dataesr/scientific-paragraphs-categorization huggingface dataset:
As noted earlier, it is difficult to compare 6 km average winds with those recorded at a single point, which will show much greater fluctuation. At around L S = 189 • , the model shows some agreement with the REMS data. Between 12:00-16:00, the REMS winds on the first two sols are in a west-northwest direction (Figure  8a ), which is similar to the model results between ∼15:00-17:00. At other times of day there is less agreement, but it can be seen by comparing winds at similar times of day in different Mars years how varied they can be. Around L S = 321 • the REMS data show winds ranging from west-southwesterly to east-southeasterly (Figure  8b ), which is in general agreement with the model. The REMS data do not show any winds with a northerly component like those in the model between 08:00-16:00.
As the vmr values are determined from the RH, these are also larger than in the model. Looking at Figure  7(a-c ), the temperatures and pressures are generally in good agreement between REMS and the model. Thus, if correct, the higher RH and vmr values in the REMS data suggest a wetter atmosphere in the late evening than in the model. This may be due to vapour diffusing into the regolith more slowly than the model predicts, or due to peak daytime vapour abundances being larger than in the model. However, the RH values at this time have large uncertainties, and so both the REMS RH and vmr values may be too high. A similar disagreement between model results and REMS vapour mixing ratios over the 17:00-00:00 period occurs in the 1D simulations of  Savijärvi et al. (2016). In terms of the spatial distribution of water in and around Gale crater, the results are similar in all the simulations that include water vapour exchange with the subsurface.
The phylogenetic position of Elizabethkingia strains selected for comparative purposes revealed the need for taxonomic reassignment for a large number of strains, as expected given recent taxonomic changes and the difficulty in differentiating Elizabethkingia species based on phenotypic characteristics. We found that several strains initially identified as E. meningoseptica are in fact E. anophelis. E. anophelis can be identified using matrix-assisted laser desorption ionization -time of flight (MALDI-TOF) analysis, but requires updated reference spectrum databases as found here and in a previous work  15. This further indicates that the clinical importance of E. anophelis was previously underestimated, in agreement with results of a recent study  15. These observations call for more research regarding E.
Libraries were prepared using the NEBnext Ultra DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA), and sequence reads were generated with the Illumina MiSeq Reagent Kit v2 and MiSeq instrument (Illumina, Inc. , San Diego, CA). For comparative purposes, we included seven isolates stored in the Pasteur Institute's collection (Collection de l'Institut Pasteur or CIP; Supplementary Data 1, isolate names starting with CIP). Strains were cultivated on trypticase soy agar at 30 °C. DNA extraction was performed using the MagNA Pure 96 robotic System with the MagNA Pure 96 DNA and Viral Nucleic Acid small volume kit (Roche Diagnostics). Libraries were constructed using the Nextera XT DNA Library Preparation kit (Illumina, Inc. , San Diego, CA) and sequenced on a NextSeq-500 instrument using a 2 Â 150 paired-end protocol.
These resulted in a set of 1,862 genes for the Wisconsin cgMLST scheme. These protein-coding genes, together with the 1,546 genes of the genus cgMLST scheme, constitute a total of 3,408 loci used for genotyping Wisconsin outbreak isolates of E. anophelis. The two cgMLST schemes are implemented in the Institut Pasteur instance of the BIGSdb database tool  49. Allele sequences and their corresponding numerical designations are publicly accessible at http://bigsdb. pasteur. fr. Phylogenetic analysis of cgMLST data. CDSs corresponding to the cgMLST schemes loci were aligned at the amino-acid level with MAFFT v. 7. 245 (ref. 50), back-translated to obtain multiple codon-based sequence alignments, and finally concatenated to obtain supermatrices of characters. This procedure was performed for (i) the entire Elizabethkingia sample (114 genomes) with the genus cgMLST scheme (1,546 loci, 554,224 aligned codons), and (ii) the Wisconsin outbreak isolates (69 genomes) by adding the dedicated cgMLST scheme to the genus one (total of 3,408 loci, 1,137,011 aligned codons).
