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Presently, the non-biodegradable polypropylene (PP) patches frequently used for hernia repair can cause fibrous tissue growth and adhesions. This study created a Janus Patch with anti-adhesion and antimicrobial properties to improve hernia repair while promoting tissue repair. The biologically active 4arm-PLGA-BLPD was initially synthesized through the modification of 4arm-PLGA with lysine, followed by the fabrication of a Janus patch using a layer-by-layer electrostatic spinning technique. This patch consisted of three layers: a repair layer composed of 4arm-PLGA-BLPD/PCL fiber membrane, a mechanical layer of 4arm-PLGA/PCL fiber membrane, and an antimicrobial layer of EMO-4arm-PLGA/PCL fiber membrane loaded with Emodin (EMO). The results showed that Janus patch exhibited notable tensile strength and elongation at break, enabling it to offer enhanced mechanical reinforcement for abdominal wall defects. In addition, it slowly releases lysine for repair and inhibits bacterial growth with EMO. In vivo experiments demonstrated that the patch effectively induced neovascularization, reduced collagen ac-cumulation, and stabilized the expression of relevant proteins through the up-regulation of MMP1 and MMP9. This facilitated successful repair of the abdominal wall defect model and prevented adhesions. In summary, the Janus patch offers both practical application and theoretical insight for hernia repair., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Natural preservation materials have long been a focus of research in the quality control of fruits and vegetables. This study aimed to develop composite films with exceptional preservation properties by utilizing chitosan (CS) as the film-forming material and incorporating onion polysaccharide (ONP) as the active component. The CS-ONP composite films were prepared, and their performance and preservation effects were evaluated. The results demonstrated that increasing the ONP content significantly enhanced the shading, antimicrobial, and antioxidant capabilities of the CS-ONP composite films. Preservation experiments revealed that the CS-ONP composite films effectively delayed the quality decline of cherry tomatoes during storage. However, despite the improvements brought by ONP, certain drawbacks persisted, such as reduced mechanical properties and alterations in surface structure. In summary, the CS-ONP composite films exhibit promising potential as novel materials for fruit and vegetable preservation. PRACTICAL APPLICATION: The spoilage of fruits and vegetables can cause huge economic losses. This study addresses this challenge by using chitosan as the film-forming substrate and adding crude onion polysaccharide as the active ingredient to create composite films. The preservation effects of these films on cherry tomatoes were studied. Although only cherry tomatoes were tested in this study, the composite films demonstrated significant potential for broader applications in fruit and vegetable preservation., (© 2024 Institute of Food Technologists.)

The surge in non-grain production on farmland (NGPF) poses significant threats to food security and land sustainability, particularly in hilly regions. However, there remains a lack of clarity on how to effectively balance grain and non-grain production in relation to land remediation. Using Wannian County as a case study, we investigate the evolution of this by leveraging high-precision land surveys and satellite imagery. Through the application of bootstrapped partial linear regression models, we identify key influencers behind each type of NGPF. In proposing land remediation solutions, we integrate the results of NGPF and land quality evaluations to identify mismatches between non-grain production and land attributes (i.e., topography, geology, soil, and land use). Our findings reveal a substantial growth in NGPF, expanding from 3838.72 ha to 5659.64 ha (2010-2020), and predominantly occurring on farmland with favorable natural conditions and connected locations such as proximity to roads, town centers, and industrial plants. Surprisingly, the basic farmland protection policy shows limited effectiveness in curbing NGPF, except for garden operations. We identify 1674 NGPF patches suitable for conversion to grain production and provide land remediation suggestions tailored to low-quality farmland with specific natural barriers, thus complementing the demand for regional non-grain production. This study thereby innovatively proposes nature-based land remediation strategies to address the non-grain production dilemma by tailoring NGPF and land quality, offering valuable insights for sustainable farmland management in China and beyond., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

PP mesh is a widely used prosthetic material in hernia repair. However, visceral adhesion is one of the worst complications of this operation. Hence, an anti-adhesive PP mesh is developed by coating porous polyvinyl alcohol (PVA) hydrogel on PP surface via freezing-thawing process method. The compressive modulus of porous PVA hydrogel coating is first regulated by the addition of porogen sodium bicarbonate (NaHCO 3 ) at various quality ratios with PVA. As expected, the porous hydrogel coating displayed modulus more closely resembling that of native abdominal wall tissue. In vitro tests demonstrate the modified PP mesh show superior coating stability, excellent hemocompatibility, and good cytocompatibility. In vivo experiments illustrate that PP mesh coated by the PVA4 hydrogel that mimicked the modulus of native abdominal wall could prevent adhesion effectively. Based on this, the rapamycin (RPM) is loaded into the porous PVA4 hydrogel coating to further improve anti-adhesive property of PP mesh. The Hematoxylin and eosin (H&E) and Masson trichrome (MT) staining results verified that the resulting mesh could alleviate the inflammation response and reduce the deposition of collagen around the implantation zone. The biomimetic mechanical property and anti-adhesive property of modified PP mesh make it a valuable candidate for application in hernioplasty., (© 2024 Wiley‐VCH GmbH.)

