Identification of MHC Class II Binders/ Non-binders using Negative Selection Algorithm

The identification of major histocompatibility complex (MHC) class-II restricted peptides is an important goal in human immunological research leading to peptide based vaccine design. These MHC class–II peptides are predominantly recognized by CD4+ T-helper cells, which when turned on, have profound immune regulatory effects. Thus, prediction of such MHC class-II binding peptides is very helpful towards epitope-based vaccine design. HLA-DR proteins were found to be associated with autoimmune diseases e.g. HLA-DRB1*0401 with rheumatoid arthritis. It is important for the treatment of autoimmune diseases to determine which peptides bind to MHC class II molecules. The experimental methods for identification of these peptides are both time consuming and cost intensive. Therefore, computational methods have been found helpful in classifying these peptides as binders or non-binders. We have applied negative selection algorithm, an artificial immune system approach to predict MHC class–II binders and non-binders. For the evaluation of the NSA algorithm, five fold cross validation has been used and six MHC class–II alleles have been taken. The average area under ROC curve for HLA-DRB1*0301, DRB1*0401, DRB1*0701, DRB1*1101, DRB1*1501, DRB1*1301 have been found to be 0.75, 0.77, 0.71, 0.72, and 0.69, and 0.84 respectively indicating good predictive performance for the small training set

Ensemble of ANN and ANFIS for Water Quality Prediction and Analysis - A Data Driven Approach

The consequences of un-clean water are some of the direst issues faced by humanity today. These concerns can be addressed efficiently if data is pre-analyzed and water quality is predicted before its effects occur. The aim of this research is to develop a novel ensemble of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using averaging ensemble technique, producing improved prediction accuracy. Measurements of different water quality parameters have been used for predicting the overall water quality, applying ANN, ANFIS and ANN-ANFIS ensemble and their results have been compared. The data used in this study is obtained by USGS online repository for the year of 2015, with a 30-minutes time interval between measurements. Root Mean Squared Error (RMSE) has been used as the main performance measure. The results depict a significant improvement in the Ensemble ANN-ANFIS model (RMSE: 0.457) as compared to both the ANN model (RMSE: 2.709) and the ANFIS model (1.734). The study concludes that the ensemble of ANN and ANFIS model shows significant improvement in prediction performance as compared to the individual models. The research can prove to be beneficial for decision making in terms of water quality improvement

GGM classifier with multi-scale line detectors for retinal vessel segmentation

Persistent changes in the diameter of retinal blood vessels may indicate some chronic eye diseases. Computer-assisted change observation attempts may become challenging due to the emergence of interfering pathologies around blood vessels in retinal fundus images. The end result is lower sensitivity to thin vessels for certain computerized detection methods. Quite recently, multi-scale line detection method proved to be worthy for improved sensitivity toward lower-caliber vessels detection. This happens largely due to its adaptive property that responds more to the longevity patterns than width of a given vessel. However, the method suffers from the lack of a better aggregation process for individual line detectors. This paper investigates a scenario that introduces a supervised generalized Gaussian mixture classifier as a robust solution for the aggregate process. The classifier is built with class-conditional probability density functions as a logistic function of linear mixtures. To boost the classifier’s performance, the weighted scale images are modeled as Gaussian mixtures. The classifier is trained with weighted images modeled on a Gaussian mixture. The net effect is increased sensitivity for small vessels. The classifier’s performance has been tested with three commonly available data sets: DRIVE, SATRE, and CHASE_DB1. The results of the proposed method (with an accuracy of 96%, 96.1% and 95% on DRIVE, STARE, and CHASE_DB1, respectively) demonstrate its competitiveness against the state-of-the-art methods and its reliability for vessel segmentation

Water-pipe smoking and albuminuria: new dog with old tricks

Water-pipe (WP) smoking is on rise worldwide for the past few years, particularly among younger individuals. Growing evidence indicates that WP smoking is as harmful as cigarette smoking. To date, most of the research has focused on acute health effects of WP smoking, and evidence remains limited when it comes to chronic health effects in relation to long-term WP smoking. Therefore, the aim of this study was to examine the association between WP smoking and albuminuria in apparently healthy individuals. This analysis was conducted on data of a population-based cross-sectional study—the Urban Rural Chronic Diseases Study (URCDS). The study sample was recruited from three sites in Pakistan. Trained nurses carried out individual interviews and obtained the information on demographics, lifestyle factors, and past and current medical history. Measurements of complete blood count, lipid profile, fasting glucose level, and 24-hour albuminuria were also made by using blood and urine samples. Albumin excretion was classified into three categories using standard cut-offs: normal excretion, high-normal excretion and microalbuminuria. Multiple logistic regression models were used to examine the relationship between WP smoking and albuminuria. The final analysis included data from 1,626 health individuals, of which 829 (51.0%) were males and 797 (49.0%) females. Of 1,626 individuals, 267 (16.4%) were current WP smokers and 1,359 (83.6%) were non-WP smokers. WP smoking was significantly associated with high-normal albuminuria (OR = 2.33, 95% CI 1.68-3.22, p-value <0.001) and microalbuminuria (OR = 1.75, 95% CI 1.18-2.58, p-value 0.005) after adjustment for age, sex, BMI, social class, hypertension, and diabetes mellitus. WP smoking was significantly associated with high-normal albuminuria and microalbuminuria when analysis was stratified on hypertension and diabetes mellitus categories. WP smoking has a strong association with albuminuria in apparently healthy individuals. More research is warranted to evaluate the temporality of this association between WP smoking and albuminuria

