dataesr/scientific-paragraphs-categorization huggingface dataset:
TCR library preparation and sequencing TCRalpha and TCRbeta cDNA libraries preparation was performed as previously described in  [19]. RNA was isolated from each sample using Trizol reagent according to the manufacturer's instructions. A universal primer binding site, sample barcode and unique molecular identifier (UMI) sequences were introduced using the 5'RACE technology with TCRalpha and TCRbeta constant segment specific primers for cDNA synthesis. cDNA libraries were amplified in two PCR steps, with introduction of the second sample barcode and Illumina TruSeq adapter sequences at the second PCR step. Libraries were sequenced using the Illumina NovaSeq platform (2x150bp read length). TCR repertoire data analysis Raw data preprocessing. Raw sequencing data was demultiplexed and UMI guided consensuses were built using migec v.
ACKNOWLEDGMENTS The study was supported by  00351. E. R and A. F. are supported by the  DFG Excellence Cluster Precision Medicine in Chronic Inflammation  ( Exc2167 ) and the  DFG  grant n. 4096610003, M. B. K. , T. M. , and A. M. W. are supported by  European Research Council   COG 724208. I. Z. M. is supported by  RFBR   19-54-12011  and  18-29-09132. D. M. C. is supported by the grant from  Ministry of Science and Higher Education of the Russian Federation   075-15-2019-1789.
Microbial community analysis DNA was extracted with FastDNATM SPIN Kit following the instructions provided by the manufacturer (MP Biomedical; Santa Ana, California -USA). Extractions were confirmed and DNA concentration was obtained using Infinite 200 PRO NanoQuant (Tecan Group Ltd. , Männedorf, Switzerland). The V3 -V4 region of the 16S rRNA gene was amplified according to Carmona-Martínez et al. ,  (2015). The community composition was evaluated using MiSeq v3 (Illumina) with 2x300 bp paired-end reads at the GenoToul platform (http://www. genotoul. fr). Sequences were retrieved after demultiplexing, cleaning and affiliation of the raw sequences using Mothur v1. 39. 5. For alignment, the SILVA 132 database was used. Sequences were submitted to GenBank under the accession No. MT000996-MT001185.
Electro-fermentation efficiency (ηEF). This coefficient corresponds to the ratio between the number of electrons passing through the electrical circuit and the number of extra-electrons recovered in the fermentation products. For EF systems, this value should be as low as possible, showing that electricity production or consumption was not predominating the metabolites production. A value close to 1 indicates direct bioelectrosynthesis or electrolysis  [8]. All statistical analyses of the data were carried out using the PAST (PAlaeontological STatistics) software v3. 22 (https://folk. uio. no/ohammer/past/).
ACKNOWLEDGMENTS We thank the  Chilean National Commission for Scientific and Technology Research (CONICYT)  for the PhD scholarship to J. T-A. We thank the  Pontificia Universidad Católica de Valparaíso (PUCV)  for providing postdoctoral funding for J. T-A. L. F was founded by  Agencia Nacional de Innovación e Investigación (ANII)  from  Uruguay , PhD scholarship. L. F was founded by a short-term scholarship from the  French Embassy.
Although there is no simple method for resolving what is the dominant source of variability in the measured Fig. 6  (a) Evaporation profiles for ethanol measured using the FDC (black squares, lower data set) and an EDB (black circles, upper data set). The lines are linear fits to the data, extrapolated back to t = 0 s for the EDB measurements. The EDB data are optical diameters, calculated from light scattering analysis. The FDC data are geometric diameters. (b) Normalised evaporation profiles for water (blue), butanol (green), propanol (red) and ethanol (black) from the FDC (datapoints) and EDB (lines). The shaded blue regions are the calculated uncertainty resulting from the uncertainty in the ambient conditions of both the FDC and EDB (shown for water, as an example). ) and evaporation rates (k) from the evaporative profiles shown in Fig.
