dataesr/scientific-paragraphs-categorization huggingface dataset:
The workflow used to update volume and estimate harvestable biomass from FAWS may help tactical planning of biomass supply and energy/heating plant establishment. Such approach is reproducible wherever aggregated inventory data and geographical layers are available. Planned harvest intensities took into account current forest practice, historical harvest rates, as well as legal limitations and best practices to ensure the simultaneous provision of wood and other ecosystem services, e. g. , carbon stocking  (Vacchiano et al. 2018b)  or hydrogeologic protection  (Vacchiano et al. 2016). We estimated how much biomass would be harvestable in the next two decades by using a "stock change" approach, e. g. , planning one-off silvicultural entries and estimating the removed volume, rather than assessing removals based on estimated future woody increment  (Sacchelli et al.
2013). Our approach is applicable in all cases where not all forests are managed, and where no harvest planning exists at the regional level, something that pre-cludes any assumption on a predetermined balance between increment and removals. If prediction needs to be carried out on longer time scales, such approach should be replaced by more comprehensive modeling tools  (Verkerk et al. 2011)  which are capable of tracking the complex age-structure feedbacks that affect forests undergoing management for more than one rotation. The assessment carried out in this study is affected by several uncertainties. From the point of view of prediction accuracy (i. e. , the degree to which the predictions reflect reality), the mean relative error of the predictions of the volume model was between -20 and -30% of plot-based forest volume across the whole validation dataset, and only -2 to +10% when excluding very sparse or young forests, which should not undermine the accuracy of the predicted biomass map for the intended purpose.
In particular, the rescaling of pixel-level volumes, based on the total volumes reported for each forest cover type in the study region, made sure that the aggregated error at forest type level was zero, at least in areas not damaged by natural disturbances (insects, wildfire) in the period 1993-2005. As pixel-level estimates likely remained affected by an error of a magnitude similar to that before updating (RMSE: 90 m 3 ha -1 ), an ad-hoc validation sampling should be carried out by establishing new forest plots where biomass is evaluated in the supply area before designing detail supply plans for any individual biomass plant. Once equipped with a high-resolution, wall-to-wall estimate of forest biomass available for energy (Fig. 5 ), stakeholders and investors can use these data to decide upon the location and power of biomass energy plants.
Despite an abundance of new studies about topologically associating domains (TADs), the role of genetic information in TAD formation is still not fully understood. Here we use our software, HiCExplorer (hicexplorer. readthedocs. io) to annotate >2800 high-resolution (570 bp) TAD boundaries in Drosophila melanogaster. We identify eight DNA motifs enriched at boundaries, including a motif bound by the M1BP protein, and two new boundary motifs. In contrast to mammals, the CTCF motif is only enriched on a small fraction of boundaries flanking inactive chromatin while most active boundaries contain the motifs bound by the M1BP or Beaf-32 proteins. We demonstrate that boundaries can be accurately predicted using only the motif sequences at open chromatin sites. We propose that DNA sequence guides the genome architecture by allocation of boundary proteins in the genome.
In this study, we sought to identify the DNA encoding behind TAD boundaries in flies. First, we develop software (HiCExplorer) to obtain boundary positions at 0. 5 kilobase resolution based on published Hi-C sequencing data from Drosophila melanogaster Kc167 cell line  15, 16. Using these high-resolution TAD boundaries, we identify eight significantly enriched DNA-motifs. Five of these motifs are known to be bound by the insulator proteins: Beaf-32, CTCF, the heterodimer Ibf1 and Ibf2, Su(Hw) and ZIPIC. We find that a large fraction of boundaries contain the motif bound by the motif-1 binding protein (M1BP)  17  , a protein associated to constitutively expressed genes. This motif has recently been found at boundaries  18, 19. The two remaining DNA-motifs have not been associated to boundaries before.
