dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). The expected outcomes are based upon the processing of SAR data as they use differential InSAR (DInSAR), coherence change detection (CCD), and time series analysis (LiCSBAS software). LiCSBAS exploits the LiCSAR data that process InSAR datasets automatically (Sentinel-1), taking advantage of high-resolution SAR sensing, in order to track ground changes and landslides. The SAR analyses aim to detect ground deformations through DInSAR and CCD investigations as they consider, for illustrative purposes, a city in Algeria struck by an earthquake (7 August 2020: Algeria, Mila): the ground deformations and displacements, in Kherba City and Grarem City (northeastern part of Mila downtown, 2 km), are investigated. The affected areas span over 3. 94 km 2 for Grarem and 2. 1 km 2 for the Kherba landslides.
Pilot zone -data and image collection The dataset used for this study is collected from the European Space Agency (ESA), via the Copernicus Open Access portal, and from the Alaska Satellite Facility (ASF DAAC, 2021). The C-band Sentinel-1 A and B, launched in 2014 and 2016, respectively, provide regular datasets. The Sentinel-1 sensors have a wavelength of 5. 546 cm  (ESA, 2021a, b) , suitable for change detection and monitoring of large areas, and are right side-looking with an incidence angle ranging approximately from 20 to 46 • (ESA,  2012). For the InSAR use, the interferometric wide (IW) swath single look complex (SLC) data are selected and processed with the open-source software SNAP (Sentinel Ap-  plications Platform). It is worth using data from many orbits to monitor the AoIs due to different oriented directions, incidence angles of satellites, and the ground topography.
The optical images of Sentinel-2 satellites are obtained from ESA, whereas downloading and processing data are done via QGIS, Semi-Automatic Classification Plugin (SCP)  (Congedo, 2021). For the Mila region, the AoI is covered by three orbits; two are ascending (66, 59) and one is descending (161) (Fig. 1 ). Since the present study intends to detect the areas influenced by landslides, many pre-event and post-event data were used. Eighteen Sentinel-1 A and 17 Sentinel-1 B images (a total of 35) were downloaded to monitor Mila's area for the period from 1 July to 26 October 2020. Table  1  summarizes the appropriate interferograms, i. e. , those having small perpendicular baselines and short temporal baselines. Tables 1-3 present all the images, with their labels as IFG-ID, Orbits, and dates of acquisition.
Time series analysis and LiCSAR data The "Looking into Continents from Space with Synthetic Aperture Radar" (LiCSAR) system automatically processes Sentinel-1 datasets for InSAR use and generates wrapped and unwrapped interferograms and coherence maps  (Lazeck et al. , 2020b) , with a final product resolution of ∼ 26. 5 m  (Lazeck et al. , 2020a). For such purposes, the open-source LiCSBAS software, adopted in the present study, is used for InSAR time series analysis based on LiCSAR data. It can generate maps of LOS displacement velocity and deformation time series for all processed frames. Furthermore, it is easy to implement and does not require high-performance computing facilities  (Morishita, 2021). In addition, the mechanism of landslides can be thoroughly studied through LiCSBAS analyses.
Optical image processing The optical sensors are passive detection means that need sunlight and clear weather conditions to exploit the data. The Sentinel-2 is a multi-spectral instrument (MSI) that measures reflected solar radiance in 13 bands with a moderate spatial resolution of 10 m in the red, green, blue, and near-infrared bands  (Laneve et al. , 2021). The optical data collected from the ESA platform (Sentinel-2) are treated and plotted using QGIS software to generate true-color images (bands 2, 3, and 4 corresponding to RGB). The present study skips the image of 3 August 2020 due to bad weather conditions, so that only the two images collected and mentioned in Table  3  were used to validate the ground changes detected by InSAR.
Optical detection To validate the SAR method results, two images from Sentinel-2 are downloaded and treated using QGIS software; the dates of the images are 30 July 2020 (a week before the main shock) and 9 August 2020 (2 d after the main shock). The optical detection shows that an important displacement affected the ground in the Kherba neighborhood, over an area of 1. 32 km 2. The landslide shape of deformation has only one toe at the lower part of the hill (blue line Fig. 16b ) compared to the CCD method results. The optical images were unable to detect the ground deformations and displacements in Grarem in which there was no apparent landslide, although there were a lot of ruptures and cracks. However, a field inspection has confirmed the results of the CCD and DInSAR analysis in terms of pattern and limits of the zone affected by the deformation (surface rupture).
