dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS Neurotoxicity data from a total of 97 patients in CARTITUDE-1 with a February 2021 cutoff date were included in this analysis, representing a median follow-up of 18 months. Detailed baseline characteristics of patients in CARTITUDE-1 have been previously published  [12]. Patients' median age was 61 years, 59% were male, and 24% had high-risk cytogenetic profile-defined as the presence of del17p, t(14:16), and t(4:14). Patients had received a median of six prior lines of therapy; 88% were triple-class refractory, 42% were penta-drug refractory, and 99% were refractory to their last line of therapy. Total CAR T-cell neurotoxicities, including ICANS and other CAR T-cell neurotoxicities, were observed in 20. 6% of patients; nine (9. 3%) had grade 3/4 events and one (1. 0%) had a grade 5 event.
MNTs in ongoing trials with cilta-cel More than 150 patients were subsequently dosed across the ciltacel clinical development program since the implementation of monitoring and patient management strategies as described above. As of the April 15, 2021 data cutoff, one patient in the multicohort phase 2 CARTITUDE-2 study (NCT04133636) developed other neurotoxicity, presenting with a cluster of symptoms characteristic of MNTs. As previously described, CARTITUDE-2 is evaluating cilta-cel safety and efficacy in various clinical settings for patients with MM  [17]. This patient (male; 44 years of age), who was enrolled in cohort B (one prior line of therapy, early relapse), had characteristics that were identified among patients in CARTITUDE-1 as associated factors for MNTs such as high tumor burden at baseline, worsening disease burden despite bridging therapy, development of grade 4 CRS, and high CAR T-cell expansion and persistence.
Cilta-cel is being evaluated in earlier line settings in the phase 3 studies of CARTITUDE-4 (NCT04181827) and CARTITUDE-5 (NCT04923893), which will further inform the risk factors for CAR T-cell neurotoxicities, including MNTs. Patients completing CARTITUDE-1 and other cilta-cel clinical trials will be enrolled in a long-term follow-up study for continued monitoring for up to 15 years. A planned observational postauthorization safety study will focus on areas including characterization and evaluation of identified risks, and long-term safety. Thus, the knowledge base about cilta-cel treatment of MM will continue to grow as data emerge on longer term efficacy and safety, mechanism of action, and patient perspectives.
ACKNOWLEDGEMENTS This study was funded by  Janssen Research & Development, LLC  and  Legend Biotech, Inc. Medical writing support was provided by  Jaya Kolipaka of Eloquent Scientific Solutions , and funded by  Janssen Global Services, LLC. The authors thank the patients who participated in the study and their families and caregivers, the physicians and nurses who cared for patients and supported this clinical trial, the staff members at the study sites, and the staff members involved in data collection and analyses.
Observation and Data Reduction We present new cycle 6 observations of Oph 163131 in band 6 (1. 3 mm, Project: 2018. 1. 00958. S; PI: Villenave). The spectral setup was divided in three continuum spectral windows of rest frequency 229. 0, 243. 5, and 246. 0 GHz, and a fourth spectral window including the 12 CO J = 2 -1 transition at 230. 538 GHz. The line spectral window has a native velocity resolution of 0. 64 km s -1. The data were obtained on June 8, 2019, with baselines ranging from 80 m to 16 km. The total observing time on source was about 2 hr and 30 minutes. The raw data were calibrated using the CASA pipeline version 5. 4. 0  (McMullin et al. 2007) , and the rest of the data processing was done with CASA 5. 6. 1. To produce the final images, we combined our cycle 6 observations with lower-angular-resolution observations from cycle 4 (Project 2016.
For each set of parameters, we compute the 1. 3 mm continuum image, SED, and 0. 6 μm scattered-light image. All maps are convolved by a representative PSF before being compared to the data. We use the HST PSF generated by the TinyTim software package  (Krist et al. 2011 ) for the 0. 6 μm image. For the ALMA data, we simulate the real interferometric response by producing synthetic images using the CASA simulator. For each model, we first generate synthetic visibility files for all three observing times and configurations, using the CASA task simobserve. We then produce synthetic images of these visibilities using the tclean function with the same weighting parameters as for the observations. Previous modelings of Oph 163131 by  Wolff et al. (2021)  have demonstrated that some degree of vertical settling is needed to reproduce both the scattered-light and millimeter images.