Polypropylene (PP) mesh is the most widely used prosthetic material in hernia repair. However, the efficacy of implanted PP mesh is often compromised by adhesion between viscera and PP mesh. Thus, there is a recognized need for developing an anti-adhesive PP mesh. Here, a composite hydrogel coated PP mesh with the prevention of adhesion after hernia repair was designed. The composite hydrogel coating was prepared from polyvinyl alcohol (PVA) and hyaluronic acid (HA) by using the freezing-thawing (FT) method. To overcome the shortcoming of the long time of the traditional freezing-thawing method, a small molecule 3,4-dihydroxyphenylacetic acid (DHPA) was introduced to promote the formation of composite hydrogel. The as-prepared composite hydrogel coating displayed modulus more closely resembling that of native abdominal wall tissue. In vitro studies illustrated that the resulting meshes showed excellent coating stability, hemocompatibility, and non-cytotoxicity. In vivo experiments using a rat abdominal wall defect model demonstrated that the composite hydrogel coated PP mesh could prevent the formation of adhesion, alleviate the inflammatory response, and reduce the deposition of collagen around the damaged tissue. These disclosed results manifested that the PP mesh coated with HA/PVA composite hydrogel might be a promising application in preventing adhesion for hernia repair., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Ethnopharmacological Relevance: Rehmannia glutinosa Libosch. (RGL) is a famous ethnic medicine contained in antidepressant Chinese medicine formulas and is traditionally clinically used for depression. We have recently confirmed that RGL enhanced synaptic plasticity in a mouse model of Chinese medical syndrome and that catalpol may be the representatively pharmacological component responsible for its improvement in synaptic plasticity and treatment of depression. Impaired synaptic plasticity is closely linked to major depression. Tyrosine kinase receptor B (TrkB) signaling has recently been discovered as a key pathway for synaptic plasticity improvement and antidepressant discovery. However, to date, it is unknown whether the target of catalpol to improve synaptic plasticity involves TrkB and whether its antidepressant mechanism involves synaptic plasticity mediated by TrkB signaling., Aim of Study: This study aims to elucidate the potential antidepressant target and mechanisms of catalpol, the main active compound of RGL, through TrkB signaling-mediated synaptic plasticity., Materials and Methods: We have recently predicted through molecular networking strategy (including network pharmacology, molecular docking, and molecular dynamics simulation) that catalpol may exert its antidepressant effects by regulating TrkB signaling and thus modulating essential synaptic plasticity proteins. Then, this study used classic behavioral tests, targeted diagnostic reagents, Nissl and Golgi staining, immunohistochemical analysis, immunofluorescence analysis, Western blot, enzyme-linked immunosorbent assay, and Real-time PCR to confirm the potential target and signaling of catalpol to improve synaptic plasticity for the treatment of depression., Results: The data showed that catalpol could improve synaptic plasticity and depressive behaviors, and its action pathway was predicted to involve TrkB signaling. Subsequently, the blockade of TrkB abolished the improvement of synaptic plasticity by catalpol and its antidepressant properties, which validated that TrkB signaling was the key pathway for catalpol to improve synaptic plasticity and exert antidepressant properties. Inhibition of COX-2 was likely to be a necessary facilitator for the antidepressant efficacy of catalpol via the TrkB target and TrkB-mediated synaptic plasticity., Conclusion: TrkB signaling-mediated synaptic plasticity plays a key role in the antidepressant properties of catalpol. This study provides critical information for the development of new and targeted antidepressant therapies or treatment strategies by catalpol. However, considering the existence of sex differences in depression (female depression is 2-3 times than that of males) and not exploring the antidepressant sex specificity of catalpol is a limitation, we will investigate the sex specificity of the antidepressant effects and molecular mechanisms of catalpol on sex-specific animals in the future to provide a preclinical basis for more accurate and targeted medication of catalpol., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Recently, template-based trackers have become the leading tracking algorithms with promising performance in terms of efficiency and accuracy. However, the correlation operation between query feature and the given template only achieves accurate target localization, but is prone to state estimation error, especially when the target suffers from severe deformation. To address this issue, segmentation-based trackers are proposed that use per-pixel matching to improve the tracking performance of deformable objects effectively. However, most of the existing trackers only match with the target features of the initial frame, thereby lacking the discrimination for handling a variety of challenging factors, e.g., similar distractors, background clutter, and appearance change. To this end, we propose a dynamic compact memory embedding technique to enhance the discrimination of the segmentation-based visual tracking method that can well tell the target from the background. Specifically, we initialize a memory embedding with the target features in the first frame. During the tracking process, the current target features that have certain correlation with the existing memory are updated to the memory embedding online. To further improve the tracking accuracy for deformable objects, we use a weighted point-to-global matching strategy to measure the correlation between the pixelwise query feature and the whole template, so as to capture more detailed deformation information. Extensive evaluations on six challenging tracking benchmarks including VOT2016, VOT2018, VOT2019, GOT-10K, TrackingNet, and LaSOT demonstrate the superiority of our method over recent remarkable trackers. Besides, our tracker outperforms the excellent segmentation-based trackers, i.e., D3S and SiamMask on the DAVIS2017 benchmark. The code is available at https://github.com/peace-love243/CMEDFL.

Background: Fluoxetine is often used as a well-known first-line antidepressant. However, it is accompanied with hepatogenic injury as its main organ toxicity, thereby limiting its application despite its superior efficacy. Fluoxetine is commonly traditionally used combined with some Chinese antidepressant prescriptions containing Rehmannia glutinosa (Dihuang) for depression therapy and hepatoprotection. Our previous experiments showed that co-Dihuang can alleviate fluoxetine-induced liver injury while efficiencies, and catalpol may be the key ingredient to characterize the toxicity-reducing and synergistic effects. However, whether co-catalpol can alleviate fluoxetine-induced liver injury and its toxicity-reducing mechanism remain unclear., Purpose: On the basis of the first recognition of the dose and duration at which pre-fluoxetine caused hepatic injury, co-catalpol's alleviation of fluoxetine-induced hepatic injury and its pathway was comprehensively elucidated., Method and Results: The hepatoprotection of co-catalpol was evaluated by serum biochemical indexes sensitive to hepatic injury and multiple staining techniques for hepatic pathologic analysis. Subsequently, the pathway by which catalpol alleviated fluoxetine-induced hepatic injury was predicted by network pharmacology to be predominantly the inhibition of ferroptosis. These were validated and confirmed in subsequent experiments with key technologies and diagnostic reagents related to ferroptosis. Further molecular docking showed that activating transcription factor 3 (ATF3) and ferroptosis suppressor protein 1 (FSP1) were the the most prospective molecules for catalpol and fluoxetine among many ferroptosis-related molecules. The critical role of ATF3/FSP1 signaling was further observed by surface plasmon resonance, diagnostic reagents, transmission electron microscopy, Western blot, real-time PCR, immunofluorescence, and immunohistochemistry. Results showed that fluoxetine directly bound to ATF3 and FSP1; agonisting ATF3 or blocking FSP1 abolished the alleviation of catalpol on fluoxetine-induced liver injury, and both exacerbated ferroptosis. Moreover, co-catalpol significantly enhanced the antidepressant efficacy of fluoxetine against depressive behaviours in mice., Conclusion: The hepatic impairment properties of fluoxetine were largely dependent on ATF3/FSP1 target-mediated ferroptosis. Co-catalpol alleviated fluoxetine-induced hepatic injury while enhancing its antidepressant efficacy, and that ATF3/FSP1 signaling-mediated inhibition of ferroptosis was involved in its co-administration detoxification mechanism. This study was the first to reveal the hepatotoxicity characteristics, targets, and mechanisms of fluoxetine; provide a detoxification and efficiency regimen by co-catalpol; and elucidate the detoxification mechanism., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

Hydrogel adhesives with integrated functionalities are still required to match their ever-expanding practical applications in the field of tissue repair and regeneration. A simple and effective safety strategy is reported, involving an in situ injectable polymer precursor and visible light-induced cross-linking. This strategy enables the preparation of a hydrogel adhesive in a physiological environment, offering wet adhesion to tissue surfaces, molecular flexibility, biodegradability, biocompatibility, efficient hemostatic performance, and the ability to facilitate liver injury repair. The proposed one-step preparation process of this polymer precursor involves the mixing of gelatin methacryloyl (GelMA), poly(thioctic acid) [P(TA)], poly(acrylic acid)/amorphous calcium phosphate (PAAc/ACP, PA) and FDA-approved photoinitiator solution, and a subsequent visible light irradiation after in situ injection into target tissues that resulted in a chemically-physically cross-linked hybrid hydrogel adhesive. Such a combined strategy shows promise for medical scenarios, such as uncontrollable post-traumatic bleeding., (© 2024 Wiley-VCH GmbH.)

