dataesr/scientific-paragraphs-categorization huggingface dataset:
In spite of the need to simplify identification of children with SAM, the demand for more investigation into the clinical and physiological significance of this diagnostic discrepancy and other consequences of shifting to MUAC-only programming have been largely ignored  [9, 11, 16, 17]. SAM management programs are primarily interested in reducing short-term death of SAM children, and therefore one of the critical elements in the debate around MUAC-only programming is the extent to which mortality risks differ between the defined categories. Recently, an empirical study pooling data from >70,000 children admitted for SAM treatment, as well as a meta-analysis using 21 datasets concluded that there was no difference in mortality risk between those children identified with a low MUAC and those with a low WHZ  [18, 19].
In addition, we aimed to describe the mortality hazard of those children who would be excluded from treatment under those programs (i. e. children with single deficits of either only MUAC <115mm or only WHZ <-3 without concurrently having the other deficit), as well as those satisfying both criteria. We obtained rare longitudinal cohort datasets of children in the community not enrolled into nutritional programs, which had all the required anthropometric measurements and regular follow-up to determine survival. The effect of age, sex, and concomitant stunting on these relative mortality hazards were also examined, as well as the effect of anthropometric deficits on death due to specific causes.
Statistical analyses Data were analyzed with Stata (version 15; StataCorp LP, College Station, Texas). We pooled the 3 datasets, and calculated all Z-scores according to WHO 2006 Child Growth Standards  [26]. Because we categorized Z-scores, we did not delete extreme values, as it is very likely that an extreme value would be below -3 in reality and would therefore be correctly assigned to the severe category. However, only few values were <-7 for HAZ and WAZ, and <-6 for WHZ and could be checked manually. Three values for HAZ (0. 01%), 13 values for WHZ (0. 03%) and 6 values for WAZ (0. 01%) were regarded as implausible and deleted together with their corresponding raw value(s) for height and/or weight. We report case-fatality rates (CFR) defined as numbers of death per 100 child-months. We also report deaths per 10,000 children per day, which is the unit that is usually used when assessing death rates in humanitarian situations.
Results There were 17,520 children in the three original datasets. Of the 68,888 measurements, 6,151 measurements were done when the child was younger than 6 months, 7,995 when the child was older than 59 months, and at 10 time points the age of the child was missing; thus, these data were excluded from this analysis. At 5,731 time points either WHZ and/or MUAC were not recorded, leaving 49,001 time points provided by 15,060 children for analysis. This resulted in a total of 143,512 person-months of observation available for analysis. Of the eligible children, 49% were female. There were 749 deaths recorded (5. 0% of the study population) for which children had records of their anthropometric indicators at the start of the measurement period. Mortality was higher in the Senegal dataset (10.
4%) than in the two other datasets. Of the children that died, 48% were female and the median age at death was 26 months (interquartile range 18-35 months). The mean WHZ (SD) was -0. 22 (1. 03) in the DRC cohort, -0. 86 (1. 03) in the Senegal cohort, and -0. 41  (1. 15)  in the Nepal cohort. The children in Nepal had the highest prevalence of stunting and wasting based on HAZ and WHZ; those in DRC had a higher percent with a low absolute MUAC and MUAC-for-age Z-score. Table  2  shows the main cohort characteristics. Of all measurements points with data available for MUAC and WHZ, 2,317 (4. 7%) were <115mm for MUAC, 865 (1. 8%) were <-3 Z-scores for WHZ, and 527 (1. 1%) were under the cut-off points for both MUAC and WHZ. Of all measurements categorized as SAM, 338 measurements (13%) were under the cut-off for WHZ only and thus would be excluded from SAM diagnosis using MUAC as sole indicator (Fig  1A ).
The analysis by McDonald et al. [35]  examined children from one week of age and followed the children for about one year (0. 7 to 1. 6 years). Their mortality data are likely to be dominated by infants, particularly small-for-gestational-age infants, who have a high mortality rate, especially in the first month of life. Our failure to find that concomitant stunting increased mortality may therefore be explained by the difference in the age range examined in the two studies. It would be useful for McDonald et al. [35]  to re-examine their database to differentiate stunting and mortality in infants and children within different age ranges to see if this difference in findings can be reconciled. Nevertheless, if in children older than 6 months stunting does not augment the risk of death from SAM then this argument used by others to promote a MUAC-only program is without merit.
Strength and limitations A main strength of our study is that we were able to gather rare community-based cohort studies that included all required measures and assess them using the WHO recommended standards. These studies are more likely to have minimized the risk of a selection bias compared to health facility based studies. However, selection bias in the original studies cannot be excluded. Publications reporting the original methods state that in Senegal 88. 5% and in Nepal 96% of all the children of eligible ages were enrolled into the studies. However, in the Nepal study, we received a sub-set of the original study with 6,112 of the 28,630 children selected. We do not have any information on what criteria the selection of this sub-set was based. In the DRC dataset, the proportion of eligible children included is unknown although it is reported that 16 out of 52 villages in the study area were randomly selected and all mothers and children in these villages were enrolled into the study  [45].
