Phylogenetics of HCV: Recent advances

Hepatitis C virus (HCV), a virus present in human population from indefinite time period, has affected millions of people globally, by causing liver infection which in majority of cases leads to chronicity, cirrhosis, end stage liver disease and hepatocellular carcinoma (HCC). The disease burden is expected to increase in the developing and under developed world in future. The distribution of HCV genotypes is changing, as are the modes of transmission. Evolution of HCV is a highly dynamic process as it exploits all known mechanisms of genetic variation including recombination and mutation, to ensure its survival. It occurs both through multiple processes of adaptive selection that drive sequence change and through drift, in which phenotypically neutral sequence changes accumulate over time without altering the phenotype or behaviour of the virus. However, despite its potential to change rapidly, the longer-term evolution of HCV appears to be remarkably conservative. Phylogenetic and statistical models of viral evolution are useful in reconstructing mutational pathways of drug resistance. The two major divisions of viral heterogeneity include genotypes and quasispecies. The rate of nucleotide changes varies significantly among the different regions of the viral genome. The present HCV classification is incomplete, as new genotypes and variants are being identified till yet. Diversification of HCV occurred over time but with different rates. Host immune pressure is thought to be a main factor driving diversification in HCV quasispecies. Core and hypervariable regions are more diverse while 5' un-translated region (UTR) and 3' UTR are highly conserved across the genotypes.Keywords: HCV, phylogeny, 5' UTR, viral evolution, recombination, quasispeciesAfrican Journal of Biotechnology Vol. 9(36), pp. 5792-5799, 6 September, 201

CONSTITUTIVE MODELLING OF SANDS UNDER MONOTONIC LOADING

This paper presents the drained and undrained behavior of soils using a modified version of the original cam clay constitutive model. The strain hardening behavior of soils is one of the major challenges in geotechnical engineering. The constitutive equations are numerically integrated over fixed time steps to apply effective stress to the derived elastoplastic soil model. Convergence of solution is controlled by a constitutive relation, namely the associated flow rule. This study provides step by step Python and octave programs to solve for q"-" p by solving the associated non-linear system. The problem is formulated by assuming small strains in the elastic region and large strains in the plastic region. The transition from over-consolidated to normally consolidated states is predicted to be smooth by this elastoplastic model. The model is recognized and solved as a boundary value problem with only two effective stress variables namely q"-" p which is an approximation of three-dimensional invariants

Formation of chitosan nanoparticles to encapsulate krill oil (Euphausia superba) for application as a dietary supplement

Encapsulation of krill oil (KO), a rich source of eicosapentanoic (EPA) and docosahexanoic acid (DHA) was carried out in chitosan-TPP (tripolyphosphate) nanoparticles using a newly developed two-step process (i.e, formation of emulsion and later electrostatic interaction of chitosan with TPP). The encapsulation of KO in chitosan nanoparticles (CSNPs) was confirmed by using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and Thermo gravimetric analysis (TGA) techniques. Loading capacity (LC) and encapsulation efficiency (EE) of the obtained particles were about 9 – 25 and 33 – 59 % respectively, when the initial KO content was in the ratio of 0.25 – 1.25 g/g of Chitosan. Bulk KO showed less protection to oxidation and showed more formation of hydroperoxides during first week as noted by FTIR. However, KO loaded CSNPs showed better prevention of KO towards oxidation with less hydroperoxide formation even after two weeks of storage at elevated temperature (45 oC). The obtained KO-loaded CSNPs were irregular in shape with an average particle diameter of < 130 nm as observed by SEM. The results obtained confirmed the suitability of the emulsion and later electrostatic interaction of CS with TPP for the formation of KO loaded CSNPs with greater EE & LC, which will enhance their usage in the Food and Pharmaceutical industrie

APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm

Hypokalaemic periodic paralysis in patients presenting with severe limb paralysis at PUMHSW Nawabshah

Objective: To determine the hypokalemic periodic paralysis rate in patients presenting with severe limb paralysis at PUMHSW Nawabshah. Methodology: This descriptive was conducted Medical department of Peoples Medical College &amp; hospital Nawabshah from October 2017 to April 2018. All the patients having age 20-50 years of either gender with severe limb paralysis at Intensive care unit &amp; medical ward of Peoples Medical College Hospital Nawabshah were included. Demographics information was obtained. After clinical examination along with detailed medical history regarding hypokalemic periodic paralysis (HPP) and severe limb paralysis, patients were subjected to relevant investigations especially potassium and x-rays. Data was collected via self-made proforma. Results: Total of 150 patients were studied; their mean age was 33.4±5.69 years.  22(14.7%) study subjects were female and 128(85.3%) were male patients. Hypokalemic periodic paralysis was seen in 77(51.3%) patients, presenting with severe limb paralysis. There was significant impact of age and gender on frequency of Hypokalemic periodic paralysis. Conclusion: Hypokalemic periodic paralysis (HPP) is a significant factor of acute flaccid paralysis, as well as prompt management and early recognition of this condition would give pleasing result and in some cases, it would prevent additional attacks

Functional Foods and Human Health: An Overview

Functional food is a whole ingredient or a part of food that used as food for specific therapeutic purposes. It is divided into two wide categories: Conventional and modified functional foods. Conventional functional Foods are composed of natural or whole-food ingredients that provide functional substances while modified functional is food or food products in which add additional ingredients for specific health purposes. Plant-based food such as fruits, vegetables, herbs, cereals, nuts and beans contain vitamins, minerals, fiber, omega-3 fatty acids, antioxidants and phenolic compounds that play a functional role in the human body against chronic diseases including cancer, cardiovascular and GIT-related disease. Some other foods or food products like juices, dairy products, fortified eggs and seafood are composed of functional components. Fish contain omega-3 fatty acids (EPA and DHA) that are played a functional role in heart health and brain development

