dataesr/scientific-paragraphs-categorization huggingface dataset:
4E, ). The variability across individuals increased significantly after 90 years (Fig. 5 ), and VOR gain declined steadily thereafter (-0. 8%/year). Local and global adaptation shaped the input drive of PCs and MVN neurons to compensate for afferent losses during lifetime (Supp. Fig. 3 ). To compare our results against human cross-sectional data  (Li et al. , 2015; Matiño-Soler et al. , 2015; McGarvie et al. , 2015) , we analyzed VOR gain changes across decade age bands (Fig. 6B ). Each age band included 400 simulated individuals. A multiple comparison post hoc test showed that VOR function began to decline during the 80-89 age band, and it continued to decrease thereafter (ANOVA F (7,3032) = 736. 2, p < 10 -9 ). Then, given the large interindividual variability observed at older ages (Fig.
Funding EU  Human Brain  Project Specific Grant Agreement 3 to ER ( H2020-RIA. 945539 ) and to FN NEUSEQBOT (891774). EU and Andalucía Regional Government (Spain)  to ER CEREBIO ( P18-FR-2378 ) and to NRL ( A-TIC-276-UGR18 ). The  Spanish Ministry of Science and Innovation to ER INTSENSO  ( MICINN-FEDER-PID2019-109991GB-I00 ) and to NRL SPIKEAGE (MICINN- PID2020-113422G A-I00 ). It was also supported by the  French Government  via the Chair SILVERSIGHT to AA ( ANR-14-CHIN-0001 & ANR-18-CHIN-0002 ), the  LabEx LIFESENSES  to  AA  ( ANR-10-LABX-65 ), and the  IHU FOReSIGHT  to  AA  ( ANR-18-IAHU-01 ). ' All authors approved the version of the manuscript to be published.
Building a high-confidence set of archaic variants and SNPs annotation To curate list of high-confidence archaic SNPs to analyse for possible functional contributions to present-day Papuans, we took advantage of a recently described dataset of both archaic and non-archaic haplotypes segregating within 56 individuals of Papuan ancestry  [5]. Due to incomplete lineage sorting and recombination, a large fraction of variants included in these archaic haplotypes is not expected to be of archaic origin  [8]. To account for this while enriching for variants of evolutionary interest we applied a series of filtering steps to our starting list of 7,791,042 genomic SNPs from  [5] , which the authors had previously classified as either archaic (aSNPs, segregating within either Denisovan or Neanderthal haplotypes) or non-archaic (naSNPs, segregating in all other haplotypes).
To gain insights into the consequences of archaic introgression in Papuans, we have analysed a previously published dataset of 56 present-day genomes sampled across the island of New Guinea, representing the Fig 1. aSNP introgression across the Roadmap Epigenomics data. Heatmap of the Denisovan and Neanderthal aSNPs impact across each chromatin state/cell type combination. Only significant differences (i. e. FDR-corrected Fisher's exact test p < 0. 05 are coloured). Asterisks indicate genome-wide significant enrichment/depletion of aSNPs for the given state (see Fig. S1  and Supp Table  1 ). marks  [25]. Conversely, Neanderthal variants are significantly enriched within Het, but significantly depleted from Enh and promoters (TssAFlnk) (Fig. S1  and Supp Table  1 ). We then sought to understand whether these observations were consistent across the entire Roadmap Epigenomics dataset, or driven by a subset of cell types.
2, respectively). We first intersected these variants with significant eQTLs (qval < 0. 05) from GTEx v8  [27]. Only 24 Denisovan, 50 Neanderthal and 515 modern human variants (0. 28%, 0. 69% and 0. 87% of testable SNPs, respectively) are eQTLs in GTEx. To ensure these results were not biased towards variants that might be at high frequency only in Papuans, we repeated this analysis focusing on all testable SNPs in our data. This time we found 82 Denisovan, 169 Neanderthal and 1,248 modern human SNPs (0. 10%, 0. 33% and 0. 37% of the entire set of SNPs, respectively) within GTEx. Given the well-recognised bias towards individuals of European ancestry within the GTEx dataset, these findings both support the need to include under-represented populations into genetic surveys, and reaffirm that the majority of our introgression assignments are correct, and, as expected, that our variants are geographically restricted.
