dataesr/scientific-paragraphs-categorization huggingface dataset:
Because eating habits are inseparably linked with people's physical health, effective behaviour interventions are highly demanded to promote healthy eating among older people. The aim of this systematic review was to identify effective diet interventions for older people and provide useful evidence and direction for further research. Three electronic bibliographic databases-PubMed, Scopus and Web of Science Core Collection were used to conduct a systematic literature search based on fixed inclusion and exclusion criteria. English language peer-reviewed journal articles published between 2011 and 2016 were selected for data extraction and quality assessment. Finally, a total of 16 studies were identified. The studies' duration ranged from three weeks to seven years. The majority of studies were carried out in European countries.
Literature Search Strategy Electronic bibliographic databases-PubMed, Scopus, and Web of Science Core Collection were used for conducting this literature search. The search strategy was based on a clear and careful selection of key words and terms. After repeated attempts and adjustments, the final search strategy was built and is shown as follows: intervention* AND (diet* OR nutrition OR healthy eating) AND (elder* OR senior* OR old*). To collect the latest studies, each study's publication time was restricted to range from January 2011 to February 2016. The search filter was set on human beings and language was restricted to English. References from reviews and systematic reviews were also checked manually for further screening in case they were not identified during the whole search process.
Quality Assessment Following Cochrane's guideline, risk of bias was assessed by at least two reviewers independently, then the agreed assessment was further entered into the software Review Manager 5. 3. 5 (The Nordic Cochrane Centre, Copenhagen, Denmark). If agreement was not achieved, a third reviewer would contribute to the assessment. In total, there are seven domains for quality assessment: (1) Random sequence generation; (2) Allocation concealment; (3) Blinding of participants and personnel; (4) Blinding of outcome assessment; (5) Incomplete outcome data;  (6)  Selective reporting;  (7)  Other bias (other source of bias could put the study at a high risk of bias in certain circumstances, e. g. , carry-over in cross-over trials, baseline imbalance). Each judgement has three options: low risk, high risk, and unclear risk.
Study Selection A total of 5162 results were obtained from three databases after applying the predefined search strategy (see Section 2. 1). After removing duplicates, 4072 papers were left for screening. Initially, 183 papers were retained after checking each paper's title and abstract. After full-text reading and screening, the remaining 19 articles (one literature review, seven systematic reviews, and eleven articles) were retrieved, read, and studies that met the inclusion criteria were retained for data extraction. An additional five relevant studies were identified by checking the references from included reviews and added to the final analysis. In total, 16 unique studies were selected for data extraction and data analysis. Figure  1  shows the progress of screening. A total of 5162 results were obtained from three databases after applying the predefined search strategy (see 2.
1). After removing duplicates, 4072 papers were left for screening. Initially, 183 papers were retained after checking each paper's title and abstract. After full-text reading and screening, the remaining 19 articles (one literature review, seven systematic reviews, and eleven articles) were retrieved, read, and studies that met the inclusion criteria were retained for data extraction. An additional five relevant studies were identified by checking the references from included reviews and added to the final analysis. In total, 16 unique studies were selected for data extraction and data analysis. Figure  1  shows the progress of screening.
Study Quality Assessment The quality of the included studies was assessed in terms of risk of bias in this systematic review. Figure  2  shows the risk of bias across all included studies. The low risk of bias (above 50% of the studies) were due to adequate random sequence generation and allocation concealment (selection bias), sufficient outcome data (attrition bias), and adequate reporting of results (reporting bias). Apart from other bias, blinding of participants (performance bias) was the one with the higher unclear risk. Although high risk of bias was identified in each domain across all included studies, it was of relatively small magnitude. Figure  3  shows the risk of bias summary, including assessment of each risk item in each study. None of the studies were assessed as fully low risk of bias, and one study was assessed as unclear or high risk regarding different domains  [35].
