dataesr/scientific-paragraphs-categorization huggingface dataset:
tabaci s. s Tobacco whitefly Danaus Monarch plexippus butterfly b OGCs Orthologous gene clusters A comparison of 23 arthropod taxa including the six new B. tabaci s. l. new genomes. Analysis was performed via Orthofinder (v2. 4. 0) [51], by providing canonical protein-coding sequences as input. The Orthofinder pipeline implemented both MSA and phylogenetic gene tree reconstruction with default settings. All B. tabaci s. l. gene sets were generated via the Ensembl gene annotation pipeline. Newly generated B. tabaci s. l. were first released via Ensembl Metazoa (release e103) (https:// metaz oa. ensem bl. org). For previously published B. argentifolii and B. tabaci s. s. re-annotated datasets see Additional File 5. The protein-coding gene (PCG) set of T. vaporariorum was obtained from WhiteflyDB (http:// www.
Acoustic data analysis The bat calls present in the raw acoustic data, which totalled 481 h, were identified using custom built code in Python version 2. 7  (Rossum et al. 1995). The code used to do this can be found at https:// github. com/ Iqbal Bhalla/ BatCl assifi er. git. There are limited acoustic libraries for Indian bats  (Wordley et al. 2014) , none extending as far as Assam. Without such a library to use as reference, species level identification of recorded calls was not possible, leading us to classify calls into acoustic sonotypes  (López-Baucells et al. 2019; Roemer et al. 2021). We determined through manual inspection of the recordings that we had recorded three classes of sonotypes: (i) Constant Frequency (CF), (ii) Frequency modulated-Quasi Constant frequency (FM-QCF), and (iii) Quasi-Constant frequency (QCF).
2014 ); (ii) minimum and maximum frequencies-the lowest and highest frequencies that contained 5% of the energy of FMAXE; (iii) bandwidth-difference in frequency between the minimum and maximum frequencies; (iv) call length-the time interval between the point that the call first crosses 5% FMAXE, and when it last crosses FMAXE; and (v) average amplitude-the average amplitude of the call. All recordings were divided into five-second intervals. One bat pass was defined as an interval containing more than two calls of the same sonotype  (Millon et al. 2015). The classified calls were then processed in R version 3. 63 (R Core Team 2020) to reclassify based on FMAXE and bandwidth those few calls that had been misclassified. The resulting dataset was then filtered to remove false positives and bats that were recorded prior to the recorders being put in place.
Acknowledgements We are grateful to  Dr. Smarjit Ojah ,  Monish Thappa ,  Mr. Sanjib Goswami , and  Snigdha Das  for invaluable support throughout the fieldwork. We also thank the  Divisional Forest Officer, Nagaon District  for supporting and encouraging the work and the farmers who welcomed  Bhalla  into their homes. Bhalla  thanks his funders, the  Rhodes Trust  for supporting and funding this project, as well as  Jesus College, University of Oxford , and the  School of Geography and the Environment, University of Oxford , for providing financial support. Razgour was funded through a  Natural Environment Research Council   Independent Research Fellowship  ( NE/ M018660/3 ).
In this study, we analyze the role of pre-strom proxies in modulating RC and explain stormflow volume variability. To do so, we compare different basin pre-storm indices (e. g. , API, pre-storm river discharge, surface and root-zone soil moisture, as well as total water storage anomalies, TWSA, coming from hydrological models, reanalysis data and land surface models ingesting satellite soil moisture observations) to explain observed RC variability across historical European floods. We consider the impact of climate, season, land cover, topography, and the hydrological signatures of the basins and use correlation and regression analyses and datasets of precipitation and river discharge observations  from 284 basins across Europe, from which we extracted more than 60000 flood events. This work goes beyond the analysis of  Crow et al.
Study area and basin characteristics The examined 284 basins result from the stringent screening process of more than 3900 stations across Europe (see below) and range between 507 and 19920 km 2 in size (see  Figure SI 2). This is a range of areas similar to the one used by other studies  (Merz and Blöschl, 2009)  and guarantees relatively small uncertainty due to the spatial interpolation of rainfall (for smaller basins) and the small spatial variability of RC (for larger basins). The basin selection was carried out with the aim of maximizing the geographical coverage and the long-term availability of daily streamflow records (i. e. , from 1980 to 2015 at least) collected from the Global Runoff Data Center (GRDC, https://www. bafg. de/GRDC/EN/01_GRDC/grdc_node. html). The selected basins passed a careful quality control aimed at excluding stations with unreliable time series due to human regulations (e.