6   aerodynamic diameter of the dried particles, analysis of the morphology of the dry particles provides an indication of their uniformity. The evolving evaporation rates for these datasets are shown in more detail in Fig. 8. The general trend is a steady decrease in evaporation rate following droplet generation, deviating away from the d 2 law, until the onset of crystallisation. The decrease is most extreme at 40% RH and can be rationalised by considering the difference between the water vapour pressures at the droplet surface and in the gas phase, which govern the evaporation rate. As the water evaporates, the surface concentration of the solute increases which leads to a decrease in the vapour pressure difference and, hence, the evaporation rate. This does not occur as dramatically for droplets evaporating at a lower RH because a significant difference between the vapour pressures is maintained until efflorescence occurs.
Patient Selection A retrospective review of prospectively collected data on consecutive cases of women with a history of BS who were admitted in an ambulatory procedure during pregnancy between January 2013 and July 2020 in a tertiary care hospital with more than 2600 annual deliveries was done. For this study, we included consecutive pregnant women with a history of RYGB or SG with complete clinical and biological nutritional assessments performed in our institution. We excluded women with a history of others types of BS (i. e. , laparoscopic gastric banding, duodenal switch), women without gestational age dating (crown-to-rump length at a first-trimester ultrasound examination or fetal biometry before 24 weeks), multiple pregnancy, women with medical-indicated second trimester termination of pregnancy, and with intra uterine death and fetal loss before 22 weeks.
Maternal and Perinatal Outcome Maternal sociodemographic characteristics, information regarding pregnancy followup, and standard perinatal outcomes were collected prospectively by one obstetrician (GD), from a prospectively maintained database of women who were included in the study. Maternal characteristics (age, geographic origin, type of BS, pre-pregnancy BMI, parity, pre-existing type 1 or 2 diabetes, medical history of chronic hypertension, and smoking during pregnancy) and antepartum variables (time to conception from bariatric surgery, GDM, pregnancy-induced hypertension/pre-eclampsia, intrahepatic cholestasis of pregnancy, nutritional supplementations after blood tests, gestational weight gain (GWG), and specific complications in pregnancy due to BS) were collected. Pre-pregnancy BMI was calculated (kilograms per square meter) from the database with the use of height and weight before the beginning of the pregnancy.
Prolonged pregnancy was determined by gestational age ≥ 41(+0) weeks  [25]. PPH was defined by a blood loss ≥ 500 mL, assessed with a collector bag placed just after birth. The neonatal outcome collected included birth weight, fetal sex, birth weight z-score (BWz), cephalic perimeter, umbilical artery blood gas values that were routinely measured, and immediate neonatal data that were recorded with a systematic pediatrician examination after delivery: 5 min Apgar score, need for resuscitation or intubation, admission to the neonatal intensive care unit (NICU), and neonatal death. BWz were calculated according to French published references, adjusted for sex and term at birth  [26]. Neonatal morbidity was a composite variable, defined by at least one of the following criteria: Preterm delivery (<37 weeks), 5 min Apgar score less than 7, pH less than 7.
Statistical Analysis The finalized dataset of included pregnant women was entered into EXCEL spreadsheets (all patients' identifiable data were excluded) and migrated into the Software for Statistics and Data Science (STATA, version 16. 0, StataCorp LLC, College Station, TX, USA) for analyses. Continuous variables were described by their mean ± SD and compared between the groups by the Student's t-tests (or Mann-Whitney U test when appropriate) and categorical variables were described by percentages and compared between the groups by the chi-squared test (or Fisher's exact test when appropriate). No formal sample size was calculated as we analyzed data on all cases (RYGB and SG) over a specified time period. The maternal and neonatal outcome and the serum micronutrients values were compared according to the type of surgery.
