dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation with real-world data We simulated tissue heterogeneity in silico with a transcriptomics study in Canis lupus familiaris (domestic dog) by Briggs et al. [19]. In this study the authors constructed a compendium of canine normal tissue gene expression, which includes 39 samples of 10 tissues from four dogs (four samples of liver, kidney, heart, lung, cerebrum, lymph node, spleen, jejunum, and skeletal muscle; three samples of pancreas). Prior to simulation experiments we first applied BioQC to the dataset to test whether its results match information provided by the authors. In  [19] , the authors performed hierarchical clustering of both canine and human data and reported a 'remarkable similarity' of normal tissue gene expression profiles between the two organisms. Indeed, BioQC confirmed the consistency in 36 (92%) cases in which canine tissues reported by the authors match the highest-ranking human tissues reported by BioQC (Table  1  in Additional file 3: Document 2).
The asymmetry is likely caused by the relatively high expression of heartenriched genes compared with small-intestine-specific genes in the respective tissue (Figure  4  in Additional file 3: Document 2). Despite of the asymmetry, we observe a strong leap of small-intestine signature's rank as its proportion further increases. In practice when one of several heart samples is contaminated by jejunum, an aberrant higher rank of the jejunum signature can be a warning sign of tissue heterogeneity to the software user. Following this example, we mixed all pairs of canine tissues and found that on average BioQC is able to detect heterogeneity with 20% or more contamination (enrichment score 3. 0 or rank 10, Figure  3  in Additional file 3: Document 2). Tissues like liver and heart with highly expressed enriched genes can be more easily detected as sources of contamination with the proportion as low as 12%.
Case study with whole-organ profiling data We applied BioQC to a dataset generated in a Roche research program. In this study twenty-five mouse wholekidney samples were taken after diverse treatment regimen including operation and medication (details in Additional file 4: Document 3). Genome-wide gene expression profiling was performed with Affymetrix Mouse Genome 430 2. 0 microarray. Results of BioQC are visualized in Fig. 2a. As expected, kidney-enriched genes ranked first among all tissue signatures in all samples. In three samples (index 23, 24 and 25), however, the pancreas signature scored much higher than in other samples. As we examined genes in the signature, we observed substantial expression of many genes including insulin (INS), glucagon (GCG), and pancreatic carboxypeptidase A1 (CPA1) in the three samples (Fig.
Extrapolating from the observation that BioQC needs about one second to analyze 100 genome-wide gene expression profiles, we estimate that it would take BioQC only about six hours to analyze the entire GEO database, which collects about 2. 0 million gene expression profiles as of November 2016, with one CPU-thread (excluding the time of downloading, preprocessing, etc. ). In light of its favorable scalability and sensitivity, we believe BioQC is suitable for small-and large-scale gene expression studies. We expect tissue signatures to be applicable across closely related species as it was shown elsewhere that within mammalians the expression patterns of protein-coding genes are more conserved between species than between major organs  [21, 22]. The human derived tissue signatures are already applied successfully to tissue expression data from multiple vertebrate model species including macaque, pig, dog, rodents, and zebrafish (data not shown).
The simulation study using dog tissue expression data exemplifies such an analysis. We have integrated BioQC in our gene expression analysis pipeline since three years to routinely detect tissue heterogeneity in internal and external studies. It has raised warning flags in many datasets independent of the target tissue of profiling, organism, experiment design, profiling platform and laboratory. While a few suspects were validated such as the ones in our case study, most findings were regrettably not followed up due to limited capacity and material unavailability. With more expression profiling data generated worldwide everyday, we wish that BioQC can help experimentalists and data analysts alike to improve the quality and reproducibility of gene expression studies.
Availability of data and materials The BioQC package is written in the programming languages R and C. It is freely available from Bioconductor [23] with the GPL-3 license. The package is tested in major operating systems including Unix, Linux, Mac OS and Windows. The source code and datasets supporting the conclusions of this article are available on the BioQC website  [24]. The dog compendium data is downloaded from the NCBI Gene Expression Omnibus (GEO) database with the accession number GSE20113  [25]. GTEx data was downloaded from  [26].
