dataesr/scientific-paragraphs-categorization huggingface dataset:
K. ). Data were acquired and integrated with Texture Exponent software from the same manufacturer. Each sample was placed horizontally and centered under the probe before measurement. The two compression cycles were achieved using a flat ended aluminum cylinder plunger (i. e. probe P/75; diameter, 50 mm) to 50% of its initial height. The trigger force was 0. 049 N and the test speed was set at 1. 0 mm s À1. All analyses were carried out at a room temperature of 26 C. The force deformation curve was evaluated according to Bourne (2002) and Rosenthal (1999). 34, 35 Hardness is defined as the maximum force on the first compression cycle. Cohesiveness is defined as a ratio between the positive area under the force-deformation curve of the second and the first compression cycles. Springiness is defined as the ratio between distances from the onset to peak force of the second to the first compression cycle.
Data analysis The chemical data (starch, proteins and sugars) and TPA texturometer data (hardness, cohesiveness and springiness) were analyzed using XLSTAT version 19. 03. 44616. A one-way ANOVA was used to analyze the differences in evaluated characteristics between genotypes. A Pearson correlation coefficient analysis was used to determine the correlation coefficients between different chemical and texture characteristics. The relationship between chemical and TPA parameters was illustrated by a multivariate analysis (principal component analysis, PCA). The results of the PCA were represented by two plots, one for attributes and one for the varieties, with clusters based on hierarchical clustering on principal components (HCPC). The HCPC first built a hierarchical tree. The sums of within-cluster inertia values were calculated for each partition.
Spectra collection and sample selection NIR spectroscopy analyses were carried out in the food processing laboratory of INRAE's Tropical Animal Research Unit, UR143, in Guadeloupe (France). Two replicates of yam flour samples were scanned with a FOSS-NIRSystems model 6500 scanning monochromator (FOSS-NIRSystems, Silver Spring, MD, USA) equipped with an autocup. The spectroscopic procedures and data recording were conducted with ISIscan(TM) software (FOSS, Hillerød, Denmark). Each flour was placed in a small ring cup 36 mm in diameter, and reflectance spectra from 400 to 2500 nm were recorded at 2 nm intervals. Each spectrum represented the average of 32 scans. 36 Each sample was scanned twice with two independent cups, in order to minimize the effect of particle size. The average spectrum of each sample was used for further chemometric analysis.
Calibration and validation of regression models For all traits but hardness, modeling was done using WinISI software v4. 10. 0 (FOSS, NIR spectroscopy, Denmark) using a modified partial least squares regression (M-PLS). First, spectra and data outliers were eliminated following the Shenk and Westerhaus (1993) procedure. 36 Before developing a calibration model, two spectral outlier elimination cycles were set up on the 174 samples using the center algorithm, which calculated the Global H distance (GH) with a cutoff of GH ¼ 3. In this study, an outlier is defined as a sample that does not conform to the bulk of the population in terms of the spectral data. The samples were then divided into a calibration set (3/4) and a validation set (1/4). To select appropriate and representative samples of the calibration set, the SELECT algorithm was applied on the spectra.
The choice of the best pretreatment parameters and combination was based on the standard error of calibration. Mathematical pretreatment (MP D, G, S1, S2 ) and pretreatment algorithm selected were MP 2,8,8,1 followed by MSC for dry matter, starch and protein; MP 2,8,8,2 followed by WMSC for sugar; and MP 2,4,4,1 followed by SNV for springiness and cohesiveness. Hardness was calibrated with the ChemFlow (https://chemproject. org/ChemFlow) open source software. Data pretreatment used a standard normal variate followed by the Savitzky-Golay algorithm with first order derivative, 13 window size and second degree polynomial. The multivariate distances were used as criteria for removing outliers (i. e. samples in the population that were more variable based on the spectra features). Then, principal component analysis (PCA) was performed followed by the calibration using nonlinear estimation by iterative partial least squares regression (NIPALS),  37  with 4-fold cross validation and 20 latent variables.
