dataesr/scientific-paragraphs-categorization huggingface dataset:
SNP Genotyping Data Analysis Using PLINK v1. 9  (Chang et al. , 2015) , we removed SNPs that: (i) were typed with probes mapping to several genomic locations (N = 12,440), (ii) presented a poor genotype clustering (GenTrain score < 0. 35; N = 809), (iii) had the same chromosomal position as another SNP in dbSNP b138 (N = 6,968), (iv) were not reported in dbSNP b138 (N = 5,311), (v) presented a call rate < 95% (N = 79,310), (vi) were monomorphic in our sample (N = 652,385), (vii) were located on the sex chromosomes (N = 50,994), and (viii) presented a Hardy-Weinberg p < 10 À3 in AFB or EUB populations (N = 4,007). After quality-control filtering, we retained a total of 3,489,108 SNPs. The SNP call rate for the 200 individuals was 99. 8% on average, ranging from 98. 89% to 99. 99%. No evidence was found for 2 nd -degree cryptic relatedness (kinship coefficient > 0.
Whole-Exome Data Analysis Read-pairs were processed according to the GATK Best Practice recommendations. Read-pairs were first mapped onto the human GRCh37 genome with BWA v. 0. 7. 7  (Li and Durbin, 2009) , and reads duplicating the start position of another read were marked as duplicates with Picard Tools v. 1. 94 (http://broadinstitute. github. io/picard). We used GATK v. 3. 2. 2  (DePristo et al. , 2011)  for base quality score recalibration (''BaseRecalibrator''), insertion/deletion realignment (''IndelRealigner''), and SNP and insertion/deletion discovery for each sample (''Haplotype Caller''). Individual variant files were combined with ''GenotypeGVCFs'' and filtered with ''VariantQualityScoreRecalibration. '' Individual coverage was 52. 32 3 on average, ranging from 33. 84 to 100. 59 3, and individual breadth of coverage at 5 3 was 92.
Imputation of Genome-wide SNP and Exome Data Before merging the Omni5 and exome datasets, we first checked genotype concordance for 169,406 SNPs common to the two platforms. We flipped alleles for 8,025 SNPs with incompatible allelic states, and removed 119 SNPs with alleles that remained incompatible after allele flipping. The total concordance rate was 99. 66%. The concordance rates for each of the 200 individuals exceeded 99%, confirming an absence of errors during DNA sample processing. Of the 8,155 SNPs with discordance rates > 1%, 296 were due to C/G or A/T SNPs, and high genotype concordance between the two DNA typing technologies was restored by allele flipping. The remaining 7,881 SNPs were removed. The entire Omni5 and exome datasets (3,489,108 and 471,740 SNPs, respectively) were then merged, yielding a final concordance rate of 99.
93%, for a total of 3,782,260 SNPs. Before imputation, we phased the data with SHAPEIT2  (Delaneau et al. , 2013) , using 500 conditioning haplotypes, 50 MCMC iterations, 10 burn-in and 10 pruning iterations. SNPs and allelic states were then aligned with the 1,000 Genomes Project imputation reference panel (Phase 1 v3. 2010/11/23). We removed 8,705 SNPs with identical positions in our data and in the reference panel but incompatible alleles, even after allele flipping, and 4,137 SNPs with C/G or A/T alleles. Genotype imputation was performed with IMPUTE v. 2  (Howie et al. , 2009) , considering 1-Mb windows and a buffer region of 1 Mb. Of the 38,098,530 SNPs obtained after imputation, we removed SNPs that: (i) presented an information metric below 0. 8 (N = 18,085,215), (ii) had a duplicate (N = 59,914), (iii) presented a call rate < 90% (N = 329,910), and (iv) were monomorphic (N = 4,053).
The final imputed dataset included 19,619,457 SNPs. To evaluate imputation accuracy, we estimated correlation coefficients R 2 between true genotypes (i. e. , obtained by Illumina array genotyping or exome sequencing) and imputed genotypes for the same SNPs (i. e. , obtained by artificially removing genotyped SNPs from the data before imputation and then imputing them). In very good agreement with recent studies  (Auton et al. , 2015) , the average correlation coefficient was 95. 6% across all genotyped SNPs with information metric > 0. 8 (93. 6% for SNPs with MAF < 0. 10 and 97. 7% for SNPs with MAF > 0. 10). This shows that our stringent quality filters ensure that only accurately imputed SNPs are analyzed.