The ratio of SAM prevalence by diagnostic category in the DRC study seems somewhat unusual with a very low proportion of children with low WHZ in comparison to both the Senegal and Nepal study in this analysis, and other community-based studies  [3, 4, 9]. Based on the original report, the occurrence of edema was low in the DRC study (on average 0. 1% at each time point)  [45]. Edema often appears acutely before death, and the incidence might not have been captured adequately in the 3-monthly surveys in the DRC study. Although we do not have data on edema for the Senegal and the Nepal cohort, this is not likely to explain the discrepancy between the cohorts and the surveys. When repeating the main Cox regression analysis excluding the DRC dataset, the estimates were similar and we would have arrived at the same conclusions (results not shown).
This could have biased our estimates of death rates. Another limitation refers to the question of whether the datasets we received were thoroughly cleaned. When we checked for consistency at subsequent time points, we found that many anthropometric measurements were not plausible based on criteria defined prior to our analysis (see S1 Table ). The surprising number of data-points that we had to eliminate during the initial cleaning calls into question the quality control of the original measurements. At the time these studies were conducted it was considered that increasing sample size would counteract the effect of random measurement error; we now recognize this to be an incorrect assumption  [52]. Nevertheless, estimates of mortality hazard (HR) did not change substantially when we applied quite stringent cleaning criteria (S1 Table ) and therefore we are confident that this would not have affected the conclusions presented in this analysis.
Acknowledgments We would like to acknowledge the principal investigators of the original studies:  Jan Van den Broeck  for the DRC cohort,  Michel Garenne  for the Senegal cohort, and  Keith West  for the Nepal cohort. The DRC study was supported by the  Centre de Developpement Integrale-Bwamanda , and funding was provided by the  Flemish Inter-University Council (Vlaamse Interuniversitaire Raad) , the  Belgian Administration for Development Cooperation , and the  Nutricia Research Foundation. Catherine Schwinger is affiliated to the  Centre for Intervention Science in Maternal and Child Health (CISMAC) , which is funded by the  Research Council of Norway through its Centres of Excellence funding scheme  (project number  223269 ), the  University of Bergen (UiB) ,  Norway.
Dietary Intake Assessment Dietary data were collected using three 24 h recalls, one of which was on the weekend, randomly distributed within a 2 week period. Participants were not previously informed of the call day to limit changes in eating habits for the survey. Specific questionnaires were used to investigate salt intake to describe the use of salt in cooking and salt added with a salt shaker during meals. Additional data were collected concerning the usual consumption of episodically consumed foods. In this paper, diet intake is reported based on the 24 h recalls (g/day). Consumption of alcoholic beverages was assessed using a combination of an annual propensity questionnaire and 24 h recalls (g/day). The DASH-style diet, as previously developed by Fung et al. [17] , includes 8 dietary components which should be increased (fruits, vegetables, legumes, low-fat dairy, whole grains) or minimized (salt, sweetened beverages, red and processed meats).
Socioeconomic Status Sociodemographic and economic data were collected by a questionnaire administered face-to-face during the first home visit. Duration of work was categorized in two classes: fixed-term contract and indefinite contract. Education level was determined according to the International Standard Classification of Education (ISCED)  [19]  and was then classified into three levels: High school diploma or less (≤13 years of education), undergraduate degree (14-16 years of education), and postgraduate degree (≥17 years of education). From the baseline questionnaires, we used the following sociodemographic variables: marital status (i. e. , single (single/separated/widowed or divorced, and couple: marital or non-marital) and household income (i. e. , <1600; 1600-2500; 2500-4600; >4600 euros per month).
Statistical Analysis For the participants who underwent a health examination, the probability of inclusion was calculated to consider the complex survey design. In addition, to consider a potential participation bias, calibration by age, education diploma, and the presence of at least one child in the household, according to French national census data and the period of collection, was performed separately for each gender. Calibration was carried out using the SAS macro program CALMAR (CALibration on MARgins), and census data came from the National Institute for Statistics and Economic Studies (Insee). Descriptive analyses were performed for the entire population and for each gender using counts and percentages or means ± standard deviations (SDs) for quantitative variables, or frequencies and percentages for categorical variables for the entire population and for each gender.
First, we used a linear regression to evaluate the association between the DASH score and alcohol consumption with BP parameters (as continuous variables) and second, in same models, we included all the nutrient parameters of the DASH score and alcohol consumption to evaluate the independent determinants of BP parameters. Then, quintiles of the DASH score and alcohol consumption were constructed to evaluate their effects on BP gain or loss. The results were reported for each model as the square partial correlation coefficient, r 2 , which was used to describe the contribution of each parameter to BP variability. All tests were 2-sided; p < 0. 05 was considered statistically significant. Statistical analyses were done using SAS software (version 9. 4; SAS Institute, Carry, NC, USA).
