dataesr/scientific-paragraphs-categorization huggingface dataset:
Nutritional consequences of COVID-19 disease The COVID-19 patients with the most severe forms as seen in the ICU are more frequently elderly and with comorbidities  [16]  and therefore at major risk of malnutrition and sarcopenia  [17, 18]. In the absence of nutritional data specific to COVID-19, the following considerations are proposed from the data related to severe respiratory infections: -Severe respiratory infections induce inflammatory syndrome and hypercatabolism, with increased energy expenditure linked to ventilatory work, in turn responsible for increased energy and protein requirements;  Therefore, the nutritional screening at admission should be based on: - -Patient's or relatives' interview to determine recent weight loss before admission and body mass index. If possible, food intake may also be quickly and easily assessed with semi-quantitative methods: an analogue scale between 0 and 10/10  [22]  or consumed portions (0, ¼, ½; 1) during the last lunch or dinner, as did in the NutritionDay survey  [23] ; taking < 7/10 should alert to likely malnutrition  [22].
Conclusions In recent years, responses to toxic metals in aquatic animals were studied in various experimental conditions which helped to confirm that they trigger common toxicity pathways. In this context, various datasets showed that molecular regulation at the gene, protein, and metabolite level indicated physiological adaptation. Metals induce an early response as evidenced by alterations both at structural and functional levels of different organs, including enzymatic and genetic effects. Thus, they affect redox homeostasis, energy metabolism, and the innate immune system of exposed biota and/or potentially increase susceptibility to multiple types of disease (Figure  2 ). Moreover, the analysis of these endpoints, notably in multi-biomarker strategies, seems highly promising in identifying molecular signatures that will allow the early and sensitive detection of metal contamination.
e. , far above human dietary levels for the equivalent food-grade forms; these aspects could elicit different effects on the gut microbiome. It also appears that interpretations of findings from animals to humans are very limited due to differences in gut microbiota composition and activity. However, as 85% of the 16S rRNA sequence dataset for the mouse microbiota represents genera that are not present in humans  [199] , the use of GF mice inoculated with the human microbiota seems to be a good model for investigation of the chronic impact of foodborne NPs on bacteria that colonize the human intestine. Some studies used a custom colon reactor in this way, but the exposure time of the microbiota to the NPs was short  [113, 117]  and hence was not representative of chronic exposure conditions.
Study Population and Survey Design We used data from a previously described two-stage (household and community) cluster survey originally designed to estimate dengue seroprevalence in Bangladesh. 8 In brief, from December 2015 through January 2016, 70 communities from a total of 97,162 in the 2011 national census were selected with probability proportional to each community's population. In rural areas, communities were defined as villages, whereas in urban areas they were defined as wards (i. e. , neighborhoods). In each selected household, study staff identified the household head, described the study and invited them to participate in the study. If the household head agreed to participate, all household members older than 6 months of age were invited to take part. Within each community, study staff continued enrolling households until at least 40 serum samples were collected from at least 10 households.
In supplementary analyses (supplement), we also estimated the 100-and 200-day seroincidence rates. As the time between sampling an individual and their last infection increases, the sensitivity of the random forest and vibriocidal tests tend to decrease. We model this time-varying sensitivity using a logistic regression with a cubic polynomial for time since an individual's infection (up to 1 year post-infection) with data from a previously published cohort study. 7, 10 We then we combined the specificity of each test and the time-varying sensitivity, assuming constant risk in the previous year, to adjust our estimates of seroincidence in sampled communities. To estimate seroincidence in the sampled population while correcting for test sensitivity and taking into account the sampling design, we applied a Bayesian hierarchical model, implemented in Stan.
All individual and household-level data were collected directly from the survey questionnaires. Population density,  13  travel time (to the nearest city),  14  distance to a major water body, altitude and a poverty index  15  were from publicly available sources. We included spatial correlation using a Matern covariance model and estimated the joint posterior density using INLA. 12 Odds ratios and corresponding 95% highest posterior density intervals (referred to as 95% CI throughout) were based on 1,000 draws from the posterior. In sensitivity analyses we included models that had either household-level or community-level random effects with or without the Matern spatial covariance function. All analyses were performed in R. Data and source code to reproduce analyses are available at https://github.