Populations Genetic Structure Two methods were used to infer the genetic structure of our population set of 100 African-descent and 100 European-descent Belgians (AFB and EUB, respectively). Because both methods assume linkage equilibrium among SNPs, we pruned the datasets for SNPs in linkage disequilibrium (LD), using PLINK v1. 9  (Chang et al. , 2015). Specifically, we removed SNPs in 50-SNP windows that present LD r 2 > 0. 5 (''-indep-pairwise 1000 10 0. 5'' option). The first model-based approach, ADMIXTURE  (Alexander et al. , 2009) , estimates the proportions of each individual's genome originating from K ancestral populations, K being specified a priori. This analysis was performed on 229,320 independent SNPs and 789 individuals from 22 populations, including EUB and AFB, together with a selection of representative populations from sub-Saharan Africa, North Africa, the Near East and Europe  (Altshuler et al.
, 2010; Behar et al. , 2010; Patin et al. , 2014). eQTL Mapping For expression quantitative trait loci (eQTL) mapping, only variants with a minor allele frequency (MAF) R 0. 05 in the population studied were retained in the analysis, resulting in a set of 10,278,745 SNPs (i. e. , corresponding to the merged genotyping and imputed SNP dataset; 8,913,090 SNPs in Africans and 6,178,808 SNPs in Europeans,). We mapped eQTLs with the MatrixEQTL R package  (Shabalin, 2012). PC1 and PC2 of the genotype matrix were included in the model to account for possible population stratification. The inclusion of additional PC in the model (up to PC6) was tested and showed highly consistent results (i. e. , correlation of -log 10 p-values of eQTL > 0. 95). For each gene, SNPs were considered ''local,'' likely cis-acting, if they were located less than 1Mb away from the gene transcription start or end site.
They were otherwise considered to be trans-acting. eQTL mapping was performed separately for each population and condition, and false-positives due to outliers were prevented by discarding, from the analysis, eQTL associations that did not pass a p-value threshold of 10 À3 for local eQTLs, and 10 À5 for trans-eQTLs in Kruskal-Wallis rank tests. For both cis-and trans-eQTLs, FDR was computed by mapping eQTLs on 100 datasets with genotypes permuted within each population. We then kept, after each permutation, the most significant p-value per gene, across all conditions and populations. Finally, we computed the false discovery rate associated with each p-value threshold in cis or in trans, and subsequently selected the p-value threshold that provided a 5% FDR, leading to p = 7. 67 3 10 À7 and p = 2.
Finally, we used simulations to evaluate FDR among the set of genes with at least one ASE/ASR event. We generated 1,000 null datasets, by randomly reassigning reads to the alternative and reference allele with equal probability, and estimated the number of genes with at least one significant ASE or ASR event at each p-value threshold. We then computed FDR as the ratio of the average number of genes with ASE in our resampling to the observed number of genes with ASE at the same p-value threshold.
ASE Enrichment in Rare Coding Variants For each exonic SNP for which we quantified ASE, we used Variant Effect Predictor (VEP)  (McLaren et al. , 2010)  with Ensembl v. 70 Transcript Annotation to identify the set of transcripts overlapping the variant, and Cufflinks FPKM to identify the most strongly expressed overlapping transcript in the individual/condition concerned. VEP annotation was then used to classify variants, according to the most abundant transcript, as synonymous (synonymous_variant/ non_coding exon_variant), missense (missense_variant) or nonsense  (stop_gained, stop_lost, initiator_codon_variant). Enrichment in rare coding variants was then assessed using Fisher's exact test comparing each category with synonymous variants.
ACKNOWLEDGMENTS This project was funded by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  grant agreement  281297  (to L. Q. -M. ). We thank  Macrogen Inc. for the use of their RNA-sequencing facilities. M. R. was supported by a  Marie Sk1odowska-Curie fellowship  ( DLV-655417 ). M. Dannemann and J. K. are supported by the  Max Planck Society  and a grant from the  Deutsche Forschungsgemeinschaft  ( SFB 1052 , project  A02 ).
