dataesr/scientific-paragraphs-categorization huggingface dataset:
Acoustic Measurements and Data Processing Acoustic data were collected using a Biosonics single-beam scientific echosounder transmitting at 38 kHz. The transducer was mounted at the end of a steel pole affixed to the side of the R/V Atlantis during NAAMES 1, 2, 3 and in a transducer well in the hull of the R/V Atlantis during NAAMES 4. The transducer was not calibrated in situ: calibrations were performed by the manufacturer before NAAMES 1, 2, and 3. While having uncalibrated data would be problematic if we were trying to estimate fish biomass, our analysis of anomalies in backscatter is not affected by this issue. Furthermore, we opted for comparing seasonal anomalies of acoustic backscattering to remove any effect caused by inter-annual, seasonal variability and transducer location. Pulse length and pinging period were of 2 and 0.
2 ms, respectively during NAAMES 2, 3, and 4 and were adjusted according to different conditions during NAAMES 1. Data were collected using the Biosonics DT-X system and converted and analyzed using a set of custom-written Matlab/Octave scripts. First, the time-dependent range and volume backscattering, S v , were calculated from 10-pings medians and corrected for the time-varying soundspeeds estimated using temperature and salinity measured at the surface using the shipboard thermosalinograph. Second, we followed the method described by De Robertis and Higginbottom (  2007 ) to quantify and mitigate background noise and the methods described by  Ryan et al. (2015)  to filter out the majority of impulsive and transient noise, as well as signal attenuation due to surface bubbles. Finally, echograms were visually inspected and the pings corresponding to unrealistic patterns were flagged.
ACKNOWLEDGMENTS We thank the captains and crew onboard of the  R/V Atlantis  for their assistance and enabling safe operation during the four expeditions. We would also like to thanks  Roland Proud ,  Elizabeth Phillips , and  Sophie Fielding  for their advice about the treatment of acoustics data and  Marnie Joe Zirbel  for the classification of the midwater trawl samples. We are also grateful to  Pascal Rivière  for the valuable discussions and to the two reviewers for their suggestions. This study has been conducted using E. U. Copernicus Marine Service Information (CMEMS).
Phenolic compounds identification by UPLC-ESI/qTOF/MS The extracts were characterized by ultra-performance liquid chromatography-electrospray ionization/quadrupole-time-of-flight high definition mass spectrometry technique (UPLC-ESI/qTOF/MS). Both samples were filtered (0. 22 μm) with an injection volume of 3 μL, by using a BEH C18 1. 7 μm column (Acquity UPLC, Waters, Mildford MA, USA) at 30 C, the flow rate was of 0. 3 mL/min with the next solvent system: acetonitrile (solvent A) and Water pH 2. 5 with trifluoracetic acid (solvent B) 15 % at 0 min, 35 % A at 6. 97 min, 35 % at 15 min. Then, the samples were nebulized by ESI-Q-ToF (Xevo G2-XS QTof, Waters, Milford MA, USA) and the ions were collected between 100 to 1500 Da, in positive (þ) mode with the MassLynx software (v4. 1, Waters, Milford, MA, USA).
All the data were analyzed in the MestReNova software (v12. 0. 20910, Mestrelab Research S. L. )  (Anguiano-Sevilla et al. , 2018). The ESI conditions were a positive ion mode capillary voltage 32 Kv; SAMPLIng cone 42; source offset 80; and N2 cone gas flows: cone gas 50 1/h: desolvation gas 5000 MS/MS cone voltage: 35V and energy collision: 25V dry gas  (Carballo-Uicab et al. , 2019).
Molecular docking analysis Molecular docking studies were employed to explore the potential interaction/binding between ligands and proteins involved in T2DM. Twenty-four ligands were selected according to the results of UPLC-ESI/ qTOF/MS that represent the major compounds found in the extracts. Metformin and sitagliptin were used as controls. On the other hand, we investigated the principal target proteins that participate in diabetes pathogenesis. In total, 13 proteins were identified. Their three-dimensional structure was obtained from the Protein Data Bank (PDB) database and their function was acquired from the literature (Table  1 ). Docking calculations were carried out using DockingServer (https://www. dockingserver. com/) (Bikadi and Hazai, 2009)  and BIO-VIA Discovery Studio 2017 (http://www.
