dataesr/scientific-paragraphs-categorization huggingface dataset:
Drs Habtemariam and McGregor were previously employed by and are shareholders of Alnylam Pharmaceuticals. Dr Magen receives research funding, consultancy fees, and nonfinancial support from Alnylam Pharmaceuticals. The remaining authors declare that they have no relevant financial interests. Acknowledgments: The authors thank the patients, the patients' families, investigators, study staff, and collaborators for their participation in the lumasiran clinical studies. Data sharing: Deidentified individual participant data that support these results will be made available in a secure-access environment 12 months after study completion and when the product and indication have been approved for no less than 12 months in the US and the EU. Access will be provided contingent upon the approval of a research proposal and the execution of a data sharing agreement.
This paper presents retrospective simulations and future projections of daily time series of discharge and stream temperature for 52 278 reaches over the Loire River basin (10 5 km 2 ) in France, using a physical process-based thermal model coupled with a hydrological model. Retrospective simulations over the 1963-2019 are based on the Safran meteorological reanalysis over France. 21st century projections are based on a subset of the DRIAS2020 ensemble projection dataset, derived from the Euro Cordex data set through the ADAMONT statistical bias correction. Such a dataset at this large scale and high spatial resolution stands out from existing datasets, and is the first one in France derived from a physical process-based thermal model.
They also found the greatest increase in large rivers, while riparian shading mitigated the increase in Tw in small mountainuous streams. Additionally, climate-induced changes in Tw could also help us to predict the vulnerability of aquatic species to climate change  (Lee et al. , 2020). This paper, using outputs of the T-NET physical process-based thermal model coupled with the EROS semi-distributed hydrological model, presents daily time series of Q and Tw from the past to future at the reach scale over the Loire River basin (10 5 km 2 ), one of the largest in Europe. Section 2 briefly describes how such a dataset was produced using the above models. Retrospective simulations based on the Safran reanalysis over the 1963-2019 period are then presented in Sec. 3. 1. Future Q and Tw projections derived from the DRIAS2020 climate projection dataset, are then presented in Sec.
Finally, hourly outputs of T-NET (Q and Tw) are averaged at the daily scale. Models used here do not consider the influence of water-abstractions and impoundments i. e. simulate natural Q and Tw time series. Detailed information on models principles, input data, calibration and validation can be found in  Thiéry and Moutzopoulos (1995) ;  Thiéry (2018)  and shortly in  Seyedhashemi et al. (2022b)  for EROS, and  Beaufort et al. (2016a) ;  Loicq et al. (2018) ;  Seyedhashemi et al. (2022b)  for T-NET. For both EROS and T-NET, meteorological data (i. e. Ta, P, PET, Hns, Hla, RH and W) for retrospective simulations  (1963-2019) and projections (1976-2100)  come from from different datasets described below. Nevertheless, meterological datasets share the same 8-km grid resolution. All reaches within a grid cell are assigned meteorological data values in that grid cell.
Projections (1976-2100) The DRIAS2020 climate projection dataset  (Soubeyroux et al. , 2020)  has been recently released over France through the DRIAS portal (see http://www. drias-climat. fr/. It comprises an ensemble of climate projections under 3 Representative Concentration Pathways used in the fifth IPCC Assessment Report (IPCC, 2014) derived from the larger EUROCORDEX dataset using Regional Climaet Models (RCM) over Europe. This ensemble id downscaled over France to the 8-km Safran grid and biascorrected with respect to the Safran reanalysis with the ADAMONT method  (Verfaillie et al. , 2017). In this study a subset of 3 contrasted future projections (GCM+RCM) are used to sample the dispersion of the full ensemble of 12 GCM+RCM projections from the DRIA2020 dataset. The 3 selected projections include a warm and wet couple of models (IPSL-CM5A/MRWRF381P), an intermediate one (CNRM-CM5-LR/ALADIN63), and a hot and dry couple (HadGEM2/CCLM4-8-17).
, 2022b). The potential applications of the proposed dataset over the past and future are manifold. This can be employed to understand spatio-temporal variability in Q and Tw, to assess the synchronicity of extermes (e. g. studies of  Arismendi et al. , 2013; Arevalo et al. , 2020) , to better explain and predict the possible spatial distribution of aquatic communities (e. g. study of  Picard et al. , 2022 , who used the current dataset), and to assess the various stresses on freshwater habitat due to climate change (e. g. Lee et al. , 2020).