Conclusions and recommendations In this paper, active and passive space-based satellite data are used to monitor and study the impact of natural hazards (earthquakes and landslides) on struck areas. The Cband Sentinel-1 SAR datasets (active sensing) and optical images of Sentinel-2 data (10 m spatial resolution) were used in this study to investigate the area, and the passive images were used only to validate the active sensing results. For the InSAR processing, the use of DInSAR, CCD methods, and the LiCSBAS tools has been able to generate a detailed time series analysis of ground changes. InSAR techniques have proven their efficiency to extract useful geodetic information, such as the ground movement and track surface deformation over large areas with centimetric accuracy in coherent change cases.
Code availability. The code was created by  Morishita et al. (2020). The LiCSBAS package is available online: https://github. com/ yumorishita/LiCSBAS  (Morishita, 2022). availability. The data in this work are freely provided from the European Space Agency (ESA) and are available online through the Copernicus program https://scihub. copernicus. eu/dhus/#/home (ESA Copernicus, 2022). Or ASF DAAC https://search. asf. alaska. edu/ (ASF DAAC, 2022), and COMET-LiCS Sentinel-1 InSAR Portal https://comet. nerc. ac. uk/COMET-LiCS-portal/, (COMET, 2022). Author contributions. TS processed and analyzed the data and provided interpretation and redaction. MA and AM supervised the work and provided advice and feedback throughout the process. ML processed the data, analyzed the data, and provided interpretation.
The 20th annual Database Issue of Nucleic Acids Research includes 176 articles, half of which describe new online molecular biology databases and the other half provide updates on the databases previously featured in NAR and other journals. This year's highlights include two databases of DNA repeat elements; several databases of transcriptional factors and transcriptional factor-binding sites; databases on various aspects of protein structure and protein-protein interactions; databases for metagenomic and rRNA sequence analysis; and four databases specifically dedicated to Escherichia coli. The increased emphasis on using the genome data to improve human health is reflected in the development of the databases of genomic structural variation (NCBI's dbVar and EBI's DGVa), the NIH Genetic Testing Registry and several other databases centered on the genetic basis of human disease, potential drugs, their targets and the mechanisms of protein-ligand binding.
NAR Database Issues in September 1994, then in January 1996, and each January after that. In the past 20 years, the Database Issue has gradually grown in size before stabilizing at the level of 180 articles. However, despite the almost 10-fold increase in the number of published articles, the key topics of the current issue remain largely the same as 20 years ago. This issue again features articles from GenBank and the European Nucleotide Archive (formerly the EMBL Data Library), which, together with the DNA Data Bank of Japan, form   (1) (2) (3) (4). Just as 20 years ago, there are updates from SWISS-PROT and PIR (now combined into UniProt) and PROSITE  (5, 6). Continuing the tradition of featuring well-curated databases of RNA sequences, this issue includes an update on SILVA, a widely used comprehensive database of bacterial, archaeal and eukaryotic 16S/18S and 23S/28S rRNA sequences  (7) , and a description of Protist Ribosomal Reference database (PR2), a new database that catalogs small subunit rRNA sequences from unicellular eukaryotes  (8).
An update on the Ribosomal Database Project, a constant feature of the NAR Database Issue since 1991  (9) , was last published in 2009  (10). Other RNA databases in this issue include an update on Rfam  (11) , the universally acclaimed database of RNA families, as well as several databases on long non-coding RNA, microRNA and their targets. An update of MODOMICS, a database on RNA modification, is now supplemented by RNApathwaysDB, a database of RNA maturation and decay pathways developed by the same group  (12, 13). As before, this issue presents several transcription factor (TF) databases. Two of them cover TFs themselves: TFClass offers a classification of human TFs, while NPIDB presents structural information on DNAprotein and RNA-protein complexes  (14, 15). Several other databases collect information on the TF-binding sites.
