Integrating HTML tables using semantic hierarchies and meta-data sets

There are rich data resources residing in available materials websites, and most of these data resources are shown in the form of HTML tables. However, it is difficult to distinguish the attributes and values because of the semi-structured feature of HTML tables. Therefore, identifying attributes in HTML tables is the key issue for the information acquisition. In this paper, based on sibling comparison, a method for materials knowledge extraction from HTML tables is proposed, which consists of three steps: acquiring sibling tables, identifying table pattern and extracting table data. We show how to use [Formula: see text]-measure to find the appropriate thresholds for matching of tables from materials websites when acquiring sibling tables. Further, we propose a strategy named FRFC (i.e. the First Row matching and First Column matching) to distinguish attributes and values, so that table pattern is identified. Moreover, the data from HTML tables is extracted based on their corresponding table patterns and mapped to a predefined schema, which will facilitate the population to materials ontology. The proposed approach is applicable to circumstances, where an attribute in the table may span multiple cells and matched attributes in sibling tables are more. We acquire desired accuracy ([Formula: see text]%) through using FRFC for identifying table pattern. The time about extraction may not increase significantly with increasing number of documents and cells in tables, so our approach is effective to process a large number of documents. A prototype named MTES is developed and demonstrates the effectiveness of our proposed approach.

Millions of HTML tables containing structured data can be found on the Web. With their wide coverage, these tables are potentially very useful for filling missing values and extending cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. As a prerequisite for being able to use table data for knowledge base extension, the HTML tables need to be matched with the knowledge base, meaning that correspondences between table rows/columns and entities/schema elements of the knowledge base need to be found. This paper presents the T2D gold standard for measuring and comparing the performance of HTML table to knowledge base matching systems. T2D consists of 8 700 schema-level and 26 100 entity-level correspondences between the WebDataCommons Web Tables Corpus and the DBpedia knowledge base. In contrast related work on HTML table to knowledge base matching, the Web Tables Corpus (147 million tables), the knowledge base, as well as the gold standard are publicly available. The gold standard is used afterward to evaluate the performance of T2K Match, an iterative matching method which combines schema and instance matching. T2K Match is designed for the use case of matching large quantities of mostly small and narrow HTML tables against large cross-domain knowledge bases. The evaluation using the T2D gold standard shows that T2K Match discovers table-to-class correspondences with a precision of 94%, row-to-entity correspondences with a precision of 90%, and column-to-property correspondences with a precision of 77%.

In this paper, we propose a novel method for extracting information from HTML tables with similar contents but with a different structure. We aim to integrate multiple HTML tables into a single table for retrieval of information containing in various Web pages. The method is designed by extending tree-structured LSTM, the neural network for tree-structured data, in order to extract information that is both linguistic and structural information of HTML data. We evaluate the proposed method through experiments using real data published on the WWW.

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract Appropriate modulation of escape behaviors in response to potentially damaging stimuli is essential for survival. Although nociceptive circuitry has been studied, it is poorly understood how genetic contexts affect relevant escape responses. Using an unbiased genome-wide association analysis, we identified an Ly6/α-neurotoxin family protein, Belly roll (Bero), which negatively regulates Drosophila nociceptive escape behavior. We show that Bero is expressed in abdominal leucokinin-producing neurons (ABLK neurons) and bero knockdown in ABLK neurons resulted in enhanced escape behavior. Furthermore, we demonstrated that ABLK neurons responded to activation of nociceptors and initiated the behavior. Notably, bero knockdown reduced persistent neuronal activity and increased evoked nociceptive responses in ABLK neurons. Our findings reveal that Bero modulates an escape response by regulating distinct neuronal