Salt Intake Our study also identified a gender difference in the association between BP and nutritional factors. A few observational studies have reported this discrepancy but with inconsistent results  [6, 22, 28]. We found that BP was associated with salt intake in women but not with men, when adjusted for all parameters. Several hypotheses can explain this finding. Data from a controlled dietary trial suggested that low dietary sodium intake may be more effective in reducing BP among women due to their higher salt sensitivity compared with men  [29]. Dietary questionnaires only covered salt intake from foods. Total consumption was estimated by adding a constant for cooking meals and eating, which could have reduced the inter-individual variation and thus could made the potential correlation more difficult to identify.
Strengths and Limitations The main strength of our study is that participants of the ESTEBAN survey are representative of the general French population. The data were collected using standardized protocols which add validity to our study's results. However, some limitations are present in our study. The self-administered questionnaire on weight history may have been subject to memory biases which would alter patient answers about social difficulties, recent financial limitations, refraining from care, weight variation, and age at which this variation occurred. Medical history and comorbidities were collected from insurance database notifications, self-reporting and physician assertions during medical examination in health centers. They were less subject to misinterpretation and under or over-reporting.
But nowadays, the method of choice is genome sequencing and comparative genomics. Since the first genome of strain PSU-1 produced in 2005 by Sanger technology  (Mills et al. 2005) , Next Generation Sequencing (NGS) technologies have made it possible to compare genomic sequences of 14 strains in 2012  (Borneman et al. 2012a ), 57 in 2015 (Campbell-Sills et al. 2015 ), 196 in 2016 (Sternes & Borneman 2016) , and more than 220 genomes are now available in databanks. Phylogenomics analyses have confirmed the population structure and phylogenetic lineages previously suggested by MLST. They have also revealed new strains lineages and allowed the discovery of some correlations with the regions or products of origin. Comparative genomics investigations have started to unravel the genetic characteristics of the strains, shedding new light on their adaptation to different environments.
Quantitative data collection and analysis We used the quantitative data collected through a household survey across three different projects conducted in Bangladesh, Nepal and Tajikistan. In the case of Nepal, the survey was designed by the research team for the purpose of the impact evaluation of a development project implemented by an international non-governmental organisation (INGO). The surveys for these three projects did not specifically aim at exploring relationships between food security and women's empowerment but collected data on agricultural practices, water management and on different components of food security and women's empowerment/agency (Table  1 ). We therefore used a heterogeneous set of variables as indicators of women's empowerment and food security. Our intention was not to compare results across countries but rather to explore different types of relationships between empowerment and food security across a wide range of contexts.
Qualitative data collection and analysis The qualitative component of our study considers food insecurity as a process, exploring some of the structural causes that create food insecurity. In Nepal, the qualitative study was conducted between November 2015 and May 2016 as part of the BRACED project under which also the quantitative data were collected. We selected two communities in Dadeldhura and Doti districts, based on the diversity of caste and ethnic groups among the beneficiaries and different types of interventions conducted. The INGO envisioned in this project multiple pathways linking women's empowerment and increased food security: women's enhanced access to water for both domestic and productive uses and women's membership in vegetable farming groups would allow them engaging in irrigated homestead vegetable gardening.
Literature annotation Some PRNP variants can be identified as highly penetrant by their extreme enrichment in cases over population controls, but many variants are too rare in both groups for meaningful comparison. 4 We therefore reviewed primary literature for 70 reportedly pathogenic PRNP variants (table S1, doi. org/10. 5281/zenodo. 2535761) to determine which had evidence of at least one family with at least 3 affected individuals in a pattern consistent with Mendelian segregation, or had a documented case with a de novo mutation. We identified 27 such variants, deemed likely high-penetrance variants. The remainder were seen in isolated patients with a negative or unknown family history, or have population allele frequencies inconsistent with high penetrance. 4 These variants will include both benign and low-risk variants.
This gives a conservative estimate that 221 × 75% × 36% = ;60 individuals alive in the United States today who meet the criteria we use in our power calculations. In the supplementary Discussion (doi. org/10. 5281/zenodo. 2535761), we consider in detail several other approaches to estimating the number of PRNP mutation carriers available for trials. Statistics, source code, and data availability Analyses were conducted using custom R 3. 5. 1 scripts run in RStudio 1. 1. 456. We present a public GitHub repository at github. com/ericminikel/prnp_onset (including figure, tables, Discussion, and Methods including the details of power calculations and simulations) (doi. org/10. 5281/zenodo. 2535761) as well source code in the R programming language and life tables for each mutation. These data and code are sufficient to reproduce most analyses and figures herein.
