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Hyaluronic acid and zwitterionic hydrogels are soft materials with poor mechanical properties. The unique structures and physiological properties make them attractive candidates for ideal hydrogel dressings, but the crux of lacking satisfying mechanical strengths and adhesive properties is still pendent. In this study, the physical cross-linking of dipole–dipole interactions of zwitterionic pairs was utilized to enhance the mechanical properties of hydrogels. The hydrogels have been prepared by copolymerizing methacrylate hyaluronic (HAGMA) with carboxybetaine methacrylamide (CBMAA) (the mass ratio of [HAGMA]/[CBMAA] is 2:5, 1:5, 1:10, or 1:20), obtaining HA-CB2.5, HA-CB5.0, HA-CB10.0, or HA-CB20.0hydrogel. Therein, the HA-CB20.0hydrogel with a high CBMAA content can generate a strong dipole–dipole interaction to form internal physical cross-links, exhibit stretchability and low elastic modulus, and withstand 99% compressive deformation and cyclic compression under strain at 90%. Moreover, the HA-CB20.0hydrogel is adhesive to diverse substrates, including skin, glass, stainless steel, and plastic. The synergistic effect of HAGMA and CBMAA shows strong anti-biofouling, high water absorption, biodegradability under hyaluronidase, and biocompatibility.

This article proposes a novel deep learning-based framework for hyperspectral band selection, named Cross-Scene Self-Representative Network (CSSRnet). The proposed method leverages the rich labels of the source domain (SD) to guide the band selection in the target domain (TD). To the best of our knowledge, CSSRnet is the first deep-learning-based solution for cross-scene hyperspectral band selection. First, the CSSRnet employs contextual attention mechanism to capture the latent features of SD and TD. It combines the self-attention mechanism with convolutional operations to capture static and dynamic contextual information. Then, the self-representative layer provides the self-representative coefficient of SD and TD. Subsequently, the maximum mean difference (MMD) is utilized to align the self-representative coefficients of both SD and TD. To enhance the representativeness and precision of these coefficients, we introduce different tasks for the SD and TD branches. Finally, a suitable band subset is selected based on a ranking method that evaluates each band’s importance by considering its self-representative coefficient matrix. Experiments are carried out to assess the efficacy of CSSRnet. These experiments focus on evaluating classification accuracy across various cross-scene datasets, the utility of cross-scene concepts, and the practical application in coastal wetland. Experimental results confirm the effectiveness of CSSRnet.

While fusion of hyperspectral images (HSIs) with low spatial resolution and multispectral images (MSIs) with high spatial resolution has achieved significant success, high-quality fusion between noisy images has always been challenging. In this article, we propose a domain transform model driven by deep learning for anti-noise hyperspectral and multispectral image fusion (DTAFN). This marks the first time that wavelet decomposition theory is combined with deep learning for noise reduction in hyperspectral and MSI fusion. DTAFN initially decomposes hyperspectral and MSIs into frequency components and constructs a novel feature interaction fusion module (FIFM). This module, while using MSIs to guide the removal of noise from HSIs, also achieves the fusion of spatial and spectral information. Furthermore, it maps the fused features to a lower dimensional subspace to enhance computational efficiency. Additionally, we introduce a spatial-spectral self-attention mechanism to optimize the reconstructed frequency components using the subspace features. In the end, the wavelet inverse transform is used to reconstruct the clean fused image. It is worth noting that the extraction of the subspace is considered a process of nonlinear low-rank component extraction, which, to a certain extent, suppresses noise signals. Numerous experiments of mixed noise image fusion are carried out, and the experimental results show that DTAFN can obtain high-quality fusion results, is robust, and superior to the state-of-the-art methods.

Spectral super-resolution (SSR) aims to improve the spectral resolution of images from multispectral imagery or even red, green, blue (RGB) images. However, the majority of existing SSR methods do not fully exploit the spatial and texture features in RGB images, which would lead to the image unreal and distort of the high-frequency details in the reconstructed SSR images. In this study, a hybrid spectral and texture attention pyramid network (STANet) is proposed to reconstruct hyperspectral images (HSIs) with RGB bands of remote sensing images as input. More specifically, a learnable texture feature extraction module is proposed, aiming to make full use of the texture features in the RGB images, which are important in the subsequent spectral reconstruction. Furthermore, to better reconstruct the correlations between various spectral channels, a spatial-spectral-constrained cross-attention module is introduced. Finally, a novel spectral-texture fusion method is proposed, which successfully alleviates the problem of insufficient deep interaction among multiple deep features. On three remote sensing datasets, STANet demonstrates state-of-the-art performance, with its peak signal-to-noise ratio (PSNR) exceeding the suboptimal methods by 0.7266, 0.6724, and 0.6 dB, respectively. The results of the land-cover classification experiment using the reconstructed HSI further demonstrated the performance of the STANet algorithm.

