dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We acknowledge  J. Lewandowski ,  M. Parkes ,  A. Kaser ,  D. Thomas  and members of the Smith and Rinn labs for helpful discussion,  J. Sowerby  for experimental help and  D. Seyres  for ChIP advice. Jurkats were a gift from  Stefan Marciniak , pRSCgfp-hAIM2 was a gift from  Emad Alnemri  (Addgene # 51666 ), CBFRE-EGFP was a gift from  Nicholas Gaiano  (Addgene # 17705 ), and pOTTC407-pAAV EF1a eGFP was a gift from  Brandon Harvey  (Addgene # 60058 ). We thank  NIHR BioResource  volunteers for their participation, and acknowledge  NIHR BioResource centres, NHS Blood and Transplant , and  NHS  staff for their contribution. This work was supported by the  Wellcome Trust  ( Intermediate Clinical Fellowship  to J. C. L,  105920/Z/14/Z ;  Senior Fellowship  to C. W. , WT107881),  Crohn's and Colitis UK  ( M2018/3 ), the  National Institute for Health Research (Cambridge Biomedical Research Centre  at the  Cambridge University Hospitals NHS Foundation Trust ), the  Howard Hughes Medical Institute  ( Gilliam Fellowship  to A.
Data availability The data sets produced in this study, including raw and processed files, are available in the following databases: • MPRA data: Gene Expression Omnibus GSE135925 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE135925). • ChIP-Seq data: Gene Expression Omnibus GSE136092 (https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE136092). Expanded View for this article is available online. For more information TNFAIP3 (OMIM): https://www. omim. org/entry/191163 GWAS catalog: https://www. ebi. ac. uk/gwas/ Author website: https://www. citiid. cam. ac. uk/james-lee/.
Microsatellite analyses. We used previously validated SSRs as highly polymorphic markers to provide continuity with our published Ae. albopictus population data and because their use allowed accurate biogeographic data analyses especially at macrogeographic scale. Indeed, the availability of a population SSR genotype dataset was a reliable resource for ABC simulations as SSRs represented a good balance between being well characterized (i. e. in terms of mutation model) and having sufficient genetic diversity to provide statistical power, as we previously assessed  31. We used 11 highly informative SSR loci applied to 702 individuals from 25 localities to produce a SSR genotyping dataset with no major bias in statistical analyses  69. On this basis, genomic DNA extracted from each mosquito  70  was genotyped at the following SSR loci: Aealbmic1, 2, 3, 5, 6, 9, 11, 14, 15, 16 and 17  31.
These loci were chosen due to their high polymorphism and because they are spread across the genome (they map to different scaffolds of the Ae. albopictus Foshan genome (ref. 71 and our unpublished data)). They have proven to be efficient markers to detect variability, even in relatively small samples  32. PCR amplifications and fragment identifications were performed as previously described  31. In order to account for genotyping errors, automated binning of allele lengths was performed with TANDEM v1. 09  72  followed by manual checking. When microsatellite amplification was not successful, or allele scoring was unclear, a new DNA extraction was performed. The generated SSR genotype dataset, in addition to offering continuity with previous studies, allows the formulation of reliable models of recent evolutionary history because of their well-documented mutation rates  73  , high molecular diversity and high minor allele frequencies.
Variability and genetic diversity analyses. Linkage disequilibrium between pairs of loci in each sample (100 batches, 1000 iterations per batch) and deviation from within-population Hardy-Weinberg equilibrium (HWE) at each locus/sample combination were tested with GENEPOP V. 4. 2  74. The statistical significance was assessed following Bonferroni corrections  75. Variation within each locality was estimated in terms of average number of alleles (na), private alleles (np) and their respective frequencies (Ap) using GenAlEx 6. 5  76. The average number of alleles (na/n) and private alleles (np/n) were also computed at the individual level. Gene diversity (Hs) and allelic richness (Rs) were computed with the program FSTAT V. 2. 9. 3. 2  77. Observed (Ho) and unbiased expected heterozygosity (uHe) values and Pairwise-F ST were computed using Microsatellite Analyzer (MSA) V.
