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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. 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. 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. We found IMB5036 binds to KSRP and overcomes multidrug resistance via gasdermin E (GSDME)-dependent pyroptosis. [Display omitted] [ABSTRACT FROM AUTHOR]

• A half instance normalization residual dense network (HINRDNet) is proposed for passive millimetre wave image restoration. • Deblurring and denoising U-Net (DBNU-Net) is designed by employing sum dense HINBlock (SDHB) and concatenate dense HINBlock (CDHB). • Quantitatively and qualitatively experimental results validate the effectiveness of the proposed HINRDNet method. During the imaging process of passive millimeter wave (PMMW), due to the long wavelength of millimeter wave, the small aperture of the antenna, and the introduction of noises, the obtained PMMW image are degraded by blur and noise simultaneously. To address these problems, we propose a passive millimeter-wave image restoration network called as HINRDNet which includes a denoising module (DNM), deblurring and denoising U-Net (DBNU-Net), and an image reconstruction module (RCM). In order to fully utilize local features, the proposed network is designed based on Half Instance Normalization Block, and is mainly composed of the densely connected sum dense HINBlock (SDHB) and the concatenate dense HINBlock (CDHB) block from low step to high step. In addition, the higher level features information is combined with low level features for global feature cross fusion. Finally, we construct remote sensing and infrared simulation datasets to train the network, and test it on simulated datasets and real PMMW degraded images. The experimental results for the simulated degraded images and the real PMMW degraded images demonstrate that our proposed method outperforms the existing state-of-the-art methods in terms of visual perception and evaluation metrics. [ABSTRACT FROM AUTHOR]

• Proposed a novel enhancement framework for uneven intensity correction. • Base image enhancement with spatially adaptive gamma correction. • Different adaptive correction parameters for enhancing multi-scale detail layers. Low or uneven luminance results in low contrast of near-infrared and optical remote sensing images, making it challenging to analyze their contents. Traditional image enhancement methods cannot simultaneously take detail preservation, contrast enhancement, and brightness improvement into account. In order to cope with this problem, this paper proposes a spatially adaptive multi-scale image enhancement (SAMSIE) scheme, including three key procedures: First, nonsubsampled contourlet transform (NSCT) is employed to decompose a low-contrast image into multi-scale layers. Second, a spatially adaptive Gamma correction strategy based on improved histogram equalization is proposed to enhance the base layer which is used as a guide layer. Third, an adaptive enhancement operator is proposed to enhance fine details. Finally, a high-contrast optical infrared image is obtained by the inverse NSCT with usage of these enhanced layers. The effectiveness of the proposed SAMSIE method is validated by both visualization assess and the evaluation of three quantitative indexes including discrete entropy (DE), contrast gain (CG), and mean brightness improvement (MBI), with comparison of the state-of-the-arts. [ABSTRACT FROM AUTHOR]

L 0 regularization is highly suitable for estimating the latent image in the blur kernel estimation step of the two-step process of blind image deblurring. To tackle L 0 regularization problem, the method randomly selects gradient truncation threshold or only depends on the experience of repeated experiments, which often leads to the damage of edges in the latent image and inaccurate blur kernel. The proposed blind deblurring model based on edge-preserving of L 0 -regularized gradient (EP-L 0 RG) prior has the great adaptability to the parameters selection for retaining the edges. The experimental results state that the proposed method not only reduces the blindness and difficulty of the parameter settings, but also estimates a more accurate kernel and improves the deblurring performance. Comparative experiments demonstrated that the proposed method provides the optimal and quantitative results. [ABSTRACT FROM AUTHOR]

X-ray angiograms, which suffer from low-contrast and noise, need to be improved by both the image enhancement and denoising techniques. However, the goals of these two tasks usually conflict, which makes it difficult to efficiently combine the enhancement and denoising in one scheme. To deal with this problem, we propose a novel spatial-frequency filtering (SFF) scheme for high-contrast X-ray cardiovascular angiogram image restoration. The proposed scheme includes the following four parts: (1) low-quality X-ray angiogram images are decomposed into the base layer with strong structures and redundant parts by employing a relative total variation (RTV) method. (2) the base layer is enhanced by a proposed improved histogram equalization (IHE) method. (3) The detail layer with weak structures is extracted by using a short-time Fourier transform (STFT) and is further enhanced by a proposed adaptive correction parameter. (4) The improved image is the combination of results obtained by the base layer and the detail layer. Experimental results in both qualification and quantitation validate that the proposed method performs better than the state-of-the-arts on contrast enhancement, structure preservation, and noise reduction. [ABSTRACT FROM AUTHOR]

• Tracking strategy for contact point detection when switching the contact wire. • Initial strategy for the first frame determination after switching the contact wire. • Quantitative and qualitative results validating the effectiveness of our strategy. It is a key point to accurately and stably detect the pantograph-catenary contact point for its geometric parameter measurement from infrared images under complex backgrounds (e.g. , cloud, cross-bridge, curved contact wire, in particular, the contact wire switching time). In order to complete the task of accurate and robust contact point detection, this paper proposes an progressive detection strategy, called as joint horizontal-vertical enhancement and tracking (JHVET), including three crucial parts: Firstly, an input infrared image is decomposed into a horizontal image layer and a vertical image layer by a horizontal-vertical enhancement operator, and the potential contact point is located by an extensive random sample consensus (RANSAC) algorithm. Secondly, an updated tracking approach is proposed to deal with the contact point detection problem in the contact wire switching time. Finally, an initial scheme is proposed to determine the first contact wire after switching the contact wire. Experimental results verify the effectiveness of the proposed JHVET method in both quantitation and qualification. Especially, the performance with high detection accuracy (98.23%), low average pixel error (0.523 pixel), and satisfactory detection rate (over 108 fps) yielded by the proposed JHVET method is very suitable for its extensive application. [ABSTRACT FROM AUTHOR]

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 C 2 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.

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.

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.

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.

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 (MoS 2 ) 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 MoS 2 .

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.

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., (© 2022 Wiley-VCH GmbH.)

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., (© 2022. The Author(s).)

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, 18 F-(2 S ,4 R )4-fluoroglutamine ([ 18 F]FGln) and 18 F-fluoroboronoglutamine ([ 18 F]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 [ 18 F]FBQ-C 2 is a stable Gln PET tracer by adding two more methylene groups to the side chain of [ 18 F]FBQ. [ 18 F]FBQ-C 2 was synthesized with a good radiochemical yield of 35% and over 98% radiochemical purity. [ 18 F]FBQ-C 2 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 [ 18 F]FBQ-C 2 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 [ 18 F]FBQ-C 2 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 [ 18 F]FBQ-C 2 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, [ 18 F]FBQ-C 2 can be used to perform high-contrast Gln imaging of tumors and can serve as a PET tracer for clinical research.

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.