dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank  US006/CREFRE INSERM/UPS  ( Toulouse, France ),  Dr D'Angelo  and  Dr Zanoun  for assistance and advice with imaging ( Cellular Imaging Facility Rangueil-I2MC/TRI Platform ) and the zootechnical core facility ( Anexplo-Genotoul platform ) for their technical assistance. We especially thank  Mrs Bonnel ,  Mrs André ,  Mrs Achard  and  Mr De Vecchi  ( STROMALab ) for help in experiment managing,  Mr Martinez  for help with laser microdissection experiments and the  FRAIB laboratory  ( CNRS-FR3450 ,  Castanet-Tolosan, France) ,  Prof. Belenguer  for sharing TOM20-antibody,  Dr Pradère  for mice experiments and  Dr Monsarrat ,  Dr Dani  and  Dr Mourier  for helpful discussions. We thank  Dr Kazak  for critical reading of the manuscript. JCP is grateful to INSERM for funding a temporary fulltime researcher position.
Funding and additional information This work was supported by the  European Union   FP7  project  DIABAT  ( HEALTH-F2-2011-278373 ), " La Société Française de Nutrition", "l'Agence Nationale pour la Recherche" (Projet- ANR-18-CE18-0006 ) and " The Inspire Program " (supported by grants from the  Region Occitanie/Pyrénées-Méditerranée  (Reference number: 1901175), the  European Regional Development Fund (ERDF)  (Project number:  MP0022856 )). E. C. was supported by  NIH   DK123095  and  Claudia Adams Barr Program. A-K. B. S. and L. Pel. were supported by the  LabEx TRAIL  ( ANR-10-LABX-57 ). MetaboHub-MetaToul (Metabolomics & Fluxomics facilities, Toulouse, France , http://www. metatoul. fr) is supported by the  ANR  grant  MetaboHUB - ANR-11-INBS-0010.
To test whether wounding-induced sleep requires nas-38 or the innate immune response, we measured movement quiescence following mechanical wounding in nas-38, sta-2, and AMP mutants. nas-38 mutations did not affect movement quiescence in adults (Figure  S6D ). sta-2(ok1860) and the multi nlp/cnc knockout reduced movement quiescence after wounding by half (Figure  5E ). These data indicate that nas-38(ok3407) during molting, and injury of the adult epidermis, are separate triggers that both activate innate immunity to cause sleep. Thus, we identified innate immunity as a novel molecular pathway for promoting sleep in C. elegans. We next tested whether innate immunity is required for heat stress-induced sleep. sta-2(-)did not reduce heat shock-induced movement quiescence. No effect was observed either in mutants for npr-12 (Figure  S6E ) that lack the cognate receptor for NLP-29  [57] , suggesting that the signaling pathways triggering heat stress-induced sleep and woundinginduced sleep are at least partially distinct.
L1 lethargus in the differential interference contrast (DIC) burst mode (explained below) was imaged with a 40x objective. The microscope setup consisted of the following components: Either a Nikon TiE inverted microscope was used that was equipped with an automated XY stage (Prior, Nikon), an LED system (CoolLED), Andor iXon electron multiplying charge-coupled device (EMCCD) camera (512x512 pixels), kinetic trigger device, automated Prior XY stage, Andor iQ2 or iQ3 or NIS Elements Advanced Research software and a home-made sample holder with a heat lid. Or a Nikon Ti2 inverted microscope was used that was equipped with an LED system (Lumencor Sola II), a Photometrics Prime 95B sCMOS camera (1608x1608 pixels), NIS Elements Advanced Research software and a custom-made sample holder with a heating lid.
Image analysis Analyzing functional Ca 2+ -images From functional Ca 2+ sensor images, we extracted two parameters: 1) the worm's locomotion velocity based on the movement of the neuron center and 2) the neuronal activity. Both parameters were determined by a custom-made MatLab (MathWorks) routine, similar to the one previously described  [23]. For RIS depolarizing, the RIS activity was the average of the 30 highest pixel intensities in an 11 pixel x 11 pixel area around the detected pixel with the highest intensity. These 30 pixels represent the RIS cell body very robustly, which was determined empirically.