Once the best number of clusters, K, was inferred, CLUMPP software  82  was used to merge the 20 runs and the results were plotted in DISTRUCT  83. Analyses of demographic History. The Approximate Bayesian Computation (ABC) method, as implemented in the software DIYABC v2. 1, was used to disentangle the complex invasion patterns of Ae. albopictus at the macro-geographic level. This approach allows comparisons between competing hypotheses regarding the divergence of the populations on a global scale. DIYABC v2. 1 implements a standard ABC method (ABC-LDA) which, although being more expensive computationally, was shown to perform comparably (for posterior probability) or slightly better (for prior error rate) than the more recent random forest method (ABC-RF)  84  when large reference tables are used  85  , as in our case.
This index helps to discriminate populations that have experienced recent losses in genetic variability from those that have been stable for a long time. One million simulated datasets were generated for each scenario. Competing scenarios were compared by calculating their posterior probabilities using a polychotomous logistic regression on the 1% of simulated data closest to the observed data  88. Confidence in scenario choice was evaluated by computing type I and type II errors  89. Once the most likely scenario was identified for each analysis, the posterior distributions of genetic and demographic parameters were estimated. This was achieved by computing a local linear regression on the 1% of the simulated data closest to our observed dataset, after applying a logit transformation to the parameters value.
The infectious status of head and saliva samples was determined by titration assays  90. The blood meal titres, viral strains, days post-infection (dpi) as well as DE and TE data are summarized in Supplementary Table  4. Viral strains. Six CHIKV strains isolated from human cases were used in infection assays. Five strains belonged to the ECSA genotype: CHIKV_0621 (Accession number AM258992) and CHIKV_05115 (Accession number AM258990) isolated in La Réunion, CHIKV_1909 (Accession number FR846305) from Southeast France, CHIKV_DRC and CHIKV_Congo from Congo  38. All these ECSA strains were split into two different genotype groups according to the amino acid substitution at 226 position of E1 envelope protein: the group named 'ECSA' that contains the substitution E1-226A (Alanine) and the group named 'ECSA E1-226V' that contains a Valine at the same position.
The primary targets of both exercise training and BA supplementation are the muscles of ambulation. Nevertheless, it seems reasonable to hypothesise that an enhanced bioavailability of carnosine in the body, by means of BA supplementation, may have an antioxidative effect in both the lungs and the brain. Interestingly, on oral supplementation, carnosine is taken up by the PEPT2 transporter at the lung membrane, resulting in enhanced carnosine levels in lung tissue. 25 26 Likewise, elevations in carnosine in all compartments of the brain were measured after BA supplementation in rats. 27 These elevations in carnosine increased the resiliency to stress and were inversely associated with anxiety index. To date, the safety and efficacy of BA supplementation during usual care (not combined with exercise training) in patients with COPD has only been assessed once (NCT02770417).
05; β=0. 90). 6 urthermore, a sample size calculation for the required muscle biopsies is included. Based on the unpublished findings of De Brandt et al (NCT02770417; effect size: 1. 39), a group size of n=15 is required for each group in each trial, including an estimated dropout rate of 25% (α=0. 05; β=0. 90). Therefore, the aim will be to perform muscle biopsies in 30 patients with COPD (15 for the BA group and 15 for the placebo group) in each trial.
Data management and statistical analyses In order to minimise missing data, only essential information will be collected at each visit, user-friendly casereport forms have been developed, and the majority of the tests and questionnaires will be performed in the presence of a researcher. The study variables will be tested for normality. For all data, point measures and measures of variability will be provided. Demographic variables (such as age, sex, educational level, smoking history) will be compared between patients in the BA and placebo group, using unpaired t-test or Mann-Whitney U Open access test, as appropriate, for continuous variables and χ 2 tests for categorical variables. To evaluate the changes in exercise tolerance, lowerlimb muscle function, muscle carnosine, muscle and systemic BA, taurine and histidine, muscle, systemic and pulmonary markers for inflammation and oxidative stress and cognitive function, a 2×2 general linear model repeated measures analysis of variance (ANOVA) will be performed, with 'supplementation group' (BA, PL) as between-subjects factor and 'time' (PRE vs POST) as a within-subjects factor.