Ethics and role of the funding source This study was approved by the icddr,b ethical review board (protocol number PR-14058) and this secondary analysis was reviewed and deemed exempt from review by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board. All adult participants provided written informed consent to participate in the study. Parents/guardians of all child participants provided written, informed consent on their behalf. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Acknowledgements This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership coordinated by  TDR , The Special  Programme for Research and Training in Tropical Diseases  hosted at the  World Health Organization. The training model is based on a course developed jointly by the  International Union against Tuberculosis  and  Lung Disease  and  Médecins Sans Frontières-Luxembourg (LuxOR). The specific SORT IT program that led to these publications included a joint implementing partnership between TDR, the  Institute of Tropical Medicine, Antwerp, Belgium , the  University of Gondar, Ethiopia , The WHO country office in Ethiopia,  Médecins Sans Frontières, Luxembourg (LuxOR) , and The  International Union Against Tuberculosis and Lung Disease ,  Paris France.
To determine the population for analysis in the present study, we used 1998-2002 as the study baseline, corresponding to the years when detailed data on nut consumption were first available. We focused on this relatively early time period for baseline, since we were concerned that, at older ages, underlying health could influence diet choices and lead to bias in analyses. Moreover, scientifically, many chronic diseases and health conditions take years to develop, and thus, it is biologically likely that risk factors at earlier timepoints are most important to later health. We defined healthy aging from the 2012 follow-up questionnaire, which was the first time we collected information simultaneously on chronic diseases, memory concerns, physical function, and mental health from the full cohort.
Among the 55,318 nurses who responded to the questions on healthy aging in 2012, we excluded 13,353 women who had already been diagnosed with one of the 11 major chronic diseases of interest (see list below) at study baseline and 7,479 women who did not provide baseline dietary data in 1998 and 2002, as well as 555 women who were missing adequate data to calculate diet quality (AHEI score). us, 33,931 participants were included in analyses.
Acknowledgments e Nurses'  Health Study  was supported by the  NIH  (grant  UM1CA186107 ). An unrestricted research award from the  California Walnut Commission , Sacramento, CA, US, was provided to  Francine Grodstein. A fellowship award from the  Instituto de Salud Carlos III Miguel Servet in Spain  (grant  CP12/03299 ) was given to  Aleix Sala-Vila.
Puncta immediately adjacent to POMC Cre:tdTomato neurons (arrows) were counted after 3D rendering using Imaris software. (D) Ratio of VGlut1:VGAT immunoreactive puncta adjacent to POMC Cre:tdTomato neuron somata. Pre, n = 62 cells from 3 mice; 1 h SD, n = 71 cells from 3 mice, 1 h HFD, n = 56 cells from 3 mice. (E) Image of arcuate POMC Cre:tdTomato neurons (in the dark) acquired by electron microscopy at 36,000 and 330,000 magnification. Glial elements surrounding POMC Cre:tdTomato neuron somata are pseudocolored in magenta. (F) Quantification of the astrocytic coverage on POMC Cre:tdTomato neurons according to the prandial state. Glial ensheathment on POMC Cre:tdTomato neurons was measured with ImageJ software on digitalized electron microscopy images acquired at the original magnification of 330,000.
Pre, n = 14 cells from 3 mice; 1 h SD, n = 22 cells from 4 mice; 1 h HFD, n = 28 cells from 4 mice. See also Table  S2. Data are means ± SEM. Statistical analyses were performed on labeled cells. Asterisks indicate statistical differences between groups: ***p < 0. 001. In (B) and (D), data were analyzed using Kruskal-Wallis test followed by Dunn's test. In (F), data were analyzed using ANOVA followed by Newman-Keuls test. Kim et al. , 2014). Here we tested whether postprandial elevation of blood glucose mediated the neuroglial remodeling we observed on a standard diet. We first continuously monitored glycemia of mice following food consumption by telemetry using glucometer implants. Despite some differences in postprandial blood glucose excursions that reflect the specific glycemic index of the diets, glycemia increased with either a standard diet or HFD, peaking similarly 20-22 min after food introduction, confirming a regulated postprandial hyperglycemic response after food consumption (Figure  3A ; Figure  S3A ).
