dataesr/scientific-paragraphs-categorization huggingface dataset:
We considered three levels of sample overlap: 0% (independent), 25% (moderate) and 50% (high), with 0%, 25% and 50% samples in each study being present in the other study. Details on the simulation studies can be found in the "Methods" section. First, we compare GhostKnockoff (GhostKnockoff-S and Ghost-Knockoff-M, where S or M represents single and multiple knockoff (five knockoff copies per variant), respectively) with the second-order knockoff generator proposed by Candès et al. that requires individuallevel data (IndividualData Knockoff-S). We also extend the secondorder knockoff generator to the multiple knockoff setting and include it in the comparison (IndividualData Knockoff-M). The methods that require individual level data were applied to an oracle pooled dataset that only contains unique samples.
In the Supplementary Materials, we discuss the consistency between the estimated study correlations and similarities in the design of these studies. We present the results of the meta-analyses of the nine studies in Fig. 3C. We define two loci as independent if they are at least 1Mb away from each other. We adopt the most proximal gene's name as the locus name, recognizing that it is not necessarily the most likely causal gene. Our analysis identified in total 34 loci significant at FDR 0. 05 and 50 loci significant at FDR 0. 1. Supplementary Table  2  summarizes the 34 loci at FDR 0. 05. The results show that most of the associations exhibit suggestive signals in individual studies, and most identified loci, including existing and new ones, have the same direction of effects across all studies, except very few loci where one dataset has an opposite direction of effect, although not significant, relative to other studies.
We used this fixed effect model instead of a random effect model because the number of clusters is small relative to the total number of cells. Among the 50 proximal genes, 38 had expression measurements in the scRNAseq dataset. We considered p-value threshold 0. 05 for suggestive signals and a more stringent Bonferroni correction 0. 05/38 = 0. 0013 for significant signals (more details on the analyses are available in the "Methods" section). We show the scRNAseq results (-log10(p-value) vs. log2 fold change) in Fig. 4A. Overall, we observed a consistently higher proportion of differentially expressed genes for the proximal genes compared to all other 23496 genes that are observed in the scRNAseq data (background genes), across the 14 cell types (Fig. 4B ). Specifically, we found that 25/38 (65.
Phenome-wide analysis of UK Biobank data We applied GhostKnockoff separately to each of 1403 binary phenotypes from the UK Biobank data with 408,961 white British participants (European ancestry). In this analysis, GhostKnockoff reduces to a knockoff inference based on summary statistics from a single study. We collected existing Z-scores calculated by Zhou et al. (2018) using SAIGE, a method that controls for case-control imbalance and sample relatedness  15. GhostKnockoff was applied to each phenotype separately to select associated genetic variants at FDR 0. 1. Similar to the AD genetics analysis, we restrict the analyses to common and lowfrequency variants with minor allele frequency >1%. We aim to illustrate two properties of GhostKnockoff inference that are systematically different from a conventional GWAS analysis.
marginal test) (Fig. 5 ). GhostKnockoff identifies generally more loci per disease phenotype (4. 18-fold more discoveries on average, Figs. 5A, C ) relative to GWAS. This is not surprising given that we identify significant variants at a more liberal threshold of FDR 0. 1. More interesting, Ghost-Knockoff identifies less genetic variants within each locus (52% less variants on average after accounting for LD, Fig. 5B, C ), even though FDR control is more liberal than the FWER control. We additionally present the results stratified by phecode category (phecodes are grouped into different categories as in SAIGE) in Fig. 5D , E  15. Again, we observed that the proposed method consistently exhibits more loci and less genetic variants within each locus across disease categories. To evaluate the functional effect of the identified variants, we performed functional enrichment analysis using 19 functional scores included in regBase  37  , including: CADD  38  , DANN  39  , FATHMM-MKL  40  , FunSeq2  41  , Eigen 42 , Eigen_PC 42 , GenoCanyon  43  , FIRE  44  , ReMM  45  , LINSIGHT  46  , fitCons 47 , FATHMM-XF  48  , CScape  49  , CDTS  50  , DVAR  51  , FitCons2  52  , ncER  53  , Orion  54  and PAFA  55.