The sustained casing pressure (SCP) caused by the sealing failure of cement sheaths has become one of the main threats to the safe and efficient production of high-temperature and high-pressure (HTHP) gas wells. An actual HTHP gas well was used as an example, and the temperature calculation models were established. Then, the temperature–pressure coupling finite element coupling models of casing-cement sheath-formation assembly for the whole well were established, and the factors affecting the integrity were examined. Finally, the maximum production and wellhead annular pressure based on the constraints of cement sheath integrity was analyzed. The results indicate that the natural gas would transfer energy to the wellbore assembly during production; the greater the production, the greater the temperature fluctuation of the cement sheath, which was unbeneficial to the integrity, the radial and tangential stresses of the cement sheath increase and decrease, respectively, with depth. The cement sheath near the ground was at risk of tensile failure, which could result in tangential tensile cracks. The cement sheath near the packer was susceptible to compression failure, resulting in radial compression cracks, reducing production and wellhead annular pressure were beneficial to relieve the stress state. Compared to the production, the wellhead pressure has a greater impact, it is necessary to control the wellhead pressure in a reasonable range to ensure integrity.

Using evolutionary search and first-principles methods, we predict new two-dimensional XN (X = C, Si, Ge, Sn) monolayers. They are confirmed to be indirect-gap semiconductors and are stable energetically and dynamically. In particular, the SiN monolayer shows a moderate gap of ∼2 eV and isotropic mechanical characteristics. The CN monolayer possesses a wide band gap of ∼6 eV, maximal Young’s modulus (∼550 N/m), and minimal Poisson’s ratio (∼0.12). The electron mobility of the SnN monolayer can be as large as ∼1.55 × 104cm2·V–1·s–1, exceeding that of previously reported black phosphorene (104cm2·V–1·s–1). Besides, the SnN and GeN monolayers also exhibit good light absorptions and excellent catalytic performances in terms of Gibbs free energies. These suggest that the XN (X = C, Si, Ge, or Sn) monolayers have potential applications in nanoelectronic and optoelectronic devices.

Thermal transport properties of PtX2(X = S, Se, and Te) monolayers under different sample lengths, temperatures, vacancy densities, and tensile strains are investigated by using reverse nonequilibrium molecular dynamics simulations. It is found that the thermal conductivities (TCs) of PX2show an isotropic behavior along the two in-plane directions due to the high lattice symmetry. Besides, the TCs increase with the increase of sample length, exhibiting an obvious size dependence. In addition, an increase of either the temperature or the tensile strain leads to a strong reduction in the TCs of PtX2. Moreover, the TCs of PtX2are found to be very sensitive to the presence of defects in the structures, showing a rapid decrease with the increase of vacancy density, especially at small defect densities. The underlying mechanisms for the reduction of the TCs are also analyzed by the calculations of the atomic heat flux distribution and vibrational density of states in PtX2. The findings from this work are helpful for the thermal management of PtX2-based devices and the thermoelectrical applications of PtX2.

Accumulating evidence indicates a correlation between circadian dysfunction and genomic instability. However, whether the circadian machinery directly regulates DNA damage repair, especially in double-strand breaks (DSBs), remains poorly understood. Here, we report that in response to DSBs, BMAL1 is activated by ATM-mediated phosphorylation at S183. Phosphorylated BMAL1 is then localized to DNA damage sites, where it facilitates acetylase CLOCK to load in the chromatin, regulating the acetylation of histone H4 (H4Ac) at DSB sites. In this way, the BMAL1-CLOCK-H4Ac axis promotes the DNA end-resection to generate single-stranded DNA (ssDNA) and the subsequent homologous recombination (HR). BMAL1 deficient cells display defective HR, accumulation of unrepaired DSBs and genome instability. Accordingly, depletion of BMAL1 significantly enhances the sensitivity of adrenocortical carcinoma (ACC) to DNA damage-based therapy in vitro and in vivo. These findings uncover non-canonical function of BMAL1 and CLOCK in HR-mediated DSB repair, which may have an implication in cancer therapeutics.

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Spatial–temporal–spectral fusion (STSF) of remote sensing imagery can produce data with the highest spatial and spectral resolution, only as well as fine temporal resolution, by integrating images with complementary information in both the temporal and spectral domains. Accuracy of temporal variation is an important guarantee for achieving fidelity fusion in STSF. However, current STSF methods estimate the temporal variation only by utilizing the temporal variation between the observed multispectral image (MSI) and the relationship between MSI and hyperspectral image (HSI), which is difficult to obtain accurate temporal variation. To address this problem, this article proposes a coupled temporal variation information estimation and resolution enhancement for remote sensing image STSF (CTVRE-STSF). The temporal variation information estimation model estimates the temporal variation of the target image, while the resolution enhancement model provides additional constraints for estimating the temporal variation. For the temporal variation information reconstruction model, we build a temporal variation information estimation based on a generalized linear mixed model and use the temporal variation between MSIs. In addition, a resolution enhancement model is constructed to estimate the temporal variation of the target image by incorporating relevant prior knowledge. The introduction of the resolution enhancement model in the prior provides additional constraints on the estimation of the temporal variation high-dimensional information, thus facilitating the resolution improvement. The experimental results on two real datasets demonstrate the effectiveness and superiority of our proposed method over current state-of-the-art methods, especially in terms of spectral fidelity.

