dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis sets Descriptive baseline characteristics were based on the full analysis set (all randomized participants). All available eGFR measurements from the full analysis set were included (in-trial data), regardless of whether participants discontinued treatment, developed kidney failure, or experienced a fatal event. All safety analyses were performed using in-trial data from the full analysis set and summarized overall and per subgroup. Analyses of eGFR and UACR across visits eGFR and UACR changes over time (per visit) were assessed using mixed models for repeated measurements with treatment group, subgroup, trial, and the interaction between treatment group and subgroup as fixed factors, and baseline value as a covariate, all nested within visit. The change from baseline in eGFR and the change from baseline in log-transformed UACR were evaluated at week 80 and 83 in the semaglutide and placebo groups, respectively.
Time to persistent reduction in eGFR Time to persistent reductions in eGFR was based on the Modification of Diet in Renal Disease equation (used for the inclusion criteria and the adjudicated eGFR reduction component in the nephropathy endpoint in SUSTAIN 6) corresponding to the eGFR decline thresholds $30%, $40%, $50%, and $57%, and it was assessed in the overall population and the 2 eGFR subgroups using both pooled data and data separated by trial (SUSTAIN 6 and PIONEER 6). A persistent eGFR decline was defined as the time from randomization to the first visit at which the eGFR change threshold was attained, with this value confirmed at the subsequent visit. If no subsequent visit occurred, confirmation of the eGFR value was not required. Hazard ratios (HRs; semaglutide:placebo) and 95% CIs for time to persistent reductions in eGFR were estimated using a Cox proportional hazards model, with treatment and eGFR subgroups and the interaction between treatment group and eGFR subgroups as fixed factors, stratified by trial.
Supplementary analyses in relation to annual eGFR slope To explore whether the treatment effect on eGFR may be explained by changes in other parameters, 2 supplementary analyses were conducted using the random slope model-one adjusted for change from baseline in HbA 1c across trial visits, and one adjusted for changes from baseline in HbA 1c , body weight (BW), and systolic blood pressure (SBP), and baseline diuretic use (yes/no) across trial visits. A supplementary analysis excluding eGFR data before week 14 (PIONEER 6) or week 16 (SUSTAIN 6) was also conducted to assess the chronic effect of semaglutide on annual eGFR slope, as an early decline in eGFR was observed with GLP-1RAs in participants with eGFR >60 ml/min per 1. 73 m 2 in prior reports. 23 Hence, in the chronic slope analyses, change from week 14 or 16, respectively, was evaluated, with eGFR at week 14 or 16 considered the baseline.
Safety Within each eGFR subgroup, the proportions of participants experiencing AEs, serious AEs, severe AEs, severe hypoglycemic AEs, or acute kidney injury with semaglutide and placebo were comparable (Table  2 ). Within each eGFR subgroup, the proportions of participants experiencing GI AEs or discontinuing treatment prematurely due to AEs were higher among those was defined as the time from randomization to the first visit in which the value from the subsequent visit was confirmed by fulfilling the same relative reduction from baseline as the value from the previous visit. If no subsequent visit was performed, the confirmation was not required. Data from participants with a baseline eGFR <30 ml/min per 1. 73 m 2 were included in the analyses, and they are presented for the overall analyses but not for the subgroup analyses.
These findings were achieved together with no increase in the risk of AEs or new safety concerns identified. Whether the difference in annual eGFR slopes translates into reduced kidney disease events with semaglutide remains to be determined, along with the mechanisms behind any potential benefits on kidney outcomes. More evidence is therefore needed from randomized controlled trials with kidney disease endpoints as the primary outcome. The ongoing A Research Study to See How Semaglutide Works Compared to Placebo in People With Type 2 Diabetes and Chronic Kidney Disease (FLOW; NCT03819153) and A Research Study to Find Out How Semaglutide Works in the Kidneys Compared to Placebo, in People with Type 2 Diabetes and Chronic Kidney Disease (REMODEL; NCT04865770) trials of once-weekly s. c. semaglutide will address these evidence gaps on the use of and mechanisms behind GLP-1RA treatment for CKD in T2D.