2, we find that the Stokes number of particles emitting at millimeter wavelengths must be greater than St > 1. 7 × 10 -3 at 100 au. Finally, we note that the settling prescription implemented in this section depends on many parameters (e. g. , grain size distribution, settling prescription, same turbulence in all the disk, constant grain opacity,. ) and that it might be necessary to add more complexity to the models in order for them to be able to simultaneously reproduce the millimeter, scattered-light images, and SED. Nevertheless, the high-resolution data sets available for the highly inclined disk in Oph 163131 allows us to obtain independent measures of scale height for multiple dust sizes, which is extremely valuable to test and improve current dust settling models.
Summary and Conclusions In this paper, we present new high-angular-resolution ALMA continuum and 12 CO millimeter images of the highly inclined disk Oph 163131. The 12 CO image can be described by two distinct regions: (1) an inner region (R  150 au) where the disk describes a clear X shape, with a linear increase of the gas emission height with radius as of z/r ∼ 0. 3, and (2) a flat outer region (for R > 200 au), with uniform brightness and temperature. In contrast with the 12 CO emission, the scatteredlight surface brightness of HST 0. 6 μm data appears flat at all radii. This indicates that scattered light comes from the outer regions of the disk, due to the high optical depth of micronsized particles in Oph 163131. We find that the heights of scattered-light and 12 CO emission are similar at large radii, which suggests that both components are emitted at similar altitudes above the midplane.
Figure3. Left: 12 CO continuum-subtracted moment 0 map, Right: 12 CO moment 1 map and continuum 5σ and 10σ contours in white. We illustrate the beam sizes with ellipses in the bottom left corner of each panel ( 12 CO beam in black-both panels, and continuum beam in white within the CO beam-right panel only). The cube used to make these figures is available in Zenodo at 10. 5281/zenodo. 6371758.
Sequencing mode was set as 20 dark cycles followed by 80 bases in single end reads (SR80). Sequencing data was processed using our custom pipeline, 5C-Pro, available at https://github. com/bioinfo-pf-curie/5C-Pro. Briefly, single-end sequencing reads were first trimmed to remove Illumina adapters and aligned on an in silico reference of all pairs of forward and reverse primers using the bowtie2 software  (Langmead and Salzberg, 2012). Aligned reads were then directly used to infer the number of contacts between pairs of forward and reverse primers, thus providing a 5C map at the primer resolution. Based on our previous experiments, inefficient primers were discarded from downstream analysis. Quality controls of the experiments were then performed using the HiTC BioConductor package  (Servant et al.
, 2012). Data from biological replicates were pooled (summed) and binned using a running median (window=30kb, final resolution=6kb). We normalized 5C contacts for the total number of reads and filtered out outlier probes and singletons, as previously described  (Hnisz et al. , 2016; Nora et al. , 2012; Smith et al. , 2016). We also developed a novel method to exclude noisy contacts in the 5C maps, called "neighbourhood coefficient of variation", available at https://github. com/zhanyinx/ Coefficient_Variation. Considering that the chromatin fiber behaves as a polymer, the contact frequency of a given pair of genomic loci (e. g. i and j) cannot be very different from those of fragments i±N and j±N if N is smaller (or in the order of) than the persistence length of the chromatin fiber. Hence, a given pixel in the 5C map (which is proportional to the contact frequency between the two corresponding loci) can be defined as noisy if its numerical value is too different from those corresponding to neighboring interaction frequencies.
Accession numbers All next-generation sequencing data generated in this study has been deposited in the Gene Expression Omnibus (GEO) under the accession number GSE124596 (5C data for E14 cell line) and GSE180617 (5C data for 245kb-INV cell line). 5C data for E14 cell line (used as control) has been published previously  (Galupa et al. , 2020)  but control and mutant samples were collected and processed in parallel. ----------To review GEO accession GSE180617: Go to https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE180617 Enter token gdelimmatputvod into the box ----------       The interaction frequency between Xite-Tsx is ~5-fold lower in the inverted allele (~50 counts) than in the WT (~250 counts, and within the region where contact frequency is dominated by genomic distance).
