Efficient algorithms for analyzing large scale network dynamics: Centrality, community and predictability

Large scale networks are an indispensable part of our daily life; be it biological network, smart grids, academic collaboration networks, social networks, vehicular networks, or the networks as part of various smart environments, they are fast becoming ubiquitous. The successful realization of applications and services over them depend on efficient solution to their computational challenges that are compounded with network dynamics. The core challenges underlying large scale networks, for example: determining central (influential) nodes (and edges), interactions and contacts among nodes, are the basis behind the success of applications and services. Though at first glance these challenges seem to be trivial, the network characteristics affect their effective and efficient evaluation strategy. We thus propose to leverage large scale network structural characteristics and temporal dynamics in addressing these core conceptual challenges in this dissertation. We propose a divide and conquer based computationally efficient algorithm that leverages the underlying network community structure for deterministic computation of betweenness centrality indices for all nodes. As an integral part of it, we also propose a computationally efficient agglomerative hierarchical community detection algorithm. Next, we propose a network structure evolution based novel probabilistic link prediction algorithm that predicts set of links occurring over subsequent time periods with higher accuracy. To best capture the evolution process and have higher prediction accuracy we propose multiple time scales with the Markov prediction model. Finally, we propose to capture the multi-periodicity of human mobility pattern with sinusoidal intensity function of a cascaded nonhomogeneous Poisson process, to predict the future contacts over mobile networks. We use real data set and benchmarked approaches to validate the better performance of our proposed approaches --Abstract, page iii

A CORRELATIVE STUDY ON SPINDLE CELL SARCOMA WITH CYTO-HISTOLOGICAL GRADING BY CONVENTIONAL METHODS IN AN INDIAN TERTIARY CARE TEACHING HOSPITAL

Objective: Spindle cell sarcomas constitute an important component of soft tissue sarcomas, where accurate grading is more important than histologic subtypes to plan treatment strategies and determine prognosis. To assess whether histologic criteria for grading sarcomas could be applied to fine needle aspiration biopsy (FNAB) specimens of adult spindle cell sarcomas without knowledge of sarcoma subtypes. Furthermore, correlate this grading with histologic Federation Nationale Des Centres de lutte contre le cancer (FNCLCC) grading system and find out the percentage of accuracy of FNAB grading. Methods: Hundred cases were studied by fine needle aspiration cytology (FNAC), among them 50 cases found to be spindle cell sarcoma; in only 42 cases surgical specimens were available. Each aspiration being spread into two or more slides. Subsequently, biopsy was done and studied. Technique used - grading of FNAB specimens and corresponding surgical specimens. Result: Grading of FNAB specimens done taking into account four parameters - nuclear atypia, nuclear variation (nuclear contour, chromatin, nucleoli, and nuclear overlap), mitotic figure, and necrosis. Grading of surgical specimens done by FNCLCC system. The overall results were correlated for both FNAC and biopsy concordance and statistically analyzed. From there the sensitivity and specificity were calculated as 95.23% and 80.76%, respectively. Conclusion: Histologic criteria for grading sarcomas in adults could be applied to FNAC specimens of spindle cell sarcomas with 88% accuracy. While comparing the diagnosis given by FNAC and biopsy, it revealed the true positive were 40 cases, true negative 42 cases, false positive 10 cases, and false negative (FN) 2 cases.Â

Eldo-care: EEG with Kinect sensor based telehealthcare for the disabled and the elderly

Telehealthcare systems are nowadays becoming a massive daily helping kit for elderly and disabled people. By using the Kinect sensors, remote monitoring has become easy. Also, the sensors' data are useful for the further improvement of the device. In this paper, we have discussed our newly developed “Eldo-care” system. This system is designed for the assessment and management of diverse neurological illnesses. The telemedical system is developed to monitor the psycho-neurological condition. People with disabilities and the elderly frequently experience access issues to essential services. Researchers today are concentrating on rehabilitative technologies based on human-computer interfaces that are closer to social-emotional intelligence. The goal of the study is to help old and disabled persons with cognitive rehabilitation using machine learning techniques. Human brain activity is observed using electroencephalograms, while user movement is tracked using Kinect sensors. Chebyshev filter is used for feature extraction and noise reduction. Utilizing the autoencoder technique, categorization is carried out by a Convolutional neural network with an accuracy of 95% and higher based on transfer learning. A better quality of life for older and disabled persons will be attained through the application of the suggested system in real time. The proposed device is attached to the subject under monitoring

Quality of private and public ambulatory health care in low and middle income countries: systematic review of comparative studies

Paul Garner and colleagues conducted a systematic review of 80 studies to compare the quality of private versus public ambulatory health care in low- and middle-income countries

Insulin-like growth factor-I expression is not increased in the retina of diabetic BB/W-rats

A combination of immunocytochemistry, in situ hybridization and ligand binding were used to investigate the localization of IGF-I and its receptor in the retina of diabetic and non-diabetic BB/W-rats. Immunocytochemical localization revealed the presence of IGF-I in retinal pigment epithelium, ganglion cells, Muller cell processes and in microvessels. In most sites immunoreactivity was increased in the diabetic retina compared to that of non-diabetic BB/W-rats. In microvessels, however, immunoreactivity was decreased in diabetes. In situ hybridization using an antisense IGF-I riboprobe provided evidence of IGF-I synthesis in all retinal layers with a similar grain density in diabetic and non-diabetic rats. Autoradiographic localization of IGF-I receptors, using [125I]-IGF-I binding, demonstrated a diffuse localization in all retinal layers, with an increase in diabetic animals. These findings suggest that IGF-I synthesis is not altered in the diabetic retina, and that the increased immunoreactivity of IGF-I detectable in the various layers of the retina from diabetic rats may be due to an increased uptake of blood-derived IGF-I suggested by increased receptor density in diabetic rats.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29042/1/0000075.pd

Nursing Personnel Planning for Rural Hospitals in Burdwan District, West Bengal, India, Using Workload Indicators of Staffing Needs

Lack of appropriate human resources planning is an important factor in the inefficient use of the public health facilities. Workforce projections can be improved by using objective methods of staffing needs based on the workload and actual work undertaken by workers, a guideline developed by Peter J. Shipp in collaboration with WHO\u2014Workload Indicators of Staffing Need (WISN). A cross-sectional study was carried out to estimate the nursing stuff requirement for the rural hospitals and provide a quantitative description of imbalances, if there is any, in the allocation at the district level during 2011. The average WISN turns out to be 0.35 for entire district, which means only 35% of the required nurses is available or 65% understaffed. So, there is an urgent need for more allocations and deployment of staff so that workload can be tackled and evenly distributed among all nursing personnel

The Aetiology of Diabetic Neuropathy: the Combined Roles of Metabolic and Vascular Defects

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75362/1/j.1464-5491.1995.tb00544.x.pd

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses