dataesr/scientific-paragraphs-categorization huggingface dataset:
The estimation was performed by Maximum likelihood estimation using a stochastic approximation EM algorithm implemented in the software Monolix (http://www. lixoft. com). Selection of the compartment effect on parameters (β, δ, P) as well as random effects and covariates on the statistical model (  2 ) was performed by the estimation of several models that were successively compared according to the corrected Bayesian information criterion (BICc) (to be minimized). After the removal of random effect on the viral production ( ) allowing the reduction of the variance on the two other random effects, all combinations of compartment effects were evaluated, leading to the final selection of a single effect on P ( ). Then, the effect of group intervention was independently added on model parameters among β, δ, P and c.
Acknowledgments: We would like to thank  J. Guedj  and  O. Terrier  for fruitful discussions on the model definition. We thank  S. Langlois ,  J. Demilly ,  N. Dhooge ,  P. Le Calvez ,  M. Potier ,  J. M. Robert ,  T. Prot , and  C. Dodan  for the NHP experiments;  L. Bossevot ,  M. Leonec ,  L. Moenne-Loccoz ,  M. Calpin-Lebreau , and  J. Morin  for the RT-qPCR, ELISpot and Luminex assays, and for the preparation of reagents; A-S. Gallouët,  M. Gomez-Pacheco  and  W. Gros  for NHP T-cell assays and flow cytometry;  B. Fert  for her help with the CT scans;  M. Barendji ,  J. Dinh  and  E. Guyon  for the NHP sample processing;  S. Keyser  for the transports organization;  F. Ducancel  and  Y. Gorin  for their help with the logistics and safety management;  I. Mangeot  for here help with resources management and B.
Targat contributed to data management. The monkey and syringe pictures in Fig. 1 was created with BioRender. com. This work was supported b IN R and the Investisse ents d' venir progra ,  Vaccine Research Institute (VRI) , managed by the  ANR  under reference  ANR-10-LABX-77-01. MA has been funded by  INRIA  PhD grant. The  Infectious Disease Models and Innovative Therapies (IDMIT)  research infrastructure is supported b the " rogra e Investisse ents d' venir", anaged b the NR under reference  ANR-11-INBS-0008. The  Fondation Bettencourt Schueller  and the  Region Ilede-France  contributed to the i ple entation of ID IT's facilities and i aging technologies used to define volume of respiratory tract. The NHP study received financial support from  REACTing , the  Fondation pour la Recherche Medicale  (FRM; AM-CoV-Path).
5% frequency) with large effect sizes (OR > 2. 1; rs562281690 near EPHA3 and rs115924439), highlighting the importance of inclusion of common and rare indels, structural and single variants in GWAS. We performed replication with INVENT+MVP VTE association summary statistics (N=42,032 discovery cases)  (Thibord et al. , 2022). All 38 associated loci were tested in the replication dataset (rs187506383 was used as a proxy for rs536995174 [linkage disequilibrium = r 2 >0. 8], the other 37 index variants were available). 35 of the 38 lead SNPs from the discovery meta-analysis were nominally significant in the replication, with 30 significant at a Bonferroni threshold of 1. 4x10 -3. This is significantly more replicated loci than we would expect by chance (binomial test p-value 0. 00024). Five of the 30 replicated lead SNPs are in loci considered to be potentially novel.
All of the variants showed consistent direction of effect between discovery and replication cohorts, with a Pearson's correlation of discovery and replication effect sizes of 0. 93 (95% CI 0. 87, 0. 96) (Supplementary Figure  5 ). Due to a potential overlap of individuals between eMERGE samples in INVENT and BioVU samples in GBMI, we also compared INVENT summary statistics to GBMI summary statistics without BioVU as a sensitivity analysis (Supplementary Figure  5 ) and observed similar results (Pearson's correlation 0. 92, 95% CI 0. 84-0. 96). Additionally, when meta-analyzing the replication summary statistics with leave-BioVU-out summary statistics from GBMI, 35 loci still show a combined P-value of <5x10 -8 (Supplementary Table  4 ). Furthermore, the broad agreement between replication and discovery datasets gives us confidence in the phenotype used for GBMI, despite inclusion of nurse-verified, self reported cases in INVENT+MVP but not in GBMI.
