dataesr/scientific-paragraphs-categorization huggingface dataset:
sweat glands caused volume depletion and abnormally low serum sodium, chloride, and potassium in the children with CF (26), and we presume the same mechanism was responsible for the electrolyte abnormality episodes in the father of Family B. Of note are reports of 3 children who presented with dehydration and/ or hypochloremic metabolic alkalosis (salt-loss syndrome) and were thereafter identified to have F312del (reported as ΔF311 or DF311) and other severe CF-causing variants  (7, 8). Elevated sweat chloride concentration was a consistent feature in all 25 individuals with F312del paired with a CF-causing variant reported in databases. Thus, F312del presents a notable deviation from the robust correlation between CFTR chloride transport and sweat chloride concentration and the well-accepted correlation between elevated sweat chloride concentration and development of progressive CF disease leading to mortality (i.
e. , progressive obstructive lung disease and pancreatic exocrine insufficiency)  (19, 27, 28). The diagnosis of CF is based on the presence of characteristic clinical features along with evidence of CFTR dysfunction, which is typically demonstrated by an elevated sweat chloride concentration  (29). A fraction of the 25 individuals in databases bearing F312del and a CF-causing variant warrant a diagnosis of CF as they exhibited exocrine pancreatic insufficiency and/or decreased pulmonary function (FEV 1 % predicted below 80%). Variable presence of lung and pancreatic disease may be due to environmental factors, such as exposure to second-hand smoke  (30, 31)  or inequitable receipt of healthcare related to socioeconomic status  (32). Alternatively, other variants that affect CFTR function may be present in some but not all CFTR genes bearing F312del.
Acknowledgments We wish to thank the individuals who underwent rigorous evaluation and contributed their tissue samples and clinical data to this study, as well as the patients, caregivers, and families at CF centers for their contributions to patient registry databases. This work was supported by  NIH  grant  R01DK44003  (to GRC);  CF Foundation  grants  CUTTIN13A1 ,  CUTTIN5XX0 , and  CUTTIN16IO  (to GRC),  GORALS19Y5  (to JLG),  Bridge18XX0  (to RJB), and  SHARMA19I0  (to NS); and  CF Foundation Therapeutics  grant  SHEPPA14XX0  (to DNS). Address correspondence to:  Garry R. Cutting , 733 North Broadway, MRB 559, Baltimore, Maryland 21287,  USA. Phone: 410. 955. 1773;Email: gcutting@jhmi. edu.
The Genetic Disease Screening Program in California (52) provided clinical details from 6 individuals who underwent CF NBS, were found to harbor F312del and a severe CF-causing variant, and were subsequently diagnosed with CF. Haplotype analysis. Haplotype analysis was performed on WGS data to determine the molecular background of F312del in 2 individuals from the CFGP. Variants within CFTR with greater than 15% minor allele frequency in the study were phased on the University of Michigan Imputation Server with Eagle v2. 4, using 1000G Phase3 v5 as the reference panel. The haplotypes were imputed using VCFtools v0. 1. 13. Assessment of F312del-CFTR expression and function. Primary nasal cell culture, transient transfection, creation of stable cells, measurement of CFTR function, IB, and patch-clamp techniques have been described previously  (19, 37, (53) (54) (55) (56) (57) (58).
Therefore, investigating drought's role in the DFS and its temporal trend may provide important insights into the understanding of future phenological variations with climate change. Thus, we suggest that over the past several decades, the autumn DFS may have exhibited a non-stationary response to drought with global warming and frequent drought events and such effects may shorten the previously predicted prolonged DFS driven by continuous warming at the end of this century. To this end, we detected the changing response of DFS to drought using a moving window method, analysed the possible reasons and improved DFS modelling and prediction. Two sources of DFS observations, three independent water availability indicators and other climatic and plant physiological datasets were used here: (1) 71,478 ground phenological observations at 905 sites in Europe recorded since the 1940s (Supplementary Fig.