Methods We conducted a cross-sectional electronic survey evaluating the structure and resources of ASTs in surgical departments. The survey was designed by a multidisciplinary team of investigators including an epidemiologist, a surgeon, an infectious diseases physician, a pharmacologist, and a microbiologist. The questionnaire was piloted among five physicians for face and content validity. The 24-item self-administered questionnaire collected information from multidisciplinary experts-mostly physicians-about characteristics and composition of the hospital team, implementation of local procedures, availability of antimicrobial use monitoring and surveillance systems, presence of an ASP, and related interventions (Additional file 1). An electronic invitation with a link to the survey was sent to 831 physicians: 173 physicians who participated in the AGORA project  [1] , and a large number (658) of international experts in the fields of antimicrobial stewardship, infection control, and infections in surgery identified after a thorough investigation using the PubMed database.
The survey was Internetbased (using http://www. docs. google. com). Participation was voluntary but not anonymous; however, the confidentiality of respondents and their choices was ensured. No incentives were provided to the respondents. The study was open for 6 weeks between September 30 and November 11, 2016. Reminders were sent to all those who had not replied after 1 and 3 weeks. Due to the characteristics of the survey, a response rate ranging between 15 and 25% was expected. Data were entered in an Excel database (Microsoft Corporation, Redmond, Washington, USA) and analyzed using Stata 11. 0 software package (StataCorp, College Station, TX). Descriptive analyses included medians and interquartile ranges (IQR) for continuous variables or frequency (%) for categorical variables The two-sided chi-square or Fisher's exact test was used for categorical variables, as appropriate.
, 2014; Farinelli et al. , 2015). From these 66 individuals, we identified a total of 61 different genotypes using microsatellite markers and then matched these genotypes in the worldwide Olive World Germplasm Bank of INRA Marrakech assessed with the same markers. This simple analysis revealed no less than 14 cases where the genotypes associated with a given variety name were different among collections, representing a major discrepancy that demonstrates the unreliability of vernacular names. Hence, previous studies including those published by the Bervillé et al. group were based on poorly identified varieties, which is likely to have generated considerable uncertainty in the results. We believe that it will now be important for the community working on Olive trees to generate a public database of trees whose genetic identity has been ascertained by a common set of molecular markers, ideally also including their position in a reference orchard and a voucher DNA sample that could be exchanged among users.
| CONCLUSIONS The mating system of the Olive tree has remained a controversial issue in the literature, but many of the previously published studies have been based on a poorly genetically controlled experimental design. Given 1) the vast uncertainty around the genetic identity of vernacular varieties, 2) the massive risk of contamination associated with commonly used pollination protocols and 3) the importance of checking for stigma receptivity in controlled crosses [all important features that were not adequately taken into account in the studies cited by the Breton et al. (  2017 ) comment], we are confident that the time is ripe for new standards to be set in the scientific community. We can only encourage authors of this comment as well as any other researchers having doubts about the actual existence of DSI and the absence of asymmetrical incompatibility reactions in Olive trees, to carefully assess reproducibility of the output data of their experimental crosses, to control for pollen contamination with paternity analyses and to use positive pollination controls of stigma receptivity.
Reads were filtered for QC and artifacts using the fastX toolbox, and then the reads were paired up using an adapted python script (https://github. com/enormandeau/Scripts/blob/master/fastqCombinePairedEnd. py). The reads were aligned to the genome (GRCh38, GENCODE v22  (Harrow et al. , 2012 )) using STAR (2. 4. 2a)  (Dobin et al. , 2012). The data has been deposited in GEO/SRA under accession number GSE84891.
Network analysis using neighbor-voting To measure the information content of the network, we use the performance of the n-fold cross validation task of a neighbor voting algorithm. If we can hide known information about genes in a gene set and then "learn" this information from the network, then our network has, to a degree, information that is reflective of the known biology of that GO term. This is based on the "guilt-by-association" principle, which states that genes with shared functions should be connected preferentially in the network. The reported performance metric from this task is the averaged AUROC (area under the ROC curve) for each group across the n-folds. We used the Bioconductor package EGAD  (Ballouz et al. , 2016)  and GO to perform this analysis on the frequency of co-expression network.
