dataesr/scientific-paragraphs-categorization huggingface dataset:
Each simulated MSA is derived from a different ancestor, randomly assigned from the emission probabilities at each position of the HMM model of the real MSA. For each of the three real alignments, we simulated thirty MSAs of approximately 250, 500, 750, and 1,000 sequences, for a total of 360 simulated alignments. The final number of sequences in the simulated alignments can be slightly different from the number requested, due to the necessity to maintain the same ratio between branches and tree topology during the simulation. The statistical properties of the simulated and real alignments, as well as examples of the phylogenetic trees, are shown in Supplementary Figs  S1  and S2. In MSAvolve, correlated substitutions may occur by random mutations (stochastic noise), recombination or functional/structural demands (positions forced to coevolve), and, in our simulations, the 15 per cent of positions in the sequence were selected randomly as covarying pairs.
2013) , where the required mutations rtL180M and rtM204V appear first, followed by mutation rtS202I/G  (Locarnini and Yuen 2010; Villet et al. 2007 ). These 'temporal' events in the mutational process are observed by BIS2 when considering a subset of sequences where rt202 and 712 (in RNaseH) coevolve and where rtG202 and Q712 appear in the context of rtM180 and rtV204, but not vice-versa (cluster 58, Fig. 5 bottom ). In other words, in our dataset, mutations rtG202 and Q712 never appear in sequences without the mutations rtM180 and rtV204, and this fact suggests that the temporal pattern is due to drug pressure. rtM180 and rtV204 appeared with the variant rtS202 þ L712 too. The same 'temporal' pattern identifies coevolution between the known ETV resistance mutation rtT184L and the variant rtS75T þ rtP95T (Cluster 20, Fig.
All reported clusters related to LMV resistance present at least two known resistance mutations and they are encountered with very low frequency in treatment-naı ¨ve individuals, making this hypothetical situation even less likely to occur  (Choi, Lee, and Kim 2018). On the methodological side, it should be observed that our Pol analysis has been realised fully automatically. BIS2TreeAnalyzer systematically applied BIS2 to all subtrees of the distance tree for the available viral sequences of each HBV genotype. Clusters of coevolving positions were automatically handled and combined to evaluate the confidence. This automaticity makes BIS2TreeAnalyzer a suitable approach to compute coevolution in any dataset of conserved sequences, not limited to any particular taxon. The analysis was realised on the full set of sequences in the reference repository database of HBV sequences.
Yet, coevolution signals that BIS2TreeAnalyzer detected depend on the sequences present in the database. The future availability of large quantities of HBV sequences from patients will allow to reach a thorough overview of the landscape of evolutionary solutions taken by HBV to resist drugs. BIS2TreeAnalyzer can help the systematic identification of these combinations of substitutions, making accessible the writing of the large catalogue of mutational variants, the temporal patterns regulating their appearance and their effects. In this respect, an analysis of 639 HBV whole-genome sequences obtained from eleven patients showed that adaptation to LMV is not associated with strong bottleneck events  (Thai et al. 2012) , implying that drug resistance is a complex trait that is much more complex than previously described because it is encoded in the full HBV genome rather than a few substitutions in the RT domain.
Funding The French 'Agence Nationale de la Recherche sur le SIDA et les hepatites virales' (ANRS CSS4 ECTZ25224-2017-19), the Institut Universitaire de France, LabEx CALSIMLAB (public grant ANR-11-LABX-0037-01 constituting a part of the 'Investissements d'Avenir' program-reference: ANR-11-IDEX-0004-02). Conflict of interest: None declared.
Furthermore, often, pedigree inbreeding is also ignored in the computation of the inverse relationship matrix (A-inverse) using  Henderson's (1976)  rules, which results in an approximated BLUP and a further level of approximation for the computation of modelbased individual accuracies. As a result, even for small data sets where exact computations of PEVs are feasible, reported accuracies are often approximate. The objective of this paper is to present correct ways of considering inbreeding in the computation of accuracy and compare with results from ignoring inbreeding, in a single step GBLUP and traditional pedigree-based BLUP context, using two datasets: a dairy cattle data set with genotyped individuals (using BLUP and SSGBLUP) and a closed rabbit selection line with a large number of generations and steady increase of inbreeding (using BLUP).