Protein preparation for dockings Docking calculations were carried out on 13 proteins models involved in T2DM pathogenesis. The crystalline structures were downloaded from Protein Data Bank website (https://www. rcsb. org/pdb/home/home. do) and saved in pdb format. The ID's of selected proteins are listed below (Table  1 ). Essential hydrogen atoms, Kollman united atom type charges, and solvation parameters were added with the aid of AutoDock tool. Affinity (grid) maps of 20 Â 20 Â 20 Å grid points and 0. 375 Å spacing were generated using the Autogrid program. AutoDock parameter setand distance-dependent dielectric functions were used in the calculation of the van der Waals and the electrostatic terms, respectively  (Morris et al. , 1998).
Ligand and energy minimization The MMFF94 force field  (Halgren, 1998)  was used for energy minimization of ligands molecules using DockingServer. Gasteiger partial charges were added to the ligand atoms. Non-polar hydrogen atoms were merged, and rotatable bonds were defined. The ligands were downloaded from the PubChem software (https://pubchem. ncbi. nlm. nih. gov).
Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease. However, high quality RNA sequencing (RNA-seq) datasets along with clinical data with long follow-up allowing discovery of biochemical recurrence (BCR) biomarkers are small and rare. In this study, we propose a machine learning approach that is robust to batch effect and enables the discovery of highly predictive signatures despite using small datasets. Gene expression data were extracted from three RNA-Seq datasets cumulating a total of 171 PCa patients.
Data were re-analyzed using a unique pipeline to ensure uniformity. Using a machine learning approach, a total of 14 classifiers were tested with various parameters to identify the best model and gene signature to predict BCR. Using a random forest model, we have identified a signature composed of only three genes (JUN, HES4, PPDPF) predicting BCR with better accuracy [74. 2%, balanced error rate (BER) = 27%] than the clinico-pathological variables (69. 2%, BER = 32%) currently in use to predict PCa evolution. This score is in the range of the studies that predicted BCR in single-cohort with a higher number of patients. We showed that it is possible to merge and analyze different small and heterogeneous datasets altogether to obtain a better signature than if they were analyzed individually, thus reducing the need for very large cohorts.
To reduce costs and continue to improve prognostic, omics data are promising. With the decreasing price of RNA sequencing (RNA-seq), the accessibility of affordable technologies [e. g. , MinION from Oxford Nanopore Technologies  (Menegon et al. , 2017) ], the available PCa cohorts and the efficient computational approaches, transcriptomics is becoming a valuable resource to identify biomarkers  (Nikitina et al. , 2017). The rapid development of omics technology has led to the availability of many omics databases  (Marx, 2013; Almeida et al. , 2014; Stephens et al. , 2015) , including The Cancer Genome Atlas Program (TCGA)  (Tomczak et al. , 2015)  and those of the International Cancer Genome Consortium (ICGC)  (International Cancer Genome Consortium Hudson et al. , 2010) , thus opening an opportunity to apply and test machine learning algorithms  (Li et al.
, 2016). These algorithms have been utilized as an aim to model the progression and treatment of cancerous conditions, and resulted in effective and accurate decisionmaking  (Kourou et al. , 2015). However, many of the datasets results from patients cohorts that were either rather small and/or had insufficient follow-up of clinical history which limit their use for clinical outcome prediction. Hence, there is a challenge to set up predictive models that could anticipate the event of BCR, thus predicting the evolution of cancer, immediately after surgery. Consequently, we propose here a method to discover a transcriptomic signature that could be used to predict BCR events using a combination of datasets to increase the discovery potential. To this purpose, we applied specific preprocessing and cleaning steps on three RNA-seq datasets and established a machine learning protocol.