On the X chromosome, common variant p. Thr194Ala in F9 known as F9 Malmö (rs6048)  (Bezemer et al. , 2009)  was associated in our dataset, as well as a variant ~200kb upstream of BCOR (rs3002417). An intronic variant of FUNDC2 (rs17328181) previously nominally associated with VTE (p-value=2x10 -7 )  (Lindström et al. , 2019)  was also associated. FUNDC2 is known to be involved in platelet survival  (Ma et al. , 2019)  and is ~29kb from the 5' end of F8, a gene in which genetic mutations cause Factor VIII deficiency or hemophilia A and thrombosis  (Kamphuisen et al. , 2001). Using our integrative gene prioritization approach we identified eleven genes known to be involved in blood clotting (F2, F5, F9, F7, F10, F11, FGG, PROC, PROCR, PROS1, VWF), including genes known to regulate blood clotting factors from functional analyses but not previously identified by GWAS (e.
Statistical analysis Unless otherwise noted, analysis was performed in R. Packages for data visualization and statistical tests included: isoplotR  (Vermeesch, 2018)  ggplot2  (Wickham, 2016) , MASS(Venables WNRipley), data. table  (Dowle and Srinivasan, 2021) , ggthemes  (Arnold, 2016) , dplyr  (Wickham et al. , 2022). Code is available https://github. com/bnwolford/gbmi_vte.
After laser injury, larvae were anesthetized and individually processed with lysis buffer (10 mM Tris-Cl, pH 8. 0; 2 mM EDTA, 2% Triton X-100, and 100 µg/mL Proteinase K) at 55°C for at least 30 minutes, followed by heat inactivation at 95°C for 5 minutes. Crude lysate was subjected to polymerase chain reaction (PCR) using primers flanking each sgRNA target site to confirm successful genome editing. Statistical analysis was performed with the ggpubr package in R v4. 1. 1. Pairwise Wilcoxon signed rank test was performed for each gene to compare time to occlusion of uninjected controls and sgRNA injected embryos. Injection with no sgRNA was used as a negative control. A sensitivity analysis was performed by pooling uninjected control measurements and comparing this distribution to that of each sgRNA injected gene using Wilcoxon signed rank test.
Acknowledgments The authors acknowledge the biobank participants, recruitment teams and project managers of the  Global Biobank Meta-analysis Initiative  for providing their data for biomedical research, providing data aggregation, management, and distribution services in support of the research reported in this publication (especially  Sinéad Chapman  and  Bethany Klunder ). We acknowledge  BioBank Japan  ( Yukinori Okada ,  Koichi Matsua , and  Masahiro Kanai ),  BioMe  ( Ruth Loos ,  Judy Cho ,  Eimear Kenny ,  Michael Preuss , and  Simon Lee ),  BioVU  ( Nancy Cox  and  Jibril Hirbo ),  Canadian Partnership  for Tomorrow ( Philip Awadalla  and  Marie-Julie Fave ),  China Kadoorie  ( Robin Walters ,  Kuang Lin , and  Iona Millwood ),  Colorado Center for Personalized Medicine  ( Kathleen Barnes ,  Michelle Daya , and  Chris Gignoux ),  deCODE Genetics  ( Kári Stefánsson  and  Unnur  Þorsteinsdóttir),  East London Genes & Health  ( David A van Heel ,  Sarah Finer , and  Richard Trembath ),  Estonian Biobank  ( Andres Metspalu ,  Reedik Mägi ,  Tõnu Esko , and  Priit Palta ),  FinnGen  ( Aarno Palotie ,  Mark Daly ,  Samuli Ripatti ,  Mitja Kurki , and  Juha Karjalainen ), Generation Scotland ( Caroline Hayward  and  Riccardo Marioni ),  HUNT  ( Kristian Hveem ,  Cristen Willer , and  Sarah Graham ,  Ben Brumpton , and  Brooke Wolford ),  Lifelines  ( Serena Sanna  and  Esteban Lopera ),  Michigan Genomics Initiative  ( Sebastian Zoellner ,  Michael Boehnke ,  Lars Fritsche , and  Anita Pandit ), Million Veteran Program ( Christopher J.
