MOTIVATION: Heart failure (HF), a major cause of morbidity and mortality, necessitates precise diagnostic and prognostic methods. RESULTS: This study presents a novel deep learning approach, Transformer-based Analysis of Images of Tissue for Effective Remedy (TRAITER), for HF diagnosis and prognosis. Using image segmentation techniques and a Vision Transformer, TRAITER predicts HF likelihood from cardiac tissue cell nuclear morphology images and the potential for left ventricular reverse remodeling (LVRR) from dual-stained images with cell nuclei and DNA damage markers. In HF prediction using 31 158 images from 9 patients, TRAITER achieved 83.1% accuracy. For LVRR prediction with 231 840 images from 46 patients, TRAITER attained 84.2% accuracy for individual images and 92.9% for individual patients. TRAITER outperformed other neural network models in terms of receiver operating characteristics, and precision-recall curves. Our method promises to advance personalized HF medicine decision-making. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at the following link: https://github.com/HamanoLaboratory/predict-of-HF-and-LVRR.

Summary form only given. Grid computing is increasingly being viewed as the next phase of distributed computing. Built on pervasive Internet standards, grid computing enables organizations to share computing and information resources across department and organizational boundaries in a secure, highly efficient manner. Grid computing originates in eScience and its early development was driven to a large extent by the requirements of large-scale computing and efficient sharing of huge datasets. eBusiness requirements led to the adoption of emerging Web services technologies - initially developed for distributed business application integration. Therefore grid computing can be applied to enterprise computing within and across organizations and pave the way for utility computing. I present major Grid computing projects for both eScience and eBusiness in Japan (including NaReGI and Business Grid) and several key Grid products and applications (including CyberGRIP and Resource Coordinator both developed by Fujitsu). The Global Grid Forum (GGF) is leading the standardization of grid computing. In particular, GGF has developed the Open Grid Services Architecture (OGSA) and is working throughout the industry to champion this "architectural blueprint" and the associated specifications that will enable the pervasive adoption of grid computing for eBusiness and eScience worldwide

The FACOM ALPHA is the first and only commercially dedicated processor for Lisp and Prolog manufactured in Japan. This paper discusses the evaluation of the FACOM ALPHA for Lisp execution when compared with a general-purpose computer. The CPU use rate of machine instructions and the utilization ratio of the hardware resources are discussed. This paper also explains the interaction between the garbage collector and the virtual memory system, which improves system response time.

ALPHA is a dedicated machine designed for high-speed list processing. In this article, we describe a highly effective stack which can support a value cache and virtual stack, and high-speed garbage collection algorithm for virtual memory. These new ideas have been studied in ALPHA. ALPHA is designed as a back end processor for a large computer under TSS. ALPHA allows TSS users to do more high-speed list processing than a large computer does.