dataesr/scientific-paragraphs-categorization huggingface dataset:
Case detection and data collection: all suspected cholera cases among refugees and the neighbouring community were referred to cholera treatment centres (CTC). We identified the cases by visiting the CTC and reviewing line lists and medical records. We collected retrospective clinical, demographic and epidemiological data using structured case report. Data was uploaded and fed directly to an excel database. Inclusion criterion was any person who fit the case definition of cholera and sought treatment at the cholera treatment centre and whose records could be found in patient records or line lists. Exclusion criterion was anyone who did not match the inclusion criteria. Statistical analysis: we analysed data using STATA version 15 (StataCorp, College Station, TX). We used frequencies and percentages to describe the socio-demographic characteristics of the study population.
Statistical analyses All statistical analyses were carried out using R-Studio software version 1. 1. 442 [32]. The data were pooled across species, and basic descriptive statistics, including arithmetic mean, median, standard deviation (SD), minimum and maximum, were obtained for each parameter. As no variable was normally distributed, non-parametric statistical tests were used. Mann Whitney test was performed for all the quantitative parameters (age, iron, AST and CK activities, cholesterol, total proteins, albumin, SOD and GPx) in order to compare BR and WR. Graphic assessment of the SOD and GPx data showed a potential discriminant threshold (>1500 g/Hb and >300 U/gHb, respectively) between the 2 populations of rhinos. As a result, these data were transformed into categorical variables ("Normal" versus "High") for the statistical analysis.
When it comes to maintaining hydration, examining compliance with standards and norms expressed either as water intakes in mL/d or as ratio on water intakes to calories (kcal/d) would be of additional interest. The question was whether any population subgroup was failing to maintain adequate water intakes, as specified by the Institute of Medicine (IOM). The present analyses were based on the nationally representative NHANES 2011-2016 dietary intakes database for children (4-18y) and adults (≥19y) in the US, including the most recent 2015-2016 cycle  [14]. The present goals were to examine consumption patterns for water and other beverages by socio-demographic variables, including education and incomes and to explore consumption trends between 2011 and 2016. The adequacy of total water intakes, as compared to the IOM recommendations was also examined.
Dietary intake databases Consumption data for drinking water, beverages, and foods came from three cycles of the nationally representative NHANES, corresponding to years 2011  -12, 2013-2014, and 2015-2016 [15]. The three NHANES cycles provided a nationally representative sample of 7453 children (aged 4-18y) and 15,263 adults (aged >19y). The NHANES 24-h recall uses a multi-pass method, where respondents reported the types and amounts of all food and beverages consumed in the preceding 24-h from midnight to midnight  [16]. The multi-pass method was conducted by a trained interviewer using a computerized interface  [17]. Respondents first identified a quick list of foods and beverages consumed. The time and occasion for each food item was also obtained. A more detailed cycle then recorded the amounts consumed, followed by a final probe for any often-forgotten foods (beverages, condiments).
The present results were for mL of water content from water and selected beverages and not for the volume of the beverages themselves (which may not be 100% water). Moisture from foods was calculated as well. The USDA Food and Nutrient Database for Dietary Studies (FNDDS) was used to calculate the energy content of the diet based on caloric beverages and solid foods  [19]. These data were used to calculate the ratios of water intakes (mL/d) to energy intakes (kcal/d) for each NHANES participant.
Statistical analyses The survey-weighted mean intakes of total water were evaluated overall and by age group, gender, race/ethnicity, and family income-to-poverty ratio. All analyses accounted for the complex survey design of NHANES and reflect dietary behaviors of the US adult population from 2011 to  16. Analyses of what proportion of NHANES 2011-2016 participants met the IOM recommendations for adequate hydration were based on habitual water intakes established using the National Cancer Institute method  [24, 25]. The probability of water consumption was estimated to be 1. Subsequent analyses specified the consumption-day amount using linear regression and intake data from 24-h recalls. The consumption of tap and bottled water was evaluated separately for the entire population and for population subgroups.
Survey-weighted means and corresponding standard errors were obtained. Hypothesis testing was based on a linear trend test which treats the NHANES cycles as a continuous variable. This trend test may be sensitive to extreme values in either the first (2011-12) or last (2015-2016) cycle and may not reflect weak non-linear trends. All analyses were conducted using SAS software, version 9. 4 (SAS Institute Inc. , Cary NC, USA) by using SURVEYREG, SURVEY-MEANS and SURVEYFREQ procedures.