Denisovan aSNPs might alter gene regulation in immune-related cells Given the observed enrichment within CRE-associated chromatin states, particularly for Denisovan variants, we next sought to characterise the potential for our variants (both modern and archaic) to alter gene regulation, and functional mechanisms by which this may occur. To this end, we examined the potential of high-frequency variants within CREs to disrupt known transcription factor binding sites (TFBSs), assessing their impact on 690 different position weight matrices (PWMs) drawn from the from HOCOMOCO v. 11  [28]  and Jaspar2018  [29]  databases. After consolidating variants predicted to disrupt the same motif across the two databases (see Methods), we found that 3,538 Denisovan, 2,886 Neanderthal and 24,747 modern human variants (41.
Denisovan TFBS-disrupting SNPs active in immune cells are mostly Papuan-specific Recent studies have shown the existence of a genetic structure characterising the Indonesian archipelago, with Denisovan ancestry showing a marked west to east cline  [5, 31]. We therefore investigated whether our entire subset of TFBS-disrupting variants also show different frequencies between different human groups. To this extent, we defined aSNPs and naSNPs in genotype data from 63 individuals from western Indonesia, also part of  [5] , as above, and compared allele frequencies between the two datasets. 2,387 and 18,493 variants respectively of Neanderthal and modern human ancestry are shared between the two populations (82. 7% and 74. 7%, respectively), but this number falls to 1,683 (47. 6%) when considering Denisovan aSNPs (Fig.
In this study, we have taken advantage of a recently published dataset  [5]  to investigate the landscape of archaic introgression in individuals of Papuan ancestry, the functional consequences of which remained poorly understood. This has allowed us to characterise the putative contribution of Denisovan DNA, which is known to account for up to 5% of the genome of present-day Papuans  [4, 43] , while also comparing it to that of Neanderthal DNA and regional variants. We specifically analysed all of our variants across multiple cell types and functional chromatin states aiming to account for the strong dependency on the cells' 1) chromatin landscapes  [25] ; 2) developmental stages  [44]  and 3) experienced environmental stimuli  [45] , when assessing the potential activities of introgressed alleles.
Variant filtering and selection To curate a high confidence set of SNPs segregating in our dataset, we began by intersecting genome-wide genotype data from all 56 Papuan individuals with the set of high confidence Neanderthal and Denisovan haplotypes identified in this group, as defined by  [5]  (for detailed methods and sample information see  [5] ). Due to incomplete lineage sorting, these regions may still contain SNPs that are not Denisovan or Neanderthal-specific, thus we applied a series of stringent filtering steps, as follows. All singletons were discarded. Variants were initially labelled as aSNPs or naSNPs depending on whether they fell within archaic haplotypes. Only SNPs lying outside both sets of archaic haplotypes were considered potential naSNPs, with the remaining variants were sorted into Denisovan aSNPs or Neanderthal aSNPs based on the inferred ancestry of the haplotype of origin.
Variant impact on TFBSs To characterise the impact of our sets of SNPs on transcription factor binding sites (TFBSs), we focused on variants that fell within TssA, TssAFlnk, TxFlnk, Enh and EnhG in at least one cell type and define the union of these regions as putative cis-regulatory elements (CREs). To assess whether any of our SNPs might disrupt any TFBS we used the motifbreakR R package (v 2. 2. 0)  [55] , retrieving the collection of DNA motifs from Jaspar 2018  [29]  and HOCOMOCO v. 11  [28]  databases. Motifs were matched to the hg19 genome using a p-value threshold of 1 -5 , and the allelic impact on each identified motif was quantified via the built-in sum of log-probabilities algorithm, setting a background probability of 0. 3 and 0. 2 for A/T and G/C, respectively. The allelic impact on the position weigh matrices (PWMs), i.
e. ∆ PWM score, was obtained from motifbreakR and is calculated as the difference between the alternative and reference allele. Duplicated variants predicted to disrupt the same binding site across both databases were removed by retaining only the SNP-PWM association with the highest absolute ∆ PWM score. The final set of SNPs, together with their affected motifs and their predicted disruptive potential, are listed in Supp File 1 -Supp File 3. To assess the genome-wide impact of aSNPs on TFBSs and to avoid redundancy between TFs due to 22/37 similar binding preferences across motifs, we grouped all our TFs into families based on motif clustering information from  [30]. We removed 72 HOCOMOCO v11 and 93 Jaspar 2018 PWMs that were either not associated with any cluster or that have mismatches in the PWM versions between MotifDb  [56]  and  [30].