39, 40 In addition, the A Heart Disease Study of Semaglutide in Patients With Type 2 Diabetes (SOUL) CV outcomes trial (NCT03914326) with once-daily oral semaglutide includes secondary kidney disease outcomes and will provide further evidence regarding semaglutide and kidney protection. 41 SCLOSURE KRT reports receiving grants/contracts from Novo Nordisk; consulting fees from AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly & Co, Gilead, Goldfinch Bio, Janssen, Novo Nordisk and Travere; honoraria from AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Gilead, Goldfinch Bio, and Novo Nordisk; and support for attending meetings or travel from Eli Lilly; and is on the Board of Directors for the Kidney Health Initiative and Chair of the Diabetic Kidney Disease Collaborative Task Force, both of which are for the American Society of Nephrology.
) CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; CV, coefficient of variation; eGFR, estimated glomerular filtration rate; HbA 1c , glycated hemoglobin; N/n, number of participants; PIONEER 6, A Trial Investigating the Cardiovascular Safety of Oral Semaglutide in Subjects With Type 2 Diabetes; SUSTAIN 6, a Trial to Evaluate Cardiovascular and Other Long-term Outcomes With Semaglutide in Subjects With Type 2 Diabetes; UACR, urinary albumin-to-creatinine ratio. aData from participants with a baseline eGFR <30 ml/min per 1. 73 m 2 were included in the analyses, and they are presented for the overall analyses but not for the subgroup analyses. b Albuminuria percentage is calculated based on number of participants from SUSTAIN 6 with a UACR measurement at baseline (semaglutide, n ¼ 1616; placebo, n ¼ 1622), as these data were not collected in PIONEER 6.
ACKNOWLEDGMENTS The trials included in this analysis, and the analysis reported here, were funded by  Novo Nordisk A/S. The authors thank all the participants, investigators, and trial-site staff. The authors also thank  Vlado Perkovic  for providing input on the interpretation of the analysis and editorial input on the manuscript outline, and  Carola Krause ,  Alexander Jones , and  Flavia Sciota  ( AXON Communications ) for medical writing and editorial assistance (funded by  Novo Nordisk A/S).
2, 3, 5, and 6). For better characterization of the mobile (or chromosomally integrated) elements encoding AMR, we also performed long-read sequencing on 16 XDR S. sonnei isolates (one to two isolates per XDR genomic cluster). The XDR S. sonnei isolates contained one to three plasmids carrying AMR genes (Supplementary Table  3 ). Plasmid sizes ranged from 8379 to 106,936 kb. A small ∼ 8 kb (either non-typable or typed as PTU-E63) plasmid encoding resistance to streptomycin (strA and strB genes), sulfonamides (sul2) and tetracyclines (tet(A)) was found in 12 of 16 XDR isolates from all four genotypes. This plasmid was highly similar to pMHMC-012 (GenBank accession no. CP053763) (Supplementary Fig. 1 ) from a S. sonnei isolate collected from a GBMSM patient in Boston, USA in 2017 (ref. 18 ).
Most of the other AMR plasmids belonged to IncFII (PTU-FE) and were related to pKSR100 (GenBank accession no. LN624486)-an The S. sonnei reference genome 53G (GenBank accession numbers NC_016822) was also included in addition to the 3140 genomes from the French National Reference Center for E. coli, Shigella, and Salmonella (Supplementary Data 1). The circular phylogenetic tree (in blue) is rooted on S. flexneri 2a strain 2457T and its branch length has been shortened by a factor of 100 (indicated by the double slash) to improve vizualisation. S. sonnei lineages L1 to L4, clade 3. 6, and subclade 3. 6. 1 are indicated. The rings show the associated information (see key) for each isolate, according to its position in the phylogeny, from the innermost to the outermost, in the following order: (1) selected S.