00) and 0. 39 (P = 0. 00), respectively (Fig. 1c, e ). The analyses from satellite-observed DFS were in line with that from SM and proportions for T sen + and T senwere 33. 1% and 21. 5% for SPEI (Fig. 1d ). These proportions differed slightly for VPD (30. 0% and 21. 4%) (Fig. 1f ). Detailed results based on the first-layer SM indicator for plant functional types are shown in Supplementary Fig. 6. In addition, the results of SM, SPEI and VPD showed overall agreements in their spatial distributions (Supplementary Fig. 7 ). We also performed analyses using different sizes of moving window and the results were consistent (Supplementary Fig. 8 ). Overall, two independent phenological datasets and three drought indicators suggest an increased impact of drought on DFS for the past several decades in the Northern Hemisphere.
Given the larger geographic coverage and better representation of the diverse climate conditions and plant functional types in the satellite dataset and considering the dominant role of SM as a dryness stress  16  , we used these two datasets to explore the underlying mechanisms of the increased impact of drought on DFS. We grouped regions with enhanced or weakened DFS sensitivity, T sen + and T sen -, respectively and found that both experienced comparable warming of 0. 4 °C decade -1 over the past 34 years (Fig. 2a ). However, the precipitation trends were significantly different in areas with enhanced, compared with weakened, DFS sensitivity. Areas that exhibited T sen + had almost 13% mean lower precipitation increase (Fig. 2b ). This shortage of moisture supply caused different strategies for balancing photosynthesis and water loss because the isohydricity (the degree to which plants regulate their water status and a lower value indicating stricter regulation) of the T sen + was significantly lower (9.
Articles Nature Climate ChaNge methods Site-level ground LUD and DFS observations. Ground leaf unfolding date (LUD) and DFS observations across Europe were represented by the date of first visible leaf stalk (BBCH scale 32 = 11) and date of autumnal leaf colouring (50%) (BBCH = 94), respectively, compiled by the Pan European Phenology project (PEP725) (http://www. pep725. eu/). Consecutive records for at least 30 years for a given site and species were selected after choosing a moving window size of 15 years on the basis of the satellite data. As a result, a total of 71,478 site-year records were available for 9 plant species at 905 sites during the period 1946-2015. Due to the data availability, analyses based on SM and VPD started from 1958 to 2015. A more detailed description is presented in Supplementary Table  2.
Satellite-derived LUD and DFS data. Satellite LUD and DFS for 1982-2015 in the Northern Hemisphere (>30° N) were determined using the 15-d NDVI time series from the third generation of the Global Inventory Modeling and Mapping Studies (GIMMS) from the Advanced Very High-Resolution Radiometer (AVHRR), that is, the GIMMS NDVI3g dataset. This dataset has been widely used to model phenology over the Northern terrestrial ecosystems. We reconstructed the NDVI time series by reducing the snow and cloud impacts on the NDVI temporal trajectory before extracting LUD and DFS. First, we only focused on the vegetated pixels with clear seasonal variations and excluded those non-vegetated and agricultural lands. The pixels were selected on the basis of the multiple standards developed by ref. 33. This also helps to solve the concerns on the landcover change issue because pixels showing low NDVI value (<0.
Water availability indicators. We used three individual water availability indicators, including SM, SPEI and VPD to test the changing response of DFS to drought. For the SM data, three sets of SM data were used to satisfy the needs of different time spans of the site DFS, satellite DFS and projected data. (1) The monthly ERA5-Land SM data (m 3 m -3 ) for the period 1982-2015 was derived from the European Center for Medium-Range Weather Forecast (ECMWF) ERA5-Land reanalysis dataset  39  , which was produced by incorporating model data together with global observations using the physics laws. It started from 1981 to the present with a grid size of 9 km and a vertical resolution of four soil layers: 0-7 cm, 7-28 cm, 28-100 cm and 100-289 cm. (2) The monthly TerraClimate SM data  40  (mm) for 1958-2015 with a spatial resolution of 1/24° were obtained to match the longer duration of ground DFS observations.
It was synthesized by combining the WorldClim with the Climatic Research Unit Time-series v. 4. 0 (CRU_TS v. 4. 0) and the Japanese 55-yr reanalysis using the climatologically aided interpolation. (3) To predict future DFS, the mass of water in the upper soil layer of 0-10 cm (kg m -2 ) for 2081-2100 was obtained from the higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM1. 2-HR) under the scenarios of SSP 1-2. 6 and SSP 5-8. 5 (ref. 41 ; https://esgf-node. llnl. gov/search/cmip6/). The SPEI comprises the standardized difference between precipitation and potential evapotranspiration (PET), that is, the climatic water balance, during a fixed period  42. The SPEI data at any given month can be calculated at multiple timescales or aggregation times, so, the SPEI at the n-month time scale (n-month SPEI) provided an overall climatic water balance over n continuous months.