Acknowledgments: This systematic review was supported by the  VeggiEAT  project (received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under the Grant Agreement No. FP7-612326-VeggiEAT ),  FoodSMART  project (funded from the  European Union 's  Horizon 2020 Programme  under Grant agreement No. 643999 ),  ELDORADO  project 'Preventing malnourishment and promoting well-being in the elderly at home through personalized cost-effective food and meal supply' (grant  4105-00009B ,  Innovation Fund Denmark) , and project  CALM  ' Counteracting Age-related Loss of Skeletal Muscle Mass '(calm. ku. dk). First author Xiao Zhou is presently supported by the  Chinese Scholarship Council.
Statistical Analysis All experiments have been repeated, the quantification of the fluorescence was performed by measuring the fluorescent intensity of the staining, which was normalized by cell area. Lipid peroxidation in cells was quantified according to manufacturer guideline with the ratios of the signal from the 590 to 510 channels. Statistical analyses were performed using JMP software (SAS, Cary, NC, USA). Normality testing of the data was performed with the Shapiro-Wilk test. The one-way analysis of variance (ANOVA) was used to determine whether there was any significant difference between the means of two or more independent groups. Difference between two means was performed with Student's t-test. A p-value ≤ 0. 05 was considered statistically significant (*), p-value ≤ 0. 01 as very significant (**) and p-value ≤ 0.
Acknowledgements The authors thank  M. Lardière  for English-language review. This work was funded by the  Fondation Méditerranée Infection  and by  Malaria Research  and  Training Center-Département d'épidémiologie et des affections parasitaires/Unité mixte de recherche internationale  ( MRTC-DEAP/UMI3189 ) Centre national de la recherche scientifique (CNRS) and  Fondation Mérieux (FMERIEUX)  grants.
The midline survey was conducted in the same 30 districts in 2013. The detailed reports including methodology of these surveys are available in the public domain  [12]. A subset of the data from these surveys was used to answer some specific research questions that were operationally relevant. These studies were written-up as separate manuscripts (n = 6) Table  3. OR questions/topics that were undertaken in the second OR course (2011-2012) the status of their publication in international peer-reviewed scientific journals. and published in peer-reviewed journals  [13] [14] [15] [16] [17] [18]. The findings were also presented at The Union's World Conferences on Lung Health (years 2011-2016). OR studies (n = 8) were also done to answer specific research questions using routine monitoring data from the Global Fund Project  [19] [20] [21] [22] [23] [24] [25] [26].
To assess the aerobic/anaerobic balance in stools we previously developed a database "List of Prokaryotes according to their Aerotolerant or Obligate Anaerobic Metabolism" previously published in  [4]  (v1. 1) and updated in  [14]  (v1. 2provided as Table  S1 ). To test such a link between fecal redox (mV) and aerotolerant/strict anaerobic balance, different variables were analyzed. Two quantitative variables were obtained by measurement (redox potential, ratio of the metagenomic relative abundance of aerotolerant species on the relative abundance of strict anaerobes). Normal and log-normal distributions were tested by the Kolmogorov-Smirnov test. Automatic distribution fitting was also used to confirmed log-normal distribution. When a log-normal distribution was identified, the natural logarithm of the variable was used instead of the variable itself.
Two qualitative variables were also defined: positive redox (redox potential > 0 mV) and aerobic predominance (relative abundance of reads corresponding to aerotolerant species > relative abundance of reads corresponding to strict anaerobic species). Qualitative variables were compared using the bilateral khi-square test. Comparison of quantitative variables between groups were analyzed using student t test or Mann-Whitney test according to the normal distribution test. Finally, a dose-dependent relationship between quantitative variables was tested by correlation and linear regression. Statistical analyses were performed using GraphPad Prism version 8. 00 for Windows, (GraphPad Software, La Jolla California USA) and XLSTAT 2018. 2 (Addinsoft, Paris, France).
Conversely, aerotolerant reads predominated in children with malnutrition (MR; marasmus, KW; kwashiorkor). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) Values for all groups followed a normal distribution according to Kolmogorov-Smirnov test except the group "French teenagers with anorexia nervosa" for which the sample size was too small. microbiota alteration  [1, 14, 15]. This index can be calculated for any metagenome with taxonomic assignment, notably by using our database (List of Prokaryotes according to their Aerotolerant or Obligate Anaerobic Metabolism available at our website: http://www. mediterranee-infection. com/article. php?larub=143&titre=base-dedonnees -Table  S1 ). The MAPI is easily understandable (MAPI > 0 corresponds to an aerotolerant predominance, MAPI < 0 corresponds to a strict anaerobic predominance).
