DNA methylation analysis of peripheral blood mononuclear cells in diagnosing breast cancer from benign breast lesions.

Background: With the increasing incidence of breast lesions, the differential diagnosis between benign lesions and breast cancer (BCa) has become a big challenge. Host peripheral blood mononuclear cells (PBMCs) could undergo changes in DNA methylation upon disease progression. However, the clinical value of DNA methylation of PBMCs in differentiating benign lesions and BCa is still unclear.

Robust Multifunctional Films with Excellent EMI Shielding, Anti-Peeling, and Joule Heating Performances Enabled by an Encapsulated Highly Conductive Fabric Strategy.

Recently, the issue of electromagnetic pollution has become increasingly prominent. Flexible polymer films with various conductive fillers are preferred to address this problem due to their highly efficient and durable electromagnetic interference (EMI) shielding performance. However, their applications are restricted by the unbalanced and insufficient electromagnetic wave absorption and shielding capabilities, as well as the weak interlayer bonding force.

Dual Responsive Magnetic DCR3 Nanoparticles: A New Strategy for Efficiently Targeting Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a major cause of cancer deaths globally. Unlike traditional molecularly targeted drugs, magnetically controlled drug delivery to micro/nanorobots enhances precision in targeting tumors, improving drug efficiency and minimizing side effects. This study develops a dual-responsive, magnetically controlled drug delivery system using PEGylated paramagnetic nanoparticles conjugated with decoy receptor 3 (DCR3) antibodies.

Surface Activation and Characterization of Basalt Fiber by Plasma Treatment and Its Interfacial Adhesion with Epoxy.

The weakness of the fiber-matrix interface restricts the practical application of basalt fiber (BF) as a reinforcing material. In order to improve the interfacial adhesion between the BF and epoxy matrix, surface activation of the BF was carried out using low-pressure O and H-Ar plasma under various conditions. The interfacial shear strength (IFSS), evaluated by a micro-droplet de-bonding test, was adopted to demonstrate the bonding effects at the BF/epoxy interphase.

Design of Acoustic Absorbing Structures for Mercurous Halide-Based Acousto-Optic Tunable Filters.

For the acousto-optic tunable filter (AOTF)-based spectral imaging systems, the diffraction efficiency of the AOTF is a primary factor affecting system throughput. Moreover, the distribution of the acoustic field within the AOTF fundamentally determines the device's diffraction efficiency. Thus, the design of an AOTF device including a transducer and absorber to achieve a uniform acoustic field distribution plays an important role in improving diffraction efficiency.

A Progressive Loss Decomposition Method for Low-Frequency Shielding of Soft Magnetic Materials.

Energy loss in shielding soft magnetic materials at low frequencies (1-100 Hz) can cause fluctuations in the material's magnetic field, and the resulting magnetic noise can interfere with the measurement accuracy and basic precision physics of biomagnetic signals. This places higher demands on the credibility and accuracy of loss separation predictions. The current statistical loss theory (STL) method tends to ignore the high impact of the excitation dependence of quasi-static loss in the low-frequency band on the prediction accuracy.

Enhancement of Magnetic Shielding Based on Low-Noise Materials, Magnetization Control, and Active Compensation: A Review.

Magnetic-shielding technologies play a crucial role in the field of ultra-sensitive physical measurement, medical imaging, quantum sensing, etc. With the increasing demand for the accuracy of magnetic measurement, the performance requirements of magnetic-shielding devices are also higher, such as the extremely weak magnetic field, gradient, and low-frequency noise. However, the conventional method to improve the shielding performance by adding layers of materials is restricted by complex construction and inherent materials noise.

Analysis of Umbilical Artery Hemodynamics in Development of Intrauterine Growth Restriction Using Computational Fluid Dynamics with Doppler Ultrasound.

Intrauterine growth restriction (IUGR), the failure of the fetus to achieve his/her growth potential, is a common and complex problem in pregnancy. Clinically, IUGR is usually monitored using Doppler ultrasound of the umbilical artery (UA). The Doppler waveform is generally divided into three typical patterns in IUGR development, from normal blood flow (Normal), to the loss of end diastolic blood flow (LDBF), and even to the reversal of end diastolic blood flow (RDBF).

Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review.

This review systematically analyzes the latest advancements in preprocessing techniques for Electrocardiography (ECG) and Magnetocardiography (MCG) signals over the past decade. ECG and MCG play crucial roles in cardiovascular disease (CVD) detection, but both are susceptible to noise interference. This paper categorizes and compares different ECG denoising methods based on noise types, such as baseline wander (BW), electromyographic noise (EMG), power line interference (PLI), and composite noise.