Novartis, not related to the current projects, and personal fees for consultancies and lectures from  AstraZeneca ,  Boehringer Ingelheim ,  Chiesi ,  GlaxoSmithKline ,  Novartis  and TEVA. EBdV reports personal fees for consultancies and lectures from  Boehringer Ingelheim ,  Chiesi ,  Mylan ,  Novartis , Vivisol and TEVA, not related to the current projects. FNS reports speakers fees from  AstraZeneca ,  Chiesi ,  Menarini ,  Mundipharma  and  Novartis ; reports consultancy fees from GSK; and reports service on an advisory board for  AstraZeneca ,  Chiesi ,  GSK  and  Novartis , all outside the submitted work. MH reports research grants from  Bastide Medical , not related to the current project; personal fees from  AstraZeneca  for participation to scientific lectures; financial support for congress participation from  SOS Oxygène ,  Eole Santé ,  Boehringer Ingelheim ,  GlaxoSmithKline ,  AstraZeneca ; and hospitalities during local scientific meetings from  ALK-Abelló ,  Actelion Pharmaceuticals France ,  Vifor Fresenius Medical Care Renal Pharma ,  Sanofi Aventis France ,  Novartis Pharma ,  LVL Medical Sud, Chiesi, SOS Oxygene Mediterranee.
[6] Sequential doping was performed in a Jacomex glovebox (p O2 < 1 ppm and p H2O < 1 ppm) using anhydrous acetonitrile (F 4 TCNQ and F 6 TCNNQ) and nitromethane (FeCl 3 ) following the protocol described in reference. [4] All solvents were used as received from Sigma Aldrich. Charge conductivity and Seebeck coefficients were probed using a Keithley 4200 source meter and a Lab Assistant Semi-Probe station along and IPA (open circles) and a fraction of samples was subsequently drawn through a DMSO bath (filled circles). Adapted with permission. [3] Copyright 2019, American Chemical Society b) Experimental data on PBTTT from Ref. [5]  showing the anisotropy of the electrical conductivity and thermopower as a function of the alkyl side chain length at a doping time of 6 min. Adapted with permission.
Introduction Mycoplasma (M. ) bovis is one of the currently described more than 130 species of the bacterial genus Mycoplasma and one of the major causative agents of bovine mycoplasmosis  [1]. It was first reported in a case of mastitis in 1961 in the USA  [2]  and since then has been increasingly associated with a broad range of clinical manifestations, such as pneumonia (as one of the etiological agents of the bovine respiratory disease, BRD), arthritis, otitis media, and genital disorders. It is distributed worldwide, with variable prevalence, and considered to be one of the major emerging pathogens of cattle in elsewhere in the world. Additionally, the diversity of our M. bovis study population was assessed in terms of susceptibility to the commonly used veterinary antibiotics in order to assess the potential for antibiotic resistance lineages in Nordic European countries, from where data are scarce.
All our recent isolates seem to radiate from ST5, either by a short (ST12, 19, 184, 185) or a greater evolutionary distance (one lineage with ST14, 18, and 42 and another one with ST94-adh1). Isolates from all over the world (as seen in entries from PubMLST) share this similarity to ST5, confirming a worldwide distribution. Isolates sharing STs that are more genetically distant from ST5, such as ST14, 18, and 42 (France and the Netherlands in our data set), are also noted to be geographically and temporally widespread, appearing in the US in strains from 1994 and more recently in central Europe and Israel, according to the PubMLST data. As a consequence of the absence of the adh-1 gene, we were unable to further investigate the closest common ancestor to subtype ST94-adh1 subtype. With the new Register scheme (MLST-1b, Figure  S3 ), the resulting spanning tree appears similar in structure, despite comprising a more limited strain set.
