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Summary: Methicillin-resistant Staphylococcus aureus (MRSA) infection, a major cause of hospital- and community-acquired pneumonia, still has a high mortality rate. Extracellular vesicles (EVs), as crucial mediators of intercellular communication, have a significant impact on infectious diseases. However, the role of EVs from alveolar macrophages (AMs) in MRSA pneumonia remains unclear. We report that AMs phagocytose MRSA and release more EVs in mice with MRSA pneumonia. EVs from AMs harboring phagocytosed MRSA exhibit significant proinflammatory effects and induce necroptosis by delivering tumor necrosis factor α (TNF-α) and miR-146a-5p. Mechanically, the upregulated miR-146a-5p in these EVs enhances the phosphorylation of RIPK1, RIPK3, and MLKL by targeting TNF receptor-associated factor 6 (TRAF6), thereby promoting TNF-α-induced necroptosis. The combination of a TNF-α antagonist and an miR-146a-5p antagomir effectively improves the outcomes of mice with MRSA pneumonia. Overall, we reveal the pronecrotic effect of EVs from MRSA-infected AMs and provide a promising target for the prevention and treatment of MRSA pneumonia.

Doxorubicin (Dox) is an anti-tumor drug with a broad spectrum, whereas the cardiotoxicity limits its further application. In clinical settings, liposome delivery vehicles are used to reduce Dox cardiotoxicity. Here, we substitute extracellular vesicles (EVs) for liposomes and deeply investigate the mechanism for EV-encapsulated Dox delivery. The results demonstrate that EVs dramatically increase import efficiency and anti-tumor effects of Dox in vitro and in vivo, and the efficiency increase benefits from its unique entry pattern. Dox-loading EVs repeat a “kiss-and-run” motion before EVs internalization. Once EVs touch the cell membrane, Dox disassociates from EVs and directly enters the cytoplasm, leading to higher and faster Dox import than single Dox. This unique entry pattern makes the adhesion between EVs and cell membrane rather than the total amount of EV internalization the key factor for regulating the Dox import. Furthermore, we recognize ICAM1 as the molecule mediating the adhesion between EVs and cell membranes. Interestingly, EV-encapsulated Dox can induce ICAM1 expression by irritating IFN-γ and TNF-α secretion in TME, thereby increasing tumor targeting of Dox-loading EVs. Altogether, EVs and EV-encapsulated Dox synergize via ICAM1, which collectively enhances the curative effects for tumor treatment.

Abstract Besides participating in diverse pathological and physiological processes, extracellular vesicles (EVs) are also excellent drug‐delivery vehicles. However, clinical drugs modulating EV levels are still lacking. Here, we show that proton pump inhibitors (PPIs) reduce EVs by enhancing macropinocytosis‐mediated EV uptake. PPIs accelerate intestinal cell endocytosis of autocrine immunosuppressive EVs through macropinocytosis, thereby aggravating inflammatory bowel disease. PPI‐induced macropinocytosis facilitates the clearance of immunosuppressive EVs from tumour cells, improving antitumor immunity. PPI‐induced macropinocytosis also increases doxorubicin and antisense oligonucleotides of microRNA‐155 delivery efficiency by EVs, leading to enhanced therapeutic effects of drug‐loaded EVs on tumours and acute liver failure. Mechanistically, PPIs reduce cytosolic pH, promote ATP6V1A (v‐ATPase subunit) disassembly from the vacuolar membrane and enhance the assembly of plasma membrane v‐ATPases, thereby inducing macropinocytosis. Altogether, our results reveal a mechanism for macropinocytic regulation and PPIs as potential modulators of EV levels, thus regulating their functions.

Abstract Homeobox A13 (HOXA13) has been verified as an oncogen in some malignancies. However, its role in nasopharyngeal carcinoma (NPC) is still unclear. This study aims to explore the role of HOXA13 in NPC and its underlying mechanism. The mRNA expression of HOXA13 in NPC was obtained from the GSE53819 and GSE64634 datasets in the Gene Expression Omnibus (GEO) database. MTT, colony formation and transwell assays and xenograft tumour models were used to investigate the effects of HOXA13 on NPC HNE1 cells in vitro and in vivo. The expression of HOXA13, epithelial-mesenchymal transition-transcription factor (EMT-TF) Snail and matrix metalloproteinase 2 (MMP-2) was detected by immunohistochemistry, quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting. The results showed that HOXA13 was upregulated in NPC. Silencing HOXA13 suppressed the proliferation, migration, and invasion of HNE1 cells, which inhibited tumour growth, while overexpression of HOXA13 induced the opposite effects. In addition, the expression of Snail and MMP-2 at the transcriptional and protein levels was associated with the expression of HOXA13. In summary, our results suggest that HOXA13 plays a role as a cancer-promoting gene in NPC. The underlying mechanism may be related to the upregulation of Snail and MMP-2.

