dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional annotation To derive basic functional annotation information, we annotated all variants included in 95% credible sets from ancestry-specific and trans-ethnic meta-analyses with the Variant Effect Predictor (VEP)( https://useast. ensembl. org/info/docs/tools/vep/index. html), compiling both all consequences and the most severe consequence for Ensembl/GENCODE transcripts. We also specifically annotated rare coding variants using VEP (defined as any variant with MAF <1% in a given analysis, with a GC-corrected P-value <5x10 -9 , and annotated as a missense_variant, stop_gained, stop_lost, splice_donor, or a splice_acceptor, regardless of fine-mapping results). We removed all variants with a GC-corrected P-value <5x10 -9 in EA, in the MHC region, and, in analyses including individuals with at least some African ancestry, on chromosome 1 for neutrophils and total WBC count and for RBC traits near the chromosome 11 b-globin and the chromosome 16 a-globin loci.
Phenome-wide association study (pheWAS) analysis UK Biobank (UKBB). We extracted pheWAS results for a list of 5552 variants in UKBB ICD PheWeb hosted at the University of Michigan (Accessed 21 August 2019). To account for severe imbalance in case-control ratios, we selected the output from the SAIGE analyses (http://pheweb. sph. umich. edu/SAIGE-UKB/) based on 408,961 samples from White British participants  (Zhou et al. , 2018). In total, 1403 phecodes were tested for association. All results were downloaded using R, and were parsed and organized into data table format using the data. table, rvest, stringr, dplyr and tidyr packages. Biobank Japan (BBJ). We performed a pheWAS for the lead variants identified by the trans-ethnic meta-analyses. From the list of all the significantly associated variants with blood cell-related traits, we extracted those genotyped or imputed in the BBJ project  (nSNP = 4, 255).
Next, we curated the phenotype record of the disease status and clinical values for the same individuals analyzed in the discovery phase  (nindiv = 143,988). Then, we performed the logistic regression analyses for 22 binary traits (20 diseases and 2 behavioral habits) which had a sufficient number of case samples (ncase = 2,500). Regression models were adjusted for age, sex and 20 principal components as covariates. Trait-specific covariates are described elsewhere  (Kanai et al. , 2018). BioVU. BioVU is the biobank of Vanderbilt University Medical Center (VUMC) that houses deidentified DNA samples linked to phenotypic data derived from electronic health records (EHRs) system of VUMC. The clinical information is updated every 1-3 months for the de-identified EHRs. Detailed description of program operations, ethical considerations, and continuing oversight and patient engagement have been published  (Roden et al.
FUNDING This research was funded by grants from the  Spanish "Agencia Estatal de Investigación" and European FEDER Funds  to: AD, MJL, and OB ( AGL2016-78922-R ); by the  Fundación Ramón Areces  ( CIVP18A3888 ) to AD, JG-Z, JT-C, ML, MC, and FV. Also supported by  CIBER de Fisiopatología de la Obesidad y Nutrición (CIBERObn)  to JM, and by  POR FESR   3S4H  to FV. CIBERObn. is an initiative of ISCIII, Spain. MCL and MR-R were recipients of contracts from the  Consejería de Educación, Juventud y Deporte de la Comunidad de Madrid ,  Fondo Social Europeo , and  Iniciativa de Empleo Juvenil YEI  ( PEJD-2016/BIO-2781  and  PEJD-2018-POST/BIO-8933 , respectively).
Acknowledgements The authors thank the  ERC  ( Advanced Research Grant   SUPRA-DAPT 290585 ), the  ANR  Grant DYNAFUN as well as the  University of Strasbourg Institute for Advanced Study (USIAS)  and the  Commissariat à l' Énergie Atomique et aux Énergies Alternatives (CEA)  for nancial support. AC thanks the  CEA  for a doctoral fellowship,  Pr. Jack Harroweld  for discussions,  Dr Jean-Louis Schmitt ,  Cyril Antheaume  and  Dr Bruno Vincent  for helpful discussions of NMR and HRMS data as well as  Corinne Bailly , and  Dr Lydia Karmazin  for the X-ray crystallographic structure determinations. AC thanks  Dr Anne Boos ,  Ilsah El Masoudi  and  Pascale Ronot  for ICP-AES analyses.
