dataesr/scientific-paragraphs-categorization huggingface dataset:
b. Reference Generally, the forecast quality is assessed by comparing against observations  (Coelho et al. 2019; Wilks 2019). However, in the absence of a serially complete and spatially coherent observed dataset, reanalysis is used as a reference in forecast verification  (Kalnay 2003). In this study, we use ERA5 reanalysis  (Hersbach et al. 2020)  as reference. ERA5 reanalysis is a fifth-generation high-resolution (hourly output, 31-km horizontal grid spacing) reanalysis produced using 4D-Var data assimilation and the CY41R2 version of the Integrated Forecast System of the ECMWF  (Hersbach et al. 2020). We retrieve ERA5 reanalysis of T2m, zonal and meridional components of 100-m wind speed, and geopotential at 500 hPa on a global grid between January 1979 and January 2021 at the same spatial and temporal resolution as the forecasts.
The data are retrieved from the Climate Data Store of the Copernicus Climate Change Services  (Raoult et al. 2017). The 100-m wind speed and geopotential height are computed as previously described. Although ERA5 reanalysis shows cold biases in representing surface temperature over the Iberian Peninsula and the Mediterranean  (Johannsen et al. 2019) , it represents the means and extremes well over most of Europe (e. g. ,  Simmons et al. 2021; Velikou et al. 2022). Although ERA5 reanalysis severely underestimates the mean winds over complex terrain, it represents the variability of wind speed more realistically compared with other reanalysis datasets over Europe (e. g. ,  Ramon et al. 2019; Jourdier 2020; D örenkämper et al. 2020; Brune et al. 2021; Molina et al. 2021; Murcia et al. 2022). Inspite of the biases, the representation errors of ERA5 reanalysis are small, and hence acceptable for verification  (Ramon et al.
Methodology a. Redundancy analysis RDA is a multivariate statistical technique that attempts to find lower-dimensional patterns of linear dependence between two multivariate datasets (i. e. , between predictor and predictand) maximizing the coefficient of determination of linear regression  (von Storch et al. 1999; Wang and Zwiers 2001; Tippett et al. 2008; Wilks 2014 ). There exist several other methods to find linearly coupled patterns between two multivariate datasets, notably canonical correlation analysis (CCA) and maximum covariance analysis (MCA). RDA, unlike CCA or MCA, is asymmetric in the treatment of two datasets as it identifies one as the predictor and the other as the predictand. This way, the patterns derived from RDA are specifically tailored for use in multilinear regression models: P 5 p 1,1 p 1,2 … p 1,t p 2,1 • … p 2,t • • … • p m,1 • … p m,t ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ and Q 5 q 1,1 q 1,2 … q 1,t q 2,1 • … q 2,t • • … • q n,1 • … q n,t ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠.
We observed that using lagging 15-year climatology (i. e. , the most recent 15-yr period as climatology) performs better relative to using lagging 20-yr climatology (i. e. , the most recent 20-yr period) in alleviating cold biases in temperature forecasts that are derived from the ongoing climate warming (not shown). This observation is consistent with the ones previously seen in the literature (e. g. ,  Wilks 2013; Wilks and Livezey 2013; Wilks 2014). Therefore, we compute Z500, T2m, and U100 anomalies by removing lagging 15-year mean climatology from the observed weekly mean. The climatological data used to compute anomalies correspond to the same week and month of the year as the observation. Although U100 shows no particular trend, we retain the 15-yr period for computing U100 anomalies to make the inter-variable comparison consistent.
D. This work contributes to the Energy4Climate Interdisciplinary Centre (E4C) of Institut Polytechnique de Paris and Ecole des Ponts ParisTech, supported by 3rd Programme d'Investissements d'Avenir (ANR-18-EUR-0006-02). The authors acknowledge the data center Ensemble de services pour la recherche à l'Institut Pierre-Simon Laplace (ESPRI) for their help in storing and accessing the data. Finally, the authors thank the editor and the two reviewers for carefully reviewing the work. Data availability statement. The archived ECMWF extended-range forecasts and reforecasts are published under Creative Commons Attribution 4. 0 International (CC BY 4. 0). However, a data access fee may be applicable. For further information, kindly refer to https://www. ecmwf. int/. The ECMWF ERA5 reanalysis data are publicly available and can be accessed through the Climate Data Store of the Copernicus Climate Change Services upon registration.
