dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure3: The performance of iterative-PopPUNK at multiple levels. A) the comparison iterative-PopPUNK clusters with the genealogical tree (from 100 samples) at four levels: 1%, 20%, 80%, and 99% of the MACD. These green and orange faces represent matched clusters with tree nodes respectively. For these unmatched clusters, the tree leaves are annotated using different colors. B). 1). The average number of clusters while moving the cutoff lines in iterative-PopPUNK tree. The value is calculated by the total number of estimated clusters divided by total number of genomes at each level; 2). The accuracy of estimated clusters at each level. The accuracy is calculated by the total number of matched clusters (with tree nodes) divided by the total number of estimated clusters. The chosen example dataset has a recombination rate r=0.
Capillary zone electrophoresis (CZE) CZE experiments were performed on a PA 800 Plus Pharmaceutical Analysis System apparatus (SCIEX) equipped with a diode array UV/vis detector operating between 200 and 300 nm and located at the outlet side of the capillary. Data acquisition and processing were performed using the 32 Karat software supplied by SCIEX, and electropherograms at 214 nm were analyzed. A fused-silica capillary 75 μm internal diameter (Polymicro Technologies, Caudebec les Elboeuf, France), 30 cm total length (from the inlet to the outlet) and 20 cm effective length (from the inlet to the UV detection window) was employed. Capillary temperature was controlled by coolant at 25. 0 • C. All solutions were filtered before use with a 0. 45 μm pore size regenerated cellulose filter (Alltech, Templeuve, France).
Prediction of protein hydrophobicity from primary sequence analysis Online protscale software from the expasy platform was used. The software can be found via the following internet link as it was on January 01, 2022: https://web. expasy. org/protscale/ For prediction of hydrophobicity, a seven amino acids smoothing average window was used for analysis by using the algorithm "Kyte and Doolittle". Hydrophobic amino acid corresponding score can be found at the following internet link: https://web. expasy. org/protscale/pscale/H phob. Doolittle. html. The sequences for capsid protein of GA, Qβ and MS2 phages were all from the UniProtKB data base with the following reference numbers: GA: P07234 (CAPSD_BPGA); MS2: P03612 (CAPSD_BPMS2); Qβ: P03615 (CAPSD_BPQBE).
Acknowledgement We gratefully acknowledge  Pr R. Duval , faculty director of  Faculté de Pharmacie de Nancy (Université de Lorraine, Nancy, France ), for access to the capillary electrophoresis instrument and  Pr. I. Clarot  ( EA 3452 CITHEFOR, Université de Lorraine, Nancy, France ) for valuable discussions. We thank also  J. Challant  ( UMR 7564 LCPME ,  CNRS-Université de Lorraine, Nancy, France)  for assistance in phages production and purification as well as the colleagues of the laboratory for the English revision of the manuscript. Finally, we acknowledge the anonymous Reviewers as well as  Ian J. Molineux  (Editor) for their muchappreciated contributions in improving this paper.
A tnR threshold value of ≥0. 2 and the request for specific co-purification in at least two anti-Nephrin/Neph1 APs finally identified an ensemble of 29 proteins (out of 1750 proteins detected) that robustly co-purified with Nephrin and Neph1 and thus defined the bona fide interactome (or proteome) of these slit-spanning SD core constituents (Fig. 1D , Table  1 ). With exception of tyrosine kinase Fyn and proteins Dendrin, ROBO2, and MAGI1,2  [29] [30] [31] [32] [33]  , none of the identified interactors have yet been associated with Nephrin or Neph1 in public databases or literature. Eight interactors have previously been ascribed to podocyte function/biology (mostly through their gene knockout, underlined in Table  1 ). Detailed inspection of the interactome showed a number of intriguing features (Table  1 ): First, most of the interactome constituents are membrane proteins of different classes (majority with a single membrane-spanning domain); the others are soluble proteins with documented membrane-attachment(s) via lipid anchors or known binding to membrane proteins (through WW or PDZ-domain(s)).
1D , Table  1 ). Beyond these SD components, however, the Podocin interactome contained a number of additional proteins (Fig. 2D , Table  2 ), in line with the distinct subcellular localization of Podocin compared to the exclusive localization of Nephrin/ Neph1 to the slit between podocyte FPs (Fig. 2B ). Interestingly, the majority of these Podocin-specific interactors are either members of different (sub)-families of solute-carrier (SLC)-type transporters  44  or other transporting systems (ABC-A9, MFSD1) and scaffolding proteins (tetraspanin CD81). The individual transporters differ in their substrate(s) ranging from organic anions/cations to amino acids or vitamins and, as yet, none of them has been related to Podocin in public databases and literature. Together, these interactome data strongly suggest that Podocin serves as a more universal cytoplasmic anchor targeting not only the SD and its associated protein network, but also integral membrane proteins with diverse functions.
