dataesr/scientific-paragraphs-categorization huggingface dataset:
The analysis was performed with the package ALDEx2  [36]  on the R programming environment  [37]. We obtained more than 600 ASVs after the de novo taxonomic annotation. The data sets were filtered (i. e. , rare ASVs with less than 10 total reads and detected in less than 30% of samples of each data set were removed) and then collapsed at genus level, obtaining 324 features on which the ALDEx2 pipeline was applied. Venn diagrams (using the online tool at http://bioinformatics. psb. ugent. be/webtools/Venn/) were used to calculate the number of genera shared by the three data sets (hereafter ubiquity analysis). We also performed a Detrended Canonical Analysis (DCA) using the taxonomic profile of each data set, looking for the ordination of the individual tick specimens of the three data sets into coherent groups (i.
These sets would persist under changing combinations of bacterial taxa. For each data set, we calculated the SparCC-correlations  [41]  between the number of reads of pairs of co-occurring organisms, removing those between the range -0. 3 to +0. 3 because these are considered to be non-significant correlations. We built networks of co-occurring taxa and calculated their modularity by the Louvaine algorithm  [42]  using Gephi 0. 92 (available at www. gephi. org, accessed October, 2019). In our application, modularity represents probabilistic modules of taxa that tend to co-occur more frequently among them than with other taxa. We selected for further procedures the three larger modules of taxa emanating from the network of each data set. We translated the modules of co-occurring taxa in each data set to their functional profile and also explored the taxa contribution to the predicted metabolic pathways.
Lower Functional Diversity in the Microbiome of Borrelia Infected Larvae Compared to Uninfected Nymphs The functional metabolic profiling resulting from PICRUSt2 genome prediction produced a total of 342 metabolic pathways in the three data sets (Figure  4a ). Of these, a core of 283 (82. 7%) pathways was recorded in the microbiome of every tick in the three data sets). A total of 330 pathways (96. 5%) were shared by uninfected nymphs, whereas tick microbiomes of Borrelia-infected larvae differed from the others in 21 and 9 metabolic pathways, respectively (6. 1 and 2. 6%). Borrelia-infected larvae lack 15. 4% of pathways present in uninfected nymphs. However, it is out of the scope of this manuscript to elucidate whether missing pathways are associated with Borrelia infection or developmental stage.
SNP methodology The identification of tilapia species is of importance for the management of farmed and wild stocks due to the existence of multiple tilapia species and potential hybrids. A panel of ten validated species-diagnostic SNP assays exploiting KASP technology were applied to confirm the genotype of individuals from the Molobicus breeding programme. The number of SNPs applied was a compromise based on the expected species contribution, information gained and minimising the cost within a large-scale breeding programme. KASP technology was chosen for the SNP assays due to the flexibility, low cost and ease of use compared to array-based platforms  (Semagn et al. , 2014). The HotShot DNA extraction method was able to generate crude DNA template suitable for the PCR assay and offer equivalent performance (data not shown) and a rapid alternative to the longer salt precipitation protocol yielding purified DNA.
, 2015)  and the Enhancer of Polycomb Homolog 1 (EPC1) as possible candidate genes involved in osmoregulation  (Gu et al. , 2018; Su et al. , 2020). The advent of high throughput sequencing and completion of the genome assembly for commercially important tilapia species such as O. niloticus  (Conte et al. , 2017)  should help accelerate the identification and genetic manipulation of key traits. SNP datasets are available for the three species included here and Red tilapia  (Kajungiro et al. , 2019; Van Bers et al. , 2012; Xia et al. , 2014). This technology was demonstrated in a later whole genome sequencing study able to locate the signatures of selection in multiple LGs most common in non-coding regions as well as known growth-related pathways in genetically improved tilapia lines  (Xia et al.
Components (DAPC)Discriminant Analysis of Principal Components (DAPC) was conducted using R/adegenet based on the ten SNP markers. The dataset included the 151 samples from the present study and the genotypes of 60 individuals representing the three pure tilapia species of O. niloticus, O. mossambicus and O. aureus (Syaifudin et al. , 2019)  as the reference populations. DAPC analysis was able to clearly separate the pure species of O. niloticus (coloured dark orange), O. mossambicus (dark blue) and O. aureus (green) into three distinct groups using both component comparisons (Figure2). From the current study, the GIFT samples genotyped (light orange) were positioned as a broad cluster closer to O. niloticus than to O. mossambicus. The feral O. mossambicus (light blue), although with an elliptical distribution, overlapped the reference O.
