dataesr/scientific-paragraphs-categorization huggingface dataset:
45%) for rain rates higher than 1 mm/h (resp. 3 mm/h). On rainless areas, performances are similar with a small reduction of the RMSE by 2. 7%. However, since the buoys have a time resolution of ten minutes, some quick sub-mesoscale processes, such as gust fronts, are difficult to register. The limited spatial range of the buoys also makes it challenging to observe rare phenomena. Future work should address these concerns. APPENDIX A secondary dataset was created differing with the main dataset by Eq. 4. Here, the balancing policy is defined as: ∀x, P (x) = P + (x) = P -(x) (Eq. 8) The distributions P + and P -are presented in Fig. 10. The balancing is performed for every wind speed, which lead to remove 84% of the rain patches -especially at 5 and 10 m/s-since the number of rain patches at 8 m/s is limited.
This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully e MODEL AND CHANNELS RAIN RATE Balanced Dataset RMSE Neutral Dataset Balanced Dataset PCC Neutral Dataset [0, 1[mm/h ♢ 1. 38 [0. 016] ♢2. 40 [0. 019] ♢ 89. 9% [0. 19%] ♢ 74. 2% [0. 42%] I [1, 3[mm/h ♢ 1. 64 [0. 046] ♢2. 78 [0. 038] ♢ 92. 7% [0. 39%] ♢ 79. 5% [0. 58%] [VV] [3, 10[mm/h ♢ 1. 59 [0. 038] ♢3. 18 [0. 070] ♢92. 7% [0. 38%] ♢ 78. 9% [0. 62%] ≥ 10mm/h ♢2. 12 [0. 052] ⋆3. 27 [0. 135] ♢81. 7% [1. 03%] ♢ 74. 3% [0. 90%] [0, 1[mm/h ♢ 0. 87 [0. 009] ♢2. 26 [0. 019] ♢ 96. 2% [0. 06%] ♢ 77. 7% [0. 24%] II [1, 3[mm/h ♢ 1. 04 [0. 086] ♢2. 87 [0. 061] ♢ 97. 2% [0. 47%] ♢ 77. 6% [0. 24%] [VV, INC, WDIR] [3, 10[mm/h ♢1.
DUSQ analysis For quantification, only the sequences that originate from 5 0 to 3 0 sequencing are used, following the algorithm described in Figure  2. Sequences are sorted according to their quality score, and those with a Phred score below 20 are discarded. Simultaneously, the UMI of each sequence is trimmed and added at the end of the sequence name using the Fastp tool. 22 The sequences thus obtained are then aligned by the Bowtie2 program  23  with the corresponding pULD reference sequence.
The infection rate of these cells was similar to that observed in MT4R5 cells but with a 1 day delay in time. This observation is consistent with the fact that reverse transcription is slower in quiescent cells, as previously described. 13 nterestingly, the pULD half-lives determined for these cells were 11. 1 (Figure  4C  Therefore, DUSQ allowed us to demonstrate that the pULD half-life in resting T cells is up to 20-fold higher than in MT4R5s and up to 4-fold higher than in MDMs. Finally, to assess DUSQ suitability for pULD quantification in cells from patients infected with HIV-1, DNA was extracted from PBMCs and/or spleen mononuclear cells (SMCs) from 6 untreated patients chronically infected with HIV-1 (Figure  5 ). In these samples, pULDs were not detected using LM-PCR associated with qPCR (data not shown).
Limitations of the study As a proof of concept for the use of DUSQ to quantify pULDs in patients, we included blood and spleen samples from patients who were untreated and chronically infected with HIV-1. Virological data were not available for these historical samples. However, they were taken from patients splenectomized for HIV-related disease, suggesting a poor virological control and an active viral replication. In order to assess pre-integrative latency in patients, it could be important to include patients on long-term antiretroviral therapy. Resting CD4 + T cell (CD4 + CD69 À CD25 À HLA-DR -) were isolated by magnetic bead separation (EasySep Human Resting CD4 + T cell Isolation Kit, Stemcell), according to the manufacturer protocol. For MDM differentiation, PBMC were seeded at a concentration of 1.
