Site Directed Mutagenesis of a Putative Protease Cleavage Site Within the 105 Kilodation Protein of Southern Bean Mosaic Virus

There are four open reading frames (ORF) in three positive-sense translational phases of Southern Bean Mosaic Virus (SBMV). ORF2 codes for a 105 KDa polyprotein which is proposed to be autocatalytically cleaved into smaller functional proteins. Amino acid sequence homology between the SBMV and poliovirus, foot and mouth disease virus, and cowpea mosaic virus reveal that SBMV has a similar genomic organization to picornaviruses with a conserved order: Vpg (Viral protein, genome-linked )-protease-replicase. Two Gln-Ser (QS) amino acid pairs within the 105 KDa polyprotein, C75= 930-936, C60= 1305-1310, are proposed to be the cleavage sites based on 1) similar structural arrangement around the two QS pairs to that of known cleavage sites of picornaviruses and potyviruses; 2) computer prediction of the cleavage products from the SBMV RNA sequence; 3) observed in vitro translation products. In order to investigate the role of the C60 QS pair in the proteolytic processing of the 105 KDa polyprotein, a mutagenic oligo was designed to create various amino acid substitutions in the QS pair. A coupled in vitro transcription/translation system was used to produce the protein products of both wildtype and mutants. During the procedure, tritiated leucine was incorporated into the protein products. The protein products were separated by SDS-PAGE and the cleavage patterns were detected after autoradiography. Two substitutions of the C60 QS pair-Pro-Val (PS) in mutant6, Gin-Pro (QP) in mutant19-reduced the yield of the 60 KDa protein by different levels. This result supports the hypothesis that the C60 QS is a cleavage site

Blockchain-assisted Undisclosed IIoT Vulnerabilities Trusted Sharing Protection with Dynamic Token

With the large-scale deployment of industrial internet of things (IIoT) devices, the number of vulnerabilities that threaten IIoT security is also growing dramatically, including a mass of undisclosed IIoT vulnerabilities that lack mitigation measures. Coordination Vulnerabilities Disclosure (CVD) is one of the most popular vulnerabilities sharing solutions, in which some security workers (SWs) can develop undisclosed vulnerabilities patches together. However, CVD assumes that sharing participants (SWs) are all honest, and thus offering chances for dishonest SWs to leak undisclosed IIoT vulnerabilities. To combat such threats, we propose an Undisclosed IIoT Vulnerabilities Trusted Sharing Protection (UIV-TSP) scheme with dynamic token. In this article, a dynamic token is an implicit access credential for an SW to acquire an undisclosed vulnerability information, which is only held by the system and constantly updated as the SW access. Meanwhile, the latest updated token can be stealthily sneaked into the acquired information as the traceability token. Once the undisclosed vulnerability information leaves the SW host, the embedded self-destruct program will be automatically triggered to prevent leaks since the destination MAC address in the traceability token has changed. To quickly distinguish dishonest SWs, trust mechanism is adopted to evaluate the trust value of SWs. Moreover, we design a blockchain-assisted continuous logs storage method to achieve the tamper-proofing of dynamic token and the transparency of undisclosed IIoT vulnerabilities sharing. The simulation results indicate that our proposed scheme is resilient to suppress dishonest SWs and protect the IoT undisclosed vulnerabilities effectively.Comment: 10 pages,12 figure

Preparation and Properties of 1, 3, 5, 7-Tetranitro-1, 3, 5, 7-Tetrazocane-based Nanocomposites

A new insensitive explosive based on octahydro-1, 3, 5, 7-tetranitro-1, 3, 5, 7-tetrazocine (HMX) was prepared by spray drying using Viton A as a binder. The HMX sample without binder (HMX-1) was obtained by the same spray drying process also. The samples were characterised by Scanning Electron Microscope, and X-ray diffraction. The Differential Scanning Calorimetry and the impact sensitivity of HMX-1 and nanocomposites were also being tested. The nanocomposite morphology was found to be microspherical (1 μm to 7 μm diameter) and composed of many tiny particles, 100 nm to 200 nm in size. The crystal type of HMX-1 and HMX/Viton A agrees with raw HMX. The activation energy of raw HMX, HMX-1 and HMX/Viton A is 523.16 kJ mol-1, 435.74 kJ mol-1 and 482.72 kJ mol-1, respectively. The self-ignition temperatures of raw HMX, HMX-1 and HMX/Viton A is 279.01 °C, 277.63 °C, and 279.34 °C, respectively. The impact sensitivity order of samples is HMX/Viton A < HMX-1 < raw HMX from low to high.Defence Science Journal, Vol. 65, No. 2, March 2015, pp.131-134, DOI:http://dx.doi.org/10.14429/dsj.65.784

