dataesr/scientific-paragraphs-categorization huggingface dataset:
Two virulence factor (VF) databases are included this year. The first, the well-established VFDB  (37)  reports a new automated tool, VFanalyzer, for the identification of VFs in complete or draft bacterial genomes. It combines sequence similarity searches with a consideration of genomic context to achieve performance that is reportedly comparable with human curation. The new Victors database  (38)  has manually curated information from over 5000 VFs and a broader scope--encompassing bacterial, viral, parasitic and fungal VFs--than comparable resources. Human and model organism genomics again has a strong presence, starting with the second of the Issue's Breakthrough Articles. Vibrism DB (39) contains anatomical, gene expression and co-expression data at different ages of the mouse brain presenting the information in ways including interactive 3D visualization in a browser.
More than 170 000 individual expression maps covering coding and non-coding transcripts are included and co-expression can be viewed in anatomical context or in a network format. The database covers many more transcripts than similar resources, requires few brains to profile expression and cleverly allows users to share URLs that encode scene-setting parameters enabling easy sharing of visualizations. Elsewhere in the section, the major resources Ensembl  (40)  and the UCSC Genome Browser  (41)  present their usual updates and are joined by GENCODE  (42). A new arrival, the Trips-Viz transcriptome browser  (43)  allows for mapping of Ribo-Seq and mRNA-seq data onto individual transcripts from seven model organisms. Popular model organism databases such as FlyBase  (44) , ZFIN  (45)  and Plan-Mine  (46)  are joined by the new resource iDOG  (47)  which contains an impressive variety of data relating to domestic dogs and other canids, and which is designed to appeal to interested lay people as well as expert researchers.
The ArrayExpress functional genomics database marks 15 years since its first appearance in this journal with an update  (48)  reporting the increasing proportion of submissions coming from single cell studies and the challenges of capturing appropriate metadata. A large number of databases in the areas of human genomic variation, diseases and drugs are included in this Issue. The popular returning database GWAS Catalog  (49)  now includes studies from over 3500 publications, while a pair of new databases--EWAS Atlas  (50)  and EWASdb (51)--each service the fast-growing area of Epigenome-Wide Association Studies. The new database PopHuman-Scan  (52)  processes population genomics data from last year's PopHuman database  (53)  revealing almost 3000 candidate human genome regions under positive selection.
They are linked to relevant literature in the database while users are also invited to contribute their own candidate regions for curation. CADD (54) is a major new arrival and is a very popular measure for predicting the deleteriousness of genome variants. Its classification model is trained using a large number of features, including protein, genome and epigenome information. The deleteriousness of both single nucleotide variants and short insertions or deletions can be predicted. A new database, AWESOME  (55) , specifically addresses the impact of single nucleotide variants on protein post-translational modifications, considering the effects of around 1 million variants from dbSNP  (56)  on six different modifications. The venerable OMIM database, linking genes and phenotypes such as inherited disorders, reports continued strong growth of around 300 new phenotypes per year such that over 6000 phenotypes are now linked to around 4000 genes  (57).
Several cancer databases are covered, including the popular curated catalogue of cancer mutations COSMIC  (58). The 3D protein structural consequences of cancer-related mutations are covered in COSMIC-3D and in the dedicated resource Cancer3D  (59). The roles of long non-coding RNAs in cancer are the subject of the returning database Lnc2Cancer (60), while disease in general is covered by the similarly popular and hugely expanded LncRNADisease  (61). In vivo study of cancers will be facilitated by the new Cell Model Passports database (62) which contains standardized infor-mation for over 1200 cell models enabling a rational choice of cell model for a particular purpose to be made. Plant databases include the return of the Genome Database for Rosaceae, celebrating 15 years (63), and the new arrival of the Cucurbit Genomics Database (64), covering important crops such as pumpkin, melon and cucumber.
Another new arrival, CMAUP  (65) , aims to catalogue the chemical compounds present in plants of different kinds (edible, medicinal, garden etc. ) and distributions, linking them to effects on human proteins, pathways and diseases. It includes over 5000 plants and almost 50 000 ingredients. Two important glycobiology databases are included in the last section. The returning glycosciences. DB (66) reports a new interface and new search options, including the ability to search with glycan (sub-)structures. The newcomer UniLectin3D (67) contains structural information on lectins, their ligands and their interactions. It features a wide range of visualization options and ample links out to related resources. This section also contains two major proteomics databases: iProX  (68) , a new Chinese contributor to the ProteomeXchange consortium and jPOSTdb  (69).