Acknowledgements We would like to thank all members of the  Gallego Romero and McCarthy groups as well as Ashlee Hutchinson (University of Melbourne ) for their helpful discussions and comments on the manuscript. We also wish to acknowledge all of the study participants who generously consented to genome sequencing in the original study, and the leadership of  Herawati Sudoyo  and  Chelzie Crenna Darusalam  ( Eijkman Institute for Molecular Biology ) in generating these datasets. This work was supported by an award from 18/37 the  Leakey Foundation  and by  Australian Research Council Discovery  Project  DP200101552 , both to I. G. R.
Sparse models such as Sparse Component Analysis exploit sparsity on the coefficients  Gribonval and Lesage (2006). It can be noted that while all those factorization techniques aim at providing interpretable representations, they are typically identifiable under strict conditions not necessarily met in practice  Donoho and Stodden (2003) ;  Fu et al. (2018) ;  Cohen and Gillis (2019). It should be noted that the most important underlying hypothesis in matrix factorization is the linear dependency of the input data with respect to the templates/principal components stored in matrix A following the notations of Equation  1. In practice, to compute for instance NMF, one solves an optimization problem of the form argmin A≥0, B≥0 Y -AB T 2 F (5) which is nonconvex with respect to A, B jointly but convex for each block.
The resulting FISTA algorithm is denoted as Block FISTA. Note that after Block FISTA returns a candidate solution, this solution's support is extracted, truncated to be of size k columnwise using hard thresholding, and an unbiased MSC solution is computed with that fixed support. Algorithm 2 FISTA for Block LASSO (Block-FISTA) Input: data Y , dictionary D, mixing matrix B, regularization ratio α ∈ [0, 1] r , sparsity level k, initial value X. Output: estimated codes X, support S. Precompute D T D, D T Y B and B T B if memory allows. Compute λ i,max as in Proposition 5, and set λ i = α i λ i,max , λ = [λ 1 ,. , λ r ] Compute stepsize η = 1 σ(D) 2 σ(B) 2 Initialize Z = X, β = 1. while stopping criterion is not reached do X old = X X = S ηλ Z -η(D T DZB T B -D T Y B) β old = β β = 1 2 (1 + 1 + 4β 2 ) Z = X + β old -1 β (X -X old ) end while Estimate the support S = S(X) Set X as the least squares solution following Section 3.
Dictionary-based matrix factorization with application to matrix completion Let us study the following Dictionary-based Matrix Factorization model: argmin X∈R d×r , ∀i≤r, Xi 0≤k, B∈R m×r Y -DXB T 2 F. (28) Low-rank factorizations have been extensively used in machine learning for matrix completion  (Candès and Recht, 2009)  In remotely acquired hyperspectral images, missing rows in the data matrix are common as they correspond to corrupted pixels. Moreover, it is well-known that many hyperspectral images are approximately low-rank. Therefore in this toy experiment, a small portion of the Urban hyperspectral image is used to showcase the proposed inpainting strategy. Urban is often considered to be between rank 4, 5 or 6 (Zhu, 2017). I will use r = 4 hereafter. Urban is a collection of 307x307 images collected on 162 clean bands.
Furthermore, each component in the CPD may use different splines while the method of  (Timmerman and Kiers, 2002)  uses the same splines for all the components. Hand-crafting is still required to build the dictionary, but one does not have to fear introducing an inadequate spline. An advantage of B-splines is that they are nonnegative, therefore one can compute nonnegative DCPD by imposing nonnegativity on the sparse coefficients X as explained in Section 3. 4. Imposing nonnegativity in the method of  (Timmerman and Kiers, 2002)  is not as straightforward albeit doable  (Cohen et al. , 2015). For this study the toy fluorescence spectroscopy dataset "amino-acids" available online 9  is used. Its rank is known to be r = 3, and dimensions are 201 × 61 × 5. Fluorescence spectroscopy tensors are nonnegative, low-rank and feature smooth factors.
