dataesr/scientific-paragraphs-categorization huggingface dataset:
cloud/Enrichr/) or the R package gprofiler2 (v0. 2. 0)  [16] , which provides an interface to the gene list functional profiling toolset, g:Profiler (https://biit. cs. ut. ee/gprofiler/gost). Classical GO term enrichment analysis is provided via the R package topGO (v2. 38. 1)  [17]. Metabolic pathway enrichment analysis for the metabolites in the constructed subnetworks is performed using platform-specific annotations of metabolites into classes and super-/sub-pathways where this information is available. Contingency tables are calculated and then tested using Fisher's exact test (function fisher. test with alternative = 'greater' from R package stats). Multiple testing correction is performed using p. adjust (method = 'fdr'). For a full list of tools and packages that are used in this project please refer to the Supplementary Material.
Overview of the Alzheimer's disease (AD) Atlas The AD Atlas is a comprehensive, network-based catalogue of results from large omics studies that is accessible via an interactive, network-based user interface (www. adatlas. org). It was built by inferring relationships between biological entities (e. g. metabolites, SNPs, genes) from large-scale studies, resulting in a highly complex collection of data stored in network format (Figure  1A ). To increase data accessibility and downstream interpretability, the data was summarized and extracted into a simplified data view (Section 2. 3). This summarized network representation is accessible through the user interface and consists of four node types; (I) metabolites, mapped across available platforms where possible, (II) genes, including information on associated transcripts, SNPs and proteins (Supplementary Material), (III) traits, which describe AD (endo-)phenotypes and biomarkers, and (IV) meta-traits, which are collections of traits loosely following the A-T-(N)-(C) classification  [18].
Various different relationship types interconnect these entities (Supplementary Figure  1B  and Supplementary Table  1 ). Integrating over 20 different studies and analyses, the AD Atlas includes information on 20,353 protein-coding genes, 8,615 proteins, 997 metabolites and 31 unique AD-related traits. Traits include cerebrospinal fluid (CSF) and imaging biomarkers, partially with different covariate settings (adjustment for APOE genotype), stratifications or stagings for neuropathologies, amounting to 59 traits in total (Supplementary Table  3 ). Biological entities and traits are linked by over 1. 5 million relationships, representing statistical associations inferred from large-scale quantitative data from population-based cohorts and AD case-control studies. A more detailed summary of the data compiled in the AD Atlas can be seen in Table  1.
Metabolite-centric subnetworks User provides metabolite or set of metabolites as input. Traits and genes that are associated with the metabolite of interest (provided as biochemical name) are extracted. Furthermore, all relationships between these entities are included in the resulting network. The AD Atlas is a multi-omics resource that enables the integration and analysis of heterogeneous omics datasets in the context of Alzheimer's disease. A. Step-wise multi-omics integration approach underlying the AD Atlas. Using statistical analysis, such as association analysis and partial correlation, omics data collected in large population-based studies is used to infer biological relationships between (inter-omics) and within (intra-omics) omics layers. Links to AD (endo-)phenotypes from large-scale case/control or biomarker studies enable multi-omics exploration in the context of AD.
Molecular subnetwork of lipid metabolism and transport identifies known repositioning candidates In recent years, multiple genetic risk factors for late-onset Alzheimer's disease (LOAD) have been identified through GWAS  [10]. The earliest was the discovery of the genetic risk exerted by the ε4 allele of apolipoprotein E (APOE)  [39] , which was followed by the identification of several risk variants in clusterin (CLU, also referred to as APOJ)  [40, 41]. Both proteins are involved in lipid metabolism and transport and we hypothesized that these mechanisms could potentially be targeted by available repositioning candidates. We therefore queried the AD Atlas using the two genes as input and expanded to the 1-step functional neighborhood defined by gene co-expression, co-regulation data and protein co-abundance data (Supplementary Material).
