dataesr/scientific-paragraphs-categorization huggingface dataset:
Prognostic value of MCP-counter scores For a given cancer type, several datasets, sometimes based on different platforms, were collected. MCP-counter estimates were first computed for each dataset individually. The resulting scores were then Z-transformed for each dataset individually, leading to similar distributions of the scores across datasets. Then, univariate Cox proportional hazards models for OS were fitted separately in each dataset using the related Z-transformed MCP-counter scores. To aggregate the resulting estimates (beta values) across datasets, we used the meta-analytical R package meta (function metagen), using a fixed-effect model (as the scores in each series follow the same distribution). This function weights the independent estimates using an inverse-variance weighting.
Microenvironment-based tumor classifications MCP-counter estimates were first computed for each dataset individually. The resulting scores were then Ztransformed for each dataset individually, leading to similar distributions of the scores across datasets. Datasets from the same cancer were then merged and all MCPcounter variables were binarized using a median cut (leading to "high" and "low" samples for each variable and for each cancer according to their relative position from the cancer's median value). We selected three tumor classifications from the literature (using B and T cells in lung adenocarcinoma, fibroblasts and cytotoxic lymphocytes in colorectal cancer, and macrophages and cytotoxic lymphocytes in breast cancer). For each of these three cancers, we concatenated the binarized scores for the two variables of interest, leading to four classes (highhigh, high-low, low-high, low-low).
Additional files Additional file 1: Table  S1. Microenvironment cell populations (MCP) dataset. Discovery series. Affymetrix Human Genome U133 Plus 2. 0. Table  S2. The selected transcriptomic markers and their expression across MCP datasets. fc fold change, auc area under the ROC curve, Fcspec specific fold change (see "Methods"). 133P2 is the discovery MCP series; 133A and HG1 are the validation MCP series. Restricted. TM. set indicates whether the corresponding probe set is only in the complete set of markers (No) or if it is included in both the complete and the restricted set (Yes) (see Methods). Table  S3. Result of the screening process on the discovery and validation series. For each non-root node (categories) represented in Additional file 2: Figure  S1  or listed in the rows of Additional file 1: Table  S13 , and applying the screening process described in the "Results" and "Methods" sections, we report the number of markers identified on each series, the number of distinct datasets containing positive samples for this node, and the number of positive samples.
The overlap between markers identified by this process on pairs or triplets of MCP series is reported with the corresponding Fisher's exact test p value. Table  S4. Microenvironment cell populations dataset. Validation series 1. Affymetrix 133A. Table  S5. Microenvironment cell populations dataset. Validation series 2. Affymetrix HuGene ST 1. 0. Table  S6. Non-hematopoietic human tumor datasets. The numbers of GEP per expression platform and per tumor type are listed, as well as the number samples annotated for overall survival (OS). Table  S7. Non-hematopoietic human primary tumors dataset 1. Affymetrix Human Genome U133 Plus 2. 0. Table  S8. Nonhematopoietic human primary tumors dataset 2. Affymetrix 133A. Table  S9  Non-hematopoietic human primary tumors dataset 3. TCGA, Illumina HiSeq.
Reproducibility of transcriptomic marker expression patterns between the discovery and validation series. Transcriptomic marker expression quartiles in peripheral blood mononuclear cells (PBMC), CD3 + T cells, CD4 + T cells, CD8 + T cells, NK cells, Tγδ cells, B cells, pDC (plasmacytoid Dendritic Cells), Granulocytes, Neutrophils, Eosinophils, Monocytes, Macrophages, myeloid dendritic cells (mDC), mast cells, endothelial cells, fibroblasts, and cancer cell lines in the MCP discovery series and MCP validation series. Figure S4. AUC for each MCP-counter score on MCP discovery and validation series. The TM sets used here are the "complete" sets, which do not use any information from the validation series. Figure S5. Expression pattern of the BLK gene across MCP samples. Figure S6. Coexpression of transcriptomic markers specific for the same cell population in the three MCP datasets.
Heat map representation of correlation matrices. The TM sets used here are the "complete" sets, which do not use any information from the validation series. Figure S7. Coexpression of transcriptomic markers specific for the same cell population in the three tumor datasets. Heat map representation of TM correlation matrices. TM were ordered using hierarchical clustering with complete linkage and Pearson's "distance". Figure S8. Reproducibility of the coexpression patterns within malignancies. Heat map representation of TM correlation matrices within each malignancy. The color scale is the same as in Additional file 2: Figure S6 as well as the order of the features.
