Remote sensing image dehazing using generative adversarial network with texture and color space enhancement.

Remote sensing is gradually playing an important role in the detection of ground information. However, the quality of remote-sensing images has always suffered from unexpected natural conditions, such as intense haze phenomenon. Recently, convolutional neural networks (CNNs) have been applied to deal with dehazing problems, and some important findings have been obtained. Unfortunately, the performance of these classical CNN-based methods still needs further enhancement owing to their limited feature extraction capability. As a critical branch of CNNs, the generative adversarial network (GAN), composed of a generator and discriminator, has become a hot research topic and is considered a feasible approach to solving the dehazing problems. In this study, a novel dehazed generative adversarial network (GAN) is proposed to reconstruct the clean images from the hazy ones. For the generator network of the proposed GAN, the color and luminance feature extraction module and the high-frequency feature extraction module aim to extract multi-scale features and color space characteristics, which help the network to acquire texture, color, and luminance information. Meanwhile, a color loss function based on hue saturation value (HSV) is also proposed to enhance the performance in color recovery. For the discriminator network, a parallel structure is designed to enhance the extraction of texture and background information. Synthetic and real hazy images are used to check the performance of the proposed method. The experimental results demonstrate that the performance can significantly improve the image quality with a significant increment in peak-signal-to-noise ratio (PSNR). Compared with other popular methods, the dehazing results of the proposed method closely resemble haze-free images.

Emergence of the isotropic Kitaev honeycomb latticeα-RuCl3and its magnetic properties.

We present a comprehensive investigation of the crystal and magnetic structures of the van der Waals antiferromagnetα-RuCl3using single crystal x-ray and neutron diffraction. The crystal structure at room temperature is a monoclinic (C2/m). However, with decreasing temperature, a remarkable first-order structural phase transition is observed, leading to the emergence of a rhombohedral (R3-) structure characterized by three-fold rotational symmetry forming an isotropic honeycomb lattice. On further cooling, a zigzag-type antiferromagnetic order develops belowTN=6∼6.6K. The critical exponent of the magnetic order parameter was determined to beß=0.11(1), which is close to the two-dimensional Ising model. Additionally, the angular dependence of the magnetic critical field of the zigzag antiferromagnetic order for the polarized ferromagnetic phase reveals a six-fold rotational symmetry within theab-plane. These findingsreflect the symmetry associated with the Ising-like bond-dependent Kitaev spin interactions and underscore the universality of the Kitaev interaction-dominated antiferromagnetic system.

Nano- and Microstructures of Collagen-Nanocellulose Hydrogels as Engineered Extracellular Matrices.

The extracellular matrix (ECM) is the fundamental acellular element of human tissues, providing their mechanical structure while delivering biomechanical and biochemical signals to cells. Three-dimensional (3D) tissue models commonly use hydrogels to recreate the ECM in vitro and support the growth of cells as organoids and spheroids. Collagen-nanocellulose (COL-NC) hydrogels rely on the blending of both polymers to design matrices with tailorable physical properties. Despite the promising application of these biomaterials in 3D tissue models, the architecture and network organization of COL-NC remain unclear. Here, we investigate the structural effects of incorporating NC fibers into COL hydrogels by small-angle neutron scattering (SANS) and ultra-SANS (USANS). The critical hierarchical structure parameters of fiber dimensions, interfiber distance, and coassembled open structures of NC and COL in the absence and presence of cells were determined. We found that NC expanded and increased the homogeneity in the COL network without affecting the inherent fiber properties of both polymers. Cells cultured as spheroids in COL-NC remodeled the hydrogel network without a significant impact on its architecture. Our study reveals the polymer organization of COL-NC hydrogels and demonstrates SANS and USANS as exceptional techniques to reveal nano- and micron-scale details on polymer organization, which leads to a better understanding of the structural properties of hydrogels to engineer novel ECMs.

Effect of temperature on the conformation and functionality of poly(N-isopropylacrylamide) (PNIPAM)-grafted nanocellulose hydrogels.