sonnei genotypes; (2) antimicrobial susceptibility testing (AST) for ciprofloxacin; (3) AST for azithromycin; (4) AST for thirdgeneration cephalosporins; (5) and the XDR isolates (with the XDR cluster names, X1 to X13, in red). The scale bar indicates the number of substitutions per variable site (SNVs). Due to a deliberate strategy to enrich our genomic dataset with azithromycin-resistant, ciprofloxacin-resistant, and third-generation cephalosporinresistant isolates collected before 2017, the year in which routine genomic surveillance began in France (see "Methods"), there is an overrepresentation of these resistances in this figure with respect to the global population of 7121 isolates studied. epidemic MDR plasmid encoding resistance to AZM with a low fitness cost for its bacterial hostpreviously described in S.
6. 1. 1. 1_CipR. SEA and 3. 6. 1. 1. 2_CipR. MSM5) (Fig. 3 , Supplementary Table  2 , and Supplementary Figs. 2  and 3 ). Some of our ESBL plasmids were also similar to certain plasmids described in previous studies. Hence, the bla CTX-M-27 -carrying p202008564-6 plasmid (cluster X10, genotype 3. 6. 1. 1. 2_CipR. MSM5) displayed 99. 6% nucleotide identity to p893916 (GenBank accession no. NZ_MW396858), from a S. sonnei isolate collected in London, UK, in 2020 (Supplementary Fig. 2 )  20. The second form of the ESBL plasmid found in cluster X10 isolates-and represented by p202008118-4-probably resulted from an insertion sequence (IS)-driven deletion or acquisition of the azithromycin-resistance gene, erm(B) (Supplementary Fig. 2 ). The bla CTX-M-134 -carrying p202000562-4 plasmid (cluster X9, genotype 3.
6. 1. 1. 2_CipR. MSM5) displayed 90. 3% nucleotide identity to p3123885 (GenBank accession no. CP049164), from a S. sonnei isolate acquired in Israel in 2019 (Supplementary Fig. 4 )  9. Finally, the bla CTX-M-3 -carrying p201908234-4 plasmid (cluster X11, genotype 3. 6. 1. 1. 2_CipR. MSM5) displayed 99. 7% nucleotide identity to p711-69 (GenBank accession no. CP049176), from a S. sonnei isolate acquired in Turkey in 2019 (Supplementary Fig. 3 )  9. In two S. sonnei isolates, 201701093 (cluster X1, genotype 3. 6. 1. 1_CipR) and 201908033 (cluster X6, also 3. 6. 1. 1_CipR), the bla CTX-M-15 ESBL gene was not located on a plasmid but on the bacterial chromosome (Fig. 3 , Supplementary Table  3 ). In isolate 201908033, the ESBL gene was part of a ∼ 42 kb genomic island described in Supplementary Fig.
sonnei isolates were received over this time period) belong to genotype 3. 6. 1. 1. 2_CipR. MSM5 and contain the ESBL bla CTX-M-27 gene, suggesting that the epidemic X10 strain has continued its intensive circulation in France for a third year. This European epidemic strain was recently identified in the US and Australia  25. A descriptive epidemiological study of the UK outbreak (72 cases) revealed that 21 of the 27 cases interviewed (78%) were HIV-negative GBMSM and users of HIV pre-exposure prophylaxis (PrEP)  26. These individuals also reported having high-risk sex in England or Europe during the incubation period. Due to the few therapeutic options left, the recommended oral antimicrobial therapy, when required, was either pivmecillinam or Fig. 6  | Acquisition of selected genes encoding resistance to third-generation cephalosporins in our genomic dataset.