Acknowledgments This work was partly supported by the  MASTS pooling initiative  (The  Marine Alliance for Science and Technology for Scotland)  funded by the  Scottish Funding Council  (grant reference  HR09011 ) and contributing institutions. The authors also acknowledge the  Commonwealth Scholarship Commission (UK)  for their financial assistance (CSC reference  BDCA-2013-5 ). Support was also provided by the  CGIAR Research Program on Fish Agri-Food Systems (FISH)  led by  WorldFish. The program is supported by contributors to the  CGIAR Trust Fund. Publication cost was covered by the  University of Stirling APC  fund.
Sequencing was performed at the Molecular Research LP (MR DNA; Texas, USA) on a MiSeq™ System following the manufacturer's guidelines. Amplicon sequence data were sorted by the sample and demultiplexed using demux for QIIME 2 (version 2018. 11; Bolyen et al. 2018). Sequences were screened for quality, trimmed at 450 bp after removal of primer sequences and assigned as amplicon sequence variants (ASVs) [58] using DADA2  [59]. Taxonomy of the ASVs was determined using a pre-trained, naїve Bayes classi er [60] and the q2-feature-classi er plugin  [61]. The classi er was trained on the target 480 bp region of sequences in the Greengenes 13_8 99% database. ASV clusters were arranged in a phylogenetic tree using FastTree [62, 63] and visualised using Interactive Tree of Life  [64]. The feature table, metadata and taxonomic classi cations were exported from QIIME 2 in.
Statistical analysis The exported feature table and phylogenetic tree were imported into R version 3. 5. 2 (R Core Team 2019) and stored as a phyloseq object [66] for downstream analyses. All ASVs not assigned to phylum were ltered from the data, and those designated as chloroplasts or cyanobacteria were removed and stored as a separate object for further analysis. Samples were rare ed to minimum sampling depth for diversity analyses; however, non-rare ed data were used for multivariate modelling  [67, 68]. Multivariate generalised linear models were used to test for signi cant differences in bacterial communities among host sh species, trophic guild and location along intestines using mvabund in R [69, 70]. Bacterial taxa were grouped by class when examining microbiome changes along the length of the intestinal tract.
Bacterial community data were tted to negative binomial distributions and tested using log-likelihood ratios (LRT) via 999 simulations using Monte Carlo resampling. A nested analysis of variance (ANOVA) used to test the role of gut location when accounting for species variation. Traditional distance-based ordination methods to visualise variation across communities, such as non-metric multidimensional scaling (NMDS) and principal coordinate analysis (PCoA) may confound trends  [71]. To avoid these issues, the R package boral  [72]  was utilised to explain the bacterial community composition of each sample through a set of latent variables. Bacterial community data were tted to a negative binomial distribution, and the model was run with two latent variables to account for residual variation for each of the major phyla detected in the samples.
Sequence abundance and taxonomy for each ASVs representing more than 1% of total sequences. Accession numbers for closest GenBank sequences (similarity given in brackets) are supplied. Different levels of ASV richness were detected for each host sh species. Dischistodus perspicillatus has the greatest mean richness of ASVs, with a total of 322 ± 17 ASVs per individual. The species with the lowest ASV richness are C. atripectoralis and A. sexfasciatus with 47 ± 21 and 30 ± 8 ASVs per individual,.
, Photobacterium damselae, Vibrio harveyi, Vibrio ponticus and other Vibrio sp. were prevalent amongst the damsel shes sampled in this study. These bacteria represent potential pathogenic members of Vibrionacaea and have been detected in many shes of aquaculture importance, including Chromis punctipinnis [81], Lutjanus argentimaculatus [82], Seriola dumerili [83], Scophthalmus maximus [84, 85], Sparus aurata [86], Solea quinqueradiata [87] and Solea senegalensis [88]. Although identi ed as Vibrio harveyi in the GreenGenes database, GenBank revealed there was high similarity of these sequences to other members of the Harveyi clade, such as Vibrio owensii [83]. There are thought to be up to 11 species of Vibrio belonging to this clade [89], most of which are pathogens of sh, shrimp and coral [90-92].