HYPOTHESIS: Poly(N-isopropylacrylamide) [PNIPAM]-grafted cellulose nanofibers (CNFs) are new thermo-responsive hydrogels which can be used for a wide range of applications. Currently, there is no clear understanding of the precise mechanism by which CNFs and PNIPAM interact together. Here, we hypothesize that the physical crosslinking of grafted PNIPAM on CNF inhibits the free movement of individual CNF, which increases the gel strength while sustaining its thermo-responsive properties. EXPERIMENTS: The thermo-responsive behaviour of PNIPAM-grafted CNFs (PNIPAM-g-CNFs), synthesized via silver-catalyzed decarboxylative radical polymerization, and PNIPAM-blended CNFs (PNIPAM-b-CNFs) was studied. Small angle neutron scattering (SANS) combined with Ultra-SANS (USANS) revealed the nano to microscale conformation changes of these polymer hybrids as a function of temperature. The effect of temperature on the optical and viscoelastic properties of hydrogels was also investigated. FINDINGS: Grafting PNIPAM from CNFs shifted the lower critical solution temperature (LCST) from 32 °C to 36 °C. Below LCST, the PNIPAM chains in PNIPAM-g-CNF sustain an open conformation and poor interaction with CNF, and exhibit water-like behaviour. At and above LCST, the PNIPAM chains change conformation to entangle and aggregate nearby CNFs. Large voids are formed in solution between the aggregated PNIPAM-CNF walls. In comparison, PNIPAM-b-CNF sustains liquid-like behaviour below LCST. At and above LCST, the blended PNIPAM phase separates from CNF to form large aggregates which do not affect CNF network and thus PNIPAM-b-CNF demonstrates low viscosity. Understanding of temperature-dependent conformation of PNIPAM-g-CNFs engineer thermo-responsive hydrogels for biomedical and functional applications.

Recent Progress of 2D Layered Materials in Water-in-Salt/Deep Eutectic Solvent-Based Liquid Electrolytes for Supercapacitors.

Supercapacitors are candidates with the greatest potential for use in sustainable energy resources. Extensive research is being carried out to improve the performances of state-of-art supercapacitors to meet our increased energy demands because of huge technological innovations in various fields. The development of high-performing materials for supercapacitor components such as electrodes, electrolytes, current collectors, and separators is inevitable. To boost research in materials design and production toward supercapacitors, the up-to-date collection of recent advancements is necessary for the benefit of active researchers. This review summarizes the most recent developments of water-in-salt (WIS) and deep eutectic solvents (DES), which are considered significant electrolyte systems to advance the energy density of supercapacitors, with a focus on two-dimensional layered nanomaterials. It provides a comprehensive survey of 2D materials (graphene, MXenes, and transition-metal oxides/dichalcogenides/sulfides) employed in supercapacitors using WIS/DES electrolytes. The synthesis and characterization of various 2D materials along with their electrochemical performances in WIS and DES electrolyte systems are described. In addition, the challenges and opportunities for the next-generation supercapacitor devices are summarily discussed.

Synthesis and Characterization of Supermagnetic Nanocomposites Coated with Pluronic F127 as a Contrast Agent for Biomedical Applications.

Nanomedicine has garnered significant interest owing to advances in drug delivery, effectively demonstrated in the treatment of certain diseases. Here, smart supermagnetic nanocomposites based on iron oxide nanoparticles (MNPs) coated with Pluronic F127 (F127) were developed for the delivery of doxorubicin (DOX) to tumor tissues. The XRD patterns for all samples revealed peaks consistent with Fe3O4, as shown by their indices (220), (311), (400), (422), (511), and (440), demonstrating that the structure of Fe3O4 did not change after the coating process. After loading with DOX, the as-prepared smart nanocomposites demonstrated drug-loading efficiency and drug-loading capacity percentages of 45 ± 0.10 and 17 ± 0.58% for MNP-F127-2-DOX and 65 ± 0.12 and 13 ± 0.79% for MNP-F127-3-DOX, respectively. Moreover, a better DOX release rate was observed under acidic conditions, which may be credited to the pH sensitivity of the polymer. In vitro analysis demonstrated the survival rate of approximately 90% in HepG2 cells treated with PBS and MNP-F127-3 nanocomposites. Furthermore, after treatment with MNP-F127-3-DOX, the survival rate decreased, confirming cellular inhibition. Hence, the synthesized smart nanocomposites showed great promise for drug delivery in liver cancer treatment, overcoming the limitations of traditional therapies.