Calibration and validation of classification models In order to evaluate the feasibility of screening genotypes for quality when regression models were performing poorly, continuous texture attributes were binarized based on a variable's average (i. e. values strictly higher than the average were considered to have the traits, while others not). Classification models were then fitted in order to predict belonging to binary classes of hardness, cohesiveness, springiness and moldability. The classification modeling strategy was based on convolutional neural network (CNN). 0] [41] [42]  The first advantage of CNN comes when classes are not linearly separable. In such cases the linear classifiers may be outperform by more sophisticated models able to deal with nonlinearity. Moreover, the innovation of convolutional neural networks rely on their ability to automatically learn together a large number of filters specific to a training dataset under the constraints of a specific predictive modeling problem, such as spectra classification.
39 The result is highly specific features that can be detected anywhere on input spectra. For the calibration of these models, all the spectra were kept without removing spectral outliers. Pretreatments, calibration and validation were carried out using python language (v3. 6, https://www. pyth on. org) with a Keras framework (v2. 1. 5, https:// keras. io/) and a TensorFlow backend (v1. 6. 0, https://www. tensorflow. org). Samples were divided into a calibration set (60) and a validation set (21) using constrained random sampling keeping the same proportion of each class in the two sets. First, a data (i. e. sample) augmentation was applied to the calibration set only. For each original sample, 30 synthetic spectra were generated using a combination of random translation and rotation of the original spectra.
Then spectra presenting absorbance values higher than 1 or lower than 0 were discarded. This allow going from 61 to more than 800 spectra. Noised data augmentation is a common technique used in deep learning to reduce overfitting of small dataset. 43 econdly, we made a feature (i. e. spectral) augmentation on calibration and validation sets. We apply an all possibilities approach combining and keeping the different pretreated spectra, including the original one. For each sample, feature augmentation was applied by generating 12 new spectra using pretreatments based on Haar transform, Gaussian derivatives, SVG, SNV, and different degrees of MSC. A convolutional neural network composed of three convolutional layers followed by two dense layers was fitted to the calibration data. Binary cross entropy was used as the loss function.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was made possible by the  RTBfood  project funded by the  Bill and Melinda Gates Foundation  [OPP1178942], for which we should like to thank the various leaders. The authors acknowledge funding provided by the  ERDF   Cavalbio  project, financed by  R egion Guadeloupe  and the  European Union , which contributed to field preparation and maintenance.
com/ licen se). For deuteranopia we used Coblis-The Color BLIndness Simulator (https:// www. colorblind ness. com/ coblis-color-blind ness-simul ator/) and checked the results with the Colorblind Web Page Filter (https:// www. toptal. com/ desig ners/ color filter). The lens brunescence filter is inspired by  30  : we used Adobe Lightroom classic version 10. 0 with a yellow filter and darkening to simulate a Kodak yellow filter. For more details, see the "Results", "Materials and Methods" and Supplementary Materials. proto-language  31  ), exclusion of possible alternative explanatory factors (such as cultural complexity, environmental influences), lack of modeling of causal pathways, and the statistical methods used. Therefore, we combine multiple explanatory factors and potential confounds, including language contact generally (since specific data about the borrowing of words for blue is not available for most languages under consideration) and language family, subsistence strategy and population size (as proxies for cultural complexity  32, 33  as systematic, quantitative data about cultural artefacts and dyeing techniques across cultures does not exist), latitude (which partly determines UV-B incidence), climate, ecology and distance to large bodies of water (as proxies for various environmental influences) using a comprehensive database and advanced statistical methods.
Here, we not only test the hypothesis that there is a negative relationship between UV-B incidence and the population frequency of red-green color deficiency using advanced methods and controls, we also include the potential influence of subsistence strategy. In summary, we test two main hypotheses, one linking the existence of a dedicated word for blue to UV-B incidence while simultaneously controlling for other factors, and a second concerning the selective pressures against abnormal red-green vision in different environments. We do so on a large dataset of 142 populations (Fig. 1  Panel A) speaking languages from 32 families across the world, for which we collected information about the existence of a dedicated word for blue, the incidence of ultraviolet light, and the frequency of red-green color deficiency, as well as data on elevation, population size, subsistence strategy, climate, ecology, distance to large bodies of water, and several additional variables concerning the populations and physical environments they inhabit (see Methods and Supplementary Materials).
