dataesr/scientific-paragraphs-categorization huggingface dataset:
ε are variations between observed and predicted values. The responses are defined as follows: Y 5-CQA = C 5-CQA × V/m (2) where C 5-CQA is the concentration of 5-CQA measured by HPLC, V the solvent volume recovered and m the dry mass of biomass used for the extraction. The Y 3. 5-diCQA yield was calculated in the same way. For Y DPPH , the antioxidant activity was calculated as described below in  (5). Commercial software MODDE v. 12. 0 (Umetrics AS, Umeå, Sweden) was used to generate the D-optimal design and analyse the experimental data. The effects of each factor were represented by effect diagrams. The model significance was tested using an analysis of variance (ANOVA). The optimal conditions were calculated by the optimizer software tool based on the Nelder-Mead simplex method. The two optima were carried out in triplicate to check compliance with the model.
HPLC Quantification of the CQAs Extracts were acidified with one drop of HCl to precipitate proteins and filtered through a 0. 20 µm filter (Interchim, Uptidisc PTFE). CQAs were quantified by reversed-phase UHPLC-DAD (Ultimate 3000; Dionex, ThermoFisher) equipped with a DAD 3000 diode array detector. Chromatograms were recorded and processed with Chromeleon 6. 8 Software. CQAs were separated by a Halo AQ-C18 (Advanced Materials Technology, Wilmington, USA), heated at 35 • C. Elution was performed using a mobile phase composed of 1% formic acid in water (solvent A) and acetonitrile (solvent B) according to the following gradient: 0-1 min 2% B; 1-5. 5 min from 2 to 10% B; 5. 5-7 from 10 to 15% B; 7-8. 5 min 15% B; 8. 5-11 min from 15 to 25% B; 11-13. 5 min from 25 to 80% B; 13. 5-14 min 80% B and 14-15 min from 80 to 2% B.
Study population A cross-sectional study based on data extracted from the Research on Nutritional and Health Status of Primary Education Students in the city of Lokossa-Benin. Data were collected from a representative sample of sixth-year primary schoolchildren, attending public and private schools located in rural and urban areas of Lokossa, between December/2018 and January/2019. This municipality is located in the Mono Department, in the southern region of the country, where infrastructure and services are largely concentrated. According to the 2013 population census, 45% of Lokossa population was aged less than 15 years  (14). This sample comprised exclusively schoolchildren in the last year of primary school, since those in previous years would not be able to properly answer the questionnaire used for data collection.
Schoolchildren in this sample were selected using conglomerate probabilistic sampling with two strata (urban and rural). Schools (conglomerates) with probability of selection proportional to the number of schoolchildren were chosen in each stratum. All sixth-year schoolchildren attending the selected schools on the day of data collection were invited to participate. Sampling selection was based on the list of schools and number of schoolchildren enrolled in the sixth year of the previous school year (2017/2018), provided by Mono Education Department. Outcomes of interest were estimated with a level of significance of 5%, prevalence of condition of interest of 10%, and maximum error of 3. 25%. A design effect of 1. 25 was also accounted for based on similar previous investigations  (15). A calculation method accounting for maximum possible sample size (Sample Size for Finite Populations, fcp) was also adopted.
Data collection The following instruments were used to build the structured questionnaire adopted in the present survey: 1) Global School-Based Student Health Survey (GSHS-Benin 2009), based on the WHO standard GSHS instrument  (16)  ; 2) Brazilian National Adolescent School-based Health Survey (PeNSE-2015), of the Brazilian Ministry of Health  (17)  ; 3) Study on Cardiovascular Risks in Adolescents (ERICA), carried out in Brazil  (18)  ; and 4) the questionnaire used to assess the double burden of malnutrition during school age in urban areas of Burkina Faso-2008-2009  (19). Firstly, the above instruments were assessed, and the questions were selected and revised contemplating common lexis and culture of Benin schoolchildren. The order of the questions and the instructions on how to respond them was also revised considering the education level of the study sample.
Nutritional status Body Mass Index (BMI) was calculated using body weight in kilograms divided by the square of height in meters. Heightfor-age (HAZ) and BMI-for-age (BAZ) indices were calculated, standardized according to Z-scores and categorized according to WHO cut-offs for children and adolescents aged 5 to 19 years  (21). Indicators were estimated using the software WHO AnthroPlus (World Health Organization, Switzerland). Thinness and stunting were defined as BAZ<-2 Z-score and HAZ<-2 Z-score, respectively.