Ethics statement This study was based exclusively on bacterial isolates and associated metadata collected under the mandate for laboratory-based surveillance awarded by the French Ministry of Health to the National Reference Center for Escherichia coli, Shigella and Salmonella (NRC-ESS). As a result, neither informed consent nor approval from an ethics committee was required. Data collection and storage by the NRC-ESS was approved by the French National Commission for Data Protection and Liberties ("Commission Nationale Informatique et Libertés (CNIL)"; approval number: 1474659).
25 µg/ 23. 75 µg), streptomycin (STR, 10 µg), amikacin (AKN, 30 µg), gentamicin (GEN, 10 µg), tetracycline (TET, 30 µg), nalidixic acid (NAL, 30 µg), ofloxacin (OFX, 5 µg) or pefloxacin (PEF, 5 µg), ciprofloxacin (CIP, 5 µg), and azithromycin (AZM, 15 µg). Susceptibility to AZM was tested systematically only from April 2014. Resistance to 3GCs was defined as resistance to ceftazidime, cefotaxime, or ceftriaxone. For isolates resistant to NAL, CIP, AZM, or 3GCs, we confirmed the DD data by determining the minimum inhibitory concentrations (MICs) of these drugs with Etest strips (AB Biodisk, Solna, Sweden; bioMérieux, Marcy L'Etoile, France). The Clinical and Laboratory Standards Institute (CLSI) criteria were then used for the final interpretation  31. As a means of distinguishing Shigella isolates susceptible to ciprofloxacin (minimum inhibitory concentration [MIC] ≤ 0.
Total DNA was extracted with the MagNA Pure DNA isolation kit (Roche Molecular Systems, Indianapolis, IN, USA), in accordance with the manufacturer's recommendations. Whole-genome sequencing was performed as part of routine procedures at the FNRC-ESS, and at the Mutualized Platform for Microbiology (P2M) at Institut Pasteur, Paris. The libraries were prepared with the Nextera XT kit (Illumina, San Diego, CA, USA) and sequencing was performed with the NextSeq 500 system (Illumina) generating 150 bp paired-end reads. All reads were filtered with FqCleanER version 21. 06 (https://gitlab. pasteur. fr/ GIPhy/fqCleanER) with options -q 15 -l 50 to eliminate adaptor sequences and discard low-quality reads with phred scores below 15 and a length of less than 50 bp  32. Assemblies were generated with SPAdes version 3.
Phylogenomic analysis We included in the phylogenomic analysis 3140 S. sonnei genomic sequences from isolates and historical strains of the FNRC-ESS (Supplementary Data 1) originating from 3109 S. sonnei isolates collected between 2005 and 2021 and 31 isolated before 2005, to enrich the dataset with rare lineages of S. sonnei (L1, L2, and L4)  6, 8, 10, 16. The paired-end reads were mapped onto the reference genome of S. sonnei 53G (GenBank accession numbers NC_016822)  16  with Snippy version 4. 6. 0/BWA-MEM version 0. 7. 17 (https://github. com/tseemann/ snippy). SNVs were called with Snippy version 4. 6. 0/Freebayes version 1. 3. 2 (https://github. com/tseemann/snippy) under the following constraints: mapping quality of 60, a minimum base quality of 13, a minimum read coverage of 4, and a 75% read concordance at a locus for a variant to be reported.
An alignment of core genome SNVs was produced in Snippy version 4. 6. 0 for phylogenetic inference. Repetitive regions (i. e. , insertion sequences, tRNAs) in the alignment were masked (https://doi. org/10. 26180/5f1a443b19b2f)  15. Putative recombinogenic regions were detected and masked with Gubbins version 3. 2. 0 (ref. 35 ) (default settings, except -f 32). A maximum likelihood (ML) phylogenetic tree was built from an alignment of 59,295 chromosomal SNVs, with RAxML version 8. 2. 12, under the GTR model, with 200 bootstrap values  36. The final tree was rooted on the S. flexneri 2a strain 2457T genome (GenBank accession no. AE014073) and visualized with iTOL version 6 (https://itol. embl. de)  37. We also used the same phylogenetic approach to compare our 164 XDR S. sonnei isolates with 69 previously published XDR (or inferred to be XDR) S.
