dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, this final state does not differentiate between single top quark and top quark pair production. A measurement of top quark production was also performed by the LHCb collaboration in the µb b and eb b final states using the same dataset  [5] , where the top quark pair (tt) production cross-section was measured with a precision of 40%. The measurements are in agreement with the SM prediction, calculated to a precision of 25% to 30% with MCFM  [6]  using the CT10 PDF set  [7] , and are limited by uncertainties on the b-tagging efficiency and the background composition. In 2015 and 2016, a data sample corresponding to an integrated luminosity of 1. 93 fb -1 was collected by the LHCb experiment at a centre-of-mass energy of 13 TeV. The increased energy leads to an increase of a factor of ten in the tt production cross-section within the LHCb acceptance with respect to collisions at a energy of 8 TeV  [8].
Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers. The online event selection is performed by a trigger, which consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. In this paper, the presence of a muon is used to select candidate events at both stages of the trigger; the hardware stage selects muons with a transverse momentum p T > 1. 76 GeV and the subsequent software trigger requires that a muon with p T > 12 GeV is present. To estimate the trigger, reconstruction and selection efficiencies, to determine background contributions, and to compare the selected data sample to theoretical expectations, simulated pp collisions are generated using Pythia8  [11, 12].
More details are given in Ref. [26]. Lepton isolation criteria is applied by rejecting events where the transverse component of the vector sum of the momentum of all reconstructed charged particles within ∆R < 0. 5 of either lepton is greater than 5 GeV. The leptons are also required to be consistent with originating from a common primary vertex, satisfying IP < 0. 04 mm. After applying the full selection, a total of 44 events are retained in the data sample. In addition to the signal, several physics processes contribute to the selected data sample, either through the presence of an identical final state, or through the misidentification of one or more of the final state objects. The following background processes are considered. • Lepton misidentification where the muon and electron candidates are produced through the misidentification of one or two hadrons.
Cross-section calculation The cross-section, σ tt , is measured in the fiducial region defined by the p T , η, and ∆R requirements placed on the muon, electron and b-jet candidates, and is calculated using the formula σ tt = N -N bkg L • ε •F res , ( 1 ) where N is the total number of candidates selected in data, N bkg is the sum of the expected background contributions, F res is a resolution factor that accounts for migrations in to and out of the fiducial region, ε is the efficiency to reconstruct and select the signal events, and L is the integrated luminosity of the data sample. The instantaneous luminosity is measured continuously during the acquisition of physics data by recording the rates of several selected reference processes. The crosssection of these processes is measured during a dedicated data-taking calibration period, using a beam-gas imaging method specific to the LHCb detector  [27, 28] , which gives a luminosity calibration with an uncertainty of 3.
The signal yield is determined by a fit to the invariant mass of the reconstructed B hadron before and after the tagging requirement is applied. A similar level of agreement between data and simulation is observed as in the previous studies, and consequently the same uncertainty is applied. The selection efficiency refers to the efficiency of the isolation and impact parameter requirements applied to the leptons. The impact parameter efficiency is obtained from Pythia8 simulation where the impact parameter distribution is smeared using factors obtained from a comparison of Z → µ + µ -events in data and simulation. The difference between the efficiency obtained before and after the smearing procedure is taken as a systematic uncertainty. The efficiency of the isolation requirement is obtained directly from Pythia8 simulation, with a systematic uncertainty applied to account for differences in data and simulation.
Conclusion The cross-section for top quark pair production in the forward region at LHCb has been measured using the µeb final state, where the presence of a muon, electron and b-jet are used to identify tt candidates. The cross-section is measured in two fiducial regions: where fiducial requirements are placed on the final state objects, and where fiducial requirements are placed on the top quarks themselves. The latter fiducial cross-section is obtained by scaling the former by an extrapolation factor obtained from aMC@NLO. The measurement precision of 20% is comparable to prior measurements of top quark production at LHCb in Run 1 and to the precision of the theoretical predictions. The measurement uncertainty is dominated by the statistical precision of the data sample and the knowledge of the b-tagging efficiency.