Image pairs in under-illuminated scenes along with the presence of complex light sources often result in strong flare artifacts in images, affecting both image quality and the performance of downstream visual applications. Removing lens flare and ghosts is a challenging issue, particularly in low-light environments. Existing methods for flare removal are mainly restricted by inadequate simulation and real-world capture, resulting in singular categories of scattered flares and unavailable reflected ghosts. Therefore, a comprehensive deterioration procedure is crucial for generating a dataset for flare removal. We propose a methodology based on spatial position relationships for generating data pairs with flare deterioration, which is supported by theoretical analysis and real-world evaluation. Our procedure is comprehensive and realizes the similarity of scattered flares and the symmetric effect of reflected ghosts. We also construct a real-shot pipeline that respectively processes the effects of scattering and reflective flares, aiming to directly generate data for end-to-end methods. Experimental results demonstrate that our methodology adds diversity to existing flare datasets and constructs a comprehensive mapping procedure for flare data pairs. Our method facilitates the data-driven model to achieve better restoration in flare images and proposes a better evaluation system based on real shots, thus promoting progress in the area of real flare removal.

Nighttime image dehazing presents unique challenges due to the unevenly distributed haze caused by the color change of artificial light sources. This results in multiple interferences, including atmospheric light, glow, and direct light, which make the complex scattering haze interference difficult to accurately distinguish and remove. Additionally, obtaining pairs of high-definition data for fog removal at night is a difficult task. These challenges make nighttime image dehazing a particularly challenging problem to solve. To address these challenges, we introduced the haze scattering formula to more accurately express the haze in three-dimensional space. We also proposed a novel data synthesis method using the latest CG textures and lumen lighting technology to build scenes where various hazes can be seen clearly through ray tracing. We converted the complex 3D scattering relationship transformation into a 2D image dataset to better learn the mapping from 3D haze to 2D haze. Additionally, we improved the existing neural network and established a night haze intensity evaluation label based on the idea of optical PSF. This allowed us to adjust the haze intensity of the rendered dataset according to the intensity of the real haze image and improve the accuracy of dehazing. Our experiments showed that our data construction and network improvement achieved better visual effects, objective indicators, and calculation speed.

Abstract This paper presents a generative adversarial network (GAN) with patch match algorithm to realize a high‐quality digital zooming using two camera modules with different focal lengths. In dual camera system, shorter focal length module produces the wide‐view image with the low resolution. On the other hand, the longer focal length module produces the tele‐view image via optical zooming. The long‐focal image contains more details than short‐focal image and can be used to guide short‐focal image to reconstruct high frequency part. Firstly, a feature extraction block (FEB) is advanced to extract feature of long‐focal image and short focal‐image to reconstruct a wide‐view image with different resolutions. Next, a patch match algorithm is integrated into convolution neural networks (CNN) to fuse information of long‐focal with short‐focal image and generate a new fused image. Finally, the fused image and short‐focal image are merged with a feature fusion block (FFB) to predict high‐resolution images. In addition, generative adversarial networks are used for filtering information integrated by previous network and output the zoomed image. Extensive experiments on benchmark datasets show that our algorithm achieves favorable performance against state‐of‐the‐art methods.

Remediation of environmental toxic pollutants has attracted extensive attention in recent years. Microbial bioremediation has been an important technology for removing toxic pollutants. However, microbial activity is also susceptible to toxicity stress in the process of intracellular detoxification, which significantly reduces microbial activity. Electroactive microorganisms (EAMs) can detoxify toxic pollutants extracellularly to a certain extent, which is related to their unique extracellular electron transfer (EET) function. In this review, the extracellular and intracellular aspects of the EAMs’ detoxification mechanisms are explored separately. Additionally, various strategies for enhancing the effect of extracellular detoxification are discussed. Finally, future research directions are proposed based on the bottlenecks encountered in the current studies. This review can contribute to the development of toxic pollutants remediation technologies based on EAMs, and provide theoretical and technical support for future practical engineering applications.