These include Factorbook, a database of TFbinding data from the ENCODE project; HOCOMOCO, a collection of human TF-binding sites; CTCFBSDB, a database of CCCTC-binding factor (CTCF)-binding sites; RegulonDB, a database of transcriptional regulation in E. coli; and SwissRegulon, a database of regulatory sites in human, mouse and yeast genomes and in model bacteria  (16) (17) (18) (19) (20). The structural databases featured in this issue all show a trend towards a better integration and cross-referencing tools. This refers both to the updates of well-known databases, such as the RCSB Protein Data Bank (PDB), CATH and PDBTM, and to such databases as EBI's SIFTS, a joint effort of UniProt and PDBe to provide a residue level mapping of their entries and supplement it with annotation from other public databases; Genome3D, a recent collaborative project aiming to provide structural annotation from CATH and SCOP to the genomic sequences; and dcGO, which develops domain-centric ontologies to link protein domains with functions, phenotypes and diseases  (21) (22) (23).
HUMAN DISEASE GENOMICS-THE NEXT FRONTIER? As discussed earlier  (27) , the original GDB did not survive the influx of the new data and multiple changes of ownership. Nevertheless, we now have a wide variety of databases that cover different aspects of human genome and genomes of model organisms. This issue features annual updates from Ensembl and ENCODE projects and from the UCSC Genome Browser and the Japanese H-InvDB database  (28) (29) (30) (31). The model organism databases are represented by the updates to FlyBase, Mouse Genome database, Xenbase and ZFIN  (32) (33) (34) (35). Two new databases, RhesusBase and NHPRTR, present extensive genome and RNAseq data for non-human primates, including great apes, old world monkeys, new world monkeys and prosimians  (36, 37). These data could go a long way towards establishing monkeys as model organisms for comparative genomics studies.
One more database is dedicated to a more distant relative of human, the urochordate Oikopleura dioica  (38). A potentially important development is the construction of two new databases of repetitive DNA elements, Dfam and SINEBase  (39, 40). Along with the industry standard Repbase Update  (41, 42)  and monthly RepBase Reports (http://www. girinst. org/repbase/reports/), these databases promise to contribute to a better understanding of eukaryotic repeat elements. With the abundance of databases providing valuable tools for genome analysis, there is a clear trend towards bringing genomics 'from the bench to the bedside', i. e. using genomic data for a better understanding and, hopefully, better treatment of human disease. A number of projects, including ClinSeq (http://www. genome. gov/ 20519355), DDD (http://www.
ddduk. org/) and UK10K (http://www. uk10k. org/) are working towards these goals, and several databases featured in this issue represent important steps in this direction. Last year's issue introduced the GWASdb database of human genetic variants identified by genome-wide association studies  (43). GWAS Central, established in 2007 as HGVbaseG2P  (44) , has been revamped and now includes data from over 1000 studies. Now, a joint article from NCBI and EBI describes their databases of genomic structural variation, dbVar and DGVa  (45). These databases cover diverse variation data including inversions, insertions and translocations that are >50 bp in length. NCBI is also developing ClinVar (http://www. ncbi. nlm. nih. gov/clinvar/), a database of relationships between human gene variation and the observed health status  (46).
The task of streamlining the genetic tests that provide such information is taken up by the recently created NIH Genetic Testing Registry, a database of genetic tests and laboratories that perform them, with detailed information about what exactly is measured in each test and its analytic and clinical validity  (47). The impact of the genomic data on developing targeted approaches for fighting disease is particularly evident in the case of cancer. This issue features updates from three great databases, the UCSC Cancer Genome Browser  (48) , the Atlas of Genetics and Cytogenetics in Oncology and Haematology  (49)  and the TP53 website [(50), the first update of the database on tumor factor p53 mutations since 1997]. In addition, there are two new databases dedicated to studying cancer at the level of specific cell lines.
CURATION OF THE NAR DATABASE COLLECTION During the past 20 years, all databases featured in the NAR Database Issues were added to the NAR online Molecular Biology Database Collection, available at http://www. oxfordjournals. org/nar/database/a/. With the annual attrition rate of <5%, this Collection has been steadily growing and, in 2012, exceeded 1400 database entries  (53). It was clear that the list was due for a serious clean-up, and one of the authors (XMFS) devised and set in motion a semi-automated procedure to identify obsolete and non-responsive websites. Remarkably, >90% of the databases listed in the last year's release of the online Collection were found to be functional. Corresponding authors of close to a hundred non-responsive resources had been contacted and 44 websites (3.