Forecast Bias Adjustment The bias-adjusted ensemble member x * k for any forecast at a given lead time is The Wilcoxon signed-rank test is a nonparametric hypothesis test that is used to investigate whether the considered data samples are derived from the same population or generating process  (Wilcoxon 1945; Conover 1971; Wilks 2019 ). The test assesses for possible differences in location (i. e. , rank) between members of a paired dataset. Here, the test statistic is based on ranks rather than numerical values of the data. In this study, we consider a total of 103 forecasts initialized in the boreal winter between December 2016 and February 2020. We define the null hypothesis as "there is no difference in fair-CRPSS between dynamical and hybrid predictions," and the alternate hypothesis as "hybrid predictions have higher fair-CRPSS than dynamical predictions.
, 1992). The effect of optogenetic stimulations on targeted GoCs are recorded in loose-cellattached (holding at 0 mV) with 3-4 M electrodes filled with an intrapipette solution containing (in mM): 140 NaCl, 2. 34 KCl, 1. 25 NaH2PO4, 10 HEPES, 1. 3 CaCl2, 1. 1 MgCl2, pH adjusted to 7. 4 with 1 M NaOH. For all experiments, GrCs are recorded in whole cell configuration and voltage clamp mode (holding at -70 mV) with 7-8 M electrodes filled with an intracellular solution containing (in mM): 110 CsCl, 20 TEA-Cl, 10 HEPES, 6 NaCl, 10 EGTA, 0. 2 CaCl2, 4 ATP-Mg, 0. 4 GTP-Na, pH adjusted to 7. 4 with CsOH at 1 M. The data were acquired with an EPC10 amplifier (HEKA), sampled at 20 KHz and filtered at 8 or 3 KHz.
Statistics The statistics were made with the SciPy library available on Python  (Virtanen et al. , 2020). The data are presented as mean ± SD unless otherwise stated in the text. All population difference significance was assessed by a bilateral non-parametric Mann-Whitney ranked test due to the small size of our groups and their no monotone distribution. Paired comparisons were calculated by a non-parametric Wilcoxon bilateral test when appropriated. All correlations have been calculated with the Spearman method and the corresponding two-sided p-value were calculated with a t statistic. When correlations are significant, they are represented with the linear regression on the figure and the slope is reported in the text. The significance of the variance difference between GlyT2(+) and GlyT2(-) pairs have been tested by running a Conover test of equal variance (Wolfram function on Mathematica 12).
Acknowledgments We thank  Boris Barbour ,  Clément Léna ,  Johnathan Bradley ,  Mariano Casado , and  Vincent Villette  for discussion on the manuscript and suggestion for the data analysis. DD was financed by a  MESRI Ph. D. fellowship, Labex memolife  and  ANR  grant  Glubrain3A. This study was supported by the grants Equipe  FRM DEQ20140329498  and  ANR-17-CE16-014-03 GluBrain3A , by  CNRS  and by  INSERM. We gratefully acknowledge the  IBENS imaging facility (IMACHEM-IBiSA ), member of the  French National Research Infrastructure France-BioImaging  ( ANR-10-INBS-04 ), which received support from the  "Fédération pour la Recherche sur le Cerveau-Rotary International France " (2011) and from the  program « Investissements d'Avenir »   ANR-10-LABX-54   MEMOLIFE. We also thank the teams of the animal facility and acute transgenesis facility (PFL2) of IBENS.
Data Acquisition The experimental X-ray absorption spectra presented here were collected in the scope of a community proposal as the first user-beamtime at the SCS instrument. The intensities of the two beams generated by the beam-splitting zone plate were recorded using a FastCCD detector  34  with 1920×960 pixels of 30×30 µm 2. The intensities of both beams were integrated over a region of interest (ROI) corresponding to 350×350 pixels each to retrieve the signal and reference intensities for each FEL shot. The high beam divergence due to the zone plate focusing distributed the signal on a 4 mm wide square on the detector 1 m downstream of the sample, thus greatly decreasing the fluence incident per detector area in order to avoid detector saturation. The gas-attenuator was filled with varying low pressure of nitrogen gas to regulate the transmission through the beamline to the required fluence.
Since a full intensity profile could not be measured from the shot craters, the concept of the effective area of the focal size is used. This area connects the peak fluence F 0 with the overall pulse energy E pulse , i. e. , A e f f = E pulse F 0 , (B1) and it can be defined for an arbitrary spot profile. To characterize the effective focal size, the reference membranes were analyzed using SEM. Fig. 12  shows two SEM images: one overview image of an entire membrane and one example of a high-resolution image of a single imprint. Such high-resolution images were taken of 85 selected spots which were associated with the corresponding FEL photon diagnostics data by matching the reconstructed movement of the sample stage to the pattern of imprints on the sample. The reference membranes were chosen for the imprint analysis since their X-ray absorbance can be considered constant for the scanned photon energies.