Datasets The dairy cattle data set involves milk yield from the Uruguayan Holstein national genetic evaluation. Dairy cattle records were provided by the Uruguayan National Dairy Herd Improvement (MU -Mejoramiento y Control Lechero Uruguayo, Montevideo Uruguay) and genealogical information was provided by the National Herdbook (ARU -Asociacion Rural del Uruguay, Montevideo, Uruguay). Data consisted of 305 DIM milk yields from 925,821 records of 377,612 cows from lactation 1 to 5 since 1990 to 2018. Pedigree file was created using 3 generations of ancestors backwards from either phenotyped or genotyped animals and consisted of 511,576 animals. Figure  1  shows the average level of inbreeding for cows with phenotypes. The model used was a single trait model with repeated records for milk yield which included fixed effects of herd-year-season, lactation-age, and random effects of permanent environment and animal additive genetic effect.
Rabbit The effect of ignoring inbreeding in the relationship matrices is dramatic, and shown in Figure  5. In fact, the assumed inbreeding is much higher, which in turn leads to underestimation of  * as shown below. The overall correlation between  and  * is 0. 92. For this dataset, given strong selection, we present average computed reliabilities per generation. Figure  6  (top) shows stabilization of reliability ( = 1 - (1+  )  2 ) after a few generations. As the line becomes inbred, there is an increase in both  = ( - ̂) and an increase in () = (1 + )  2. However, if  * is computed (ignoring inbreeding coefficients in the setup of  -1 ) then  * increases too much, leading to apparently decreasing reliabilities ( # = 1 - * (1+  )  2 ) with time (middle). This trend is even more marked if reliabilities are computed using the wrong denominator, (i.
ACKNOWLEDGMENTS Work supported by  FEDER  ( Poctefa funds , project  ARDI ),  INRA   SelGen metaprogram  (project  GDivSelGen ), and project Lomas  CYT FCA 22 ,  Universidad Nacional de Lomas de Zamora (Argentina). This project has received funding from the  European Unions '  Horizon 2020 Research & Innovation programme  under grant agreement  N°772787  -SMARTER. We thank reviewers for insightful comments.
This work was supported by  VetAgro Sup  in the form of grants to GC. Domes Pharma provided support in the form of salaries for authors AC, ES and JP. Domes Pharma also provided support in the form of research materials for this study. Some strains were provided by the  CGC , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.
Benz at 20, 50, and 100 µM significantly exerted cytotoxicity on the preformed tumoroids (Figure  2G ), and, from the cytotoxicity data, IC 50 of Benz was calculated to be 16. 5 µM. These data suggest that Benz could be effectively cytotoxic on preformed tumors. We next examined the cytotoxicity of Benz by measuring the release of lactate dehydrogenase (LDH), a marker of cell damage and death, from the LuM1 tumoroids. Benz at 20 µM induced the release of LDH from tumoroids for 48 to 72 hours treatment periods, but not for 24 hours (Figure  S1 ). The release of LDH was concomitant with the significant loss in cancer cell viability for the 48 to 72 hours treatment periods. We next examined the effects of Benz on tumoroid formation by the human colon cancer cell line, HCT116 cells. Benz at 20 µM significantly inhibited the tumoroid growth of HCT116 for 24, 48, and 72 hours treatment periods (Figure  2H, I ).
Clinical Significance of DAT and STAT Next, we searched the database to determine whether genetic alterations in the DAT/SLC6A3 gene could be clinically significant. The genetic amplification of DAT/SLC6A3 was frequently found in 14% of castration-resistant prostate cancer (CRPC, known as neuroendocrine tumor), 12% of pancreatic cancer, 13% of lung squamous cell carcinoma (SCC), 11% of lung adenocarcinoma, 12% of esophagus cancer, 12% of sarcoma, 9% of bladder cancer, and 8% of ovarian cancer (Figure  7A , Figure  S7 ). A high expression of DAT/SLC6A3 mRNA was found in renal cell carcinoma, lung adenocarcinoma, prostate cancer, lung SCC, pancreatic cancer, ovarian cancer, stomach cancer, esophagus cancer, sarcoma, and head and neck cancer (Figure  S8 ). Cancer cases with DAT/SLC6A3 alteration(s) were significantly correlated with a poorer prognosis of cancer patients as compared to the no alteration group, as shown by the overall survival Kaplan-Meier estimate (Figure  7B , Table  1 , Figure  S9 ).