Power Resilience Through Deployment & Integration of Microgrid with Traditional Grid station

The microgrid technology development and deployment is emerging widely throughout the world as future of power system. Microgrid connectivity with currently installed utility grids in our environment to gain required power parameters including resilience, reliability and quality is the core subject analyzed here. This research is based on analyzing various microgrids installed around the world and their usefulness to the customers. Present power supply setup for different small to medium size industries in Multan Industrial Estate through traditional utility grid has been studied. Integration of current power sources available for individual industrial units with microgrid has been studied

Cyber Secure Framework for Smart Agriculture: Robust and Tamper-Resistant Authentication Scheme for IoT Devices

Internet of Things (IoT) as refers to a network of devices that have the ability to connect, collect and exchange data with other devices over the Internet. IoT is a revolutionary technology that have tremendous applications in numerous fields of engineering and sciences such as logistics, healthcare, traffic, oil and gas industries and agriculture. In agriculture field, the farmer still used conventional agriculture methods resulting in low crop and fruit yields. The integration of IoT in conventional agriculture methods has led to significant developments in agriculture field. Different sensors and IoT devices are providing services to automate agriculture precision and to monitor crop conditions. These IoT devices are deployed in agriculture environment to increase yields production by making smart farming decisions and to collect data regarding crops temperature, humidity and irrigation systems. However, the integration of IoT and smart communication technologies in agriculture environment introduces cyber security attacks and vulnerabilities. Such cyber attacks have the capability to adversely affect the countries&rsquo; economies that are heavily reliant on agriculture. On the other hand, these IoT devices are resource constrained having limited memory and power capabilities and cannot be secured using conventional cyber security protocols. Therefore, designing robust and efficient secure framework for smart agriculture are required. In this paper, a Cyber Secured Framework for Smart Agriculture (CSFSA) is proposed. The proposed CSFSA presents a robust and tamper resistant authentication scheme for IoT devices using Constrained Application Protocol (CoAP) to ensure the data integrity and authenticity. The proposed CSFSA is demonstrated in Contiki NG simulation tool and greatly reduces packet size, communication overhead and power consumption. The performance of proposed CSFSA is computationally efficient and is resilient against various cyber security attacks i.e., replay attacks, Denial of Service (DoS) attacks, resource exhaustion

Growth kinetics, fatty acid composition and metabolic activity changes of Crypthecodinium cohnii under different nitrogen source and concentration

Abstract The effect of varying concentrations of the nitrogen source on the growth kinetics, lipid accumulation, lipid and DHA productivity, and fatty acid composition of C. cohnii was elucidated. Growth of C. cohnii was in three distinct growth stages: cell growth, lipid accumulation and a final lipid turnover stage. Most of lipids were accumulated in lipid accumulation stage (48–120 h) though, slow growth rate was observed during this stage. NaNO3 supported significantly higher lipid content (26.9% of DCW), DHA content (0.99 g/L) and DHA yield (44.2 mg/g glucose) which were 2.5 to 3.3-folds higher than other N-sources. The maximum level of C16–C18 content (% TFA) was calculated as 43, 54 and 43% in lipid accumulation stage under low nitrogen (LN, 0.2 g/L), medium nitrogen (MN, 0.8 g/L) and high nitrogen (HN, 1.6 g/L) treatments, respectively. Cultures with LN, by down-regulating cell metabolism, trigger onset of lipogenic enzymes. Conversely, NAD+/NADP+-dependent isocitrate dehydrogenase (NAD+/NADP+-ICDH) were less active in LN than HN treatments which resulted in retardation of Kreb’s Cycle and thereby divert citrate into cytoplasm as substrate for ATP-citrate lyase (ACL). Thereby, ACL and fatty acid synthase (FAS) were most active in lipid accumulation stage at LN treatments. Glucose-6-phosphate dehydrogenase (G6PDH) was more active than malic enzyme (ME) in lipid accumulation stage and showed higher activities in NaNO3 than other N-sources. This represents that G6PDH contributes more NADPH than ME in C. cohnii. However, G6PDH and ME together seems to play a dual role in offering NADPH for lipid biosynthesis. This concept of ME together with G6PD in offering NADPH for lipogenesis might be novel in this alga and needed to be explored

Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO

The Internet of Things (IoT) supports human endeavors by creating smart environments. Although the IoT has enabled many human comforts and enhanced business opportunities, it has also opened the door to intruders or attackers who can exploit the technology, either through attacks or by eluding it. Hence, security and privacy are the key concerns for IoT networks. To date, numerous intrusion detection systems (IDS) have been designed for IoT networks, using various optimization techniques. However, with the increase in data dimensionality, the search space has expanded dramatically, thereby posing significant challenges to optimization methods, including particle swarm optimization (PSO). In light of these challenges, this paper proposes a method called improved dynamic sticky binary particle swarm optimization (IDSBPSO) for feature selection, introducing a dynamic search space reduction strategy and a number of dynamic parameters to enhance the searchability of sticky binary particle swarm optimization (SBPSO). Through this approach, an IDS was designed to detect malicious data traffic in IoT networks. The proposed model was evaluated using two IoT network datasets: IoTID20 and UNSW-NB15. It was observed that in most cases, IDSBPSO obtained either higher or similar accuracy even with less number of features. Moreover, IDSBPSO substantially reduced computational cost and prediction time, compared with conventional PSO-based feature selection methods