dataesr/scientific-paragraphs-categorization huggingface dataset:
For further developments, it appears necessary to:  Extend the dataset since the samples are usually heterogenous. Indeed, it would be desirable to integrate more samples to obtain a better representativeness. In addition, the dataset seems not large enough to perform other classification algorithms as the logistic-regression one, which would be useful to compare both algorithms and to conclude on the best method to use.  Define more precisely the discriminant power of each of the magnetic parameters because the prediction accuracy is highly impacted by noisy data or outliers  (Rhys, 2020). Finally, it appears that we need to test more sophisticated algorithms which provide a way to include non-available values for some parameters (NA). For now, the model is not able to deal with missing data.
Some of these clinical trials are highlighted below. Phase 1/2 clinical trial NCT03131908 is studying the combination of PD-1 inhibitor pembrolizumab and PI3Kβ inhibitor GSK2636771 in PD-1 refractory patients with metastatic melanoma and PTEN loss  [431]. Besides, phase 1/2 clinical trial NCT04688658 is focussing on the combination of PD-1 inhibitor nivolumab and PI3Kδγ inhibitor duvelisib in patients with unresectable melanoma that have already progressed on PD-1 therapy, with clinical response in at least one patient in preliminary results  [432]. Moreover, phase 2 clinical trial NCT03484819 is evaluating the combination of nivolumab and PI3Kαδ inhibitor copanlisib in relapsed/refractory diffuse large B-cell lymphoma or primary mediastinal large B-cell lymphoma. This clinical study is also trying to characterize the effects of this combination on the tumor microenvironment and immune response  [433].
Acknowledgements H. E. is supported by a  NUS Medicine   Postdoctoral Fellowship  funded by the  Ministry of Education (MOE)  and the  Social Sciences Research Council (SSRC) of Singapore. D. B. L. T. is supported by grants from the  Singapore National Medical Council  ( A-0006179-00-00-NMRC OF YIRG ) and from the  Singapore Ministry of Education  ( A-0006237-00-00-MOE GAP  Funding). BG is supported by the  Medecin-Chercheur Parrainage program of Gustave Roussy Cancer Center. The mTOR inhibitor research in the laboratory of D. A. F. is supported by a  Reach Grant  from  Alex's Lemonade Stand Foundation.
e. , venlafaxine-d6 diluted in ACN, 10 ng/mL) was spiked in each sample. The sample was then vortexed for 10 min and extracted twice with 1. 8 mL MTBE. Following the centrifugation step, the organic layers were evaporated under gentle steam of nitrogen at 35 • C and dry extracts were dissolved in H 2 O/MeOH (50/50, v/v). A volume of 10 µL was injected into the LC-HRMS analytical system. Analytical conditions: The LC-HRMS system was based on the Vanquish ® LC pump and autosampler coupled to a QExactive Focus ® mass spectrometer equipped with a heated electrospray ionization (HESI) probe operating in polarity switching mode (ThermoFisher Scientific, Bremen, Germany). The analytical system was managed using TraceFinder ® 4. 0 software (ThermoFisher Scientific). LC was performed on an Accucore ® Phenyl Hexyl (100 × 2.
Nitrogen was used for spray stabilization, for collision-induced dissociation experiments in the HCD cell, and as the damping gas in the C-trap. The automatic gain control target was fixed to 1E5 for MS/MS experiments. Transient time was fixed to 120 and 50 ms for full MS and MS/MS scans, respectively. The mass spectrometer was calibrated in the positive and negative modes twice weekly prior to analyses. Data were acquired in full-scan in data-dependent MS2 (ddMS2) mode. In this mode, a full-scan acquisition was performed at a resolution of 70,000 (at m/z 200) between m/z 100 and 1000, followed by the acquisition of MS/MS spectra, at a resolution of 17,500 with an isolation window of m/z 2. The acquisition of MS/MS spectra was based on a list of ~1500 compounds including venlafaxine, O-desmethyl-venlafaxine, N-desmethyl-venlafaxine, and the internal standard venlafaxine d6.
Acquisition of MS and MS/MS data was managed using Thermo Scientific TraceFinder ® software. Data processing: for the targeted quantitative analyses, the data processing step was performed using TraceFinder ® software to generate the calibration curve and to quantify venlafaxine, O-desmethyl-venlafaxine, and N-desmethyl-venlafaxine. Pharmacokinetic analyses: The plasma concentration-time profile of venlafaxine and its two main metabolites (O-desmethyl-venlafaxine and N-desmethyl-venlafaxine) were analyzed using a standard non-compartmental method (Phoenix WinNonlin ® software version 8. 3, Certara USA, Inc. , Princeton, NJ, USA). The peak plasma concentration (C max ) and time to peak plasma concentration (T max ) for each compound were determined directly from measured data. The area under the plasma concentration-time curve (AUC last ) was calculated using measured data points from the time of administration to the time of the last quantifiable concentration (C last ) via a linear trapezoidal rule.
