Search Results (3,608)

In recent years, the near-infrared (NIR) fluorescence theranostic system has garnered increasing attention for its advantages in the simultaneous diagnosis- and imaging-guided delivery of therapeutic drugs. However, challenges such as strong background fluorescence signals and rapid metabolism have hindered the achievement of sufficient contrast between tumors and surrounding tissues, limiting the system’s applicability. This study aims to integrate the pegylation strategy with a tumor microenvironment-responsive approach. A novel esterase-activated EPR strategy prodrug, OBHSA-PEG-DCM, was designed. This prodrug links OBHSA, a protein degrader capable of efficient ERα protein degradation, to the PEG-modified fluorescent group (dicyanomethylene-4H-pyran, DCM) via an ester bond. This integration facilitates targeted drug delivery and enhances the retention of the fluorescent group within the tumor, allowing distinct in vivo tumor imaging periods. Experimental results show that, benefiting from overexpressed esterase in cancer cells, OBHSA-PEG-DCM can be efficiently hydrolyzed, releasing OBHSA and pegylated DCM. OBHSA demonstrated potent inhibition against MCF-7 cells (IC₅₀ = 1.09 μM). Simultaneously, pegylated DCM exhibited remarkable in vivo imaging capabilities, lasting up to 12 days in mice, due to the enhanced permeability and retention (EPR) effect. OBHSA-PEG-DCM holds promise as a theranostic agent for ERα-positive breast cancer, offering both therapeutic and diagnostic capabilities. Importantly, this study highlights the utility of pegylated NIR fluorophores for long-circulating drug delivery systems, addressing current challenges in achieving high-contrast tumor imaging and effective targeted drug release. Full article

The dynamically developing field of implantology requires researchers to search for new materials and solutions. In this study, TiNbZr samples were investigated as an alternative for popular, but potentially hazardous TiAl6V4. Samples were etched, sandblasted, subjected to PEO, and covered in AgNP suspension. Simultaneously, SEM images were taken, and the wettability and roughness of the surface were measured. Samples covered in AgNPs were subjected to biological trials. A six-day measurement of human fibroblast proliferation was conducted to assess biocompatibility, and the population of E. coli and S. aureus was measured over eight hours. Results showed that the TiNbZr PEO surface is biocompatible with human fibroblast cells and promotes growth. However, deposited AgNPs exhibited only slight effectiveness in decreasing bacterial growth over the first two hours. The results suggest that the method of surface preparation is sufficient and might promote osseointegration. On the other hand, more efficient and reliable methods of application of AgNPs should be researched Full article

Background/Objectives: The increased SNR available at 7T combined with fast readout trajectories enables accelerated spectroscopic imaging acquisitions for clinical applications. In this report, we evaluate the performance of a Hadamard slice encoding strategy with a 2D rosette trajectory for multi-slice fast spectroscopic imaging at 7T. Methods: Moderate-TE (~40 ms) spin echo and J-refocused polarization transfer sequences were acquired with simultaneous Hadamard multi-slice excitations and rosette in-plane encoding. The moderate spin echo sequence, which targets singlet compounds (i.e., N-acetyl aspartate, creatine, and choline), uses cascaded multi-slice RF excitation pulses to minimize the chemical shift dispersion error. The J-refocused sequence targets coupled spin systems (i.e., glutamate and myo-inositol) using simultaneous multi-slice excitation to maintain the same TE across all slices. A modified Hadamard slice encoding strategy was used to decrease the peak RF pulse amplitude of the simultaneous multi-slice excitation pulse for the J-refocused acquisition. Results: The accuracy of multi-slice and single-slice rosette spectroscopic imaging (RSI) is comparable to conventional Cartesian-encoded spectroscopic imaging (CSI). Spectral analyses for the J-refocused studies of glutamate and myo-inositol show that the Cramer Rao lower bounds are not significantly different between the fast RSI and conventional CSI studies. Linear regressions of creatine/N-acetyl aspartate and glutamate/N-acetyl aspartate with tissue gray matter content are consistent with literature values. Conclusions: With minimal gradient demands and fast acquisition times, the 2.2 min to 9 min for single- to four-slice RSI acquisitions are well tolerated by healthy subjects and tumor patients, and show results that are consistent with clinical outcomes. Full article