Promoter Analysis A 588-bp cDNA fragment of the Mmp9 promoter region between -569 and +19 was cloned via genomic PCR from a tail of a BALB/c mouse and sequenced, as described  [2]. Human and murine Mmp9 promoter sequences were also obtained from the Eukaryotic Promoter Database. Transcription factor binding sites were predicted using PROMO (ver. 8. 3), as described  [51]. Sequences were aligned using NCBI BLAST. STAT binding sequences were analyzed according to consensus sequences  [52, 53].
Acknowledgments: This paper is dedicated to the memory of one of our mentors,  Professor Ken-ichi Kozaki , who passed away on May 29, 2016. The authors thank  Kazuya Arai  and  Manabu Itoh  at  JSR Corporation  for conceptualization of 3D-culture-based phenotypic screening on NCP,  Keisuke Nakano  and  Eriko Aoyama  for valuable illuminating discussion and encouragement,  Kisho Ono  and  Kazumi Ohyama  for technical assistance.
Study design This 6-week, parallel two-arm, exploratory randomized controlled trial (RCT) was prospectively registered on July 3, 2015 with ClinicalTrials. gov (NCT02322112) as part of a large parallel four-arm RCT that aimed to compare the effects of four structurally distinct fibers (i. e. , arabinoxylan, acacia gum, resistant starch type-IV, and MCC) on the gut microbiota and human health, referred to as The Alberta FYBER (Feed Your Gut Bacteria morE fibeR) Study (for original registration we refer to  [82] ). In response to requests by reviewers of a grant application, which advised against including a premarket fiber ingredient in a larger human trial, the arabinoxylan arm was separated from the original RCT on October 26, 2016 and data from the 15 subjects that completed the protocol were analyzed independently.
Upon enrollment, subjects were then assigned to the next available randomization number by a study investigator blinded to these predetermined allocation sequences. Thirty-eight subjects were enrolled in the study and instructed to consume their corresponding supplement for 6 weeks at a daily fiber dose of 25 g for females and 35 g for males, provided strictly as either arabinoxylan or MCC. Half daily doses were provided for the first 2 days of treatment (12. 5 g for females and 17. 5 g for males), as this was shown by pilot data to ease diet incorporation. After 1 week of treatment, subjects returned to provide a second fecal sample and to assess protocol adherence (visit 3), which was also assessed during their third week of treatment (visit 4). A final visit was required at endpoint (6 weeks of treatment) to provide the third and final fecal sample and to assess overall protocol adherence (visit 5).
Baseline dietary intake and anthropometric assessment Subjects were asked to maintain their habitual diet and physical activity level during the intervention study. Baseline dietary intake was assessed by the online Canadian Diet History Questionnaire II Past Month (C-DHQ II), a food frequency questionnaire adapted for the Canadian population from the validated US-DHQ II  [84]. Subjects' responses were analyzed using Diet*Calc software (Version 1. 5. 0) and the C-DHQ II-specific nutrient database; previously updated to include eight new food group variables representing Canada's 2007 Food Guide serving-size-equivalents  [85]. Prior to statistical analyses, C-DHQ II extracted data were assessed for extreme outliers using methods described by Kipnis and colleagues  [86] , and then calorie-adjusted using methods described by Willett and Stampfer  [87].
5 μm film thickness, Restek Corporation, Pennsylvania, USA) with a flame ionization detector, and quantified by calculating response factors for each SCFA relative to 4-methyl-valeric acid using injections of pure standards. Total SCFA concentrations were determined as the sum of acetate, propionate, and butyrate, while the relative percentage of each SCFA was determined by dividing these individual SCFAs by total SCFAs. Total branched short-chain fatty acid concentrations were determined as the sum of isobutyrate and isovalerate. Fecal moisture content was determined by drying raw fecal material overnight in an oven at 103 °C. DNA extraction, 16S ribosomal RNA (rRNA) gene amplicons sequencing, and data processing for microbiota analysis Bacterial DNA was extracted from fecal homogenates in phosphate-buffered saline (1:10) using the QIAamp DNA Stool Mini Kit (QIAGEN, Hilden, Germany) as previously described  [91].