However, they remain relatively expensive, time-consuming, and require expertise for interpretation. Hence, for M. bovis, the existence of the pubmlst. org database remains an asset for the whole scientific community. This study has contributed to the public database (both the genomic and MLST databases) by including a wider range of isolates from Europe. For instance, previously, only 33 isolates in the PubMLST set of 448 isolates studied by Register et al. were of European origin  [28]. We have been able to include older isolates, collected prior to 2000, that are currently less well represented in the MLST scheme and genomic data. Understanding the epidemiology of these old isolates is undoubtedly important. One current genotype (or subtype) in France (strain F9160) clustered with old isolates, suggesting it could result from a re-emergence of an old clone that had continued to circulate for years in an unusual host or in an environmental reservoir.
[24] , polC genes were extracted with simulate_PCR [52] aligned with MUSCLE  [53]  and attributed a subtype (st) in comparison to those defined previously  [24, 31, 32]. Trees were built using FastTree  [54] , an approximate maximum-likelihood algorithm. All comparative genomic data were agglomerated and plotted using R v3. 4. 4. Mutational hotspots associated with resistance according to Sulyok  [10]  were investigated by extracting the 16S and 23S rRNA genes and aligning them using MUSCLE  [53]  to the corresponding gene from Escherichia coli strain K-12 substrain MG1655 (GenBank accession number CP014225). The individual positions were then checked for mutations relative to the PG45 type strain. The presence of resistance genes was investigated in silico by performing a BLAST analysis using the Comprehensive Antibiotic Resistance Database (CARD; https://card.
Participants were also advised on how to opt out of the survey by sending a text with the word leave at any time. As a final orientation step, participants were provided an informational sheet and advised on how to increase privacy during the study period and avoid unintended loss of confidentiality, such as activating cell phone passwords, deleting all text message surveys, responding only to the study's phone number, and answering in a quiet and private space. Participants were also informed of the study's security protocol that included separating cell phone numbers from identifying information; selecting a platform, such as TextIt. In, that did not require handing over participants' names, addresses, or other identifying information to a mobile database software company; anonymizing phone numbers with a random code at the end of the study to render numbers invalid for future use; and using encrypted and compliant channels of TextIt.
Text Survey Design We used TextIt. In to create, send, and receive text messages from participants. TextIt. In is an online service for building text messaging apps using a visual and interactive flow. The text message survey was powered by Twilio, a cloud communications platform, using a study-sponsored phone number. We then developed an online logic tree to order how survey questions would be texted to the participants. Figure  1  shows an excerpt of the branch logic used in the question tree. Each of the 14 questions was sent sequentially and in the same order as a single text message after the prior question had been answered. To facilitate responsiveness and data quality, the text messaging app included automated reminders and quality check prompts. Participants had 24 hours to complete each weekly text message survey.
Measures Data were collected in August and September 2017. Participants received the same 14 questions as text messages each week, regardless of their responses to the week's prior text message survey. Table  1  lists the questions used in the survey of this formative study. The set of questions was adapted from previous studies of economic empowerment and costs associated with HIV preventive and treatment services  [42] [43] [44] [45] [46] [47] [48] [49] [50] [51]  and included questions developed by the study team, specifically for African American young adults experiencing homelessness and unemployment  [52] [53] [54]. All questions were in English and reviewed by the study team, piloted with young adults, and revised as necessary prior to utilization. The questions referred to the last 7 days, equivalent to the prior week.
Analysis To analyze the results of the text message survey, we first created a database in Excel that included: a cell phone number for each participant; a participant unique study ID; demographic data relating to participants' age, gender, education level, years living in Baltimore, number of hours worked per week, and number of children living in and out of the household, the date and time of study enrollment, the date and time of all outgoing and/or incoming messages, and the numerical, textual, or free-from text message response to each of the 14 text message questions each week. Secondly, we calculated the study's primary feasibility measures, as listed above. Third, we calculated the frequencies of sexual and economic behaviors per the specific responses for each weekly question. Finally, lessons learned from the study's process evaluation were analyzed over 5 implementation domains: acceptability, enrollment and registration, responsiveness, data quality, and data access.