Surprisingly, we find that depletion of Beaf-32 has no major effect on chromosome organisation, while the depletion of M1BP leads to cell arrest in M-phase and dramatically affects the Hi-C results. Using machine learning methods based on the acquired DNAmotif information, we could accurately distinguish boundaries from non-boundaries and identify TAD boundaries that were missed when using only Hi-C data. Our results suggest that the genome architecture of flies can be explained predominantly by the genetic information. We have implemented the methods for Hi-C data processing, TAD calling and visualization into an easy to use tool called HiCExplorer (hicexplorer. readthedocs. io). To facilitate exploration of available Hi-C data, we also provide an interactive online database containing processed high-resolution Hi-C data sets from fly, mouse and human genome, available at http://chorogenome.
The TAD separation score track (Methods section) depicts a normalized measure of the contacts between two flanking regions (10-40 kb, depicted by gray line, blue line depict mean score). The boundaries, estimated using the TAD separation score are shown as vertical lines. The following tracks show normalized ChIP-seq coverage for the known boundary proteins CP190, Beaf-32, and Su(Hw) on Kc167 39 and CTCF  15. The following tracks contain ChIP-chip data for histone modifications from modEncode  28. The image was generated using HiCExplorer. This particular region was selected because many different TADs could be seen; other regions can be browsed at http://chorogenome. ie-freiburg. mpg. de. b Histogram of the distance of a boundary to the nearest CP190 (common insulator protein co-factor) peak.
The ranking of feature importance showed that indeed open promoters are required for boundaries (Fig. 5a ). For the motifs, the feature importance ranking follows the abundance of the motifs at boundaries: M1BP and Beaf-32 show the highest importance scores, followed by motif-6, ZIPIC, and motif-8. Some features, like GAF and Pita motifs were found to be negatively associated with boundaries at promoters (Supplementary Figure  5a ). Although regularized models used here are less prone to overfitting compared to other machine learning methods, we further protect against overfitting by 10-fold cross validation during training. We then tested model accuracy on an independent test data set. The classifiers showed a sensitivity and specificity over 71% on the test data (Fig. 5b ). We also classified open chromatin regions distant from promoters, using only motif scores on these sites as features (Fig.
Resources to explore TADs and associated genomic features. During our research, we developed processing and analysis tools for chromosome conformation. Our tool-suite, called HiCExplorer, simplifies the Hi-C data pre-processing, quality controls, contact matrix transformation, and TAD calling into a few easy steps (Fig. 6a ). HiCExplorer is open source and is available at https://github. com/deeptools/HiCExplorer/. Importantly, HiCExplorer can be used with other pipelines and processing tools as we have built-in import/export functions covering commonly used Hi-C data formats. To facilitate analysis, we have integrated HiCExplorer into the Galaxy platform  33. With HiCExplorer, we made available our efforts to create meaningful and accurate visualizations of Hi-C data that can integrate other data sources, examples of which can be seen throughout this manuscript.
Further information can be found at the associated documentation (http://hicexplorer. readthedocs. io), which includes a full analysis workflow and detailed description of the tools. Our tools would be beneficial to users that do not routinely perform expensive and technically challenging Hi-C experiments. They enable quick visualization of specific genes or regions of interest in the context of TADs and loops, to understand gene regulation. We provide a resource called the Chorogenome Navigator (Fig. 6b ) (http://chorogenome. ie-freiburg. mpg. de/), which includes Drosophila, Mouse, and Human Hi-C data sets, already processed by HiCExplorer, along with associated gene annotations, histone marks and other TAD/boundary annotations. The underlying program called HiCBrowser (https://github. com/deeptools/HiCBrowser/), is also freely available to be used as a standalone browser, where users can include their own genomic tracks.
This difference is caused by the quality of the ChIP-seq data that can produce spurious peaks. For example, we could observe a significant enrichment of CTCF at boundaries in the ChIP-Seq data from Wood et al. 39  but not in the CTCF ChIP-seq data from Li et al. 15. On the other hand, both ChIP-seq data sets show significant enrichment if we only consider ChIP-seq peaks that contain the CTCF motif, (Supplementary Figure  3j ). For CTCF, and in general for ChIP-seq experiments in flies, 'phantom peaks' are known to occur at active promoters  25. Thus, to avoid misleading results our analyses are based on motif presence when possible and for ChIP-Seq data sets, we use significance threshold along with motif binding affinity for analysis (instead of taking a significance cutoff alone). We observe that boundary strength is associated with the chromatin states of flanking TADs and particular motif combinations, but is not associated with the number of co-occurring boundary motifs.
