Extracting and Cleaning RDF Data

The RDF data model has become a prevalent format to represent heterogeneous data because of its versatility. The capability of dismantling information from its native formats and representing it in triple format offers a simple yet powerful way of modelling data that is obtained from multiple sources. In addition, the triple format and schema constraints of the RDF model make the RDF data easy to process as labeled, directed graphs. This graph representation of RDF data supports higher-level analytics by enabling querying using different techniques and querying languages, e.g., SPARQL. Anlaytics that require structured data are supported by transforming the graph data on-the-fly to populate the target schema that is needed for downstream analysis. These target schemas are defined by downstream applications according to their information need. The flexibility of RDF data brings two main challenges. First, the extraction of RDF data is a complex task that may involve domain expertise about the information required to be extracted for different applications. Another significant aspect of analyzing RDF data is its quality, which depends on multiple factors including the reliability of data sources and the accuracy of the extraction systems. The quality of the analysis depends mainly on the quality of the underlying data. Therefore, evaluating and improving the quality of RDF data has a direct effect on the correctness of downstream analytics. This work presents multiple approaches related to the extraction and quality evaluation of RDF data. To cope with the large amounts of data that needs to be extracted, we present DSTLR, a scalable framework to extract RDF triples from semi-structured and unstructured data sources. For rare entities that fall on the long tail of information, there may not be enough signals to support high-confidence extraction. Towards this problem, we present an approach to estimate property values for long tail entities. We also present multiple algorithms and approaches that focus on the quality of RDF data. These include discovering quality constraints from RDF data, and utilizing machine learning techniques to repair errors in RDF data

Query Optimization for On-Demand Information Extraction Tasks over Text Databases

Many modern applications involve analyzing large amounts of data that comes from unstructured text documents. In its original format, data contains information that, if extracted, can give more insight and help in the decision-making process. The ability to answer structured SQL queries over unstructured data allows for more complex data analysis. Querying unstructured data can be accomplished with the help of information extraction (IE) techniques. The traditional way is by using the Extract-Transform-Load (ETL) approach, which performs all possible extractions over the document corpus and stores the extracted relational results in a data warehouse. Then, the extracted data is queried. The ETL approach produces results that are out of date and causes an explosion in the number of possible relations and attributes to extract. Therefore, new approaches to perform extraction on-the-fly were developed; however, previous efforts relied on specialized extraction operators, or particular IE algorithms, which limited the optimization opportunities of such queries. In this work, we propose an on-line approach that integrates the engine of the database management system with IE systems using a new type of view called extraction views. Queries on text documents are evaluated using these extraction views, which get populated at query-time with newly extracted data. Our approach enables the optimizer to apply all well-defined optimization techniques. The optimizer selects the best execution plan using a defined cost model that considers a user-defined balance between the cost and quality of extraction, and we explain the trade-off between the two factors. The main contribution is the ability to run on-demand information extraction to consider latest changes in the data, while avoiding unnecessary extraction from irrelevant text documents

Heat requirement of pomegranate fruit: a case study on Shishe-Kab cultivar

The aim of this study was to determine growing degree day (GDD) for pomegranate fruit Shishe-Kab cultivar and study the phonological stages of fruit from bloom to harvest. A completely randomized block design was carried out on the research orchard of the Faculty of Agriculture, University of Birjand, 2017. During the May to October, the diameter and length of the fruits and calyx were recorded using the non-destructive method by a digital caliper on the tree. Next fruit samples were randomly taken from the trees to determine fresh and dry weight. Furthermore, by using the metrological data, the thermal requirement based on the growth degree day (GDD) has been determined from April until harvest day. The effective heat requirement for Shishe-Kab cultivar that was calculated from blooming to reach maturity was 2560.95. The highest cumulative temperature was recorded in the commercial harvest date. By receiving this degree day, fruit reached the highest fresh and dry weight, length and also diameter at the end of growing season. The results indicated that all fruit characteristics significantly increased from the first recording day till the end, except the ratio of fruit length to diameter. A slight decrease in growth rate was presented in fruit diameter and length, which was concomitant with their seed hardening. Results showed that calyx diameter and length of pomegranate fruit has a slow continues liner growth pattern, fruit length and diameter exhibited a double sigmoid growth curve, while the fresh and dry weight followed a single sigmoidal curve. By determining the fruit growth pattern under climatic conditions, it is possible to determine the length of growing season and the critical stages of growth for proper management in the garden

