dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyses were evaluated by (a) all AMs, (b) amoxicillin, (c) azithromycin, (d) amoxicillin-clavulanate and (e) phenoxymethylpenicillin. Rare OTUs (summarised to < 20 counts in all samples) were filtered out in DESeq2 analysis. Analyses were adjusted for age, gender, language and other AMs. We report log 2 fold change per unit of change of AM purchase, i. e. , per increment unit AM. The metagenome functional profiling was predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States-PICRUSt2 (v2. 0. 0-b. 2)  [58] , with sequences and count file after preclustering from the mothur pipeline as input. Pathways were predicted using the MetaCyc database. Differentially present pathways between low and high AM groups were analysed with welch test using STAMP (Version 2.
Each cryovial contains a defined number of parasites (suspensions of tachyzoites or cysts). The security of the resources is ensured by the permanent temperature monitoring of the storage enclosures: freezers are temperature monitored by a software and the liquid nitrogen level in tanks is regularly monitored by the BRC staff. Moreover, the collection is duplicate in Reims and Limoges. The following data are recorded on BRCLims software  [3]  and are available for each strain: genotype (microsatellite typing, and for some of them haplogroup membership), mouse virulence, host and tissue origin, date of isolation, geographical origin and disease. To order a strain, a request must be submitted at the BRC Toxoplasma scientific council (crb. toxobs@unilim. fr) by filling an order form available on the website.
and Brunet et al. [21, 22]. GP60 subtypes were identified according to the protocol described by Sulaiman et al. [23]. A nested PCR was performed using primers: AL3531 (5 -ATAGTCTCCGCTGTATTC-3 )/AL3533 (5 -GAGATATATCTTGGTGCG-3 ) and secondly AL3532 (5 -TCCGCTGTATTCTCAGCC-3 )/LX0029 (5 -CGAACCACATTACAAATGAAGT-3 ). Thermocycling conditions were: 94 • C for 3 min, followed by 40 cycles of 94 • C for 45 s, 54 • C for 45 s and 72 • C for 60 s and a final step at 72 • C for 7 min. Sequencing was performed using an AB3500 automated sequencer (Applied Biosystems, Illkirch, France). Statistical analyses were performed using Chi2 tests whenever applicable (expected number >5). Statistical analysis was performed using the SAS program (version 9. 4).
Annual Distribution of Reported Cases A similar annual distribution was observed for cases reported by public hospitals and those reported by private laboratories with a large summer peak from July to October, especially in August and September (Figure  1 ). Cases occurred in summer (July to October) in 71% of immunocompetent patients and in 39% of immunocompromised patients (data not shown). Microorganisms 2020, 8, x FOR PEER REVIEW 3 of 17 Statistical analyses were performed using Chi2 tests whenever applicable (expected number >5). Statistical analysis was performed using the SAS program (version 9. 4).
ACKNOWLEDGMENT The authors thank the urban health centers of  Chinthalbasti and Banjarahills, Government of Telangana, India ; rural health center, Digwal,  Piramal Health Care, Hyderabad, India; rural clinics, Institute for Rural Health Studies, Hyderabad, India ;  Vasavya Mahila Trust ,  Shamshabad, Hyderabad, India ; and the  Association for Promoting Social Action, Hyderabad, India.
Acknowledgements The work was supported by the  Leenaards Foundation  (grant  4390  to L. K. , C. T. , and F. S. ), the  Swiss National Science Foundation  (Sinergia grant  CRSII5_180323  to F. S. and C. T. ), and the  Werner Siemens-Stiftung. The mouse model work was done at the  Institute for Antiviral Research at Utah State University. Funding was provided by the  National Institute of Health  contract number  HHSN272201700041I ,  Task Order A30 , from the  Respiratory Diseases Branch, Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA.
Randomization After signing the study consent (taken by an appropriately trained physician), participants will be randomized in one of the two treatment arms: (a) GEMRITUX treatment, (b) personalized treatment. Randomization will be balanced (1:1). Centralized block randomization will be conducted by the Department of Clinical Research and Innovation at Nice University Hospital. Due to small study sample and a high number of participating centers, it does not seem appropriate to account for the center as a stratification parameter. Randomization will be integrated in the electronic case report file (e-CRF) devised specifically for the study with Open Clinica R software. Using their personal access details to login, the investigators will provide the necessary patient information (i. e. , the first letters of their first and last names and their month and year of birth) for random allocation to treatment by an online randomization module (RedCap R ).