0™  [34, 35]. Data processing was performed using MZmine 2. 53 as previously described  [36, 37]. Briefly, MS and MS/MS spectral data were extracted using a mass detection noise level set at 1E5 and 5E3, respectively. Then, extracted ion chromatograms were systematically built using the ADAP algorithms (minimum group size of 4 scans, a group intensity threshold of 500,000, and an m/z tolerance of 10 ppm)  [38]. The ADAP wavelets chromatogram deconvolution algorithm was then applied and set at the following parameters: signal-to-noise ratio: 6; coefficient/area ratio: 40; peak duration range: 0. 05-1. 5 min; and retention time wavelet range: 0. 0-0. 15 min. De-isotope chromatograms were grouped using the isotopic peaks grouper algorithm set at m/z and t R tolerances of 10 ppm and 0. 1 min, respectively.
The peak alignment of samples was then achieved using the join aligner method with parameters set at m/z and t R tolerances of 10 ppm and 0. 15 min, respectively. MS/MS scans were associated with the corresponding MS scans using an m/z and t R tolerance of 10 ppm and 0. 15 min, respectively. The gap-filling process was performed on the peak list using the so-called module "same RT and m/z range gap filler" with m/z tolerance of 10 ppm. These data processing steps led to a matrix listing the peak intensities for each analyzed sample associated with a unique m/z and retention time value. It also generated a file (. mgf format) containing the MS/MS spectra data, allowing us to generate the molecular network. Molecular network generation: The MNs were generated using the feature-based molecular networking workflow of the Global Natural Products Social platform and using MetGem software  [26, 39].
MNs were built using LC-MS/MS data obtained together from all analyzed plasma samples. The following settings were used to build the network: minimum pairs Cos: >0. 60; parent ion mass tolerance: 0. 02 Da; fragment ion mass tolerance: 0. 02; network topK: <150; minimum matched peaks: 4; and minimum cluster size: 2. The library spectra inquiries were performed using the same parameter values as those defined for the network building. The MNs were finally visualized and annotated using Cytoscape 3. 4. 0™ software (San Diego, CA, USA)  [40]. Identification of exogenous and endogenous metabolites: The dereplication step of endogenous and exogenous metabolites was obtained using the GNPS database library and an in-house database. Furthermore, the structure assignment of venlafaxine and its metabolites as well as endogenous plasma metabolites was based on MS and MS/MS data, using a tolerance window of 5 and 15 ppm, respectively.
Pharmacokinetics of Venlafaxine, O-desmethyl-venlafaxine, and N-desmethyl-venlafaxine Metabolites Venlafaxine and its demethylated metabolites O-desmethyl-venlafaxine and N-desmethylvenlafaxine were quantitatively determined in the 26 available plasma samples of the patient over a 25-day period (Figure  2 ). Identification of exogenous and endogenous metabolites: The dereplication step of endogenous and exogenous metabolites was obtained using the GNPS database library and an in-house database. Furthermore, the structure assignment of venlafaxine and its metabolites as well as endogenous plasma metabolites was based on MS and MS/MS data, using a tolerance window of 5 and 15 ppm, respectively. Annotations were supported by experimental chromatographic retention time (tR) values. SIRIUS 5. 4 was used to support molecular formula determination and metabolite identification  [41].
On the same MS/MS spectra, the product ion at m/z 121. 0650 allowed for the identification of the methoxyphenyl radical of venlafaxine. Based on the MS/MS spectra inspection, the peak detected at t R of 2. 70 min in the ion chromatogram at m/z 294. 2064 was identified as the hydroxylation of the cyclohexyl radical of venlafaxine. Sixteen venlafaxine metabolites have been annotated using LC-HRMS data acquired in both ESI positive and negative ion modes, including six phase-I and ten phase-II metabolites. The analytical characteristics of these metabolites including the retention times and the exact mass of parent and diagnostic product ions are listed in Supplementary Table  S1. For each metabolite, the annotation level was reported according to standard metabolomic initiative guidelines. Phase-II metabolites were also detected in the negative ion mode and corresponding MN.
