dataesr/scientific-paragraphs-categorization huggingface dataset:
Association between WDDS-10 and MPA Figure  2  illustrates non-adjusted associations between WDDS-10 and MPA (see Supplemental table  6  for details of the number of pregnant women consuming various numbers of food groups by dataset). The WDDS-10 was significantly and positively associated with the MPA in every dataset (all P <0. 001) (Table  3 ). Unadjusted regression coefficients ranged from 0. 079 (95% CI: 0. 070, 0. 088) to 0. 309 (95% CI: 0. 250, 0. 367) and was 0. 168 (95% CI: 0. 157, 0. 178) for the pooled sample. The unadjusted models explained between 14% and 33% of the MPA variance, and 28% in the pooled sample. In models including total energy intake (kcal/d) as covariate, associations were attenuated in all datasets J o u r n a l P r e -p r o o f but remained highly significant.
Food group indicator performance and identification of thresholds The AUC value in the pooled sample was 0. 78 (95% CI: 0. 75, 0. 80), which indicates an acceptable predicting power, and ranged from 0. 61 to 0. 81 across datasets which indicates a low to good performance in predicting a MPA >0. 60, except for BF1 where the 95% CI (0. 43, 0. 78) included 0. 50 which indicates no statistically significant predictive power (Table  4 ). In the sensitivity and specificity analyses in the pooled sample, the threshold of WDDS-10 ≥5 food groups had the best performances in predicting an MPA >0. 60 (i. e. both sensitivity and specificity >0. 60 and percentage of individuals correctly classified >0. 70) with good sensitivity (62%) and very good specificity (81%) and percentage of individuals correctly classified (79%).
The threshold of ≥4 food groups showed slightly lower performances with very good sensitivity (84%), but fair enough specificity (55%) and a moderate percentage of correctly classified participants (58%). The threshold of ≥6 food groups had lower performances with low sensitivity (32%), but very good specificity (93%) and percentage of correctly classified participants (87%). The other thresholds had worse classification properties. However, findings were heterogeneous across datasets. In summary, when balancing sensitivity, specificity and percentage of correct classification, the most acceptable food group consumption threshold for predicting a MPA >0. 60 was WDDS-10 ≥4 in BF1, BF2, and BF3, ≥5 in India and Nepal, and ≥6 in Bangladesh. The three distinct scenarios from our robustness analyses returned similar findings, confirming both the observed heterogeneity across countries and also that the threshold of J o u r n a l P r e -p r o o f WDDS-10 ≥5 food groups had the best performance in predicting an MPA >0.
Discussion Following the approach used for developing and validating MDD-W among non-pregnant women  (12, 13) , we analyzed six dietary datasets to determine the minimum number of food groups consumed, out of the 10 food groups of the MDD-W, which best discriminates between higher versus lower micronutrient adequacy among pregnant adolescent girls and women in four LMICs. At least half of the women in each dataset had PAs of six micronutrients at zero, highlighting the urgency of an emphasis on diet quality and nutrient adequacy population group. Consequently, pregnant adolescent girls and women had low nutrient adequacy, with median MPA values ranging from 0. 09 to 0. 43 across the datasets. These findings are consistent with those reported among lactating women, who also face higher nutrient requirements, where the MPA ranged from 0.
23 to 0. 50 in nine datasets from resource-poor settings  (12, 13). As with other population subgroups  (12, 13) , the WDDS-10 was significantly and positively associated with MPA in each dataset. Similarly to the results found during the initial validation of MDD-W for non-pregnant women  (12, 13) , our analyses showed that across the pooled sample a threshold of 5 or more food groups had the best performance in classifying pregnant adolescent girls and women as having a minimally acceptable level of dietary micronutrient adequacy (i. e. , MPA >0. 60). Nevertheless, we found evidence of heterogeneity across datasets, both in terms of dietary patterns and in the optimal threshold of WDDS-10 to predict a minimally acceptable level of micronutrient adequacy (which varied from 4 to 6). Pulses and dairy were more commonly consumed in South Asian countries, whereas nuts, seeds, and green leafy vegetables were more commonly consumed in Burkina Faso.