This revealed an up-regulation on the transcript levels of both APOE and CLU as well as their functional neighborhood in individuals with AD compared to healthy individuals (133 In order to utilize the potential of such networks towards identification of drug repositioning candidates, we subsequently performed gene set enrichment analysis on all the genes in this subnetwork using molecular drug signatures from the EnrichR database  [42]  (left hand panel under "Enrichment analysis -Gene set enrichment -enrichR"). Although this is a rather simplistic approach that ignores effect direction or strength, we were able to identify multiple candidate drugs that have been previously proposed and tested in clinical trials among the top hits, i. e. their associated lists of genes affected in drug screens overlapped most significantly with the APOE/CLU context network.
This analysis revealed that of the 20 genes, 15 show a dysregulation at the mRNA level with 14 in opposing direction to the drug (up-regulated in AD) and only one showing downregulation. Another interesting candidate that was identified by this analysis is Candesartan, an angiotensin receptor blocker typically used for the treatment of hypertension. Of the 21 genes that are affected by the drug in the subnetwork, more than half also display a perturbed transcriptional signature in AD (13 up-regulated and one down-regulated in the TCX of AD patients; Supplementary Figure  4B ). Candesartan has recently been studied in a phase II trial (NCT02646982) to investigate its effect on individuals with mild cognitive impairment that are positive for AD biomarkers. Although, no results have been published for this trial, previous clinical trials suggest beneficial neurocognitive effects following Candesartan treatment in older individuals with hypertension and mild cognitive impairment  [50, 51].
Therefore, we focused the enrichment analysis again on drug induced signatures of down-regulated gene expression (gene set Drug Perturbations from GEO down from EnrichR). This analysis yielded multiple significant hits (lower table in Figure  4B ), including Citalopram, a selective serotonin reuptake inhibitor used in the treatment of depression. Using the AD Atlas, we further investigated whether the genes perturbed by this agent are also perturbed in AD and if its effects on transcript levels are in opposing direction. Citalopram affects a total of 16 genes in the trait-centric subnetwork and more than half are differentially expressed in AD (nine up-and one down-regulated). Furthermore, Escitalopram, the (S)-stereoisomer of Citalopram, is currently being studied as a treatment for agitation in AD patients in a phase III trial (NCT03108846) and has also entered a phase I trial as a cognitive enhancer (NCT03274817).
Safety and tolerability of Etarnecept in AD has been established in a small randomized, placebo-controlled, double-blind phase II trial (NCT01068353)  [54]  but the drug has not been studied in a phase III trial, despite multiple large observational studies indicating a reduced risk of AD among patients treated with Etarnecept  [55]. Further supporting evidence is provided by a second generation TNFα inhibitor, selective for the soluble form of TNFα, that has also shown promising results in preclinical studies  [56, 57]  and was recently investigated in a Phase I trial (NCT03943264). Both Etarnecept and Citalopram seem to affect the immunoregulatory human leukocyte antigen (HLA) complex, which has been implicated in neurodegenerative diseases, including AD  [23, 58]. In conclusion, the AD Atlas enables the generation of global, disease-related molecular networks without the need to perform complex data analysis.
These networks can be used in downstream analysis within the web-based user interface, for instance to identify plausible repositioning candidates using gene set enrichment. In addition, the networks and lists of contained entities can be directly downloaded for offline follow-up analyses. Despite coming from diverse sources and cohorts, the totality of data collected in the AD Atlas can provide valuable insights into the underlying mechanisms of disease, as we demonstrate with the identification of Etarnecept and Citalopram as promising repositioning candidates through potential modulation of neuroinflammatory pathways.
Next, we used the gene set enrichment analysis within the AD Atlas to functionally characterize the pathways targeted by statins. Here, we found a significant enrichment for the TYROBP causal network (EnrichR, WikiPathways, PFDR = 7. 89e-15), an immune-and microglia-specific module that has been implicated in late-onset Alzheimer's disease (LOAD)  [68]. This enrichment is driven by the co-expression network surrounding ITGAL (Figure  5B ), also known as CD11a, a subunit of the integrin leukocyte function associated antigen-1 (LFA-1) which is involved in a variety of immunerelated functions  [69]. Interestingly, only a subset of statins (Rosuvastatin, Lovastatin, Simvastatin, Pitavastatin) target ITGAL which may explain the heterogenous results reported in studies. Our analysis showcases how the AD Atlas can be utilized to investigate candidate drugs in an AD context.