The selection of SD network constituents to be analyzed was based on interactors being not reported before, showing documented expression (of orthologues) in flies, and displaying interesting links in databases and literature. In wildtype flies, the uptake of FITC and RFP was highly effective, while it was largely decreased or even abolished in flies where expression of the Drosophila orthologue of Neph1, Kirre, or of Nephrin, SNS, had been knocked down by RNAi (Fig. 4B, D ). Interestingly, knocking down the orthologues of the non-slit-spanning constituents MERTK, ANPRC, MAGI1, ITM2B, or its known interaction partner protein A4  23  also resulted in a significant decrease of the FITC/RFP-uptake, albeit to distinct degrees (Fig. 4B, D ). While, the removal of MERTK and ANPRC reduced the FITC-uptake by about 35% and 20%, respectively, the impairment induced by ITM2B and A4 knock-down was more than 55% of the effect obtained for Kirre with FITC or RFP (Fig.
Alteration or removal of this signaling will result in defective filtration as observed with the MERTK knock-down (Fig. 4B ) and thus eventually lead to proteinuria similarly as observed for reduction/ablation of the SD core components (Fig. 3 ). Defective function(s) as further hints for the relevance of the identified interactome constituents come from first genetic analyses (Supplementary A Schematic representation of the hypothetical molecular appearance of the rodent SD before (upper panel; Grahammer et al. 7  ) and after interactome analysis (lower panel). Protein constituents are drawn about to scale (using data from literature and databases, as well as from AlphaFold  59  ), and illustration was restricted to structurally distinct constituents (legend on the right) to highlight the complexity of the SD and the associated protein network.
RNA Isolation and qPCR RNA was isolated using the acidic GTC-Phenol-Chloroform method as previously described  55. RNA purity was assessed with the help of the 260/280 nm ratio and only ratios between 1. 8 and 2. 0 were accepted. After DNase digest, the RNA was reverse transcribed using the iScript Reverse Transcription Supermix (BioRad, California, USA) following instructions of the manufacturer. qPCR was performed in a CFX Connect Real-time PCR Detection System (BioRad, California, USA) using SYBR Green as a fluorophore. Each sample was measured in triplicate and normalized to a reference housekeeping gene using the ΔΔC T method in the CFX Manager Software 3. 1 (BioRad, California, USA).
net/, version 3. 0. 6906). Peak lists were searched against the UniProtKB/ Swiss-Prot database (rat, mouse, and human entries; release 2019_11, 2022_01) using Mascot 2. 6. (Matrix Science, UK). Initially, preliminary searches with high peptide mass tolerance (±50 ppm) were performed and after linear shift mass recalibration using in-house developed software, mass tolerance was reduced to ±5 ppm for final searches. Fragment mass tolerance was set to ±0. 8 Da (ion trap MS/MS spectra). One missed trypsin cleavage and common variable modifications including S/T/Y phosphorylation were accepted for peptide identification. The peptide significance threshold was set to p < 0. 05. Proteins identified by only one specific MS/MS spectrum or representing exogenous contaminations such as keratins or immunoglobulins were eliminated.
Protein quantification. Label-free quantification of proteins was carried out using a recently developed procedure  25, 57. Briefly, peptide signal intensities (peak volumes, PVs) from FT full scans were extracted and offline mass calibrated using MaxQuant v1. 6. 3 (http://www. maxquant. org). Then, peptide PV elution times were pairwise aligned using LOESS regression (reference times dynamically calculated from the median peptide elution times over all aligned datasets). PVs were then assigned to peptides based on congruent m/z and elution times (±1 min/2-3 ppm) as obtained directly or indirectly from MS/MS-based identification using in-house developed software as described  16. In a second step, protein-specific PV data matrices (peptides versus MS runs) were subjected to analysis of their internal consistency (between runs and with regard to their relative peptide ionization efficiencies).