Resistance gene analysis The presence and type of acquired antibiotic resistance genes (ARGs) were determined with ResFinder version 4. 0. 1 (https://cge. cbs. dtu. dk/ services/ResFinder/)  38  , Sonneityping/Mykrobe version 0. 9. 0 (https:// github. com/katholt/sonneityping)  15, 34 on SPAdes assemblies. The presence of mutations in genes encoding resistance to quinolones (gyrA, parC) was also investigated by analyzing the sequences assembled de novo with BLAST version 2. 2. 26.
23 (ref. 41 ) to generate a high-quality sequence composed of chromosomal and plasmid sequences. The plasmids were then annotated with Prokka version 1. 14. 5 (https://github. com/tseemann/prokka)  42 and corrected manually. Plasmids were aligned and visualized with BRIG version 0. 95 (http://sourceforge. net/projects/brig)  43.
Acknowledgements This study was supported by the  Institut Pasteur  (F. -X. W. and A. F. ), Santé publique France (F. -X. W. ), and the  French government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases ' (grant number  ANR-10-LABX-62-IBEID ) (F. -X. W. ). We thank  C. Parsot  for his expert advice on the virulence plasmid of Shigella spp. We also thank all the corresponding laboratories of the Article https://doi. org/10. 1038/s41467-023-36222-8Nature Communications | (2023) 14:462.
Data availability The publicly available sequences used in this study are available in GenBank under accession numbers CP053763, NC_016822, AE014073, NZ_MW396858, LN624486, CP049176, CP049164, CP049174, CP049186, CP053751. Short-read sequence data generated in this study were submitted to EnteroBase (https://enterobase. warwick. ac. uk/) and to the European Nucleotide Archive (ENA, https://www. ebi. ac. uk/ena/) under study number PRJEB44801. Whole-genome assemblies have been deposited in FigShare (https://doi. org/10. 6084/m9. figshare. 21594033. v1). All the accession numbers of the short-read sequences produced and used in this study are listed in Supplementary Table  2  and Supplementary Data 1. The plasmid sequences obtained were deposited in GenBank (https://www. ncbi. nlm. nih. gov/genbank/) under accession numbers OP038267-OP038301 and OP038303 (Supplementary Table  3 ).
Number of TH positive cells and fraction of these cells containing alpha-synuclein aggregates was quantified. Data from 8-12 animals per group were subsequently compared with one-way ANOVA or Brown-Forsythe ANOVA test, followed by Fisher LSD test or Welch t-test. Results: In multiple static rods test, PFF+SAL mice exhibited increased rotation time as compared to SHAM+SAL group (by 45%, p<0. 05), MK-0677 at 1mg/kg i. p. and 2mg/kg p. o. rescued motor behaviour (p<0. 01 and p<0. 05 respectively). Likewise, travel time was longer for PFF+SAL mice compared to control group (by 23%, p<0. 05) but the effect was again alleviated by MK-0677 at 1mg/kg and 2mg/kg. PFF+SAL group had also decreased number of TH+ neurons in substantia nigra (by 28%, p<0. 05) which was rescued by MK-0677 at 0. 2mg/kg (p<0.
Genomic engineering tools were developed in cell cultures and validated in mouse neurons. All resulting data underwent statistical analysis for normality of distribution and significance test. First, we analyze the time course of global EEF1A and EEF1A2 variant loss in a PDGF-hSyn mouse model overexpressing alpha-synuclein compared to wild type littermates. Several ages from 2 to 42 weeks of age were selected to precisely pinpoint the start of the protein depletion from synapses. Furthermore, studies identifying the EEF1A1 and EEF1A2 phosphorylation status were conducted to more precisely identify functions involved in synucleinopathy as their profile varies depending on the protein variant. EEF1A proteins conduct different roles in the cell. While EEF1A1 is predominantly involved in protein translation functions, the EEF1A2 seems to be more involved in other cellular functions such as actin bundling, autophagy and apoptosis, nuclear export and protein degradation, including misfolded proteins shuttling into aggresome  [2].