rAAV8/GFAP-Ha-hM4D(Gi)-IRES-mCitrine particles express the Gicoupled hM4D receptor and mCitrine reporter under control of GFAP promoter. (A) Representative images showing mCitrine immunoreactivity (green) overlapping with glial fibrillary acidic protein (GFAP) staining (red). Images were acquired with a confocal microscope using 310 and 363 oil immersion objectives. 3V, third ventricle. Scale bar, 30 mm. (B) Representative images showing adeno-associated virus (AAV)-transduced mCitrine-positive cells (green) enwrapping arcuate Tomato-positive POMC Cre:tdTomato somata (red). Images were acquired with a 363 oil immersion objective on a confocal microscope, and volume rendering was done using Imaris software. Scale bar, 30 mm. (C) Quantification of astrocytic coverage on POMC Cre:tdTomato neurons during the postprandial state in hM4D(Gi)-transduced mice fed an SD diet and treated with NaCl or 1 mg/kg CNO (i.
, 2018; Lam et al. , 2017; Sohn and Williams, 2012; Williams et al. , 2010). Thus, our data suggest that taking into account the prandial state of animals could minimize the apparent functional heterogeneity of POMC neurons. In this study, we used chemogenetic tools to interrogate the function of hypothalamic astrocytes. As already reported by others  (Jones et al. , 2018) , we found that artificial chemogenetic manipulation with the inhibitory hM4D(Gi) receptor rapidly reduces intracellular cyclic AMP in GFAP-expressing cells such as astrocytes. We also found that CNO injection in mice transduced with the inhibitory DREADD induced robust glial retraction around POMC somata. This is consistent with previous pharmacological studies showing that astrocyte stellation is regulated within minutes by intracellular cyclic AMP level  (Vardjan et al.
Continuous monitoring of feeding behavior Food intake was recorded in undisturbed mice using the automated BioDAQ System (Research Diets, Inc. ). Mice were habituated for one week to single housing in a regular cage and feeding through a food hopper. Cages contained enrichment and bedding material. Water was given ad libitum from regular bottles placed on the top of the cage. Mice showed normal food intake and body weight gain within 3 days of habituation. During habituation and experiments, food hoppers were closed for 2 hours before light off. Data were collected continuously during experiments and were analyzed using the BioDAQ DataViewer software (Research Diets, Inc. ). For analysis, the minimum food amount was filtered at 0. 02 g and the minimum inter-bout interval, which separates two feeding bouts, was defined as 5 s.
Continuous monitoring of glycemia Telemetric devices (Data Sciences International) were implanted according to the manufacturer instructions. Briefly, mice were anesthetized with isoflurane and received ip injection of 10 mg/kg buprenorphine (Buprecareâ 0. 3 mg/ml) and 10 mg/kg ketoprofen (Ketofenâ 10%). The sensor (HD-XG; Data Sciences International) was placed into the left carotid toward the aortic arch. After surgery, animals were allowed to recover at 35 C and received a daily ip injection of ketoprofen (Ketofenâ 10%) for 3 days. During a 7-day recovery period, individualized mice were carefully monitored for body weight and behavior and had facilitated access to food. Implanted animals were then installed on their own receiver. An initial calibration was accomplished through an oral glucose tolerance test (2 g/kg of 30% glucose).
Open Field Test An open field test was used to assess locomotor activity and anxiety-like behavior in mice. The open field consisted of a plexiglas chamber (43. 5 cm x 43. 5 cm x 30. 5 cm) placed inside a frame containing infrared photobeams sources and sensors, which detect the presence of animal in a spatial and temporal manner along the three axis (x, y, z) (Activity Monitor system; Med Associates, Inc. ). Animal activity was monitored and analyzed with the Activity Monitor software (Med Associates, Inc. ). For analysis, arena was divided into two zones: center and periphery. For trial, food was removed 2 hours at the end of the light period, as indicated before. Mice received a single injection of either NaCl or CNO solution intraperitoneally, one hour before light off. At light off, animals were fed for 3 hours using standard diet and then placed into the arena, in a randomized corner.
4 Na 2 -GTP (E K+ = À100. 7 mV; E Cl À = À72. 0 mV; pH = 7. 2-7. 3, adjusted with KOH 1N; osmolarity 290 mOsmol/L, adjusted with sucrose) in presence of picrotoxin (25 mM) and tetrodotoxin (TTX; 500 nM). For recording of mIPSC in whole-cell configuration (voltage-clamp À65 mV), electrodes were filled with a cesium-chlorure Intracellular Solution (Cs-IS), which contain (in mM) 140 CsCl; 3. 6 NaCl, 1 MgCl 2 , 10 HEPES; 0. 1 Na 4 -EGTA, 2 Na 2 -ATP, 0. 4 Na 2 -GTP; (E K+ = -N; E Cl À = 3. 68 mV; osmolarity = 280 mOsmol/L, adjusted with sucrose; pH = 7. 2-7. 3 adjusted with NaOH 1N) in presence of TTX (500 nM). Electrical activities were recorded with an amplifier (EPC10; Heka Elektronik) connected to an acquisition interface (LIH 8+8; Heka Elektronik). Acquisitions were done with Patchmaster software (Heka Elektronik) and data were analyzed with Clampfit software (10.