Further studies are needed to confirm this link. Redox measurement should be standardized (great variability between probes, depending on the diluent used (tap water has a redox of +200 mV), impact of exposure time to ambient air and stool preservation, possible direct measurement in the rectum  [20] , etc…). The measurement of MAPI will be improved by culturomics  [30]  analysis to define an oxidative stress resistance profile for reads not currently assigned to the species. Moreover, this database could be completed at the level of the operational taxonomy units (OTUs) by considering that if all the cultivated species corresponding to a metagenomic cluster have the same oxidative stress resistance profile, all the OTUs of this cluster have this oxidative stress resistance profile (example: Lachnospiraceae are all strictly anaerobic).
Acknowledgments We thank  Eric ADEHOSSI ,  Dipankar BACHAR ,  Caroline BLANC-TAILLEUR ,  Souleymane BRAH ,  Aldiouma DIALLO ,  Alexandre FABRE ,  Saber KHELAIFIA ,  Jean-Christophe LAGIER ,  Catherine ROBERT ,  Cheikh SOKHNA , and  Maryam TIDJANI-ALOU  for providing samples, measurements and technical help.
Funding This work has received financial support from the  French Government  through the  Agence Nationale pour la Recherche (ANR) , including the " Programme d'Investissement d'Avenir"  under the reference Méditerranée Infection  10-IAHU-03. This work was supported by  Région Provence Alpes Côte d'Azur and European funding FEDER PRIMMI (Fonds Européen de Développement Régional -Plateformes de Recherche et d'Innovation Mutualisées Méditerranée Infection).
Microarray Data Extraction and Analysis Fluorescence intensity data was extracted from. tif files using GenePix Pro v. 5. 1 software (Molecular Devices) and a proprietary *. gal file using adaptive diameter (70-130%) circular alignment based on 230 µm features and exported to Excel (v. 2010, Microsoft, Redmond, WA, USA) as a text file, where subsequent data processing and analysis was performed. Local background was subtracted, and background corrected median feature intensity (F532median-B532 or F635median-B635) was used for each feature intensity value. The median of six replicate features per subarray was handled as a single data point for graphical and statistical analysis. Data were normalized to the mean for three replicate microarray slides subarray by subarray using subarray total intensity mean.
Statistical and Hierarchical Clustering Analysis The Mann-Whitney U test was used to estimate statistical significance between two different conditions, with a p-value of <0. 05 considered significant. Bar charts of H. pylori and TFF1 binding of human mucins were generated using GraphPad Prism, version 5 (La Jolla, CA, USA). To determine the correlation between H. pylori, TFF1, and lectin binding of human gastric and colonic mucins, a log transformation of each data set was performed prior to plotting and calculation of the Spearman correlation coefficient using the R statistical software environment, version 1. 1. 442, (Vienna, Austria). Lectin data scaled to a fluorescence intensity maximum of 50,000 relative fluorescence units (RFU) was subjected to unsupervised clustering with complete linkage and Euclidean distance using Hierarchical Clustering Explorer v3.
pylori bound intensely to gastric mucins, in particular GM1, GM2, GM4, GM8, GM11 and GM12 with greatest binding intensity observed with GM8. The adhesion of H. pylori to gastric and colonic mucins was compared to the TFF1 interaction with the ggplot2 package in R. There was a strong correlation between the gastric mucins to which TFF1 bound and those to which H. pylori strain P12 bound (rho = 0. 81), p = 0. 0007; Figure  3b  and Table  1 ). The congruent interaction suggests that TFF1 and H. pylori might bind to similar structures of gastric mucins. Due to the lack of binding of TFF1 to colonic mucins, there was no association between TFF1 and H. pylori binding to colonic mucins (rho = 0. 37, p = 0. 2). Figure  2. Binding of TFF1 dimer to gastric and colonic mucins. Mucin microarrays containing human gastric (GM) and colonic mucins (CM) were incubated with TFF1 dimer (20 µg/mL).