Library pool preparation and sequencing The concentration of each library sample was determined using the KAPA Library Quantification Kit (Roche Sequencing Solutions). Each library sample was diluted to a concentration of 2nM and 5mL of each dilution was added to the library pool. The pool underwent a quality check using a Bioanalyzer High Sensitivity DNA Analysis (Agilent Technologies). Library pool was loaded on a MiSeq Reagent Kit v2 (300-cycles, Illumina) and sequenced in a double 150 cycle paired-end run. Collected reads were exported in Fastq format after processing with the Aozan software  41  and analyzed using the Galaxy Europe server. 38.
QUANTIFICATION AND STATISTICAL ANALYSIS All data are presented as mean ± SD except on Figure  5  where they are presented as mean ± SEM of n replicated experiment as indicated in the figure legends. Unpaired two-tail t test, linear regression and one phase decay non-linear regression were performed using Graphpad Prism 6 Software. Linear regression is represented as a dotted line with its 95% confidence interval; R 2 and p of the regression are indicated on the figure. For Unpaired two-tail t test p value is indicated on the figure. One phase decay non-linear regression are indicated as dotted line on the figure; half-life (t 1/2 ), goodness of the fit (r 2 ) and 95% confidence interval (IC95%) are indicated on the figure.
ACKNOWLEDGMENTS H. M. R. was the recipient of Universitede Paris' doctoral fellowship. This work was carried out with the support of the Institut de Medecine et d'Epidemiologie Appliquee (IMEA), Viiv Healthcare France, and the French Agence Nationale de recherche sur le SIDA et les hepatites (ANRS, France). The authors express their gratitude to GENOM'IC, CYBIO, and HISTIM core facilities of the Institut Cochin, INSERM U1016, for their expert and helpful collaboration. The authors would like to thank both healthy volunteers and patients for their participation in the study. The Galaxy server that was used for some calculations is in part funded by Collaborative Research Centre 992 Medical Epigenetics (DFG grant SFB 992/1 2012) and German Federal Ministry of Education and Research (BMBF grants 031 A538A/A538C RBC, 031L0101B/031L0101C de.
This approach also highlights a new class of pathogenic variant hot spot at positions that are evolutionarily conserved yet enriched in missense variants. We found 5,086 positions in 766 domain families were missense depleted (covering 365,300 residues in the human proteome) and 13,490 positions in 3,591 families were enriched in missense variants (340,829 residues in human) and provide a map of residue-level population constraint in human. Comparison to 61,214 three dimensional structures from the Protein Data Bank, covering 40,394 sequences from 3,661 Pfam domains, and over 10,000 variants labelled pathogenic in ClinVar provides validation of these sites in context with structure and pathogenicity. Application to the GPCR family identifies 17 sites important for ligand binding specificity as well as sites key to function in all members of the GPCR family.
We apply this method to classify positions in thousands of families from the Pfam  54  database, and assess the properties of each class with respect to features from experimentally determined protein three-dimensional structures  53, 55  including solvent accessibility, protein-protein and protein-ligand interfaces and pathogenic variants from ClinVar  56. We then combine our population constraint score with a conventional measure of evolutionary conservation to reveal the structural and functional properties of a new classification of residues.
We similarly define missense enriched (MES > 1; p < 0. 1) and missense neutral positions (p ≥ 0. 1), which represent different grades of polymorphic positions. We found 5,086 positions in 766 families met the threshold of missense depletion (covering 365,300 residues in the human proteome) and 13,490 positions in 3,591 families that were enriched in missense variants (340,829 residues in human). In the next few paragraphs, we describe the structural and functional features of these positions with reference to 61,214 three dimensional structures from the Protein Data Bank 55 , covering 40,394 sequences from 3,661 Pfam domains, and over 10,000 variants labelled pathogenic in ClinVar  56. We also compare their properties to comparable sets of evolutionarily conserved and unconserved residues, defined based on their Shenkin diversity (see Methods p.
This suggests the position is important for function and may indicate specificity determining sites. Examination of ligand interactions, protein-protein interfaces and pathogenic variants at UMD sites is consistent with this view (Figure  4  and Supplementary Table  2 ). Figure  4A  shows UMD positions are enriched in ligand binding compared to UMN or UME in all but core sites. UMD positions are also enriched for protein-protein interaction sites (PPIs) at surface and part-exposed sites compared to UMN or UME (Figure  4B ). UMD sites are also enriched in pathogenic variants in all RSA classes compared to UMN or UME sites (Figure  4C ). Examples of UMD sites contributing to known specificity include ligand binding residues in class A GPCRs (Pfam ID: PF00001; Figure  5 ) and nuclear receptor ligand binding domains (PF00104; Figure  6 ).
