dataesr/scientific-paragraphs-categorization huggingface dataset:
However, both long-and short-term interventional studies involving lean or obese populations showed rather contradictory results  [25]. In all cases, accurate estimation of BCAA dietary intake is an absolute requirement. BCAA intake was also included as part of the working hypothesis in the European MEDIGENE (standing for Mediterranean Genes) program (FP7-279171) that focused on MetS and long-term complications in Mediterranean countries (https://cordis. europa. eu/project/rcn/101810_en. html). MEDIGENE investigators were also faced with a wide variety of food items and recipes in addition to language barriers  [26, 27]. In dietary investigations, both the Food Frequency Questionnaire (FFQ) and 24-hour dietary recall (24HDR) recently benefitted from innovations in computer science, including food pictures, camera and tape recordings, or food bar code readers  [28] [29] [30].
Several computer programs have emerged as both interactive computer-and web-based technologies  [31, 32]. The best known are US Department of Agriculture (USDA) automated multiple pass method, the University of Minnesota's nutrition data system for research, computer-assisted 24HDR method (EPIC-soft) developed in the frame of the European Prospective Investigation into Cancer and Nutrition study and the Oxford WebQ  [33] [34] [35] [36] [37]. In France, the NutriNet initiative was applied for wide ranges of human diseases, including MetS  [38]. To solve problems raised by the variability of more than 11 countries in the MEDIGENE program and to efficiently estimate BCAA contents in food items, the objective of this study was to develop a new BCAA database embedded in a software system named MEDIPAD (standing for Mediterranean iPAD tablet computer) that can manage clinical, nutritional, and genetic anthropological data (https: //cordis.
europa. eu/result/rcn/195753_fr. html). The program was written in partnership with Intactile Design SA (France) and replaced the former Système Informatique Diététique Intégrée (SIDI) software developed by P Than Chi and L Monnier (Lapeyronie Hospital, CHU, Montpellier, France)  [39]. Since MEDIPAD offers new facilities for food administration, in this paper we briefly present the new program and how we developed the BCAA database together with the evaluation of applicability of this program on 4559 24HDR from Saint Pierre Institute (SPI) in Palavas (France), and 119 available surveys from MEDIGENE in Mediterranean countries.
Computer Program For 24HDR collection, the new software package MEDIPAD was written using PHP (http://www. php. net) and MySQL (http://dev. mysql. com) for the database. It is composed of several functional interacting blocks as shown in Figure  1. The dietary record block has been linked with the database with clinical, biological, and genetic data and has been designated MEDIGENE Anthropologic and Genetic Database (MAGDB). The practitioner can access the program from an iPAD or laptop computer via MEDIGENE portal using username and password and is thus able to securely introduce new patient data from dietary surveys into the database. To introduce new food items, the following information is required by the administrator: (1) name of the food/recipe in French, English, Spanish or Romanian; (2) description of the item and comments if necessary; (3) food group according to MEDIPAD or other groups created by the administrator; and (4) food portion found in the database or created by the administrator.
The nutritional composition of each item is expressed in 100 g of edible portion. During the nutritional assessment, the dietitian can introduce a new food item or recipe that is not present in MEDIPAD by depositing a provisional written comment and/or a full description of ingredients and preparation procedures. The administrator can then filter and check added items, validate, and complete registration with the nutritional composition and related information. For mixed-dish items, the nutritional composition was calculated with the former SIDI software (this action is not allowed in MEDIPAD). In contrast, The MEDIPAD program permits creation of new food groups. For this aim, the administrator should provide the name of subproject in the consortium and food groups. In a second step, the administrator should assign each food item in the database to a food group.
BCAA (Branched-Chain Amino Acids) Database The database was developed based on the French (CIQUAL) dietary tables (https://pro. anses. fr/TableCIQUAL/index. ht). Because this database does not contain amino acids in food composition and for MEDIGENE goals, BCAA content in foods was compiled for 1331 food items from the 2012 CIQUAL version. The strategy of the compiler was similar to that proposed by Suga et al. [40]  based on similarities in food items by name with the description and nutrient composition and, in particular, taking the protein content into account. Nutrient tolerances per 100 g of food were used to estimate how close is a food item to the reference. For protein, nutrient tolerances were considered ±5 g  [41]. The nitrogen value was estimated by dividing protein content by the nitrogen-to-protein conversion factor.