Distribution of summarized TM sets on microenvironment series. Boxplots showing the distribution of scores computed with TM sets from Bindea et al. (left) and MCP-counter complete TM sets (right) across the three MCP series. For each TM set, boxplots are colored red for "positive" samples, blue for "negative" samples, and gray for "mixed" samples. Figure S10. Reproducibility of the malignancy mean abundance estimates across the three primary human tumor meta-datasets. Means of MCP-counter scores across malignant tissues. For each platform, columns were Z-transformed. Dark red denotes highest and dark blue lowest Z-scores.
Acknowledgements We wish to acknowledge members of the teams 10, 13, and 15 of the  Cordeliers Research Center , the  Centre d'Imagerie Cellulaire et de Cytométrie "CICC" form of the Cordeliers Research Center , and the " Plateforme Biopuces et Séquençage " of the IGBMC for their respective technical and scientific expertise. The efforts of the  Gene Expression Omnibus, ArrayExpress , the expression project for Oncology, and the  International Genomics Consortium , and all the teams that shared their GEP results are greatly acknowledged. This work involved the support of the  French national program Cartes d'Identité des Tumeurs® (CIT)  funded and developed by the  Ligue Nationale Contre le Cancer. We thank  Julien Calderaro  for his advice and insightful comments. Funding This work was supported by the  "Institut National de la Santé et de la Recherche Médicale" , the  University Paris-Descartes , the  University Pierre et Marie Curie , the  Institut National du Cancer  ( 2011-1-PLBIO-06-INSERM 6-1 ),  CARPEM (CAncer Research for PErsonalized Medicine) ,  Labex Immuno-Oncology  ( LAXE62_9UMS872 FRIDMAN ), the  Fondation ARC pour la recherche sur le cancer , the  Cancéropôle Ile-de-France ,  Institut National du Cancer  ( 2011-1-PLBIO-06-INSERM 6-1 , PLBIO09-088-IDF-KROEMER ), the  Universidad de los Andes School of Medicine (NAG) ,  Colciencias (NAG).
Availability of data and materials The three microenvironment GEP series have been deposited as third-party reanalyses under GEO accession code GSE86370. mRNA mixtures were deposited under accession code GSE64385. The MCP-counter package used in this work has been deposited at http:// dx. doi. org/10. 5281/zenodo. 61372 and the source code is freely available at http://github. com/ebecht/MCPcounter. The software is licensed under GNU General Public Licence v3.
Coexpression of transcriptomic markers specific for the same cell population in the three MCP datasets. Heat map representation of correlation matrices. The TM sets used here are the "complete" sets, which do not use any information from the validation series. Figure S7. Coexpression of transcriptomic markers specific for the same cell population in the three tumor datasets. Heat map representation of TM correlation matrices. TM were ordered using hierarchical clustering with complete linkage and Pearson's "distance". Figure S8. Reproducibility of the coexpression patterns within malignancies. Heat map representation of TM correlation matrices within each malignancy. The color scale is the same as in Additional file 2: Figure S6 as well as the order of the features.
Virtualization is the main technique to assure multi-tenancy and service isolation. Supporting native virtualization extensions, advanced RISC machinebased SoCs fully satisfy those constraints. On the other hand, existing VIMs, such as OpenStack and hypervisors such as the Linux KVM are already adopted by the industry as de facto a standard. The convergence point of these technologies is the KVM port to the advanced RISC machine platform. Likewise, an accelerated network between VNFs is crucial for the near native performance of the NFVI. A virtual switch (vSwitch) is the key component to ensure interconnection between VNFs and their communication with the outside world. In fact, virtual switches outperform their hardware counterparts in some scenarios, such as VM-to-VM communication. They are also more flexible when it comes to extending their functionalities.
Indeed, virtual switches can benefit from different hardware accelerators available on the host, for example, cryptochips and embedded hardware bridges. The effect is significant host CPU offloading, allowing to increase VM density. In the scope of the SESAME project, we implement an accelerated user-space vSwitch solution based on the SnabbSwitch  [6]  network framework and open data plane. The virtual switch takes advantage of the LuaJIT (Lua Just-In-Time) compiler dynamic optimisations of the code during execution. Open data plane, on the other hand, provides application program interface for accessing a big number of hardware and software devices such as NICs and hardware accelerators. The fact that the vSwitch runs entirely in user space implies there is no performance penalty, otherwise related to context switching.
