Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to mild cognitive impairment (MCI) and dementia. Clinical diagnosis of PD mainly depends on characteristic motor symptoms, which can lead to misdiagnosis, underscoring the need for reliable biomarkers. Early detection of PD and effective monitoring of disease progression are crucial for enhancing patient outcomes.

Anti-Icing Organogel Enables Quasi-Homogeneous Supercooling Preservation of Mouse Hearts.

Supercooling preservation holds great promise for extending the storage limits of organs. However, supercooled systems are susceptible to stochastic ice nucleation, which can cause fatal damage to the organs. In this study, an organogel interface composed of nanoscale polydimethylsiloxane and dimethyl-silicone oil is proposed, which presents a significant energy barrier for ice nucleation, comparable to that of homogeneous nucleation.

Enhancing colorectal polyp classification using gaze-based attention networks.

Colorectal polyps are potential precursor lesions of colorectal cancer. Accurate classification of colorectal polyps during endoscopy is crucial for early diagnosis and effective treatment. Automatic and accurate classification of colorectal polyps based on convolutional neural networks (CNNs) during endoscopy is vital for assisting endoscopists in diagnosis and treatment.

Isolation and Characterization of Phage TSW001 and Its Application on Large Yellow Croaker.

is a common spoilage bacterium found in refrigerated fish. In this study, a virulent bacteriophage was isolated from wastewater using AS08 as the host, and it was designated as TSW001. Based on morphological characterization and whole-genome analysis, bacteriophage TSW001 was classified within the genus .

Stress-induced and anchoring-programmed smectic layer architectures.

The large-area architecture of ordered stimuli-responsive systems is of vital importance in nanotechnology and functional materials. However, the entropy-driven self-organization of soft matters remains a fundamental challenge. Here, we adopt mechanical stress to regulate the layered structures of smectic A liquid crystals.

Weighted Errors-in-Variables Modelling for Detection of Cortico-Muscular Couplings.

Functional cortico-muscular couplings are commonly assessed through cortico-muscular coherence (CMC) analysis, a measure of linear dependency between electroencephalogram (EEG) and electromyogram (EMG) signals. However, the presence of noise in EEG and EMG signals may exceed the strength of synchronous components, posing challenges in reliably detecting CMC. This study introduces an approach based on weighted errors-in-variables (EIV) modelling to extract relevant versions of cortical and muscular signals governing movement control from noisy EEG and EMG signals, aiming to enhance co-herence estimation.

Dynamic Manipulation of Ice Nucleation by an Electrochemical Reaction.

In this report, we present a simple and effective method for manipulating ice nucleation through an electric-field-induced electrochemical reaction. By applying an electric field strength of 2 kV/m on water between a tin anode and platinum cathode, a significant increase of 7 °C in the nucleation temperature of water was observed. This threshold electric field strength is considerably lower than those reported in previous studies on electro-freezing.

Dynamic photomask directed lithography based on electrically stimulated nematic liquid crystal architectures.

Lithography technology is a powerful tool for preparing complex microstructures through projecting patterns from static templates with permanent features onto samples. To simplify fabrication and alignment processes, dynamic photomask for multiple configurations preparation becomes increasingly noteworthy. Hereby, we report a dynamic photomask by assembling the electrically stimulated nematic liquid crystal (NLC) into multifarious architectures.

A Critical Analysis of the Opportunities and Challenges of Phage Application in Seafood Quality Control.

Seafood is an important source of food and protein for humans. However, it is highly susceptible to microbial contamination, which has become a major challenge for the seafood processing industry. Bacteriophages are widely distributed in the environment and have been successfully used as biocontrol agents against pathogenic microorganisms in certain food processing applications.

Self-healing spiral phase contrast imaging.

Spiral phase contrast imaging alleviates the information load by extracting the geometric features of objects and is one of the most representative branches of instant imaging processing. The self-healing capacity of edge detectors can enhance their robustness to obstacles in practical applications. Here, a self-healing spiral phase contrast imaging scheme is proposed and experimentally demonstrated by a liquid crystal edge detector combining a spiral phase, an axicon phase, and a lens phase.

Cross-frequency cortex-muscle interactions are abnormal in young people with dystonia.

Sensory processing and sensorimotor integration are abnormal in dystonia, including impaired modulation of beta-corticomuscular coherence. However, cortex-muscle interactions in either direction are rarely described, with reports limited predominantly to investigation of linear coupling, using corticomuscular coherence or Granger causality. Information-theoretic tools such as transfer entropy detect both linear and non-linear interactions between processes.

Subband Independent Component Analysis for Coherence Enhancement.

Objective: Cortico-muscular coherence (CMC) is becoming a common technique for detection and characterization of functional coupling between the motor cortex and muscle activity. It is typically evaluated between surface electromyogram (sEMG) and electroencephalogram (EEG) signals collected synchronously during controlled movement tasks. However, the presence of noise and activities unrelated to observed motor tasks in sEMG and EEG results in low CMC levels, which often makes functional coupling difficult to detect.

