Artificial Visual System for Stereo-Orientation Recognition Based on Hubel-Wiesel Model.

Stereo-orientation selectivity is a fundamental neural mechanism in the brain that plays a crucial role in perception. However, due to the recognition process of high-dimensional spatial information commonly occurring in high-order cortex, we still know little about the mechanisms underlying stereo-orientation selectivity and lack a modeling strategy. A classical explanation for the mechanism of two-dimensional orientation selectivity within the primary visual cortex is based on the Hubel-Wiesel model, a cascading neural connection structure.

Double Attention: An Optimization Method for the Self-Attention Mechanism Based on Human Attention.

Artificial intelligence, with its remarkable adaptability, has gradually integrated into daily life. The emergence of the self-attention mechanism has propelled the Transformer architecture into diverse fields, including a role as an efficient and precise diagnostic and predictive tool in medicine. To enhance accuracy, we propose the Double-Attention (DA) method, which improves the neural network's biomimetic performance of human attention.

Learning Dendritic-Neuron-Based Motion Detection for RGB Images: A Biomimetic Approach.

In this study, we designed a biomimetic artificial visual system (AVS) inspired by biological visual system that can process RGB images. Our approach begins by mimicking the photoreceptor cone cells to simulate the initial input processing followed by a learnable dendritic neuron model to replicate ganglion cells that integrate outputs from bipolar and horizontal cell simulations. To handle multi-channel integration, we utilize a nonlearnable dendritic neuron model to simulate the lateral geniculate nucleus (LGN), which consolidates outputs across color channels, an essential function in biological multi-channel processing.

Exploring the subtle and novel renal pathological changes in diabetic nephropathy using clustering analysis with deep learning.

To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease is required. We performed invariant information clustering (IIC)-based clustering on glomerular images obtained from nephrectomized kidneys of patients with and without diabetes. We also used visualizing techniques (gradient-weighted class activation mapping (Grad-CAM) and generative adversarial networks (GAN)) to identify the novel and early pathological changes on light microscopy in diabetic nephropathy.

Nonlinear excitation of energetic particle driven geodesic acoustic mode by resonance overlap with Alfvén instability in ASDEX Upgrade.

The Alfvén instability nonlinearly excited the energetic-particle-driven geodesic acoustic mode on the ASDEX-Upgrade tokamak, as demonstrated experimentally. The mechanism of the energetic-particle-driven geodesic acoustic mode excitation and the mode nonlinear evolution is not yet fully understood. In the present work, a first-principles simulation using the MEGA code investigated the mode properties in both the linear growth and nonlinear saturated phases.

A Learning Dendritic Neuron-Based Motion Direction Detective System and Its Application to Grayscale Images.

In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On-Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells.

A novel multivariate time series forecasting dendritic neuron model for COVID-19 pandemic transmission tendency.

A novel coronavirus discovered in late 2019 (COVID-19) quickly spread into a global epidemic and, thankfully, was brought under control by 2022. Because of the virus's unknown mutations and the vaccine's waning potency, forecasting is still essential for resurgence prevention and medical resource management. Computational efficiency and long-term accuracy are two bottlenecks for national-level forecasting.

Electron Heating in High Mach Number Collisionless Shocks.

The energy partition in high Mach number collisionless shock waves is central to a wide range of high-energy astrophysical environments. We present a new theoretical model for electron heating that accounts for the energy exchange between electrons and ions at the shock. The fundamental mechanism relies on the difference in inertia between electrons and ions, resulting in differential scattering of the particles off a decelerating magnetically dominated microturbulence across the shock transition.

Disruption of international trade and its propagation through firm-level domestic supply chains: A case of Japan.

This study simulates how the disruption of imports from various regions affects the total production of the importer economy. We particularly incorporate the propagation of the economic effect through domestic supply chains using data on more than one million firms and four million supply chain ties in Japan. Our findings are summarized as follows.

Single-layer perceptron artificial visual system for orientation detection.

Orientation detection is an essential function of the visual system. In our previous works, we have proposed a new orientation detection mechanism based on local orientation-selective neurons. We assume that there are neurons solely responsible for orientation detection, with each neuron dedicated to detecting a specific local orientation.

Maximum Lyapunov exponent-based multiple chaotic slime mold algorithm for real-world optimization.

Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem. However, it still suffers from issues of missing the optimal solution or collapsing to local optimum when facing complicated problems.

Simulation of supply chain disruptions considering establishments and power outages.

In this paper, we simulate the economic loss resulting from supply chain disruptions triggered by the Great East Japan Earthquake (GEJE) in 2011, applying data from firm-level supply chains and establishment-level attributes to an agent-based model. To enhance the accuracy of the simulation, we extend data and models in previous studies in four ways. First, we identify the damage to production facilities in the disaster-hit regions more accurately by using establishment-level census and survey data and geographic information system (GIS) data on the damages caused by the GEJE and subsequent tsunami.

Massive platelet-rich thrombus formation in small pulmonary vessels in amniotic fluid embolism: An autopsy study.

Objective: To identify pulmonary/uterine thrombus formation in amniotic fluid embolism (AFE).

Design: Retrospective, observational.

Setting: Nationwide.