Methods Hi-C processing. Different Hi-C data available for D. melanogaster were downloaded from GEO and processed using HiCExplorer (http://hicexplorer. readthedocs. io/). The list of data sources can be seen in Supplementary Table  3. Each mate of the Hi-C sequencing read pairs was aligned separately using bwa mem with parameters '-E50 -L0'. The E parameter is the gap extension penalty, which is set high to avoid gapped alignments. This is because a fraction of the reads from a Hi-C experiment contain sequences from two distinct genomic positions. By increasing the gap extension penalty we promote the aligner to map the two parts of the read separately instead of trying to map the read to a single location. The L parameter is the penalty for 5′ and 3′ clipping which we set to zero to favor such clipping for the same reason as before.
Thus, noticing that these two marks are complementary for active regions we classified TADs having predominantly these marks as 'active'. For the other clusters we used the same categories as 29 : the cluster of TADs with H3K9me3 was labeled as 'HP1' (Heterochromatin Protein 1); the cluster with H3K27me3 was labeled 'repressed' or 'PcG' (Polycomb group) and the cluster with no mark was labeled as inactive. Analysis of transcription at boundaries. In order to analyse transcription at boundaries, we downloaded ribo-depleted RNA-Seq data from modENCODE  57. All data were mapped to the Drosophila (dm3) genome using HISAT2 (v2. 0. 4)  58  and the reads were summarized per gene using featureCounts (v1. 5. 0. p1)  59  using options '-p --primary -Q 10'. We used data from Kc167 cells, along with 14 different developmental stages, ranging from embryo to adult.
We only used data produced in 2014 in order to avoid batch effects and further confirmed the data quality by clustering the samples by Euclidean distance (Supplementary Figure  2a ). We normalized each sample by library size, averaged the counts for replicates, and finally used log transformed counts for all the analysis. In order to be able to compare all samples from mixed sex, genes on sex chromosomes were excluded for analysis. In total 10,449 autosomal genes were used for expression and variability analysis. Genes were considered expressed if they have a normalized log-count >0. Similar results were obtained using the more stringent cutoff of log counts ≥10, genes with boundary promoters = 45. 84% and non-boundary promoter 18. 36% (p-value = 1. 58E -96, Fisher's exact test). Variability was assessed using coefficient of variation of a gene across all developmental stages along with Kc167 cells.
The gene body was defined as the region between 50 bp downstream of the transcription start site and the gene end. We used the maximum coverage for the promoters and the median coverage for the gene body. Processing of ChIP-seq data. The data sources are listed in Supplementary Table  4. For each ChIP-seq data used, we downloaded the respective fastq files and aligned them in the dm3 assembly using Bowtie2  63. MACS2 was used to identify peaks for each of the proteins  61. For the respective data sources we downloaded input sequences and aligned them as the ChIP-seq data. bamCompare and bam-Coverage from deepTools2  56  were used to create normalized coverage bigwig files. MEME-chip  20  was used to identify motifs based on the MACS2 peaks. The resulting motifs can be seen in Supplementary Table  1.
For this we use the peaks from the DNAse-seq data  28. Boundary prediction and feature ranking. We performed boundary prediction at all Drosophila promoters using motif TRAP scores for various transcription factors and DNAse-seq signals as features. We utilized methods ranging from simple to complex (linear models, logistic regression, random forest, and stochastic gradient boosting), with the primary purpose to rank the features by importance in boundary prediction. Pre-filtering was done to remove highly correlated features (pearson R > 40%). Linear model and random forest was performed using the package Caret 65 , while logistic regression was performed using package glmnet  66  in R. Linear model was used with stepwise feature selection algorithm to predict boundary score from features by selecting the combination of features that minimizes the Akaike Information Criteria (AIC).