Data availability The daily Q and Tw in retrospective simulation over the 1963-2019 period and under 7 projections over the 1976-2100 period are available for T-NET hyrographic network (52 278 reaches) under the Attribution-NonCommercial 4. 0 International (CC BY-NC 4. 0) in NetCDF file format through: https://doi. org/10. 57745/LBPGFS  (Seyedhashemi et al. , 2022a). Ta and other desired meteorological variables corresponding to each reach can be extracted from the closest grid cell to the reach of the Safran reanalysis database (available upon request from Météo-France) for the retrospective simulation and of the DRIAS-2020 dataset for projections (see DRIAS: http://www. drias-climat. fr/, portail partenarial Météo-France, IPSL, Cerfacs, and  Soubeyroux et al. , 2020). Author contributions.
HS developed the dataset and prepared the manuscript. DT ran the EROS model and provided discharge data for both past and future. All co-authors contributed to the manuscript. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. The authors would like to thank Météo-France for providing the Safran reanalysis data. This work was performed in the course of a doctoral project at the University of Tours, funded by the European Regional Development Fund (Fonds Européen de.
https://doi. org/10. 5194/essd-2022-451 Preprint. Discussion started: 12 January 2023 c Author(s) 2023. CC BY 4. 0 License. This data paper presented and described daily Q and Tw reconstructions over the 1963-2019 period as well as projections over the 1976-2100 period for 52 278 reaches over the Loire River basin (10 5 km 2 ) using a physical process-based T-NET thermal model coupled with the EROS hydrological model. Daily Q and Tw are projected under three contrasted downscaled and bias-corrected climate projections (GCM/RCM) including warm and wet (IPSL-CM5A/MRWRF381P), intermediate (CNRM-CM5-LR/ALADIN63), and hot and dry (HadGEM2/CCLM4-8-17) models from the DRIAS-2020 dataset (Soubeyroux et al. , 2020), under three Representative Concentration Pathways (RCPs) from the fifth report of IPCC (IPCC, 2014).
As shown below, our approach can detect TLS which are otherwise excluded in step 4. We distinguish two phases: training and deployment. Our supervised training approach, detailed in the Methods section and sketched in Fig. 2 , takes just a few hours of training on a single CPU. It requires an initial dataset of Oð10 4 Þ full NEB calculations, whose collection is the most time-consuming part of the training phase. Once training is complete, the ML model can be deployed to identify new TLS. Its workflow is similar to the standard one, with some major improvements. It proceeds with the following steps: (1)-(3) The first 3 steps are similar to the standard procedure to obtain a collection of inherent structures from a dynamical exploration. (  4 ) Apply the ML model to all possible pairs of IS to predict which pairs form a DW potential.
The first ensemble is trained to classify DW (Methods, Sec. IV F), which is a necessary condition for TLS. A DW is defined when the minimum energy path between the two IS resembles a quartic potential, as sketched in Fig. 1b. For the pairs that are predicted to be DW, a second model ensemble (Methods, Sec. IV G) is used to predict the quantum splitting (QS), which determines if the pair is a TLS or not. The dataset is constructed by comparing all the pairs of inherent structures (IS), focusing on the M atoms that displace the most between two IS (highlighted by colors: bright, resp. dark, indicate small, resp. large particle radius). Specific features are extracted to construct the input vector X. We then train a classifier to predict whether a pair of inherent structures forms a double well potential (DW) or not.
062, 0. 07, and 0. 092, respectively. In order to have comparable data at the three temperatures, for T f = 0. 092 we only consider a subset of glasses corresponding to 30920 IS pairs. The results of the TLS search are summarized in the red columns of Tab. 1. Overall, the standard procedure reaches a rate of TLS found per NEB calculation of 4 × 10 -3 , 13 × 10 -3 , and 8 × 10 -3 with increasing T f. We compare these numbers with those obtained with our iterative training procedure applied to the same data, see green columns of Tab. 1. We immediately notice two major improvements. First, the overall number of TLS that we find from the same dataset is more than twice larger. Second, the ratio of TLS per NEB calculation is more than 15 times larger, corresponding to 62 × 10 -3 , 211 × 10 -3 , and 194 × 10 -3 with increasing T f.
