Practical Byzantine Consensus for Internet-of-Things

Use of IoT/WSN assisted smart-systems in the current age is making our living much more easier. However, components of such systems bear a high chance of getting compromised which may result in a substantial damage or loss. Use of fault tolerant consensus protocols provides a way towards solving this problem. Existing solutions for IoT/WSN systems mostly assume simple non-Byzantine node failures which is not enough to solve the problem. To combat the presence of smart devices with malicious intention, Byzantine fault tolerance support is highly essential in building trustworthy decentralised system. Byzantine fault tolerance has not been addressed much in the context of IoT/WSN because of its inherent requirement of extensive data sharing among the nodes. In this work, we approach to bring a solution to the problem using synchronous communication. In particular, we recast the well-known \textit{Practical Byzantine Fault Tolerant} (PBFT) consensus strategy to an efficient form that is suitable for use in IoT/WSN systems. We demonstrate that our proposed design can work upto 80% faster and consume upto 82% lesser energy compared to a naive implementation of the strategy in publicly available IoT/WSN testbed having 45 nodes.<br>

Practical Byzantine Consensus for Internet-of-Things

Use of IoT/WSN assisted smart-systems in the current age is making our living much more easier. However, components of such systems bear a high chance of getting compromised which may result in a substantial damage or loss. Use of fault tolerant consensus protocols provides a way towards solving this problem. Existing solutions for IoT/WSN systems mostly assume simple non-Byzantine node failures which is not enough to solve the problem. To combat the presence of smart devices with malicious intention, Byzantine fault tolerance support is highly essential in building trustworthy decentralised system. Byzantine fault tolerance has not been addressed much in the context of IoT/WSN because of its inherent requirement of extensive data sharing among the nodes. In this work, we approach to bring a solution to the problem using synchronous communication. In particular, we recast the well-known \textit{Practical Byzantine Fault Tolerant} (PBFT) consensus strategy to an efficient form that is suitable for use in IoT/WSN systems. We demonstrate that our proposed design can work upto 80% faster and consume upto 82% lesser energy compared to a naive implementation of the strategy in publicly available IoT/WSN testbed having 45 nodes.<br>

RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data

S-palmitoylation is a reversible covalent post-translational modification of cysteine thiol side chain by palmitic acid. S-palmitoylation plays a critical role in a variety of biological processes and is engaged in several human diseases. Therefore, identifying specific sites of this modification is crucial for understanding their functional consequences in physiology and pathology. We present a random forest (RF) classifier-based consensus strategy (RFCM-PALM) for predicting the palmitoylated cysteine sites on synaptic proteins from male/female mouse data. To design the prediction model, we have introduced a heuristic strategy for selection of the optimum set of physicochemical features from the AAIndex dataset using (a) K-Best (KB) features, (b) genetic algorithm (GA), and (c) a union (UN) of KB and GA based features. Furthermore, decisions from best-trained models of the KB, GA, and UN-based classifiers are combined by designing a three-star quality consensus strategy to further refine and enhance the scores of the individual models. The experiment is carried out on three categorized synaptic protein datasets of a male mouse, female mouse, and combined (male + female), whereas in each group, weighted data is used as training, and knock-out is used as the hold-out set for performance evaluation and comparison. RFCM-PALM shows ~80% area under curve (AUC) score in all three categories of datasets and achieve 10% average accuracy (male—15%, female—15%, and combined—7%) improvements on the hold-out set compared to the state-of-the-art approaches. To summarize, our method with efficient feature selection and novel consensus strategy shows significant performance gains in the prediction of S-palmitoylation sites in mouse datasets.

In Silico Prediction of Novel Inhibitors of SARS-CoV-2 Main Protease through Structure-Based Virtual Screening and Molecular Dynamic Simulation

The unprecedented pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening global health. SARS-CoV-2 has caused severe disease with significant mortality since December 2019. The enzyme chymotrypsin-like protease (3CLpro) or main protease (Mpro) of the virus is considered to be a promising drug target due to its crucial role in viral replication and its genomic dissimilarity to human proteases. In this study, we implemented a structure-based virtual screening (VS) protocol in search of compounds that could inhibit the viral Mpro. A library of >eight hundred compounds was screened by molecular docking into multiple structures of Mpro, and the result was analyzed by consensus strategy. Those compounds that were ranked mutually in the ‘Top-100’ position in at least 50% of the structures were selected and their analogous binding modes predicted simultaneously in all the structures were considered as bioactive poses. Subsequently, based on the predicted physiological and pharmacokinetic behavior and interaction analysis, eleven compounds were identified as ‘Hits’ against SARS-CoV-2 Mpro. Those eleven compounds, along with the apo form of Mpro and one reference inhibitor (X77), were subjected to molecular dynamic simulation to explore the ligand-induced structural and dynamic behavior of Mpro. The MM-GBSA calculations reflect that eight out of eleven compounds specifically possess high to good binding affinities for Mpro. This study provides valuable insights to design more potent and selective inhibitors of SARS-CoV-2 Mpro.

Distributed Adaptive Formation Control for Underactuated Quadrotors with Guaranteed Performances

This paper investigates a distributed adaptive formation control problem for underactuated quadrotors with guaranteed performances. To ensure a robust and stable formation pattern with predefined behavior bounds, by transforming the original constrained formation synchronization error dynamics into an equivalent unconstrained one, a prescribed performance mechanism is introduced in the translational loop to render the formation regulation as a prior. Based on the graph theory and Lyapunov stability analysis, a state estimator-based minimal learning parameter (SE-MLP) neuroadaptive consensus strategy is developed for follower quadrotors to achieve a distributed cooperative formation with prescribed tracking abilities via exchanging local information with neighbors. The presented control scheme has the following salient merits: 1) the formation synchronization errors can be guaranteed within pre-assigned bounds with desired transient behaviors despite of uncertain disturbances; 2) by using a state estimation error to update neural network (NN) parameters, rather than the tracking error that widely applied in traditional NN approximators, and with the help of MLP technique, the proposed SE-MLP observer capable of decreasing the computational complexity can achieve a fast identification of lumped disturbances without causing high-frequency oscillations even using a large adaptive gain, and the transient solutions of L 2 norm of the differential of neural weights are established to illustrate the mechanism of SE-MLP observer in reducing chattering behaviors. Simulation results are given to validate the efficiency of developed technique.

Cavitron ultrasonic surgical aspirator for mitral annular decalcification

Mitral valve surgery can be especially difficult in patients with mitral annular calcification. Not only is it associated with higher morbidity and mortality, but there is no consensus strategy for the treatment of mitral annular calcification, which may render some patients inoperable. We describe a case of severe mitral annular calcification successfully treated with the cavitron ultrasonic surgical aspirator. We demonstrate the proper technique for and the efficacy of using this instrument for decalcification of the mitral annulus. The video tutorial includes the patient presentation, preoperative and postoperative imaging, and the proper surgical technique for cavitron ultrasonic surgical aspirator-assisted mitral annular decalcification. With the proper technique, the cavitron ultrasonic surgical aspirator may mitigate the risk of surgical complications associated with severe calcification of the mitral annulus.