We combine anthropometric and socio-economic data from the Indian National Family Health Survey 2015/2016 (hereafter NFHS-4) with granular information on air pollutant concentrations, wind direction, wind speed, and weather variables from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) from NASA's atmospheric reanalysis of satellite data. This unique dataset allows us to estimate the causal impact of air pollution on child growth by exploiting the quasi-random variation in pollution attributable to the transportation of pollutants by wind. We focus on three criteria pollutants, viz. fine particulate matter (PM 2. 5 ), carbon monoxide (CO), and ozone (O 3 ), and show their disparate impacts on height-and weight-related child growth outcomes. Based on a long time-series of hourly data, we build aggregate measures of frequency of wind direction and pollution concentrations that reflect the personal exposure of each child in our sample from the gestational period till the time of the interview.
Anthropometric and socio-economic data We use data from India's NFHS-4 conducted in 2015/2016 -the most recent available -collected through the Demographic and Health Surveys (DHS) Program. For each child in the NFHS-4 dataset, we match cell-level environmental variables that reflect personalized averages over the lifetime of the child. To account for the well-known impacts of pollution and climate variables also during the gestation period (Goyal   2 While there have been three previous waves of the NFHS, NFHS-4 is the first that provides geocoordinates of the primary sampling units (PSUs) where interviewed households reside. These coordinates are geomasked for ethical reasons, using a displacement buffer of up to two kilometers for urban PSUs and up to five kilometers for rural ones. As our main unit of analysis is cells with longitudinal-latitudinal resolution of 0.
625 • × 0. 5 • , such geomasking is not a big concern for our specifications. 3 Given India's latitudinal and longitudinal coordinates, cell areas vary between 3,157 and 3,798 km 2 , or polygons with edges of roughly 69 × 55 km. A. Balietti et al. and Canning, 2017; Sanders, 2012), we average hourly data starting nine months before the birth of the child 4  up to the date of the interview. Our results are robust to including only the period after the actual birth. 5 ur final dataset consists of 223,049 children aged 0-5 years old for which we have complete anthropometric measures and relevant socio-economic information. Table  1  presents key summary statistics characterizing the children in our sample. All child characteristics reflect averages at the child level, while household characteristics reflect household-level statistics.
, 2019). 6 To obtain a representative quantification of air pollution, we use satellite data from NASA's second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) database, with a longitudinal-latitudinal resolution of 0. 625 • × 0. 5 • and available at hourly frequency. 7 Accounting for all cells where at least one PSU has been surveyed by the NFHS-4, our spatial structure consists of 987 cells. 8 M 2. 5 is the standard indicator to measure the impacts of air pollution on health (WHO, 2016) and, often, the only included measure of air pollution in empirical analyses. Depending on location and emission sources, major composites of PM 2. 5 can include windblown mineral dust (DS 2. 5 ), sea salt (SS 2. 5 ), organic carbon (OC), black carbon (BC), and particles derived from the oxidation of primary gases like sulfur and nitrogen oxide, such as sulfate (SO 4 )  (Perraud et al.
Weather variables Data on precipitation, temperature, and wind speed and direction comes from the MERRA-2 Surface Flux Diagnostics datasets, also available at 0. 625 • ×0. 5 • spatial resolution and hourly frequency. Similarly to the procedure followed in the case of air pollution variables, we match the weather outcomes with the NFHS-4, by constructing averages representative of the lifetime horizon of each child, beginning nine months prior to the reported birth date until the date of interview when the anthropometric measures have been taken. The key weather variable for our identification strategy is wind direction, and in particular its temporal and spatial variation. Wind direction frequency is fairly equally distributed on average in our sample, with only wind from the East being slightly less observed.
Thus, they reflect a snapshot of a child's health status, shaped by all genetic, environmental, and behavioral factors that have impacted the child's development since conception and until the date of measurement. The pollution concentrations are computed for each child separately and reflect averages of hourly measurements in the cell of residence  from child conception until the date of the survey. In this sense, computed pollution concentrations are equal for all children born on the same month and year in cell. Hence, our results quantify the impact of pollution on the average child residing in the cell. The granularity of our data allows us to control for a rich set of socio-economic and environmental factors that can impact child growth (  ). We control for gender and age to capture remaining variation in the outcome variables across these two dimensions.