National regulation in terms of privacy, data protection and resource sharing was introduced 20 to 30 years ago, and it is thus outdated. The growth of a digital ecosystem based on dramatic changes to technology and global linked in many countries was not predicted by policy makers or regulators. Undoubtedtly, the coming wave of 5G innovations will have a concrete exploitation and socio-economic impact by 2020, through the deployment of the so-called 5G infrastructure. However, 5G will be much more that the next step beyond 4G, as it is expected to be the core functional system of our modern digital society and economy, thus generating a truly converged and tremendously dense communication infrastructure, integrating IT systems (e. g. processing and storage) with plentiful network resources.
Data deposition: A complete list of the Lactobacillus rhamnosus strains used in the manuscript and the GenBank accession numbers can be found in supplementary table  S1 , Supplementary Material online. The genomes will become available when the manuscript is accepted. In addition, four public strains genomes (strains GG, HN001, Lc705, ATCC 21052) were downloaded and processed with the same bioinformatics pipeline as the newly sequenced genomes.
Lactobacillus rhamnosus is an anaerobic, facultative heterofermentative rod-shaped microorganism that is encountered in the fermentation of food and feed-raw materials  (Bergey 2009) , but also frequently found in the human and animal intestinal  (Heilig et al. 2002) , oral and uro-genital system (Hummelen and Fernandes 2010). Among its most studied strains, L. rhamnosus GG (ATCC 53103) stands apart as the most documented probiotic strain (887 scientific articles and 58 patents by 2015-according to the Scopus Database). Different health and industrial benefits were associated with different strains of L. rhamnosus, but due to the lack of controlled comparative studies, it remains to be established if and to what extent all these effects are strain-specific  (Douillard, Ribbera, Ja ¨rvinen, et al.
Metabolic Mapping-KEGG The draft metabolic network and associated gene-reaction relationships of the pan, core and clades' genomes was constructed using a KEGG automatic annotation server (KAAS)  (Moriya 2007) , according to the KEGG database  (Kanehisa et al. 2009 ) using the standard settings and mapping against 40 typical prokaryotes genomes. Then, manual checking of the metabolic maps obtained from the KAAS annotation identified the relevant metabolic diversity and gaps. Substrate specificity of PEP group translocation (PTS) transporters was predicted based on homology to annotated PTS genes (the best hit in the NCBI nr database) and based on their genomic context, focusing on genetically linked genes with predicted functions related to enzyme and regulatory functions in carbohydrate metabolism.
CRISPR Identification and Characterization CRISPR (clustered regularly interspaced short palindromic repeat) loci and associated cas genes were identified using a combination of homology to previously identified cas genes and their corresponding CRISPR repeats  (Brouns et al. 2008)  and by de novo identification using the CRISPR Recognition Tool (CRT)  (Bland et al. 2007). Spacer homologies to foreign genetic elements were assessed using BLASTN  (Altschul et al. 1997)  on two databases: one created with all L. rhamnosus genomes presented in this study and the NCBI complete nucleotide collection nr  (Pruitt et al. 2007). Nucleotide conservation between CRISPR spacers and corresponding protospacers in phages, plasmids, and chromosomal sequences were visualized using Ugene  (Okonechnikov et al.
Orthology Estimation The orthology prediction was performed for all genomes (5 public and 35 sequenced by 454 GS FLX in this study) using sequence clustering and search program Usearch v. 6. 0. 307 (Edgar 2010). To define orthologous groups (OGs), all the predicted open reading frame (ORF) amino acid sequences of all the strains were first ordered by length using the "sortbylength" command, and then clustered with the "clus-ter_smallmem" command using identity threshold of 0. 5 and E-value cut-off of 1e À 30. Thus, the members of each resulting ORF sequence cluster shared at least 50% amino acid identity over their entire sequence lengths based on the longest sequence. The longest sequence in each cluster was chosen as the representative of the OG. The pan-genome was thus defined as the set of representative sequences of the OGs.