Morphometric study for determination of neuronal perimeter and glial coverage was computed with the open source ImageJ software on digitalized images acquired at x 6,000-8,000 original magnification. For synaptic puncta analysis, microphotographs from confocal microscopy were analyzed with Imaris software (Bitplane). Surface rendering was done for each channel prior to contact analysis. Contacts between pre-synaptic puncta and POMC neurons were determined using the ''Surface Surface Contact Area'' plug-in (Imaris, Bitplane). To assess somatic puncta, numbers of contacts between VGAT-positive elements and VGluT1-positive elements were counted on a single tdTomato-positive soma. To assess peri-somatic puncta, contacts between synaptic puncta and tdTomato-positive fibers were determined on 3 regions of interest (ROI) per image.
ACKNOWLEDGMENTS We thank the animal facility of the  CSGA (Dijon) , especially  Anne Lefranc ,  Martin Bertomeu ,  Laurence Decocq ,  Alexia Mathou , and  Virginie Cadiou , for animal care. We also thank the animal facility of the  University Paris Diderot (Paris)  for animal care. We thank the  DImaCell Imaging Facility , especially  Jeannine Lherminier  and  Christine Arnould , for precious help. We thank  Dr. Bruno Patris  for fruitful discussions regarding behavior studies and  Dr. Vincent Gigot  for image analysis. We gratefully acknowledge  Dr. Serge Luquet  for valuable advice and thoughtful input. We also thank  Dr. Sylvian Bauer  for stimulating discussions regarding study of the prandial state in mice. This work was supported by  Agence National pour la Recherche (ANR)  grant  ANR-13-JSV1-0003-01 , the  French ''Investissements d'Avenir'' program , project  ISITE-BFC  (contract  ANR-15-IDEX-0003 ), and  Region Bourgogne  grant  FEDER 2010 O0431SGO003S03440  (to A.
B. ); by the  IDEX UCA Academia 3 program  (to C. C. ); by  Fondation de la Recherche Medicale/Equipe FRM   DEQ20150331738  and  ANR ''Investments for the Future'' LABEX SIGNALIFE program  reference  ANR-11-LABX-0028_01  (to C. C. and J. -L. N. ); by  FRM Camille Woringer Award   2016  and  CAPES/COFECUB Sv 848-15  (to J. -L. N. ); and by  FRM subvention DRM20101220421 and Medisite Foundation  award  2017  (to C. R. ). R. D. was supported by The Continuous Glucose Telemetry Award 2018 sponsored by Data Sciences International.
In M6 mice, we used the geometric mean of three internal control genes (Betaactin, ribosomal protein lateral stalk subunit P0 (36B4) and Hypoxanthine phosporibosyl-transferase 1 (HPRT1)) to normalize gene expression, using geNorm software as already described  19. In E18. 5 fetuses, D0 and D7 mice, ribosomal r18S RNA was used as a housekeeping gene, since all other normalization genes varied during renal development. The primer sequences for these internal control genes are presented in Table  1. The relative expression of each gene is expressed as the ratio of attomoles of specific gene per geometric average of control gene expression as determined by geNorm (see above) or femtomole of r18S in pups. The final results represent relative expression normalized to that obtained in samples from control mice at each age, which was arbitrarily set at one.
Acknowledgements The authors are indebted to  Clement Journé  and to the  FRIM facility  ( Fédération de Recherche en Imagerie Multi-modalités, Faculté de Médecine X. Bichat, Paris, France ) for the systolic blood pressure measurements. The authors would like to acknowledge the  French Pediatric Society (SFP) , the  French Pediatric Endocrinology Society (SFEDP)  and the  French Endocrinology Society (SFE)  for the grants awarded to  Laurence Dumeige. Marc Lombès  and  Laetitia Martinerie  received funding from the  PremUP Foundation ,  INSERM, University Paris-Sud , from the  French Pediatric Endocrinology Society (SFEDP)  and from the  Académie de Médecine (Nestlé Waters Prize). The sponsors or funding organizations had no role in the design or conduct of this research. Laurence Dumeige  has received a grant from the  French Pediatric Society (SFP) , the  French Pediatric Endocrinology Society (SFEDP)  and the  French Endocrinology Society (SFE).