In fact factors are smooth on two modes related to excitation and emission spectra, therefore a double-constrained DCPD is also of interest. It boils down to solving minimize  where D (A,B) and k (A,B) are the respective dictionaries of sizes 201 × 180 and 61 × 81 and sparsity targets for modes one and two. The third mode factor contains relative concentrations  (Bro, 1998). Additional Gaussian noise is added to the data so that the effective SNR used in the experiments is -8. 7dB. Because CPD is identifiable, in particular for the amino-acids dataset, nnCPD of the noisy data is used for initialization, computed using Hierarchical Alternating Least Squares  (Phan and Cichocki, 2011). We compare sparse coding one or two modes of the output of HALS with AO-DCPD (i. e. AO-DLRA used to compute DCPD) with smoothness on one mode, two modes, and on two modes with nonnegativity (AO-nnDCPD).
A. Vavasis. On the complexity of nonnegative matrix factorization. SIAM Journal on Optimization, 20(3):1364-1377, 2010. Y. Xu. Alternating proximal gradient method for sparse nonnegative Tucker decomposition. Mathematical Programming Computation, 7(1):39-70, mar 2015. arxiv:1302. 2559. F. Zhu. Hyperspectral unmixing: ground truth labeling, datasets, benchmark performances and survey. arXiv preprint arXiv:1708. 05125, 2017. L. Zhuang and J. M. Bioucas-Dias. Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3):730-742, 2018.
S. A. Vavasis. On the complexity of nonnegative matrix factorization. SIAM Journal on Optimization, 20(3):1364-1377, 2010. Y. Xu. Alternating proximal gradient method for sparse nonnegative Tucker decomposition. Mathematical Programming Computation, 7(1):39-70, mar 2015. arxiv:1302. 2559. F. Zhu. Hyperspectral unmixing: ground truth labeling, datasets, benchmark performances and survey. arXiv preprint arXiv:1708. 05125, 2017. L. Zhuang and J. M. Bioucas-Dias. Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3):730-742, 2018.
DISCUSSION In this large-scale multi-national study, we reported a mortality prediction model for patients hospitalized with COVID-19 that retained accuracy across healthcare systems and countries. Building on the growing literature of COVID-19 mortality prediction, our study is unique in leveraging international cohorts to validate the generalizability of the prediction model, which has the following specific features. First, a predictive model containing nine commonly measured laboratory test values performed better than the model containing 17 laboratory test values: CRP, creatinine, white blood cell count, lymphocyte count, AST, ALT, total bilirubin, neutrophil count, and albumin. From a list of 17 laboratory tests associated with worse outcomes in patients with COVID-19 based on prior reports 6 , we selected the subset of nine tests based on their low rate of missing data in our data set.
To mitigate the quality issue of EHR data, we performed extensive and iterative quality controls at each participating healthcare system with local collaborators and centrally to address potential imprecision due to healthcare system-specific variations in data extraction and incompleteness of datasets (e. g. , incomplete mapping of local EHR codes to desired data elements). These critical quality control steps, which are often underappreciated in multi-center EHR data research, further differentiate the 4CE research efforts from other COVID-19 research efforts. Second, we observed a significant level of heterogeneity in the predictiveness of individual laboratory tests and the locally trained mortality risk models across the participating healthcare systems. The heterogeneity could result from differences in patient population, clinical practice and EHR system.
Participating sites Data were available from 39,969 patients from 284 hospitals (affiliated with 16 sites) across four countries: France, Germany, Spain, and the United States. See Supplementary Table  2  for details about participating sites. Several sites collected data from multiple hospitals. In the United States, 170 medical centers of the US Department of Veterans Affairs were grouped into five regional divisions called Veterans Integrated Service Networks. Fig. 4  Schematic of the federated EHR-based study involving healthcare systems from three countries. Each site generated three data tables (comma-separated files) containing patient level data: 1) local patient clinical course indicates which days the patient was in the hospital and when the patient died; 2) local patient observation includes first three-character ICD9/10 diagnosis code and laboratory tests, where laboratory test has a numerical value; 3) local patient summary contains demographic variables including age, sex and race.
We evaluated the ability of a biomarker and demographics-based algorithm to predict mortality using admission data. We removed patients who died at admission. We developed mortality risk prediction models using a set of nine common laboratory tests with missing rates <30% at most sites, adjusting for demographic variables and the Charlson comorbidity index. We derived the risk models by fitting penalized Cox proportional hazards model. We evaluated the accuracy of the risk models for predicting mortality by t-days since admission based on the timespecific AUC  9. We used the 10-fold cross-validation to estimate the AUC when evaluating the model performance within each local site. The mortality risk prediction model was not trained at Spain because the data were not available at the time when we collected the model training results.