However, because these factors are frequently present in other data, genes jointly affected by these conditions should co-vary across previous data and thus not generate artefactual outliers. We assess each family in a separate batch (library preparation and sequencing run). This holds likely technical variation constant in each family and independent across families, so that gene-level recurrence is not expected to differ from the null. By way of analogy, our experimental design resembles the analysis of de novo variants in DNA analyses, in which as many factors as possible are held constant in the control group for the proband (e. g. , siblings, parents). For the case-control version of the analysis, we take all the parents as controls, and all the probands as cases, and perform the standard DE analysis.
Module detection and outlier analysis To measure the joint differential activity of a set of genes, we extract the sub network of these genes from the frequency of co-expression network. Then, taking threshold on the median value, we use this binary network as distance matrix, and perform a hierarchical clustering of the genes. This clustering returns genes that are closer in distance, and we use this dendrogram to define modules within the data. We used the R dynamicTreeCut  (Langfelder et al. , 2008)  package to select modules within the data of a minimum cluster size 2. We used these clusters or modules to define our co-expression blocks, and filtered away blocks greater than size 5, to keep "functional-outliers". We calculate the significance of overlap using Fisher's exact test (phyper in R).
We calculate the significance of recurrence of the differentially expressed genes using the binomial test (pbinom in R), and then correcting for multiple tests using either Bonferroni (FWER) or Benjamini-Hochberg (FDR, p. adjust in R). Code and the network is available for download from our github repository (https://github. com/sarbal/redBlocks). For the downsampling version of the recurrence analysis, we calculate the significance of recurrence as above, but this time for a combination of the probands. The recurrence or adjusted p-values were used to rank the genes. where each entry is the edge weight, or visually as a graph. A functional assay, or a gene list, allows us to look at a subset of genes. In most cases, researchers look for functional enrichment within that set. (B) Here we take those genes and instead identify those grouped together within the network.
Prepulse inhibition of the startle reflex Male WT and α5SNP littermates ranging in age from 60-75 d were used for the study. The PPI apparatus (SR-Lab, San Diego Instruments) consists of a sound-attenuating chamber and a vented Plexiglas animal enclosure (4 cm in diameter) that rests on a 20 × 13 cm Plexiglas frame. Background (70 dB), prepulse (74, 78, 82 and 90 dB) and pulse (120 dB) bursts were generated through a computer using SR-Lab software (San Diego Instruments) and presented through a speaker mounted (13 cm) above the animal enclosure. The magnitude of the startle response was measured by a piezoelectric accelerometer mounted below the Plexiglas animal enclosure. Each session was initiated with a 5-min acclimation period followed by initiation of the test session with five pulse-only trials (white noise, 120 dB/40 ms); each session also concluded with five identical pulse-only trials.
CRISPR/Cas9 nuclease RNA-guided genome editing We used CRISPR-associated Cas9 endonuclease technology for inactivation of the α5 subunit (Sigma-Aldrich). Three different gRNAs were designed by Sigma-Aldrich using an algorithm based on Bowtie sequence analysis software. gRNAs targeting a site in the first two-thirds of the coding sequence with at least 3 bp of mismatch with any other location in the host genome were proposed for testing by genomic cleavage detection assay (CEL-1 endonuclease). Assessment of the CRISPR/Cas9 on-target and off-target effects is described in the Supplementary Methods (Off target analysis using next generation sequencing) and Supplementary Tables  2  and 3. After screening of three target sites in the Chrna5 gene, the following target site was chosen: TGTCACTCAGGTGTTCCACAGG.
Two-photon data analysis Image analysis was performed offline with ImageJ software. The time series were corrected using Image Stabilizer (http://www. cs. cmu. edu/~kangli/code/Image_Stabilizer. html). Regions of interest (ROIs) were manually selected in FIJI, and detection of Ca 2+ transients of individual neurons was performed automatically by using a custom-written toolbox in MATLAB (Mathworks). For details on the analysis method, see the Supplementary Methods (Two-photon data analysis).