The secondary antibodies used were AlexaFluor 350, AlexaFluor 488, and AlexaFluor 546 (Invitrogen), at a dilution of 1:2,000. Images were collected on an Applied Precision Delta Vision inverted epifluorescence microscope using a UPlans APO 100×/1. 40 oil lens. Images were subjected to deconvolution and contrast adjustment using Applied Precision software (Softworx). For quantitative image analysis (as described in the results section) a minimum of 50 vacuoles were scored in at least three independent experiments. Super-Resolution images were acquired using the Zeiss ELYRA S1 (SR-SIM) microscope. Images were collected and processed using Zeiss Zen software. Means and standard deviations were calculated and plotted using Graph Pad Prism Version 5. 0c (La Jolla, California, USA).
Taken together, TgSMC1 localization appears intimately linked to the centromere. Lastly, we determined potential interactors of TgSMC1 by co-immunoprecipitation (Figures  3A, B ). Proteins coimmunoprecipitated with TgSMC1 were identified by LC-MS. TgSMC1's elution fraction contains a significant amount of TgCenH3, suggesting that not only do they co-localize at the centromere but they also interact physically (Figure  3C ). In contrast, TgChromo1  (Gissot et al. , 2012)  which binds chromatin immediately adjacent to the centromeres, does not co-precipitate with TgSMC1 (Figure  3C ). The ten most abundant proteins recovered in all four purifications are shown in Figure  3D. A complete list of LC-MS results can be found in Table  S1  ordered by ascending order of accession number in the T. gondii genome database  (Kissinger et al.
Design and data collection This study was designed as a multinational, multicentre, retrospective medical chart review of adult ( ≥18 years) patients diagnosed with UC or CD, treated (or previously treated) in IBDspecialised centres, and who initiated anti-TNF therapy (index date) between 01 March 2010 and 01 March 2015 (eligibility period). The observational period ranged from two years (for patients who discontinued index therapy within two years of the index date) to up to five years (for those who continued therapy beyond two years) post-index, unless the patient died. IBD-specialised centres from ten NICs were included: Argentina, Colombia, Mexico (LatAm); Russia, Saudi Arabia, Turkey (RME); China, Singapore, South Korea, and Taiwan (APAC). Patients diagnosed with intermediate/unspecified type of IBD; were part of an IBD-related clinical trial during the observational period; received an anti-TNF agent for any non-UC or non-CD condition or outside of the labelled dosing regimen; had undergone a total colectomy pre-index (UC patients only); or whose medical records were unavailable, were excluded.
All potentially eligible patients at each site were screened and randomly selected for enrolment. Data were abstracted from either paper or electronic medical records (dependent on country) by site personnel and entered into a secure electronic data capture form. Patient characteristics assessed at index included demographics, IBD history, comorbidities (including, but not restricted to, extra-intestinal manifestations [EIMs], direct consequences of IBD, chronic conditions caused by IBD treatments and infections), history of any type of non-biologic therapy and concomitant use of aminosalicylates, corticosteroids and immunosuppressants, corticosteroiddependence/intolerance status (based on clinician opinion), disease location, disease behavior (CD), and documented presence of complications (CD).
15 , 16 , 18 , 24 It may be beneficial for physicians to identify early, the patients exhibiting these high-risk profiles for suboptimal response in order to explore alternative biologic therapies which may provide better treatment outcomes. Further research is warranted, through large prospective cohort studies, to identify patient profiles at high-risk of suboptimal response to anti-TNF therapy and better guide therapeutic decisions to a patient-tailored approach. Inherent limitations of the retrospective design of the EXPLORE study could have impacted the observed incidence of suboptimal response. Information was collected from medical records from which data necessary to identify cases of suboptimal response may likely have been incomplete (e. g. reasons for discontinuation, dose escalation, hospitalization).
We also performed 16S rRNA gene sequencing as previously described  [8]. Sequence analysis (1388 nt) showed that the isolate belonged to the genus Peptoniphilus, but to an as yet unknown species. Indeed, a similarity table constructed using utilities implemented in Biological sequence alignment editor (BioEdit) software (http://www. mbio. ncsu. edu/ bioedit/bioedit. html) revealed that the type strains of Peptoniphilus coxii (97. 9% of 16S rRNA gene sequence identity) and Peptoniphilus ivorii (94. 6%), as well as the type strains of the two non validated species 'Peptoniphilus urinimassiliensis' (96. 6%) and 'Peptoniphilus pacaensis' (96. 1%) were the most closely related species of the clinical isolate [9e12]. However, the highest 16S rRNA gene sequence identity observed between the clinical isolate (strain 1804121828, GenBank accession number: MK945758) and the type strain of Peptoniphilus coxii was below the threshold for species identification, i.
