dataesr/scientific-paragraphs-categorization huggingface dataset:
Analytical methods The chemical composition of the diets was analysed using the following procedures: 1) dry matter was determined after drying at 105°C for 24 h, 2) protein content (N×6. 25) was determined by the Kjeldahl method after acid digestion, 3) fat was determined by petroleum ether extraction (Soxtherm), 4) gross energy was determined in an adiabatic bomb calorimeter (IKA, Heitersheim Gribheimer, Germany), 5) ash content was determined by incinerating the samples in a muffle furnace at 600°C for 6h, and 6) starch content was measured by an enzymatic method (InVivo Labs, France). Plasma glucose levels were determined by using a commercial kit (Biomérieux, Marcy l'Etoile, France) adapted to a microplate format according to the manufacturer's instructions. In silico analysis Orthologous Pck1, Pck2, Fbp1 and G6pc genes in the rainbow trout genome (3) were collected from the SIGENAE database (http://www.
sigenae. org) using the BLAST tool (all accession numbers are given in brackets in Figure  1 ). Sequences from other species were collected from the Ensembl Genome database (Ensembl release 77 -October 2014, http://www. ensembl. org) or Genbank. The Genomicus software program, version 01. 01 (http://www. genomicus. biologie. ens. fr/genomicus-trout-01. 01/cgi-bin/search. pl) was used to confirm their identities by establishing syntenic analysis (Fig. 2 ). Phylogenetic analyses were performed using the MEGA package version 5 software (52), as previously described  (11). The phylogenetic trees, based on full-length amino acid deduced sequences, were built using the Neighbor-Joining (NJ) method and confirmed by the Minimum evolution method (data not shown). The reliability of the inferred trees was estimated using the bootstrap method with 1,000 replications.
Branchiostoma floridae Pck (Genbank accession XP_002593974), Oikopleura dioica Fbp (Genbank accession CBY10016) and Latimeria chalumnae G6pc (Ensembl accession ENSLACG00000022601) protein sequences were used to root trees for Pck (Fig. 1A ), Fbp1 (Fig. 1B ) and G6pc (Fig. 1C ) phylogenetic analysis, respectively. New gene annotations were allocated according to ZFIN Nomenclature guidelines (http://zfin. org/). Percentage Identity Matrices established among paralogous teleost sequences (Table  1 ) were calculated with Clustal 2. 1 using the MUSCLE interface  (9). Total RNA extraction and cDNA synthesis Relative hepatic gene expression was determined by quantitative real-time RT-PCR. Samples were homogenised using Precellys®24 (Bertin Technologies, Montigny-le-Bretonneux, France) in 2mL tubes containing Trizol reagent (Invitro-gen, Carlsbad, CA, USA) and 2.
Melting curves were systematically monitored (temperature gradient 1. 1°C/10 s from 65-94°C) at the end of the last amplification cycle to confirm the specificity of the amplification reaction. Each PCR assay included replicate samples (duplicate of reverse transcription and PCR amplification, respectively) and negative controls (reverse transcriptase-and cDNA template-free samples, respectively). For the expression analysis, relative quantification of target gene expression was performed using the ΔCT method  (36). The relative gene expression of Rps16 (ribosomal protein 40S S6, (  28 )) was used for the normalization of measured mRNA as its relative expression did not significantly change over sampling time (data not shown). In all cases, PCR efficiency was measured by the slope of a standard curve using serial dilutions of cDNA.
Results In silico analysis of gluconeogenic genes By analysing assembly of the rainbow trout genome (3) we identified for the first time several genes sharing high sequence homology with the gluconeogenic zebrafish -1 1 -(Danio rerio) and human (Homo sapiens) orthologues: two were related to PCK, three to FBP1, and five to G6PC. A phylogenetic analysis, using full-length vertebrate protein sequences, and a syntenic analysis were then performed to clarify the identity of these rainbow trout sequences. Our Pck phylogenetic analysis showed that one of the rainbow trout sequences identified (GSONMG00082468001) was grouped with pck1 vertebrate orthologues while the other (GSONMG00059643001) was grouped with pck2 vertebrate orthologues (Fig. 1A ). We also found that both rainbow trout genes were located in two distinct syntenic groups, ccndbp1-psme1 for GSONMG00082468001 and dcaf11-psme1 for GSONMG00059643001, conserved around pck1 and pck2 loci, respectively, in zebrafish (data not shown).