Software platform. Most sites used the open source i2b2 (Informatics for Integrating Biology and the Bedside) software platform to obtain the data. More than 200 organizations worldwide use i2b2 for purposes that include identifying participants for clinical trials, drug safety monitoring, and clinical and epidemiological research. Those 4CE sites with i2b2 used database scripts to directly query their i2b2 repository, calculate the counts and statistics, and export the data files. The 4CE sites without i2b2 used the Observational Medical Outcomes Partnership (OMOP) Common Data Model or their own clinical data warehouse solutions (e. g. , Epic Caboodle) and querying tools to create the required files.
ACKNOWLEDGEMENTS G. W. reports funding from  NCATS   UL1TR002541 ,  NCATS UL1TR000005 , and  NLM R01LM013345. S. M. and J. K. report funding from  NCATS   5UL1TR001857-05  and  NHGRI 5R01HG009174-04. Z. X. reports funding from  NINDS   R01NS098023. G. O. reports.
The similarity of task structures across the three brain parts could be obtained merely by the difference of task-specific sensorimotor information. To exclude such a possibility, we first extracted visual features using the motion energy (ME) model, auditory features using the modulation transfer function (MTF) model, and motor features using the button response (BR) model (see Motion energy features, Modulation transfer function features, and Button response features subsections in Methods for detail) and concatenated those features to obtain sensorimotor features. We then performed the encoding model analyses 50 times within the training dataset using sensorimotor features and excluded the reliably predicted voxels (having a prediction accuracy of at least r = 0. 3) from further analyses (sensorimotor voxels, Supplementary Fig.
To interpret cognitive factors related to those tasks, we first performed principal component analysis (PCA) on the averaged weight matrix of the task-type model concatenated across six subjects. The resultant PCs were then associated with independent cognitive factors using a metadata-based reverse inference analysis. For each of the PC score maps, we calculated Pearson's correlation coefficients with the 715 reverse inference maps taken from the Neurosynth database  46. The top and bottom 10 terms of each PC provided their objective interpretations (Supplementary Tables  2, 3 ). For the sake of intelligibility, we only presented the results of the top five PCs. For the cerebellum, PC1 was associated with introspection and emotion terms on the positive side (top 10 terms; "theory mind," "disgust") and executive function and motor terms on the negative side (bottom 10 terms; "working memory," "motor").
3a , Supplementary Fig. 3 ; Subcortex, Fig. 4a , Supplementary Fig. 4 ; see the visualization for the cortical voxels in our previous study  45  ). The top five PCs explained 30. 1% of the variances in the cerebellum weight matrix and 25. 0% of the variances in the subcortex weight matrix (Figs. 3b, 4b ). The 2D map based on the cerebellum showed consistent task organization with the metadata-based interpretation of PCs. Introspection tasks ("ImagineFuture", "RecallFace") are colored in red and located on the right side (i. e. , on the positive side of the PC1). Tasks related to the executive function ("CalcHard", "PropLogic") are colored in green and located on top (i. e. , on the positive side of the PC2). Language tasks ("Sarcasm", "WordMeaning") are colored in blue. The 2D map obtained based on the subcortex also showed consistent task organization with the metadata-based interpretation of PCs.
Emotional tasks ("RateHappyPic", "RateDisgustPic") are colored in red and located on the right side (i. e. , on the positive side of the PC1). Memory tasks ("LetterFluency", "RecallFace") are colored in green and located on top (i. e. , on the positive side of the PC2). Motor tasks ("PressOrdHard", "PressLeft") are colored in blue. To scrutinize representational differences in each subregion of the cerebellum and subcortex, we visualized average weight values of 103 tasks in each subregion (Figs. 3c-d, 4c-d ). Based on the Fig. 1  Experimental design and analyses. Some figures were modified from one of our previous studies  45. a The subjects performed 103 naturalistic tasks while the brain activity was measured using functional magnetic resonance imaging. Ten example tasks were shown. b Schematic of the encoding model fitting using the sparse task-type and continuous cognitive factor features.