dataesr/scientific-paragraphs-categorization huggingface dataset:
Randomization and Blinding The blinding and randomization process was as follows: JMB added 1 % squalamine to five out of 20 randomly selected tubes containing 20 g of petrolatum-based ointment. The tubes were anonymized and identified with a unique number from 1 to 20. Only JMB knew the randomization key; he was neither involved in the drug administration nor in the clinical monitoring of patients. Twenty patients received daily topical application (either squalamine or placebo) during 3 weeks. The treatment evaluated and placebo had the same visual appearance. Neither the clinician in charge of the patient's treatment follow-up nor the patient knew the exact nature of the administered treatment. The unblinding occurred after the ''freezing'' of the database before the statistical analysis.
Statistical Analysis Patients were randomized in a 1:3 ratio in the group treated with squalamine or in the placebo group. To test the difference between 2 % cure rate in placebo group and 80 % cure rate in the squalamine group, with a 5 % type I error and 90 % power, number needed to treat was 20 patients: five in the squalamine group and 15 in the placebo group. All data were collected onto case report forms that were kept confidential and checked daily by a study supervisor. Data were entered into a MS ACCESS TM database and checked by the supervisor. The data were presented as mean (±standard deviation) for quantitative variables and percentages for qualitative variables. The primary endpoint was complete cure. The secondary endpoints were safety, mycological cure (negative mycology), and partial clinical response (hair-growth score and/or reduction in the diameters of lesion).
The proportions of patients with an endpoint in either squalamine or placebo treatment groups were compared using the Fisher's exact test. The same analysis was performed considering each lesion of tinea capitis, using the generalized estimating equations option to account for the non-independence of multiple tinea capitis lesions in a given patient. Statistical analyses were performed using SAS, version 9. 2 (SAS Institute, Cary, NC). All tests were two-sided with a p \ 0. 05 significance level.
Results The study was conducted in May 2011. Twenty male patients aged 6-15 years were included. Five were treated with topical squalamine and 15 with placebo. The data at days 0, 7, and 21 of both squalamine and placebo groups are tabulated in Table  1. There were no lost to follow-up. The patients' baseline characteristics were homogenous among treatment groups (Table  1 ). At day 0, there were 10 and 27 lesions of tinea capitis, mean number of lesions per child 2 ± 1. 2 and 1. 8 ± 0. 8 (p [ 0. 05), in the squalamine and placebo groups, respectively. Microsporum audouinii was the predominant pathogen in this study. The MIC of terbinafine was\0. 25 mg/l in all 21 isolates. All patients in the squalamine group were infected by M. audouinii. The means of MICs of squalamine were 11. 2 ± 4. 4 and 12.
Firstly, a differential LC-MS-based metabolomics approach was used to provide an overview of all the UV-induced modifications of the chemical compositions of the extracts studied. Several biomarkers, including characteristic phytoalexins of each species, were putatively identified based on molecular formula assignment from the high-resolution MS (HRMS) data recorded. Secondly, variations in the antioxidant activity were used to obtain an initial indication of the UV-C-induced chemical changes in a crude extract. Radical-scavenging activity was assessed with both DPPH and ABTS assays, and antioxidant activity was assessed with the oxygen radical absorbance capacity (ORAC) assay.
The LC-MS data, recorded in both positive (PI) and negative (NI) electrospray ESI ionisation modes, were processed using MZmine 2. 10 to extract features characterised by their m/z ratio, retention time and area. Then, principal component analysis (PCA) was used as an exploratory step prior to multivariate supervised analysis (Figure  1B ). To evaluate rapidly when major modifications occur, a few leaves were analysed 24, 48 and 72 h after UV-C exposure. The quenching and extraction of leaves 48 hours after UV-C radiation revealed the most significant metabolomic variations. Differences between control and treated leaves were less pronounced when sampling was performed after 24 h. However, 72 h after radiation, degradation of the leaf surface was noticeable at many locations, and the variation between biological replicates was significant (data not shown).
