RISK FACTORS OF HUMAN RABIES IN SOUTH ASIA: A SYSTEMATIC REVIEW

Background Rabies as a zoonotic viral disease is one of the neglected tropical diseases with high incidence among the poorest communities of least developed and developing countries of Africa and Asia. Aim This study aims to investigate the risk factors of human rabies in south Asia, with focus on Bangladesh, India and Pakistan. Method A systematic review approach was adopted, which included studies that identified the risk factors of human rabies in the three south Asian countries from 2007 to 2016. Electronic databases searched include PsycINFO, PubMed Central [PMC] and Cumulative Index of Nursing and Allied Health Literature [CINAHL]. Appropriate data screening was carried out to extract relevant articles. Finally, the articles were quality appraised and synthesized with a narrative synthesis approach. Result Eight relevant studies were finally identified, with either moderate or high quality. The studies identified one or more risk factors of human rabies. The findings include; animal bite mostly from certain stray animals (dog, cat, monkey and rat). Secondly, poor knowledge/awareness of the people about human rabies, thus people were ignorant of the need to seek for immediate treatment following animal bites. Thirdly, poor traditional/cultural practices following bites from infected animals. Fourthly, socioeconomic factors and finally, poor use of preventive measures against rabies. Conclusion Based on the findings, it is concluded that most of the factors predisposing to rabies infection in south Asia are preventable, hence; Government authorities, non-governmental organizations and philanthropists should be more committed toward increasing awareness about the consequences of the infection as well as providing free and accessible treatments across each country. Keywords: risk, factors, rabies, Bangladesh, India, Pakista

Reservoir Characterization and Modelling with Diagenetic Trends of carbonates of the Kawagarh Formation: A Section exposed in the Kala-Chitta Range, Pakistan

Present study is focused on the diagenetic studies and reservoir characterization of the Cretaceous KawagarhFormation exposed in the Gandab village, Kala-Chitta range, north-western Himalayan Fold-and-Thrust belt, Pakistan.The formation is composed of argillaceous limestone and dark grey marls. A total of thirty-three representativecarbonate rock samples were collected at equal intervals of three meters. Various diagenetic features includingcementation, micritization, pyrite precipitation, neomorphism, fracturing, sparitization and stylolitization were observedin the studied rocks which occur in the marine, meteoric and deep burial diagenetic environments respectively. Suchdiagenetic features control the reservoir quality of the rock unit. Porosity types include mostly vuggy and fracture,while minor stylolitic porosity were noted with quantity ranging from 2.66% to 3.88%. The carbonates of KawagarhFormation are highly fractured, but the filling of these fractures due to precipitation of calcite or micritic mud hasgreatly reduced its reservoir potential, while some unfilled fractures, stylolites and vuggs are the dominant factors thatenhance the reservoir potentiality of the Kawagarh Formation. However, the porosity values still do not mark the levelof reservoir rock. These diagenetic studies revealed very less chances for hydrocarbon accumulation as no significantporosity values have been observed and overall reservoir potential is characterized as poor

Discrimination in Algorithmic Decision Making: From Principles to Measures and Mechanisms

