dataesr/scientific-paragraphs-categorization huggingface dataset:
Optical/near-infrared imaging We have supporting data from the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS;  Grogin et al. 2011 ) with images obtained with the Wide Field Camera 3 / infrared channel (WFC3/IR) and UVIS channel, along with the Advanced Camera for Surveys (ACS;  Koekemoer et al. 2011. The area covered by this survey lies in the deep region of the CANDELS program (central one-third of the field). The 5-σ detection depth for a point-source reaches a magnitude of 28. 16 for the H 160 filter (measured within a fixed aperture of 0. 17  Guo et al. 2013). The CANDELS/Deep program also provides images in 7 other bands: the Y 125 , J 125 , B 435 , V 606 , i 775 , i 814 and z 850 filters, reaching 5-σ detection depths of  28. 45, 28. 35, 28. 95, 29. 35, 28. 55, 28.
Mid/far-infrared imaging Data in the mid and far-IR are provided by the Infrared Array Camera (IRAC;  Fazio et al. 2004) at 3. 6, 4. 5, 5. 8, and 8 µm, Spitzer Multiband Imaging Photometer (MIPS; Rieke et al. 2004 ) at 24 µm, Herschel Photodetector Array Camera and Spectrometer (PACS,  Poglitsch et al. 2010 ) at 70, 100 and 160 µm, and Herschel Spectral and Photometric Imaging REceiver (SPIRE,  Griffin et al. 2010 ) at 250, 350, and 500 µm. The IRAC observations in the GOODS-South field were taken in February 2004 and August 2004 by the GOODS Spitzer Legacy project (PI: M. Dickinson). These data have been supplemented by the Spitzer Extended Deep Survey (SEDS; PI: G. Fazio) at 3. 6 and 4. 5 µm  (Ashby et al. 2013)  as well as the Spitzer-Cosmic Assembly Near-Infrared Deep Extragalactic Survey (S-CANDELS;  Ashby et al.
Complementary ALMA data As the GOODS-South Field encompasses the Hubble Ultra Deep Field (HUDF), we take advantage of deep 1. 3-mm ALMA data of the HUDF. The ALMA image of the full HUDF reaches a σ 1. 3mm = 35 µJy  (Dunlop et al. 2017) , over an area of 4. 5 arcmin 2 that was observed using a 45-pointing mosaic at a tapered resolution of 0. 7. These observations were taken in two separate periods from July to September 2014. In this region, 16 galaxies were detected by  Dunlop et al. (2017) , 3 of them with a high SNR (SNR > 14), the other 13 with lower SNRs (3. 51 < SNR < 6. 63). 1. Summary of the observations. The slice ID, the date, the number of antennae, the time on target, the total time (time on target + calibration time), the resolution and the 1-σ noise of the slice are given.
Radio imaging We also use radio imaging at 5 cm from the Karl G. Jansky Very Large Array (VLA). These data were observed during 2014 March -2015 September for a total of 177 hours in the A, B, and C configurations (PI: W. Rujopakarn). The images have a 0 31 × 0 61 synthesized beam and an rms noise at the pointing centre of 0. 32 µJy. beam -1  (Rujopakarn et al. 2016 ). Here, 179 galaxies were detected with a significance greater than 3 σ over an area of 61 arcmin 2 around the HUDF field, with a rms sensitivity better than 1 µJy. beam -. However, this radio survey does not cover the entire ALMA area presented in this paper.
Acknowledgements. We wish to thank  Ivo Labbé  for sharing with us the IRAC/GREATS images and  Bahram Mobasher  for sharing the spectroscopic redshift of AGS4 with us. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2015. 1. 00543. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. RD and NN gratefully acknowledge the support provided by the  BASAL Center for Astrophysics and Associated Technologies (CATA)  through grant  PFB-06   Etapa II. Support for BM was provided by the  DFG priority program 1573  "The physics of the interstellar medium".
Study population In the present study we retrospectively analysed 861 consecutive patients with type 2 diabetes who were referred as outpatients to nephrologists in our hospital during the 2000-13 period (Centre Hospitalier Universitaire, Hopital Bretonneau, Tours, France). The study was approved by the Ethics Committee of Human Research of our hospital (approval number 2016-21). Type 2 diabetes was defined according to the American Diabetes Association (ADA) criteria  [10]. Data were retrieved from files at baseline and during follow-up. They included age, gender, comorbid conditions and diabetes duration. At baseline, systolic and diastolic blood pressures and body mass index (BMI) were measured and information regarding the use of antihypertensive drugs [including renin-angiotensin system (RAS) blockers], glucose-lowering agents, lipid-lowering agents and antiplatelet medications were collected.