Differences between DW and TLS With our ML-driven exploration of the energy landscape we can focus the numerical effort on DW and favorable TLS candidates, while processing a larger number and/or longer exploration trajectories. This allows us to consider a larger set of independent glasses of the same type as those treated in ref. 35, which is particularly relevant for ultrastable glasses generated at the lowest temperature T f = 0. 062. While in ref. 35 the collection of 61 TLS required more than 14,000 NEB calculations, we are able to identify 872 TLS running 11 iterations of iterative training using only a total of 5500 NEB calculations in addition to the ~6000 used for pretraining. In the next section we analyze these results to discuss the nature of TLS. The database of glasses that we analyze with iterative training contains 5 times more IS than in ref.
Dataset and features construction for ML approach The first step of our machine learning approach is the evaluation of a set of static quantities for all the available IS. This set consists of: potential energy, particle positions, averaged bond-orientational order parameters  59  determined via a Voronoi tessellation, that we denote {q 2 , q 3 …q 12 }, and finally particle radii. The cost of this operation scales as the number of available states N IS , but we use these quantities to calculate the features of ∼ N 2 IS pairs. A detailed analysis reported in Supplementary Note 1, shows that the bond-orientational parameters and the particle sizes are not very useful for the ML model. Since their calculation is slower than all the other features, we do not include them in the final version of the ML approach.
2 ), where the prediction of the first stack E ð0Þ qs = 1=n GB P n GB k c k G k ðx i Þ= P k c k is concatenated to x i and used as input for the following stack. At the same time, we also perform k-fold bagging  66  , which consists in splitting the data in k subsets used to train k copies of each model with different data. This has shown to be particularly effective in improving the prediction for small datasets  60. In order to train the model we first collect a set of E qs examples. The size of this training set is discussed in the Supplementary Fig. 2  where we find that the minimum number is around 10 4. We can use some of the data already collected in previous work in ref. 35 for the training. Moreover, since we are interested in estimating with more precision the lowest values of E qs we train the model to minimize the following loss function L = P n i = 1 w i E qs,true À E qs,predicted 2 n P n i = 1 w i , ð5Þ which is a weighted mean-squared error.
Acknowledgements We thank  E. Flenner ,  G. Folena ,  M. Ozawa ,  J. Sethna ,  S. Elliott ,  G. Ruocco , and  W. Schirmacher  for useful discussions. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the program Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. This project received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program , Grant no. 723955 -GlassUniversality (FZ), and from the  Simons Foundation  (# 454933 , L. B. , # 454955 , F. Z. , #454951 D. R. ) and by a  Visiting Professorship  from the  Leverhulme Trust  ( VP1-2019-029 , L. B. ). C. S. acknowledges support from the  Herchel Smith Fund  and  Sidney Sussex College, University of Cambridge.
Population structure analysis Population structure analyses were performed with the SNPs dataset of a homogeneous group of capercaillies. All the European samples not coming from the Iberian Peninsula (n = 28) were included in this group but, in order to maintain sampling homogeneity across different populations, 8 samples were selected from the Pyrenees together with 8 from the Cantabrian Mountains, so that this analysis included 44 samples in total, as indicated in Table  S1. The dataset contained 8,627 SNPs. The Structure 2. 3. 4 program  (Pritchard et al. 2000)  was used with the correlated model and 300,000 generations of calculation after a burn-in of 30,000 generations. The number of populations (K) considered ranged from 2 to 6. A total of 10 runs per K were performed (only one run did not converge properly, at K = 5, and this was removed as all the others converged to the same result).
The Evanno method  (Evanno et al. 2005 ) was used to identify the most supported number of populations from the ∆K graph. The uncorrelated model of Structure was also used for comparison. In addition, the program was tested with a 6,740 SNPs dataset from which singletons were removed (with MAF = 0. 0125), as suggested in other works  (Linck and Battey 2019). Population structure was also analyzed with the program PopCluster (Wang 2022) using the 8627 SNPs dataset and K ranging from 1 to 6. Principal component analysis (PCA) was performed on the 8627 SNPs dataset of the homogeneous group of capercaillies using the KING 2. 2. 5 program  (Manichaikul et al. 2010).