We also constructed a complex (but largely theoretically motivated and conservative) path model (Fig. 2  Panel A) linking latitude and blue through UV-B and controlling for potential confounds. This model fitted the data very well ( χ 2 (1) = 0. 4 , p = 0. 53 ; CFI = 1. 0 , TLI = 1. 05 , NNFI = 1. 05 , RMSEA = 0. 0 ) and, importantly, showed that the effect of latitude on blue is fully mediated not only by UV-B ( β latitude→UVB = -0. 96 , p < 0. 001 , and β UVB→blue = -0. 65 , p = 0. 032 ), but also by subsistence, population size and distance to lakes. The presence of a distinct term for blue was predicted by information in the full dataset using Bayesian mixed-effects logistic regression with family and macroarea as random effects, the conditional random trees, the random and conditional random forests, and the Support Vector Machines, with very little difference between them (accuracy from 76% for random forests to 91% for the Bayesian mixed-effects model).
08) and showed that UV-B has both a direct effect on daltonism ( β = -0. 31 , p < 0. 001) and one mediated by blue ( β UVB→blue = -0. 55 , p < 0. 001; β blue→daltonism = 0. 4 , p < 0. 001). Adding subsistence to the model improved the fit further ( χ 2 (1) = 1. 6 , p = 0. 45; CFI = 1. 000, TLI = 1. 011, NNFI = 1. 011, RMSEA = 0. 0). It added a significant influence of subsistence ( β subsistence→daltonism = 0. 38 , p < 0. 001), but removed the influence of blue to daltonism ( β blue→daltonism = 0. 11 , p = 0. 32). Converging evidence across analysis methods predicted daltonism on the full dataset, but this performance did not generalize well. Moreover, the importance of the predictors varied across methods. Nevertheless, UV-B tended to be among the most important predictors, along with blue and some genetic distance measures (see Fig.
Finally, our data also supported an interaction between acquired blue-green and inherited red-green color deficits: populations closer to the equator, more affected by lens brunescence, speaking languages without a dedicated word for blue have a lower frequency of people with red-green color deficiency. The data also suggested that populations of hunter-gatherers tend to have a lower incidence of abnormal red-green color perception. While this hypothesis requires further testing, it would support the idea that certain cultural practices and subsistence strategies might have higher demands on color perception, thereby generating selective pressures against inherited color perception deficits. Some caution is required in interpretation, however, given limitations in the current study. First, our dataset contained only 14 groups classified as hunter-gatherers, which, even if representative of the current distribution of communities, is too small to allow strong conclusions.
Methods and materials We collected data from 142 populations, extending and re-checking earlier databases  9  considerably, especially concerning information about the color lexicon and the incidence of abnormal red-green color perception. Each population was uniquely identified by its Glottolog code  49  of the primary language spoken and its geographic location. According to Glottolog, these languages belong to 32 language families (the most represented being Indo-European (41), Atlantic-Congo (19) and Afro-Asiatic (13)) distributed across 6 macroareas (Africa (31), Australia (2), Eurasia (79), Papunesia (9), North America (9) and South America (  12 )). We also obtained the geographic location of the putative origins of each family  49, 50. The geographic locations were cos-transformed: cos(longitude) ranges between -1.
7% of the variance and reflecting low seasonality, wet and hot climates, PC2, 24. 7%, high seasonality, hot and dry climates, and PC3, 8. 6%, unclear interpretation). We calculated the incidence of ultraviolet light (UV) using the data from NASA Total Ozone Mappi ng Spect romet er (TOMS) for the year 1998 (in order to replicate the procedure in 9 ): these contain daily measures of UV radiation received by the human body at four wavelengths (305 nm, 310 nm, 320 nm, and 380 nm) in J/m 2 , taking into account the thickness of the ozone layer in the stratosphere, the amount of cloud cover, the elevation, and how high the sun is in the sky. Here, we use the whole-year means and standard deviations for UV-A (315 nm to 400 nm), UV-B (280 nm to 315 nm) and the full spectrum. Moreover, we obtained each population's log(size) (from  52  ) and subsistence strategy, dichotomized into populations whose subsistence mode is primarily based on hunting, fishing, gathering and/or foraging ('HG') and populations with subsistence modes centered around food production (' AGR'), using data from  44, [53] [54] [55].