15  As the NFHS-4 collected data over two years (2015/2016), we also control for the year of interview. As a measure of a household's 13 Table A. 2 in the Appendix presents summary statistics regarding the overall, between, and within variation in wind direction over 90-degree wind angle bins using monthly data over 2010-2016 for all cells. 14 Section A. 4 in the Appendix provides details on the spatial variation in prevailing winds and on how this relates to air pollution concentrations. financial resources, we include the 5-digit wealth index provided by the NFHS-4, which is expected to account for the household's access and usage of various resources, including nutrition and health care. Similarly, to control for location-specific economic development across India, we include the nightlights index of the corresponding primary sampling unit (PSU).
In Appendix B, we include results from the same exercise for all 30 regions, with both four and eight cardinal directions to illustrate the variation in pollution. In all cases, the share of wind from the different directions is a strong predictor of local PM 2. 5. The illustrative case above suggests that there is no bias due to weak instruments. The results of the first-stage estimation in our benchmark model (Eq. (  4 )) using the entire dataset are presented in Section B. 2 in the Appendix. Furthermore, the first-stage Anderson-Rubin and the Kleibergen-Paap F -statistics in the table of results in Section 5. 1 also reinforce that there is no weak instrument bias.
Note: The table presents a list of existing studies that investigate the height premium in earnings using large-scale longitudinal data for a set of developing and developed countries. Studies include different sets of covariates in their specifications. We measure the impact of an increase in the mass concentrations of PM 2. 5 on adult height as: Height ′ 𝑎 -Height 𝑎 = (𝑠𝑑 𝑎 × 𝜌 × HAZ ′ 𝑐 + Median 𝑎 ) -(𝑠𝑑 𝑎 × 𝜌 × HAZ 𝑐 + Median 𝑎 ) = 𝑠𝑑 𝑎 × 𝜌 × β1 (7) where Height ′  is the height of an adult that has been exposed to pollution in childhood and their corresponding HAZ score in childhood is HAZ ′. β1 is the change in the HAZ score of children at an increase in the mass concentration of PM 2. 5. Fig. 7 (Panel A ) plots the estimated reduction in adult height at an increase by one standard deviation in PM 2.
Conclusions The detrimental effects of air pollution on children's health are becoming increasingly salient, particularly in India, where the bad air quality is a constant problem, exacerbated by the high prevalence of solid fuel combustion. We rely on a unique dataset formed by merging the latest available National Family Health Survey (NFHS-4) in India with satellite information on ambient air pollution, wind direction and speed, and related weather variables. We use the quasi-random variation in pollution induced by wind direction. Correcting for endogeneity, this paper is, to the best of our knowledge, the first to move away from correlation analysis and take steps toward demonstrating the causal link between air pollution and chronic restrictions in the physical development of children.
Acknowledgments We are grateful to the editor and three anonymous reviewers for their many insightful comments and suggestions. Special thanks go also to  Timo Goeschl ,  Rohini Pande , and research fellows at the  Harvard Kennedy School. We thank  Marek Bojko  for excellent research assistance. We would also like to thank seminar participants at the  University of Manchester , the  University of Glasgow , the third annual meeting of the  Sustainable Energy Transitions Initiative , the annual conferences of the  Research Group on Development Economics in Zürich and Göttingen , the 17th Nordic Conference in Development Economics, the 25th Annual Conference of the  European Association of Environmental and Resource Economists  for helpful feedback, 30th congress of the European Economic Association, and the  2020 Economic Science Association Meeting.
Procedure The study was conducted between July and September in 2018. On Rarotonga island, data were collected in the main village center, Avarua, whereas on Penrhyn island, data were collected in the two main villages of Omoka and Te Tautua. Participants' taste preferences were tested individually using the suprathreshold taste testtaste sprays  (Walliczek et al. , 2016). Five sprays stored in 10 ml brown glass bottles with an atomizer were used. Bottles contained the following substances, each diluted in 10 ml distilled water: (1) Sweet: 1 g d-saccharose; (2) Sour: 0. 5 g citric acid; (3) Salty: 0. 75 g NaCl; (4) Bitter: 0. 005 g quinine hydrochloride, and (5) Umami: 0. 04 g Mononatriumglutamat. The concentrations of each substance reached suprathreshold levels of stimulation as used in clinical tests of gustatory sensitivity  (Hummel et al.
