dataesr/scientific-paragraphs-categorization huggingface dataset:
65-Å resolution it was not possible for the authors to state definitively whether the imine or trans-enamine breakdown product was observed. Other crystal structures in which hydrolyzed tazobactam has been modeled into native class A b-lactamases, such as BlaC (PDB code 6H2I, 2. 75-Å resolution  [33] ), BS3 (4A5R, 2. 1-Å resolution [unpublished data]), and SHV-1 1VM1, 2. 02-Å resolution  [28] , are of too low a resolution and/or feature insufficiently defined inhibitorderived acylenzyme for definitive assignment of the tautomeric state. In the highest-resolution tazobactam complex structure previously available (GES-2, PDB code 3NIA  [30]  [Fig. S10 ]), the inhibitor-derived product binds in an orientation almost identical to that seen in CTX-M-15, except that in the GES-2 complex the flexible triazole ring is flipped by a rotation at C-6.
Diffraction data for inhibitor soaks and cocrystals were collected at Diamond Light Source at beamline I03 (CTX-M-15:tazobactam-soak, apo-CTX-M-15 G238C , and apo-CTX-M-15 G238/G239_Y240insA ), Soleil Proxima-2A (apo-CTX-M-15 E166Q ), Soleil Proxima-1 (CTX-M-15:tazobactam and CTX-M-15 E166Q :tazobactam cocrystals), ALBA BL13-XALOC (CTX-M-15:enmetazobactam and CTX-M-15 E166Q :enmetazobactam cocrystals), and DLS I04 (CTX-M-15 K73A :enmetazobactam cocrystals and apo-CTX-M-15 K73A ). Data were integrated in DIALS  (49)  and scaled in Aimless in the CCP4 suite  (50) , with the exception of CTX-M-15 G238/G239_Y240insA apo data, which were integrated and scaled in autoproc  (51). Phases were calculated by molecular replacement in Phaser (52) using PDB code 4HBT  (34)  as the starting model. Structures were completed with iterative rounds of manual model building using WinCoot (53) and refined using Phenix  (54).
Proteolytic peptides (;300 ng) were desalted and concentrated with a C 18 symmetry trap column (Waters) and separated using a C 18 BEH130 reverse-phase column (Waters) with a linear gradient 0% to 42% mobile phase B (0. 1% formic acid and acetonitrile) versus mobile phase A (100% water and 0. 1% formic acid). Peptides were introduced into the nano-electrospray source at a capillary voltage of 2. 0 kV. MS1 full mass spectra were acquired in the Orbitrap mass analyzer (resolution, 120 K). Tandem mass spectra were generated in the linear ion trap mass analyzer by collision-induced dissociation of peptide ions at a normalized collision energy of 35%. The resulting MS/MS spectra were searched against a CTX-M-15 protein database using Mass Matrix software with the mass errors of 10 ppm and 0. 8 Da for MS1 and MS/MS scans, respectively.
ACKNOWLEDGMENTS We thank  Philipp Knechtle  and  Adam Belley  ( Allecra Therapeutics ) for helpful discussions. We thank  William Shepard  and  Leonard Chavas  at the Proxima-2A and Proxima-1 beamlines, respectively, at the SOLEIL synchrotron for assistance with data collection and processing. CTX-M-15:enmetazobactam and CTX-M-15 E166Q :enmetazobactam cocrystal diffraction data were collected at BL13-XALOC beamline at  ALBA  synchrotron with the collaboration of  ALBA  staff. We thank  Diamond Light Source  for access to beamlines I03 and I04 (proposal 23269) that contributed to the results presented here and the staff of the  Diamond macromolecular crystallography village  for their help. These studies were supported by research grants from  Allecra Therapeutics  to J. S. and R. A. B. and from the  Medical Research Council  to C.
Data availability. Coordinates and structure factors have been deposited and are available at the Protein Data Bank under the following accession numbers: 6Z7J (CTX-M-15:enmetazobactam cross-link), 6Z7K (CTX-M-15:tazobactam soak/acylenzyme), 7BDS (CTX-M-15:tazobactam cross-link), 6Z7H (CTX-M-15 E166Q :enmetazobactam cross-link), 7BDR (CTX-M-15 E166Q :tazobactam cross-link), 7QQ5 (CTX-M-15 K73A : enmetazobactam), 6Z7I (CTX-M-15 E166Q ), 7QQC (CTX-M-15 K73A ), 7R3R (CTX-M-15 G238C ), and 7R3Q (CTX-M-15 G238C/G239_Y240insA ).