The length of the deduced amino acid sequence was therefore not appropriate for inclusion in the phylogenetic analysis. However, this truncated trout sequence was submitted to a BLAST search against several sequenced teleost genomes (zebrafish, medaka (Oryzias latipes), fugu (Takifugu rubripes) and tetraodon (Tetraodon nigroviridis)) available in the Ensembl database. In all species, the hit with the highest score matched the fbp1a gene (data not shown). Finally, no informative syntenic conservation was found around fbp1a or around fbp1b2 because of the small size of the scaffolds. Nevertheless, fbp1b1, borne by a larger scaffold, was located in the gas1-C9-dab2-thsb4b syntenic group conserved in the zebrafish, medaka, stickleback (Gasterosteus aculeatus), tetraodon and fugu around the fbp1b gene (data not shown).
Temperature and salinity The PFB (4H-JENA © ) was fixed on the wet laboratory bench, close to the Cytosense, in order to share the same water inlet. This instrument recorded temperature and conductivity (from which salinity was computed) from the clean water supplied by the ship's seawater pumping system at a frequency of one sample every minute. Within the PFB data set, only data related to automated SFC analyses were selected for plotting temperature-salinity diagrams.
Mixed layer depth Daily water column temperature mapping was obtained from the Forecasting Ocean Assimilation Model 7 km Atlantic Margin model (FOAM AMM7), available from the My-Ocean database (http://www. myocean. eu. org/). Model output temperature depths were as follows: 0,  3, 10, 15, 20, 30, 50, 75, 100, 125  and 150 m. Average mixed layer depth (MLD) on the five sampling days was calculated from daily temperature data sets. MLD was defined as the depth associated with an observed temperature difference of more than 0. 2 • C with respect to the surface (defined at 10 m;  de Boyer Montégut et al. , 2004).
Statistics Statistics were run in R software (CRAN, http://cran. r-project. org/). Before running correlation and comparison tests on the different in situ sensors (for Chl a and total TFLR), the Shapiro normality test was run. When data did not follow a normal distribution, a Wilcoxon signed rank test was applied. Correlations between data were defined using Spearman's rank correlation coefficient. As the PHYSAT approach is based on the link between specific RA spectra (in terms of shapes and amplitudes) and specific phytoplankton composition, the set of remotely sensed data was separated into distinct groups with similar RAs. The PHYSAT RA found over the studied area and matching the in situ SFC samples was differentiated by applying a k-means clustering partitioning method (tested either around means  (Everitt and Hothorn, 2006)  or around menoids  (Kaufman and Rousseeuw, 1990) ).
PHYSAT anomalies and SFC phytoplankton community composition, extrapolation to the non-turbid classified waters in the North Sea Considering our database of coincident SFC in situ and MODIS remotely sensed observations, a total of 56 matching points were identified, from which only 38 points corresponded to non-turbid classified waters. Matching points between in situ sampling and remote sensing pixels for the purpose of the PHYSAT empirical calibration were selected in the daytime period 06:00-18:00. Additional samples col- Table  1. Minimal, maximal, average and standard deviation of abundance (cells cm -3 ) for each defined phytoplankton cluster followed by the size-estimated (µm) average ± standard deviation values. Cluster's name Abundance Average abundance ± SD Average size ± SD min-max (cells cm -3 ) (cells cm -3 ) (µm)   and 8 ).
Discussion Water mass dynamics generates patchiness which modifies phytoplankton community structure and makes it difficult to follow a population over time and at a basin scale. In this context, the hourly observation of phytoplankton at the singlecell and community level and its daily spatial structure resolution from extrapolation using PFT remote sensing mapping can help to follow spatial distribution of phytoplankton communities. The improvement of PFT mapping, i. e. from dominant groups to the community structure resolution, is one of the ideas generated in this paper. This paper shows for the first time that SFC data sets can be used for labelling PHYSAT anomalies at the daily scale. The SFC is a powerful automated system aimed to be implemented in several vessels of opportunity and monitoring programs for future PHYSAT anomalies identification at the daily scale and at the community structure level.