Active Control of Alfvén Eigenmodes by Externally Applied 3D Magnetic Perturbations.

The suppression and excitation of Alfvén eigenmodes have been experimentally obtained, for the first time, by means of externally applied 3D perturbative fields with different spatial spectra in a tokamak plasma. The applied perturbation causes an internal fast-ion redistribution that modifies the phase-space gradients responsible for driving the modes, determining, ultimately their existence. Hybrid kinetic-magnetohydrodynamic simulations reveal an edge resonant transport layer activated by the 3D perturbative field as the responsible mechanism for the fast-ion redistribution.

An Extension Network of Dendritic Neurons.

Deep learning (DL) has achieved breakthrough successes in various tasks, owing to its layer-by-layer information processing and sufficient model complexity. However, DL suffers from the issues of both redundant model complexity and low interpretability, which are mainly because of its oversimplified basic McCulloch-Pitts neuron unit. A widely recognized biologically plausible dendritic neuron model (DNM) has demonstrated its effectiveness in alleviating the aforementioned issues, but it can only solve binary classification tasks, which significantly limits its applicability.

Robustness and resilience of supply chains during the COVID-19 pandemic.

Using a unique firm-level data set from Asia, this study examines what determined the robustness and resilience of supply chain links, that is, the ability of maintaining links and recovering disrupted links by substitution, respectively, when firms faced economic shocks due to the spread of the coronavirus disease (COVID-19). We find that a supply chain link was likely to be robust if the link was between a foreign-owned firm and a firm located in the foreign-owned firm's home country, implying that homophily on a certain dimension generates strong ties and thus supply chain robustness. We also find that firms with geographic diversity of customers and suppliers tended to increase their transaction volume with one partner while decreasing the volume with others.

Has Covid-19 permanently changed online purchasing behavior?

Unlabelled: This study examines how the COVID-19 pandemic has affected online purchasing behavior using data from a major online shopping platform in Japan. We focus on the effect of two measures of the pandemic, i.e.

Evaluation of the one-step nucleic acid amplification assay for detecting lymph node metastasis in patients with cervical and endometrial cancer: A multicenter prospective study.

Objective: This multicenter study aimed to evaluate the accuracy of the one-step nucleic acid amplification (OSNA) assay in diagnosing lymph node metastasis (LNM) in patients with cervical and endometrial cancers.

Methods: Surgically removed LNs from patients with cervical and endometrial cancer were sectioned at 2-mm intervals along the short axis direction and alternately examined using the OSNA assay and conventional histopathological examination. Ultrastaging (200-μm LN sections) was performed for metastatic LNs using hematoxylin and eosin staining and immunostaining with an anti-CK19 antibody in cases where the OSNA assay and histopathological examination (performed using 2-mm LN sections) results showed discordance.

Prevalence of sarcopenia in patients with gynecological cancer.

Background: The aim of the study was to investigate a prevalence of sarcopenia in patients with gynecological cancer in accordance with current diagnostic criteria of sarcopenia.

Methods: A series of 513 patients with gynecological cancer who were intended to newly receive initial or salvage treatment were recruited in a prospective study. Eligible patients were examined with dual energy X-ray absorptiometry and underwent handgrip strength test and the Short Physical Performance Battery before treatment.

Artificial Visual System for Orientation Detection Based on Hubel-Wiesel Model.

The Hubel-Wiesel (HW) model is a classical neurobiological model for explaining the orientation selectivity of cortical cells. However, the HW model still has not been fully proved physiologically, and there are few concise but efficient systems to quantify and simulate the HW model and can be used for object orientation detection applications. To realize a straightforward and efficient quantitive method and validate the HW model's reasonability and practicality, we use McCulloch-Pitts (MP) neuron model to simulate simple cells and complex cells and implement an artificial visual system (AVS) for two-dimensional object orientation detection.

Initial screening by immunohistochemistry is effective in universal screening for Lynch syndrome in endometrial cancer patients: a prospective observational study.

Background: Few prospective reports of universal screening for Lynch syndrome exist for patients with endometrial cancer. In this study, we performed immunohistochemical staining for DNA mismatch repair-related genes (MLH1, MSH2, MSH6 and PMS2), to determine the extent to which Lynch syndrome can be diagnosed in endometrial cancer patients through universal screening.

Methods: We recruited 116 consecutive patients assumed to have uterine corpus malignancy from October 2019 to February 2021 in a prospective observational study.

Combined evaluation of Fibrosis-4 index and fatty liver for stratifying the risk for diabetes mellitus.

Aims/introduction: To investigate whether the Fibrosis-4 index can help stratify the risk of diabetes mellitus in patients with fatty liver disease.

Materials And Methods: Based on fatty liver disease and Fibrosis-4 index (cut-off value 1.3), we retrospectively divided 9,449 individuals, who underwent at least two annual health checkups, into four groups stratified by sex: normal; high Fibrosis-4 index without fatty liver disease; low Fibrosis-4 index with fatty liver disease; and high Fibrosis-4 index with fatty liver disease.

A Two-Stage Method Based on Multiobjective Differential Evolution for Gene Selection.

Microarray gene expression data provide a prospective way to diagnose disease and classify cancer. However, in bioinformatics, the gene selection problem, i.e.