Discussion GhostKnockoff can be applied to commonly available summary statistics from conventional GWAS to improve the power to identify additional, potentially weaker, associations and to prioritize the causal variants at each locus. Additionally, GhostKnockoff can be applied to meta-analysis of possibly overlapping studies. In applications to phenome-wide analyses of UK Biobank data and a meta-analysis of Alzheimer's disease studies, we identified loci that were missed by conventional marginal association tests with improved precision. The additional discoveries are supported by functional enrichment analyses and single-cell transcriptomic analyses. These results demonstrate the improved performance of GhostKnockoff in distinguishing small effect loci that are potentially functional from noisy background genome.
In fact, the proposed estimation of study correlation is valid if the correlation of genome-wide Z-scores is correctly inferred. Our proposed method for estimating the correlation is data driven. If other factors increase/ decrease the correlation, we think that the data-driven estimation will remain valid. However, we do require that i-values from different datasets are valid, and correctly reflecting the same disease-genetic association. If the p-values in the original study are deflated or inflated, the meta-analysis results can be biased subsequently. In addition, the dependency factor for knockoff generation was derived based on sample overlapping. In practice, we found that the analysis based on this dependency factor reasonably reflects other factors, but the theoretical guarantee will require future investigations.
,s p Þ is a diagonal matrix obtained by solving the following convex optimization problem: minimize X p j = 1 |1 À s j |, subject to 2Σ À D k 0, s j ≥ 0,1 ≤ j ≤ p ( ð11Þ This way, we can directly simulate the knockoff Z-scores from a multivariate normal distribution. For a given dataset, the knockoff A, B Comparison between conventional GWAS and GhostKnockoff. C Summary of (A) and (B). For each phenotype, we calculated the ratio between the total number of identified loci/ the average number of proxy variants per shared locus by GhostKnockoff and by conventional GWAS (capped at 500 for better visualization). Panel (C) presents the average ratio (as in (A) and (B)) across 1403 phenotypes. The standard error is calculated as standard deviation of the ratio ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi total number of phenotypesÀ1 p.
D Distribution of the number of identified loci. We present boxplot (median and 25%/75% quantiles) for each disease category. E For loci identified by both conventional GWAS and the proposed method, we present median and 25%/75% quantiles of the number of identified variants per locus. For visualization purposes, we present the results for disease phenotypes with ≥ 5 loci identified by either conventional GWAS or the proposed multiple knockoff inference for panels (D, E). F Functional score of variants identified by GhostKnockoff compared to that of genome-wide background variants. Each data point in the boxplot corresponds to the average score of one disease category. The boxplot presents median and 25%/75% quantiles. Z-scores will follow the same distribution as those calculated based on generating individual-level knockoffs.
It is a convex optimization problem with a unique solution which can be efficiently solved by standard software such as the CVXR package in R. With the proposed weights, we revise the calculation above of the dependency factor as γ = ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 + N Nef f À Nef f N v u u t , Nef f N = P k w 2 k P 1 ≤ i,j ≤ K w i w j cor:S ij ð43Þ It is worth noting that the form of γ was derived for weights ffiffiffi ffi n k N p. Theoretically, the exact form of γ should be further revised to reflect the change in weights, which can be complicated to compute in practice. Here we use this form as an approximation, and we found that it empirically controls FDR very well in the presence of sample overlap. Practical strategy for study-specific rare variants Another possible and often overlooked complication in meta-analyses of genetic studies is the unequal coverage of variants across studies due to different genotyping platforms and/or different imputation panels for individual studies, which results in the presence of "studyspecific" rare variants (MAF < 0.