Background: The accurate identification and evaluation of lymph nodes by CT images is of great significance for disease diagnosis, treatment, and prognosis., Purpose: To assess the lymph nodes' segmentation, size, and station by artificial intelligence (AI) for unenhanced chest CT images and evaluate its value in clinical scenarios., Material and Methods: This retrospective study proposed an end-to-end Lymph Nodes Analysis System (LNAS) consisting of three models: the Lymph Node Segmentation model (LNS), the Mediastinal Organ Segmentation model (MOS), and the Lymph Node Station Registration model (LNR). We selected a healthy chest CT image as the template image and annotated 14 lymph node station masks according to the IASLC to build the lymph node station mapping template. The exact contours and stations of the lymph nodes were annotated by two junior radiologists and reviewed by a senior radiologist. Patients aged 18 and above, who had undergone unenhanced chest CT and had at least one suspicious enlarged mediastinal lymph node in imaging reports, were included. Exclusions were patients who had thoracic surgeries in the past 2 weeks or artifacts on CT images affecting lymph node observation by radiologists. The system was trained on 6725 consecutive chest CTs that from Tianjin Medical University General Hospital, among which 6249 patients had suspicious enlarged mediastinal lymph nodes. A total of 519 consecutive chest CTs from Qilu Hospital of Shandong University (Qingdao) were used for external validation. The gold standard for each CT was determined by two radiologists and reviewed by one senior radiologist., Results: The patient-level sensitivity of the LNAS system reached of 93.94% and 92.89% in internal and external test dataset, respectively. And the lesion-level sensitivity (recall) reached 89.48% and 85.97% in internal and external test dataset. For man-machine comparison, AI significantly apparently shortened the average reading time (p < 0.001) and had better lesion-level and patient-level sensitivities., Conclusion: AI improved the sensitivity lymph node segmentation by radiologists with an advantage in reading time., (© 2023. Italian Society of Medical Radiology.)

Ethnopharmacological Relevance: Dioscorea bulbifera L. (Rhizoma Dioscoreae Bulbiferae; RDB) is commonly used as an expectorant and cough suppressant herb but is accompanied by severe hepatotoxicity. Using the juice of auxiliary herbs (such as Glycyrrhiza uralensis Fisch. (Glycyrrhizae Radix et Rhizoma; GRR) juice) in concocting poisonous Chinese medicine is a conventional method to reduce toxicity or increase effects. Our previous study found that concoction with GRR juice provided a detoxifying effect against the major toxic hepatotoxicity induced by RDB, but the principle for the detoxification of the concoction is unknown to date., Aim of the Study: The principle of concoction was investigated by using the processing excipient GRR juice to reduce the major toxic hepatotoxicity of RDB, and the efficacy of RDB as an expectorant and cough suppressant was enhanced., Materials and Methods: In this study, common factors (RDB:GRR ratio, concocted temperature, and concocted time) in the concoction process were used for the preparation of each RDB concocted with GRR juice by using an orthogonal experimental design. We measured the content of the main toxic compound diosbulbin B (DB) and serum biochemical indicators and performed pathological analysis in liver tissues of mice to determine the best detoxification process of RDB concocted with GRR juice. On this basis, the biological mechanisms of target organs were detected by Western blot and enzyme-linked immunosorbent assay at the inflammation and apoptosis levels. Further, the effects of RDB on expectorant and cough suppressant with GRR juice were evaluated by the conventional tests of phenol red expectorant and concentrated ammonia-induced cough. Lastly, the major compounds in the GRR juice introduced to RDB concoction were determined., Results: RDB concocted with GRR juice significantly alleviated DB content, serum alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase levels, and improved liver pathological damages. The best detoxification process was achieved by using an RDB:GRR ratio of 100:20 at 120 °C for 20 min. Further, RDB concocted with GRR juice down-regulated the protein levels of nuclear factor kappa B (NF-κB), cyclooxygenase 2 (COX-2), and Bcl-2 related X protein (Bax) in the liver and enhanced the expectorant and cough suppressant effects of RDB. Finally, liquiritin (LQ) and glycyrrhizic acid (GA) in the GRR juice were introduced to the RDB concoction., Conclusion: Concoction with GRR juice not only effectively reduced the major toxic hepatotoxicity of RDB but also enhanced its main efficacy as an expectorant and cough suppressant, and that the rationale for the detoxification and/or potentiation of RDB was related to the reduction in the content of the main hepatotoxic compound, DB, the introduction of the hepatoprotective active compounds, LQ and GA, in the auxiliary GRR juice, as well as the inhibition of NF-κB/COX-2/Bax signaling-mediated inflammation and apoptosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Recent learning-based methods demonstrate their strong ability to estimate depth for multi-view stereo reconstruction. However, most of these methods directly extract features via regular or deformable convolutions, and few works consider the alignment of the receptive fields between views while constructing the cost volume. Through analyzing the constraint and inference of previous MVS networks, we find that there are still some shortcomings that hinder the performance. To deal with the above issues, we propose an Epipolar-Guided Multi-View Stereo Network with Interval-Aware Label (EI-MVSNet), which includes an epipolar-guided volume construction module and an interval-aware depth estimation module in a unified architecture for MVS. The proposed EI-MVSNet enjoys several merits. First, in the epipolar-guided volume construction module, we construct cost volume with features from aligned receptive fields between different pairs of reference and source images via epipolar-guided convolutions, which take rotation and scale changes into account. Second, in the interval-aware depth estimation module, we attempt to supervise the cost volume directly and make depth estimation independent of extraneous values by perceiving the upper and lower boundaries, which can achieve fine-grained predictions and enhance the reasoning ability of the network. Extensive experimental results on two standard benchmarks demonstrate that our EI-MVSNet performs favorably against state-of-the-art MVS methods. Specifically, our EI-MVSNet ranks 1 st on both intermediate and advanced subsets of the Tanks and Temples benchmark, which verifies the high precision and strong robustness of our model.

Generalized Zero-Shot Learning (GZSL) aims at recognizing images from both seen and unseen classes by constructing correspondences between visual images and semantic embedding. However, existing methods suffer from a strong bias problem, where unseen images in the target domain tend to be recognized as seen classes in the source domain. To address this issue, we propose a Prototype-augmented Self-supervised Generative Network by integrating self-supervised learning and prototype learning into a feature generating model for GZSL. The proposed model enjoys several advantages. First, we propose a Self-supervised Learning Module to exploit inter-domain relationships, where we introduce anchors as a bridge between seen and unseen categories. In the shared space, we pull the distribution of the target domain away from the source domain and obtain domain-aware features. To our best knowledge, this is the first work to introduce self-supervised learning into GZSL as learning guidance. Second, a Prototype Enhancing Module is proposed to utilize class prototypes to model reliable target domain distribution in finer granularity. In this module, a Prototype Alignment mechanism and a Prototype Dispersion mechanism are combined to guide the generation of better target class features with intra-class compactness and inter-class separability. Extensive experimental results on five standard benchmarks demonstrate that our model performs favorably against state-of-the-art GZSL methods.