Effects of Venlafaxine on Plasma Endogenous Metabolites Besides venlafaxine and its sixteen identified metabolites, untargeted analysis data allowed for the detection of 214 various endogenous metabolites including amino acids, dipeptides, organic acids, organic amines, vitamins, and lipids. The annotation step was performed by querying in-house and GNPS database libraries. The proposed metabolite annotation was manually inspected and validated. Correlations between plasma concentrations of annotated metabolites and venlafaxine were negative for 77 compounds and positive for 50 other compounds (Supplementary Table  S2 ).
Effects of Venlafaxine on Plasma Endogenous Metabolites Besides venlafaxine and its sixteen identified metabolites, untargeted analysis data allowed for the detection of 214 various endogenous metabolites including amino acids, dipeptides, organic acids, organic amines, vitamins, and lipids. The annotation step was performed by querying in-house and GNPS database libraries. The proposed metabolite annotation was manually inspected and validated. Correlations between plasma concentrations of annotated metabolites and venlafaxine were negative for 77 compounds and positive for 50 other compounds (Supplementary Table  S2 ). Several amino acids were positively correlated with plasma venlafaxine, including phenylalanine (r = 0. 76, p < 0. 0001), glutamine (r = 0. 75, p < 0. 0001), tyrosine (r = 0.
Briefly, indirect immunofluorescence on rat brain sections was used as a screening technique, further confirmed by a cell-based assay (CBA) using HEK293 cells expressing the GluN1 and GluN2b subunits of the NMDAR. Diagnosis was only established when both techniques were positive, but followup evaluations exclusively included in-house CBA. Antibody titers were assessed with endpoint dilutions on CBAs, starting from 1/10. To ensure that the study cohort was representative of the disease and to reduce selection bias, we compared the clinical characteristics of the patients included in this study with a control cohort of 104 different, randomly chosen, patients with anti-NMDAR encephalitis from the French Reference Center with all the aforementioned data available (eTable 1, links. lww. com/NXI/A821) but without known NMDAR-Ab status during follow-up.
Study Funding This work has been developed within the  BETPSY  project, which is supported by a public grant overseen by the  Agence Nationale de la Recherche (ANR)  as part of the  second Investissements d'Avenir program  ( ANR-18-RHUS-0012 ), and has been performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of the  Université de Lyon  operated by the  ANR. This study was also supported by the  Andalusian Network of Clinical and Translational Research in Neurology (Neuro-RECA)  (code:  RIC-0111-2019 ) and the  Instituto de Salud Carlos III (ISCIII, Fondo de Investigaciones Sanitarias ,  PI22/00596 ).
Acknowledgements We are grateful to  Brigitte Manship  for proofreading of the paper. This work was supported by institutional grants from  INSERM/CNRS , La ligue contre le cancer nationale et régionale (F. L. ),  Labex Dev2Can  (F. L),  Fondation Bettencourt Schueller (Impulscience) (F. L. ),  INCa  (F. L. ),  ANR  (F. L. ),  Labex   DEV2CAN  (F. L. ),  Fondation ARC  (F. L. , A. H. ),  Centre Léon Bérard  (F. L. , A. H. ),  Fondation pour la recherche médicale  (N. C. , G. F. , F. L. ) and  Institut Convergence PLAsCAN ,  ANR-17-CONV-0002.
Acknowledgements We thank members of the "Stress  adaptation and tumor escape" laboratory  for their support. We benefited from technical support from the  Cytometry Core facility (CytoCell)   of Nantes University. L. Nocquet was a recipient of  French ministry of Higher Education , Research and Innovation and was supported by a fellowship from  Ligue contre le cancer  (CD44). This work was supported by  INCa (SIRIC ILIAD ,  INCa-DGOS INSERM -ITMO Cancer-18011 ), and departmental committee of  LIGUE Contre le Cancer  CD44, CD53 and CD22. P. P. J. 's laboratory is labelized by the  Ligue Nationale Contre le Cancer (LNCC). P. P. J. acknowledges the  Association Ruban Rose  for providing support.
DNA isolation and genotyping We isolated DNA from 50 mg of tissue using CTAB custom DNA isolation protocol for 96-well plate format with 2. 4 M NaCl lysis buffer for fruits and 1. 4 M NaCl lysis buffer for leaves  (Larue et al. 2021d ). We checked the quality of the DNA with a spectrophotometer Thermo Scientific NanoDrop 8000. We characterized each sample at 79 SNP using Agena MassARRAY Platform  (Larue et al. 2021d ) and checked the raw data using MassARRAY Typer Analyzer 4. 0. 26. 75 (Agena Biosciences) to exclude loci with weak signal (i. e. , too many missing values) or Fig. 3  Aerial photographs of the five studied orchards. Orchard A is isolated. All trees belong to "Marigoule" cultivar. In Orchard B almost all trees are "Marigoule" cultivar. This orchard is surrounded by a chestnut forest.