Taxonomies from phylum to genus level were assigned using the entire sequence set by the Ribosomal Database Project Classifier  [93]. OTUs were assigned taxonomy by using the Silva database (release 132  [94] ), and sequence identity at species level was confirmed using 16S rRNA gene databases on EzBioCloud  [95] , IMG/MER  [96] , and NCBI  [97]  platforms. Prior to ordination and statistical analysis, OTU count data were converted into relative abundance and also CLR transformed to correct for compositionality  [98]. Considering all fecal samples, OTUs with an average relative abundance below 0. 15% were removed. This approach resulted in exactly 100 OTUs (referred to as "all OTUs"), which were used in downstream analyses, accounting for 88. 1% of the approximately 1 millioncurated reads.
Bacterial community analysis To explore the effect of fiber on the bacterial community, we assessed overall β-diversity, dissimilarity between and within individuals, and α-diversity. To assess overall β-diversity, Euclidean distance between bacterial communities was first calculated from CLR-transformed data of all OTUs and then visualized using non-metric multidimensional scaling (vegan  [99]  and ggplot2  [100]  packages). Differences in the communities of arabinoxylan and MCC groups at specific time points were compared by PERMANOVA using the Adonis function in vegan  [99]. Euclidean distances were used to calculate inter-subject (between subjects at the same time point) and intra-subject (within subjects, but at different time points) dissimilarities. Differences in inter-subject diversity were determined within each treatment group relative to baseline using GEE models (geepack package  [101] ) followed by Bonferroni correction.
Relationships between bacterial community and SCFA responses with microbiota and diet features To explain the individualized response of the fecal microbiota to fiber, MLR analyses were employed using R. In order to perform the analysis, dimensionality of the microbiota and diet data were reduced by PCA into PC1, PC2, and PC3, which represents the largest proportion of the inter-individual variability and captures the most information on microbiota and dietary variation. Microbiota compositional and SCFA response variables were used as dependent variables. Baseline and shifts of PC variables, CARGs, OTUs, and diet data were used as predictors. Subset selection in regression was applied to choose the best combination of predictors using the sequential replacement algorithm (leaps package  [107] ).
Therefore, each MLR model presented only contained the top one or two predictors that explained the response variable the best. Dietary and microbiota-related predictors were treated separately in different models, and total grains, whole grains, and total fiber intake were used as single dietary predictors. All models were adjusted by fiber dose/sex and p values were corrected by FDR with statistical significance considered at q < 0. 05. To estimate the quality of each model in predicting the same dependent variable, AICc values were calculated using the AICcmodavg package  [108]. AICc values were then converted to relative percentages by assigning the highest AICc value as 100%, and then remaining AICc values were calculated by AICc value Highest AICc value x 100. Thus, lower AICc values indicate higher quality models.
Residuals for all linear regression models were plotted to check for homogeneity of variance and normality. Availability of supporting data While metadata have been included as Additional file 11: Table  S5 , 16S rRNA gene amplicons sequencing data have been deposited in the National Center for Biotechnology Information Sequence Read Archive and are available for download under BioProject PRJNA564636. The R scripts used in this study are available at GitHub (https://github. com/BioKhoi/Arabinoxylan_ study_Microbiome_Journal).
Extracted DNA was then purified on silica membrane and eluted in 100 µL. Then, 16S metagenomics 16SrDNA variables regions V1 to V3 were amplified using primer 27F (AGAGTTTGATCCTGGCTCAG) and 534R (ATTACCGCGGCTGCTGG); 16s DNA sequencing was performed on Illumina Miseq using paired-end technology. All sequence processing was performed using QIIME2 suite (https://qiime2. org/) using the default settings. After quality filtering, read pairs were merged by overlapping and clustered and chimeric sequences were removed. Operational taxonomic units (OTUs) were assigned to sequences against Silva 132 rDNA database. To evaluate whether the active ingredient affects populations of five bacterial species (S. epidermidis, S. aureus, S. hominis, C. acnes, and C. granulosum, and one genus Corynebacterium), genus/species quantification using quantitative (real-time) PCR (qPCR) was carried out.
This modification will not allow distinction between SLST types A1 and A6 or SLST types E3 and E7. In both cases, these variants belong to the same phylotype. All sequence processing was performed using QIIME2 suite (https://qiime2. org/) with default settings. After quality filtering, read pairs were merged by overlapping and clustered and chimeric sequences were removed. SLST (Single Locus Sequence Type) was assigned to sequences against an SLST reference database (http://medbac. dk/slst/pacnes) [31].