Tissue adhesives have attracted intense and increasing interest due to their multiple biomedical applications. Despite the rapid development of adhesive hydrogels, huge challenges remain for materials that can ensure strong adhesion and seal hemostasis in aqueous and blood environments. To address this issue, we have developed an innovative design of PAA-based coacervate hydrogel with strong wet adhesion capability through a simple mixture of PAA copolymers with oxidized-carboxymethylcellulose (OCMC), and tannic acid (TA) as the main components, and structurally enhanced with natural clays (Laponite XLG). The absorbed TA provides solid adhesion to dry and wet substrates via multiple interactions, which endows the XLG-enhanced coacervate with the desired underwater adhesive strength. More importantly, the dielectric constant is introduced to evaluate the polarity of the tested samples, which may be used as guidance for the design of mussel-inspired adhesives with even better underwater adhesive properties. In vivo hemorrhage experiments further confirmed that the hydrogel adhesive dramatically shortened the hemostatic time to tens of seconds. Overall, the persistent adhesion and acceptable cytocompatibility of the hydrogel nanocomposite make it a promising alternative suture-free approach for rapid hemostasis at different length scales and is expected to be extended to clinical application for other organ injuries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

We tackle the problem of establishing dense correspondences between a pair of images in an efficient way. Most existing dense matching methods use 4D convolutions to filter incorrect matches, but 4D convolutions are highly inefficient due to their quadratic complexity. Besides, these methods learn features with fixed convolutions which cannot make learnt features robust to different challenge scenarios. To deal with these issues, we propose an Efficient Dynamic Correspondence Network (EDCNet) by jointly equipping pre-separate convolution (Psconv) and dynamic convolution (Dyconv) to establish dense correspondences in a coarse-to-fine manner. The proposed EDCNet enjoys several merits. First, two well-designed modules including a neighbourhood aggregation (NA) module and a dynamic feature learning (DFL) module are combined elegantly in the coarse-to-fine architecture, which is efficient and effective to establish both reliable and accurate correspondences. Second, the proposed NA module maintains linear complexity, showing its high efficiency. And our proposed DFL module has better flexibility to learn features robust to different challenges. Extensive experimental results show that our algorithm performs favorably against state-of-the-art methods on three challenging datasets including HPatches, Aachen Day-Night and InLoc.

The mainstream of image and sentence matching studies currently focuses on fine-grained alignment of image regions and sentence words. However, these methods miss a crucial fact: the correspondence between images and sentences does not simply come from alignments between individual regions and words but from alignments between the phrases they form respectively. In this work, we propose a novel Decoupled Cross-modal Phrase-Attention network (DCPA) for image-sentence matching by modeling the relationships between textual phrases and visual phrases. Furthermore, we design a novel decoupled manner for training and inferencing, which is able to release the trade-off for bi-directional retrieval, where image-to-sentence matching is executed in textual semantic space and sentence-to-image matching is executed in visual semantic space. Extensive experimental results on Flickr30K and MS-COCO demonstrate that the proposed method outperforms state-of-the-art methods by a large margin, and can compete with some methods introducing external knowledge.

Establishing effective correspondences between a pair of images is difficult due to real-world challenges such as illumination, viewpoint and scale variations. Modern detector-based methods typically learn fixed detectors from a given dataset, which is hard to extract repeatable and reliable keypoints for various images with extreme appearance changes and weakly textured scenes. To deal with this problem, we propose a novel Dynamic Keypoint Detection Network (DKDNet) for robust image matching via a dynamic keypoint feature learning module and a guided heatmap activator. The proposed DKDNet enjoys several merits. First, the proposed dynamic keypoint feature learning module can generate adaptive keypoint features via the attention mechanism, which is flexibly updated with the current input image and can capture keypoints with different patterns. Second, the guided heatmap activator can effectively fuse multi-group keypoint heatmaps by fully considering the importance of different feature channels, which can realize more robust keypoint detection. Extensive experimental results on four standard benchmarks demonstrate that our DKDNet outperforms state-of-the-art image-matching methods by a large margin. Specifically, our DKDNet can outperform the best image-matching method by 2.1% in AUC@ 3px on HPatches, 3.74% in AUC@ 5 ° on ScanNet, 7.14% in AUC@ 5 ° on MegaDepth and 12.32% in AUC@ 5 ° on YFCC100M.

Intracerebral hemorrhage (ICH) is the most devastating subtype of stroke, nevertheless specific treatments with conclusive clinical benefit in improving outcomes of ICH remain lacking. The present study applied dl-3-n-butylphthalide (NBP), a compound approved for the treatment of ischemic stroke and rarely studied in ICH, to an experimental animal model of ICH, aiming to evaluate the therapeutic effects of NBP on ICH and the potential mechanisms. The results showed that rats receiving NBP administration exhibited a structural and functional restoration of brain after ICH mainly manifested as alleviation of neuronal apoptosis, suppression of neuroinflammation and oxidative stress, neurovascular remodeling, and eventually improvement of neurological deficits. In addition, several protein targets of NBP were revealed, which mainly play molecular functions of ribonucleoside triphosphate phosphatase activity, pyrophosphatase activity, hydrolase activity and GTPase activity, and participate in the biological process of brain development by regulating the formation of cellular components such as spindles, polymeric cytoskeletal fibers, microtubules and synapses, through mediating pathways such as VEGF signaling pathway, Fc epsilon RI signaling pathway, ECM-receptor interaction, Fc gamma R-mediated phagocytosis, peroxisome and so on, guiding the mechanism exploration of NBP therapy to some extent. Taken together, the study added some new evidence to the application of NBP in ICH treatment., Competing Interests: Declaration of competing interest None., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Visual tracking aims to estimate object state in a video sequence, which is challenging when facing drastic appearance changes. Most existing trackers conduct tracking with divided parts to handle appearance variations. However, these trackers commonly divide target objects into regular patches by a hand-designed splitting way, which is too coarse to align object parts well. Besides, a fixed part detector is difficult to partition targets with arbitrary categories and deformations. To address the above issues, we propose a novel adaptive part mining tracker (APMT) for robust tracking via a transformer architecture, including an object representation encoder, an adaptive part mining decoder, and an object state estimation decoder. The proposed APMT enjoys several merits. First, in the object representation encoder, object representation is learned by distinguishing target object from background regions. Second, in the adaptive part mining decoder, we introduce multiple part prototypes to adaptively capture target parts through cross-attention mechanisms for arbitrary categories and deformations. Third, in the object state estimation decoder, we propose two novel strategies to effectively handle appearance variations and distractors. Extensive experimental results demonstrate that our APMT achieves promising results with high FPS. Notably, our tracker is ranked the first place in the VOT-STb2022 challenge.

Cherry tomatoes are highly well-liked and have a lot of nutritional value. However, the edible value of cherry tomatoes rapidly declines as their storage duration is extended. Pleurotus citrinopileatus polysaccharide (PCP) is a kind of polysaccharide obtained from P. citrinopileatus by water extraction. The effects of PCP were investigated to identify a way to maximally postpone cherry tomato degradation. The results showed that PCP had inhibitory effects on all 10 tested strains, and the inhibitory effect on Pseudomonas aeruginosa was the strongest. PCP could effectively reduce the weight loss rate and malondialdehyde accumulation of cherry tomatoes during storage, weaken the activity of polyphenol oxidase, and delay the decline of hardness, titratable acid content, and V C content compared with untreated cherry tomatoes. PCP solution at a concentration of 2 g/L exerted the best preservation effects. Therefore, PCP can potentially contribute to the preservation of vegetables and fruits.