The rise of algorithmic decision making in a variety of applications has also raised concerns about its potential for discrimination against certain social groups. However, incorporating nondiscrimination goals into the design of algorithmic decision making systems (or, classifiers) has proven to be quite challenging. These challenges arise mainly due to the computational complexities involved in the process, and the inadequacy of existing measures to computationally capture discrimination in various situations. The goal of this thesis is to tackle these problems. First, with the aim of incorporating existing measures of discrimination (namely, disparate treatment and disparate impact) into the design of well-known classifiers, we introduce a mechanism of decision boundary covariance, that can be included in the formulation of any convex boundary-based classifier in the form of convex constraints. Second, we propose alternative measures of discrimination. Our first proposed measure, disparate mistreatment, is useful in situations when unbiased ground truth training data is available. The other two measures, preferred treatment and preferred impact, are useful in situations when feature and class distributions of different social groups are significantly different, and can additionally help reduce the cost of nondiscrimination (as compared to the existing measures). We also design mechanisms to incorporate these new measures into the design of convex boundary-based classifiers.Die Vielzahl der Anwendungen, die Algorithmen immer stärker an Entscheidungsprozessen beteiligen, wächst stetig. Dadurch werden Bedenken über die potenzielle Diskriminierung bestimmter gesellschaftlicher Gruppen aufgeworfen. Die Aufnahme von Nichtdiskriminierungszielsetzungen bei der Gestaltung algorithmischer Entscheidungs- bzw. Klassifizierungssysteme hat sich jedoch als grosse Herausforderung herausgestellt. Zum einen sind die nötigen Berechnungen komplex und zum anderen sind die existierenden Metriken unzureichend, um Diskriminierung in bestimmten Situationen rechnerisch zu erfassen. Das Ziel dieser Arbeit ist es, diese Problematik anzugehen. Als erstes stellen wir einen Decision Boundary-basierten Kovarianzmechanismus vor, der genutzt werden kann, um existierende Diskriminierungsmetriken (also Disparate Treatment und Disparate Impact) beim Entwurf von gängigen Klassifizierungsalgorithmen einzusetzen. Der Ansatz kann für jeden konvexen Boundary-basierten Klassifizierungsalgorithmus in Form konvexer Constraints formuliert werden. Als nächstes definieren wir neue Diskriminierungsmetriken. Unsere erste Metrik namens Disparate Mistreatment kommt in Situationen zum Einsatz, in denen die Referenzdaten nicht zugunsten einer sozialen Gruppe verzerrt sind. Die übrigen beiden Metriken namens Preferred Treatment und Preferred Impact sind für Situationen konzipiert, in denen die Feature- und Klassenverteilungen unterschiedlicher sozialer Gruppen stark voneinander abweichen. Sie können dabei helfen, die Kosten von Nichtdiskriminierung im Vergleich zu bestehenden Metriken zu reduzieren. Wir zeigen ebenfalls, wie diese neuen Metriken in konvexen Boundary-basierten Klassifizierungsalgorithmen genutzt werden können

Characterizing Information Diets of Social Media Users

With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of information were traditional news organizations, which broadcast the same carefully-edited information to all consumers over mass media channels. Whereas, now, in online social media, any user can be a producer of information, and every user selects which other users she connects to, thereby choosing the information she consumes. Moreover, the personalized recommendations that most social media sites provide also contribute towards the information consumed by individual users. In this work, we define a concept of information diet -- which is the topical distribution of a given set of information items (e.g., tweets) -- to characterize the information produced and consumed by various types of users in the popular Twitter social media. At a high level, we find that (i) popular users mostly produce very specialized diets focusing on only a few topics; in fact, news organizations (e.g., NYTimes) produce much more focused diets on social media as compared to their mass media diets, (ii) most users' consumption diets are primarily focused towards one or two topics of their interest, and (iii) the personalized recommendations provided by Twitter help to mitigate some of the topical imbalances in the users' consumption diets, by adding information on diverse topics apart from the users' primary topics of interest.Comment: In Proceeding of International AAAI Conference on Web and Social Media (ICWSM), Oxford, UK, May 201

Moderating Role of Country Governance in the Relationship between Technological Innovation and Inward Foreign Direct Investment

Due to the significance of Foreign Direct Investment (FDI) in economic development, a growing body of literature aims to analyze its determinants. In this regard, this study examines the role of technological innovation in attracting FDI and explains how country governance affects thisrelationship. For empirical analysis, we analyzed panel data from a wide range of developed and emerging economies for a period of 24 years, stretching from 1993 to 2016. We used the random effect model to obtain results after applying the Hausman test. We examined the relationshipbetween technological innovation, governance (by investigating governance indicators individually), FDI and how governance moderates the relationship between technological innovation and FDI in emerging and developed economies. The findings indicated that technological innovationin the host country is important for attracting inward FDI, regardless of the recipient country’s developmental level. For developed economies, political stability showed a strengthening effect on inward FDI. However, for both emerging and developed economies, all the other governance indicators weakened the technological innovation and FDI nexus

Comorbidity of COVID-19 related Fatalities in Tertiary Care Hospitals of Rawalpindi, Pakistan