The current water environment management of China has gradually shifted from reaching water quality standards to preserving water ecological health, and the control focus has extended from traditional nitrogen and phosphorus pollutants to micropollutants, such as heavy metals and emerging pollutants. However, the precise traceability and regulatory system of micropollutants in different regions’ watersheds was not yet mature. A five-step strategy of risk assessment and management was proposed this study to successfully construct a list of industrial priority pollutants and a list of 376 priority control discharge enterprises in 13 administrative regions of China’s Taihu Lake Basin. Firstly, a preliminary list of 78 pollutants was determined with reference to the emission standards of major regional industrial pollution sources, relevant national environmental regulations and literature reports. Secondly, 22 types of priority control pollutants were re-screened based on the analytical results of surface water samples obtained from 26 monitoring sites in the study area. Then the environmental risk values of the re-screened pollutants were calculated referring to the Chemical Hazard Evaluation for Management Strategy (CHEMS-1) method. Next, the regional environmental risk baseline value was determined and the risk value equivalent (EY) for each pollutant was calculated. Finally, according to the EY results, the regional priority control pollutants were identified, and the priority control discharge enterprises were confirmed retroactively. The results of this study can provide methodological support and a scientific basis for the precise control of micropollutants in surface water from differentiated regions.

Image registration (or image alignment), the problem of aligning multiple images with relative displacement, is a crucial step in many multiframe image restoration algorithms. To solve the problem that most existing image registration approaches can only align two images in one inference, we propose a split-attention multiframe alignment network (SAMANet). Pixel-level displacements between multiple images are first estimated at low-resolution scales and then refined gradually with the increase in feature resolution. To better integrate the interframe information, we present a split-attention module (SAM) and a dot-product attention module (DPAM), which can adaptively rescale the cost volume features and optical flow features according to the similarity between features from different images. The experimental results demonstrate the superiority of our SAMANet over state-of-the-art image registration methods in terms of both accuracy and robustness. To solve the “ghosting effect” caused by pixelwise registration, we designed two “ghost” removal modules: warping repetition detection module (WRDM) and attention fusion module (AFM). WRDM detects “ghost” regions during the image warping process without increasing the time complexity of the registration algorithm. AFM uses an attention mechanism to rescale the aligned images and enables the registration network and the subsequent image restoration networks to be trained jointly. To validate the strengths of the proposed approaches, we apply SAMANet, WRDM and AFM to three image/video restoration tasks. Extensive evaluations demonstrate that the proposed methods can enhance the performance of image restoration algorithms and outperform the other compared registration algorithms.

Due to the limitation of hardware, infrared (IR) images have low-resolution (LR) and poor visual quality. Image super-resolution (SR) is a good solution to this problem. In this paper, we present a new convolution network (CNN) to improve the spatial resolution of infrared (IR) images. Our network is able to restore fine details by decomposing the input image into low-frequency and high-frequency domains. In low-frequency domains, we reconstruct image structure by deep networks. In high frequency domains, we reconstruct IR image details. Furthermore, we proposed another network to remove artifacts. Additionally, we propose a new loss function using visible (VIS) images to enhance the details of IR images. In training phase, we use VIS images to guide IR image restoration and in testing phase we get SR IR images with LR IR images input only. We optimize our deep network with a targeted function which penalizes images at different semantic levels using the corresponding terms. Besides, we build a dataset where paired LR-VIS images on the same scene are captured by a camera with both infrared and visible light sensors which both sensors have the same optical axis. Extensive experiments demonstrate that the proposed algorithm achieves superior performance and visual improvements against the state-of-the-arts.

In this study, hydrogenated tungsten oxide(WO3-x) was synthesized. Through experiments, it was found that the material could effectively use infrared light to generate free radicals and effectively remove trimethoprim(TMP). Among them, O2·-and H2O2 are the main types of free radicals. SEM, TEM, XRD, UV–Vis-IR diffuse reflection, and repeatability experiments proved the high efficiency of infrared light utilization and the stability of the material itself. Besides, nine degradation products and two pathways of TMP in the system were first discovered.

