit{Thalassiosira oceanica} \\ 
+      & & NC\_015403.1 & \textit{Fistulifera sp} \\ 
+      & & NC\_016731.1 & \textit{Synedra acus} \\ 
+      & & NC\_016735.1 & \textit{Fucus vesiculosus} \\ 
+      & & NC\_018523.1 & \textit{Saccharina japonica} \\ 
+      & & NC\_020014.1 & \textit{Nannochloropsis gadtina} \\ 
+      \hline
+      % Entering second group
+      \parbox[t]{1mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{F1}}} &
+      \multirow{3}{*}{3} & NC\_000925.1 & \textit{Porphyra purpurea} \\ 
+       &  & NC\_001840.1 & \textit{Cyanidium caldarium} \\ 
+      & & NC\_006137.1 & \textit{Gracilaria tenuistipitata} \\ 
+      \hline
+      % Entering third group
+      \parbox[t]{1mm}{\multirow{17}{*}{\rotatebox[origin=c]{90}{Green Algae}}} &
+      \multirow{17}{*}{17} & NC\_000927.1 & \textit{Nephroselmis olivacea} \\ 
+      & & NC\_002186.1 & \textit{Mesotigma viride} \\ 
+      & & NC\_005353.1 & \textit{Chlamydomonas reinhardtii} \\ 
+      & & NC\_008097.1 & \textit{Chara vulgaris} \\ 
+      & & NC\_008099.1 & \textit{Oltmannsiellopsis viridis} \\ 
+      & & NC\_008114.1 & \textit{Pseudoclonium akinetum} \\ 
+      & & NC\_008289.1 & \textit{Ostreococcus tauri} \\ 
+      & & NC\_008372.1 & \textit{Stigeoclonium helveticum} \\ 
+      & & NC\_008822.1 & \textit{Chlorokybus atmophyticus} \\ 
+      & & NC\_011031.1 & \textit{Oedogonium cardiacum} \\ 
+      & & NC\_012097.1 & \textit{Pycnococcus provaseolii} \\ 
+      & & NC\_012099.1 & \textit{Pyramimonas parkeae} \\ 
+      & & NC\_012568.1 & \textit{Micromonas pusilla} \\ 
+      & & NC\_014346.1 & \textit{Floydiella terrestris} \\ 
+      & & NC\_015645.1 & \textit{Schizomeris leibleinii} \\ 
+      & & NC\_016732.1 & \textit{Dunaliella salina} \\ 
+      & & NC\_016733.1 & \textit{Pedinomonas minor} \\ % 
+      \hline
+      % Entering fourth group
+      \parbox[t]{1mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{F2}}} &
+      \multirow{3}{*}{3} & NC\_001319.1 & \textit{Marchantia polymorpha} \\ 
+       &  & NC\_004543.1 & \textit{Anthoceros formosae} \\ 
+      & & NC\_005087.1 & \textit{Physcomitrella patens} \\ %
+      \hline
+      % Entering fifth group
+      \parbox[t]{1mm}{\multirow{2}{*}{\rotatebox[origin=c]{90}{F3}}} &
+      \multirow{2}{*}{2} & NC\_014267.1 & \textit{Kryptoperidinium foliaceum} \\ 
+      & 
+      & NC\_014287.1 & \textit{Durinskia baltica} \\ 
+      \hline
+
+      % Entering sixth group
+      \parbox[t]{1mm}{\multirow{2}{*}{\rotatebox[origin=c]{90}{F4}}}     &
+      \multirow{2}{*}{2} & NC\_001603.2 & \textit{Euglena gracilis} \\ 
+      &  & NC\_020018.1 & \textit{Monomorphina aenigmatica} \\ 
+       \hline
+       % Entering seventh group
+       \parbox[t]{1mm}{\multirow{5}{*}{\rotatebox[origin=c]{90}{Ferns}}}     &       \multirow{5}{*}{5}
+       & NC\_003386.1 & \textit{Psilotum nudum} \\ 
+      & & NC\_008829.1 & \textit{Angiopteris evecta} \\
+      &  & NC\_014348.1 & \textit{Pteridium aquilinum} \\ 
+      & & NC\_014699.1 & \textit{Equisetum arvense} \\ 
+      & & NC\_017006.1 & \textit{Mankyua chejuensis} \\ 
+      \hline
+    % Entering tenth group
+       & & & \\
+      \parbox[t]{1mm}{\multirow{1}{*}{\rotatebox[origin=c]{90}{F5}}}
+ & 1 & NC\_007288.1 & \textit{Emiliana huxleyi}\\
+       & & & \\
+ \hline
+      % Entering eleventh group
+      \parbox[t]{1mm}{\multirow{2}{*}{\rotatebox[origin=c]{90}{F6}}}
+       & \multirow{2}{*}{2} & NC\_014675.1 & \textit{Isoetes flaccida} \\
+      & & NC\_006861.1 & \textit{Huperzia lucidula} \\
+      \hline
+    \end{tabular}
+  \end{minipage}    
+  \begin{minipage}{0.60\textwidth}
