dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole transcriptome sequencing and analysis Transcriptomic analysis for each condition was performed using 3 independent RNA preparations. The RNA samples were first treated using Epicenter Bacterial Ribo-Zero kit to remove the rRNA content. Then depleted rRNA fraction was used to construct strand-specific single end cDNA libraries according to manufacturers' instructions using Truseq Small Stranded Total RNA sample prep kit, Illumina. Libraries have been sequenced by Illumina HiSeq2000 sequencer. Cleaned sequences were aligned to the reannotated C. difficile strain 630  58  for the mapping of the sequences using Bowtie (Version 2. 1. 0). DEseq2 (version 1. 8. 3) was used to perform normalization and differential analysis using BHISG values as a reference for reporting the expression data of BHISG with 240 µM of DOC.
ACKNOWLEDGEMENTS The authors thank  Dr. A. L. Sonenshein  for helpful discussion,  Dr. J. Peltier  and  Dr. G. Vedantam  for providing cwp19 mutant and the PS-II anti-sera, respectively, and  Dr. T. Fontaine  for the sugars analysis. This work was funded by the  Institut Pasteur and the "Integrative Biology of Emerging Infectious Diseases" (LabEX IBEID)  funded in the framework of the  French Government's "Programme Investissements d'Avenir ". T. D. and Y. T. are postdoctoral fellows from " Bourses Roux " funded by the  Institut Pasteur and the LabEX IBEID , respectively.
Study population The ESPOIR cohort is a nationwide prospective cohort study of adults conducted under the umbrella of the French Society of Rheumatology. The cohort was constituted by asking general practitioners and rheumatologists to refer patients with early arthritis to one of the 14 university hospitals participating in the ESPOIR cohort project. The protocol has been described in detail elsewhere  [14]  (ClinicalTrials. gov NCT03666091). Briefly, patients were eligible if they had a definite or probable clinical diagnosis of RA or a diagnosis of undifferentiated arthritis with potential for progression to RA. Patients were included if they were 18 to 70 years old and had swelling of ≥ 2 joints for 6 weeks, symptom duration < 6 months, and no prior treatment with DMARDs or glucocorticoids.
As sensitivity analyses, we conducted the same analysis plan in the entire ESPOIR cohort, restricted to patients fulfilling ACR/EULAR 2010 criteria for RA and having been treated by at least 1 DMARD in their first year of follow-up. We also tested alternative outcome measures than EULAR response in seronegative patients, with DAS28 remission and DAS28 remission/low disease activity at 12 months being used. Statistical analysis involved using SPSS v15. P < 0. 05 was considered statistically significant.
Patient characteristics From the 813 patients included in the ESPOIR cohort, 24 had missing data regarding RF-and CCP-tests. Of the 789 with available data, 384 (48. 7%) were "seronegative" for both autoantibodies (RF and ACPA), while 405 had at least 1 positive test from RF and CCP tests. Twentyone patients could not be classified according to ACR EULAR 2010 criteria due to missing data (i. e. , 3 had sufficient criteria even without information regarding serologic status to be classified having RA). From these 792 patients, 645 (79. 3%) were classified RA, 399 (61. 9%) of them being seropositive, and 246 (38. 1%) seronegative. From the 147 not being classified RA, 21 (14. 3%) were seropositive and 126 (85. 7%) seronegative (Fig. 1 ). We included 172 patients in the current study (n = 58 had not received a csDMARD during the first year, n = 19 were lost to follow-up, n = 29 had missing data).
Predictors of radiographic outcome at 12 months Radiographic data were available for 149 patients at 1 year. In total, 15 (10%) and 9 (6%) showed a progression of at least 1 point and 5 points, respectively, in mTSS at 1 year. On multivariable analysis, the probability of radiographic progression by a least 1 mTSS point at 1 year was significantly increased in patients with erosions on baseline radiographic assessment and was decreased in those with > 10 tender joints. The only characteristic associated with a radiographic progression of at least 5 mTSS points at 1 year was the presence of erosions at baseline (OR = 5. 42 [95% CI 1. 14-25. 7], p = 0. 03).
