dataesr/scientific-paragraphs-categorization huggingface dataset:
The comparison between the spatially-averaged rainfall data and observed data, over several regions of Morocco  [32] , showed that averaging th data over large surfaces led to a reduced visibility of climatic variability and the main climatic signals, but the main signals remained visible whatever the size of the region used for averaging. This supports the idea that the results visible with, both, observed and gridded data sets are quite consistent. All monthly grids built during this study are available for free on the HydroSciences Montpellier website and our aim is to contribute to the maintenance of an accurate database for climatic scenarios (http: //www. hydrosciences. org/spip. php?rubrique1387/). These grids are already available on the Researchgate website: https://www. researchgate.
net/project/Monthly-rainfall-gridded-data-set-for-Africa. As it is very difficult to gather observed data covering the whole continent since 2000, it is improbable that an update of this study might be performed, knowing that, since then, a number of rainfall gridded product have been released on the basis of satellite-derived rainfall estimations  [35] , which help fill the gap between the needs of researchers for recent data and the unavailability of a free international observed rainfall database. The length of the time-series was, thus, adapted for a comparison of historical time-series and rainfall time-series from the GCM/RCM reanalysis, or for study of time-series for regions where data are difficult to obtain, even for historical periods.
Since 2000, the HSM_SIEREM database was only used and enriched by the teams of HSM in Montpellier, with no institutional mandate for commitment to the management or the dissemination of this base. For Eastern and Southern Africa, HSM gathered data through its commitment in the IHP (International Hydrological Program) of UNESCO and mainly in the FRIEND programs (Flow Regimes from International Experimental and Network Data), as HSM managed the implementation and hosting of the databases of the different African FRIEND groups. Many other international programs that the HSM collaborated on in Africa, allowed the IRD hydrologists access to more recent data. The CRU is specifically acknowledged, as they had developed a collaboration with the hydrologists of HSM which let them access the rainfall data in the areas where HSM did not have any access.
Acknowledgments: We thank the  CRU of the University of East Anglia  for the exchange of raw data at the early stage of this study. We thank the  IRD  and the  HSM Laboratory of Montpellier  for the financial and technical support; and the  FRIEND program  ( UNESCO/IHP ) for their collaboration through the managers of the databases of the different regional programs in Africa (MEDFRIEND, FRIEND  AOC, FRIEND Nile, FRIEND Austral Africa). We thank all the national meteorological services of African countries for their continuous work of data collection, although, we regret that only a few of them accepted to update the data series for the 1990s.
Fitting the chemical shift changes using the global parameters allows the extrapolation of the incomplete datasets to 0 M GdmCl, thereby providing estimates of d 1 H N and d 15 N H values where none could be determined experimentally. The accuracy of these estimates was determined to be 0. 03 ppm for d 1 H N and 0. 3 ppm for d 15  ), m = À2. 8 ± 0. 2 (6. 5 ± 0. 4 kJ mol À1 M À1 ), and K D (Cl) = 410 ± 40 mM (14 kJ mol À1 ). The mean c 2 was 12. 9 with 24 degrees of freedom. (Figure  2B ), because the root-sum of squares function does not account for the direction of chemical shift changes. The sequence distribution of the resulting chemical shift changes corresponding to each transition is shown in Figure  S2. The derived K D value for the interaction with Cl À (410 mM) is consistent with weak, non-specific binding, which is focused at hotspots around the KRR sequence (residues 355-357) and around residue A390, which in the Myc-Max crystal structure correspond to the N-terminal basic H1 region and the turn region, respectively.
The a-Helical Structure Is Populated at Low Denaturant Concentrations The changes in chemical shift with denaturant suggest that the conformational distribution of the polypeptide chain is changing. Whereas some of these changes can be ascribed to weak interactions with Cl À or Gdm + , the cooperative transition is consistent with structural changes that alter the exposure of hydrophobic surface area  (Scholtz et al. , 2009). The backbone 1 H, 15 N, and 13 C chemical shifts were analyzed to determine whether these changes corresponded to a change in secondary structure propensity (SSP). Full datasets (d 1 H N , d 15 N H , d 13 C a , and d 13 C b ) were available at 1. 6, 2. 4, and 3. 2 M GdmCl, with additional data at 0 M for residues 352-395. The Dd 13 C values have a strong correlation with Dd 15 N H values for the same residue (Figure  S3 ) and so d 13 C a and d 13 C b were extrapolated back to 0 M GdmCl using the fitted Dd 15 N H values from the analysis above, which allowed estimation of SSPs for residues 400-437.