Patient Data Gathered Patient data will be collected and directly entered in the patient e-CRF at different time points: inclusion, randomization (D0), month-3 (M3), month-6 (M6), month-9 (M9), month-12 (M12), month-18 (M18), and last follow-up at month-24 (M24). The data collected will be the following: consultation date; demographic data (month and year of birth, gender); history of MN; paraclinic tests to eliminate secondary MN when performed (body scan, anti-nuclear antibodies, HBV, HCV serological tests); current treatment and changes in concomitant treatments; clinical examination (weight, blood pressure, edema); serious adverse events; blood sample (urea, creatinine, electrolytes (Na, K, Cl, HCO3), protein, albumin, full blood count); urine collection on a spot morning sample (protein, albumin, urea, creatinine); centralized assays for both groups (two dry tubes of 5 ml of blood and two EDTA tubes of 2 ml of blood will be shipped to Nice at room temperature for PLA2R1-Ab, epitope profile determination, rituximab serum levels, anti-rituximab antibodies, and IL-35, B and T cells counts) (Table  2 ).
Sample and Data Collection and Management Non-fasted blood samples will be shipped on the same day at room temperature to the Laboratory of Immunology in Nice University Hospital, overseen by Dr Seitz-Polski, analyzed and results will be provided to the study site within 48 h. All samples will be retained after analysis (except if consent is withdrawn) to pursue other research goals and will be stored at -80 • C in a secured freezer located in the Laboratory of Immunology in Nice University Hospital. No genetic study will be performed on the samples. The Nice University Hospital where the collection is stored will be responsible for declaring it to the Research minister and the Agence Régionale d'Hospitalization. The cell bank has all of the informatics tools and software necessary for conformance with the national bioethical laws.
The rules governing sample anonymity will be followed. In accordance with Good Clinical Practice, at the end of the trial, all documents relating to the protocol will be archived for a period of 15 years by the principal investigator. They will be located in a lockable room providing adequate safeguards against fire, water damage, light, or malice. A case report file (CRF) will be specifically designed for study data collection. The Clinical Research Assistant responsible for the sponsorship of the study and the Data Manager of the Department of Clinical Research and Innovation at Nice will design the CRF, in coordination with, and under the responsibility of, the principal investigator. The study data will be recorded in the electronic record (e-CRF), implemented by the Data Manager of the Department of Clinical Research and Innovation using Open Clinica R software from the finalized paper CRF.
Analysis Strategy The statistical analyses will be performed by the biostatistician of the Department of Clinical Research and Innovation at Nice University Hospital. Before each analysis is performed, the conditions for the application of the tests that used will be verified. The various tests will be considered significant at a threshold of 5% (unless otherwise specified). Continuous variables will be described using the number of observations (N), arithmetic mean (Mean), standard deviation, minimum (MIN), median (Median), and maximum (MAX) values. Categorical variables will be summarized by absolute (N) and relative frequencies (%). The statistical analyses will be performed using SAS Enterprise Guide 5. 1 software (Copyright (c) 2012 by SAS Institute Inc. , Cary, NC, USA). The study patients will be analyzed according to the intention to treat principle (ITT).
Each patient will be analyzed as a part of the group to which he or she was assigned at randomization. A per protocol analysis will also be performed, though the results of this analysis cannot be substituted for those of the intention to treat analysis. The "last observation carried forward" (LOCF) strategy will be used to impute the missing data for the principal objective and, when possible, for the secondary objectives. According to the CONSORT 2010 statements, a flow diagram of the progress through the phases (enrollment, allocation, follow-up, and analysis) of this study will be presented. The number of screening failures and reasons for screening failures will be summarized. The characteristics of patients lost to follow-up over the course of the study will be reported.
Bacterial strains, constructs and culture conditions All in vitro and in vivo experiments were performed using L. interrogans serovar Manilae strain UP-MMC-NIID LP. Leptospira were cultured under normal laboratory conditions at 30   (21)  medium unless otherwise noted. For growth curves, stationary phase bacteria were diluted 200fold into a 30 ml EMJH in 50 ml flask with agitation at 100 rpms. Aliquots were removed at the listed timepoints and the absorbance at 420nm was measured daily with an Infinite M Nano spectrophotometer and i-control 2. 0 software (Tecan) until strains reached stationary phase. For experiments requiring plating, concentrated EMJH was mixed with 1. 2% (solid) or 0. 6% (semisolid) final volume agar noble. For semisolid plating, a small divot was gouged into the agar surface into which 5L of mid-log phase Leptospira in EMJH was pipetted.