The FAO-56 Penman-Monteith (PM) algorithm is used to calculate PET in SPEI  43  and it has been proved that SPEI outperformed other drought indices in detecting drought events, such as the standardized precipitation index (SPI) and the Palmer drought severity index (PDSI)  44, 45. Here, we adopted the timescales of 1-12 months to detect the effect of water status during the previous single year  46. The SPEI data used here were derived from the SPEIbase v. 2. 5 dataset (https://spei. csic. es/ database. html), which was calculated using the recently released 4. 03 version of CRU_TS  47. It has a spatial resolution of 0. 5°, a monthly time interval and covers the historical period of 1901-2015. Monthly VPD data (hPa) from 1958 to 2015 were derived from the TerraClimate dataset  40  for both site and satellite-derived DFS analyses.
Other climatic and plant physiological datasets. Daily temperature product for 1982-2015 from the NASA Global Land Data Assimilation System (GLDAS)  48, 49  was used to identify the snow-covered pixels when extracting LUD and DFS from the GIMMS NDVI3g time series and was also used to build DFS models. These data have a spatial resolution of 0. 25° and a 3-h temporal interval. To predict DFS under future scenarios, we also obtained daily temperature (2081-2100) from the MPI-ESM1. 2-HR dataset under both SSP 1-2. 6 and 5-8. 5 scenarios. Monthly temperature and precipitation data for 1982-2015 were acquired from the CRU_TS v. 4. 03 (ref. 47 ). WUE was determined as the ratio of GPP to evapotranspiration. GPP was produced by a revised light use efficiency model with a temporal resolution of 8 d and a pixel size of 0.
05° (ref. 50 ). The monthly evapotranspiration data were generated on the basis of a modified process-based land surface evapotranspiration/heat fluxes algorithm, with a resolution of 8 km and spanning from 1982 to 2013  51. Both data were obtained from Scientific Data (https://www. nature. com/sdata/). The monthly T dew data (1982-2015) were derived from the ERA5-Land dataset with a spatial resolution of 0. 1°. We acquired the iso/anisohydry data which were produced on the basis of the Ku-Band backscatter data from QuikSCAT  52. These have a pixel size of 0. 05° and cover the whole globe. Extreme isohydric behaviour is indicated by a value near zero, while a value >1 suggests extreme anisohydric behaviour. The Ku-Band VOD data for 1987-2015 at a pixel size of 0. 25° were derived from the VOD Climate Archive (VODCA)  53.
This dataset was produced using VOD observations from multiple sensors. The maximum root length at a grid of 0. 0083° was obtained from the EartH2Observe  25  and the canopy height was obtained from the Global 1 km Forest Canopy Height dataset (https://webmap. ornl. gov/ogc/dataset. jsp?dg_id=10023_1). Calculation of T sen. To detect the temporal changes in the sensitivity of DFS to drought, we adopted a moving window method. Given the 34 years of satellite observation in our study, the window size was set from 13 years to 23 years. For a fixed window size, we computed the drought sensitivity of DFS using a nonlinear regression method within each moving window. Take SM as an example: DFS = a (SM) + b (LUD) + c (Tss) + d(SM × LUD) + e (SM × Tss) + ε (4) In equation (  4 ), DFS represents the ground-or satellite-derived DFS time series, SM and Tss are mean soil moisture over its preseason length and springsummer mean temperature time series, respectively, and LUD represents the ground-or satellite-derived leaf unfolding dates.
Acknowledgements This work was funded by the  Strategic Priority Research Programme of the Chinese Academy of Sciences  ( XDA19040103 ), the  National Natural Science Foundation of China  ( 42125101 ) and the  CAS Interdisciplinary Innovation  ( JCTD-2020-05 ). J. P. and P. C. were funded by  European Research Council Synergy  grant  ERC-SyG-2013-610028 IMBALANCE-P. J. P. was also financially supported by the  Fundación Ramon Areces  grant  ELEMENTAL-CLIMATE , the  Spanish Government  grant  PID2019-110521GB-I00  and the  Catalan Government  grant  SGR 2017-1005.