In practice, this instrument is used in combination with pattern recognition algorithms for the identification part. Concretely, any application often starts by carrying out several measurements with the targeted compounds. We call this first measurement session 'Session 1'. Each gas sensor generates a time series reflecting the interaction between the sensor and the compound. This time series can be directly used for further analysis or identification  (Skov and Bro, 2005)  but a more common practice is to extract one or two features. One measurement leads then to a vector (by considering all the gas sensors) which is assumed to be characteristic of the compound and which is called a 'signature'. After Session 1, the database of signatures is used to train a classifier which will then predict the identity of future samples.
These versions of X and X 1 are called X ⊥ and X 1,⊥. A good direction would then super-impose X ⊥ and X 1,⊥ , meaning that we would have only R different clusters. A bad direction would likely create additional clusters in the subspace. To check if X ⊥ and X 1,⊥ are superimposed or not, a good figure of merit is the within-class covariance matrix. Note X⊥ the combination of the two datasets X ⊥ and X 1,⊥ : S w = R r=1 n∈r (x n,⊥ -µ r ( X⊥ ))(x n,⊥ -µ r ( X⊥ )) T (6) with µ r ( X⊥ ) the centroid of the class r in the orthogonal subspace, computed from X⊥. S w evaluates how far from each other the samples belonging to a same class are. The greater the within-class covariance is, the more spread-out the classes are. To have only a single number, we take w = Tr(S w ). The lower w is, the better X ⊥ and X 1,⊥ are superimposed.
This environment is scanned by a robot which carries the eNose. By following a predefined path, the robot goes over the VOC sources and the eNose detects them. The path is repeated multiple times to obtain a substantial data set at each measurement session. The setup and the response (after the baseline subtraction) of the instrument after one lap are reported in Figure  4. Temperature and humidity are not controlled during the experiments (temperature variations belong to [20. 4°C, 34. 5°C] and humidity variations to [14. 2%, 40. 3%]) and compounds concentration is neither controlled, nor measured. To have a better idea of the experiments, we encourage the reader to watch the video in footnote 2. In this video, a larger setup is used but the idea remains the same. Data set. The responses of the gas sensors look like peaks due to the very short time injection (∼ 1 sec).
However, some of the sessions have been generated with a similar, but larger, setup containing 12 different compounds in 24 different cups, including the studied compounds 2. For these sessions, we extracted from the data the 3 compounds of interest. In Table  1 , the column R indicates the number of compounds actually present in the session. In Table  1 , we also report some classification results (SVM column). They correspond to the ability of the eNose to tell the 3 compounds apart when the training and the testing are performed in the same session: this corresponds to the 'naive' solution explained in Introduction and we call them intra-session scores. They have been obtained after a dimensionality reduction with PCA using 5 components and after a 5-fold cross validation using a linear SVM.
Artificial data set In Figure  5a  (left), we present a random realization of the performed simulations. In order to cope with the non-linearity that we introduced, we segment X as a series of non-overlapping windows of size ∆ t = 9. For one simulation, it corresponds to a total of 28 time windows. Each time window is considered as a measurement session of 9 × Q samples (Session i) which needs to be drift corrected. We recall that Q, the number of compounds in Session i, varies from 2 to R = 6. We compare the proposed method to 2 other methods: PCA-CC from  Artursson et al. (2000)  working under the Calibrant Scenario and the method of Di Carlo and Falasconi (2012) working under the Blind Scenario. The methods considering the Multi-Session Scenario are not used here since the first sessions will not contain enough information about the future directions (d(t) is rather complex).