DNA was then purified by phenol-chloroform extraction and ethanol precipitation, pellets were resuspended in 1× TE buffer + 1 mg/ml RNase A and incubated for 30 min at 37 • C. 5 g of DNA was then sonicated using the Covaris S220 Focused Ultrasonicator to yield an average fragment size of 300 bp (140 W peak incidence power, 10% duty factor, 200 cycles per burst, 7 • C). The DNA was purified and processed for sequencing according to manufacturer instructions (TruSeq™ Nano DNA High Throughput Library Prep Kit, illumina). All data was processed as previously  (28, 29)  and the 3C fastq files have been deposited on the SRA database (BioProject ID PR-JNA631225). Codes and functions used to generate the figures from the raw data are available online (https://github. com/koszullab/E coli analysis).
SMRT sequencing Genomic DNA of Leptospira strains was extracted with the Genomic tip 100 g kit (Qiagen) from a 35 ml culture according to manufacturer protocols and the complete genome sequence was obtained using SMRT (Pacific Biosciences) technology. PacBio sequencing was performed at the Génome Québec Innovation Centre (McGill University, Montreal, Canada) using a Pacific BioScience RS II system. The resulting reads of each strain were de novo assembled with Unicycler version 0. 4. 7  (31). The sequences of the replicons were then linearized with various tools of EMBOSS version 6. 6. 0. 0 (32) and polished with the Resequencing protocol of SMRTLink version 6. 0. 0. 47841 (https://www. pacb. com). The sequencing reads of the different data sets were subsequently mapped on the genome sequences of the WT strain and analyzed at the methylation level with the Base Modification and Motif Analysis protocol, also through SMRTLink with default parameters (95% confidence interval bounds, P-value cutoff = 0.
001, minimum methylation fraction = 0. 3 and minimum Qmod score = 30) (Supplementary Table  S6 ). The CTAG motifs were identified in the genomic sequences of the WT strain using the fuzznuc tool from the EMBOSS suite. The unmethylated motifs were determined by subtracting the methylated motifs identified by SMRTLink from the set of all of the CTAG motifs found by fuzznuc. The reads were deposited in SRA under accession numbers SRR10257437, SRR10257436 and SRR10257435 for the L. interrogans wild-type, lomA mutant and complemented strains, respectively.
4. 6-p3 (37) from Subreads package (parameters: -t CDS -g locus tag -s 1). Count data were analyzed using R version 3. 5. 1  (38)  and the Bioconductor package DESeq2 version 1. 20. 0 (39). The normalization and dispersion estimation were performed with DESeq2 using the default parameters and statistical tests for differential expression were performed applying the independent filtering algorithm. A generalized linear model was set in order to test for the differential expression between WT, complemented strain, and lomA. For each pairwise comparison, raw P-values were adjusted for multiple testing according to the Benjamini and Hochberg (BH) procedure  (40)  and genes with an adjusted P-value lower than 0. 05 were considered differentially expressed (Supplementary Table  S7 ). The genes that are differentially expressed were grouped based on COG (Clusters of Orthologous Groups).
Quantitative reverse transcriptase PCR (RT-qPCR) RNA samples were prepared as for transcriptomic analyses. After the successful purification of RNA and elimination of contaminating DNA, RNA was converted into cDNA using the iScript Advance cDNA synthesis kit (BioRad) according to manufacturer's direction using 2500 ng of RNA per sample. For RT-qPCR flaB was used as the reference gene (See Supplementary Table  S5 ). This gene was verified to be unchanged in the transcriptomic data as well as empirically in the lab. Samples were prepared with SsoFast Eva-Green Supermix (Biorad) and thermal cycling and quantitation was performed using a C1000 thermal cycler with attached CFX96 real-time system (Biorad). Results were obtained and analyzed using the CFX manager 3. 1 software.