Data Treatment and Analysis To evaluate LC-MS data, the PI and NI datasets were compared (RT: ±0. 2 min, m/z: ±10 ppm) after removing features identified as adducts or ion complexes (Venn diagram, Figure  2A ). The number of features detected varied significantly according to the species studied and the ionisation mode used. It was interesting that only 8% to 12% of detected features were common to PI and NI modes, thus emphasising the complementary information provided by both ionisation processes (Figure  2A ). To incorporate all these data, the PI and NI datasets from each species were concatenated, and multivariate data analysis (low level data fusion) was performed  [28, 29]. Unit variance scaling was used to reduce the effect of differential sensitivity between the ionisation modes. Overall, PCA displayed two well-separated clusters corresponding to control and UV-C-treated leaves along the first principal component for the three species studied (Figure  2B ).
Global Estimation of the Leaf Metabolome Modifications upon UV-C Treatment Following PCA, LC-MS datasets underwent OPLS-DA. A 7-fold procedure was used to cross-validate discriminant models of each species (V. vinifera model: R2Y = 0. 98, Q2Y = 0. 92; C. Antarctica model: R2Y = 0. 99, Q2Y = 0. 96; C. sativa model: R2Y = 0. 99, Q2Y = 0. 96). The VIP scores were used to rank the features according to their contribution to the discriminant model. Only VIP scores greater than one were retained for further analysis, because they are the most relevant for explaining the Y response (i. e. , UV-C leaf treatment response)  [30]. Then, significant features were subjected to an unpaired T-test (α = 0. 05), and only fold changes greater than five between control and UV-C treated samples were retained for further analysis.
Biomarkers of UV-C Radiation Significant features were annotated based on accurate HRMS exact mass spectral data and the use of heuristic filters  [31]  after deconvolution and adduct removal. Molecular formula determination and further cross-searching based on chemotaxonomy information were then performed. Putative identification was achieved using the molecular formula together with the botanical genus and family as queries in the Dictionary of Natural Products database  [32]. Unidentified metabolites were then matched using Lipidmaps  [33]  and the Plant Metabolic Network  [34] , generic databases related to plant metabolism. These labels correspond to level 2 and 3 IDs according to the Metabolite Identification Task Group  [35]. Altogether, the putative identification of approximately 50% of the significant features (>5-fold changes) in each species was achieved (bar plots, Figure  3 ).
For instance, a systemic induction of phosphatidic acid and lysophospholipids was found in wounded tomato leaves  [40]. The possible connection of phospholipids to jasmonates is illustrated by the fact that silencing phospholipase D in rice limits the induction of jasmonic acid levels  [41]. Although the untargeted LC-MS profiling conducted here did not reveal any induction of jasmonates, our data reveal some evidence of cell membrane reconfiguration upon UV-C radiation. a For the DNP database, the molecular formula was crossed-filtered using the genus name or family name. The number of hits is indicated in brackets; b In the case of more than one hit, the annotation is indicative of a compound characteristic of the class; * Comparison with pure standard. The fold change indicates the ratio of intensity (up-regulation) of a given feature in the UV-C leaves compared to control.
Some of these compounds remain unknown since they are not yet listed in databases and would require more extensive investigations to be identified. For instance, de novo induction of oxidised products compounds is expected since UV-C are known to increase ROS levels  [10]. This is illustrated by the high level of astringin detected in Vitis vinifera which is the oxydised product of piceid. Several of the identified compounds are secondary metabolites with assessed biological activities. For instance, induced cinnamic acid amides in C. sativa leaves are known to play a role in cell wall reinforcement after tissue disruption  [48]. Stilbenes induced in Vitaceae species contribute to a constitutive defence against microbial diseases  [49]  and powdery mildew infestation  [50]. The antioxidant properties of stilbenes are also well documented  [51] , and in particular, several studies have shown stronger antiradical scavenging activities for cyclised stilbenes  [52].
Acknowledgments We thank  S. Bertrand  for his help in coding a specific Excel macro. J. -L. W is thankful to the  Swiss National Science Foundation (SNF)  grant No. 200020_146200  for supporting his plant metabolomics studies. We gratefully acknowledge the first nine  Grands Crus de Bordeaux  for the salary of Sylvain Schnee:  Château Ausone ,  Château Cheval blanc, Château Haut-Brion ,  Château Lafitte Rothschild ,  Château Latour ,  Château Margaux ,  Château Mouton Rothschild ,  Château Petrus  and  Château d'Yquem.