Precipitation, soil moisture, evaporation products and ancillary datasets Precipitation, temperature, and snow water equivalent (SWE) data used in this study were obtained from the ERA5 reanalysis dataset  (Hersbach et al. , 2020). ERA5 is particularly suitable for this study due to its homogeneity, demonstrably good performance over Europe  (Bandhauer et al. , 2021) , and permissive usage license  (Anon, 2020). We acquired ERA5 data from 1979-2019 at a 0. 25 • (about 30 km) resolution for an hourly time step and summed them to obtain daily estimates. Soil moisture data were obtained from the following reanalysis, hydrological models, and remote sensing observations: 1. ERA5 reanalysis (1979-2019, 0. 25 • ) over three soil layers: 0-7 cm, 7-28 cm and 28-100 cm. These data were acquired at an hourly timestep and averaged to obtain daily data.
In an evaluation study using 25 soil moisture networks including those in Europe, ERA5 was shown to be superior to other reanalysis datasets  (Li et al. , 2020). 2. Sacramento hydrological Model (SAC) surface and root zone soil moisture obtained via the calibration of the SAC model against observed streamflow time series. We used ERA5 precipitation and temperature for calculating potential evaporation via the Haymon formulation  (Lu et al. , 2005). SAC model has been used extensively for operational stream flow forecasting within medium-sized (about 1000 km 2 ) river basins  (Burnash, 1995)   (Camici et al. , 2018; De Santis et al. , 2021). Note that, being a conceptual model, SAC does not assign specific surface and root zone specific depths to its soil moisture states. 3. The Global Land Evaporation Amsterdam Model (GLEAM,  Martens et al.
Despite being based on a combination of model and remote sensing information, GLEAM is based on satellite observations and is therefore assumed here to be representative of satellite soil moisture datasets such as the SMAP Level 2 and 3 products -with quasi-daily temporal resolution that only cover the period after 2015 (note that CCI soil moisture is available since 1979, but it is not usable here due to its noticeable number of missing values prior to 2001). Other ancillary data were collected to describe the relatively static basin attributes (Fig. 1 ): 1. Land cover and soil texture as indexed by the curve number (CN ). CN was estimated by using the dataset of  Hong et al. (2007)  extracted from the MODIS land cover classification and the hydrological soil group map are used to estimate the CN by indexing in the standard lookup tables of the Natural Resources Conservation Service  Ponce and Hawkins, 1996.
𝑛 with 𝑛 = [-10, +10] excluding zero. 𝑘 = 0. 000546 was the one providing the best agreement (i. e. , Pearson correlation coefficient) between event runoff coefficients and antecedent condition proxies. The tests with different 𝑘 values showed a low sensitivity to the magnitude of 𝑘 itself. The source code of the function to separate event water from runoff is freely distributed via https://hydrographpy. readthedocs. io/en/latest/index. html. 2. To distinguish between two close flood events we calculated for each basin the concentration time Tc as: 𝑇 𝑐 = 𝐿∕0. 6 with 𝐿 = 1. 19𝐴𝑏 0. 33 representing the lag time for the basin and Ab the basin area as proposed by  Melone et al. (2002). The coefficient (1/0. 6) was derived from the formulation of the concentration time as proposed by  Simas (1996) , that can be assumed valid for natural basin conditions given an approximately uniform spatial distribution of runoff.
Basin classification The selected basins cover many parts of Europe and are classified by their different land cover, climate, soil type, and hydrological signatures. As a result, systematically examining the influence of each individual factor on the coupling strength between RC and the different pre-storm condition proxies is challenging. To overcome this issue, we performed a basin classification using the k-means clustering algorithm  (Lloyd, 1982). This algorithm is among the oldest and most classical unsupervised learning algorithms used to partition datasets. It aims at portioning observations into K clusters defined by centroids, where K is chosen a priori. The algorithm has been applied in many fields due to its intuitive basis, high efficiency, and easy implementation (Chong, 2021).