For new items, we used the crude protein (N × 6. 25) from CIQUAL while for the reference items, protein content with the corresponding nitrogen-to-protein conversion factor was used. The strategy of compiling consisted of several methods: Method 1. Similarities were searched in other food table compositions, in particular from USDA food composition tables (https://ndb. nal. usda. gov), Danish Food Composition Databank (http://www. foodcomp. dk) and Canadian Nutrient File (https://food-nutrition. canada. ca). Amino acid values were adjusted considering differences in the nitrogen (N) of each food item using the following equation  [40] : BCAA value of food in question = BCAA value of reference food × N of food in question/N of reference food Method 2. When data were not found in international food composition tables, we considered published data in which food items have been analyzed by high-performance liquid chromatography (HPLC) or gas chromatography (GC).
BCAA (Branched-Chain Amino Acids) Database The database was developed based on the French (CIQUAL) dietary tables (https://pro. anses. fr/ TableCIQUAL/index. ht). Because this database does not contain amino acids in food composition and for MEDIGENE goals, BCAA content in foods was compiled for 1331 food items from the 2012 CIQUAL version. The strategy of the compiler was similar to that proposed by Suga et al. [40]  based on similarities in food items by name with the description and nutrient composition and, in particular, taking the protein content into account. Nutrient tolerances per 100 g of food were used to estimate how close is a food item to the reference. For protein, nutrient tolerances were considered ±5 g  [41]. The nitrogen value was estimated by dividing protein content by the nitrogen-to-protein conversion factor.
For new items, we used the crude protein (N × 6. 25) from CIQUAL while for the reference items, protein content with the corresponding nitrogen-to-protein conversion factor was used. The strategy of compiling consisted of several methods: Method 1. Similarities were searched in other food table compositions, in particular from USDA food composition tables (https://ndb. nal. usda. gov), Danish Food Composition Databank (http:// www. foodcomp. dk) and Canadian Nutrient File (https://food-nutrition. canada. ca). Amino acid values were adjusted considering differences in the nitrogen (N) of each food item using the following equation  [40] : BCAA value of food in question = BCAA value of reference food × N of food in question/N of reference food Method 2. When data were not found in international food composition tables, we considered published data in which food items have been analyzed by high-performance liquid chromatography (HPLC) or gas chromatography (GC).
Populations and Ethical Statement Applicability of MEDIPAD program with the BCAA database was evaluated on dietary surveys from 119 subjects in the MEDIGENE program and 2957 subjects from archives of SPI. The study design was cross-sectional and for this study we stratified patients by function of dietary intake of BCAA (quartiles) and by function of BMI (normal weight and obese). In the SPI survey, patients were divided into three groups based on growth charts (0-18 years old) from the Programme National Nutrition Santé (PNNS) (http://inpes. santepubliquefrance. fr/CFESBases/catalogue/pdf/IMC/courbes_enfants. pdf): (1) normal body weight; (2) overweight; and (3) obese and in some cases regrouped in two categories: Normal weight and obese. In MEDIGENE adult populations, the BMI cut-off value for obesity was 30 kg/m 2.
National Ethical Committees approved the study protocols, and informed consent was obtained from each patient in accordance with the Helsinki Declaration  [42]. All collections were reported to the Ministère de l'Enseignement Supérieur de la Recherche et de l'Innovation (MESRI) in France under CODECOH # DC-2014-2226. Populations recruited during MEDIGENE program (2012-2016) was multiethnic and included patients from France, Greece, Turkey, and Romania for which we had available 24HDR (n = 128) registered in MEDIPAD and performed by a professional dietitian via the MEDIGENE portal. As standard, we registered one week day, one weekend day and the day before the consultation. Patients were recruited for MetS studies, and diagnoses were based on the National Cholesterol Education Program (NCEP) and Adult Treatment Panel-III (ATP-III) criteria  [43]  as the presence of three or more of the following criteria: waist circumference, triglyceride and High-density lipoprotein (HDL) levels, the presence of blood pressure, hyperglycemia or medication for the last four criteria.