Flow cytometry on day 25 splenocytes Cells were stained with antibodies to the following markers: anti-CD4 (W3/25, Biolegend), anti-CD25 (OX-39, Biolegend), anti-foxp3 (FJK-16s, ebioscience), anti-CD11b (M1/70 BD Pharmingen), anti-CD103 (M290 BD Pharmingen), anti-MHCII (OX-6, Biolegend) and anti-CD172 (OX-41, Biolegend). Flow cytometry data collection was performed on MACSQuant Analyzers (Miltenyi Biotec). Data were analysed using VenturiOne (AplliedCytometry) software. In vitro OVA cell restimulation After washing, cells were seeded on 24-well plates at 2610 6 cells per well in Cerottini culture medium (Dulbecco modified Eagle medium without phenol red supplemented with 8% heat-inactivated FCS, 36 mg/l asparagine, 116 mg/l arginine, 10 mg/l folic acid, 1 g/l 4-[2-hydroxyethyl]-1-piperazineethanesulfonic acid, 0.
Cytokines measurement Cytokines were measured in supernatant of primary cell cultures of spleen and MLN, and on jejunal tissue segments. Frozen jejunal fragments were suspended in RIPA buffer (0. 5% deoxycholate, 0. 1% SDS and 1% Igepal in TBS) containing complete anti protease cocktail (Roche), and protein concentrations were measured using BCA optima kit (Interchim). IL13 and Growth-Regulated Oncogene/Keratinocyte Chemoattractant (GRO/KC) in jejunal tissues were measured using Bio-Plex Pro Assays kit (Biorad) following the manufacturer procedure, and results expressed in pg of cytokine per mg of protein loaded. Commercial ELISA kits (Duoset R&D Systems, Lille, France) were used for IFNc, IL4, or IL10 cytokine measurements in primary cell culture supernatants or jejunal tissues. Data were expressed in pg of cytokine per ml of supernatant of cell culture media.
This work was supported by grant  ANR-10-CESA-005  from  Agence Nationale de la Recherche  (http://www. agence-nationalerecherche. fr/)(France) and AP AlimH 2008 from  Institut National de la Recherche Agronomique  (http://www. alimh. inra. fr/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments Financial support from the  National Science Center  (Grant No. N N204 247340 ) is acknowledged. The research was carried out with equipment purchased with financial support from the  European Regional Development Fund  within the framework of the  Polish Innovation Economy Operational Program  (contract no. POIG. 0 2. 01. 00-12-0 23/08 ). O. M. acknowledges the financial support from the project  Interdisciplinary PhD Studies ''Molecular sciences for medicine '' (co-financed by the  European Social Fund  within the  Human Capital Operational Programme ). The authors gratefully acknowledge  Wiesław Knap  for performing the ICP-MS experiments. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
5-HT transporter (SERT) binding Serotonin transporter (SERT) binding was determined on brain sections, cut as above, using ( 3 H)-paroxetine (Perkin Elmer, UK) as previously described  [61]. Slides were then exposed to ( 3 H)-sensitive film (Amersham Hyperfilm MP, GE Healthcare, UK) at -80°C for 6 weeks. Analysis of autoradiographs was performed by measuring the signal over the area of interest with densitometry software (MCID Basic 7. 0, Imaging Research, Inc. ). The background was subtracted.
Acknowledgements We are grateful to our funding sources, including  Wellcome Trust  (grant  083192/Z/07/Z ; PMJ, JRS, AC), the  European Research Council  ( FP7  grant  260463 ; AC), the  National Institute of Mental Health  ( NIH  Award  MH086539 ; CAL), the  Israel Science Foundation (AC)  and the  Legacy Heritage Biomedical Science Partnership (AC). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institute of Mental Health  or the National Institutes of Health.
CLINICAL POPULATIONS We used a sample of convenience from two clinical conditions known to affect hand function as a first exploration of the clinical utility of this paradigm. Our goal was not to diagnose or evaluate treatment, but simply collect cross-sectional data from patients suffering from these conditions. For these clinical groups, participants were excluded if they were undergoing treatment for injury or surgery and had not been released by their surgeon or physical/occupational therapist to participate in everyday ADL, had a concurrent injury or pathologic condition that caused pain or discomfort in the tested limb during physical activity and/or at rest, had clinical, surgical, physical, cognitive, or other conditions that may have prevented their ability to perform the tasks proposed in this study, including the clinical restriction decided by the surgeon or therapist, or were unable to complete the protocol.