Following on from jPOSTrepo, also published in the Database Issue  (70) , jPOSTdb subjects the original raw data to a standardized protocol. Post-translational modifications and protein isoforms can be visualized with further options allowing differential expression analysis and protein set enrichment with respect to KEGG pathways and Gene Ontology terms. Finally, it is always a pleasure to welcome databases addressing entirely new kinds of data. Such is the case of AleDB (71) which records the results of Adaptive Laboratory Evolution experiments whereby microbes are grown in defined conditions and the mutations responsible for improved phenotypes are tracked.
NAR ONLINE MOLECULAR BIOLOGY DATABASE COLLECTION With the 26th release of the NAR online Molecular Biology Database Collection (which is freely available at http:// www. oxfordjournals. org/nar/database/c), we feature 66 new resources. We continue to monitor the collection to ensure the information is still relevant and resources are running, contacting authors when repeated downtime is detected. Thanks to this verification process we have updated 506 database entries, removing 147 obsolete or discontinued databases. We are happy to include new databases in the Collection and we encourage authors of resources published elsewhere to contact us. Such suggestions should be addressed to XMF at xose. m. fernandez@gmail. com and should include database summaries in plain text, organized in accordance with the http://www.
//bigd. big. ac. cn/ewasEpigenome-Wide Association Studies EWASdb http://www. bioapp. org/ewasEpigenome-Wide Association Studies Atlas FusionGDB https://ccsm. uth. edu/FusionGDBFusion Gene annotation DataBase gcMeta https://gcmeta. wdcm. orgMicrobiome research data Genome Properties https://www. ebi. ac. uk/interpro/genomepropertiesPathways and other properties represented by sets of protein families HACER http://bioinfo. vanderbilt. edu/AE/HACERHuman ACtive Enhancer to interpret Regulatory variants HmtVar https://www. hmtvar. uniba. itHuman mitochondrial DNA variants iDOG http://bigd. big. ac. cn/idogDog data on genes, SNPs, diseases etc. iProX http://www. iprox. orgProteomics datasets jPOSTdb https://jpostdb. orgJapan ProteOme STandard environment KinaMetrix http://kinametrix. comProtein kinase models, conformations and ligands liqDB http://bioinfo5.
coding potential and proteomes Pancan-meQTL http://bioinfo. life. hust. edu. cn/Pancan-meQTL Methylation quantitative trait loci in cancer PDX Finder http://www. pdxfinder. org Patient-derived xenograft models PED http://bigd. big. ac. cn/ped Plant Editosome Database PreMedKB http://fudan-pgx. org/premedkb Precision medicine knowledgebase piRTarBase http://cosbi5. ee. ncku. edu. tw/piRTarBase piRNA targeting sites Plasmid Atlas http://www. patlas. site Plasmid annotations and metadata PLSDB https://ccb-microbe. cs. uni-saarland. de/plsdb Plasmid annotations and metadata PopHumanScan https://pophumanscan.
It will also enable the study of thermal and scattering properties of the atmosphere as the planet orbit around the star. The main purpose of this paper is to estimate an optimal list of targets observable by ARIEL or a similar mission in ten years time and quantify a realistic mission scenario to be completed in 4 year nominal mission lifetime, including the commissioning phase. To achieve the mission objectives, the sample should include gaseous and rocky planets with a range of temperatures around stars of different spectral type and metallicity. With this aim, it is necessary to consider both the already known exoplanets and the "expected" ones yet to be discovered. The data collected by Kepler allow to estimate the occurrence rate of exoplanets according to their size and orbital periods.
We used the instrument parameters of the ARIEL payload as designed during the phase A study. To increase the efficiency of our simulations we used a Python tool as a wrap of the ESA Radiometric Model, so we could test different mission configurations that fulfil the mission science objectives. The results were validated with ExoSim, a time domain simulator used for the ARIEL space mission, but thanks to its modularity it can be used to study any transit spectroscopy instrument from space or ground. ExoSim has been developed by  Sarkar et al. (2016) ;  Sarkar and Pascale (2015) ;  Pascale et al. (2015)  (see App A).