pylori bound intensely to gastric mucins, in particular GM1, GM2, GM4, GM8, GM11 and GM12 with greatest binding intensity observed with GM8. The adhesion of H. pylori to gastric and colonic mucins was compared to the TFF1 interaction with the ggplot2 package in R. There was a strong correlation between the gastric mucins to which TFF1 bound and those to which H. pylori strain P12 bound (rho = 0. 81), p = 0. 0007; Figure  3b  and Table  1 ). The congruent interaction suggests that TFF1 and H. pylori might bind to similar structures of gastric mucins. Due to the lack of binding of TFF1 to colonic mucins, there was no association between TFF1 and H. pylori binding to colonic mucins (rho = 0. 37, p = 0. 2).
Acknowledgments: We would like to acknowledge the help of  Ronan O'Connell ,  Ailin Rogers  and  Des Winters ,  School of Medicine, University College Dublin  for help in obtaining tissue for isolation of mucin. Funding: This research was funded by the  Irish Research Council for Science, Engineering and Technology ,  GOI Postgraduate Scholarship , to  Ciara Dunne , by  Science Foundation Ireland , grant  08/SRC/B1393 , to  Marguerite Clyne  and  Lokesh Joshi  and by the  German Science Foundation  project  A04 in CRC-1181  to  Steffen Backert. Author Contributions: C. D. , F. E. B. M. and M. C. were involved in the study concept and design. C. D. , J. N. , G. D. , M. K. and C. L. performed experiments and were involved in data acquisition and analysis. L. J. , S. C. , H. E. , S. B. , C. R.
g. , through cholera interaction with other common acute or chronic diseases,  1  antibiotic pretreatment that may alter disease course, and different intestinal microbial flora). Because of the common lack of laboratory diagnostic capacity in Africa outside the national capital cities,  [11] [12] [13]  clinicians and public health staff must rely more on clinical case definitions. The African Cholera Surveillance Network (Africhol) was launched in 2009 with funding from the Bill & Melinda Gates Foundation. African Cholera Surveillance Network was implemented in 11 countries through 2015 and was designed to assess cholera burden in enhanced surveillance sites and outbreak sites using clinical diagnosis and subsequent laboratory confirmation. 14 To assess the performance of different cholera case definitions in Africa, we used data from the Africhol surveillance network and outbreak sites to analyze the clinical signs of suspected cholera cases by age group using positive bacterial culture as the gold standard.
In addition to surveillance for endemic disease within the enhanced surveillance zones, outbreak investigations were conducted when the national surveillance system reported cholera cases in sites not included in the enhanced surveillance zones. The same methodology (tools, case definition, and training) as in the enhanced surveillance areas was used. Because of budgetary constraints, we focused Data collection. Data were collected on a standardized case report form at each participating treatment center by trained clinical staff used by the National MoH; forms included epidemiological, clinical, behavioral, and laboratory information. Participating treatment centers were all specialized diarrheal disease treatment facilities, either CTCs or diarrheal treatment wards at regional referral hospitals.
We provided standard training to clinical staff at study sites in the evaluation of patients during for the presence of dehydration, difficulty breathing, altered consciousness, dry mucous membranes, and coma. Vomiting, diarrhea, watery stools, rice water stools, and mucous or bloody stools were  assessed at the health facility through clinical examination. Patients self-reported symptoms such as nausea, abdominal pain, leg cramps, and number of stools during the 24 hours before admission. Deaths were only ascertained when they occurred in the clinic. All data were entered into the Africhol database. Culture confirmation. In principle, our study aimed to collect rectal swabs from each suspected cholera case. Practical constraints in the field (e. g. , clinical staff becoming overwhelmed during the peak of an outbreak) prevented realization of this goal.