Propuesta en Supply Chain Management y Logística en la empresa Tecnitrauma S.A

Durante el diplomado de profundización se llevó a cabo el análisis operacional de la empresa Tecnitrauma S.A la cual tiene sede en la ciudad de Barranquilla/Atlántico y en donde uno de los objetivos principales era el análisis e implementación de mejoras en los elementos del modelo referencial de las cadenas de suministro y Logística. Esta empresa es prestadora de servicios médicos especializados y está enfocada en el campo de la traumatología en donde ofrecen productos relacionados sobre Ortesis y Osteosíntesis. Al abordar los distintos elementos del diplomado se pudo evidenciar mejoras en los procesos operacionales los cuales se documentaron y de iguales formas algunas ya se encontraban en operación y se procedió a su actualización. Este proceso fue llevado a cabo por el grupo 24 del curso de Diplomado de Supply Chain Management y Logística de la UNAD y fue posible concluirlo gracias a la disposición, conocimiento y entrega de cada uno de los presentes y sobre todo al acompañamiento recibido por la Universidad.During the in-depth diploma, the operational analysis of the company Tecnitrauma S.A, which is based in the city of Barranquilla/Atlántico, was carried out, where one of the main objectives was the analysis and implementation of improvements in the elements of the model Reference of the supply chains and Logistics. This company is a provider of specialized medical services and is focused in the field of traumatology where they offer related products on Orthosis and Osteosynthesis. By addressing the different elements of the diploma, it was possible to show improvements in the operational processes that were documented and in the same way some were already in operation and they were updated. This process was carried out by the 24th group of the UNAD Supply Chain Management and Logistics Diploma course and it was possible to conclude it thanks to the willingness, knowledge and dedication of each of those present and especially the accompaniment received by the University

Prevalence of Malnutrition among Iranian Pediatric Patients before and After Hospitalization (2015 To 2017): A Multicenter Study

Background: Malnutrition undermines the beneficial outcomes of clinical interventions and also increases hospital costs. Therefore, this study aimed to estimate the prevalence of malnutrition through a multicenter observational study at the time of admission and discharge in Iranian hospitalized children and adolescents. Methods: The present cross-sectional study was performed on children and adolescents aged one month to 18 years from three Iranian public tertiary pediatric hospitals located in different cities of Iran. To determine the participants’ nutritional status, Z-score of the weight for height (for those with 1month to 5years of age) and Z-score of BMI (for ≥5 to 18-year-old patients) were calculated using the WHO growth standards. Data were analyzed using SPSS version 23. Results: Information about 1499 patients was collected. At the time of admission, 64% of the participants had a good nutritional status, 15.5% were at high risk of wasting, 8.4% were wasted, and 12.1% were severely wasted. Among 295 malnourished patients, the nutritional status of 182 patients (63%) had been improved at the time of discharge. Also, 23% of all subjects with normal nutritional status at the admission time (85 participants), were at risk of malnutrition at discharge. The prevalence of moderate and severe malnutrition at the discharge time was about 20%. Conclusion: More than one-third of the hospitalized children had moderate or severe malnutrition or were at high risk. Although the prevalence of malnutrition decreased somewhat during hospitalization, some children were not malnourished at the time of admission and were malnourished at discharge

Prevalence of Dysglycemia Among Coronary Artery Bypass Surgery Patients with No Previous Diabetic History

