dataesr/scientific-paragraphs-categorization huggingface dataset:
Only ions with the "peptide preferable" profile were analyzed for MS/MS. Data were acquired using Xcalibur software (Thermo Scientific). The raw mass spectrometry data were uploaded to the PRIDE public site (accession no. PXD019288). Data analysis and statistics for mass spectrometry. For protein identification and quantification, we used the MaxQuant software version 1. 6. 3. 3  (25). We used Andromeda search incorporated into MaxQuant to search MS/MS spectra against the UniProtKB database of the E. coli K-12 proteome (2018 Uniprot release). The enzyme specificity was set to trypsin, allowing cleavage N terminal to proline and a maximum of two miscleavages. Peptides had to have a minimum length of seven amino acids to be considered for identification. Carbamidomethylation was set as a fixed modification, and methionine oxidation was set as a variable modification.
A false-discovery rate (FDR) of 0. 05 was applied at the peptide and protein levels. An initial precursor mass deviation until 4. 5 ppm and fragment mass deviation until 20 ppm were allowed. Only proteins identified by more than two peptides were considered (Table  S1 ). To quantify changes in protein expression, we utilized LFQ using the MaxQuant default parameters  (25). The Perseus software was used for statistical analysis, defining significantly different protein profiles as well as for visualization  (63). For functional annotation analysis, the DAVID web server (https://david. ncifcrf. gov) was used. Statistical analysis of the sequence features (amino acid propensity, hydrophobicity, net charge, and others) was done using in-house scripts. The hydrophobicity and instability were calculated using the GRAVY index scale (64) and as described by Guruprasad et al.
ACKNOWLEDGMENTS This work was supported by  ANR  ( CHAPCOP-ANR-19-CE44-0018 ),  Région PACA  and  HTS BIO Company (L. Z. 's PhD funding) , the  BIP-laboratory, FrenchBIC  for L. Z. 's accommodation in Grenoble, and a PhD sandwich scholarship ( CHE, Israel ) for L. Z. O. S. acknowledges  Labex ARCANE  and  CBH-EUR-GS  ( ANR-17-EURE-0003 ) for financial support. D. R. and R. F. thank the  Israel Science Foundation  ( 1765/13  and  1537/18 ) for support, and U. J. was supported by  GM122506. We thank  H. Mejri ,  R. Majumdar ,  O. Reille ,  L. Bernard , and  M. Ranchoux  for technical support during their internship, as well as  A. Zellat  for ICP-OES experiments. We thank  J. Bardwell  for kindly giving us the JPP114 and JP539 strains. We thank  B. Amenta  for writing the scripts used for data analysis.
Materials and Methods: A secondary analysis of two prospective cohorts conducted in a tertiary pediatric intensive care unit in France was undertaken, and additional data were retrospectively collected. Children included were those (0-18 years) admitted to the pediatric intensive care unit with a minimum of one phosphorus, potassium, and/or magnesium assay and who received exclusive or supplemental nutritional support. Undernourished children (body mass index z-score < -2 standard deviations) were considered at risk of refeeding syndrome. The ASPEN critiera were used to identify those with probable refeeding syndrome. Results: A total of 1,261 children were included in the study, with 199 children (15. 8%) classified as undernourished, who were at risk of refeeding syndrome. Of these, 93 children were identified as having probable refeeding syndrome, giving an overall incidence of 7.
The following data were extracted from the studies by Valla et al. (10, 17)  or from electronic patient records and compared among groups: child characteristics (age, sex, type and diagnosis of admission, Pediatric Logistic Organ Dysfunction I (PELOD I) and Pediatric Index of Mortality Score II (PIM II) severity scores  (10, 17) , respiratory support (duration of invasive ventilation and noninvasive ventilation), length of stay, acquired infections as defined by the Center for Disease Control  (10)  and mortality), electrolyte determination, nutritional support including supplementation (P, K, and/or Mg), and confounding factors. To describe children with probable RS, categorical variables (including levels of RS severity) were expressed as absolute (n) and relative (%) frequencies. The distribution of quantitative variables was assessed by means of their mean, standard deviation, median, interquartile range, skewness, kurtosis, histogram, and box plot.