The ortholog data were managed using R v. 2. 15. 3 with Biostrings package and in-house scripts. Since most of the genomes are in a draft state, ORF prediction might have missed protein-coding sequences (CDSs). To detect the eventually missing genes, CDSs were aligned against each of the genomes using tBLASTn. CDSs that had a hit with 95% identity and query length or had the best alignment on the edge of a contig were classified as being present in the corresponding genome.
Search for L. rhamnosus-Specific Genes (Orphan OGs) To identify L. rhamnosus-specific genes, we created a database of the protein sequences of all completely sequenced bacterial genomes present in the NCBI nr database. All known L. rhamnosus genomes were excluded from this set. The final database composed of 3,505,217 proteins from 1,047 genomes. We compared all the genes belonging to L. rhamnosus strains to this database using BLASTP with an initial identity threshold of 0. 5 and E-value cut-off of 1e À 30, the same value used to create the OG matrix. Genes that had no blast hit against any of the proteins in the database were considered to be L. rhamnosus specific. The genes specific for the species' core and pan gene sets were analyzed using the same approach.
Variome Classification Based on Presence-Absence of Genes Genomes and OGs were clustered in R (R Development Core Team 2008) using the complete method based on the Manhattan distance of presence-absence matrix. A subset of 10% of core genes was added to the variable genome in order to identify the least variable OG clusters. The output was visualized with heatmap. 2 from the gplots package  (Warnes 2012).
The variable gene content of the analyzed genomes was investigated using the gene presence/absence matrix and visualized by creating a heatmap using R (fig. 1 ) and used to construct a genome-relatedness or "pan-genome" tree based on variable genome content (variome tree; fig. 1 ). The eight clade-classification identifies L. rhamnosus unique and clade-specific OGs (supplementary table  S3 , Supplementary Material online). Species-specific orphans OGs (OOGs) that are uniquely present in L. rhamnosus and missing in all other bacteria (based on the entries in the NCBI nr database), amounted to 49% of the L. rhamnosus pangenome. The present study identified 232 OOGs in the L. rhamnosus group, among which a substantial amount are small-sized ORFs (more than 10% have less than 75 amino acids), suggesting that the number of OOGs may be somewhat overestimated due to sequencing gaps and/or overprediction of ORFs, also supported by an observed larger size of the pan-genome compared to other LAB species (see above).
casei or L. paracasei. Another example of HGT is the operon responsible for taurine transport (LGG_00171 to LGG_00177) that is lacking from strains of clades 6 and 8. A cladogram obtained from the multiple alignment of all similar sequences from NCBI's nr database (supplementary fig. S4 , Supplementary Material online) for the ABC transporter showed that it is shared by some strains of the L. casei group, with the closest homologue in L. farciminis, suggesting that this species could be the source of multiple HGT events in L. rhamnosus. A recent comparative genomics study reported that L. rhamnosus GG (clade 4) has the highest number of predicted transport proteins among the L. casei group representatives  (Toh et al. 2013 ). However, the current analysis revealed that strains of clades 3 and 4 encode even a larger number of transport genes, making these strains of L.
2005 ) play a prominent role in genetic mobility within a strain's genome (intracellular mobility), but also between bacterial genomes of different strains (intercellular mobility) and form one of the driving forces in the evolution of organisms  (Liu et al. 2009). Plasmids. Three plasmids of L. rhamnosus have previously been described, pLC001 (strain LC705), pLR001 and pLR002 (strain HN001)  (Morita et al. 2009). Next to the search for these known plasmids, novel plasmid identification relied on the detection of contigs that contain plasmid-associated genes, particularly focusing on replication functions. The latter approach failed to identify novel plasmid entities in the genome database created in this study. Notably, strains belonging to clades 4 and 7 did consistently appear to lack plasmids, which was also the case for individual strains in the other clades (Lrh5 from clade 1, Lrh11 from clade 6, and ATCC7469, ATCC8530, Lrh23, Lrh12, and Lrh15 from clade 8; fig.