Empirical power and FDR simulations For each replicate, we first generated two overlapping studies (2500 individuals per study) with genetic data on 2000 common and rare genetic variants randomly selected from a 1Mb region near the APOE region (chr19:44909011-45912650; hg38) in the ADSP study. We then restricted the simulations to variants with minor allele counts >25 to ensure stable calculation of summary statistics (e. g. p-values). Since the simulations here focus on method comparison to identify relevant clusters of tightly linked variants, we simplify the simulation design by keeping one representative variant from each tightly linked cluster. Specifically, we applied hierarchical clustering such that no two clusters have cross-correlations above a threshold value of 0. 75 and then randomly choose one representative variant from each cluster to be included in the simulation study.
; (2) The genome-wide meta-analysis of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls) by Jansen et al. 24 ; (3) The genome-wide meta-analysis of clinically diagnosed AD (21,982 cases, 41,944 controls) by Kunkle et al. 25 ; (4) The genome-wide meta-analysis by Schwartzentruber et al. 26 , aggregating Kunkle et al. 2019 and UK Biobank based on a proxy AD phenotype; (5) In-house genome-wide associations study of 15,209 cases and 14,452 controls aggregating 27 cohorts across 39 SNP array datasets, imputed using the TOPMed reference panels 27 ; (6, 7) Two whole-exome sequencing analyses of data from The Alzheimer's Disease Sequencing Project (ADSP) by Bis et al.
Data availability The manuscript used summary statistics from existing studies from the UK Biobank available at https://pheweb. org/UKB-SAIGE/. The summary statistics from each GWAS for Alzheimer's disease can be found at https://ctg. cncr. nl/software/summary_statistics, https://www. niagads. org/datasets, and https://www. ebi. ac. uk/gwas/. Specifically, (1)  The genome-wide survival association study performed by Huang      8 ) In-house whole-exome sequencing analysis of ADSP (NIAGADS ID: NG00067. v5); (9) In-house whole-genome sequencing analysis of ADSP (NIAGADS ID: NG00067. v5). The single-cell RNASeq data for the candidate genes are available in the GEO database under accession code GSE163577. The results of our analysis of UK Biobank and AD genetics can be downloaded at: zihuaihelab.
Measurement bench A measurement bench was designed in-house with the purpose of measuring full-field through-thickness deformation along with frictional properties of skin strips in contact with a cylindrical probe (Figure  1 ). The probe axis is parallel to the sample top surface and normal to the imaging plane and the direction of travel in friction experiments. The principle of the measurement was to acquire pictures of the side of the sample with a camera during its interaction with the probe. Deformations were later computed using DIC using the open source Matlab software Ncorr  [39]. The skin sample was held in the centre of the device by a system that allowed it to be stretched. The top, front and back surfaces of the samples were free, the bottom surface rested on the device and the side ones were used to apply tension in the sample.
The camera was equipped with a manual zoom lens (MLH-10X, CBC America, Cary, North Caroline, USA), and a 40mm lens extension tube. In this configuration, as the resolution of the camera was 1260x980 pixels, the width of 1 pixel was approximately 6µm, giving a total field of view of 7. 56mm × 5. 88mm. A light source using LED (MultiLED QT, GS Vitec, Gründau, Germany), which power was adjustable using an external control table (MultiLED G8, GS Vitec, Gründau, Germany), was located on the other side of the sample, allowing it to be back lit. A preliminary study has determined that if the samples are more than 4mm wide, the light does not pass through them sufficiently to have a good image quality. The force sensor, the motorized displacement table and the camera were connected to a computer. A LabView program was created in order to coordinate all the devices of the bench and ensure good controlling of the acquisition process.
Image processing Before DIC can be applied, it was necessary to apply some filtering to the images. This last step aims to improve the contrast in order to enhance the speckle pattern quality. First, very high frequency noise was removed using a rectangular window filter. Then, the low frequencies were removed from the images in order to enhance the contours. The application of filters creates intermediate gray levels so this is why the images were converted into 16-bit gray level format to further improve the details. An example of image obtained after filtering is shown in figure 2. The DIC was performed using the open source Matlab software Ncorr  [39]. A preliminary experiment was carried out to determine the optimal size for subset radius. For all 72 experiments, the reference situation was a picture of the unloaded sample.
