dataesr/scientific-paragraphs-categorization huggingface dataset:
Since both mouse and human tRNA [Ser]Sec structures were resolved  ( 38 , 63 ) , a high confidence for RNAComposergenerated models was expected. The generated pdb files were then visualized and analyzed using Chimera UCSF software  ( 64 ). Our findings indica te tha t the C65G mutation indeed disrupts one G:C base pair but without significantly affecting the overall ar chitectur e of the 3D structur e, as illustrated in Figure  1  B and Supplementary Movies S1 and S2. Howe v er, the loss of a G:C base pairing may impair the global stability of the molecule, and further investigation is r equir ed to confirm this hypothesis.
Localization of tRNA [Ser]Sec within the UGA translating ribosome: potential implications of C65G mutation Recently, the first structure of the 80S ribosome as it decodes the selenocysteine UGA codon, the selenosome ( 15 ), has been solved by cryo-EM (PDB: 7ZJW). This study reveals the structure of various components of the Sec insertion machinery and their respecti v e positioning and interplay within the translation context. The structure of ribosome-bound tRNA [Ser]Sec has been specifically elucidated, wherein the C65 residue, found to be mutated in patients, is located at the contact interface with EFSec (Supplementary Figure  S6 ). The dedica ted elonga tion factor EFSec indeed binds tRNA [Ser]Sec , with its D1, D2 and D3 domains, loca ted a t the GTPase-associated center, contacting the acceptor arm, and D4 interacting sim ultaneousl y with the variable arm of the tRNA and the apical loop of the SECIS element.
ACKNOWLEDGEMENTS We thank  Dr Pierre Ray  ( CHU de Grenoble, UFR de Biochimie et G én étique Mol éculair e, Gr enoble, France ) f or very inf ormative discussions about genetic mutations, and  Dr Yann Ponty [Laboratoire d'Informatique de l' École Polytechnique  ( LIX CNRS UMR 7161 ),  Palaiseau, France ] for useful insights about 3D structures.
FUNDING The  Agence Nationale de Recherche sur le SIDA  et les h épatites virales (ANRS); the  Institut National de la Sant é et de la Recherche M édicale (INSERM) ; the  Centre National de la Recherche Scientifique (CNRS) ; the  École Normale Sup érieure (ENS) de Lyon  ' Emerging Project ' [L. C. in 2016 and T. O. in 2018]; the  CNRS MITI programs 'Isotop'  and ' Metallo-Mix '; the  ANRS  [to C. V. ]; the  Uni v ersit é Claude Bernard Lyon 1  [to O. G. ]; the  Fondation pour la Recherche M édicale  [to P. M. ]; the  Agence Nationale de la Recher che  [ ANR-10-LABX-0036  NETRNA  Fr ench National Program Investissement d'Avenir (Labex NetRNA )].
ACKNOWLEDGMENTS The authors wish to thank the animal facility of the  Institut Jacques Monod. We acknowledge the  ImagoSeine facility , member of the  France BioImaging infrastructure  supported by the  French National Research Agency  ( ANR-10-INSB-04 , "Investments for the future"), for help with confocal microscopy. We are grateful to  Vanessa Ribes  for the kind gift of antibodies,  Adrien Calvairoly  for help with collecting Ascl1 mutants, and members of the  Pierani laboratory  for helpful discussions. F. C. is an Inserm researcher. A. P. is a CNRS investigator. This work was supported by grants from the  Agence Nationale de la Recherche  ( ANR-2011-BSV4-023-01 ) and the  Fondation pour la Recherche Médicale (FRM)  ( INE20060306503  and «Equipe FRM DEQ 20130326521») to A. P.
Processing of samples was performed at 4°C and followed by incubation with GST-GGA3-PBD for 1 hour. For western immunoblotting, anti-ARF1 rabbit monoclonal IgG as primary antibody was diluted at 1:2000 in Intercept buffer prior to incubation at -4°C for 24 hours under agitation. The blot was then incubated with IRDye 800CW Goat antirabbit antibody at 1:15 000 dilution for 1 hour at room temperature under agitation. Western blot analysis results were imaged using Odyssey Sa IFred scanner (LI-COR, Bad Homburg, Germany), equipped with Imaging Studio (Analysis Software V. 4. 0). Results were quantified using Empiria Studio V. 1. 3 software. The experiment was performed three times. Whole blood was collected on PAXgen tube and total RNA was extracted from patient's lymphoblastoid cell lines using PAXgene Blood RNA kit (Qiagen).
