dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction performances (in terms of average pseudo-R 2 obtained with SVM regression) over all q output variables for the three benchmark datasets versus the number of features included in the regression, d (along the regularization path for the sparse penalty or along the feature ranking, depending on the method). Direct prediction performances with the multivariate lasso are also given when possible. For "Nutrimouse", the red dot corresponds to the features selected by regularized CCA. "Nutrimouse" "Diogenes" "TCGA" increase of availability of Ppe and the fact that government provided support to vulnerable persons were found by both approaches. Similarly, surveillance, the increase of isolation and quarantine facilities and providing international help were also found weakly influential measures by both approaches.
DISCUSSION Overall, the quality of the selection performed with the new method presented in this article is very satisfactory: for the unsupervised case, UKFS obtains results that are far better than the simplest and fastest methods based on scoring and results comparable to methods using an a priori information on a number of cluster. Note that, in real-life applications, this information is usually not available and, as shown in the experiments, performances of such methods are strongly impeded by an incorrect assumption on this hyperparameter. Since UKFS does not require such an a priori knowledge, its use is advantageous is very general situations, where there is no strong a priori on the dataset structure. For the kernel output case, the situation is very similar, with an exception: the very simple Relief method outperforms, on some datasets, the performance of KOKFS but with a performance quality that is very varying from one dataset to the other.
A specific feature of KOKFS could explain both its improved performance compared to HSIC methods (that are also kernel methods) and the varying performance of Relief: in Relief, the features are selected independently, which leads to a strong redundancy when the input dataset has a large number of features (and this drawback impacts less Relief when the number of features is small to moderate). This is the same feature that explains the better performance of UKFS (and the other multivariate unsupervised methods) compared to the simplest score based methods (lapl and SPEC). This explanation is supported by the good tradeoff between performance and redundancy of features obtained for both methods and all datasets for the proposed approach and thus indicates that it is probably best suited when the original number of features is large and that the correlation structure between these features is strong.
On the opposite, multivariate methods ours are able to account for colinearities between features and to reduce redundancy in selected features, especially for datasets with very large dimensions, but they do not scale well. UKFS and KOKFS are particularly sensitive to large sample size (large n as in "TCGA"), as most kernel methods. To a lesser extent, they are also impacted by large dimensions of inputs (large p as in "Carcinom", "Koren", "Diogene" and "TCGA") because of the need to recompute the full kernel at each iteration step (contrary to HSIC methods for instance). As a consequence, the running time is not very good on small to moderate size datasets ("Nutrimouse" and "Diogene"). However, while accounting for the multivariate aspect of output features, the kernel output method is almost insensitive to large dimensions of outputs (large q), because the output kernel is computed only once.
It is thus more efficient for large (input and output) dimensions than multivariate lasso ("Diogene") and is the only multivariate method for which the training was not possible for the large sample size and large dimension dataset ("TCGA"). Note that the running time of another multivariate feature selection method, the Autoencoder, is also very prohibitive (almost 1 hour for the smallest dataset, which is not realistic for computational biology data that are usually large). Note that Autoencoder would scale better if run on GPU (only CPU computations were performed for the sake of fair comparison) but the algorithm presented in this work is based on operations that have already been implemented in GPU for modern frameworks such as Pytorch. Therefore, it would benefit from a similar speedup.
This made it possible for us to film the heads of the mice from above  (15)  and to analyze the amplitude of protraction-retraction for the left and right whiskers together. 3) Dark tunnel: The mice were placed in a small dark tunnel (6 cm long). They remained calm within the tunnel, performing very few head movements, facilitating the recording of very clear whisker movements. Two experimenters blind to group performed computer analyses of the films on D1, D3, D7, D10, D14, D16, D25, D30, and D60 after surgery. Scores were attributed as previously described (  16 ), but with a slight adjustment of the classical scores according to whisker movements in the different devices, based on intermediate scores (Table  3 ). A precise evaluation of protraction movements was performed by video capture system (NCH Videopad software), making it possible to determine the complete amplitude recovery of the protraction.