activities in ABLK neurons. Editor's evaluation This convincing and fundamental study uses unbiased genome-wide association analysis to identify a gene, called Bero, encoding Ly6/α-neurotoxin family protein, that affects the way larval Drosophila respond to nociceptive stimuli. This discovery is followed up by the identification of neurons in which Bero function is relevant for the modulation of nociceptive behaviour, namely the abdominal leucokinin-producing neurons. These neurons are activated by nociceptive sensory neurons and can initiate escape behavior. In these neurons Bero modulates both persistent and evoked activities. This elegant work will be of interest to neurobiologists working on genes, neural circuits, and behaviour. https://doi.org/10.7554/eLife.83856.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Appropriate escape behavior in response to threatening stimuli is essential for organismal survival (Branco and Redgrave, 2020; Burrell, 2017; Chin and Tracey, 2017; Im and Galko, 2012; Peirs and Seal, 2016). In both vertebrates and invertebrates, the modulation of escape behavior depends on environmental contexts as well as intensity and modality of stimuli, which are sensed and processed in a multilayered neuronal network, subject to genetic variation, that integrates external and internal sensory stimuli (Burnett et al., 2016; Jennings et al., 2014; Mu et al., 2012; Ohyama et al., 2015). Within the network, peptidergic neuromodulation plays a critical role in neuronal information processing (Nässel and Winther, 2010; Taghert and Nitabach, 2012). When Drosophila melanogaster larvae encounter noxious stimuli, such as harsh mechanical stimulations intrinsic to attacks by parasitoid wasps, they show stereotypic escape behaviors composed of an initial abrupt bending and subsequent corkscrew-like rolling (Hwang et al., 2007; Onodera et al., 2017; Tracey et al., 2003). In larvae, the noxious stimuli are mainly sensed by class IV dendritic arborization neurons (Class IV neurons) underneath the body wall (Tracey et al., 2003). In addition, a type of tracheal dendrite neuron (v’td2 neurons) and class III dendritic arborization neurons (Class III neurons) are known to detect noxious light stimuli and noxious cold stimuli, respectively (Imambocus et al., 2022; Turner et al., 2016). Recent studies have identified several important downstream components of the nociceptive neural circuitry (Ohyama et al., 2015; Takagi et al., 2017; Yoshino et al., 2017). In parallel, several genes and neurotransmitters have been characterized as context-dependent modulators of the behaviors (Burgos et al., 2018; Dason et al., 2020; Hu et al., 2017; Kaneko et al., 2017). While most of these studies identified the nociceptive neural circuitry by connectomic approaches, we used a genetic strategy to provide additional insights into the nociceptive modulation. Importantly, we discovered that D. melanogaster larvae displayed an extensive natural variation in nociceptive escape behavior. This broad behavioral variation allowed us to perform an unbiased genome-wide association (GWA) analysis to explore genetic variants affecting the nociceptive escape behaviors. In this study, we have demonstrated that an Ly6/α-neurotoxin family protein, Belly roll (Bero), modulates the neuronal excitability of peptidergic interneurons that initiates and facilitates Drosophila nociceptive escape behaviors. We first identified the bero gene through GWA analysis and validated its role in regulating nociception using pan-neuronal bero knockdown and null mutant animals. By searching for bero-expressing neurons, we identified abdominal leucokinin-producing neurons (ABLK neurons) that have been reported to be involved in controlling larval escape behaviors (Hu et al., 2020; Imambocus et al., 2022). We then found that bero knockdown in ABLK neurons resulted in enhanced escape behaviors, similar to bero mutants. Furthermore, we demonstrated that ABLK neurons responded to the activation of nociceptors and initiated nociceptive escape behavior. Notably, bero knockdown in ABLK neurons resulted in reduced persistent activities and increased evoked nociceptive responses in ABLK neurons, implying an essential role of bero in regulating neuronal activities. Based on our results regarding the ABLK neurons, we propose that these neurons may integrate external and internal sensory stimuli, thereby orchestrating nociceptive escape behaviors. Results Natural variation in nociceptive responses in wild-type strains of the Drosophila melanogaster Genetic Reference Panel Upon noxious thermal stimulation, Drosophila larvae show stereotypical nociceptive responses: bending and rolling behavior (Figure 1A; Tracey et al., 2003). Using the Heat Probe