To our knowledge, this is the first study to measure the incidence of RS in critically ill children, including all types of nutritional intakes, and based on the recent ASPEN consensus definition (4). This study also differentiated the incidence of RS among all included children and those at risk. Using the ASPEN definition, this study is the first to generate data on the severity levels of RS. The sample size was large and included participants from two prospective cohorts admitted to the PICU for over two years. This supports the external validity of this study. Anthropometric data were collected in a way that also favored their validity and reliability [see references  (10, 17) ]. In addition, the use of electronic patient records for daily energy intake (NS, glucose, and propofol), supplementation (P, K, and/or Mg), and confounding factors was reliable and reflected the quantities administered to the patients.
Statistical Analysis Data were analyzed using SPSS 25. 0 (IBM Corp. , Armonk, NY, USA). Verification of normal distribution was completed with a Kolmogorov-Smirnov test. The homogeneity of variances was studied with an F-test. Data from gilts were analyzed using the Student's t test. To analyze fetal data, effects of diet (control vs. treatment) and sex (female vs. male) on developmental traits, adiposity, FA composition, oxidative stress, and metabolic status were assessed using three-factor ANOVA for diet, sex, and litter size. Due to the bias between treatment and litter size previously described  [20] , litter size was also considered an effect. Animals were grouped by their litter sizes after determining the mean number of fetuses per litter (9. 25 ± 1. 42 piglets per sow) and defining small litters as those with ≤9 piglets and large litters as those with >9 piglets.
Data Analysis and Quantification The identification of the peptides in the database was carried out using Proteome Discoverer v2. 5 software (Thermo Scientific, Waltham, MA, USA) using a 1% FDR and the Uniprot database restricted to Sus scrofa taxonomy (released 2021/02) and contaminants (released 2017). The enzyme was set to trypsin with two missed cleavages allowed. The dynamic modifications were oxidation in methionine, acetylation in protein N-terminus, Met-loss in methionine, and Met-loss+Acetyl in methionine. The carbamidomethylation of cysteines and the TMT in peptide N-terminus and in K were set as the static modifications. The precursor and fragment mass tolerance were set to 100 ppm and 0. 6 Da. Quantification of the proteins was based on the intensity of the reporter ions derived from the TMT labelling.
Only unique peptides with SPS mass matches higher than 55% were used for quantification. The TMT intensity of the reporter ions was normalized using the total abundance of each TMT label to minimize the error due to the different protein load of each channel. Statistical analysis to determine the differential proteins and peptides was performed using DanteR software (http://omics. pnl. gov/software/danter accessed on 14 July 2021). Detailed information on how this software works can be found at http://omics. pnl. gov/sites/defa$\upmu$lt/files/DanteR_Overview. pdf (accessed on 14 July 2021). An ANOVA analysis of control versus treated samples using the normalized data has been performed using DanteR software. The variability introduced by the labelling and by the sex of the animals from which the samples were obtained is included in the linear model.
Gene Ontology and Bioinformatic Analysis For protein names and gene ontology (GO) classifications, PANTHER version 16. 0 software (http://pantherdb. org/ accessed on 22 September 2021) was used, together with the UniProt databases (http://www. uniprot. org/ accessed on 22 September 2021)  [40]. Complete GO and GO slims were run. GO slims are cut-down versions of the GO ontologies containing a subset of the terms in the whole GO. They give a broad overview of the ontology content but exclude the details of the specific fine-grained terms. For pathway analysis, the Reactome platform version 77 was used (https://reactome. org/ accessed on 23 September 2021) [41], as well as the Kegg Mapper tool version 5. 0 (https://www. genome. jp/kegg/mapper. html accessed on 23 September 2021)  [42]. For protein interaction network analyses, the identified proteins were analysed with STRING version 11.