Model update is an important module in target trackers. It plays an important role in adaptive tracking. Many researches have proven that different model update strategies should be adopted, when tracking in different scenes, especially in occlusion and deformation. Though many strategies have been proposed in recent years, few of them make high improvement and good combination on trackers. In this paper, we first proved there is a close relationship between the tracking scenes and the response maps. Then, we proposed an adaptive model update strategy for calculating model update rate based on the response map. Many experiments have been done to compare the proposed model update strategy with some state-of-the-art strategies, and the results have shown that the proposed model update strategy outperforms the best model update strategy by 7% on the test of Kernel Correlation Filter tracker. Furthermore, the proposed model update strategy was evaluated on some state-of-the-art correlation filter trackers. Results have shown the proposed strategy was well integrated into many trackers, and improved the tracking accuracy effectively.

This study examined the regulation of phenotype and function of natural killer (NK) cells by nasopharyngeal carcinoma (NPC) cells with metastasis-associated in colon cancer-1 (MACC1) overexpression. Thirty patients with NPC and 20 healthy subjects were included. Peripheral blood (5 mL) was collected, and NK cells were purified using Ficoll and negative separation. NPC HONE1 cells were transfected with pcDNA3.1-MACC1 or negative control plasmids, and culture supernatant was collected for co-culture with NK cells. Flow cytometry was used to determine the expression of activated and inhibitory receptors on the cells. Western blotting was used to determine the expression of proteins. The results showed that the ratio of NK cells in the peripheral blood of NPC patients was elevated, but the activity and tumor-killing effect of NK cells were reduced. MACC1 promoted the escape of HONE1 cells from killing by NK cells. HONE1 with overexpression of MACC1 down-regulated the expression of NKG2D in NK cells, inhibited the proliferation of NK cells and escaped the immune surveillance by NK cells. MACC1 down-regulated the expression of NKG2D in NK cells by up-regulating the expression of TGF-β1 in HONE1 cells, thereby inhibiting the activity of NK cells. MACC1 inhibited the cell cycle of NK cells via TGF-β1 pathway. The study demonstrated that MACC1 elevated the expression of TGF-β1 by NPC HONE1 cells, thereby inhibiting NKG2D expression and G1/S transition in NK cells. MACC1 helped NPC HONE1 cells avoid killing by NK cells.

The COVID-19 pandemic brought profound changes to all corners of society and affected people in every aspect of their lives. This survey-based study investigated how household food related matters such as food sourcing and consumption behaviors of 2,126 Chinese consumers in different age groups changed approximately two months into the COVID-19 quarantine. A new food sourcing mechanism, community-based online group grocery-ordering (CoGGO), was widely adopted by households, particularly among the youngest group studied (18-24 years of age). The same group showed a higher confidence in the food supply system during the quarantine and a greater propensity for weight gain while staying-at-home. The more mature age group (≥35 years of age) showed heightened vigilance and awareness, with fewer grocery-shopping trips, a higher tendency for purchasing extra food, and less tendency to waste food. Survey findings of the new food-sourcing mechanism, attitudes to food, and changes in behavior among different age groups provide valuable insights to guide policies and management interventions to address matters pertaining to food supply and distribution, food access and household food security, and food waste reduction.

Proper features matter for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present the dual-branch feature fusion network (DBFFNet), a simple effective framework mainly composed of three modules: global information perception module, local information concatenation module and refinement fusion module. The local information of a salient object is extracted from the local information concatenation module. The global information perception module exploits the U-Net structure to transmit the global information layer by layer. By employing the refinement fusion module, our approach is able to refine features from two branches and detect salient objects with final details without any post-processing. Experiments on standard benchmarks demonstrate that our method outperforms almost all of the state-of-the-art methods in terms of accuracy, and achieves the best performance in terms of speed under fair settings. Moreover, we design a wide-field optical system and combine with DBFFNet to achieve salient object detection with large field of view.

BaF2–TiO2 nanocomposite material (hereinafter called the composite) was prepared by a sol–gel method. The composite surface area, morphology and structure were characterized by Brunauer–Emmett–Teller method, X-ray diffraction analysis and a scanning electron microscopy. The results showed that BaF2 and TiO2 form a PN-like structure on the surface of the composite. Composites were used to catalyse the degradation of methyl orange by irradiation with ultraviolet light, γ-rays and an electron beam (EB). It was demonstrated that the composite is found to be more efficient than the prepared TiO2 and commercial P25 in the degradation of methyl orange under γ-irradiation. Increasing the composite catalyst concentration within a certain range can effectively improve the decolorization rate of the methyl orange solution. However, when the composite material is used to catalyse the degradation of organic matter in the presence of ultraviolet light or 10 MeV EB irradiation, the catalytic effect is poor or substantially ineffective. In addition, a hybrid mechanism is proposed; BaF2 absorbs γ-rays to generate radioluminescence and further excites TiO2 to generate photo-charges. Due to the heterojunction effect, the resulting photo-charge will produce more active particles. This seems to be a possible mechanism to explain γ-irradiation's catalytic behaviour.