+   \setlength{\tabcolsep}{4pt}
+   \begin{tabular}{|p{0.2cm}|p{0.2cm}|p{1.45cm}|p{2.4cm}|}
+      \hline
+      {{F.}}&{{\#}} & {{Acc. No}} & {{Scientific Name}} \\ 
+      \hline
+
+      % Entering eighth group
+      \parbox[t]{1mm}{\multirow{45}{*}{\rotatebox[origin=c]{90}{Angiosperms}}}  
+      & 
+      \multirow{45}{*}{45} & NC\_007898.3 & \textit{Solanum lyopersicum} \\
+      & & NC\_001568.1 & \textit{Epifagus virginiana} \\ 
+      & & NC\_001666.2 & \textit{Zea Mays} \\ 
+      & & NC\_005086.1 & \textit{Amborella trichopoda} \\ 
+      & & NC\_006050.1 & \textit{Nymphaea alba} \\ 
+      & & NC\_006290.1 & \textit{Panax ginseng} \\ 
+      & & NC\_007578.1 & \textit{Lactuca sativa} \\ 
+      & & NC\_007957.1 & \textit{Vitis vinifera} \\ 
+      & & NC\_007977.1 & \textit{Helianthus annuus} \\ 
+      & & NC\_008325.1 & \textit{Daucus carota} \\ 
+      & & NC\_008336.1 & \textit{Nandina domestica} \\ 
+      & & NC\_008359.1 & \textit{Morus indica} \\ 
+      & & NC\_008407.1 & \textit{Jasminum nudiflorum} \\ 
+      & & NC\_008456.1 & \textit{Drimys granadensis} \\ 
+      & & NC\_008457.1 & \textit{Piper cenocladum} \\ 
+      & & NC\_009601.1 & \textit{Dioscorea elephantipes} \\ 
+      & & NC\_009765.1 & \textit{Cuscuta gronovii} \\ 
+      & & NC\_009808.1 & \textit{Ipomea purpurea} \\ 
+      & & NC\_010361.1 & \textit{Oenothera biennis} \\ 
+      & & NC\_010433.1 & \textit{Manihot esculenta} \\ 
+      & & NC\_010442.1 & \textit{Trachelium caeruleum} \\ 
+      & & NC\_013707.2 & \textit{Olea europea} \\ 
+      & & NC\_013823.1 & \textit{Typha latifolia} \\ 
+      & & NC\_014570.1 & \textit{Eucalyptus} \\ 
+      & & NC\_014674.1 & \textit{Castanea mollissima} \\ 
+      & & NC\_014676.2 & \textit{Theobroma cacao} \\ 
+      & & NC\_015830.1 & \textit{Bambusa emeiensis} \\ 
+      & & NC\_015899.1 & \textit{Wolffia australiana} \\ 
+      & & NC\_016433.2 & \textit{Sesamum indicum} \\ 
+      & & NC\_016468.1 & \textit{Boea hygrometrica} \\ 
+      & & NC\_016670.1 & \textit{Gossypium darwinii} \\ 
+      & & NC\_016727.1 & \textit{Silene vulgaris} \\ 
+      & & NC\_016734.1 & \textit{Brassica napus} \\ 
+      & & NC\_016736.1 & \textit{Ricinus communis} \\ 
+      & & NC\_016753.1 & \textit{Colocasia esculenta} \\ 
+      & & NC\_017609.1 & \textit{Phalaenopsis equestris} \\ 
+      & & NC\_018357.1 & \textit{Magnolia denudata} \\ 
+      & & NC\_019601.1 & \textit{Fragaria chiloensis} \\ 
+      & & NC\_008796.1 & \textit{Ranunculus macranthus} \\ 
+      & & NC\_013991.2 & \textit{Phoenix dactylifera} \\ 
+      & & NC\_016068.1 & \textit{Nicotiana undulata} \\ 
+      \hline
+      % Entering ninth group
+      \parbox[t]{1mm}{\multirow{7}{*}{\rotatebox[origin=c]{90}{Gymnosperms}}}
+      & 
+      \multirow{7}{*}{7}& NC\_009618.1 & \textit{Cycas taitungensis} \\ 
+      & & NC\_011942.1 & \textit{Gnetum parvifolium} \\ 
+      & & NC\_016058.1 & \textit{Larix decidua} \\ 
+      & & NC\_016063.1 & \textit{Cephalotaxus wilsoniana} \\ 
+      & & NC\_016065.1 & \textit{Taiwania cryptomerioides} \\ 
+      & & NC\_016069.1 & \textit{Picea morrisonicola} \\ 
+      & & NC\_016986.1 & \textit{Gingko biloba} \\ 
+      \hline
+     \end{tabular}
+  \end{minipage}
+ 
+  \scriptsize 
+  \noindent where lineages F1, F2, F3, F4, F5, and F6 are 
+  \textit{Red Algae},
+  \textit{Bryophytes}, 
+  \textit{Dinoflagellates},
+  \textit{Euglena},
+  \textit{Haptophytes}, and \textit{Lycophytes} respectively.
+  \normalsize
+  \end{table}
+  
\ No newline at end of file

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