Method for Augmenting Side-Scan Sonar Seafloor Sediment Image Dataset Based on BCEL1-CBAM-INGAN.

In this paper, a method for augmenting samples of side-scan sonar seafloor sediment images based on CBAM-BCEL1-INGAN is proposed, aiming to address the difficulties in acquiring and labeling datasets, as well as the insufficient diversity and quantity of data samples. Firstly, a Convolutional Block Attention Module (CBAM) is integrated into the residual blocks of the INGAN generator to enhance the learning of specific attributes and improve the quality of the generated images. Secondly, a BCEL1 loss function (combining binary cross-entropy and L1 loss functions) is introduced into the discriminator, enabling it to focus on both global image consistency and finer distinctions for better generation results. Finally, augmented samples are input into an AlexNet classifier to verify their authenticity. Experimental results demonstrate the excellent performance of the method in generating images of coarse sand, gravel, and bedrock, as evidenced by significant improvements in the Frechet Inception Distance (FID) and Inception Score (IS). The introduction of the CBAM and BCEL1 loss function notably enhances the quality and details of the generated images. Moreover, classification experiments using the AlexNet classifier show an increase in the recognition rate from 90.5% using only INGAN-generated images of bedrock to 97.3% using images augmented using our method, marking a 6.8% improvement. Additionally, the classification accuracy of bedrock-type matrices is improved by 5.2% when images enhanced using the method presented in this paper are added to the training set, which is 2.7% higher than that of the simple method amplification. This validates the effectiveness of our method in the task of generating seafloor sediment images, partially alleviating the scarcity of side-scan sonar seafloor sediment image data.

DS-SIAUG: A Self-Training Approach Using a Disrupted Student Model for Enhanced Side-Scan Sonar Image Augmentation.

Side-scan sonar is a principal technique for subsea target detection, where the quantity of sonar images of seabed targets significantly influences the accuracy of intelligent target recognition. To expand the number of representative side-scan sonar target image samples, a novel augmentation method employing self-training with a Disrupted Student model is designed (DS-SIAUG). The process begins by inputting a dataset of side-scan sonar target images, followed by augmenting the samples through an adversarial network consisting of the DDPM (Denoising Diffusion Probabilistic Model) and the YOLO (You Only Look Once) detection model. Subsequently, the Disrupted Student model is used to filter out representative target images. These selected images are then reused as a new dataset to repeat the adversarial filtering process. Experimental results indicate that using the Disrupted Student model for selection achieves a target recognition accuracy comparable to manual selection, improving the accuracy of intelligent target recognition by approximately 5% over direct adversarial network augmentation.

Multi-Scale Marine Object Detection in Side-Scan Sonar Images Based on BES-YOLO.

Aiming at the problem of low accuracy of multi-scale seafloor target detection in side-scan sonar images with high noise and complex background texture, a model for multi-scale target detection using the BES-YOLO network is proposed. First, an efficient multi-scale attention (EMA) mechanism is used in the backbone of the YOLOv8 network, and a bi-directional feature pyramid network (Bifpn) is introduced to merge the information of different scales, finally, a Shape_IoU loss function is introduced to continuously optimize the model and improve its accuracy. Before training, the dataset is preprocessed using 2D discrete wavelet decomposition and reconstruction to enhance the robustness of the network. The experimental results show that 92.4% of the mean average accuracy at IoU of 0.5 (mAP@0.5) and 67.7% of the mean average accuracy at IoU of 0.5 to 0.95 (mAP@0.5:0.95) are achieved using the BES-YOLO network, which is an increase of 5.3% and 4.4% compared to the YOLOv8n model. The research results can effectively improve the detection accuracy and efficiency of multi-scale targets in side-scan sonar images, which can be applied to AUVs and other underwater platforms to implement intelligent detection of undersea targets.

Multiscale Bayes Adaptive Threshold Wavelet Transform Geomagnetic Basemap Denoising Taking Residual Constraints into Account.

