dataesr/scientific-paragraphs-categorization huggingface dataset:
Y. M. P. receives support from the  Dutch Heart Foundation (CVON PRIME). B. P. H. is funded by the  BHF   Intermediate Fellowship  ( FS/ICRF/21/26019 ). D. P. O'R. is supported by the  MRC  ( MC_UP_1605/13 ) and  BHF  ( RG/19/6/34387 ). R. A. d. B. is supported by the  Dutch Heart Foundation  ( 2020B005 ), by the  Leducq Foundation (Cure-PLaN) , and by the  European Research Council  ( ERC CoG 818715 ). I. C. receives support from the  Dutch Heart Foundation  ( CVON 2015-12 eDETECT ). P. M. M. received funding from the  Edmond J. Safra Foundation  and  Lily Safra  and an  NIHR   Senior Investigator Award , the  UK Dementia Research Institute , which receives its funding from  UK DRI Ltd. , funded by the  MRC ,  Alzheimer's Society , and  Alzheimer's Research UK  and the  Imperial College British Heart Foundation Centre of Excellence.
J. -C. T. holds the  Canada Research Chair  in personalized medicine and the  University of Montreal  endowed research chair in atherosclerosis, and he is also the principal investigator of the  Montreal Heart Institute André  and  France Desmarais  hospital cohort funded by the  Montreal Heart Institute Foundation. A. G. has received support from the  BHF ,  European Commission  [ LSHM-CT-2007-037273 ,  HEALTH-F2-2013-601456 ] and  TriPartite Immunometabolism Consortium [TrIC]-NovoNordisk Foundation  [ NNF15CC0018486 ],  BHF-DZHK  ( SP/19/2/344612 ). C. R. B. received support from  EJP-RD  ( LQTS-NEXT ,  ZonMW  project  40-46300-98-19009 ) and the  Leducq Foundation  (project  17CVD02 ). HW is member of the  Oxford BHF Centre   for Research Excellence  ( RE/13/1/30181 ). HW and JW are supported by  CureHeart , the  British Heart Foundation's   Big Beat Challenge award  ( BBC/F/21/220106 ).
Data availability Data from the Genome Aggregation Database (gnomAD, v. 2. 1. 1)  are available at https://gnomad. broadinstitute. org. Data from the UKB can be requested from the UKB Access Management System (https://bbams. ndph. ox. ac. uk). Data from the GTEx consortium are available at the GTEx portal (https://gtexportal. org). Published snRNA-seq data are available at the Broad Single Cell Portal (singlecell. broadinstitute. org) and at the Cellxgene tool website (https://cellxgene. cziscience. com/collections/e75342a8-0f3b-4ec5-8ee1-245a23e0f7cb/private). Other datasets generated during and/or analyzed during the current study can be made available upon reasonable request to the corresponding authors. Individual level data sharing is subject to restrictions imposed by patient consent and local ethics review boards.
Variants were retained where meta-analysis came from 2 or more studies and had sample size >5,000 for sarcomere-negative and >2,500 for sarcomere-positive. The final dataset included 9,492,702 (all comer), 7,614,734 (HCMSARC+)   and 9 ,226,079 (HCMSARC-) variants after filtering. The results of the all-comer HCM GWAS meta-analysis and stratified analyses are presented in Table  1, Supplementary Figure 1  and Supplementary Table 2. A false discovery rate (FDR) 1% P value cut-off was derived from the all-comer, HCMSARC+ and HCMSARCsummary statistics using Simes method (Stata 10. 1) and the corresponding P-values were 8. 5x10 -6 , 1. 6x10 -6 and 7. 8x10 -6 respectively. Using the 1% FDR P value thresholds, we then performed a stepwise model selection to identify 1% FDR independently associated variants using GCTA.
We then selected the trait with the highest genetic correlation with HCM for each cluster (strain circ , LVESVi and LVconc) to include in MTAG together with HCM. Only SNPs included in all meta-analyses (that is HCM and LV traits) were used in MTAG. The coded/noncoded alleles were aligned for all 4 studies before MTAG, and multi-allelic SNPs were removed. All summary statistics refer to the positive strand of GRCh37 and, as such, ambiguous/palindromic SNPs (having alleles A/T or C/G) were not excluded. Regression coefficients (beta) and their standard errors were used as inputs for MTAG. The maxFDR was calculated as suggested by the MTAG developers. 16  MaxFDR calculates the type I error in the analyzed dataset for the worst-case scenario. We estimated the gain in statistical power by the increment in the effective sample size (Neff).