Condomless sex was a common risk behavior, with 14%-75% of participants reporting condomless sex at least once in a given week. There were no reports of sex exchange for money, food, or housing. Using other noncondom prevention methods was also low (11% in week 3). Participants were more likely to respond yes to the last two sexual behaviors of the survey, which were about discussing HIV testing with any of their sex partners (43%-67%) and receiving an HIV test in the past week (14%-43%). Participants used the skip response infrequently and only during weeks 1 and 2. When used, skip was most common for questions relating to sex exchange and money spent on HIV prevention services or products (Table  5 ). The dataset analyzed during this study is publicly available  [55].
Improvements in exporting and coding the messaging app's data would increase efficiency. Finally, given that text messaging technology is constantly evolving as are young adults' cell phone behaviors, including the option to respond to surveys via instant messaging or other text messaging apps may improve participation. The study's small sample size was a limiting factor, as the study was not designed to determine efficacy or estimate prevalence of economic and sexual risk behaviors. Rather, the study aimed to conduct a rapid test cycle using a small group of young adults with a preliminary goal of assessing instrument feasibility for a larger intervention trial. We additionally selected young adults who had a working cell phone, were literate, and were receiving residential services at the participating CBOs.
Acknowledgments The authors would like to thank the  EMERGE (Engaging Microenterprise for Resource Generation and Health Empowerment) study group , which consists of the EMERGE study participants and graduate student research assistants at the  Johns Hopkins University Bloomberg School of Public Health. The authors are also grateful to the directors, managers, and staff at  YO!Baltimore (Elizabeth Torres Brown ,  Burgundi Allison ,  Sherry Kizielewicz , and  Kierra Sanders ) and AIRS ( Anthony Butler ,  Monifa Riggins , and  Irisha Burrell ) and  Jamison Merrill , who made this work possible. This research was funded through resources and services provided by the  National Institute of Mental Health (NIMH)  (grant:  K01MH107310 , principal investigator:  Larissa Jennings Mayo-Wilson ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIMH.
The current classification of proteases provided by the MEROPS database clusters caspases, meta-and paracaspases to the same family, C14, within the CD clan (https://www. ebi. ac. uk/ merops/). All members of C14 family are annotated to possess aspartate P1 cleavage specificity, and the family is further split into two subfamilies: C14A (caspases) and C14B (meta-and paracaspases). Importantly, the MEROPS approach of grouping proteases into families or subfamilies is based on statistically significant similarities of the amino acid sequence within the peptidase domain or part thereof, without considering their biochemical properties  (Rawlings et al. , 2018). Being valuable for high-throughput protease classification, this approach, however, has substantial drawbacks if implemented without further adjustment.
Instead caspases, meta-and paracaspases should be separated into three corresponding groups within the family and each group should be properly annotated by providing its key biochemical and structural characteristics. We kindly request curators of MEROPS database to make corresponding changes. Since structure and substrate specificity of prokaryotic caspase-like proteases named "orthocaspases" remain largely unknown  (Klemenčič et al. , 2015)  we leave their classification and nomenclature open until their structural and biochemical properties have been clarified.
Conclusions: By enhancing the interpretability of genomic k-mer-based antibiotic resistance prediction models, our approach improves their clinical utility and hence will facilitate their adoption in routine diagnostics by clinicians and microbiologists. While antibiotic resistance was the motivating application, the method is generic and can be transposed to any other bacterial trait. An R package implementing our method is available at https://gitlab. com/biomerieux-data-science/clustlasso.
Last but not least, it is hazardous to predict susceptibility when no marker is detected because the resistance marker may be novel and databases incomplete. Building AMR prediction models is now more and more addressed from the supervised machine learning (ML) standpoint: given a set of genomes with associated reference phenotypes (provided by phenotypic AST methods  [20] ), one seeks a prediction rule allowing inference of the resistance or susceptibility of a novel strain from genomic features. While ML methods are also hindered by a completeness limitation because the set of genomes may not be representative of the genomic diversity of the whole species, they have the ability to identify novel markers or marker combinations in situations where no or limited prior knowledge is available and hence are becoming more popular in this context.