Contact information was provided for each person responsible for certain activities. There were also articles about Shenten and about the Yungdrung Bon Association in an effort to make its functioning transparent, as well as news about Triten Norbutse Monastery, events regarding the Yungdrung Bon tradition worldwide, and the activities of local groups. The newsletter has evolved and changed since 2006, focusing today mostly on the calendar of activities of Geshes and lamas in the West. It is a periodic newsletter now sent by email to all members registered in the Shenten database. Typically, the newsletter begins with a letter from the president of the Shenten Association, followed by news regarding the center and the calendar of activities, retreats, and teachings related to Shenten's Geshes and lamas.
SURVEY METHODOLOGY Deep learning for genomics is a rapidly evolving field. We did our best to gather many of the studies published so far focusing on the use of deep neural networks that take as input genomic sequences. To ensure an unbiased analysis of literature, a comprehensive analysis of published articles was carried using the following online databases: Medline (PubMed), Science Direct (http://sciencedirect. com) database, and Google Scholar database. We used the following keywords: deep learning, neural networks, genomics, DNA sequence and then gathered articles together with articles that were cited within the recovered articles.
To avoid these pitfalls, testing the predictions with experimental cues is needed to confirm or not the computational predictions. The computational predictions are just one line of evidence about the true annotation. The strength of that evidence depends on how well the model has predicted out-ofdistribution instances in the past and how different the distribution of interest is to the training distribution and to other tested distributions. The best solution in an unknown case would be to get a small dataset of experimental annotations to be used to validate and/or fine tune the models.
Unveiling sequence determinants of genomic annotations We have seen above that when trained appropriately on large datasets, neural networks are capable of predicting annotations in new contexts. These neural networks in some ways mimic the machinery of DNA binding: metaphorically, they both "read" the DNA sequence and "annotate" it, with computationally predicted biochemical labels or molecules. Such a neural network can be treated as an emulation of the DNA binding machinery and used for in silico experiments to generate hypotheses and theories, like any other model. The real benefit comes from the ease to perform new "experiments" with this neural network and from the possibility to reveal how the model makes its predictions. In other words, the advantage of studying a biological mechanism with a neural network is twofold.
This analysis has an important drawback: many variants are linked, i. e. their co-occurrence is significantly more frequent, but within these linked variants, some have no role in creating the phenotype. In other words, GWAS is prone to false positives. Here again, deep learning can be used to prioritize variants, i. e. trying to find the variants responsible for the trait. In order to uncover how the DNA sequence drives the local assembly of various chromatin context we review below the different experimental datasets that have been studied using a deep learning based approach.
The network obtained at the time the best results on this task. Other teams have, since then, improved the quality of predictions on the same experimental dataset and same testset  (Tayara & Chong, 2019; Quang & Xie, 2016). The Basenji network  (Kelley et al. , 2018)  was also used to predict the effect of variants. This network predicts the outcome of 2,307 ChIP-seq experiments on histone marks, 949 DNase-seq experiments, and 973 CAGE experiments in human. The predictions of the model in the presence of variants known to alter gene expression were compared to predictions obtained with non-variant sequence. Again, this network, although trained on a unique reference genome, is able to predict a change of the chromatin context at these loci. The model is for instance able to predict the effect of a variant (rs78461372) on the two surrounding genes, one of which is located 13 kbp away.
Splicing, translation and polyadenylation of RNA  Cheng et al. (2019 Cheng et al. ( , 2021) )  developed a neural network to predict gene splice sites from the RNA sequence. Analysis of the effect of variants using this network shows its utility in understanding the genomic causes of autism. They used the dataset provided by  Zhou et al. (2019)  that we presented earlier and targeted 3,884 mutations that are near exons. They demonstrated that the disruption score of mutations as provided by their model is significantly higher in the affected group than in their unaffected siblings (Wilcoxom rank sum test, p_value = 0. 0035). Once again the effect is larger in brain tissues. Jaganathan et al. (2019)  confirmed the relevance of the use of neural networks for the study of gene splicing in the context of intellectual disability and autism.