Datasets We retrieved three different RNA-Seq datasets of radical prostatectomy specimens with the associated clinical features. The first dataset is from TCGA cohort in the Prostate Adenocarcinoma (PRAD) project. The second dataset (GSE54460) is from a cohort constituted by  Long et al. (2014)  and the third dataset was provided by Prof C. Collins from the Vancouver Prostate Cancer Center (VPCC)  (Wyatt et al. , 2014). Quality of the BCR event data is dependent on patient clinical follow-up. A patient followed only a few weeks or months after surgery without showing BCR would be considered as a non-BCR case. These cases are a bias since the patient could have experienced a BCR event after the period of follow-up. Consequently, we discarded from our analysis the patients with no BCR whose follow-up was inferior to 60 months.
TCGA-PRAD Dataset Data from 498 samples were initially recovered from the PRAD project on the TCGA data portal 1. According to the TCGA Research Network (Cancer Genome Atlas Research Network, 2015) 131 samples must be discarded because of the presence of RNA degradation, as we did. We also ignored samples with less than 40% of tumor cells (column percent_tumor_cells in clinical file) and follow-up inferior to 60 months. We ended up with 52 samples after these filters.
GSE54460 Dataset The data were downloaded from NCBI website (GEO accession GSE54460) where sequencing and clinical data from 106 patients FIGURE 1 | Pipeline workflow. Quality control of raw data sequencing files is measured, then trimmed to remove their adaptors. Patient metadata are then filtered to keep only BCR patients with long follow-up. Retained sequences are then mapped, quantified and normalized. Finally, a machine learning approach is used to analyze the data to obtain a gene expression predictive signature and a model. were recovered. After selecting cases with a minimum of 60 months of follow-up, we ended-up with 96 patients of whom 54 had a BCR.
VPCC Dataset We obtained the raw fastq files and clinical data from 85 patients, available at European Nucleotide Archive of the EMBL-EBI under accession PRJEB6530. Patients treated with hormonal therapy before radical prostatectomy were removed because this treatment strongly alters RNA expression. After selecting patients for minimal follow-up we ended up with 23 patients of whom five experienced a BCR. The baseline characteristics of the resulting individual and combined cohorts after selection of eligible cases are summarized in Table  1.
Quality Control, Alignment and Gene Expression The quality of the raw fastq files from the TCGA cohort was measured using FastQC  (Andrews et al. , 2010) (v0. 11. 5 ) and Trimmomatic  (Bolger et al. , 2014) (v0. 32). A threshold quality per base of 30 (based on Phred 33) and a minimal length of 40 bases were applied. The transcriptomes were then mapped on GrCH38. p7 using Kallisto  (Bray et al. , 2016) (v0. 43. 0). The software Kallisto was used to estimate isoform counts, adjusted for the amount of bias in the experiment to ensure a coherent no-naive mapping. Default paired end parameters indicated in kallisto's manual were used. The index needed to run Kallisto is provided on the official github repository 2  , but can be manually created. Consequently, we computed gene counts with tximport  (Soneson et al.
, 2015)  (Figure  2 ). The Ensembl gene identifiers were converted with Biomart tools  (Kinsella et al. , 2011; Smedley et al. , 2015)  from transcript ID to gene ID. For both GSE54460 and VPCC datasets, we processed the raw fastq files using the same method as for the TCGA dataset. However, in GSE54460 the ribosomal sequences were still present within the reads, so we separated these sequences from the mapped reads and removed them. After mapping procedure, 29820 Ensembl genes were found in TCGA-PRAD dataset, 28704 in GSE54460 dataset and 32334 in VPCC dataset. The difference of number of Ensembl genes detected is explained by the sequencing depth of the datasets. A total of 25504 Ensembl genes were common to all sets and were retained for the analysis.
Normalization The gene expression data were normalized with the RUV method (Gagnon-Bartsch and Speed, 2012;  Risso et al. , 2014)  in each dataset separately following the default protocol indicated in the RUVseq package vignette. RUVg uses negative control genes [housekeeping genes (HKG)], assumed not to be differentially expressed. In order to normalize properly we selected in the literature a set of specific HKG candidates for PCa  (de Kok et al. , 2005; Ohl et al. , 2005; Chua et al. , 2011; Vajda et al. , 2013) : ACTB, PPIA, GAPDH, PGK1, GUSB, RRN18S, and RPL13A. The expression of these genes was tested by RT-qPCR in a series of 50 prostate tumors and the genes were shown to be stably expressed between tumor samples. We excluded from the final list the ribosomal genes RRN18S and RPL13A because ribosomal RNAs were removed from our RNA-seq datasets.