Here, we use an active learning approach  15, 16  to explore, optimize and understand the impact of cell-free composition on protein production in cell-free systems. We demonstrate that sufficient amount of data can be obtained to train a machine learning algorithm, achieve high quality predictions and increase protein production by 34 times. We next show that only 20 informative compositions are enough to train our machine learning model and obtain accurate predictions. This approach enables to maximize protein production on different cell lysates with minimal experimental effort. To study cell-free systems productivity, we developed an automatable strategy coupling an acoustic liquid handling robot (Echo 550, Labcyte, USA) and a plate reader (Infinite MF500, Tecan, USA) to measure ~4000 cell-free reactions (including controls and triplicates) and provide data to train a machine learning model.
A row stand for one mix composition, the colour code is the same as in panel a. g, Results of a mutual information analysis, using the 1017 compositions, of the relationship between the yield and each chemical compound. Next, we investigated whether protein production in cell-free using lysates made in other conditions (different experimentalists, using a different strain or supplemented with antibiotics) could be quickly predicted with a one-step method (Fig. 2a ). We selected 102 cell-free compositions representative of the 1017 already tested with the original lysate (see methods, Supplementary Fig. 2a ). Amongst the 102 compositions, 20 were used to train the model and 82 to test its predictive accuracy (Fig. 2a ). The challenge lies in the model's ability to accurately predict a large diversity of yields based on a small training dataset.
General script descriptions. All scripts mentioned below were written in Python (version 3. 6. 5), executed in Jupyter notebooks (version 1. 0. 0). Scripts are available online at github (https://github. com/brsynth). The libraries numpy and csv were used to handle files between different scripts. We used scitkit-learn 22 (version 0. 19. 1) for all model training. Concentrations to instructions workflow. The details of those scripts are described in the READme file of the ECHO_handling_scripts of our code. Roughly, it proceeds in 4 steps: -Complete concentrations: Taking as input a file containing only concentrations of interest for the machine learning algorithm, it adds information of values that are constant across all conditions, such as the lysate quantity. -Concentration to volume: This file converts a csv file concentrations -to a file of volumes one wants to test (in triplicates).
Data analysis. We provide a script to map the fluorescence quantification (see fluorescence quantification above) to the tested concentrations with well names (last step of concentrations to instructions workflow above). We performed outlier removal based on the following criteria: if the coefficient of variation, amongst 3 replicates, was higher than 30%, we removed the value farthest from the other 2. This concerned 27 values of our 1017 values tested during the active learning. Those are identified in the online data on Github with the third value of fluorescence is set to -1. This script also outputs csv files allowing for visualisation of where the outliers are, in order to spot potential border effects. It also separately outputs the outliers for further analysis. Data normalisation.
For each model, we train it 10 times (models_number) using the whole dataset at the moment (e. g. 3x102 values at the 3rd iteration). Training the model multiple times allows for optimising for random weight initialization of the model. We keep the best model (with the highest regression from scitkit-learn R 2 score). -Multilayer perceptrons give the best results (random forests and linear regressions were also investigated early on). They are trained with the default parameters from scitkit-learn except the following parameters: maximum iteration of 20000, adaptive learning rate, adam solver, early stopping and the following layers: (10, 100,100, 20) -We obtain mean and standard error from our predictions by taking the mean and standard error from the n results generated by our ensemble of n models.
A high std value stands for an uncertain yield value. We output compositions for full exploitation, full exploration and maximisation of the above formula but use the third option for the rest of the workflow. We are therefore querying points with both high yield and uncertainty. Model statistics. For model statistics presented in Fig. 1e , we used the same models as described in the active learning section above, but using 5 fold cross validation instead of the whole training set. The full dataset is separated into 5 subsets then the 25 models are trained on 4 subsets, and used to predict the 5th, where scores are obtained. This is done 5 times, once on each subset. The scores presented in Fig. 1e  are the mean and standard deviation of those 5 scores.