cDNA was generated from 1 μg RNA with the addition of random hexamers and oligo dT primers using the SuperScript II Reverse Transcriptase (ThermoFisher Scientific). Impact on transcription of the c. 384+1G>T substitution was then characterised by PCR Sanger sequencing on cDNA according to standard procedures, using primers localised in exons 3 and 5 of ARF1 gene, (F= ACCG TGGA GTAC AAGA ACATCAGC, R= ACTT CTGG TTCC GGAG CTGAT). Structural consequence of missense variants were predicted using SwissModel 11 (V. 4. 1. 0), DynaMut2  12  and visualised with Mol*. 13.
Molecular analysis Thirteen different de novo variants in ARF1 (NM_001658. 4; ENST00000272102. 10) were identified in 17 individuals: 10 variants, 1 splice variant and 1 frameshift variant. (Nota bene: the missense p. (Phe51Leu) was caused by two different variants, c. 153C>A and c. 151T>C. ) All variants were absent from gnomAD 14 (V. 2. 1. 1; V. 3. 1. 2) or deCAF  15  and the 10 missense variants were predicted to be deleterious by multiple in silico tools (see 'Discussion' section and online supplemental figure  1A ). The frameshift variant p. (Asp72Thrfs*17) is predicted to elicit nonsense-mediated decay according to the 50 nucleotides rule,  16  and is identified by the Loss-Of-Function Transcript Effect Estimator (V. 1. 0. 3  14  ) to result in a loss of function with high confidence.
Data analysis The differences in the FA composition (in %, considering only the FA representing >1 % of total FA) were analysed by permutational analysis of variance (PERMANOVA,  Anderson, 2014)  using Euclidean distances as dissimilarity values among individuals and considering species, muscle tissues and lipid fractions as factors. Principal component analyses (PCA) were performed to visualise the variation in FA profile composition between species (sardine and anchovy), muscle tissues (white and red muscles) and lipid fractions (neutral and polar lipids). The FA content of fish fillet was calculated as follows, considering the relative proportions of red and white muscle: FA fish fillet = a * FA white muscle + (1-a)* FA red muscle (1) with a= 0. 71 for sardine and a= 0. 83 for anchovy to consider the relative proportions of red and white muscles of each species  (Greer--Walker et al.
Funding information This work is a contribution to the  OMEGA  project, supported by  ISblue  project , Interdisciplinary graduate school for the blue planet  ( ANR-17-EURE-0015 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir " embedded in France 2030. MMR was funded by the  PHYSIOAL  project funded by  ISblue  and by a grant from the  Regional Council of Brittany  ( SAD program ).
| Participants In experiment 1 (VAS method), we recruited 38 normal-weight (NW: 20 < body mass index, BMI < 25 kg/m 2 ) and 44 volunteers living with obesity (OB: BMI > 30 kg/m 2 ). In experiment 2 (RT method), 43 normal-weight (NW: 20 < BMI < 25 kg/m 2 ) and 38 volunteers living with obesity participated (OB: BMI > 30 kg/m 2 ) (Table  S1 ). We performed a power analysis with Gpower (v3. 1. 9. 7) using the data acquired during a pre-test on 16 participants. The alpha and beta parameters were set up respectively at 5% and 80%. The analysis suggested a minimum of 40 participants to reach significance level for both VAS and RT experiments. In both experiments, the participants were scaled and weighed at the end of the first session. They should not eat, drink or smoke at least 1:30 h before the sessions.
ACKNOWLEDGMENTS We would like to thank for their technical and logistic support on the experiment:  Noëlle Béno , and  Françoise Durey  from the  ChemoSens platform of CSGA , and  Chantal Septier ,  Claire Follot ,  Gaia Maillard ,  Marianela Santoyo Zedillo ,  Marie Simon ,  Yue Ma  and  Anaïs Guilbert  from the  FFOPP team of CSGA. We would like to thank  Firmenich (Geneva, Switzerland)  for providing the bacon odorant composition.
Funding information  ISITE-BFC, Grant/Award Number :  ANR-15-IDEX-0003 ;  Institut National de Recherche pour l'Agriculture, l'alimentation et l'Environnement (INRAE) , Grant/Award Number: half-PhD-funding;  Agence Nationale de la Recherche (ANR) :  AROMA, Grant/Award Number :  ANR-20-CE21-0004-01.