Structural basis underlying the synergism of NADase and SLO during group A Streptococcus infection.

Group A Streptococcus (GAS) is a strict human pathogen possessing a unique pathogenic trait that utilizes the cooperative activity of NAD+-glycohydrolase (NADase) and Streptolysin O (SLO) to enhance its virulence. How NADase interacts with SLO to synergistically promote GAS cytotoxicity and intracellular survival is a long-standing question. Here, the structure and dynamic nature of the NADase/SLO complex are elucidated by X-ray crystallography and small-angle scattering, illustrating atomic details of the complex interface and functionally relevant conformations. Structure-guided studies reveal a salt-bridge interaction between NADase and SLO is important to cytotoxicity and resistance to phagocytic killing during GAS infection. Furthermore, the biological significance of the NADase/SLO complex in GAS virulence is demonstrated in a murine infection model. Overall, this work delivers the structure-functional relationship of the NADase/SLO complex and pinpoints the key interacting residues that are central to the coordinated actions of NADase and SLO in the pathogenesis of GAS infection.

Multilevel Graph Matching Networks for Deep Graph Similarity Learning.

While the celebrated graph neural networks (GNNs) yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node-node interactions, however, ignoring the rich cross-level interactions (e.g., between each node of one graph and the other whole graph). In this article, we propose a multilevel graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. In particular, the proposed MGMN consists of a node-graph matching network (NGMN) for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese GNN to learn global-level interactions between two input graphs. Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks. Compared with previous work, multilevel graph matching network (MGMN) also exhibits stronger robustness as the sizes of the two input graphs increase.

A polytherapy based approach to combat antimicrobial resistance using cubosomes.

A depleted antimicrobial drug pipeline combined with an increasing prevalence of Gram-negative 'superbugs' has increased interest in nano therapies to treat antibiotic resistance. As cubosomes and polymyxins disrupt the outer membrane of Gram-negative bacteria via different mechanisms, we herein examine the antimicrobial activity of polymyxin-loaded cubosomes and explore an alternative strategy via the polytherapy treatment of pathogens with cubosomes in combination with polymyxin. The polytherapy treatment substantially increases antimicrobial activity compared to polymyxin B-loaded cubosomes or polymyxin and cubosomes alone. Confocal microscopy and neutron reflectometry suggest the superior polytherapy activity is achieved via a two-step process. Firstly, electrostatic interactions between polymyxin and lipid A initially destabilize the outer membrane. Subsequently, an influx of cubosomes results in further membrane disruption via a lipid exchange process. These findings demonstrate that nanoparticle-based polytherapy treatments may potentially serve as improved alternatives to the conventional use of drug-loaded lipid nanoparticles for the treatment of "superbugs".

Non-conventional superconductivity in magnetic In and Sn nanoparticles.

We report on experimental evidence of non-conversional pairing in In and Sn nanoparticle assemblies. Spontaneous magnetizations are observed, through extremely weak-field magnetization and neutron-diffraction measurements, to develop when the nanoparticles enter the superconducting state. The superconducting transition temperature TC shifts to a noticeably higher temperature when an external magnetic field or magnetic Ni nanoparticles are introduced into the vicinity of the superconducting In or Sn nanoparticles. There is a critical magnetic field and a critical Ni composition that must be reached before the magnetic environment will suppress the superconductivity. The observations may be understood when assuming development of spin-parallel superconducting pairs on the surfaces and spin-antiparallel superconducting pairs in the core of the nanoparticles.

V-Fuzz: Vulnerability Prediction-Assisted Evolutionary Fuzzing for Binary Programs.