From these relations between PV values, expected PV values were calculated to derive a measure of accuracy for each PV value that was subsequently used as a weighting factor. In parallel, time-and run-dependent detectability thresholds were estimated for each measured or missed PV value in the matrix. Global protein references were then obtained from the aggregation of qualified PV data in each protein matrix, termed protein reference ridges (basically vectors reflecting the maximum protein coverage of MS/MS-identified and quantified peptides with their relative ionization efficiencies). Protein quantification was finally achieved by weighted fitting of its measured peptide PVs to its reference ridge. In case no (consistent) peptide PVs could be identified, an apparent protein detection limit was determined using the detectability thresholds of the three best ionizing peptides fitted to the protein reference ridge.
Molecular abundances were calculated as abundance norm spec values  16  from the fitted protein ridges. The molecular abundances determined by this procedure are basically comparable to MaxQuant LFQ values (where all data is fitted without weighting) but more accurate, in particular for proteins quantified from sparse PV data. To determine specificity in affinity purifications (APs), ratios of molecular abundances of proteins in WT versus control APs were calculated, logarithmized (basis 10) and normalized to the abundance ratio of the respective target (=maximum 100%) and to the estimated distribution of the background proteins (=minimum 0%) as shown in Supplementary Fig. 5. The resulting target-normalized ratios and molecular abundances were collectively inspected using the BELKI software suite (Jordan, J, and Haupt, A (2021).
Belki: Version 2. 0 (v2. 0), Zenodo. https://doi. org/10. 5281/zenodo. 4670785). For anti-Nephrin/ Neph1 APs (Fig. 1 ) three target-unrelated ABs were used for control APs (rabbit IgG (Millipore, 12-370, lot 2295402); anti-PODXL (Abcam, ab205350, lot GR268901-3); anti-SLC34A3 (Biorbyt, orb313245, lot BS6202)). Visualization of tnR-values by t-distributed stochastic neighbor embedding (tSNE, Fig. 2C ) suggested a minimum tnR of 0. 20 as indicator for positive interaction. For anti-Podocin APs (Fig. 2 ), APs from membrane fractions prepared from Podocin KO animals with additional depletion of the small remainders of the target protein via the a1 anti-Podocin AB were used as controls and a minimum tnR of 0. 25 was used as indicator for positive interaction. FITC-uptake assay in Drosophila nephrocytes D.
Collected embryos were aged for 48 hours at 29 °C. Nephrocytes of L3 larva were dissected, transferred onto a cover slip, and subjected to a 30 s pulse of FITC labeled albumin. For each experiment 10 dissected larvae were used. Using a confocal microscope (A1 CLEM Eclipse TI. /Nikon) equipped with a ×60 oil-immersion objective, fluorescent and DIC images of each nephrocyte package were taken. Blinded to the genotype 4 randomly chosen cells of nephrocyte package were used to quantify the uptake efficiency using the ImageJ Software (NIH, USA). Each condition was repeated twice. RFP-accumulation assay in Drosophila nephrocysts D. melanogaster stocks were cultured on standard cornmeal molasses agar food and maintained at 25 °C. The Drosophila homologs of mammalian genes were identified using the DIOPT DRSC Integrative Ortholog Prediction Tool (Harvard Medical School).
Acknowledgements We thank  G. Walz  for fruitful discussions and critical reading of the manuscript,  J. Jordan  for help with the evaluation of meAP-MS data, and  Charlotte Meyer ,  Natalie Wernet , and  Valerie Oberüber  for excellent technical support. This work was supported by grants from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) SFB  1140  ( A01 ,  A03  (to B. F. , T. B. H. , F. G. ),  SFB 1453  (project-ID 431984000 (to B. F. and  A. Kö. ),  SFB 1192  ( B02 ,  B08 ,  B09  (to F. G. , T. B. H. ),  SFB 1381  (project- ID 403222702  (to B. F),  FA 332/16-1  (to B. F. , F. G. ),  GR 3933/1-1  (to F. G. ),  HU 1016/8-2 , 11-1, 12-1  (to T. B. H. ) and FOR 2143 (to A. Ku. ).
QTL identification in greenhouse conditions Greenhouse bioassays are mostly carried out with no or a few experimental replicates. These conditions enabled the identification of Rpv1  (Merdinoglu et al. , 2003) , Rpv3. 3  (Vezzulli et al. , 2019) , Rpv28  (Bhattarai et al. , 2021) , Ren1  (Hoffmann et al. , 2008) , Ren1. 2  (Riaz et al. , 2020) , and Run1  (Pauquet et al. , 2001)  (Table  5 ). Phenotyping in the greenhouse also confirmed QTL identified under vineyard conditions. This occurred for Rpv3. 1  (Welter et al. , 2007) , Ren3/Ren9 (van Heerden et al. , 2014), Ren4  (Ramming et al. , 2011) , and Ren6 and Ren7  (Pap et al. , 2016). Only Ren11 was not confirmed with greenhousecollected data  (Karn et al. , 2021). At the same time, greenhouse assays showed a lower number of minor QTLs, confirming those from the vineyard data, such as Rpv11  (Welter et al.