<p>Abstract</p> <p>Background</p> <p>Dysglycemia is a major risk factor for atherosclerosis. In many patient populations dysglycemia is under-diagnosed. Patients with severe coronary artery disease commonly have dysglycemia and there is growing evidence that dysglycemia, irrespective of underlying history of diabetes, is associated with adverse outcome in coronary artery bypass graft (CABG) surgery patients, including longer hospital stay, wound infections, and higher mortality. As HbA1c is an easy and reliable way of checking for dysglycemia we routinely screen all patients undergoing CABG for elevations in HbA1c. Our hypothesis was that a substantial number of patients with dysglycemia that could be identified at the time of cardiothoracic surgery despite having no apparent history of diabetes.</p> <p>Methods</p> <p>1045 consecutive patients undergoing CABG between 2007 and 2009 had HbA1c measured pre-operatively. The 2010 American Diabetes Association (ADA) diagnostic guidelines were used to categorize patients with no known history of diabetes as having diabetes (HbA1c ≥ 6.5%) or increased risk for diabetes (HbA1c 5.7-6.4%).</p> <p>Results</p> <p>Of the 1045 patients with pre-operative HbA1c measurements, 40% (n = 415) had a known history of diabetes and 60% (n = 630) had no known history of diabetes. For the 630 patients with no known diabetic history: 207 (32.9%) had a normal HbA1c (< 5.7%); 356 (56.5%) had an HbA1c falling in the increased risk for diabetes range (5.7-6.4%); and 67 (10.6%) had an HbA1c in the diabetes range (6.5% or higher). In this study the only conventional risk factor that was predictive of high HbA1c was BMI. We also found a high HbA1c irrespective of history of DM was associated with severe coronary artery disease as indicated by the number of vessels revascularized.</p> <p>Conclusion</p> <p>Among individuals undergoing CABG with no known history of diabetes, there is a substantial amount of undiagnosed dysglycemia. Even though labeling these patients as "diabetic" or "increased risk for diabetes" remains controversial in terms of perioperative management, pre-operative screening could lead to appropriate post-operative follow up to mitigate short-term adverse outcome and provide high priority medical referrals of this at risk population.</p

A survey of relationship between anxiety, depression and duration of infertility

BACKGROUND: A cross sectional study was designed to survey the relationship between anxiety/depression and duration/cause of infertility, in Vali-e-Asr Reproductive Health Research Center, Tehran, Iran. METHODS: After obtaining their consents, 370 female patients with different infertility causes participated in, and data gathered by Beck Depression Inventory(BDI) and Cattle questionnaires for surveying anxiety and depression due to the duration of infertility. This was studied in relation to patients' age, educational level, socio-economic status and job (patients and their husbands). RESULTS: Age range was 17–45 years and duration and cause of infertility was 1–20 years. This survey showed that 151 women (40.8%) had depression and 321 women (86.8%) had anxiety. Depression had a significant relation with cause of infertility, duration of infertility, educational level, and job of women. Anxiety had a significant relationship with duration of infertility and educational level, but not with cause of infertility, or job. Findings showed that anxiety and depression were most common after 4–6 years of infertility and especially severe depression could be found in those who had infertility for 7–9 years. CONCLUSIONS: Adequate attention to these patients psychologically and treating them properly, is of great importance for their mental health and will improve quality of their lives

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases

Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

Atrial fibrillation affects more than 33 million people worldwide and increases the risk of stroke, heart failure, and death. Fourteen genetic loci have been associated with atrial fibrillation in European and Asian ancestry groups. To further define the genetic basis of atrial fibrillation, we performed large-scale, trans-ancestry meta-analyses of common and rare variant association studies. The genome-wide association studies (GWAS) included 17,931 individuals with atrial fibrillation and 115,142 referents; the exome-wide association studies (ExWAS) and rare variant association studies (RVAS) involved 22,346 cases and 132,086 referents. We identified 12 new genetic loci that exceeded genome-wide significance, implicating genes involved in cardiac electrical and structural remodeling. Our results nearly double the number of known genetic loci for atrial fibrillation, provide insights into the molecular basis of atrial fibrillation, and may facilitate the identification of new potential targets for drug discovery