6%, while logistic regression and random forest performed predictions with around 73-78% accuracy. After obtaining the best model in each scenario, we ranked the features by their importance in prediction, using the 'varImp' function from Caret. 'varImp' selects a variable importance predictor based on the model type, which is calculated for each parameter in the model (https://topepo. github. io/caret/variable-importance. html). Briefly, the importance score for linear model is the absolute value of the t-statistic for the model parameter, for lasso, it is the absolute value of final coefficients, for gbm it is the relative influence score as described in Friedman  67  , and for random forest it is the difference between the classification error-rate for the out-of-bag portion of data and a permuted predictor variable, averaged over all trees and normalized by the standard deviation of the differences (https://www.
Cells were then incubated overnight at 4 °C with a 1:500 dilution of anti-H3 Ser10 phosphorylated antibody (Abcam, ab5176) and washed twice with permeabilization buffer. Cells were pelleted and resuspended in 400 µl of permeabilization buffer. One microliter of secondary antibody (anti-rabbit conjugated with Alexa 633 fluorophore, A21070, Life Technologies) was added and cells were incubated for 2 h at room temperature (protected from light). After two washes using the permeabilization solution, cells were resuspended in 500 µl of PBS and treated with 5 µl of RNase A for 30 min at room temperature. Nuclei were stained using 1 µg/ml of DAPI prior sorting. Samples were analyzed by flow cytometry using BD LSRFortessa instrument. The data have been analyzed using FACSDiva. Hi-C processing of Beaf-32 and M1BP knockdowns.
Acknowledgements We would like to thank  David Gilmour  ( PennState University ) for providing the M1BP antibody. Also, we would like to thank  Ulrike Bönisch  and her team at the  Deep-Sequencing Facility  and  Andreas Würch  from the  FACS  facility of the  MPI-IE. We further thank  Wilhelm Rüsing  for IT support,  Diana Santacruz  for critical reading of the manuscript and  Gina Renschler  for the help in interpreting FACS results. We also thank  Victor Corces Lab  whose published data have been instrumental in this research. This work was supported by the  German Research Foundation  [ SFB 992  " Medical Epigenetics "] awarded to A. A. and T. M. and a grant from the  Federal Ministry of Education and Research  through the  German Epigenome Programme DEEP  [ 01KU1216G ] awarded to T. M.
Code availability. HiCExplorer code is available online at: https://github. com/ deeptools/HiCExplorer/. HiCBrowser code is available at: https://github. com/ deeptools/HiCBrowser Data availability. The processed data from ChIP-Seq and Hi-C samples obtained from online sources can be found at: http://chorogenome. ie-freiburg. mpg. de/ data_sources. html. Sequencing data for the in situ Hi-C experiments described in this article are available at NCBI GEO under accession: GSE97965.
The grey and white matter volumes are known to reduce with age. This cortical shrinkage is visible on magnetic resonance images and is conveniently identified by the increased volume of cerebrospinal fluid in the sulci between two gyri. Here, we replicated this finding using the UK Biobank dataset and studied the genetic influence on these cortical features of aging. We divided all individuals genetically confirmed of British ancestry into two sub-cohorts (12,162 and 3,435 subjects for discovery and replication samples, respectively). We found that the heritability of the sulcal opening ranges from 15 to 45% (s. e. = 4. 8%). We identified 4 new loci that contribute to this opening, including one that also affects the sulci grey matter thickness. We identified the most significant variant (rs864736) on this locus as being an expression quantitative trait locus (eQTL) for the KCNK2 gene.
Materials -Subjects The present analyses were conducted under UK Biobank data application number 25251. The UK Biobank is a health research resource that aims to improve the prevention, diagnosis and treatment of a wide range of illnesses. Between the years 2006 and 2010, about 500,000 people aged between 45 and 73 years old, were recruited in the general population across Great Britain. In this work, we used the data released on January 2018, consisting of 20,060 subjects with a T1-weighted MRI. We included 15,040 in our discovery sample and 5,020 in our replication sample. The subjects were separated according to their availability in NIFTI format. In January 2018, replication sample was available only in DICOM format. The UK Biobank genetic data underwent a stringent QC protocol, which was performed at the Wellcome Trust Centre for Human Genetics  (Bycroft et al.
