Sars-cov-2 envelope and membrane proteins: structural differences linked to virus characteristics?

The Coronavirus Disease 2019 (COVID-19) is a new viral infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). Genomic analyses have revealed that SARS-CoV-2 is related to Pangolin and Bat coronaviruses. In this report, a structural comparison between the Sars-CoV-2 Envelope and Membrane proteins from different human isolates with homologous proteins from closely related viruses is described. The analyses here reported show the high structural similarity of Envelope and Membrane proteins to the counterparts from Pangolin and Bat coronavirus isolates. However, the comparisons have also highlighted structural differences specific of Sars-CoV-2 proteins which may be correlated to the cross-species transmission and/or to the properties of the virus. Structural modelling has been applied to map the variant sites onto the predicted three-dimensional structure of the Envelope and Membrane proteins

Support to Design for Air Traffic Management: An Approach with Agent-Based Modelling and Evolutionary Search

This paper presents a methodology to manage the support to design in ATM operations. We propose a workflow for the design of ATM solutions in a performance-based setting. The methodology includes the evaluation of the impact on human behaviour and exploits a combination of different paradigms, such as Agent-Based Modelling and Simulation, and Agent-Based Evolutionary Search. We prove the soundness of the methodology by carrying out a real case study, which is the transition from Direct Routing to Free Routing in the Italian airspace. The validation results exhibit limited errors for the assessment of the performance metrics under evaluation. Furthermore, the optimization of sector collapsing/decollapsing configuration is discussed to demonstrate the effectiveness of the implemented engines

Functional balance at rest of hemispheric homologs assessed via normalized compression distance

Introduction: The formation and functioning of neural networks hinge critically on the balance between structurally homologous areas in the hemispheres. This balance, reflecting their physiological relationship, is fundamental for learning processes. In our study, we explore this functional homology in the resting state, employing a complexity measure that accounts for the temporal patterns in neurodynamics. Methods: We used Normalized Compression Distance (NCD) to assess the similarity over time, neurodynamics, of the somatosensory areas associated with hand perception (S1). This assessment was conducted using magnetoencephalography (MEG) in conjunction with Functional Source Separation (FSS). Our primary hypothesis posited that neurodynamic similarity would be more pronounced within individual subjects than across different individuals. Additionally, we investigated whether this similarity is influenced by hemisphere or age at a population level. Results: Our findings validate the hypothesis, indicating that NCD is a robust tool for capturing balanced functional homology between hemispheric regions. Notably, we observed a higher degree of neurodynamic similarity in the population within the left hemisphere compared to the right. Also, we found that intra-subject functional homology displayed greater variability in older individuals than in younger ones. Discussion: Our approach could be instrumental in investigating chronic neurological conditions marked by imbalances in brain activity, such as depression, addiction, fatigue, and epilepsy. It holds potential for aiding in the development of new therapeutic strategies tailored to these complex conditions, though further research is needed to fully realize this potential

Simulation-Based Evolutionary Optimization of Air Traffic Management

In the context of aerospace engineering, the optimization of processes may often require to solve multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities, possibly involving highly-expensive objective function evaluations. In Air Traffic Management (ATM), the optimization of procedures and protocols becomes even more complicated, due to the involve-ment of human controllers, which act as final decision points in the control chain. In this article, we propose the use of computational intelligence techniques, such as Agent-Based Modelling and Simulation (ABMS)and Evolutionary Computing (EC), to design a simulation-based distributed architecture to optimize control plans and procedures in the context of ATM. We rely on Agent-Based fast-time simulations to carry out offline what-if analysis of multiple scenarios, also taking into account human-related decisions, during the strategic or pre-tactical phases. The scenarios are constructed using real-world traffic data traces, while multiple optimization variables governed by an EC algorithm allow to explore the search space to identify the best solutions. Our optimization approach relies on ad-hoc multi-objective performance metrics which allow to assess the goodness of the control of aircraft and air traffic regulations. We present experimental results which prove the viability of our approach, comparing them with real-world data traces, and proving their meaningfulness from an Air Traffic Control perspective

Normalized compression distance to measure cortico-muscular synchronization

The neuronal functional connectivity is a complex and non-stationary phenomenon creating dynamic networks synchronization determining the brain states and needed to produce tasks. Here, as a measure that quantifies the synchronization between the neuronal electrical activity of two brain regions, we used the normalized compression distance (NCD), which is the length of the compressed file constituted by the concatenated two signals, normalized by the length of the two compressed files including each single signal. To test the NCD sensitivity to physiological properties, we used NCD to measure the cortico-muscular synchronization, a well-known mechanism to control movements, in 15 healthy volunteers during a weak handgrip. Independently of NCD compressor (Huffman or Lempel Ziv), we found out that the resulting measure is sensitive to the dominant-non dominant asymmetry when novelty management is required (p = 0.011; p = 0.007, respectively) and depends on the level of novelty when moving the nondominant hand (p = 0.012; p = 0.024). Showing lower synchronization levels for less dexterous networks, NCD seems to be a measure able to enrich the estimate of functional two-node connectivity within the neuronal networks that control the body

