Germ-line mutations in BRCA1 gene contribute to a majority of familial breast and ovarian cancers. A group of 23 Tamil Nadu (south India) patients with positive family history for breast and ovarian cancer were screened for BRCA1 mutations by conformation sensitive gel electrophoresis (CSGE) followed by sequencing. In the present study, we report a novel 1307delT mutation in exon 11 of BRCA1 gene in a 43-year-old woman of Indian origin with breast cancer. This mutation gives rise to a premature stop codon at amino acid residue 409 and also creates a novel DdeI restriction site. The same mutation was also detected in the patient's maternal uncle and his son through extended family analysis. The 1307delT is a novel mutation that has not been documented in any population or published report to the best of our knowledge. Identification of this novel mutation stresses the need for developing a database of BRCA1 mutations, which will aid in breast cancer screening in this population.
A material-tailored special concrete composite that uses a synthetic fiber to make the concrete ductile and imposes strain-hardening characteristics with eco-friendly ingredients is known as an “engineered geopolymer composite (EGC)”. Mix design of special concrete is always tedious, particularly without standards. Researchers used several artificial intelligence tools to analyze and design the special concrete. This paper attempts to design the material EGC through an artificial neural network with a cross-validation technique to achieve the desired compressive and tensile strength. A database was formulated with seven mix-design influencing factors collected from the literature. The five best artificial neural network (ANN) models were trained and analyzed. A gradient descent momentum and adaptive learning rate backpropagation (GDX)–based ANN was developed to cross-validate those five best models. Upon regression analysis, ANN [2:16:16:7] model performed best, with 74% accuracy, whereas ANN [2:16:25:7] performed best in cross-validation, with 80% accuracy. The best individual outputs were “tacked-together” from the best five ANN models and were also analyzed, achieving accuracy up to 88%. It is suggested that when these seven mix-design influencing factors are involved, then ANN [2:16: 25:7] can be used to predict the mix which can be cross-verified with GDX-ANN [7:14:2] to ensure accuracy and, due to the few mix trials required, help design the SHGC with lower costs, less time, and fewer materials.
This research focuses on estimating the ACC (axial compression capacity) of concrete-filled double-skin tubular (CFDST) columns. The study utilised algorithms and ‘six’ evaluation methods (XGBoost, AdaBoost, Lasso, Ridge, Random Forest Regressor and artificial neural network (ANN) architecture-based regression) to study the empirical formulae and utilise the parameters as the research’s features, in order to find the best model that has higher and accurate reliability by using the RMSE and R2 scores as performance evaluation metrics. Thus, by identifying the best model in empirical formulae for estimating the ACC of CFDST, the research offers a reliable model for future research. Through findings, it was found that, out of the existing evaluation metrics, the ABR for AFRP, GFRP and Steel; RFR for CFRP; and RR for PETFRP were found to be the best models in the CFDST columns.
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