8 Although solanezumab had no significant effect on the primary outcome in this study, solanezumab was favored across multiple other measures. 8 The anti-amyloid treatment in asymptomatic AD study (NCT02008357) is currently exploring high-dose solanezumab (1600 mg Q4W) in preclinical AD patients. 11 lumetric magnetic resonance imaging (vMRI) and atrophy assessments are essential tools in characterizing effects of candidate treatments on brain volume in AD trials. Inclusion of imaging endpoints is useful in assessing treatment safety and efficacy. 12 Rates of whole brain volume (WBV) and ventricular volume (VV) changes are sensitive markers of neurodegeneration and used as biomarker outcome measures in trials of potential disease-modifying therapies. 12 4] [15] [16] [17]  In EXPEDITION3, solanezumab did not significantly alter WBV change in mild AD compared to placebo, although patients treated with solanezumab showed numerically less WBV change than placebo.
18 e aim of this post hoc analysis was to investigate the effects of solanezumab on global brain volume measures in patients with mild or moderate AD, using vMRI data from EXPEDITION, EXPEDITION2, EXPEDITION3, and a pooled EXPEDITION dataset. WBV and VV change, measured in cm  3  , were estimated at 80 weeks using either boundary shift integral (BSI)  20  (EXPEDITION and EXPEDITION2) or tensor-based morphometry (TBM)  [21] [22] [23]  (EXPEDI-TION3). BSI uses signal changes at the boundary between cerebrospinal fluid and brain tissue between baseline and follow-up scans to determine change in brain volume, hence requiring two segmentations to generate the brain/ventricular boundary across visits. 20 M measures change globally within a region of interest defined at baseline. 24 lume measures from all trials were pooled after confirming strong agreement (Lin's concordance correlation coefficient; between 0.
5. From the 380 domestic male samples, 354 with a call rate of >95% were kept. From the 34 wild goats, two Italian ibexes and four Iranian bezoars had call rates of only 90. 7%-94. 4%, but this did not appear to affect their phylogenetic positions or the corresponding bootstrapping values (see below), so these were retained in the data set. 6. From the 29,035 SNPs, 12,540 had a call rate of <99% in the 388 male goats (354 domestic and 34 wild) and were discarded. Allele-sharing distances between individuals were calculated using plink or mega7  (Tamura et al. , 2011) , and visualized in neighbour-joining (NJ) trees by using the program splitstree4  (Huson & Bryant, 2006). For calculating bootstrapping values in an NJ tree of the 80 domestic and 20 bezoar haplotypes and as an outgroup one markhor, we selected 2867 SNPs with MAF >5% and used the program mega7.
| DISCUSS ION One of the benefits of the current availability of WGS data sets is the access to an abundance of sequence variants, which allow a comparison of individuals or populations for several purposes. This is especially useful for the analysis of Y-chromosomal diversity, which was previously restricted by the availability of Y-chromosomal markers. The male-specific part of the Y-chromosome constitutes the longest haplotype in the mammalian genome and may serve as a marker for mammalian paternal lineages. Here, we combined the data set of the VarGoats project with published data and genotyped diagnostic SNPs in male goat samples from several sources. The Y-chromosomal phylogeny of wild and domestic goats is in agreement with the Y-chromosomal tree on the basis of AMELY and ZFY gene fragments  (Pidancier et al.
ACK N OWLED G EM ENTS The VarGoats project sequencing effort has been funded mainly by France Génomique "Call for high impact projects" (ANR-10-INBS-09-08) and by the BBSRC Global Challenges Research Fund Data and Resources Grant BBS/OS/GC/000012F awarded to the Roslin Institute for Reference genome and population sequencing of African goats. Detailed funding sources of the VarGoats project are described in Denoyelle et al. (2021). This study was supported by the Croatian Science Foundation (Project ANAGRAMS-IP-2018-01-8708) "Application of NGS in assessment of genomic variability in ruminants" and by the European Union (projects ECONOGENE QLK5-CT2001-02461). We are grateful to Dr E.
Funding information Croatian Science Foundation , Grant/ Award Number:  ANAGRAMS-IP-2018-01-8708 ;  European Union , Grant/Award Number:  QLK5-CT2001-02461 ;  France Génomique , Grant/Award Number:  ANR-10-INBS-09-08 ; the  BBSRC Global Challenges Research Fund Data and Resources , Grant/Award Number:  BBS/ OS/GC/000012F.