This could be explained by geographical and J o u r n a l P r e -p r o o f temporal differences, such as food availability, prices, budgets, and preferences. For example, each dataset only captured certain months of the year while seasonality could affect food availability and thus dietary diversity in these contexts  (39, 40). In terms of differences in thresholds, it should be noted that even in the validation study that led to adopt the MDD-W there were differences across datasets regarding the best threshold that predicted a MPA>0. 60 which varied from 4 to 6 as in the present study  (12). Various food (sub)groups contribute more or less to the MPA than others and/or can be consumed in larger or smaller quantities according to the context. This heterogeneity is not specific to pregnant adolescent girls and women.
When recommending the threshold of 5 food groups, that work best in the pooled sample in this study as well as across the 9 datasets of the MDD-W validation study  (12) , we are pretty confident that this threshold would most likely minimize the gap to the true, context-specific and also probably season-specific optimal threshold that remains unknown in many contexts but was found in the range of 4 to 6 in most if not all published studies  (12, 17, 41). Measuring characteristics of diets and monitoring of their changes at global and national levels are needed to support governments in establishing policies and programmes to promote healthy diets, to assess the effectiveness of their actions and hold them accountable. This is the spirit behind the development of the MDD-W  (12, 13). Although MDD-W is already widely collected in large multi-topic surveys, such as Demographic and Health Surveys and Gallup World Poll, it only reflects dietary diversity which is one, albeit indispensable, subconstruct of healthy diets  (42, 43).
Other promising metrics were recently designed to assess in a synthetic manner several subconstructs of healthy diets. The Global Diet Quality Score (GDQS), for example, is based on the consumption of 25 food groups that are globally important contributors to nutrient intake, on the one hand, and/or to non-communicable disease risk, on the other hand  (44). Although it has been validated using several datasets J o u r n a l P r e -p r o o f from various contexts, the validation was performed against several outcomes and by comparisons with the performance of other metrics and not directly to nutrient adequacy. In addition, the GDQS has not yet been widely used in large surveys, probably because some appraisal of quantities or portions consumed is needed for its construction. The Global Dietary Recommendations (GDR) score is another recently developed synthetic metric that was designed to assess the adherence to a dietary pattern respecting 11 global dietary recommendations from WHO, which include dietary factors protective against noncommunicable diseases  (45).
First, despite our efforts to obtain datasets from a diversity of contexts, our study only includes data from rural contexts in four LMICs among two regions (sub-Saharan Africa and South Asia). Although our findings are not globally representative, they are consistent with other analyses among non-pregnant women from more settings  (12, 13). Furthermore, the rural locations included in our study are settings where valid scores are arguably much needed, as they typically have a high burden of undernutrition and low dietary diversity  (15, 39, 46, 47). In the meantime, more datasets should be made available in settings where a reasonable proportion of pregnant adolescent girls and women reach an acceptable MPA, so that the best predictors of acceptable MPA can be further J o u r n a l P r e -p r o o f studied.
For example, in the BF1 sample of our study, only 11 (2. 4%) pregnant women reached an MPA ≥0. 60, which strongly limits the search for the best dichotomous indicator predicting higher MPA. Another limitation concerns the use of an external within-person variance estimate to calculate the MPA in four of the six datasets. This results in more reliable prevalence estimates than when using a single day recall  (36) , but the use of within-person variance estimates from repeated measures within the samples is preferable  (35). Although we tried to find and use an external estimate of within-person variance from a relevant food intake survey, we were limited in our ability to find studies with the same geographical (e. g. for India, the region of the external estimate study is 1500 km away from that of the dataset) or temporal (different seasonality between BF1 and BF3) characteristics.
Acknowledgements We are grateful to  Kripa Rajagopalan  ( Cornell University ) and  Rupak Shivakoti  ( Columbia University ) for sharing the within-person variance estimate from their FFQ validation study among pregnant women living with or without HIV in Pune, India. The authors' responsibilities were as follows: EOV, SED, DBB, GTH-C, EL, MS, YMP, and CL designed the study; AA, EB, LD, AG, GTH-C, HH-F, SK, SSK, PHN, NMS, LMT and RRZ provided the datasets; DBB harmonized the datasets; SBB analyzed data; EOV and SBB drafted the figures, tables, and manuscript, and the other authors provided critical review; all authors read and approved the final manuscript.