Study Population All children clinically diagnosed with PFAPA (periodic fever, aphthous stomatitis, pharyngitis and cervical adenitis), CAPS (Cryopyrin-associated periodic syndrome) or uAID (undefined autoinflammatory disease) attending the pediatric rheumatology consultation of Western Switzerland at the Lausanne University Hospital and the Geneva University Hospital, from November 2009 to November 2017, had a genetic analysis in the 4 major genes associated with monogenic periodic fevers: MEFV, TNFRSF1A, MVK, NLRP3. The data were collected from the retrospective module of the JIR (Juvenile Inflammatory Rheumatism) cohort network. The JIR cohort is an international database which includes patients presenting an inflammatory rheumatism starting in childhood; only the center of Western Switzerland among the JIR network participated in this study.
One more patient presenting with recurrent fevers and chronic glomerulonephritis followed in the adult immunology consultation was added in this data collection. An additional and independent cohort of patients with recurrent fevers was identified from the AID-registry, which is part of the AID-Net (Network for autoinflammatory diseases), a research initiative funded by the German Federal Ministry of Education and Research (BMBF) und supported by the German Rheumatism Research Center (DRFZ) and the German society for pediatric rheumatology (GKJR)  (21). Patient data was retrospectively documented between July 2009 and October 2017. All patients with a diagnosis of CAPS or PFAPA and availability of genetic analysis in the four fever genes either provided by the centers or analyzed in Muenster in samples of the Biobank.
Comparison of Q703K-Positive and Q703K-Negative uAID Patients: No Statistically Significant Differences in the Phenotype of Patients Comparisons of the clinical characteristics between the Q703Kpositive and -negative uAID patients with complete datasets were performed in the JIR cohort and AID-Net cohort, where all uAID patients underwent genetic analysis, and is shown in Table  4. No statistically significant differences were found in the prevalence of clinical characteristics, disease complications, nor the positivity of familial history. No Role for Q703K in Monocyte-Derived IL-1β Secretion but Trend in Higher TNF-α Production We compared the cytokine secretion between 6 Q703K-positive and 6 Q703K-negative healthy parents. As shown in Figure  1 , the production of IL-1β, TNF-α or IL-6 were not significantly different between monocytes from Q703K positive and negative asymptomatic parents (Q703K positive: 4583.
ACKNOWLEDGMENTS We thank  Volker Hilger  (IT management) and  Nicole Weyandt  (study nurse) from the  AID-registry ,  Gregoire Simon  for cell culture experiments,  Ilaria Bernabei  for her contribution on laboratory data illustration and  Francois Hofer  for the JIRcohorte. The authors thank all study nurses and colleagues of all participating centers for documentation in the AID registry in Germany and in JIRcohorte, as well as the patients and parents.
Tsimane health and life history project dataset Mixed cross-sectional and longitudinal data were collected from 2004 to 2014 as part of the ongoing Tsimane Health and Life History Project (THLHP; http://tsimane. anth. ucsb. edu/index. html, (13 July 2020, date last accessed))  [27, [31] [32] [33]. Pregnancy status was determined during medical visits based on date of last menses, with urinary pregnancy tests administered by the physician when pregnancy was suspected. Pregnancies were cross-validated against subsequent annual demographic and census interviews that ascertained children's birth dates, permitting detection of pregnancies occurring between medical visits and pregnancies that went undetected during previous physician examinations  [33]. Venous blood drawn into heparinized vacutainers was analyzed immediately after collection.
Total leukocyte counts were determined with a QBC Autoread Plus dry hematology system (QBC Diagnostics) and relative fractions of neutrophils, eosinophils, lymphocytes, basophils and monocytes were determined manually by microscopy with a hemocytometer. Blood drawn into serum vacutainers was allowed to clot and then centrifuged to separate serum, which was frozen in liquid nitrogen and then transported on dry ice for analysis at the University of California, Santa Barbara (UCSB). Serum samples were then analyzed to determine CRP concentration at the UCSB Human Biodemography lab via enzyme immunoassay (ELISA)  [34] , using a protocol validated against the same University of Washington lab responsible for NHANES data analyses  [31]. For this study, the THLHP dataset included pregnant and non-pregnant cycling women aged 18-45 years with total and differential leukocyte count (WBC) and/or CRP concentration.
