Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a whole is required. In this study, we try to find the high-impact core regulating processes and factors involved in dementia’s protein–protein interaction network. We also explore various aspects related to its stability and signal propagation. Using gene interaction databases such as STRING and GeneMANIA, a principal dementia network (PDN) consisting of 881 genes and 59,085 interactions was achieved. It was assortative in nature with hierarchical, scale-free topology enriched in various gene ontology (GO) categories and KEGG pathways, such as negative and positive regulation of apoptotic processes, macroautophagy, aging, response to drug, protein binding, etc. Using a clustering algorithm (Louvain method of modularity maximization) iteratively, we found a number of communities at different levels of hierarchy in PDN consisting of 95 “motif-localized hubs”, out of which, 7 were present at deepest level and hence were key regulators (KRs) of PDN (HSP90AA1, HSP90AB1, EGFR, FYN, JUN, CELF2 and CTNNA3). In order to explore aspects of network’s resilience, a knockout (of motif-localized hubs) experiment was carried out. It changed the network’s topology from a hierarchal scale-free topology to scale-free, where independent clusters exhibited greater control. Additionally, network experiments on interaction of druggable genome and motif-localized hubs were carried out where UBC, EGFR, APP, CTNNB1, NTRK1, FN1, HSP90AA1, MDM2, VCP, CTNNA1 and GRB2 were identified as hubs in the resultant network (RN). We finally concluded that stability and resilience of PDN highly relies on motif-localized hubs (especially those present at deeper levels), making them important therapeutic intervention candidates. HSP90AA1, involved in heat shock response (and its master regulator, i.e., HSF1), and EGFR are most important genes in pathology of dementia apart from KRs, given their presence as KRs as well as hubs in RN.

Sensor node authentication and integrity of data are of utmost importance in many decision-making processes in wireless sensor networks, with the intent that every necessary data must be digitally signed by its sensor node to send data cautiously on wireless network. No one should unlock the data other than the sink node, since only the sink node can have privilege for the verification of data. The key generation process and/or the verification process is certainly a difficult job, and it would wear out the sensor having minimal operational resources. This paper studies and evaluates different types of digital signature algorithms, such as ECDSA, RSA, OTS, Lightweight digital signature and identity-based signature, and compares their key sizes, computational and energy cost in WSN. It has been found that elliptic curve digital signature algorithm has significant benefit over other public-key algorithms in WSN, as it minimizes computational time, amount of data transmission and storage.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic component. This research aims to identify key genes associated with autism spectrum disorder using a hybrid deep learning approach. To achieve this, a protein–protein interaction network is constructedand analyzed through a graph convolutional network, which extracts features based on gene interactions. Logistic regression is then employed to predict potential key regulatorgenes using probability scores derived from these features. To evaluate the infection ability of these potential key regulator genes, a susceptible–infected (SI) model, is performed, which reveals the higher infection ability for the genes identified by the proposed method, highlighting its effectiveness in pinpointing key genetic factors associated with ASD. The performance of the proposed method is compared with centrality methods, showing significantly improved results. Identified key genes are further compared with the SFARI gene database and the Evaluation of Autism Gene Link Evidence (EAGLE) framework, revealing commongenes that are strongly associated with ASD. This reinforces the validity of the method in identifying key regulator genes. The proposed method aligns with advancements in therapeutic systems, diagnostics, and neural engineering, providing a robust framework for ASD research and other neurodevelopmental disorders.

Brain tumor classification is a crucial task in medical image analysis due to the complexity of the neurological system. With the rapid advancements in deep learning techniques, particularly in medical imaging, there is growing potential to enhance the accuracy and efficiency of brain tumor diagnosis using the magnetic resonance imaging (MRI). This paper proposes an optimized and low-computation deep learning model built on the backbone MobileNetv2 convolutional neural network architecture to classify the brain tumors into three categories: glioma, meningioma, and pituitary tumors. The model is trained, validated, and tested using a dataset of T1-weighted contrast-enhanced brain MR images (T1W-CE MRI). Preprocessing steps are incorporated to enhance the classification efficiency. We evaluate the model's performance on both equally and unequally distributed classes of the images and achieve an accuracy of 92.23% and 93.59%, with F1 scores of 92.21% and 93.65%, respectively, for both the distributions. The experimental results demonstrate that the proposed model efficiently classifies the brain tumors using the MR images and achieves superior accuracy to the latest literature methods and state-of-the-art models: "ResNet50, VGG16, NASNetMobile, InceptionResNetV2, and InceptionV3". Thus, the proposed model may help assisting the radiologists in fast and better diagnoses.

JOURNEY TO SAFE BLOOD Critically ill patients who have lost blood must receive blood transfusions in order to survive, but could encounter adverse effects during this process. Any adverse incident that occurs during or after a transfusion of whole blood or one of its components and is not associated with any other factor is considered […]

SARS-CoV-2 is a highly virulent and deadly RNA virus causing the Covid-19 pandemic and several deaths across the world. The pandemic is so fast that any concrete theory of sudden widespread of this disease is still not known. In this work, we studied and analyzed a large number of publicly available SARS-CoV-2 genomes across the world using the multifractal approach. The mutation events in the isolates obey the Markov process and exhibit very high mutational rates, which occur in six specific genes and highest in orf1ab gene, leading to virulent nature. f ( α ) analysis indicated that the isolates are highly asymmetric (left-skewed), revealing the richness of complexity and dominance by large fluctuations in genome structure organization. The values of H _q and D _q are found to be significantly large, showing heterogeneous genome structure self-organization, strong positive correlation in organizing the isolates, and quite sensitive to fluctuations in and around it. We then present multiple-isolates hosts-virus interaction models, and derived Price equation for the model. The phase plane analysis of the model showed asymptotic stability type bifurcation. The competition among the mutant isolates drives the trade-off of the dominant mutant isolates, otherwise confined to the present hosts.