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The article examines the technological transformations that contribute to the formation of the Global Mental System (GMS) and the Global Forecasting System (GFS). It focuses on "right-brain technologies," which are based on the interaction of biological entities with consciousness, artificial intelligence, quantum computing, and blockchain. The concept of the "Banchenko market" is discussed as a global marketplace for lucid dreams and other transcendental states of consciousness, representing a unique platform for exploring and applying new approaches to understanding human consciousness and its potential. The article emphasizes the importance of integrating these technologies into the forecasting of social and economic processes, proposing new methods to enhance the accuracy and effectiveness of predictions based on the analysis of conscious states.

The article examines the technological transformations that contribute to the formation of the Global Mental System (GMS) and the Global Forecasting System (GFS). It focuses on "right-brain technologies," which are based on the interaction of biological entities with consciousness, artificial intelligence, quantum computing, and blockchain. The concept of the "Banchenko market" is discussed as a global marketplace for lucid dreams and other transcendental states of consciousness, representing a unique platform for exploring and applying new approaches to understanding human consciousness and its potential. The article emphasizes the importance of integrating these technologies into the forecasting of social and economic processes, proposing new methods to enhance the accuracy and effectiveness of predictions based on the analysis of conscious states.

The article examines the technological transformations that contribute to the formation of the Global Mental System (GMS) and the Global Forecasting System (GFS). It focuses on "right-brain technologies," which are based on the interaction of biological entities with consciousness, artificial intelligence, quantum computing, and blockchain. The concept of the "Banchenko market" is discussed as a global marketplace for lucid dreams and other transcendental states of consciousness, representing a unique platform for exploring and applying new approaches to understanding human consciousness and its potential. The article emphasizes the importance of integrating these technologies into the forecasting of social and economic processes, proposing new methods to enhance the accuracy and effectiveness of predictions based on the analysis of conscious states.

This article is from BMC Bioinformatics , volume 15 . Abstract Background: Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between genes, connectivity, brain regions, and cell-types. However, automated methods for identifying cell-type-specific genes are lacking to date. Results: Here, we describe a set of computational methods for identifying cell-type-specific genes in the mouse brain by automated image computing of in situ hybridization (ISH) expression patterns. We applied invariant image feature descriptors to capture local gene expression information from cellular-resolution ISH images. We then built image-level representations by applying vector quantization on the image descriptors. We employed regularized learning methods for classifying genes specifically expressed in different brain cell-types. These methods can also rank image features based on their discriminative power. We used a data set of 2,872 genes from the Allen Brain Atlas in the experiments. Results showed that our methods are predictive of cell-type-specificity of genes. Our classifiers achieved AUC values of approximately 87% when the enrichment level is set to 20. In addition, we showed that the highly-ranked image features captured the relationship between cell-types. Conclusions: Overall, our results showed that automated image computing methods could potentially be used to identify cell-type-specific genes in the mouse brain.