Street-scale module The Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is a street-scale air quality model developed by  Kim et al. (2018)  to simulate the concentrations in the urban canopy. The variation of pollutants simulated on the street scale was mainly caused by two modules in the model: a street-canyon module and a street-intersection module. The chemical transformation of pollutants was modeled using the CB05 chemical kinetic mechanism  (Yarwood et al. , 2005). In MUNICH v1. 0, the pollutant concentrations in streets are computed assuming stationarity in a street segment. The mass flux balance was used to calculate the concentration. Using the stationary assumption can lead to numerical instability in the MUNICH simulations. A new algorithm was developed to reduce the numerical instability by calculating the concentration without the stationary assumption  (Lugon et al.
, 2020). More details of the mechanism of MUNICH in street calculations can be found in  Kim et al. (2018) ; it has been widely used in European countries  (Kim et al. , 2018; Lugon et al. , 2020; Mario et al. , 2020). As the input file for MUNICH, the street information and the 297,211 building locations and floors in Beijing were obtained from the online map website Gaode Map-China's most popular mapping source, and its point of interest data were acquired via the application programming interface provided by the official website of Gaode Map (https://lbs. amap. com/, last access: January 10, 2022). In this study, we assumed the height of each floor to be 3 m to calculate building heights (Fig. 1.
Downscaling module coupling the regional and street-scale modules NAQPMS and MUNICH were coupled offline: first, the NAQPMS was run over Beijing, and the regional concentrations of NO x , O 3 , and VOCs were simulated by NAQPMS without Beijing traffic grid emissions, avoiding double-counting the traffic emissions in regional-and street-scale models. The background concentration of each street needed in MUNICH was obtained from NAQPMS using an urban-street coupler. In the coupler, the background concentrations of NO x and O 3 in each street segment were obtained from the NAQPMS bottom layer, and the background concentration of each street segment was obtained from the nearest grid data simulated by NAQPMS with a 3-km horizontal grid spacing. The meteorology simulated by the WRF was adapted to street canyons in MUNICH  (Kim et al.
On-road traffic emission module Reliable high spatiotemporal resolution emission data could significantly improve the quality of input data in the air quality model for simulations at the urban street level. The real-time on-road emission (ROE) model was developed by  Wu et al. (2019)  to calculate urban street-level on-road emissions using real-time traffic speed data obtained from the Gaode Map navigation application. The model is divided into four modules: the crawler module for the traffic speed data on roads, the preprocessing module for preparing road information and parameterization data, the calculation module for road emissions, and the output module for emission inventories, which are the input data of the street-scale model. The model crawls real-time traffic speed data for every road in the study area and calculates the traffic flow of every road using the Underwood volume calculation model  (Underwood, 1961).
The formula used in the model is shown in Eq. (  1 ): V = k m × u × ln u f u (1) where u is the traffic speed (km/h), u f is the free speed on the road (km/h), k m is the traffic density (veh/km), and V is the traffic flow at speed u (veh/h). In this study, the traffic flow of motorways, trunk roads, and local roads in Beijing was manually counted from the collected traffic video records, and the corresponding traffic speed was crawled from the Gaode Map navigation application. The localized empirical parameters developed based on these observation data were k m = 159 and u f = 88, k m = 173 and u f = 54, and k m = 210 and u f = 35 in motorways, trunk roads, and local roads, respectively. In the emission calculation module, the emission rate for each street segment was calculated according to the vehicle fleet information, vehicle emission factors, and road length in the street.
The calculated emission pollutants included NO x , CO, hydrocarbons (HC), PM 2. 5 , and PM 10. The formula is shown in Eq. (2). s,t = ∑ n v=1 EF s,v × V v,t × L (2) where Emis s, t is the emission rate of pollutant s at time t (g/h), EF s, v is the emission factor of pollutant s for vehicle category v (g/km), V v, t is the traffic flow of vehicle v at time t (veh/h), and L is the length of the street segment (km). In this study, the total number of different vehicle categories in the ROE was updated, as shown in Table  S1. The output module summarizes the data calculated using the emission calculation module and saves the format of the emission data required by the street-scale air quality model. More detailed information on the ROE model can be found in  Wu et al. (2019).
3b. Monthly anthropogenic emissions of 0. 1 o × 0. 1 o resolution were obtained from the Multi-resolution Emission Inventory for China (MEIC, http://www. meicmodel. org/dataset-mix. html, last access: January 10, 2022), including from the power, industry, residential, vehicle, and agricultural sectors. Biogenic emissions with 0. 25 o resolutions were provided by the Model of Emissions of Gases and Aerosols from Nature (MEGAN v2. 0)  (Guenther et al. , 2006) , developed by the National Center for Atmospheric Research (NCAR). Emissions from biomass burning were calculated from the Fire Inventory from the NCAR (FINN v1) emissions reported by  Wiedinmyer et al. , (2011). FINN provides daily global emissions with a resolution of 0. 01 o × 0. 01 o based on satellite observations for detecting active fires as thermal anomalies and land-cover changes  (Wiedinmyer et al.
, 2011). The emission data were adjusted using the sparse matrix operator kernel emissions (https://zenodo. org/record/1421403#. Yd-5wNFByUk, last access: January 10, 2022) modeling system to make grid data that could be consistent with simulation settings. To isolate the impact of high-resolution vehicle emissions and the street-scale model, three scenarios were implemented (Table  1 ). First, the regional model simulation scenario with MEIC static emissions (NAQPMS-MEIC), and second, the regional model simulation scenario with high spatial-temporal traffic emissions (NAQPMS-ROE); the hourly emissions in each street were considered and summed up to the grid emission for NAQPMS, such that the hourly variation and spatial variation of traffic emissions were provided in NAQPMS-ROE. This scenario was compared with NAQPMS-MEIC to understand the impact of the improvement of vehicle emissions on the simulation by the regional model.
Observation data The ground sites used for comparison are shown in Fig. 3b. Meteorological observations, including temperature, relative humidity, wind speed, and wind direction, at four stations were collected from the China Meteorological Administration and used for comparison with the meteorological simulations (Fig. S1 ). The hourly measurement data for O 3 and NO 2 were obtained from the Chinese National Environment Monitoring Center (CNEMC, http://www. cnemc. cn/, last access: 13, 2022), and the hourly NO observations were provided by the Beijing Municipal Ecological and Environmental Monitoring Center (http://www. bjmemc. com. cn/index. jsp, last access: January 13, 2022). During the study period, the observed daily maximum O 3 concentration exceeded 150 μg/m 3 from September 25 to September 29, 2020, in Beijing, decreasing to 50-75 μg/m 3 during the holidays (October 1 to 8, 2020).
CompOSE (CompStar Online Supernovae Equations of State) is an online repository of equations of state (EoS) for use in nuclear physics and astrophysics, e. g. , in the description of compact stars or the simulation of core-collapse supernovae and neutron-star mergers, see https://compose. obspm. fr. The main services, offered via the website, are: a collection of data tables in a flexible and easily extendable data format for different EoS types and related physical quantities with extensive documentation and referencing; software for download to extract and to interpolate these data and to calculate additional quantities; webtools to generate EoS tables that are customized to the needs of the users and to illustrate dependencies of various EoS quantities in graphical form. This manual is an update of previous versions that are available on the CompOSE website, at arXiv:1307.
Part I. Introduction 1 What CompOSE can do and what not The online service CompOSE provides information and data tables for different equations of state (EoS) ready for further use in astrophysical applications, nuclear physics and beyond. See reviews  [2] [3] [4] [5]  for general introductions. In general, EoS are available from more-or-less sophisticated theoretical models or parametric descriptions using different representations of the data. In some cases, e. g. , for the calculation of properties of cold neutrons stars, simple piecewise polytropes, speed-of-sound parameterizations or other functional forms with a dependence on a single variable like the baryon density are used. This EoS class allows a fast computation of the required quantities and a simple change of the parameters is often possible.