DATA AVA I L A B I L I T Y S TAT E M E N T Dideoxy sequences of DBY, ZFY and SRY segments representing different haplogroups are accessible via the NCBI codes MF741774. 1 to MF74182. 1 and MG545047. 1 to MG545050. 1. The preliminary data set or 5356 genotypes in 70 mainly Asian and Moroccan male goats in plink format can be accessed via https://osf. io/ngx7u/ ?view_ only=64b4f 8bf78 794cd db5f4 7a3e3 e3d4534. For the VarGoats data set, see www. goatg enome. org/vargo ats. html and Denoyelle.
Acknowledgments-We acknowledge  Dr Thierry Peyrard , head of the  Centre National de Référence pour les Groupes Sanguins , at the  Institut National de la Transfusion Sanguine , for authorizing the thawing of rare blood samples GIL-null and Colton-null and  Dr Eduardo Mendez ,  Instituto de Química Biológica, Universidad de la República , for useful discussions. Author contributions-L. T. and M. N. M. conceptualization; L. T. and M. N. M. methodology; I. M. -C. and M. A. O. formal analysis; F. O. , A. C. L. , D. S. , C. A. , L. T. , and M. N. M. investigation; I. R. -G. , I. M. -C. , and M. A. O. resources; F. O. , A. C. L. , L. T. , and M. N. M. writing-original draft; F. O. , A. C. L. , I. R. -G. , I. M. -C. , M. A. O. , A. D. , L. T. , and M. N. M. writing-review & editing; L. T. and M.
N. M. supervision; A. D. , L. T. , and M. N. M. project administration; A. D. , L. T. , and M. N. M. funding acquisition. Funding and additional information-The authors acknowledge financial support from  Agencia Nacional de Investigación e Innovación, Uruguay  (grants  FCE_2017_136043  to L. T. and M. N. M. and  FMV_2019_155597  to L. T. and I. R. -G. , and a fellowship to A. C. L. ) and to  Comisión Sectorial de Investigación Científica  (grants Iniciación a la  Investigación Científica  to F. O. and C. A. ; and  Programa Grupos   I+D_2014_C632-348  and  _2018_47  to A. D. ). F. O. received a scholarship from  Comisión Académica de Posgrados, Universidad de la República.
Data collected Questions were asked using a face-to-face questionnaire administered by a physician to assess sexual behavior and addictive substance use: socio-demographic data and gold mining activity, number of partners during the last year; condom use at last sexual intercourse; whether last sexual intercourse was commercial; cost of last condom used; past history of HIV testing; tobacco, alcohol, and drug consumption. The questionnaire was based on similar questionnaires used in the context of gold mining  [2, 17]  and on validated questionnaires from reference health institutions  [18] [19] [20] [21]. The S1 Dataset presents the questionnaire in English and Portuguese (the language used with the participants), the origin of the questions, the data collected, definitions, and the modalities of the variables used in the analyses.
Statistical methods Data were analyzed using standard tests with a p-value<0. 05. Univariate and multivariate analysis were carried out with Stata13 software. Multiple logistic regression models included variables with a p-value<0. 2 in univariate analysis. The prevalence of STIs in 2015 and 2019 was calculated and presented graphically as a histogram with a 95% confidence interval. The design, setting, inclusion criteria, data collection procedure, and biological examinations (quantity of blood samples, methods, and testing laboratory) were identical in the 2015 and 2019 cross-sectional studies so that results could be compared  [8].
Ethics A written informed consent form was collected for each participant. Since recruitment took place on the Surinamese and Brazilian borders, authorizations were obtained from their respective ethics committees (The CMWO-Commissie voor Mensgebonden Wetenschappelijk Onderzoek-in Suriname: N˚DVG-738; the Fiocruz Ethics Committee in Brazil: N2. 560. 415). The authorization to import human biological samples to FG was obtained from the French Ministry of Education and Research, process N˚IE-2019-1099. The database was anonymized and declared to the French Data Protection Authority, CNIL.
1. 5) as basecaller. The quality of the Illumina sequencing data was assessed using FastQC v0. 11. 9 and trimmomatic v0. 38 for adapter clipping, quality trimming (LEAD-ING:3 TRAILING:3 SLIDINGWINDOW:4:15), and minimum length exclusion (>50 bp). The quality of the Nanopore reads was assessed using Nanoplot v1. 28. 2, and Porechop v0. 2. 3 was used for barcode clipping and NanoFilt v2. 6. 0 for filtering on quality (Q > 8) and length (>500 bp). The genomes were reconstructed using the hybrid assembler Unicycler v0. 4. 8. Single Nucleotide polymorphism (SNP) calling was done using snippy v4. 4. 5 against the PGAP annotation of isolate Kp36336. The assemblies were visually inspected using Bandage  28  and the plasmid elements were checked using BLASTn against the NCBI plasmid database (accessed in October 2020).