Populations for the achieved dietary records (n = 4559) of SPI (Palavas, France) concerned children and young adults of 3 to 24 years (n = 2957) investigated for obesity since this institute is specializes in obesity care in young adults and children. The dietary investigation was obtained by face-to-face interviews with a specialized dietician. As standard, all records concerned one day at the time of hospitalization. For many records we also used an additional week day and another weekend using the SIDI program (P. Garandeau and P. Than Chi) between 2007 and 2011  [39]. From the entire collection, for this study the type 1 diabetic patients were excluded, and for the remaining patients, we used available data on age, sex, and BMI values (kg/m 2 ), but no data were available on insulin resistance or MetS.
Dietary and Physical Activity Assessment The MEDIPAD nutritional software can collect 24HDR face-to-face interview data by trained personal (dietician/physician) for one to three days via a web connection with the MEDIGENE server. During the interview, a patient is questioned about the quantity of food items with reference to the food portions during each meal: (1) breakfast; (2) lunch; (3) dinner and (  4 ) snacks (screenshots are illustrated in Figures  S1  and S2 ). The administrator has access to the clinical database and can anonymously collect clinical and dietary data in addition to any clinician's comments. The general architecture of the nutritional block is depicted in Figure  1. To assess physical activity levels, the global physical activity questionnaire (GPAQ) was introduced in French, English, and Spanish  [44].
Macronutrient intake, vitamins, and minerals are also provided with recommended daily intake using ANSES (French Agency for Food, Environmental and Occupational Health & Safety) as the reference  [46]. A summary questionnaire concerning socioeconomic and demographic data, dietary habits, lifestyle factors, and changes in dietary habits during immigration were also included. Data derived from SPI were introduced in MEDIPAD using MySQL and Python scripts. Underand over-reporters of energy intake were excluded based on physical activity levels assessed by the GPAQ also included in MEDIPAD  [46]. For missing data on physical activity, outliers of energy intake were considered >4000 Kcal and <1000 Kcal. Basal metabolic rate (BMR) was calculated using the Harris & Benedict formula  [47].
Description of BCAA Database For the BCAA database, the chief section in the MEDIPAD program is the section for food administration (Food Admin). Through this interface, the administrator is able to obtain the list of food items, groups, and food composition and can manage the following additional functions: (1) manage the list of portions; (2) check the list of nutritional components; (3) create new food groups for research purposes; (4) manually add new food items or recipes; and (5) import food items as bulk in. csv file (screenshots are illustrated in Figure  1 ). The French CIQUAL database 2012 containing 1331 food items was updated with up to 2137 food items by adding several parameters: 1. Food items (n = 430) were completed from previous observations in SIDI program between 1992 and 2012 as compiled by Dr.
2. For the BCAA database, the chief section in the MEDIPAD program is the section for food administration (Food Admin). Through this interface, the administrator is able to obtain the list of food items, groups, and food composition and can manage the following additional functions: (1) manage the list of portions; (2) check the list of nutritional components; (3) create new food groups for research purposes; (4) manually add new food items or recipes; and (5) import food items as bulk in. csv file (screenshots are illustrated in Figure  1 ). Greek food items and traditional dishes (n = 141) were added from the composition table of foods and Greek dishes of the Hellenic Health Foundation (http://www. hhf-greece. gr/tables/home. aspx?l=en). The'source was "Composition tables of foods and Greek dishes", 3rd edition  [49]  Repartition of food items such as the method's function used for BCAA content in CIQUAL along with the number of items in each group are indicated in Table  1.