DISCUSSION Previous work on chemically denatured proteins demonstrated that they rapidly interconvert between denatured states with random coil behavior and more compact, molten globule-like states, with the population of the more compact forms increasing as conditions become more native-like  (Candotti et al. , 2013; Cliff et al. , 2009; Schulman et al. , 1997). NMR measurements reflect the average behavior of the ensemble, and measurements under a range of conditions can allow the contribution of sub-populations to be deconvolved. Molten globulelike states frequently have very poor NMR characteristics, with low resonance dispersion and fast relaxation (leading to broad resonances and signal attenuation), and so the CDT method allows the determination of otherwise hidden behavior. The data here show that the Myc bHLH-LZ domain is an IDP with such molten globule-like behavior.
Gradient selective, sensitivity-enhanced HSQC spectra had a spectral width of 20 ppm (1622 Hz) and apparent acquisition time of 60 ms for the indirect dimension. Spectra were processed using Topspin 3. 2 software (Bruker Corp). Protein backbone resonance assignment experiments were recorded for Myc at 3. 2 M GdmCl, comprising two-dimensional 15 N-1 H HSQC and triple resonance experiments (H)N(CA)NNH, HNCACB, HNCA, HNCO, HN(CO)CA and HN(CO)CACB spectra. All spectra were collected using Echo/Antiecho-TPPI gradient selection, which was efficient at suppressing signals from GdmCl and other buffer components. Non-uniform sampling (NUS) was used to optimize resolution of the indirect dimensions in the available experiment time. NUS acquired data were process using MDD algorithm within Topspin. Spectra were visualised and analysed using CCPN Analysis 2.
3. Two-dimensional 1 H-15 N HSQC spectra were collected at a range of GdmCl concentrations (from 0 to 3. 2 M with 0. 2 M step). In order to confirm validity of the resonance assignment transfer between denaturant concentrations HNCACB spectra were collected for 2. 4, 1. 6 and 0 M GdmCl. This led to unambiguous assignment of Myc at native-like conditions even for very low intensity peaks. In addition, a sodium chloride titration of Myc between 0 and 1. 2 M (with 0. 2 M step) was collected. To characterize the Myc self-association and aggregation, protein concentrations (0, 0. 025, 0. 05, 0. 1, 0. 2, 0. 4, 0. 8 mM) were varied and 1 H-15 N HSQC spectra recorded, repeated at 0, 0. 6, 1 and 2 M GdmCl. Paramagnetic relaxation enhancement (PRE) data were collected for diamagnetic and paramagnetic samples at a range of GdmCl concentrations (0 to 3.
QUANTIFICATION AND STATISTICAL ANALYSIS Data were processed in Topspin 3. 2 and quantified in CCPN Analysis 2. 3. Data fitting was by Levenberg-Marquadt non-linear least squares optimisation using in-house routines interfacing with Numerical Python. Chemical shifts were analysed as follows: the total reaction scheme for the interaction between Myc and GdmCl can be described by Scheme 1, as shown in the main text. Because the aggregation process is slow and irreversible, it doesn't contribute to chemical shifts. In addition, the dissociation constant for Gdm + binding to the unfolded state is so high (>4M) that the effect can be approximated by a linear function. Thus, the chemical shift data can be analysed according to the following simplified scheme: e2 Structure 27, 1537-1546. e1-e4, October 1, 2019 K D(Cl) is the binding constant for chloride and K N/D is equilibrium constant between the denatured and the native disordered states.
No significant difference between the fit-lines and the data points resulted. The efficacy of using the global parameters to allow extrapolation to zero denaturant for residues with significant signal attenuation under such conditions was tested by omitting data points below 0. 6 M GdmCl for complete data sets and repeating the fitting procedure. The RMS deviation between the fitted values and the recorded chemical shifts was 0. 03 ppm for dH and 0. 3 ppm for dN. These uncertainties were propagated into the estimates of 13 C chemical shifts. 13 C a and 15 N H chemical shifts changes for each residue were found to correlate, and so the maximum 15 N shift defined by data fitting was used to calculate the 13 C a shifts at 0 M GdmCl used for SSP. For residues with complete datasets, omitting the 0 M GdmCl point and repeating the process gives a RMS difference from the recorded value of 0.