Age at onset in genetic prion disease We reasoned that any preventive trial with a clinical endpoint in genetic prion disease would derive most of its statistical power from individuals with high penetrance PRNP variants. Literature review (see Methods) revealed 27 PRNP variants with evidence for high penetrance. The top 3-E200K, P102L, and D178N-collectively explain 85% of high-penetrance cases in a recent case series  4  (figure  S1 , doi. org/10. 5281/zenodo. 2535761). Each of these arises from a CpG transition (a C to T DNA change where the adjacent base is G), a type of variant that occurs by spontaneous mutation 10-100× more often than other mutation types,  16, 17  explaining the recurrence of these 3 mutations on multiple PRNP haplotypes in families worldwide. 4, 18, 19 herefore, regardless of the population studied, these 3 variants are likely to account for a large fraction of genetic prion disease cases with high-penetrance variants.
We focused our analysis primarily on individuals with these 3 variants, aggregating age at onset or age at death data on 1,001 individuals from 9 study centers worldwide (tables 1 and S2 [doi. org/10. 5281/zenodo. 2535761]), encompassing both direct clinical reports and family histories (see Methods), and including censored individuals. Statistics on 93 individuals with the next 4 mutations most common in cases-5-OPRI (insertion of 5 extra octapeptide repeats), 6-OPRI, P105L, and A117V-are included in table  S3  (doi. org/10. 5281/zenodo. 2535761). We used these data to compile life tables and computed the annual hazard-risk of onset in each year of life-for each mutation (supplementary life tables, doi. org/10. 5281/zenodo. 2535761). 1] [22]  An implication of this variability is that high lifetime risk arises not from certain onset at a specific age, but from modest risk in any given year of life, accumulated over many decades of exposure.
This poses a challenge for following presymptomatic individuals to onset in a preventive clinical trial, as it is difficult to ascertain a group of individuals for whom onset is imminent. For example, even at age 57, an E200K individual has only a 5% probability of disease onset occurring in any given year. This means that 20 person-years of follow-up for E200K individuals around this age would be expected to result in only one observed disease onset. Annual hazards do rise with age, but as they reach high levels, the number of surviving individuals also dwindles (figure  1 ). For the 3 most common mutations, the annual hazard remains below 10% until after the majority of people have already died (figures 1 and S3 and supplementary life tables, doi. org/10. 5281/zenodo. 2535761). Similarly, the median number of years until onset, conditioned on an individual's current age, remains ≥5 years until after the median age at onset has passed (supplementary life tables, doi.
org/10. 5281/zenodo. 2535761). The next 4 most common mutations have tighter age at onset distributions, and so reach higher annual hazards sooner (figure  S3 , doi. org/ 10. 5281/zenodo. 2535761), but these mutations are also much rarer, accounting for only 10% of cases with a highpenetrance variant. 4 wer for randomized preapproval trials with a clinical endpoint Based on the age-dependent hazards calculated for each mutation, we set out to determine how many individuals would need to enroll in order to power prevention trials with an endpoint of disease onset. While younger individuals or those with a mutation of modest penetrance might seek to enroll in trials or take a preventive drug, they would not contribute much statistical power to an endpoint of clinical onset. We therefore chose to base our power calculations on individuals with the 3 most common high-penetrance mutations between age 40 and 80.
org/10. 5281/zenodo. 2535761); and a postmarketing surveillance program might allow newly drugtreated individuals to enter the program on a rolling basis, replacing any who withdraw. The effects of these assumptions (table  3 ) are collectively to reduce the number of individuals required to demonstrate efficacy of a drug with HR of 0. 5 from 813 to 37. At the same time, the number of individuals available for a trial might increase, because an approved drug should have broader geographic reach than a preapproval trial, being limited only by the availability of medical personnel capable of prescribing and administering it rather than by a set number of trial sites; a treatment might improve awareness and diagnosis of the disease; and a treatment might stimulate more individuals to pursue predictive genetic testing.
For instance, of people at 50/50 risk for a PRNP mutation, currently only 23% pursue predictive testing, 15 compared to 60% (2. 6× higher) for In each case, the calculation is for a hazard ratio of 0. 5, the n indicated is for 80% power at the p = 0. 05 threshold, and all assumptions other than those indicated in the table are the same as for table  2. The number of individuals required for postmarketing studies is determined by simulation (supplementary Discussion, doi. org/10. 5281/zenodo. 2535761). BRCA1 or BRCA2 mutations,  30  which are considered medically actionable. 31 Thus, a postmarketing study could be adequately powered with available numbers of individuals for a HR of 0. 5 (table  3 ) and, over a range of assumptions, would bring power requirements into closer alignment with the number of available individuals (figure  S6  and supplementary Discussion, doi.