Non-pregnant women who were not regularly menstruating due to either lactational amenorrhea or menopause were excluded from analyses. Given these inclusion criteria, the initial THLHP sample contained 1198 observations (cycling N ¼ 942; pregnant N ¼ 256). The number of pregnant women sampled was lower than the number of cycling women, and pregnant participants were, on average, younger than cycling women. Based on previously reported Tsimane interbirth intervals and THLHP oversampling of people over age 40 during some rounds of data collection, this initial sampling distribution roughly followed the expected 3:1 ratio of cycling to pregnant participants. To address potential sampling biases, we randomly selected a subset of cycling women from the initial dataset with an agedistribution matching that of the pregnant subset.
We did this by dividing the pregnant subset into 6-year age groups and using the sample function in R to pick a proportional sample of cycling women from each age bracket. Since not all individuals had recorded data for both CRP and WBC, we conducted this age-matching process separately for participants with complete WBC differential data and those with CRP data. Across the final aggregated dataset used for this study, slightly more individuals were sampled during the second and third trimester compared to the first, although not more than would be expected by chance (v 2 ¼ 3. 66, df ¼ 2, P > 0. 1). Pregnant and cycling Tsimane women exhibited comparable body mass index (BMI) (t ¼À 0. 73, df ¼ 497. 01, P-value ¼ 0. 46), while median parity (i. e. number of live births) was higher among pregnant women (t ¼À3.
20, df ¼ 454. 55, P-value <0. 01). Although median parity was comparatively lower within the cycling sample, this was likely due to the mixed cross-sectional and longitudinal nature of the THLHP collection schedule rather than disproportionate sampling of individuals with undiagnosed fertility issues. Based on the ratio of Tsimane women experiencing lactational amenorrhea to those who are currently pregnant (at any given age), lactational amenorrhea usually lasts between 12 and 14 months. Considering the average interbirth interval in this population is $30 months  [28] , these estimates indicate that most Tsimane women experience $7to 9 months of regular cycling between each pregnancy. In our entire sample of non-pregnant cycling Tsimane women, the median number of recorded live births was 4 (range ¼ 0-11), providing further evidence that by-and-large these individuals were simply in-between pregnancies when these particular data were collected.
NHANES dataset We compiled data from NHANES (2003-16) (https://wwwn. cdc. gov/nchs/nhanes/, (14 July 2020, date last accessed)), limiting our sample to pregnant and cycling women between ages 18 and 45 years with complete data on WBC differential and/or CRP concentration. To ensure as comparable of a selection process as possible between THLHP and NHANES samples, individuals in the NHANES dataset were included in the cycling group if they were not pregnant or breastfeeding at the time of the study, reported having regular menstrual cycles throughout the last year, and were not currently using hormonal contraception. No exclusions were based on medical diagnoses. CRP was measured using nephelometry, while total and differential leukocyte counts were measured using the Coulter method. All participants in the NHANES dataset were measured only once.
According to these inclusion criteria and dataset characteristics, the initial NHANES dataset selected for this study contained 1652 observations (cycling N ¼ 1375; pregnant N ¼ 277). The same method used to age-match groups within the THLHP dataset was utilized to age-match cycling and pregnant subsets within the NHANES dataset, for both WBC differential and CRP subsamples. Pregnancy was determined by participant report or urine test at the time of exam, which may explain why women in their first trimester were significantly under-sampled relative to women in later pregnancy (v 2 ¼ 25. 15, df ¼ 2, P < 0. 001). Overall, pregnant US women exhibited higher BMI (t ¼À 2. 59, df ¼ 490. 17, P-value <0. 01) and lower median parity (t ¼ À13. 91, df ¼ 487. 45, P-value <0. 001) than non-pregnant women.
For complete descriptive statistics, see Table  2. Given population-level differences in age (t ¼À 13. 73, df ¼ 1460. 40, P-value <0. 001), BMI (t ¼ 18. 41, df ¼ 1957. 40, P-value <0. 001) and parity (t ¼À36. 27, df ¼ 1107. 40, P-value <0. 001), we did not attempt to match sampling distributions across populations, as this would have substantially reduced our overall sample for analysis. Instead, we controlled for age, BMI and parity in all statistical models. The final dataset is available at https://doi. org/10. 25349/D94C77, (14 July 2020, date last accessed).