Phenotyping under in vitro conditions The evaluation of grapevine resistance in the laboratory or in vitro conditions requires limited resources and space for plants and pathogens, and allows for an increase of genotypes to be evaluated and of bioassay replicates. One or multiple experiments per year can be performed  (Schwander et al. , 2012; Divilov et al. , 2017; Possamai et al. , 2021)  and can integrate phenotypic dataset from the greenhouse or vineyard  (Vezzulli et al. , 2019; Riaz et al. , 2020; Bhattarai et al. , 2021). For these reasons, the in vitro phenotyping strategy is considered as an effective and practical tool for screening large populations and for identifying resistant accessions among grapevine collections  (Brown et al. , 1999; Deglène-Benbrahim et al. , 2010; Miclot et al.
The MIAPPE (www. miappe. org) checklist consists of attributes that can be classified within the following eight categories: (1) general metadata (dataset identifier, objectives of the experiment and authors); (2) location and timing of the experiment; (3) biosource (plant material identification, pathogen); (4) environmental descriptors (for vineyard, greenhouse and environment chamber) such as aerial conditions, light, fertilizing and watering; (5) treatments (environmental properties, artificial treatments); (6) experimental design (experimental units could be a single plant, plot and groups of plants); (7) samples collection, processing and management protocols; and (8) observed variables described by three basic attributes: trait name, measurement method, and notation scale. Furthermore, authors relied on annotations that agree with publicly available ontologies using proper dataset formats  (ISA-Tab; Rocca-Serra et al.
𝐴𝐴 ) is the data set of the SB biases calculated for CESM (respectively CMIP6) from assumption A. By assuming that the CESM SB A biases are representative of the SB A biases from any single model multi-run ensemble in the CMIP6 ensemble, R A allows to quantify the relative weights of the biases from inter-run or inter-model biases, based on assumption A. However, it is not possible to assume such a representativity of the CESM ensemble. Hence, more rigorously, R A quantifies the relative weights of the biases from the CESM inter-run biases over the CMIP6 inter-model biases from assumption A. The R A values are plotted in function of the time period in Figure  5  for the four ssumptions and the four seasons. The 90% confidence interval of each ratio is also computed via a bootstrap of 75% of the and    6  values, repeated 100 times.
Data Availability Statement All CMIP6 GCM simulations used in this article  (Boucher et al. , 2018; Seferian, 2018; Shiogama et al. , 2019; Swart et al. , 2019; Tang et al. , 2019; Voldoire, 2018 Voldoire, , 2019;; Volodin et al. , 2019; Wu et al. , 2018; Yukimoto et al. , 2019)  and listed in Table 1 can be downloaded through the Earth System Grid Federation portals. Instructions to access the data are available at: https://pcmdi. llnl. gov/CMIP6/Guide/dataUsers. html. The CESM Large Ensemble Simulations can be downloaded from the CESM Large Ensemble Community Project website: https:// www. cesm. ucar. edu/projects/community-projects/LENS/.
Predictors Neural Networks can deal with large datasets at low computational time. During their self optimisation, they are able to select the important variables and regions for the prediction. In this way, a large number of raw predictors can be given to the learning algorithm, with minimum prior selection (which could introduce some bias) or statistical prework (which might delete some of the information). Several ESD studies  (Lemus-Canovas and Brands 2020; Erlandsen et al. 2020)  show that the right combination of predictors depends strongly on the target region and the season. The RCM domains are often composed of very different regions in terms of orography, land types, distance to the sea, etc. , j} ∈ [[1 , I]] × [[1 , J]] k, l Spatial indexes over target grid {k, l} ∈ [[1 , K]] × [[1 , L]] t Temporal index, daily t ∈ ℕ x 2-D variables index List of 2D variables : V2D z 1-D variables index List of 1D variables : V1D X Set of 2D input for the emulator X t,i,j,x ∈ {ℕ × [[1 , I]] × [[1 , J]] × V2D} Z Set of 1D input for the emulator Z t,z {ℕ × V1D} Y Target: daily near-surface temperature Y t,k,l ∈ {ℕ × [[1 , K]] × [[1 , L]]} F Downscaling function of the RCM F Emulator: estimation of F For these reasons, we decided to give all potentially needed predictors to the emulator and leave the algorithm to determine the right combination to be used to predict each RCM grid point.
