Search Results (3)

For a highly beneficial mutation A at locus 1 spreading in a very large population, we have analyzed the scenario that at a closely linked locus 2 a second beneficial mutant B arises before A has fixed. Under the assumptions that the fitness of B is greater than that of A and that A- and B-carrying chromosomes can recombine at some rate $r$, recombinants AB may form and eventually fix. We present explicit formulas for the fixation time of AB under additive fitness of the mutants as a function of the frequency $X_2\left(0\right)$ of A at the time when B is introduced. Our analysis suggests that the effect of interference between the beneficial mutations is most pronounced for small values of $X_2\left(0\right)<0.1.$ Furthermore, we identify a threshold value for $r$, above which recombination speeds up fixation. Using published simulation data, we also describe the genomic footprint of competing beneficial mutations. At neutral sites between the two linked selected loci, an excess of intermediate-frequency variants may occur when interference is strong, i.e.,$X_2\left(0\right)$ small. Finally, we discuss under which circumstances this scenario may be encountered in real sequences from recombining genomic regions. Full article

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in this study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we assessed the performance of eight deep learning models: long short-term memory (LSTM), a convolutional neural network (CNN), bidirectional LSTM (BiLSTM), CNN Attention, a bidirectional gated recurrent unit (BiGRU) CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market. Full article

Small malignant tumor foci arising from benign lesions are rare but offer a unique opportunity to investigate the genomic evolution that occurs during malignant transformation. In this study, we analyzed 11 colorectal and 10 gastric adenoma–carcinoma pairs, each of which represented malignant tumors (carcinomas) embedded in benign lesions (adenomas) found in the same patient. Whole-exome sequencing revealed that mutation abundance was variable across different cases, but comparable between adenoma–carcinoma pairs. When mutations were classified as adenoma-specific, carcinoma-specific, or common, adenoma-specific mutations were more enriched with subclonal mutations than were carcinoma-specific mutations, indicative of a perturbation in mutational subclonal architecture (such as selective sweep) during malignant transformation. Among the recurrent mutations in colorectal cancers, APC and KRAS mutations were common between adenomas and carcinomas, indicative of their early occurrence during genomic evolution. TP53 mutations were often observed as adenoma-specific and therefore likely not associated with the emergence of malignant clones. Clonality-based enrichment analysis revealed that subclonal mutations of extracellular matrix genes in adenomas are more likely to be clonal in carcinomas, indicating potential roles for these genes in malignant transformation. Compared with colorectal cancers, gastric cancers showed more lesion-specific mutations than common mutations and higher levels of discordance in copy number profiles between matched adenomas and carcinomas, which may explain the elevated evolutionary dynamics and heterogeneity of gastric cancers compared to colorectal cancers. Taken together, this study demonstrates that co-existing benign and malignant lesions enable the evolution-based categorization of genomic alterations that may reveal clinically important biomarkers in colorectal and gastric cancers. Full article