Mold-free self-assembled scalable microlens arrays with ultrasmooth surface and record-high resolution.

Microlens arrays (MLAs) based on the selective wetting have opened new avenues for developing compact and miniaturized imaging and display techniques with ultrahigh resolution beyond the traditional bulky and volumetric optics. However, the selective wetting lenses explored so far have been constrained by the lack of precisely defined pattern for highly controllable wettability contrast, thus limiting the available droplet curvature and numerical aperture, which is a major challenge towards the practical high-performance MLAs. Here we report a mold-free and self-assembly approach of mass-production of scalable MLAs, which can also have ultrasmooth surface, ultrahigh resolution, and the large tuning range of the curvatures.

Physics-model-based neural networks for inverse design of binary phase planar diffractive lenses.

The inverse design approach has enabled the customized design of photonic devices with engineered functionalities through adopting various optimization algorithms. However, conventional optimization algorithms for inverse design encounter difficulties in multi-constrained problems due to the substantial time consumed in the random searching process. Here, we report an efficient inverse design method, based on physics-model-based neural networks (PMNNs) and Rayleigh-Sommerfeld diffraction theory, for engineering the focusing behavior of binary phase planar diffractive lenses (BPPDLs).

A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision.

In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.

Computational prediction and characterization of cell-type-specific and shared binding sites.

Motivation: Cell-type-specific gene expression is maintained in large part by transcription factors (TFs) selectively binding to distinct sets of sites in different cell types. Recent research works have provided evidence that such cell-type-specific binding is determined by TF's intrinsic sequence preferences, cooperative interactions with co-factors, cell-type-specific chromatin landscapes and 3D chromatin interactions. However, computational prediction and characterization of cell-type-specific and shared binding sites is rarely studied.

DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps.

Unravelling Causal Relationships Between Cortex and Muscle with Errors-in-variables Models.

Corticomuscular communications are commonly estimated by Granger causality (GC) or directed coherence, with the aim of assessing the linear causal relationship between electroencephalogram (EEG) and electromyogram (EMG) signals. However, conventional GC based on standard linear regression (LR) models may be substantially underestimated in the presence of noise in both EEG and EMG signals: some healthy subjects with good motor skills show no significant GC. In this study, errors-in-variables (EIV) models are investigated for the purpose of estimating underlying linear time-invariant systems in the context of GC.

Cylindrical vector beam multiplexer/demultiplexer using off-axis polarization control.

The emergence of cylindrical vector beam (CVB) multiplexing has opened new avenues for high-capacity optical communication. Although several configurations have been developed to couple/separate CVBs, the CVB multiplexer/demultiplexer remains elusive due to lack of effective off-axis polarization control technologies. Here we report a straightforward approach to realize off-axis polarization control for CVB multiplexing/demultiplexing based on a metal-dielectric-metal metasurface.

Theoretical realization of single-mode fiber integrated metalens for beam collimating.

Optical fiber facet has rapidly emerged as a powerful light-coupling platform for integrating metasurfaces with miniaturized footprint and multifarious functionalities, through direct lithographic patterning or decal transfer. However, the fiber integrated metasurfaces investigated so far have been usually limited to high refractive index (RI) materials, thus leading to severe impedance mismatch at the fiber/metasurface interface and low efficiency. Here we report a single-mode fiber (SMF) integrated metalens based on low-RI material.

Multiscale Wavelet Transfer Entropy With Application to Corticomuscular Coupling Analysis.

Objective: Functional coupling between the motor cortex and muscle activity is commonly detected and quantified by cortico-muscular coherence (CMC) or Granger causality (GC) analysis, which are applicable only to linear couplings and are not sufficiently sensitive: some healthy subjects show no significant CMC and GC, and yet have good motor skills. The objective of this work is to develop measures of functional cortico-muscular coupling that have improved sensitivity and are capable of detecting both linear and non-linear interactions.

Methods: A multiscale wavelet transfer entropy (TE) methodology is proposed.

Synthesis and Electrochemical Performance of ZnSe Electrospinning Nanofibers as an Anode Material for Lithium Ion and Sodium Ion Batteries.

ZnSe nitrogen-doped carbon composite nanofibers (ZnSe@N-CNFs) were derived as anode materials from selenization of electrospinning nanofibers. Electron microscopy shows that ZnSe nanoparticles are distributed in electrospinning nanofibers after selenization. Electrochemistry tests were carried out and the results show the one-dimensional carbon composite nanofibers reveal a great structural stability and electrochemistry performance by the enhanced synergistic effect with ZnSe.

Altered electroencephalography functional connectivity in depression during the emotional face-word Stroop task.

Objective: Depression is a severe mental disorder. However, the neural mechanisms underlying affective interference (difficulties in directing attention away from negative distractors) in depression patients are still not well-understood. In particular, the connections between brain regions remain unclear.