Experiments To evaluate our proposed framework, we design two sets of experiments. The first set aims to study the scalability of our proposed algorithms to solve the different job seeker and job provider optimization problems as the number of jobs, the number of platforms and the number of worker groups increase. For such experiments, we rely on purely synthetic data. The second set of experiments aim to qualitatively analyze the solutions provided by our algorithms for the different problems and for that we use semi-synthetic data generated from a real-world online platform. We divide this section as follows. First, we explain how the semi-synthetic dataset (used in qualitative experiments) is generated. We then describe the different experiments (both scalability and qualitative ) and their results for the job seeker problems.
Data Generation To simulate multiple, semi-synthetic platforms, we use the TaskRabbit dataset from  [1] , and generate eight different "worlds" from it using interventions. An intervention is a sampling of the initial dataset's workers such that the sampled "world" matches a specific distribution of protected attributes (in our case either on gender or ethnicity). When generated, each of the obtained worlds is treated as a separate platform. The resulting dataset, consisting of the original TaskRabbit data and the eight new worlds, is saved to files for ease of reuse, and we refer to these nine platforms collectively as the alternative worlds. The worlds world1 to world4 are created based on gender interventions from the original world as follows: world1 has percentages of males and females switched compared to the original; world2 is composed of 50% males and 50% females; world3 is composed of 30% males and 70% females; and finally world4 is composed of 70% males and 30% females.
Unconstrained Problem Scalability Experiments To compare the performance of our two Unconstrained Job Seeker algorithms at various scales, we built a fullysynthetic dataset consisting of 5000 jobs and 70 platforms. Each job in each platform is represented as a file, containing a ranked list of its fictional workers. The number of these workers for each job-platform pair is a random value between 0 and 50. In addition, each worker is assigned values for two protected attributes, also at random. Then, the corresponding index files for the top-k algorithm are built from the generated data. On this new dataset, we run both the naive and topk algorithms we implemented, using increasing values of |J|, |P |, and k on each run 3. To compute fairness values, we use the two metrics defined in  [1] , namely Earth Mover Distance (EMD) and Exposure.
instances as follows. First, we assign to each job in J a set of 2 n -1 = 3 fairness values, selected at random between 1000 and 9999, and a cost value selected at random between 50 and 149. Next, each platform p in P is assigned a budget limit b p , where: b p = 100 × |J| |P | + and where is a random integer between 0 and 49. The point of the above formula is to roughly even out the budget limits across platforms, while still having some fluctuation in the b p values. The goal is then to solve these problem instances using each of the algorithms considered, as well as a generic ILP solver (OR-Tools), while recording each method's solving times and optimality gaps. Starting with exact algorithms, the three methods we are comparing are: -The BB algorithm by Fisher et al. [9] , following the pseudo-code in  [18] ; -The BB algorithm by Karabakal et al.
temporal forecasting blended model A B S T R A C T Islands in tropical regions have high potential for solar energy, but the weather conditions in these areas are complex, with high fluctuations in the amount of sunlight received over time and across different locations, making it difficult to predict solar irradiance accurately. In a preliminary study, two spatio-temporal technics STVAR (spatio-temporal autoregressive) and CMV (cloud motion vector) showing a good predictive performance in literature, were assessed in this challenging environment. The strengths and the weaknesses of different models for different conditions/locations were presented. In this paper, we focus on the validation STVAR/CMV blends for the same satellite-derived irradiance dataset. In a first step, the research of the equation defining the blended model is investigated, highlighting a linear combination of irradiance predicted from CMV and STVAR by least-squares fit, as being optimal.