Elucidating the Interacting Domains of Chandipura

The nucleocapsid (N) protein of Chandipura virus (CHPV) plays a crucial role in viral life cycle, besides being an important structural component of the virion through proper organization of its interactions with other viral proteins. In a recent study, the authors had mapped the associations among CHPV proteins and shown that N protein interacts with four of the viral proteins: N, phosphoprotein (P), matrix protein (M), and glycoprotein (G). The present study aimed to distinguish the regions of CHPV N protein responsible for its interactions with other viral proteins. In this direction, we have generated the structure of CHPV N protein by homology modeling using SWISS-MODEL workspace and Accelrys Discovery Studio client 2.55 and mapped the domains of N protein using PiSQRD. The interactions of N protein fragments with other proteins were determined by ZDOCK rigid-body docking method and validated by yeast two-hybrid and ELISA. The study revealed a unique binding site, comprising of amino acids 1–30 at the N terminus of the nucleocapsid protein (N1) that is instrumental in its interactions with N, P, M, and G proteins. It was also observed that N2 associates with N and G proteins while N3 interacts with N, P, and M proteins

The investment decision-making process from a risk manager's perspective: a survey

Purpose – The purpose of this paper is to identify associations between various inputs in the investment decision process of Saudi Arabian risk managers (RMs). Design/methodology/approach – The paper reports the views of 81 RMs in Saudi Arabia regarding their approach to investment risk and uses these as inputs into conditional independence graphs. Findings – Saudi RMs favour their experience and personal judgment over mathematical projections and statistical models when considering investment risk. A need remains for an efficient risk-modeling framework for the banking system that has more practical value than those which have emerged to date. Originality/value – The paper provides novel insights on issues such as the extent to which risk management is dealt with in practice via personal experience rather than statistical-based projections. The findings also shed light on the level of satisfaction amongst RMs and regulators with the incentives provided in the Saudi Arabian environment, and the importance placed on guidance from the nation's leading regulatory institution.Decision making, Risk assessment, Risk management, Saudi Arabia

DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem

Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 4.0 ecosystem. In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move. However, there are various challenges for the design of patient driven healthcare recommender systems. Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods. For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem. DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health. On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes

Not Available

Not AvailableNot AvailableNot Availabl