dataesr/scientific-paragraphs-categorization huggingface dataset:
Calculation and validation of founder probabilities We used the package R/qtl2  (Broman et al. 2019)  to determine founder probabilities of the MAGIC DH lines using the 600K genotype data and the cross type "riself16". We then filtered down the founder probabilities from all 503,902 sites to represent founder recombination blocks for interval mapping. Markers for the QTL F approach were filtered based on linkage disequilibrium using an iterative approach where a marker was dropped if the R 2 value of the founder probabilities between it and the previous marker was greater than 0. 95. After filtering, a total of 4,578 sites were kept to represent founder recombination blocks in the MAGIC DH lines. Due to the fact that the actual crossing scheme and the cross type input into R/qtl2 differed (DH lines rather than RILs), we wanted to assess the accuracy of the founder probabilities.
This was done by simulating lines using the actual crossing scheme and assessing the performance of the calc_genoprobs function of R/qtl2 in correctly identifying the founder genotype (Fig. 2c ). We developed an R package (R Core Team 2017), magicsim (https:// github. com/sarahodell/magicsim) to simulate the lines using the maize consensus genetic map from  Ogut et al. (2015)  to generate approximate recombination rates across the chromosome. We simulated 100 MAGIC populations constituting 325 lines and assessed founder assignment accuracy as the average percentage of SNPs where the predicted founder was the same as the actual founder. Fig. 1. The crossing scheme of the MAGIC population; 16 inbred maize lines were crossed in a funnel crossing scheme, and then the 8-way hybrids were outcrossed for 3 generations.
Calculation of IBD and haplotype probabilities The identification of regions of shared genetic sequence between founder pairs allows collapsing of founders into haplotypes. IBD was measured from the 600K SNP data of the founders using the software RefinedIBD with a sliding window of 10 cM and a minimum IBD segment length of 0. 2 cM  (Browning and Browning 2013). The resulting segments of pairwise IBD between each of the 16 founders were used to identify distinct haplotype blocks. We did this by moving along the chromosome, starting a new haplotype block when a segment of pairwise IBD between founders started or ended (Supplementary Fig. 2 ). Then, within blocks, we grouped all founders that were in IBD with one another into a haplotype. Within blocks, the founder probabilities for founders that shared a haplotype were summed to obtain haplotype probabilities.
Association and QTL mapping The R package GridLMM  (Runcie and Crawford 2019)  was used to run association mapping using the 3 different models of representing the QTL allelic state. The function GridLMM_ML was used with the "ML" option. The following 3 models were approximated by fitting each locus independently. The 3 methods differed in the X matrix used in the mixed linear model. The biallelic model (GWAS SNP ) is: y ¼ l þ x Si b Si þ Zu þ (1) where y is the response variable, l is the global mean, x Si is an n Â 1 genotype vector for SNP i with reference and alternate alleles represented as 0 and 1, respectively, b Si is the effect size of the alternate allele, Z is the design matrix, u $ Nð0; r 2 u KÞ is the random effects of markers across the rest of the genome using the genomic relationship matrix, K, and is the error.
The genomic relationship matrix, K was generated from the 600K SNP data using a leave-one-chromosome-out method. The same K matrix was used for all 3 models. The founder model (QTL F ) is: y ¼ l þ X Fi b Fi þ Zu þ (2) where X Fi is a n Â f -1 matrix for marker i and x fni is the probability that at site i, individual n was derived from founder f, and b Fi is the effect size of each founder allele. The ancestral haplotype model (QTL H ) is: y ¼ l þ X Hi b Hi þ Zu þ (3) where X Hi is an n Â h -1 matrix for marker i and x hni is the probability that at site i, individual n has ancestral haplotype h and b Hi is the effect size of each haplotype allele. Significance cutoffs for P-values were obtained using permutation testing, taking the 5% cutoff from 1,000 permutations where genotypes were randomized relative to phenotypes for each method.
