dataesr/scientific-paragraphs-categorization huggingface dataset:
Altered Local Interactions at Polycomb Loci between Serum and 2i-Cultured mESCs Polycomb is responsible for forming selfinteracting topologically associated domains (TADs) at Hox loci as detected by chromosome conformation capture methods  (Kundu et al. , 2017; Noordermeer et al. , 2011; Williamson et al. , 2014). To assess whether changes in 3D chromatin organization occur in 2i cells at regions other than Hox loci, we employed in situ Hi-C (Lieberman-Aiden  et al. , 2009; Rao et al. , 2014)  to assay genome-wide chromatin interactions from E14 mESCs grown in serum/LIF and in 2i/LIF, generating two independent Hi-C datasets for each condition. Cluster analysis of local insulation profiles showed separation of the two culture conditions, but the differences were small (Figure  S2A ). Similar analysis of the eigenvector tracks revealed a larger effect of culture conditions on compartmentalization (Figure  S2B ).
5 embryos  (Du et al. , 2017; Ke et al. , 2017 ; Zhang et al. , 2018). While we cannot be sure of the polycomb distribution across the genome at this stage of embryogenesis in vivo, consistent with a DNA hypomethylated state, we observe high levels of enrichment for CTCF-associated loops in these datasets, but no enrichment at sites corresponding to RING1B-associated loops (Figure  4D ). However, enrichment of RING1B-associated loops appears very prominently later in embryogenesis at E6. 5 in cells of both the epiblast and visceral endoderm, when DNA methylation is very high (Figure  4D )  (Zhang et al. , 2018).
High-performance liquid chromatography (HPLC) confirmed that high CpG DNA methylation levels are retained in 3B3L cells cultured in 2i (Figure  5A ). We note that Uhrf1 transcript levels in 2i were similar for both WT and 3B3L mESCs more consistent with the Prdm14 repression model of hypomethylation in 2i, rather than a model involving downregulation of Uhrf1  (von Meyenn et al. , 2016). Consistent with the model where DNA methylation focuses polycomb targeting, ChIP-sequencing revealed that the maintenance of serum-level DNA methylation levels under 2i conditions in 3B3L cells also resulted in the observed H3K27me3 deposition being largely retained at polycomb target loci (Figures  5B  and 5C ). The H3K27me3 profiles at CGI that we observed in our rescued 3B3L cells parallel those observed when de novo Dnmt activities were reintroduced in Dnmt3a À/À , Dnmt3b À/À hypomethylated mESCs  (King et al.
The adaptor-ligated library was then amplified (10 cycles) and size-selected using two rounds of AMPure XP bead (Beckman Coulter) capture to size-select fragments approximately 100-250bp in length. Samples were pooled at a 1:1 ratio and sequenced on an Ion Proton P1 microwell chip (Thermo Fisher). Mapping and data normalization were carried out as described previously  (Thomson et al. , 2015). In short, reads were mapped to the reference genome using the Torrent TMAP software. The data were then binned into 200bp windows across the genome and normalized by total read count. Raw sequencing datasets from published WT E14 mESCs in both serum and 2i were processed in a similar manner  (Marks et al. , 2012) : NCBI GSE23943.
A gradient of 0%-90% B was run over 4 mins, where B is acetonitrile and A is 20 mM ammonium carbonate. Mass spectra were acquired in negative mode on a Thermo Q Exactive, scanning from 300 to 350 m/z at resolution 70k. AGC target was set to 1x 106 and maximum ion time 100ms. Data were analyzed using AssayR  (Wills et al. , 2017). In Situ Hi-C Hi-C was performed largely as described  (Rao et al. , 2014)  with minor modifications. Briefly, 2-5x10 6 mESCs were crosslinked in 1% formaldehyde for 10 mins, snap-frozen and stored at À80 C. After permeabilization in lysis buffer (0. 2% Igepal, 10 mM Tris-HCl pH 8. 0, 10 mM NaCl, 1x Halt Protease inhibitor cocktail) nuclei were isolated in 0. 3% SDS in NEBuffer 3 at 62 C for 10 min. SDS was quenched with 1% Triton X-100 at 37 C for 1 h, then the nuclei were pelleted and resuspended in 250 ml DpnII buffer with 600 U DpnII.
QUANTIFICATION AND STATISTICAL ANALYSIS FISH Image Analysis Volocity software (PerkinElmer) was used to capture, process, and analyze the images. Images were deconvolved using the Restoration module, using the constrained iterative algorithm. Image analysis was carried out using the Quantitation module. For analysis of data from ESCs, each dataset consisted of 70-155 measurements. For analysis of Hox probes in blastocysts, 686 alleles from 14 embryos were analyzed. Control inter-probe distances were measured from 100 alleles from 2 blastocysts. The statistical analysis of inter-probe distance distributions was determined using a Mann-Whitney U Test. Mean inter-probe distances for all FISH data are shown in Table  S1  and p values are listed in Table  S2.
