CHRONIC KIDNEY DISEASE – A MULTI-CENTER STUDY IN KARACHI, PAKISTAN

Objective: Chronic kidney disease is growing at alarming rate in developing countries like Pakistan. The aim of the study was to find out the major factors leading to this disease and to carry out the comparative analysis of the effectiveness of allopathic and homoeopathic medicines in treatment of chronic kidney disease.Methods: A multi-center study was carried out in five different centers from 2009-2014. The study was carried out by interviewing the patients, noting down their vitals and reviewing their records. Evaluation of the data was done considering age, sex and co-morbidities associated with renal failure.Results: Significant results were observed. Patients of age groups 46 to 60 (48%) and 30 to 45 (21%) were found to suffer more from chronic kidney disease. Hypertension was found as the most frequently occurring co-morbidity along with chronic renal failure followed by diabetes.Conclusion: The current study will be beneficial in bringing awareness in general public and thereby reducing the increasing burden of end-stage kidney disease

Robust Brain Age Estimation via Regression Models and MRI-derived Features

The determination of biological brain age is a crucial biomarker in the assessment of neurological disorders and understanding of the morphological changes that occur during aging. Various machine learning models have been proposed for estimating brain age through Magnetic Resonance Imaging (MRI) of healthy controls. However, developing a robust brain age estimation (BAE) framework has been challenging due to the selection of appropriate MRI-derived features and the high cost of MRI acquisition. In this study, we present a novel BAE framework using the Open Big Healthy Brain (OpenBHB) dataset, which is a new multi-site and publicly available benchmark dataset that includes region-wise feature metrics derived from T1-weighted (T1-w) brain MRI scans of 3965 healthy controls aged between 6 to 86 years. Our approach integrates three different MRI-derived region-wise features and different regression models, resulting in a highly accurate brain age estimation with a Mean Absolute Error (MAE) of 3.25 years, demonstrating the framework's robustness. We also analyze our model's regression-based performance on gender-wise (male and female) healthy test groups. The proposed BAE framework provides a new approach for estimating brain age, which has important implications for the understanding of neurological disorders and age-related brain changes.Comment: Published at the 15th International Conference on Computational Collective Intelligenc

Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues

Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps

Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers

Analysis of the fairness of machine learning (ML) algorithms recently attracted many researchers' interest. Most ML methods show bias toward protected groups, which limits the applicability of ML models in many applications like crime rate prediction etc. Since the data may have missing values which, if not appropriately handled, are known to further harmfully affect fairness. Many imputation methods are proposed to deal with missing data. However, the effect of missing data imputation on fairness is not studied well. In this paper, we analyze the effect on fairness in the context of graph data (node attributes) imputation using different embedding and neural network methods. Extensive experiments on six datasets demonstrate severe fairness issues in missing data imputation under graph node classification. We also find that the choice of the imputation method affects both fairness and accuracy. Our results provide valuable insights into graph data fairness and how to handle missingness in graphs efficiently. This work also provides directions regarding theoretical studies on fairness in graph data.Comment: Accepted at IEEE International Conference on Big Data (IEEE Big Data

Simulation of Error Control Schemes for Wireless and Satellite ATM

Abstract This paper summarizes the results of a set of simulations of error control schemes for transmission of ATM cells in Wireless and Satellite ATM networks. For Wireless ATM networks, we consider a scheme which incorporates separate FECs for ATM cell header and payload. This scheme is an effective scheme and achieves a trade-off between coding rate and bandwidth. A concatenated FEC scheme (RSCCC) is considered for Satellite ATM networks

Mershon and Humanities Institute Faculty Panel

Streaming video requires RealPlayer to view.Should we alter our strategy, stay the course, bring our troops home, increase their number? Afghanistan: The Choices is an interdisciplinary discussion of the way forward for the United States in Afghanistan, with a panel of leading Ohio State experts (Richard Herrmann, Peter Mansoor, John Mueller, and Alam Payind), moderated by Fred Andrle. In August 2009, Gen. Stanley McChrystal, commander of U.S. and NATO forces in Afghanistan, submitted a report to be reviewed by President Barack Obama and his top national security advisers. In his report, Gen. McChrystal gave a grim assessment of the conflict in Afghanistan and later requested 40,000 more troops be sent to fight the Taliban-led insurgency, bringing the total number of U.S. and NATO forces in Afghanistan to 108,000. The possibility that more troops will be needed to ensure success in Afghanistan has presented the Obama administration with a number of challenges and new considerations in dealing with a conflict that Obama has called the central front in the war on terror. Besides facing pressure from both conservatives and members of his own party, Obama is also facing new questions over the credibility of the Afghan government compounded with mounting American and NATO casualties. Afghanistan: The Choices will explore the future of the United States involvement in Afghanistan through an interdisciplinary discussion of strategies for success in Afghanistan. The panelists will discuss and answer questions relating to the application of a more comprehensive counterinsurgency strategy, implications of the U.S. presence on the Taliban insurgency, the level of U.S. forces in Afghanistan, and the national security interest of the United States in Afghanistan.Ohio State University. Mershon Center for International Security StudiesOhio State University. Institute for Collaborative Research and Public HumanitiesOhio State University. Middle East Studies CenterEvent Web page, streaming video, event photo

Cytotoxicity, Morphology and Chemical Composition of Two Luting Cements: An in Vitro Study

Objective: To assess the cytotoxicity, surface morphology, elemental compositions and chemical characterization of two commonly used luting cement. Material and Methods: The two luting types of cement used were Elite Cement® and Hy-Bond Resiglass®. Freshly mixed (n=6) and set form (n=6) of each cement was placed in medium to obtain extracts. The extract from each sample was exposed to L929 mouse fibroblasts (1x104cells/well). Alamar Blue Assay assessed cell viability. Surface morphology and elemental composition were evaluated using scanning electron microscopy and energy dispersive spectroscopy. The chemical characterization was performed by Fourier Transform Infrared Spectroscopy. One-way ANOVA and post-hoc Tukey analysis were conducted to assess results. Results: Hy-Bond Resiglass® was the more cytotoxic of the two types of cement in both freshly mixed (68.10 +5.16; p<0.05) and set state (87.58 +4.86; p<0.05), compared to Elite Cement® both freshly mixed (77.01 +5.45; p<0.05) and set state (89.39 +5.66; p<0.05). Scanning electron microscopy revealed a more irregular and porous structure in Hy-Bond Resiglass® compared to Elite Cement®. Similarly, intense peaks of aluminium, tungsten and fluorine were observed in energy dispersive spectroscopy in Hy-Bond Resiglass. Conclusion: All these three elements (aluminium, tungsten and fluorine) have cytotoxic potential. The Fourier transform infrared spectroscopy revealed the presence of hydroxyethyl methacrylate in Hy-Bond Resiglass®, which has a cytotoxic potential