Malaysian macroalga Padina australis Hauck attenuates high dose corticosterone-mediated oxidative damage in PC12 cells mimicking the effects of depression

Oxidative damage has been associated with the pathophysiology of depression. Macroalgae are equipped with antioxidant defense system to counteract the effects of free radicals. We explored the use of Malaysian Padina australis to attenuate high dose corticosterone-mediated oxidative damage in a cellular model mimicking depression. Fresh specimen of P. australis was freeze-dried and extracted sequentially with hexanes, ethyl acetate and ethanol. The extracts were screened for their phytochemical contents and antioxidant activities. Ethanol extract demonstrated the most potent antioxidant capacity and was selected for subsequent assays against high dose corticosterone of 600 µM-mediated oxidative damage in the rat pheochromocytoma (PC12) cells. The corticosterone reduced the cell viability, glutathione (GSH) level, aconitase activity, and mitochondrial membrane potential (MMP); and increased the lactate dehydrogenase (LDH) release, intracellular reactive oxygen species (ROS) level and apoptosis. However, the extent of oxidative damage was reversed by 0.25–0.5 mg/mL ethanol extract suggesting a possible role of P. australis-based antioxidants in the mitochondrial defense against constant ROS generation and regulation of antioxidant pathway. The effects were similar to that of desipramine, a tricyclic antidepressant. Our findings indicate that P. australis can be developed as a mitochondria-targeted antioxidant to mitigate antidepressant-like effects

Design of Cost-effective Printer to Print tactile Images for the Visually Impaired

Teaching text for visually impaired people is not a difficult task. But, learning the subjects like Science, Geography etc requires the perception of two dimensional images. This is very difficult for the visually impaired people. They cannot be easily taught images and graphics. In developed countries, the visually impaired students are taught about the images from their childhood days. So, they are familiar with image representations. One way to teach graphics is to use tactile images. Tactile graphics allow the visually impaired to perceive two-dimensional imagery easily. But, printing tactile graphics is costlier. Hence, the visually impaired people in developing and under developing countries grow up without any exposure to tactile images. To address this issue, the paper proposes a new approach for designing a new printer that helps to print high quality tactile images with low cost

Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification

Deep learning-based medical image analysis is an effective and precise method for identifying various cancer types. However, due to concerns over patient privacy, sharing diagnostic images across medical facilities is typically not permitted. Federated learning (FL) tries to construct a shared model across dispersed clients under such privacy-preserving constraints. Although there is a good chance of success, dealing with non-IID (non-independent and identical distribution) client data, which is a typical circumstance in real-world FL tasks, is still difficult for FL. We use two FL algorithms, FedAvg and FedProx, to manage client heterogeneity and non-IID data in a federated setting. A heterogeneous data split of the cancer datasets with three different forms of cancer—cervical, lung, and colon—is used to validate the efficacy of the FL. In addition, since hyperparameter optimization presents new difficulties in an FL setting, we also examine the impact of various hyperparameter values. We use Bayesian optimization to fine-tune the hyperparameters and identify the appropriate values in order to increase performance. Furthermore, we investigate the hyperparameter optimization in both local and global models of the FL environment. Through a series of experiments, we find that FedProx outperforms FedAvg in scenarios with significant levels of heterogeneity

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

In comparison to the competitors, engineers must provide quick, low-cost, and dependable solutions. The advancement of intelligence generated by machines and its application in almost every field has created a need to reduce the human role in image processing while also making time and labor profit. Lepidopterology is the discipline of entomology dedicated to the scientific analysis of caterpillars and the three butterfly superfamilies. Students studying lepidopterology must generally capture butterflies with nets and dissect them to discover the insect’s family types and shape. This research work aims to assist science students in correctly recognizing butterflies without harming the insects during their analysis. This paper discusses transfer-learning-based neural network models to identify butterfly species. The datasets are collected from the Kaggle website, which contains 10,035 images of 75 different species of butterflies. From the available dataset, 15 unusual species were selected, including various butterfly orientations, photography angles, butterfly lengths, occlusion, and backdrop complexity. When we analyzed the dataset, we found an imbalanced class distribution among the 15 identified classes, leading to overfitting. The proposed system performs data augmentation to prevent data scarcity and reduce overfitting. The augmented dataset is also used to improve the accuracy of the data models. This research work utilizes transfer learning based on various convolutional neural network architectures such as VGG16, VGG19, MobileNet, Xception, ResNet50, and InceptionV3 to classify the butterfly species into various categories. All the proposed models are evaluated using precision, recall, F-Measure, and accuracy. The investigation findings reveal that the InceptionV3 architecture provides an accuracy of 94.66%, superior to all other architectures