Acknowledgements. This study was funded by the  DYMPAHY  (Development of a DYnamic observation system for the assessment of MArine water quality, based on PHYtoplankton analysis) INTERREG IVA "2 Mers Seas Zeeën" European cross-border project, co-funded by the  European Regional Development Fund (ERDF)  and  French  ( ULCO-CNRS-UL1 ), English (Cefas) and Dutch (RWS) partners. We thank the captain and crew of the  RV Cefas Endeavour , as well as  Anne-Hélène Rêve  for chlorophyll a bench-top analysis. We also thank  Rodney Forster  for his invitation on board the ship during the  EU   FP7 PROTOOL (PROductivity TOOLs : Automated Tools to Measure Primary Productivity in European Seas) cruise. We are also grateful to our funding sources -the  CNRS  and the  CNES-TOSCA / PHYTOCOT  project. The authors thank  NASA/GSFC/DAAC  for providing access to daily level-3 MODIS binned products.
Spacer oligonucleotide typing, or spoligotyping, is a genotyping method used to study the epidemiology of the MTBC and it is based on the presence or absence of 43 spacers at the direct repeat locus  [24]. The visualization of spoligotype patterns based on an estimated evolutionary history is a procedure to predict emerging strains or genotypes associated with elevated transmission and can be appropriate for medium-large spatio-temporal scale analysis. This method was used to determine if M. tuberculosis strains were spreading faster than the background transmission rate  [25]  and implemented in DESTUS (Detecting Emerging Strains of Tuberculosis Using Spoligotypes), which is included in spolTools (http://www. emi. unsw. edu. au/spoltools/). This method was designed to be used with self-contained datasets corresponding to specific regions, rather than composite data from different countries or collection periods.
Genome sequence assembly and annotation High quality overlapping reads were merged using FLASH (Magoc et al. , 2011) and then assembled using Velvet  [32]  with k-value = 97 (S1 Table ). Contigs were annotated using BG7  [33, 34]  (S2 Table ). For annotation, a set of 191,017 reference proteins was used including (a) all Uniprot proteins from M. bovis and M. tuberculosis, (b) a set of bacterial antibiotic resistance related Uniprot proteins selected using the GO annotation terms "antibiotic resistance" and "response to antibiotic" and a selection of proteins based on similarity to the proteins of ARDB  [35] , and (c) all Uniprot proteins with Enzyme Code (EC) from MTBC. For whole genome comparative analysis, the 4 genomes were then aligned against the M. bovis reference genome sequence (AF2122/97; http://www.
ncbi. nlm. nih. gov/nuccore/31791177) using Differences program (Turrientes et al. , 2010) that allows comparisons at the whole genome level and particularly the detection of substitutions, insertions or deletions of any length and at any region of the genome. Genome sequence information and annotation was deposited in GenBank under the accession numbers CDHF01000001-CDHF01000049, CDHG01000001-CDHG01000059, CDHH01000001-CDHH01000094 and CDHE01000001-CDHE01000118 for MB1-MB4 isolates, respectively  [36].
Graphical representation and editing of the phylogenetic trees were performed with EvolView (v 198. 3)  [38]. The alignments obtained by MAFFT were used to perform codon alignments using HIV database website (www. hiv. lanl. gov; [42] ). Non-synonymous (dN) and synonymous (dS) nucleotide substitutions were classified using the SNAP method  [43]  implemented in HIV database website  [42]. SNPs were identified by pairwise comparison of MB1-MB4 and M. bovis AF2122/97 using SNAP.
Sequence analysis of Rv0050 To identify non-synonymous mutations that may be associated with virulence and/or transmission, the orthologs of peptidoglycan assembly protein locus Rv0050 (H37Rv) in strains MB1-MB4 were compared to the equivalent locus in animal isolates (BCG Pasteur 1173P2, AF2122/97, 09-1192) and human isolates (Bz 3115, B2 7505) of M. bovis available in the Gen-Bank. The M. bovis strains were selected among all strains for having distinct Rv0050 locus and/or distinct source of isolation. The presence of a putative signal peptide and cleavage site in the Rv0050 locus was analyzed with a previously validated program for their prediction in M. tuberculosis (SignalP; http://www. cbs. dtu. dk/services/SignalP-3. 0/)  [46]. Results were also confirmed with two other programs, Signal-Blast (http://sigpep.