Assay (HPA; Tracey et al., 2003), we noticed that there is a large difference in the latency of rolling behavior (rolling latency) between the two commonly used strains, w1118 and Canton-S (Figure 1B). In order to evaluate natural variation in nociceptive responses, we took advantage of the Drosophila melanogaster Genetic Reference Panel (DGRP; Huang et al., 2014; Mackay et al., 2012), a collection of wild-caught Drosophila lines. We quantified the rolling behavior in 38 representative inbred wild-type strains of DGRP and observed a considerable variation in the behavior: the strain-specific median rolling latency ranged from 2.83 s to 10 s and the occurrence rates of rolling behavior (rolling probability) within 10 s ranged from around 30% to almost 100% among these strains (Figure 1C and D). We categorized the rolling probability into three response classes (rolling probability in 2, 5, and 10 s) based on previous studies (Onodera et al., 2017; Terada et al., 2016; Tracey et al., 2003) and found that the three response classes are highly correlated with one another (Figure 1—figure supplement 1A). To identify genetic variants associated with the behavioral variation, we performed multiple trials of GWA mapping analyses using the publicly available web-based analysis tool, DGRP 2.0 (Huang et al., 2014), with four statistical metrics that characterize larval rolling behavior and are mutually dependent to some extent (Figure 1E, Figure 1—source data 1, ‘Materials and methods’). We then identified in total 37 single-nucleotide polymorphisms (SNPs), two deletions, and three insertions in or near 36 candidate genes (Figure 1—source data 2), at a threshold p-value of 1.0 × 10–5, a commonly used threshold p-value in previous studies with DGRP (Figure 1F, Figure 1—figure supplement 1B; Mackay and Huang, 2018). Figure 1 with 1 supplement see all Download asset Open asset Natural diversity in nociceptive rolling escape behavior in wild-type strains of Drosophila melanogaster. (A) A schematic representation of the Heat Probe Assay (see ‘Materials and methods’ for details). (B) The latency of rolling escape behavior (rolling latency) is different between two wild-type strains, w1118 and Canton-S (CS) (46°C, [w1118] n = 40, [CS] n = 38; 48°C, [w1118] n = 32, [CS] n = 33; 50°C, [w1118] n = 33, [CS] n = 33; ***p<0.001, Wilcoxon rank-sum test). Violin plots provide a kernel density estimate of the data, where the middle circle shows the median, a boxplot shape indicates 25th and 75th percentiles, and whiskers to the left and right of the box indicate the 90th and 10th percentiles, respectively, in this and the following figures. Each data point represents an individual larva. See source data tables for detailed genotypes in this and the following figures. (C) The rolling latency of 38 representative Drosophila melanogaster Genetic Reference Panel (DGRP) lines in ascending order (n = 30 or 31 larvae/line). Boxplots indicate the median and 25th and 75th percentiles, and whiskers to the left and right of the box indicate the 90th and 10th percentiles, respectively, in this and the following figures. (D) The rolling probability of 38 representative DGRP lines in ascending order (n = 30 or 31 larvae/line). The stacked bar chart indicates the rolling probability within 2, 5, and 10 s, respectively. (E) A diagram showing experimental procedures for the genome-wide association (GWA) analysis. (F) GWA analysis for median rolling latency of the rolling escape behavior. The p-values (−log10 transformed) are shown on the y axis. The gray dotted line marks the nominal p-value threshold (1.0 × 10–5). Each data point corresponds to an individual genetic variant (single-nucleotide polymorphism, deletion, or insertion). Data points are arranged by relative chromosome (genomic) position, the color code indicates the respective chromosome to which they belong. See also Figure 1—source data 1 and Figure 1—figure supplement 1A. Figure 1—source data 1 Summary table of genotypes, statistical testing, and graph data for Figure 1. https://cdn.elifesciences.org/articles/83856/elife-83856-fig1-data1-v1.zip Download elife-83856-fig1-data1-v1.zip Figure 1—source data 2 Genome-wide association results for rolling behavior. https://cdn.elifesciences.org/articles/83856/elife-83856-fig1-data2-v1.zip Download elife-83856-fig1-data2-v1.zip Identification of CG9336/belly roll (bero) through an RNA interference knockdown screen of candidate genes To explore impacts of these candidate genes on nociceptive responses, we carried out a secondary functional screen by pan-neuronal RNA interference (RNAi)-mediated gene knockdown. By evaluating 17 RNAi strains that were currently available, we found that pan-neuronal knockdown of the CG9336 gene resulted