Statistical analysis The statistical data analysis was performed with (SPSS 2011) SPSS 20 for Windows (SPSS, Inc. , Chicago, IL). The normality of data distribution and homogeneity of variances were assessed using the Shapiro-Wilk test and the Levene test, respectively. Data were analysed with one-way ANOVA, with the treatment as the fixed effect. Means were separated using a Tukey post-hoc test. The results were presented as mean values and pooled SEM. The results were considered statistically significant when associated with a lower probability than 5%. The Se data measured on the breast muscle pertaining to L-SeMet and OH-SeMet were evaluated in Exp 3 using linear regression analysis separately on the Se feed supplementation with the NC as a common intercept. The effect of L-SeMet and OH-SeMet supplementation on the Se concentration in the tissue was expressed by means of regression equations and correlation coefficients, which allowed an estimate to be obtained of the relative biological value of the two Se sources.
This has led to the identification of more than 250 subspecies defining genes common to both analyses. The majority of these genes are arranged in clusters called genomic islands. We further reduced the number of diagnostic targets by excluding sequences having high BLAST similarity to other mycobacterial species recently added to the National Center for Biotechnology Information database. Genes identified as diagnostic following these bioinformatic approaches were further tested by DNA amplification PCR on an additional 20 M. avium subspecies strains. This combined approach confirmed 86 genes as Map-specific, seven as Maa-specific and three as Mah-specific. A single-tube PCR reaction was conducted as a proof of concept method to quickly distinguish M. avium subspecies strains. With these novel data, researchers can classify isolates in their freezers, quickly characterize clinical samples, and functionally analyze these unique genes.
A forth subspecies has been reported (silvaticum), but the lack of circulating strains and sequences in public databases suggests this subspecies designation may be artifactual  (2). Map is the causative agent of Johne's disease, which is a chronic intestinal ailment of ruminants that results in significant economic loss to dairy, meat and wool industries  (3, 4). Mah is an opportunistic pathogen of swine and humans that manifests most commonly as a pulmonary infection in humans  (5) , while Maa is primarily a pathogen of birds  (6). In this report, the terms Map and non-Map are used to divide the M. avium subspecies. This is due to the focus on diagnostic Map sequences, which are major tools for research and surveillance of Johne's disease. Conversely, very few studies have reported sequences that are diagnostic for non-Map subspecies.
To identify all remaining sequences specific for Map and new diagnostic sequences for Maa and Mah, complete genomes from several strains were compared and candidate genes identified. Two independent bioinformatic analyses were first conducted to obtain subspecies specific genes in M. avium. These analyses included Roary  (23)  and PanOCT  (24)  pan genome tools, which identify core and accessory genes for a group of closely related organisms. PanOCT works by joining homologous and conserved gene neighborhoods to cluster orthologous proteins while Roary preclusters highly similar protein sequences before BLASTP similarity searching. PanOCT analysis yields a variety of comparative data about the input genomes  (24) , but one of the more useful outputs are the match tables. These tables list orthologous clusters of genes along with information on the cutoff metrics that would result in genes being declared absent in the match table.
In silico Identification of Subspecies Specific Genes PanOCT output includes a match table of all genes in every input genome (Genbank flatfile format) with orthologs arranged in rows and genomes arranged in columns. The input genomes included the 29 M. avium genomes reported in Tables 1, 2 of (2). When orthologs are not present in a genome(s), this is interpreted as one of several possibilities: the amino acid percent identity is <35%, the BLAST E-value was >1 × 10 -5 , the minimum percent match length of subject and query (default is 1%) was not met, the frame-shift overlap parameter (default is 1. 33) triggered a non-match, there were more than 20 amino acids at the beginning or end of a match that were missing, and finally, there was not at least one match that confirmed a protein fragment/frame-shift.
Primer Design Primers were designed using the National Center for Biotechnology Information (NCBI) primer search software (https://www. ncbi. nlm. nih. gov/tools/primer-blast/). Parameters that differed from the defaults included minimum product size of 300 bp, maximum product size of 800 bp, optimal primer  If the open reading frames were small and adjacent to each other, a forward primer was designed in one coding sequence and the reverse primer was designed in the neighboring coding sequence. This approach provided more sequence to design optimal primer pairs and lessened the number of PCRs to be conducted. In one case, a primer pair was designed that spanned three small coding sequences, MAP_RS10970, MAP_RS10975, and MAP_RS10980.
