Polarization-independent narrowband photodetection with plasmon-induced thermoelectric effect in a hexagonal array of Au nanoholes.

Photodetectors are crucial for modern technologies such as optical communications, imaging, autonomous vehicles, and machine vision. However, conventional semiconductor-based photodetectors require additional filtering systems due to their broad spectral response, leading to increased costs and complexity. Here, we present a narrow spectral response photodetector using hexagonally arranged plasmonic Au nanohole structures, eliminating the need for optical filters.

Nowcasting Vector Mosquito Abundance and Determining Its Association With Malaria Epidemics in South Korea.

Since a resurgence occurred in 1993, malaria has remained an endemic disease in the Republic of Korea (ROK). A major challenge is the inaccessibility of current vector mosquito abundance data due to a 2-week reporting delay, which limits timely implementation of control measures. We aimed to nowcast mosquito abundance and assess its utility by evaluating the predictive value of mosquito abundance for malaria epidemic peaks.

Reciprocal rescue of Wolfram syndrome by two causative genes.

Wolfram syndrome (WS) is marked by juvenile-onset diabetes mellitus, optic atrophy, diabetes insipidus, and sensorineural hearing loss. The causative genes, WFS1 and CISD2, correspond to WS types 1 and 2, respectively. Here, we establish their mutual indispensability for inositol 1,4,5-triphosphate receptor (IPR) activity, demonstrating their ability to restore reduced IPR activity in WFS1- or CISD2-deficient mammalian cells.

Identification of Gas Mixture Components with Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data.

Chemical vapor sensors are essential for various fields, including medical diagnostics and environmental monitoring. Notably, the identification of components in unknown gas mixtures has great potential for noninvasive diagnosis of diseases such as lung cancer. However, current gas identification techniques, despite the development of electronic nose-based sensor platforms, still lack sufficient classification accuracy for mixed gases.

Silicon-Based Biosensors: A Critical Review of Silicon's Role in Enhancing Biosensing Performance.

This review into recent advancements in silicon-based technology, with a particular emphasis on the biomedical applications of silicon sensors. Owing to their diminutive size, high sensitivity, and intrinsic compatibility with electronic systems, silicon-based sensors have found widespread utilization across healthcare, industrial, and environmental monitoring domains. In the realm of biomedical sensing, silicon has demonstrated significant potential to enhance human health outcomes while simultaneously driving progress in microfabrication techniques for multifunctional device development.

Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.

Monitoring and assessing the progression of symptoms in neurodegenerative diseases, including Alzheimer's and Parkinson's disease, are critical for improving patient outcomes. Traditional biomarkers, such as cerebrospinal fluid analysis and brain imaging, are widely used to investigate the underlying mechanisms of disease and enable early diagnosis. In contrast, digital biomarkers derived from phenotypic changes-such as EEG, eye movement, gait, and speech analysis-offer a noninvasive and accessible alternative.

Global Lipidomics Reveals the Lipid Composition Heterogeneity of Extracellular Vesicles from Drug-Resistant .

: The rise of drug-resistant strains presents a significant challenge in the treatment of Leishmaniasis, a neglected tropical disease. Extracellular vesicles (EVs) produced by these parasites have gained attention for their role in drug resistance and host-pathogen interactions. : This study developed and applied a novel lipidomics workflow to explore the lipid profiles of EVs from three types of drug-resistant strains compared to a wild-type strain.

Three-dimensional surface lattice plasmon resonance effect from plasmonic inclined nanostructures via one-step stencil lithography.

Plasmonic nanostructures allow the manipulation and confinement of optical fields on the sub-wavelength scale. The local field enhancement and environmentally sensitive resonance characteristics provided by these nanostructures are of high importance for biological and chemical sensing. Recently, surface lattice plasmon resonance (SLR) research has attracted much interest because of its superior quality factor (-factor) compared to that of localized surface plasmon resonances (LSPR), which is facilitated by resonant plasmonic mode coupling between individual nanostructures over a large area.