This research was supported by grants  AGL2014-56305  and  IPT-2011-0735-010000  from  Ministerio de Economía y Competitividad, Spain , and the  European Union   FP7 ANTIGONE  grant  278976  and  Horizon 2020 COMPARE Grant 377/14. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Overview of the Model The model is a simple network of neurons organized as one 2D layer of 100 × 100 cells (Figure  1A ) and connected according to a 2D Mexican hat kernel (Figure  1B ). Our model is close to those of  Arai et al. (1993) ,  Marino et al. (2012 ), Trappenberg et al. (2001)  and  Wilimzig et al. (2006). Nevertheless, the critical differences are that we implemented a MH with a short range of inhibition and that we used spiking neurons (Figure  1C ) allowing to set up different synaptic decay times for inhibition and excitation (see Section Parameter Choice). Finally, we did not implement the logarithmic compression of space that is observed in the SC to remain general. The model is implemented in Python 2. 7 (http://www. python. org/) using the library BRIAN, a spiking neuron network simulator  (Goodman and Brette, 2008, 2009).
The code source for all the following simulations can be found at: https://github. com/Nodragem/SuppData-MHLimitations-Selection. The spiking neuron model  (Lapicque, 1907; Brunel and van Rossum, 2007)  used here is a simplification of conductancebased integrate-and-fire  (Hodgkin and Huxley, 1952; Shadlen and Newsome, 1998). Activity of each neuron of this network can be described with the following equations:                        τ m ∂V (n, t) ∂t = -(V-V 0 ) -g e (V-V e ) -g i (V -V i ) (a) ∂g e (n, t) ∂t = - g e τ e + α e n ′ f δ t f n ′ -t w e n ′ , n + α s w s F s (b) ∂g i (n, t) ∂t = - g i τ i + α i n ′ f δ t f n ′ -t w i n ′ , n (c) withV = V r if V > V t (1) Equation (a) describes the time course of the membrane potential V for all neuron n (Figure  1C , column 3).
We applied our feature selection method on five public datasets in oncology, and assessed the prediction performances of the optimal signatures as input to the diagonal linear discriminant analysis (DLDA) classifier. They were at the same level or better than the best-reported ones. The predictions were robust, and the signatures were almost always significantly smaller. We studied in more details the performances of our predictive modeling on two breast cancer datasets to predict the response to a preoperative chemotherapy: the performances were higher than the previously reported ones, the signatures were three times smaller (11 versus 30 gene signatures), and the genes member of the signature were known to be involved in the response to chemotherapy. conclusIons: Defining molecular signatures as the optima of a bi-objective function that combined the signature size and the interclass distance was well founded and efficient for prediction in oncogenomics.
Introduction The important amount of data that are generated by highthroughput technologies increased the need for computational analysis. 1 Among these technologies, microarray chips are used to measure the expressions of tens of thousands mRNAs simultaneously. Supervised and unsupervised classifications are the two broad methods for analyzing the gene expressions measured by these devices. Supervised classification methods are the major tool for extracting quantitative information from the datasets: the analyst selects representative variables from a training dataset (feature selection), designs a classifier relying on these variables (model design), then assesses the predictor performances on independent datasets from similar platforms (statistical validation) or on the same dataset by cross-validation procedures.
These methods are independent of the classification models used afterwards for delivering the predictions. Conversely, wrapper methods  4  search the set of features for optimal subsets of a given specific classifier. An objective function value is computed for each subset according to the performances of this particular classifier assessed on the learning dataset. Thus, the classifier is part of the valuation criterion, if not the valuation function itself. Finally, embedded methods perform the feature selection while designing the classification model. Typical examples are decision trees which have built-in mechanisms for performing feature selection while constructing the tree. Regarding the possible use of these classifiers in clinical routine, it is of upmost importance to minimize the number of genes in the molecular signatures because predictors relying on small sized signatures would be easier to implement into cheap low-throughput technologies.