Further functional annotation, antibiotics resistance genes, prophage elements, and genomic islands were annotated using respectively eggNOG-mapper v2. 0. 0 29 , ABRicate v1 (https://github. com/tseemann/abricate, accessed in October 2020), PHASTER  30  (https://phaster. ca, accessed in October 2020), and Island-Viewer 4  31. Unless specified otherwise, programs were run using the default parameters. The sequence type and capsule type of the strain were determined using the software mlst to query the pubMLST database  32  (https://github. com/ tseemann/mlst, accessed in October 2020) and Kaptive v0. 7. 3  33  , respectively. The genomic datasets were deposited in the NCBI databases. The initial Illumina and Nanopore sets of reads are available in the SRA database via the accession numbers mentioned in Supplementary Data 5.
36. Genome assembly was performed using SPAdes (Galaxy v3. 12. 0 + Galaxy1)  37. Next, using MEGA X v10. 1. 2, the phage genome was aligned to Klebsiella phage vB_KpnM_KP15, which was identified as the closest type species by BLASTn against the NCBI nucleotide database (accessed in March 2021). Finally, the phage genome was annotated using RASTtk (PATRIC v3. 6. 12)  38 nd manually curated by BLASTp against the NCBI protein database (accessed in March 2020). The genome was screened for tRNAs using tRNAscan-SE 2. 0 and was visualised as a genome map using EasyFig 2. 2. 2  39. We used VirulenceFinder v2. 0 40 , ABRicate v1 (https://github. com/tseemann/abricate, accessed in October 2020) and manually verified the annotated proteins to screen for lysogeny-related proteins. The genomes of the originally selected variant and the pre-adapted variant of phage M1 used in therapy were analyzed for distinguishing mutations using the variant caller snippy v4.
Data availability The Illumina and Nanopore sequencing datasets of the Klebsiella pneumoniae genomes were deposited in the Sequence Read Archive (SRA) database under accession codes SRR13350373-SRR13350378. The genome of isolate Kp36336 was deposited in the GenBank database under accession codes CP066511-CP066515. The Illumina data of the wild type and the adapted phages can be accessed via the NCBI SRA database under the accession codes SRR15604575 and SRR15694574, respectively. The genome sequence of phage vB_KpnM_M1 was deposited in the GenBank database under accession code MW448170. Source data are provided with this paper. The authors declare that all other data supporting the findings of this study are available within the article and its Supplementary Data files. Source data are provided with this paper.
The size of the resulting products was consistent with the expected size distribution (a broad peak between 300 and 500 bp). Clustering and DNA sequencing using the Nova-Seq6000 v1. 5 was performed according to the manufacturer's protocols including a concentration of 1. 1 nM of DNA, two flow cells, and NovaSeq control software NCS v1. 7. Image analysis, base calling, and quality check was performed with the Illumina data analysis pipeline RTA3. 4. 4 and Bcl2fastq v2. 20. Expression levels of the transcripts (20 million paired end reads per sample) were quantified against the mouse reference genome (Ensembl GRCm38. p6, containing 22. 519 coding genes) using TopHat version 2. 0. 14.
RNA sequencing analysis The obtained count-tables were used for further differential gene expression analysis using Rstudio statistical software (version 4. 1. 0). Control samples for DD4 and DD10 were compared by differential gene expression using DESeq2 (version 1. 30. 0), adjusting for the sampling day per individual independent experiment to extract log2FC values of genes with at least one count in the analysis (=20. 335). Differentially Expressed Genes (DEGs) were obtained by filtering results for p < 0. 001 and log2FC > 1. 5. Functional enrichment analysis of genes in Gene Ontologies (GO) terms (biological processes GO BP5) was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID; https://david. ncifcrf. gov/ consulted in September 2021, version 6. 8) as a gene list with genes of at least one count in the analysis.
GO-terms were clustered using the 'functional annotation clustering' tool and summary names (see supplementary material) were obtained with help of http://amigo/geneontology. org/amigo based on their treeview. The compound treated samples were analysed in a similar manner. First, for visualisation, principal component analysis (PCA) was performed on count data after filtering out the 0 values and applying a Variance Stabilizing Transformation (VST). Additionally, data was corrected for differences between experiments. Determination of DEGs was performed using DESeq2 after filtering results for p < 0. 001 and log2FC > 0. 5 and the number of up and downregulated genes per condition were determined compared to the control (DMSO) values. PCA plots and heatmaps were generated in RStudio (version 4.