2% of the total) have been approved for deletion. About 100 entries in the Collection have been updated by receiving corrected URLs, summaries highlighting recent developments, or some other changes in the deposited data. Although deletion of 40 databases was well within the average drop-off rate and was hardly surprising, further analysis revealed that most of these resources were not lost. Instead, in the normal course of database evolution, they have been integrated into larger projects. For example, a couple of segmental duplications databases were merged into the Database of Genomic Variants (54), NAR Database Collection entry no. 655, while the NCBI's Cancer Chromosomes database has been merged into dbVar [described in detail in this issue,  (45) ]. Further, improved annotation of the human genome made redundant a number of resources that covered specific areas of the genome (e.
g. the IXDB with its physical maps of human chromosome X). In one instance, the ExDom database of exon-intron structures of genes in seven eukaryotic genomes  (55)  had to be removed from the Collection, as it has taken the commercial route and does not provide a free version anymore, although the author's company offered a discounted version for academic users. Unfortunately, the tightening budgets  (56)  might force other databases to follow the same path. In total, the NAR online Molecular Biology Database Collection now includes 1512 databases sorted into 14 categories and 41 subcategories. The authors wishing to have their databases, published elsewhere, to be included in the Collection are welcome to contact XMFS directly.
ac. uk/dgva/ dcGO httpDoBISCUIT http://www. bio. nite. go. jpof glycan 3D structures H2DB http://tga. nig. ac. jp/h2db/ Heritability data with trait-associated genomic loci HBVdb http://hbvdb. ibcp. fr/HBVdb/ A knowledge database for the Hepatitis B Virus HemaExplorer http://servers. binf. ku. dk/shs/ Gene expression profiles in haematopoiesis HEXEvent http://hertellab. mmg. uci. edu/cgi-bin/HEXEvent/ HEXEventWEB. cgi Human Exone Splicing Events HOCOMOCO http://autosome. ru/HOCOMOCO, http://cbrc. kaust.
Database name URL Brief description PR2 http://ssu-rrna. org/ Protist Ribosomal reference database prePPI http://bhapp. c2b2. columbia. edu/PrePPI Predicted and experimentally determined protein-protein interactions for yeast and human PTMcode http://ptmcode. embl. de/ Functional associations between posttranslational modifications within proteins Quorumpeps http://quorumpeps. ugent. be/ A database of quorum-sensing peptides RhesusBase http://www. rhesusbase. org/ A Knowledgebase for the Monkey Research Community RiceFREND http://ricefrend. dna. affrc. go. jp/ Rice Functionally Related gene Expression Network Database RNApathwaysDB http://rnb. genesilico. pl/ A database of RNA processing pathways SecReT4 http://db-mml. sjtu. edu. cn/SecReT4/ Type IV Secretion system Resource SEVA http://seva.
cnb. csic. es/SEVA/ Standard European Vector Architecture: a collection of plasmids to analyse complex prokaryotic phenotypes SIFTS http://www. ebi. ac. uk/pdbe/docs/sifts/ Structure Integration with Function, Taxonomy and Sequences SINEBase http://sines. eimb. ru A database of short interspersed elements (SINEs) SomamiR http://compbio. uthsc. edu/SomamiR/ Somatic mutations that impact microRNA targeting in cancer Spermatogenesis Online http://mcg. ustc. edu. cn/sdap1/spermgenes/ Spermatogenesis-related genes SpliceAid-F http://mi. caspur. it/SpliceAidF/ Human splicing factors and their RNA-binding sites Spliceosome Database http://spliceosomedb. ucsc. edu/ Spliceosome genes and proteins, splicing complexes StreptomeDB http://streptomedb. pharmaceuticalbioinformatics. de Antibiotic, anti-tumour and immunosuppressant drugs produced by Streptomyces spp.
SwissBioisostere http://www. swissbioisostere. ch/ Molecular replacements for ligand design SwissSidechain http://www. swisssidechain. ch/ Non-natural amino acid sidechains for protein engineering SynSysNet http://bioinformatics. charite. de/synsysnet/ Synapse proteins, their structures and interactions TCMID http://www. megabionet. org/tcmid/ Traditional Chinese Medicine Integrated Database TFClass http://www. edgar-wingender. de/huTF_ classification. html Human transcription factors classified according to their DNA-binding domains TissueNet http://netbio. bgu. ac. il/tissuenet/ Tissue distribution of protein-protein interactions TOPPR http://iomics. ugent. be/toppr/ The Online Protein Processing Resource TSGene http://bioinfo. mc. vanderbilt. edu/TSGene/ Tumor Suppressor Gene database UCNEbase http://ccg.