Sensory Characterization of Dark Chocolate Samples A clustered heatmap (Figure  1 ) allows for a summarized visualization of the raw (not pretreated data, as in not centered nor scaled) mean sensory scores of all 54 chocolate samples evaluated in this study, along with insights into the correlations that exist among the different attributes that were evaluated (sweetness, bitterness, acidity, astringency, cocoa, yellow fruits, red fruits, citrus, dried fruits, nutty, winey, black olives, green, earthy, floral, woody, spicy, and roasted). In Appendix A, Table  A1 , additional information on the overall sensory results obtained from the chocolate dataset is presented. of major interest for the industry, notably in the case of highly-sought-after attributes, such as fruity or floral. The focus of this study is to identify key volatile aroma compounds that may enable differentiation within a selection of dark chocolate samples derived from Trinitario beans grown in the Dominican Republic, as well as to ultimately develop sufficiently accurate predictive models that would allow the discrimination of samples with regard to their flavor potential.
Sensory Characterization of Dark Chocolate Samples A clustered heatmap (Figure  1 ) allows for a summarized visualization of the raw (not pretreated data, as in not centered nor scaled) mean sensory scores of all 54 chocolate samples evaluated in this study, along with insights into the correlations that exist among the different attributes that were evaluated (sweetness, bitterness, acidity, astringency, cocoa, yellow fruits, red fruits, citrus, dried fruits, nutty, winey, black olives, green, earthy, floral, woody, spicy, and roasted). In Appendix A, Table  A1 , additional information on the overall sensory results obtained from the chocolate dataset is presented. It can be seen that cocoa, acidity, bitterness, and astringency are attributes highly correlated with each other. Moreover, cocoa is the attribute that displays the highest overall mean sensory scores, which would indicate that the majority of the chocolate samples display a flavor profile that has strong cocoa notes.
Identification and Quantification of Volatile Aroma Compounds Present in the Dark Chocolate Samples Volatile compounds present in the 54 dark chocolate samples were determined by GC-MS; 34 known compounds were identified to be present from well-defined peaks shown in the retrieved spectra. An unidentified 35th compound was found in several of the samples, but it could not be associated to any of the spectra already in the NIST Mass Spectral Library. All identified volatiles, together with the odor descriptors commonly associated individually to each, are listed in Table  1. In Appendix A, Table  A2 , additional information is presented on the relative concentrations obtained for each of the identified volatile compounds among the chocolate sample dataset. A PCA was performed on the relative concentrations of the identified volatile compounds.
While the two main components (F1 and F2) were able to explain 54. 74% of the variance among the samples, the contribution of the third component to the explanation of the variance of the dataset is also worth discussing. This is because the third component appears to explain a sufficiently large amount of variance (as seen in the scree plot pertaining to this PCA, shown in Appendix A, Figure  A2 ). Together, F1 and F3 are capable of explaining up to 53. 18% of the dataset variance. In Figure  3 , the PCA biplot showing the distribution of the chocolate samples in the space of F1 and F2, as well as in the space of F1 and F3, is presented, together with the variable loadings, corresponding to all volatile compounds. In relation to the biplot on the F1 and F3 components, it must be noted that the dimension of the third component appears to be mostly represented by one single chocolate sample (contributing to 25.
Identification of Key Aroma Compounds Based on Their Impact on the Sensory Perception of Dark Chocolate Samples A partial least squares (PLS) regression was performed in an attempt to obtain a global view of the whole dataset. This allows the principal relationships existing between groups of volatile compounds (explanatory variables) whose concentrations may be able to predict mean sensory scores (response variables) to be summarized. The variables were centered and reduced. The quality of the PLS model obtained is explained using a bar plot presented in Appendix A, Figure  A3 ; it shows that while the first two components summarize the correlations between explanatory and dependent variables well, the third component may still provide additional information. The obtained bi-dimensional correlation plot on axes t1 and t2 is presented below, in Figure  4.
Materials and Methods, training models were built and optimized by cross-validation. Predictive models were then built for the best fitted training models. The model performance metrics for each of the obtained models are presented in Table  2. The five volatile compounds that had the largest impact in the building of each model (most important variables) were retrieved for reporting. A Pearson correlation analysis was performed on the raw data in order to understand the nature of the correlations existing between the explanatory (volatile compounds) and the response variables (sensory attributes). Both the most important compounds per predictive model and their corresponding Pearson correlation coefficients (obtained from the raw data) are presented in Table  3. Acetaldehyde seems to be a key compound in predicting multiple distinct models, and it is either positively or negatively related to an increase in the attribute's intensity.