The information about color vocabulary specifically concerns the existence of a specific term for blue (a dichotomous variable, 'no'/'yes'), and we also collected data about the frequency of abnormal red/green color perception in males excluding, if specified, tritanopia, and those samples that do not distinguish between males and females, or with fewer than 50 individuals (see Fig. 1  Panel A and Supplementary Materials for details). Finally, to control for past demography and selection (about which we do not have direct information here), we estimated the overall pairwise genetic distances between populations, estimated by the fixation index F ST. We used a set of genetic markers aimed at forensic applications (FROG-kb) from the ALFRED database 56 with ultrametric missing value imputation  57, 58  , followed by classic multi-dimensional scaling (MDS), retaining the first 10 dimensions (goodness-of-fit 10.
All analyses were done using R  59. We used five main classes of methods in our analyses (see the Supplementary Materials). First, in order to understand the geographical patterning of our data, we tested it against complete spatial randomness 60, 61 using the the χ 2 test based on quadrat counts, and the G, F and K functions, and we also estimated its spatial autocorrelation using Moran's I (with either the inverse of the shortest geographical distance "as the crow flies", or the nearest neighbor distance on the Delaunay triangulation). Second, we fitted Bayesian mixed-effect regression models (as implemented by R's package brms 62 using Stan 63 ) with family and macroarea as random effects, and explanatory variables and potential confounds as predictors. For blue we performed logistic regression, and for daltonism we performed beta regression (after replacing the 0.
0% values by 0. 01%); we used model selection based on Bayes Factors (BF), leave-one-out cross-validation (LOO), the Widely Applicable Information Criterion (WAIC), and K-Fold Cross-Validation (KFOLD, with K = 10). Third, we conducted mediation analysis using a Bayesian mixed effects model (brms with Stan) approach which allowed us to include random effects. Fourth, we used path analysis (as implemented by lavaan 64 ) to test more complex models involving multiple potential predictors (but without controlling for the non-independence due to family and macroarea). Finally, we quantified the predictive capacity of a set of variables using Bayesian mixed-effects regression (brms with Stan), conditional inference trees (ctree  65  in package partykit 66 ), random forests (package randomForest 67 ) and conditional random forests (cforest in package partykit) and Support Vector Machines (rminer package  68  ).
Acknowledgements We thank  Delwin Lindsey  and  Angela Brown  for making available their database. DD was funded by the  Netherlands Organisation for Scientific Research (NWO) VIDI  grant  276-70-022  ( 2012-2017 ) and the  IDEXLYON  ( 16-IDEX-0005 ) Fellowship grant ( 2018-2021 ). MJ was funded by the  IDEXLYON  ( 16-IDEX-0005 ) Fellowship grant ( 2018-2021 ) and the  Doctoral School "Neurosciences and Cognition" (ED 476-NSCo) of the University of Lyon  ( 2020-2023 ). DD and MJ are grateful to the  ASLAN  project ( ANR-10-LABX-0081 ) of the  Université de Lyon , for its financial support within the  French program  "Investments for the Future" operated by the  National Research Agency (ANR).
However, the detrimental modulatory action of Zn 2+ is mediated by other targets than NMDARs, which become accessible to Zn 2+ owing to the profound morphological changes that take place upon DA depletion. In the context of DA depletion, Zn 2+ action on GluN2A-NMDARs is rather beneficial because the aberrant increase in NMDAR signaling impairs motor learning. Funding: This work was supported by the Centre National de la Recherche Scientifique (CNRS), Aix-Marseille University (AMU) and Institut National de la Santé et de la Recherche Médicale (INSERM). We thank the Association France Parkinson (AFP), the Fondation de France (FF) for financial support. J. S. was supported by the Fondation Universitaire A*MIDEX and the Association France Parkinson (AFP). Institutional Review Board Statement: All experimental procedures were conducted with the approval of the French national ethics committee (CE071) and in accordance with the EEC (2010/63/ UE) guidelines for care and use of laboratory animals.