Acknowledgments We are grateful to  Isabelle Grandjean  for help and advice with animal management; to  Patricia Diabangouaya  for help with mouse genotyping; to  Chris Gard  for help with gene expression analysis; to  Denis Krndija  for critical reading of the manuscript. We thank all members of the  Heard lab  for advice, support, and helpful comments and discussions. We are also thankful to facilities at the  Institut Curie , including the  Mouse Facility , the  BDD team of PICT-IBiSA , the  ICGex NGS platform  (in particular  Sónia Lameiras  and  Sylvain Baulande ), the  Genomics Platform  (in particular  David Gentien ,  Cécile Reyes ,  Audrey Rapinat  and  Benoit Albaud ) and the  Bioinformatics Platform. We acknowledge the  Zhang lab  for sharing plasmids, and the  ENCODE Consortium  and the  Bruneau, Ren, Sharp, Stamatoyannopoulos and Young labs  for generating datasets used in this study.
Funding: This work was supported by fellowships from  Région Ile-de-France (DIM Biothérapies)  and  Fondation pour la Recherche Médicale  ( FDT20160435295 ) to RG;  ERC Advanced Investigator award  ( ERC-2014-AdG no. 671027 ),  Labelisation La Ligue ,  FRM  ( DEI20151234398 ),  ANR   DoseX 2017, Labex DEEP  ( ANR-11-LBX-0044 ), part of the  IDEX PSL  ( ANR-10-IDEX-0001-02 PSL ) and  ABS4NGS  ( ANR-11-BINF-0001 ) to EH.
Background: Studies focusing on dietary pesticides in population-based samples are scarce and little is known about potential mixture effects. We aimed to assess associations between dietary pesticide exposure profiles and Type 2 Diabetes (T2D) among NutriNet-Santé cohort participants. Methods: Participants completed a Food Frequency Questionnaire at baseline, assessing conventional and organic food consumption. Exposures to 25 active substances used in European Union pesticides were estimated using the Chemisches und Veterinäruntersuchungsamt Stuttgart residue database accounting for farming practices. T2D were identified through several sources. Exposure profiles were established using Non-Negative Matrix Factorization (NMF), adapted for sparse data. Cox models adjusted for known confounders were used to estimate hazard ratios (HR) and 95% confidence interval (95% CI), for the associations between four NMF components, divided into quintiles (Q) and T2D risk.
The organic food consumption was then calculated by matching to the modalities with respective percentage of, 0, 25, 50, 75, and 100. Weighting and sensitivity analyses for the Org-FFQ have been published elsewhere  [30]. All food and beverage items were combined into 33 food groups. Nutritional values were obtained from a published food composition database  [33]. A global proportion (as weight) of organic food in the diet was calculated as well as the proportion of organic food for each food group.
Pesticide exposure assessment Dietary exposure to pesticides was calculated by combining dietary intakes of each adult with pesticide residue concentration values in plant foods using contamination data from the European Union reference laboratory for pesticides, Chemisches und Veterinäruntersuchungsamt (CVUA) Stuttgart  [34]. Contamination data for conventional and organic food products were both available in the database, details can be found in Supplementary Material 1. Among compounds available in this database, 25 commonly used pesticides were chosen, given both their frequency of detection above the Maximum Residue Levels (MRL) when sufficient data were available, and their frequency above Acceptable Daily Intake (ADI) otherwise, as detailed in Baudry et al. 2019  [35]. Pesticides authorized and widely used in organic farming processes (e.
g. natural pyrethrins, spinosad) were also included. The 264 Org-FFQ items were broken down into 442 ingredients (comprising at least 5% of at least one food item). Animal-based ingredients were excluded, as CVUA encompassed plant-based ingredients only. Indeed, plant-based foods have notably more frequent and higher pesticides residues levels than foods of animal origin  [36]. The resulting 180 plant ingredients were linked to the CVUA database and then were assigned a contamination value in organic and conventional farming modes (as the mean of corresponding data point). A description of the different steps for the decomposition and matching process is shown in Supplementary Material 2. The final 180 ingredients are listed in Supplementary Material 3. For each ingredient/pesticide pair in conventional and organic farming, frequency of detection and frequency of quantification were determined using the formula as follows: These frequencies were then used to determine the censoring rate in order to apply the most adapted methodology, following EFSA and WHO's recommendations  [37, 38].
