MEF2A (myocyte enhancer factor-2A) is a transcription factor of the MEF2 family. It has been recognized as the cause of coronary artery disease in the absence of any other clinical characteristic. It ...is involved in vascular development and is most commonly found in the coronary artery endothelium. The goal of this case-control study was to see if there was a link between polymorphisms in the MEF2A gene and coronary artery disease. A case-control study was carried out to look into the possible significance of MEF2A polymorphisms as a risk factor for coronary artery disease. This research included 225 patients and 225 healthy controls. A biochemical examination was carried out to evaluate the risk factors for developing this condition. The polymorphisms of Mef2A (1250 C > T in exon 8 and 452 G > T, 481 A > G in exon 11) were found using the PCR-RFLP technique. All identified risk variables, such as hypercholesterolemia, diabetes mellitus, and hypertriglyceridemia, were shown to be statistically significant in the current study for coronary artery disease occurrence. The most polymorphisms were found in MEF2A 1250 C > T, MEF2A 452 G > T, and MEF2A 481 A > G. The genotyping results for MEF2A 1250, MEF2A 452, and MEF2A 481 were (X2 = 2.985; P = 0.235), (X2 = 4.371; P = 0.112), and (X2 = 4.025; P = 0.134), respectively. In conclusion, we identified a much higher incidence of MEF2A in people with coronary artery disease, and MEF2A may play a crucial role in cardiovascular pathophysiology. Patients and controls had considerably different genetics and frequency of alleles.
Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user ...submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%.
Ulvan is a natural sulfated polysaccharide obtained from marine green algae composed of 3-sulfated rhamnoglucuronan as the main component. It has a unique chemical structure that rich of L-rhamnosa, ...D-glucuronic acid, and L-iduronic acid. Ulvan has a similar structure to glycosaminoglycans (GAGs) in mammals including chondroitin sulfate, dermatan sulfate, and heparan sulfate that has broad range applications for many years. Here, we provide an overview of ulvan based hydrogels for biomedical applications. Hydrogels are one of ulvan advances in polymer science for application in drug delivery, tissue engineering, and wound healing. This review presented an overview about functional information of ulvan based hydrogels and the promising potential in biomedicals collected from published papers in Scopus, PubMed, and Google Scholar. Other important aspects concerning properties, hydrogel-forming mechanisms, and ulvan based hydrogel developments were reported as well. As conclusion, ulvan showed interesting properties in forming hydrogels and promising advances in biomedical applications.
Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time ...of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing systems in terms of both computation and communication costs. An application can be considered as a task graph represented using Directed Acyclic Graphs (DAG). Due to the heterogeneous system, each task has different execution time on different processors. The primary concern in this problem domain is to reduce the schedule length with minimum complexity of the scheduling procedure. This work presents a couple of hybrid heuristics, based on a list and guided random search to address this concern. The proposed heuristic, i.e., Hybrid Heuristic and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HHG) uses Genetic Algorithm and a list-based approach. This work also presents another heuristic, namely, Hybrid Task Duplication, and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HTDG). The present work improves the quality of initial GA population by inducing two diverse guided chromosomes. The proposal is compared with four state-of-the-art methods, including two evolutionary algorithms for the same task, i.e., New Genetic Algorithm (NGA) and Enhanced Genetic Algorithm for Task Scheduling (EGA-TS), and two list-based algorithms, i.e., Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). Results show that the proposed solution performs better than its counterparts based on occurrences of the best result, average makespan, average schedule length ratio, average speedup, and the average running time. HTDG yields 89% better results and HHG demonstrates 56% better results in comparisons to the four state-of-the-art task scheduling algorithms.
In this research, we have investigated doubly singular ordinary differential equations and a real application problem of studying the temperature profile in a porous fin model. We have suggested a ...novel soft computing strategy for the training of unknown weights involved in the feed-forward artificial neural networks (ANNs). Our neuroevolutionary approach is used to suggest approximate solutions to a highly nonlinear doubly singular type of differential equations. We have considered a real application from thermodynamics, which analyses the temperature profile in porous fins. For this purpose, we have used the optimizer, namely, the fractional-order particle swarm optimization technique (FO-DPSO), to minimize errors in solutions through fitness functions. ANNs are used to design the approximate series of solutions to problems considered in this paper. We find the values of unknown weights such that the approximate solutions to these problems have a minimum residual error. For global search in the domain, we have initialized FO-DPSO with random solutions, and it collects best so far solutions in each generation/ iteration. In the second phase, we have fine-tuned our algorithm by initializing FO-DPSO with the collection of best so far solutions. It is graphically illustrated that this strategy is very efficient in terms of convergence and minimum mean squared error in its best solutions. We can use this strategy for the higher-order system of differential equations modeling different important real applications.
