An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions.</p

Localizing Epileptogenic Zone from High Density EEG Data Using Machine Learning

Drug-resistant focal epilepsy is the failure of antiepileptic drugs scheduled to obtain epileptic free brain activities. In human brain, cerebral hemispheres are the most commonly involved brain regions in epilepsy. In case of antiepileptic drugs failure, surgical treatment is the best cure possible. However, correct localization of epileptogenic region is a challenging task for neurologists, while for computer scientists, automatic localization is. This research work’s aim is to explore the functional activities of all brain regions in drug-resistant focal epileptic patients and achieve high accuracy for the classification of epileptogenic region (ER) with the high-density electroencephalographic (hdEEG) data. The proposed system includes frequency analysis for feature extractions followed by individual subject’s registration of hdEEG signals with anatomical brain images for most precise localization of ER possible. The datasets attained from feature extraction process are then preprocessed for class imbalanced and then evaluated using different machine learning algorithms including the techniques under Bayesian networks, Lazy networks, Meta techniques, Rule based systems and Tree structured algorithms. Considering human brain as stationary object as well as dynamic object, frequency-based and time-frequency based features were considered in 12 subjects respectively. Through this novel approach, 99.70% accuracy is achieved to classify ER from healthy regions using KSTAR and using IBK algorithm, 91.60% accuracy has been achieved to classify generator from propagator

Diffusion MRI compartmental model analysis of DSI data

Recently proposed compartmental models can providenew micro-structural indexes to characterize tissue microstructuralproperties from diffusion MRI data. The state-of-theartof compartmental models of brain is Neurite OrientationDispersion and Density Imaging (NODDI), which is able todescribe the diffusion process in both intra-cellular and extracellularspace in the human brain. In our work we applied thismodel to a group of human Diffusion Spectrum Imaging (DSI)data and we strongly believe that the computation of its indexescan provide new insights of the differences between gray matter(GM), white matter (WM) and cerebrospinal fluid (CSF)

Biosynthesized Iron Oxide Nanoparticles (Fe3O4 NPs) Mitigate Arsenic Toxicity in Rice Seedlings

Arsenic (As) contamination has emerged as a serious public health concern worldwide because of its accumulation and mobility through the food chain. Therefore, the current study was planned to check the effect of Bacillus subtilis-synthesized iron oxide nano particles (Fe3O4 NP) on rice (Oryza Sativa L.) growth against arsenic stress (0, 5, 10 and 15 ppm). Iron oxide nanoparticles were extracellular synthesized from Bacillus subtilis with a desired shape and size. The formations of nanoparticles were differentiated through UV-Visible Spectroscopy, FTIR, XRD and SEM. The UV-Visible spectroscopy of Bacillus subtilis-synthesized nanoparticles showed that the iron oxide surface plasmon band occurs at 268 nm. FTIR results revealed that different functional groups (aldehyde, alkene, alcohol and phenol) were present on the surface of nanoparticles. The SEM image showed that particles were spherical in shape with an average size of 67.28 nm. Arsenic toxicity was observed in seed germination and young seedling stage. The arsenic application significantly reduced seed germination (35%), root and shoots length (1.25 and 2.00 cm), shoot/root ratio (0.289), fresh root and shoots weight (0.205 and 0.260 g), dry root and shoots weight (6.55 and 6.75 g), dry matter percentage of shoot (12.67) and root (14.91) as compared to control. Bacillus subtilis-synthesized Fe3O4 NPs treatments (5 ppm) remarkably increased the germination (65%), root and shoot length (2 and 3.45 cm), shoot/root ratio (1.24) fresh root and shoot weight (0.335 and 0.275 mg), dry root and shoot weight (11.75 and 10.6 mg) and dry matter percentage of shoot (10.40) and root (18.37). Results revealed that the application of Fe3O4 NPs alleviated the arsenic stress and enhanced the plant growth. This study suggests that Bacillus subtilus-synthesized iron oxide nanoparticles can be used as nano-adsorbents in reducing arsenic toxicity in rice plants

Bacillus subtilis Synthesized Iron Oxide Nanoparticles (Fe3O4 NPs) Induced Metabolic and Anti-Oxidative Response in Rice (Oryza sativa L.) under Arsenic Stress