Effects of Maternal Supplementation with HTX on the Foetal Hippocampal Proteome To identify the proteins differentially expressed in the hippocampus between the two study conditions, twenty samples were used: ten of each condition (Ctrl and HTX) and sex (M and F). The samples were from the same foetuses used in the previous study  [39]. The identification of the peptides in the database was carried out using the Proteome Discoverer v2. 5 software using a 1% FDR and the Uniprot database restricted to the taxonomy and contaminants of Sus scrofa. A total of 5745 proteins were identified, 4479 with at least 2 peptides. An ANOVA analysis of the control (Ctrl) versus the treated (HTX) samples was performed using the normalized data, and an FC of > 1. 5 (upregulated) or an FC of < 0. 67 (downregulated) and a p-value of less than 0.
Conceptualization, A. G. -B. and A. B. ; methodology, N. Y. , J. P. -V. , M. V. -G. and C. G. -C. ; formal analysis and investigation, N. Y. ; data curation, N. Y. ; writing-original draft preparation, A. B. ; writing-review and editing, N. Y. , A. G. -B. and A. B. ; supervision, A. G. -B. and A. B. ; project administration, A. G. -B. and A. B. ; funding acquisition, A. G. -B. and A. B. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Spanish Ministry of Economy and Competitivity (AGL2015-68463-C2-2-P to A. B. and projects AGL2013-48121-C3-R and AGL2016-79321-C2-1-R to A. G. -B. ), and co-funded by FEDER. N. Y. and M. V. -G. were the recipients of a graduate student fellowship from the Spanish FPU National Program Grant (Ministry of Science, Innovation and Universities; number FPU17/01881 and FPU14/01285) and C.
Author Contributions: Conceptualization, A. G. -B. and A. B. ; methodology, N. Y. , J. P. -V. , M. V. -G. and C. G. -C. ; formal analysis and investigation, N. Y. ; data curation, N. Y. ; writing-original draft preparation, A. B. ; writing-review and editing, N. Y. , A. G. -B. and A. B. ; supervision, A. G. -B. and A. B. ; project administration, A. G. -B. and A. B. ; funding acquisition, A. G. -B. and A. B. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Spanish Ministry of Economy and Competitivity (AGL2015-68463-C2-2-P to A. B. and projects AGL2013-48121-C3-R and AGL2016-79321-C2-1-R to A. G. -B. ), and co-funded by FEDER. N. Y. and M. V. -G. were the recipients of a graduate student fellowship from the Spanish FPU National Program Grant (Ministry of Science, Innovation and Universities; number FPU17/01881 and FPU14/01285) and C.
The Leeds Food Preference Questionnaire (LFPQ), a computer-based task for measuring reward responses (liking/wanting) and preferences for images of food, is a widely used tool. However, no cultural adaptation studies to date have addressed its validity and repeatability in a test-retest design. The present study aimed to develop a Japanese version of the LFPQ (LFPQ-J); examine its outcomes under fasted and fed states; and test its reproducability after one week. An online survey containing foods that were either low-fat sweet, high-fat sweet, low-fat savoury or high-fat savoury was first conducted among a sample of 200 Japanese adults (100 men and 100 women) to develop and validate a culturally appropriate food image database. Sixty participants (30 men and 30 women) then participated in two identical trials where they completed the LFPQ-J under fasted and fed states (immediately after a standardised meal), at least one week apart.
Methods To meet the aims of the present study, two steps were performed: the development of a culturally suitable Japanese version of the task and the validation of the task, according to a standardised procedure recommended in a previous review  (Oustric et al. , 2020). For the former, a food image (i. e. , picture) database was created and validated for the Japanese population, and all the text from the task was translated into Japanese. The latter was an experimental procedure which was conducted to test the sensitivity and repeatability of this new version of the LFPQ for a Japanese adult population (LFPQ-J).
Development of Japanese version of the LFPQ (LFPQ-J) The LFPQ is a computer-based task to assess the different components of food preference and food reward. Sixteen ready-to-eat food images which are common in the Japanese diet were chosen from a validated food image database (Supplemental Fig. 1 ). The images were categorised into four categories including HFSA, LFSA, HFSW and LFSW. This categorisation is based on the final set of sixteen food images with four foods in each category via the food validation. Text from the original version of the LFPQ  (Finlayson et al. , 2007)  was translated into Japanese by one of the authors, a Japanese native speaker with advanced level of English language proficiency, and Japanese version of text was back-translated into English by a bilingual English-Japanese speaker who works at a scientific language editing company to strengthen the validity of the translated task.