1. 08, as implemented in    FUMA 22  , including gene-set and tissue expression analyses. We used Gene Ontology (GO) gene sets from the Molecular Signatures Database (MSigDB, v. 6. 2) for the gene-set analysis and the Genotype-Tissue Expression project(GTEx, v. 8) for the tissue specificity analysis. The results of MAGMA analyses are shown in SupplementaryTable 11 (gene-set analyses) and Supplementary Table 12(tissue specificity analyses). Cardiac cell type heritability enrichment analysisGene programs derived from single nuclei RNA sequencing (snRNA-seq) were used to investigate heritability enrichment in cardiac cell types and states using the sc-linker framework. 21  This approach uses snRNA-seq data to generate gene programs that characterize individual cell types and states. These programs are then linked to genomic regions and the SNPs that regulate them by incorporating Roadmap Enhancer-Gene Linking 50,51 and Activity-by-Contact models52,53.
625° longitude by 0. 5° latitude. Detailed information can be found in  Gelaro et al. (2017). To assign the gridded MERRA-2 data to the simulations for the collocated MODIS and CALIOP (referred to as https://doi. org/10. 5194/egusphere-2023-103 Preprint. Discussion started: 6 February 2023 c Author(s) 2023. CC BY 4. 0 License. "MODIS-CALIOP") observations, we first obtain the geolocation and time of all grid cells of MERRA-2 data. Then, we find the spatially and temporally closest grid cell with each MODIS-CALIOP pixel. 185 As the retrieval is implemented over oceans only, which is explained in Section 2. 3, we obtain the level-2 sea surface temperature (SST) retrieved based on the Advanced Microwave Scanning Radiometer -Earth Observing System Sensor (AMSR-E) onboard Aqua (ceased operation in December 2011), and its successor (launched in May 2012), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard GCOM-W1 that follows Aqua's orbit.
The radiative transfer models 205 The foundation of a LUT-based retrieval method is an accurate radiative transfer model. For the radiative transfer simulation of terrestrial TIR radiation under clear atmospheric conditions, atmospheric gaseous absorption is critical. In this study, we use the version-2 CRTM developed by the US Joint Center for satellite data Assimilation (JCSDA) as the foothold for our retrieval  (Chen et al. , 2012; Han, 2006). The transmittance coefficients in CRTM are first trained by applying regression algorithms to the line-by-line integrated transmittances for numerous atmospheric 210 profiles  (McMillin et al. , 2006). Afterward, the gaseous absorption component can achieve an accuracy as high as the line-by-line transmittance but consumes far less computational time  (Ding et al.
Dust refractive indices and dust shape The RI of dust, determined by dust mineral compositions, has a profound impact on dust scattering properties and 270 therefore the retrieval results  (Sokolik and Toon, 1999). Ideally, the dust RI should be retrieved simultaneously with other properties of dust. However, given the highly limited information content from the three MODIS TIR bands, a retrieval of dust RI is not possible, at least in this study. It should be noted that most previous studies also used preassumed dust RI, often one or two simple global constants, in their retrievals including widely used operational aerosol retrieval products  (Capelle et al. , 2018; Zhou et al. , 2020). 275 Nevertheless, in this study, we try to incorporate the spatial variability of dust RI in our retrieval by using two newly developed datasets.
One is a state-of-the-art dust RI database developed by Di Biagio et al. (  2017 ) (referred to as the "Di-Biagio RI"), which provides dust RIs retrieved based on surface soil samples collected in nineteen arid and semiarid sites from worldwide dust source regions. The Di-Biagio RI database provides the observational basis for 280 accounting for the regional dependence of dust RI. The other is the fractional contribution over oceans supplied by various dust source regions from the DustCOMM-2021 dataset developed by  Kok et al. (2021a) , which is used to assign dust RIs from different source regions to the observed dust aerosol over oceans. Details of the dust RI assignments are presented in Appendix C.
The higher the aspect ratio, the greater the non-sphericity. 295 The spheroid shape assumption is a first-order approximation of dust non-sphericity  (Mishchenko et al. , 1997; Dubovik et al. , 2002)  and is widely used for non-spherical aerosol retrievals  (Levy et al. , 2013; Kahn et al. , 2010). To seek a broader application of this study to others, we stick to the spheroid assumption with the size-independent aspect ratio distribution from  Dubovik et al. (2006)  for the retrieval. Nonetheless, the retrieval based on a more advanced non-spherical dust optical properties database, such as the hexahedral shape  (Saito et al. , 2021) , will be evaluated in 300 future studies. By assuming dust particles with spheroidal shape, we calculate the dust single-particle optical properties for each a priori dust RI using the T-matrix method  (Mishchenko et al.