Planetary masses and densities To simulate a realistic planetary population we need to consider a distribution of masses given a planetary radius. The planetary mass affects directly the surface gravity and therefore the scale height (H) of the atmosphere: H = k T µ g (5) The mass estimate is not a trivial task, given the range of planetary densities observed today. We used a Python tool written by  Chen and Kipping (2016)  to estimate the mass of all the planets in our simulated sample. In the ARIEL planetary sample there are both known and simulated planets. Chen and Kipping (2016)  use the currently known planets to derive the statistical distribution of the mass of a given planet when its radius is known. Thus, for each planet in our initial sample, the mass is randomly drawn following this distribution except for known systems.
Benchmark generation and synthesis As explained in Sect. 2, in this work, we generate large MPSoC benchmarks through locally developed DSX tool. This tool takes three parameters as input. First parameter contains the description of MPSoC architecture and the instantiations of all the components contained in the architecture. For example, this parameter contains number of cluster in the architecture, number of processors inside each cluster, size and type of other interface components like UART, RAM etc inside each cluster. Second parameter then gives details of all the components of the architecture and also defines the interface between them. Third parameter finally defines the software application graph with variable number of tasks running on different processors of MPSoC architecture. Once these parameters are given to the DSX tool, it automatically generates MPSoC architecture's synthesizable files.
Routing time comparison results Apart from frequency comparison, we also perform routing time comparison between six interconnect topologies. By the routing time, we mean the time taken to perform inter-FPGA routing step as the rest of the steps do not make any difference for different interconnect topologies. Normalized routing time comparison results for three FPGA board are shown in Fig. 21. These results are obtained by running the inter-FPGA routing tool on a 64 bit linux server with 16 cores each running at 3 GHz and having 64 GB RAM. It can be seen from this figure that for almost all benchmarks, topology 2 gives the best routing time results while topology 5 gives the worst routing time results. Since topology 2 is based on generic two point and multi point direct connections only, its hyper graph is simple and Pathfinder quickly finds the conflict free solution.
The intelligent vehicle can plan the global behaviors and motions by reflecting the distant area's EOGM downloaded from the cloud. The accuracy and reliability of EOGM in the cloud will be improved by merging the multiple EOGMs from several intelligent vehicles. Also, vehicles without perception sensors can use the EOGM generated by other vehicles if they only have an Internet connection. For the cloud sharing of multi-vehicle EOGMs, there are two technical issues to be addressed. First, the EOGM must have a standard data format to be shared with other vehicles and to deal with the large-scale area. In the small area such as an indoor space, the EOGM is able to be implemented with single rectangular matrix data structure. However, the single rectangular matrix is not efficient format to cover the large-scale area for multiple vehicles.
Second, evidential update method of EOGM is required to merge the new EOGMs updated from multiple on-road vehicles and old EOGM in the cloud. The evidential update also must take into account the aging factor of the old EOGM. The main contribution of this paper is to propose a cloud update framework of evidential occupancy grid maps (EOGMs) for multiple intelligent vehicles in the large-scale real road environment. Each intelligent vehicle creates the EOGM for their moving trajectory based on GraphSLAM using LiDARs, motion sensors, and low-cost GPS receiver. The created EOGMs are uploaded and merged to EOGM in the map cloud. A standard tiling method of geodetic quad-tree tile system is applied to manage the EOGMs. The tiling method provides a common interface and data format to manage the large-scale EOGMs.
To construct the edge constraint of the measurement model, the error z,t between the measurement z t and measurement model h(x t , m) must be evaluated. However, since the z t and h(x t , m) are EOGM data type, the error z,t cannot be directly calculated with an analytical function. Instead of evaluating the error z,t , the similarity evaluation between two EOGMs based on the evidential scoring function is applied to construct the measurement model constraint. The evidential scoring function estimates the degree of matching of two EOGMs based on the sum of the Occupied masses in all cells of merged EOGM, as described in  (9). The merged EOGM is obtained by applying the conjunctive combination rule (4) for the two EOGMs, m 1 = z t and m 2 = h(x t , m). Based on the scoring function, the best matching pose x t which has the maximum score is evaluated, as described in  (10).