Evolving network structure of academic institutions

Today’s colleges and universities consist of highly complex structures that dictate interactions between the administration, faculty, and student body. These structures can play a role in dictating the efficiency of policy enacted by the administration and determine the effect that curriculum changes in one department have on other departments. Despite the fact that the features of these complex structures have a strong impact on the institutions, they remain by-and-large unknown in many cases. In this paper we study the academic structure of our home institution of Trinity College in Hartford, CT using the major and minor patterns between graduating students to build a temporal multiplex network describing the interactions between different departments. Using recent network science techniques developed for such temporal networks we identify the evolving community structures that organize departments’ interactions, as well as quantify the interdisciplinary centrality of each department. We implement this framework for Trinity College, finding practical insights and applications, but also present it as a general framework for colleges and universities to better understand their own structural makeup in order to better inform academic and administrative policy

A semantic driven approach for consistency verification between requirements and FMEA

Consistency within the system life cycle is difficult to guarantee, due to the cross of different skills and requirements, often expressed by means of different languages. In particular, in safety-critical systems consistency between software requirements and safety analysis requires checks to guarantee that safety engineer needs are feasible and implemented by the system. Failure Mode and Effects Analysis (FMEA) is a systematic technique to analyze the failure modes of components, evaluating their impact and their mitigation actions, which are procedures to be implemented by operators or by the system itself (usually by the software). Although the actual efforts to centralize system information in a structured way, safety analysis is not tied in a structured manner to other systems, in particular to software. This paper proposes an automatic approach to check consistency between FMEA and software requirements with a bit effort of formalization. The approach models FMEA and software requirements with Resource Description Framework (RDF) triplets and checks their consistency on the basis of consistency rules

A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones

In recent years, the drone market has had a significant expansion, with applications in various fields (surveillance, rescue operations, intelligent logistics, environmental monitoring, precision agriculture, inspection and measuring in the construction industry). Given their increasing use, the issues related to safety, security and privacy must be taken into consideration. Accordingly, the development of new concepts for countermeasures systems, able to identify and neutralize a single (or multiples) malicious drone(s) (i.e., classified as a threat), has become of primary importance. For this purpose, the paper evaluates the concept of a multiplatform counter-UAS system (CUS), based mainly on a team of mini drones acting as a cooperative defensive system. In order to provide the basis for implementing such a system, we present a review of the available technologies for sensing, mitigation and command and control systems that generally comprise a CUS, focusing on their applicability and suitability in the case of mini drones

COVID-2019: the role of the nsp2 and nsp3 in its pathogenesis

Last December 2019, a new virus, named COVID-2019 causing many cases of severe pneumonia was reported in Wuhan, China. The virus knowledge is limited and especially about COVID-2019 pathogenesis. The Open Reading Frame 1ab (ORF1ab) of COVID-2019 has been analyzed to evidence the presence of mutation caused by selective pressure on the virus. For selective pressure analysis fast-unconstrained Bayesian approximation (FUBAR) was used. Homology modelling has been performed by SwissModel and HHPred servers. The presence of transmembrane helical segments in Coronavirus ORF1ab nsp2 and nsp3, was tested by TMHMM, MEMSAT and MEMPACK tools. Three-dimensional structures have been analyzed and displayed using PyMOL. FUBAR analysis revealed the presence of potential sites under positive selective pressure (p-value < 0.05). Position 723 in the COVID-2019 has a serine instead a glycine residue, while at aminoacidic position 1010 a proline instead an isoleucine. Significant (p < 0.05) pervasive negative selection in 2416 sites (55%) was found. The positive selective pressure could account for some clinical features of this virus compared to SARS and Bat SARS-like CoV. The stabilizing mutation falling in the endosome-associated-protein-like domain of the nsp2 protein could account for COVID-2019 high ability of contagious, while the destabilizing mutation in nsp3 proteins could suggest a potential mechanism differentiating COVID-2019 from SARS. These data could be helpful for further investigation aimed to identify potential therapeutic targets or vaccine strategy, especially in the actual moment when the epidemic is ongoing and the scientific community is trying to enrich knowledge about this new viral pathogen. This article is protected by copyright. All rights reserved