Source locations of water and other beverages Most bottled water and other beverages (1076 mL/d or 75% of total) came from the store. Of this, 339 mL/d came from store-bought bottled water and 737 mL/d came from store bought other beverages. Figure  4  shows the distribution of store bought other beverages by category. Smaller amounts of other beverages and virtually no bottled water were obtained from restaurants (fullservice [FSR] and quick-service [QSR]), work/school or someone else. Tap water consumption (589 mL/d) was almost evenly split between tap water at home (288 mL/d) and tap water away from home (301 mL/d); as previously noted, it was not possible to differentiate tap water more finely due to the manner of data collection.
First, total dietary water intakes from drinking water, other beverages, and foods have remained stable from 2011 to 2016. Total amounts of water and other beverages have also remained constant. We now show for the first time that the observed significant drop in SSB consumption has been offset by an increased consumption of plain drinking water. On the average, about 62% of drinking water came from the tap, a major increase from 56% observed in the 2005-2010 NHANES database  [11]. Second, and contrary to expectations, tap water consumption was higher at higher incomes, whereas the consumption of bottled water was higher at lower incomes. Consistent with previous findings, non-Hispanic whites and higher income groups in the present study consumed the largest amounts of tap water  [11]. By contrast, Mexican Americans drank the most bottled water and the least tap water.
66 µM SYTO9, a cell-permanent green dye that label all bacteria, and 10-µM propidium iodide (PI), a cell-impermeant nucleic acid dye that contrasts dead bacteria in red (Invitrogen, Carlsbad, CA, USA). SYTO9 and PI were excited with an argon laser set at 488 nm, and the emitted fluorescence was collected in the range 500 to 550 nm for the green signal and 650 to 70 nm for the red signal. For each biofilm analyzed, a series of 512 × 512 pixel images was acquired every micrometer on the whole biofilm thickness. Three-dimensional projections of the biofilm were reconstituted using IMARIS software. Quantitative biovolumes of the biofilms were extracted from HCS-CLSM images using ImageJ [17].
Statistics Results are expressed as the mean ± the standard error of the mean (SEM) of three experiments with duplicate determinations. The statistical analyses were performed using GraphPad Prism version 8 (GraphPad Software, San Diego, CA, USA). One-way ANOVA was carried out, followed by Dunnet's or Tukey's multiple comparisons test. For the three-factor analysis, a two-way ANOVA followed by Sidak's multiple comparison test was used. p-values < 0. 05 were considered significant.
Analysis of Biofilm Formation by the Selected Lactobacilli Using Confocal Laser Scanning Microscopy (CLSM) We analyzed the biofilm formation by the selected strains, in microscopic-grade 96-well plates, at 0, 4, and 24 h by using HCS-CLSM. The representative images were treated with IMARIS dedicated software (Figure  3a ). Virtual shadow projections of the biofilm are represented on the right side of the images. The three isolates, LC03, LR04, and LGG, formed short chains that were visible after the adhesion (0 h). The chains formed by the LC03 strain were over-winded. For these three strains, the morphotypes observed were not specific to the adherent populations, as they were also observed for the planktonic bacteria in the stationary phase. After a short period of incubation (4 h), the adherent chains of the three isolates elongated to form an aerial structure.
05 to 15 Hz) and converted into a digital signal (MacAdios analog-to-digital 14-bit converter; GW-Instruments, Somerville, MA) at a sampling rate of 100 Hz. Calibration of the plethysmograph was done by injecting 100 µl of air into the measurement chamber via a Hamilton syringe. A homemade software (derived from Superscope II software; GW-Instruments, Somerville, MA) detected the onset and amplitude of each breath and calculated the breath duration (and thus the respiratory frequency, Rf) and the tidal volume (VT) was calculated according to the Drorbaugh and Fenn's equation  [18, 19]. Minute ventilation (VE) was calculated as the product of Rf by VT. Estimations of the concentrations of O2 and CO2 inside the animal chamber were obtained from the outflow concentrations using an infrared CO2 analyzer and a paramagnetic O2 analyzer (Arelco, Fontenay-sous-Bois, France), calibrated with known gas mixtures.
