An Efficient Multiscale Spatial Rearrangement MLP Architecture for Image Restoration.

The effective use of long-range information can yield improved network performance, which is very important for image restoration. Although local window-based models have linear complexity and can be feasibly applied to process high-resolution images, a single-scale window has a limited receptive field and is less efficient for encoding long-range context information. To address this issue, this paper presents a single-stage multiscale spatial rearrangement multilayer perceptron (MSSR-MLP) architecture that can obtain information at different scales within a local window. Specifically, we propose a simple and efficient spatial rearrangement module (SRM) that moves information outside the local window to the inside of the local window so that long-range dependencies can be modeled using only a window-based fully connected (FC) layer. The SRM can extend the local receptive field of a window-based FC layer without introducing additional parameters and FLOPs. Utilizing several spatial rearrangement modules with different step sizes, we design an efficient multiscale spatial rearrangement MLP architecture for image restoration. This design aggregates multiscale information to achieve improved restoration quality while maintaining a low computational cost. Extensive experiments conducted on several image restoration tasks demonstrate the efficiency and effectiveness of our method. For example, it requires only ~4.3% of the FLOPs needed by SwinIR for Gaussian gray image denoising, ~13.9% of the FLOPs needed by C2 PNet for single-image dehazing and ~18.9% of the FLOPs needed by MAXIM for single-image motion deblurring but achieves better performance on each of these restoration tasks.

Adaptive anisotropic pixel-by-pixel correction method for a space-variant degraded image.

Large field-of-view optical imaging systems often face challenges in the presence of space-variant degradation. The existence of degradation leads to target detection and recognition being difficult or even unsuccessful. To address this issue, this paper proposes an adaptive anisotropic pixel-by-pixel space-variant correction method. First, we estimated region acquisition of local space-variant point spread functions (PSFs) based on Haar wavelet degradation degree distribution, and obtained initial PSF matrix estimation with inverse distance weighted spatial interpolation. Then, we established a pixel-by-pixel space-variant correction model based on the PSF matrix. Third, we imposed adaptive sparse regularization terms of the Haar wavelet based on the adaptive anisotropic iterative reweight strategy and non-negative regularization terms as the constraint in the pixel-by-pixel space-variant correction model. Finally, as the correction process is refined to each pixel, the split-Bregman multivariate separation solution algorithm was employed for the pixel-by-pixel spare-variant correction model to estimate the final PSF matrix and the gray value of each pixel. Through this algorithm, the "whole image correction" and "block correction" is avoided, the "pixel-by-pixel correction" is realized, and the final corrected images are obtained. Experimental results show that compared with the current advanced correction methods, the proposed approach in the space-variant wide field correction of a degraded image shows better performance in preserving the image details and texture information.

Intra-block pyramid cross-scale network for thermal radiation effect correction of uncooled infrared images.

Thermal radiation effects can greatly degrade the image quality of uncooled infrared focal plane array detection systems. In this paper, we propose a thermal radiation effect correction network based on intra-block pyramid cross-scale feature extraction and fusion. First, an intra-block pyramid residual attention module is introduced to obtain fine-grained features from long-range IR images by extracting cross-scale local features within the residual block. Second, we propose a cross-scale gated fusion module to efficiently integrate the shallow and abstract features at multiple scales of the encoder and decoder through gated linear units. Finally, to ensure accurate correction of thermal radiation effects, we add double-loss constraints in the spatial-frequency domain and construct a single-input, multi-output network with multiple supervised constraints. The experimental results demonstrate that our proposed method outperforms state-of-the-art correction methods in terms of both visual quality and quantitative evaluation metrics.

UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios.

Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. However, the high proportion of small objects in UAV images and the limited platform resources lead to the low accuracy of most of the existing detection models embedded in UAVs, and it is difficult to strike a good balance between detection performance and resource consumption. To alleviate the above problems, we optimize YOLOv8 and propose an object detection model based on UAV aerial photography scenarios, called UAV-YOLOv8. Firstly, Wise-IoU (WIoU) v3 is used as a bounding box regression loss, and a wise gradient allocation strategy makes the model focus more on common-quality samples, thus improving the localization ability of the model. Secondly, an attention mechanism called BiFormer is introduced to optimize the backbone network, which improves the model's attention to critical information. Finally, we design a feature processing module named Focal FasterNet block (FFNB) and propose two new detection scales based on this module, which makes the shallow features and deep features fully integrated. The proposed multiscale feature fusion network substantially increased the detection performance of the model and reduces the missed detection rate of small objects. The experimental results show that our model has fewer parameters compared to the baseline model and has a mean detection accuracy higher than the baseline model by 7.7%. Compared with other mainstream models, the overall performance of our model is much better. The proposed method effectively improves the ability to detect small objects. There is room to optimize the detection effectiveness of our model for small and feature-less objects (such as bicycle-type vehicles), as we will address in subsequent research.

Wafer-scale δ waveguides for integrated two-dimensional photonics.

The efficient, large-scale generation and control of photonic modes guided by van der Waals materials remains as a challenge despite their potential for on-chip photonic circuitry. We report three-atom-thick waveguides-δ waveguides-based on wafer-scale molybdenum disulfide (MoS2) monolayers that can guide visible and near-infrared light over millimeter-scale distances with low loss and an efficient in-coupling. The extreme thinness provides a light-trapping mechanism analogous to a δ-potential well in quantum mechanics and enables the guided waves that are essentially a plane wave freely propagating along the in-plane, but confined along the out-of-plane, direction of the waveguide. We further demonstrate key functionalities essential for two-dimensional photonics, including refraction, focusing, grating, interconnection, and intensity modulation, by integrating thin-film optical components with δ waveguides using microfabricated dielectric, metal, or patterned MoS2.

IMB5036 overcomes resistance to multiple chemotherapeutic drugs in human cancer cells through pyroptosis by targeting the KH-type splicing regulatory protein.

AIMS: IMB5036 is a pyridazinone compound with antiproliferative and antitumour activity against hepatoma and pancreatic cancer. In this study, we attempted to identify the target protein of IMB5036 and test its potential for overcoming multidrug resistance and inducing pyroptosis. MATERIALS AND METHODS: We examined the effects of IMB5036 on cancer cells by in vitro assays, a molecular docking model and in vivo tumour models. We performed pull-down experiments using biotinylated IMB5036 and identified the binding proteins. Gene knockdown were used to investigate the oncogenic role of KH-type splicing regulatory protein (KSRP). Western blot was used to detect for mechanism-associated molecules. KEY FINDINGS: IMB5036 could overcome resistance to multiple chemotherapeutic drugs at the cellular level and in vivo. Furthermore, IMB5036 was not a P-glycoprotein (P-gp) substrate and downregulated the expression of P-gp. We identified KSRP as a binding protein of IMB5036. The knockdown of KSRP inhibited the proliferation of MCF7 and MCF7/adriamycin (MCF7/ADR) cells. In addition, IMB5036 induced pyroptosis in both MCF7 and MCF7/ADR cells via KSRP. SIGNIFICANCE: We found IMB5036 binds to KSRP and overcomes multidrug resistance via gasdermin E (GSDME)-dependent pyroptosis.

DSM Extraction Based on Gaofen-6 Satellite High-Resolution Cross-Track Images with Wide Field of View.

Digital Surface Model (DSM) is a three-dimensional model presenting the elevation of the Earth's surface, which can be obtained by the along-track or cross-track stereo images of optical satellites. This paper investigates the DSM extraction method using Gaofen-6 (GF-6) high-resolution (HR) cross-track images with a wide field of view (WFV). To guarantee the elevation accuracy, the relationship between the intersection angle and the overlap of the cross-track images was analyzed. Cross-track images with 20-40% overlaps could be selected to conduct DSM extraction. First, the rational function model (RFM) based on error compensation was used to realize the accurate orientation of the image. Then, the disparity map was generated based on the semi-global block matching (SGBM) algorithm with epipolar constraint. Finally, the DSM was generated by forward intersection. The GF-6 HR cross-track images with about 30% overlap located in Taian, Shandong Province, China, were used for DSM extraction. The results show that the mountainous surface elevation features were retained completely, and the details, such as houses and roads, were presented in valleys and urban areas. The root mean square error (RMSE) of the extracted DSM could reach 6.303 m, 12.879 m, 14.929 m, and 19.043 m in valley, ridge, urban, and peak areas, respectively. The results indicate that the GF-6 HR cross-track images with a certain overlap can be used to extract a DSM to enhance its application in land cover monitoring.