Rather, we showed that our bi-objective function had exactly n + 1 non-dominated optima (Pareto sets) that we could compute by ranking the probesets according to their contributions to the interclass distance. Therefore, the optimization problem could be solved by a non-combinatorial approach where both the gene valuation function and the gene ranking process were direct consequences of the bi-objective optimization. The second step was to compute, among n + 1 optimal signatures, the one whose size was optimal regarding the classifier model. We achieved this computation with a wrapper approach. 1, 4 This computation too was non combinatorial because conducted in the very small set of n + 1 optimal signature (not in the huge set of all the signatures). We assessed our predictive modeling on seven datasets in oncology.
In all the experiments reported in the present article, the threshold value was set to 95 percent of the maximum accuracy value measured on the learning set of cases. On a quasi-plateau of length greater than or equal to 2 (ie, ul + 1 $ 2), we define the size of the locally optimal nonsingular predictor as k l l u [ , ] * =. Then, the optimal predictor was the smallest locally optimal non-singular predictor of highest accuracy. Its size was the minimum of the k l u values. datasets. We compared the performances of our predictors to those of the major published ones on seven different datasets in oncology. The purpose of this benchmarking was to assess the relevancy and predicting power of the optimal signatures our method unveiled. To this end, we computed the signatures on a significant number of different prediction problems in oncogenomics and compared their performances (using a DLDA classifier) to the state-of-the-art published predictive modelings.
Each of them was a two-class dataset, whose characteristics are summarized in Table  1. First, we used five datasets of tumors in different tissues (Datasets I-V) to evaluate the performance of our predictors. We invite the reader to refer to the cited articles for further explanations of the different methods and protocols that were used in the reported studies. In a second stage, we conducted a detailed study on two breast cancer datasets (Datasets VI-VII). cross validation. Three different cross validation techniques were used in this study: 1) k-FOLD CROSS-VALIDATION (k-fold CV) which consists of splitting the dataset into k sets of equal length, choosing k -1 sets as training sets for building the model, and testing the performances on the remaining set. This training/testing procedure was performed times with the k unique different combinations, and the average performances were computed.
2) REPEATED RANDOM SUB-SAMPLING is a cross-validation procedure very similar to the k-fold CV. The main difference is that instead of splitting the dataset only once then assessing the performances on the randomly created subsets, it splits the dataset randomly several times. Therefore the average performances can be computed on a broader number of simulated subsets, enhancing the robustness of the results. 3) LEAVE-ONE-OUT CROSS-VALIDATION is a specific case of the k-fold CV, taking k as the total number of samples, which implies that the model is built on k -1 samples and tested on the last one, this operation being repeated times. Since there are only partitions of k -1 samples, this method is run only once, and the total number of FP, TP, FN, and TN (whose sum is k) gives the performance results.
results We have applied our predictive modeling (Methods: Experimental Protocol (Non-biased)) to seven different microarray datasets in oncology. Five of them were used for benchmarking, and two of them for a thorough analysis of breast cancer signatures. evaluation of the performance of the predictors unveiled by the bi-objective optimization. We first performed a benchmarking on different datasets to objectively assess the performances of the signatures predicted by the bi-objective optimization. To this end, we have applied our predictive modeling to Datasets I-V (Methods: Datasets) and conducted two kinds of cross-validation procedures (Methods: Cross-validation): 1) a three-fold cross-validation whose results are reported in Table  2 , and 2) a leave-one-out crossvalidation procedure whose results are in Table  3.
The results outlined three interesting features: 1) the signature sizes (between 3 and 10 probesets in average), were rather small compared to what was commonly reported in the literature (most of the time containing between 20 and 50 genes); this property being exemplified in Table  4  where we compared different published classifiers to our model, 2) the performances were high and concordant (high accuracy, with both high specificity and sensitivity, see Terminology and Abbreviations). Only the brain cancer dataset seemed to harbor relatively weaker performances, but one still had ∼68% accuracy with signatures of ∼10 probesets. 3) The two different cross validation protocols had very similar results, which strengthen the confidence in the robustness of the predictions. We computed the δ -filtered signatures 100 times for different random training/testing subsets on Datasets I-V (Methods: 3-fold CV).