Food Items as Function of BCAA Content To validate the soundness of the database's compiled food items, we used a repertoire of 4559 surveys from SPI and compared those surveys to the entire CIQUAL database. Food items were stratified as functions of BCAA content percentiles (g/100 g) as indicated in Table  2  and Figure  S3. Six categories were generated: (1) A: zero content; (2) B: 0-0. 232; (3) C: 0. 233-1. 083; (4) D: 1. 084-2. 658; (5) E: 2. 659-4. 168; and (6) F: >4. 168 g/100 g. Percentiles of CIQUAL food items were in accordance with those used in the large SPI repertoire. Thus, in the first category (A), we mainly find the food groups without proteins such as fats and oils, sugar or confectioneries, and drinks. In the last category (F), we found items with the highest BCAA content consisting of animal products with the several food subgroups: (1) cheese (30.
Major Contributors to BCAA Intake in SPI Surveys In the next step and to further validate our new database using population data, we focused on SPI surveys considering age categories from 9 to 13 and from 14 to 18 years, stratified by BCAA quartiles. After exclusion of over-and under-reporters along with aberrant values or missing data, we had 24HDR from 2957 subjects. Two-thousand, one-hundred and seventy-five (1161 females/1014 males) and 782 (501 females/281 males) subjects were included in the age groups of 9 to 13 and 14 to 18 years, respectively. Their clinical profiles are indicated in Table  S2. When the intake of total BCAA and isolated amino acids were studied by logistic regression using the stratification of values by quartiles, subjects at the highest quartiles of BCAA intake had proportional in the OR of association with obesity (Table  3 ).
Evaluation of BCAA Intake in MEDIGENE Compared to Other Studies A second application of the database was performed to compare data from MEDIGENE to other studies in the literature that used a similar methodology. Clinical profiles of these patients with a diagnosis of MetS are indicated in Table  S5. In brief, there were no statistical differences between females and males for BMI, insulin resistance and other components of MetS. The energy intake was higher in men than in women (p < 0. 006, Mann Whitney). The mean BCAA intake in MEDIGENE collection was compared between normal weight and obese subjects. In females, obese subjects had a higher intake of isoleucine, leucine, and valine compared to normal weight subjects (p < 0. 0328, <0. 0071, and <0. 052, respectively). After adjusting for energy intake, significance was lower for leucine (p < 0.
Discussion In this paper, we described a new database with BCAA content in food items. This database may be used for dietary surveys using 24HDR as part of the novel computer software in the MEDIGENE European program for the study of insulin resistance in Mediterranean countries. The MEDIPAD program is advantageous as it helps clinicians or researchers enter clinical, biological, and 24HDR for Mediterranean countries. The project was initiated with the goal of studying Gene × Diet interactions in the pathogenesis of insulin resistance. So far, the major advantage of the program was the new section of food administration that can manage data for mixed-dish recipes, modifying items as a function of country and language, and providing a contact with investigators in various countries. The Mediterranean area displays diverse dietary habits and data collection based on many different food databases more or less specific to countries is a difficult task.
The necessity of MEDIPAD program emerged from the multitude of countries and clinical hospitals that were involved in this study but also from new aspects of immigration of ethnic groups into another county (such as Albanians into Greece) and areas in which dietary surveys should be performed in native and immigrant populations. The European MEDIGENE project involved Mediterranean countries (Spain, France, Italy, Greece, Croatia, Albania, Romania, Turkey, Russia, Algeria, Morocco, and Tunisia) and was intended to study genetic and environmental risk factors involved in the pathogenesis of insulin resistance syndrome. Therefore, the best way to harmonize the dietary data collections was to create a program common to all countries with a unique database, including a database for food items. To help the interviewer, we conceived a web-based design that can introduce data from countries using a laptop computer or iPAD on a unique server implemented in France.
The MEDIPAD requires a secure web (https) connection that allows entry of nominal data. At present, the program benefitted from a specialized interviewer, a dietician, or a nutritionist in addition to the contribution of a food administrator for completion of the database, manipulation, and standardization. MEDIPAD was written in French, English, Spanish, and Romanian (Arabic is missing in the actual version). Informatics administration is under the responsibility the Département de Systèmes d'Information of Montpellier University (France) at which a dedicated IT staff assured conditions for archiving and data backup. In this project, we focused on 24HDR since multiple recordings on several days are frequently used as reference nutritional instruments for accurate dietary assessments in connection with metabolic disorders  [33, 52].
