An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images

This paper proposes an automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images. It is a challenging issue to segment leukocytes under uneven imaging conditions since features of microscopic leukocyte images change in different laboratories. Therefore, this paper introduces an automatic robust method to segment leukocyte from blood microscopic images. The proposed robust segmentation technique was designed based on the fact that if background and erythrocytes could be removed from the blood microscopic image, the remainder area will indicate leukocyte candidate regions. A new set of features based on hematologist visual criteria for the recognition of malignant leukocytes in blood samples comprising shape, color, and LBP-based texture features are extracted. Two new ensemble classifiers are proposed for healthy and malignant leukocytes classification which each of them is highly effective in different levels of analysis. Experimental results demonstrate that the proposed approach effectively segments leukocytes from various types of blood microscopic images. The proposed method performs better than other available methods in terms of robustness and accuracy. The final accuracy rate achieved by the proposed method is 98.10% in cell level. To the best of our knowledge, the image level test for acute lymphoblastic leukemia (ALL) recognition was performed on the proposed system for the first time that achieves the best accuracy rate of 89.81%.

Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients

Airway and vessel characterization of bronchiectasis patterns in lung high-resolution computed tomography (HRCT) images of cystic fibrosis (CF) patients is very important to compute the score of disease severity. We propose a hybrid and evolutionary optimized threshold and model-based method for characterization of airway and vessel in lung HRCT images of CF patients. First, the initial model of airway and vessel is obtained using the enhanced threshold-based method. Then, the model is fitted to the actual image by optimizing its parameters using particle swarm optimization (PSO) evolutionary algorithm. The experimental results demonstrated the outperformance of the proposed method over its counterpart in R-squared, mean and variance of error, and run time. Moreover, the proposed method outperformed its counterpart for airway inner diameter/vessel diameter (AID/VD) and airway wall thickness/vessel diameter (AWT/VD) biomarkers in R-squared and slope of regression analysis.

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

Leaching of Metals in Coastal Technosols Triggered by Saline Solutions and Labile Organic Matter Removal

Medicinal Plants with Multiple Effects on Diabetes Mellitus and Its Complications: a Systematic Review

This systematic review describes evidence concerning medicinal plants that, in addition to exerting hypoglycemic effects, decrease accompanying complications such as nephropathy, neuropathy, retinopathy, hypertension, and/or hyperlipidemia among individuals with diabetes mellitus (DM).

Improvements on the hybrid Monte Carlo algorithms for matrix computations

In this paper, we present some improvements on the Markov chain Monte Carlo and hybrid Markov chain Monte Carlo algorithms for matrix computations. We discuss the convergence of the Monte Carlo method using the Ulam–von Neumann approach related to selecting the transition probability matrix. Specifically, we show that if the norm of the iteration matrix T is less than 1 then the Monte Carlo Almost Optimal method is convergent. Moreover, we suggest a new technique to approximate the inverse of the strictly diagonally dominant matrix and we exert some modifications and corrections on the hybrid Monte Carlo algorithm to obtain the inverse matrix in general. Finally, numerical experiments are discussed to illustrate the efficiency of the theoretical results.

An inter-subunit disulfide bond of artemin acts as a redox switch for its chaperone-like activity

Encysted embryos of Artemia are among the most stress-resistant eukaryotes partly due to the massive amount of a cysteine-rich protein termed artemin. High number of cysteine residues in artemin and their intramolecular spatial positions motivated us to investigate the role of the cysteine residues in the chaperone-like activity of artemin. According to the result of Ellman's assay, there are nine free thiols (seven buried and two exposed) and one disulfide bond per monomer of artemin. Subsequent theoretical analysis of the predicted 3D structure of artemin confirmed the data obtained by the spectroscopic study. Native and reduced/modified forms of artemin were also compared with respect to their efficiency in chaperoning activity, tertiary structure, and stability. Since the alkylation and reduction of artemin diminished its chaperone activity, it appears that its chaperoning potential depends on the formation of intermolecular disulfide bond and the presence of cysteine residues. Comparative fluorescence studies on the structure and stability of the native and reduced protein revealed some differences between them. Due to the redox-dependent functional switching of artemin from the less to more active form, it can be finally suggested as a redox-dependent chaperone.

Capabilities of the Monte Carlo Simulation Codes for Modeling of a Small Animal SPECT Camera

This study aims to compare Monte Carlo-based codes' characteristics in the determination of the basic parameters of a high-resolution single photon emission computed tomography (HiReSPECT) scanner.

Computational technique for simulating variable-order fractional Heston model with application in US stock market

Prevalence of different metabolic phenotypes of obesity in Iranian children and adolescents: the CASPIAN V study

Pediatric metabolic disorders are a major health problem. The prevalence of child and adolescent metabolic disorders particularly obesity has globally shown a growing pattern. The aims of this study were to estimate the prevalence of different metabolic phenotypes of obesity in children and adolescents.