Genetic algorithm in ab initio protein structure prediction using low resolution model : a review

Proteins are sequences of amino acids bound into a linear chain that adopt a specific folded three-dimensional (3D) shape. This specific folded shape enables proteins to perform specific tasks. The protein structure prediction (PSP) by ab initio or de novo approach is promising amongst various available computational methods and can help to unravel the important relationship between sequence and its corresponding structure. This article presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. As a review, the essence of twin removal, intelligence in coding, the development and application of domain specific heuristics garnered from the properties of the resulting model and the protein core formation concept discussed are all highly relevant in attempting to secure the best solution

Age related muscle texture variation between Cobb-500 and Ross broiler strain

Meat characteristics of Cobb-500 and Ross boiler strains in terms of histomorphometry of myocyte, fat tissue and connective tissue were studied. Two representative muscles from breast (Pectoralis thoracis and Supracoracoideus) and two from thigh (Ilitibialis lateralis and Iliotibialis cranialis) were selected. Thicker myofiber in breast and thinner myofiber in thigh were found in Ross strain. The perimysial thickness significantly differed among the muscles. The perimysial thickness of breast and thigh muscle at 28th day and thigh muscle at 35th day of Cob-500 were found higher that indicate more toughness of representative muscles. Thick and broad bundles of collagen fiber were observed in perimysium of Ilitibialis lateralis and thinner but broad bundles were in perimysium of Pectoralis thoracis muscle. At 35th day of age the endomysial thickness was found same in both strains but at 28th day of age it was higher in Cobb-500 than that of Ross strain. The intramuscular fat deposited mainly within perimysium as cluster and the number (per focus) and the size of adipocyte diameter was differed among the muscles. Adipocytes diameter was recorded highest (24.14±1.33 μ) in pectoralis thoracis muscle of Ross boiler and 22.01±1.74 μ second in position in Cobb-500. The lowest diameter 15.62±0.87 μ) was recorded in case of iliotibialis lataralis muscle of Ross boiler

Thermal expansion and temperature variation of elastic constants of Li(H,D) and Na(H,D,) systems

Consiglio Nazionale delle Ricerche (CNR). Biblioteca Centrale / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Fatty acid modulation of endothelial activation

Predicting the minimum energy protein structure from its amino acid sequence, even under the rather simplified HP lattice model, continues to be an important and challenging problem in computational biology. In this paper, we propose a novel initial population generation strategy for evolutionary algorithm incorporating domain knowledge based on the concept of maximum hydrophobic core formation for Protein structure prediction (PSP) problem. The proposed technique helps the optimization process to commence with diverse seeds and thereby aids in converging to the global solution quickly. The experimental results, conducted on PSP problem using HP benchmark sequences for 2D square and 3D cubic lattice model, demonstrate that the proposed evolutionary algorithm with new core-based population initialization technique is very effective in improving the optimization process in terms of convergence as well as in achieving the optimal energy

Cost-informed operational process support

The ability to steer business operations in alignment with the true origins of costs, and to be informed about this on a real-time basis, allows businesses to increase profitability. In most organisations however, high-level cost-based managerial decisions are still being made separately from process-related operational decisions. In this paper, we describe how process-related decisions at the operational level can be guided by cost considerations and how these cost-informed decision rules can be supported by a workflow management system. The paper presents the conceptual framework together with data requirements and technical challenges that need to be addressed to realise cost-informed workflow execution. The feasibility of our approach is demonstrated using a prototype implementation in the YAWL workflow environment

Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters

The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (5% and 50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.This work was supported by Qatar National Research Fund (QNRF) under Grant UREP28-144-3-046 and Qatar University Emergency Response Grant (QUERG-CENG-2020-1) through Qatar University. Open Access publication is funded by Qatar National Library (QNL).Scopu