Objectives: To assess the COVID-19 associated fatalities with respect to demographics, comorbidity, critical illness, and length of hospital stay in tertiary care hospitals. Subjects & Methods: A retrospective hospital data-based research was done among 216 COVID-19 associated mortalities registered in 4 tertiary care hospitals Holy Family Hospital (HFH), Benazir Bhutto Hospital (BBH), District Head Quarters Hospital (DHQ) and Rawalpindi Institute of Urology & Transplantation (RIU & T) affiliated with Rawalpindi Medical University from 29th March-15th June 2020. The data was gathered by consecutive sampling pertinent to demographics, hospital stay, comorbidity, critical illness, and ventilator or oxygen support. The length of hospital stay among fatalities with and without comorbidity was compared by an independent sample z-test. Data were analyzed by using SPSS version 25.0. Results: Of the total 216 COVID-19 related mortalities, 150(69.4%) were males and 66(30.6%) were females. The mean age of fatalities was 55.66 ± 13.97 years. About 76.7% of dying males were 41-70 years old while 56.1% of females dying of COVID-19 were 41-60 years old. Most (60.8%) of study subjects had hypertension followed by diabetes (53.8%), Ischemic Heart Disease (17.5%), and respiratory disorders (12.3%). About 75% of the critically ill patients needed a ventilator for respiratory support. Length of hospital stay was determined to have a statistically insignificant association (P > 0.10) with the presence or absence of comorbidity among COVID-19 patients. Critical illness had a highly significant association (P < 0.000) with ventilator support among COVID-19 related mortalities. Conclusion: People 41-70 years should preferably adopt stringent precautions for protection against COVID-19. Comorbid states chiefly hypertension, diabetes, cardiac and respiratory diseases need special consideration amid COVID-19 pandemic to abstain from adverse health outcomes

What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations

With over 500 million tweets posted per day, in Twitter, it is difficult for Twitter users to discover interesting content from the deluge of uninteresting posts. In this work, we present a novel, explainable, topical recommendation system, that utilizes social annotations, to help Twitter users discover tweets, on topics of their interest. A major challenge in using traditional rating dependent recommendation systems, like collaborative filtering and content based systems, in high volume social networks is that, due to attention scarcity most items do not get any ratings. Additionally, the fact that most Twitter users are passive consumers, with 44% users never tweeting, makes it very difficult to use user ratings for generating recommendations. Further, a key challenge in developing recommendation systems is that in many cases users reject relevant recommendations if they are totally unfamiliar with the recommended item. Providing a suitable explanation, for why the item is recommended, significantly improves the acceptability of recommendation. By virtue of being a topical recommendation system our method is able to present simple topical explanations for the generated recommendations. Comparisons with state-of-the-art matrix factorization based collaborative filtering, content based and social recommendations demonstrate the efficacy of the proposed approach

Correlation of Atrial Fibrillation with Left Atrial Volume in Patients with Mitral Stenosis. a Single Centre Study From Pakistan

Background: Rheumatic heart disease has a strong association with mitral valve stenosis. Atrial fibrillation is one of the most common complications of this condition and is a poor prognostic factor. Early detection and prompt management of atrial fibrillation can help to improve the quality of life and increase the life expectancy of the patients. We carried out this study to investigate the significance of left atrial volumetric changes in mitral stenosis and its correlation with atrial fibrillation. Methodology: We audited the data of 60 patients of rheumatic heart disease who had mitral valve stenosis. The patients were randomized into atrial fibrillation (Group A) and normal sinus rhythm (Group B). We conducted this cross-sectional analytical study at Cardiology Department, Mayo Hospital, Lahore, from 1st February 2017 to 31st January 2018. We only included those patients who consented to be a part of this study and fulfilled our predefined inclusion criteria. Left atrial volume was measured by prolate ellipse method and biplane methods on echocardiography. The Data was analyzed on SPSS v20. Results: Sixty patients were included in the study. Among the subjects, thirty-six (60%) were males, and twenty-four (40%) were females. Atrial fibrillation was noted in 43.33% of the patients of mitral valve stenosis. There was a marked difference in the mean volume of the left atrium among the two groups. We observed that the mean area of the mitral valve for Group A patients was larger than that of patients in Group B. Our study showed an inverse correlation between left atrial volume and mitral valve area among Group A patients. Conclusion: Patients of mitral stenosis are at an increased risk of developing atrial fibrillation if the left atrial volume is increasing. All patients with mitral stenosis should have routine echocardiography & measurement of left atrial volumes, so that proper treatment can be started if the left atrial volume is increasing, to prevent atrial fibrillation