Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI

The known regional abnormality of the dorsolateral prefrontal cortex (DLPFC) and its role in various neural circuits in schizophrenia has given prominence to its importance in studies on the dysconnection associated with schizophrenia. Abnormal functional connectivities of the DLPFC have been found during various goal-directed tasks; however, the occurrence of the abnormality during rest in patients with schizophrenia has rarely been reported. In the present study, we selected bilateral Brodmann's area 46 as region of interest and analyzed the differences in the DLPFC functional connectivity pattern between 17 patients with first-episode schizophrenia (FES) and 17 matched controls using resting-state fMRI. We found that the bilateral DLPFC showed reduced functional connectivities to the parietal lobe, posterior cingulate cortex, thalamus and striatum in FES patients. We also found enhanced functional connectivity between the left DLPFC and the left mid-posterior temporal lobe and the paralimbic regions in FES patients. Our results suggest that functional dysconnectivity associated with the DLPFC exists in schizophrenia during rest. This may be partially related to disturbance in the intrinsic brain activity.     

Acute and chronic toxic effects of bisphenol a on Chlorella pyrenoidosa and Scenedesmus obliquus

The acute and chronic toxic effects of Bisphenol A (BPA) on Chlorella pyrenoidosa (C. pyrenoidosa) and Scenedesmus obliquus (S. obliquus) were not well understood. The indoor experiments were carried out to observe and analyze the BPA‐induced changes. Results of the observations showed that in acute tests BPA could significantly inhibit the growth of both algae, whereas chronic exposure hardly displayed similar trend. Superoxide dismutase (SOD) and Catalase (CAT) activities of both algae were promoted in all the treatments. Chlorophyll a synthesis of the two algae exhibited similar inhibitory trend in short‐term treatments, and in chronic tests C. pyrenoidosa hardly resulted in visible influence, whereas in contrast, dose‐dependent inhibitory effects of S. obliquus could be clearly observed. The experimental results indicated that the growth and Chlorophyll a syntheses of S.obliquus were more sensitive in response to BPA than that of C. pyrenoidosa, whereas for SOD andCAT activities, C. pyrenoidosa was more susceptible. This research provides a basic understanding of BPA toxicity to aquatic organisms. © 2012 Wiley Periodicals, Inc. Environ Toxicol 29: 714–722, 2014.     

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, \(k\) -nearest neighbor ( \(k\) -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.