For each supermatrix of characters, the phylogenetic analysis was performed using IQ-TREE  51  with the codon evolutionary model being selected to minimize the BIC criterion, that is,  52  for the Elizabethkingia and for the Wisconsin outbreak samples, respectively. GY þ F þ G 4 and GY þ F Mapping-based SNP analysis. To assess variation of the entire genome including intergenic regions for phylogenetic analysis of the outbreak isolates, we used a read mapping approach. All read sets were mapped against the same reference outbreak genome sequence (CSID 3015183678) as used for core-genome and cgMLST locus definitions. Read mapping, SNP calling and preliminary filtering were completed using the RedDog phylogenomics pipeline (https://github. com/katholt/RedDog)  53. Because we were primarily interested in phylogenetic analysis of the conserved regions of the Elizabethkingia genomes, SNP sites at which mapping and base calling could be confidently conducted in o95% of isolates were excluded from further analysis (most of these were located in a 77 kbp region that was deleted in four isolates that were derived from the same patient), as were SNPs located in either putative phage-associated or repeated regions of the reference genome, as detected by Phaster  54  or the nucmer algorithm of MUMmer v3 (ref.
The concatenated alignment of these SNP alleles was used to generate a maximum likelihood phylogenetic tree for the outbreak isolates using IQ-TREE  51  (see Supplementary Fig. 2 , Supplementary Method 3. 2 and Supplementary Data 7). SNPs were mapped back to the tree using FastML v3. 1 and the details of each substitution mutation (branch, ancestral allele, derived allele) were extracted from the marginal sequences output file (Supplementary Data 2). The coding effects of the SNPs, inferred using the annotated reference genome, was defined using the parseSNPtable. py script in RedDog and analysed using R. BEAST analyses. Date estimates of all nodes were derived using BEAST v. 2. 3. 1 (refs 57,58) on the cgMLST supermatrix of aligned nucleotide characters (Supplementary Data 8) with the GTR þ G 4 þ I nucleotide evolutionary model (one per codon position) and lognormal relaxed-clock model.
1. 2 (ref. 62). A CDS was included in a family if it was homologous to at least one CDS already in the family. SiLiX parameters were set to consider two CDSs as homologs if their aligned part had at least 60% (Elizabethkingia genus) or 80% (E. anophelis) sequence identity and included 480% of the smallest CDS. The pan-genomes of Elizabethkingia and of the outbreak isolates were determined independently. Detection of capsular gene clusters. To identify capsular gene clusters, we used our previous approach  2. In brief, we performed a keyword search of the Pfam database v. 29. 0 (http://pfam. xfam. org) for protein profiles involved in capsular polysaccharide production such as glycosyl transferases, ABC transporters, Wzx flippase and Wzy polymerase. We then performed a search of these profiles in Elizabethkingia genomes using HMMER3 v.
3. 1b1 (ref. 63) with the E-values o10 À 4 and a coverage threshold of 50% of the protein. After the identification of a putative capsular cluster across all genomes, several proteins within the cluster did not match any of the previously selected protein profiles. For completeness, we searched these proteins for known functional domains against the PFAM database using the command hmmscan included in the software HMMER3, and recorded their family and/or annotation (see Supplementary Fig. 7 , and regions 5 and 6 of Supplementary Data 3). Antimicrobial resistance and virulence-associated genes. Acquired antimicrobial resistance genes were detected using HMMER3 v. 3. 1b1 to screen genome sequences against the ResFams (Core v. 1. 2), a curated database of antimicrobial resistance protein families and associated profile hidden Markov models with the cut_ga option  64  (Supplementary Data 4).
Virulence-associated genes were identified by screening genome sequences against the VFDB 2016 (ref. 65) using BLASTP v. 2. 0 (minimum 40% identity with E-value o10 À 5 ), as in (ref. 5) (Supplementary Data 5). Detection of mobile genetic elements. ICEs were identified and classified using MacSyFinder v. 1. 0. 2 (ref. 66) with TXSScan profiles  67. CRISPR-Cas systems were searched using MacSyFinder v. 1. 0. 2 with Cas-Finder profiles  66  and CRISPR-Finder  68  , with default parameters. Integrons were searched using IntegronFinder v. 1. 4 with -local_max option  69  , and prophages using VirSorter v. 1. 0. 3 on RefSeqDB only  70  and PhageFinder v. 4. 6 (ref. 71). Antimicrobial susceptibility testing. Antimicrobial susceptibility testing was performed by Kirby Bauer disk diffusion method (http://www.