Second, it is very effective in terms of memory utilization because only the global EOGM in the area of interest with the certain tile size need to be created and loaded into the memory. Figure  7  shows an example of the geodetic quad-tree tile-based global EOGM, which has same local mapping configuration with the Figure  3. Compared to Figure  3  with one large rectangular global EOGM, the quad-tree tile based management is more efficient for the memory utilization because the global EOGM for areas of no interest do not need to be updated. Third, since all quad-tree tiles have a unique quad-key, the searching and data management of the global EOGM for cloud update is straightforward. Finally, the most important thing is the quad-tree tile system provides the common interface and data format for the global EOGM, which is compatible for all around the world.
Funding Acquisition, M. S. Funding: This work was financially supported by the  BK21 plus program  ( 22A20130000045 ) under the  Ministry of Education, Republic of Korea , the  Industrial Strategy Technology Development Program  (No. 10039673 ,  10060068 ,  10079961 ), the  International Collaborative Research and Development Program (N0001992)  under the  Ministry of Trade, Industry and Energy (MOTIE Korea ), and  National Research Foundation of Korea (NRF)  grant funded by the  Korean government (MEST)  (No. 2011-0017495 ).
Such a vivid experimental status is complemented by a significant progress in the theoretical description of DVCS. In particular, such new developments like NLO  [15] [16] [17] [18] [19] [20] [21] [22] , finite-t and mass  [23]  corrections are now available. Except DVCS, a variety of other exclusive processes has been described to provide access to GPDs, in particular: Timelike Compton Scattering  [24] , Deeply Virtual Meson Production  [25] , Heavy Vector Meson Production  [26] , Double Deeply Virtual Compton Scattering  [27, 28] , two particles  [29, 30]  and neutrino induced exclusive reactions  [31] [32] [33]. For some of those processes experimental data have been already collected, while other processes are expected to be probed in the future. The phenomenology of GPDs is much more involved than that of EFFs and PDFs.
Our original approach allows to utilize many basic properties of GPDs at the level of CFFs fits. We analyze PDFs, but also EFF and DVCS data, that is, we combine information coming from (semi-)inclusive, elastic and exclusive measurements. The analysis is characterized by a careful propagation of uncertainties coming from all those sources, which we achieved with the replica method. Obtained results allow for nucleon tomography, while the extracted subtraction constant may give some insight into the distribution of forces acting on partons inside the nucleon. This work is done with PARTONS  [42]  that is the open-source software framework for the phenomenology of GPDs. It serves not only as the main component of the fit machinery, but it is also utilized to handle multithreading computations and MySQL databases to store and retrieve experimental data.
(31) Here, the capital letters in the subscripts of observables names denote beam and target polarizations, respectively, with U standing for "Unpolarized" and L standing for "Longitudinally polarized". We also analyze data for "Transversely polarized targets", which are distinguished by the subscript T. These data are provided for two moments, sin(φ -φ S ) and cos(φ -φ S ), which are distinguished by the corresponding labels in the superscripts, as for instance in A -,sin(φ-φ S ) (x Bj , t, Q 2 , φ). Furthermore there are observables probing only the beam charge dependency (subscript C), and those combining cross sections measured with various beam charges to drop either the DVCS or interference contribution (subscripts I and DVCS, respectively). A different group consists of Fourier-like observables related to specific modulations of the φ angle.
Ansatz We present in this study a global extraction of CFFs. According to the terminology used within the GPD community, "global" refers to constraining parameters of an assumed CFF functional forms from various measurements on a wide kinematic range. On the contrary in local extractions, CFFs are extracted as a set of disconnected values in bins of ξ and t (see for instance Ref. [34, 35] ). We restrict our analysis to the LO approximation and we neglect any contribution coming from higher-twist effects and kinematic (target mass and finite-t) corrections. We adopt the description of cross sections in terms of DVCS and BH amplitudes by Guichon and Vanderhaeghen, used for phenomenology for instance in Refs. [71, 72]  and publicly available in the open-source PARTONS framework  [42]. We point out, that the limited phase space covered by available data, the precision of those data and the plenitude of involved dependencies force us to keep the parameterizations as simple as possible.
As the contribution coming from ∆q sea is subdominant, we neglect it entirely. For valence quarks, similarly to the GPD H, we allow the profile function to be different for u val and d val , while for the skewness function one has a u val H = a d val H ≡ a q val H. For E and E only the valence quarks are considered. For the GPD E all free parameters for both profile function and forward limit given by Eq. (  64 ) are fitted to EFF data. For the GPD E the normalization parameter N E is fitted to DVCS data. In total, we fit 9 parameters to EFF data (see Tab. 2) and 13 parameters are constrained by DVCS data (see Tab. 5).