Hi-C Data Analysis Reads were processed using distiller (https://github. com/mirnylab/distiller-nf) on the high-performance computing cluster of the University of Edinburgh (Eddie). Mapping was performed to the mm9 genome build (Table  S4 ). Hi-C pairs with exactly matching coordinates were removed as PCR or optical duplicates (pcr_dups_max_mismatch_bp: 0). Pairs with mapq < 30 were not used. The output statistics information and Cooler files (https://github. com/mirnylab/cooler) were used in downstream analyses  (Abdennur and Mirny, 2019). 1000 bp resolution Cooler files were used to create multi-resolution files for visualization in HiGlass. We only used balanced matrices for our analyses. We performed pileup analysis using coolpup. py (Flyamer et al. , 2019). Briefly, we took all regions of interest in the Hi-C maps, e.
, 2014)  from dekkerlab/shrink-donut-dotfinder (commit 377106e). This was applied with default settings (except for lower FDR threshold of 0. 1) to reanalyzed mapq R 30 filtered mESC Hi-C data at 5 kb, 10 kb and 25 kb resolution to find areas of local enrichment of interactions between loci up to 20 Mb away. Calls from different resolutions were combined using a custom script following the HiCCUPS merging procedure. Annotated dots were then filtered by intersecting with published CTCF peaks  (Bonev et al. , 2017) , and/or RING1B peaks  (Illingworth et al. , 2015)  using bedtools pairtobed after widening the peaks using bedtools slop. For local interaction density analysis, we used 5 kb resolution data and 25 kb windows. For each window we determined average observed/expected number of interactions (excluding the first two diagonals, so we averaged 6 pixels per window).
Value of the center pixel is shown in the top left corner of each heatmap. (D) Pileups at loops called in mESC Hi-C data (Bonev et al. , 2017) , using our serum and 2i Hi-C data, and compared to published Hi-C data from ICM (Du et al. , 2017; Zhang et al. , 2018) , E3. 5 embryos (Ke et al. , 2017) , and from the epiblast (Epi) and visceral endoderm (VE) of the E6. 5 embryo and the ectoderm (Ect) at E7. 5 (Zhang et al. , 2018). Shown are all loops (All), those associated with CTCF peaks but not RING1B peaks (CTCF), and those associated with RING1B peaks (RING1B), but not CTCF peaks. Association is determined by the highest enriched pixel in the loop being within 5 kb of a ChIP-seq peak on both ends, while lack of a peak on at least one of the sides is treated as no association. Value of the center pixel is shown in the top-left corner of each heatmap.
ACKNOWLEDGMENTS We are grateful to  Nezar Abdennur ,  Anton Goloborodko , and  Maxim Imakaev  for advice on Hi-C data analysis, and to  Sergey Venev  for prompt help with loop annotation. We are grateful to  Johanna Gassler  for advice with FISH on mouse embryos. We thank other W. A. B. and R. R. M. lab members for discussions. K. M. was funded by a PhD studentship from the  UK Medical Research Council (MRC). I. M. F. was funded by a PhD studentship from the  Darwin Trust. Work in S. P. 's lab  is supported by the  BBSRC , and  BHF. Work in the R. R. M. lab is funded by an  MRC   University Unit grant  ( MC_PC_U127574433  and  MC_UU_00007/17 ). I. R. A. is funded by  MRC University Unit grants  ( MC_PC_U127580973  and  MC_UU_00007/6 ). Work in the W. A. B. lab is funded by an  MRC   University Unit grant  ( MC_UU_00007/2 ).
PQ-QM constructs its TF by fitting parametric distributions to the datasets and composing closed-form expressions for the corresponding CDFs; KDDM estimates the probability density functions (PDFs) of the datasets non-parametrically, integrates them to get CDFs, and constructs a TF by fitting a spline to corresponding CDF quantiles (details in  McGinnis, et al. , 2015). A variant of the empirical quantile-mapping method is the CDF-t technique  (Vrac et al. , 2012). It first estimates the CDFs of "pseudo" reference data over the projection time period (either future or evaluation time period) before applying a distribution-derived quantilemapping between the CDF of the model simulations and that of pseudoreferences, both for the projection period. Then, based on this estimation and on the projection model data CDF, a distribution-based quantile mapping is performed.