We only considered the first event even when it occurred several times during followup. A stepwise descending procedure was used to determine every final multivariate model (all conventional variables were included in the models: all univariate significant variables were included in a maximized multivariate model, then we determined an optimized model with a backward procedure). Kaplan-Meier curves were constructed and compared using a log-rank test. Analyses were performed using SAS version 9. 3 (SAS Institute, Cary, NC, USA). A P-value <0. 05 was considered to be statistically significant.
crop. cnr. it). Our study uses two CROP offshore seismic sections (M4 and M5) of the Ionian Sea. We made use of Kingdom Ò software (copyright IHS Inc. ) to build a Geographic Information System (GIS) seismic grid database, to do a geological interpretation of seismic profiles, and to perform 2-D models of stratigraphic horizons. Faults were interpreted on seismic reflection profiles (providing location and separation of these structures), mapped in a GIS environment, and displayed as lines on structure contour maps. Due to a relatively strict spacing between  2-D seismic lines, it was possible to recognize and link major faults based on their geometry, dip direction, and amount of throw. The final step was the construction of the 3-D digital models using the Vu-PACK module that permits more accurate interpretations and controls opacity, color, and lighting of volumes, horizons, and faults.
The human face represents a combined set of highly heritable phenotypes, but knowledge on its genetic architecture remains limited, despite the relevance for various fields. A series of genome-wide association studies on 78 facial shape phenotypes quantified from 3dimensional facial images of 10,115 Europeans identified 24 genetic loci reaching study-wide suggestive association (p < 5 Â 10 À8 ), among which 17 were previously unreported. A follow-up multi-ethnic study in additional 7917 individuals confirmed 10 loci including six unreported ones (p adjusted < 2. 1 Â 10 À3 ). A global map of derived polygenic face scores assembled facial features in major continental groups consistent with anthropological knowledge. Analyses of epigenomic datasets from cranial neural crest cells revealed abundant cis-regulatory activities at the faceassociated genetic loci.
Facial phenotypes The current study included seven cohorts, totaling 18,032 individuals. Demographic characteristics and phenotyping details for all cohorts are provided in Materials and methods and Supplementary file 1 -Table  1. A variety of different phenotyping methods are available for 3D facial image data, where some approaches  (Claes et al. , 2018)  require access to the underlying raw image datasets, which often cannot be shared between different research groups. In order to maximize sample size via a collaborative study, we focused on linear distance measures of facial features that can be accurately derived from facial image datasets. In our cohorts with 3D facial surface images, we focused on distances between a common set of thirteen well-defined facial landmarks (Figure  1 ).
GWASs and replications We conducted a series of GWASs and meta-analyses to test the genetic association of 7,029,494 autosomal SNPs with 78 face phenotypes in RS, TwinsUK, ALSPAC, and PITT, totaling 10,115 subjects of European descent, used as the discovery dataset. Inflation factors were in an acceptable range in all meta-analyses (average l = 1. 015, sd = 0. 006; Figure  1 -figure supplement 4), and were thus not further considered. A matrix spectral decomposition analysis estimated the effective number of independent facial traits as 43, which corresponds to a Bonferroni corrected study-wide significant threshold of 1. 2 Â 10 À9. A power analysis showed that under this threshold, our discovery data had over 90% power to detect an allelic effect explaining 0. 54% variance for individual phenotypes (Figure  1 -figure supplement 5A).
Five out of the 24 loci showed genome-wide significant association (p<5e-8) with non-facial phenotypes and/or diseases in the GeneATLAS database  (Canela-Xandri et al. , 2018)  (Supplementary file 1 -Table  10 ), suggesting pleiotropic effects. Six out of the 24 loci showed a high degree of diversity among the major continental groups in terms of both allele frequency differences (>0. 2) and Fst values (empirical p<0. 05), including 1p31. 2 RPE65, 2q36. 1 PAX3, 4q28. 1 INTU, 16q12. 1 SALL1, 16q12. 2 RPGRIP1L and 17q24. 3 CASC17 (Supplementary file 1 -Tables  11  and 12 ). Three out of the 24 face-associated loci showed signals of positive selection in terms of the integrated haplotype scores (iHS, empirical p<0. 01), including RPE65, RPGRIP1L and CASC17 (Supplementary file 1 -Table  12 ). None of the 24 loci overlapped with the previously reported signals of singleton density score (SDS, empirical p<0.