Results are presented by age group and duration of hospitalization. We did not analyze data by site as with 27 different sites case counts were often low, increasing the risk of type 1 errors. To assess the association between individual symptoms and a positive stool culture for V. cholerae, we conducted an analysis limited to patients with a culture result available and aged 1 year or more. Children aged less than 1 year were excluded because of a small sample size (Table  3 ). All symptom variables were dichotomous. We present unadjusted results from univariate analysis for the association between each individual symptom or sign and culture-confirmed cholera. For each individual symptom or sign that predicted culture positivity on univariate analysis at a significance level of P £ 0. 10, we created a multivariate adjusted model that simultaneously adjusted for gender, age (entered as a linear  variable), and country (using DRC as the referent category as a large majority of cases were reported from DRC).
We created a separate model for each symptom or sign as symptoms and signs were highly correlated with each other. We considered a multilevel analysis but decided against it because of the small number of countries in our dataset  16  ; as noted earlier, instead we opted for stratification to assess the exact degree to which results varied. Missing data represented between 2. 4% and 18. 6% of cases for individual signs and symptoms (Table  2 ) and these missing data were evenly distributed over time and across analyzed age groups. Based on this, we decided not to apply multiple imputations for missing data. Data analysis was conducted by Stata version 12 (StataCorp, College Station, TX). Considering the availability of symptoms for each suspect cholera case recorded in the Africhol database, we used three different categories of case definitions: 1.
Acknowledgments: We thank all the focal points involved in the project:  Didier Bompangue ,  Mireille Dosso ,  Sakoba Keita ,  Lamine Koivogui ,  Abiba Kere Banla ,  Ilesh Jani ,  Issa Makumbi  and  Peter Mmbuji ; all the country coordinators, surveillance officers, laboratory coordinators and technicians, and country data managers. We acknowledge all the physicians, nurses, and clinical staff of each site, all the patients and their families for taking the time to respond to our surveillance questionnaire. We also thank all the members of the  Africhol consortium  and the other members of the Africhol coordination team:  Aline Munier ,  Souleymane Sakande ,  Issaka Ouedraogo ,  Themba Mhlanga ,  Leonard Heyerdahl ,  Richard Wood , and  Alexandre Blake. Financial support: The  Africhol  project received funding from the  Bill & Melinda Gates foundation  (grant number:  OPPGH5233 ), administered by the  Agence de Medecine Preventive (AMP) ,  Paris, France.
For morphometric analysis, 10 images of the ileum of each animal were randomly captured and analyzed using ImageJ software (version 1. 8. 0). Villi height and the crypt depth were measured vertically from the tip of villi to the base of the adjacent crypt. Additional cuts in the paraffinized samples from the ileum were stained by the Periodic Acid-Schiff (PAS), technique to determine the number of goblet cells in the tissues  (Prisciandaro et al. , 2011). Ten random field images of each sample were made using the 40× objective and the intact goblet cells were counted using ImageJ software (version 1. 8. 0) and expressed as the number of cells per high-power field (hpf) (40×, 108. 2 µm 2 ).
The intervention foods were distributed in sachets of daily rations, adjusted for age of the child. The sachets had identical WinFood logos with cooking instructions. The intervention lasted for nine months, during which the caregivers were provided with a batch of daily rations of food until the next scheduled follow-up. Local health staff in each village visited the mothers weekly for supervision and support, and could always be consulted for questions. ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T 10 The fat and fatty acid content of the intervention food were estimated, based on the fat and fatty acid content of the food components obtained from laboratory analyses (for fish and spider) and food composition data  [20]  (for all other food materials). The maximum fatty acid intake was calculated from the estimated maximum intake of the intervention food.
Intake calculation Food consumption was estimated from the food sachets returned at the monthly follow-up visit. All caregivers at all visits reported that the distributed foods had been eaten only by the infant and not shared with the household. The empty, partially empty and unopened sachets were collected and weighed to estimate the amount of food consumed every month. We did not collect any information about food waste, thus do not have information about the actual take of the intervention products. Hence, we can only estimate the maximum intake. The total fat content of the intervention foods was obtained from laboratory analyses. The fatty acid contents were calculated from the fatty acid composition of the different food ingredients, which was based on analyses for fish (Esomus longimanus, Paralauba typus and mixed batches of common indigenous small fish), spider (Haplopelma species) and database information (frida.