SPNet: a size-variant progressive network for aero-optical thermal radiation effects correction.

When an aircraft is flying at a high speed, the airflow meets the optical cover and is compressed, resulting in aero-optical thermal radiation effects that degrade image quality. In this paper, based on the inherent characteristic that the degrade level of the thermal radiation bias field remains consistent regardless of image size, a size-variant progressive aero-optical thermal radiation effects correction network (SPNet) is proposed. First, SPNet uses two sub-networks to progressively correct degraded image, first and second sub-networks are responsible for learning coarse and accurate thermal radiation bias fields respectively. Second, we introduce the multi-scale feature upsampling module (MFUM) to leverage the multi-scale information of the features and promote inter-channel information interaction. Third, we propose an adaptive feature fusion module (AFFM) to dynamically fuse features from different scales by assigning different weights. At last, a multi-head self-attention feature extraction module (MSFEM) is proposed to extract global information feature maps. Compared with state-of-the-art thermal radiation effects correction methods, experiments on both simulated and real degraded images demonstrate the performance of our proposed method.

Multi-scale thermal radiation effects correction via a fast surface fitting with Chebyshev polynomials.

In an uncooled infrared imaging system, thermal radiation effects are caused by the heat source from the target or the detection window, which affects the ability of target detection, tracking, and recognition seriously. To address this problem, a multi-scale correction method via a fast surface fitting with Chebyshev polynomials is proposed. A high-precision Chebyshev polynomial surface fitting is introduced into thermal radiation bias field estimation for the first time, to the best of our knowledge. The surface fitting in the gradient domain is added to the thermal radiation effects correction model as a regularization term, which overcomes the ill-posed matrix problem of high-order bivariate polynomials surface fitting, and achieves higher accuracy under the same order. Additionally, a multi-scale iterative strategy and vector representation are adopted to speed up the iterative optimization and surface fitting, respectively. Vector representation greatly reduces the number of basis function calls and achieves fast surface fitting. In addition, split Bregman optimization is used to solve the minimization problem of the correction model, which decomposes the multivariable optimization problem into multiple univariate optimization sub-problems. The experimental results of simulated and real degraded images demonstrate that our proposed method performs favorably against the state of the art in thermal radiation effects correction.

Regulation of the sensitivity of hepatocarcinoma cells by ORMDL3, to sorafenib by autophagy.

Serum orosomucoid1-like protein 3 (ORMDL3) is a membrane protein in the endoplasmic reticulum, known to regulate many important signal transduction processes and autophagy regulation, but it is unclear whether it is involved in the intratumoral microenvironment and cancer drug resistance. Our present study found that silencing ORMDL3 increases the inhibitory effect of sorafenib on the viability and proliferation in HCC cells, and increases the sensitivity of HCC cells to sorafenib. In addition, silencing ORMDL3 can increase ROS levels by inhibiting autophagy, thereby increasing sorafenib-induced apoptosis of HCC cells. Further, our study also found that ORMDL3 silencing inhibits autophagy through the PERK-ATF4-Beclin1 pathway, thus affecting sorafenib sensitivity. The in vivo effects of sorafenib were tested by xenografting using nude mice. It showed that silencing ORMDL3 in HCC cells could increase the inhibitory effect of sorafenib on the growth of tumors. This is the first report to describe the relationships among ORMDL3, autophagy, and sorafenib resistance. This study provides available targets that might have a synergetic effect with sorafenib.

Radiotherapy-Induced Cleavage of Quaternary Ammonium Groups Activates Prodrugs in Tumors.

Cleavage chemistry offers a new chance to activate chemotherapeutic prodrugs in a tumor-selective manner, yet developing spatiotemporally controllable cleavage chemistry with deep tissue penetration is still a great challenge. Herein, we present a novel radiotherapy-triggered cleavage chemistry that enables controlled drug release in tumors. Quaternary ammonium groups are identified as masking groups that can be efficiently removed by hydrated electrons (e-aq ) from water radiolysis. The subsequently released tertiary amines can be anti-cancer toxins or readily release functional molecules via 1,6-elimination. This radiotherapy-induced cleavage works successfully in living cells and tumor-bearing mice, showing remarkable treatment efficacy when the mice are given carfilzomib prodrug and radiotherapy. This strategy provides a new perspective for combinational radiochemotherapy, which is the first-line treatment for over 50 % of cancer patients.