Mutual information calculation. Mutual information is a method to quantify the mutual dependence between two variables. This concept is intrinsically linked to the concept of entropy and is especially useful to quantify non-linear relationships between variables. More information on the theory behind this method can be found in the review 'estimation of mutual information  23  and in sci-kit learn documentation  22. It was calculated using the feature_selection. mutual_info_regression function from scitkit-learn 22 (version 0. 19. 1) between each feature and the yield (compounds_effect_analysis/mutual_information_analysis jupyter notebook) with default parameters. Identification of informative points. To identify the most informative points, we proceeded in the following manner: We did 1000 iterations of the following procedure: -Randomly sample n combinations from the dataset (n=20 out of a dataset of 102 values for Fig.
Acknowledgements O. B is supported by  Genopole  ' Allocation Recherche 2017' and CRI Paris 'Short-term Fellows '. M. K is supported by  DGA  ( French Ministry of Defense ) and  Ecole Polytechnique. PS is supported by  ANR  (grant number  ANR-18-CE33-0015 ). A. C. B acknowledge funding provided by the  ANR SINAPUV  grant number  ANR-17-CE07-0046. A. P is supported by  INRA  ( National Institute for Agricultural Research ) and an  idEx interdisciplinary scholarship  from the  University of Paris-Saclay. J-L. F acknowledges support from  BBSRC/EPSRC  (grant number  BB/M017702/1 ). The chemogenomic and  Biological Screening Platform  of.
To address the need for a measure to assess PEI symptoms and impacts, a 45-item PEI-specific PRO (PEI-Q) was developed through a literature review and qualitative concept elicitation interviews with clinicians (n ¼ 10) and patients (n ¼ 61 PEI patients with CF/CP) in France, Germany and the UK. Content validity was then assessed through cognitive debriefing interviews with patients in the same countries, and 18 items were deleted due to conceptual redundancy or lack of relevance, resulting in a 26-item measure. This study evaluated the psychometric properties of the 26-item PEI-Q using data from an observational study. Analyses aimed to support possible item reduction and finalise scoring, and to evaluate the psychometric validity of the resulting scores.
Study design This was a multi-centre observational study in France, Germany, Spain and the UK which aimed to collect quantitative data from approximately 325 participants (205 PEI adult and adolescent patients, 60 patients with diarrhoea-specific irritable bowel syndrome (IBS-D) and 60 healthy controls). Analysis was conducted in two phases: stage 1 used dimensionality analyses to assess item performance and support item reduction and scoring development; stage 2 assessed the measurement properties of the resulting PEI-Q scores. The study was conducted in accordance with the Declaration of Helsinki. Ethical oversight was provided by the National Research Ethics Service in the UK and the Freiburger Ethics commission review board in Germany. In Spain, site-specific approval was obtained at the University Hospital of Santiago de Compostela.
Eligibility criteria Patients were eligible to participate if they had an expert clinician-confirmed diagnosis of PEI and either CF or CP. CP patients had to have been on PEI treatment for six years or less. Both CP and CF patients had to have experienced PEI symptoms within the past three months. All patients were adults (18 years or more) apart from a subset of CF adolescents with PEI (aged 12e17 years) who were also included. Adult IBS-D patients and healthy controls were included to provide comparison groups for evaluating the discriminant properties of the measure. Patients with IBS-D were included if they met the ROME III criteria (at the time of data collection the Rome IV had not yet been published) for IBS, and experienced PEI-like bowel symptoms at least three days per month over the past three months.
For each domain, sensitivity and specificity statistics were also calculated to establish the optimal cut-point on the domain and total scores, to discriminate between PEI and other participants. A good cut-point was the response option that achieved a sensitivity and specificity of 80% or more, fair between 65 and 80% and poor <65%  [28]. patients (25. 7 years) in part due to the inclusion of adolescents in the CF sample (Supplementary Table  1 ). There were higher proportions of females in the CF (female: 64. 8%, male: 35. 2%) and IBS-D (female: 71. 0%, male: 29. 0%) samples but more males than females in the CP sample (male: 69. 2%, female: 30. 8%). Most participants in the UK, Germany and Spain were Caucasian/White (range: 64. 8e88. 3%); ethnicity data were not collected in France as it is considered inappropriate.