This process was repeated 5 times to avoid being trapped in local minima. These final 10000 structures were used as a starting pool to fit the PRE and chemical shift data simultaneously. ASTEROIDS was used to produce equivalent ensembles containing 100 structures (5000 evolution steps were used in the genetic algorithm). PREs were calculated from 1 H R 2 values estimated from the 1 H-e -distance in each conformation, the INEPT delay, the conformational sampling of the spin label relative to the backbone and the estimated correlation time of the dipole interaction (5ns), as described elsewhere  (Salmon et al. , 2010). Structure 27, 1537-1546. e1-e4, October 1, 2019 e3 Long-range order was assessed in the final ensembles by calculating distance values normalized against the average distance values calculated for the chemical shift (CS) based ensemble: D ij = log d ij d 0 ij ! where d ij is the distance between residues i and j in the final (CS-PRE) calculated ensemble and d 0 ij is distance between residues i and j for the CS calculated ensemble.
N H (see the STAR Methods). A B Figure 2. Response of Myc to Denaturant (A) Primary structure of Myc bHLH-LZ domain with residues color coded according to category of behavior, mostly hyperbolic (blue), mostly sigmoidal (red), and showing both transitions (green), with the secondary structure boundaries taken from the Myc-Max crystal structure PDB:1NKP (B, basic helix; H1, helix 1; H2, helix 2; LZ, leucine zipper heptad repeats). Circle radii reflect the uncertainty in the chemical shift measurements (0. 005 ppm for d 1 H N and 0. 02 ppm for d 15 N H ). (B) Titration behavior of amide crosspeaks for a selection of residues. The top row shows positional changes in 1 H-15 N HSQC spectra with the axes origin indicating the starting chemical shift (lowest concentration of GdmCl giving a visible peak).
Images are captured at different stages of the mechanical loading, usually starting from the unloaded state as a reference configuration. Raw data at each pixel or voxel are related to a physical quantity depending on the camera sensor, in the visible, infrared or X-ray ranges for instance. Quantitative thermomechanical analysis is then possible by image processing, providing data such as displacements, strains, stresses or temperatures. Full-field measurement techniques have been carried out for various types of material, but applications to discrete media remain relatively scarce compared with continuous media. This can primarily be explained by the specific loading devices that are usually required for cohesionless materials, making instrumentation by cameras a more complex process. Secondly, the intrinsically three-dimensional (3D) nature of media such as sands requires measurements in the bulk, limiting the number of techniques available: X-ray tomography combined with volumetric digital image correlation  [9] [10] [11] , computerized X-ray tomography  [12, 13] , flash X-ray shadowgraphy  [14] , magnetic resonance imaging  [15, 16] , radar-based sensing  [17]  and positron emission particle tracking  [18].
Stress fields can be also obtained from certain full-field measurement techniques. Shear stresses can be measured by photoelasticimetry using particles made of birefringent materials  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41]. It is possible to identify contact force networks by processing the photoelastic data  [42]. Beyond measurements in the visible range, infrared (IR) thermography has been employed to measure hydrostatic stress fields in granular materials by Thermoelastic Stress Analysis  [43] [44] [45]. It is possible to reveal thermoelastic couplings  [45]  as well as material self-heating, which can mainly be attributed to interparticle friction  [46] [47] [48]. In the present study, another physical principle is employed to provide information on the mechanical response of granular systems at the local scale: mechanoluminescence.
Image acquisition began four seconds before starting the loading. The first images were used to define the reference state. Temporal synchronization between image acquisition and mechanical loading was performed manually with a precision of 1 s (due to the image acquisition frequency) using the times at maximum loading and maximum light emission. Figure  5a  shows an example of an image taken at 300 N for the ML longitudinal specimen. The specimen appears in green. Photographs were then converted to greyscale, as performed for instance in Ref. [62]. Matlab software was used for this purpose using the rgb2gray command, leading to pixel values between 0 (black) and 1 (white). The analysis below is based on the change in light intensity with respect to the reference configuration. The reference image was built by averaging three images captured just before starting the new load-unload cycle.
