ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called "matching dependencies" (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language "LogiQL" -an extended form of Datalog supported by the "LogicBlox" platform- for all activities related to data processing, and the specification and enforcement of MDs.Comment: Final journal version, with some minor technical corrections. Extended version of arXiv:1508.0601

Epidemiology, clinical characteristics, and outcome of hospitalized COVID-19 patients in Kurdistan Province, Iran

BACKGROUND: The present study aimed to evaluate the epidemiology, clinical characteristics, and outcome of confirmed and suspected hospitalized coronavirus disease 2019 (COVID-19) cases in Iran hospitals affiliated with the Kurdistan University of Medical Sciences, Sanandaj, Iran. METHODS: This cross-sectional study was performed on all confirmed and suspected hospitalized COVID-19 cases in hospitals affiliated with the Kurdistan University of Medical Sciences between March and September 2020. Required data were obtained from the Hospital Intelligent Management System of hospitals. Independent t-test, chi-square test, Fisher's exact test, and one-way analysis of variance (ANOVA) were used for univariate analysis. Variables with P-value < 0.3 in univariate analysis were entered into the multivariate model, and the adjusted odds ratio (AOR) was calculated. RESULTS: Out of 9176 cases, 3210 cases (35.03%) were confirmed with COVID-19. The mean and standard deviation (SD) of age of the cases was 56.5 ± 19.3 in the confirmed and 57.5 ± 20.6 in the suspected cases. The confirmed and suspected cases’ mortality rate was 15.0% and 10.2%, respectively. In both groups, the most common symptoms of admission to the hospital were respiratory distress, coughing, fever, and muscular pain. The variables of older age, male gender, being transferred to hospitals by ambulance, intensive care unit (ICU) hospitalization, being intubated, blood oxygen saturation level less than 93, and having an underlying disease were statistically associated with an increased chance of death. CONCLUSION: The mortality rate among both confirmed and suspected hospitalized COVID-19 cases was significant, and this rate was higher for the confirmed cases. Death-related risk factors should be considered in resource allocation, management, and patient prioritization to reduce the outcome of death

Enforcing relational matching dependencies with datalog for entity resolution

Entity resolution (ER) is about identifying and merging records in a database that represent the same real-world entity. Matching dependencies (MDS) have been introduced and investigated as declarative rules that specify ER policies. An ER process induced by MDS over a dirty instance leads to multiple clean instances, in general. General answer sets programs have been proposed to specify the MD-based cleaning task and its results. In this work, we extend MDS to relational MDS, which capture more application semantics, and identify classes of relational MDS for which the general ASP can be automatically rewritten into a stratified Datalog program, with the single clean instance as its standard model

Enforcing relational matching dependencies with datalog for entity resolution

Entity resolution (ER) is about identifying and merging records in a database that represent the same real-world entity. Matching dependencies (MDS) have been introduced and investigated as declarative rules that specify ER policies. An ER process induced by MDS over a dirty instance leads to multiple clean instances, in general. General answer sets programs have been proposed to specify the MD-based cleaning task and its results. In this work, we extend MDS to relational MDS, which capture more application semantics, and identify classes of relational MDS for which the general ASP can be automatically rewritten into a stratified Datalog program, with the single clean instance as its standard model

Phytotoxins Produced by Two Biscogniauxia rosacearum Strains, Causal Agents of Grapevine Trunk Diseases, and Charcoal Canker of Oak Trees in Iran

Biscogniauxia rosacearum, recognized for the first time as a pathogen involved in grapevine trunk diseases in Paveh (west of Iran) vineyards, produced meso-2,3-butanediol (1) as the only phytotoxin. Nectriapyrone (2), (3R)-5-methylmellein (3), (3R)-5-methyl-6-methoxymellein (4), and tyrosol (5) were instead produced as phytotoxins from a strain of the same fungus isolated from oak trees in Zagros forests of Gilan-e Gharb, Kermanshah Province. They were identified comparing their 1H and 13C NMR, ESIMS, and specific optical rotation data with those already reported in the literature. The phytotoxicity of metabolites (1–5) was estimated by leaf puncture assay on Quercus ilex L. and Hedera helix L., and by leaf absorption assay on grapevine (Vitis vinifera L.) at a concentration of 5 × 10−3 and 10−3 M. Tested on grapevine, meso-2,3-butanediol (1) and (3R)-5-methyl-6-methoxymellein (4) resulted to be the most phytotoxic compounds. On Q. ilex, nectriapyrone (2) and tyrosol (5) showed severe necrosis at the highest concentration while none of the compounds (1–5) was active on H. helix. Furthermore, the phytotoxicity of compounds 3 and 4 was also compared with that of some related natural melleins to perform a structure-activity relationship (SAR) study. The results of this study were also discussed

Phytotoxins produced by Didymella glomerata and Truncatella angustata, associated with grapevine trunk diseases (GTDs) in Iran

Didymella glomerata and Truncatella angustata associated with grapevine trunk diseases (GTDs) in Iran, were grown in vitro to evaluate the production of phytotoxic metabolites as potential pathogenicity determinants. 2,5-Dihydroxymethylfuran and (+)-6-hydroxyramulosin were isolated from the culture filtrates of D. glomerata and T. angustata, respectively. They were identified by physical and spectroscopic (essentially 1 D and 2 D 1H and 13C NMR and ESIMS) methods and X ray analysis. Both compounds induced significant necrosis and curling on the leaves of the host plant Vitis vinifera L. and the effects were concentration dependent. No effect was observed on the leaves of the non-host Solanum lycopersicum L.. plant