Multi-layer ear-scalp distillation framework for ear-EEG classification enhancement.

Background: Ear-electroencephalography (ear-EEG) holds significant promise as a practical tool in brain-computer interfaces (BCIs) due to its enhanced unobtrusiveness, comfort, and mobility compared to traditional steady-state visual evoked potential (SSVEP)-based BCI systems. However, achieving accurate SSVEP classification with ear-EEG remains a major challenge due to the significant attenuation and distortion of the signal amplitude.

Objective: Our aim is to enhance the classification performance of SSVEP using ear-EEG and to increase its practical application value.

Open-window MSR Design with Active Magnetic Compensation Coil based on COMSOL Multiphysics.

Magnetic shielding room (MSR) is one of the necessary equipment for measuring magnetocardiography (MCG) and magnetoencephalography (MEG). The traditional closed MSR only focuses on the indicators of the shielded magnetic field, and ignores the patient's feelings within the MSR. In this paper, an open-window MSR is proposed to solve the problem that patients have difficulty in communicating with the outside of closed MSR and have closed fear.

Precision Ice Detection on Power Transmission Lines: A Novel Approach with Multi-Scale Retinex and Advanced Morphological Edge Detection Monitoring.

Line icings on the power transmission lines are dangerous risks that may lead to situations like structural damage or power outages. The current techniques used for identifying ice have certain drawbacks, particularly when used in complex environments. This paper aims to detect lines on the top and bottom in PTLI with low illumination and complex backgrounds.

The Adversarial Robust and Generalizable Stereo Matching for Infrared Binocular Based on Deep Learning.

Despite the considerable success of deep learning methods in stereo matching for binocular images, the generalizability and robustness of these algorithms, particularly under challenging conditions such as occlusions or degraded infrared textures, remain uncertain. This paper presents a novel deep-learning-based depth optimization method that obviates the need for large infrared image datasets and adapts seamlessly to any specific infrared camera. Moreover, this adaptability extends to standard binocular images, allowing the method to work effectively on both infrared and visible light stereo images.

Analysis of Cushioned Landing Strategies of Cats Based on Posture Estimation.

This article addresses the challenge of minimizing landing impacts for legged space robots during on-orbit operations. Inspired by the agility of cats, we investigate the role of forelimbs in the landing process. By identifying the kinematic chain of the cat skeleton and tracking it using animal posture estimation, we derive the cushioning strategy that cats use to handle landing impacts.

Development of a Novel Tailless X-Type Flapping-Wing Micro Air Vehicle with Independent Electric Drive.

A novel tailless X-type flapping-wing micro air vehicle with two pairs of independent drive wings is designed and fabricated in this paper. Due to the complexity and unsteady of the flapping wing mechanism, the geometric and kinematic parameters of flapping wings significantly influence the aerodynamic characteristics of the bio-inspired flying robot. The wings of the vehicle are vector-controlled independently on both sides, enhancing the maneuverability and robustness of the system.

Ultrasound Stimulation Modulates Microglia M1/M2 Polarization and Affects Hippocampal Proteomic Changes in a Mouse Model of Alzheimer's Disease.

Background: The effectiveness of ultrasound stimulation in treating Alzheimer's disease (AD) has been reported in previous studies, but the underlying mechanisms remain unclear. This study investigated the effects of ultrasound stimulation on the proportion and function of microglia of different phenotypes, as well as on the levels of inflammatory factors. Additionally, it revealed the alterations in proteomic molecules in the mouse hippocampus following ultrasound stimulation treatment, aiming to uncover potential new molecular mechanisms.

Optimizing Molecular Passivation in MAPbI Perovskites: Impact of Dipole Moments and Structural Parameters.

In the realm of perovskite materials, organic molecules situated at the A site play a critical role in stabilizing the structure through specific orientations and weak interactions with the inorganic framework. These polar interactions significantly influence the optoelectronic properties of perovskites, and the introduction of polar molecules can disrupt the inherent polarization, thereby altering the material performance. The research primarily focuses on the relationship between the length and width of these organic cations and their polar inductive effects.

Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations.

Background: Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients.

Methodology for quality risk prediction for milk powder production plants with domain-knowledge-involved serial neural networks.

In dairy enterprises, predicting product quality attributes that are influenced by operating parameters is a major task. To reduce quality loss in production, a prediction-based quality control method is proposed in this study. In particular, a serial neural network was designed, and an innovative quality risk prediction methodology based on the integration of SNN and domain knowledge was created.