They used data coming from 4,293 individuals with intellectual disabilities, 3,953 individuals with autism spectrum disorder and 2,073 unaffected siblings. De novo mutations that are predicted to disrupt splicing are enriched 1. 51-fold in intellectual disability and 1. 30-fold in autism spectrum disorder compared to healthy controls. Translation initiation of mRNAs does not always occur at the canonical AUG codon, as shown by the recent QTI-seq method which precisely maps translation initiation sites  (Gao et al. , 2015). These data have paved the way for the use of deep learning to predict these initiation sites. Zhang et al. (2017)  developed a network capable of predicting initiation sites from mRNA sequences. By interpreting their network using input optimization methods, they highlighted the importance of Kozak sequences around AUG codons, confirming the previously established role of these sequences  (Kozak, 1989).
Species classification Bioinformatics tools used to determine a short sequence's species of origin, such as BLAST, align and compare this sequence with sequences from different reference species. These tools are therefore increasingly slow as the number of reference species increases. Deep learning approaches do not suffer from the same problem. Indeed, once a network is trained to attribute a species to a DNA sequence, the prediction will always take the same amount of time. This advantage also comes at a cost: adding a new reference species to the database would require retraining the network. That retraining process is much more complicated, error-prone and demanding in terms of computational resources than adding a new sequence for BLAST. Networks have nevertheless already been developed for this specific application.
Highly multiplexed single-molecule RNA-FISH methods (MER-FISH, seqFISH)  [24, 25]  and in situ sequencing methods (FISSEQ, StarMAP)  [26, 27]  provide direct spatial and quantitative measurements, but their detection power remains limited to a couple of thousands of genes simultaneously. Spatially resolved capture of mRNA via barcoded beads (Slide-seq, Stereo-seq  [28, 29] ) offers high spatial resolution for tissue sections, but in their current state, these strategies do not provide single-cell resolution and yield sparse datasets that can impede the detection of lowly expressed genes. The combination of spatial transcriptomic methods, both high-resolution imaging and laser capture microdissection, and scRNA-seq has been successfully applied in numerous contexts (e. g. [30, 31] ). However, these approaches remain technically challenging, require specialized equipment and reagents and may be inconvenient to explore large samples.
The same effect is visible by re-clustering single cells using only non-spatially regulated genes (Fig. 3D , Fig. S8 ). We observed that the new graph projection grouped cells together by annotated cell types, reducing the striking position-driven dispersal of the initial projection (Fig  1C ). Correspondingly, cells from different positions (as shown by their color indexes) were considerably more intermixed (the original spatial signal is almost abolished for the AP axis). To further investigate the interplay between cell position and cell fate, we used fastTopic  [50, 51]  to model each cell as a mixture of different (k = 8) topics which allowed to describe cell states as combinations of more or less independent modules (Fig. 3E ). This uncovered a spectrum of combinatorial topic utilization consistent with the continuum of cell states highlighted by MetaCell.
Clustering and annotation of the mutant dataset was done as for the wild-type reference. We first examined the expression of genes known to be associated directly or indirectly with the mutation and Fgf8 in the AER. As expected, Fgf8 expression was abolished in the ectoderm and Dpcd, a bystander gene included in the DEL(PolL-SHFM) deletion, was almost undetectable in the mutant cells (Fig. 4B ). We could additionally observe changes reported by previous analyses of the mutant by candidate gene approaches using in situ hybridization, including a compensatory upregulation of Fgf4 in the AER  [12]. We then sought to characterize how the reduction of FGF signaling from the AER impacted the different limb bud cell populations. Given the spatial-and cell type-level resolution offered by TATOO-seq, we could investigate changes at multiple levels including the correct specification of the different cell types as well as changes in their transcriptomes and spatial distributions.
Considering the smaller size of the mutant limbs, we first examined whether the differences present in the mutant limbs reflected a global developmental delay. For this comparison, we generated scRNAs-seq from E10. 5 wild-type forelimbs (embryos n = 4, limbs n = 4; 2912 cells total after QC) and examined several molecular markers indicative of limb developmental progression in the different datasets (Fig. S11 ). These comparisons did not yield evidence suggesting a general heterochrony of limb development in the mutant limbs. To compare the cell populations present in the wild-type and mutant limbs, we clustered together two datasets, using a coarser clustering strategy with the Seurat package  [59]. For each cluster, we computed the relative fraction of wildtype and mutant cells (Fig. 4C ).