Conditional correction of temperatures is a step towards bias adjustment of multiple variables, for example as introduced by the final two general purpose multivariate methods. The N-dimensional Multivariate Bias Correction (MBCn; Cannon, 2018) is a form of multivariate quantile mapping that is based on an image processing technique proposed by  Pitié (2005 Pitié ( , 2007)). MBCn adjusts the simulated dependence structures to match observations while modifying the temporal sequencing of the climate simulations enough to match the observed multivariate distribution. This is achieved by iteratively applying a random orthogonal rotation to the datasets followed by univariate-quantile-mapping of the rotated marginal distributions. Multi-sites and multi-variables can be adjusted simultaneously.
This paper addresses the problem of efficient simulations of the signal backscattered from buried structures. The proposed modelling of subsurface soil structure is based on a geologicallyand hydrologically-motivated approach by using of real geological database. Six different Soil Typological Units have been used to model the inhomogeneous and geological soil structures as well as for moisture profile simulation. We use the semi-empirical pedotransfer function to calculate the Complex Relative Effective Permittivity in frequency range of 0. 3-1. 3 GHz. The subsurface penetration capabilities have been analysed with the regard to attenuation along the propagation path in a layered structure. We use the backward-propagation matrix and ray-tracing method to compute the backscattered signals from five targets located at five different depths in order to analyse the defocusing effects due to EM wave propagation.
Subsurface modelling From the geological point of view, the soil is made up of the mineral particles, organic matter, water and air  (Behari, 2005; Food and Agriculture Organization of the United Nations, 1985). Soil composition is a key point to derive the dielectric properties for realistic simulations of the backscattered signal. Geological databases, such as the real geological database SPADE2, are a valuable source of information about the soil textural composition and inhomogeneity  (Kedzierawski, Le Caillec, Czarnecki, & Pasternak, 2012). In many works, for instance  (Kedzierawski et al. , 2012; Kuo & Moghaddam, 2007; Lambot, Slob, van den Bosch, Stockbroeckx, & Vanclooste, 2004; Le Caillec et al. , 2011) , the soil has been modelled as homogeneous and planar layers with different heights, inner textural composition and thus EM properties.
Geological layer Files from the SPADE-2 database contain the content of sand s d clay c l , silt s t and the bulk density at non uniform depths (named horizons). In order to perform the EM propagation simulations, we first define the z depth vector (of random step), for which the soil texture is approximated (see below). It starts at z 0 ¼ 0:0m and is iteratively sampled as follows: z nþ1 ¼ z n À h n ( 3 ) where n is the layer number. Total number of layers is N Z ¼ 1509 and maximum considered depth of the soil is Z max ¼ 15:0317m. The individual soil layer l n is modelled as a geologically uniform, thin and planar layer located between two following interfaces. The last (absorbing) soil layer l N Z starts at the z max; (see Figure  1 ). In order to convey the inhomogeneity within a geological horizon, we assume that h n layer height has a Gaussian distribution of mean μ h ¼ 0:01m and standard deviation σ h % 0:0017m.
c l z ð Þ þ s t z ð Þ þ s d z ð Þ ¼ 100%; , the c l z ð Þ clay and s d z ð Þ sand content are considered as two Clay c l z ð Þ and sand s d z ð Þ contents are given by Gaussian distributions, where μ c (μ s ) is a mean value of clay (sand) content in the horizon containing the n th layer (values extracted from the database file) and σ c (σ s ) is the standard deviation of clay c l z ð Þ (sand s d z ð Þ). We chose σ c ¼ σ s ¼ 0. 17% for avoiding nega- tive values. Additionally, the STU contains information about the ρ b bulk density average of the dry soil in each horizon. However, due to the approximation of soil texture at z n , ρ b z ð Þ and p z ð Þ, the porosity, are calculated based on clayc l z ð Þ and sand s d z ð Þ content as follows  (Behari, 2005) : ρ b z ð Þ ¼ 3:455 R z ð Þ R z ð Þ ¼ 25:1 À 0:21 s d z ð Þ þ 0:22 c l z ð Þ ½ 0:3018 (4) p z ð Þ ¼ 1 À ρ b z ð Þ ρ s ¼ ρ s À ρ b z ð Þ ρ s where ρ s is the density of the rock in the soil.
DESIGN, SETTING, AND PARTICIPANTS Genetic association study in which rare variants were identified by whole-exome sequencing in unrelated individuals of European ancestry from the Alzheimer's Disease Sequencing Project (ADSP). Data were analyzed between March 2017 and September 2018. MAIN OUTCOMES AND MEASURES Minor alleles genome-wide and in 95 genes previously associated with AD, AD-related traits, or other dementias were tabulated and filtered for predicted functional impact and occurrence in participants with AD but not controls. Support for several findings was sought in a whole-exome sequencing data set comprising 19 affected relative pairs from Utah high-risk pedigrees and whole-genome sequencing data sets from the ADSP and Alzheimer's Disease Neuroimaging Initiative. RESULTS Among 5617 participants with AD (3202 [57.