The associated phenotypes of the replicated SNPs demonstrated a clear pattern of facial symmetry that is the same association was observed on both sides of the face, while such a symmetric pattern was less clear for most phenotypes involved in the non-replicated loci (Figure  2 -figure supplements 7-17). Several novel replicated loci showed effects on the similar sets of facial shape phenotypes; for example, 1p36. 22 CASZ1 and 4q28. 1 INTU (Figure  2A  and C ) both affected the distances between pronasion and alares. Moreover, several novel replicated loci affected phenotypes in multiple facial regions for example 4q32. 1 C14orf64 and 12q24. 1 TBX3 (Figure  2D  and E ) both affected many distances among eyes, alares and mouth. It should be noted however, that the discovery dataset is of European ancestry while the replication dataset is multi-ethnic and the facial phenotypes could not be obtained in the same way as in the discovery set; hence, the replication results were rather conservative, that is the possibility of false negatives in this replication study cannot be excluded.
Integration with previous literature knowledge In our discovery GWAS dataset, we also investigated 122 face-associated SNPs reported in previously published facial GWASs  (Adhikari et al. , 2016; Cha et al. , 2018; Claes et al. , 2018; Cole et al. , 2016; Lee et al. , 2017; Liu et al. , 2012; Paternoster et al. , 2012; Pickrell et al. , 2016; Shaffer et al. , 2016). Out of those, a total of 44 (36%) SNPs showed significant association on either the same set of facial phenotypes or at least one facial phenotypes in a combined test, after Bonferroni correction for the number of SNPs (adjusted p<0. 05) (Supplementary file 1 -Table  8 ). Notably, none of the 5 SNPs reported in a previous African GWAS  (Cole et al. , 2016)  was replicated in our European dataset. This is unlikely explained by the allele frequency differences between Sub-Saharan African (AFR) and Europeans (EUR) (absolute frequency differences between AFR and EUR were less than 0.
Expression of face-associated genetic loci in embryonic cranial neural crest cells Embryonic cranial neural crest cells (CNCCs) arise during weeks 3-6 of human gestation from the dorsal part of the neural tube ectoderm and migrate into the branchial arches. They later form the embryonic face, consequently establishing the central plan of facial morphology and determining species-specific and individual facial variation  (Bronner and LeDouarin, 2012; Cordero et al. , 2011). Recently, CNCCs have been successfully derived in-vitro from human embryonic stem cells or induced pluripotent stem cells  (Prescott et al. , 2015). Taking advantage of the available CNCC RNA-seq data  (Prescott et al. , 2015)  and other public RNA-seq datasets compiled from GTEx  (Lonsdale et al. , 2013)  and ENCODE (ENCODE Project Consortium, 2012), we compared 24 genes, which were nearest to the top-associated SNPs at the 24 face-associated genetic loci identified in our discovery GWAS meta-analysis, with the background genes over the genome for their expression in CNCCs and 49 other cell types.
5 Â 10 À23 , Figure  4B ). Expression levels of the genes nearest to the 24 top-associated SNPs (see Table  1 ) in 50 cell types were displayed in boxplots based on published data from the study of  Prescott et al. (2015) , the GTEx database  (Lonsdale et al. , 2013)  and the ENCODE database (ENCODE Project Consortium, 2012). The cell types included CNCC, 20 other tissue cells, 20 primary cells and nine embryonic stem cells. (A) Boxplots of normalized RNA-seq VST values for the 24 genes (in orange) and all genome background genes (~50,000 genes, in blue). Expression difference between the genome background genes and the 24 genes was tested in each cell type using the unpaired Wilcoxon rank-sum test. Distribution of the expression levels in CNCC showed smaller variation than those of several embryonic stem cells which showed higher median.
Cis-regulatory signals in face-associated genetic loci It is becoming increasingly clear that cis-regulatory changes play a central role in determining human complex phenotypes  (Carroll, 2008; Wray, 2007). In our study, apart from those at KIF6 and ROR2, all identified face-associated DNA variants were non-exonic. With preferential expression pattern validated in CNCCs, we explored the potential cis-regulatory activity of the face-associated DNA variants using the CNCC epigenomic mapping datasets  (Prescott et al. , 2015). This includes ChIPseq data on transcription factor (TFAP2A and NR2F1) binding, general coactivator (p300) binding, or histone modifications associated with active regulatory elements (H3K4me1, H3K4me3, and H3K27ac), as well as ATAC-seq data on chromatin accessibility. Abundant entries of cis-regulatory signals were detected surrounding the face-associated loci.