Application to individual protein families We have recently applied the general principles described above to the Ankyrin protein repeat family to identify the positions most critical to fold and functional interactions  67. Here we present detailed analyses of the class A GPCRs (PF00001), the nuclear receptor ligand binding domains (LBDs; PF00104) and the cytochrome P450s (PF00067). These were selected owing to their medical and biological significance, and the observation of at least one clearly explainable association between UMD positions and a known function. For GPCRs and LBDs this was the observation of several UMDs in their ligand binding pockets and for the P450s it was the site of missense depleted positions at a heteroprotein interface. Although these features formed part of the selection criteria, all the other functional associations discussed were only discovered following detailed research and so these accounts should be representative of what can be discovered when there is some a priori knowledge that missense depletion in the family has functional associations.
A clear example of this contrast is the allosteric sodium binding site, which includes CMDs 3x39 and 7x45, reflecting the constancy of sodium binding in class A GPCRs versus the receptor specificity required in the ligand binding pocket. In addition to these sites, a UMD position is sometimes observed in complex with the allosteric sodium (e. g. , N131 in OPRD PDB ID: 4n6h), this position is also involved in ligand binding (Table  2 ), raising the possibility that the site may modulate receptor specific aspects of the activity of the allosteric sodium. Another example is the well-known "toggle switch" 6x48 (Figure  5F -G; W336) that plays a pivotal role in the universal activation dynamics shared by class A GPCRs. The toggle itself is CMD, but in its vicinity are other CMDs, such as 5x47 (F243), that interact with the toggle and are likely to contribute to the critical outward movement of the cytoplasmic part of TM6 (Figure  5F-G ).
The rest of the CMD positions in this cluster bridge the coactivator binding site with the hormone binding pocket and are therefore likely to be important in their coupling. Given the apparent clustering of the UMD and CMD sites in this family, we were curious if they possessed any distinctive properties with respect to sequence covariation. Although eight of the ten UMD sites were among the 133 strongly coupled sites identified by the EVcouplings 79 webserver (Supplementary Table  4 ), as well as all seven CMDs, the distribution of evolutionary coupling scores among CMDs, UMDs, and other residues did not show any statistically significant differences (Kruskal-Wallis χ 2 = 1. 28, df = 2, p = 0. 53). This suggests that the sequence covariation patterns in this dataset do not consistently differentiate between UMD, CMD.
E. Enrichment of BioLiP curated ligand interactions amongst positions in the nuclear receptor ligand binding domain Pfam (PF00104) in different regions of the conservation plane. Table 3. Summary of variant, conservation and structural analysis of the UMD and CMD positions in the Nuclear Receptor Ligand Binding Domain Pfam (PF00104). Positions are labelled with respect to the column index of the multiple sequence alignment and the corresponding residues in the STF1 receptor used as our reference example in Figure  6.
644 In this work, we have presented a unified approach to the analysis of population constraint 645 and evolutionary conservation in protein domain families, which highlights structurally and 646 functionally important residues, including interfaces and pathogenic sites. First, we 647 demonstrated that population missense variants at homologous sites in protein domains 648 were subject to similar constraints compared to substitutions throughout evolution. We 649 then calculated population constraint at each site within all human protein containing Pfam 650 domains using a new missense enrichment score (MES) and data from gnomAD. This revealed over 5,000 positions in 766 protein domains -covering over 365,000 residues of the human proteome -that were depleted in missense variants. Missense depleted positions were enriched in buried residues, protein-ligand and protein-protein interfaces, and pathogenic variants, and in these ways, they were analogous to evolutionarily conserved positions.