( 81 ) where i = p, n. • the squared charge radius of neutron: r 2 nE = 6 dG n E (t) dt t=0. ( 82 ) Experimental data for those observables and for G n E (t) come from the sources summarized in Table  1. For the selection of observables and related data we follow Ref. [81] , which deals with the subject in great detail. In total, the used sample of data consists of 178 data points covering the range of 0. 017 GeV 2 ≤ |t| ≤ 31. 2 GeV 2. Our fit to those data ends for the central PDF replica with χ 2 /ndf = 129. 6/(178 -9) ≈ 0. 77. The fit is repeated for all other PDF replicas and for 100 instances of replicated data to propagate the uncertainties of EFF data to the analysis of CFFs. A single instance of replicated data is generated with the following prescription: v i ± ∆ tot i replica j -----→ rnd j (v i , ∆ tot i ) ± ∆ tot i , ( 83 ) where v i is the measured value associated to the experimental point i.
We will back to these problems in Sec. 9. We apply two kinematic cuts on experimental data: Q 2 > 1. 5 GeV 2 , ( 85 ) -t/Q 2 < 0. 25. ( 86 ) The purpose of those cuts is to restrict the phasespace covered by experimental data to the deeply virtual region where one can rely on the factorization between GPDs and the hard scattering kernel. The values of those cuts have been selected with the correspondence to KM analysis  [36] , which may help to compare results of both analyses in the future. Fig. 4 : Elastic Form Factor data listed in Table  1  and fitted according to the text. The gray bands indicate 68% confidence level for uncertainties coming from EFF data. The corresponding bands for unpolarized PDFs are indicated by the labels. For data points provided with systematic uncertainties, the inner bars represent statistical uncertainties, while the outer ones are for the quadratic sum of statistical and systematic uncertainties.
Uncertainties In this analysis all the uncertainties are evaluated with the replica method, which for a sufficiently large set of replicas allows one to accurately reproduce the probability distribution of a given problem. We distinguish four types of uncertainties on the extracted parameterizations of CFFs. They origin from: i) DVCS data, ii ) unpolarized PDFs, iii ) polarized PDFs) and iv ) EFFs. Each type of uncertainty is estimated independently, as described in the following. The uncertainties coming from DVCS data are estimated with a set of 100 instances of replicated data. Similarly to the analysis of EFFs, see Sec. 6, a single instance of replicated data is generated with the following prescription: v i ± ∆ tot i replica j -----→ rnd j (v i , ∆ tot i ) ± ∆ tot i × rnd j (1, ∆ norm i ).
Namely, we repeat our fit to DVCS data separately for each PDF replica, that is 100 times for unpolarized PDFs and 100 times for polarized PDFs. A cos iφ C i = 1 t 4 / 4 3 2008 [120] A cos iφ C i = 0, 1 x Bj 18 / 24 A sin(φ-φ S ) cos iφ U T ,DVCS i = 0 A sin(φ-φ S ) cos iφ U T ,I i = 0, 1 A cos(φ-φ S ) sin iφ U T ,I i = 1 4 2009 [121] A sin iφ LU,I i = 1, 2 x Bj 35 / 42 A sin iφ LU,DVCS i = 1 A cos iφ C i = 0, 1, 2, 3 5 2010 [122] A +,sin iφ U L i = 1, 2, 3 x Bj 18 / 24 A +,cos iφ LL i = 0, 1, 2 6 2011 [123] A cos(φ-φ S ) cos iφ LT ,DVCS i = 0, 1 x Bj 24 / 32 A sin(φ-φ S ) sin iφ LT ,DVCS i = 1 A cos(φ-φ S ) cos iφ LT ,I i = 0, 1, 2 A sin(φ-φ S ) sin iφ LT ,I i = 1, 2 7 2012 [124] A sin iφ LU,I i = 1, 2 x Bj 35 / 42 A sin iφ LU,DVCS i = 1 A cos iφ C i = 0, 1, 2, 3 8 CLAS 2001 [14] A -,sin iφ LU i = 1, 2 - 0 / 2 9 2006 [125] A -,sin iφ U L i = 1, 2 - 2 / A similar method is used to evaluate the uncertainties coming from EFF parameterizations.