Acknowledgements We thank  D. Mornico  of  Institut Pasteur  and  V. Nyak  of  CDC  for assistance with submission of sequence data to public repositories. We would also like to thank the  State Health Departments of Michigan and Illinois  for contributing strains and information for the cases outside of the  State of Wisconsin. The efforts of laboratory  staff in both DHQP and DHCPP are greatly appreciated. This work was supported by  Institut Pasteur ,  French government 's  Investissement d'Avenir program Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (grant  ANR-10-LABX-62-IBEID ), and the  Advanced Molecular Detection (AMD) initiative at CDC. O. R. was supported by a fellowship from  Fondation pour la Recherche Medicale  (grant number  ARF20150934077 ).
Data availability. Reads for all outbreak isolates and complete genome sequences of outbreak isolates CSID 3015183678, CSID 3015183684, CSID 3000521207 and CSID 3015183681 were submitted to NCBI, associated with project ID PRJNA315668. Reads and draft genome sequences for strains Po0527107 and V0378064 (ref. 2) were submitted to the European Nucleotide Archive and are available under their respective project IDs, PRJEB5243 and PRJEB5242. Reads for strains CIP 78. 9, CIP 60. 59, CIP 104057, CIP 108654, CIP 79. 29, CIP 80. 33 and CIP 108653 were submitted to the European Nucleotide Archive and are available under project ID PRJEB14302. In addition, every genome sequence assembled during this study is available in the Institut Pasteur instance of the BIGSdb database tool dedicated to Elizabethkingia (http://bigsdb.
Validation cohort: To confirm the association between the SLC28A3 gene variant (rs10868152) and Tac dose response, we analyzed an independent cohort of 189 KTRs from Necker Hospital for whom DNA was available for genotyping and who had provided informed consent. The demographic and clinical information was collected prospectively in the DIVAT (Donn ees Informatis ees et VAlid ees en Transplantation) database at 3, 12 and 24 mo after transplantation. The demographic and clinical characteristics of this cohort are described in Table  S2. Regulatory aspects: The institutional review board at each participating center approved the study design, and written informed consent was obtained from all patients. The clinical and research activities reported were consistent with the principles of the Declaration of Istanbul, as outlined in the Declaration of Istanbul on Organ Trafficking and Transplant Tourism.
Genotyping and annotation Genomic DNA was purified using the QIAamp DNA purification system (Qiagen, Hilden, Germany). DNA samples were genotyped for 16 561 SNPs using a customized Illumina SNP genotyping assay (Illumina, San Diego, CA) designed to capture the genetic variation of 1653 key drug pathway genes (including phase I and II drug metabolism enzymes, drug transporters, drug targets, and drug receptors). SNP selection was performed by tagging functional SNPs with tagSNP using the HapMap database  (14)  with a pairwise tagging cutoff of R 2 =0. 8. SNPs were selected to characterize the main haplotypes within the white population (95% of haplotypic diversity) according to the following criteria: Genes were defined by their position on human genome 36 (National Center for Biotechnology Information); the minor allelic frequency had to be 5%; and for SNPs carrying the same information, a "score design" allowed selection of the final SNPs (defined by technical criteria related to the chip and established by the manufacturer).
These differences in distribution were significant (p ≤ 0. 05) with parametric tests (repeated-measures ANOVA) and with the linear mixed model (p ≤ 0. 001). On the basis of the SNPs present in the microarray, rs10868152 was not found in linkage disequilibrium with other variants of genes involved in drug metabolism (in contrast to CYP3A5) (Figure  5C ). In addition, the 1000 Genomes Project and HapMap genotype databases indicate that rs10868152 was not in linkage disequilibrium with other genes in black and white populations. To confirm the role of this variant in Tac metabolism, we tested the statistical difference between the distributions of the log (Tac C 0 /dose) for the three possible genotypes in an independent cohort of 189 KTRs from the Necker Hospital under Tac treatment and found that Tac C 0 /dose was significantly lower in this group compared with carriers of the CT or CC genotype (Figure  6 ).
Although we did not achieve significant performance using our two single-feature selection techniques alone (data not shown), our ensemble feature-selection scheme did. A possible improvement of our approach would be the use of nonlinear kernel-based PLS regression, which has been shown to be more effective in defining the correlation between drug response and gene expression  (19). We provided evidence of a role for the SLC28A3 gene variant rs10868152 in the modulation of Tac pharmacokinetic parameters. Importantly, this association was validated in an independent cohort of KTRs, and the variant is not in linkage disequilibrium with either the other genes variants in the chip or the 1000 Genomes Project and HapMap genotype databases. Moreover, SLC28A3 has broad specificity for pyrimidine and purine nucleosides, and variants of the gene have been associated with variation in the responses to ribavirin, gemcitabine and anthracyclines  (21, 22).