STRENGTH-DEXTERITY TEST The SD test is well described elsewhere  (3) (4) (5) (6) (7) (8) (9) (10) (11) (12). Briefly, it involves using the fingertips to compress as far as possible a slender spring, prone to buckling. This requires control of fingertip motions and force vectors at very low force levels (Figure  1A ). It was conducted with a custom spring (Century Springs Corp. , Los Angeles, CA, USA) outfitted with two miniature compression load cells (ELB4-10, Measurement Specialties, Hampton, VA, USA). The load cells were connected to a signal-conditioning box and USB-DAQ (National Instruments, Austin, TX, USA), sampled at 2000 Hz using custom Matlab (The Mathworks, Natick, MA, USA) software, and calibrated with a deadweight procedure. Participants were asked to compress the spring in a controlled way at their own pace to the point of maximal instability they can sustain (i.
DATA ANALYSIS AND VARIABLE DESCRIPTIONS The dependent variables for the SD and LED tests are defined in Table  1. Linear regressions, two-tailed t -tests, and analysis of variance (ANOVA) were applied to the data set, as appropriate, to identify and quantify the relationships between test performance, age, gender, and dominance and to compare performance between clinical and control populations. Significance was set at p < 0. 05 for all analyses. Matlab R2013a and SPSS version 22 (IBM, Armonk, NY, USA) were used for these analyses.
FINGER SD TEST WITH CONTROL SUBJECTS IN THE SELF-REPORTED DOMINANT HAND We tested for the effects of age and gender on finger dexterity in the self-reported dominant hand of 147 healthy individuals between the ages of 20 and 88 years. When needed, some variables (F f , Ḟf , Ff , and RMS f ) were transformed using the natural logarithm function to meet the assumptions of normality required for parametric statistics. As shown in Table  2 , an ANOVA with finger compression force as the dependent variable and age and gender as factors performed on the transformed data revealed a significant effect by both age (p = 0. 017) and gender (p = 0. 021). Furthermore, we report no gender effects on the compression dynamics ( Ḟ f , F f ,      A linear regression of finger compression force with respect to age, grouped by gender, is shown in Figure  3.
FINGER SD TEST WITH CLINICAL SUBJECTS We compared performance on the SD test (F f , Ḟf , Ff , and RMS f ) between clinical patients diagnosed with either CMC OA or PD and a subset from our dataset of 29 healthy, age-matched volunteers (10M, 19F; 65. 6 ± 9. 7 years, 48 hands) with no history of hand injury or disease or neurological disorder. Interestingly, we found no significant differences in finger compression force among groups, however we found differences between the clinical and control groups in compression dynamics ( Ḟf , Ff , and RMS f ) during the sustained compression as illustrated in Figure  4. We found no differences in compression dynamics between the PD and CMC OA groups; however, both groups showed significant differences from the control participants (p < 0. 001), indicating distinctly different dynamical behavior during manipulation in these clinical populations (Table  2 ).
LEG LED TEST WITH CONTROL SUBJECTS IN THE RIGHT LEG Mirroring the work on finger dexterity, we also tested for effects of age, gender, and dominance on leg dexterity in the right leg of 188 healthy individuals from 14 to 92 years. In order to account for the age and gender effects on body weight, which may influence leg compression force, we included body mass index (BMI) in the analysis. The data were normally distributed, and an ANOVA with leg compression force as dependent variable, age and gender as factors, and BMI as a covariate showed that compression force is strongly affected by both age (p = 0. 005) and gender (p = 0. 002; Table  2 ), but not by BMI (p = 0. 198). Furthermore, ANOVA on the force dynamics ( Ḟl , Fl , and RMS l ) during sustained compression showed no effect of gender, age, or BMI.
ACKNOWLEDGMENTS We thank  Dr. Sudarshan Dayanidhi ,  Veronica Stern ,  Narissa Casebeer ,  Alison Hu ,  Analiese DiConti ,  Jonathan Lerner ,  Na-Hyeon (Hannah) Ko ,  Oliver Krenn ,  Stefanie Kernbeiss ,  Veronica Frontull ,  Martin Zarfl ,  Benjamin Gondolatsch ,  Markus Posch ,  Daniel Lorenzi ,  Florian Melmer , and  Stefan Dilitz  for their assistance with data collection. We also thank  Drs. Beth Fisher  and  Giselle Petzinger  and  Carolee Winstein  for subject recruitment and protocol development,  Veronica Lothan  for chart collection, and  Alexander Reyes  for hardware development. Funding Sources:  NIDRR  grant  H133E080024 ;  NSF  grant  EFRI-COPN 0836042  and  NIH  grants  AR050520  and  AR052345  to  Francisco J. Valero-Cuevas.