We used the multiple-comparisons Tukey-Kramer test in order to determine which pairs of mean ranks were significantly different. Animals were not excluded from the analysis. The number of mice needed for our study was determined by preliminary experiments in anesthetized mice and pilot experiments in awake mice. Box plots, histograms and empirical cumulative distribution functions (Kaplan-Meier estimate) were used to compare the different distributions of neural activity between the mouse types. For each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, and the whiskers extend to the most extreme data points not considered outliers. The ends of the whiskers represent the lowest data value still within 1. 5 times the IQR (interquartile range) of the lower quartile and the highest data value still within 1.
5 times the IQR of the upper quartile. On the empirical cumulative distribution plots, 95% lower and upper confidence bounds were computed with Greenwood's formula. Behavioral data-Statistical analysis was performed using Prism (GraphPad) software. Social interaction task differences were assessed using unpaired Student's t test. Because the variance between genotypes for the social index and interaction time measures were not equal, unpaired Student's t test with Welch's correction was used. Two-way repeatedmeasures ANOVA with Sidak post hoc test was used for the PPI data analysis. All data other than the aforementioned social interaction tests met the assumptions of the chosen statistical test: normality and equal variances.
Acknowledgments F. K. is a scholar of the  Pasteur Paris University Doctoral Program (PPU)  and received a stipend from the  Stavros Niarchos Foundation. This work was supported by the  CNRS UMR 3571 , the  Fondation de la recherche médicale (FRM  grant  DPA20140629803 ), the  Agence nationale de la recherche (ANR) ,  the Laboratoire d'Excellence BIO-PSY  (including salary support to F. K. , AAP Fin de thèse 2015), the programme PasteurInnov  2012 , by the  FP7 ERANET programme NICO-GENE , Grant Agreement n009 BLANC  20092009BLANC 20 , by the  European Commission   FP7   RTD  Project  HEALTH-2009-Neurocyp. 08-202088  Grant  242167 , by the  French National Cancer Institute  Grant  CANCEROPOLE IDF 2016-1-TABAC-01-IP-1  MASKOS (all to U. M. ), and  NIH   CA089392  and  DA015663  (to J. S. ).
The laboratories of U. M. , B. S. G. and D. A. D. are part of the  École des Neurosciences de Paris Ile-de-France RTRA network. U. M. and D. A. D. are members of the  Laboratory of Excellence, LabEx BIO-PSY. As such this work was supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02. B. S. G. is member of the  Laboratory of Excellence, LabEx IEC. B. S. G. acknowledges support from the  Russian Academic Excellence Project  ' 5-100 '. We would like to thank  S. Pons  for valuable support and discussions on lentiviral constructs, and  M. Soudant  for production of lentiviral vectors. We also acknowledge the  Pasteur Institute Shared Neuroscience Department imaging facility  funded by  Ile de France Domaine d'Intérêt Majeur (DIM/NeRF).
FTTY [26 mOD min -1 ]) and are represented using a continuous gradient from 100% (highest rate, green) to 0% (black). Row headings show the P4 residue and column headings show the P3-P2-P1 sequence for each peptide (colored green for each protease's optimal substrate). Proteolytic activity in stratum corneum extracts was also studied using substrates for (c) KLK7 (KHLY-pNA) or (d) trypsin-like KLKs (Ac-YASR-pNA). Substrate cleavage (detected by measuring the absorbance at 405 nm) is shown on the y-axis and time (hours) is shown on the x-axis. Data points represent the mean ± SEM from three experiments performed in triplicate, each experiment using a separate protein extract. Inhibitor treatments were also included to gauge the level of substrate cleavage that could be attributed to the target protease(s).
Subjects and study design EPIC is an ongoing multicenter, prospective cohort study investigating the relationships of dietary and lifestyle factors with cancer and other chronic diseases carried out in 23 centers in 10 European countries: Denmark, France, Germany, Greece, Italy, The Netherlands, Norway, Spain, Sweden and the United Kingdom (UK). Participants were mostly recruited from the general population. The full cohort comprises 519,978 participants including 153,457 men, most aged 35-70 years, recruited between 1992 and 2000. The details of the study design used in the EPIC study have been described elsewhere. 15  study flowchart is shown in Supporting Information Figure  1. Since only women were recruited in France, Norway, Naples (Italy) and Utrecht (The Netherlands), these centers where not included in the current study, and data from 19 centers in 8 countries were included in this analysis.