These data suggest that the chromosomal alterations seen in the lomA mutant are unlikely to cause the specific alterations of transcription in these cells. X overproduction is physiologically deleterious. Phenotypes of strains overexpressing srpA, srpB, srpB and sigX in a wild-type background. An empty vector, pMaGro, was used as a control. (A) Growth curve demonstrating similar growth rates for the control, srpA, srpB and srpC overexpressing strains and growth defect for the sigX strain. The strains were grown in liquid EMJH at 30 • C (n = 3) (B) Expansion in semisolid EMJH. Soft agar plates were inoculated with L. interrogans strains and the colony diameter was measured. The average colony diameter measured for the sigX and srpB overexpressing strains was significantly smaller than for the wild-type (empty) and srpA and srpC overexpressing strains (n = 6).
(C) Survival curve of hamsters infected intraperitoneally with 10 6 leptospires (n = 8) showing 100% survival for animals infected with the sigX overexpressing strain and survival rates statistically equal to the empty vector control in the other three lipoprotein overexpression mutants. (D) RT-qPCR results for the X regulon comparing the srp and sigX transcript levels in strains overexpressing each gene reveal that X controls the expression of the three srp genes as well as its own but the Srp proteins do not affect any of the other genes. Data were obtained from the RNA of three independent cultures from each strain.
ACKNOWLEDGEMENTS We thank  Drs. Matsunaga  and  Haake  for providing plasmid pRAT724 and  Dr. Koszul  for feedback on CCMs. We thank  Dr. Linda Grillova  for aid with bioinformatic analysis and critical reading of the manuscript. We also thank  Drs. Simonetta Gribaldo  and  Nadia Benaroudj  for critical reading of the manuscript.
Data collection Detailed data for scrub typhus from 2003-2018 were obtained from the National Disease Surveillance System (R506)  [31]. Brief annual summary reports from 1980-2002 were also available and collected. Probable and confirmed cases were reported according to the case definition, based on ICD-10: A75. 3, and outlined in Table  1  below. Cases were reported from governmental healthcare facilities including provincial hospitals, district hospitals and primary care units. Reporting from private healthcare facilities also occurred, albeit to a lesser extent. Reports for each year from 2003-2018 were obtained and collated into a single dataset. Information on the number of cases per province each month, population, annual incidence rate per 100,000 population (AIR), age groups, sex and occupation were collected.
Additionally, less detailed information from annual summary reports from 1980-2002 were extracted and data collated into the main dataset. Regional administrative boundaries were obtained from the National Statistical Office, Ministry of Information and Communication Technology. As a case study, we obtained detailed reporting data for Chiangrai province from the Ministry of Public Health, the province with the highest number of reported cases from 2003-2018. Data included patient demographics, location and type of healthcare facilities, and residential data to the sub-district and village level. Administrative boundaries of the province including districts and sub-districts as well as population size were obtained from the Ministry of Natural Resources and Environment, Thailand (2012). Average monthly temperature (˚C) and total monthly rainfall (mm) data for Chiangrai at a central representative weather station were obtained from the Thai Meteorological Department, Ministry of Information and Communication Technology.
National burden of scrub typhus in Thailand From 2003-2018, there were a total of 103,345 cases of scrub typhus reported to the national disease surveillance system. During this period, the number of cases per year ranged from 2,928 in 2005 to 10,952 in 2013 and the annual incidence rate/100,000 population (AIR) ranged from 4. 71 in 2005 to 17. 09 in 2013. The median male to female ratio (IQR) was 1:0. 74 (0. 71-0. 76) and the largest groups by occupation (median, IQR) were agricultural workers at 44. 80%  (42. 65-45. 88 ), labourers at 17. 95%  (15. 77-19. 22 ) and students at 14. 83% (13. 89-16. 04). Data on the annual number of cases from 1980-2002 and incidence rate/100,000 population from 1985-2002 were also obtained. Cases per year ranged from 17 in 1980 to 5,094 in 2001 and AIR rose from 0.