Values are percentages (except for the AUC values); AUC, area under the curve; CI, confident interval; BF1, rural Burkina Faso dataset (2017/2019/2020); BF2, rural Burkina Faso dataset (2020); BF3, rural Burkina Faso dataset (2019/2021). PCC, percentage correctly classified; WDDS-10, 10-food group Women Dietary Diversity Score. J o u r n a l P r e -p r o o f.
Data and sample restrictions This study is based on data from the Demographic and Health Surveys (DHS), collected through a collaboration between the United States Agency for International Development (USAID) and its technical partners, including ICF International. The main objectives of DHS data are to track demographic, health and nutrition patterns in developing countries to facilitate public health and development research and enable informed decision-making in these domains. The data provide a nationally representative snapshot of a wide range of population, health, and nutrition indicators collected from women aged 15-49 and their children under five. These data allow for comparing countries over time as the DHS program applied standard procedures and methodologies. No ethical approval is required to use the DHS data as they are anonymous, do not constitute personal data, and are publicly downloadable from the DHS website.
In this study, we focus on data available for the Asian continent. As shown in Table  S1  in Supplementary Materials, we first compiled data from 62 DHS collected in 20 countries distributed across South Asia (6 countries), Europe & Central Asia (7 countries), and East Asia & the Pacific (7 countries), including low-income, lower-middleincome and upper-middle-income countries. However, due to data availability and comparability constraints for constructing our development level and household wealth indicators, we finally could pool 42 DHS covering 15 countries from 1990 to 2017. We argue that the period and the number of countries covered are large enough to reflect a potential shift in the wealth gradient of BF practices according to different stages of development and time. After sample restrictions, the whole dataset is composed of 3,030,568 observations.
Control variables The econometric models (defined below) include a comprehensive set of covariates that the literature has hypothesized to be important determinants of BF behaviors  (Bhandari et al. , 2019; Chandhiok et al. , 2015; Gayatri and Dasvarma, 2020; Hazir et al. , 2013; Kounnavong et al. , 2013; Ogbo et al. , 2019; Rahman et al. , 2020; Senarath et al. , 2012; Um et al. , 2020; Yadanar et al. , 2020). The infant's characteristics include sex, age (in months), birth order, birth interval (in months), size at birth (5-point score), and information about multiple births. Obstetric data include the wantedness of pregnancy, the number of antenatal visits, the mode of delivery (C-section or not), and the place of delivery (at home or not). The mother's characteristics include her age (in years), marital status (married or not), number of children and level of education (no education; primary; secondary; higher).
With the alternative OLS model including interaction terms between household wealth quintiles and survey years (from 1990 to 2017), we investigate potential temporal dynamics in the process of social convergence of BF practices. Fitted coefficients resulting from these estimates are presented in Fig. 4  (full regression results in Table  S11  in Supplementary Materials). For EBF, we observe a clear reversal of the wealth-BF association across time. More precisely, while wealth inequality in EBF was substantial at the beginning of the period, with higher rates observed among the poorest households, we note a social convergence in the 2000s driven by an increase in EBF prevalence  Source: Authors from Asian DHS data (rounds 2 to 7). Notes: Dots refer to the marginal effects, and brackets to standard errors.
Full regression results are available in Table  S10  in Supplementary Materials. Samples of children vary according to the dependent variable (see Table  2  for age intervals). Control variables are: child sex, child age, child's size at birth, birth order, birth interval, multiple births, child wantedness by parents, number of antenatal visits, delivery place (at home or not), delivery mode (C-section or not), mother's completed education, mother's age, mother's marital status, number of children in the household, the living place of the household (rural or urban), and dummies of DHS rounds (round 2 to 7). Source: Authors from Asian DHS data (rounds 2 to 7). greater among rich households than among poor households. As a result, wealth-related disparities slowly reversed during the 2010s, EBF becoming more prevalent among the wealthiest households.