Functional Analysis of Genes All Roary Map-specific protein sequences of each gene (155 total) were extracted using an in-house python script (Supplementary Materials) based on Biopython (https:// biopython. org). KEGG orthologs for each protein sequence were assigned using kofamscan tool (https://github. com/ takaram/kofam_scan) based on HMM profile search with default parameters. Kofamscan results were parsed using another in-house python script (searchKEGG. py). Briefly, for each gene, we selected the best result based either on the ratio value compute as score / threshold_score where available or on the lower e-value. K numbers were then used to retrieve the complete KEGG hierarchies and functional classes were assigned based on the KEGG hierarchies. For functional analysis using eggNOG, all Roary Map-specific protein sequences were again extracted.
Thus, it was excluded by Roary analysis. The 41 genes present by Roary analysis but absent by PanOCT is due to the different clustering methods used by these bioinformatic tools. However, it is of interest to further explore this subset of 41 genes to understand which bioinformatic approach might be better for defining these gene targets. BLAST analysis of these 41 genes against the NCBI database showed reasonably strong homologies to other, more distantly related, Mycobacterium species (Supplementary Table  3 ). These results suggest that PanOCT may be better suited to consider more evolutionarily distant strains than Roary. Nonetheless, 114 Map-specific gene candidates are present in both analyses (Supplementary Table  2 ). This result excludes Map-specific repetitive sequences, such as IS900, which are not annotated in the E1 and E93 genome sequences  (2).
BLAST Similarity and in silico PCR Analysis The 114 Map-specific genes were next analyzed by similarity searches against other complete mycobacterial genomes including members of the M. tuberculosis complex and nontuberculosis mycobacteria closely related to the MAC complex  (25, 26). Several Map-specific genes showed surprisingly strong similarity to mycobacterial genomes that were recently added to public sequence databases (Supplementary Table  6 ). These include M. marseillense, M. lepraemurium, M. branderi, and Mycolicibacterium doricum. In silico PCR using primers designed for this study (Supplementary Table  1 ) were tested on the strongest matching genome sequences obtained from BLAST analysis and amplification products of the correct size were obtained for nine Map genes (Supplementary Table  6 ).
Removing these nine targets reduced the list of Map genes to 104 that are defined as Map-specific following PanOCT, Roary and in silico PCR analysis. Within that group of 104 genes, 54 were highly specific to Map as they had no matches in the NCBI sequence database (indicated by "no result" in Supplementary Table  6 ). Two additional M. avium genomes, Map DSM 44135  (27)  and Maa ATCC 25291  (28) , were published after this study began and in silico PCR on those genome sequences shows agreement with subspecies specificity for all genes except FCV17_RS22495, a Mah gene that yielded a correct size product in Maa ATCC 25291 (Supplementary Table  4 ). This translational start for Telford was downstream of that for the rest of the Map genomes.
Since agreement of both pipelines was required, the more conservative method would drive the final list of targets. Is the conservative assessment by PanOCT too strict or is Roary too liberal? Or is it somewhere in between? The only way to answer the question of whether the conservative nature of PanOCT was warranted would be to test those genes identified only by Roary to determine their diagnostic specificity. When the 41-gene subset from Roary analysis was further analyzed by BLAST against the NCBI database, most of these genes (31 of 41) showed reasonably strong homologies to other, more distantly related, Mycobacterium species (Supplementary Table  3 ). Therefore, PanOCT might be the better bioinformatic tool to use in this approach. There are several possible reasons for the non-specific amplification of selected genes that were reported as specific by PanOCT and Roary analysis.
All three of these sequences were also identified in this study as Map-specific by Roary, PanOCT, and PCR analysis. One area where annotations have improved dramatically has been in G+C rich organisms like the mycobacteria. RefSeq annotation has identified new coding sequences that were previously hidden in the same genome because original annotation software either missed them or called distinctly different start sites or reading frames for the same gene. Because of the consistency in the RefSeq annotation pipeline, genomes can now be directly compared at the gene feature level. Several subspecies specific genes identified in this study do not list an outdated locus tag, but only the updated RefSeq locus tag. Among the Map-specific genes, 17 do not have an original locus tag designation (Table  5 ), representing 20% of the Map-specific genes identified in this study that might have been "missed" or changed significantly from the initial sequence annotation  (37).
