OEKG: The Open Event Knowledge Graph

Accessing and understanding contemporary and historical events of global impact such as the US elections and the Olympic Games is a major prerequisite for cross-lingual event analytics that investigate event causes, perception and consequences across country borders. In this paper, we present the Open Event Knowledge Graph (OEKG), a multilingual, event-centric, temporal knowledge graph composed of seven different data sets from multiple application domains, including question answering, entity recommendation and named entity recognition. These data sets are all integrated through an easy-to-use and robust pipeline and by linking to the event-centric knowledge graph EventKG. We describe their common schema and demonstrate the use of the OEKG at the example of three use cases: type-specific image retrieval, hybrid question answering over knowledge graphs and news articles, as well as language-specific event recommendation. The OEKG and its query endpoint are publicly available

Ferroresonance case study in a distribution network and the potential impact of DERs and CVR/VVO

Ferroresonance in single-phase, line-to-line connected transformers in an ungrounded distribution system with delta-connected capacitors is possible but has not been reported in recent literature. In this paper, a high voltage event that actually occurred in an ungrounded distribution network with multiple distribution transformers has been simulated for lessons learned. The hypothesis was that ferroresonance was the cause of the overvoltage event in the network after a single-phase event led to sustained voltages of 1.45 p.u. Simulations were performed and ferroresonance was found to be the possible cause for the overvoltages. One of the prevention solutions found to avoid the overvoltages was to balance the loads on the three phases. The increased deployment of CVR/VVO strategies leads to circuit configurations similar to that studied in this paper and could lead to an increased likelihood of ferroresonance, if it is not fully understood and addressed. The impact of additional single-phase solar inverters on the low voltage side of the transformers was also studied. The results obtained show that the effective loading of the transformers, which is the difference in the actual load connected and the output from the DER, proved to be the deciding factor for initiation of ferroresonance in such networks

Clinical and histopathological profile of lesions of umbilicus

Background: Although lesions of umbilicus are encountered in clinical practice on a regular basis, surgical intervention is rarely required for them. Umbilical tissues are uncommonly received for histopathology. They formed 0.18% of the total specimens received. Aims: To study the clinical and histopathological characteristics of umbilical lesions received in the Surgical Pathology Department of a tertiary care hospital. Materials and Methods: This is a 2-year retrospective study. Records of the cases were reviewed and the histopathology slides were reassessed. Results: A total of 15 cases were found between the age range of 9 months and 45 years, with a male preponderance. Complaints of umbilical discharge/wet umbilicus and umbilical mass were the most common. In four cases, the umbilical lesion was associated with underlying congenital anomaly. Umbilical sinus (four cases) and umbilical granuloma (three cases) were the most common histopathological diagnosis. Two of the sinuses were pilonidal sinuses, which are rare lesions in this location. Conclusion: Umbilical mass or discharge associated with abdominal symptoms requires careful evaluation for congenital anomalies. Pilonidal sinus should be considered in a young hirsute patient with wet umbilicus

Epithelial ovarian tumors: Clinicopathological correlation and immunohistochemical study

Background: Ovarian cancer is the third leading site of cancer among women, trailing behind cervix and breast cancer. Aim: This study was undertaken to analyze the immunohistochemical (IHC) profile of estrogen receptors (ER), progesterone receptors (PR), Ki-67, and p53 in various ovarian epithelial tumors and attempt correlation with clinical and histopathological findings. Materials and Methods: The present study was conducted over a period of 4 years. A technique of manual tissue array was employed for cases subjected for IHC. The primary antibodies used were ER, PR, p53, and Ki-67. A correlation was attempted between histopathological and IHC findings. Results were subjected to statistical analysis. Software program "the primer of biostatistics 5.0" was used for calculation of interrelationships between the analyzed ER, PR, p53, and Ki-67 expression and histological factors by Pearson′s Chi-square test. The results were considered to be significant when the P < 0.05. Results: There were 110 cases of surface epithelial ovarian tumors (SEOT) encountered over the period of 4 years. The expression of ER was more in malignant tumors (13/16, 81.25%) than borderline (9/12, 75%) and benign (20/82, 24.39%). As compared to ER, the expression of PR was more in benign (51/82, 62.19%) than borderline (8/12, 66.67%) and malignant tumors (9/16, 56.25%). The expression of PR was more in benign tumors than borderline and malignant tumors. However, this was not statistically significant (Chi-square = 0.335 with 2 degrees of freedom; P = 0.846). The expression of p53 was less in benign (5/82, 6.1%) than borderline (9/12, 75%) and malignant tumors (13/16, 81.25%). The expression of Ki-67 was more in malignant (4/82, 4.88%) than borderline (10/12, 83.33%) and benign tumors (15/16, 93.75%). In all the above cases, the difference was statistically significant (P < 0.05). There was statistically significant difference in the expression of ER, PR, p53, and Ki-67 in the patients with age <40 years and above 40 years (P = 0.912). A positive correlation was observed in p53 expression and tumor grade. Similar correlation was seen in Ki-67 and tumor grade. It was also noted that mean Ki-67 labeling index (Li) had also increased with tumor grade. In the case of serous tumors, ER was expressed in all high- and low-grade tumors. The expression of PR was more in low-grade tumors than high-grade ones. P53 expression was seen in all high-grade tumors and 33.34% of low-grade tumor. The Ki-67 Li was more in high-grade tumors than low-grade tumors. Expression of ER, p53, and Ki-67 was higher in tumor showing metastasis. The mean Ki-67 Li was also higher in metastasizing tumors. However, PR expression was less in metastasizing tumors than nonmetastasizing tumors. Conclusion: IHC marker report of ER, PR status, and Ki-67 if included in each pathology report will pave the way for better understanding of biological behavior and modify treatment strategies