A Cationic Micelle as In Vivo Catalyst for Tumor-Localized Cleavage Chemistry.

The emerging strategies of accelerating the cleavage reaction in tumors through locally enriching the reactants is promising. Yet, the applications are limited due to the lack of the tumor-selectivity for most of the reactants. Here we explored an alternative approach to leverage the rate constant by locally inducing an in vivo catalyst. We found that the desilylation-induced cleavage chemistry could be catalyzed in vivo by cationic micelles, and accelerated over 1400-fold under physiological condition. This micelle-catalyzed controlled release platform is demonstrated by the release of a 6-hydroxyl-quinoline-2-benzothiazole derivative (HQB) in two cancer cell lines and a NIR dye in mouse tumor xenografts. Through intravenous injection of a pH-sensitive polymer micelles, we successfully applied this strategy to a prodrug activation of hydroxyl camptothecin (OH-CPT) in tumors. Its "decaging" efficiency is 42-fold to that without cationic micelles-mediated catalysis. This micelle-catalyzed desilylation strategy unveils the potential that micelle may act beyond a carrier but a catalyst for local perturbing or activation.

Side Chain Optimization Remarkably Enhances the in Vivo Stability of 18F-Labeled Glutamine for Tumor Imaging.

Similar to glycolysis, glutaminolysis acts as a vital energy source in tumor cells, providing building blocks for the metabolic needs of tumor cells. To capture glutaminolysis in tumors, 18F-(2S,4R)4-fluoroglutamine ([18F]FGln) and 18F-fluoroboronoglutamine ([18F]FBQ) have been successfully developed for positron emission tomography (PET) imaging, but these two molecules lack stability, resulting in undesired yet significant bone uptake. In this study, we found that [18F]FBQ-C2 is a stable Gln PET tracer by adding two more methylene groups to the side chain of [18F]FBQ. [18F]FBQ-C2 was synthesized with a good radiochemical yield of 35% and over 98% radiochemical purity. [18F]FBQ-C2 showed extreme stability in vitro, and no defluorination was observed after 2 h in phosphate buffered saline at 37 °C. The competitive inhibition assay results indicated that [18F]FBQ-C2 enters cells via the system ASC and N, similar to natural glutamine, and can be transported by tumor-overexpressed ASCT2. PET imaging and biodistribution results indicated that [18F]FBQ-C2 is stable in vivo with low bone uptake (0.81 ± 0.20% ID/g) and can be cleared rapidly from most tissues. Dynamic scan and pharmacokinetic studies using BGC823-xenograft-bearing mice revealed that [18F]FBQ-C2 accumulates specifically in tumors, with a longer half-life (101.18 ± 6.50 min) in tumor tissues than in other tissues (52.70 ± 12.44 min in muscle). Biodistribution exhibits a high tumor-to-normal tissue ratio (4.8 ± 1.7 for the muscle, 2.5 ± 1.0 for the stomach, 2.2 ± 0.9 for the liver, and 17.8 ± 8.4 for the brain). In conclusion, [18F]FBQ-C2 can be used to perform high-contrast Gln imaging of tumors and can serve as a PET tracer for clinical research.

Aero-optic thermal radiation effects correction with a low-frequency prior and a sparse constraint in the gradient domain.

Sparse representation is commonly used in correction models to reduce thermal radiation effects. If the correction problem is restricted to a sparse representation problem, residual aero-optic thermal radiation effects will appear in the corrected results. To accurately estimate the thermal radiation bias field, the low-frequency information of the thermal radiation bias field is explored. We propose a correction model to integrate the low-frequency constraint on the thermal bias field and gradient sparse constraint on the latent clear image. A split Bregman alternating iterative algorithm is used to solve the minimization problem of the correction model. The experimental results show that the proposed method can effectively remove thermal radiation effects and greatly improve image quality for infrared focal plane detection.