Mock-up pragmatic study on the impact performance of self-compacting concrete incorporating sea sand.

Self-Compacting Concrete (SCC) allows for the use of non-desalted sea sand as a fine aggregate, but the durability of triple mix SCC with partial sea sand replacement remains unclear. To optimize binder and fine aggregate replacements, tests for consistency, setting times, soundness, compressive strength, and Ultrasonic Pulse Velocity were performed. Six SCC variations, incorporating 30 Class F Fly Ash (FA), 5 Ground Granulated Blast Furnace Slag (GGBS), and various fine aggregate combinations, were evaluated for their fresh, mechanical, microstructural, and durability properties.

Machine learning based predictive model for the analysis of sequence activity relationships using protein spectra and protein descriptors.

Accurately establishing the connection between a protein sequence and its function remains a focal point within the field of protein engineering, especially in the context of predicting the effects of mutations. From this, there has been a continued drive to build accurate and reliable predictive models via machine learning that allow for the virtual screening of many protein mutant sequences, measuring the relationship between sequence and 'fitness' or 'activity', commonly known as a Sequence-Activity-Relationship (SAR). An important preliminary stage in the building of these predictive models is the encoding of the chosen sequences.

A Comparative Study on Single and Multiple Point Crossovers in a Genetic Algorithm for Coarse Protein Modeling.

The protein structure prediction problem is a holy grail for life science researchers. Computational protein structure prediction involves the folding of protein sequence (string) into the tertiary structure, called the native protein structure. The hydrophobic polar (HP) model is one of the basic models used to investigate the protein folding mechanism at the coarse level.

A Review of Protein Structure Prediction Using Lattice Model.

The prediction of the tertiary structure of a protein from its primary sequence is a long-standing problem for life science researchers. Solutions to this problem have a direct impact on other research areas such as design and discovery of new drugs, understanding the sequenced genomic data, solving the puzzle of protein structure, and understanding the folding mechanism. Solving protein structure prediction (PSP) is considered a cornerstone for life science research to reveal the mechanism of cellular processes; it has been an open problem for past six decades.

A Novel Framework for Coarse Protein Structure Prediction.

Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation.

Genetic variants of NQO1 gene increase bladder cancer risk in Indian population and meta-analysis.

quinone oxidoreductase (NQO1) is cytosolic enzymes that plays a role in protection against natural and xenobiotic quinones. This enzyme also protects cells from oxidative damage by preventing the generation of reactive oxygen species and reducing environmental carcinogens. The study included 200 bladder cancer (BC) patients and 200 healthy control individuals that had been matched by age and sex, and were of similar ethnicity.