A ubiquitous and reversible phosphorylation is important for molecular signaling cascades, regulated by the transient interaction of protein kinases. The coupled folding and phosphorylation determining substrate specificity re-calibrates the interactive environment of intrinsically disordered regions (IDRs). There are over 50 computational methods for predicting IDRs in the proteome, yet achieving an accurate depiction remains an ongoing challenge. In this study, we present a standardized and kinase-centric approach for IDR prediction within the human kinome, employing a long short-term memory deep learning framework that achieves a high predictive performance (AUC = 0.97). The web server is now publicly accessible at: https://ciods.in/kindisorder. Our workflow begins with proteome-wide IDR prediction and proceeds with the categorization of short and long IDR segments, followed by an in-depth analysis of their distribution relative to the kinase domain regulatory core. We evaluated the conservation of these IDRs across all 137 human kinase families, computing a trend-setting conservation index to identify both conserved and variable disorder patterns. Through this framework, we uncovered 1039 functional disorder region hotspots that correlate with dynamic conformational shifts, phosphorylation sites, functional motif enrichment, and mutation impact embedded within IDRs. To further validate their regulatory significance, we conducted biophysical profiling of conserved and variable IDRs. Finally, we developed a structural integrity framework to link these IDRs to their influence on intrinsic signaling cascades and substrate specificity. This study offers a comprehensive functional characterization of IDRs in the human kinome, providing a valuable resource for exploring kinase regulation and opportunities in drug repurposing.

Purpose: During the current pandemic scientists, researchers, and health professionals across the globe are in search of new technological methods for tackling COVID-19. The magnificent performance reported by machine learning and deep learning methods in the previous epidemic has encouraged researchers to develop systems with these methods to diagnose COVID-19. Methods: In this paper, an ensemble-based multi-level voting model is proposed to diagnose COVID-19 from chest x-rays. The multi-level voting model proposed in this paper is built using four machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and K-Nearest Neighbor (KNN). These algorithms are trained with features extracted using the ResNet50 deep learning model before merging them to form the voting model. In this work, voting is performed at two levels, at level 1 these four algorithms are grouped into 2 sets consisting of two algorithms each (set 1 — SVM with linear kernel and LR and set 2 — RF and KNN) and intra set hard voting is performed. At level 2 these two sets are merged using hard voting to form the proposed model. Results: The proposed multilevel voting model outperformed all the machine learning algorithms, pre-trained models, and other proposed works with an accuracy of 100% and specificity of 100%. Conclusion: The proposed model helps for the faster diagnosis of COVID-19 across the globe.

Encryption is one of the techniques that ensure the security of images used in various domains like military intelligence, secure medical imaging services, intranet and internet communication, e-banking, social networking image communication like Facebook, WhatsApp, Twitter etc. All these images travel in a free and open network either during storage or communication; hence their security turns out to be a crucial necessity in the grounds of personal privacy and confidentiality. This article reviews and summarizes various image encryption techniques so as to promote development of advanced image encryption methods that facilitate increased versatility and security.

Abstract This paper considers a generalized version of the stochastic spanning tree problem in which edge costs are random variables and the objective is to find a spectrum of optimal spanning trees satisfying a certain chance constraint whose right‐hand side also is treated as a decision variable. A special case of this problem with fixed right‐hand side has been solved polynomially using a parameteric approach. Also, the same parametric method without increasing the complexity order has been extended to include the right‐hand side also as a decision variable. In this paper, two different methods are given for solving the generalized problem. First, a different parametric method better than the earlier one is given. Then, a method that makes use of the efficient extreme points of the convex hull of the mappings of all the spanning trees in a bicriteria spanning tree problem is presented. But it is shown that in the worst case the bicriteria method is superior. © 1993 by John Wiley & Sons, Inc.