To improve the centroid extraction accuracy and efficiency of high-dynamic star sensors, this paper proposes a multi-centroid localization method based on the prior distribution of star trail projections. First, the mapping relationship between attitude information and star trails is constructed based on a geometric imaging model, and an endpoint centroid group extraction strategy is designed from the perspectives of time synchronization and computational complexity. Then, the endpoint position parameters are determined by fitting the star trail grayscale projection using a line spread function, and accurate centroid localization is achieved through principal axis analysis and inter-frame correlation. Finally, the effectiveness of the proposed method under different dynamic scenarios was tested using numerical simulations and semi-physical experiments. The experimental results show that when the three-axis angular velocity reaches 8°/s, the centroid extraction accuracy of the proposed method remains superior to 0.1 pixels, achieving an improvement of over 30% compared to existing methods and simultaneously doubling the attitude measurement frequency. This demonstrates the superiority of this method in high-dynamic attitude measurement tasks. Full article

Autonomous driving vehicles have strong path planning and obstacle avoidance capabilities, which provide great support to avoid traffic accidents. Autonomous driving has become a research hotspot worldwide. Depth estimation is a key technology in autonomous driving as it provides an important basis for accurately detecting traffic objects and avoiding collisions in advance. However, the current difficulties in depth estimation include insufficient estimation accuracy, difficulty in acquiring depth information using monocular vision, and an important challenge of fusing multiple sensors for depth estimation. To enhance depth estimation performance in complex traffic environments, this study proposes a depth estimation method in which point clouds and images obtained from MMwave radar and cameras are fused. Firstly, a residual network is established to extract the multi-scale features of the MMwave radar point clouds and the corresponding image obtained simultaneously from the same location. Correlations between the radar points and the image are established by fusing the extracted multi-scale features. A semi-dense depth estimation is achieved by assigning the depth value of the radar point to the most relevant image region. Secondly, a bidirectional feature fusion structure with additional fusion branches is designed to enhance the richness of the feature information. The information loss during the feature fusion process is reduced, and the robustness of the model is enhanced. Finally, parallel channel and position attention mechanisms are used to enhance the feature representation of the key areas in the fused feature map, the interference of irrelevant areas is suppressed, and the depth estimation accuracy is enhanced. The experimental results on the public dataset nuScenes show that, compared with the baseline model, the proposed method reduces the average absolute error (MAE) by 4.7–6.3% and the root mean square error (RMSE) by 4.2–5.2%. Full article

It is very well-known that the reliable coupling relationship between water area and water level is very important in analyzing the risks of floods and droughts for big lakes, such as Dongting Lake, especially when remote sensing images are absent and in situ measurements cannot be carried out. To obtain this relationship, two types of mathematical models—polynomial regression (PR) based on the least square algorithm and machine learning regression (MLR) based on the BP (Backpropagation) neural network algorithm—are constructed using the water area data extracted from multiple temporal remote sensing images and water levels recorded at several representative hydrological stations for nearly 30 years. In this study, Dongting Lake is divided into three parts: East Dongting Lake (EDL), South Dongting Lake (SDL), and West Dongting Lake (WDL). This is because water slope exists on its surface, which is formed by several inflow rivers and the high and low terrain. To calculate the total water area of this lake, two ways are put forward by choosing the water levels: from EDL, SDL, and WDL in their turn; or from all three simultaneously. In other words, three univariate and one multivariate regression. For PR, there are perfect coefficients of determination (most nearly 0.95, the smallest being 0.76), which is in line with regression test relative errors (between 0.27% and 6.7%). For MLR, which was initially applied to this problem, the best node number (10 for the first way, 8 for the second way) in the hidden layer of the neural network is adaptively chosen, with coefficients of determination (similar to PR), together with training and testing error performances (between 1% and 10%). These results confirm the validity and reliability of them. The regression and prediction results on the two models are better than the documented way (only focus on the water level of EDL). These results can provide some references for researchers and decision makers in studying similar big Lakes. Full article

Researchers have already used vibration data and deep learning methods, such as Convolutional Neural Networks (CNNs), to detect structural damage. Moreover, some researchers have employed image-based displacement sensors (such as the template matching and edge detection methods) to obtain structural vibration information. It is necessary to verify whether deep learning methods can detect minor damage inside beams, for example, small hollowing in concrete. In addition, there is an urgent need to develop an effective image-based displacement sensor that can simultaneously detect a large number of reliable vibration data from different measurement points. In this study, the vibration data of two beam-ABAQUS models were used as the input data for a newly designed deep learning-based structural health monitoring method. There were 500 vibration samples for each case, and the peak of vibrations was several millimeters. The proposed CNN model can locate damage positions in beams with high accuracy (close to 100%), and the damage sizes are 3 cm and 6 cm. Laboratory experiments were carried out on four beams with different damage. The optimized displacement sensor developed based on the edge detection method was used to detect the displacement of the beams. Each beam had 200 vibration data, and there were 800 vibration data in total. These vibration data were used as input data to train the proposed deep learning architecture, and satisfactory accuracy was achieved in detecting the damage of the beams with an accuracy of 97%. The training process is satisfactory in that the training loss and validation loss dropped very quickly. Full article