MALDI-TOF MS is a powerful and rapid identification tool; however, databases are currently incomplete and optimization of current databases for anaerobes is ongoing [18e20]. MS was unable to identify our clinical isolate and 16S rRNA gene sequence analysis was required revealing the clinical isolate to belong to an unknown species and showing a still underestimated diversity in this genus. A formal description of this new species based on a polyphasic taxonomy approach has been undertaken. Members of the different human microbiota, Peptoniphilus spp. have been reported in a large variety of human endogeneous polymicrobial infections due to the pathogenesis process of such infections, i. e. , polymicrobial infections involving members of the contiguous microbiota through contamination of initially sterile anatomical sites.
Clinical relevance of Peptoniphilus spp. has been mainly demonstrated after isolation from skin and soft tissues, chronic wounds (pressure ulcer, diabetic foot wounds), osteoarticular samples, genitourinary (vaginal infections) and respiratory tract (pleural empyema, chronic rhinosinusitis)  [2]. Anaerobic infections remain rare in patients with prosthetic joints and mostly involved species originating from the cutaneous microbiota like Cutibacterium (formerly Propionibacterium) acnes and F. magna [21e24]. Despite Peptoniphilus spp. have been previously identified during osteoarticular  [21, 25, 26]  and soft tissue infections  [27, 28] , we were unable to find a case similar to that described herein, i. e. , surgical site infection following PJI, among the 122 publications retrieved in the PubMed database using the "Peptoniphilus" search term (July 8, 2019).
None. Ezakiella peruensis strain M6. X2 T (KJ469554) Neighbor-joining phylogenetic tree showing the relationship between the 16S rRNA gene sequences of Peptoniphilus strain 1804121828 T (type strain of the proposed novel species 'Peptoniphilus nemausus') and of species, either validated or not, in the genus Peptoniphilus. Alignment length was 1166 nt. Names for effectively published but non-validated species are indicated between quotes. GenBank accession numbers are indicated in parentheses. Bootstrap support was computed after 1000 reiterations. Bootstrap values are indicated at the corresponding nodes when >70%. Ezakiella peruensis was used as the outgroup microorganism. * indicates species with uncertain taxonomic status, as P. senegalensis and 'P. rhinitis' might be synonym species of P.
Acknowledgments The authors thank  Nathalie Jeanney  for excellent technical assistance. Authors belong to the  Centre Regional des Infections Ost eo-Articulaires complexes (CRIOAc) Sud M editerran ee (AP, NC, CL, BM , and JPL), to the  Clinique du Pied Diab etique Gard Occitanie (NC, CL, BM , and JPL) and/or to the  FHU INCh (F ed eration Hospitalo-Universitaire Infections Chroniques, Aviesan ) (all authors). We thank the  Nîmes University Hospital  for its structural, human and financial support through the award obtained by our team during the internal call for tenders « Th ematiques phares ».
' Fig. 1. Neighbor-joining phylogenetic tree showing the relationship between the 16S rRNA gene sequences of Peptoniphilus strain 1804121828 T (type strain of the proposed novel species 'Peptoniphilus nemausus') and of species, either validated or not, in the genus Peptoniphilus. Alignment length was 1166 nt. Names for effectively published but non-validated species are indicated between quotes. GenBank accession numbers are indicated in parentheses. Bootstrap support was computed after 1000 reiterations. Bootstrap values are indicated at the corresponding nodes when >70%. Ezakiella peruensis was used as the outgroup microorganism. * indicates species with uncertain taxonomic status, as P. senegalensis and 'P. rhinitis' might be synonym species of P. tyrrelliae and P. lacydoensis, respectively.
DNA extraction and microsatellite amplification DNA extraction was carried out according to an MTAB protocol, as previously described in  [40] , from wild pearl millet inflorescences and cultivated pearl millet leaves after plants were grown from seeds in a greenhouse. Twelve polymorphic SSRs were used for genotyping, as described in  [41]. A total of 271 individuals from the 1976 pearl millet collection (i. e. 21 individuals/accession on average; range 12 to 29), 309 individuals from the 2016 pearl millet collection (i. e. 24 individuals/accession on average; range 6 to 31), and 642 individuals from the wild pearl millet collection (i. e. 18 individuals/accession on average; range 3 to 36) were kept for subsequent analysis. The average percentage of missing data per individual was 1. 75% (S1 Table ).