Estimation of effect sizes We used the R package lme4qtl to calculate standard errors of effect sizes relative to the population mean  (Ziyatdinov et al. 2018). For the QTL F model, effect sizes were dropped for individual founders at some sites if there were fewer than 5 MAGIC lines that had a probability greater than 0. 8 for 1 of the founders. This same filtering was done with QTL H effect sizes for sites with low representation of particular haplotypes. This was to ensure that effect sizes for individual founders and haplotypes could be effectively estimated. We confirmed that effect sizes calculated by GridLMM and lme4qtl matched one another, with the correlations in effect sizes between the 2 methods greater than 0. 99.
MAGIC population We developed a 16-parent MAGIC population using temperate inbred maize lines representative of the diversity of the Flint and Dent heterotic groups of North America and Europe (Fig. 1 ). We genotyped 344 MAGIC DH lines from the population with a 600K SNP genotyping array and measured 7 phenotypes across 6 environments from the MAGIC F1s. Using the phenotype data from the 6 environments, we calculated BLUP scores for each of the MAGIC lines. PCA analysis of the MAGIC lines and the 16 founders and tester suggested that the MAGIC lines maintained much of the genetic variation possessed by the founders, without overwhelming bias toward any particular founders (Supplementary Fig. 3 ). In addition, the minor allele frequencies of SNPs in the 16 founder compared to in the MAGIC lines suggested that lower frequency SNPs in the founders were either maintained or brought up in frequency in the MAGIC lines, which aids in the estimation of SNP effect sizes (Supplementary Fig.
IBD and MAGIC haplotypes In some cases, the model which inferred founder identity in the MAGIC lines had high uncertainty, with probabilities split approximately equally between 2 founders. We hypothesized that this uncertainty was due to the 2 founders having very similar genetic sequence at those regions, such that the model struggled to differentiate the 2. To assess the genetic similarity of the founders, we calculated pairwise IBD between all founders using the software RefinedIBD  (Browning and Browning 2013). Areas of uncertainty in founder probabilities of the DH lines were associated with regions of IBD between 2 or more founder lines in that region of the chromosome (Supplementary Fig. 5 ). The results showed that a total of 1. 81 Gb (86%) and 1,367. 5 cM (92. 7%) of the genome were in IBD between at least 2 different founders.
2013). Different means of determining ancestral haplotype blocks from parental sequences have been used, with clusthaplo  (Leroux et al. 2014) , an extension of the software MCQTL  (Jourjon et al. 2005) , being a commonly used algorithm in recent studies. Bayesian frameworks have also been implemented in real (P erez-Enciso 2003) and simulated  (Bink et al. 2012 ) multiparent populations. Giraud et al. (2014)  used both a haplotype-and founderbased approach in 2 nested association mapping populations of Northern European flint and dent maize lines created by  Bauer et al. (2013)  and genotyped with a 50K SNP array  (Ganal et al. 2011). Giraud et al. (2014)  used clusthaplo to determine haplotype blocks based on IBD between parents and used discrete founder and haplotype values in their models.
Interestingly, the performance of bi-allelic, founder, and ancestral haplotype models differs across studies. Giraud et al. (2014)  found that their haplotype model outperformed the founder and SNP models in terms of the number of QTL identified using EU-NAM Flint and Dent maize populations. In contrast,  Garin et al. (2020)  found that in the EU-NAM Flint population, the bi-allelic model detected a larger number of unique QTL, compared to parental or ancestral haplotype models. Bardol et al. (2013)  found that in 2 multiparent dent populations, their biallelic model and ancestral haplotype model generally outperformed the parental linkage model, although benefits of these models varied by dataset. The performance of the 3 models seems to depend heavily on the diversity of the parents used to generate the population.
Data availability Genotypic data will be made available through FigShare. Phenotypic and environmental data will be made available at FigShare associated with our companion paper  (Hudson et al. in prep)  (tracking number G3-2021-402688) (https://figshare. com/s/ 5ee8337defdef63b04ce). Supplementary files are available at FigShare. Supplementary File 1 contains a detailed description of the 16 founders and the crossing scheme used to develop the MAGIC population. Supplementary File 2 shows Manhattan plots similar to Fig. 5, a-c  for individual phenotypes and environments. Supplementary File 3 shows founder effect size plots for vgt1 similar to Fig. 6  for individual environments. Supplementary File 4 contains tables of all QTL identified in the study, their locations, and their effect sizes estimated from the 3 models.