Discussion The PEI-Q is the first-known PEI-specific PRO developed specifically for use in PEI patients. The analyses described in this study involved evaluating the psychometric properties of the PEI-Q using data collected from an observational, non-interventional study with treated PEI patients (CF and CP patients), IBS-D patients and healthy controls in the UK, France, Germany and Spain. These results support reliability, validity and ability to discriminate based on severity or diagnosis of PEI, in a sample of treated patients from four European countries. There are concerns that PEI is under-diagnosed and potentially under-treated  [10] , leading to a more severe symptom experience, greater impacts on HRQoL  [11, 12]  and potentially less treatment satisfaction. The PEI-Q could be of value in clinical practice to help monitor symptoms and impacts in a standardized way and improve patient-physician communication.
The key distinction is that the frequency of abdominal pain required has increased from 'three days per month' to 'at least one day per week'. This may mean that patients with a Rome III diagnosis may not meet the Rome IV criteria for IBS-D  [14]. There is the possibility that some patients included in the IBS-D sample also had PEI, due to the overlap in symptom presentation. However, as the IBS-D patients were recruited by specialist clinicians at clinical sites with extensive experience identifying both PEI and IBS-D, it is considered unlikely that these clinicians would have misdiagnosed PEI patients as having IBS-D. The two-week follow-up produced good evidence of test-retest reliability, however data were not collected before and after an intervention, to confirm the ability of the PEI-Q to detect changes over time.
Analysis of longitudinal data from an intervention sample will be needed to establish clinically meaningful change thresholds in PEI-Q scores and generate evidence to support the ability of PEI-Q scores to detect changes over time. The study included PEI patients with CF or CP, as these are the most common causes of PEI. Additionally, patients in the sample were receiving treatment for their condition. However, it is recommended that the PEI-Q is further evaluated in patients with other causes of PEI such as pancreatectomy, pancreatic cancer or acute pancreatitis and untreated and newly diagnosed PEI patients to confirm reliability and validity in those populations  [15, 16]. Future studies should also explore the cross-cultural validity of the PEI-Q in additional countries. Although the psychometric evaluation performed here was limited to the UK, Germany, France and Spain, recruited patients had diverse demographic characteristics, so findings are likely representative of the PEI experience among CF/CP patients across a range of countries.
Acknowledgements Professors Johnson, Staab ,  Dominguez-Munoz ,  Levy and Lerch  and  Dr. Connett  provided input into the study design, reviewed and provided input into the psychometric analyses and results and provided input into the clinical relevance of symptoms and impacts to PEI. Mr. Arbuckle ,  Ms Williamson ,  Ms Johnson  and  Ms Simpson  were employees of Adelphi Values during the conduct of the study, a healthcare outcomes research company which was paid by  Abbott  to conduct this research. They were involved in all research activities, leading the study design, development of study documents, data collection and analysis and write up of the data. All authors contributed to the writing of the manuscript and reviewed and approved the final manuscript.
Myostatin, a negative regulator of skeletal muscle mass in animals, has been shown to play a role in determining muscular hypertrophy in several livestock species, and a high degree of polymorphism has been previously reported for this gene in humans and cattle. In this study, we provide a characterization of the myostatin gene in the dromedary (Camelus dromedarius) at the genomic, transcript and protein level. The gene was found to share high structural and sequence similarity with other mammals, notably Old World camelids. 3D modeling highlighted several non-conservative SNP variants compared to the bovine, as well as putative functional variants involved in the stability of the myostatin dimer. NGS data for nine dromedaries from various countries revealed 66 novel SNPs, all of them falling either upstream or downstream the coding region.
Precursor Prediction The SignalP 4. 1 1 , the Combined Signal Peptide Predictor (CoSiDe) 2 and the Signal-3L 2. 0 3 online tools were interrogated for predicting the most probable location of the signal peptide cleavage site. (5ntu. pdb), were thus aligned by using ClustalW (see  Pierri et al. , 2010, and references therein)  for investigating chemical-physical properties of the amino acid regions showing variants between the human and the dromedary sequences. Accession numbers of the sequences considered in this study, together with additional details concerning the databases and the online tools used in this study, are summarized in Supplementary Table  S1. Then, SPDBV was used for generating a 3D model of the dromedary myostatin protein according to protocols described in  Pierri et al.