Fuzzing is a technique of finding bugs by executing a target program recurrently with a large number of abnormal inputs. Most of the coverage-based fuzzers consider all parts of a program equally and pay too much attention to how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this article, we design and implement an evolutionary fuzzing framework called V-Fuzz, which aims to find bugs efficiently and quickly in limited time for binary programs. V-Fuzz consists of two main components: 1) a vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which are more likely to arrive at the vulnerable locations, guided by the vulnerability prediction result. The experimental results demonstrate that V-Fuzz can find bugs efficiently with the assistance of vulnerability prediction. Moreover, V-Fuzz has discovered ten common vulnerabilities and exposures (CVEs), and three of them are newly discovered.

Distributions of Deuterated Polystyrene Chains in Perforated Layers of Blend Films of a Symmetric Polystyrene-block-poly(methyl methacrylate).

We have examined the spatial distributions of polymer chains in blend films of weakly segregated polystyrene-block-poly(methyl methacrylate) [P(S-b-MMA)] and deuterated polystyrene (dPS). By fine-tuning the composition (ϕPS+dPS = 63.8 vol %) of the total PS/dPS component and annealing temperature (230 and 270 °C), P(S-b-MMA)/dPS blend films mainly form perforated layers with a parallel orientation (hereafter PLs//). The distributions of dPS in PLs// were probed by grazing-incidence small-angle neutron scattering (GISANS) and time-of-flight neutron reflectivity (ToF-NR). GISANS and ToF-NR results offer evidence that dPS chains preferentially locate at the free surface and within the PS layers for blend films that were annealed at 230 °C. Upon annealing at 270 °C, dPS chains distribute within PS layers and perforated PMMA layers. Nevertheless, dPS chains still retain a surface preference for thin films. In contrast, such surface segregation of dPS chains is prohibited for thick films when annealed at 270 °C.

Research on insulator defect detection algorithm of transmission line based on CenterNet.

The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.

Targeted delivery of LM22A-4 by cubosomes protects retinal ganglion cells in an experimental glaucoma model.

Glaucoma, a major cause of irreversible blindness worldwide, is associated with elevated intraocular pressure (IOP) and progressive loss of retinal ganglion cells (RGCs) that undergo apoptosis. A mechanism for RGCs injury involves impairment of neurotrophic support and exogenous supply of neurotrophic factors has been shown to be beneficial. However, neurotrophic factors can have widespread effects on neuronal tissues, thus targeting neurotrophic support to injured neurons may be a better neuroprotective strategy. In this study, we have encapsulated LM22A-4, a small neurotrophic factor mimetic, into Annexin V-conjugated cubosomes (L4-ACs) for targeted delivery to injured RGCs in a model of acute IOP elevation, which is induced by acute IOP elevation. We have tested cubosomes formulations that encapsulate from 9% to 33% LM22A-4. Our data indicated that cubosomes encapsulating 9% and 17% LM22A-4 exhibited a mixture of Pn3m/Im3m cubic phase, whereas 23% and 33% showed a pure Im3m cubic phase. We found that 17% L4-ACs with Pn3m/Im3m symmetries showed better in-situ and in-vitro lipid membrane interactions than the 23% and 33% L4-ACs with Im3m symmetry. In vivo experiments showed that 17% L4-ACs targeted the posterior retina and the optic nerve head, which prevented RGCs loss and improved functional outcomes in a mouse model of acute IOP elevation. These results provide evidence that Annexin V-conjugated cubosomes-based LM22A-4 delivery may be a useful targeted approach to prevent the progression of RGCs loss in glaucoma. STATEMENT OF SIGNIFICANCE: Recent studies suggest that the therapy of effectively delivering neurotrophic factors to the injured retinal ganglion cells (RGCs) could promote the survival of RGCs in glaucoma. Our present work has for the first time used cubosomes as an active targeted delivery system and have successfully delivered a neuroprotective drug to the damaged RGCs in vivo. Our new cubosomal formulation can protect apoptotic cell death in vitro and in vivo, showing that cubosomes are a promising drug carrier system for ocular drug delivery and glaucoma treatment. We have further found that by controlling cubosomes in Pn3m phase we can facilitate delivery of neuroprotective drug through apoptotic membranes. This data, we believe, has important implications for future design and formulation of cubosomes for therapeutic applications.