Selected Literature A total of 33 pieces of literature were selected for the literature review. Table  1  presents the number of selected literature based on the search term used. The various literature was collected from different research databases and sources. Namely, the sources of the literature were SpringerLink, ResearchGate, arXiv, ACM, HAL Open Archive, IEEE Xplore, IntechOpen, MDPI, and organizations or companies. Figure  1  shows the percentage of how much literature was collected from each research database or source. Following the selection of literature, information was gathered to answer the established research questions. The extracted content is discussed in the following sections.
Introduction The prediction of 3D protein structures by the AlphaFold project is clearly a breakthrough for scientific and medical research  [1]. These structures, which now cover almost the entire human proteome, can be downloaded freely from the European Bioinformatics Institute database (https:// www. ebi. ac. uk) and from the Uniprot server (https://www. uniprot. org). This long awaited technological breakthrough has been compared to the sequencing of the human genome, and the benefits are expected to be just as spectacular. However, without lessening this biotechnological achievement in any way, certain questions and potential limitations of this strategy should be considered. While discussing with several colleagues about the publication recounting the success of AlphaFold, we realized that most of them believed that the problem of the protein structure had been completely solved.
Indeed, this is what media and social networks have extensively relayed and commented on. Thus, many scientists consider browsing the AlphaFold database to download the structure of proteins of their interest, convinced that the AlphaFold structure is equivalent to the pdb structures obtained by crystallization and X-ray diffraction, cryo-electron microscopy, or nuclear magnetic resonance (NMR). Therefore, it seemed interesting to us to wonder about the possible limitations of the AlphaFold algorithm. We present several representative examples of such proteins whose 3D structure is not accurately predicted by AlphaFold. Based on this analysis, we propose a new concept which complements current models for 3D protein structure. This new paradigm, coined "epigenetic dimension of protein structure," takes into account any parameter that is not encoded in the amino acid sequence of the protein.
This paradigm illustrates how difficult it is to make an accurate prediction of the 3D structure of a protein which may, in some cases, remain an elusive Grail. Yet this does not mean that AlphaFold models are useless, but that we should use them with caution. When downloading an AlphaFold file, a careful analysis of the pLDDT is mandatory, since only regions with pLDDT > 90 have a sufficient confident score. These regions are highlighted in blue in the Uniprot database. Regions that are colored in orange and yellow may not be resolved and are represented as large loops with poor biological significance. For membrane proteins, it is necessary to localize the TM domains to ensure that membrane topology is respected. The particular case of IDPs may be solved by merging the protein and a known ligand to obtain structural information on the folding process of these proteins.
Integrating satellite data at different resolutions (i. e. , spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono-and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i. e. , Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.
e. , entire spectra) approach, e. g. ,  [6] [7] [8] 13]. For instance, spectral datasets based on multi-and hyper-spectral images have been used by researchers to quantify the minerals and/or physicochemical properties of the soil. Spectral indices are based on the physical analysis of spectral reflectances, such as the slope or absorption band depth value, and by combining spectral reflectance from two or more wavelengths that indicate the relative abundance of features of interest, which allows the quantifying of minerals or physicochemical properties of the soils  [14]. For example, the absorption band depth values centered at 2206 and 2341 nm can be used to estimate the clay  [15]  and calcium carbonate (CaCO 3 )  [16]  contents, respectively, whereas chromophores and iron oxides cause wide, weakly expressed absorption bands at wavelengths less than 1000 nm  [17].
Soil Dataset and Sampling Collection Soil samples were taken at a 0-5 cm depth during the soil studies conducted between October 2008 and November 2010. Five sub-samples were taken at random at every soil sample site inside a 10 × 10 m 2 square plot. The central geographical position was registered using a Garmin GPS navigation system and georeferenced. The clay content (granulometric fraction inferior to 2 µm) was measured using the pipette method (method NF 31-107, particle size distribution by sedimentation) after drying, homogenizing, and sieving to 2 mm of all the samples  [30]. [32] , and Sentinel-2A MSI  [33]  were used for the purpose of this study (Table  1 ). The Landsat-TM and OLI images were level-1 precision terrain (L1TP) products. The ASTER images were level-1 precision terrain, corrected and registered, at-sensor radiance (AST_L1T) products.