The generated subnetwork is shown in Figure  6A. All SMs are associated with at least two genes at a genome-wide significance level. Interestingly, these particular SMs are not directly linked to each other through partial correlations. Also, while the associated genes are partially interlinked by coregulation edges pointing to the same genetic loci, the individual "SM-gene-clusters" are not interlinked. These aspects may point to their involvement in distinct pathways or pathway steps. Furthermore, 35 of the 36 AD-related traits are associated to SM C16:0 related genes, which include AD risk genes APOE and APOC1. The SM (OH) C14:1 component shows no direct or indirect trait associations, while SM C20:2 is directly associated with the clinical dementia rating -sum of boxes score. Overlay of differential gene and protein expression data using the visualization options provided by the AD Atlas reveals an up-regulation at the mRNA level of 5 of 22 genes (APOE locus (APOE, APOC1), SPTLC3, SYNE2 and CERS4) and down-regulation of one gene (MTHFD1), which displays an opposing effect at the protein level (up-regulation).
Post mortem data suggests that ROCK protein levels are elevated in AD brains  [93]  and preclinical data from in vitro and in vivo studies, including animal models of AD, indicate that Fasudil may be able to reduce the burden of tau protein  [94]  and promote an anti-inflammatory microglial phenotype  [95, 96]. Currently, two ongoing clinical phase II trials are investigating the use of Fasudil in tauopathies (NCT04734379) and the effectiveness of an oral formulation of Fasudil in patients with dementia (NCT04793659). Interestingly, a previous study reported experimental evidence linking androgen levels through androgen receptor signaling to the levels of miRNA-135a which targets ROCK, providing a potential mechanistic model integrating the different omics entities contained in the AD Atlas-derived subnetwork  [97].
Discussion We here present the AD Atlas, a network-based resource that consolidates experimental data from large cohort studies, building a global molecular view of AD. Integrating data in a largely hypothesisfree and data-driven manner, the AD Atlas enables researchers to explore the disease in a multiomics context starting from any genes, metabolites or phenotypes of interest. Through a browserbased interface to the AD Atlas network, we provide researchers of varying backgrounds and interests an easy-to-use resource for AD knowledge discovery. The molecular network underlying the AD Atlas is derived using large-scale population-based data and genomic annotation databases and provides a generalized framework to study multi-omics relationships globally. Using association data from large-scale studies on diverse aspects of AD, including hundreds to hundreds of thousands of individuals, this framework is then transformed into an integrated multi-omics knowledgebase for markers of AD.
These data include recent large genetic association (meta-)analyses of AD and AD biomarkers from NIAGADS and other large efforts, as well as differences in transcriptomics, proteomics, and metabolomics markers observed in AD or in relation to AD endophenotypes. The latter are predominantly based on data generated on thousands of human blood and brain tissue samples from multiple brain regions using state-of-theart technologies and analyzed using standardized processing pipelines through the AMP-AD program and partnering initiatives. By integrating multiple independent datasets of the same analysis type (e. g. GWAS on the same traits conducted in different cohorts), we build additional data confidence by independent replication and make this data available to the user. Through its network-based user interface, available at www.
Besides dynamic generation and interactive exploration of networks, we additionally interlink entities to external databases and provide downstream analysis tools, including the overlay of experimental data on differently expressed genes and differentially abundant proteins, as well as gene set and pathway enrichment analysis. Building upon and extending previous efforts that have provided multi-omics integration solutions for multi-disease drug repositioning  [100, 101] , genome-guided computational analysis of AD  [102]  and multi-omics annotation of individual targets  [103] , the AD Atlas provides users with an easy-touse and fully network-based research platform. We have demonstrated the utility and flexibility of this resource to answer a wide array of research questions, from drug repositioning efforts to explorative analysis and contextualization of AD findings.