Functional annotation of the loci To obtain information on the role of the region upstream of KCNK2, we investigated the gene expression QTL using the GTEx database (GTEx Analysis Release V7 (dbGaP Accession phs000424. v7. p2)) (GTEx Consortium 2017). We found that the variants rs864736 and rs14526008, which are in LD, are significant eQTLs for the KCNK2 gene expression, with significant multi-tissue meta-analysis RE2 (random-effects model 2)  (Han and Eskin 2011)  pvalues of 9. 7•10 -6 and 5. 7•10 -9 , respectively. Figure  4  presents the effect size in various tissues of rs864736 allelic configuration on KCNK2 gene expression. Even though the association barely reaches nominal significance in single brain tissue due to low sample sizes (~80-140 subjects), it overall emphasizes strong effects across brain tissues.
Various FE commercial software, such as ABAQUS and MSC. MARC, were used to simulate CAF process and all these FE models were built based on the creep-ageing constitutive models of corresponding alloys. However, no publication has been found on the process modelling of an alloy with the asymmetric creep-ageing behaviour, which is important for its CAF applications for the prediction of formed shapes and the springback compensation of the die-design. One of the key techniques for determining the prediction efficiency and accuracy of FE models for the forming process is how the particular constitutive model of the alloy is solved and implemented  [17] [18] [19]. Both implicit and explicit methods were adapted to solve the ratedependent constitutive model for FE simulations  [20] [21] [22] [23].
However, the creep strain rate evolves and can change by order of magnitude during CAF, and the time step size required for convergence in the explicit solution is determined by the maximum creep strain rate, thus reducing the efficiency of the FE simulation. In addition, CAF process is a typical quasi-static problem, whose process lasts for many hours, and springback is essential to be considered. Hence FE models with implicit method can provide better accuracy and efficiency for the CAF process simulation. Some implicit FE models for CAF process have been built based on the CREEP subroutine in ABAQUS  [13, 28] , a local iteration procedure was defined in the software to repeatedly call the subroutine to implicitly integrate only the creep strain equation in each time step, while the convergence of other state variables was hard to be fully guaranteed, especially for the recently developed unified models in which more comprehensive state variables were introduced.
Unified creep-ageing model implementation in FE solver A commercial FE software PAM-STAMP was used for FE simulation in this study. The implicit integration algorithm discussed in the above sections to solve the unified creep-ageing constitutive model was implemented into the software by a set of subroutines in the "userdefined material type 184"  [37]. All material constants were assigned through the "Input Phase" subroutine, the calculation process was coded and completed in the "Initialisation Phase" and "Solver Phase" subroutines and the results were provided through the "Output Phase" subroutine.
In the personal-computer ecosystem, malware developers commonly exploit executable packing and other code obfuscation techniques to generate a large number of polymorphic variants of the same malicious application  [4] ,  [5]. As a consequence antivirus (AV) software are struggling to keep their signature database up-to-date, and AV scanners suffer from a considerable quantity of false negatives  [6]. Moreover, the malicious code is often reused and customized to fit different needs. For example, a developer may reuse the rootkit installation code, while replacing the modules that provide network connectivity to a Command-and-Control server. By the end of 2010s, the Android ecosystem is facing a similar scenario, although the situation is exacerbated by the simplicity of malicious repackaging  [7].
That is an alteration of the original application installation package (i. e. , the APK file), where legitimate applications are reverse engineered, modified to include malicious code, signed with a new signature, and eventually distributed for download. Since applications consist of bytecode, changes are relatively easy to implement and ad-hoc tools assist the procedure  [8] ,  [9]. The growth of Android malware created a major challenge for AV vendors to efficiently handle new samples and accurately label them. Due to the practical impossibility of manually analyzing the thousands of suspicious samples received every day, a large fraction is left unlabeled, delaying the signature generation. While malware variants can be generated at a high pace, they are likely to perform similar malicious activities when executed.