Sleep detection Quiescence bouts in all conditions and ages had to last for at least 1 min to be classified as sleep. Empirically determined cut-off parameters were used for sleep detection. All data were smoothed using a 1 st degree polynomial local regression model over 5 time points using the in-build smooth function in MatLab. Image subtraction data of L1 lethargus measurements were normalized to the median of the highest 50 values and had to be below 0. 2 to be counted as sleep. Data of wounded adults were normalized to the median of the highest 20 values. Image subtraction values had to be below 0. 2 and velocity data from RIS GCaMP tracking had to be below 0. 1 to be classified as sleep. Image subtraction data of adults after heat shock-induced nlp-29 expression were normalized to the median of the highest 500 values and the threshold was set to 0.
Acknowledgments We thank  Ines Lewandrowski ,  Sinem Öztas ,  Juliane Haase ,  Jerome Belougne  and  Shizue Omi  for help with some experiments. We thank  Dong Yan ,  National BioResource Project Japan , and the  Caenorhabditis Genetics Center  supported by the  National Institutes of Health Office of Research Infrastructure Programs  ( P40 OD010440 ) for strains. The CRISPR strains were generated by  SunyBiotech and Knudra. We thank  Hirofumi Toda  for discussing the role of nemuri. This work was supported by the  Max Planck Society  ( Max Planck Research Group "Sleep and Waking "), by an  European Research Council   Starting Grant  (ID:  637860 , SLEEPCONTROL), the  French National Research Agency  ( ANR-16-CE15-0001-01 ), by the  « Investissements d'Avenir » French Government program  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University -A*MIDEX  and institutional grants from  CNRS ,  Aix Marseille University , and INSERM to the CIML.
Observational challenges and missing links From the missions that have been or are being used to gain insights into Jupiter's radiation belts (Table  1 ), none was actually designed to explore them in a comprehensive way. To minimize radiation exposure, spacecraft orbits or operations were planned in ways that avoid the belts' core region. This region is located inward of Io's orbit, at ~5. 9 RJ and at low magnetic latitudes (1 RJ = 71492 km, a jovian radius). Pioneer 10, 11  and Voyager 1 reached deeper than Io, offering just brief snapshots of the belts from their flyby trajectories. Future missions JUICE and Europa Clipper will not reach deeper than Europa's orbit, at ~9. 5 RJ. For most spacecraft that passed through the inner radiation belts, their instruments suffered from saturation and radiation damage, rendering many of their data unusable or very challenging to calibrate  [Vogt et al.
These carbohydrates include, among others, short-chain fermentable carbohydrates (fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP)  [18] , and the metabolism of these may vary based on gut microbiota capacity to encode enzymes for metabolizing food glycans (CAZy or carbohydrate-active enzymes). We previously showed that the gut microbiota is associated with symptom severity in IBS and identified a microbial signature for IBS severity that was not associated with the intake of macronutrients  [19]. Although considered to be associated with symptom generation in individuals with IBS, details about how the combination of diet and gut microbiota affects symptoms remain unknown  [19] [20] [21]. Therefore, in this study, we investigated the relation between digestive symptoms and extensive datasets of diet and the gut microbiota of individuals with IBS, with a focus on those with severe symptoms, further referred to as severe IBS, making use of a whole-metagenomics sequencing approach and categorized dietary intake based on a 4-day food diary.
Dietary intake All subjects completed a paper-based diet record, in which all foods and drinks consumed during four consecutive days (Wednesday-Saturday) were reported. Oral and written instructions were given to the patients on how to record their dietary intake, and patients were told to keep to their regular diet during the recording days. The type of food and the time at which it was consumed were noted, with quantification in grams, according to the use of household utensils (e. g. , tablespoons) or the number of slices, for example. Cooking methods and the contents of food labels were noted where applicable. All diet records were entered into a special version of Dietist XP 3. 1 software (Kostdata. se, Stockholm, Sweden), which calculates the energy and nutrient composition of foods.
The software was linked to a Swedish Food Composition Database provided by the National Food Agency in Sweden (https://www. livsmedelsverket. se/) and to a Swedish database with FODMAP content, developed in-house  [25]. This database contained information about fructose, fructan, lactose, galactooligosaccharide (GOS), and polyol content (g/100g) from published sources  [26]. All diet records were entered into the software by a trained dietician. Excess fructose levels were calculated from data for fructose and total monosaccharide content from diet records. Glucose and fructose are the dominant monosaccharides in foods. If glucose content was higher than fructose, then the excess fructose variable was assigned a value of 0 (for each separate meal). For the reported intake of FODMAPs, outliers were defined as values exceeding the mean ± 4 SD.
