Non-aqueous electrowetting liquid lens with centimeter-level large aperture based on dielectric failure suppression principle.

Liquid lens offers a novel approach to achieving large depth of field, wide viewing angle, high speed, and high-quality imaging in zoom optical systems. However, the aperture and reliability limit the lens's performance in various optical applications. The liquid material is crucial for the reliability of the large-aperture liquid lens.

Multiplexed fiber meta-tip-based circular polarimetry for label-free pathological analysis of ischemic stroke.

Significance: We present an optical technology for a full-process label-free method for brain slice screening. This proposed multiplexed circular polarimetric method has the advantages of simple operation and high accuracy which may provide easily accessible evidence for the future diagnosis of related diseases.

Aim: One of its missions is to provide a quantifiable, reproducible analysis methodology that can replace or supplement traditional qualitative, subjective pathological analysis.

An adaptive stacking generalization integrated with Raman spectroscopy feature enhancement algorithm for fine glioma grading identification.

For different grades of brain gliomas, it is crucial for clinicians to rapidly and accurately develop personalized treatment strategies intraoperatively to improve surgical outcomes and enhance the quality of life for patients. Raman fiber miniature spectroscopy detection can provide detailed information about the properties of biomolecules. This technique offers several advantages, including non-invasiveness, real-time detection, intelligence, high precision, and the potential for early diagnosis.

Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study.

Background: Gastrointestinal bleeding is a serious adverse event of coronary artery bypass grafting and lacks tailored risk assessment tools for personalized prevention.

Objective: This study aims to develop and validate predictive models to assess the risk of gastrointestinal bleeding after coronary artery bypass grafting (GIBCG) and to guide personalized prevention.

Methods: Participants were recruited from 4 medical centers, including a prospective cohort and the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

Thermal Atoms Facilitate Intensity Clipping Between Vectorial Dual-Beam Generated by a Single Metasurface Chip.

Manipulating vector beams is pivotal in fields such as particle manipulation, image processing, and quantum communication. Flexibly adjusting the intensity distribution of these beams is crucial for effectively realizing these applications. This study introduces a vectorial dual-beam system utilizing thermal atoms as the medium for modulating the intensity profile of vector beams.

Decoding the Spatiotemporal Dynamics of Neural Response Similarity in Auditory Processing: A Multivariate Analysis Based on OPM-MEG.

The brain represents information through the encoding of neural populations, where the activity patterns of these neural groups constitute the content of this information. Understanding these activity patterns and their dynamic changes is of significant importance to cognitive neuroscience and related research areas. Current studies focus more on brain regions that show differential responses to stimuli, but they lack the ability to capture information about the representational or process-level dynamics within these regions.

Investigating the effects of calibration errors on the spatial resolution of OPM-MEG beamformer imaging.

The use of optically pumped magnetometers (OPMs) has provided a feasible, moveable and wearable alternative to superconducting detectors for magnetoencephalography (MEG) measurements. Recently, the widely used beamformer imaging technique has greatly improved spatial accuracy of MEG in the field of source reconstruction of neuroimaging. The spatial resolution of the source reconstruction using beamformer imaging technique was explored in the present study.

Improving the Sensitivity of a Dark-Resonance Atomic Magnetometer.

The combination of unmanned aerial vehicles and atomic magnetometers can be used for detection applications such as mineral resource exploration, environmental protection, and earthquake monitoring, as well as the detection of sunken ships and unexploded ordnance. A dark-resonance atomic magnetometer offers the significant advantages of a fully optical probe and omnidirectional measurement with no dead zones, making it an ideal choice for airborne applications on unmanned aerial vehicles. Enhancing the sensitivity of such atomic magnetometers is an essential task.

High-Accuracy Intermittent Strabismus Screening via Wearable Eye-Tracking and AI-Enhanced Ocular Feature Analysis.

An effective and highly accurate strabismus screening method is expected to identify potential patients and provide timely treatment to prevent further deterioration, such as amblyopia and even permanent vision loss. To satisfy this need, this work showcases a novel strabismus screening method based on a wearable eye-tracking device combined with an artificial intelligence (AI) algorithm. To identify the minor and occasional inconsistencies in strabismus patients during the binocular coordination process, which are usually seen in early-stage patients and rarely recognized in current studies, the system captures temporally and spatially continuous high-definition infrared images of the eye during wide-angle continuous motion, and is effective in inducing intermittent strabismus.

Comparison of magnetocardiography and coronary computed tomographic angiography for detection of coronary artery stenosis and the influence of calcium.

Objectives: This study aimed to compare the diagnostic performance of magnetocardiography (MCG) and coronary computed tomography angiography (CCTA) in detecting coronary artery stenosis in relation to coronary calcification.

Methods: A total of 587 patients who underwent invasive coronary angiography (ICA) with both CCTA and MCG between September 1, 2022, and August 31, 2023, were included. The patients were divided into three subgroups based on their coronary artery calcium score (CACS), namely less than 100, 100-400, and 400 and above, as determined by the Agatston score.

A Fluorescence/Colorimetric Synergistic-Enhanced Type-I Heterostructured MOF@QDs for Both Multi-Depth Food-Freshness Prediction and Extra Preservation.

Spoiled food has significantly impacted the global economy and public health, which increases worldwide concern about monitoring and preserving food freshness. Herein, a multi-functional type-I heterojunction (Eu@ZMC) is designed by europium metal-organic framework (EuMOF), zinc oxide quantum dots (ZnO QDs), and chlorogenic acid (CGA). Eu@ZMC achieves ratiometric fluorescent/colorimetric sensing of pH and biogenic amines to detect freshness.