To achieve high-precision geomagnetic matching navigation, a reliable geomagnetic anomaly basemap is essential. However, the accuracy of the geomagnetic anomaly basemap is often compromised by noise data that are inherent in the process of data acquisition and integration of multiple data sources. In order to address this challenge, a denoising approach utilizing an improved multiscale wavelet transform is proposed. The denoising process involves the iterative multiscale wavelet transform, which leverages the structural characteristics of the geomagnetic anomaly basemap to extract statistical information on model residuals. This information serves as the a priori knowledge for determining the Bayes estimation threshold necessary for obtaining an optimal wavelet threshold. Additionally, the entropy method is employed to integrate three commonly used evaluation indexes-the signal-to-noise ratio, root mean square (RMS), and smoothing degree. A fusion model of soft and hard threshold functions is devised to mitigate the inherent drawbacks of a single threshold function. During denoising, the Elastic Net regular term is introduced to enhance the accuracy and stability of the denoising results. To validate the proposed method, denoising experiments are conducted using simulation data from a sphere magnetic anomaly model and measured data from a Pacific Ocean sea area. The denoising performance of the proposed method is compared with Gaussian filter, mean filter, and soft and hard threshold wavelet transform algorithms. The experimental results, both for the simulated and measured data, demonstrate that the proposed method excels in denoising effectiveness; maintaining high accuracy; preserving image details while effectively removing noise; and optimizing the signal-to-noise ratio, structural similarity, root mean square error, and smoothing degree of the denoised image.

ATG-101 Is a Tetravalent PD-L1×4-1BB Bispecific Antibody That Stimulates Antitumor Immunity through PD-L1 Blockade and PD-L1-Directed 4-1BB Activation.

Immune checkpoint inhibitors (ICI) have transformed cancer treatment. However, only a minority of patients achieve a profound response. Many patients are innately resistant while others acquire resistance to ICIs. Furthermore, hepatotoxicity and suboptimal efficacy have hampered the clinical development of agonists of 4-1BB, a promising immune-stimulating target. To effectively target 4-1BB and treat diseases resistant to ICIs, we engineered ATG-101, a tetravalent "2+2″ PD-L1×4-1BB bispecific antibody. ATG-101 bound PD-L1 and 4-1BB concurrently, with a greater affinity for PD-L1, and potently activated 4-1BB+ T cells when cross-linked with PD-L1-positive cells. ATG-101 activated exhausted T cells upon PD-L1 binding, indicating a possible role in reversing T-cell dysfunction. ATG-101 displayed potent antitumor activity in numerous in vivo tumor models, including those resistant or refractory to ICIs. ATG-101 greatly increased the proliferation of CD8+ T cells, the infiltration of effector memory T cells, and the ratio of CD8+ T/regulatory T cells in the tumor microenvironment (TME), rendering an immunologically "cold" tumor "hot." Comprehensive characterization of the TME after ATG-101 treatment using single-cell RNA sequencing further revealed an altered immune landscape that reflected increased antitumor immunity. ATG-101 was well tolerated and did not induce hepatotoxicity in non-human primates. According to computational semimechanistic pharmacology modeling, 4-1BB/ATG-101/PD-L1 trimer formation and PD-L1 receptor occupancy were both maximized at around 2 mg/kg of ATG-101, providing guidance regarding the optimal biological dose for clinical trials. In summary, by localizing to PD-L1-rich microenvironments and activating 4-1BB+ immune cells in a PD-L1 cross-linking-dependent manner, ATG-101 safely inhibits growth of ICI resistant and refractory tumors. SIGNIFICANCE: The tetravalent PD-L1×4-1BB bispecific antibody ATG-101 activates 4-1BB+ T cells in a PD-L1 cross-linking-dependent manner, minimizing the hepatotoxicity of existing 4-1BB agonists and suppressing growth of ICI-resistant tumors. See related commentary by Ha et al., p. 1546.

Manipulating Oxygen Vacancies by K+ Doping and Controlling Mn2+ Deposition to Boost Energy Storage in ß-MnO2.