Lower blood oxygen content after ethanol withdrawal in adult CPEE rats We next investigated whether this persistent hypoventilation was associated with a change in arterial blood oxygen parameters. One month after ethanol withdrawal, the acidbase equilibrium and the hematocrit in CPEE rats were similar to that of rats never exposed to ethanol (p>0. 05, Table  2 ). Nonetheless, the total blood oxygen content was significantly lower when compared to control rats (p<0. 05, table  2 ), suggesting a long-term alteration of the blood oxygenation status. This was not associated to changes in oxygen saturation (Pearson product moment correlation, P>0. 05). A subset of animals was tested for both plethysmography and blood sample analysis (n=8, including 3 CPEE withdrawn rats). For these rats, there was a significant positive correlation between resting minute ventilation and total oxygen content (data not shown, Pearson product moment correlation, p<0.
Subjects and Setting Patients with sudden olfactory dysfunction as an isolated symptom, of as the first symptom in association with other otolaryngologic symptoms developed at a later stage, were recruited through a public call of the University of Mons (Mons, Belgium). After evaluation of inclusion and exclusion criteria, they were included in the study and their clinical data collected using a patient-reported outcome questionnaire. The following inclusion criteria were considered: patients presenting sudden olfactory dysfunction at the onset of the COVID-19, patients who are native French-speaker, and patients clinically able to fulfill the questionnaire. The following exclusion criteria were considered: patients with olfactory or gustatory dysfunctions before the COVID-19 epidemic; patients with a history of chronic rhinosinusitis or nasal polyposis; and history of nasal surgery and pregnancy.
Test environments Each panelist evaluated one beer sample per day in an individual sensory booth in the R&D center at Asahi Breweries, Ltd. During the evaluation session, the panelists were not allowed to drink water to avoid disrupting evaluations of the beer samples. They were only allowed to eat non-topping salt crackers plain crackers with no salt topping while they were drinking the beer samples. All the panelists, including the expert panelists in Phase 1, were instructed to drink the beer samples freely in the way they usually consumed beer. Use of p. 10 phones or personal computers (PCs) for work was prohibited during the sessions, but magazines were available in the sensory booths for the participants to read. Following the instructions displayed on the PC screens in the isolated sensory booth, each panelist performed sensory and hedonic evaluations throughout the session, and the data were captured using the TimeSens© software (INRA, Dijon, France).
Dynamic evaluation of wanting Just before the panelists wanted to have the 3 rd sip, they evaluated how much they "want to drink it continuously" on a linear rating scale ranging from "I do not want to drink it continuously at all" to "I want to drink it continuously very much. " The panelist's indication on the linear rating scale was converted in the TimeSens© software to a score ranging from 0 to 10 for data analysis purposes (the same procedure was used for the measurements described in section 2. 7. ). They repeated this wanting evaluation every 3 sips, just before the 6 th , 9 th , 12 th sips, and so on. Since the main focus of this study was to measure "the will of drinking" for beer drinkability, the greatest importance was placed on this dynamic evaluation of "wanting to drink continuously.
This structure assumes that the covariance between two wanting scores from two sips of the same evaluation decreases with the number of sips in between these two sips. The least square mean (LS-mean) and standard error were subsequently computed for each product, for each sip, and for each sip by each product, which enabled interpretation of the statistical differences among products, among sips, and among sips by each product, respectively. This analysis was accomplished using PROC MIXED in SAS 9. 4.
Cohorts and study variables FORCE was initially formed from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium. FORCE is an ongoing consortium project to study relationships of fatty acid biomarkers with health outcomes (http://force. nutrition. tufts. edu/) [19] [20] [21]. The current project included 17 prospective studies (cohorts and nested case-control or casecohort studies). These studies agreed to participate after confirming the inclusion criteria met: recruitment of adults aged 18 years or over and without prevalent diabetes at the time of fatty acid assessment; available data of circulating or adipose 16:0, 16:1n7, 18:0, and 18:1n9; and ascertainment of incident T2D (S1 Text). Other cohorts participated in FORCE for other projects  [19] [20] [21]  but did not contribute to this study because incident T2D were not ascertained.
: I have read the journal's policy and the authors of this manuscript have the following competing interests: JYW and RM report research support from Unilever for other projects on fatty acid biomarkers. LCDG reported receiving ad hoc consulting fees from the Life Sciences Research Organization. CH reported receiving fees for a conference from Novartis. IAB reported involvement in a research project partly funded by Unilever. JGR received research grants from Amarin and Astra-Zeneca. DM reported receiving ad hoc honoraria from Bunge, Pollock Institute, and Quaker Oats; ad hoc consulting for Foodminds, Life Sciences Research Organization, Nutrition Impact, Amarin, AstraZeneca, Winston, and Strawn LLP; membership in Unilever North America Scientific Advisory Board; and chapter royalties from UpToDate.