Statistical analysis Data were analyzed in SPSS v. 26 with the level of significance set to α = 0. 05. A generalized linear mixed model (GLMM) was used to test whether accuracy in identifying tastes (binary identification score: correct vs incorrect) varied as a function of island (Penrhyn vs Rarotonga) for each taste (sour, sweet, salty, umami, bitter) (Hypothesis 1). Both factors were included in the model as fixed factors and intercepts were allowed to randomly vary as a function of subject ID (random factor). We computed two linear mixed models (LMMs) that included island (Penrhyn vs Rarotonga) and taste (sour, sweet, salty, umami, bitter) and the interaction of these two terms as fixed factors, with taste preference (Hypothesis 2) and frequency of ingestion (Hypothesis 3) as dependent variables.
Limitations The modest sample size in this study, due to the nature of field work on small, isolated and remote human populations, limits statistical power to explore only moderate to strong effects. Thus while more than 20% of adults (greater than 15 years old) living on Penrhyn at the time of data collection took part in our research  (Cook Islands Statistical Office, 2018) , very subtle differences between Penrhyn and Rarotonga inhabitants may have gone undetected. To maximize the statistical power of our analyses we employed a within study design wherein each of five tastes was judged by each participant, resulting in a multilevel statistical model with five tastants nested within individual participant IDs. Such an approach requires smaller sample sizes, such as those that can be expected with remote field data  (Bell et al.
Data gathering and statistical analysis Data gathering was conducted from December 2021 to March 2022. With the only exception of Malta, where FB from artisanal bakeries were considered, nutritional data were taken from the labels of FB marketed at the major retailers in the surveyed countries. Samples were chosen not taking into account any topping or coating. From the nutritional labels, energy (kcal/100 g), total fat (g/100 g), saturated fatty acids-SFA (g/100 g), carbohydrates (g/100 g), sugars (g/100 g), fiber (g/100 g); protein (g/100 g), and salt (g/100 g) were extracted and recorded. The statistical analysis was performed using STATGRAPHICS CENTURION XVII software (Version 17. 2, Virginia, USA). Marketed products were categorized for the gluten presence, type (single or double layer), type of flour (white, whole grain and blends), ingredients (fat, sugar), leavening agent (yeast, sodium bicarbonate), process aids (hydrocolloids, emulsifier), preservatives (sorbate or propionate salts or acids) and secondary ingredients.
In this regard, a significant (p < 0. 05) association was found between the inclusion of brown wheat and other non-wheat cereals in increasing the final dietary fiber content. Lebanese, Spanish, and Italian FB significantly (p < 0. 05) stood out for their higher fiber amount. Same trend was observed for the protein content, in which the three afore-mentioned countries showed the highest content, but no significancy (p < 0. 05) was found. The average protein content of the Mediterranean FB was around 9 g/100 g. Owing to the large datasets, a principal component analysis (PCA) was employed to improve the exploration of the FB Mediterranean market. Principal component 1 (PC1) was able to explain the 32. 3% of the data variability and products were mainly separated by their fat content. In fact, samples higher in total fat, saturated fat and salt are located in the right upper quadrant and principally represent products from Spanish and Italian markets.
Acknowledgements This paper is supported by the  PRIMA program  under grant agreement No. 2031 , project  Flat Bread of Mediterranean area: INnovation & Emerging process & technology (Flat Bread Mine ). The  PRIMA program  is an Art. 185 initiative supported and funded under  Horizon 2020 , the  European Union 's  Framework Programme for Research and Innovation. The results and content found on this paper reflects only the author's view. The  PRIMA Foundation  is not responsible for any use that may be made of the information it contains.
