Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and identifying variants. A patient's HIV strain can be classified as susceptible or resistant to 17 different treatments. The FGD-MCNN transforms DNA genotype and HIV data into mathematical metrics, providing valuable insights into treatment-resistant HIV strains through pooling analysis. With remarkable accuracy, the FGD-MCNN deep learning system predicts HIV medication resistance using behavioral and genome-wide data from the HIV database. DNA patterns can be classified as resistant or susceptible by 17 antiretroviral drugs, providing valuable information for treatment planning and medical judgment. The model's parameter values illustrate the connections between neurons and the complex webs observed in the data have been examined. This study improves treatment effectiveness and expands the knowledge of HIV/AIDS.
The development of new building materials, able of absorbing more energy is an active research area. Engineering Cementitious Composite (ECC) is a class of super-elastic fiberreinforced cement composites characterized by high ductility and tight crack width control. The use of bendable concrete produced from Portland Limestone Cement (PLC) may lead to an interest in new concrete mixes. Impact results of bendable concrete reinforced with steel mesh and polymer fibers will provide data for the use of this concrete in areas subject to impact loading. The experimental part consisted of compressive strength and impact resistance tests along with a result comparison with unreinforced concrete. Concrete samples, with dimensions of 100×
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Stainless steel (AISI 304) has good electrical and thermal conductivities, good corrosion resistance at ambient temperature, apart from these it is cheap and abundantly available; but has good mechanical properties such as hardness. To improve the hardness and corrosion resistance of stainless steel its surface can be modified by developing nanocomposite coatings applied on its surface. The main objective of this paper is to study effect of electroco-deposition method on microhardness and corrosion resistance of stainless steel, and to analyze effect of nanoparticles (Al2O3, ZrO2 , and SiC) on properties of composite coatings. I
... Show MoreThe development of new cephalosporins with improved activity against resistant microbes, such as, MRSA (methicillin resistant Staph. aureus), P. aeruginosa, is of high potential. Chemical synthesis of two new series of thiadiazole linked to cysteine (series 1) and cephalosporins containing thiadiazole linked to cysteine through disulfide bond (series 2) were achieved. The chemical structures of the synthesized compounds were confirmed using spectral (FT-IR, 1H-NMR) and elemental microanalysis. The incorporation of privileged chemical moieties, such as, thiadiazole, Schiff base, cysteine and sulfonamide, has been found to have great contribution to the antimicrobial activities. Compounds of series 1 (1
... Show MorePseudomonas aeruginosa, a ubiquitous environmental organism, is a difficult-to-treat opportunistic pathogen due to its broad-spectrum antibiotic resistance and its ability to form biofilms. In this study, we investigate the link between resistance to a clinically important antibiotic, imipenem, and biofilm formation. First, we observed that the laboratory strain P. aeruginosa PAO1 carrying a mutation in the oprD gene, which confers resistance to imipenem, showed a modest reduction in biofilm formation.We also observed an inverse relationship between imipenem resistance and biofilm formation for imipenem-resistant strains selected in vitro, as well as for clinical isolates.We identified two clinical isolates of P. aeruginosa from the sputum
... Show MorePermeability is an essential parameter in reservoir characterization because it is determined hydrocarbon flow patterns and volume, for this reason, the need for accurate and inexpensive methods for predicting permeability is important. Predictive models of permeability become more attractive as a result.
A Mishrif reservoir in Iraq's southeast has been chosen, and the study is based on data from four wells that penetrate the Mishrif formation. This study discusses some methods for predicting permeability. The conventional method of developing a link between permeability and porosity is one of the strategies. The second technique uses flow units and a flow zone indicator (FZI) to predict the permeability of a rock mass u
... Show MorePolyhydroxyalkanoates (PHAs) have gained much attention as biodegradable polymers, many efforts are being made to minimize the cost of PHAs by finding cheap carbon source depending on the type of microorganism and fermentation conditions. The aims of this study were to evaluate the effects of different glucose concentrations and other important conditions on the PHA production by Bacillus cereus isolated from soil. Polyhydroxyalkanoates PHAs accumulated by soil microorganisms were examined by screening the isolated bacteria using Sudan B Black and Nile Blue staining process. A Gram positive strain was identified using the 16s rRNA gene, deposited in the NCBI GenBank sequence database. Different growth conditions (favorite glucose concentrat
... Show MoreChurning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date. A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s
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