Acknowledgments The lead author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted, and that any discrepancies from the study as planned have been explained. Ulf Riserus ,  Jennifer G. Robinson ,  Cecilia Samieri ,  Mackenzie Senn ,  David S. Siscovick ,  Sabita S. Soedamah-Muthu ,  Nona Sotoodehnia ,  Qi Sun ,  Michael Y. Tsai ,  Tomi-Pekka Tuomainen ,  Matti Uusitupa ,  Lynne E. Wagenknecht ,  Nick J. Wareham ,  Jason H. Y. Wu ,  Renata Micha ,  Rozenn N. Lemaitre ,  Dariush Mozaffarian ,  Nita G. Forouhi.
No specific funding was received for this study. This manuscript does not reflect opinions or conclusions of any funding agency. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. FI, NGF, and NJW were funded by the  United Kingdom Medical Research Council Epidemiology Unit  core grant ( MC_UU_12015/5  and  MC_UU_12015/1 ); NJW,  NGF  and  AK acknowledge National Institute for Health Research (NIHR) Biomedical Research Centre Cambridge  ( IS-BRC-1215-20014 ); NJW is an NIHR Senior Investigator; IAB, by the  European Union  ( FP7  and  Horizon2020 ) and the  Dutch Scientific Organization (ZonMW) ; CS, by the  Foundation Plan Alzheimer ; and AAK, by the  NIH  training grant ( 3T32DK007703  and  T32CA009001 ). Alpha Omega Cohort was funded by the  Netherlands Heart Foundation  (grant  2000T401 ), the  NIH  ( NIH/ National Heart, Lung, and Blood Institute [NHLBI] , and  ODS , grant  R01HL-076200 ), and  Unilever R&D, Vlaardingen  (margarine production and distribution).
The  Cardiovascular Health Study (CHS)  was supported by contracts  HHSN268201200036C ,  HHSN268200800007C ,  HHSN268201800001C ,  N01HC55222 ,  N01HC85079 ,  N01HC85080 ,  N01HC85081 ,  N01HC85082 ,  N01HC85083 ,  N01HC85086 , and grants  R01-HL-085710 ,  U01HL080295  and  U01HL130114  from the  NHLBI , with additional contribution from the  National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by  R01AG023629  from  NIA. The  Framingham Heart Study  was supported by the  NHLBI  in collaboration with  Boston University  (Contract No. N01-HC-25195 ). The Health Professionals Follow-up Study was funded by  UM1 CA167552 ,  R01 HL35464 ,  AA11181 ,  HL35464 ,  CA55075 ,  HL60712 , and  P30 Nationale pour la Solidarite et l'Autonomie. The  Uppsala Longitudinal Studies of Adult Men 50  and  70  were funded by the  Swedish Research Council for Health, Working Life and Welfare, Uppsala City Council, Swedish Research Council , and  Swedish Diabetes Foundation (UR).
Disclosures This article was based on a series of meetings organized and sponsored by Kyowa Kirin International. Medical writing support was provided by ApotheCom and funded by Kyowa Kirin International. FA consults for and has received lecture fees from KKI. JB consults for Kyowa Kirin International, Amgen, and Bayer and has received lecture fees and grants from Kyowa Kirin International. MLB was previously employed at the University of Florence, Italy, in the last 2 years. She has consulted for Alexion, Amolyt, Bruno Farmaceutici, Calcilytix, Kyowa Kirin, and UCB and has received honoraria, grants, or speaker fees from Amgen, Bruno Farmaceutici, Calcilytix, Kyowa Kirin, Abiogen, Alexion, Echolight, Eli Lilly, SPA, Theramex, and UCB. PF consults for Kyowa Kirin/Ultragenyx and has received lecture fees and a research grant from Ultragenyx.
She is spokesperson for the German network of rare bone diseases, member of the ECTS Action group of Rare Bone Diseases, member of the ESE Education committee and representative in the PARAT programme of ESE, and treasurer of the German Society of Osteology. MV consults for Astra Zeneca, Vifor Pharma, Otsuka, and Kyowa Kirin. He previously consulted for Medice and Amgen, and has received lecture fees and grant support from Amgen, Vifor, and Fresenius Medical Care. He has participated on a Data Safety Monitoring Board or Advisory Board in the OASIS and DIPAK trials. He is on the KDIGO committee on CKD-MBD and the chair of the ERA-EDTA working group on CKD-MBD. CW consults for Kyowa Kirin, Ardelyx, and Medice, and previously consulted for Chugai. He has received lecture fees in the last 2 years from Kyowa Kirin, Medice, Advicenne, and Salmon Pharma and grant support from Chugai.