in significant enhancement of the rolling probability (Figure 2A). To further test whether the phenotype was specifically caused by suppression of CG9336, we generated two distinct short-hairpin RNAs (shRNAs) against CG9336 (shRNA#1 and #2; Figure 2B; ‘Materials and methods’), validated the efficiency of shRNA#2 (Figure 2—figure supplement 1A and B; ‘Materials and methods’), and found that both shRNAs caused a comparable enhancement (Figure 2E, Figure 2—figure supplement 1C). We also performed three sets of effector-control tests for UAS-bero shRNA#2 with different genomic backgrounds. In each group, both the UAS-bero shRNA#2 control animal and the corresponding controls exhibited comparable rolling behavior, which ensures that any observed phenotypes were not due to leaky expression from the UAS transgene (Figure 2E). We then designated the gene as belly roll (bero), after a high-jump style in athletics. bero encodes a member of the Ly6/α-neurotoxin protein superfamily, which is characterized by a three-finger structure, including various membrane-tethered and secreted polypeptides (Loughner et al., 2016). Using several bioinformatics tools, we predicted a signal peptide sequence at the amino terminus, two possible glycosylation sites, a GPI-anchorage site, a putative transmembrane region at the carboxyl terminus, and five putative disulfide bridges, which are typical structural features of the Ly6/α-neurotoxin protein superfamily (Figure 2C, Figure 2—figure supplement 1D; Almagro Armenteros et al., 2019; Baek et al., 2021; Eisenhaber et al., 1999; Gupta and Brunak, 2001; Krogh et al., 2001). A three-dimensional model of Bero was generated by protein structure prediction using AlphaFold2 (Jumper et al., 2021; Figure 2D). Figure 2 with 2 supplements see all Download asset Open asset bero negatively regulates nociceptive rolling escape behavior. (A) A secondary functional screen by pan-neuronal RNA interference. The rolling latency of each UAS-RNAi line was measured. Pan-neuronal knockdown of CG9336/bero reduces rolling latency (Heat Probe Assay, n = 30 or 31 larvae/genotype; ****p<0.0001, Wilcoxon rank-sum test). (B) A schematic representation of bero-associated SNP (2L_20859945_SNP; minor allele, cytosine; major allele, thymine; minor allele frequency = 0.4167). (C) A schematic representation of unprocessed and mature Bero protein. SP, signal peptide; TM, transmembrane region. Orange lines mark the predicted disulfide bonds. Yellow lines mark the predicted N-glycosylation sites. Pink lines mark the predicted GPI-modification site. See also Figure 2—figure supplement 1D. (D) A three-dimensional protein structure prediction of unprocessed Bero protein by AlphaFold2. Red region, signal peptide; blue region, transmembrane region; orange sticks, predicted disulfide bonds; yellow region, predicted N-glycosylation sites; pink region, predicted GPI-modification site. (E) Rolling latency of nSyb>attP2 control (n = 63), pan-neuronal bero knockdown animals (nSyb>bero RNAishRNA#2, n = 65), and the corresponding three sets of effector-controls (w1118 background: attP2 control, n = 64, bero RNAishRNA#2, n = 60; Canton-S background: attP2 control, n = 60, bero RNAishRNA#2, n = 60, y w background: attP2 control, n = 55, bero RNAishRNA#2, n = 55). **p<0.01; n.s., nonsignificant; Wilcoxon rank-sum test. The genetic backgrounds are controlled (see source data tables for detailed genotypes in this and the following figures). (F) Rolling latency of Canton-S control (n = 38), bero heterozygous (beroKO/+, n = 38), and homozygous (beroKO/beroKO, n = 37) mutant animals. **p<0.01, ***p<0.001, Wilcoxon rank-sum test. The bero KO strain had been outcrossed to Canton-S for 11 generations. Figure 2—source data 1 Summary table of genotypes, statistical testing, and graph data for Figure 2. https://cdn.elifesciences.org/articles/83856/elife-83856-fig2-data1-v1.zip Download elife-83856-fig2-data1-v1.zip The bero-associated SNP, 2L_20859945_SNP, is located in the second intron of CG9336 isoform A (FBtr0081427) and comprises two nucleotide variations in the DGRP core 40 lines: a minor allele, cytosine, and a major allele, thymine (Figure 2B). We found that most of the 38 DGRP core lines with the bero minor allele (C) showed higher responsiveness than those with the major allele (T) upon noxious thermal stimulations (Figure 2—figure supplement 2A). Based on these observations and our bero knockdown results, we speculated that animals with the bero minor allele (C) might have lower expression levels of bero compared with those possessing the major allele (T). Thus, using reverse transcriptase PCR, we compared the expression levels of bero in the larval central nervous system (CNS) among four DGPR strains that showed extremely