Application of an isolation-with-migration model To estimate population split times, effective population sizes and migration rates in an isolation-with-migration (IM) framework, we used the program IMa3 version 1. 12  (Hey et al. 2018) , with similar conditions as in a previous work  (Balmori-de la Puente et al. 2022). We considered the six geographical populations of Fig. 1 , which were also supported by the Structure analysis: Pyrenees, Cantabrian Mountains, Alps, Balkans, Carpathians, and Western boreal zone. Only individuals of the homogeneous group (Table  S1 ) were used. In addition, three individuals that were genetically very distant from their respective populations in the PCA analysis were excluded, as their presence in these populations may be due to recent migration events. The population topology used, based on a TreeMix analysis without migration  (Pickrell and Pritchard 2012) , was: ((Pyrenees, Cantabrian), (Alps, (Balkans, (Carpathians, Boreal)))).
The population tree figure was generated with the IMfig program  (Hey 2010 ). The significance of population migration rates (2Nm, effective number of migrant genes per generation) was based on a log-likelihood-ratio test  (Nielsen and Wakeley 2001). To scale parameters to demographic units, we used a mutation rate of 1. 91 × 10 -9 mutations/site/year, determined for the chicken genome using four-fold degenerate sites  (Nam et al. 2010). The chicken belongs to the same family, Phasianidae, as the capercaillie, so it is likely that they have similar mutation rates. In addition, we have shown in a previous work that four-fold degenerate sites have similar mutation rates to ddRAD loci (Balmori-de la Puente et al. 2022). The per-base mutation rate was multiplied by 145 to obtain the mutation rate of the loci.
Relatedness and inbreeding coefficient Individual inbreeding coefficients and pairwise relatedness coefficients were estimated from the SNPs dataset with the R library related 1. 0  (Pew et al. 2015) , using the full ninestates identity-by-descent model. Confidence intervals (95%) of the relatedness coefficients were calculated using 100 bootstraps over loci and these coefficients were considered significant if the lower limit of the confidence interval was greater than 0. Estimates of inbreeding and relatedness coefficients require information on the background allele frequencies of the population, so these were only calculated for the Cantabrian and Pyrenean populations, where the higher number of samples made this estimation possible.
The analysis after filtering out singletons again supported K = 2 and gave similar results to those obtained with the full SNPs dataset (Figure  S1b ). These results indicated that the Evanno result is robust under different conditions. The population structure analysis performed with PopCluster was highly consistent with that of Structure with the correlated model from K = 2 to K = 5 (Figure  S2 ), although at K = 6 the two Iberian groups were not split, In addition, the most supported number of populations according to this method was K = 5, which differs from the Structure result. A PCA based on the SNP allele frequencies of the homogeneous set of capercaillies, in which the first two components explain 15. 1% and 11. 3% of the variance, respectively, arranged most of the samples in a similar way to their geographical distribution (Fig.
Acknowledgements We thank the following people for providing samples:  Florian Kunz  ( Institute of Wildlife Biology and Game Management, Austria ),  Gabriel de Pedro  ( Junta de Castilla y León, Spain ),  Iván Afonso  ( Conselh Generau d'Aran, Spain ),  Javier Quesada  ( Banc de Teixits, Museu de Ciències Naturals de Barcelona, Spain ),  Jordi Solá  ( Gobierno de Andorra ),  Laura Kvist ,  Jouni Aspi  ( University of Oulu, Finlandia ),  María Suárez  ( Tragsatec, Spain ),  Pedro de la Cruz  ( Spain ),  Rainer Ploner  ( Südtiroler Landesverwaltung, Land-und Forstwirtschaft, Italy ),  Rubén Moreno-Opo  ( Ministerio para la Transición Ecológica y el Reto Demográfico, Spain ), the  Mountain Galliform Observatory of the French Pyrenees (France ),  Rudi Suchant  and  Annette Kohnen  ( Forstliche Versuchs-und Forschungsanstalt Baden-Württemberg, Germany ),  Tomasz Strzała  ( Wroclaw University of Environmental and Life Sciences, Poland ),  Petar Zhelev  ( Forestry University, Bulgaria ), and  Dalibor Ballian  ( Forestry Faculty, Bosnia ).