Acknowledgements Gina Mennen  was supported by the  French National Association for Research and Technology (ANRT)  through a  CIFRE PhD  grant no. 2018-0682 , which was co-supervised by  Aldert Piersma ,  Nina Hallmark ,  Marc Pallardy ,  Remi Bars , and  Helen Tinwell. Aldert Piersma  was funded by the  Dutch Ministry of Health, Welfare and Sports. We would like to thank  Jeroen Pennings  for a critical review of the manuscript.
Search Strategy A comprehensive search of the literature including the biomedical databases PubMed (NLM), EMBASE (Elsevier), Scopus (Clarivate), Cochrane Library (Cochrane Collaboration) and Web of Science (Clarivate) was initially conducted on July 2019 by MA and SA in close collaboration with a medical librarian specialized in systematic reviews (LÖ). The complete search was updated in March 2022. Grey literature sources were not covered, as only peer-reviewed, published papers were considered for this review. PubMed and PubMed's MeSH were used to systematically identify search terms and to develop a search strategy. The search term inclusion was reviewed by experts (AB and EHA) and the search string was peer reviewed by LÖ before it was adapted and applied to search all selected databases.
All search terms were searched in a combination of the search fields "article title", "abstract" and "MeSH"/"thesaurus". No filters or limitations to study design, publication dates or language were applied to the search, to ensure optimal information retrieval and to capture eventual pre-indexed materials. Hand screening of reference lists in the final papers was also conducted independently by MA and SA to ensure literature saturation. This yielded no additional references. Finally, the Predatory Reports from Cabell's International  [18]  was consulted (MA and SA) to ensure the academic quality of the selected papers published in open access journals. A search log with detailed search documentation and results for all databases is available in Appendix A. Appendices B and C are the PRISMA-S  [19]  and PRISMA 2020 checklists  [17]  for reporting, respectively.
Selection Process All records identified in the literature search were uploaded to the systematic review software Covidence  [20]  for automatic de-duplication and blinded screening by two reviewers (SA and MA). A third reviewer (AB) resolved any conflicts reported by the software. The screening was done in two stages. Initial screening of titles and abstracts was conducted by two reviewers in accordance with the pre-specified inclusion and exclusion criteria. The two reviewers then independently evaluated the full-text papers that matched the eligibility criteria and passed initial screening. An expert (AB) resolved any conflicts detected by the software. A PRISMA flow diagram with the details for the screening and selection process is available in Figure  3. The pre-set eligibility criteria were used to determine whether the articles fitted the focus of the review, i.
Data Extraction An Excel sheet was designed based on data to be extracted, including details of the study design, animals, details including pre-or post-treatment, method, and duration of sensitization and AS, interventions, and our specified outcomes of interest. The primary outcome is the normalization of arterial blood pressure, measured in mmHg. The secondary outcome is animal survival, measured in minutes. The two reviewers independently performed data extraction from the published articles. Inconsistencies were resolved by discussion between the two reviewers. In case of missing data, or data found only in graphs/diagrams but not in text, authors were contacted for the original data. Corresponding author emails were unavailable for some papers. Only one author of the four contacted regarding data for 11 articles responded to our request.
Data Synthesis and Statistical Analysis The purpose of our data extraction was to compile experimental data from different studies that address the role of NO pathways in AS animal models. However, data heterogeneity prevented a meta-analysis or even reliable statistical analysis. For example, studies using the same animal species used different medications. Even when stratified by medication tested, medication dosages and times of administration differed with respect to antigen challenge. For all included studies, a qualitative assessment of results was performed.
We isolated viruses from positive samples in embryonated eggs, then performed molecular characterization by whole-genome sequencing using an Illumina MiSeq system (https://www. illumina. com) as described elsewhere  (7). We obtained ON870413-47). The isolates shared a high nucleotide sequence identity of 98. 62%-99. 91% and amino acid similarity among one another. To determine the origin of the Benin H5N1 viruses, we performed phylogenetic analysis for all 8 genomic segments from the 5 isolates using the maximumlikelihood method and inferred phylogenetic trees using IQ-TREE version 2. 1. 2 software (http://www. iqtree. org) with automatic model selection and 1,000 nonparametric bootstrap replicates  (8). Our analysis revealed that the Benin HPAI H5N1 viruses were closely related to H5N1 viruses isolated in Nigeria in 2021 and, to a lesser extent, to viruses detected in Lesotho in 2021 and Europe in 2020-2021, suggesting a possible transmission route from Nigeria to Benin.