quick or slow behavioral responses. We found that animals with the minor allele have lower expression levels of bero (Figure 2—figure supplement 2B). Using online sequence analysis based on JASPAR 2022 (Castro-Mondragon et al., 2022), we predicted that the neighboring nucleotide sequences of the bero-associated SNP might be a target site of certain transcription factors that control the expression level of bero in CNS (Supplementary file 2). Furthermore, we generated a bero null mutant line using CRISPR-mediated mutagenesis, which deleted the entire protein-coding sequence (Figure 2—figure supplement 1E; ‘Materials and methods’), and observed in HPA experiments that both bero heterozygous (beroKO/+) and homozygous (beroKO/beroKO) mutant animals displayed an enhanced rolling escape behavior (Figure 2F). Taken together, these results strongly suggest that bero expression in neurons negatively regulates nociceptive escape behavior. bero is expressed in several subgroups of peptidergic neurons in the larval CNS We next sought to find which neurons express bero in the larval CNS. To do this, we examined the overlap between bero-expressing neurons labeled in a protein-trap strain bero-YFP (CPTI-001654; Lowe et al., 2014), in which the endogenous Bero protein is fused with a yellow fluorescent protein variant mVenus, and candidate neurons labeled by a CD4:tdTomato fusion protein driven by distinct GAL4-drivers. We found no bero-YFP expression in the Class IV neurons (nociceptors) or in the downstream neurons that have been reported to be required for the nociceptive escape behavior (Figure 3—figure supplement 1A; Ohyama et al., 2015). Consistent with this, bero knockdown specifically in the corresponding neurons did not alter the rolling behavior (Figure 3—figure supplement 1B). Instead, we observed bero-YFP expression in several groups of peptidergic neurons specified by the bHLH transcription factor dimmed (dimm), including insulin-producing cells (IPCs) and a subset of Eclosion hormone-producing neurons (EH neurons) in the larval brain, and abdominal leucokinin-producing neurons (ABLK neurons) in the lateral region of abdominal ganglions in the larval ventral nerve cord (VNC; Figure 3A and B). In support of our observations, a recent single-cell transcriptomic atlas showed that bero/CG9336 is selectively enriched in peptidergic neurons (Corrales et al., 2022). Bero is also expressed in certain non-neuronal tissues, such as midline glia (Figure 3A). However, we did not study Bero function in the glia in this study. Figure 3 with 1 supplement see all Download asset Open asset Expression of bero in ABLK neurons plays an essential role in the negative regulation of nociceptive behavior. (A) Confocal image stacks (maximum projection) showing fluorescence of the endogenous Bero reporter, Bero-YFP (green), and the colocalized peptidergic neurons with GAL4, UAS-CD4-tdTomato (magenta) in third-instar larvae. Scale bars, 50 μm. (B) A schematic representation of bero-expressing neurons. IPC, insulin-producing cells; EH, eclosion hormone-producing neurons; ABLK, abdominal leucokinin-producing neurons; SEG, the subesophageal ganglion; VNC, the ventral nerve cord. (C) Confocal image stack showing endogenous Bero expression (labeled by Bero-YFP, yellow), LK neurons (Lk-GAL4.TH, UAS-CD4-tdTomato, magenta), and leucokinin (labeled by anti-Lk, cyan) in a third-instar larva. Bottom and left panels show an XZ and YZ cross-section of the ABLK somatic region (locations of the cross-sections are indicated by horizontal and vertical gray lines in the primary image), respectively. Middle panels show magnified views of the boxed regions: Individual optical sections depicting ABLK neurons (ABLK neurons are indicated by A1–A7 label, and an image of A7 with enhanced intensity is showed), Right: image stack depicting SELK neurons (indicated by arrowheads), and image stack depicting ABLK neurites (an image with enhanced intensity is showed). Scale bars: 50 μm; 10 μm or 20 μm for the magnified view. (D) Confocal image stack showing the dendrite (labeled by ABLK >DenMark, magenta) and axon terminal markers (labeled by ABLK >syt:eGFP, green) in ABLK neurons in a third-instar larva. Scale bars: 50 μm; 20 μm for the magnified view. (E) Rolling latency of LK-specific bero knockdown larvae (Lk>bero RNAishRNA #2, n = 73) and control (Lk>attP2, n = 75). Rolling latency of Eh-specific bero knockdown larvae (Eh>bero RNAishRNA #2, n = 38) and control (Eh>attP2, n = 41). Rolling latency of Ilp2-specific bero knockdown larvae (Ilp2>bero RNAishRNA #2, n = 40) and control (Ilp2>attP2, n = 39). ***p<0.001; n.s., nonsignificant; Wilcoxon rank-sum test. (F) Rolling latency of ABLK-specific bero overexpression larvae (ABLK>Bero:FLAG, n = 72), control (ABLK>myr:GFP, n = 73), and the corresponding effector-controls (y v background: myr:GFP control, n = 60, Bero:FLAG, n = 61). **p<0.01; n.s., nonsignificant; Wilcoxon rank-sum test. Figure 3—source data 1 Summary table of genotypes, statistical testing, and graph data for Figure 3. https://cdn.elifesciences.org/articles/83856/elife-83856-fig3-data1-v1.zip Download elife-83856-fig3-data1-v1.zip bero expression in ABLK neurons negatively regulates nociceptive rolling escape behavior To identify the particular subgroups of neurons that require bero expression for negative regulation of nociceptive escape behavior, we examined rolling escape behavior in larvae with cell type-specific RNAi knockdowns of bero. We found that knockdown of bero using Lk-GAL4, a leucokinin-producing neuron-specific driver, caused enhanced rolling escape behavior (Figure 3E). In contrast, knockdown of bero using either Eh-GAL4, a neuron-specific driver, or an driver, did not in any in rolling escape behavior (Figure 3E). LK neurons can be into four distinct subgroups in the larval LK neurons lateral LK neurons subesophageal LK neurons and abdominal LK neurons et al., bero is expressed in all of ABLK neurons and is to not expressed in any LK neurons (Figure 3A and Consistent with this, the of bero-YFP labeled neurons were for (Figure of individual optical sections showed that Bero on both the and internal of ABLK neurons (Figure We expressed the dendrite and axon terminal namely and in ABLK neurons (Figure that Bero both and of the neurons. To the role of bero expression in ABLK neurons in rolling behavior, we to ABLK-specific bero overexpression using an et al., 2010; 2016; Figure 3—figure supplement 1C). We observed that bero overexpression rolling behavior (Figure that bero expression in ABLK neurons plays an essential role in the negative regulation of nociceptive behavior. In contrast, in the effector-control both the control animal and the corresponding controls showed comparable levels of rolling behavior (Figure we our analysis on the of ABLK neurons and the functional of bero in these neurons. bero persistent activities and evoked nociceptive responses in ABLK neurons We next whether ABLK neurons respond to of To test this, we expressed a blue variant of et al., in and a fluorescence et al., in LK neurons. We responses in ABLK neurons in and observed that cell and of ABLK neurons displayed persistent of (Figure and Figure supplement which was with a previous study et al., Notably, activation of the nociceptors evoked evoked neuronal activities in ABLK neurons, that these neurons respond to nociceptive (Figure and Hu et al., We used larvae on a with no additional all as controls activation in the A was also in the control larvae on a with no additional the the of the blue light (Figure This indicates that the be to the persistent in ABLK neurons. Figure with 2 supplements see all Download asset Open asset bero is for the of persistent activities and suppression of evoked nociceptive activity in ABLK neurons. (A) results of responses showing the persistent signal of ABLK neurons in control and bero knockdown larvae bero RNAishRNA (B) A of the of persistent activities of ABLK neurons in control (n = neurons from three and bero knockdown larvae (n = neurons from three ****p<0.0001, Wilcoxon rank-sum test. (C) of the nociceptive responses of ABLK neurons in control and bero knockdown larvae bero RNAishRNA after activation of Class IV neurons Scale bars, 10 μm. (D) for responses see ‘Materials and methods’ for of ABLK neurons in control n = neurons from three and bero knockdown larvae bero RNAishRNA #2, n = neurons from three upon activation of Class IV neurons light is indicated by at and for blue and gray indicate (E) for responses of ABLK neurons in control n = neurons from three and bero knockdown larvae bero RNAishRNA #2, n = neurons from three upon activation of Class IV neurons. (F) of responses of ABLK neurons in control n = neurons from three n = neurons from three and bero knockdown larvae n = neurons from three n = neurons from three upon activation of Class IV neurons n.s., nonsignificant; test followed by test. Figure data 1 Summary table of genotypes, statistical testing, and graph data for Figure Download To further the of bero in ABLK neurons, we whether knockdown of bero persistent activities nociceptive responses of the neurons. We found that bero knockdown almost persistent activities (Figure and it increased the of evoked nociceptive responses (Figure and that bero knockdown enhanced rolling escape behavior in the HPA experiments (Figure we speculated that the evoked nociceptive responses in ABLK neurons are important for rolling escape behavior. the phenotype of the bero knockdown on persistent