Statistical analyses All analyses were conducted in R 3. 6. 2 (https://cran. r-project. org/, (14 July 2020, date last accessed)) using the brms package  [35]. For each outcome variable, we estimated the populationspecific effects of reproductive status (i. e. non-pregnant cycling, trimester 1, trimester 2 or trimester 3), adjusting for fixedeffects of age, BMI and parity and, when applicable, a random effect of participant ID, to account for repeated measures per participant in the mixed cross-sectional and longitudinal THLHP sample. Due to population-specific differences in immune marker distributions, THLHP and NHANES eosinophil count, monocyte count and monocyte proportion were modeled separately using discrete priors and different distribution families. Among the Tsimane, raw eosinophil counts were logtransformed and then estimated using a Gaussian model, while eosinophil counts among US women followed a negative binomial distribution and were modeled as such.
Due to dataset restrictions, we were limited to quantifying large-scale differences in immune cell populations and a singular measure of acute inflammation (with a small THLHP sample size), excluding other relevant and more finegrained indices of immune function (e. g. cytokine profiles, proportion of regulatory T cells, neutrophil oxidative bursts). Lastly, while the ecological landscape currently inhabited by Tsimane women presumably more closely resembles the environmental conditions that prevailed before the agricultural transition and industrialization, the Tsimane do not serve as a universal representation of other past or contemporary non-industrialized populations. Rather, this study highlights the need for future studies to consider variation both across and within nonindustrialized contexts, especially since myriad factors combine to produce mosaic ecologies.
These three proteins, albeit small, are unusually immunogenic and contain 285 of the total 1. 603 epitopes described in M. tuberculosis  [31]. In line with this observation, our recent preclinical studies have demonstrated that immunity against ESAT-6 and CFP-10 conferred after MTBVAC vaccination remarkably correlate with improved vaccine efficacy relative to BCG Danish  [32]. After an extensive preclinical testing in animal models  [19,33À36] , MTBVAC was the first and unique live attenuated M. tuberculosis vaccine approved to enter into clinical trials in 2012  [37]. After successfully completed phases Ia (NCT02013245)  [38]  and Ib (NCT02729571)  [39] , currently, two phases IIa trials are ongoing in South Africa, one in newborns using MTBVAC as a prime vaccine (NCT03536117) and the other on in adults where MTBVAC is tested as a boost for BCG (NCT02933281).
European Commission  ( TBVAC2020 ,  H2020-PHC-643381 ),  Spanish Ministry of Science  ( RTI2018-097625-B-I00 ),  Instituto de Salud Carlos III  ( PI18/0336 ),  Gobierno de Arag on/Fondo Social Europeo  and the  French National Research Council  ( ANR-10-LABX-62-IBEID ,  ANR-16-CE35-0009 ,  ANR-16-CE15-0003 ).
Funding sources This work was supported by the  European Commission Horizon 2020  ( TBVAC2020 ,  H2020-PHC-643381 ), the  Spanish Ministry of Science  ( RTI2018-097625-B-I00 ),  Instituto de Salud Carlos III  ( PI18/ 0336 ) and  Gobierno de Arag on/Fondo Social Europeo , as well as by the  French National Research Council ANR  ( ANR-10-LABX-62-IBEID ,  ANR-16-CE35-0009 ,  ANR-16-CE15-0003 ). IP was recipient of a " DGA-Fondo Social Europeo " grant. IP was also recipient of a "Programa  CAI-Ibercaja Estancias de Investigaci on"  ( CM3/18 ) and a " FEMS Research and Training grant " ( FEMS-GO-2018-118 ) to perform an internship at  Integrated Mycobacterial Pathogenomics Unit at the Institut Pasteur of Paris.
We present an approach to assess the disease ecology of rickettsial species by investigating open databases and by using data science methodologies. First, we explored the epidemiological trend and changes of human rickettsial disease epidemics over the years and compared this trend with knowledge on emerging rickettsial diseases given by published reviews. Second, we investigated the global diversity of rickettsial species recorded in humans, domestic animals and wild mammals, using the Enhanced Infectious Disease Database (EID2) and employing a network analysis approach to represent and quantify transmission ecology of rickettsial species among their carriers, arthropod vectors or mammal reservoirs and humans. Our results confirmed previous studies that emphasized the increasing incidence in rickettsial diseases at the onset of 1970.