dataesr/scientific-paragraphs-categorization huggingface dataset:
Food Frequency Questionnaire The food list for the FFeQ was developed by the investigators based on existing national food questionnaires  [14] [15] [16] [31] [32] [33] [34]  and data from the second national study of individual food intakes of French adults  [35]  to ensure that all food groups contributing to at least 5% of the mean energy, macronutrient, vitamin, or mineral intake of the French population were represented in the FFeQ,  [16]. The FFeQ was self-administered online. The questionnaire asked participants to report their usual dietary intake over the past year. In epidemiological studies, one-year memory FFQ are mainly used because they assess long-term diets (diets tend to remain stable year on year) and season variability of intakes is considered  [6]. The questionnaire was divided into two parts: The first part comprised 40 food groups.
The second part was specific to the study population. It was composed of 10 questions. Six were qualitative questions about eating habits (eg, meal frequency, socialization during meals, source of food supplies), and four questions were used to obtain nutritional data, of which two provided more detailed information about some food groups from the first part of the questionnaire (ie, fish and soft drinks). In total, 44 items were used to obtain the nutritional data (see Multimedia Appendix 4). Daily intakes for each food group item were computed: frequencies were converted into numbers of servings per day and multiplied by the portion size. An ad hoc composition table was developed using data from the INCA2 French representative population survey  (35)  to estimate the percentage of contribution of each food included in a food group item.
Nutritional data were then obtained using the French food composition database established by the French Data Centre on Food Quality (Ciqual, last updated in 2013)  [37]. Besides nutritional and diet context information, information on sex, birth date, and anthropometric data was elicited. It also questioned participants about potential changes in their food habits during the past year due to specific situations (diet, pregnancy, a move, surgery or depression). The FFeQ was adapted for laptops, tablets and smartphones. Compared to the paper version of the questionnaire  [8] , the online version presented here had a higher data quality thanks to alerts, restricted answers and automated checks. The design of the FFeQ consisted of one web page per food item and a pilot study previously demonstrated an average time of 30 minutes to complete the questionnaire.
24-Hour Recalls The reference method used to compare results from the FFeQ consisted of six 24-hour recalls carried out every two months during the year between the first and the second FFeQ. Study participants were asked to recall all foods and beverages consumed on the previous day (due to logistics, data for Saturdays were collected on Mondays). Participants were not informed in advance of the day of the recall. To account for intra-individual variation (because dietary habits may differ according to weekdays or weekends and seasons), all days and all seasons were covered by the recalls as recommended  [6]  ie, days for recalls were randomly selected every two months per participant. Phone interviews were carried out by a trained dietitian who entered the data into the Nutrilog Software (v3.
Relative Validity To study relative validity, data evaluated by the second FFeQ (FFeQ2) were compared with the mean of the 24-hour recalls, since both methods covered the same period. A list of concordance was established between food group items from the FFeQ and food items provided by 24-hour recalls. Few rarely consumed foods declared during the 24-hour recalls were not covered by the FFeQ items and were not taken into consideration. Unadjusted Spearman correlation coefficients were calculated for food groups. Unadjusted and energy-adjusted Pearson correlation coefficients were calculated for nutrient intakes. Energy-adjusted coefficients, corrected for attenuation for within-person variation in the reference method (deattenuated coefficients)  [6, 38] , were produced. Energy adjustment was performed using the residual method  [6].
Reproducibility To evaluate reproducibility, data obtained from the first and second FFeQs (FFeQ1 and FFeQ2) were compared. For food groups, unadjusted Spearman correlation coefficients and intraclass correlation coefficients (ICC) were estimated. Unadjusted and energy-adjusted Pearson correlation coefficients as well as ICC were calculated for nutrient intakes. Nutrient intakes were logarithmically transformed before analysis, to improve the normal distribution. The level of agreement in ranking subjects between the two FFeQs (in terms of food group and nutrient intakes) was examined through cross-classification into quintiles. All statistical analyses were performed on SAS 9. 4 (SAS Institute Inc, Cary, NC, USA). A P value <. 05 was considered statistically significant.
Acknowledgments This work was supported by a grant from the  French Research Institute in Public Health (IReSP)  and the French multi-organism thematic Institutes in Public Health (ITMO Santé Publique). AA is supported by a doctoral grant from the  French National Cancer Institute. GF is supported by the  French Research Agency (ANR, Agence Nationale de la Recherche)  via an  "Investissement d'Avenir" grant (investment for the future grant ,  ANR-10-COHO-0006 ) that supports the E4N study. The  IReSP, ITMO Santé Publique , and  ANR  had no role in the design, analysis or writing of this article. The authors are grateful to all participants for providing the data used in this relative validity and reproducibility study. We thank  Maryvonne Niravong  and  Françoise Clavel-Chapelon  for their advice and contributions.