The partitioning is based on iteratively finding clusters (i. e. , groups of basins) that minimize the sum of squared distance between the points of the cluster (within-cluster sum of square WCSS) and its centroid, and simultaneously maximize the distance between cluster centroids. As unsupervised learning, the algorithm detects patterns in the dataset using predefined distances such as Euclidian and exploits them. In order to find an optimal number of clusters, we used the concept of Silhouette score  (Rousseeuw, 1987). In the method of  (Rousseeuw, 1987) , each cluster is represented by a so-called silhouette (see  Figure SI 8  in the supplement) which shows tightness and separation of each cluster for different number of clusters. The silhouette score is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation) (see supplementary material Section Supplementary Information 1.
RFR can handle complex interactions among variables, and is not affected by multi-collinearity  (Breiman, 2001). It can assess the effects of all explanatory variables simultaneously, and automatically ranks the importance of these variables in descending order  (Rodriguez-Galiano et al. , 2012). However, the main limitation of RFR is its inability to extrapolate values beyond the range of the training dataset, which makes it inappropriate for capturing hydrological extremes. For a detailed description of the mathematical formulation for the RFR model, the reader is referred to  Breiman (2001) ,  Liaw and Wiener (2002). In this study, the open source scikit-learn RandomForestRegressor function was used https://scikitlearn. org/stable/modules/generated/ sklearn. ensemble. RandomForestRegressor.
To determine the above mentioned parameters we ran a grid search function using the GridSearchCV function of the same scikit-learn package (https://scikit-learn. org/stable/modules/ generated/sklearn. model_selection. GridSearchCV. html) by letting the 𝑛 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑜𝑟𝑠 varying between 1000 and 5000 with steps of 100 and the number of features to vary between 1 and 10 with steps of 1. The logic to start from 1000 for the 𝑛 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑜𝑟𝑠 is because  (Rodriguez-Galiano et al. , 2014)  demonstrated that the error was minimum and stable when considering at least 1000 trees, while, for the number of features the values were selected based on the recommendation of  Breiman (2001)  who suggested using 𝑚 -where 𝑚 equals the number of predictors (six in this case) divided by 3. The optimal number of these two parameters was then found based on the minimum mean squared error obtained in the validation sets of each basin.
Although we used state-of-the-art calibration techniques, such calibration entails some levels of subjectivity, so that no general conclusions should be drawn for this particular result. The correlations in Fig. 5  are in any case generally low. This is not surprising since we examined a wide spectrum of basins characterized by different topography, climate, land cover and hydrological behaviors (see Section 2. 1 and Fig. 1 ) and because we did not separate between seasons (and therefore main flood generation mechanisms). On top of that, uncertainties in this analysis arise from the quality of precipitation and river discharge datasets, as well as the use of daily data -that can limit the identification of the exact timing of the flood peaks and thus the correct identification of precipitation and pre-storm proxies.
Limitations of the study Our results focus on European basins only, and are limited by the size of the examined basins and by the resolution of the used datasets. We call for further large-scale experiments in other parts of the world and for further investigation at finer scales, for example with better, more explicit and more controlled consideration of local factors, such as basin features, hydrological regimes, and anthropogenic influences. Indeed, the large-scale relationships found in this multibasin study may not hold for individual basins, as various underlying physical processes can impact basin hydrological behavior, such as specific geological and climatological conditions, anthropogenic influences, complex hydrological regime, and more local basin processes. In this respect, our basin classification could be sub-optimal especially across northern latitudes as also highlighted by  Kuentz et al.
Finally, the basin size considered in this study and the daily granularity of the analysis can hide the small-scale temporal variations of hydrological conditions. In this respect, our analysis could be strengthened by focusing on a selected sample of basins with higher temporal-and spatial-resolution data. Nevertheless, the fact that we found a clear statistical large-scale signal of dominant correlations between different pre-storm proxies and RCs for different datasets from different data sources and for a variety of basins with different local  features, suggests that, at the examined scales, our conclusions are generally relevant for Europe and its main climate regions.
Summary and conclusions Understanding basin response to precipitation is crucial for flood forecasting, hydrological data assimilation, hydrological model calibration, and for better predicting the impact of climate change on future floods. In this study, we carried out a large-scale analysis considering 284 basins located in different regions of Europe. We extracted more than 60000 flood events that occurred in the period 1980-2015, and studied the coupling strength between RC and different proxies of pre-storm conditions (namely, antecedent precipitation index, surface and root zone soil moisture derived from hydrological, land surface model ingesting satellite surface soil moisture observations and reanalysis, pre-storm river discharge, snow water equivalent, and total water storage anomalies).