Machine Learning There are multiple approaches to treat biological data in a machine learning workflow  (Al-Jarrah et al. , 2015; Makridakis et al. , 2018). Many machine learning libraries exist, in various programming languages, such as MLR in R  (Lesmeister, 2015) , Scikit-Learn  (Garreta and Moncecchi, 2013)  in python and WEKA  (Hall et al. , 2009)  in Java. We chose the MLR (v2. 8) package in R to set up our work. Our general workflow is described in Figure  3.
Validation Strategy We performed a resampling to assess the performance of the learning algorithm, avoid over-optimistic results and get a more robust measure of the performance of our model. The entire dataset was split into a random stratified (i. e. , class balance preserved) training and testing sets, 1000 times, hence the classification algorithm is trained and tested on different sets. The measure of performance is an aggregated value (e. g. , average) of the individual performance on the test set. Because we have no repeated measures and independent variables (i. e. , the patients) we chose the subsampling method which is also the best in general in different benchmarks but is less effective computationally  (Bischl et al. , 2012). The resampling strategy was run 200 times with a split of 2/3 for training and 1/3 for test sets.
Feature Selection Feature selection was performed to reduce dimensionality to improve prediction performances by removing uninformative features, which has been proven successful in other studies  (Novakovic et al. , 2011). There are different approaches to identify relevant features  (Hira and Gillies, 2015; Singh and Sivabalakrishnan, 2015; Raza and Qamar, 2019). We chose information gain ranking, an entropy based method, that can handle both numerical (e. g. , gene expression) and categorical data (e. g. , clinical data). In MLR this method relies on the package FSelector which is an entropy based selection method  (Lin, 1991; Coifman and Wickerhauser, 1992). The detailed formula of our metrics.
Model and Features Selection Following our machine learning pipeline (Figure  3 ), we first reduced the dimension of the dataset and removed noninformative features to obtain 400 top ranked features to train and benchmark 13 models (Figure  4 ). We observed that the random forest (RF) algorithm  (Ho, 1995)  performed best on our data. The classical RF was chosen as the main model for our further analysis. Since our goal was to identify a very short genomic signature we looked up the BER rate and other metrics while varying the number of selected features, from 1 to 400, used in the model. We observed that the BER and MMCE dropped rapidly with a few features selected (<3) then oscillated around 0. 27 (Figure  5 ). The MCC and the accuracy (ACC) went up rapidly and stabilized in the same way.
Comparison of Omics and Clinic Models We compared the potential of omic data versus clinical data to assess the ACC of our omics model. A RF model for the clinical data (Grade, stage, and PSA) and a merged model combining clinic and omics data were set up following the same protocol used for the omics data. For the clinical model the best BER obtained was 0. 311 and for the mixed model the best BER obtained was 0. 276 (Table  4 ).
Single Cohort Performance To further assess the performance of the three-gene model obtained with the combined dataset, we also performed the analysis with the individual cohorts. We used the RF algorithm iterated on the 50 best features from Information Gain on the three datasets evaluated by leave one out group validation (i. e. , two datasets for training, one for testing), and the combined dataset evaluated by resampling (see section "Validation Strategy"). The results are displayed in Figure  9  and show that the combined dataset offers better and more stable performances.
to identify multigenic signatures containing a reasonable number of potential targets. The identified genes could be eventually verified in other cohorts or by experimental validations. One key point should be to add gradually smaller datasets to control the signature stability with various experiments and technologies. Integrate too large cohorts in this approach will imbalance model parameters in favor of that cohort, then all the advantages of using several small dataset will be lost. This approach has the advantage of offering a small research team the opportunity to integrate their own work in a larger view. After integrating more dataset, a set up in a specific technology such as TaqMan probe to evaluate gene expression could be proposed as diagnosis and maybe to develop drugs  (Laetsch et al.