For example, by investigating ADassociated genes, as well as those targeted by drug repositioning candidates proposed in the literature, we highlight the potential of the AD Atlas to guide computational drug repositioning efforts. Using two established AD risk genes, APOE and CLU, we were able to identify multiple proposed repositioning candidates and show that, even without adding pathway information, extensive data on drug effects, or detailed information on effect directions, the AD Atlas is able to capture disease-relevant information and guide drug prioritization. Information on differentially expressed entities (DEG/DEP) can additionally be overlaid to explore the extent of dysregulation and prioritize drug repositioning candidates that are known to induce transcriptional effects opposing the observed direction in disease  [38].
In addition to this hypothesis-driven approach to drug repositioning, we showed that the AD Atlas also allows the generation of data-driven disease networks that allow a more global and unbiased view on the disease. Furthermore, we explored the proposed benefits of statins for AD therapy and identified a link to neuroinflammation for a subset of compounds targeting ITGAL. Beyond applications related to drug discovery, the AD Atlas also provides a valuable platform to contextualize prior analysis results and hypotheses, by enabling researchers to easily validate and complement their findings with information from multiple heterogeneous resources and publications within the scope of one resource. For example, we extended the findings of a previous analysis that implicated three sphingomyelins in AD by adding additional layers of multi-omics data to gain further insights into the involvement of these metabolites in AD.
Limitations and outlook The AD Atlas in its current form provides extended access to large-scale multi-omics data generated by the AMP-AD initiative, where single biological entities are embedded into their molecular multilevel context. As such, in its current version the resource has several limitations. First, the underlying molecular framework is derived primarily in a data-driven way, which leads to a largely bias-free network representation of molecular data. Although the confidence in these datadriven representations will increase through inclusion of additional omics-studies, we neglect a large body of curated knowledge from experts and public databases on, for example, biochemical pathways or drug-targets. As we continue to extend the AD Atlas, integrating such data will be a valuable addition and provide a further layer of evidence.
Second, in the web interface we have so far only applied simplistic, minimal edge filtering criteria, i. e. using genome-wide, gene-wise or study-specific significance thresholds. While the underlying database contains potentially relevant evidence at even finer granularity (up to a raw p-value ≤ 0. 05), this data is currently not accessible. Similarly, additional information, such as study sample size or number and consistency of effects reported in different studies are currently not compared or meta-analyzed. To this end, we aim to develop more sophisticated, context-dependent edge weighting and filtering criteria, as well as more comprehensive analysis reports. Third, based on the currently limited availability of research data obtained in diverse populations/ethnicities, the data used to generate the AD Atlas are also not representative of these populations, which reduces the generalizability and transferability of the integrated molecular observations.
DATA AVAILIBILITY The AD Atlas is accessible via the user interface at adatlas. org. A comprehensive listing of the exact data sources that were integrated in the AD Atlas is given in the Supplementary Material. The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal. org). Data used in the preparation of this article were further obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (https://adni. loni. usc. edu).
P. M. D. has received research grants (through Duke University) from Lilly, Avanir, Bausch, Alzheimer's Drug Discovery Foundation. P. M. D. has received advisory fees from Verily, Otsuka, Genomind, Cogniciti, Clearview, VitaKey, Neuronix, Neuroglee, and Transposon Therapeutics. P. M. D. owns shares or options in UMethod, Evidation Health, Transposon, Marvel Biome, and Advera Health. P. M. D. serves on the board of Apollo. P. M. D. is a co-inventor (through Duke) on patents relating to dementia biomarkers, metabolomics, and therapies. K. N. , A. J. S. , R. K. D. , G. K. , and M. A. are coinventors on patents on applications of metabolomics in Alzheimer's disease. A. J. S. has been supported by multiple grants from the National Institute on Aging (NIA), National Cancer Institute (NCI), and National Collegiatie Athletic Association (NCAA)/Department of Defense (DoD) as well as collaborative research support from Eli Lilly unrelated to the work reported here.
