A Secure IoT-Based Cloud Platform Selection Using Entropy Distance Approach and Fuzzy Set Theory

With the growing emergence of the Internet connectivity in this era of Gen Z, several IoT solutions have come into existence for exchanging large scale of data securely, backed up by their own unique cloud service providers (CSPs). It has, therefore, generated the need for customers to decide the IoT cloud platform to suit their vivid and volatile demands in terms of attributes like security and privacy of data, performance efficiency, cost optimization, and other individualistic properties as per unique user. In spite of the existence of many software solutions for this decision-making problem, they have been proved to be inadequate considering the distinct attributes unique to individual user. This paper proposes a framework to represent the selection of IoT cloud platform as a MCDM problem, thereby providing a solution of optimal efficacy with a particular focus in user-specific priorities to create a unique solution for volatile user demands and agile market trends and needs using optimized distance-based approach (DBA) aided by Fuzzy Set Theory

miRNAs in Herpesvirus Infection: Powerful Regulators in Small Packages

microRNAs are a class of small, single-stranded, noncoding RNAs that regulate gene expression. They can be significantly dysregulated upon exposure to any infection, serving as important biomarkers and therapeutic targets. Numerous human DNA viruses, along with several herpesviruses, have been found to encode and express functional viral microRNAs known as vmiRNAs, which can play a vital role in host–pathogen interactions by controlling the viral life cycle and altering host biological pathways. Viruses have also adopted a variety of strategies to prevent being targeted by cellular miRNAs. Cellular miRNAs can act as anti- or proviral components, and their dysregulation occurs during a wide range of infections, including herpesvirus infection. This demonstrates the significance of miRNAs in host herpesvirus infection. The current state of knowledge regarding microRNAs and their role in the different stages of herpes virus infection are discussed in this review. It also delineates the therapeutic and biomarker potential of these microRNAs in future research directions

A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising

Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to eliminate the corrosive Gaussian additive white noise from computed tomography (CT) images while preserving the fine details. The proposed approach is synthesized by amalgamating the concept of method noise with a deep learning-based framework of a convolutional neural network (CNN). The corrupted images are obtained by explicit addition of Gaussian additive white noise at multiple noise variance levels (σ = 10, 15, 20, 25). The denoised images obtained are then evaluated according to their visual quality and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics for denoised CT images are then compared with their respective values for the reference CT image. The average PSNR value of the proposed method is 25.82, the average SSIM value is 0.85, and the average computational time is 2.8760. To better understand the proposed approach’s effectiveness, an intensity profile of denoised and original medical images is plotted and compared. To further test the performance of the proposed methodology, the results obtained are also compared with that of other non-traditional methods. The critical analysis of the results shows the commendable efficiency of the proposed methodology in denoising the medical CT images corrupted by Gaussian noise. This approach can be utilized in multiple pragmatic areas of application in the field of medical image processing

A novel method for detecting conformation-dependent β-glucans—single, double, and triple-helical from Shiitake mushroom

This pioneering study explores the structural intricacies of therapeutic β-glucan in Shiitake (Lentinula edodes), i.e. Lentinan (LNT). Lentinan, a neutral polysaccharide [β-(1,3; 1,6) glucan], exists in three forms; single, double, and triple-helical, but conformation-dependent bioactivity studies are lacking. In this context, we meticulously assessed indigenous Shiitake accessions from Northeast India, unveiling the conformational spectrum of LNT through an innovative pipeline. The experiment approached the simultaneous estimation of total glucan (TG), triple helical glucan (THG), and single-double helical glucan (SDG). Profiling revealed the exceptional LNT content in DMRO-623 (TG: 46.74%, SDG: 9.34%, THG: 37.39%) which emerged as the highest documented to date. Beyond the culinary delight, this research and the novel approach to LNT quantification will create a pivotal platform for advanced mushroom research, offering prospects for novel discoveries, innovative applications, and therapeutic potential.</p