In the active condition, the odor delivery onset at the snout location (measured with a PID; 44 ms latency after shuffle valve switch) was synchronized with the moment where the gratings reached their most extended position. As mice in the active condition tend to whisk to fetch the approaching grating cued by the sound of the linear stage, whiskers could make first contact with the gratings up to 400 ms before odor delivery. In the passive condition, the odor delivery at the snout was synchronized with the first possible whisker contact with the sweeping gratings. In both stimulation conditions, the odors were delivered at a fixed time, independently of respiration. All parts of the stimulation system were timed and synchronized with a data acquisition card (USB-6343-BNC, National Instruments) and MATLAB scripts (Mathworks).
Images were acquired at 31. 5 Hz during trials of 11 s interleaved with an 8 s interval. For the multi-odor experiment in the passive context (Fig. 7 ), layer 2/3 barrel cortex twophoton imaging was performed with an acousto-optic microscope (Karthala) combined with a pulsed laser (Insight, SpectraPhysics). The objective was a 16× (N16XLWD-PF, Nikon). Images were acquired from four plans at 19. 1 Hz per plan interleaved by 60 µm with fields of view of 478 × 478 µm. Calcium imaging data analysis. Data analysis was performed using Python scripts. Motion artifacts, region of interest selection, and signal extraction were carried out using Suite2p  55. Neuropil contamination was subtracted by applying the following equation: F cor (t) = F(t) -0. 7 F n (t). Then the change in fluorescence ΔF/F 0 was computed as (F cor (t) -F 0 )/F 0 , where F 0 is estimated as the 8th percentile of F for each trial.
The same k-fold cross-validation procedure was applied. Each session was used to obtain a crossvalidated accuracy with and without shuffling. Significance was assessed by comparing the mean accuracy of the real and shuffled data (Wilcoxon signed-rank test). The data were z-scored to reach significance in the results presented in Supplementary Fig. 4. The same procedure was applied for the multi-class classification presented in Fig. 7  except that linear Support Vector Machines (SVM) were used. For the categorization of amyl acetate and ethyl butyrate mixtures, we used an exponential radial basis function kernel. The results presented in Fig. 7  do not depend on the regularization parameter which was fitted for each dataset to maximize decoding at stimulus presentation while keeping performance at baseline to chance levels.
Facial behavior analysis. Facial movements were monitored with a CCD camera (GC651MP, Smartek Vision) equipped with a 50 mm objective (Fujinon, HF50HA-1B, Fujifilm), recording at 20 Hz. Video and two-photon acquisition were synchronized with a common trigger. Principal components were extracted using the singular value decomposition provided by the Facemap algorithm (https://github. com/MouseLand/facemap). Linear multi-class SVMs trained on the 500 first principal components of each imaging session were used to classify odor identity from behavior. Statistical analysis. All quantification and statistical analysis were performed with Python scripts. Plotting relied on Matplotlib and Seaborn. Statistical assessment was performed with non-parametric tests implemented in the Statistical functions module reported in figures and legends together with mean and 95% confidence intervals, the number of samples used for the test, and the nature of the samples (number of sessions for between sessions assessment and number of trials for within sessions assessment).
Acknowledgements We thank  Sophie Bagur  for helpful comments on the manuscript. This work was supported by the  Fondation pour la Recherche Médicale  (FRM grant number  ECO20170637482  to A. R. ), the  International Human Frontier Science Program Organization  ( CDA-0064-2015 ), the  European Research Council  ( ERC CoG 770841 ) and the  Paris-Saclay University , the  Fondation pour l'Audition ,  FPA IDA02  and  APA 2016-03. B. B. acknowledges the support of the  Fondation pour l'Audition  to the  Institut de l'Audition.
Statistical analysis The mean values of total phenolic content, total flavonoid content, IC 50 for the DPPH scavenging activity, % of inhibition of ferrous ion chelation, IC 50 for the α-amylase inhibition activity, IC 50 for the α-glucosidase inhibition activity were calculated for the commercial and homemade PM samples. Mann-Whitney U test was used to compare these mean values between the commercial and homemade pomegranate molasses and spearman's correlation analysis was performed to examine the correlation between each of total phenol and total flavonoid contents and IC 50 of DPPH scavenging capacity, % of inhibition of ferrous ion chelation, IC 50 for α-amylase inhibition and IC 50 for α-glucosidase inhibition. The statistical analysis was carried out using the statistical package for social sciences (SPSS) version 23.