Original study It was a cross-sectional, community-based study using multistage probability sampling, of a representative sample of 501 adult Lebanese men and women (age ≥ 18 years) residing in Greater Beirut that were recruited between February and June 2014. The objective of the study was to assess the level of Bisphenol A among the residents in Greater Beirut. The study and the current analysis were approved by the Institutional Review Board (IRB) of the American University of Beirut. The methods were carried out in accordance with the relevant guidelines and regulations, and all participants signed an informed consent form. Face-to-face interviews, anthropometric measurements and blood withdrawal for laboratory tests were carried out by very well-trained study personnel. The questionnaire included data on: (1) demographic and socioeconomic characteristics: age, gender, marital status, education, occupation, crowding index based on number of residents and rooms in the dwelling, and monthly income bracket per family; (2) lifestyle: current smoking intake to include regular cigarettes and narghileh (a type of hookah pipe commonly smoked in Lebanon), alcohol intake, caffeine intake, physical activity (using the short version of the International Physical Activity Questionnaire (IPAQ)  [35] ) and questions on sleeping habits; (3) medical history for chronic diseases: such as type 2 diabetes mellitus, hypertension and dyslipidemia.
Statistical analysis Data were entered into SPSS version 23. 0 (IBM, USA) and a P <0. 05 was considered to indicate statistical significance. RTL was categorized into 3 groups based on statistical grounds (tertiles). Data were computed as means ± standard deviation (SD) for continuous variables, and as numbers and percent for categorical variables. In addition to the questions on whether patients were diagnosed with type 2 diabetes mellitus, whether they were treated for diabetes and the associated laboratory values, a "definite diabetes" variable was computed for subjects who were diagnosed with diabetes and/or both fasting blood sugar (FBS) was ≥126 mg/dL and HbA1C was ≥6. 5%  [39]. Similarly, for hypertension, a "definite hypertension" variable was computed for those who were diagnosed with hypertension or had an abnormal blood pressure reading upon recruitment (systolic blood pressure≥140 mm/Hg or diastolic blood pressure≥90 mm/Hg)  [40].
ACKNOWLEDGMENTS This project has received funding from the  European Union   H2020  program  MSCA-ITN-SUPUVIR  under grant agreement  722380 , the  EIPHI Graduate School  ( ANR-17-EURE-0002 ), the I- SITE BFC Program  ( ANR-15-IDEX-0003 ), and the  Conseil R égional de Bourgogne Franche-Comt  é. The authors thank  V. Pecheur ,  A. N. Ghosh ,  C. Lechevalier ,  L Gauthier-Manuel ,  A. Mosset , and  C. Billet  for technical support.
The suspension was mixed and incubated at 37 C for 30 minutes, washed again in PBS, and the pellet was resuspended in 500 μL of PBS before flow cytometric analysis using an FACSCalibur (Becton Dickinson & Co, Franklin Lakes, New Jersey). Data were collected for 10,000 events through a gated region from each sample (CellQuest Pro software; Becton Dickinson & Co), and the mean fluorescence intensity (MFI) was obtained. The A, B, or both antigen RBC surface expression was designated as negative for an MFI < 10 and positive for any MFI ≥ 10. Incubation time, quantity of reagent, or blood samples were decided based upon experience from the laboratory and manufacturer instructions. 1, 20.
An evidence-based and objective definition of MToP outcome is urgently needed, especially as MToP is used increasingly around the world. Therefore, this study endeavours to develop a consensus around a set of relevant and welldefined first trimester MToP outcomes, as recommended by the Core Outcomes in Women's and Newborn Health initiative  [6]. It may be considered a first step for the Standardizing Abortion Research Outcomes project, dedicated to producing, disseminating and implementing a core outcome dataset for medical and surgical abortion research  [7].
Background White-rot fungi are a group of wood decay fungi that play a major role in carbon cycling. They are the ultimate degraders of highly recalcitrant raw lignocellulosic substrates in forest ecosystems. From a biotechnological aspect, their natural capacities are particularly suited for plant biomass conversions such as the production of biobased products from renewable raw materials  [1]. Whiterot fungi can degrade all plant cell wall polymers through the concerted secretion of complex sets of hydrolytic and oxidative enzymes. These enzymes belong to enzyme families including glycoside hydrolases (GH), carbohydrate esterases (CE), pectate lyases (PL), and auxiliary activity oxidoreductases (AA) as classified in the Carbohydrate Active Enzyme database (CAZy; https ://www. CAZy. org;  [2] ).