, 2021). Despite their importance for the main quality attributes of boiled yam, these pathways have been very little studied. Over the last decade, hundreds of genomes have been released. Likewise, much efforts have been put on improving the genome annotation in order to extract biological knowledge from genomic sequences  (Marks et al. , 2021). However, annotating a genome is time and resources consuming. Automatic pipelines can produce inaccurate genome annotation and their results often require manual curation  (Bolger et al. , 2017). In non-model species, this can be especially true, since several genes lack a precise functional annotation, relying on public databases and gene similarity only. The use of comparative analysis allows to find homologous and orthologous genes from different species and within the species of interest  (Mota et al.
The pentose and glucuronate interconversions pathway include most genes (150) and EC numbers (73), followed by starch and sucrose metabolism (136 genes, 76 EC numbers) and flavonoid biosynthesis metabolism (83 genes, 28 EC numbers). To complement the set of annotated genes, a comparative genomics approach was employed using a comparison of 45 diverse plant genomes  (Mota et al. , 2020)  (Supplemental Table  3 ). Groups of orthologous predicted proteins from different plants (OG), were used to infer the function of proteins that were not annotated by other methods. Since the clustering of proteins in OG does not depend on their previous function annotation but rather on their protein sequence, this method can uncover missing genes from a keyword search. The orthology analysis resulted in a total of 58,916 OG from 1,706,645 proteins (weblink dataverse Cirad).
alata, we narrowed these groups down to the three pathways targeted in this study. For most of the OG found in the three pathways, at least one gene from each of the 45 species was present, or the majority of them (weblink dataverse Cirad; Supplemental Figure  4B ). These OG were not exclusive to any particular phylogenetic clade, genus, or species, indicating the importance of these proteins in biological processes for all plant species. Although present in all species, the number of proteins for each OG was uneven, which could be the result of both bias originating from assembly and annotation discrepancies, as well as duplication events that occurred after the differentiation of certain species (in-paralogs)  (Pont et al. , 2011). The comparative analysis was not used to increase the number of EC numbers for these pathways but to complete the set of previously annotated genes  (Bredeson et al.
Candidate gene association studies for quality traits in D. alata To get a deeper insight in the involvement of the genes annotated from the three metabolic pathways on the tuber quality, we conducted a candidate gene association study. We used phenotypic data collected from two-years field trials on 45 genotypes among the 125 D. alata genotypes. They were selected to cover the genetic diversity and to minimize the genetic structure. A set of 10 traits related to starch content, colour indices (Brown Index, the Hue for purple and the Browning over time) and texture were measured. Texture was measured with two different types of probes. A conical probe was used to measure the total area (N sec), which reflects the total force required to penetrate the sample at a constant speed. A flat plate probe was used during a double compression cycle to measure hardness, gumminess, springiness, and cohesiveness.
We annotated orthologous groups using the conserved domains, which facilitated the correction of incomplete or outdated gene assignments, such as for Malectin-like genes. In addition, based on the orthology analysis we found gene structure errors, such as for OG0082990 and OG0083049 (weblink CIRAD dataverse), which calls for manual curation of important gene families to further improve the functional annotation of D. alata genome. We found that various gene ontology categories were enriched from the genes under selection between the three identified genetic groups. The most enriched pathways under selection were transmembrane transport, protein phosphorylation, response to wounding, hormone signalling, lignin biosynthetic process, known to play important physiological roles in defence response during plant-stress interactions  (Zhang and Sun, 2021).
Plant material This study used 125 genotypes from Dioscorea alata and two outgroup genotypes, belonging to D. rotundata and D. trifida species, collected in 19 different countries. Genotypes were selected to cover a maximum of worldwide diversity (Figure  1 ; Supplemental Table  1 ) based on previous studies  (Sharif et al. , 2020). These genotypes were used to find genomic polymorphism, compared to the D. alata reference genome. From the whole set used in this study, a total of 45 D. alata accessions harbouring a high genetic diversity were planted from 2018 to 2020 in May each year and harvested at full maturity in the following year between January and March at three different locations in Guadeloupe (Godet, Roujol and Duclos). The three locations have contrasting pedoclimatic conditions  (Cormier et al.