Acknowledgments Medical writing support was provided by  Afsaneh Khetrapal of ApotheCom, London, UK , and funded by  Kyowa Kirin International. Authors' roles: All authors (FA, JB, MLB, PF, MKJ, OM, AR, MR, HS, DT, MV, and CAW) were involved in the writing, reviewing, and editing of the manuscript. All authors approved the final version of the manuscript. Open access funding enabled and organized by Projekt DEAL.
Procedure Patients were seen at baseline for enrollment, initial data collection and treatment at day-0, and again for daily followup during a 14 day period. Each day, patients received a standardized clinical examination and when possible, a nasopharyngeal sample was collected. All clinical data were collected using standardized questionnaires. All patients in the Marseille center were proposed oral hydroxychloroquine sulfate 200 mg, three times per day for ten days (in this preliminary phase, we did not enroll children in the treatment group based on data indicating that children develop mild symptoms of COVID-19  [4]  ). Patients who refused the treatment or had an exclusion criteria, served as controls in the Marseille centre. Patients in other centers did not receive hydroxychloroquine and served as controls.
ET-1 measurement by EIA ET-1 concentration in the OM primary culture medium was measured using a commercial EIA kit (Cayman Chemical, Ann Arbor, MI, USA) according to the manufacturer instructions. Plates were read at 405 nm and compared to a standard curve. Data are presented either as the raw values of ET-1 concentrations or divided by the slope of NADH oxidation in the presence of intracellular LDH content. This last measure accounts for the production of ET-1 relative to the cellular population (n = 5/4/5 for day in vitro 3 (DIV3), DIV5 and DIV7 respectively from two independent primary cultures). qPCR Total RNA was extracted from OM primary culture cells using the Trizol method and subsequently treated with DNase I. OligodT first strand cDNA were synthesized from 5 μg total RNA by the Superscript II reverse transcriptase (Invitrogen, Cergy Pontoise, France) following the manufacturer recommendations.
Statistical analysis Group data presented in the text and figures are expressed as mean ± standard error of the mean (SEM). Statistical significance was assessed using non-parametric Mann-Whitney U tests, one-way or two-way analysis of variance (ANOVA) followed by Bonferroni multiple comparison post-hoc tests (GraphPad Prism 5. 0, GraphPad software Inc. , La Jolla, San Jose, CA, USA). A probability value of P < 0. 05 was set for significant differences.
Acknowledgments: We would like to thank  Birte Nielsen  ( Neurobiologie de l'olfaction, Institut National de Recherche Agronomique (INRA), Université Paris-Saclay, Jouy-en-Josas, France ) for her help in improving the manuscript,  Patrice Dahirel  and  Aurelien Raynaud  ( Neurobiologie de l' olfaction, Institut National de Recherche Agronomique (INRA), Université Paris-Saclay, Jouy-en-Josas, France ) for animal care and treatments;  Stéphanie Rimbaud ,  Denise Grébert ,  Sacha Muszlak  ( Neurobiologie de  l' olfaction,  Institut National de Recherche Agronomique (INRA) ,  Université Paris-Saclay , Jouy-en-Josas, France) and  Nicolas Martin  ( MSc Student, Université Versailles St Quentin, France ) for technical help.
Mass spectrometry The samples analyzed by ssNMR were also analyzed by matrix-assisted laser desorption/ionization (MALDI)-time-of-flight (TOF) MS. For the measurements, an ionic preparation of 2,5-dihydroxybenzoic acid (DHB) and N,N-dimethylaniline (DMA) was used as the MALDI matrix, as described in  (Ropartz, 2011). Briefly, the matrix consists of a mixture of DHB and DMA (DHB 100 mg. ml -1 , in H2O/acetonitrile/ DMA (1:1:0. 02)) and was mixed with the samples in a 1:1 ratio (v/v). The mixture (1 μL) was then deposited on a polished steel MALDI target plate. MALDI measurements were then performed on an Autoflex Speed MALDI-TOF/TOF spectrometer (Bruker Daltonics, Bremen, Germany) equipped with a Smartbeam laser (355 nm, 1000 Hz) and controlled using the Flex Control 3. 0 software package. The mass spectrometer was operated with positive polarity in reflectron mode.