The Sentinel-2A MSI image was a level-1C top-of-atmosphere (TOA) reflectance data product. One scene of the Landsat-TM, Landsat-OLI, and Sentinel-2A data and two scenes of the ASTER data were selected to cover the entire study area and were registered on 14 August 2008, 27 July 2013, 30 August 2015, and 03 July 2004, respectively, (Table  1 ). The multispectral satellite images were chosen during the summer season to avert the soil moisture caused by rain, cloud cover, and the impact of green vegetation. All images were radiometrically calibrated, atmospherically corrected, and converted to surface reflectance using the QUick Atmospheric Correction (QUAC) method  [34]. The blue, green, red, and NIR1 bands of the Sentinel-2A image (i. e. , bands 2, 3, 4, and 8A, respectively) were resampled to 20 m using the resample toolbox and the nearest neighbor technique  [35]  in ENVI software to guarantee that all Sentinel-2A spectral bands had an equal spatial resolution.
Multi-Sensor Satellite Data Fusion of Spectral Index Images All satellite clay spectral index images (i. e. , TM_SI, OLI_SI, AST_SI, and S2A_SI) were fused on the only common area to form a single image with 30 m of spatial resolution, which reunited the four clay spectral index images, named Fusion_SI. This data fusion integrated the clay spectral index images derived from several multispectral remote sensing datasets at different spatial, spectral, and temporal resolutions to collect more abundant information. The spectral index processing and data fusion were implemented using the ENVI software.
Spatial Regression Models The predictions of the soil clay content using TM, OLI, AST, S2A, and fusion images were performed following two approaches: (a) using the spectral index of each multispectral image (i. e. , TM_SI, OLI_SI, AST_SI, S2A_SI, and Fusion_SI) and (b) the VNIR-SWIR spectral bands of each multispectral image (i. e. , TM_S, OLI_S, AST_S, S2A_S, and Fusion_SB), as predictors, for a total of ten predictor images (Figure  2 ). The soil sample datasets for each image were split into training (75%) and validation (25%) using the Unscrambler software package  [42]. The response variables (i. e. , Y clay , clay content) were ordered in increasing rank. The lower clay content sample was first attributed to a validation set, followed by the next three samples in the training set. The process was then carried out by successively placing the next sample in the validation set and the subsequent three samples in the training set.
Spatial Structure Analysis The spatial structure of the predicted clay contents was evaluated by examining their variogram characteristics, which quantified their spatial dependence. The semi-variance for the soil clay values to be displayed on a variogram was calculated using the formula  [50] : γ(h) = 1 2N(h) N(h) ∑ i=1 {z(x i ) -z(x i + h)} 2 ( 3 ) where h is the lag distance, γ(h) is the semi-variance of the soil clay content at a lag distance h, N(h) is the total population of sample pairs at lag distance h, z is the value of the clay content, x is the position of point i on the landscape, and z(x i + h) is the clay value at the position x i + h. The variograms were calculated for the observed values dataset and maps estimated by spectral index (i. e. , TM_SI, OLI_SI, AST_SI, and S2A_SI), spectral bands (i.
Bare Soil Images and Spectral Pattern Descriptions The TM image had the highest bare soil pixels percentage (63. 14%), followed by the S2A (57. 37%), AST (52. 50%), and OLI (41. 62%) images (Figure  3 ). The bare soil's surface variation was related to the different acquisition years of the multispectral scenes. According to the soil line analysis, the OLI image best described the bare soil patterns in the study region, with the highest coefficient of determination (R 2 = 0. 90). In contrast, the AST image had the lowest representation (R 2 = 0. 79) of all the examined images (Figure  3 ). The four multispectral remote sensing datasets had 34. 6% of the common bare soil pixels. The canonical correlation analysis between these common bare soil pixels showed that the TM and OLI, as well as the OLI and S2A, had the highest coefficient (0.
