Diesel exhaust particle exposure exacerbates ciliary and epithelial barrier dysfunction in the multiciliated bronchial epithelium models.

Airway epithelium, the first defense barrier of the respiratory system, facilitates mucociliary clearance against inflammatory stimuli, such as pathogens and particulates inhaled into the airway and lung. Inhaled particulate matter 2.5 (PM2.5) can penetrate the alveolar region of the lung, and it can develop and exacerbate respiratory diseases. Although the pathophysiological effects of PM2.5 in the respiratory system are well known, its impact on mucociliary clearance of airway epithelium has yet to be clearly defined. In this study, we used two different 3D in vitro airway models, namely the EpiAirway-full-thickness (FT) model and a normal human bronchial epithelial cell (NHBE)-based air-liquid interface (ALI) system, to investigate the effect of diesel exhaust particles (DEPs) belonging to PM2.5 on mucociliary clearance. RNA-sequencing (RNA-Seq) analyses of EpiAirway-FT exposed to DEPs indicated that DEP-induced differentially expressed genes (DEGs) are related to ciliary and microtubule function and inflammatory-related pathways. The exposure to DEPs significantly decreased the number of ciliated cells and shortened ciliary length. It reduced the expression of cilium-related genes such as acetylated α-tubulin, ARL13B, DNAH5, and DNAL1 in the NHBEs cultured in the ALI system. Furthermore, DEPs significantly increased the expression of MUC5AC, whereas they decreased the expression of epithelial junction proteins, namely, ZO1, Occludin, and E-cadherin. Impairment of mucociliary clearance by DEPs significantly improved the release of epithelial-derived inflammatory and fibrotic mediators such as IL-1ß, IL-6, IL-8, GM-CSF, MMP-1, VEGF, and S100A9. Taken together, it can be speculated that DEPs can cause ciliary dysfunction, hyperplasia of goblet cells, and the disruption of the epithelial barrier, resulting in the hyperproduction of lung injury mediators. Our data strongly suggest that PM2.5 exposure is directly associated with ciliary and epithelial barrier dysfunction and may exacerbate lung injury.

Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing.

Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.

Retention-aware zero-shifting technique for Tiki-Taka algorithm-based analog deep learning accelerator.

We present the fabrication of 4 K-scale electrochemical random-access memory (ECRAM) cross-point arrays for analog neural network training accelerator and an electrical characteristic of an 8 × 8 ECRAM array with a 100% yield, showing excellent switching characteristics, low cycle-to-cycle, and device-to-device variations. Leveraging the advances of the ECRAM array, we showcase its efficacy in neural network training using the Tiki-Taka version 2 algorithm (TTv2) tailored for non-ideal analog memory devices. Through an experimental study using ECRAM devices, we investigate the influence of retention characteristics on the training performance of TTv2, revealing that the relative location of the retention convergence point critically determines the available weight range and, consequently, affects the training accuracy. We propose a retention-aware zero-shifting technique designed to optimize neural network training performance, particularly in scenarios involving cross-point devices with limited retention times. This technique ensures robust and efficient analog neural network training despite the practical constraints posed by analog cross-point devices.