655 Figure  10  shows the comparisons of 5° by 2° gridded seasonal mean MODIS-CALIOP DAOD10μm with the two IASI DAOD10μm over the three dust-transport regions from 2013 to 2017. MODIS-CALIOP DAOD10μm over NA and IO are highly correlated and consistent with IASI-LMD DAOD10μm with R = 0. 9,0. 8 and k = 0. 9,1. 1, respectively, as shown in Figures  10a  and 10b. However, for optically thin dust (e. g. , DAOD < 0. 1), IASI-LMD DAOD10μm is systematically ~0. 02 greater than MODIS-CALIOP DAOD10μm as shown from the intercepts of the linear regression 660 in Figures  10a  and 10b. In addition, the two datasets have poor agreement over NP (R = 0. 2). In contrast, MODIS-CALIOP DAOD10μm achieves a better correlation over the dust-transport regions (including NP) than the comparison with IASI-LMD DAOD10μm (see Figures  10d-10f ).
It is mainly because both retrievals mostly avoid contamination from sub-pixel clouds and background aerosols, which should be the reason for the high bias of the IASI-LMD optically thin dust (DAOD10μm < 0. 1) DAOD10μm. However, IASI-ULB DAOD10μm is 40%-60% lower than that of 665 MODIS-CALIOP and IASI-LMD DAOD10μm over the three dust-transport regions (k = 0. 6,0. 5,0. 4 in Figures  10d-10f ). Although the discrepancy of the two IASI-based DAOD10μm is non-negligible, the two datasets achieve good agreements with AERONET because the different assumptions of TIR-to-VIS DAOD ratios offset the DAOD10μm 670 difference. As they assumed similar dust RIs (e. g. , OPAC RIs) and spherical dust, the disagreement of the DAOD10μm could be due to the different a priori dust PSDs. IASI-ULB assumes a monomodal dust PSD with a geometric mean radius at 0.
The 2013 case validates the Deff retrieval in both the short-range (Cape Verde) and long-range (the Caribbean Sea) transport regions by comparing with SALTRACE dust PSD and demonstrates the retrieval's capability of revealing the transport 800 process of dust coarse-mode particle size in a better spatiotemporal resolution than in-situ measurements. The results convince us that the DAOD10μm and Deff retrieval dataset can provide a better constraint on regional and global LW DRE uncertainties due to DAOD and dust PSD. However, an assumption of dust RI is still needed. We apply the retrieval to five-year MODIS-CALIOP data from 2013 to 2017 and compare the DAOD retrieval with 805 IIR-based and IASI-based retrieval. As an improved version compared with the IIR-based retrieval, the MODIS-CALIOP retrieval reduced ~50% of DAOD uncertainty and achieves good consistency (R = 0.
Secondly, our retrieval is still not applicable for observations over land due to the uncertainties from land surface temperature and emissivity. Nonetheless, with more reliable databases of land surface characteristics, this portable retrieval algorithm can be easily extended to cover the dust source regions. Thirdly, the limited spatial coverage of CALIOP restrains the application of our data to regional studies. Extending the retrieval to off-CALIOP-track MODIS pixels is 835 recommended for future studies. Figures  A1a  and A1c  show the BT discrepancies (referred to as "dBT") between the simulations and the observations for daytime and nighttime cloud-free and clean cases over oceans at three MODIS TIR bands. Figures  A1b  and A1d  show the corresponding discrepancies of the cloud-free spectral BT differences (BTD) between 11 μm and 12 μm (blue curve, referred to as "dBTD11-12") and that between 8.
However, the DAOD532nm uncertainty contributed by LR is beyond this study and will not be discussed. Readers are referred to  Kim et al. (2020)  for details. It should be noted that the DustCOMM-2021 is a climatological dataset. As such the uncertainties included in this dataset cannot be propagated into not propagable for instantaneous observational retrievals. Hence, in this study, 940 instead of scaling the Di-Biagio RI's fractional contribution to form a new dust RI, we select the source region if its DAOD fractional contribution exceeds 0. 1 and assign the corresponding Di-Biagio RIs within the selected source region for our retrieval. Figure  C1  shows the nine regimes over oceans with fractional contribution greater than 0. 1 for the nine defined dust source regions in summer (see Figures S2 to S4 for other seasons).
The Di-Biagio RIs are assigned to the nine source regions based on their geolocations. For observations in each season within each regime, 945 the retrieval will assume the dust originated from the identified dust source regions and choose the corresponding Di-Biagio RIs. Note that the nine regimes can overlap, meaning that the observation over a particular grid cell covered by multiple regimes will assume multiple RIs from these regimes. The uncertainty due to the variation of multiple RIs is evaluated in Section 3. 2. Spatiales (CNES) and by the CNRS via the Labex L-IPSL, which is funded by the ANR (grant no. ANR-10-LABX-0018). The computations in this study were performed at the UMBC High Performance Computing Facility (HPCF). The facility is supported by the US National Science Foundation through the MRI program (grant nos.