Acknowledgments CM, SM, LB and SC were supported by  4DMED-Hydrology European Space Agency  project  ESA 4D-MED  ( 4000136272/21/I-EF ) and  DTE-Hydrology Evolution  ( 4000136272/21/I-EF -CCN N. 1 ). FM was supported by the  CARIPARO Foundation  through the  Excellence Grant  2021 to the '' Resilience '' Project. USDA ARS  is an equal opportunity employer.
𝑈 𝑍𝑇 𝑊 𝐶, 𝑅𝑍 𝑆𝐴𝐶 = 𝐿𝑍𝐹 𝑃 𝐶 + 𝐿𝑍𝑇 𝑊 𝐶 + 𝐿𝑍𝐹 𝑆𝐶). Details related to the calibration and application of the SAC model to the basins are reported in the supplementary material. We found comparable river discharge simulation performance to other model studies in Europe based on similar forcing datasets basin-scale surface and root zone soil moisture (𝑆𝑆𝑀 𝑆𝐴𝐶 = 𝑈 𝑍𝐹 𝑊 𝐶 +. Soil moisture accounting in the model is based on the estima- tion of different soil water states: upper-zone free water con- tent (𝑈 𝑍𝐹 𝑊 𝐶), upper-zone tension water content (𝑈 𝑍𝑇 𝑊 𝐶), lower-zone tension water content (𝐿𝑍𝑇 𝑊 𝐶), lower-zone free primary water content (𝐿𝑍𝐹 𝑃 𝐶), and lower-zone free supple- mental water content (𝐿𝑍𝐹 𝑆𝐶) which we combined to obtain.
Whole exome sequencing (WES) data of IAV-infected patients identifies a number of genes with putative loss-of-function (pLOF) variants, including structural scaffold protein AHNAK, that are associated with severe influenza disease and significantly change in our cellular proteomic data. In addition, we perform functional genomic screening of host targets identified in our proteomic dataset and discover 54 human genes that regulate IAV infection, two of which are also important for SARS-CoV-2 replication (AHNAK and COPB1). Lastly, we test 37 host proteintargeting compounds from our proteomic data and from previously published SARS-CoV-2 phosphorylation data  19  against pH1N1, H3N2 and H5N1 IAV, and find 16 compounds that suppress replication of multiple IAV strains, 5 of which also show antiviral activity against SARS-CoV-2.
For PPIs that are shared between multiple IAV proteins or cell types, the maximum MiST score from either IAV protein or cell type is reported in the network for each strain. IAVhuman PPIs are depicted (dark grey lines), and human-human PPIs are identified (light grey lines) as curated in the CORUM 130 database. Human protein complexes (yellow halo) are labeled as described in CORUM 130 , and biological processes (pink halo) are labeled as described by GO terms. with H5N1 NEP in NHBE cells (Supplementary Fig. 4 , Supplementary Data 1), and reciprocal pulldown of endogenous AHNAK in H5N1infected NHBE cells co-purified NEP with an enrichment of log2 fold change 2 compared to an IgG1 control pulldown in H5N1-infected NHBE cells (Supplementary Fig. 5C, D , Supplementary Data 1). We also demonstrate that AHNAK and NEP both localize to the cytoplasm in IAV-infected NHBE cells (Supplementary Fig.
Patient exome sequencing identifies gene variants encoding proteins that are regulated in AB and PH during IAV infection To investigate the clinical implications of our proteomic datasets, we explored the correlation between proteins we found to be regulated in cell models of IAV infection and patient responses to IAV. To this end, we obtained de-identified human blood samples following informed consent from individuals at five eMERGE study sites. We used principal components analysis (PCA) to characterize the genetic ancestry of the study population and identified 495 individuals of genetically-identified European Ancestry, of whom 161 were hospitalized with severe influenza infection and 334 served as outpatient controls (Fig. 4A ). We used International Classification of Diseases, Ninth Revision (ICD-9) codes as the phenotypic trait for the analysis.
Whole exome sequencing of the 495 participants achieved 97% coverage of targeted bases at a depth of ≥ 20x. In total across all participants, we identified 3,621,267 variants in 22 million base pairs across the coding regions of 22,621 genes, of which 90% were rare variants (minor allele frequency (MAF) < 1%). There was no evidence of site-specific effects or other systematic biases in the analysis of the filtered data. In order to investigate whether proteins from our proteomics study impinged on genes with variants associated with severe influenza disease, we searched for predicted loss-of-function (pLOF) variants in our proteomic datasets. We focused on pLOF variants as these are not only easier to predict  [75] [76] [77]  , but have more understandable molecular consequences than gain-of-function or synonymous mutations.