Genetic structure and paternity analyses We identified all samples having the same multilocus genotype using "Multilocus Matches" function from GENALEX 6. 503 (Peakall and Smouse 2012) and carefully inspected the results manually. We kept only one copy of each genotype for the remaining analyses. We used the Bayesian clustering analysis software STRU CTU RE  (Pritchard et al. 2000)   We then assigned all samples from the five production orchards (adult trees and seeds) to the three chestnut species and their hybrids (Appendix 6. 1: b). We had developed the molecular markers with the goal to differentiate the different species of chestnuts and their hybrids  (Larue et al. 2021d). Therefore, variation of admixture coefficients across runs was very small (results not shown), and we kept only six iterations for the STRU CTU RE analyses.
Statistical analyses We performed all statistical analyses with R software (v3. 6. 6; R Core Team 2013). We calculated the corrected fruit set with basic functions implemented in R and produced the illustrations with the ggplot2 (v3,6,3; Wickham 2016), ggthemes (v4. 2. 4; Arnold 2016), and cowplot (v1. 1. 1; Wilke 2020) packages.
Genotyping success and genetic data curation Genotyping results show that all sampled adult trees belong to the expected cultivar, "Marigoule" or "Bouche de Bétizac. " We assigned the genotyped adult trees to the different chestnut species and hybrids with the software STRU CTU RE. We successfully genotyped 547 of the 601 fruits collected (91%) at more than 33 SNPs, allowing an even representation of all orchards and providing enough genetic power for our objectives (Table  2 ).
Acknowledgements Production orchards were selected with the help of  N. Lebarbier. We warmly thank the orchard owners,  P. Lacoste ,  M. Duberteix ,  M. Feneteau , and  P. Gay  for allowing us to use their production orchards for this study. We thank  C. Lalanne ,  J. Dudit , and  M. Martin-Clotte  for their invaluable assistance to prepare samples for DNA isolation. SNP development and genotyping were performed at the  Genome Transcriptome Facility of Bordeaux (PGTB)  with the help of  E. Guichoux ,  M. Massot ,  A. Delcamp ,  C. Boury , and  L. Dubois.
Funding This paper was part of the PhD of CL. The Cifre thesis ( Conventions Industrielles de Formation par la Recherche ) was supported by the  ANRT (Association Nationale de la Recherche de la Technologie , convention Cifre N°  2018/0179 ). It was funded by  Invenio , the  Région Nouvelle-Aquitaine  ( Regina  project N°  22001415-00004759  and chestnut pollination project N°  2001216-00002632 ), by  INRAE  ( Institut National de Recherche pour l' Agriculture et l'Environnement). Further analyses and writing were supported through  ANR  (project  ANR-21-PRRD-0008-01 ). The PGTB facility, where part of the work was conducted, benefits from grants from the  Conseil Régional d' Aquitaine  no. 20030304002FA  and  20040305003FA , from the  European Union FEDER  no. 2003227  and from  Investissements d' Avenir  ( ANR-10-EQPX-16-01 ).
On day 3, mice were fasted overnight. The test was performed on day 4 in the morning. Ex4 in WD mice and Ex9 in RD mice were infused in the OB 13 min prior to the behavior test. The test cage contained 5 cm of fresh bedding and was equipped with a video camera. A piece of chocolate was buried 2 cm beneath the surface of the bedding in a random location. Latency time (sec) was defined as the amount of time between the introduction of the mouse to the cage and the retrieval of the little piece of chocolate after foraging. Only mice that found the cookie were included in the statistics. Latency was manually counted using the Kinovea video tool (open platform, https:// www. kinovea. org/). 4. 6. Statistical methods Data are given as means AE standard error of the mean (SEM). Statistical analysis was performed using unpaired Student's t test or twoway ANOVA after normality was assessed by a ShapiroeWilk test.
2. 2 (https://www. ebi. ac. uk/Tools/msa/clustalo/). FOXD2 protein structure and PDB files were absent in the RCSB Protein Data Bank (https://www. rcsb. org/). Therefore, the pre-existing model based in AlphaFold database (https://alphafold. ebi. ac. uk/entry/O60548) was used. Gibbs free energy minimization of the wild type protein structure was carried out with RepairPDB command of FoldX software  16  with the following syntax: foldx --command=RepairPDB --pdb= AF-O60548-F1-model_v2. pdb --water=CRYSTAL. Four in silico tools, which estimate the impact of missense variants on protein stability, and calculate the changes of unfolding Gibbs free energy, ΔΔG were employed. Protein stability predictions were calculated with DynaMut2 17 , INPS-3D  18  , FoldX  16  and PremPS  19  using the AlphaFold structure of FOXD2 as input PDB file.