In the future, one area in general structure prediction where population variation could be useful is for proteins with limited homology that generate shallow alignments. For example, AlphaFold normally requires an alignment of around 30 sequences to build a good model and continues to benefit from additional sequences up until alignments of around 100 sequences  13. As population variation datasets grow, these data could supplement shallow alignments, especially as population data become available for more species as combining these data within our framework is possible. Indeed, combining population variants from other species within our framework is an exciting proposition since it would allow a systematic comparative analysis of population polymorphism at proteome scale. There are some examples where it has been empirically observed that population variation from different species can be correlated  89  , but a general survey would have implications for the range of homology inference and could answer questions like for a given gene or domain, what is the best model organism? Finally, the application of our framework to specific protein families will be useful in general biological research and drug-development applications, for example, to prioritise candidate sites from fragment screens or in protein families that have become structurally illuminated with the AlphaFold DB  90.
Mapping population variants to Pfam alignments (Pfam-gnomAD) Protein family alignments were downloaded from Pfam (v31)  91, 92  and parsed using Biopython (v1. 66, with patches #768 #769)  93. Alignments with at least one human sequence were filtered to exclude TrEMBL sequences and leave the higher confidence SwissProt sequences. This yielded a set of 6,042 alignments containing 392,703 sequences -including 44,530 human sequences -that we will refer to as PfamSP. Population variants from the gnomAD Version 2 dataset were mapped to the human sequences in PfamSP. For each sequence in an alignment, genomic mappings were retrieved from Ensembl  94, 95  via the Ensembl API and variants in these regions were loaded from a Σ𝑋 !"##$%#$ = 𝛽 ) 𝑛 '*!+% + 𝛽 ), 𝑛 '*!+% * 𝑉 &'$%("% This model treats the evolutionary divergence as a modifier of the rate of missense variation per human residue.
Acknowledgements We thank  Drs Chris Cole  and  Melanie Volk  who contributed to early ideas in previous versions of this work and  Dr Marek Gierlinski  for his advice on statistical matters. We also thank the  University of Dundee High Performance Computing Team  for supporting the computing infrastructure on which this research was carried out as well as  Dr Jim Procter  and the  Jalview development  team for discussion of alignment/structure data visualisation.
72  refined model derived from PDB ID: 7wc4 69. B. The side-extended pocket of 5-HT2A with bound monoolein. Residues within 5 Å of monoolein are shown as sticks. C. Prostaglandin E2 receptor subtype EP3 (EP3) bound to prostaglandin E2 (PGE2; green) in PDB ID: 6ak3 70. Tan coloured ribbon indicates portion of TMX outside the Pfam region. D. Enrichment of BioLiP curated ligand interactions amongst positions in Class A Rhodopsin GPCRs in different regions of the conservation plane. E. 5-HT2A ribbon illustration highlighting distribution of UMDs and CMDs across TM helices and relative to membrane (grey = not in Pfam). ECL1 and ECL2 are expanded to show UMDs and CMDs in these loops. F and G. Conformational changes in 5-HT2A upon receptor activation in the vicinity of the W336 "toggle" position.
Delineating biogeographical regions can provide important insights into the processes shaping large-scale species distribution patterns. Here we aimed to 1) identify global marine benthic biogeographical regions for ophiuroids extending from shallow waters to the deep sea and 2) quantify the importance of contemporary environmental conditions and geological features in shaping the delineated biogeographical regions. We delineated marine benthic biogeographical regions using a bipartite network analysis applied to a historical dataset of brittle stars. We then examined the faunal exchanges between the regions, and applied random forest models to evaluate the relative role of contemporary environmental conditions and tectonic configuration underlying the proposed biogeographic scheme. We propose ten main large biogeographical regions across the benthos.
Description of the data and sampling units Our database is a global compilation based on the distribution of 2201 species from the class Ophiuroidea, with historical records from the last 130 years  (Stöhr et al. 2023). The data consist mostly of voucher specimens stored in museums and examined by experts; see the Supporting information for more information. There are in total 95 559 records, of which 44% are from below 200 m depth extending to 8135 m depth (Supporting information). Of the deep-sea records, 15% were collected between 1976 and 2018 on cruises organised by the Tropical Deep Sea Benthos program, which also facilitated an international network of taxonomists to work on the specimens  (de Forges et al. 2021). We evaluated the completeness profiles of our database across several spatial resolutions to find an optimal resolution for the analysis.