STAR+METHODS KEY RESOURCES   (Trapnell et al. , 2012)   http://cole-trapnell-lab. github. io/cufflinks/cuffdiff/ ComBat, sva R package  (Johnson et al. , 2007)  https://www. bioconductor. org/ WGCNA  (Langfelder and Horvath, 2008)  https://www. bioconductor. org/ PASTAA  (Roider et al. , 2009)  http://trap. molgen. mpg. de/cgi-bin/pastaa. cgi.
Regulatory Elements and Transcription Factor Binding Sites Regulatory features were extracted from Ensembl Regulatory Build v80  (Zerbino et al. , 2015) , which contains regulatory element predictions based on open chromatin regions and histone marks from ENCODE and the Roadmap Epigenomics datasets  (Ernst and Kellis, 2015; Kundaje et al. , 2015). SNPs overlapping a regulatory element were then classified into four categories: promoter, promoter flanking, enhancer, and CTCF binding sites. Similarly, ENCODE uniformly processed transcription factor binding site (TFBS) clusters (V3)  (Ernst and Kellis, 2015)  were downloaded from UCSC, and their overlap with the physical position of all SNPs was determined. We then used Fisher's exact test to assess the eQTL enrichment of specific TFBS or regulatory elements, considering the peak-SNP at each locus, or a randomly selected SNP if multiple SNPs in perfect LD were found.
This archaic haplotype, tagged by the aSNP rs12436322, has re-introduced the ancestral allele of rs6574138 in Europeans (i. e. all individuals not carrying the archaic haplotypes have the derived allele), which is also present in Africans and associated with PNMA1 expression. SNP rs6574138 overlaps with ENCODE binding sites, consistent with its putative functional role in the regulation of PNMA1 expression in both Europeans and Africans. (E) Inferred history of the ancestral and derived alleles at rs6574138 in modern human populations, up to their most recent common ancestor. Our data suggest that rs6574138 predates the split between Neandertals and modern humans and that the derived allele of this variant was fixed in early Europeans. The ancestral allele of rs6574138 was then reintroduced in Europeans by introgression of the Neandertal haplotype tagged by rs12436322, and is responsible for the variability in PNMA1 expression in the contemporary European population.
Vehicular communication involves two categories; the first one is related to road safety while the second is related to transport ergonomics  [4]. Vehicular networks (V2X) (Figure  1 ) involve two cases, which are Vehicle to Infrastructure (V2I)  [5]  (Figure  2 ) and Vehicle to Vehicle (V2V)  [5]  (Figure  3 ). V2I covers exchanged data between road infrastructure (such as traffic lights and signs sidewalls) and vehicles. The V2V covers systems that focus on communicating two vehicles without considering an access point control. It is through Wireless Access in Vehicular Environment (WAVE) or the IEEE 802. 11p communication standard that the IEEE community has incessantly worked on the improvement of V2X communication. Actually, it is interested in both the physical PHY layer of specifications for Dedicated Short  Range Communication (DSRC) and Medium Access Control (MAC) of vehicular communication (Figure  4 ).
(a) We begin with rearranging the pilot subcarrier patterns. (b) Finally, we have proposed two supplementary pilot subcarriers in order to optimize the IEEE 802. 11p channel estimation technique. The rest of the paper is structured as follows. Section 2 presents a review of the literature pertaining to the evaluation of the performance of the IEEE 802. 11p communication standard. As for Section 3, it provides a description for the transmission process architecture of this standard, taking operational parameters into consideration. The scenarios and results from the first Matlab-based simulation sets are given in Section 4 which also presents the experimental hardware.
As in the V2I scenario and as demonstrated in Figure  5 , the same starting transmission point was restricted in this experiment for both vehicles. So, at the reception unit, the packet reception performance was obtained. Figure  12  shows the effect of the high speed range as a function of the PLR on V2V communication. Such results  offer remarkable feasible insights that can be summarized as follows. Indeed, the application of low data rates prompts more important effects of node mobility on vehicular communication than higher data rates. According to the results presented in Figures  12  and 13 , an increase from 5% to 11. 5% in PLR can be obtained for the data rates of 3 and 27 Mbits/s at 50 km/h, respectively. Eventually, the quality of transmission degrades with the increase of the node speed; that is, the PLR increases from 6% to 18% for 40 km/s and 220 km/h, respectively, for the BPSK 1/2.