Assessment of dietary intake and other predictor variables At baseline, information was collected on lifestyle, health status, socio-demographic characteristics, anthropometry and medical history. 15 Information on dietary intake during the year before recruitment was collected by country-or centerspecific validated dietary questionnaires. Most centers used self-administered dietary questionnaires, however, in Greece, Ragusa (Italy), and Spain participants were interviewed by trained staff members. In Malm€ o (Sweden) dietary intake was assessed using a modified diet history method which combined information from a self-administered diet questionnaire, a 7-day food registration and a 1-hr interview. With the purpose of improving the comparability of dietary data across the participating centers, dietary intakes from the questionnaires were calibrated using a standardized, computer-based, 24-hr dietary recall method in an 8% random sample of the whole EPIC cohort.
Some strengths and limitations of the present study should be considered. The major strengths of this study include the prospective design, the large number of total prostate cancer cases and prostate cancer deaths and the large amount of grade and stage information, and the reliable identification of prostate cancer cases through cancer registries and/or verified medical records. The Gleason grade was based on data available from biopsies and surgical pathology and there may be some misclassification because of changes in grading over time. The dietary questionnaires in all EPIC centers were validated and dietary intakes were calibrated using measures from a standardized 24-hr diet recall method, with the aim of correcting for over and under-estimation of dietary intake. 17 We were able to look at eight subgroups of fruit and vegetables, although we were not able to look at further subtypes, such as grapes and berries, because median intakes were very low.
ACKNOWLEDGMENTS This study was partly supported by the  Federal Office for the Environment (FOEN) ,  Switzerland , the  Inspection de la pêche du Canton de Vaud, Switzerland , the  Ministère de l'Ecologie, de l'Energie, du Développement Durable et de la Mer, France , the  Haute-Savoie Departmental Federation of Fisheries, France , and  La Maison de la Rivière ,  Tolochenaz, Switzerland. A. Caudron contribution was provided by the  Fédération de la Pêche et de Protection des Milieux Aquatiques de la Haute-Savoie  in the  INRA-UMR CARRTEL research station of Thonon, France. The fish scales are part of the  OLA fish scale collection © SOERE OLA, INRA UMR CARRTEL Thonon-les-Bains  (www6. inra. fr/soere-ola). We thank  R. Burri ,  H. Collin ,  G. Evanno ,  J. Goudet , and  C. Wedekind  for their helpful advices;  F.
We repeated the DCA analysis on two subsets restricted to either bacterial or eukaryotic sequences, to further investigate the origin of the detected coevolutionary signal. The results obtained on the bacterial subset (Figure  3 -figure supplement 1A) are in perfect agreement with the results obtained on the full dataset, whereas no strong coevolutionary couplings are detected using the eukaryotic subset (Figure  3 -figure  supplement 1B ). This observation indicates that the coevolutionary signal of the observed Hsp70/Hsp40 interface mostly originates from the bacterial sequences in the dataset.
Coarse-grained simulations Simulation protocol We used the coarse-grained model introduced in  Kim and Hummer (2008)  to simulate the binding of DnaJ to DnaK constructs. Both proteins were treated as rigid bodies, at a resolution of one bead per residue centered on the C a atoms. We modeled NBD(ADP) using the structured region (residues 4-380) of ADP-bound E. coli DnaK (pdb: 2kho  [Bertelsen et al. , 2009] ), whereas we relied on the X-ray structure of ATP-bound E. coli DnaK (pdb: 4jne  [Qi et al. , 2013] ) for both NBD(ATP) (residues 4-380) and FL(ATP) (residues 1-600). The J-domain was modeled based on the structured region of the E. coli DnaJ (pdb: 1xbl  [Pellecchia et al. , 1996] , residues 2-70). We defined the structured part of the J-domain, by aligning multiple  J-domains (pdb:1xbl, 4j7z, 2m6y, 2n04, 2qsa, 2lgw, 2och, 2dn9, 2dmx, 1hdj, 1faf, 2ctw)  and considering the common structured part.