ACKNOWLEDGEMENTS This work was supported by the  National Institutes of Health/National Institute on Aging (NIA)  through grants  1RF1AG057452 ,  1RF1AG058942 ,  1RF1AG059093 ,  1U01AG061359 ,  1U19AG063744 , and  1R01AG069901. K. S. was supported by the  Biomedical Research Program at Weill Cornell Medicine in Qatar, a program  funded by the  Qatar Foundation. P. M. D. is supported by grants from the  National Institutes of Health (NIH) , the  Cure Alzheimer's Fund , the  Steve Aoki Foundation  and the  Karen L Wrenn Trust. A. K. G. is supported by the  NIA  ( U24AG061340 ,  NIA RF1AG057443 ). Data collection and sharing for this project was funded by the  Alzheimer's Disease Metabolomics Consortium (National Institute on Aging   R01AG046171 ,  RF1AG051550  and  3U01AG024904-09S4 ), the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ).
As the star is behind the coronagraph during our observations, it is unfeasible to obtain unsaturated images of the star to use as a photometric reference. Hence, here we establish whether or not the satellite spots serve as appropriate photomet- ric references and a proxy for measurements of unsaturated stellar images. While satellite spots appear relatively circular when using a narrow-band filter, with the broadband  filter they appear as roughly elliptical spots. Thus, to determine satellite spot position in each frame in our time series data, we fit a 2-dimensional elliptical Gaus-sian to each of the four satellite spots using the astropy photutils modeling package  (Bradley et al. 2019). We then extracted aperture photometry using the astropy photutils aperphot package in an elliptical aperture with semi-major and semi-minor axes of 4 and 2 pixels, rotated to the correct angle to recover the flux from each satellite spot, and centered on the satellite spot positions from the 2-d elliptical Gaussian fits.
Correlations between satellite spots Following  Apai et al. (2016) , we calculate the degree of correlation between individual satellite spots using the Spearman's correlation test. The Spearman's correlation test ranks two sets of data in order of ascending data values, and then assesses how correlated the ranked sets of data are -this is the Spearman  value. If the datasets are perfectly correlated (a higher data value in one dataset always corresponds to a higher value in the other), this will produce a  value of 1. Perfectly anti-correlated datasets will produce a  value of -1. The Spearman's correlation test requires that the relationship between two variables follows a monotonic function, unlike a Pearson correlation test which specifically requires a linear relationship. We used spearmanr from from scipy.
2006; Lafrenière et al. 2007 ) observation for that planet-star separation. We found that speckle evolution in our datasets was sufficiently rapid that frames taken too far before or after a given frame were not useful to include in building the PSF using PCA, thus the frames selected for each temporal bin were run through the PCA analysis separately. The parallactic angle range we adopt for each temporal bin ensures that for each frame in the temporal bin, a sufficient number of frames taken far enough away in parallactic angle are available to build an accurate PSF model without significant self-subtraction. Each of our observations covered >70 • of sky rotation for HR 8799, over 4-5 hours; the parallactic angle coverage necessary for 20 pixels of on-sky rotation will vary according to the planet-star separation.
Since we have not removed the stellar PSF, the background varies considerably as a function of radius at the position of the planet, hence we chose to use a quite small aperture herethe 3-pixel aperture is sufficiently small to minimize variation in the background at the planet position. To correct for this radiallydependent background, we subtracted both the median of an annulus Figure  6. Spearman  coefficients between satellite spots, with no division by the mean lightcurve. A  value of 1 implies perfect correlation; a  value of -1 implies perfect anti-correlation. Conditions were particularly excellent for our August 2018 datasets; our satellite spot photometry thus covered a limited range of values, meaning that correlation between satellite spots is not a particularly good proxy for the quality of the observation.