Symbolic integration by integrating learning models with different strengths and weaknesses

Integration is indispensable, not only in mathematics, but also in a wide range of other fields. A deep learning method has recently been developed and shown to be capable of integrating mathematical functions that could not previously be integrated on a computer. However, that method treats integration as equivalent to natural language translation and does not reflect mathematical information. In this study, we adjusted the learning model to take mathematical information into account and developed a wide range of learning models that learn the order of numerical operations more robustly. In this way, we achieved a 98.80% correct answer rate with symbolic integration, a higher rate than that of any existing method. We judged the correctness of the integration based on whether the derivative of the primitive function was consistent with the integrand. By building an integrated model based on this strategy, we achieved a 99.79% rate of correct answers with symbolic integration

Predicting the future direction of cell movement with convolutional neural networks.

Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell movement. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future direction of cell movement from current cell shape, and can be used to automatically identify those morphological features that influence future cell movement

Deep Learning for Non-Invasive Determination of the Differentiation Status of Human Neuronal Cells by Using Phase-Contrast Photomicrographs

Regenerative medicine using neural stem cells (NSCs), which self-renew and have pluripotency, has recently attracted a lot of interest. Much research has focused on the transplantation of differentiated NSCs to damaged tissues for the treatment of various neurodegenerative diseases and spinal cord injuries. However, current approaches for distinguishing differentiated from non-differentiated NSCs at the single-cell level have low reproducibility or are invasive to the cells. Here, we developed a fully automated, non-invasive convolutional neural network-based model to determine the differentiation status of human NSCs at the single-cell level from phase-contrast photomicrographs; after training, our model showed an accuracy of identification greater than 94%. To understand how our model distinguished between differentiated and non-differentiated NSCs, we evaluated the informative features it learned for the two cell types and found that it had learned several biologically relevant features related to NSC shape during differentiation. We also used our model to examine the differentiation of NSCs over time; the findings confirmed our model’s ability to distinguish between non-differentiated and differentiated NSCs. Thus, our model was able to non-invasively and quantitatively identify differentiated NSCs with high accuracy and reproducibility, and, therefore, could be an ideal means of identifying differentiated NSCs in the clinic

The COX-2/PGE(2) pathway suppresses apical elimination of RasV12-transformed cells from epithelia

At the initial stage of carcinogenesis, when RasV12-transformed cells are surrounded by normal epithelial cells, RasV12 cells are apically extruded from epithelia through cell competition with the surrounding normal cells. In this study, we demonstrate that expression of cyclooxygenase (COX)-2 is upregulated in normal cells surrounding RasV12-transformed cells. Addition of COX inhibitor or COX-2-knockout promotes apical extrusion of RasV12 cells. Furthermore, production of Prostaglandin (PG) E-2, a downstream prostanoid of COX-2, is elevated in normal cells surrounding RasV12 cells, and addition of PGE(2) suppresses apical extrusion of RasV12 cells. In a cell competition mouse model, expression of COX-2 is elevated in pancreatic epithelia harbouring RasV12-exressing cells, and the COX inhibitor ibuprofen promotes apical extrusion of RasV12 cells. Moreover, caerulein-induced chronic inflammation substantially suppresses apical elimination of RasV12 cells. These results indicate that intrinsically or extrinsically mediated inflammation can promote tumour initiation by diminishing cell competition between normal and transformed cells. Sato et al find that in an epithelial cell sheet containing some RasV12-transformed cells, expression of cyclooxygenase-2 and production of its downstream product prostaglandin E2 are increased in normal cells surrounding transformed cells, and suppress extrusion of the latter. This study sheds light on how transformed cells are eliminated from epithelia