activities in ABLK neurons, we performed a behavioral analysis of and found that bero knockdown did not affect larval (Figure supplement persistent in ABLK neurons of bero knockdown animals to be enhanced compared to controls (Figure Figure supplement 1B). We that the used for in nociceptive Furthermore, ABLK neurons with bero knockdown displayed increased to these nociceptive than which may provide a possible the neural activity may not Thus, we compared activity the we have that bero plays a critical role in not a higher level of persistent also the evoked nociceptive responses of the ABLK neurons, thereby the nociceptive escape behavior (Figure of evoked activity in ABLK neurons is for the control of rolling escape behavior To test whether of ABLK activity to escape behaviors, we activated the neurons using a et al., We first activated neurons and found that light a rolling behavior that was observed in control larvae (Figure and B). We then the to selectively express in ABLK or SELK neurons (Figure activation of ABLK neurons almost resulted in not rolling bending behavior, which is as a initial of the entire sequence of escape (Figure Figure supplement 1A and B). In contrast, activation of SELK neurons rolling behavior in all larvae. Figure with 1 supplement see all

Relational database management systems (RDBMSs) have been a common option to manage structured data over the past decades. In recent years, with the prevalence of big data applications, vast unstructured and semi-structured data are generated, deeply challenging the relational model used in RDBMSs. For this reason, a wide spectrum of NoSQL databases are developed for managing unstructured, semi-structured or structured data. For example, graph database management systems (GDBMSs) are commonly used as an important category of NoSQL databases, to manage sophisticated graph data as well as relational data. Nonetheless, as claimed in existing literatures, both RDBMSs and GDBMSs are capable of managing graph data and relational data, the boundaries of them still remain unclear. In this paper, we propose a unified benchmark for RDBMSs and GDBMSs, to evaluate them under the same metrics, and report which category is better in different application scenarios. We conduct extensive experiments over the unified benchmark, and report our findings: (1) RDBMSs are significantly faster for aggregations and order by operations, (2) GDBMSs are shown to be superior for projection, multi-table join and deep recursive operations, (3) GDBMSs and RDBMSs are comparable for two-table join and shallow recursive operations.

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The Health Care Network (HCN ) related information of a country is one of the most influential fields of data sharing and integrating with disparate data sources of physical world and compatible health care organizations on the exponentially growing World Wide Web (WWW ) for improving and maintaining health related services of mass people. Although these accessible HCN related unstructured or semi-structured data in multiple heterogeneous repositories on the web originate challenges to develop a smarter method in finding specific information using traditional keyword-based search engines. Moreover, it is also a formidable task to harmonize spatial data with our HCN for exploring implicit knowledge that assist people in making their decisions regarding of health services and it is getting researchers' attention at a rapid pace. In this regard, we applied the Physical-Cyber-Social (PCS) computing approach on HCN that demonstrates the feasibility of semantic web technologies to represent domain information in the machine understandable format and we also integrate data from accessible Cyber Space Data Sources (CSDSs) to address these challenges. We published our HCN related data using Resource Description Framework (RDF ) to build a knowledge-base. We call this repository as HCN - BD, which is HCN related machine understandable data source of Bangladesh. This yields a huge knowledge graph with 0.25 million nodes, i.e. RDF triples to facilitate reasoning important information pattern by a graph based query language, SPARQL. Furthermore, we integrate this semantic repository with our generic spatial knowledge-base, Geo - Bangladesh, a machine understandable geographic data source related to the administrative structure of Bangladesh, to examine health care locations using SPARQL and therefore, we achieved the semantic interoperability in our Linked Open Data (LOD) application. Moreover, our research effectively addresses nearest health care centers using real time spatial data from Global Positioning System (GPS) and maps these locality to increase searching ability by mass people.