Measurement of Nasal Deviation A frontal photograph of each animal was taken at baseline, before surgery. Two experimenters then took frontal photographs on the surgery table or in the tunnel, for the animals of each group, on D1, D3, D7, D10, D14, D16, D25, D30, D35, and D38 after surgery. Image J software was used to determine the changes in nasal position as follows (Figure  1 ): a line (the "inter-iris line") was drawn linking the center of the pupil of the right eye and the center of the pupil of the left eye. A second line, perpendicular to the first, was then drawn midway between the irises. A third line was added connecting the outer edges of the two nostrils. The angles between the line perpendicular to the inter-iris line and the line connecting the nostrils were measured.
The number of motoneurons labeled with FG was quantified with Image J software (version 1. 52). We counted the labeled motoneurons in the lateral and intermediate nuclei of each section according to a well-defined protocol: only cells displaying intermediate or intense FG labeling with a diameter of at least 20 µm were counted. Motoneurons were counted from all consecutive sections. Corrections were made for double counting according to the method of Abercrombie  (17). The Neurotrace (NT) counterstaining was used to identify the location of the FG-labeled motoneurons in the various subnuclei of the facial complex (Figure  2 ).
Heterotrophic bacterial counting was performed on samples that were incubated with SYBR Green I (1 × 10 -4 dilution of the commercial stock solution) for 15 min in the dark. In addition, the abundances of picoeukaryotes, Synechococcus, and Prochlorococcus were directly determined by FCM without staining  [33]. For all these samples, a suspension of yellow-green fluorescent beads (1 µm in diameter; Molecular Probes) was added as an internal standard, and all data were analysed with FCS Express V3 software (De Novo Software, http:// www. denovosoftware. com/). The typical scatter plots of each microbial group were shown in Fig. S1  and additional details about the FCM analysis and data treatment can be found in the previous reports  [34, 35].
Subsamples (1 ml) were collected to enumerate the viral abundance at 0, 3, 6, 9, and 12 h of incubation. The lytic and lysogenic VP (viruses ml -1 h -1 ) was estimated through viral accumulation in each 12 h incubation with the VIPCAL online program (http://www. univie. ac. at/nuhag-php/vipcal/)  [39]. Furthermore, the VP R was calculated as VP R %h À1 ð Þ¼ VP viruses ml À1 h À1 ð Þ VA0 100% , where VA 0 represents the viral abundance at 0 h in the VP experiment. The BS was defined as BS ¼ VPðviruses ml À1 h À1 Þ ΔBAðcells ml À1 h À1 Þ , where ΔBA represents the amount of bacterial death determined from the reduction in bacterial abundance per hour during the incubation of the VP experiments, and is calculated by using the equation ΔBA ¼ BAmax 1 ÀBAmin 1 tmin 1 Àtmax 1 þ þ BAmax n ÀBAmin n tmin n Àtmax n =n.
Statistical analysis Using the SPSS (19. 0) software package (SPSS Inc. , Chicago, IL, USA), Pearson's correlation analysis was applied to assess the degree of correlation among the biotic or abiotic parameters investigated, and Student's t-test (paired design) was performed to assess the significant difference in the (lytic and lysogenic) VP R , VD R , BS, FLC and VMM between surface water and transplantation treatments. A p value < 0. 05 was used to indicate statistical significance. In addition, distance-based multivariate analysis for a linear model using forward selection (DISTLM-forward) was used to test the relationships between viral and environmental parameters in Primer 6 software with the PERMANOVA + package (Primer-E, Plymouth, United Kingdom).
ACKNOWLEDGEMENTS The authors wish to thank  Prof. Farooq Azam  of  University of California San Diego  for his constructive reviews to improve the manuscript and  Prof. Jun Sun  of  Tianjin University of Science and Technology  for providing some environmental data. This work was supported by the  National Natural Science Foundation of China  ( 42188102 ,  91951209 ,  41906085 ,  41861144018 ) and the  China Postdoctoral Science Foundation  ( 2019M662236 ,  2020T130066ZX ). Markus Weinbauer  was supported by the international collaborative project on " Marine Biogeochemistry and Ecotoxicology " funded by the  Program of Introducing Talents of Discipline to Universities  ( BP0719030 ).