ACKNOWLEDGMENTS We acknowledge European XFEL in Schenefeld, Germany, for provision of X-ray free-electron laser beamtime at the SCS instrument and would like to thank the staff for their assistance. Funding by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) -Project -ID 278162697 -SFB 1242  and the  Helmholtz Association  (grant  VH-NG-1105 ) is gratefully acknowledged. Access to Synchrotron SOLEIL through proposal ID  20160880  for characterization of static properties of the Ni films is acknowledged. Parts of this work were funded by the  Swiss National Science Foundation  (Grant No. PZ00P2-179944 ).
cdc. gov/poxvirus/mpox/response/ 2022/demographics-TPOXX. html  March 8, 2023). Anecdotal evidence suggests good efficacy in humans with monkeypox, but placebo controlled human clinical trials are ongoing in the United States (STOMP trial, https://actgnetwork. org/studies/a5418study-of-tecovirimat-for-human-monkeypox-virus-stomp/), in the Democratic Republic of Congo (https://clinicaltrials. gov/ct2/ show/record/NCT05559099), and in Brazil https://clinicaltrials. gov/ct2/show/NCT05597735?term=tecovirimat&cond=mon-keypox&draw=2&rank=4. Another drug approved for smallpox that can be used against MPXV is Brincidofovir, (Tembexa), a lipid-conjugated prodrug of cidofovir, an analog of 2 0 -deoxycytidine that inhibits the viral DNA polymerase  [64] [65] [66] [67]. However, liver function needs to be monitored carefully in person receiving this drug, especially when coadministered with HIV drugs cobicistat and fostemsavir.
Introduction. 00 2. Diagnostics and surveillance. 00 3. Vaccines. 00 4. Therapeutics. 00 5. Action Plan: What needs to be done. 00 6. Conclusions. 00 Declaration of Competing Interest. 00 and > 3,000 each in Germany, the UK, France, Columbia, Mexico, and Peru, with 110 countries reporting cases (including 99 nonendemic countries). There have been 112 deaths recorded as of March 28, 2023, and the infections are continuing globally with 28 countries reporting cases in the preceding 21 days. Deaths have been associated with encephalitis, uncontrolled infections in patients with advanced HIV, septic shock, and other comorbidities. It is possible that co-infection with various other viruses might also exacerbate the disease. Complete genome sequences from over 1,400 samples have been deposited in open access public databases (e.
1. To our knowledge a polymorphism at this position has not previously been described. This nucleotide change was confirmed in another laboratory using next-generation sequencing (AllType NGS reagent, One Lambda Inc. , Canoga Park, California) performed on the Ion S5 platform (Thermo Fisher Scientific, Waltham, Massachusetts) and the reads were analyzed using the TypeStream Visual Software version 3. 0 (One Lambda Inc. , Canoga Park, California). The nucleotide sequence has been submitted to GenBank (accession number: OQ148664) and in the IPD-IMGT/HLA Database (Submission number: 10064765). The name C*03:632 has been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System in January 2023. This follows the agreed policy that, subject to the conditions stated in the most recent Nomenclature Report 2 names will be assigned to new sequences as they are identified.
Clinical data on the patients including age, sex, disease duration, expanded disability status scale (EDSS), and multiple sclerosis severity score (MSSS) were registered. All had a baseline cerebral MRI scan, and a follow-up scan 2 years later to evaluate radiological disease activity by number of new white matter lesions in the brain  [4]. A relapse was defined according to the 2017 McDonald diagnostic criteria  [28]. Clinical disease activity at baseline was evaluated by number of relapses 1 year prior to the baseline visit and was dichotomized as clinically not active (CNA, no relapse) or clinically active (CA, one relapse or more). Number of relapses, worsening in EDSS, new white matter lesions on MRI, and NEDA-3 (no evidence of disease activity, i. e. , no relapses, no new/ enlarging white matter MRI lesions and stable EDSS) were monitored during the follow-up period of 2 years.
Measurements were performed using a BD Fortessa LSRII flow cytometer (BD Biosciences), and data were acquired using BD FACSDi-VaTM software. A threshold value of 200 was applied on the FITC (530/30 nm) channel. Fluorescence intensity at green (530/30 nm, FITC), blue (450/50 nm, Pacific Blue), yellow (575/26 nm, PE), and red (695/40 nm, PerCP-Cy5-5) fluorescence channels as well as forward-and side-scattered (FSC and SSC) light intensities were collected. Measurements were performed at a pre-set flow rate of 0. 5 μL/s. Data were processed in R using flowcore package in R Studio. Fixed gating strategy separated the microbial fluorescent events from the fecal sample background (Additional file 2: Fig. S1 ). Individual bacterial cell counts are given in Additional file 1: Table  S2.