The novel rotating rectangular aperture (RRA) system provides a good solution for space-based, large-aperture, high-resolution imaging tasks. Its imaging quality depends largely on the image synthesis algorithm, and the mainstream multi-frame deblurring approach is sophisticated and time-consuming. In this paper, we propose a novel full-aperture image synthesis algorithm for the RRA system, based on Fourier spectrum restoration. First, a numerical simulation model is established to analyze the RRA system’s characteristics and obtain the point spread functions (PSFs) rapidly. Then, each image is used iteratively to calculate the increment size and update the final restored Fourier spectrum. Both the simulation’s results and the practical experiment’s results show that our algorithm performs well in terms of objective evaluation and time consumption.

Abstract Rather than the conventional concept of viewing conductive carbon black (CB) to be chemically inert in microbial electrochemical cells (MECs), here we confirmed the redox activity of CB for its feasibility as an electron sink in the microbial battery (MB). Acting as the cathode of a MB, the solid-state CB electrode showed the highest electron capacity equivalent of 18.58 ± 0.46 C/g for the unsintered one and the lowest capacity of 2.29 ± 0.48 C/g for the one sintered under 100% N2 atmosphere. The capacity vibrations of CBs were strongly in coincidence with the abundances of C=O moiety caused by different pretreatments and it implied one plausible mechanism based on CB’s surface functionality for its electron capturing. Once subjected to electron saturation, CB could be completely regenerated by different strategies in terms of electrochemical discharging or donating electrons to biologically-catalyzed nitrate reduction. Surface characterization also revealed that CB’s regeneration fully depended on the reversible shift of C=O moiety, further confirming the functionality-based mechanism for CB’s feasibility as the role of MB’s cathode. Moreover, resilience tests demonstrated that CB cathode was robust for the multi-cycles charging-discharging operations. These results imply that CB is a promising alternative material for the solid-state cathode in MBs.

We develop a high-performance anode formed from carbonized cow dung for bioelectrochemical systems. Thermal gravimetric analysis showed that the CD carbonization process started at 300°C and ended at approximately 550°C; the weight was reduced by 51%. After a heat-treatment at 800°C for 2 h, the treated CD featured a good conductivity and a high specific surface area. The maximum current density of 11.74 ± 0.41 A m-2 was achieved by CD anode (heated at 800°C), which remained relatively stable from more than 10 days. This study shows that a valuable anode material can be produced through conversion of CD by high-temperature carbonization. This approach provides a new way to alleviate environmental problems associated with CD.

Chitin biomass, a rich renewable resource, is the second most abundant natural polysaccharide after cellulose. Conversion of chitin biomass to high value-added chemicals can play a significant role in alleviating the global energy crisis and environmental pollution. In this review, the recent achievements in converting chitin biomass to high-value chemicals, such as 5-hydroxymethylfurfural (HMF), under different conditions using chitin, chitosan, glucosamine, and N-acetylglucosamine as raw materials are summarized. Related research on pretreatment technology of chitin biomass is also discussed. New approaches for transformation of chitin biomass to HMF are also proposed. This review promotes the development of industrial technologies for degradation of chitin biomass and preparation of HMF. It also provides insight into a sustainable future in terms of renewable resources.

In this work, we use ultrasonication and chemical etching agents to assist preparation of metal current collectors with nano-scale pores on the surface. Four different current collectors (copper foil, copper foam, aluminum foil, and aluminum foam) are prepared. The preparation parameters, ultrasonic time and etching agent concentration, are investigated and optimized accordingly. The morphologies of the as-prepared current collectors are observed under a scanning electronic microscope. Soft-packed lithium ion batteries with various current collectors are fabricated and tested. The prepared lithium ion batteries show good long-term cycle stability. The nanoporous structure of the current collector has little impact on the improvement of battery capacity under slow charging/discharging rates but has a positive impact on capacity retention under fast charging/discharging rates.