The Relationship Between Big Five Personality Traits and Psychotic Experience in a Large Non-clinical Youth Sample: The Mediating Role of Emotion Regulation

Objective: Despite a long history of interest in personality traits and psychosis, the association between personality traits and psychotic experiences in the general population is not yet well understood. One possible factor that could influence the degree of distress from psychotic experiences is emotion regulation. The purpose of this study was to explore whether the association between personality and psychotic symptoms is already apparent in non-clinical youth as well as the mediating role of emotion regulation strategies between personality traits and psychotic experiences.Methods: Three thousand one hundred and forty seven college students were surveyed via self-report questionnaires measuring the Five-Factor model of personality, emotion regulation strategies, and psychotic experiences.Results: Neuroticism was found to be significantly positively correlated with psychotic experiences, while Extraversion, Openness, Agreeableness, and Conscientiousness were found to be significantly negatively correlated. Both the suppression and reappraisal strategies mediated the relationship between personality traits and psychotic experiences.Conclusion: Our findings suggest that youth with certain personality traits are more likely to have psychotic experiences. The reappraisal emotion regulation strategy could serve as a protective factor against the distress of psychotic experiences

Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection

Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks, but it is challenging to comprehensively reflect complex correlations between AUs via hand-crafted settings. There are alternative methods that employ a fully connected graph to learn these dependencies exhaustively. However, these approaches can result in a computational explosion and high dependency with a large dataset. To address these challenges, this paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection. This method adaptively learns and updates AU correlation graphs by efficiently leveraging the characteristics of different levels of AU motion and emotion representation information extracted in different stages of the network. Moreover, this paper explores the role of multi-scale learning in correlation information extraction, and design a simple yet effective multi-scale feature learning (MSFL) method to promote better performance in AU detection. By integrating AU correlation information with multi-scale features, the proposed method obtains a more robust feature representation for the final AU detection. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on widely used AU detection benchmark datasets, with only 28.7\% and 12.0\% of the parameters and FLOPs of the best method, respectively. The code for this method is available at \url{https://github.com/linuxsino/Self-adjusting-AU}.Comment: 13pages, 7 figure

Visualizing Causality in Mixed Reality for Manual Task Learning: An Exploratory Study

Mixed Reality (MR) is gaining prominence in manual task skill learning due to its in-situ, embodied, and immersive experience. To teach manual tasks, current methodologies break the task into hierarchies (tasks into subtasks) and visualize the current subtask and future in terms of causality. Existing psychology literature also shows that humans learn tasks by breaking them into hierarchies. In order to understand the design space of information visualized to the learner for better task understanding, we conducted a user study with 48 users. The study was conducted using a complex assembly task, which involves learning of both actions and tool usage. We aim to explore the effect of visualization of causality in the hierarchy for manual task learning in MR by four options: no causality, event level causality, interaction level causality, and gesture level causality. The results show that the user understands and performs best when all the level of causality is shown to the user. Based on the results, we further provide design recommendations and in-depth discussions for future manual task learning systems

A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC

Vanadium-Based Superconductivity in a Breathing Kagome Compound Ta2V3.1Si0.9

Superconductivity in V-based kagome metals has recently raised great interest as they exhibit the competing ground states associated with the flat bands and topological electronic structures. Here we report the discovery of superconductivity in Ta2V3.1Si0.9 with a superconducting transition temperature Tc of 7.5 K, much higher than those in previously reported kagome metals at ambient pressure. While the V ions form a two-dimensional breathing kagome structure, the length difference between two different V-V bonds is just 0.04, making it very close to the perfect kagome structure. Our results show that Ta2V3.1Si0.9 is a moderate-coupled superconductor with a large upper critical field that is close to the Pauli limit. DFT calculations give a van-Hove-singularity band located at Fermi energy, which may explain the relatively high Tc observed in this material.Comment: 19 pages, 5 figure