To characterize the sub-populations of CD4 + T cells in this high-dimensional analysis, we rst mapped our transcriptomic data to a reference human PBMC dataset using Azimuth  41  , thereby delineating 6 subsets of CD4 + T cells (Fig. 1a, b ). To further elucidate the heterogeneity in our dataset, we performed a graph-based clustering analysis using Seurat, which uncovered 16 clusters. Eight of these clusters (Clusters 1 to 8) were enriched in cells annotated as CD4 naïve by Azimuth and expressing genes associated with a naïve CD4 + T cell pro le (e. g. , CCR7 and SELL), 5 (Clusters 10 to 14) were enriched in activated CD4 + T cells (CD69), including one with high NF-kB signaling (Cluster 10; NFKB1). The nal 3 clusters encompassed a mix of naïve and activated cells, including one cluster delineated by viral sensing gene transcripts (Cluster 9; IFIT2, IFIT3), one cluster enriched in Treg cell transcripts (Cluster 15; FOXP3) and another with mitotic cells (Cluster 16; TRBC1) (Extended Data Fig.
Identi cation of Notch pathway genetic variants in MIS-C. To investigate underlying genetic factors that may predispose to MIS-C versus acute pediatric COVID-19, we performed gene-enrichment tests for rare variants (stop-gain/start-loss, frameshift deletions/insertions and canonical splicing mutations) using 8626 pathways from Gene-Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases (8,299 and 327 respectively). We collected genome and exome sequences on 39 MIS-C and 24 acute pediatric COVID-19 subjects, which we compared with 8 different datasets comprising 4,682 exomes collected at the Boston Children's Hospital, including 4 rare disease categories, obesity, myopathy, autism-ADHD and Immune de ciency/dysregulation  42  (see Methods section). All samples were processed using the Variant Explorer (VExP) Pipeline with the same set parameters to avoid bias in the selection of the rare variants  43.
We performed a Fisher test for each group in the different GO and KEGG pathways. Furthermore, we validated these results by Monte-Carlo simulation testing as an unbiased stochastic approach to test for enrichment in genetic variants along individual pathways in the MIS-C group versus the sum total of the clinical comparison groups used for the Fischer tests, as described in the Methods section. Results showed that several in ammation and autoimmunity pathways were signi cantly enriched in rare mutations (≤ 10 in 280,000 chromosomes) in MIS-C versus acute pediatric COVID-19 and the other comparison groups (Fig. 3a, b , Dataset 1). Several of those pathways contained NOTCH related genes. Speci c NOTCH pathway mutations predicted to be damaging and linked to those pathways were identi ed included NOTCH2, NOTCH4 and RPBJL (Dataset 2).
Overall, these results supported the presence of an underlying genetic predisposition for MIS-C. To validate the above ndings from our initial cohort, we screened 88 additional patients with MIS-C from the U. S. multicenter Overcoming COVID-19 Network for mutations in Notch-related genes (see methods in Supplement) 9, 44. Rare damaging mutations were identi ed in NUMB and NUMBL, encoding closely conserved eponymous proteins that negatively regulate Notch receptor signaling and tra cking  36, 45, 46, 47. We further analyzed three mutation in the phosphotyrosine binding (PTB) domain of NUMB (NM_001005745. 1:c. 280C > T; p. Leu94Phe), NUMBL (NM_004756. 5:c. 236G > T,NP_004747. 1:p. Ser79Ile) and NUMBL (NM_004756. 5:c. 262G > A,NP_004747. 1:p. Val88Met) (Fig. 3c  and Dataset 3). These mutations, which were either not found in the Genome Aggregation Database (gnomAD) (NUMB  Leu94Phe  and NUMBL Ser79IIe ; gnomAD = 0) or very rarely so (NUMBL Val88Met ; gnomAD = 4), were predicted to impair NUMB and NUMBL regulatory functions  36, 46, 47.
Acknowledgment We would like to acknowledge  David A. Williams, MD, Lucinda Williams , DNP, RN, PNP,  Myriam Armont, PhD ,  Leah Cheng , MA and other research staff who made major contributions to the Taking on COVID-19  Together Study at Boston Children's Hospital. We would also like to acknowledge the patients and families who participated by contributing samples for these studies. This work was supported by a  National Institutes of Health (NIH)  grants  R01 AI115699 ,  R01 AI065617 , and  R01 AI06561720S1  to T. A. C. ,  NIH   K08 AR073339 ,  NIH   P30 AR070253  and  All Arthritis Grant  from the  Arthritis National Research Foundation  (to L. A. H. ),  NIH   R01DK1300465  to J. C. and  R01AI139633  to R. S. G and by  Istanbul University BAP  Project number  TOA-2020-35899  to G. D. Supported by Grant by  Italian Ministry of University and Research (MUR)   FISR2020IP_04530  to R.