2 and 6. 3 months of follow-up in average, respectively. data collection Preconceptional data Demographic, socioeconomic and household characteristics were collected at inclusion (table  1 ). At this occasion, screening for malaria, urinary schistosomiasis, Chlamydia trachomatis and Neisseria gonorrhoeae was performed. Haemoglobin (Hb) and markers of inflammation levels were determined. The first day of LMP was recorded and a urinary pregnancy test (One-Step; International Holding, Germany) was performed each month. Anthropometric measurements were collected every 3 months, and dietary intakes were assessed twice during follow-up.
Body composition, assessed by bioelectrical impedance analysis method, was measured by the multifrequency body composition analyser Tanita MC-780 with a four-electrode arrangement paired on hands and feet. A quality control of anthropometric data was performed periodically by a senior research scientist in nutrition. Dietary intakes were assessed by 24-hour recalls using the standard multiple-pass methodology. 24 A comprehensive list of the most common food items and recipes was developed from a preliminary study of the diet habits of the study area. This list was used to identify foods mentioned by the women as consumed during the interview. Amounts of food consumed were estimated by household measures consisted of precalibrated utensils, food pictures of different portion sizes and monetary value of known food portions sold on the study area markets.
Funding This study was supported by the  French National Research Agency (ANR ,  ANR-13-JSV1-0004 , grant 2013) and the  Fondation de France  (no:  00074147 , grant  2017 ). MA was funded by the  Réseau doctoral de l'Ecole des Hautes Etudes en Santé Publique  ( EHESP ) for PhD scholarship and received a prize from the  Fondation des Treilles  (http://www. les-treilles. com/ en/).
Quantitative real-time PCR (qPCR) Quantitative real-time RT-PCR was used to determine the mRNA expression of genes of interest (see Supplementary Methods for details). Lysates were prepared from FAC-treated cells or control cells and total RNA was isolated using Tri-reagent (Sigma) following standard protocols. The quality of the RNA isolated was ascertained and found to be consistently high. Sequences of all primers used are listed in Supplementary Table  1. Expression levels of genes of interest were normalized to that of Rpl19 which was used as the reference gene. MIQE check-list and qPCR primer validation data are provided in Supplementary Table  3  and Supplementary Table  4  respectively.
Statistical analyses Statistical Package for Social Scientists (SPSS), version 16. 0, was used for all statistical analyses. The Kruskal-Wallis test was used to look for effects of the various treatments on the parameters of interest. Pairwise comparisons were done using the Mann-Whitney test. Correlation analysis was done by Spearman's correlation test. A p value of < 0. 05 was used to indicate statistical significance in all cases. Data was obtained from at least three independent experiments (primary hepatocytes isolated from at least 3 different mice), with each incubation done in duplicates or triplicates, under the specified conditions.
In addition, AMPK inhibits glucagon-induced increase in G6pc expression by activating phosphodiesterase-mediated cAMP degradation  [23]. Therefore, iron-induced activation of Akt and AMPK pathways can explain the decrease in HGP and G6pc expression in this setting. The rate of glucose uptake by hepatocytes is chiefly regulated by glucokinase (GK)  [56]. Insulin increases hepatocyte glucose uptake by increasing transcription of the gene for glucokinase (Gck)  [57]. Consistent with this, our data shows that insulin induced mRNA expression of Gck; however, in the presence of iron, this effect was attenuated, while treatment of the cells with only iron did not affect Gck mRNA levels (Fig. 5E ). This tends to suggest that glucose uptake may not be affected in hepatocytes treated with iron. We attempted to estimate glucose uptake in hepatocytes in this setting using a 2-deoxyglucose (2-DG) uptake assay (Cosmo Bio, Japan).
FUNDING This project was supported by the  Italian Institute of Health , under the frame of E-Rare-2, the  ERANet for Research on Rare Diseases "RescueCFTRpreclinic"  (to LM and GK). GK is supported by the  Ligue contre le Cancer Comité de Charente-Maritime  (équipe labelisée);  Agence National de la Recherche (ANR) -Projets blancs ;  ANR  under the frame of E-Rare-2; Association pour la recherche sur le cancer (ARC);  Cancéropôle Ile-de-France ;  Chancelerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche M. dicale (FRM) ; a donation by Elior; the  European Commission (ArtForce) ; the  European Research Council (ERC) ;  Fondation Carrefour; Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology ; the  RHU Torino Lumière ; the  Searave Foundation ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE)  and the  SIRIC Cancer Research and Personalized Medicine (CARPEM).
g. record keeping) alongside more commonly required quantitative measures, such as water quality. 12 In addition to demonstrating tangible improvements attributable to WSPs, these measures may also aid ongoing quality control efforts (e. g. toward achieving performance goals). Tracking the same indicators consistently over time and over multiple water systems can also enable large-scale comparisons (e. g. meta-analyses) and help to clarify the time frame needed to both achieve and observe results. In this study, we investigated the potential value of several operational performance indicators used for a WSP at a drinking water utility in southwestern France, seeking to validate and supplement outcomes reported in the literature. We collected approximately six years (2012-2017) of operational performance data and used both qualitative and quantitative analyses to describe trends.
