Deep learning is being increasingly applied for obtaining digital microscopy image data of cells. Well-defined annotated cell images have contributed to the development of the technology. Cell morphology is an inherent characteristic of each cell type. Moreover, the morphology of a cell changes during its lifetime because of cellular activity. Artificial intelligence (AI) capable of recognizing a mouse-induced pluripotent stem (miPS) cell cultured in a medium containing Lewis lung cancer (LLC) cell culture-conditioned medium (cm), miPS-LLCcm cell, which is a cancer stem cell (CSC) derived from miPS cell, would be suitable for basic and applied science. This study aims to clarify the limitation of AI models constructed using different datasets and the versatility improvement of AI models. The trained AI was used to segment CSC in phase-contrast images using conditional generative adversarial networks (CGAN). The dataset included blank cell images that were used for training the AI but they did not affect the quality of predicting CSC in phase contrast images compared with the dataset without the blank cell images. AI models trained using images of 1-day culture could predict CSC in images of 2-day culture; however, the quality of the CSC prediction was reduced. Convolutional neural network (CNN) classification indicated that miPS-LLCcm cell image classification was done based on cultivation day. By using a dataset that included images of each cell culture day, the prediction of CSC remains to be improved. This is useful because cells do not change the characteristics of stem cells owing to stem cell marker expression, even if the cell morphology changes during culture.

Artificial intelligence (AI) technology for image recognition has the potential to identify cancer stem cells (CSCs) in cultures and tissues. CSCs play an important role in the development and relapse of tumors. Although the characteristics of CSCs have been extensively studied, their morphological features remain elusive. The attempt to obtain an AI model identifying CSCs in culture showed the importance of images from spatially and temporally grown cultures of CSCs for deep learning to improve accuracy, but was insufficient. This study aimed to identify a process that is significantly efficient in increasing the accuracy values of the AI model output for predicting CSCs from phase-contrast images. An AI model of conditional generative adversarial network (CGAN) image translation for CSC identification predicted CSCs with various accuracy levels, and convolutional neural network classification of CSC phase-contrast images showed variation in the images. The accuracy of the AI model of CGAN image translation was increased by the AI model built by deep learning of selected CSC images with high accuracy previously calculated by another AI model. The workflow of building an AI model based on CGAN image translation could be useful for the AI prediction of CSCs.

A general purpose Cellular Array Processor(CAP) with distributed frame buffers for fast parallel subimage generation has been developed. CAP consists of many processor elements called cells. A cell has video memory for subimage storage, a window controller to map each subimage to an area on the monitor screen, and communication devices, in addition to ordinary microcomputer components such as MPU, RAM, and ROM. Image data in a cell is directly displayed via the video bus. The mapping pattern and the position on the screen of subimages can be changed dynamically. Various hidden surface algorithms can be implemented in CAP using mapping patterns appropriate for the algorithm.Our goal is an efficient interactive visual solid modeler. We adopted a general CSG hidden surface algorithm that enables display of both Boundary representation and Constructive Solid Geometry. A technique for hidden surface removal of general CSG models, requiring less memory space for large models in many cases, has been proposed. This technique subdivides the model into submodels by dividing the CSG tree at union nodes. Imagse of each submodel are generated by a CSG or a z-buffer algorithm. If a submodel is just a primitive, it is processed by the z-buffer algorithm, otherwise by the CSG algorithm. Hidden surface removal between submodels is done by comparing the z values for each pixel which are saved in the z-buffer.

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An efficient all-to-all communication algorithm for torus and mesh networks, A2AT, was proposed. A2AT schedules message sending sequence so that all links are fully used by exploiting function of concurrent message transfer in the node. By using A2AT, the hop count of messages equals the maximum number of messages sharing a link in their routes for all message transfers. A2AT can therefore maintain synchronization without the need for phasing operation such as an MPI barrier. When the VOQ which is an ideal configuration for A2AT was used, communication times for mesh/torus network obtained by A2AT were roughly 1.20 and 1.09 times higher, on average, than those of the ideal times. When the networks had the minimum number of virtual channels and a small buffer, assuming a practical network, A2AT was able to reduce communication times by 12.5% and 36.0% compared with those of the conventional algorithm. When two controllers are used, A2AT reduced 28.2% and 55.7% communication time with those by A2AND on 15×15×15 (=3,375 nodes) mesh and torus networks respectively (18.6% and 44.8% in average). A2AT also reduced 15.1% and 41.9% of communication time with those by A2AND on the same mesh and torus networks respectively (14.4% and 37.5% in average) when six controllers are used.