Moreover, pleiotropic effects of TBX15 on skeleton, for example stature, were previously demonstrated by a large-sized GWAS  (Wood et al. , 2014). Loss of function of TBX15 in mice has been shown to cause abnormal skeletal development  (Singh et al. , 2005). Thus, the fact that these three previously reported face-associated loci were identified in our discovery GWAS, but were not confirmed in our replication analysis, likely is explained by relatively low statistical power, especially for the European replication dataset (N = 1,101) being most relevant here as these three genes were highlighted in Europeans in the previous and in our study. Moreover, for the European QIMR samples in the replication set, no direct replication analysis was possible due to the availability of 2D images, but not 3D images as in the discovery dataset.
Individuals were excluded from further analysis based on incorrect sex assignments; heterozygosity (0. 345 for the Sanger data and 0. 330 for the LabCorp data); individual missingness (>3%); cryptic relatedness (>10% IBD) and non-European ancestry (detected by a multidimensional scaling analysis seeded with HapMap two individuals). The resulting post-quality control dataset contained 8237 individuals. The post-quality control ALSPAC children were combined with the ALSPAC mothers cohort  (Fraser et al. , 2013)  and imputed together using a subset of markers common to both the mothers and the children. The combined sample was pre-phased using ShapeIT (v2. r644)  (Delaneau et al. , 2012)  and imputed to the 1000 Genomes reference panel (Phase 1, Version3)  (Auton et al. , 2015)  using IMPUTE3 V2.
Pittsburgh 3D Facial Norms (PITT) sample, United States The current study included 2195 individuals from the 3D Facial Norms dataset  (Weinberg et al. , 2016). These individuals were recruited at four US sites (Pittsburgh, Seattle, Houston, and Iowa City), were of self-identified European ancestry, and ranged in age from 3 to 49 years. Exclusion criteria included any individuals with a history of significant facial trauma, congenital defects of the face, surgical alteration of the face, or any medical conditions affecting the facial posture. DNA was extracted from saliva samples and genotyped along with 72 HapMap control samples for 964,193 SNPs on the Illumina (San Diego, CA) HumanOmniEx-press+Exome v1. 2 array plus 4,322 SNPs of custom content by the Center for Inherited Disease Research (CIDR).
After applying these filters, we obtained a dataset of 709 samples with 810,648 SNPs for the Uyghurs. The chip genotype data were firstly phased using SHAPEIT  (O'Connell et al. , 2014). IMPUTE2  (Howie et al. , 2009)  was then used to impute genotypes at un-genotyped SNPs using the 1000 Genomes Phase three data as reference. Finally, a total of 6,414,304 imputed SNPs passed quality control and were combined with 810,648 genotyped SNPs for further analyses.
Average admixture proportions for this sample were estimated as: 48% European, 46% Native American and 6% African, but with substantial inter-individual variation. After all genomic and phenotypic quality controls this study included 5958 individuals. The genetic PCs were obtained from the LD-pruned dataset of 93,328 SNPs using PLINK 1. 9. These PCs were selected by inspecting the proportion of variance explained and checking scatter and scree plots. The final imputed dataset used in the GWAS analyses included genotypes for 9,143,600 SNPs using the 1000 Genomes Phase I reference panel. Association analysis on the imputed dataset were performed using the best-guess imputed genotypes in PLINK 1. 9 using linear regression with an additive genetic model incorporating age, sex and five genetic PCs as covariates.
9. For the analysis of the X-chromosome, genotypes were coded 0, 1, and two as per the additive genetic model for females, and coded 0, two for males in order to maintain the same scale between sexes. Note that in the meta-analysis of all cohorts, the X-chromosome was not included. For CANDELA, PLINK 1. 9 was used to perform the primary genome-wide association tests for each phenotype using multiple linear regression with an additive genetic model incorporating age, sex, BMI and five genetic PCs as covariates. Association analyses were performed on the imputed dataset with two approaches: using the best-guess imputed genotypes in PLINK, and using the IMPUTE2 genotype probabilities in SNPTEST v2. 5. Both were consistent with each other and with the results from the chip genotype data. GWAS results with using best-guess genotypes were used in the subsequent meta-analysis.