Specific consideration for some invasive procedures Neuraxial anesthesia Two multicenter studies have published findings on residual perioperative DOAC concentrations. Both concluded that 48 h without treatment might not guarantee the absence of residual anticoagulant effect at the time of the We suggest realizing these procedures at trough levels of direct oral anticoagulants (e. g. avoiding the intake the morning of the procedure) b Awaiting results of BRUISECONTROL-2 trial (NCT01675076) to decide whether device procedures can be safely realized on direct oral anticoagulants  procedure in around 15% of patients and suggested that the period of stopping a DOAC before very high bleeding risk procedures requiring complete hemostatic function such as neuraxial anesthesia or intracranial surgery should be prolonged  [16, 54].
Several observational studies from clinical trials or national registries have shown a higher bleeding risk without reduction of TE with the use of heparin bridging in patients taking DOACs or warfarin  [79]. However, in most studies heparin bridging and anticoagulant arrest were not standardized. Furthermore, the invasive procedures had mainly low to moderate bleeding risks and patients at high TE risks were again underrepresented in these trials. Therefore the ongoing PAUSE trial (NCT02228798) which aims to recruit 3300 patients on DOACs in a perioperative setting following a standardized protocol without heparin bridging and with residual DOAC concentration measurements on the day of the invasive procedure, may be able to recruit sufficient high-TE risk patients to strengthen the conclusions of the previous trials.
To reverse the effects of rivaroxaban at a daily dose of 20 mg, the recommended bolus of andexanet alpha is 800 mg IV (30 mg per minute) followed by a continuous infusion of 8 mg/min for 2 h (960 mg in total). For apixaban at a dose of 5 mg twice a day, the recommended bolus of andexanet alpha is 400 mg IV (30 mg per minute) followed by a continuous infusion of 4 mg/ min for 2 h (480 mg in total). No serious adverse events or thrombotic complications were recorded in the ANNEXA-A and ANNEXA-R studies, which included 101 patients receiving andexanet alpha. Recently, results from the phase III trial, the « Prospective, Open-Label Study of Andexanet Alfa in Patients Receiving a Factor Xa Inhibitor Who Have Acute Major Bleeding » (ClinicalTrials. gov number, NCT02329327) have been published. The authors evaluated 67 patients with acute major bleeding within 18 h of the last FXa inhibitor administration.
Aims: The MARLINA-T2D study (ClinicalTrials. gov, NCT01792518) was designed to investigate the glycaemic and renal effects of linagliptin added to standard-of-care in individuals with type 2 diabetes and albuminuria. Methods: A total of 360 individuals with type 2 diabetes, HbA1c 6. 5% to 10. 0% (48-86 mmol/ mol), estimated glomerular filtration rate (eGFR) ≥30 mL/min/1. 73 m 2 and urinary albumin-tocreatinine ratio (UACR) 30-3000 mg/g despite single agent renin-angiotensin-system blockade were randomized to double-blind linagliptin (n = 182) or placebo (n = 178) for 24 weeks. The primary and key secondary endpoints were change from baseline in HbA1c at week 24 and time-weighted average of percentage change from baseline in UACR over 24 weeks, respectively. Results: Baseline mean HbA1c and geometric mean (gMean) UACR were 7.
The MMRM included baseline HbA1c, baseline log10 (UACR), baseline HbA1c by visit and baseline log10 (UACR) by visit as linear covariates and treatment, visit and visit by treatment interaction as fixed classification effects. Similar MMRM models were fitted for subgroup analyses (Table  S1 ). The percentage of participants with baseline HbA1c ≥7. 0% who achieved HbA1c <7. 0% at week 24 was analysed post hoc using a logistic regression model in which treatment was a factor and continuous baseline HbA1c and continuous baseline log10 (UACR) were covariates. The key secondary endpoint was evaluated using an analysis of covariance (ANCOVA) of data from the FAS, with baseline HbA1c and baseline log10 (UACR) as linear covariates and treatment as a fixed classification effect; the last-observation-carried-forward (LOCF) approach was used to replace missing data (including values obtained after glycaemic rescue therapy was started).