If the patients are interested in the study, an appointment is made to provide more detailed information and to answer questions. When the patient agrees to participate in the study, the informed consent is signed before obtaining the medical record at admission to guarantee privacy. Lastly, after obtaining informed consent the patients are screened by the primary investigator to assure inclusion. After group allocation and before starting treatment (T0), pre-intervention tests are performed to assess baseline values of primary and secondary outcome measures. At week 3, an assessment (T1) of only primary outcomes is performed. Within one day after completing the intervention (T2), data will be collected for all e cacy outcomes (see outcome measures section). Same data will be collected at 3 months after the end of the intervention (T3), and again at 6 months (T4).
During the 5-week intervention period, each session data will be collected regarding to intervention adherence (number of sessions and duration), physiotherapy intensity, which exercises were performed for each patient and incidences. All visits and e cacy assessments are performed at the rehabilitation center where patients have been initially treated for 5 weeks (whenever possible during routine clinical follow-up visits). Only when the patient is not able to attend personally the site due to a medical condition, the assessment is taken place at home. All data are recorded on-line using an electronic data form Clinapsis®, available from the study coordinating center. All investigators were trained in the use of the application and have a help guide, as well as a consultation service directly with the logistics coordinator of the study.
Access to the study database is restricted to authorized study personnel by password. No adverse events were recorded in other studies with core stability exercises. However, all adverse events occurring after entry into the study and until hospital discharge will be recorded. Through the combination of our electronic data form (Clinapsis®), instantaneous electronic validation, the data coordinating centre daily visual cross-validation of the data for complex errors, and regular onsite monitoring, the quality and completeness of the data will be re ective of the state of the art in clinical trials.
Patients chose ve levels of severity (1, no problem; 2, slight problem; 3, moderate problem; 4, severe problem; and 5, unable to/extreme problem) in ve dimensions (mobility, self-care, usual activity, pain/discomfort, and depression/anxiety) and rated their overall health status via the EQ-VAS. 7) rate of falls is measured by a speci c registry created speci cally for this study. The outcome is de ned as the average number of falls per patient during the intervention period and follow-up. 8) gait speed assessed by BTS G-Walk. It is a wireless system consisting of an inertial sensor composed by a triaxial accelerometer, a magnetic sensor, and a triaxial gyroscope that positioned on L5-S1 vertebrae. From the data acquired, the system extrapolates all spatial-temporal gait. The patient walks during one minute (10-m) without aids.
Statistical Analysis The main analysis population will be de ned by intention-to-treat, comprising all randomised participants with a baseline assessment, regardless of later events such as protocol violations, missing data or loss to follow-up. Missing data will be imputed and the impacts of imputation on results will be explored. Secondary analyses will be conducted restricted to the population of participants who followed the study protocol and had no missing data. General linear models with repeated measures design will be used to test the effect of CORE program (with or without TENS) on the change in the outcome of interest (total S-TIS 2. 0 score or any other outcome) between baseline and follow up (5 weeks), using a repeated measures design with 2 levels (baseline, 5 weeks), and a 2-level factor testing the intervention (CORE, usual care).
All reports, data collection, process, and administrative forms will be identi ed by a coded ID (identi cation) number only to maintain participant con dentiality. All records that contain names or other personal identi ers, such as locator forms and informed consent forms, will be stored separately from study records identi ed by code number. All local databases will be secured with passwordprotected access systems. Forms, lists, logbooks, appointment books, and any other listings that link participant ID numbers to other identifying information will be stored in a separate, locked le in an area with limited access. Any modi cations to the protocol which may impact on the conduct of the study, potential bene t of the patient or may affect patient safety, including changes of study objectives, study design, patient population, sample sizes, study procedures, or signi cant administrative aspects will require a formal amendment to the protocol.