A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

Coherent multiwave mixing is in demand for optical frequency conversion, imaging, quantum information science, etc., but has rarely been demonstrated in solid-state systems. Here, we observed three- and five-wave mixing (5WM) in a c -axis growth zinc oxide microwire on a Au film with picosecond pulses in the near-infrared region. An output 5WM of 4.7 × 10 -7 μW, only 2-3 orders smaller than the three-wave mixing, is achieved when the excitation power is as low as 1.5 mW and the peak power density as weak as ∼10 7 W/cm 2 . The excitation power dependence of 5WM agrees well with the perturbation limit under the low intensity but exhibits a strong deviation at a high pumping power. This extraordinary behavior is attributed to the cooperative resonant enhancement effect when pumping in the near-infrared range. Our study offers a potential solid-state platform for on-chip multiwave mixing and quantum nonlinear optics, such as generating many-photon entangled states or the construction of photon-photon quantum logic gates.

Weakly supervised object localization (WSOL) aims to predict both object locations and categories with only image-level class labels. However, most existing methods rely on class-specific image regions for localization, resulting in incomplete object localization. To alleviate this problem, we propose a novel end-to-end task-aware framework with a transformer encoder-decoder architecture (TAFormer) to learn class-agnostic foreground maps, including a representation encoder, a localization decoder, and a classification decoder. The proposed TAFormer enjoys several merits. First, the designed three modules can effectively perform class-agnostic localization and classification in a task-aware manner, achieving remarkable performance for both tasks. Second, an optimal transport algorithm is proposed to provide pixel-level pseudo labels to online refine foreground maps. To the best of our knowledge, this is the first work by exploring a task-aware framework with a transformer architecture and an optimal transport algorithm to achieve accurate object localization for WSOL. Extensive experiments with four backbones on two standard benchmarks demonstrate that our TAFormer achieves favorable performance against state-of-the-art methods. Furthermore, we show that the proposed TAFormer provides higher robustness against adversarial attacks and noisy labels.

Although injectable hydrogels with minimally invasive delivery have garnered significant interest, their potential applications have been restricted by a singular property. In this study, a supramolecular hydrogel system with improved adhesion was constructed through host-guest interactions between alginate and polyacrylamide. The maximum tensile adhesion strength between the β-cyclodextrin and dopamine-grafted alginate/adamantane-grafted polyacrylamide (Alg-βCD-DA/PAAm-Ad, namely AβCDPA) hydrogels and pigskin reached 19.2 kPa, which was 76 % stronger than the non-catechol-based control hydrogel (β-cyclodextrin-grafted alginate/adamantane-grafted polyacrylamide, Alg-βCD/PAAm-Ad). Moreover, the hydrogels demonstrated excellent self-healing, shear-thinning, and injectable properties. The required pressure to extrude the AβCDPA 2 hydrogel from a 16G needle at a rate of 2.0 mL/min was 67.4 N. As the polymer concentration and adamantane substitution degree increased, the hydrogels exhibited higher modulus, stronger network structure, and lower swelling ratio and degradation rate. Encapsulating and culturing cells within these hydrogels demonstrated good cytocompatibility. Therefore, this hydrogel can serve as a viscosity extender or bioadhesive, and as a carrier material to deliver encapsulated therapeutic substances into the body through minimally invasive injection methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Introduction: Cancer selectivity, including targeted internalization and accelerated drug release in tumor cells, remains a major challenge for designing novel stimuli-responsive nanocarriers to promote therapeutic efficacy. The hypoxic microenvironment created by photodynamic therapy (PDT) is believed to play a critical role in chemoresistance. Methods: We construct dual-responsive carriers ( DA NP CT ) that encapsulate the photosensitizer chlorin e6 (Ce6) and hypoxia-activated prodrug tirapazamine (TPZ) to enable efficient PDT and PDT-boosted hypoxia-activated chemotherapy. Results and discussion: Due to TAT masking, DA NP CT prolonged payload circulation in the bloodstream, and selective tumor cell uptake occurred via acidity-triggered TAT presentation. PDT was performed with a spatially controlled 660-nm laser to enable precise cell killing and exacerbate hypoxia. Hypoxia-responsive conversion of the hydrophobic NI moiety led to the disassembly of DA NP CT , facilitating TPZ release. TPZ was reduced to cytotoxic radicals under hypoxic conditions, contributing to the chemotherapeutic cascade. This work offers a sophisticated strategy for programmed chemo-PDT., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Feng, Zhang, Gao and Sun.)

In weakly supervised (WSAL) and unsupervised temporal action localization (UAL), the target is to simultaneously localize temporal boundaries and identify category labels of actions with only video-level category labels (WSAL) or category numbers in a dataset (UAL) during training. Among existing methods, attention based methods have achieved superior performance in both tasks by highlighting action segments with foreground attention weights. However, without the segment-level supervision on the attention weight learning, the quality of the attention weight hinders the performance of these methods. In this paper, we propose a novel Uncertainty Guided Collaborative Training (UGCT) strategy to alleviate this problem, which mainly includes two key designs: (1) The first design is an online pseudo label generation module, in which the RGB and FLOW streams work collaboratively to learn from each other. (2) The second design is an uncertainty aware learning module, which can mitigate the noise in the generated pseudo labels. These two designs work together to promote the model performance effectively and efficiently by exchanging information between RGB and FLOW streams. Extensive experimental results on two benchmark datasets with three attention based methods demonstrate the effectiveness of the proposed method, e.g, more than 7.0% performance gain for mAP@IoU=0.5 on THUMOS14 dataset.

Polypropylene (PP) mesh has been widely used in hernia repair as prosthesis material owing to its excellent balanced biocompatibility and mechanical properties. However, abdominal adhesion between the visceral and PP mesh is still a major problem. Therefore, anti-adhesive PP mesh was designed with poly(vinyl alcohol) (PVA) hydrogel and liposomes drug delivery system. First, PVA hydrogel coating was formed on the surface of PP mesh with freezing-thawing processing cycles (FTP). Subsequently, the lyophilized PVA 10 -c-PP was immersed in rapamycin (RPM)-loaded liposome solution until swelling equilibrated to obtain the anti-adhesion mesh RPM@LPS/PVA 10 -c-PP. It was demonstrated that the hydrogel coating can stably fix on the surface of PP mesh even after immersed in PBS solution at 37 °C or 40 °C for up to 30 days. In vitro cell tests revealed the excellent cytocompatibility and the potential to inhibit cell adhesion of the modified PP mesh. Moreover, the anti-adhesive effects of the RPM@LPS/PVA 10 -c-PP mesh was evaluated through in vivo experiments. The RPM@LPS/PVA 10 -c-PP mesh exhibited less adhesion than original PP mesh throughout the duration of implantation. At 30 days, the adhesion score of RPM@LPS/PVA 10 -c-PP mesh was 1.37 ± 0.75, however the original PP was 3 ± 0.71. Furthermore, the results of H&E and Masson trichrome staining proved that the RPM@LPS/PVA 10 -c-PP mesh showed slighter inflammation response and significant looser fibrous tissue surrounded the PP filaments as compared to the native PP. The current findings manifested that this type of RPM@LPS/PVA 10 -c-PP might be a potential candidate for anti-adhesion treatment. DATA AVAILABILITY: Data will be made available on request., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Point cloud shape correspondence aims at accurately mapping one point cloud to another point cloud with various 3D shapes. Since point clouds are usually sparse, disordered, irregular, and with diverse shapes, it is challenging to learn consistent point cloud representations and achieve the accurate matching of different point cloud shapes. To address the above issues, we propose a Hierarchical Shape-consistent TRansformer for unsupervised point cloud shape correspondence (HSTR), including a multi-receptive-field point representation encoder and a shape-consistent constrained module in a unified architecture. The proposed HSTR enjoys several merits. In the multi-receptive-field point representation encoder, we set progressively larger receptive fields in different blocks to simultaneously consider the local structure and the long-range context. In the shape-consistent constrained module, we design two novel shape selective whitening losses, which can complement each other to achieve suppression of features sensitive to shape change. Extensive experimental results on four standard benchmarks demonstrate the superiority and generalization ability of our approach to existing methods at the similar model scale, and our method achieves the new state-of-the-art results.