Case ascertainment Participants declared health events through a yearly health status questionnaire and a dedicated webservice at any time of the study. All medical records were compiled and examined by dedicated physicians. Physicians of participants declaring major health events were contacted to collect additional information if necessary. A medical expert committee validated these major health events. Moreover, declared health data were merged with medico-administrative registers of the national health insurance system (Système National d'Information Inter-Régimes de l' Assurance Maladie [SNIIRAM] databases) to validate and provide information on health events. Mortality data from the French Centre for Epidemiology Medical Causes of Death database (CépiDC) were also used. Therefore, diabetes cases were identified using a multi-source approach, i.
e. T2D self-reported during follow-up along with declaration of the use of T2D medication. Matching with the medico-administrative databases of the French National health insurance (SNIIRAM database) allowed us to correct potential errors. In this study, we considered as cases all T2D cases diagnosed between baseline (i. e. the date of completion of the Org-FFQ in 2014) and October 6 th 2020. Prevalent cases of type 1 diabetes and T2D were removed from the analysis.
Statistical analyses A flowchart for the study sample selection is presented in Fig. 2. For the present study, participants who completed the Org-FFQ between June and December 2014 (N = 37,685), with no missing covariates for basal metabolic rate computation (N = 37,305), who were not detected as under-or over-reporters (N = 35,196), living in France with available data on dietary scores (N = 34,442), who were free of type 1 diabetes or T2D when they completed the Org-FFQ (N = 33,013), were selected. The detection method for under and over-reporters was based on the comparison between energy intake and energy requirement as previously performed in other studies  [30, 50]. Dietary pesticide exposure profiles were analyzed using Non-Negative Matrix factorization (NMF) (detailed in Supplemental Material 5), specially adapted for nonnegative data with excess zeros, developed by Lee et al.
Sensitivity analyses were performed to test for consistency of findings. A model excluding early cases (within 1 year after baseline), a model excluding CVD events before or during follow-up, and a model excluding baseline metabolic abnormalities (hypertension or dyslipidaemia) were performed as sensitivity analyses. Additional models were adjusted for provegetarian score and three scores assessing quality of foods, cDQI, pDQI and aDQI (data not shown)  [49]. Two-sided tests were used. Data management and statistical analyses were performed using SAS (version 9. 4; SAS Institute, Inc. ). The NMF analysis was performed using R's NMF package  [56].
It is possible that risk alpha inflated with multiple comparisons. However, our analyses were hypothesis-driven, supported by available data in animal or mechanistic studies in the literature and the number of outcomes and subgroup analyses were limited. Other limitations, inherent to the pesticide database, could be mentioned: for instance, data were not available for animal products (with generally low levels of pesticides) and the database did not contain measures for copper or sulfur-based products, widely used in organic farming. Measures were performed in Germany, but products from all over European Union were tested. Pesticide exposure was estimated based on dietary intakes, since the financial burden limits biomarker monitoring in very large cohorts. Some urinary metabolites measures were available for a limited subsample of the cohort (300 individuals).
The pesticide mixtures found in this study could be administrated to animals in order to better understand underlying mechanisms. If confirmed by other studies, these findings may help to understand the role of dietary pesticide exposure in major chronic diseases' incidence. These results would have important implications for developing prevention strategies for the whole population, through regulation or dietary guidelines. et des Libertés (CNIL 908,450 and 909,216). All participants provided their informed consent with an electronic signature, and this study is registered in ClinicalTrials. gov (NCT03335644).
Acknowledgements We especially thank  Cédric Agaesse  (manager),  Alexandre De-Sa  and  Rebecca Lutchia  (dietitians);  Thi Hong Van Duong ,  Younes Esseddik  (IT manager),  Régis Gatibelza ,  Jagatjit Mohinder  and  Aladi Timera  (computer scientists);  Julien Allegre ,  Nathalie Arnault ,  Laurent Bourhis ,  Nicolas Dechamp  and  Fabien Szabo de Edelenyi, PhD  (manager) (data-manager/statisticians);  Sandrine Kamdem  (health event validator);  Maria Gomes  ( Nutrinaute support ) for their technical contribution to the  NutriNet-Santé study  and  Nathalie Druesne-Pecollo, PhD  (operational manager). We also thank the  CVUAS  for the pesticide residue database and  Noémie Soton  for her contribution to the data management of the CVUA database. We warmly thank all of the dedicated and conscientious volunteers involved in the NutriNet-Santé cohort.