Aqueous zinc-ion batteries (ZIBs) have gained wide attention for their low cost, high safety, and environmental friendliness in recent years. ß-MnO2, a potential cathode material for ZIBs, has been restricted by its small channels for efficient charge storage. Herein, ß-MnO2 nanorods with oxygen vacancies are fabricated by a K+-doping strategy to improve the performance of ZIBs. The assembled batteries exhibit a capacity of 468 mAh g-1, a power density of 2605 W kg-1, and an energy density of 179 Wh kg-1, which outperforms most reported ZIBs. Such a performance is owing to the synergistic combination of the oxygen vacancies in ß-MnO2 and concurrent deposition of ε-MnO2 from Mn2+ in the electrolyte. Furthermore, superior cycling stability with negligible capacity decay in these batteries is demonstrated over 1000 cycles at a high current of 2 A g-1. This study reveals the importance of oxygen vacancies and Mn2+ deposition effect in understanding the mechanism of charge storage in MnO2-based ZIBs.

Elastomeric Nanodielectrics for Soft and Hysteresis-Free Electronics.

Elastomeric dielectrics are crucial for reliably governing the carrier densities in semiconducting channels during deformation in soft/stretchable field-effect transistors (FETs). Uncontrolled stacking of polymeric chains renders elastomeric dielectrics poorly insulated at nanoscale thicknesses, thereby thick films are usually required, leading to high voltage or power consumption for on/off operations of FETs. Here, layer-by-layer assembly is exploited to build 15-nm-thick elastomeric nanodielectrics through alternative adsorption of oppositely charged polyurethanes (PUs) for soft and hysteresis-free FETs. After mild thermal annealing to heal pinholes, such PU multilayers offer high areal capacitances of 237 nF cm-2 and low leakage current densities of 3.2 × 10-8 A cm-2 at 2 V. Owing to the intrinsic ductility of the elastomeric PUs, the nanofilms possess excellent dielectric properties at a strain of 5% or a bending radius of 1.5 mm, while the wrinkled counterparts show mechanical stability with negligible changes of leakage currents after repeated stretching to a strain of 50%. Besides, these nanodielectrics are immune to high humidity and conserve their properties when immersed into water, despite their assembly occurs aqueously. Furthermore, the PU dielectrics are implemented in carbon nanotube FETs, demonstrating low-voltage operations (< 1.5 V) and negligible hysteresis without any encapsulations.

A Mosaic Method for Side-Scan Sonar Strip Images Based on Curvelet Transform and Resolution Constraints.

Due to the complex marine environment, side-scan sonar signals are unstable, resulting in random non-rigid distortion in side-scan sonar strip images. To reduce the influence of resolution difference of common areas on strip image mosaicking, we proposed a mosaic method for side-scan sonar strip images based on curvelet transform and resolution constraints. First, image registration was carried out to eliminate dislocation and distortion of the strip images. Then, the resolution vector of the common area in two strip images were calculated, and a resolution model was created. Curvelet transform was then performed for the images, the resolution fusion rules were used for Coarse layer coefficients, and the maximum coefficient integration was applied to the Detail layer and Fine layer to calculate the fusion coefficients. Last, inverse Curvelet transform was carried out on the fusion coefficients to obtain images in the fusion area. The fusion images in multiple areas were then combined in the registered images to obtain the final image. The experiment results showed that the proposed method had better mosaicking performance than some conventional fusion algorithms.

Recent Advances on Conductive 2D Covalent Organic Frameworks.

As a burgeoning family of crystalline porous copolymers, covalent organic frameworks (COFs) allow precise atomic insertion of organic components in the topology construction to form periodic networks and ordered nanopores. Their 2D networks bear great similarities to graphene analogs, and therefore are essential additions to the 2D family. Here, the electronic properties of conductive 2D-COFs are reviewed and their bonding strategies and structural characteristics are examined in detail. The controlling approaches toward the morphologies of conductive 2D-COFs are further explored, followed by a discussion of their applications in field-effect transistors, photodetectors, sensors, catalysis, and energy storage. Finally, research challenges and forthcoming developments are projected. The resulting survey reveals that the extended porous 2D organic networks with conductive properties will provide great opportunities and essential innovations in various electronics and energy-related fields.