Background/Objectives: Performing hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC) on the same specimen slide provides advantages that include specimen conservation and the ability to combine the H&E context with biomarker expression at the individual cell level. We previously used invisible deposited chromogens and dual-camera imaging, including monochrome and color cameras, to implement simultaneous H&E and IHC. Using this approach, conventional H&E staining could be simultaneously viewed in color on a computer monitor alongside a monochrome video of the invisible IHC staining, while manually scanning the specimen. Methods: We have now simplified the microscope system to a single camera and increased the IHC multiplexing to four biomarkers using translational assays. The color camera used in this approach also enabled multispectral imaging, similar to monochrome cameras. Results: Application is made to several clinically relevant specimens, including breast cancer (HER2, ER, and PR), prostate cancer (PSMA, P504S, basal cell, and CD8), Hodgkin’s lymphoma (CD15 and CD30), and melanoma (LAG3). Additionally, invisible chromogenic IHC was combined with conventional DAB IHC to present a multiplex IHC assay with unobscured DAB staining, suitable for visual interrogation. Conclusions: Simultaneous staining and detection, as described here, provides the pathologist a means to evaluate complex multiplexed assays, while seated at the microscope, with the added multispectral imaging capability to support digital pathology and artificial intelligence workflows of the future. Full article

The phase separation of high-density polyethylene (HDPE)–polypropylene (PP) blends was studied using atomic force microscopy in tapping mode to obtain height and phase images. The results are compared with those from scanning electron microscopy imaging and are connected to the thermomechanical properties of the blends, characterised through differential scanning calorimetry, dynamic mechanical analysis (DMA), and tensile testing. Pure PP, as well as 10:90 and 20:80 weight ratio HDPE–PP blends, showed a homogeneous morphology, but the 25:75 HDPE–PP blends exhibited a sub-micrometre droplet-matrix structure, and the 50:50 HDPE–PP blends displayed a more complex co-continuous nano/microphase-separated structure. These complex phase separation morphologies correlate with the increased loss modulus (viscous properties) of the corresponding blends as measured by DMA, demonstrating the potential for the creation of strong and simultaneously tough, energy-absorbing materials for numerous applications. Full article

High-impedance fault detection poses significant challenges for distribution network maintenance and operation. We propose a dual-path neural network for high-impedance fault detection. To enhance feature extraction, we use a Gramian Angular Field algorithm to transform 1D zero-sequence voltage signals into 2D images. Our dual-branch network simultaneously processes both representations: the CNN extracts spatial features from the transformed images, while the GRU captures temporal features from the raw signals. To optimize model performance, we integrate the Crested Porcupine Optimizer (CPO) algorithm for the adaptive optimization of key network hyperparameters. The experimental results demonstrate that our method achieves a 99.70% recognition accuracy on a dataset comprising high-impedance faults, capacitor switching, and load connections. Furthermore, it maintains robust performance under various test conditions, including different noise levels and network topology changes. Full article

Food safety is gaining increasing attention worldwide. Currently, low-density organic foreign objects such as insects are extremely challenging to detect using conventional metal detectors and X-ray inspection systems. This study aimed to develop a visible-near-infrared single-pixel imaging (Vis-NIR-SPI) method to detect small insects inside food. The advantages of Vis-NIR light include its ability to analyze samples non-destructively and measure multiple components simultaneously and quickly, while SPI is robust against dark noise, high scattering, and high equipment costs. The experimental results demonstrated that (1) the newly designed system effectively reduces scattering effects from the highly scattering sample (intralipid 20%), allowing for the capture of information beyond the capabilities of a charge-coupled-device camera; (2) insects positioned behind ham and bread were readily detectable using the imaging reconstruction algorithm; and (3) even for chocolate samples with very high light absorption, only 1 uncontaminated sample out of 100 was mistakenly classified as contaminated, yielding an overall accuracy of 99%. This high level of accuracy underscores the potential of the Vis-NIR-SPI method to provide reliable detection while maintaining sample integrity. Furthermore, this method is cost-effective, offering a practical and efficient solution to improve quality control processes and consumer trust in the food industry. Full article