Study Population and Data Pipeline The Alzheimer's Disease Sequencing Project (ADSP) performed whole-exome sequencing (WES) on DNA samples obtained from participants of non-Hispanic European ancestry (EA) and a group of Caribbean Hispanic individuals that was deemed too small for inclusion in this study. A total of 5617 participants with AD met National Institute of Neurological and Communicative Diseases and Stroke/ Alzheimer's Disease and Related Disorders Association criteria for possible, probable, or definite AD after clinical and/or neuropathologic examination  9  and 4594 controls were cognitively normal. The ADSP participants were selected using a risk score based on age, sex, and APOE ε4 carrier status to maximize cases most likely to have AD risk variants and controls most likely to have AD protective variants.
Rare Variant Analysis in an Independent Data Set To extend and enhance the discovery of novel associations, we evaluated WES data obtained from 19 AD-affected first-or second-cousin pairs identified in the Utah Population Database belonging to a pedigree with a statistical excess of AD risk. These pedigrees are genealogically independent at least as far back as the early 1800s. Details of the Utah Population Database, case classification, and identification of high-risk pedigrees have been published elsewhere. 18 A series of steps that included filters for sharing among affected relatives, frequency in several public next-generation sequencing databases, pathogenicity, and relevance to AD pathologic factors resulted in 130 variants exome wide for further evaluation (eMethods in the Supplement).
Protein Homologic Modeling and Pathway Analysis Protein homologic modeling was performed for several high-impact variants in NOTCH3 with BLAST-P, version 2. 7. 1, 12 SWISS-MODEL, SMTL version 2019-02-13 (PDB release 2019-02-08),  13  and Maestro, version 11. 2 software. 14 Additional details of the modeling procedures are provided in the eMethods in the Supplement. A high-confidence (confidence score >0. 7) human protein-protein interaction network was then created with version 10 of the STRING database for NOTCH3 and its ligand JAG1. 15 The set of genes forming the protein network was tested for gene-set enrichment using Protein Analysis Through Evolutionary Relationships pathways and the Fisher exact test with false discovery rate multiple test correction. 16.
Discussion We identified several rare variants that have a high probability of damage to protein structure and may increase AD risk. These variants were not detected in previous analyses of the same ADSP WES data set that were agnostic with respect to functional impact of the variants and conducted using current statistical testing approaches. 7 Our focus on variants observed in participants with AD but not controls yielded results that are often undetected by traditional genetic association methods that cannot evaluate empty cells, regardless of sample size or frequency of variants among cases. Several of our top-ranked results confirm previously identified AD associations with rare variants, including PSEN1 rs63749824  29  and rs63750592, 30 SORL1 rs139710266,  31  MAPT rs63750424,  32  and ABCA7 rs145987355,  33  which suggest that novel findings identified by our approach may be robust.
Two of our novel findings offer additional evidence of shared genetic mechanisms between AD and other rare dementia syndromes, namely CADASIL and Nasu-Hakola disease. Our study also suggests that participants with AD have a significantly higher burden of deleterious rare coding variants in known AD, AD-related, or other dementia genes compared with controls. This observation generalizes previous findings in SORL1,  31, 34, 35  MAPT,  32, 36  TREM2,  4, 37, 38  and ABCA7 33,39-41 that both common and rare variants in the same gene may independently contribute to AD risk. We observed the rare NOTCH3 rs149307620 allele in 11 participants with AD and 1 participant with mild cognitive impairment, but not in controls, in the combined ADSP WES, ADSP WGS, and Alzheimer's Disease Neuroimaging Initiative WGS data sets.
The most remarkable finding from analysis of the Utah high-risk pedigree WES data set was rare NOTCH3 rs140914494 and rs141402160 alleles in a pair of affected half first-cousins. These mutations in exons 4 (rs140914494), 5 (rs141402160), and 6 (rs149307620) are located in the JAG1 binding site and involve the gain or loss of an alanine residue (Table  1 ). Based on this evidence alone, it is unclear whether one or both of the rs140914494 and rs141402160 mutations, which are likely in cis given their probable inheritance from a single common ancestor, have a role in AD. In contrast, the rs114447350 and rs112197217 variants are located near the end of the coding sequence (exons 33 and 21, respectively) and may be clinically benign  42  and thus unlikely to be causally related to AD. Many other NOTCH3 variants have been associated with CADASIL that typically replace the wild-type amino acid with a cysteine residue or replace a highly conserved cysteine residue with another amino acid,  20, 26, 27  although there are several exceptions.
