Effective pixel classification of Mars images based on ant colony optimization feature selection and extreme learning machine

one of the most important tasks of Mars rover, a robot which explores the Mars surface, is the process of automatic segmentation of images taken by front-line Panoramic Camera (Pancam). This procedure is highly significant since the transformation cost of images from Mars to earth is extremely high. Also, image analysis may help Mars rover for its navigation and localization. In this paper, a new feature vector including wavelet and color features for Mars images is proposed. Then, this feature vector is presented for extreme learning machine (ELM) classifier which leads to a high accuracy pixel classifier. It is shown that this system statistically outperforms support vector machine (SVM) and k-nearest neighbours (KNNs) classifiers with respect to both accuracy and run time. After that, dimension reduction in feature space is done by two proposed feature section algorithms based on ant colony optimization (ACO) to decrease the time complexity which is very important in Mars on-board applications. In the first proposed feature selection algorithm, the same feature subset is selected among the feature vector for all pixel classes, while in the second proposed algorithm, the most significant features are selected for each pixel class, separately. Proposed pixel classifier with complete feature set outperforms prior methods by 6.44% and 5.84% with respect to average Fmeasure and accuracy, respectively. Finally, proposed feature selection methods decrease the feature vector size up to 76% and achieves Fmeasure and accuracy of 91.72% and 91.05%, respectively, which outperforms prior methods with 87.22% and 86.64%

Effect of water-soluble PM10 on the production of TNF-α by human monocytes and induction of apoptosis in A549 human lung epithelial cells

Long-term exposure to airborne particles of 10 µm and less in size (PM10) in dust can lead to increased risk of diseases such as respiratory, cardiovascular, lung cancer and atherosclerosis. The aim of the study was to evaluate the effects of water-soluble PM10 particles in the Middle East Dust (MED) storm in Ahvaz, Iran, on the production of TNF-α by human monocytes. In addition, we assessed the level of induction of apoptosis in isolated A549 human pulmonary epithelial cells. For this purpose, isolated human blood monocytes (250,000 to 300,000 cell/ ml) as well as isolated human pulmonary A549 epithelial cells (100,0000 cell/ ml) were exposed to various concentrations (62.5, 125, 250, 500 µg/ml) of water-soluble PM10 particles for different incubation periods (12, 24, 48 h). The results showed that exposure to PM10 particles increased the production of TNF-α in human monocytes and promoted apoptosis induction in A549 cells, in both concentration and incubation of period-dependent manner. In conclusion, airborne dust particles in Ahvaz city contain active compounds capable of increasing production of the pro-inflammatory cytokine, TNF-α, and inducing apoptosis of lung epithelial cells. © 2021, Springer Nature Switzerland AG

Ellagic acid mitigates sodium arsenite-induced renal and hepatic toxicity in male Wistar rats

Background: The aim of this study was to investigate the effect of ellagic acid (EA) on arsenic-induced renal and hepatic toxicity in rats. Methods: A total number of 35 male Wistar rats were randomly divided into five experimental groups. Group 1 received normal saline (po). Group 2 received sodium arsenite (SA, 10 mg/kg, po) for 21 days. Group 3 received EA (30 mg/kg, po) for 14 days. Groups 4 and 5 received SA 7 days prior to EA (10 and 30 mg/kg respectively) treatment and continued up to 21 days simultaneous with SA administration. Various biochemical, histological and molecular biomarkers were measured in kidney and liver. Results: Treatment with EA (more potently at dose of 30 mg/kg) restored the SA-induced alterations in serum creatinine (Cr) and blood urine nitrogen (BUN) levels as well as the changes in aspartate aminotransferase (AST), alkaline phosphatase (ALP) and alanine aminotransferase (ALT) concentrations (all p < 0.001). Elevated levels of malondialdehyde (MDA) and nitric oxide (NO) in renal and hepatic tissue was reduced by EA treatment (all p < 0.001). Treatment with EA enhanced the glutathione (GSH) content in liver (p < 0.001) and up-regulated renal and hepatic superoxide dismutase (SOD) and glutathione peroxidase (GPx) mRNA expression (all p < 0.001). The SA-induced histopathological alterations in kidney and liver were reduced by EA treatment. Conclusion: In conclusion, the presence of EA with SA alleviated its toxic effects and EA treatment might be an effective strategy for the management of arsenic-induced renal and hepatic damage. © 2018 Institute of Pharmacology, Polish Academy of Science

Manual Tool and Semi-Automated Graph Theory Method for Layer Segmentation in Optical Coherence Tomography

Optical Coherence Tomography (OCT) is a major tool in the diagnosis of various diseases. Disease diagnosis is based on various features within the OCT images, including retinal layer positions and the distances between them and the build-up of fluid. All of these features require an expert marker in order to identify them so that the information can properly aid in the diagnosis for the patient. This process takes an incredible amount of time for the expert carry out as they need to manually trace the layers for every frame. This therefore indicates that there is a need for automation so that the expert can more easily and efficiently label the retinal layers. In this project two processes were developed. The first step is to use a semi-automated graph theory method to segment a specific layer given a rectangular region of interest, specified by the user. The output of the first process can then be corrected, where needed, using the manual tool. This method can segment layers with on average less than 1-2 pixels of error vs two expert markers

Ellagic acid mitigates sodium arsenite-induced renal and hepatic toxicity in male Wistar rats

Background: The aim of this study was to investigate the effect of ellagic acid (EA) on arsenic-induced renal and hepatic toxicity in rats. Methods: A total number of 35 male Wistar rats were randomly divided into five experimental groups. Group 1 received normal saline (po). Group 2 received sodium arsenite (SA, 10 mg/kg, po) for 21 days. Group 3 received EA (30 mg/kg, po) for 14 days. Groups 4 and 5 received SA 7 days prior to EA (10 and 30 mg/kg respectively) treatment and continued up to 21 days simultaneous with SA administration. Various biochemical, histological and molecular biomarkers were measured in kidney and liver. Results: Treatment with EA (more potently at dose of 30 mg/kg) restored the SA-induced alterations in serum creatinine (Cr) and blood urine nitrogen (BUN) levels as well as the changes in aspartate aminotransferase (AST), alkaline phosphatase (ALP) and alanine aminotransferase (ALT) concentrations (all p < 0.001). Elevated levels of malondialdehyde (MDA) and nitric oxide (NO) in renal and hepatic tissue was reduced by EA treatment (all p < 0.001). Treatment with EA enhanced the glutathione (GSH) content in liver (p < 0.001) and up-regulated renal and hepatic superoxide dismutase (SOD) and glutathione peroxidase (GPx) mRNA expression (all p < 0.001). The SA-induced histopathological alterations in kidney and liver were reduced by EA treatment. Conclusion: In conclusion, the presence of EA with SA alleviated its toxic effects and EA treatment might be an effective strategy for the management of arsenic-induced renal and hepatic damage. © 2018 Institute of Pharmacology, Polish Academy of Science