Fecal water content estimation For estimating fecal water content in stools from multiple sclerosis cases, frozen feces samples were weighed before and after freeze-drying. Freeze-drying included a primary drying performed at 0. 1hPa and 23°C for 17h and a secondary drying at 0. 05 hPa and 23°C for 3h (CoolSafe touch 15L, LaboGene, Lilleroed, Denmark). Individual data for fecal water content is given in Additional file 1: Table  S2.
Gene count table To construct a gene count table, METEOR software was used  [32] : first, reads were filtered for low quality by AlienTrimmer  [33]. Reads that aligned human genome (identity > 95%) were also discarded. Remaining reads were mapped onto the Integrated Gut Catalogue 2 (IGC2)  [34] , comprising 10. 4 million of genes, using Bowtie2  [35]. The unique mapped reads (reads mapped to a unique gene in the catalogue) were attributed to their corresponding genes. Then, the shared reads (reads that mapped with the same alignment score to multiple genes in the catalogue) were attributed according to the ratio of their unique mapping counts of the captured genes. The resulting count table was further processed using the R package MetaOMineR v1. 31  [36]. It was downsized at 12 million mapped reads to take into account differences in sequencing depth and in mapping rate across samples.
Predicted functional modules of gut bacteriome Three databases were used to predict gene functions: Kyoto Encyclopedia of Genes and Genomes (KEGG)  [39] , eggNOG  [40] , and TIGRFAM  [41]. Genes from the IGC2 catalogue were mapped with diamond  [42]  onto KEGG orthologs (KO) from the KEGG database (version 8. 9). Each gene was assigned to the best-ranked KEGG orthologs (KO) among hits with e-value < 10e-05 and bit score > 60. The same procedure was used with eggNOG (version 3. 0). The gene catalogue was searched against TIGRFAM profiles (version 15. 0) using HMMER 3. 2. 1  [43]. Then we assessed presence of KEGG modules, gut metabolic modules (GMMs)  [44]  and gut-brain modules (GBMs)  [45]  in an MGS. A functional module consists in an ensemble of KOs (or NOGs, or TIGRFAMs). Since MGS are pangenomes, their genes are divided into "core" genes (which are present in all samples harboring the MGS) or "accessory" (which might be absent from a sample even if the MGS is detected).
Analyses of viral gut microbiota The viral gut microbiota was analyzed using MiCoP  [46] , as this method is optimized to call viruses directly from the bulk metagenomics sequencing reads. As a reference dataset, MiCoP draws upon the NCBI's RefSeq Viral database  [47]. We identified a total of 150 viral species with prevalence of > 10% and relative abundance of > 0. 01% for 296 (148 multiple sclerosis cases versus 148 HC) individuals included in the dataset.
Statistical analysis applied in analyses of bacterial and viral gut microbiota All statistical analysis were performed with R v3. 6. 0  [48]. Contrasts in MGS or functional modules abundances were performed using Mann-Whitney test if two groups and Kruskal-Wallis if more than two groups. Correlations between variables (either metagenomics variables or clinical variables) were performed using Spearman's correlations. All p-values were corrected for multiple testing with the Benjamini-Hochberg method. Unless stated otherwise, a corrected p-value (q-value) is assessed as significant when under the threshold of 0. 1. Effect size was computed as the Cliff 's Delta (CD) using the package effsize v0. 7. 4  [49]. Bray-Curtis dissimilarity was computed on the log-10 transformed MGS table with the package vegan v2.
5. 7  [50]. Principal coordinates analysis (PCoA) was performed on the Bray-Curtis dissimilarity with the package ade4 v1. 7. 16  [51]. Bray-Curtis dissimilarity variance between groups was then analyzed by PERMANOVA with the function adonis from the package vegan. Covariates deconfounding was performed on each metagenomics feature with the R package metadecon-foundR v0. 1. 5 [52, 53]. Covariates included status (multiple sclerosis cases or HC, CA, or CNA, respectively), BMI, age, sex, fecal water content, and medication. When a metagenomics feature is significantly associated with at least two covariates, these covariates can be strictly deconfounded, ambiguously deconfounded, or confounded  [52]. For viral gut microbiota analysis only, differences in abundance were detected using Microbiome Multivariable Association with Linear Models (MaAslin2)  [54]  and corrected for multiple testing by Benjamini-Hochberg method.