All of the identified compounds had been previously reported in the literature as present either in dry fermented cocoa beans, roasted beans, liquor, or dark chocolate from different varieties and regions, processed under unique conditions  [14, 17, 18, 20, 32, [38] [39] [40] [41] [42] [43] [44] [45] [46]. Hence, none of the 35 identified compounds point to being unique markers that could be potentially used for differentiating the Dominican Republic chocolates that make up the sample dataset of this study in relation to other cocoa products. The predictive models obtained from the PLS regression are limited in their predictive capacity due to the reduced number of samples used, resulting in a data-dependent model. Despite this limitation, the models still display a relatively good level of predictive accuracy.
The extracted volatile compounds were analyzed using an Agilent 6890 N gas chromatography-mass spectrometer (GC-MS) equipped with a capillary column DBWAX, 60 m length × 0. 25 mm internal diameter × 0. 25 µm film thickness (Agilent, Santa Clara, California, USA). The full procedure for volatile compound extraction and identification was previously described by Assi-Clair et. al. [57]. The relative concentration of each compound was calculated based on the exact weight of the sample and on the known concentration of the compound that was added as an internal standard (butan-1-ol). Each sample was analyzed in triplicate and the mean of the three obtained concentrations per sample was recovered for reporting (in µg/g of fresh matter) and for the subsequent statistical analysis. Statistical Data Analysis-Statistical analysis, including chemometric analysis based on the principal component analysis (PCA) and on partial least squares (PLS) regression, was performed with XLSTAT STUDENT (Addinsoft, Paris, France).
PLS descriptive and predictive models were built in R, using the 'caret' package. All variables were centered and scaled. The dataset was split into training and testing sub-datasets. PLS training models were built, using the 'trainControl()' function. The training models were optimized by cross-validation following the "repeatedcv" method, by repeatedly partitioning the data into a fixed number of equally sized groups (20 folds were used) and then training and testing the model using each group as the validation set. This process was then repeated 30 times. The metric used in the cross-validation with the training data subset was the receiver operating characteristic (ROC). The best models obtained from the training data subset were then validated using the testing data subsets. This operation was repeated in a loop in order to cover the entirety of the dataset, iteratively training and testing the PLS models on different subsets of the data.
Dataset Metrics Validated Model Performance for Prediction Attribute Model Number of Components ROC Sensitivity Specificity Accuracy 95% CI Sensitivity Specificity Bitterness 0. 8508 0. 8117 0. 7300 0. 7963 (0. 6647, 0. 8937) 0. 7714 0. 8421 Astringency 0. 9263 0. 8083 0. 8608 0. 7963 (0. 6647, 0. 8937) 0. 8214 0. 7692 Citrus 0. 8146 0. 6600 0. 7283 0. 7222 (0. 5836, 0. 8354) 0. 7692 0. 6786 Acidity 0. 7621 0. 6817 0. 7008 0. 7037 (0. 5639, 0. 8202) 0. 7500 0. 6667 Red fruits 0. 8579 0. 7058 0. 7867 0. 7037 (0. 5639, 0. 8202) 0. 7407 0. 6667 Dried fruits 0. 7846 0. 6283 0. 8250 0. 6852 (0. 5445, 0. 8048) 0. 7273 0. 6562 Green 0. 8192 0. 8225 0. 6792 0. 6852 (0. 5445, 0. 8048) 0. 7143 0. 6316 Black olives 0. 6538 0. 7342 0. 5933 0. 6667 (0. 5253, 0. 7891) 0. 6562 0. 6818 Woody 0. 8038 0. 7550 0.
| Data collection questionnaires for endline surveys In all three locations, questionnaires captured data on children's and mother's dietary intakes and relevant associated variables at the household level. The questionnaires were built upon the Multiple Indicator Cluster Survey (MICS) modules. The Household questionnaire, an example of which can be found in Supporting Information 1 included data on (i) household profile, (ii) employment situation, conditions and remittance, (iii) Expenditure, (iv) credit or debt, remittances, (v) housing, (vi) food consumption score and market food access, (vii) coping strategies, (viii) household food insecurity access scale and household food provisioning, (ix) water, sanitation and hygiene, (x) health, and (xi) COVID-19 shock. The Mother and Child questionnaire, an example of which can be found in Supporting Information 1 captured data on (i) children and mother profile (e.