Experimental procedure for validation of the LFPQ-J 2. 3. 1. Participants A participant flow diagramme is shown in Fig. 1. Inclusion criteria included healthy Japanese adults 20-29 years in age who are currently non-smoking, BMI 18. 5-29. 9 kg/m 2 and not having chronic diseases. Participants of the present study were recruited between May 2021 and November 2021 through advertisements placed on the campus. Sixtyfour healthy Japanese adults provided written informed consent to participate in the study and 60 participants completed this study. We analysed 56 (26 men and 30 women) participants' data as we were unable to obtain physical activity data (i. e. , the day before each main trial) from four participants.
Calculations and statistical analysis Data were analysed with IBM SPSS Statistics for Windows, version 26. 0 (IBM Corp. , Armonk, N. Y. , USA) and GraphPad Prism 9 (GraphPad Software, Inc. La Jolla, California, USA). The absolute difference within the participants in LFPQ-J scores between the two testing occasions was analysed using Bland-Altman plots (bias, 95% limits of agreement and critical difference), and intra-personal correlation coefficients. The critical difference was set at two times the standard deviation of the difference between the first and second test days. A generalised estimated equation was used to examine between-test day differences for anthropometry measured in the morning, energy intake and physical activity level the day before each test. Statistical significance was accepted at the < 5% level.
Food image validation and final selection Results from the food image database validation data are shown in Table  1. These food images were prioritised according to the results of recognition, frequency of eating, liking, and the perception of fat content and taste, considering the variety within each category. The four Fig. 2. A schematic representation of the study protocol. After a preliminary visit, each participant underwent two, one-day laboratory-based trials (i. e. , the first and second trials). Participants reported to the laboratory at 08:30. After anthropometry, food reward was evaluated using the Leeds Food Preference Questionnaire in Japanese (LFPQ-J) before and after a standardised meal at 08:45 (Fasted state) and at 09:50 (Fed state). After a greater than one-week washout period, the participants repeated the same protocol.
Food reward Explicit liking, explicit wanting and implicit wanting of HFSA, LFSA, HFSW, LFSW, fat appeal bias and taste appeal bias under the fasted and fed states are shown in Table  2. Four data of the implicit wanting under the either fasted or fed states were invalid because the reaction time were very short (i. e. , shorter than 100 ms) or very long (i. e. , longer than 8000 ms). These data were excluded from the analyses for implicit wanting -data are therefore presented for 52 participants (fasted state, 23 men and 29 women; fed state, 22 men and 30 women).
In the cultural adaptation procedure of the development for the LFPQ-J, we created a database of food images for a wide range of age groups, and the subjective perceptions of foods of a sample of 200 Japanese adults in their 20s to 50s were surveyed. Throughout this process, the present study was able to establish 16 food images for the LFPQ-J, which were easy to recognise and familiar to the Japanese population, and met the inclusion criteria of the LFPQ  (Oustric et al. , 2020). In fact, 97% of the participants in the present study were able to conduct the LFPQ-J using the selected 16 food images. This is important as food images that are less familiar may affect food choice  (Leenaars et al. , 2016). The range of each LFPQ-J score observed in both fasted and fed states in each of the four categories in the present study was in accordance with a previous study and a recent review that have used versions of the British LFPQ  (Finlayson et al.
Children's Cohort This cross-sectional multicentric study was conducted in November 2019 among schoolchildren in Jalalabad city, the capital of Nangarhar province in eastern Afghanistan (Figure  1 ). Jalalabad has 70 schools (20 public and 50 private). Children at school were chosen to participate in this study rather than children at home in order to allow examination of both male and female children from the full socio-economic spectrum of children attending school. The sampling method for the survey was a two-stage random sampling. The necessary representative number of children within the cohort was determined using the Epi-info software (with 95% confidence interval, with a total population of 98,272 schoolchildren). A sample of 245 children was randomly selected among 5 out of 70 schools, leading to 49 students from each school.