We identified 196,832 total variants in genes corresponding to the 3658 AB, 3656 PH, and 214 PPI proteins that we detected in our proteomic datasets (Supplementary Data 3). Variants were classified as pLOF if identified as nonsynonymous exonic, frameshift substitution or stop gain/loss (MAF < 1%), and predicted deleterious from any of six annotation algorithms (Supplementary Data 3). In total, 2808 AB genes, 3092 PH genes, and 177 PPI genes had pLOF variants, however, since the power to detect singletons is limited by their low frequency, we used a gene-based collapsing method by which rare mutations are considered jointly for association analysis  78. After gene-based collapsing, 1082 AB genes, 2336 PH genes, and 118 PPI genes were tested for association with severe influenza disease (Fig.
4B ). We identified 24 AB, 49 PH and 5 PPI significant severe disease-associated genes with pLOF variants (FDR < 0. 05) (Supplementary Data 3). For further analyses between genes with pLOF variants and our proteomic dataset, we considered 95 AB, 161 PH and 7 PPI genes as moderately significant genes with pLOF variants associated with severe disease (FDR < 0. 1) to include more genes with smaller effects (Fig. 4B-D ). Looking at the overlap of our proteomic and patient datasets, we found 23 AB and 52 PH genes with pLOF variants to be significantly regulated during IAV infection (Fig. 4C, E, F ). From the list of phosphorylation sites identified in our proteomic data, we also identified pLOF nonsynonymous mutations at predicted phospho-serine, phospho-threonine, and phospho-tyrosine sites for each of the AB, PH and PPI datasets (Supplementary Data 3).
We identified phospho-variants in 75 AB genes, 146 PH genes and 6 PPI genes (Supplementary Data 3). At the convergence of our proteomic and pLOF analyses are 44 proteins with pLOF variants that were detected in all three proteomic datasets: AB, PH, and PPI (Fig. 4D ). Focusing on these proteomically detected genes with pLOF variants and looking at the average of the severe influenza disease association FDR across the three dataset tests, we find two surpassing our significance threshold (FDR < 0. 1): AHNAK and SEL1L. SEL1L interacts with H3N2 HA, and while detected in both AB and PH datasets, it is not significantly regulated during IAV infection at the protein level. In contrast, we find that AHNAK, a H5N1 NEP interactor, is significantly downregulated in protein AB by all three IAV strains in both NHBE and THP1 cells (Fig.
4E ), and has significant changes in phosphorylation at a number of phosphorylation sites in at least one cell type by at least one IAV strain (Fig. 4F ). Looking at all AHNAK predicted phosphorylation sites, as well as all of the significantly regulated phosphorylation sites in our dataset (including infinite quantification values) (Fig. 4G ), we found serine position 210 contained phosphorylation disruption mutations of serine to proline or glutamine (S210P/Q) that was significantly associated with severe disease (FDR < 0. 05) (Fig. 4H , Supplementary Data 3). Interestingly, AHNAK phosphorylation at S210 was also regulated during infection of Calu-3 cells with early-lineage or Alpha variant SARS-CoV-2  79, 80. Together, our data pinpoints how molecular regulation of AHNAK in response to infection could be reflective of the systemic effect of AHNAK as it relates to disease severity in the host.
We knocked down the 54 functional IAV host factors identified above and challenged these cells with SARS-CoV-2. Cells were assessed for viability with siRNA knockdown by Fig. 3  | Global proteomic profiling highlights 13 modulated kinases in IAV infection. A Experimental design workflow for global proteomic profiling of protein abundance (AB) and phosphorylation (PH) changes in NHBE and PMAdifferentiated THP-1 cells infected in biological duplicate with pH1N1, H3N2 or H5N1 IAV (MOI 2, four time points post-infection with time-matched mocks). B-D AB data are not available (N/A) for pH1N1 and H3N2 IAV at the 12-hour time point in THP-1 cells, as these samples did not pass MS quality control. Bar chart plotting the total number of (B) proteins from the AB dataset and (C) phosphorylation sites from the PH dataset quantified at each time point (light red=significantly increased; light blue=significantly decreased; dark red=only detected in IAV infection; dark blue=only detected in mock infection; grey=no significant change).