A Reverse Engineering Methodology for Extracting Parallelism From Design Abstractions.

Migration of code from sequential environments to the parallel processing environments is often done in an ad hoc manner. The purpose of this research is to develop a reverse engineering methodology to facilitate systematic migration of code from sequential to the parallel processing environments. The research results include the development of a three-phase methodology and the design and development of a reverse engineering toolkit (abbreviated as RETK) which serves to establish a working model for the methodology. The methodology consists of three phases: Analysis, Synthesis, and Transformation. The Analysis phase uses concepts from reverse engineering research to recover the sequential design description from programs using a new design recovery technique. The Synthesis phase is comprised of processes that compute the data and control dependences by using the design abstractions produced by the Analysis phase to construct the program dependence graph. The Transformation phase consists of processes that require knowledge-based analysis of the program and dependence information produced by the Analysis and Synthesis phases, respectively. Design recommendations for parallel environments are the key output of the Transformation phase. The main components of RETK are an Information Extractor, a Dependence Analyzer, and a Design Assistant that implement the processes of the Analysis, Synthesis, and Transformation phases, respectively. The object-oriented design and implementation of the Information Extractor and Dependence Analyzer are described. The design and implementation of the Design Assistant using C Language Interface Production System (CLIPS) are described. In addition, experimental results of applying the methodology to test programs by RETK are presented. The results include analysis of a Numerical Aerodynamic Simulation (NAS) benchmark program. By uniquely combining research in reverse engineering, dependence analysis, and knowledge-based analysis, the methodology provides a systematic approach for code migration. The benefits of using the methodology are increased comprehensibility and improved efficiency in migrating sequential systems to parallel environments

Characterization Ofp53 Gene Sequence in Exons 5-8 of the Western Mosquito Fish, Gambusia Affinis.

Cancer is a multistage disease that involves both genetic and epigenetic factors. The number of physical and chemical agents with which human beings come in contact on a regular basis is increasing everyday. Somatic mutations can result due to such exposures and trigger uncontrolled cell division resulting in cancer. The p53 tumor suppressor gene has been identified in several vertebrate species ranging from man to fish. In addition, inactivation of p53 has been observed in a wide variety of tumors. Fish models are becoming increasingly popular for assessing environmental exposure. Low background incidence of mutations, relatively low cost tumor studies, and the ability to extrapolate the results to humans makes these models viable alternatives. The Western mosquito fish (Gambusia affinis) a fresh water species (order Atheriniformes; family Poeciliidae) was chosen as a model organism for this study. Polymerase chain reaction with rainbow trout genomic DNA as a positive control was conducted. Fragments encompassing exons 5-6 and 7-8 were isolated and sequenced. Alignment of the sequences (exons 5-8) of the mosquito fish with that of rainbow trout in the similar regions revealed high homology between the species. Southern transfer of restricted genomic DNA of Gambusia affinis was conducted (target DNA). A PCR product (exons 5-6) of 450 base pairs was digoxigenin (DIG) labeled (probe DNA). In a separate set of experiments, PCR product from Gambusia affinis exons 7-8 (target DNA) was probed with another DIG-labeled PCR product (350 base pairs) from similar regions of rainbow trout genomic DNA. Hybridization of the probe and target DNA followed by chemiluminiscent detection resulted in visualization as bands on X-ray film indicating high homology between mosquito fish and rainbow trout p53 gene. While rainbow trout can function only in cold-temperatures, medaka, being exotic, is restricted to use only in the laboratory. In the present work the mosquito fish can withstand temperature ranging from \rm 40\sp\circ F{-}110\sp\circ F. In addition, this study can help promote the concept of the mosquito fish as a sentinel species for environmental monitoring and replace rainbow trout and medaka for direct validation in field

Data Integration and Predictive Analysis System for Disease Prophylaxis: Incorporating Dengue Fever Forecasts

The goal of the Data Integration and Predictive Analysis System (IPAS) is to enable prediction, analysis, and response management for incidents of infectious diseases. IPAS collects and integrates comprehensive datasets of previous disease incidents and potential influencing factors to facilitate multivariate, predictive analysis of disease patterns, intensity, and timing. We have used the IPAS technology to generate successful forecasts for Influenza Like Illness (ILI). In this study, IPAS was expanded to forecast Dengue fever in the cities of San Juan, Puerto Rico and Iquitos, Peru. Data provided by the National Oceanic and Atmospheric Administration (NOAA) was processed and used to generate prediction models. Predictions were developed with modern machine learning algorithms, identifying the one-week and four-week forecast of Dengue incidences in each city. Prediction model results are presented along with the features of the IPAS system

Data Integration and Predictive Analysis System for Disease Prophylaxis

The goal of the Data Integration and Predictive Analysis System (IPAS) is to enable prediction, analysis, and response management for incidents of infectious diseases. IPAS collects and integrates comprehensive datasets of previous disease incidents and potential influencing factors to facilitate multivariate, predictive analytics of disease patterns, intensity, and timing. IPAS supports comprehensive epidemiological analysis - exploratory spatial and temporal correlation, hypothesis testing, prediction, and intervention analysis. Innovative machine learning and predictive analytical techniques like support vector machines (SVM), decision tree-based random forests, and boosting are used to predict the disease epidemic curves. Predictions are then displayed to stakeholders in a disease situation awareness interface, alongside disease incidents, syndromic and zoonotic details extracted from news sources and medical publications. Data on Influenza Like Illness (ILI) provided by CDC was used to validate the capability of IPAS system, with plans to expand to other illnesses in the future. This paper presents the ILI prediction modeling results as well as IPAS system features

Spectral Analysis of Hand Tremors induced during a Fatigue Test

In this paper, we analyze various kinds of hand tremors in the time and frequency domain, that are induced by performing a set of hand actions. We collected the tremor data using a simple, wearable accelerometer from 15 healthy individuals that had varying levels of athleticism. The overall results presented here show that the physiologic tremors in range of 8-14 Hz are most noticeable under fatigue

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual’s plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set

Features of Physiologic Tremor in Diabetic Patients

In this paper, we estimate the effect of fatigue on physiological tremors in adults suffering from diabetes. We used a simple, wearable accelerometer to collect the acceleration data from 5 diabetic subjects with varying physical activity levels. Fatigue was induced via an intermittent submaximal isometric handgrip protocol, normalized for individual grip strength, until voluntary exhaustion. The overall results presented here show that the physiologic tremors in the range of 10-14 Hz are most noticeable under fatigue