Moreover, stresses tend to increase when the dimensions decrease, everything else being equal. In addition, the stress flow is more clearly evidenced in the smallest cylinder (S). Figure  13b  shows the variation in mean light emission in the contact zones as a function of the applied vertical force. Data were extracted as the average values over the small blue zones on either side of the contact (see image). It can be observed that the light emission of the S-L contact (in red) is lower than that of the S-M contact (in black), even if there are significant fluctuations over the course of the loading. A change in slope can be noted in the graph at about -450 N. It could be due to a yield separating non-detectable and detectable light emission as indicated above. But, similarly to the situation of figure 3-d in Ref.
Acknowledgements The authors gratefully acknowledge the  Ministère de l'Europe et des Affaires Etrangères (MEAE)  and the  Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation (MESRI)  in France, as well as the  Office of the Higher Education Commission (OHEC)  of the  Ministry of Education in Thailand. The authors also gratefully thank the  French Embassy in Thailand and Campus France  for their support during this research (PHC SIAM 2018, Project  40710SE ). Dr. P. Jongchansitto  would also like to acknowledge the financial support through the  Research Grant for New Scholar  ( MRG6080251 ) from the  Thailand Research Fund (TRF)  and  Thailand's Office of the Higher Education Commission (OHEC). Finally, the authors gratefully thank  Mr Maël Tissier ,  Sigma-Clermont Engineering School , for the elaboration of mechanoluminescent materials, as well as  Mr Clément Weigel  and  Mr Alexis Gravier ,  Sigma-Clermont Engineering School , for the manufacturing of the testing device and the molds.
INTRODUCTION Complex human phenotypes arise from a contribution of genetic and environmental factors. Genome-wide association studies (GWAS) is a key technique to link genetic loci to human phenotypes  (1). For instance, a single GWAS detected more than 400 loci associated to adult human height  (2). Given the large amount and importance of GWAS, several databases have compiled GWAS results  (3) (4) (5)  The large majority of associated SNPs fall in intronic and intergenic regions  (6). This association is likely mediated through gene regulatory regions and enhancers, as for instance, non-coding associated loci are enriched in eQTLs and DNase I hypersensitive sites (DHS)  (7, 8). Enhancers of gene expression are non-coding regions at any distance of regulated genes with particular chromatin configurations that facilitate transcription factor (TF) binding and gene expression  (9).
Supervised classification using XGBOOST XGBOOST is a software library for supervised classification based on the gradient boosting technique. The gradient boosting technique uses ensembles of regression trees constructed with the boosting meta-algorithm. The XG-BOOST software has several advantages over othe gradient boosting libraries. Regularization has been formalized in the objective function to better control over-fitting. The XGBOOST library has been optimized for large data sets. We used the python framework with the binary logistic objective function and parameters colsample bytree 1, eta 0. 3, max delta step 1, max depth 6, min child weight 1, num boost round 10 and subsample 1 for both intronic and intergenic models.
Training Training SNPs that were common (MAF > 1%), belonged to the European population and to chromosomes 1-22 were selected from the 1000 Genome data set using plink (v1. 90b4. 4 64-bit)  (29). Intronic and intergenic SNPs were defined relative to RefSeq gene annotations. For intergenic SNPs, 1 kb gene upstream regions were excluded to avoid gene promoters during the training. The following steps were carried out to create and train the model. Index SNPs were generated with plink indep option: 5, 1, 100. Linkage disequilibrium (LD) between training SNPs was computed using plink ld-snp-list limited to windows of 1000 kb, 1 000 000 SNPs and r 2 of 0. 8. SNPs were defined as associated if the SNP showed at least one significant association (P < 5 × 10 -8 ). Expression phenotypes present in the GRASP database were removed before the training step.
Analysis of binding of transcriptional regulators and mouse ontology SNPs were split according to the TAGOOS significance and annotated using non-redundant TFs from the ReMap database  (30, 31). For each TFs, the percentage of bound SNPs with either TAGOOS-significant or non-significant scores was calculated and assessed with a paired Wilcoxon test for transcriptional regulators (Figures  4C, F , 5C, D and 6C-F). The TFs were sorted according to the difference of percentage of bound SNPs with significant and nonsignificant scores and plotted (Figures  4C, F , 5C and 6C, D). In Figures  5D  and 6E , F, only the first 10 TFs were plotted. This list of TFs in Figures  4C, F , 5C and 6C, D) sorted by the binding difference between TAGOOS-significant and non-significant SNPs was split in two equal groups and submitted separately or together to the EnrichR web site  (32).
