A Computational Dissection of Spike protein of SARS-CoV-2 Omicron Variant

The emergence of SARS-CoV-2 omicron variant in late November, 2021 and its rapid spread to different countries, warns the health authorities to take initiative to work on containing its spread. The omicron SARS-CoV-2 variant is unusual from the other variants of concerns reported earlier as it harbors many novel mutations in its genome particularly with >30 mutations in the spike glycoprotein alone. The current study investigated the variation in binding mechanism which it carries compared to the wild type. The study also explored the interaction profile of spike-omicron with human ACE2 receptor. The structure of omicron spike glycoprotein was determined though homology modeling. The interaction analysis was performed through docking using HADDOCK followed by binding affinity calculation. Finally, the comparison of interactions were performed among spike-ACE2 complex of wild type, delta and omicron variants. The interaction analysis has revealed the involvement of highly charged and polar residues (H505, Arg498, Ser446, Arg493, and Tyr501) in the interactions. The important novel interactions in the spike-ACE2-omicron complex was observed as S494:H34, S496:D38, R498:Y41, Y501:K353, and H505:R393 and R493:D38. Moreover, the binding affinity of spike-ACE2-omicron complex (-17.6Kcal/mol) is much higher than wild type-ACE2 (-13.2Kcal/mol) and delta-ACE2 complex (-13.3Kcal/mol). These results indicate that the involvement of polar and charged residues in the interactions with ACE2 may have an impact on increased transmissibility of omicron variant.

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Increasing terrorist activities globally have attracted the attention of many researchers, policy makers and security agencies towards counterterrorism. The clandestine nature of terrorist networks have made them difficult for detection. Existing works have failed to explore computational characterization to design an efficient threat-mining surveillance system. In this paper, a computationally-aware surveillance robot that auto-generates threat information, and transmit same to the cloud-analytics engine is developed. The system offers hidden intelligence to security agencies without any form of interception by terrorist elements. A miniaturized surveillance robot with Hidden Markov Model (MSRHMM) for terrorist computational dissection is then derived. Also, the computational framework for MERHMM is discussed while showing the adjacency matrix of terrorist network as a determinant factor for its operation. The model indicates that the terrorist network have a property of symmetric adjacency matrix while the social network have both asymmetric and symmetric adjacency matrix. Similarly, the characteristic determinant of adjacency matrix as an important operator for terrorist network is computed to be -1 while that of a symmetric and an asymmetric in social network is 0 and 1 respectively. In conclusion, it was observed that the unique properties of terrorist networks such as symmetric and idempotent property conferred a special protection for the terrorist network resilience. Computational robotics is shown to have the capability of utilizing the hidden intelligence in attack prediction of terrorist elements. This concept is expected to contribute in national security challenges, defense and military intelligence.

Computational Predictions for OCD Pathophysiology and Treatment: A Review

Obsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is not clearly understood. Computational psychiatry is an emerging field in which behavior and its neural correlates are quantitatively analyzed and computational models are developed to improve understanding of disorders by comparing model predictions to observations. The aim is to more precisely understand psychiatric illnesses. Such computational and theoretical approaches may also enable more personalized treatments. Yet, these methodological approaches are not self-evident for clinicians with a traditional medical background. In this mini-review, we summarize a selection of computational OCD models and computational analysis frameworks, while also considering the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks or machine learning methods for modeling, analyzing and classifying patient data. Bayesian interpretations of probability for model selection were also included. The computational dissection of the underlying pathology is expected to narrow the explanatory gap between the phenomenological nosology and the neuropathophysiological background of this heterogeneous disorder. It may also contribute to develop biologically grounded and more informed dimensional taxonomies of psychopathology.

Optical and computational dissection of emergent prefrontal rewiring to encode fear memory

The prefrontal cortex regulates various emotional behaviors and memories, and prefrontal dysfunction can trigger psychiatric disorders. While untangling the internal network may provide clues to the neural architecture underlying such disorders, it is technically difficult due to the complexity and heterogeneity of the network. Here we propose an optical and computational dissection of the internal prefrontal network based on chronic two-photon imaging and a sparse modeling algorithm, which enabled the discrimination of newly emerged neuronal ensembles specifically encoding conditioned fear responses. Further graphical modeling revealed that neurons responding to the unconditioned stimulus during fear conditioning became a core of the ensembles with an enhanced capability for pattern completion, demonstrating the activity dependent rewiring upon the associative learning.