CONCLUSION By using an appropriate data transformation strategy and machine learning pipeline, we have identified a three-gene signature. With the decreasing price of RNA sequencing and its growing accuracy there are opportunities for less invasive and faster exams if the right biological variables are chosen. Other investigations on other omics data using the same machine learning approach could be undertaken, such as using miRNAs  (Kristensen et al. , 2016; Matin et al. , 2018). We also showed that it is possible to concatenate several cohorts to get stable and performing models from heterogeneous RNA-Seq PCa datasets, hence showing a robustness against batch effect. This study demonstrates the potential of taking advantage of many independent datasets produced on the same disease. Machine learning algorithms can handle the batch effect if there is the right preprocessing pipeline applied on the data.
DATA AVAILABILITY STATEMENT Publicly available datasets were analyzed in this study. This data can be found here: TCGA at GDC data portal; GEO accession GSE54460; The European Nucleotide Archive (ENA), accession number PRJEB6530 from  Wyatt et al. (2014). Moreover, the scripts developed for this study and the processed read counts are available at github. com/ArnaudDroitLab/prostate_BCR_ prediction.
Moreover, the topology of the reconstructed phylogenetic tree is suggestive of historical dissemination by this strain across both cattle and buffalo species and, at multiple geographical locations in the Marajó Island (Fig. 1 ). Next, in order to provide an adequate evolutionary background for this strain, we compared the SNP pairwise distance between the isolates of our study and a global M. bovis dataset comprised of 3 402 M. bovis genomes publicly available, along with two genomes obtained from ANSES (Maisons-Alfort, France), isolated in France and belonging to the SB0822 and SB0855 types (Supplementary Table  S1 ; Fig. 2 ). This global dataset showed an ample and continuous distribution of pairwise SNP distances, congruent with the M. bovis global phylogenetic representation of this dataset (Fig.
2 ). Across these, only eight genomes were found to be within a maximum pairwise distance of 150 SNPs from the Marajó strains. To further investigate the phylogenetic context of the the Marajó strains, a total of 240 M. bovis isolates, representative of 150 SB types (along with 18 spoligotyping patterns not found in the Mycobacterium bovis Spoligotyping Database) and originating from over 15 countries, were selected for maximum-likelihood phylogenetic analysis (Fig. 3 ). This sample of 240 M. bovis genomes included the SB0822 and SB0885 strains isolated in France with the same spoligotype pattern as the Marajó isolates and the M. bovis reference AF2122/97. The phylogenetic tree obtained confirmed the monophyletic nature of the Marajó M. bovis isolates in a global context, and, albeit unclassified as per the current rules and genetic markers associated with the different clonal complexes, these strains did form a parallel branch to the European 2 clonal complex  18  and therefore shared a more recent common ancestor with this specific clade when compared with the other clonal complexes.
embl. de). suggests that bTB epidemiology in this setting is dominated by a single strain undergoing clonal expansion across different herds of cattle and buffalos. The SB0855/SIT986 profile an uncommon one but it has been previously detected in European countries, such as France, Germany, Spain and Belgium. On the other hand, the SB0822/SIT986 profile is not as uncommon and, according to SITVIT2 and Mycobacterium bovis Spoligotyping Database (Mbovis. org), it shows widespread distribution across Europe, South America and North Africa with increased incidence in France. This latter profile has been in fact detected in Southeast Brazil (Minas Gerais and Espírito Santo states) but at a low prevalence when compared with other spoligotyping profiles  24. A possible link to France can be speculated since it has been proposed that bTB in South America has been introduced via cattle importation from Europe  25, 26.
Moreover, comparison with a global genomic dataset confirmed the uniqueness and genomic distinctiveness of the M. bovis Marajó strain herein described. Although the Marajó strains comprise a monophyletic clade, these strains are not classifiable in a known clonal complex but do in fact form a parallel branch with the European 2 strains while showing a high genetic differentiation from these. M. bovis European 2 strains are more prevalent in the Iberian Peninsula, are present at low frequencies in France and Italy, and are absent from the British Isles  18. The phylogenetic history and multivariate populational genetics analysis show that isolates detected in Germany and USA are genetically closer, arguing mostly in favor of the emergence of this strain due to cattle importation from Europe with later dissemination across Buffalo herds.