From this representation, the drift between Session 1 and the subsequent sessions is clear. If we try to predict Sessions 2 to 15 by training on Session 1 then the score reaches the chance level (33%) after Session 3 (purple line in Figure  6b ). To evaluate the correction methods, we train a linear SVM using only the data from Session 1 and we predict the corrected data from Sessions 2 to 15 (after dimensionality reduction down to 5). EMC 2 and Di Carlo's method directly return the labels ℓ i of Session i so we use them for computing the scores. However, we can also use any other classifier after correcting the data using EMC 2. For all methods, we remove only one component for the comparison to be fair. For the methods working under the Multi-Session Scenario, we use only the two first sessions.
The last method used, namely Di Carlo's method, has the same performance than the Raw method, the method which does not apply any correction. In fact, this method fails to adapt when the data shows a rather 'discontinuous' drift (in comparison to the 'continuous' impression of the simulated drift in Figure  5a left ). In other words, this method fails when a significant drift is observed between two different sessions. This method would likely perform better if sessions had been closer together in time. However, the timing of the sessions is often not controlled in the field and methods have to cope with large changes. Results for Q = 2. In practice, a training set with the R target compounds will be generated during Session 1. However, it is not realistic to expect that the R compounds will be measured every time.
Acknowledgments This work was supported by the  FUI-WISE AAP21   Minalogic  project, funded by  BPI France. This work has been partially supported by  MIAI at Univ. Grenoble Alpes  ( ANR-19-P3IA-0003 ), chair on detection of pollutants, by an  ANR  grant to  Simon Barthelmé  ( GenGP ,  ANR-16-CE23-0008 ) and by the  ANR-18-CE42-0012  OBP-optinose project.
Mixotrophy in aquatic plants: old and new evidence A gap in research We propose several hypotheses to explain the currently limited research on mixotrophy in aquatic plants. First, the belief that plants are purely autotrophic remains dominant in spite of the emerging view of a continuum between auto-and heterotrophy. Second, aquatic autotrophs have been far less studied than terrestrial ones over the past 20 years (a quick search on March 16, 2021, produced 12. 3% entries for 'plant AND physiology AND ecology' with the complementary keyword 'aquatic' on the Web of Science, KJD, Medline, RSCI, and SCIELO databases). Yet, some examples in the following text show pioneering evidence for mixotrophy in aquatic plants. Third, microorganisms, bacteria in particular, are very efficient in absorbing carbon.
Acknowledgments The authors thank the  French Ministry of Higher Education, Research and Innovation , the  University Paul Sabatier -Toulouse III (France) , the  INPT , and the  CNRS  for supporting their research. They gratefully acknowledge  Elaine Hannan  for correcting the English text. They are very grateful to  Kenji Suetsugu , to  Susanne C. Brink , and to an anonymous reviewer for their careful proofreading and suggestions.
Data Collection and Processing The RNA sequencing (RNA-seq) transcriptome data of patients with ccRCC and the corresponding clinical data and mutation profiles were downloaded from The Cancer Genome Atlas (TCGA) database. The validation datasets (GSE53757, GSE40435, GSE29609, and E-MTAB-3267) were included for analysis from the Gene Expression Omnibus (GEO) database and the European Molecular Biology Laboratory  (12) (13) (14) (15). The relative transcriptomic and clinical data of three immunotherapeutic cohorts of patients with ccRCC were obtained from the online supplementary data  (16) (17) (18). RNA-seq data of the immunotherapy cohort of bladder cancer  (19)  and melanoma (PRJEB23709 and phs000452) were collected for testing  (20, 21). The annotated response and mutational data of patients from a discovery cohort receiving ICB treatment from 4 studies were collected and consolidated to study the relationship between mutated genes and immunotherapy  (17, (22) (23) (24).