Therapeutic strategy for triglycerides, based on omega-3 acids This strategy is based on old epidemiological databases dating back to the 1970s. At that time, Bang et al. [8]  observed the Greenland Inuit and Danish controls. The Greenland Inuit are characterized by a low incidence of coronary artery disease and high levels of eicosapentaenoic acid in the vessel wall instead of arachidonic acid. In the GISSI study  [9] , 11,324 patients with myocardial infarction, 5% of whom were on statins, received 0. 9 g/day of a combination of eicosapentaenoic acid and docosahexaenoic acid in a 1. 2:1 ratio. After a 3. 5-year follow-up, there was a significant 10% decline in a combined endpoint of death, non-fatal myocardial infarction and non-fatal stroke; there was also a significant 45% decline in sudden death.
These findings provide evidence that the MiDaf16-like1 and MiSkn1-like1 genes are indeed upregulated in M. incognita under oxidative stress, and that these genes modulate defense genes in the nematode. Next, we evaluated the expression profiles of these two TFs in the different life stages of M. incognita. Initially, we mined transcriptome datasets available in a public database (NCBI SRA) generated from the egg mass, J2, J3, J4, and M. incognita females  39. These in silico results showed that the two genes are potentially expressed in all life stages of the nematode, with the MiDaf16-like1 gene having the highest number of reads (RPM) mapped in the J2 stage, while the MiSkn1-like1 gene had the largest number of reads mapped in stage J3 (Fig. 3F ). In addition, we used these same transcriptome datasets to evaluate the expression profiles of all 19 MiDaf16-like and 4 MiSkn1-like genes in the different life stages of M.
5B ), or both simultaneously (Supplemental Fig. 5C ) were downregulated in M. incognita during parasitism in single-or double-gene silencing plants, respectively. In addition, we observed that at least some defense genes from the Daf-16 and Skn-1 networks were consequently also downregulated during the parasitism of nematodes in single-or double-gene silencing plants (Supplemental Fig. 5D ). In M. incognita infecting both single-or double-gene silencing plants, the MiPRDX2-like1, MiSod3-like1, MiGPX-like1, MiGst1-like1, and MiSod1-like1 genes were simultaneously downregulated at a level of approximately 80%, with the exception of the MiGst1-like1 gene in one of the double-gene silencing plants. The in silico analyses using the same transcriptome datasets available in the public database (NCBI SRA) generated from the egg mass, J2, J3, J4, and females of M.
Material and Methods In silico analysis of Daf-16 and Skn-1 tfs from nematodes. All gene sequences of M. incognita were retrieved from BioProject ID PRJEB8714 (sample: ERS1696677)  38  from the online WormBase database version WBPS13  67. Pairwise identity matrices from nucleotide and amino acid sequences were generated using the Sequence Demarcation Tool Version 1. 2 software  68. Positional conservation of the FOXO (Forkhead box) domain was generated from multiple sequence alignment using Color Align Conservation software  69. In addition, the conserved domains in gene sequences were checked using the Conserved Domain Database (CDD)  70. Phylogenetic analyses were performed using the Phylogeny. fr web service  71. For this, sequences were aligned with MUSCLE software  72  , and the alignment was curated by the Gblocks model.
Then, phylogenetic analyses were performed using the maximum likelihood method by PhyML software using the Approximate Likelihood-Ratio test (aLRT) SH-like branch support and GTR and WAG substitution models for nucleotide and amino acid sequences, respectively. Phylogeny trees were generated and visualized by TreeDyn software also implemented in this same web service. In addition, the gene and protein sequences from other nematode species used in these sequence analyses were also retrieved from the WormBase database version WBPS13 database. The expression levels of MiDaf16-like1, MiSkn1-like1, and some genes from their networks at different stages of the M. incognita life cycle were determined using transcriptome datasets (BioProject number: PRJNA390559) retrieved from the BioSample database (NCBI) (Supplemental Table  2 ).