Hierarchical food tree and UniFrac analysis We used the hierarchical format of the Swedish Food Composition Database to categorize foods into a hierarchical tree, the food tree. Food items, their associated nutrients, and their corresponding hierarchical levels were downloaded from the Swedish Food Composition Database (https://www. livsmedelsverket. se/). These hierarchical levels corresponded to levels 3 and 4 in the food tree. We then grouped level 2 into five large categories: animal-based, plant-based, alcohol, fats, and others. Level 1 is the root of the food tree. We then divided level 4 into 85 subcategories, constituting level 5 in the food tree, as follows: 1) For each level 4 category, we extracted the nutrient content for each food. 2) We fitted a Dirichlet multinomial mixture (DMM) model based on nutrient content for each level 4 category.
Fecal sample collection and DNA extraction Fecal samples from 138 subjects were collected in RNA Later solution (Ambion, Courtaboeuf, France). Fecal DNA was extracted by mechanical lysis (Fastprep® FP120) (ThermoSavant, Illkirch, France) followed by phenol/chloroform-based extraction, as previously described  [19]. A barcoded fragment library was prepared for each sample, and DNA sequencing data were generated with SOLiD 5500xl sequencers (Applied Biosystems, Life Technologies, Villebon-sur-Yvette, France), resulting in a mean of 38 (SD 14) million sequences of 35-base single-end reads. High-quality reads were generated, with a quality score cutoff > 20. Reads with a positive match to human, plant, cow, or SOLiD adaptor sequences were removed. Filtered high-quality reads were mapped onto the MetaHIT 3.
2) Each accessory gene within the MSP with a read coverage between the 2. 5 and 97. 5% quantiles was considered to be detected. Below the 2. 5% quantile, genes were considered to be absent, and above the 97. 5% quantile, genes were considered to be present in multiple copies or to be conserved genes that might bias the estimation of coverage. A presence/ absence binary gene matrix was therefore obtained for each MSP. 3) A Jaccard index between samples was calculated from the MSP binary matrix 4) Clustering was performed, with a partition around medoids over 100 bootstraps achieved with the clusterboot function of the fpc package (bootstrap method option "subset"). The number of clusters (i. e. , MSP subspecies) was estimated based on mean silhouette width.
Statistical analysis All statistical analyses were performed with R software (version 3. 4. 1). UniFrac distances between food diaries were calculated with the phyloseq R package, using the food tree as input. Jensen Shannon divergence (JSD) between metagenomes aggregated at MSP subspecies level was calculated according to a tutorial published by Arumugam et al. [32]  implemented into BiotypeR R package (available on github tapj/BiotypeR). PERMANOVA analysis was performed with the vegan R package (Adonis, version 2. 5). Principal coordinate analysis (PCoA), coinertia analysis, and linear discriminant analysis were performed with the ade4 R package (version 1. 5). To note, PCoAs on microbiota and diet distances were computed using all available samples, while co-inertia analysis was computed on common sub-samples from PCoAs components.
S1  and S2 ). This suggests that dietary data, aggregated at the micro-and macronutrients levels, differ neither between individuals with IBS and healthy controls nor between IBS patients with different levels of symptom severity. Next, we assembled a food item tree, similar to that used by Johnson et al. [5] , clustering food items based on nutritional content rather than the food item itself (see Material and methods section, "Hierarchical food tree and UniFrac analysis"). Participants consumed 966 different food items in total, which were aggregated into three hierarchical levels, based on the National Swedish food database, to build a hierarchical tree. A fourth level based on nutrient composition was also added, resulting in 85 food-nutrient groups (Fig. 1a  and Table  S1 ). The unweighted UniFrac distance between individuals was calculated using the four hierarchical levels of dietary data and subjected to principal coordinate analysis (PCoA) (Fig.