Limitations Our study has several limitations. Because we focused on rare variants, our sample of more than 10 000 participants was inadequate to establish statistical significance. Thus, our findings require replication in independent samples. We were unable to replicate these findings in the Alzheimer's Disease Genetics Consortium genome-wide association study data set because of low and inconsistent imputation quality for these rare variants, despite the use of the large Haplotype Reference Consortium reference panel. 62 In addition, our genome-wide MAC cutoffs for focusing on particular variants were arbitrary and, therefore, some important findings may have been overlooked. It is possible that cryptic relatedness in the sample may have exaggerated some of our results; however, among the highlighted findings, the largest pairwise IBD score of 0.
Association of Rare Coding Mutations With Alzheimer Disease and Other Dementias eTable 1. Characteristics of Subjects in the ADSP WES Case-Control Dataset eTable 2. Previously Established Genes for AD, AD-Related Traits and Other Dementias eTable 3. Filtering Pipeline of Rare Variants eTable 4. High and Moderate Impact Rare Variants in Previously Established AD Genes Occurring In Ն 4 Participants With AD and No Controls eTable 5. Characteristics of AD Subjects in the ADSP WES Dataset With the NOTCH3 rs149307620 Mutation eTable 6. Characteristics of Subjects in the WGS Replication Datasets eTable 7. Characteristics of AD Subjects With the TREM2 rs104894002 Mutation (Q33X) eTable 8. High and Moderate Impact Rare Variants Genome-Wide Occurring in > 10 Participants With AD and No Controls eTable 9.
ACKNOWLEDGMENTS This work was supported by the  Singapore National Research Foundation  under  NRF RF  Award No. NRF-RF2013-08. MOE Tier 1 RG7/18 ,  MOE Tier 2 MOE2015-T2-2-007 ,  MOE2016-T2-2-153 ,  MOE2017-T2-2-136 ,  MOE Tier 3 MOE2018-T3-1-002 ,  AcRF Tier  2  MOE2017-T2-2-002 ,  NRF2017-NRF-ANR002 2DPS , and  A*Star   QTE program. Dan Tian  thanks the  National Nature Science Foundation of China  (Grant No. 21601086 ) and the  Natural Science Foundation of Jiangsu Province  ( BK20160994 ) for financial support.
Data Collection Whole-body 3D kinematic data were collected at the maximal sampling frequency of our operating system (179 Hz) using eight infrared Oqus 500+ cameras and the Qualisys Track Manager software version 2018. 1, build 4100 (Qualisys AB, Göteborg, Sweden). Forty-five and forty-one retro-reflective markers of 12 mm in diameter were used for static and running trials, respectively. They were affixed to the skin and shoes of individuals over anatomical landmarks using double-sided tape, following standard guidelines from the Project Automation Framework Running package  [13]. The 3D marker data were exported in. c3d format and processed in Visual3D Professional software version 6. 01. 12 (C-Motion Inc. , Germantown, Maryland, USA). More explicitly, the 3D marker data were low-pass filtered at 20 Hz using a fourth-order Butterworth filter.
Objective Assessment of Running Gait The 89 participants were ranked according to their DF computed from the data collected during the last 30 s of their 10-min run at 12 km•h -1. To avoid a potential bias due to sex, the number of males and females was matched between the VOL and DF groups. To do so, the number of males and females was counted in TER (N TER,M and N TER,F ) and AER (N AER,M and N AER,F ). Then, the DF values of all participants were arranged in decreasing order and by sex. DF high was composed of the N TER,M and N TER,F with the largest DF values within males and females, respectively. The remaining constituted DF low , i. e. , the N AER,M and N AER,F with the smallest DF values within males and females, respectively. Data from all 89 participants were included in the statistical analysis.
Data Data for the present study come from the 2013-2014 Health Behaviour in School-Aged Children (HBSC) study. This study was conducted in 42 nations, primarily in Europe, in collaboration with the World Health Organization' s Regional Office for Europe. It was designed to examine the health and health behaviors of adolescents across country contexts. 4 ata were collected through school-based, anonymous surveys using a standard methodology. 38 Each participating country or region used random sampling to select young people aged 11, 13 and 15, ensuring that the sample was representative of all in the age range. Fieldwork took place mainly between September 2013 and June 2014. The present analysis is limited to the 33 countries that participated in the 2013-2014 HBSC study, included the sexual behavior module for 15-year-olds and had a score on the Global Gender Gap Index in 2014.