Statistical Analysis Raw data were used to determine taxa abundances and α-diversity indices after removing OTU with less than 2 counts. Filtered and normalized data were used to compare populations between treatments and days (β-diversity) and to compare taxa abundances between the same sample groups. Abundances for five taxonomic levels (Phylum to Genus) were calculated by summing sequence numbers assigned to same taxa for the considered level. Shannon diversity index was calculated using PAST 3. 25 software (University of Oslo, Oslo, Norway), wherein index increases with diversity. Concerning the genomic quantitative study of major genus/species of skin microbiome, statistics were performed on normalized data. Normalization was performed for each sample by dividing observed taxon abundance by the sum of all taxa abundances.
Before the Treatment As shown in Figure  1 , major phyla were Actinobacteria (average 56%) followed by Firmicutes (average 32%). Cutibacterium was the most abundant genus (average 40%), followed by Staphylococcus (average 18%), Corynebacterium (average 12%), and Streptococcus (average 7%) both at D0 and D28, with no significant differences between placebo and RT extract treatment. Cosmetics 2020, 7, x FOR PEER REVIEW 6 of 14 Raw data were used to determine taxa abundances and α-diversity indices after removing OTU with less than 2 counts. Filtered and normalized data were used to compare populations between treatments and days (β-diversity) and to compare taxa abundances between the same sample groups. Abundances for five taxonomic levels (Phylum to Genus) were calculated by summing sequence numbers assigned to same taxa for the considered level.
Shannon diversity index was calculated using PAST 3. 25 software (University of Oslo, Oslo, Norway), wherein index increases with diversity. Concerning the genomic quantitative study of major genus/species of skin microbiome, statistics were performed on normalized data. Normalization was performed for each sample by dividing observed taxon abundance by the sum of all taxa abundances. Statistical comparison of taxa abundances was performed uniquely for taxa identified in at least half of tested samples. Missing values (i. e. , samples where no species or genus was detected) were replaced by a random value sampled around the minimum value +/-50%. Taxa abundances were compared using either the parametric paired t-test or the non-parametric Wilcoxon and Sign rank tests. Normality of data distribution was verified using the Shapiro-Wilk test.
Acknowledgments The authors thank  Etienne Gontier  and  Sabrina Lacomme  from the  Bordeaux Imaging Center (BIC)  for their expertise with TEM imaging and analysis, and  Gérald Clisson  from the  Laboratory of the Future (LOF)  for his help with the photolithography and profilometry of the microfluidic devices. The authors also thank  Jean Ripoche  for his guidance on zymography, and the technicians from the animal facility for their assistance.
Study design and participants Data were pooled from the BRIM-2 (NCT00949702)  [1] , BRIM-3 (NCT01006980)  [3, 4] , BRIM-7 (NCT01271803)  [5] , and coBRIM (NCT01689519)  [6, 7]  studies. Detailed methods have been previously reported. Briefly, BRIM-2 was an open-label, multicenter phase 2 trial of oral vemurafenib 960 mg twice daily  [2]. BRIM-3 was an open-label, multicenter, randomized phase 3 trial of oral vemurafenib 960 mg twice daily compared with intravenous dacarbazine 1000 mg/m 2 every 3 weeks  [3, 4]. BRIM-7 was an open-label, multicenter phase 1b doseescalation study of oral cobimetinib 60, 80, or 100 mg once daily on a 14 days on/14 days off schedule (14/14), a 21 days on/7 days off schedule (21/7), or continuously (28/0) combined with oral vemurafenib 720 or 960 mg twice daily  [5].
coBRIM was a randomized, double-blind phase 3 study of the combination of oral cobimetinib 60 mg once daily on a 21/7 schedule combined with oral vemurafenib 960 mg twice daily compared with placebo plus vemurafenib  [6, 7]. Key eligibility criteria were similar across trials; eligible patients were aged ≥ 18 years with unresectable stage IIIC or IV melanoma harboring a BRAF V600 mutation, ECOG PS of 0-1, and adequate organ function. BRIM-3 and coBRIM enrolled previously untreated patients only, whereas BRIM-2 enrolled patients who had received prior systemic treatment for advanced disease, and BRIM-7 enrolled both previously treated and untreated patients. All four trials allowed enrollment of patients with previously treated, stable brain metastases. All eligible patients (defined by each study protocol) who had PD at the time of data cutoff were included in analyses regardless of treatment assignment.