Metal-Based Nanomaterials: Fabrications, Optical Properties, and Ultrafast Photonics.

Metals are known for conductivity and luster due to the abundance of free electrons [...

A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life.

In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin coupling quality in electrophysiological sleep monitoring systems hinder the comfort and reliability of night wearing. Here, we report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals under weak device-skin coupling conditions without positioning or skin preparation requirements.

Real-time holographic camera for obtaining real 3D scene hologram.

As a frontier technology, holography has important research values in fields such as bio-micrographic imaging, light field modulation and data storage. However, the real-time acquisition of 3D scenes and high-fidelity reconstruction technology has not yet made a breakthrough, which has seriously hindered the development of holography. Here, a novel holographic camera is proposed to solve the above inherent problems completely.

Spatiotemporal pattern formation and selection induced by cross-diffusion in a cancer growth model with Allee effect.

In this paper, we study the temporal and spatiotemporal dynamics of a cancer growth model with the Allee effect. For the temporal model, we first prove its well-posedness and analyze its rich local bifurcations, such as the transcritical bifurcation, the saddle-node bifurcation, the Hopf bifurcation, and the Bogdanov-Takens bifurcation. For the diffusion system, we first prove that cross-diffusion is a key mechanism for the formation of spatial patterns.

Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data.

Regarding the transportation of people, commodities, and other items, aeroplanes are an essential need for society. Despite the generally low danger associated with various modes of transportation, some accidents may occur. The creation of a machine learning model employing data from autonomous-reliant surveillance transmissions is essential for the detection and prediction of commercial aircraft accidents.

The impact of channel density, inverse solutions, connectivity metrics and calibration errors on OPM-MEG connectivity analysis: A simulation study.

Magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs) have rapidly developed in the fields of brain function, health, and disease. Functional connectivity analysis related to the resting-state has gained popularity as a field of research in recent years. Several studies have attempted to use OPM-based MEG (OPM-MEG) for brain network estimation research; however, the choice of source connectivity analysis pipeline may lead to outcome variability.

Molecularly imprinted hydrogels embedded with two-dimensional photonic crystals for the detection of dexamethasone/betamethasone sodium phosphate.

Dexamethasone sodium phosphate (DSP) and betamethasone sodium phosphate (BSP) imprinted hydrogels embedded with two-dimensional photonic crystals (2DPC) were developed as hormones-sensitive photonic hydrogel sensors with highly sensitive, selective, anti-interference and reproducible recognition capability. The DSP/BSP molecularly imprinted photonic hydrogels (denoted as DSP-MIPH and BSP-MIPH) can specifically recognize DSP/BSP by rebinding the DSP/BET molecules to nanocavities in the hydrogel network. This recognition is enabled by the similar shape, size, and binding sites of the nanocavities to the target molecules.

Simulation Research on Low-Frequency Magnetic Noise in Fe-Based Nanocrystalline Magnetic Shields.

Depending on high permeability, high Curie temperature, and low eddy current loss noise, nanocrystalline alloys, as the innermost layer, exhibit great potential in the construction of cylindrical magnetic shielding systems with a high shielding coefficient and low magnetic noise. This study compares a magnetic noise of 1 Hz, simulated by the finite element method (FEM), of a cylindrical nanocrystalline magnetic shield with different structural parameters based on the measured initial permeability of commercial Fe-based nanocrystalline (1K107). The simulated results demonstrate that the magnetic noise is irrelevant to the pump and probe hole diameter.

Parameterization of the Differences in Neural Oscillations Recorded by Wearable Magnetoencephalography for Chinese Semantic Cognition.

Neural oscillations observed during semantic processing embody the function of brain language processing. Precise parameterization of the differences in these oscillations across various semantics from a time-frequency perspective is pivotal for elucidating the mechanisms of brain language processing. The superlet transform and cluster depth test were used to compute the time-frequency representation of oscillatory difference (ODTFR) between neural activities recorded by optically pumped magnetometer-based magnetoencephalography (OPM-MEG) during processing congruent and incongruent Chinese semantics.

Atomic spin precession electro-optic modulation detection based on guided mode resonant lithium niobate metasurfaces.

Low-frequency noise in detection systems significantly affects the performance of ultrasensitive and ultracompact spin-exchange relaxation-free atomic magnetometers. High frequency modulation detection helps effectively suppress the 1/ noise and enhance the signal-to-noise ratio, but conventional modulators are bulky and restrict the development of integrated atomic magnetometer modulation-detection systems. Resonant metasurface-based thin-film lithium-niobate (TFLN) active optics can modulate free-space light within a compact configuration.

Repairbads: An automatic and adaptive method to repair bad channels and segments for OPM-MEG.

The optically pumped magnetometer (OPM) based magnetoencephalography (MEG) system offers advantages such as flexible layout and wearability. However, the position instability or jitter of OPM sensors can result in bad channels and segments, which significantly impede subsequent preprocessing and analysis. Most common methods directly reject or interpolate to repair these bad channels and segments.

Cost-Reference Particle Filter-Based Method for Constructing Effective Brain Networks: Application in Optically Pumped Magnetometer Magnetoencephalography.

Optically pumped magnetometer magnetoencephalography (OPM-MEG) represents a novel method for recording neural signals in the brain, offering the potential to measure critical neuroimaging characteristics such as effective brain networks. Effective brain networks describe the causal relationships and information flow between brain regions. In constructing effective brain networks using Granger causality, the noise in the multivariate autoregressive model (MVAR) is typically assumed to follow a Gaussian distribution.