Single-Cell RNA Sequencing Analysis The association with m 6 A regulators was established by analyzing the genes related to the immune response in the scRNA-seq results of ccRCC  (25). The CellRanger software (version 5. 0. 0) and STAR were used for preprocessing. Principal component analysis (PCA) was run using the "RunPCA" function on the variable genes identified, and the k-nearest neighbor graph was constructed by the "FindNeighbors" function. Uniform manifold approximation and projection (UMAP) was used to visualize single-cell transcriptional profiles and clusters. Marker genes were visualized on UMAP plots using log-normalized counts.
Selection of m 6 A RNA Methylation Regulators Based on previous studies  (5, (27) (28) (29) (30) , 23 m 6 A RNA methylation regulators, namely, ALKBH5, CBLL1, FMR1, IGF2BP1/2/3, FTO, YTHDC1/2, YTHDF1/2/3, HNRNPC, LRPPRC, METTL3/14/16, WTAP, KIAA1429, RBM15/15B, ZC3H13, and HNRNPA2B1, were used for our analysis. Immunohistochemistry (IHC) images of m 6 A regulators have been used in the tissue atlas and pathology atlas panels in the Human Protein Atlas. The protein and gene expression of m 6 A regulators in normal individuals and ccRCC patients were analyzed on University of Alabama Cancer Database (UALCAN) portal.
Immune Infiltration Analysis in RCC CIBERSORT and MCP counter were used to transform the RNA-seq data into the proportion of TIICs. The MCP counter R package was used to evaluate the expression of nine TIICs types. CIBERSORT (https://cibersort. stanford. edu/) was used to quantify the 22 infiltrated immune cells according to normalized gene expression profiles, which included different types of B cells, T cells, NK cells, DC cells, and mast cells. As a verification Abbreviations: ccRCC, clear cell renal cell carcinoma; m 6 A, N6-methyladenosine; MRPRS, m 6 A regulator prognostic risk score; TMB, tumor mutational burden; mRNAs, messenger RNAs; DCs, dendritic cells; TIICs, tumor-infiltrating immune cells; ccRCC, clear cell renal cell carcinoma; TCGA, The Cancer Genome Atlas; GEO, Gene Expression Omnibus; ICB, immune checkpoint blockade; WES, whole-exome sequencing; IHC, immunohistochemistry; scRNA-Seq, single-cell RNA sequencing; PCA, principal component analysis; UMAP, uniform manifold approximation and projection; MsigDB, molecular signature database; TME, tumor microenvironment; ssGSEA, single-sample gene-set enrichment analysis; LASSO, least absolute shrinkage and selection operator; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; MHC, major histocompatibility complex; TCR, T-cell receptor; SNV, single-nucleotide variant; OS, overall survival; PFS, progression-free survival; TLSs, tertiary lymphoid structures; VEGF, vascular endothelial growth factor; mTOR, mammalian target of rapamycin; ESCA, esophageal carcinoma; LUAD, lung adenocarcinoma; PAAD, pancreatic adenocarcinoma; ACC, adrenocortical carcinoma; BLCA, bladder cancer; COAD, colon adenocarcinoma; GBM, glioblastoma multiforme; NSCLC, non-small-cell lung cancer; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain low-grade glioma; LIHC, liver hepatocellular carcinoma; MESO, mesothelioma; READ, rectum adenocarcinoma; SARC, sarcoma; STAD, stomach adenocarcinoma.