32 FOXD2 GWAS locus fine-mapping: Genome-wide association studies (GWAS) of albuminuria have identified the FOXD2 locus. 33  We therefore attempted to identify the responsible variants underlying the association signal by using the statistical fine-mapping method SuSiE. 34  SuSiE selects single nucleotide polymorphisms (SNPs) with a high probability to causally affect a given trait -here the urinary albumin-to-creatinine ratio (UACR) -even if a genetic locus contains many highly correlated genetic variants and/or multiple SNPs with a causal effect. We used the R package susieR (version 0. 12. 27) to fine-map the 1 MB genomic region centered at the SNP showing the strongest statistical association with UACR, rs1337526. For linkage disequilibrium (LD) matrix calculation, we used a genotype set of 15,000 randomly selected participants of European ancestry from the UK Biobank as in Teumer et al.
Family 1 ES of individual VI-3 (index) led to the prioritization of a homozygous frameshift variant in FOXD2 NM_004474. 4:c. 789dup, p. (Gly264Argfs*228). The variant is not listed in gnomAD (Genome Aggregation Database, v. 2. 1. 1, http://gnomad. broadinstitute. org/) and predicted to result in a protein of nearly the same length as wild type FOXD2, however, with a changed amino acid composition within the C-terminal half of the protein, leaving the DNA binding domain intact (Figure  1I ). As FOXD2 is a single-exon gene (NM_004474. 4), nonsensemediated decay due to the frameshift variant cannot be expected. See Supplementary Methods for details on filtering process of ES and Supplementary Table  1  for a list of homozygous variants at MAF 1. 0% detected with ES in the index individual. Supporting the hypothesis that FOXD2 represents an essential gene during mammalian development, no FOXD2 homozygous loss-of-function variants are reported in gnomAD v.
Importantly, homozygous Foxd2 KO mice display smaller mandibles (micrognathia or mandibular hypoplasia), as measured with distance 3-5 (Figure  3D ). Ophthalmic evaluation identified changes in the appearance of the optic disc, imaged by the OCT en face modality as a darker ring around the optic nerve head (Figure  3E ). SD-OCT images revealed that homozygous Foxd2 KO mice have posterior deformation of the optic nerve head surface (Figure  3F ). We noted different severity in the alterations of the optic nerve in these mice. The total retinal thickness was not significantly altered, only for severely affected eyes a much thinner total retinal thickness was measured (data not shown). Although macroscopy did not reveal malformations of the urinary system, microscopic analysis by standard hematoxylin-eosin (H&E) staining detected bilateral dilatation of the renal pelvis.
44  Developmental delay presenting as delayed motor milestone achievement and delayed speech development was noted in the affected individuals of family 1 and 2 (see Clinical case and Supplementary Case Report). Unfortunately, no cranial MRI data were available except for individual II-1 where enlarged ventricles and enlarged subarachnoid space were noted (see Supplementary Case Report). Furthermore, facial anomalies in the affected individuals were compatible with the previously detected expression patterns. A neuronal phenotype was not reported in Foxd2 KO mice in the literature. However, Foxd1 KO mice present with small cerebral hemispheres with reduced development of the ventral telencephalon. 9, 46, 47  Facial alterations, in turn, were not reported in Foxd2 KO mice in the literature but could be detected in the meticulously phenotyped Foxd2 KO mice of this study (Figure  3A-D ), in line with the facial dysmorphies of the affected individuals of families 1 and 2 (Figure  1D-G ; Table  1 ).
Acknowledgements We would like to thank the index individuals and their families for participation in the study and  Prof. S. Potter  for the kind gift of the mk4 cells. We further thank  Prof. Simone Sanna-Cherchi ,  Prof. Nine Knoers ,  Dr. Kirsten Renkema  and  Prof. Sophie Saunier  for kindly searching their NGS databases for cases with FOXD2 variants. tenure track funding scheme and the  ERC  starting grant  TREATCilia  (grant agreement no. 716344 ), and YL, SH, MW, MS, AK, and SA acknowledge funding from the  DFG  -Project -ID 431984000 -SFB 1453 Nephgen  and -Excellence Initiative  CIBSS -EXC-2189 -Project ID 390939984. GMC was supported by the  German Federal Ministry of Education and Research  ( Infrafrontier  grant  01KX1012  to MHdA) and  German Center for Diabetes Research (DZD) (MHdA).