Data collection and analysis Data were collected using multiple methods. Results from a standard questionnaire about reported costs, benefits, challenges, and facilitators associated with the WSP were obtained from a previous study that reported amalgamated results across 20 implementation sites but did not report findings from each site individually. 13 Notes assessing overall WSP experience were obtained from a nested study further evaluating five of the implementation sites, which included a written questionnaire, in-person site visit, and semi-structured focus group discussion regarding water quality, compliance, and health outcomes. 11 Interaction with human subjects (WSP team personnel) was approved by the Institutional Review Board (IRB) at the University of North Carolina at Chapel Hill (study #15-2118).
Written details related specifically to operational performance, critical controls, or employee practices were extracted from these sources and, in some cases, translated to English. Monthly data comprising performance indicators tracked across six categories since onset of the WSP in 2012 were shared by the WSP team lead (categories 1-3 described in Table  1 ), along with logs of alarms and water quality-specific customer complaints. Complementary indicator categories not shown in Table  1  due to privacy concerns aim to (4) ensure communication about the importance of health risk and good business practices to all stakeholders, (5) monitor the quality and the efficiency of work, and (  6 ) check control measures on water treatment plants as well as distribution networks. Progress toward these objectives is regularly reviewed and the indicators are occasionally revised by the WSP team and utility managers.
In addition, raw data from free chlorine water quality sensors at the outlet of one treatment facility were provided by the utility to model low-chlorine event dynamics. Data on performance indicators that were consistently recorded either monthly or annually over several years (Table  1 ) were plotted in Excel for visualization of trends. Statistical data analysis was conducted using SAS 9. 4 software where possible, given sufficient observations and variability in the data. Before-after comparisons used a t test (α =. 05). Trends over time in monthly count data were assessed using the GENMOD multiple regression procedure with a Poisson distribution and natural log link function. To control for seasonal variation, we introduced month as an independent categorical (class) variable in the models.
Free chlorine sensor readings at the single production facility were recorded by the utility at a frequency of approximately 5 min from 2010 to 2017. The data were divided into three periods, 'before' WSP team formation, 'during' WSP implementation, and 'after' WSP certification; for this analysis, the 'during' time period from 13 November 2012 to 20 December 2013 was discarded as a period of transition. Water quality values below the lower detection limit (<0. 01 mg/l) were set to zero. Free chlorine data likely affected by temporary plant shutdowns or sensor failures were excluded, although consistent historical records were not available to accurately document these events. Because the data were not flagged at the time of recording, the data cleaning approach was not expected to accurately discern true and false events in all cases but allowed us to simulate actual events by applying a standard procedure for both the before and after data sets.
Suspected shutdowns or sensor malfunctions were flagged by identifying events where free chlorine values equal to 0 mg/l (the WSP critical limit) were recorded for more than 24 h and then validating evidence of an external issue with the plant manager. This data cleaning procedure erred on the side of including data not definitively known to be problematic. To integrate the mixed methods of data collection and interpretation, this study took an exploratory sequential (qual → QUAN) approach. 14 That is, the initial results of the qualitative techniques informed processing of quantitative data to validate reported dynamics. Qualitative results were compiled for a single location, which was not previously disaggregated and analyzed independently in other studies. 6, 11, 13 The data integration process focused on building and merging, seeking complementarity of measures.
16 Local residents also sometimes complained about chlorine odor and taste. From the utility managers' perspective, maintaining lower limits for chlorination was important to avoid microbial regrowth in the distribution system, rather than to benefit water treatment, as the groundwater source is high quality and partially protected (a mix of deep and shallow aquifers). Managers reported that when low-chlorine alarms sound, staff would follow documented procedures to detect any problems, and production would be stopped if the problem could not be corrected. Managers stated that since most processes are automated and monitored at a central control station, it would normally take approximately half an hour to visually check the problem. To confirm the self-reported benefits and process improvements, quantitative data that potentially captured these changes were analyzed.