CNS-0821258 and CNS-1228778) and the SCREMS program (grant no. DMS-0821311), with substantial support from UMBC. We acknowledge the AERIS data infrastructure for providing access to the IASI-LMD data in this study and CNRS-LMD 970 for the development of the retrieval algorithms. We thank the ICARE Data Services Center for providing access to the IASI-ULB data in this study at http://www. icare. univ-lille1. fr/. We thank.
Acknowledgment JZ, ZZ and AG are supported by a  NASA  grant (no. 80NSSC20K0130 ) from the  CALIPSO  and  CloudSat program  managed by  David Considine. HY was supported by the  CloudSat/CALIPSO program. CR acknowledges funding from  NERC  grant  NE/M018288/1. JFK was supported by  NSF  grants  1856389  and  2151093  and the  Army Research Office  (cooperative agreement number  W911NF-20-2-0150 ). C. D. B. was supported by the  Centre National des Etudes.
https://doi. org/10. 5194/egusphere-2023-103 Preprint. Discussion started: 6 February 2023 c Author(s) 2023. CC BY 4. 0 License. lower than that of IASI-LMD DAOD10μm and the climatological Deff. The discrepancy of the two AERONETevaluated IASI DAOD10μm datasets reveals that the dependency of TIR-to-VIS DAOD ratios and the retrieved DAOD10μm to dust PSD is non-negligible, which is also proved in Z22. It highlights the importance of considering the spatiotemporal variation of dust Deff in TIR retrievals. 815 A global and climatological analysis of the five-year Deff retrievals from -60°N to 60°N over oceans is presented.
at NASA/GSFC for providing the ground-based aerosol data. The laboratory experiments to retrieve the dust refractive indices in Di Biagio et al. (2017) that feed this work had received funding from the European Union's Horizon 2020 research and innovation program through the EUROCHAMP-2020 Infrastructure Activity under grant agreement no. 730997. They were supported by the French national programme LEFE/INSU (Les Enveloppes Fluides et l'Environnement/Institut National des Sciences de l'Univers) and by the OSU-EFLUVE (Observatoire des Sciences 980 de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie) through dedicated research funding to the RED-DUST project. The authors acknowledge the CNRS-INSU for supporting the CESAM chamber as national facility as part of the French ACTRIS Research Infrastructure as well as the AERIS datacenter (https://en.
Methods Exome sequencing (ES) or screening using a macrocephaly panel was performed by all participants according to their own diagnostic protocols and platforms (See Supplemental data). Sanger sequencing was used to confirm BRWD3 variants in DNA samples from the probands and relatives. BRWD3 primers were designed based on RefSeq NM_153252. 4 to confirm the identified variant and perform family segregation analysis. International data sharing platforms like GeneMatcher (see URL) were used to assemble the cohort. The literature was also reviewed and only patients with phenotypic data available were included in our summary analysis.
Molecular results In this study, we assembled a cohort of 14 previously unreported individuals with 13 different BRWD3 variants identified through ES (12 individuals) or macrocephaly panel screening (2 individuals) (Table  1 ). These 13 variants (NM_153252. 4) included 3 nonsense, 4 frameshift, splice site, and 4 missense variants. Human Splicing Finder (HSF v3. 1) did not predict any significant splicing alteration for the c. 473T>C missense variant, but did suggest the creation of an exonic splicing silencer site leading to a potential alteration of splicing for the c. 574G>A variant. Five variants occurred de novo and 8 variants were maternally inherited. In one affected female (individual 5), X-inactivation analysis showed skewed X-inactivation (100%-0%). All the new variants have been submitted in the ClinVar public database.
Acknowledgement We thank the families for their participation. This work is supported by the  European Union  through the  FEDER Bourgogne   PERSONALISE 2019/2022  programs. Several authors are part of the ERN ITHACA. We thank  Dr Mulhern  and  Mrs Lippa  from the  Institute for Genomic Medicine, Columbia University, New York ,  NY 10032 ,  USA  for their participation to this study.
We demonstrate that genes related to oxidative phosphorylation are transcriptionally upregulated in LNM, and that these genes are also upregulated in African-American patients relative to White patients. We further show that mutations in TP53, FLT4, EYA1, CSMD3, and PCDH15 are enriched in prostate cancer metastases. Moreover, we validate our findings in a meta-analysis of publicly available datasets from twelve different studies representing 3929 primary tumors and 2721 metastases. These findings have implications for our understanding of the molecular events required for prostate metastasis.