Numerical and experimental investigation of an Archimedes screw turbine for open channel water flow application

AbstractLow‐head turbines are becoming an agricultural imperative due to their high efficiency, low cost, ability to operate at low flow rates and minimal environmental impact. Therefore, the Archimedes screw turbine (AST) can play a leading role for producing electric power, especially in Pakistan's rural areas where most of the places have less than 1 m head. In this research work, performance evaluation of AST was carried out at different flow velocities in terms of power coefficient and torque generated. Design parameters such as blade width, blade pitches, and blade rotational angles are also used for performance evaluation. For this purpose, computational fluid dynamic (CFD) analyses of AST blades were conducted at different water flow velocities (e.g., 1, 1.5, 2, 2.5, 3, and 3.5 m/s). ANSYS FLUENT was used for AST blade simulations using three different design parameters such as blade width, blade pitch, and blade rotational angles. Additionally, CFD simulations have inherent errors and uncertainties that may lead to findings and deviations from their exact or real values. To prevent these uncertainties and errors, an experimental study was also conducted to provide validation for the CFD simulation results. The results revealed from CFD simulations for optimized design parameters were then compared with experimental data. From the results, it was examined that the numerical findings were in good agreement with the experiment data. The highest power coefficient and power output values were obtained under optimized design parameters such as inner and outer diameter, blade pitch, blade width, blade rotation angles and shaft length (e.g., 40 mm, 120 mm, 130 mm, 2 mm, 60°, and 850 mm respectively). The findings can be useful to implement the AST unit for those places where the available water head is ranging from 1 to 6.5 m and a flow rate of 0.2–6.5 m3/s, especially for rural areas of Pakistan

Sequential churn prediction and analysis of cellular network users - A multi-class, multi-label perspective

We investigate the problem of churn detection and prediction using sequential cellular network data. We introduce a cleaning and preprocessing of the dataset that makes it suitable for the analysis. We draw a comparison of the churn prediction results from the-state-of-the-art algorithms such as the Gradient Boosting Trees, Random Forests, basic Long Short-Term Memory (LSTM) and Support Vector Machines (SVM). We achieve significant performance boost by incorporating the sequential nature of the data, imputing missing information and analyzing the effects of various features. This in turns makes the classifier rigorous enough to give highly accurate results. We emphasize on the sequential nature of the problem and seek algorithms that can track the variations in the data. We test and compare the performance of proposed algorithms using performance measures on real life cellular network data for churn detection. © 2017 IEEE

Renal Vein Thrombosis

ObjectiveThe aim of this article is to review the published English literature on aetiology, pathology, clinical presentation, diagnostic methods and treatment of renal vein thrombosis.Materials and methodsWe searched the published literature from Medline & Pubmed using keywords renal vein thrombosis, anti-phospholipid syndrome and nephrotic syndrome. Data was extracted from individual case reports, case series, articles on pathology, diagnostic tests, treatment modalities, and previous reviews. Case reports which did not add any new information were excluded.ResultsWe selected 60 references based on the above criteria. Renal vein thrombosis is relatively rare. CT angiography is considered the investigation of choice. Alternatives include MR angiography or renal venography in highly selected patients. As the condition is relatively uncommon, consensus on the best form of therapy for this condition has been slow to evolve. The trend in management has shifted to non-surgical therapies particularly systemic anticoagulation except in highly selected group of patients

Evaluating the toxic effects of Ficus infectoria Roxb. and Emblica officinalis Gaertn. leaf extracts on cell division and chromosomal morphology of Cicer arietinum L.

In the present study, the aqueous extracts of Ficus infectoria and Emblica officinalis leaves were evaluated for their toxic effect on cell division and chromosomal morphology of Cicer arietinum root apical meristem. The extracts were prepared by dissolving 15 gm, 30 gm and 45 gm of dry leaf powder in 1000 ml of double distilled water. The experiment was conducted in sterilized petri dishes. The results revealed that the different concentrations of aqueous extract of F. infectoria and E. officinalis caused cytotoxic and mitodepressive effects on chromosome of Cicer arietinum. The dose–dependent and statistically significant (

Heterogeneity in progression of prodromal features in Parkinson’s disease

Background: In the pre-diagnostic phase of Parkinson’s disease (PD), a range of motor and non-motor symptoms can occur. However, there is considerable variability in their onset and currently little information exists on the pattern of progression of clinical features before diagnosis. Methods: We analysed data from a survey amongst patients with PD from 11 European countries by the European Parkinson’s Disease Association. They completed questions on first occurrence of 21 pre-diagnostic features. A principal component analysis (PCA) with varimax rotation was performed to determine the co-occurrence of these features. Findings: 1,467 patients were included. Changes in movement were the most commonly reported features up to 4 years before diagnosis. However, at five or more years before diagnosis loss of sense of smell, sleep problems, fatigue and other non-motor features had been experienced most frequently. PCA of pre-diagnostic features’ duration revealed three factors with eigenvalues over Kaiser’s criterion of 1: a) a neuropsychiatric factor comprised of anxiety, depression, apathy, stress, and sleep problems; b) an axial factor defined by difficulty eating and/or swallowing problems, freezing, and falls/balance problems; and c) a motor factor with additional non-motor features. Bladder/bowel problems and tremor had low factor loadings on all components. However, in those with disease duration less than 5 years the autonomic features were associated with the axial factor and tremor loaded on both the motor and psychiatric symptom factors. Interpretation: The identified symptom complexes in the pre-diagnostic stage of PD may be reflective of a shared pattern of pathological disease progression