Because of their non-normal distribution, UACR data were log10-transformed prior to ANCOVA analyses. Similar ANCOVA models were performed for subgroup analyses, including a post hoc analysis by background therapy at baseline (ACE inhibitors or ARBs) (Table  S2 ). The odds of achieving a clinically relevant UACR response with linagliptin compared with placebo at week 24 were analysed post hoc using a logistic regression model. In this analysis, the linagliptin and placebo groups were compared for the proportion of participants with a UACR response at week 24, defined as a reduction in UACR of >20% at week 24 relative to baseline, vs those with no UACR response, defined as no change or an increase in UACR at week 24 compared to baseline. This analysis was performed on the FAS and included eligible participants with a UACR value at week 24, irrespective of introduction of glycaemic rescue therapy (OC-ROC).
MARLINA-T2D was a 24-week, randomized, double-blind, placebo-controlled, phase 3b clinical trial conducted at approximately 80 clinical centres in 12 countries: Canada, Denmark, Finland, France, Germany, Japan, the Philippines, South Korea, Spain, Taiwan, the USA and Vietnam (ClinicalTrials. gov, number NCT01792518). The study protocol was approved by independent ethics committees (IECs)/institutional review boards (IRBs) at each participating centre, and the study was conducted according to the principles of the Declaration of Helsinki and the ICH Harmonised Tripartite Guidelines for Good Clinical Practice. Eligible individuals were aged 18-80 years with type 2 diabetes, glycated haemoglobin (HbA1c) 6. 5% to 10. 0% (48-86 mmol/mol), body-mass index (BMI) ≤40 kg/m 2 and estimated glomerular filtration rate (eGFR) ≥30 mL/min/1.
ACKNOWLEDGEMENTS The authors thank the participants and staff involved in this study. Data from this study have been presented previously at the  American Diabetes Association 76th Scientific Sessions, New Orleans, Louisiana ,  June 10  to 14, 2016. Medical writing assistance, supported financially by  Boehringer Ingelheim , was provided by  Giles Brooke, PhD , CMPP, of  Envision Scientific Solutions  during the preparation of this manuscript.
A double-bed SPECT/CT was acquired immediately after WBS from the upper cervical spine to the proximal femora. SPECT images were obtained with the following parameters: 10 secondes per step acquiring 120 projections with 180°rotation for each camera head, on a 128 × 128 pixel matrix. SPECT data were reconstructed using Flash 3D (Siemens) with ordered subset expectation maximization (OSEM) (8 iterations, 16 subsets and 10 mm Gaussian post filtering). CT imaging consisted in a low-dose technique with the following parameters: modulated tube current intensity (Care4D, 90mAs), 130 kV, total collimation 6x1mm, pitch 1, and was performed on the same anatomical region as the SPECT. The estimated irradiation dose received by the patients was simulated with the CT-Expo v2. 1 package.
To test this, we examined the Immunological Genome Project (ImmGen) database, which comprises publicly available data from a collection of immune cell types in C57BL/6J mice  (13, 41). We found that within the top  (Continued on next page)  50  differentially regulated genes between the monoinfected and coinfected groups, genes specific for dendritic cells, macrophages, and monocytes were overrepresented and showed decreased mRNA levels in coinfected animals, suggesting a reduced infiltration or activation of these cells in the bladder following coinfection compared to that following monoinfection (Fig. 4C ; Table  S2 ). E. faecalis limits E. coli-mediated immune activation and promotes E. coli virulence during mixed-species CAUTI. Based on downregulation of transcripts associated with interferon regulation (oas and ifi) and monocytic chemotaxis (CCL12) during E.
3. 3) (70). Differential analysis of E. coli monospecies-infected animals and E. coli-and E. faecalis OG1RF-coinfected animals was performed using the R/Bioconductor package DESeq2 (version 1. 40. 1) (71), using default settings from that package. The NCBI file gene2refseq (downloaded 3 March 2016) was used to convert Refseq identifiers to Entrez identifiers for further analysis of Gene Otology annotations and Immunological Genome Project (ImmGen) data for functional analysis. Functional analysis of the bladder transcriptome. Processing of ImmGen expression data was performed in R 3. 2. 2 (70). Briefly, all 681 CEL files (72) were processed using the RMA method with the R/Bioconductor package oligo (version 1. 34. 0). Annotation was done using the R/Bioconductor package mogene10sttranscriptcluster.