g. , age, education for mother only), (ii) women's dietary diversity and food purchased for women, (iii) infant and young children feeding practices, (iv) child health and nutrition, including immunization, morbidity, (v) child's illness and care, (vi) food purchased for children. Data on dietary intake was collected via a qualitative 24 h recall method, that is, women were asked to recall what they ate and drank the day or night before the interview day.
| Food security and diet quality and indicators Standardised indicators were used to assess food security and child and maternal diet quality (see Supporting Information 2). Early initiation of breastfeeding (EIBF) was defined as the proportion of infants that were put on the mother's breast within an hour of birth, whereas exclusive breastfeeding (EBF) was defined as the proportion of infants that were fed on only breast milk, excluding additional water, drinks and food, within their first 6 months of life (WHO & UNICEF, 2021). Dietary data were used to calculate the indicator of minimum dietary diversity for women (MDD-W), which provides an indication of the nutrient adequacy of a woman's diet  (FAO, 2021). According to this, a woman had an adequately diverse diet if, on the previous day, she consumed foods from at least 5 out of 10 defined food groups.
Energy-dense, nutrient-poor food consumption was assessed based on consumption of products from four food groups, that is, sugary drinks, sugar-sweetened beverages, sweet and savoury snacks and fried foods, during the previous 24 h. To collect data on children's dietary intake, mothers were asked to recall what their children ate and drank for the 24 h period before the interview day. Dietary data were used to calculate indicators that described the adequacy of a child's diet, that is, minimum dietary diversity (MDD), minimum meal frequency (MMF), and minimum acceptable diet (MAD) (WHO & UNICEF, 2021). For children 6-23 months, MDD was defined as percent of children who consumed foods and beverages from at least five out of eight defined food groups during the previous day; MMF was defined as the percent of children who consumed solid, semisolid or soft foods (but also including milk feeds for nonbreastfed children) at least the minimum number of times during the previous day; and MAD was the percent of children who consumed a minimum acceptable diet during the previous day diet (  WHO & UNICEF, 2021).
| Data analysis All analyses were conducted on Stata version V. 16. 0 (StataCorp). First, prevalence estimates stratified by sociodemographic variables (e. g. , household income) were computed for all relevant child and maternal diet quality, for example, MDD, MDD-W, MMF, MAD, and food insecurity indicators, that is, FIES at the household level, using data collected during COVID-19/endline surveys. Baseline prevalence estimates were extracted from reports and differences between baseline and endline data were calculated and expressed in percentage points.
| CONCLUSION Food insecurity and diet quality among urban-poor children and mothers in all three cities worsened following the pandemic, partly due to reduced incomes, which resulted in higher household poverty, more so among households in Yangon. Although food insecurity was high across all three cities, the highest figures were seen in Quezon City. There was a decline in the proportion of children 6-23 months and mothers achieving the MDD across all three cities. Energy-dense, nutrient-poor food intakes were still reported among mothers and children in all cities, although the proportion was lower compared with baseline. To reduce food insecurity and improve diet quality, multi-sectoral initiatives are needed to: (1) use robust data management systems to streamline comprehensive eligibility criteria for food and cash aid to cover all vulnerable groups, for example, CU2, adolescent girls, and extremely poor households  (Akseer et al.
Genotyping procedure and quality controls Genetic data from the LEAP cohort participants and their families were obtained for 1566 individuals (coming from 698 families, including 51 twin families). Their genotyping was performed at the "Centre National de Recherche en Génomique Humaine" (CNRGH) using the Infinium OmniExpress-24v1 BeadChip (721114 markers) from Illumina. We performed a genetic quality control on the whole genetic LEAP cohort and the following SNPs were removed: 41,889 with missingness rate >2%, 17,832 from sexual chromosomes, 61,701 with minor allele frequency <1% and 459 with departure from Hardy-Weinberg equilibrium (HWE) with p value < 10 -6 for controls and p value < 10 -10 for cases. For individual quality control of the 1566 ASD participants, 28 individuals were removed when data missingness rate per individual >2%, and 3 individuals removed with sex discrepancies, with no individuals removed for a heterozygosity rate issue.