Dietary profile associations with gut microbiota composition and function As diet is a major driver of microbiota composition and function, we investigated whether differences, previously observed in dietary profile between study individuals, were associated with gut microbiota composition and functions. We have previously shown that enterotypes, assessed by 16S rRNA gene sequencing, could be separated into three microbiota communities based on Dirichlet multinomial mixture (DMM) modeling  [19]. Enterotypes were not associated with dietary distance (PERMANOVA, p > 0. 05), nor with meat/plant ratio (p > 0. 05) or diet quality (FSA-NPS DI, p > 0. 05), Fig. 1  Quantity and quality assessment for dietary profiles and analyses of associations with gastrointestinal symptom severity. a Food item-based hierarchical tree based on the National Swedish food database and nutrient-based clustering.
Association of microbiota function and dietary variation with clinical parameters and gas metabolism As dietary variation was better explained at the species (i. e. , MSPs) or subspecies level MSP, we further combined microbiota profile variation (JSD distance at subspecies and MSP_unassigned) with dietary profile variation. Using a co-inertia approach on PCoA components, we explored the complex association between gut microbiota, diet, and multiple explanatory variables in 79 subjects for whom both dietary and microbiota datasets were available (training set, co-inertia RV coefficient = 0. 59). Individuals with only one of these datasets (microbiota or diet, Table  1 ) constituted the test set (N = 122). All 201 study subjects (training and test sets) were projected onto the same hyperspace based on their gut microbiota and dietary profiles (Fig.
This suggests that the meat/plant ratio was the main tested factor explaining microbiota and dietary covariations independently of gene richness, diet quality, and exhaled CH 4. Although weaker, consistent correlation directions for axis A1, in both the test and training set, were detected for exhaled H 2 and IBS symptom severity. Since CH 4 metabolism is dependent on H 2 metabolism, the ratio of exhaled H 2 to exhaled CH 4 for each subject was calculated. This ratio was consistently correlated with axis 4 in both the food and microbiota in the training sets and microbiota in the test set, suggesting that H 2 /CH 4 metabolism can be explained by metagenomic and dietary profile variations. For the prediction of each clinical variable, we used a machine learning approach by constructing regression models from the first five co-inertia coordinates (40% of the variance) extracted from the microbiota and dietary datasets.
Co-inertia models trained on microbiota and validated on dietary data were assessed for robustness by regression analysis (Fig. 3c ). For example, using a model fitted on the microbiota training set, H 2 /CH 4 ratio could be predicted from the microbiota test set (Pearson r = 0. 53, p < 0. 05). Similarly, using a model fitted on the microbiota training set, the meat/plant ratio could be predicted from the dietary test set (r = 0. 69, p < 0. 05). Using another model fitted on the dietary training set, IBS symptom severity could be predicted from the dietary test set (r = 0. 28, p < 0. 05).
Availability of data and materials The datasets used and analyzed in this and the parent study  [19]  are available from https://doi. org/10. 5878/ejpj-p674 and https://doi. org/10. 5878/5rbz-ww62. Sequence data associated with this project have been deposited in EMBL under BioProject accession PRJEB34992. The source codes used in this study are available from GitHub (https://github. com/danone/zephyr. IBSFood).
Funding sources This work was supported by the  European Virus Archive GLOBAL  ( EVA-GLOBAL)  project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 871029. Fig. 2. SARS-CoV-2 infectivity overtime after lyophilisation with different stabilizers. A. SARS-CoV-2 infectivity at -20 • C. B. SARS-CoV-2 infectivity at 20 • C (Room temperature ). C. SARS-CoV-2 infectivity at 37 • C. Data represent mean ± SD of a quadruplicate. Titers are expressed as TCID 50 /mL.
Data collection Habitual dietary intake in the month prior to diagnosis was assessed using a 204-item validated, semi-quantitative food frequency questionnaire (FFQ) developed by the Division of Human Nutrition and Health of Wageningen University & Research, the Netherlands  [27, 28]. The FFQ was used to calculate a priori defined diet quality indices and to construct a posteriori data-driven dietary patterns. Demographic and lifestyle characteristics such as sex, age, weight, height, and smoking habits were assessed using self-administered questionnaires. All questionnaires were filled out prior to tumor resection. Medical information, including cancer stage, tumor location, and treatment strategies, was collected using the Dutch ColoRectal Audit  [29]. Non-fasted plasma EDTA samples were collected upon recruitment, which were intended before the start of treatment, and stored at -80 °C using a standardized protocol to ensure identical sample handling across the eleven hospitals.