Study design and patients This manuscript summarizes a part of the secondary analysis of pooled data collected in four observational studies of magnesium deficiency in pregnant women and women with hormone-related conditions: MAGIC (DIREGL06157), MAGIC2 (DIREGL06468), MAGYN (MAGNEL06863), and MAGYN2 (MAGNEL07741)  [18] [19] [20] [21]. MAGIC and MAGIC2 enrolled pregnant women (N = 1130 and N = 2117, respectively) during routine visits to maternity welfare centers. Women were included in the studies if they were > 18 years of age, were pregnant and had suspected magnesium deficiency (fatigue, muscle cramps, etc. ). The study excluded women who reported other known or obvious reasons for magnesium deficiency beside pregnancy  [19, 21]. MAGYN and MAGY N2 studies enrolled women with hormone-related conditions (N = 9168 and N = 11,424, respectively) attending outpatient clinics.
Women were included if they were 18-60 years of age and used hormonal contraception or hormone replacement therapy (HRT) or had one of the following conditions: premenstrual syndrome (PMS), climacteric syndrome without HRT, osteoporosis or other hormonal conditions (including endometriosis, polycystic ovarian disease, uterine leiomyoma, dysmenorrhea, endometrial hyperplasia). Women were excluded if they had severe conditions potentially hindering participation in the study or were receiving magnesium supplementation at baseline  [18, 20]. The present analysis included all patients who fulfilled the inclusion/exclusion criteria in the studies (Fig. 1 ). Patients with missing data, contradictory/inconsistent data or outlier data were excluded from the analysis (exclusion was performed separately for each variable of interest).
Patient characteristics, available medical history and serum magnesium test results were combined in two pooled databases ('pregnant women' and 'women with hormone-related conditions'). Medical history that was available for the two cohorts of participants included any recorded terms that were relevant for this group: for example, for the 'pregnant women' cohort, the terms included complications and complaints associated with a previous pregnancy, whereas for the cohort of 'women with hormone-related conditions' the terms included general diseases and comorbidities. The rate of magnesium deficiency was estimated using two cut-offs: 0. 66 mmol/L, the traditional lower reference limit used in the Russian Federation and other countries  [7] , and 0. 8 mmol/L, the lower reference limit that has been recommended based on the recent studies  [3, 8].
Statistical analysis Study cohorts were characterized using descriptive statistics. The cohort of pregnant women was treated as a single entity, whereas the cohort of women with hormone-related conditions was separated into subgroups according to their specific conditions. The prevalence of hypomagnesemia was assessed in both cohorts and in the subgroups of women with hormone-related conditions. All subgroups of women with hormone-related conditions were crosschecked for conflicting data on concomitant diseases, laboratory and clinical assessments; patients with missing, conflicting or improbable data were excluded from analysis. Differences in magnesium levels between cohorts and subgroups were analyzed using the chi square test, unpaired t-test and non-parametric tests. Univariate logistic regression analysis was performed to identify the risk factors and comorbid conditions associated with an increased risk of low serum magnesium level within both cohorts.
The large number of risk factors associated with hypomagnesemia in this study confirms the status of magnesium as one of the essential elements in health and supports further clinical research investigating the role of magnesium in various neurological, gastrointestinal and cardiovascular conditions. This study has several limitations. The observational studies MAGIC and MAGIC2 collected data on pregnant women with suspected magnesium deficiency, and it is not possible to generalize these results to the overall population. However, the study generated an important insight into hypomagnesemia in a larger cohort of women with hormone-related conditions (MAGYN and MAGYN2). Because of the retrospective observational design of this study, no causal links can be established between magnesium deficiency and associated conditions.