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#author("2024-12-07T08:57:04+09:00","","") This study presents a new input parameter selection and modeling procedure in order to control and predict peak particle velocity (PPV) values induced by mine blasting. The first part of this study was performed through the use of fuzzy Delphi method (FDM) to identify the key input variables with the deepest influence on PPV based on the experts' opinions. Then, in the second part, the most effective parameters on PPV were selected to be applied in hybrid artificial neural network (ANN)-based models i.e., genetic algorithm (GA)-ANN, particle swarm optimization (PSO)-ANN, imperialism competitive algorithm (ICA)-ANN, artificial bee colony (ABC)-ANN and firefly algorithm (FA)-ANN for the prediction of PPV. Many hybrid ANN-based models were constructed according to the most influential parameters of GA, PSO, ICA, ABC and FA optimization techniques and 5 hybrid ANN-based models were proposed to predict PPVs induced by blasting. Through simple ranking technique, the best hybrid model was selected. The obtained results revealed that the FA-ANN model is able to offer higher accuracy level for PPV prediction compared to other implemented hybrid models. Coefficient of determination (R2) results of (0.8831, 0.8995, 0.9043, 0.9095 and 0.9133) and (0.8657, 0.8749, 0.8850, 0.9094 and 0.9097) were obtained for train and test stages of GA-ANN, PSO-ANN, ICA-ANN, ABC-ANN and FA-ANN, respectively. The results showed that all hybrid models can be used to solve PPV problem, however, when the highest prediction performance is needed, the hybrid FA-ANN model would be the best choice.The remarkable advances in next-generation sequencing technology have enabled the wide usage of sequencing as a clinical tool. https://www.selleckchem.com/products/raptinal.html To promote the advance of precision oncology for breast cancer in China, here we report a large-scale prospective clinical sequencing program using the Fudan-BC panel, and comprehensively analyze the clinical and genomic characteristics of Chinese breast cancer. The mutational landscape of 1,134 breast cancers reveals that the most significant differences between Chinese and Western patients occurred in the hormone receptor positive, human epidermal growth factor receptor 2 negative breast cancer subtype. Mutations in p53 and Hippo signaling pathways are more prevalent, and 2 mutually exclusive and 9 co-occurring patterns exist among 9 oncogenic pathways in our cohort. Further preclinical investigation partially suggests that NF2 loss-of-function mutations can be sensitive to a Hippo-targeted strategy. We establish a public database (Fudan Portal) and a precision medicine knowledge base for data exchange and interpretation. Collectively, our study presents a leading approach to Chinese precision oncology treatment and reveals potentially actionable mutations in breast cancer.Poorly immunogenic tumors, including triple negative breast cancers (TNBCs), remain resistant to current immunotherapies, due in part to the difficulty of reprogramming the highly immunosuppressive tumor microenvironment (TME). Here we show that peritumorally injected, macroporous alginate gels loaded with granulocyte-macrophage colony-stimulating factor (GM-CSF) for concentrating dendritic cells (DCs), CpG oligonucleotides, and a doxorubicin-iRGD conjugate enhance the immunogenic death of tumor cells, increase systemic tumor-specific CD8 + T cells, repolarize tumor-associated macrophages towards an inflammatory M1-like phenotype, and significantly improve antitumor efficacy against poorly immunogenic TNBCs. This system also prevents tumor recurrence after surgical resection and results in 100% metastasis-free survival upon re-challenge. This chemo-immunotherapy that concentrates DCs to present endogenous tumor antigens generated in situ may broadly serve as a facile platform to modulate the suppressive TME, and enable in situ personalized cancer vaccination.Inherited retinal dystrophies (IRDs), displaying pronounced genetic and clinical heterogeneity, comprise of a broad range of diseases characterized by progressive retinal cell death and gradual loss of vision. By the combined use of whole exome sequencing (WES), SNP-array and WES-based homozygosity mapping, as well as directed DNA sequencing (Sanger), we have identified nine pathogenic variants in six genes (ABCA4, RPE65, MERTK, USH2A, SPATA7, TULP1) in 10 consanguineous Iranian families. Six of the nine identified variants were novel, including a putative founder mutation in ABCA4 (c.3260A>G, p.Glu1087Gly), detected in two families from Northeastern Iran. Our findings provide additional information to the molecular pathology of IRDs in Iran, hopefully contributing to better genetic counselling and patient management in the respective families from this country.Extracellular pH has been assumed to play little if any role in how bacteria respond to antibiotics and antibiotic resistance development. Here, we show that the intracellular pH of Escherichia coli equilibrates to the environmental pH following treatment with the DNA damaging antibiotic nalidixic acid. We demonstrate that this allows the environmental pH to influence the transcription of various DNA damage response genes and physiological processes such as filamentation. Using purified RecA and a known pH-sensitive mutant variant RecA K250R we show how pH can affect the biochemical activity of a protein central to control of the bacterial DNA damage response system. Finally, two different mutagenesis assays indicate that environmental pH affects antibiotic resistance development. Specifically, at environmental pH's greater than six we find that mutagenesis plays a significant role in producing antibiotic resistant mutants. At pH's less than or equal to 6 the genome appears more stable but extensive filamentation is observed, a phenomenon that has previously been linked to increased survival in the presence of macrophages.Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments.