Construction and Validation of the m 6 A Gene Signature The significant m 6 A RNA methylation regulators were established by the least absolute shrinkage and selection operator (LASSO) Cox regression (with the penalty parameter estimated by 20-fold crossvalidation). Those regulator genes with minimal lambda were integrated to build an MRPRS, and it was developed according to the expression level using univariate Cox. The "glmnet" package was used to perform the LASSO Cox regression model analysis. The limma R package's empirical Bayesian approach was applied to determine differentially expressed genes (DEGs) between high and low m 6 A scores. The significance criteria for determining DEGs were set as the adjusted p < 0. 05 and |logFC| > 1. Finally, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses using the ClusterProfiler R package based on these DEGs.
Statistical Analysis Statistical tests were carried out using R version 4. 0. 4, SPSS 25. 0 (IBM, NY, USA) and GraphPad Prism 8. 0. The expression levels of the m 6 A RNA regulators were compared with the Mann-Whitney U test in ccRCC versus normal tissues. Survival curves were generated using the Kaplan-Meier method, and the difference was compared with the log-rank test. Pearson correlation coefficient was used to compare the correlation between MRPRS and gene expression values. The "oncoplot" function of the R package "maftools" was used to determine the mutation landscape of the TCGA ccRCC cohort and immunotherapeutic cohort. The high-and low-group was divided based on the optimal cut-off value calculated by the function "surv_cutpoint" in the R package "survminer". All the R package used in this study is listed in Table  S2.
The Expressional Alteration Landscape of m 6 A Regulators in ccRCC Tissues and Cell Clusters On reviewing the literature (5, 27-30), 23 genes were found that mainly regulate m 6 A modification including 9 writers (METTL3, METTL14, METTL16, RBM15, RBM15B, CBLL1, ZC3H13, KIAA1429, and WTAP), 12 readers (FMR1, HNRNPA2B1, HNRNPC, YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, and LRPPRC), and 2 erasers (FTO and ALKBH5). We utilized the bulk TCGA-ccRCC data (529 cases of ccRCC and 74 cases of normal tissues) to analyze the expression of these m 6 A regulators, revealing that 15 out of 23 m 6 A regulators were differentially expressed (Figure  1A ; Table  S3 ), and this phenomenon was also found in two other GEO datasets (GSE53757 and GSE40435) (Figure  S1A ). In addition, the protein levels of these regulators were also evaluated from the IHC results (Figure  S1B ; Table  S4 ).
Construction, Validation, and Immunotherapy Response Evaluation of the m 6 A Regulator Prognostic Risk Score To systematically evaluate these differences in m 6 A regulators, the MRPRS was established by the LASSO Cox algorithm, the 8 regulator genes with minimal lambda were integrated to build an MRPRS (Figures  2A, B ) (Figure  S2A ), and it was developed according to the expression level using univariate Cox (Figure  S2B ). The ccRCC patients in the TCGA database were divided into the high-MRPRS group (N = 134) and the low-MRPRS group (N = 395) based on the optimal cut-off value calculated by the function "surv_cutpoint" in the R package "survminer", and the ccRCC patients in the high-MRPRS group had a significantly shorter overall survival time than that in the low-MRPRS group (p < 0. 001; Figure  2C ).
The prognostic value of MRPRS was also validated in an independent cohort (GSE29609, p = 0. 037; Figure  2D ). We continued to extend the MRPRS signature to 16 other tumor types, such as esophageal carcinoma (ESCA), lung adenocarcinoma (LUAD), and pancreatic adenocarcinoma (PAAD) (Figure  S2C ). These results present that MRPRS is negatively associated with survival outcomes. Next, we investigated the correlation between MRPRS and immunotherapy response in three independent ccRCC cohorts (PMID29301960, PMID32472114, and PMID32895571)  (16, 18, 22) , and found that the MRPRS was significantly higher in the response group than in the non-response group; the high-MRPRS group presented a markedly prolonged survival (Figures  2F-I ). Moreover, the increased MRPRS in the VCAM1 + cell cluster was positively correlated with the patients who experienced complete and mixed responses (Figure  2E ).