Funding The  NutriNet-Santé study  is supported by the following public institutions:  French Ministry of Health (DGS) ,  Santé Publique France , the  National Institute for Health and Medical Research (INSERM) , the  French National Research Institute for Agriculture, Food and Environment (INRAE) , the  National Conservatory of Arts and Crafts (CNAM)  and the  Sorbonne Paris Nord University. The BioNutriNet project was supported by the  French National Research Agency (ANR)  in the context of the 2013  Programme de Recherche Systèmes Alimentaires Durables  ( ANR-13-ALID-0001 ). This study is also supported by the  Medical Research Foundation  (FRM n°  ENV202109013962 ). Pauline Rebouillat is supported by a doctoral fellowship from  Sorbonne Paris Nord University.
Availability of data and materials Researchers from public institutions can submit a collaboration request including information on the institution and a brief description of the project to collaboration@etude-nutrinet-sante. fr. All requests will be reviewed by the steering committee of the NutriNet-Santé study. A financial contribution may be requested. If the collaboration is accepted, a data access agreement will be necessary and appropriate authorizations from the competent administrative authorities may be needed. In accordance with existing regulations, no personal data will be accessible.
General procedure for the synthesis of Diethyl hydroxyphenyl α-aminophosphonates 4a-k To the reaction mixture containing hydroxybenzaldehyde (122. 12 mg, 1 mmol), aniline (93 mg, 1 mmol), and diethylphosphite (165 mg, 1. 2 mmol) a NiSO 4. H 2 O (5 mol%) was added, it was stirred within 30 min at room temperature. The reaction progress was monitored by TLC. The catalyst was removed by filtration. The filtrate was washed by water (10 mL) and extracted with dichloromethane (10mL × 2). The organic phases were combined and evaporated in vacuum. The crude product was purified by recrystallization in hexane or by column chromatography on silica gel (ethyl acetate/hexane: 30/70) to afford majority product 4a-l. Complete experimental data have been provided (NMR spectra and HRMS). 16. 12 (dd, J 3  C-P = 13.
Hydrogen atoms were located on a difference Fourier map and introduced into the calculations as a riding model with isotropic thermal parameters. All calculations were performed by using the Crystal Structure crystallographic software package WINGX  [27]. CCDC 2079186 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data _ request/cif.
The number of angles simultaneously changed in a single step was varied from 1 to 9 for diethyl hydroxyphenyl αaminophosphonates and from 1 to 14 for diethylphosphates diethyl α-aminophosphonates and they were modified in the range of 0-180 °. As a result, after automatic removal of duplicates, for each diethyl hydroxyphenyl α-aminophosphonates were created from seven to thirteen thousand, and for each diethylphosphate diethyl α-aminophosphonates up to thirty thousand of different conformers, which were next optimized in the molecular mechanics force field AMBER99. In Stage II all structures obtained in Stage I were re-optimized in vacuum using the semi-empirical method PM7 in the program MOPAC 2016  [37]. Next, from a set of conformers obtained for each molecule, were selected three hundred of the lowest energy structures (again duplicates were removed), which were used in the next stage.
Acknowledgments The  General Directorate of Scientific Research and Technological Development (DGRSDT)  is gratefully acknowledged for financial support of this work. The technical support provided by  Emilie KOLODZIEJ  is highly appreciated. We are thankful for the collaboration of  Dr Ali Debbi  ( The Research Centre in Biotechnology, Constantine, Algeria ), and  Wroclaw Centre  for Networking and Super-computing ( http://wcss. pl), grant No. 443  where calculations have been carried out.
On another hand, on-line social networks allowed non-institutional parties to publish and promote information and create communities. Tracking news stories has been attempted with some success in recent years  [20, 22, 32]. However, for most of experiments with short messages neither datasets nor implementations are shared  [13, 27, 33, 23, 4]. With Covid-19, the world health organisation (WHO) introduced the concept of 'infodemic' as 'too much information including false or misleading information in digital and physical environments during a disease outbreak'  [34]. We take benefit of this situation and of Covid-19 Twitter News datasets to conduct comparative experiments with different corpora. In this paper, we address a few research issues and we propose a framework to track news stories in short messages.