Genetic structure A panel of complementary methods were used to assess genetic structure within and between cultivated and wild forms. We performed scaled and centred principal component analyses (PCA) using the FactomineR package v. 1. 4. 2  [43]  and factoextra package v. 1. 0. 5  [44]  in the R environment  [45]  v. 3. 6. 0. Then, we conducted Bayesian clustering analyses in STRUCTURE v. 2. 3. 4  [46]  using the admixture model  [47]  and correlated allele frequency option, with a burn-in period of 100000 steps and 500000 MCMC replicates. Ten independent runs were performed for each K, ranging from K = 1 to K = 10. The most probable K value was determined by the D. ΔK criterion  [48]  and the log-likelihood (Ln P (D | K)) plot with STRUCTURE HAR-VESTER v0. 6. 94  [49]. To investigate further the wild pearl millet genetic structure, we incorporated spatial information by performing a spatial principal component analysis (sPCA) implemented in the adegenet package v.
Temporal evolution of cultivated diversity We assessed changes in cultivated pearl millet diversity by comparing the findings of the 1976 and 2016 collections. For early-flowering groups (EF), we found 88 alleles in 1976 datasets and 90 alleles in 2006 datasets; similarly, for late-flowering groups (LF), we found 52 alleles and 47 alleles, respectively. For EF, seven private alleles were lost while nine were gained, and for LF more private alleles were lost than gained (11 vs. 6, S6 Fig). In terms of genetic diversity parameters, we found a slightly significant decrease in allelic richness between EF-76 and EF-16 (A R _ EF-76 = 5. 1 vs. A R _ EF-16 = 4. 9, Wilcoxon paired test, V = 56, P = 0. 045). For LF accessions, a significant decrease was found only for H Obs (H Obs_LF-76 = 0. 464 vs.
We are grateful to  ANR CERAO  ( ANR-13-AGRO-0002 ),  Adaptincrops (Fondation Agropolis  N  ˚1403-057 ) and the  AMMA-2050 project (NERC/ UK-AID/ Future Climate For Africa program   NE/ M020002/1 ) for funding the research activities. KFO thanks  Deutscher Akademischer Austausch Dienst  ( DAAD ) and  Institut de Recherche pour le Developpement (IRD)  for the scholarship award and for travel grants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statistical analysis and pattern matching algorithms Once this is accomplished, applying statistical methods and/or machine-learning techniques is often desired in order to identify those spectral features (peaks) associated with a given condition (candidate biomarkers or biomarker profiles), sample grouping (through hierarchical classification or principal component analysis) and classification (through the constructions of class-predictive models based on the use of data-dependent algorithms or through comparison with reference spectra contained in a database). While processing and data analysis were initially based on the use of general tools like R or SPSS (Statistical Package for the Social Sciences) softwares, requiring programming skills and implying a lack of uniformity in the criteria followed for this process, a number of user-friendly tools specific for MALDI profiling data are currently available.
In general, there are two kinds of tools regarding the type of post-processing analysis: those based on the use of a reference spectra database to which test spectra are compared, and those in which the user sets examples of the different studied classes. The first group of tools includes commercial software packages containing reference databases, and its use is mainly restricted to microorganism identification (e. g. MALDI Biotyper from Bruker Daltonics or Anagnostec SARAMIS from Shimadzu). The second group entails tools that allow both supervised and unsupervised techniques to analyze data following a clinical approach. In general, the biomarker research-oriented analysis starts with performing a statistical analysis (parametric or non-parametric) using quantitative values (normalized peak height or area).
Intact Cell MALDI-TOF Mass Spectrometry -from bacteria to mammalian cells The use of ICM-MS was firstly reported in 1996 by Holland et al. , and was applied to bacterial chemotaxonomy  [46]. In this study, it was described that ICM-MS protein profiles contained speciesspecific signatures, so they could be used as a taxonomic tool  [47]. In effect, the analysis of endogenous peptidoforms and proteoforms can be considered as a functional genomics analysis and therefore useful for phenotyping  [48]. ICM-MS has been applied in different studies for bacterial identification, aiding at the development of the field of phyloproteomics  [46, 49, 50]. Identification of the microorganisms was based on the identification of species-specific signatures in the ICM-MS spectra using bioinformatic tools and in the subsequent construction of reference spectral databanks  [51].