Contrasted bacterial taxa and predicted functional modules of the bacterial gut microbiota in multiple sclerosis cases and healthy controls Both gut bacterial gene richness and metagenomic species (MGS, hereafter termed species) richness were similar in cases and HC (Additional file 2: Fig. S3A-B ). Global bacterial microbiota composition (beta diversity) was different between cases and HC (P < 0. 001, PERMANOVA, Additional file 2: Fig. S3C ). Removal of current and former smokers from analyses did not change the result (Additional file 2: Fig. S4 ). Intriguingly, when computing pairwise PERMANOVA between treatment-based subgroups of cases and HC, we found that each subgroup was significantly different from HC apart from the treatment-naïve cases (P < 0. 05, Additional file 1: Table  S4 , Additional file 2: Fig.
The HC-related species included Haemophilus parainfluenzae, Veillonella rogosae, Victivallis vadensis, Bifidobacterium angulatum, and Streptococcus australis. Most multiple sclerosis-related species (65%) were inversely correlated with species richness, while most HC-related species (87%) were positively correlated with richness when considering the total cohort (n = 296 individuals), or specific subgroups (HC, cases or treatment-naïve cases, respectively) (Additional file 1: Table  S5 , Additional file 2: Fig. S6 ). We analyzed the predicted functional modules of the bacteriome issued from three databases: gut-brain modules (GBM), gut metabolic modules (GMM), and KEGG modules. Comparing cases and HC, we found one, five, and zero contrasted modules, respectively, all  more abundant in cases (Fig.
Alterations of the viral gut microbiota in multiple sclerosis Among the gut viral orders, Caudovirales bacteriophages dominated the viral gut microbiota in both cases and HC (Fig. 5A  and Additional file 2: Fig. S16A ). The same viral order significantly differed between the two groups (Fig. 5B ) and was also associated with treatment (Additional file 2: Fig. S16B ). To explore a potential aberration of the viral gut microbiota in cases versus HC, we tested the viral alpha diversity indices. We found a significantly higher Shannon diversity of viral species (P = 0. 022, Fig. 5C ) and slightly lower viral Chao1 richness (P = 0. 058, Additional file 2: Fig. S16C ) in cases compared to HC. In addition, we examined the beta diversity of gut viral species by principal coordinates analysis based on the Bray-Curtis distance between individual viromes of the dataset.
009, Additional file 1: Table  S7 ). Only one viral gut microbiota feature differed between cases and HC (Fig. 5E  and Additional file 1: Table  S8 ). This feature is annotated as Enterococcus phage EFC-1, the abundance of which was lower in cases and inversely associated with drug treatment (Fig. 5F ) but positively linked to the blood cell gene expression of pro-inflammatory markers including IL1B and TNF-α (Fig. 5G ). This bacteriophage is reported as a lytic or temperate phage to its bacterial host, Enterococcus faecalis, with a prevalence lower than 10% in our dataset (Additional file 2: Fig. S16G ). Of interest, multiple Lactococcus viruses were inversely linked to inflammatory markers expressed in blood cells (Fig. 5G ). Another bacteriophage, Enterobacteria phage cdtI, was positively associated with multiple sclerosis treatment, whereas the abundance of its host bacterium Escherichia coli was not altered in treated cases (Additional file 2: Fig.
Availability of data and materials Sequencing reads filtered for low quality and human contamination are available from ENA: PRJEB51635 (https:// www. ebi. ac. uk/ ena/ brows er/ view/ PRJEB 51635)  [69]  for multiple sclerosis cases, PRJEB41786 (https:// www. ebi. ac. uk/ ena/ brows er/ view/ PRJEB 41786)  [70]  for the CBMR study and PRJEB41787 (https:// www. ebi. ac. uk/ ena/ brows er/ view/ PRJEB 41787)  [71]  for the DanFunD study. Genes sequences of the IGC2 as well as MGS definition and taxonomy are available from Data INRAe (https:// doi. org/ 10. 15454/ FLANUP)  [38].
Data extraction The first and second authors independently extracted the data from five randomly sampled articles  (Mackey, 1993; Mealey & Mackey, 1990; Sparks, 2016; Valente, 2015; Wallner, Fieder, & Seidler, 2012) , and checked that the instructions led to identical extractions. Then, the first author extracted the information from the remaining articles. For each selected article, we collected: title, date of publication, author list, and journal. We then extracted every statistical test of the Trivers-Willard hypothesis within each article. Whenever a test was not clearly related to the prediction, it was not included. For example, if there was a list or table of p-values, and, at the end, the authors concluded with "overall, our results support the Trivers-Willard hypothesis", those p-values could not be included in the database because it was unclear which ones were relevant according to the coders.