Acknowledgments: We acknowledge the continuous support from the  "Terre & Planètes " team at  CEREGE. We thank  Campus France  for organizing the stay of Afghan partners in France. We are also grateful to the  Education Directorate Board of Nangahar province, Afghanistan , as well as the children who participated in the study and the parents who gave their consent. We are grateful to two anonymous reviewers for their comments that helped improve the manuscript.
Geographic distance is a critical barrier to healthcare access, particularly for rural communities with poor transportation infrastructure who rely on non-motorized transportation. There is broad consensus on the importance of community health workers (CHWs) to reduce the effects of geographic isolation on healthcare access. Due to a lack of fine-scale spatial data and individual patient records, little is known about the precise effects of CHWs on removing geographic barriers at this level of the healthcare system. Relying on a high-quality, crowdsourced dataset that includes all paths and buildings in the area, we explored the impact of geographic distance from CHWs on the use of CHW services for children under 5 years in the rural district of Ifanadiana, southeastern Madagascar from 2018-2021.
We build on previous work where we obtained accurate estimates of distance to PHCs and CHWs for every household in the district using a high-resolution spatial dataset that includes all households and transportation networks used by residents in the district  [33]. From this dataset, we calculated an aggregate measure of geographic dispersion from the CHW site for each fokontany in our study area, and we assessed its relationship with all-cause child consultations with CHWs, controlling for relevant factors. We then explored two potential solutions to geographic barriers for isolated communities in Ifanadiana: optimizing a single CHW site location with regards to distance and installing an additional second site per catchment.
Data collection Distance to primary health centers and community healthcare workers. For each fokontany, we estimated the average distance of all households in the fokontany to two types of health facilities, primary health centers (PHCs) and community health sites where CHWs are based. For this, we used a high-resolution spatial dataset available on OpenStreetMap, which was collected via participatory mapping (described in detail in Ihantamalala et al. [33] ). This dataset included all paths, roads, buildings, and residential areas (defined as groups of four or more buildings) within Ifanadiana District. Overall, the dataset included over 100,000 buildings, 20,000 km of footpaths and more than 5000 residential areas. For each residential area, we estimated the network distance to the nearest health facility of each type using the Open Source Routing Machine algorithm (http://project-osrm.
This data is routinely collected by the Pivot Community Health team. Because CHW composition was stable over the time period of this study (May 2018-May 2021), we assumed that these CHW characteristics were constant for the duration of our study. We also accounted for the introduction of a pilot initiative of proactive care in one of the communes (Ranomafana) in October 2019. Pivot teams also conduct monthly on-site supervisions of CHWs, observing multiple consultations and recording the percentage of cases of malaria, pneumonia, and diarrhea correctly managed (diagnosis, treatment, recommendations, etc. ). From these measurements, we calculated the proportion of consults correctly treated across all three diseases as a metric of quality of care. This variable was used in a supplemental analysis to examine the impact of whether the CHW quality of care modified the relationship between our geographic proxies and healthcare access.
Statistical analysis of geographic barriers to community healthcare We tested for an effect of a fokontany's geographic dispersion from the CHW site on monthly CHW consultation rates using a generalized linear mixed model. To control for factors that are known to influence CHW use rates, we included the fokontany's average household distance to the nearest PHC, the fokontany's wealth score, and characteristics of the CHWs working in the community site (age and sex). All continuous predictor variables were scaled and centered to facilitate model convergence. We standardized counts of consultations per month per fokontany into the number of monthly consultations per 1000 children under 5 years old. The model was fit with a zero-inflated negative binomial distribution with a log-link, including the fokontany and month of sampling as crossed random effects to account for the repeated sampling design of the data  [43].
Ethical statement Use of aggregate monthly consultation counts from MMoPH community health data for this study was authorized by the Medical Inspector of Ifanadiana district. It was deemed nonhuman subjects research by Harvard University's Institutional Review Board. The IHOPE longitudinal survey implemented informed consent procedures approved by the Madagascar National Ethics Committee and the Madagascar Institute of Statistics, specifically including written consent from participants aged 15 years or over. Household-level de-identified data from the IHOPE survey were provided to the authors for the current study. This research was conducted by an international team consisting of researchers, health practitioners, and international development workers with differing relations with and identities relative to the region and community of study.