Images were taken every micrometer throughout the whole biofilm depth. For visualization and processing of three-dimensional (3D) images, the ZEN 2. 1 SP1 ZEN software (https://www. zeiss. com/microscopy/int/downloads/zen. html; accessed on 18 July 2022) (Carl Zeiss Microscopy) was used. Quantitative analyses of image stacks were performed using the COMSTAT software (http://www. imageanalysis. dk/; accessed on 18 July 2022)  [47]. At least three image stacks from at least three independent experiments were used for each analysis.
Statistical Analysis The data were statistically analyzed using ordinary one-way ANOVA followed by Dunnett's multiple comparison test (anisotropy assays) or two-samples paired two-sided ttest to calculate p values with GraphPad Prism (GraphPad Prism 9 version 9. 4. 0; GraphPad Software, San Diego, CA, USA). All values were reported and plotted as means with SD or SEM of at least triplicate analyses for each experimental variable. NS = Not Significant; p > 0. 05; * p < 0. 05; ** p < 0. 01; *** p < 0. 001; **** p < 0. 0001.
Phthalates and Substitutes Did Not Deeply Alter the Production by P. aeruginosa of Two Major Virulence Factors We further investigated the effects of phthalates and substitutes on P. aeruginosa virulence by measuring the production of two major virulence factors, i. e. , pyocyanin and pyoverdine. To this aim, P. aeruginosa exposure to phthalates or substitutes was performed at concentrations ranging from 10 -3 to 10 -10 M and pyocyanin production was measured after 24 h of growth in microtiter plates, as described in the materials and methods section. In a first attempt, we assayed its production in M9 succinate medium, and neither P. aeruginosa exposure to phthalates, nor to substitutes, led to pyocyanin production alterations (data not shown). To ascertain these data, an additional assay was performed in King A medium to promote the production of pyocyanin  [54].
sepedonicus and Pectobacterium carotovorum ssp. carotovorum  [30]. Altogether, these data suggest that phthalates or substitutes may have an impact on biofilm formation. We also noticed the presence of bacterial aggregates in liquid cultures upon exposure to DEHP, DINP, DEP, DEHT, TXIB, DOIP, and DIOP, which could originate from altered bacterial surface properties  [75]. Membrane alterations, such as membrane wrinkling, were noticeable mainly upon DBP, TXIB, and DINP treatment. Membrane deformations were also reported for E. coli K12 [76] and P. fluorescens [32] upon DMP exposure. Lactobacillus plantarum displayed a smooth appearance upon DBP treatment  [77] , but no case of breakdown or lysis has ever been reported to our knowledge. Since phthalates are fat-soluble compounds, we suspected that they could interact with the bacterial membrane, which may alter the cell envelope integrity.
Funding: This study was funded by the  French National Research Program for Environmental and Occupational Heath and Safety of the French Agency for Food, Environmental and Occupational Health & Safety ANSES  ( 2018/1/223 , IMPERIAL) and the  European and Regional Development Fund, ERDF RN-BEE (Norman Region , European funds). Acknowledgments: C. D. , B. L. , M. L. C. and M. M. were supported by a training gratification from the  French National Research Program for Environmental and Occupational Health of ANSES  ( 2018/1/223 , IMPERIAL). A. T. was supported by a postdoctoral fellowship from the  French National Research Program for Environmental and Occupational Heath of ANSES  ( 2018/1/223 , IMPERIAL). M. L. was supported by an engineer contract from the  ERDF   RN-BEE research program  ( Région Normandie (France).
This work was funded by the  French National Research Program for Environmental and Occupational Health of ANSES  ( 2018/1/223 , IMPERIAL). The CBSA Lab is supported by the  Région Normandie (France) , the  European FEDER  funds, and  Evreux Portes de Normandie (France). The LBCM is supported by the  Région Bretagne (France)  and  European FEDER.