We chose to exclude these control variables from previous estimates (Table  2 ) due to the drop in the number of observations it would have implied. These alternative estimates are reported in Table  S17  in Supplementary Materials. Despite the reduction in the number of observations, the findings remain consistent with Table  2. Nonetheless, looking at Table  S17 , one could be surprised to see a positive correlation between maternal employment and optimal BF Fig. 4. Marginal effects of OLS regression of BF practices on household wealth quintiles interacted with time (including covariates). Notes: Specific wealth quintiles are calculated for each country and survey year. Time refers to the survey year the data were collected and varies from year 0 (1990) to year 27 (2017). Dots refer to the fitted coefficients and brackets to standard errors at a 0.
95% confidence level. Full regression results are available in Table  S11  of Supplementary Materials. Samples of children vary according to the dependent variable (see Table  3  for age intervals). Source: Authors from Asian DHS data (rounds 2 to 7). behaviors, which differs from the literature results  (Rivera-Pasquel et al. , 2015). However, our maternal employment/unemployment indicator is imperfect since it measures the current employment status (i. e. , when the survey was collected) and not the employment status after birth. Consequently, this surprising result could simply reflect the fact that Asian women having access to work also tend to have better access to antenatal visits and hospitals for birth than women excluded from the labor market, and so the former might be more aware of BF-related information than the latter and more likely to breastfeed.
Standard errors are clustered at the PSU-level. Robust standard errors are in parenthesis: ***p < 0. 01, **p < 0. 05, *p < 0. 1. Samples of children vary according to the dependent variable (see the line "age interval"). Control variables are: child sex, child age, child's size at birth size, birth order, birth interval, multiple births, child wantedness by parents, number of antenatal visits, delivery place (at home or not), delivery mode (C-section or not), mother's completed education, mother's age, mother's marital status, number of children in the household, the living place of the household (rural or urban), and dummies of DHS rounds (round 2 to 7). Source: Authors from Asian DHS data (rounds 2 to 7).
Data sources, settings, and participants Details of the datasets used in this study are fully described elsewhere  (Grijalva-Eternod et al. , 2017). Briefly, 30 datasets from Action Contre la Faim were received following a data appeal. These datasets contained inpatient therapeutic data of individuals enrolled between 2002 and 2008 from 34 different field sites located in 12 countries, namely Afghanistan, Burundi, the Democratic Republic of the Congo, Ethiopia, Kenya, Liberia, Myanmar, Niger, Somalia, Sudan, Tajikistan, and Uganda (see Table  S1 ). The criteria for children admitted to these centres were (1) infant too weak or feeble to suckle effectively, and/or (2) not gaining weight at home (by serial measurement of weight during growth monitoring), and/or (3) WLZ <-3, and/or (4) presenting bilateral pitting oedema.
Future research is needed to explore the added value of CIAF in other programmes/datasets and to understand if simplified approaches might be just as effective at identifying the highest-risk infants <6m (e. g. , using WAZ or MUAC alone as suggested by a recent systematic review  (Hoehn et al. , 2021). In our data, we observed that at admission, boys had a greater proportion who were concurrently wasted, stunted and underweight compared to girls and presented with a higher mortality risk. Two recent metaanalyses provide support to the nutritional disadvantages in boys, one conducted in 84 countries among older children aged 6-59 months  (Khara et al. , 2018) , and another conducted in 74 studies among children aged 0-59 months  (Thurstans et al. , 2020). However, contrary to our results, the risk of death was found to be similar in boys and girls  (Thurstans et al.
, 2023). The 2020 Global Nutrition Report also showed that boys under-five years of age had a higher prevalence of stunted and wasted compared to girls  (Micha et al. , 2020). Our study added to evidence that this difference in burden and associated mortality risk might be present at an earlier age. Many possible mechanisms might explain these sex differences  (Thurstans et al. , 2022). Possibly in response to greater biological vulnerability, boys are often prioritized in terms of parental treatment-seeking behaviours  (Mahmud et al. , 2020). Programmes might need to account for biological and social risk differentials to ensure that all children receive the care needed to survive and thrive. In our dataset, we lacked full information on the reasons for admission although, at the time covered by the datasets, being wasted was the main anthropometric criteria for admission to programme care.