Diabetic retinopathy (DR) grading from fundus images has attracted increasing interest in both academic and industrial communities. Most convolutional neural network-based algorithms treat DR grading as a classification task via image-level annotations. However, these algorithms have not fully explored the valuable information in the DR-related lesions. In this article, we present a robust framework, which collaboratively utilizes patch-level and image-level annotations, for DR severity grading. By an end-to-end optimization, this framework can bidirectionally exchange the fine-grained lesion and image-level grade information. As a result, it exploits more discriminative features for DR grading. The proposed framework shows better performance than the recent state-of-the-art algorithms and three clinical ophthalmologists with over nine years of experience. By testing on datasets of different distributions (such as label and camera), we prove that our algorithm is robust when facing image quality and distribution variations that commonly exist in real-world practice. We inspect the proposed framework through extensive ablation studies to indicate the effectiveness and necessity of each motivation. The code and some valuable annotations are now publicly available.

Ultralow thermal conductivity materials have triggered much interest due to diverse applications in thermal insulation, thermal barrier coating, and especially thermoelectrics. Two dimensional (2D) indium tellurides with ultralow thermal conductivity provide a versatile platform for tailoring the heat transfer, exploring new candidates for thermoelectrics, and achieving miniature, lightweight, and highly integrated devices. Unfortunately, their nanostructure and structure-related heat transfer properties at a 2D scale are much less studied due to difficulties in material fabrication. The ionic character between interlayers and strong covalent bonds in 3D directions impede the anisotropic growth of indium telluride flakes; meanwhile, the low environmental stability and chemical reactivity of tellurium also limit the fabrication of high-quality tellurides, thus hindering the exploration of thermal transport properties. Here, a self-modulation-guided growth strategy to synthesize high-quality 2D In 4 Te 3 single crystals with ultralow thermal conductivity (0.47 W m -1  K -1 ) is developed. This strategy can also be extended to synthesize a series of highly crystallized metal tellurides, providing excellent candidates for further application in thermoelectrics., (© 2022 Wiley-VCH GmbH.)

This study developed, a novel polypropylene (PP) mesh combined with poly (L-lactic acid) (PLA) electrospun nanofibers loaded sirolimus (SRL). The PP mesh was combined with PLA/SRL (1/0, 1/0.01, 1/0.02; mass ratios) composed electrospun membrane characterized by FTIR spectroscopy, XPS and SEM, and evaluated for cytocompatibility in vitro. In an in vivo study, a total of 84 Sprague-Dawley rats were employed to evaluate the efficacy of the novel composite PP mesh anti-adhesion, mechanical properties and inflammation. As a results, the PLA/SRL membrane could compound with PP mesh stably and load SRL. Although tensile testing showed that the mechanical properties of composite mesh decreased in vivo, the integration strength between the tissue and mesh was still able to counteract intra-abdominal pressure. Compared with the native PP mesh group, the novel PP mesh group showed a lower score for abdominal adhesion and inflammation. More importantly, the novel PP mesh completely integrated with the abdominal wall and had sufficient mechanical strength to repair abdominal wall defects., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The issue of pervasively enhanced drug resistance of pancreatic cancer is fundamental to a better understanding of gemcitabine-based chemotherapy. Currently available treatment plans involving injectable therapeutics are mainly engineered to improve the performance and broaden their applications in the domain of biomedicine. Fixed-dose-rate infusion of free gemcitabine (Gem) has drawn appropriate attention for its potent anti-tumor efficacy against various solid tumors, whereas it remains a considerable challenge to extend its application and achieve better treatment. Here, we have prepared and demonstrated a long-acting delivery system using gemcitabine and injectable in situ hydrogel for the localized treatment of pancreatic cancer. The hydrogel was prepared using polysaccharide derivatives, oxidized-carboxymethylcellulose (OCMC) and carboxymethylchitosan (CMCS) at optimal ratios by a dopamine-functionalized method for the controlled release of Gem. In vitro drug release behaviors for up to a week indicated sustained drug release of the Gem delivery system. Moreover, desirable apoptosis promotion and apparent cellular proliferation inhibition associated with the drug depot have been found in vitro against BxPC-3 pancreatic cancer cells, bringing minimized side effects to systemic normal tissues. The current findings manifested that the release out of the localized delivery platform in a sustained pattern afforded a durable gemcitabine-based chemotherapy effect and inhibited tumor metastasis more persistently after intratumoral injection of the Gem@Gel system, thereby advancing the development of novel drug-loaded materials with properties not accessed previously.

Objectives: This study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors., Methods: A total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort., Results: The MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model ( p <0.001)., Conclusions: Machine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Zhang, Ma, Zhang, Zhang and Ye.)

Multi-network hydrogels with high strength and toughness have attracted increasing attention. Herein, a hybrid hydrogel consisting of alginate, gelatin, and polyacrylamide was constructed with the combination of advantages of natural and synthetic polymers. Alginate grafted with host-guest complex of βCD/Ad-AAm was first prepared, namely Alg-βCD/Ad-AAm, then further crosslink with gelatin methacryloyl (GelMA) to form hydrogel via one-step UV light initiation. The hydrogel produced by this method has more uniform and well-crosslinked networks. The hydrogels demonstrated uniform porosity, adjustable hydrophilicity (water contact angle within 32.7-91.5°), and desired mechanical properties (maximum tensile strain of 242.8%, tensile strength of 75.9 kPa, and Young's modulus of 28.5 kPa). The hydrogel also possessed self-healing ability and pH sensitivity, showing higher mechanical tensile strength at lower pH. The temperature-adjustable viscosity of pre-gel solution (sol-gel transition point of 20.4 °C) endowed it to be 3D printed as a bioink, and the printed scaffold exhibited good resilience and toughness. Moreover, HUVEC, L929, and 3T3 cells were cultured on hydrogel surfaces for 28 days and were enveloped within the hydrogels for 3D culture, indicating excellent cytocompatibility of the hydrogels. Therefore, this hybrid hydrogel system can be used potentially in cell culture scaffold and tissue engineering., (Copyright © 2022. Published by Elsevier B.V.)

