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Using Triangular type Partially Balanced Incomplete Block Designs (PBIBD), a new method for constructing Second Order Rotatable Designs is presented in this paper. [ABSTRACT FROM AUTHOR]

A micro strip patch antenna for wireless communications had proposed in this paper. The proposed antenna operates at three frequencies at 3.3GHz, 6.3GHz and 9 GHz which are mostly used for WLAN (C band) and radar, satellite communications (X band). Two rectangular slots are placed on the ground in order at achieve dual frequencies with good return loss less than -17dB and VSWR less than 1.4. The obtained results are simulated using ANSOFT HFSS software. [ABSTRACT FROM AUTHOR]

To reduce the dimensionality of the second-order response surface design model, variance component indices under imposing and non imposing restrictions on the moment matrix toward the orthogonality are derived and presented and the same is illustrated with suitable examples in this article. [ABSTRACT FROM PUBLISHER]

An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is crucial for effective triage, referral, and follow-up therapy. While various deep learning techniques have been proposed for identifying pelvic masses, current methods are often not accurate enough and can be computationally intensive. To address these issues, this manuscript introduces an optimized Siamese circle-inspired neural network with deep linear graph attention (SCINN-DLGN) model designed for pelvic mass classification. The SCINN-DLGN model is intended to classify pelvic masses into three categories: benign, malignant, and healthy. Initially, real-time MRI pelvic mass images undergo pre-processing using semantic-aware structure-preserving median morpho-filtering to enhance image quality. Following this, the region of interest (ROI) within the pelvic mass images is segmented using an EfficientNet-based U-Net framework, which reduces noise and improves the accuracy of segmentation. The segmented images are then analysed using the SCINN-DLGN model, which extracts geometric features from the ROI. These features are classified into benign, malignant, or healthy categories using a deep clustering algorithm integrated into the linear graph attention model. The proposed system is implemented on a Python platform, and its performance is evaluated using real-time MRI pelvic mass datasets. The SCINN-DLGN model achieves an impressive 99.9% accuracy and 99.8% recall, demonstrating superior efficiency compared to existing methods and highlighting its potential for further advancement in the field., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The emergence of deep learning has paved to solve many problems in the real world. COVID-19 pandemic, since the late 2019, has been affecting lives of people across the globe. Chest CT scan images are used to detect it and know its severity in patients. The problem with many existing solutions in COVID-19 detection using CT scan images is that inability to detect the infection when it is in initial stages. As the infection can exist on varied scales, there is need for more comprehensive approach that can ascertain the disease at all scales. Towards this end, we proposed a deep learning-based framework known as Automated Deep Learning-based COVID-19 Detection Framework (ADL-CDF). It does not need a human medical expert in diagnosis as it is capable of detecting automatically. The framework is assisted by two algorithms that involve image processing and deep learning. The first algorithm known as Region of Interest (ROI)-based Image Filtering (ROI-IF) which analyses given input CT scan images of a patient and discards the ones where ROI is missing. This algorithm minimizes time taken for processing besides reducing false positive rate. The second algorithm is known as Multi-Scale Feature Selection algorithm that fits into the deep learning framework's pipeline to leverage detection performance of the ADL-CDF. The proposed framework is evaluated against ResNet50V2 and Xception. Our empirical study revealed that our model outperforms the state of the art., (© King Fahd University of Petroleum & Minerals 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

The blood-brain barrier (BBB) serves to protect the central nervous system (CNS) from damage by exogenous molecules. Japanese encephalitis (JE), caused by a neurotropic flavivirus, leads to inflammation in the CNS, neuronal death and also compromises the structural and functional integrity of the BBB. Minocycline, a semisynthetic tetracycline, has been found to be broadly protective in neurological disease models featuring inflammation and cell death and at present, is being evaluated in clinical trials. In the present study, we propose that the neuroprotective role of minocycline in experimental models of JE extends also to the protection of the BBB. Damage to the BBB was assessed by Evan's blue dye exclusion test after minocycline treatment following Japanese encephalitis virus (JEV) infection. A breakdown of the BBB occurred in mice inoculated intravenously with JEV. This resulted in leakage of protein-bound Evan's blue dye into the brain tissue. Semi-quantitative RT-PCR revealed an up-regulation of chemokine receptors and adhesion molecules following JEV infection. Immunostaining showed leukocyte and neutrophil infiltration following JEV infection. Intraperitoneal injection of minocycline, beginning 24h post-JEV infection, abrogated the effects by reducing BBB damage, decreasing expression of iNOS, Cox-2, VEGF and also by reducing the elevated level of transcript of chemokine receptors and adhesion molecules in the brain. Matrix metalloproteinases (MMPs) are known to disrupt the BBB and minocycline was found to significantly decrease the activity of MMP-9 in brain tissue homogenates. Thus, minocycline, administered at a clinically relevant time, appears to maintain blood-brain barrier integrity following JEV infection.

Chitin is an essential constituent of the insect exoskeleton, the cuticle, which is an extracellular matrix (ECM) covering the animal. It is produced by the glycosyltransferase chitin synthase at the apical plasma membrane of epidermal and tracheal cells. To fulfil its role in cuticle elasticity and stiffness it associates with proteins, thereby adopting a stereotypic arrangement of helicoidally stacked sheets, which run parallel to the surface of the animal. One approach to understand the mechanisms of chitin synthesis and organisation is to dissect these processes genetically. However, since only a few genes coding for factors involved in chitin synthesis and organisation have been identified to date using the model arthropod Drosophila melanogaster insight arising from mutant analysis is rather limited. To collect new data on the role of chitin during insect cuticle differentiation, we have analysed the effects of chitin synthesis inhibitors on Drosophila embryogenesis. For this purpose, we have chosen the benzoylphenylurea diflubenzuron and lufenuron that are widely used as insect growth regulators. Our data allow mainly two important conclusions. First, correct organisation of chitin seems to directly depend on the amount of chitin synthesised. Second, chitin synthesis and organisation are cell-autonomous processes as insecticide-treated larvae display a mosaic of cuticle defects. As benzoylphenylurea are used not only as insecticides but also as anti-diabetic drugs, the study of their impact on Drosophila cuticle differentiation may be fruitful for understanding their mode of action on a cellular pathway that is seemingly conserved between vertebrates and invertebrates.