Polyporus brumalis activates an extensive arsenal of oxidative enzymes during growth on wheat straw To identify enzymes responsible for the observed selective degradation of lignin, we investigated the evolution of transcriptomes and secretomes of P. brumalis over time by comparing a set of SSF on wheat straw for 4, 10, and 15 days. The robustness of the experimental setup was confirmed by the high consistency of the mean and distributions of the normalized log2 read counts of the transcripts among the biological replicates (Additional file 2: Figure  S1 ). We constructed network maps for the fungal transcriptome and secretome using the visual multi-omics pipeline SHIN + GO, which allowed us to overview the genome-wide transcriptomic and secretomic activities and pinpoint molecular events of interest  [7].
3 ). A total of 727 genes were differentially highly transcribed after 4-day SSF (Additional file 3: Table  S2 ; Additional file 4: Dataset S2). According to KOG predictions  [27] , these genes were mainly related to metabolism, cellular processes and signaling, information storage and processing. The importance of intracellular signaling in this adaptive response was highlighted by the induction of 14 genes coding for predicted protein kinases and five for putative transcription factors. In addition, genes involved in detoxification and excretion were upregulated at the early time point including twenty, five, and four genes coding for  predicted Cytochrome P450, Glutathione S-Transferases (GSTs), and ABC transporters, respectively, suggesting the active detoxification of compounds released from the biomass or newly generated during the wheat straw degradation (Additional file 5: Figure  S2 ).
Gene functional annotation The genome was annotated using the Joint Genome Institute (JGI) Annotation Pipeline and made publicly available via JGI fungal genome portal MycoCosm  [49]. Proteins predicted to be secreted by SignalP 4. 1. (threshold 0. 34;  [50] ), TargetP 1. 1  [51]  or Phobius 1. 01  [52]  were analyzed with pscan to withdraw proteins targeted at the endoplasmic reticulum. The retrieved proteins with no transmembrane domain according to TMHMM Server 2. 0. or a single transmembrane domain corresponding to the signal peptide were identified as predicted secreted. For CAZyme gene expert annotation, all putative proteins were compared to the entries in the CAZy database  [2]  using BLASTP. The proteins with E values smaller than 0. 1 were further screened by a combination of BLAST searches against individual protein modules belonging to the AA, GH, GT (Glycosyl Transferases), PL, CE and CBM classes (https ://www.
CAZy. org/). HMMer 3  [53]  was used to query against a collection of custom-made hidden Markov model (HMM) profiles constructed for each CAZy family. All identified proteins were manually curated. Within families, subfamilies were manually defined according to their homology relationships between members of the focal family. Class II ligninolytic peroxidases (AA2s) were annotated as LiP, MnP or VP on the basis of the presence or absence of specific amino acid residues at the substrate oxidation sites  [17]  after homology modeling using crystal structures of related peroxidases as templates and programs implemented by the automated protein homology modeling server "SWISS-MODEL"  [54]. Proteins classified as KOG "Cytochrome P450" were subject to BLASTP comparisons against the Fungal CYP Database (http://p450.
Gene family expansions/contractions CAZyme gene family expansions or contractions were analyzed using the CAFE Software v3. 1 (Computational Analysis of gene Family Evolution;  [15] ) and the numbers of genes coding for CAZymes from 18 Polyporales genomes. The selected public genomes were those from Pycnoporus cinnabarinus  [56] , Phanerochaete chrysosporium  [57] , Fomitopsis pinicola, Wolfiporia cocos and Trametes versicolor  [58]   [58]  and Heterobasidion annosum  [59]  were used as outgroups. The protein sequences deduced from these genomes were downloaded from the Mycocosm portal (https ://genom e. jgi. doe. gov/progr ams/fungi /index. jsf ), with authorization of the principal investigators when not yet published. Groups of orthologs were obtained from these genomes using orthoMCL version 2.