A relation can be conceptually viewed as a table of data. The table's heading defines the relation's name, the column headings are the attribute names, and each row corresponds to an n-tuple of data values describing a single entity. The set of values which can be used in a column is called a domain. A relational data base is composed of a set of time varying relations inter-related through common domains.

Relational Database Management Systems (RDBMS) have been very successful at managing structured data with well-defined schemas. Despite this, relational systems are generally not the first choice for management of data where schemas are not pre-defined or must be flexible in the face of variations and changes. Instead, No-SQL database systems supporting JSON are often selected to provide persistence to such applications. JSON is a light-weight and flexible semi-structured data format supporting constructs common in most programming languages. In this paper, we analyze the way in which requirements differ between management of relational data and management of JSON data. We present three architectural principles that facilitate a schema-less development style within an RDBMS so that RDBMS users can store, query, and index JSON data without requiring schemas. We show how these three principles can be applied to industry-leading RDBMS platforms, such as the Oracle RDBMS Server, with relatively little effort. Consequently, an RDBMS can unify the management of both relational data and JSON data in one platform and use SQL with an embedded JSON path language as a single declarative language to query both relational data and JSON data. This SQL/JSON approach offers significant benefits to application developers as they can use one product to manage both relational data and semi-structured flexible schema data.

In this chapter, we will cover how the Snowflake Data Cloud handles semi-structured data such as JSON and XML. As data capture and sources have grown significantly in the last few years especially, semi-structured data has also grown. JSON semi-structured data especially grew significantly with the popularity of NoSQL databases. It has become essential for organizations to be able to process semi-structured data and combine it with structured data for analysis. However, analyzing semi-structured data using traditional methods has been more difficult due to added levels of complexity. Many businesses have struggled to combine structured with semi-structured data. Most analytical cloud databases also treated semi-structured data as a second-class data, making it hard to easily use both semi-structured data and structured data.

We describe some aspects of our research in relational knowledge discovery and combinatorial scientific computing [11], with special emphasis on the relation to the research portfolio of the conceptual structures community. We have recently been developing [10, 12] a combinatorial approach to the management and analysis of large ontologies such as the Gene Ontology (GO) [6]. Our approach depends on casting the GO as a labeled partially ordered set (poset) [16], and then using scores based on pseudo-distance measures which we have developed to categorize lists of labels (in the case of the GO, genes and gene products) concerning their clustering and depth within the GO. We hold that such taxonomic semantic hierarchies serve as the core conceptual structures underlying all ontological databases, and through this work we have developed a number of what we believe to be both fundamental and novel ideas about treating such large posets as data objects, in particular the nature of distance in such structures, and the nature of level as an interval-valued property. After laying out this basic framework, we can then bring these ideas to a particular kind of poset, namely the concept lattice [5]. Considering a concept lattice as a poset, we are then prepared to develop techniques for anomaly detection in relational data by measuring the relative level of concepts vs. their cardinalities.

The Web is rich of tables (e.g., HTML tables, spreadsheets, Google Fusion Tables) that host a considerable wealth of high-quality relational data. Unlike unstructured texts, tables usually favour the automatic extraction of data because of their regular structure and properties. The data extraction is usually complemented by the annotation of the table, which determines its semantics by identifying a type for each column, the relations between columns, if any, and the entities that occur in each cell.

Natural language processing-guided meta-analysis and structure factor database extraction from glass literature

In the last decades, the problem of antibiotic resistance occupies one of the key positions in the global public health system and requires attention from the medical community. Antimicrobial resistance monitoring systems play an important role in tracking the changes in antimicrobial susceptibility for timely correction of antimicrobial therapy. The pharmaceutical industry applies epidemiological data obtained through such monitoring to the creation of new medicines and modification of the antimicrobial substances developed earlier. The article describes some of the international and Russian monitoring systems created at different times. It should be noted that during development, regional-level data are used, while a number of projects present information on a global scale. The completed comparative analysis of available systems revealed both positive aspects and parameters in need of renovation. At the same time, the standardization of collecting basic data for monitoring programs requires significant changes. The majority of systems are able to examine only a limited range of microorganisms and antimicrobials. An important point in the functioning of monitoring systems is a search for the optimal way to visualize output data in tables, interactive maps, and graphics. A significant amount of projects demand further work on the result presentation options. Constant monitoring is a significant component in modern concepts of antibiotic resistance control due to the increasing occurrence of resistant organisms.