We emphasize that the bigger the dimension of the inputs and outputs, the larger the number of the parameters to be estimated and so the bigger the training set must be. Therefore, the quality of the training set is crucial: missing or wrong values will generate some additional fluctuation and errors in the training process. Moreover, we also need to cover a sufficiently large variety of scenarios in the input variables to ensure that our training set covers a wide range of possible inputs. For all these reasons, climate simulation datasets are ideal to train deep learning networks.
ReLu ∶ R(z) = max(0, z) 1 3  The EUCP project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 776613. The simulations used in this study were carried out within the framework of the CMIP5 initiative, of the EURO-CORDEX initiative and of the COPERNICUS C3S_34b_Lot2 (PRINCIPLES) project, and are available on the ESGF nodes.
, 2006) , and (4) the introduction of a new parameter k ETC scaling NPQ activity under low light intensities, to account for the recently discovered high light-dependency of the rate constant of the epoxidase enzyme  (Blommaert et al. , 2021). The model has been implemented using MODELBASE software in  PYTHON (van Aalst et al. , 2021)  and the source code to solve the system numerically is available in GitRepository (https://gitlab. com/matuszynska/npq-diatoms2020; doi: 10. 5281/zenodo. 5865038). See Methods S1 for additional information.
, 2016). Altogether, the presence of short-and long-term regulatory mechanisms plugged to a parsimonious NPQ/PMF regulatory circuit may contribute to the required functional diversity/flexibility of light-harvesting regulation for diatoms to thrive in different aquatic environments, where light acclimation is often a major determinant for growth and survival  (Lin et al. , 2016). Table  S1  Accession number of proteins used in this study. Table  S2  List of primers used in this study. Table  S3  Parameters used in the model simulations. Table  S4  Nonphotochemical quenching features in response to high light (HL) and growth rates of Phaeodactylum tricornutum cells grown under different light and nutrient conditions.
Acknowledgements The authors would like to thank  Dr Leonardo Magneschi  ( Ingenza Limited, UK ), and  Prof. Oliver Ebenh€ oh  ( University of D€ usseldorf, Germany ) for discussions and help in the first steps of this project. The authors would like to thank  Dr Marcel Kunz  ( CEA Grenoble, France ) for technical help with the HPLC facilities. This project received funding from the  LabEx GRAL  ( ANR-10-LABX-49-01 ) and was financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche ;  CBH-EUR-GS)  ( ANR-17-EURE-0003 ). GF, MS and CG acknowledge the support by the  European Research Council (ERC) Chloro-mito  (grant no. 833184 ). EG and GF received funding from the  French Research Funding agency (ANR)  grant ' Momix ' (Projet- ANR-17-CE05-0029 ). GA and GF received funds from the  HFSP  'Photosynthesis light utilization dynamics and ion fluxes: making the link'.
XZ and GF received funds form a CEA grant on carbon circular economy. GA received funds from the  CNRS 'Momentum' program. VG received funds from The  Leverhulme Trust  ( RPG-2018-199 ). AVR received funds from The  Leverhulme Trust  ( RPG-2018-199 ), the  Biotechnology and Biological Sciences Research Council  ( BB/R015694/1 ) and  The Royal Society   Wolfson Research Merit Award  ( WM140084 ). AM received funding from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2048/1project ID 390686111 and  DFG Research Grant   MA 8103/1-1. BB acknowledges the support by the  ERC   PhotoPHYTOMIX  project (grant no. 715579 ). HH was supported by the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  (no. XDPB18 ).
RNA G-quadruplexes (RG4s) are non-canonical, disease-associated post-transcriptional regulators of gene expression whose functions are driven by RNA-binding proteins (RBPs). Being able to explore transcriptome-wide RG4 formation and interaction with RBPs is thus paramount to understanding how they are regulated and exploiting them as potential therapeutic targets. Towards this goal, we present QUADRatlas (https://rg4db. cibio. unitn. it), a database of experimentally-derived and computationally predicted RG4s in the human transcriptome, enriched with biological function and disease associations. As RBPs are key to their function, we mined known interactions of RG4s with such proteins, complemented with an extensive RBP binding sites dataset. Users can thus intersect RG4s with their potential regulators and effectors, enabling the formulation of novel hypotheses on RG4 regulation, function and pathogenicity.