Funding: TW and NPJD are funded by the  Wellcome Trust , UK, as part of the  MORU Tropical Health Network  institutional funding support. IE is funded by the  Wellcome Trust , UK, as part of a  Clinical Training Fellowship  (grant number  105731/ Z/14/Z ). SM, RK and KC are supported by the  French ANR FutureHealthSEA  ( ANR-17-CE35-0003-02 ) " Predictive scenarios of health in Southeast Asia: linking land use and climate changes to infectious diseases ". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
To investigate the viral genetic variation across the HPV16 genome potentially induced by human APOBEC3 cytidine deaminases and other mutational processes (signatures) and evaluate how these variants contribute to infection outcome (i. e. , viral clearance or carcinogenesis), we analyze HPV16 whole-genome sequence data from 5328 case-control samples. Our primary analysis includes 3579 HPV16 genomes  21, 24  from 1265 controls (women with a benign HPV16 infection defined as causing ≤ cervical intraepithelial neoplasia [CIN] grade 1 [CIN1], and/or "clearing") and 1032 CIN2, 1170 CIN3 precancer, and 112 cancer cases in the prospective NCI-Kaiser Permanente Northern California (PaP) Cohort  56. We replicate case findings in 1749 HPV16 genomes  24  , including HPV16 from 444 CIN3 precancer and cancer cases (i.
For a more direct comparison of our data with the HPV16 genome data from Hirose et al. 25  , we downloaded their 45 HPV16 genomes (GenBank accession numbers: LC368952 to LC368996) and created a 96 trinucleotide mutation-type plot after exclusion of the common evolutionary-derived HPV16 lineage-defining substitutions  68, 71  , since inclusion of lineage-defining substitutions would skew the distribution for evaluations of recent or within-host mutations. The resulting distribution of mutation types looks similar to ours (Supplementary Fig. 5 ), including specific APOBEC3-associated variants and T > C and T > G substitutions. Although, mutational signature extraction was not possible for these 45 genomes  25  due to the overall small number of variants. Interestingly, we did not detect leading/lagging or transcribed DNA strand biases or APOBEC3-associated C > G changes at TCW motifs, which correspond to the APOBEC3 COSMIC  65  SBS signature 13, as observed in human somatic genomes related to APOBEC mutagenesis  36, 39.
Our observations are consistent with previous HPV data  25, 52  and suggest a difference between HPV and human genome APOBEC3 mutagenesis. It is possible that the antiviral APOBEC3 response to HPV infection is separate or slightly different from the role of APOBEC3 in host somatic genome mutagenesis. The distribution of APOBEC3-induced mutations across the viral genome was different between the controls and precancer/ cancer cases, suggesting that sites in specific regions of the viral genome may be "hit" more often by APOBEC3, or mutations at specific sites may have more deleterious effects to the virus if absent from the cases, such as mutations in L1 and L2. Alternatively, since APOBEC3 mutations occur when the viral DNA is single stranded, the mutations observed in the L1 and L2 gene regions more frequently in the controls may reflect that these regions of the viral genome are more likely to be transcribed and single stranded in the controls with a productive infection.
This cross-sectional study included 3579 exfoliated cervical cell specimens collected at enrollment previously determined to contain HPV16 DNA  56  , including 112 cancers, 1170 CIN3, 1032 CIN2, and 1265 controls (<CIN2) in follow-up through 2015. The precancer (CIN2 and CIN3) and cancer cases were diagnosed at baseline (i. e. , at enrollment; prevalent cases) or during the study follow-up period after the baseline specimens were collected (i. e. , incident cases). The controls were defined as women having enrollment specimens with HPV16 DNA, and no histologic evidence of equivocal precancer or worse (CIN2+) during the follow-up study period according to the coded data obtained from electronic health records. Therefore, controls were the HPV16-positive women that either cleared their infections or had not progressed to CIN2+ during the follow-up study period.
We used a custom Thermo Fisher Ion Torrent AmpliSeq HPV16 panel approach to amplify the entire 7906 bp HPV16 genome, as previously described  64. In brief, the next-generation sequencing (NGS) assay used the Thermo Fisher Life Sciences' Ion Torrent S5 and a custom HPV16 Ion Ampliseq panel of 47 multiplexed primer sets. Custom overlapping degenerate primers were designed to cover the entire viral genomes for all HPV16 variant lineages. After amplification, an Ion Torrent adapter-ligated library was generated following the manufacturer's Ion AmpliSeq Library Preparation kit 2. 0-96LV protocol with slight modifications (Life Technologies, Part #4480441). Raw sequencing reads were quality and adaptor trimmed using the Torrent Suite™ Software and aligned to the HPV16 reference sequence (7906 bp, NCBI accession number NC_001526) using the Torrent Mapping Alignment Program v5.