PREDOSE: A Semantic Web Platform for Drug Abuse Epidemiology using Social Media

Objectives The role of social media in biomedical knowledge mining, including clinical, medical and healthcare informatics, prescription drug abuse epidemiology and drug pharmacology, has become increasingly significant in recent years. Social media offers opportunities for people to share opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. This paper describes the development of a novel semantic web platform called PREDOSE(PREscription Drug abuse Online Surveillance and Epidemiology), which is designed to facilitate the epidemiologic study of prescription (and related) drug abuse practices using social media. PREDOSE uses web forum posts and domain knowledge, modeled in a manually created Drug Abuse Ontology (DAO – pronounced dow), to facilitate the extraction of semantic information from User Generated Content (UGC), through combination of lexical, pattern-based and semantics-based techniques. In a previous study, PREDOSE was used to obtain the datasets from which new knowledge in drug abuse research was derived. Here, we report on various platform enhancements, including an updated DAO, new components for relationship and triple extraction, and tools for content analysis, trend detection and emerging patterns exploration, which enhance the capabilities of the PREDOSE platform. Given these enhancements, PREDOSE is now more equipped to impact drug abuse research by alleviating traditional labor-intensive content analysis tasks. Methods Using custom web crawlers that scrape UGC from publicly available web forums, PREDOSE first automates the collection of web-based social media content for subsequent semantic annotation. The annotation scheme is modeled in the DAO, and includes domain specific knowledge such as prescription (and related) drugs, methods of preparation, side effects, and routes of administration. The DAO is also used to help recognize three types of data, namely: (1) entities, (2) relationships and (3) triples. PREDOSE then uses a combination of lexical and semantic-based techniques to extract entities and relationships from the scraped content, and a top-down approach for triple extraction that uses patterns expressed in the DAO. In addition, PREDOSE uses publicly available lexicons to identify initial sentiment expressions in text, and then a probabilistic optimization algorithm (from related research) to extract the final sentiment expressions. Together, these techniques enable the capture of fine-grained semantic information, which facilitate search, trend analysis and overall content analysis using social media on prescription drug abuse. Moreover, extracted data are also made available to domain experts for the creation of training and test sets for use in evaluation and refinements in information extraction techniques. Results A recent evaluation of the information extraction techniques applied in the PREDOSE platform indicates 85% precision and 72% recall in entity identification, on a manually created gold standard dataset. In another study, PREDOSE achieved 36% precision in relationship identification and 33% precision in triple extraction, through manual evaluation by domain experts. Given the complexity of the relationship and triple extraction tasks and the abstruse nature of social media texts, we interpret these as favorable initial results. Extracted semantic information is currently in use in an online discovery support system, by prescription drug abuse researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) at Wright State University. Conclusion A comprehensive platform for entity, relationship, triple and sentiment extraction from such abstruse texts has never been developed for drug abuse research. PREDOSE has already demonstrated the importance of mining social media by providing data from which new findings in drug abuse research were uncovered. Given the recent platform enhancements, including the refined DAO, components for relationship and triple extraction, and tools for content, trend and emerging pattern analysis, it is expected that PREDOSE will play a significant role in advancing drug abuse epidemiology in future