We produced raster grids of a global extent ranging from 1 to 5° resolution ('raster' R package, www. rproject. org, Hijmans 2019) and calculated the completeness profiles at orders q = 0, 1 and 2  (Chao et al. 2020  for a detailed description) (Supporting information). Here, completeness profiles assess the fraction of detected diversity of a dataset at different orders q ≥ 0 based on the framework of Hill numbers extended to incidence data  (Hill 1973 ). Completeness at order q = 0 is an upper bound estimate of the proportion of detected species richness but, in reality, the proportion of detected species richness is inadequately estimated in most cases. Completeness at order q = 0 is disproportionately sensitive to infrequent species. Completeness at order q = 1 corresponds to the sample coverage, and treats all species equally, without disproportionately favoring either infrequent or frequent species.
Delineating biogeographical regions We applied a bipartite network analysis  (Vilhena and Antonelli 2015)  on the ophiuroid distribution data. This approach keeps track of species identity throughout the bioregionalisation process, making the method robust to differences in sampling intensities  (Edler et al. 2017 , Bloomfield 2018 , Leroy et al. 2019). The algorithm assigns each species to a specific bioregion, which facilitates describing and understanding the biological significance of clusters. We constructed an occurrence network containing both species and sites (grid cells) as 'nodes', with the R package (www. r-project. org) 'biogeonetworks' ver. 0. 1. 1 (www. github. com/biogeonetworks). Species are linked to the sites in which they occur, and species to species or site to site linkages are not permitted.
For each site, we assessed transition zones between regions by measuring the extent to which a node is connected to other biogeographical regions with the participation coefficient (PC)  (Bloomfield 2018). A high PC for a site indicates that it contains species from different regions, whereas a low PC indicates that the site contains only species from the region to which it belongs. In other words, transition zones based on high PC values are generated by overlapping fauna between regions. Also, sites that contain widely distributed species can obtain high PC values. The latter is most likely caused by sampling biases in our data set and will not be explored further. For each biogeographical region, we calculated its spatial extent (km 2 ), species richness, the number of characteristic species, the percentage of endemic species and the average PC value.
Here, each region was an independent binary response variable (sites assigned to the region were 1, sites assigned to other regions were 0). The predictors included a range of oceanographic variables from MARSPEC  (Sbrocco and Barber 2013)  and Bio-ORACLE  (Assis et al. 2018) ; total annual net primary productivity (NPP) and seasonal variation in NPP calculated from satellite data, which affect deep-sea species richness  (Woolley et al. 2016) ; and the tectonic plate configuration from  Bird (2003) , which affects the distribution of species  (Keith et al. 2013 , Leprieur et al. 2016). We also included current velocity (Bio-ORACLE) and distance to nearest shoreline (MARSPEC) as proxies for the physical environment affecting nutrient input, connectivity and dispersal. The environmental data were extracted only in areas where there were species records.
The low availability of curated deep-sea data in a global context means that ophiuroids provide a novel opportunity for us to develop global benthic biogeographical regions without a complete oversaturation of shallow water data. The completeness analyses suggest that in grid cells, which are not undersampled, the majority of frequent species have been recorded making the biogeographical regions containing these grid cells robust (Supporting information). However, most of the ocean lacks sampling, as shown by our completeness analyses with a large number of undersampled grid cells, despite using a state-of-the-art ophiuroid database (Supporting information). In the shallow benthos, seven taxonomic groups showed good agreement among them  (Kocsis et al. 2018 ) and displayed similar patterns to our study, suggesting that our findings are potentially generalisable.
Acknowledgements -The authors would like to express their gratitude towards the  Tropical Deep-Sea Benthos exploration program  and the associated cruises and taxonomists, which have been instrumental in gathering biodiversity data (https://expeditions. mnhn. fr/program/tropicaldeep-seabenthos). LV would also like to thank  Dr Marine Robuchon  and  Dr Eric Pante  for useful discussions, which contributed towards the manuscript. All authors thank the three anonymous reviewers whose comments improved the manuscript significantly.
Funding -The Census of  Marine Life  provided funds to TOH to complete the ophiuroid dataset. LV and JD were funded by the  LABEX BCDiv  ( ANR-10-LABX-03 ) grant, which also supported the latest cruises of the  Tropical Deep-Sea Benthos program :  KANACONO (10. 17600/16003900 ), KANADEEP1  ( 10. 17600/17003800 ) and  PAPUA NIUGINI  ( 10. 17600/18000841 )) that enriched the dataset. LV was also further funded by the  Norwegian Research Council  project no. 287934.