Common poly(lactide- co -glycolide) ( i -PLGA) has emerged as a biodegradable and biocompatible material in tissue engineering. However, the poor hydrophilicity and elasticity of i -PLGA lead to its limited application in tissue engineering. To this end, an amphiphilic crosslinked four-armed poly(lactic- co -glycolide) was prepared. First, four-armed PLGA (4A-PLGA) was synthesized by polymerizing l-lactide (LA) and glycolide (GA) with pentaerythritol as the initiator. Then, the hydrophilic polymer poly(glutamate propylene ester) (PGPE) was prepared through the esterification of glutamic acid and 1,2-propanediol. The hydrophilic 4A-PLGA-PGPE was finally synthesized through the condensation reaction of 4A-PLGA and PGPE with the aid of triphosgene. 4A-PLGA-PGPE was then used to prepare amphiphilic membranes by electrospinning. It was demonstrated that the mechanical properties and biocompatibility of 4A-PLGA were improved after the introduction of PGPE. Furthermore, the introduction of glutamate improved the hydrophilicity of 4A-PLGA, thus effectively promoting cell entry and adhesion, which makes the electrospun 4A-PLGA-PGPE membranes promising for tissue engineering.

Prevention of Alzheimer's disease (AD) is a global imperative, but reliable early interventions are currently lacking. Microglia-mediated chronic neuroinflammation is thought to occur in the early stage of AD and plays a critical role in AD pathogenesis. Here, oxytocin (OT)-loaded angiopep-2-modified chitosan nanogels (AOC NGs) were designed for early treatment of AD via inhibiting innate inflammatory response. Through the effective transcytosis of angiopep-2, AOC NGs were driven intravenously to cross the blood-brain barrier, enter the brain, and enrich in brain areas affected by AD. A large amount of OT was then released and specifically bound to the pathological upregulated OT receptor, thus effectively inhibiting microglial activation and reducing inflammatory cytokine levels through blocking the ERK/p38 MAPK and COX-2/iNOS NF-κB signaling pathways. Consecutive weekly intravenous administration of AOC NGs into 12-week-old young APP/PS1 mice, representing the early stage of AD, remarkably slowed the progression of Aβ deposition and neuronal apoptosis in the APP/PS1 mice as they aged and ultimately prevented cognitive impairment and delayed hippocampal atrophy. Together, the findings suggest that AOC NGs, which show good biosafety, can serve as a promising therapeutic candidate to combat neuroinflammation for early prevention of AD.

Quantification of germination characteristics between herbicide-resistant and -susceptible weeds might provide methods to control resistant weeds and permit better prediction of evolution and persistence of herbicide resistance. This study aimed to compare the germination characteristics of Asian minor bluegrass ( Polypogon fugax ) populations that are resistant or susceptible to quizalofop-p-ethyl under controlled conditions, which the resistance mechanism is involved in glutathione S-transferases (GSTs) metabolism-based resistance. No major differences in seed germination were found at diverse temperatures, pH ranges, and light conditions. However, a significant difference that seed response to a gradient of osmotic and salt stress between the resistant and susceptible P. fugax populations were found. Two stress response genes ( P5CS-1 and CDPK-2 ) in P. fugax were likely involved in germination rate as well as germination speed in response to these stresses. Subsequently, population verification demonstrated that P5CS-1 and CDPK-2 genes may be linked to the resistance mechanism. Additionally, the two genes play an important role in response to salt stress and osmotic stress as shown by transcript abundance after stress treatments. Our findings suggest that the variation of the germination characteristics in P. fugax associates with the presence of GST-endowed resistance mechanism., Competing Interests: TZ is employed by Shandong Cynda Chemical Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Peng, Lin, Zhang, Yan, Bai and Pan.)

To date, the robust and durable adhesion capability of hydrogel adhesives in wet environments remains a huge challenge. Herein, a physicochemically double-network crosslinked hydrogel matrix was prepared by mixing acrylic acid (AAc), chitosan (CS) and tannic acid (TA) as the main components and the subsequent in situ polymerization of AAc. The abundant reactive sites on the surface of the hydrogel matrix facilitate rapid, strong and repeatable adhesion to different surfaces of engineering solids and biological tissues in an aquatic environment. The formation of amide covalent bonds resulting from the addition of the bridging agent further expands the long-term application of the hydrogel in tissue repair, and the constructed hydrogel-tissue adhesive interface still has robust adhesion energy after soaking in a physiological environment for up to one month. Moreover, the hydrogel showed fantastic hemostatic performance due to its characteristics of platelet adhesion and high burst pressure. Overall, the persistent adhesion and excellent cytocompatibility of the hydrogel adhesive make it potentially applicable in medical adhesives.

Weakly Supervised Object Localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, a common limitation for available techniques based on classification networks is that they only highlight the most discriminative part of the object, not the entire object. To alleviate this problem, we propose a novel end-to-end part discovery model (PDM) to learn multiple discriminative object parts in a unified network for accurate object localization and classification. The proposed PDM enjoys several merits. First, to the best of our knowledge, it is the first work to directly model diverse and robust object parts by exploiting part diversity, compactness, and importance jointly for WSOL. Second, three effective mechanisms including diversity, compactness, and importance learning mechanisms are designed to learn robust object parts. Therefore, our model can exploit complementary spatial information and local details from the learned object parts, which help to produce precise bounding boxes and discriminate different object categories. Extensive experiments on two standard benchmarks demonstrate that our PDM performs favorably against state-of-the-art WSOL approaches.

Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. Existing methods mainly focus on learning modality-shared representations by embedding images from different modalities into a common feature space, in which some discriminative modality information is discarded. Different from these methods, in this paper, we propose a novel Modality-Specific Memory Network (MSMNet) to complete the missing modality information and aggregate visible and infrared modality features into a unified feature space for the VI-ReID task. The proposed model enjoys several merits. First, it can exploit the missing modality information to alleviate the modality discrepancy when only the single-modality input is provided. To the best of our knowledge, this is the first work to exploit the missing modality information completion and alleviate the modality discrepancy with the memory network. Second, to guide the learning process of the memory network, we design three effective learning strategies, including feature consistency, memory representativeness and structural alignment. By incorporating these learning strategies in a unified model, the memory network can be well learned to propagate identity-related information between modalities and boost the VI-ReID performance. Extensive experimental results on two standard benchmarks (SYSU-MM01 and RegDB) demonstrate that the proposed MSMNet performs favorably against state-of-the-art methods.

Weakly supervised object localization (WSOL) aims at localizing objects with only image-level labels, which has better scalability and practicability than fully supervised methods. However, without pixel-level supervision, existing methods tend to generate rough localization maps, which hinders localization performance. To alleviate this problem, we propose an adversarial transformer network (ATNet), which aims to obtain a well-learned localization model with pixel-level pseudo labels. The proposed ATNet enjoys several merits. First, we design an object transformer ( G ) that can generate localization maps and pseudo labels effectively and dynamically, and a part transformer ( D ) to accurately discriminate detailed local differences between localization maps and pseudo labels. Second, we propose to train G and D via an adversarial process, where G can generate more accurate localization maps approaching pseudo labels to fool D . To the best of our knowledge, this is the first work to explore transformers with adversarial training to obtain a well-learned localization model for WSOL. Extensive experiments with four backbones on two standard benchmarks demonstrate that our ATNet achieves favorable performance against state-of-the-art WSOL methods. Besides, our adversarial training can provide higher robustness against adversarial attacks.