Visualization system to identify structurally vulnerable links in OHT railway network in semiconductor FAB using betweenness centrality.

In semiconductor fabrication (FAB), wafers are placed into carriers known as Front Opening Unified Pods (FOUPs), transported by the Overhead Hoist Transport (OHT). The OHT, a type of Automated Guided Vehicle (AGV), moves along a fixed railway network in the FAB. The routes of OHTs on the railway network are typically determined by a Single Source Shortest Path (SSSP) algorithm such as Dijkstra's.

Capture of endogenous lipids in peptidiscs and effect on protein stability and activity.

Compared to protein-protein and protein-nucleic acid interactions, our knowledge of protein-lipid interactions remains limited. This is primarily due to the inherent insolubility of membrane proteins (MPs) in aqueous solution. The traditional use of detergents to overcome the solubility barrier destabilizes MPs and strips away certain lipids that are increasingly recognized as crucial for protein function.

Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data for Advanced Colorimetric E-Nose.

The colorimetric sensor-based electronic nose has been demonstrated to discriminate specific gaseous molecules for various applications, including health or environmental monitoring. However, conventional colorimetric sensor systems rely on RGB sensors, which cannot capture the complete spectral response of the system. This limitation can degrade the performance of machine learning analysis, leading to inaccurate identification of chemicals with similar functional groups.

Diabetic sensory neuropathy and insulin resistance are induced by loss of UCHL1 in Drosophila.

Diabetic sensory neuropathy (DSN) is one of the most common complications of type 2 diabetes (T2D), however the molecular mechanistic association between T2D and DSN remains elusive. Here we identify ubiquitin C-terminal hydrolase L1 (UCHL1), a deubiquitinase highly expressed in neurons, as a key molecule underlying T2D and DSN. Genetic ablation of UCHL1 leads to neuronal insulin resistance and T2D-related symptoms in Drosophila.

Dielectrophoretic force-induced wrinkling of graphene oxide: Enhancing electrical conductivity and expanding biosensing applications.

Graphene oxide (GO) has many advantages, making it suitable for various applications. However, it has low electrical conductivity, restricting its applicability to electrochemical biosensors. This study used dielectrophoretic (DEP) force to control the movement and deformation of GO nanosheets to achieve high electrical conductivity without the chemical reduction of oxygen functional groups.

Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review.

Researchers are interested in measuring mental stress because it is linked to a variety of diseases. Real-time stress monitoring via wearable sensor systems can aid in the prevention of stress-related diseases by allowing stressors to be controlled immediately. Physical tests, such as heart rate or skin conductance, have recently been used to assess stress; however, these methods are easily influenced by daily life activities.

Axl alleviates DSS-induced colitis by preventing dysbiosis of gut microbiota.

Axl is a tyrosine kinase receptor, a negative regulator for innate immune responses and inflammatory bowel disease (IBD). The gut microbiota regulates intestinal immune homeostasis, but the role of Axl in the pathogenesis of IBD through the regulation of gut microbiota composition remains unresolved. In this study, mice with DSS-induced colitis showed increased Axl expression, which was almost entirely suppressed by depleting the gut microbiota with antibiotics.

Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.

Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees.

Long-Term Non Anesthetic Preclinical Study Available Extra-Cranial Brain Activator (ECBA) System for the Future Minimally Invasive Human Neuro Modulation.

In recent years, electroceuticals have been spotlighted as an emerging treatment for various severe chronic brain diseases, owing to their intrinsic advantage of electrical interaction with the brain, which is the most electrically active organ. However, the majority of research has verified only the short-term efficacy through acute studies in laboratory tests owing to the lack of a reliable miniaturized platform for long-term animal studies. The construction of a sufficient integrated system for such a platform is extremely difficult because it requires multi-disciplinary work using state-of-the-art technologies in a wide range of fields.

Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress.

Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal.