An Efficient Lightweight CNN and Ensemble Machine Learning Classification of Prostate Tissue Using Multilevel Feature Analysis

Prostate carcinoma is caused when cells and glands in the prostate change their shape and size from normal to abnormal. Typically, the pathologist’s goal is to classify the staining slides and differentiate normal from abnormal tissue. In the present study, we used a computational approach to classify images and features of benign and malignant tissues using artificial intelligence (AI) techniques. Here, we introduce two lightweight convolutional neural network (CNN) architectures and an ensemble machine learning (EML) method for image and feature classification, respectively. Moreover, the classification using pre-trained models and handcrafted features was carried out for comparative analysis. The binary classification was performed to classify between the two grade groups (benign vs. malignant) and quantile-quantile plots were used to show their predicted outcomes. Our proposed models for deep learning (DL) and machine learning (ML) classification achieved promising accuracies of 94.0% and 92.0%, respectively, based on non-handcrafted features extracted from CNN layers. Therefore, these models were able to predict nearly perfectly accurately using few trainable parameters or CNN layers, highlighting the importance of DL and ML techniques and suggesting that the computational analysis of microscopic anatomy will be essential to the future practice of pathology

Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature

AN OVERVIEW OF RISK QUANTIFICATION ISSUES FOR DIGITALIZED NUCLEAR POWER PLANTS USING A STATIC FAULT TREE

Risk caused by safety-critical instrumentation and control (I&C) systems considerably affects overall plant risk. As digitalization of safety-critical systems in nuclear power plants progresses, a risk model of a digitalized safety system is required and must be included in a plant safety model in order to assess this risk effect on the plant. Unique features of a digital system cause some challenges in risk modeling. This article aims at providing an overview of the issues related to the development of a static fault-tree-based risk model. We categorize the complicated issues of digital system probabilistic risk assessment (PRA) into four groups based on their characteristics: hardware module issues, software issues, system issues, and safety function issues. Quantification of the effect of these issues dominates the quality of a developed risk model. Recent research activities for addressing various issues, such as the modeling framework of a software-based system, the software failure probability and the fault coverage of a self monitoring mechanism, are discussed. Although these issues are interrelated and affect each other, the categorized and systematic approach suggested here will provide a proper insight for analyzing risk from a digital systemclos

Development of plasma sources and diagnostics for the simulation of fusion edge plasmas

© 2022, The Korean Physical Society.Although the research on divertors and scrape-off layers (SOLs) has been not as focused on as the recent success of the Korean fusion program, a few linear plasma devices have been developed for simulating divertor and SOL plasmas: (1) diversified plasma simulator (DiPS), a versatile linear machine, has been developed for simulations of divertor and space plasmas with various electric probes, such as single, triple, and Mach Probes and gridded energy analyzer. DiPS consists of two major parts: a divertor plasma simulator with a LaB 6 DC plasma source and a space plasma simulator with a helicon RF plasma source, (2) divertor plasma simulator-1 (DiPS-1) is a part of DiPS with only a LaB 6 cathode, where a low-power laser-induced fluorescence (LIF) is added and more electric probe diagnostics are augmented; it is dedicated only for fusion edge and divertor plasmas, (3) Divertor Plasma Simulator-2 (DiPS-2) has been modified from the DiPS-1 by adding a magnetic nozzle with a limiter structure and by removing the helicon source and space chamber. DiPS-2 is a linear plasma device with a 4-inch LaB 6 cathode, the same as DiPS-1, and it is focused on the development of various diagnostics, such as those used for LIF and laser Thomson scattering (LTS) along with various electric probes, on the divertor and scrape-off plasmas and on the plasma-material interaction (PMI) research, such as that of tungsten and graphite as plasma-facing components (PFCs), (4) A Multi-Purpose Plasma (MP 2) device is a renovation of the Hanbit mirror device [Kwon et al., Nucl. Fusion 43, 686 (2003)] with the installation of two plasma sources: LaB 6 (DC) and helicon (RF) plasma sources. A honeycomb-like large-area LaB 6 (HLA-LaB 6) cathode has been developed for the divertor plasma simulation to improve the resistance against the thermal shock fragility for large (8-inch) and high density plasma generation, (5) DiPS-2 has been augmented by adding another cylindrical device, called the Dust interaction with Surfaces Chamber (DiSC) for the generation and diagnostics of dusts. This combined system (DiPS-2+DiSC) has added two more diagnostics: Laser Photo-Detachment (LPD) for dust density and laser Mie Scattering (LMS) for dust size. Moreover, dusts or negative ions have been analyzed by using electric probes and capacitive diagram gauges in Transport and Removal of Dusts (TReD) device.N

Simulations of fusion edge plasmas by linear plasma devices: physics and plasma–material interactions

© 2022, The Korean Physical Society.Because a fusion edge plasma contains various atomic and molecular processes, along with various plasma–material interactions (PMIs) for post-mortem analyses, a linear plasma device can simulate divertor and scrape-off layer (SOL) plasmas with DC edge relevant parameters, although it cannot generate a high ion temperature and toroidicity with much less power density compared to toroidal devices. The Divertor Plasma Simulator-2 (DiPS-2), a linear device with an LaB6 DC cathode, has been used for a few fusion-relevant physics experiments, including edge localized mode (ELM) simulation and edge transport of diffusion and convection. An ELM simulation has been performed by modulating the magnetic field relevant to the pressure modulation of a toroidal device, and the diffusion coefficients of free and bound presheaths have been measured in simulations of divertor or limiter transport. Moreover, the convection of the filament or the bubble expansion to the first wall has also been analyzed. In addition to various atomic and molecular processes in SOL and divertor plasmas, PMIs must be analyzed both on and beneath the surface of the plasma-facing components (PFCs) because of surface modification. Using DiPS-2 and other linear devices along with Korea Superconducting Tokamak Advanced Research (KSTAR), PMIs have been analyzed in terms of the following elements or processes: (1) boronizations, both for dust interactions with the surface chamber (DiSC) and KSTAR device, are analyzed; (2) carbon damage by the dense heat flux of DiPS-2 is experimentally investigated; (3) the density profile of the lithium injection gettering of hydrogen and its transport experiments (LIGHT-1) device is analytically calculated; (4) the effect of nitrogen on the relaxation of the heat flux to the divertor tile is experimentally analyzed; and (5) tungsten as the divertor tile material is analyzed via laser ELM simulations in terms of dust generation and surface modification.N

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field