Descriptive Statistics of Clay Soil Samples and Correlation with Bare Soil Images The entire soil sample dataset contained 262 soil measurements. Due to the different number of bare soil pixels in each image, the dataset was reduced to 221, 174, 196, 218, and 149 soil data for the TM, OLI, AST, S2A, and fusion images, respectively (Table  3 ). The descriptive statistics results of the sample datasets used for each satellite image revealed a significant degree of similarity between the clay content values of the soil sample datasets. Based on the analysis of variance (ANOVA) results, no significant variations in the analyzed clay content values were observed at the 0. 01 alpha level. The coefficient of variation (CV, reported in percent) was between 41 and 45% in all datasets. The TM soil samples had the highest CV, indicating that the TM soil sample set was the most varied in these datasets.
In all datasets, the interquartile and standard deviation ranged from 283 to 302 g/kg and 179 to 182 g/kg, respectively. Finally, a soil sample with a clay content of 772 g/kg was reported in all datasets (Table  3 ). The Pearson's correlation coefficients between the spectral bands of the TM, OLI, AST, and S2A bare soil images and the measured clay values (Table  4 ) showed the significantly correlated bands of multispectral images with the measured clay values (p-value less than 0. 05). All the spectral bands of the satellites' multispectral images revealed a negative correlation with the soil clay content values. All datasets' clay values demonstrated high correlation coefficients with the Red, RedEdge, NIR, and SWIR bands of the multispectral images (Table  4 ). The clay content was most correlated with the spectral bands TM-B7, OLI-B7, AST-B8, and S2A-B12, with r values equal to -0.
72, -0. 71, -0. 67, and -0. 72, respectively (Table  4 ). This most correlation is due to the clay minerals' characteristic hydroxyl absorption around 2200 nm, caused by a mixture of vibrations related to the OH and OH-Al-OH bonds  [15, 51]. All clay value datasets were significantly correlated with the clay spectral index of each multispectral image, although the S2A_SI had the highest correlation (r = -0. 71, Table  5 ).
Spectral Index vs. Spectral Bands Prediction Performances Based on the Auto-WEKA results, the MLP-BP was selected as the best regression model for the datasets. The MLP-BP models generated from spectral index images (i. e. , TM_SI, OLI_SI, AST_SI, and S2_SI) performed poorly regardless of the sensor (0. 43 ≤ R 2 val ≤ 0. 51, and 126. 42 ≤ RMSEP ≤ 140. 24 g/kg, Table  6 ); however, the MLP-BP model built from the spectral index image fusion (Fusion_SI) provided good performance (R 2 val = 0. 61 and RMSEP = 114. 18 g/kg, Table  6 ), outperforming the MLP-BP models built from individual spectral index images (Table  6 ). The MLP-BP models created from the multispectral images (TM_S, OLI_S, AST_S, and S2A_S) showed accurate results (0. 60 ≤ R 2 val ≤ 0. 71, and 96. 45 ≤ RMSEP ≤ 115. 64 g/kg, Table  6 ), whereas the clay prediction model built using the S2A_S spectra provided the higher performance, taking advantage of the sentinel-2 MSI sensor's ten spectral bands.
Histograms analysis of the predicted maps over the Lebna and Chiba catchments showed a normal distribution of clay content, with the exception of the histogram of predicted from the TM_SI around 500 g/kg, which represents an extremely weak density (Figure  8 ). The histograms of predicted values from the TM, AST, and S2A images showed a histogram peak of around 300 g/kg, while the histograms of predicted values from the OLI image showed a histogram peak of around 500 g/kg (Figure  8 ). This may be due to the training database's clay values, which were slightly higher than the other databases (see Table  3  and Figure  8 ). Moreover, the distribution of clay contents predicted using the spectral index images (TM_SI, OLI_SI, AST_SI, and S2A_SI) shifted to slightly higher values compared to those obtained from the VNIR-SWIR spectral bands images.