Each line was created by repeating the same protocol independently starting from the same founder population. This resulted in the creation of four lines with the same origin but with some degree of divergence due to drift. Thereafter, at the third step, individuals from generation 50 (50 males and 50 females within each line) were mated randomly to create progeny which were considered as the base population (G0) for each line of the breeding programme. The simulation analyses were performed using software developed in-house.
METHODS The study population included Hungarian residents aged 18 to 100 years who were registered in the Hungarian-COVID-  19  Registry based on the National Public Health Center (NPHC) and National Health Insurance Fund Manager (NHIFM) database on March 4, 2020, when the first case of Covid-19 infection was detected in Hungary. Exclusion criteria included any inconsistencies in data such as a person receiving two different vaccine types for primary immunization, missing information on the type of the second vaccine dose, first vaccination administered before the first potential date of administration, fewer than 14 days between the first and second doses, a date of diagnosis before the first case was officially reported, or a date of death preceding the date of first vaccination. Fewer than 1,000 cases were excluded for reasons other than age outside the predefined range.
Individuals who died were removed from the study base. The numbers of new registered infections, COVID-19 related hospitalizations, and deaths were registered for each day according to intervention, and individuals experiencing an outcome were removed from the study population of the respective analysis. Person-days for each intervention group were calculated by adding up the number of persons in each group for each day. Age was categorized as 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and 85-100 years. The presence of chronic diseases was identified based on in-and outpatient health service utilization and prescription data from the NHIFM between January 1, 2013 and March 3, 2021. The following chronic conditions were considered: cardiovascular diseases (myocardial infarction, angina, chronic heart failure, peripheral vascular disease, and stroke), diabetes mellitus (type 1 and type 2), immunosuppression (immunosuppressive therapy and transplantation), chronic pulmonary diseases (asthma and chronic obstructive pulmonary diseases), neoplasms, and chronic kidney diseases.
The definitions of chronic diseases are presented in Supplementary Table  1. Incidence rates (number of outcomes divided by person-time of observation) together with their exact 95% confidence intervals (CIs) were calculated for the period between September 13, 2021 and December 31, 2021 (Delta wave). Rate ratios with their exact confidence intervals were obtained considering unvaccinated as the reference category, using STATA (version 16. 1). Mixed effect negative binomial regression model was used to derive adjusted incidence rate ratios (IRRs) with 95% CIs for each outcome adjusted for age, sex, history of different chronic diseases, and calendar day (modelled as a random effect), which is better suited for over-dispersed count data than the traditional Poisson regression. The model is a random intercept model, which allows for different incidence rates in the reference category (i.
(2011)  revisited the explicit link between stochastic partial differential equations (SPDEs) and spatial statistics already established in  Whittle (1954 Whittle ( , 1963)). They introduced a new paradigm for the statistical analysis of spatial data that consists in modelling a spatial variable as the solution of a certain class of SPDE for which the representation on a finite grid presents interesting Markov properties, making it computable for very large grids and capable of handling very large datasets. This approach was based on the observation made in  Whittle (1954 Whittle ( , 1963) )  that, on R d , the solution of the SPDE (κ 2 -∆) α/2 U = W S , (1) is a Gaussian random field whose covariance function is proven to be a Matérn covariance. In (1) α > d/2, ∆ is the usual Laplacian, W S is a Gaussian (spatial) White Noise process and U is the unknown stationary random field.
(2021)  when the vector field B is not constant. In Fig. 2 , we compare the solution of equation (  22 ) in a square domain of R 2 at a fixed time T , with the solution of the standard equation (S) without drift term. In both cases, we start from an initial condition U (x, 0) = 0, and we approach the solution with a finite difference approximation over a bounded domain with Dirichlet boundary conditions, i. e. U (x, t) = 0 on the boundary of the domain. We use an explicit Euler scheme in time (Matlab codes are available in the Open Science Framework repository: https://osf. io/w5utd). The drift term is chosen as follows: B 1 (x 1 , x 2 , t) = 2 x 2 cos(2 π t/10)/ 0. 1 + x 2 1 + x 2 2 , B 2 (x 1 , x 2 , t) = 2 x 1 sin(2 π t/10)/ 0. 1 + x 2 1 + x 2 2. ( 23 ) The other parameter values are given in the legend of Fig.