DGT, a novel heterocyclic diterpenoid, effectively suppresses psoriasis via inhibition of STAT3 phosphorylation.

BACKGROUND AND PURPOSE: Psoriasis is a chronic immune-mediated inflammatory skin disease that easily recurs and is difficult to cure. DGT is a novel synthetic heterocyclic diterpenoid, whose structure has not been previously reported. We have investigated the action of DGT against psoriasis, specifically the hyperproliferation of epidermal keratinocytes, angiogenesis and pathogenic inflammatory responses. EXPERIMENTAL APPROACH: We investigated its pharmacokinetics in skin after topical administration. We characterized its pharmacological actions in vitro and in vivo using cell proliferation assay, cell apoptosis assay, diethylstilbestrol-induced mouse vaginal epithelial cell mitosis model, tube formation assay, cell migration assay, chick embryonic chorioallantoic membrane (CAM) assay, histological, flow cytometric analysis and imiquimod (IMQ)-induced psoriasis-like model. KEY RESULTS: DGT was found to be mainly distributed in the epidermis and dermis, which indicated that DGT was suitable as a topical treatment. DGT inhibited cell proliferation and induced apoptotic cell death of keratinocytes in vitro and in vivo. Moreover, DGT inhibited endothelial cell proliferation, tube formation and migration of in vitro angiogenesis, as well as in vivo CAM angiogenesis. In an IMQ-induced psoriasis-like skin inflammation murine model, topical application of DGT ameliorated keratinocyte proliferation and inflammatory response, especially in IL-17-related psoriasiform dermatitis. Furthermore, our results demonstrated that DGT prevented these pathological processes of psoriasis through suppression of STAT3 phosphorylation. CONCLUSION AND IMPLICATIONS: DGT has great potential as a novel therapeutic agent for the treatment of psoriatic skin disease.

Anti-H1N1 viral activity of three main active ingredients from zedoary oil.

Influenza virus is one of the most widespread infectious diseases in the world. It poses a serious public health threat to humans. With the emergence of drug-resistant virus strains, antiviral drugs are urgently needed to control virus transmission and disease progression. In this study, three main active substances-curcumol, curdione and germacrone-were isolated from the traditional Chinese medicine zedoary. They inhibited the replication of influenza A (H1N1) virus in a dose-dependent manner. After treatment with these compounds, the expression of viral protein and RNA synthesis were inhibited. In vivo, these compounds also reduced H1N1-induced lung damage and the load of virus in serum as well as whole blood cells. In a proteomic analysis, after treatment with germacrone, the expression of antiviral protein and the amount of intracellular virus were significantly reduced, further proving that germacrone can inhibit viral replication. Our experiments have shown that curcumol, curdione and germacrone can inhibit the replication of H1N1 virus; in particular, germacrone shows potential both in vitro and in vivo as a therapeutic drug.

Metal-Layer Assisted Growth of Ultralong Quasi-2D MOF Nanoarrays on Arbitrary Substrates for Accelerated Oxygen Evolution.

Controlled growth of metal-organic frameworks (MOFs) nanocrystals on requisite surfaces is highly desired for myriad applications related to catalysis, energy, and electronics. Here, this challenge is addressed by overlaying arbitrary surfaces with a thermally evaporated metal layer to enable the well-aligned growth of ultralong quasi-2D MOF nanoarrays comprising cobalt ions and thiophenedicarboxylate acids. This interfacial engineering approach allows preferred chelation of carboxyl groups in the ligands with the metal interlayers, thereby making possible the fabrication and patterning of MOF nanoarrays on substrates of any materials or morphologies. The MOF nanoarrays grown on porous metal scaffolds demonstrate high electrocatalytic capability for water oxidation, exhibiting a small overpotential of 270 mV at 10 mA cm-2 , or 317 mV at 50 mA cm-2 as well as negligible decay of performance within 30 h. The enhanced performance stems from the improved electron and ion transport in the hierarchical porous nanoarrays consisting of in situ formed oxyhydroxide nanosheets in the electrochemical processes. This approach for mediating the growth of MOF nanoarrays can serve as a promising platform for diverse applications.