There are 61 parameters calculated by the program, including protein, lipid, and sugar consumption. We have adhered to the CIQUAL database in France as the baseline to which we added 697 food items from the former program SIDI or other databases. Currently, the database is improving and includes the 2016 new CIQUAL version. The food administrator can match a new item from CIQUAL with MEDIPAD database (containing the ORIGFDCD attribute, which represents the food codes in CIQUAL) and add the new item manually or via the. csv files. Although not used in this study, another advantage of the MEDIPAD is the ability to connect patient data for dietary records with the MEDIGENE Anthropological and Genetic DataBase (MAGDB) containing genetic, anthropological, and biological information. In contrast, the program is unable to perform statistical calculations; therefore, external programs should analyze exported data.
For this aim, MEDIPAD contains facilities for exporting data as. csv files, which can be easily introduced in current statistical programs. MEDIPAD does not contain graphical displays for sizes and portions of dishes although that issue is important. Therefore, a specialized dietician or nutritionist should introduce the data. For mixed dishes, we used the former SIDI program, but this function will be soon available in accordance to algorithms that will complete the program in nutritional counselling. As shown in this study, the major advantage of MEDIPAD is the possibility to add additional nutrients by compilation of BCAA content of food items in a database, and thus allowing to investigate efficiently the role of BCAA or to test hypothesis of pathogenesis of insulin resistance. Recent studies have indicated that not only the evaluation of total protein or total BCAA in the diet is crucial in understanding gene diet interaction, but precise composition of diet for each BCAA is an important determinant of metabolic status  [10].
Along this line, particular "patterns of food" items appeared to be related with MetS  [58]  and in particular case of BCAA with addition long-term use  [14]. In another study, in France, it was also indicated that for adequate protein and amino acids intake, animal/plant protein ratio in the diet requires changes in the pattern of plant protein intake  [59]. We considered that flexibility in creating and changing database for food items would be a crucial advantage of a new computer program for nutritional analysis. Therefore, we designed a specific function of food administration. Using the same function, one can introduce other components such as polyphenol content and glycemic and insulinogenic indices of foods. Along this line, another advantage of MEDIPAD is the possibility to modify or create new food groups as a function of various scientific hypotheses in the literature and to export nutritional analysis as function of food groups.
For the compilation of BCAA, most food items were obtained from other international food composition tables, except for 109 items that were not compiled. By using five methods, we were able to compile 1331 items, among which 75% was obtained from other alimentary tables. Before publication of this database following the FAO/INFOOODS criteria, we showed that application of the new BCAA database for available records yielded comparable results  [60]. Thus, the repertoire of 4559 24HDRs was similar to that of the entire CIQUAL database, including the food items with the highest BCAA content. Similarly, BCAA intake estimations in normal weight and obese individuals or separated by gender were comparable to the results from other studies in Japan (Table  S7 ). To compare to other studies ranges of age were fixed between 27 and 65.
After excluding subjects >65 years, we obtained up to 119 available surveys. BCAA mean intake was higher in men than in women (13. 84 versus 12. 91 g/day) representing a 7% increase. Ishikawa-Takata and Takimoto  [61]  also reported higher BCAA intake in the same age category (30-64 years) with a mean of 17. 20 and 14. 70 g/day in men and women, respectively, representing a 15% increase. Similar higher intake in males (16. 10 and 16. 59 g/day) was reported by Ishihara et al. [62]  in another two cohorts. In men, the mean intake was close to the MEDIGENE data and that of Suga et al. [40, 63]  using a similar BCAA compiled database (Table  S7 ). No major differences were found among ethnic groups in MEDIGENE surveys. Our data were also comparable to another recent BCAA study concerning the contribution of food groups to the content of each amino acid  [59].