Thereafter, comprehensive and curated databases were constructed using rigorous standardization procedures and commercialized together with tools that allowed a robust identification analysis  [52, 53]. Nowadays, ICM-MS has become a valuable tool for the identification and the classification of microorganisms, including bacteria  [47]  or yeast  [54] [55] [56] , and it has been introduced in clinical routine microbiological diagnostics. Thus, this approach assists clinicians to provide a fast, accurate diagnosis associated with an adequate antimicrobial treatment in infectious diseases. Furthermore, this approach can also be applied to detect specific bacterial biomarkers, such as antibiotic resistance, and may potentially predict infectious complications or/and being used for the development of novel virulence factor-based therapies  [57].
Acknowledgements This work was supported by the  French National Infrastructure of Research CRB  anim funded by " Investissements d'avenir ",  ANR-11-INBS-0003  and from the  French National Institute of Agronomic Research. The high-resolution mass spectrometer was financed ( SMHART  project) by the  European Regional Development Fund (ERDF) , the  Conseil Régional du Centre , the  French National Institute for Agricultural Research (INRA)  and the  French National Institute of Health and Medical Research (Inserm). Laura Soler  has received the support of the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills fellowship  (under grant agreement n°  267196 ).
Pseudomonas putida KT2440 strain (DSM number 6125) was purchased from the DSMZ. The laccase enzymes used in this work were purchased from MetGen. 38 Sodium borate (SB) buffer was made of 36 mM boric acid and 10 mM sodium hydroxide. TAE buffer was made of 40 mM Tris base, 20 mM acetic acid, and 1 mM EDTA. For gel imaging, it is better to have low-intensity light produced on a completely dark background than high emission on a light-emitting background. Imaging was carried out with a ChemiDocTM MP Imaging System by exposing to light from 650 to 675 nm and measuring emission at 700-730 nm unless otherwise stated. Gel analysis was carried out using ImageJ (a program developed by the National Institutes of Health and accessible online, https://imagej. nih. gov/ij/). Using the software, gel bands were selected (with fixed selection dimensions and fixed vertical alignment) and processed using the in-built gel functions (ESI 2).
A 1 mL amount of of THF was added to dissolve the sample which was then filtered using a 0. 45 μm PTFE filter. The sample was then ready for GPC. GPC was carried out with THF as eluent at 1 mL/min using an Agilent PLgel MIXED-C column using an UltiMate 3000 Autosampler Column Compartment for sample handling and an UltiMate 3000 Photodiode Array Detector. Chromeleon software was used for sample analysis. GPC plots were normalized by expressing the arbitrary units (mAU) values as a percentage of the highest mAU value for each respective sample. Enzymatic Digestion of Lignin. Enzyme treatment was carried out in a 250 mL conical flask with a total reaction volume of 50 mL containing 5 g/L KL1 Kraft lignin, 70 mM pH 7 potassium phosphate buffer, 11. 8 units of the MetZyme LIGNO laccase enzyme mixture, and 0.
Funding We thank the  EPSRC CDT in Bioenergy at the University of Leeds  ( EP/L014912/1 ] and  Biome Bioplastics Ltd. for support of C. H. This work was supported by  ADEME  [No. 1401C0066 ] and by the  European Commission  [ ERA-IB-14-055 ] in the framework of the  LIGBIO  project. The IJPB research unit benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-552 0040-SPS ).
brigantina. Indeed, differentiated populations assigned to a given Latin species may actually represent different species placed far apart in phylogeny, as found recently for P. sibirica  (Liu et al. 2019). However, there is currently no robust data on the genetic diversity or population structure of P. brigantina. In the current study, we therefore conducted extensive sampling of P. brigantina in its natural habitat and genotyped samples using 34 nuclear markers  (Liu et al. 2019). We assessed the genetic diversity of P. brigantina and its population structure, as well as its relationship with Eurasian Armeniaca and Prunus species. We questioned the affiliation of P. brigantina to either the Armeniaca or Prunus sections. For this purpose, we performed a population genetic analysis using datasets with the main members of the Armeniaca and Prunus sections and five outgroup species from the North-American section Prunocerasus.