0. 9  [60]  with default parameters and using NCBI BLAST version 2. 4. 0 +  [61]  in conjunction with MCL version 14-137 (http://mican s. org/mcl/) under default settings. A set of 20 uni-copy genes was selected among the 25 best performing genes for Polyporales phylogenetics (  [62] ; Additional file 9: Dataset S1). The concatenated 20-gene dataset in each genome had a mean number of 28,996,9 amino acids, the shortest being for Pycci1 (28144 aa) and the longest for Stehi1 (30313 aa). Alignments were done using Mafft v7. 271  [63]  and filtered with GBLOCKS version 0. 91b  [64]. The final tree was generated with RaxML version 8. 2. 4  [65]  and PROTGAMMAWAG as substitution model.
Identification of the fungal proteins secreted during fermentation The water extracts collected after 4, 10 and 15 days of culture were filtered using 0. 22-μm pore-size polyethersulfone membranes (Vivaspin, Sartorius), diafiltered with 50 mM sodium acetate (pH 5. 0) and concentrated using a Vivaspin polyethersulfone membrane with a 10-kDa cutoff (Sartorius). LC-MS/MS analysis of the secretomes was performed as described by Navarro et al. [66]. Briefly, 10 μg of proteins was in-gel digested using a standard trypsinolysis protocol. For protein identification, online analysis of peptides was performed with a Q-exactive mass spectrometer (Thermo Fisher Scientific), using a nanoelectrospray ion source. MS/MS data were queried against the catalog of predicted proteins from the P. brumalis genome, and an in-house contaminant database, using the X!Tandem software (X!Tandem Cyclone, Jouy en Josas, France).
Transcriptome and secretome network maps Total RNA was extracted from 100 mg tissue as described in Couturier et al. [67]. RNA quantity and quality were determined using the Experion RNA Std-Sens kit (QIA-GEN). RNASeq was done using 150 bp long paired end reads obtained on Illumina HiSeq-2500 (Beckman Coulter Genomics). The quality of raw fastq reads was checked with FastQC (Simon Andrews, Babraham Bioinformatics, 2011; https ://www. bioin forma tics. babra ham. ac. uk/proje cts/) and reads were cleaned with Sickle Tool (Joshi and Fass, 2011; https ://githu b. com/najos hi/sickl e) with the following criteria: quality threshold of 20; length threshold of 20. RNA reads were aligned to the genome of P. brumalis BRFM 1820 using TopHat2 with only unique mapping allowed. Read counts were determined by HTSeq, normalized using ddsNorm from the DESeq 2 Bioconductor package  [68]  and log2 transformed.
Genome-wide profiles with integrated transcriptome and secretome data were constructed using the SHIN part of the pipeline SHIN + GO  [7]. A single self-organizing map (SOM) was generated with the normalized log2 read count of the genes from all biological replicates. The map units used were 437. The number of iterations used was 43,700 (437 × 100) times to minimize the Euclidian distances between the nodes for the optimal convergence. Two sets of omics topographies (Tatami maps) were generated based on the master SOM. Transcriptomic maps showing node-wise mean transcription were created with the averaged read count of the genes clustered in each node. Secretomic maps were made with the count of detected proteins. For each protein ID, the presence of the protein in the secretome was counted as one.
Funding This work was supported by The  French National Agency for Research  ( ANR-12-BIME-0009 ,  ANR-14-CE06-0020-01 ). The work by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. The work by  CSIC  was supported by the  Spanish Ministry of Economy, Industry and Competitiveness  ( BIO2017-86559-R ). The  HPC resources of Aix-Marseille Université  was supported by The  French National Agency for Research  ( ANR-10-EQPX-29-01 ).
RNA sequencing, transcriptome read alignment, and data analysis RNA was extracted and prepared from frozen tissue with the Qiagen RNeasy Mini Kit (Cat. 74104), according to the manufacturer's protocol. All samples were subjected to quality control on a Bioanalyzer instrument and only RNA with RIN (RNA Integrity Number) > 7 was used for sequencing. RNA sequencing libraries were prepared from 1 μg of total RNA using the Illumina TruSeq Stranded mRNA Library preparation kit that can be used to perform strand-specific sequencing. A first step of polyA selection using magnetic beads was performed to focus sequencing on polyadenylated transcripts. After fragmentation, cDNA synthesis was performed and the resulting fragments were used for dA-tailing and then ligated to TruSeq indexed adapters. PCR amplification was then performed to create the final cDNA library.