Background: Mounting evidence supports that long noncoding RNAs (lncRNAs) have critical roles during cancer initiation and progression. In this study, we report that the plasmacytoma variant translocation 1 (PVT1) lncRNA is involved in breast cancer progression., Methods: qRT-PCR and western blot were performed to detect the gene and protein expression. Colony formation would healing and transwell assays were used to detect cell function. Dual-luciferase reporter assay and RNA pull-down experiments were used to examine the mechanisms interaction between molecules. Orthotopic mouse models were established to evaluate the influence of PVT1 on tumor growth and metastasis in vivo., Results: PVT1 is significant upregulated in breast cancer patients' plasma and cell lines. PVT1 promotes breast cancer cell proliferation and metastasis both in vitro and in vivo. Mechanistically, PVT1 upregulates FOXQ1 via miR-128-3p and promotes epithelial-mesenchymal transition. In addition, PVT1 binds to the UPF1 protein, thereby inducing epithelial-mesenchymal transition, proliferation and metastasis in breast cancer cells., Conclusion: PVT1 may act as an oncogene in breast cancer through binding miR-128-3p and UPF1 and represents a potential target for BC therapeutic development., (© 2021. The Author(s).)

Poly(lactide- co -glycolide) (PLGA) has been widely used as a scaffold material for tissue engineering owing to its biocompatibility, biodegradability, and biosafety. However, lactic acid (LA) produced during PLGA degradation is prone to inflammation, which is a shortcoming that must be avoided. To this end, crystalline PLGA-PEG was synthesized here for the first time. To make the crystalline PLGA-PEG more suitable for tissue engineering, porous crystalline PLGA-PEG was prepared via the swelling behavior during recrystallization annealing. The structure and properties of the porous crystalline PLGA-PEG were confirmed by SEM, POM, and XRD. Furthermore, the swelling behavior of different PEG molecular weights was studied, and the cell viability test and alkaline phosphatase activity test showed that PLGA-PEG has good biocompatibility. Such a porous crystalline PLGA-PEG will make PLGA have a broader application prospect in bone repair.

Introduction: Delineating the margins of Oral squamous cell carcinoma (OSCC) is a critical step for optimaltumor resection. The aim of this study was to evaluate the accuracy of lesion surgical margin identification using autofluorescence visualization., Materials and Methods: Thirty patients with OSCC were included in this study. For each lesion, the fluorescence loss boundary was determined using VELscope before ablative surgical resection (with a 1.5-2cm safety margin) was performed. A total of 126 samples were obtained from 30 surgical specimens, each containing the tissue from the fluorescence loss boundary to surgical margin. The status of each sample was determined by oral pathologists and the staining intensities of Ki-67, E-cadherin, and Vimentin at the fluorescence loss boundary and surgical margin were evaluated by immunohistochemistry., Results: Fluorescence loss regions were identified in all patients. Of the 126 samples collected, HE staining identified 77 normal epithelia (61.1%), 26 mild dysplasia (20.6%), 17 severe dysplasia (13.4%) and 6 carcinomas in situ (4.9%). A significant correlation was found between the differentiation grade of tumor cells and the pathological status of the surgical marginal specimens (P<0.05). Forty-two of the 126 samples were randomly selected for further immunohistochemical staining. No significant differences were seen in Ki-67, E-cadherin, or Vimentin expression at the fluorescence loss boundary or surgical margin, however, the proteins' expression level was positively correlated with the degree of dysplasia (P<0.01)., Conclusion: Autofluorescence visualization has potential as a simple surgical margin setting device for OSCC and may help delineate the superficial area of OSCC with acceptable accuracy. However, when considering the inherent limitations of this system, we suggest that the approach should only be applied under certain conditions, such as when dealing with superficial, well-differentiated lesions., (Copyright © 2021 Elsevier B.V. All rights reserved.)

With the explosive growth of video categories, zero-shot learning (ZSL) in video classification has become a promising research direction in pattern analysis and machine learning. Based on some auxiliary information such as word embeddings and attributes, the key to a robust ZSL method is to transfer the learned knowledge from seen classes to unseen classes, which requires relationship modeling between these concepts (e.g., categories and attributes). However, most existing approaches ignore to model the explicit relationships in an end-to-end manner, resulting in low effectiveness of knowledge transfer. To tackle this problem, we reconsider the video ZSL task as a task-driven message passing process to jointly enjoy several merits including alleviated heterogeneity gap, low domain shift, and robust temporal modeling. Specifically, we propose a prototype-sample GNN (PS-GNN) consisting of a prototype branch and a sample branch to directly and adaptively model all the relationships between category-attribute, category-category, and attribute-attribute. The prototype branch aims to learn robust representations of video categories, which takes as input a set of word-embedding vectors corresponding to the concepts. The sample branch is designed to generate features of a video sample by leveraging its object semantics. With the co-adaption and cooperation between both branches, a unified and robust ZSL framework is achieved. Extensive experiments strongly evidence that PS-GNN obtains favorable performance on five popular video benchmarks consistently.

Hydrogel adhesives have been widely used in wet environments. Nonetheless, strong and stable persistent adhesion remains a challenge. Here, we report a facile yet powerful strategy to construct high-strength hydrogel adhesives for durable adhesion in a saline environment. Such a hydrogel consists of two polymer networks: a hydrophobic-associated polyacrylamide network of covalent and noncovalent cross-links and an alginate network cross-linked by divalent cations in saline. Meanwhile, polydopamine nanoparticles formed through in-situ self-polymerization are distributed evenly throughout the system to provide underwater adhesion. A low and controllable swelling rate and high compressive strength of hydrogels can be achieved via this multiple interaction strategy. Ultimately, this strategy contributes to the persistent underwater adhesion of hydrogels, and the decreasing rate of lap-shear adhesion strength of hydrogels is only 24.79 ± 8.01% after saline immersion for up to 21 days. Moreover, good cytocompatibility of hydrogels is helpful for their application in the biomedical field.

Developing a high-performance nonprecious metal electrocatalyst for water splitting is a strong demand for the large-scale application of electrochemical H 2 production. In this work, we design a facile and scalable strategy to activate titanium metal for the hydrogen evolution reaction (HER) in alkaline media through incorporating hydrogen into the α-Ti crystal lattice by H 2 plasma bombardment. Benefiting from the accelerated charge transfer and enlarged electrochemical surface area after H 2 plasma treatment, the H-incorporated Ti shows remarkably enhanced HER activity with a much lower overpotential at -10 mA cm -2 by 276 mV when compared to the pristine Ti. It is revealed that the retention of the incorporated H(D) atoms in the Ti crystal lattice during HER accounts for the durable feature of the catalyst. Density functional theory calculations demonstrate the effectiveness of hydrogen incorporation in tuning the adsorption energy of reaction species via charge redistribution. Our work offers a novel route to activate titanium or other metals by H incorporation through a controllable H 2 plasma treatment to tune the electronic structure for water splitting reactions.