Glycosphingolipid GM3 is Indispensable for Dengue Virus Genome Replication.

Dengue virus (DENV) causes the most prevalent arthropod-borne viral disease of humans worldwide. Glycosphingolipids (GSLs) are involved in virus infection by regulating various steps of viral-host interaction. However, the distinct role of GSLs during DENV infection remains unclear. In this study, we used mouse melanoma B16 cells and their GSL-deficient mutant counterpart GM95 cells to study the influence of GSLs on DENV infection. Surprisingly, GM95 cells were highly resistant to DENV infection compared with B16 cells. Pretreatment of B16 cells with synthetase inhibitor of GM3, the most abundant GSLs in B16 cells, or silencing GM3 synthetase T3GAL5, significantly inhibited DENV infection. DENV attachment and endocytosis were not impaired in GM95 cells, but DENV genome replication was obviously inhibited in GM95 cells compared to B16 cells. Furthermore, GM3 was colocalized with DENV viral replication complex on endoplasmic reticulum (ER) inside the B16 cells. Finally, GM3 synthetase inhibitor significantly reduced the mortality rate of suckling mice that challenged with DENV by impairing the viral replication in mouse brain. Taken together, these data indicated that GM3 was not required for DENV attachment and endocytosis, however, essential for viral genome replication. Targeting GM3 could be a novel strategy to inhibit DENV infection.

Effects of Different Doses of Nucleocapsid Protein from Hantaan Virus A9 Strain on Regulation of Interferon Signaling.

Hantaan virus A9 strain (HTNV A9) is an etiologic agent of hemorrhagic fever with renal syndrome in China. The virulence of the pathogenic hantaviruses is determined by their ability to alter key signaling pathways of early interferon (IFN) induction within cells. The potential role of HTNV A9 structural proteins, such as nucleocapsid (N) and envelope glycoproteins (Gn and Gc), in regulating human's innate antiviral immune response has not yet been clarified. In this study, we investigated the effect of HTNV A9 N protein on the regulation of the IFN pathway. We found that A9 N protein can influence the host innate immune response by regulating the activation of IFNß. The A9 N protein stimulates IFN response in low doses, whereas significantly inhibits IFNß production at high doses. Furthermore, A9 N protein constitutively inhibits nuclear factor kappa B activation. A high dose of A9 N protein could inhibit either Poly IC-induced IFNß or vesicular stomatitis virus-induced IFNß and interferon-stimulated gene production. Our results indicate that HTNV A9 N protein helps virus establish successful infection by downregulating the IFN response and shed new light to the understanding of the interaction between the host innate immunity and virus during Hantaan virus infection.

Molecular characterization of a defensin gene from a hard tick, Dermacentor silvarum.

BACKGROUND: Ticks are distributed worldwide and considered as vectors of many human diseases. Tick defensins, a family of antimicrobial peptides, form the first line of defense against pathogens. FINDINGS: A defensin-like gene, named Ds-defensin, was identified from a cDNA library of the hard tick Dermacentor silvarum collected from northeast China. The full-length cDNA of Ds-defensin was 225 bp, encoding a 74 amino acid peptide. The nucleotide sequence of Ds-defensin shared 98.2% similarity to putative defensin from Dermacentor marginatus. RT-PCR results suggested that Ds-defensin was extensively expressed in tick salivary gland and midgut, with a higher expression level in midgut. Ds-defensin showed broad antimicrobial activity against various Gram-positive and Gram-negative bacteria, as well as the fungus Candida albicans. CONCLUSIONS: We characterized a functional defensin from D. silvarum of China. Ds-defensin showed bactericidal activity against various Gram-positive and Gram-negative bacteria. Ds-defensin can be expected to be introduced to the medical field as a new molecule with antibacterial activity.