After qPCR quantification, sequencing was carried out using 2 × 100 cycles (paired-end reads, 100 nucleotides) on a Illumina HiSeq2500 instrument (high-output flow cells) to obtain about 200 million paired reads per sample. Reads were aligned to the human reference genome hg19/GRCh37 using TopHat2 v2. 0. 6  [13]  with the following parameters: global alignment, no mismatch in the 22 bp seed, up to three mismatches in the read, library type fr-firststrand. Gene expression values (FPKM = fragments per kilobase per million reads) were computed by Cufflinks v2. 2. 1  [14]  and further normalization between samples was performed using quantile normalization (R/Bioconductor package limma)  [15]. Gene normalization and differentially expressed gene (DEG) analysis was performed using the DESeq2 package.
A false discovery rate (FDR) of 0. 1 was used to correct for multiple testing. Ingenuity® Pathway Analysis (IPA; Ingenuity® Systems, http://www. ingenuity. com/) was used to search the relevant molecular functions, cellular processes, and canonical pathways  [16]. Differentially expressed gene lists were mapped to their corresponding gene objects in the Ingenuity® pathway knowledge base. These so-called focus genes were then used as a starting point for generating biological networks. A score was assigned to each network in the dataset to estimate the relevance of the network to the uploaded gene list. This score reflects the negative logarithm of the P value indicating the likelihood of the focus genes being randomly found together in a network. Scores ≥ 2 were considered to be significant with a 99% confidence level.
Targeted NGS and somatic mutation data collection Targeted next-generation sequencing (NGS) was applied to a custom-made panel of 91 "breast cancer-specific" genes selected for their involvement in breast cancer. This BreastCurie panel is composed of the most frequently mutated genes (mutation frequency higher than 1%) in breast cancer from the cancer genome atlas (TCGA)  [19]  and genes with potential targetable mutations. The list of genes and group of pathways are presented in Additional file 1: Table  S1. DNA was extracted from formalin-fixed paraffinembedded tissues with the NucleoSpin tissue kit (Macherey-Nagel), and 50 ng of DNA was used for NGS. Targeted sequencing was performed using Illumina Hiseq2500 technology according to the manufacturer's instructions (Illumina, San Diego, CA, USA).
Sequence data were aligned to the human reference genome (hg19) using Bowtie2 algorithm. Median depth was 607× and 87% of targets achieved 5% 100× depth. Single-nucleotide variants (SNVs) and indels were called using GATK UnifiedGenotyper with default parameters. Genes were classified as oncogenes or tumor suppressor genes based on the literature. The Catalog of Somatic Mutations in Cancer (COS-MIC) was used to confirm non-synonymous, exonic/ splice variants observed at a frequency lower than 0. 1% in the population. Moreover, non-COSMIC frameshifts, splice-site, and stop-gain variants were added for tumor suppressor genes. Further confirmation of detected variants was performed by comparison with public databases (cancerhotspot, cbioportal, tumorportal, oncoKB). Detected mutations were then classified as pathogenic variants, unknown pathogenic variants, and non-pathogenic variants.
Statistical analysis Statistical analysis was performed with GraphPad Prism (version 5. 01) software. Results were considered to be statistically significant at a p value < 0. 05 (*), < 0. 01 (**), or < 0. 001 (***). Wilcoxon's paired test was used to compare pre-NET and post-NET levels of TILs and Mann-Whitney test was used to compare pre-NET and post-NET levels of TILs between responders and non-responders. Contingency tables were constructed and Fisher's exact tests and Mann-Whitney tests were performed to compare gene mutation profiles between responders and non-responders. Follow-up was measured from the date of randomization to the date of last news for patients with no events. Relapse-free survival (RFS) was determined as the interval between initial diagnosis and detection of the first relapse regardless of its site (local, regional, or distant) and death from any cause.
qRT-PCR results Four of these genes (CHGB, TPTE, NMBR, KCNK3) were tested by qRT-PCR in 70 RNA pretreatment samples from CARMINA02 patients. NMBR and KCNK3 expression changes were in accordance with RNA sequencing analysis results, although the differences between responders and non-responders were not statistically significant (data not shown). As CHGB and TPTE expression were detected on few samples, further analysis was not performed.