Conclusion The present research work aimed to design a set of Matlabbased simulations for V2X communication relative to the IEEE 802. 11p communication standard and a supplementary set of real-world experimentations using the Arada Systems LocoMate OBU multimodal communication device. The correlations between the findings from the two sets of evaluation scenarios with regard to the performance of the IEEE 802. 11p communication standard were examined. The obtained results have confirmed that using a low rate of data is favored for more enhanced performances in the long-range communications. The findings have also shown that communications can be affected negatively by node mobility. Moreover, in the case of V2V communication, the experimental data indicate that the quality of transmission     worsens with the increase in the node speed.
Furthermore, the corrected data accuracy is heavily dependent on the flow model choice and the model's parametrization  [18]. As described in  [18] , besides the LiDAR data correction methodologies developed by research groups, also companies, such as Meteodyn WT, WindSim and Leosphere, introduced commercial LiDAR data correction algorithms. Specifically, Leosphere developed a real-time correction algorithm, known as the Flow Complexity Recognition (FCR), which is available as an add-on to their LiDAR software  [20]. Overall, an improper choice of the flow model and an incorrect model parametrization can potentially introduce additional errors to single-Doppler retrievals. This leads to an increased uncertainty in the single-Doppler retrievals. Furthermore, as the single-Doppler retrievals undergo modifications based on flow modeling results, they do not represent direct measurements of the wind vector.
Therefore, the pulse emission and the acquisition of the pulse echoes are controlled by the motion controller, which sends triggers to the dedicated hardware components. Specifically, the trigger output (i. e. , virtual stepper motor input) of the motion controller is split and at the same time directed to the RF drive and acquisition\FPGA board (see the splitting blue line in Figure  2 ). By explicitly describing the motion profiles of two real servo motors and one virtual stepper motor (using the motion programs), one single hardware component (i. e. , the motion controller) controls and synchronizes the emission, acquisition and steering. A real-time LOS speed retrieval is attained by means of double buffer memory of the acquisition\FPGA board and WindScanner Client Software (WCS) that manages the hardware resources while processing the sampled data in the FIFO mode.
WindScanner Client Software The WCS is software developed by DTU Wind Energy in the LabView environment that utilizes the aforementioned measurement process control. Essential software libraries, such as the MLE dll, and hardware drivers have been provided by Leosphere. A simplified flow diagram of the WCS is shown in Figure  4. The WCS is network-based application remotely configured by additional software residing on a physically-separated computer (master computer). There are two main loops in the software which run in parallel. One loop is handling the communication with the master computer, while the other loop is governing the measurement process. The WCS user has substantial freedom in configuring the software, thus optimizing the LRWS measurement process to suit the needs of field campaigns.
Master Computer Software The Master Computer Software (MCS) was developed by DTU Wind Energy in the LabView environment in accordance with the RSComPro. It is multi-platform software delivered as a light executable (5 MB). A simplified flow diagram of the MCS is shown in Figure  6. The MCS provides a Graphical User Interface (GUI) to design, execute and monitor field campaigns with the long-range WindScanner system (Figure  7 ). At a low level, based on the user inputs, the MCS generates, sends, receives, processes and displays the network packets. The MCS is designed for remote instrument hardware monitoring, data storage and display, and it includes a set of automatic routines that handle alert states of LRWSs. The automatic routines are programmable and allow for the setup of automatic decisions in accordance with the input parameters (e.
g. , a change in wind direction triggers the change of a scanning strategy). Furthermore, the MCS is used for the assessment of static pointing accuracy of the LRWSs through the dedicated submodule CNR mapper  [43]. Lastly, the MCS is responsible for keeping the LRWSs synchronized (see the following section). The MCS allows for the coordination of any reasonable number of remote LRWSs, as well as the coordination of other remote sensing instruments, the software of which is adapted for the network communication defined by the RSComPro  [40, 41].
While a shorter duration network fallout is not a significant problem since the LRWSs continue to run independently, a longer network outage results in loss of synchronization between the LRWSs. The previously-described campaigns were primarily intended to assess the accuracy of the long-range WindScanner system's retrievals of the wind vector. Actual mapping of the wind flow with the system consisting of three LRWS was made during the Perdigão 2015 experiment  [51]. This experiment was focused on creating a high-quality dataset of wind flow measurements for validation of wind resource estimation, wind turbine inflow and wind turbine wake models in complex terrain. The complex flow was measured in a great many points of interest, the number of which ranged from 50 up to 12,050. Several scanning strategies were designed and employed for the experiment  [51].