We urge caution in interpreting our results. First, the lower proportion of wasted infants might reflect wider clinical criteria at admission, such as the inability to feed effectively, irrespective of anthropometric status. Second, because we only studied infants already admitted to therapeutic feeding, we cannot extrapolate our results to a wider population of infants <6m or make evidence-based statements about which criteria might be best for admitting to care. Further research on these issues is certainly needed using population-based samples or datasets. Complementary research like ours is also important to understand which infantsof those admittedare the highest risk and might thus need different treatment or follow-up.
Funding Funding for the original study, which compiled this database, was made possible by the generous support of the  American  people through the  United States Agency for International Development (USAID)  and the support of  Irish Aid. MK and CSG-E gratefully acknowledge support from the  Eleanor Crook Foundation  to the  LSHTM. Funders were not involved in this article and had no influence on the design, interpretation, results, or decision to submit.
Transcriptome resources and sequence homology search Our previously published G. fossarum de novo transcriptome assembly  (Cogne et al. , 2019)  served as search database to perform the sequence homology search. A searchable database from the assembly was created using the command-line BLAST function makeblastdb. Tblastn was used to identify homolog sequences to the amphipod Hyalella atzeca MTs (HAZT008668, a putative MT-like B; HATZ008669 a putative MT-like C). A search for the Open Reading Frames was performed using the NCBI tool ORFfinder.
Prediction of 3D structure from MT protein sequences The 3D structures and the structure-based functional annotation of the two putative metallothionein ORFs identified in G. fossarum were predicted using the I-TASSER server  (Yang et al. , 2015). For each target, I-TASSER simulations generate tens of thousands of conformations (called decoys). To select the final models, I-TASSER uses the SPICKER program to cluster all the decoys based on the pair-wise structure similarity, and report up to five models that correspond to the five states of lowest free energy and therefore have the highest confidence. The confidence of each model is quantitatively measured by C-score. C-score is typically in the range of  [-5, 2] , where a C-score of a higher value signifies a model with a higher confidence.
Identification of two metallothioneins in Gammarus fossarum transcriptome We mined the transcriptome of G. fossarum by BLASTt using H. atzeca MTs sequences (HAZT008668, a putative MT-like B; HATZ008669 a putative MT-like C) as queries. We identified two contigs with partial homology, each one containing several potential open reading frames (ORFs). We screened the various ORFs identified using InterProScan from InterPro database  (Blum et al. , 2021)  and we identified one ORF in each contig predicted as a crustacean metallothionein (Table  1 ). In particular, the contig GFBF_DN110944_c4_g1_i2 codes for an ORF of 60 amino acids residues that we named as mt1. The contig GFBF_DN110487_c1_g1_i4 codes for an ORF of 71 amino acids that we named mt2 (Table  1 ). These putative mt ORFs both contain 18 cysteine residues, similarly to the majority of described MTs in other crustacean, such as decapods or the amphipod Hyalella atzeca  (Mahmood et al.
The best models for MT1 (C-score = -1. 59) and MT2 (C-score = -2. 49) predicted the presence of a α-helix of six amino acids (including one of the 18 Cys residues, Cys 46) at the C-terminus in each protein (most of the residues having a confidence score higher than 7 with 9 indicating the highest confidence) (Fig. 2 ). I-TASSER also provided a functional prediction based on the 3D models by integrating the COFACTOR and COACH softwares  (Zhang et al. , 2017; Yang et al. , 2013). In both G. fossarum MT-1 and MT-2, a cadmium-binding site involving the same four cysteine residues (Cys 40, Cys 46, Cys 50, and Cys 57) was predicted (Fig. 2A-B ). Moreover, a zinc-binding site was predicted only for MT-2, involving four different cysteine residues located in proximity of the N-terminal (Cys 5, Cys 10, Cys 17, Cys 21) (Fig.