Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.     

Efficient mining of discriminative co-clusters from gene expression data

Discriminative models are used to analyze the differences between two classes and to identify class-specific patterns. Most of the existing discriminative models depend on using the entire feature space to compute the discriminative patterns for each class. Co-clustering has been proposed to capture the patterns that are correlated in a subset of features, but it cannot handle discriminative patterns in labeled datasets. In certain biological applications such as gene expression analysis, it is critical to consider the discriminative patterns that are correlated only in a subset of the feature space. The objective of this paper is twofold: first, it presents an algorithm to efficiently find arbitrarily positioned co-clusters from complex data. Second, it extends this co-clustering algorithm to discover discriminative co-clusters by incorporating the class information into the co-cluster search process. In addition, we also characterize the discriminative co-clusters and propose three novel measures that can be used to evaluate the performance of any discriminative subspace pattern-mining algorithm. We evaluated the proposed algorithms on several synthetic and real gene expression datasets, and our experimental results showed that the proposed algorithms outperformed several existing algorithms available in the literature.     

Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection (FS) technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting DDoS flooding attacks (FA) based on ICMPv6 messages using a Binary Flower Pollination Algorithm (BFPA-FA). The proposed method (BFPA-FA) employs FS technology with a support vector machine (SVM) to identify the most relevant, influential features. Moreover, The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack scenarios. The results show that the proposed method BFPA-FA achieved the best accuracy rate (97.96%) for the ICMPv6 DDoS detection with a reduced number of features (9) to half the total (19) features. The proven proposed method BFPA-FA is effective in the ICMPv6 DDoS attacks via IDS.     

A multi-step differential transform method and application to non-chaotic or chaotic systems

The differential transform method (DTM) is an analytical and numerical method for solving a wide variety of differential equations and usually gets the solution in a series form. In this paper, we propose a reliable new algorithm of DTM, namely multi-step DTM, which will increase the interval of convergence for the series solution. The multi-step DTM is treated as an algorithm in a sequence of intervals for finding accurate approximate solutions for systems of differential equations. This new algorithm is applied to Lotka–Volterra, Chen and Lorenz systems. Then, a comparative study between the new algorithm, multi-step DTM, classical DTM and the classical Runge–Kutta method is presented. The results demonstrate reliability and efficiency of the algorithm developed.     

Phase Transformations of α-Alumina Made from Waste Aluminum via a Precipitation Technique

We report on a recycling project in which α-Al₂O₃ was produced from aluminum cans because no such work has been reported in literature. Heated aluminum cans were mixed with 8.0 M of H₂SO₄ solution to form an Al₂(SO₄)₃ solution. The Al₂(SO₄)₃ salt was contained in a white semi-liquid solution with excess H₂SO₄; some unreacted aluminum pieces were also present. The solution was filtered and mixed with ethanol in a ratio of 2:3, to form a white solid of Al₂(SO₄)₃·18H₂O. The Al₂(SO₄)₃·18H₂O was calcined in an electrical furnace for 3 h at temperatures of 400–1400 °C. The heating and cooling rates were 10 °C/min. XRD was used to investigate the phase changes at different temperatures and XRF was used to determine the elemental composition in the alumina produced. A series of different alumina compositions, made by repeated dehydration and desulfonation of the Al₂(SO₄)₃·18H₂O, is reported. All transitional alumina phases produced at low temperatures were converted to α-Al₂O₃ at high temperatures. The X-ray diffraction results indicated that the α-Al₂O₃ phase was realized when the calcination temperature was at 1200 °C or higher.     

Synthesis of a Potential Tumor Imaging Agent by Oxidative Radioiodination of Aspirin and Its Preclinical Study

Radiochemistry - Aspirin, one of nonsteroidal anti-inflammatory analgesic drugs, was labeled with 125I with a labeling yield of 85.5% under the following conditions: pH 9, 100 mg of the substrate,...     

Effects of pH and chloride concentration on pitting corrosion of AA6061 aluminum alloy

Effects of pH solution and chloride (Cl⁻) ion concentration on the corrosion behaviour of alloy AA6061 immersed in aqueous solutions of NaCl have been investigated using measurements of weight loss, potentiodynamic polarisation, linear polarisation, cyclic polarisation experiment combined with open circuit potential transient technique and optical or scanning electron microscopy.The corrosion behaviour of the AA6061 aluminum alloy was found to be dependant on the pH and chloride concentration [NaCl] of solution. In acidic or slightly neutral solutions, general and pitting corrosion occurred simultaneously. In contrast, exposure to alkaline solutions results in general corrosion. Experience revealed that the alloy AA6061 was susceptible to pitting corrosion in all chloride solution of concentration ranging between 0.003 wt% and 5.5 wt% NaCl and an increase in the chloride concentration slightly shifted both the pitting E_pit and corrosion E_cor potentials to more active values. In function of the conditions of treatment, the sheets of the alloy AA6061 undergo two types of localised corrosion process, leading to the formation of hemispherical and crystallographic pits.Polarisation resistance measurements in acidic (pH = 2) and alkaline chloride solutions (pH = 12) which are in good agreement with those of weight loss, show that the corrosion kinetic is minimised in slightly neutral solutions (pH = 6).     

Chemical Amplification of a Triphenylene Molecular Electron Beam Resist

Molecular resists, such as triphenylene derivatives, are small carbon rich molecules, and thus give the potential for higher lithographic resolution and etch durability, and lower line width roughness than traditional polymeric compounds. Their main limitation to date has been poor sensitivity. A new triphenylene derivative molecular resist, with pendant epoxy groups to aid chemically amplified crosslinking, was synthesized and characterized. The sensitivity of the negative tone, pure triphenylene derivative when exposed to an electron beam with energy 20 keV was ～ 6 × 10^–4 C cm^–2, which increased substantially to ～ 1.5 × 10^–5 C cm^–2 after chemical amplification (CA) using a cationic photoinitiator. This was further improved, by the addition of a second triphenylene derivative, to ～ 7 × 10^–6 C cm^–2. The chemically amplified resist demonstrated a high etch durability comparable with the novolac resist SAL 601. Patterns with a minimum feature size of ～ 40 nm were realized in the resist with a 30 keV electron beam.     

Axial temperature profile of ohmically heated fluid jet: Analytical model and experimental validation

Temperature profile of an ohmically heated fluid jet was determined both by experimental measurements and analytical model. Heat loss to the environment and viscous dissipation were considered negligible in the simulation results. We also assumed that electrical heating rate is zero outside the fluid jet. The model predictions yielded good agreement with the experimental infrared thermography measurements. Parameters affecting temperature distribution in the fluid jet were studied such as mass flowrate, impingement distance of the jet, heating power and specific electrical conductivity. The simulation and also experimental trials show that the principal critical process factor in fluid jet ohmic heater was the fluid level and one can deduce its great impact on the thermal treatment. This study can be used for designing and controlling this ohmic heater to ensure thermal sterilization and safety of ohmically heated food products.