Phylogenetic Analysis The analysis of the evolutionary relationships among orthologous myostatin sequences was conducted using MEGA5  (Tamura et al. , 2011). Orthologous sequences of myostatin/growth differentiation factor 8 with E-value lower than 10ˆ-55, query coverage higher than 70% and % of identical amino acids ranging between 40 and 100% were aligned by using ClustalW implemented in Jalview. For Arthropoda, Aves, and Mammalia, due to the existence of more than one hundred of sequences complying with the above criteria, we imposed a filter on the first 30 sequences for each taxonomic group. Redundant sequences with 100% identical amino acids were removed from the multiple sequence alignment. A final set of 83 protein sequences (see Supplementary Data Sheet S1) were retained for tree building.
Whole-Genome Read Processing and Alignments The 3 end of sequence reads were trimmed to a minimum phredscaled base quality score of 20 (probability of error < 1. 0%) and trimmed reads < 50 bp in length were excluded using POPOOLATION v1. 2. 2  (Kofler et al. , 2011). All processed reads were aligned to the C. ferus CB1 reference genome (Genbank accession: AGVR01040332. 1) using BWA v0. 6. 2  (Li and Durbin, 2009)  with parameters '-n 0. 01 -o 1 -e 12 -d 12 -l 32. ' Duplicate reads were removed and alignments were filtered to only include reads that were properly paired and unambiguously mapped with a mapping quality score > 20. Reads around insertions/deletions were realigned and a base quality score recalibration was performed using the Genome Analysis Toolkit (GATK) v3. 1-1 following guidelines presented by  Van der Auwera et al.
Whole-Genome Variant Identification Another set of SNVs from the realigned and recalibrated alignment files was generated using the GATK HAPLOTYPECALLER and filtering criteria as described above. SNVs on scaffolds putatively assigned to the X and Y chromosome, with a minimum allele count < 2, missing a genotype in more than five individuals, with 4 > DP > 30 per genotype, and deviating from Hardy-Weinberg equilibrium (p < 0. 0001) in VCFTOOLS v0. 1. 12b (Danecek et al. , 2011)  were further excluded. This set of SNVs was used as a training set to perform variant quality score recalibration in GATK, assigning a probability of error to the training set of 0. 1. This recalibration develops a Gaussian mixture model across the various annotations in the high-quality training dataset then applies the model to all variants in the initial dataset.
Identification and Characterization of Variants at the Myostatin Locus The publicly available Camelus ferus myostatin sequence (GenBank Accession No AGVR01040332) was BLASTed against our Old World camel genomes and the contig-8645394 (Camelus dromedarius), contig-8938518 (Camelus bactrianus) and contig-7907533 (Camelus ferus) were retrieved and used in the comparative analysis of the myostatin locus at the nucleotide level. Moreover, from the final set of SNVs described in the sub-section above, the Camelus dromedarius Single Nucleotide Polymorphisms (SNPs) falling in the contig-8645394 (Supplementary Data Sheet S2) were selected for further inspection.
Data Analysis After the thermal cycling, the plates were transferred to a droplet reader (QX200 TM ; Bio-Rad). The software package provided with the ddPCR system was used for data acquisition (QuantaSoft TM 1. 6. 6. 0320; Bio-Rad). The rejection criterium for the exclusion of a reaction from subsequent analysis was a low number of droplets measured (<10,000 per 20 µl PCR). The data from the ddPCR are given in target copies/µl reaction. The significance of differences among muscles was tested using ANOVA (Analysis of Variance).
An aliquot of 20 µg of protein for each sample was diluted in Laemmli buffer not containing DTT or β-mercaptoethanol, heated at 95 • C for 5 min, and separated by 12 % (v/v) Tris/HCl SDS/PAGE. The separated proteins were transferred to Immobilon P (Millipore) in Trans-Blot semidry electrophoretic transfer cell (Amersham Biosciences) for immunoblotting. The used primary antibody was a rabbit polyclonal anti-MSTN antibody against the C-terminal region (300-349 aa) of mouse myostatin (TA343358, OriGene; dilution 1:1000) that presented broad species reactivity, including artiodactyls. The densitometric quantification and image processing of the considered bands were carried out using Adobe Photoshop and the Image software package (version 1. 61, National Institutes of Health, Bethesda, MD, United States).