Both the sequence of the cas genes of in L. rhamnosus and their genetic organization resembles that of L. casei  (Smokvina et al. 2013) , suggesting its inheritance from a common ancestor. Notably, the presence of the CRISPR-Cas system does not seem to correlate with the number of genomic islands recognized in these different strains. Noteworthy, groups of spacer sequences within the repeat locus are specific for each of the clades 3, 4, 5, and 6 (fig. 4 , supplementary fig. S2 , Supplementary Material online) and their predicted targets (on basis of 100% sequence identity to genes in the NCBI nr database) belong to Lactobacillus phages (Lc-Nu, A2, P2, and Lrm1) and plasmids (L. casei str. Zhang plasmid plca36, Lactobacillus rennini plasmid pREN, Lactobacillus plantarum 16 plasmid Lp16C, plasmid pC30il, cryptic plasmid pLJ42, Lactobacillus helveticus H10 plasmid pH10, and Lactobacillus salivarius UCC118 plasmid pSF118-20) (supplementary table  S2 , Supplementary Material online, fig.
4 ). Remarkably, numerous spacers show sequence identity to L. rhamnosus phages (Lc-Nu, Pi2, A2) and plasmids (pLR002) present in their own as well as other clades. Overall, only 23 out of 112 of the spacer sequences targeted genetic entities from other species (the NCBI nr database) and were not found in the L. rhamnosus pan-genome, suggesting that the CRISPR system in L. rhamnosus could play a role in protection against the entry of mobile genetic elements from the same species and thereby restrict the intra strain and intra clade genetic exchange events, which could influence clade evolution by favoring the acquisition of genetic material from more distant species rather than promote the genomic convergence of the L. rhamnosus clades. This concept is also supported by the close homology of many transporter encoding genes to more distant species, such as Lactobacillus farciminis, Enterococcus faecalis, and Leuconostoc kimchii (supplementary table  S2 , Supplementary Material online).
Acknowledgments This work was supported by a  Marie Curie International Training Mobility Network (Cross-Talk , grant agreement No:  21553-2 ) awarded to CC and  Nutricia Research, Utrecht ,  The Netherlands  and grants from the  Finland Academy of Sciences  (grants  137389 ,  141140  and  1272870 ), the  Netherlands Organization for Scientific Research  ( Spinoza Award and SIAM Gravity Grant ) and the  European Research Council  ( ERC Advanced Grant 250172 MicrobesInside ). The authors would also like to thank  Stan Brouns  for the useful advice and comments that greatly improved the manuscript,  Marie Christine Degivry  for the help managing the data from the  Danone Culture Collection  and  Dr Hanna Ja  ¨rvinen for the donation of the clinical L. rhamnosus strains.
A. 2 WFIRST-AFTA SPLC The same approach we used to define a discrete, algebraic propagation model for the clear circular aperture SPLC can be applied to an arbitrary telescope aperture. However, the propagation now relies on two-dimensional Fourier transforms rather than Hankel transforms, resulting in a combination of real and imaginary scalar field components. We again code the linear program in AMPL. However, we use the Gurobi 100 package to implement the solver algorithm instead of LOQO, since it better accommodates the much larger size of the two-dimensional problem. At each stage, we expand the complex exponential of the discrete Fourier transform into cosine and sine terms; doing so reveals simplifications arising from the geometric symmetry of the telescope pupil, thereby reducing the computational complexity and speeding up the optimization.
Acknowledgments We are grateful to  Mamadou N'Diaye  and  Rémi Soummer  for helpful suggestions and conversations about coronagraph design. We also thank  Wesley A. Traub  for providing us with his program to estimate the separation-contrast distribution of radial velocity planets. This work was partially funded by  NASA Technology Development for Exoplanet Missions (TDEM)  grant number  NNX09AB96G  and by the  Jet Propulsion Laboratory of the California Institute of Technology. A. J. E. Riggs is supported by  NASA  grant number  NNX14AM06H , a  NASA Space Technology Research Fellowship. Robert J. Vanderbei  acknowledges research support from the  Office of Naval Research (ONR). 15 Contrast degradation of WFIRST-AFTA characterization design for three pointing errors, shown at λ = 719 nm, the red end of the design centered on the λ 0 = 660 nm spectrograph filter.
The aperture 4° allowed one to simultaneously see the edges of both sides of the torso when fixating the navel. Images generated with the software MakeHuman, under a PDD licence (public domain). invariantly used to perform judgments of attractiveness with front-and back-view bodies, showing the largest fixation increase among the critical features involved in physical attractiveness. This observation posits the hips as being the critical diagnostic region for evaluating female attractiveness.