Gene ontology analysis, expression analysis and SNP functional annotation To further study the involved functions and regulation relationships of the genome-wide significantly associated SNP markers and candidate genes nearby, the genes located within 100 kb up-and down-stream of the SNPs were selected to perform biological process enrichment analysis based on the Gene Ontology (GO) database. The enrichment analysis was performed using the R package 'clusterProfiler'  (Yu et al. , 2012). RNA-seq data from the study of  Prescott et al. (2015) , GTEx  (Lonsdale et al. , 2013)  and ENCODE (ENCODE Project Consortium, 2012) were used to compare the differences in gene expression levels between our face-associated genes and all genes over the genome in CNCCs. We examined entries in GWAS catalog  (Welter et al.
, 2014)  to explore the potential pleotropic effects in our face associated regions. The GTEx  (Lonsdale et al. , 2013)  dataset was used to annotate our face associated SNPs for significant single-tissue cis-eQTL effects in all 48 available tissues. We used the CNCC data from  Prescott et al. and UCSC (Aken et al. , 2016)  to perform a variety of functional annotations for the face associated SNPs in non-coding regions using the WashU Epigenome Browser, including histone modifications (H3K27ac, H3K4me1 and H3K4me3 tracks), chromatin modifier (p300 tracks), transcription factors (TFAP2A tracks), chromatin accessibility (ATACseq tracks) and conserved regions (PhyloP  (Siepel et al. , 2005)  tracks) from UCSC. To investigate the pattern of regulation within each association region in details, we calculated the log scaled quantile rank and variation of all LD SNP (r 2 > 0.
Acknowledgements We are grateful to all participants and their families who took part in the  RS, TwinsUK, ALSPAC, PITT, UYG, CANDELA , and  QIMR  studies as well as the many individuals involved in the running of the studies and recruitment, which include midwives, interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, nurses and other professional. MK additionally thanks  Maren von Ko ¨ckritz-Blickwede  for earlier discussions on PAD knockout mice. QIMR would like to thank  Natalie Garden  and  Jessica Adsett  for data collection and  Scott Gordon  for data management. The ALSPAC study was supported by The  UK Medical Research Council and Wellcome  (Grant ref:  102215/2/13/2 ) and the  University of Bristol. A comprehensive list of grants funding for ALSPAC is available online (http://www.
Methods study design and subjects This retrospective multicentre study was based on the database of the ePLM network, which includes six French memory research centres performing both clinical and biological diagnosis of neurodegenerative disorders (Besançon, Montpellier, Lille, Paris-Lariboisière, Rouen and Strasbourg). Patients were selected in the database between January 2010 and December 2015. These centres used the same diagnostic procedures and diagnostic criteria. All the patients underwent an extensive examination, including physical, neurological and neuropsychological assessments (including the MMSE for general cognitive functions), laboratory tests and brain imaging. Each centre included patients with DLB according to McKeith's and DSM-5 criteria (probable DLB, based on the existence of two core symptoms in addition to cognitive decline) for DLB demented patients.
statistical analysis Statistical analyses were carried out using Graph-Pad PRISM, V. 5 (GraphPad, San Diego, California, USA). Normally distributed data were analysed using one-way analysis of variance with Tukey's post hoc analyses to determine between-group differences. In the case of non-Gaussian-distributed parameters, we used the Kruskal-Wallis test. In the case of two independent variables, a χ 2 test was used. Receiver-operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic value of CSF parameters. ROC curve comparisons were performed using MedCalc, V. 12. 7. 0 (MedCalc Software, Ostend, Belgium).
ALMA Band 6 observations We obtained Band 6 ALMA observations of NGC 5643 on 24 December 2016 and 18 July 2017 using the 12-m array in compact (baselines between 15 and 492 m) and extended (baselines between 17 and 3700 m) configurations through the project 2016. 1. 00254. S (PI: A. Alonso-Herrero). The on-source integrations times were 11 and 36 min, respectively. We defined two spectral windows of 1. 875 GHz bandwidth (3. 9 MHz ∼ 5 km/s channels), namely one at ∼228 GHz at the observed frequency of the 12 CO(2-1) transition and the other at observed frequency of ∼230 GHz to measure the sub-millimeter continuum. We calibrated the data using the ALMA reduction software  CASA (v. 4. 7; McMullin et al. 2007). For both configurations, we used J1427-4206 as bandpass and phase calibrator. The amplitude was calibrated using J1427-4206 for the extended configuration assuming a flux density of 1.