Since the primary WSP critical control is for free chlorine, the frequency and duration of low-chlorine events at a single production facility were examined using both water quality data sets and alarm logs. The free chlorine readings at the plant outlet were considered out of compliance when below 0. 05 mg/l. The data cleaning procedure identified 12 events tied to verified plant shutdowns or sensor failures for removal, resulting in 3% missing data. After cleaning, the number of individual sensor readings was 295,664 in the before period (before 13 November 2012) and 418,027 in the after period (since 20 December 2013). Statistical analysis comparing the two time periods demonstrated a reduction in the number of low-chlorine events, maximum duration, and average duration, from 1006 events lasting 51 min on average before the WSP to 179 events lasting 36.
1), production alarms for low chlorine (objective 2. 3), distribution alarms for low chlorine (objective 3. 3), rate of sensor availability (objective 3. 4), and customer complaints for the full service area (objective 3. 5) as well as the single facility's service area (Table  1 ). The number of chlorine-related alarms in the production (objective 2. 3) and distribution systems (objective 3. 3) was tracked, and alarm logs were analyzed for long-term trends. For production, this data tracking method (including 264 alarms across all facilities) was less inclusive and showed fewer events than the high-resolution water quality data from one facility (Table  2 ). Including all production facilities, Poisson regression with a natural log link function controlling for seasonality showed a significant decrease in the number of alarms over time (β = -.
026; p <. 001), with the data leveling out at fewer than five alarms per month (Figure  1 ). In both production and distribution systems, very few of the alarms that occurred were ultimately verified, since most were due to sensor malfunction. Too few distribution system alarms occurred to enable modeling. Still, records suggested these alarms facilitated beneficial checks and corrections when needed. Customer complaints for water quality reasons (objective 3. 5) revealed an improvement in recording from the onset of WSP implementation in 2013. Prior values from 2012 or earlier were not recorded consistently and thus were excluded from the statistical analysis. Water quality-specific customer complaints in the full service area (e. g. reddish water, chlorine taste, and turbidity) decreased significantly over time (β = -.
0071, p <. 0001), a change of about 10% fewer complaints per year on average (Figure  2 ). The data suggested complaints typically peaked early in the year with a smaller spike in autumn, and within-year variability appeared to narrow over time (Figure  2 ). Water quality-specific complaints were also assessed for the single facility alone. Peaks in complaints were visible during the early part of each year (late winter to early spring) (Figure  3 ). When modeled using Poisson regression with a natural log link function, and controlling for seasonality, complaints decreased significantly over time (β = -. 0089, p =. 0008). Most of the decline took place in the first year, during the initial WSP implementation period (2013). Recorded rates of real-time water quality monitoring sensor availability (objective 3.
4) reported by the utility (Figure  4 ) show inter-annual differences, suggesting a marked improvement upon WSP implementation. The apparent slippage in 2016 was likely due to a change in the assessment method to enable improved interpretation, after which the rate improved again in 2017. Prior to 2016, raw monthly data counting the number of continuously working sensors were used, and subsequently the daily data were processed to remove inaccuracies due to periods of maintenance or expected offline activities, to more accurately measure availability. Reported rates of public utility declaration completion (objective 1. 1) were fairly consistent over time (Figure  5 ), suggesting stability or subtle improvement since 2010. This formal documentation serves to confirm the public benefit of a project under French law and is externally tracked but relevant to internal WSP goals regarding documentation and pollution control (Table  1 ).
Limitations and recommendations This study collated operational performance data sources that were not previously assessed independently or reported. Most of the performance indicators were collected beginning in 2012, so earlier records are lacking to make longer-term comparisons. Background rates of change for a larger service area or a different component of the water system (where no WSP was active) would be valuable for isolating causal relationships among observed changes. Some indicators were initiated or dropped during the study period (Table  1 ), making it difficult to assess trends. While the indicators assessed here were consistently quantified and varied enough to enable some degree of trend analysis, they were largely selfreported historical data that could be affected by reporting bias or social desirability bias.
In this study, most data were tracked independently by the utility on a daily or monthly basis and aggregated annually to enable multiyear comparison using an Excel spreadsheet. The WHO offers an Excel quality assurance tool to document steps taken to complete WSPs, which could potentially be upgraded to incorporate recommended or site-specific evaluation criteria, as well as comparison with historical data from the same location or aggregated data from other locations. More sophisticated software programs might require site-specific development for a given WSP context and would likely need to be custom-built at the utility's expense. This study demonstrated the need to apply multiple performance measures in combination, since multiple goals may need to be optimized to achieve satisfactory use of the WSP.
Record of low-chlorine alarms at all production facilities (objective 2. 3) from the onset of the WSP implementation period, including a linear trend line  Monthly water quality-specific customer complaints for a single facility recorded from the onset of WSP implementation in 2013, including linear trend line Comparison of the rate of real-time water quality monitoring sensor availability (objective 3. 4) reported each year before, during, and after WSP implementation Comparison of the rate of completion of public utility declarations (objective 1. 1) each year before, during, and after WSP implementation (2017 data not yet available).