Nanoparticle (NP) application is most effective in decreasing metalloid toxicity. The current study aimed to evaluate the effect of Bacillus subtiles synthesized iron oxide nanoparticles (Fe3O4 NPs) against arsenic (As) stress on rice (Oryza sativa L.) seedlings. Different concentrations of As (5, 10 and 15 ppm) and Bacillus subtilis synthesized Fe3O4 NPs solution (5, 10 and 15 ppm) alone and in combination were applied to rice seedlings. The results showed that As at 15 ppm significantly decreased the growth of rice, which was increased by the low level of As. Results indicated that B. subtilis synthesized Fe3O4 NP-treated plants showed maximum chlorophyll land protein content as compared with arsenic treatment alone. The antioxidant enzymes such as SOD, POD, CAT, MDA and APX and stress modulators (Glycine betain and proline) also showed decreased content in plants as compared with As stress. Subsequently, Bacillus subtilis synthesized Fe3O4 NPs reduced the stress associated parameters due to limited passage of arsenic inside the plant. Furthermore, reduction in H2O2 and MDA content confirmed that the addition of Bacillus subtilis synthesized Fe3O4 NPs under As stress protected rice seedlings against arsenic toxicity, hence enhanced growth was notice and it had beneficial effects on the plant. Results highlighted that Fe3O4 NPs protect rice seedlings against arsenic stress by reducing As accumulation, act as a nano adsorbent and restricting arsenic uptake in rice plants. Hence, our study confirms the significance of Bacillus subtilis synthesized Fe3O4 NPs in alleviating As toxicity in rice plants

Combine Effect of ZnO NPs and Bacteria on Protein and Gene’s Expression Profile of Rice (<i>Oryza sativa</i> L.) Plant

Heavy metal (HM) emissions have increased due to the impact of rising urbanization and anthropogenic activity, affecting different parts of the environment. The goal of this study is to investigate the combined effect of ZnO NPs and bacteria treatment on protein and gene expression profiles of rice plants that are grown in HMs-polluted water. Seeds were primed with Bacillus spp. (Bacillus cereus and Lysinibacillus macroides) before being cultured in Hoagland media containing ZnO NPs (5 and 10 mg/L) and HMs-contaminated water from the Hayatabad industrial estate (HIE), Peshawar, Pakistan. The results revealed that the maximum nitrogen and protein content was observed in the root, shoot, and leaf of the plant grown by combining bacteria-ZnO NPs treatment under HMs stress as compared with plant grown without or with individual treatments of ZnO NPs and bacteria. Furthermore, protein expression analysis by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS PAGE) revealed that plants that were grown in HMs-polluted water were found to be affected in contaminated water, however the combined effect of bacteria-ZnO NPs reported the more dense protein profile as compared with their individual treatments. Subsequently, plants that were grown in HMs-polluted water have the highest expression levels of stress-induced genes such as myeloblastosis (Myb), zinc-finger protein (Zat-12), and ascorbate peroxidase (Apx) while the combined effect revealed minimum expression as compared with individual treatments. It is concluded that the combined effect of ZnO NPs and bacteria lowered the stress-induced gene expression while it increased the nitrogen-protein content and protein expression in plant grown under HMs stress

Zinc Oxide Nanoparticles Enhance the Tolerance and Remediation Potential of Bacillus spp. against Heavy Metal Stress

Nanoparticles and bacteria have received a great attention worldwide due to their ability to remove heavy metals (HMs) from wastewater. The current study is aimed at finding the interaction of HMs-resistance strains (Bacillus cereus and Lysinibacillus macroides) with different concentrations (5, 10, 15, 20, and 25 mg/L) of zinc oxide nanoparticles (ZnO NPs) and how they would cope with HM stress (Pb, Cd, Cr, and Cu). The growth rate and tolerance potential of bacteria were increased at lowered concentrations (5 and 10 mg/L) of ZnO NPs against HMs while it was unaffected at higher concentrations of ZnO NPs. These findings were confirmed by minimum inhibition zone and higher zinc solubilization at lower concentrations of ZnO NPs. Scanning electron microscopy (SEM) revealed that higher concentrations of ZnO NP increased HM accumulation in bacteria cells which had a significant impact on bacterial morphology and caused pores in bacterial membrane while in the case of lower concentrations, the cell remained unaffected. These results were further supported by the less production of antioxidant enzymes (SOD, POD, and CAT), thiobarbituric acid reactive substances (TBARS), and hydrogen peroxide (H2O2) contents at lower concentrations of ZnO NPs against heavy metal stress. This study suggested that synergistic treatment of Bacillus spp. with lower concentrations of ZnO NPs enhances the tolerance potential and significantly reduces the HM toxicity