Empirical construction of dietary patterns Total intake of food items (g/d) and total energy intake (kcal/d) were calculated based on frequency of intake, number of portions, portion size, and the type of products, as recorded in the FFQ. All food items were categorized into 33 food groups that were constructed according to the Dutch food composition table 2011  [32]. Final food groups are described in Supplementary Table  S1. Total intake of food groups was recalculated to relative intake (g/d per 1000 kcal) using total energy intake to simplify comparison of participants. Principal component analysis was used to investigate data-driven dietary patterns among participants  [33]. Food group data were log-transformed using the natural logarithm and Z-standardized before performing principal component analysis.
Metabolites with > 20% missing values (n = 13), including values below the level of detection (LOD) and true missings, were removed from the dataset while remaining Fig. 1  Overview of food group loadings of the Western, Carnivore, and Prudent dietary patterns. Green and red bars represent positive and negative loading strengths, respectively. A more positive loading illustrates higher consumption of a specific food group, while a more negative loading characterizes lower consumption of the food group. Only food group loadings >|0. 2| were considered to contribute to the dietary pattern and visualized to improve readability missing values were imputed in line with previous studies  [38] [39] [40]. Briefly, missing values below the LOD were imputed by half of the batch-specific LOD and values below or above the quantitative range were replaced by the lower or upper limit of quantification, respectively.
To our knowledge, this is the first report to assess whether MRD-negative status and sustained MRD negativity in RRMM may serve as a predictive and prognostic end point for clinical outcomes in the relapsed and/or refractory disease setting. Moreover, we evaluated the degree to which MRD negativity could be sustained with daratumumab plus standard-of-care regimens for RRMM, using clinical data after an extended follow-up period of approximately 4 years.
CONTEXT Key Objective Can minimal residual disease (MRD)-negative status and sustained MRD negativity in relapsed and/or refractory multiple myeloma (RRMM) serve as a predictive and prognostic end point for clinical outcomes? We conducted an exploratory analysis using the POLLUX and CASTOR studies, which represent the largest set of MRD data prospectively collected among patients with RRMM. Knowledge Generated Patients who achieved MRD-negative status had improved progression-free survival (PFS) compared with MRD-positive patients, and sustained MRD negativity was associated with improved PFS compared with patients who obtained MRDnegative status but did not maintain MRD durability. The benefit of MRD negativity and durability occurred regardless of therapy, but daratumumab-based regimens enabled many patients with RRMM to attain deep and sustained MRD-negative responses, resulting in longer periods without disease progression.
End Points and Assessments In both studies, PFS was the primary efficacy end point. Tumor response and disease progression assessments were conducted using a central laboratory and a validated computer algorithm in accordance with IMWG response criteria. In the absence of central laboratory results, local laboratories could be used for confirmatory evaluations. In accordance with the IMWG criteria, MRD was assessed at the time of suspected CR, 3 and 6 months following confirmed CR in POLLUX, and at the time of suspected CR and at 6 and 12 months following the first treatment dose in CASTOR (at the end and 6 months after the end of Vd background therapy, respectively). Additional MRD evaluation was required every 12 months post-CR in both studies (see the Data Supplement). MRD was assessed using bone marrow aspirate samples and evaluated via NGS using the clonoSEQ assay (v.
This study focused on a sensitivity threshold of 10 25 for sustained MRD negativity based on the IMWG criteria, 1 but thresholds of 10 24 and 10 26 were also evaluated. MRD negativity rates were compared using Fisher's exact test, and the chisquare estimate of the common odds ratio was provided. In post hoc analyses of PFS and MRD status by response category among patients pooled from both studies and the daratumumab treatment arms (D-Rd and D-Vd) versus control arms (Rd and Vd), PFS was evaluated and a twosided P value was presented. Additional methods are presented in the Data Supplement.
ACKNOWLEDGMENT We thank the patients who volunteered to participate in the POLLUX and CASTOR trials and their families, as well as the coinvestigators, staff members at the trial sites who cared for them, and clinical site coordinators. We also thank  Jamie Bald  for her support in sample operations,  Linda Okonkwo  and  Chris Velas  for contributions to the study,  Huiling Pei  for critical review of the manuscript, representatives of the sponsor who were involved in data collection and analyses, and  Kristin Runkle, PhD , and  Charlotte D. Majerczyk, PhD , of  MedErgy  for medical writing and editorial assistance.