For analysis of physical properties of different materials, rectangular porous fins are used to examine the heat transformation through a system. In this paper, a metaheuristic is combined with ...neural computing modelling to study the effects of temperature changes in a porous fin model. Cuckoo search algorithm is used as an efficient optimization technique to find the best weights to reduce the mean squared error in the required temperature profile. The governing partial differential equation is converted into a non-linear ordinary differential equation subject to certain boundary conditions. Two individual cases, of silicon nitride (Si3N4) and Aluminium (Al), are considered. In the proposed procedure, the Cuckoo Search(CS) algorithm is combined with the artificial neural network (ANN), namely CS-ANN, to solve the differential equations and obtain solutions with better accuracy.
Robust modeling of a multimodal dynamic system is a challenging and fast-growing area of research. In this study, an integrated bi-modal computing paradigm based on Nonlinear Autoregressive Radial ...Basis Functions (NAR-RBFs) neural network model, a new family of deep learning with the strength of hybrid artificial neural network, is presented for the solution of nonlinear chaotic dusty system (NCDS) of tiny ionized gas particles arising in fusion devices, industry, astronomy, and space. In the proposed methodology, special transformations are introduced for a class of differential equations, which convert the local optimum to a global optimum. The proposed NAR-RBFs neural network model is implemented on bi-model NCDS represented with Van der Pol-Methiew Equation (VdP-ME) for different scenarios based on variation in dust gain production and loss for both small and large time domains. Excellent agreement for proposed bimodal computing paradigm by the result with the standard state of the arts numerical solvers is verified by attaining RMSE up to 1E−38 for the nonlinear VDP-ME. Accuracy of the proposed model in the critical time domain is also validated by convergence, stability and consistency analysis on statistics calculated from absolute error, root-mean-square error, and analysis of variance metrics.
This study aims to identify and isolate the secondary metabolites of
using GC-MS, preparative TLC, and LC-MS/MS methods, to evaluate the inhibitory potency on SARS-CoV-2 3 chymotrypsin-like protease ...enzyme, as well as to study the molecular interaction and stability by using docking and molecular dynamics simulations. GC-MS analysis suggested for the isolation of terpenoids compounds as major compounds on methanol extract of pseudostems and rhizomes. Isolation and LC-MS/MS analysis identified 5-hydro-7, 8, 2'-trimethoxyflavanone (
), (
)-hexadecyl-ferulate (
), isocyperol (
),
isobutyl-(2
,4
)-octadecadienamide (
), and nootkatone (
) from the rhizome extract, as well as from the leaves extract with the absence of
. Three known steroid compounds, i.e., spinasterone (
), spinasterol (
), and 24-methylcholesta-7-en-3
-on (
), were further identified from the pseudostem extract. Molecular docking showed that steroids compounds
,
, and
have lower predictive binding energies (MMGBSA) than other metabolites with binding energy of -87.91, -78.11, and -68.80 kcal/mole, respectively. Further characterization on the single isolated compound by NMR showed that
was identified and possessed 75% inhibitory activity on SARS-CoV-2 3CL protease enzyme that was slightly different with the positive control GC376 (77%). MD simulations showed the complex stability with compound
during 100 ns simulation time.
This manuscript describes recombinant production, characterization and structural analysis of wild-type and mutant Pcal_0029, a pyruvate kinase from Pyrobaculum calidifontis. Recombinant Pcal_0029 ...was produced in soluble and highly active form in Escherichia coli. Purified protein exhibited divalent metal-dependent activity which increased with the increase in temperature till 85 °C. Recombinant Pcal_0029 was highly thermostable with no significant loss in activity even after an incubation of 120 min at 100 °C. The enzyme exhibited apparent S0.5 and Vmax values of 0.44 ± 0.05 mM and 840 ± 39 units, respectively, towards phosphoenolpyruvate. These values towards adenosine-5′-diphosphate were 0.5 ± 0.07 mM and 870 ± 26 units, respectively. In silico structural analysis and comparison with the characterized enzymes revealed the presence of eight conserved regions. Two substitutions, K130E and S155G, resulted in a 10-fold decrease in activity. Secondary structure analysis indicated similar structures for the wild-type and the mutant enzymes. Bioinformatics analysis revealed disruption of interatomic interactions and hydrogen bonds, leading to a decreased flexibility and solvent accessibility, which may have led to decrease in activity. To the best of our knowledge, Pcal_0029 is the most thermostable pyruvate kinase reported so far. Moreover, this is the first study on the role of non-catalytic residues in a pyruvate kinase.
PDF