Patient samples All patient samples were derived from a clinical trial (NCT01808222) conducted at our institution to determine the sensitivity and specificity of [18F]-fluciclovine PET imaging for the detection of significant occult metastatic disease in patients with unfavorable intermediate-risk or high-risk prostate cancer  [26]. These patients had no definitive findings of systemic metastases on conventional imaging such as CT, MR, and bone scan. Patients underwent radical prostatectomies and extended pelvic lymph node dissections. In this trial, 56 prostate cancer patients were enrolled based on criteria that correspond to a 50-80% PSA failure rate within high or very high-risk groups (T3a, Gleason score 8-10, or PSA greater than 20 ng/ml) and underwent radical prostatectomy and extended pelvic lymph node dissection.
All available FFPE blocks were collected from Emory University Hospital (EUH) pathology services and coded to remove any personal health information (PHI). For three of the cases, the metastatic LN blocks were not available and were excluded from the study and for three other cases, no LN metastases exceeded the minimum size of 4mm and were also excluded, leaving a total of 43 cases available for RNA and DNA extraction. Clinical metadata is provided in Supplementary Table  S1. Race was determined by self-identification.
1 based on F-tests. GATK funcotator was used to annotate mutations and generate mutation annotation format (MAF) files. These MAF files were analyzed using the R package maftools  [33]  to identify recurrent mutations and patterns to build mutational profiles. BAM files were also analyzed using HATCHet  [34] , an algorithm to compute tumor heterogeneity, copy number aberrations (CNAs), and whole-genome duplications (WGDs) used to investigate tumor evolution and metastatic seeding patterns.
Results To investigate the molecular changes, mutations, and signaling pathways that characterize the initial steps of prostate cancer metastasis to the lymph nodes, we analyzed 19 discrete LNM, 97 primary tumor foci, 39 benign lymph nodes, and 10 normal adjacent prostate tissue samples from 43 patients enrolled in a clinical trial (NCT01808222) of extended lymphadenectomy and metastasis excision guided by [18F]-fluciclovine PET imaging for high risk, newly diagnosed prostate cancer conducted at our institution  [26]  (Supplementary Table  S1 ). These patients fell within high or very high-risk groups (T3a, Gleason score 8-10, or PSA greater than 20 ng/ml). Of these patients, 16 had LN metastases at least 4 mm in diameter, and 20 patients had no metastases. For metastatic patients, we performed RNA and Whole Exome Sequencing (WES) on three primary tumor foci, one benign LN, and all LN metastases greater than 4mm in diameter (see Methods).
To better quantify the OXPHOS pathway activation in our RNAseq datasets, we constructed an OXPHOS score based on the z-score normalized gene expression values of the 105 genes in the leading edge of the OXPHOS gene set analyses. We computed the OXPHOS score for all samples and compared the expression in normal, primary tumor, and LNM samples and found increased OXPHOS scores as tumors progressed (Figure  1D ). Interestingly, FGFR3 was highly correlated with OXPHOS gene expression, including expression of IDH1 (Figure  1E , p < 2. 2e-16) and IDH2 (p = 3. 7e-14), which is critical for and modulates the assembly of the entire OXPHOS system  [37]. We noted that FGFR3 was expressed at higher levels in AA samples than in EA samples in our cohort (Figure  1F ). To investigate whether this observation was confirmed in an independent cohort, we also analyzed the association of FGFR3 mRNA expression with %African Ancestry data from The Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) dataset  [1]  using data available on cBioPortal  [38, 39]  and determined that FGFR3 was one of the genes most strongly associated with %African (%AFR) ancestry (Spearman rho = 0.
Tumor Heterogeneity Analysis identifies subclones suggesting gain of chr8 prior to chr8p loss To examine subclones within each sample, we performed HATCHet analysis. The HATCHet software tool can compute tumor heterogeneity  [34] , copy number aberrations (CNAs), and wholegenome duplications (WGDs) to investigate tumor evolution and metastatic seeding patterns. We applied HATCHet analysis to our WES dataset and observed that most samples had subclonal populations (Figure  4 ). Patient PCM034 which had three LNM was of particular interest and had three subclones. Clone 1 was enriched in all three LNM and was characterized by gain of chromosomal arm 8q (containing c-MYC) and loss of chromosomal arm 8p (containing NKX3. 1). The other subclones exhibited gain of chr8 but no loss of 8p, suggesting that gain of chr8 may have preceded loss of 8p during tumor evolution in this patient.