vital-it. ch/UCNEbase/ Ultraconserved non-coding elements and gene regulatory blocks UUCD http://uucd. biocuckoo. org/ Ubiquitin and ubiquitin-like conjugation database ValidNESs http://validness. ym. edu. tw/ Validated nuclear export signals-containing proteins Voronoia4RNA http://proteinformatics. charite. de/voronoia4rna/ tools/v4rna/index Packing of RNA molecules and complexes WDDD http://so. qbic. riken. jp/wddd/ Worm Developmental Dynamics Database WholeCellKB http://wholecellkb. stanford. edu/ Pathway and genome database of Mycoplasma genitalium for whole-cell modelling WormQTL http://www. wormqtl. org Natural variation data in Caenorhabditis spp. YM500 http://ngs. ym. edu. tw/ym500/ smRNA-seq database for miRNA research ZInC http://research. nhgri. nih. gov/zinc.
Telomere Association Analysis Images were analyzed with specific software NEMO  (Iannuccelli et al. , 2010) , distributed under the creative commons license that can be freely downloaded from https://forge-dga. jouy. inra. fr/ projects/nemo. Segmentations and 3D measurements between signals (center-to-center distance) were done as described in  Lahbib-Mansais et al. (2016). Euclidean distances were computed with respect to the x, y and z resolutions. Given the resolution on the z axis, at least three pixels corresponding to 0. 9 μm (0. 3 × 3) were required for a high resolution between two separate signals; consequently, 1 μm was chosen as the upper cutoff for associated signals. For each combination of telomeres, nuclei were only analyzed when four signals (corresponding to the chosen telomeres of two chromosomes) were present.
Considering the high number of unassembled scaffolds in the pig genome Sscrofa11. 1 version and given the fact that samples from both genders were collected, we focused our analysis on the 18 assembled autosomes to avoid potential effects of the sexual chromosomes on the results. • Interaction matrices were displayed using Juicebox  (Durand et al. , 2016)  and HiTC R/Bioconductor package v1. 18. 1  (Servant et al. , 2012). • Interaction matrices were normalized per chromosome using the non-parametric iterative correction and eigenvector decomposition (ICE) method when needed  (Imakaev et al. , 2012 ). • Replicability between interaction matrices was assessed using the replicability index of  Yang et al. (2017)  as implemented in the R/Bioconductor package hicrep. • Maximal resolution was computed following  Rao et al.
A/B Compartments Detection A and B compartments were obtained using the PCA approach described in  Lieberman-Aiden et al. (2009) , as implemented in the R/Bioconductor package HiTC  (Servant et al. , 2012). A/B compartment identification was performed on intrachromosome interaction matrices at 500 Kb resolution on individual interaction matrices [n 3 (replicates) × 2 (groups) × 18 (autosomes)] and on the merged interaction matrix (n 18 autosomes). Boundaries between A and B compartments were identified according to the sign of the first PC (eigenvector). Bins that were not assigned to any compartment due to a lack of data in some samples were not considered in subsequent integrative analyses. As an additional control, A/B compartments were also obtained by using the eigenvalue method of the Juicer tool  (Durand et al.
Detection of Differential Interactions A differential analysis was performed to extract interactions that were significantly differentially connected between the two groups (90 and 110 days of gestation). This analysis was performed on raw count data from the 18 autosomes at the 500 Kb resolution (the differential analysis was thus performed with two groups and n 3 replicates in each group). A method similar to the one described in  Lun and Smyth (2015) , with some adaptations, was used to perform this task. In brief (see Supplementary Methods for more details): • Low count interactions with less 30 reads across the six samples (5 reads per sample on average) were discarded from the analysis. • Interaction values were normalized using a non-linear normalization method  Ballman et al. (2004)  based on a fast cyclic loess algorithm implemented in the R/ Bioconductor package csaw  (Lun and Smyth, 2016 ).
• Differential analysis was performed using a Generalized Linear Model (GLM) based on the Negative Binomial (NB) distribution with a group fixed effect (two-level factor: 90/110 days). The model was estimated with the implementation of the R/Bioconductor package edgeR  (Robinson et al. , 2010; McCarthy et al. , 2012)  and log ratio tests were used to assess the significativity of the group effect on each bin pair interaction. p-values were genome wide corrected using  (Benjamini and Hochberg, 1995)  procedure to control the False Discovery Rate (FDR).