a b s t r a c t Rotavirus gastroenteritis is one of the leading causes of diarrhea in Indian children less than 2 years of age. The 116E rotavirus strain was developed as part of the Indo-US Vaccine Action Program and has undergone efficacy trials. This paper reports the efficacy and additional safety data in children up to 2 years of age. In a double-blind placebo controlled multicenter trial, 6799 infants aged 6-7 weeks were randomized to receive three doses of an oral human-bovine natural reassortant vaccine (116E) or placebo at ages 6, 10, and 14 weeks. The primary outcome was severe (≥11 on the Vesikari scale) rotavirus gastroenteritis. Efficacy outcomes and adverse events were ascertained through active surveillance. We randomly assigned 4532 and 2267 subjects to receive vaccine and placebo, respectively, with over 96% subjects receiving all three doses of the vaccine or placebo.
Statistical analyses Analyses were done by Quintiles using SAS ® Version 9. 2. Efficacy analysis is presented for the per-protocol (PP) population. The PP population included all subjects who received the same treatment for all three doses of vaccine orplacebo within the a priori defined windows and who reported episodes of diarrhea occurring more than 14 days after the third dose. For each endpoint within the three age windows (from more than 14 days after third dose to the end of age 1 and 2 years and for age 1-2 year period), only the first event was counted for each subject. The follow up period associated with each event was calculated as time to occurrence of that event or date of dropout or the date of completion of follow up. Efficacy estimates for first year of life include events that occurred till one year of age and efficacy for the second year includes events occurring between 1 and 2 years.
Acknowledgements This trial was funded by the  Department of Biotechnology , and  Biotechnology Industry Research Assistance Council, Government of India, New Delhi, India ; by a grant from the  Bill & Melinda Gates Foundation  (number  52714 ) to PATH, USA; by the  Research Council of Norway, UK Department for International Development ; by  National Institutes of Health, Bethesda, USA ; and by  Bharat Biotech International, Hyderabad, India. We thank infants and families who willingly participated in the trial; local governments for the support extended to the study team; pediatricians in referral hospitals who provided care to enrolled infants; data management, project management, medical monitoring, and pharmaco-vigilance teams at  Quintiles (India) ; the clinical data operations and biostatistics team at  Quintiles (South Africa and UK) ;  Jean-Michel Andrieux  ( ANTHA Clinical Quality Consulting, France ) for quality assurance audits at the three sites and the central investigation laboratory, and  Monica McNeal  ( Cincinnati Children's Hospital Medical Centre, USA ) for the laboratory audits;  VK Paul  and the neonatal unit at  All India Institute of Medical Sciences (New Delhi, India) ;  V M Katoch  ( Indian Council of Medical Research, India) ;  K Vijay Raghavan  ( Department of Biotechnology, Government of India );  N K Ganguly  ( Indian Council of Medical Research, India) ;  Krishna Ella  ( Bharat Biotech International, Hyderabad, India ) for sustained mentoring and support to this innovation; the  National Institute of Allergy and Infectious Diseases (NIAID) at National Institutes of Health (NIH) ,  USA , and  Centers for Diseases Control, USA ;  Stanford University, USA; and Centre for International Health, University of Bergen, Norway ;  and committees and departments of the Government of India's Ministry of Health and Family Welfare and Ministry of Science and Technology  for their guidance and encouragement.
Acknowledgements. This work was supported by the  MACC, MACCII  projects (funded by the  European Commission  under the  EU Seventh Research Framework Programme , contract number  218793 ), the  ADOMOCA  project (funded by the  French LEFE INSU program ) and the  TOSCA program  (funded by the  French CNES aerospace agency). We thank  NASA  for providing MLS and OMI satellite ozone products. OMI analyses used in this paper were produced with the Giovanni online data system, developed and maintained by the  NASA GES DISC. We acknowledge the Ether French atmospheric database (http://ether. ipsl. jussieu. fr)for providing IASI data and the  NOAA ESRL Global Monitoring Division  (http://esrl. noaa. gov/gmd)for providing surface ozone measurements. We acknowledge the strong support of the  European Commission, Airbus , and the Airlines that carried the MOZAIC equipment free of charge and performed the maintenance since 1994.