An example of the acquired measurements are given in Figures  11  and 12. The dataset is currently being used by several research groups for model validation  [52, 53]. In the previous paragraphs of this section, we described the measurement accuracy of the long-range WindScanner system. Over the course of experiments, we have been investigating the pointing accuracy of the LRWSs. More specifically, prior to all measurement campaigns the static pointing accuracy of the LRWSs was tested. Usually, several thin survey poles and other landmarks of interest (i. e. , hard targets) were mapped using the CNR mapper  [43]  and compared with the reference position readouts made by a theodolite and/or differential GPS. The CNR mapper creates a stack of PPI or RHI scans (TV scan), the execution of which steers the laser beam to map a hard target (Figure  13a ).
Both CW scanning CDLs and scanning Doppler radars require a tight synchronization among their fundamental processes for the same reasons as pulsed scanning CDLs do (i. e. , knowing when and where information on the flow is retrieved). In accordance with the measurement process control, the WCS has been developed. The measurement process control and the WCS provide users significant freedom in configuring the long-range WindScanner. Users are able to set up arbitrary and time-controlled scanning strategies. Arbitrary and time-controlled scanning strategies are essential for realizing a synchronized multi-LiDAR instrument. LRWSs are the first and, so far, the only pulsed scanning CDLs that can perform this type of strategy. However, even though the WCS has a dedicated module for automatic scanning strategies generation, this module can only generate a limited number of scanning strategies.
The arbitrary strategies are manually parametrized, and this process requires solid knowledge of the kinematics of the scanner head and how the motion controller executes the kinematic routines. We are currently working on an extension of the aforementioned module that will simplify and automatize the generation of arbitrary and time-controlled scanning strategies. We have formed the long-range WindScanner system, a multi-LiDAR instrument, by employing several LRWSs and wirelessly coordinating them using a remote master computer. Since the coordination is achieved by an exchange of approximately 1-kB network packets, a mobile network can be adopted for the communication between the master computer and LRWSs. An effective software solution for synchronizing LRWSs in the long-range WindScanner system has been devised.
Acknowledgments: Financial support for the development of the long-range WindScanners has been supported by the  Danish Agency for Science, Technology and Innovation  through the Grant No. 2136-08-0022. The first author work has been additionally funded by the  Marie Curie   FP7-ITN-WAUDIT Project , under the Grant No. 238576. Authors would like to acknowledge skilled work of  Guillaume Gorju  (former Leosphere) on the optical design of the long-range WindScanner. Furthermore, the authors would like to acknowledge the expert work of  Steen Andreasen  from  IPU, Lyngby, Denmark , and  Jon Howard  from  Heason , Slinfold, U. K. , on the design of the long-range WindScanner scanner head. Furthermore, the authors would like to acknowledge  Juan-José Trujillo ,  Davide Trabucchi  and  Jörge Schneemann  from  ForWind  institute for their contribution to the development of the Remote Sensing Communication Protocol.
We describe here a new method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell type and tissue specific way (FUN-LDA) by integrating diverse epigenetic annotations for specific cell types and tissues from large scale epigenomics projects such as ENCODE and Roadmap Epigenomics. Using this unsupervised approach we predict tissue-specific functional effects for every position in the human genome. We demonstrate the usefulness of our predictions using several validation experiments. Using eQTL data from several sources, including the Genotype-Tissue Expression project, the Geuvadis project and Twin-sUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap.
This class includes methods such as ChromHMM  [8, 14, 15]  and Segway  [16] , based on Hidden Markov Models (HMMs) and Dynamic Bayesian Networks respectively. ChromHMM is based on complete pooling of data from multiple tissues and fitting a single model to this superdataset, while Segway is based on fitting separate models to data from each tissue (no pooling). Various extensions of these early segmentation approaches have been proposed. Several approaches have focused on better modeling the read count data using Poisson-lognormal and negative multinomial distributions  [17, 18] , while others have focused on better modeling of the correlations among related cell types and tissues  [19, 20, 21]. Yet another approach attempts to improve the HMM parameter estimation procedure in ChromHMM by replacing the EM algorithm with a spectral learning procedure  [22].