The diameter of roots is pivotal for studying subsurface root structure geometry. Yet, directly obtaining these parameters is challenging due their hidden nature. Ground-penetrating radar (GPR) offers a reproducible, nondestructive method for root detection, but estimating diameter from B-Scan images remains challenging. To address this, we developed the CycleGAN-guided multi-task neural network (CMT-Net). It comprises two subnetworks, YOLOv4-Hyperbolic Position and Diameter (YOLOv4-HPD) and CycleGAN. The YOLOv4-HPD is obtained by adding a regression header for predicting root diameter to YOLOv4-Hyperbola, which achieves the ability to simultaneously accurately locate root objects and estimate root diameter. The CycleGAN is used to solve the problem of the lack of a real root diameter training dataset for the YOLOv4-HPD model by migrating field-measured data domains to simulated data without altering root diameter information. We used simulated and field data to evaluate the model, showing its effectiveness in estimating root diameter. This study marks the first construction of a deep learning model for fully automatic root location and diameter extraction from GPR images, achieving an “Image Input–Parameter Output” end-to-end pattern. The model’s validation across various dataset scales opens the way for estimating other root attributes. Full article

Approximately 36% of patients with cervical cancer present with regional nodal metastasis at diagnosis, which is associated with adverse survival outcomes after definitive treatment. In the modern era of chemoradiotherapy (CRT) and image-guided adaptive brachytherapy (IGABT), where excellent local control is achieved for patients with locally advanced cervical cancer (LACC), nodal failure remains a major challenge to cure. To optimize treatment outcomes for node-positive LACC and reduce the incidence of nodal failure, various treatment approaches have been explored, including methods of surgical nodal staging or dissection, RT dose escalation strategies, such as intensity-modulated radiotherapy (IMRT) with simultaneous integrated boost (SIB) to involved nodes, and elective treatment of subclinical para-aortic (PAO) disease. Additionally, there is growing interest in emerging precision RT techniques, such as magnetic resonance-guided radiotherapy (MRgRT) and proton therapy, which may allow for further improvement in the therapeutic ratio. This review outlines the various methods of detection of nodal metastasis, treatment options for node-positive LACC, techniques of nodal radiotherapy and their clinical evidence in efficacy and toxicity profiles. Furthermore, recent advances in systemic therapy and promising novel therapeutic directions that may shape the management of node-positive LACC are discussed. Full article

In today’s competitive higher education sector, institutions increasingly rely on international rankings to secure financial resources, attract top-tier talent, and elevate their global reputation. Simultaneously, these universities have expanded their presence on social media, utilizing sophisticated posting strategies to disseminate information and boost recognition and engagement. This study examines the relationship between higher education institutions’ (HEIs’) rankings and their social media posting strategies. We gathered and analyzed publications from 18 HEIs featured in a consolidated ranking system, examining various features of their social media posts. To better understand these strategies, we categorized the posts into five predefined topics—engagement, research, image, society, and education. This categorization, combined with Long Short-Term Memory (LSTM) and a Random Forest (RF) algorithm, was utilized to predict social media output in the last five days of each month, achieving successful results. This paper further explores how variations in these social media strategies correlate with the rankings of HEIs. Our findings suggest a nuanced interaction between social media engagement and the perceived prestige of HEIs. Full article

In this paper, we propose a zoom auxiliary imaging lens based on the four-component mechanical zoom method for a modulation transfer function (MTF) test system. The auxiliary imaging lenses of the current MTF test system typically use fixed-focus optical systems, which are unable to meet the test scenarios of fast and batch measurement and measure lenses with an extensive focal length range. Compared with the fixed-focus auxiliary imaging lens, the zoom auxiliary imaging lens can simultaneously satisfy the measurement of wide-angle and telephoto miniature lenses without losing measurement accuracy. The entrance pupil distance of the zoom lens is greater than that of traditional lenses, and it is constant for each focal length of the zoom lens. The zoom lens uses an intermediate real image surface to obtain the perfect image quality and lower the diameter of the rear group. Additionally, the zoom lens dynamically adjusts magnification to optimize image size and align with the detector’s pixel resolution, thereby preventing undersampling and enhancing measurement precision. The optical design is optimized for stability, delivering high resolution and minimal aberrations across the zoom range. The image quality of the zoom lens is nearly at the diffraction limit at each focal length, which significantly reduces the impact of the auxiliary lens on MTF test results, enhancing both flexibility and accuracy. This design is particularly well suited for testing miniature lenses in optoelectronic technology applications. Full article