Motif analysis Input sequences were built with a 100 bp sequence around each SNP with either the RefSeq allele or the most common alternative allele. Sequences of TAGOOS-significant or non-significant SNPs with the RefSeq allele were used to create the background model with Markov parameter 1. A library of non-redundant motifs based on RSAT matrix-clustering and the HOCOMOCO database was used  (33, 34). The matrix-scan tool of the RSAT suite was run to detect significant motifs (P < 1 × 10 -4 )  (35). The motif density was plotted in 10 bp bins of the 100 bp sequences (Figure  4G, H ).
Data sets, scores and software SNPs were downloaded from here: GRASP (3), NCBI db-SNP  (36) , 1000 Genomes databases  (37) , GWAS Catalog (5), ClinVar  (38) , a height GWAS (2) and a cleft lip association study  (11)  (Supplementary Table  S3 ). Annotations were downloaded from here: expressed enhancers (39), eQTLs  (40) , ReMap  (30, 31) , RoadMap  (41)  and H3K27ac from the Young laboratory (42) (Supplementary Table  S3 ). Training and test pipelines were implemented with Snakemake (Supplementary Figure  S1 )  (43). SNP RS identifiers were converted to coordinate bed files using the UCSC mysql server  (44). Coordinate bed files were converted to fasta sequences using the UCSC twoBitToFa tool  (44). Peak bed files were manipulated using bedtools (v2. 26. 0)  (45). The Integrative Genomics Viewer was used to browse the genome  (46).
Heatmaps (pheatmap package), correlogram (corrplot package), factor analysis and statistical tests were carried out with the R software. The TAGOOS scores, P-values, negative decimal logarithms of the P-values, annotations and UCSC tracks are provided here: https://tagoos. readthedocs. io. The developer documentation and scripts to download the data sets, annotate the SNPs, train the model and score the genome can be found here: https://github. com/aitgon/tagoos.
8) or not with associated SNPs from the GRASP database (3). Then, we annotated 1000 genome database SNPs with 4684 molecular features from various public databases related to noncoding gene regulatory sequences such as eQTLs, H3K27ac or transcription factors (Figure  1 , Table  1 , Supplementary Tables  S3  and S4 ). Index SNPs were annotated with the linkage disequilibrium (LD) r 2 between the index SNP and the annotated SNP and the annotation (Figure  1A ). This so-called training data set was used to train the XGBOOST algorithm  (47). Preliminary analysis showed that the most relevant chromatin marks were different in intronic and intergenic regions. Therefore we created two models for intronic and intergenic regions based on the 22 somatic chromosomes. To find optimal parameters and calculate the performance, we carried out a leave-one-chromosome-out crossvalidation procedure where a model was trained based on a set of chromosomes and the model was tested in a different chromosome.
The average area under the curve (AUC) performance for the associated SNPs (P < 5 × 10 -8 ) from the GRASP database versus random SNPs was ∼0. 65 for both the intronic and intergenic SNPs (Supplementary Figure  S2 ). This result suggests that the training data set is able to produce models that enrich unseen associated SNPs. Then we annotated intronic and intergenic regions genome-wide with selected features and computed the TA-GOOS scores (Figure  1A ). To evaluate the TAGOOS score with unseen GWAS SNPs, we downloaded the GWAS Catalog and removed SNPs present in the GRASP database  (5)   1B ). In the same region, GWAVA and TA-GOOS performances were roughly equivalent with AUC 0. 6 (Figure  1B ). These AUC values were generally consistent with a recent survey of functional SNP priorization tools for priorization of GWAS SNPs that included IW-Scoring, Eigen/EigenPC, DeepSea, Funseq2, LINSIGHT, FATHMM-NC, GWAVA, CADD, ReMM and FitCons tools.
In this survey, none of the tools reached AUC values of 0. 6 for priorization of GWAS versus randomly selected noncoding SNPs from the 1000 genomes database  (27). The difference between the IW-Scoring AUC performance here (AUC 0. 7) (Figure  1B ) and the original reported AUC 0. 6 (27) might arise, because in the original publication of IW-Scoring, no difference between intergenic and intronic SNPs was made and the negative data set came from the 1000 genome database  (27). Another strategy to enrich functional SNPs within a linkage disequilibrium (LD) block is to annotate the LD blocks with gene regulatory annotations  (11). A particularly popular method is to learn so-called chromatin states such as active enhancers based on annotations related to gene regulation using Hidden Markov Models  (11, 48).