A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing.

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

Substrate-dependent arrangements of the subunits of the BAM complex determined by neutron reflectometry.

In Gram-negative bacteria, the ß-barrel assembly machinery (BAM) complex catalyses the assembly of ß-barrel proteins into the outer membrane, and is composed of five subunits: BamA, BamB, BamC, BamD and BamE. Once assembled, - ß-barrel proteins can be involved in various functions including uptake of nutrients, export of toxins and mediating host-pathogen interactions, but the precise mechanism by which these ubiquitous and often essential ß-barrel proteins are assembled is yet to be established. In order to determine the relative positions of BAM subunits in the membrane environment we reconstituted each subunit into a biomimetic membrane, characterizing their interaction and structural changes by Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) and neutron reflectometry. Our results suggested that the binding of BamE, or a BamDE dimer, to BamA induced conformational changes in the polypeptide transported-associated (POTRA) domains of BamA, but that BamB or BamD alone did not promote any such changes. As monitored by neutron reflectometry, addition of an unfolded substrate protein extended the length of POTRA domains further away from the membrane interface as part of the mechanism whereby the substrate protein was folded into the membrane.

Infrared and visible image fusion method of dual NSCT and PCNN.

To solve the problem that the details of fusion images are not retained well and the information of feature targets is incomplete, we proposed a new fusion method of infrared (IR) and visible (VI) image-IR and VI image fusion method of dual non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN). The method makes full use of the flexible multi-resolution and multi-directional of NSCT, and the global coupling and pulse synchronization excitation characteristics of PCNN, effectively combining the features of IR image with the texture details of VI image. Experimental results show that the algorithm can combine IR and VI image features well. At the same time, the obtained fusion image can better display the texture information of image. The fusion performance in contrast, detail information and other aspects is better than the classical fusion algorithm, which has better visual effect and evaluation index.

Phytantriol-Based Cubosome Formulation as an Antimicrobial against Lipopolysaccharide-Deficient Gram-Negative Bacteria.

Treatment of multidrug-resistant (MDR) bacterial infections increasingly relies on last-line antibiotics, such as polymyxins, with the urgent need for discovery of new antimicrobials. Nanotechnology-based antimicrobials have gained significant importance to prevent the catastrophic emergence of MDR over the past decade. In this study, phytantriol-based nanoparticles, named cubosomes, were prepared and examined in vitro by minimum inhibitory concentration (MIC) and time-kill assays against Gram-negative bacteria: Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Phytantriol-based cubosomes were highly bactericidal against polymyxin-resistant, lipopolysaccharide (LPS)-deficient A. baumannii strains. Small-angle neutron scattering (SANS) was employed to understand the structural changes in biomimetic membranes that replicate the composition of these LPS-deficient strains upon treatment with cubosomes. Additionally, to further understand the membrane-cubosome interface, neutron reflectivity (NR) was used to investigate the interaction of cubosomes with model bacterial membranes on a solid support. These results reveal that cubosomes might be a new strategy for combating LPS-deficient Gram-negative pathogens.

Room Temperature Magnetic Memory Effect in Cluster-Glassy Fe-doped NiO Nanoparticles.

The Fe-doped NiO nanoparticles that were synthesized using a co-precipitation method are characterized by enhanced room-temperature ferromagnetic property evident from magnetic measurements. Neutron powder diffraction experiments suggested an increment of the magnetic moment of 3d ions in the nanoparticles as a function of Fe-concentration. The temperature, time, and field-dependent magnetization measurements show that the effect of Fe-doping in NiO has enhanced the intraparticle interactions due to formed defect clusters. The intraparticle interactions are proposed to bring additional magnetic anisotropy energy barriers that affect the overall magnetic moment relaxation process and emerging as room temperature magnetic memory. The outcome of this study is attractive for the future development of the room temperature ferromagnetic oxide system to facilitate the integration of spintronic devices and understanding of their fundamental physics.