The use of chemometric techniques with spectral data for sorting plastics to improve recycling rates have gained more attention in recent years. However, insufficient representation of polymer spectra in spectral databases has been one of the barriers to the further development of these techniques. This work aims to develop a polymer spectra dataset that builds upon existing spectral databases on two fronts. Firstly, the data collected includes Laser-induced Breakdown Spectroscopy (LIBS) data in addition to more commonly available Infrared (IR) and Raman data. Secondly, the dataset includes unaged and weathered conditions of the same sample. In total, the dataset includes 732 spectra, with the LIBS, IR and Raman spectra of 122 unique samples, both before and after accelerated weathering. The data collected were qualitatively analyzed and visualized. Further work will explore the effect of using hybrid spectroscopic methods on chemometrics analysis results.

Due to sensor design limitations and the influence of weather factors, it is currently challenging to obtain remote sensing images with high temporal, spatial, and spectral resolution. Spatial–temporal–spectral fusion aims to integrate the temporal, spatial, and spectral information from multiple sources of remote sensing images to reconstruct a remote sensing image with high temporal, spatial, and spectral resolution. Existing methods typically require at least three types of data to achieve spatial–temporal–spectral fusion. However, acquiring remote sensing data observed at the same time poses significant difficulties. The major challenge lies in effectively utilizing hyperspectral images (HSIs) with low spatial and temporal resolution and multispectral images with high temporal and spatial resolution to reconstruct remote sensing images with high temporal, spatial, and spectral resolution. To address the aforementioned issues, we propose a novel unsupervised 3-D tensor subspace decomposition network. Our method incorporates the theory of 3-D tensor subspace decomposition, utilizing a 3-D hyperspectral/multispectral tensor subspace extraction network to predict the hyperspectral tensor subspace features with low spatial resolution missing at other times (to better understand, the missing moment is defined as time 2). Subsequently, the 3-D hyperspectral tensor subspace reconstruction network is employed along with the time 2 hyperspectral tensor subspace features with low spatial resolution and the time 2 MSI to reconstruct the time 2 HSI with high spatial resolution. In the experiment, we utilize three simulated datasets and two real datasets to evaluate the fusion performance of our proposed method. The results demonstrate that our method achieves high-quality fusion results, exhibits comparable performance, and has robustness and practicality.

Image registration aims to eliminate the geometric deviation between multisource data with the same range, and to promote the collaborative application of data. In recent years, spaceborne hyperspectral (HS) and multispectral (MS) data have been widely used in Earth observation. However, the difference in the number of bands, spatial resolution, and spectral resolution puts forward higher requirements on the registration algorithm. The key to HS and MS image registration is to extract more common key points, weaken and eliminate the difference of radiation and spatial texture information to build superior descriptors, and achieve high-precision matching of key points. This article introduces a new robust HS and MS registration method based on common deep feature subspaces. We first construct the common deep feature subspace extraction network to extract consistent edge features and common subspace images of the image pair. Then, the Harris algorithm is used to extract key points from consistent edge features between images, which reduces the impact of spatial resolution differences between images. Besides, the SIFT descriptor and subspace images are used to describe key points, which reduces the impact of radiation differences between images. Finally, Euclidean distance is used for the initial matching of key points, and the affine matrix is calculated after the outliers are eliminated, and image registration is performed. We perform experiments on spaceborne HS and MS datasets of different spatial resolutions and comparisons with state-of-the-art methods. Experimental results show that our method can obtain satisfactory registration results and is robust.

A single helicopter-based high frequency (HF) antenna is proposed for direction finding (DF) using rotor-blade modulation. Without integrating additional antenna structures, a landing gear is utilized as a coupling element to excite a combination of significant characteristic modes of the helicopter through characteristic mode (CM) analysis. Different from traditional DF systems where multiple antennas required, incoming signal receptions are achieved by a single radiator in this work. The diversity in antenna radiation patterns is realized by incorporating main rotor-blade rotations. The incident signals can be measured as the blade rotates from 0$^\circ $ to 75$^\circ $, where the helicopter-mounted antenna with each rotation angle is equivalent to one antenna element in a conventional DF antenna array. Correlation coefficients of complex CM and excited radiation patterns between the antennas with different blade rotation angles are calculated to demonstrate pattern diversity. Both Cramer–Rao bound and multiple signal classification algorithm are investigated using the complex CM and excited radiation patterns to validate the azimuthal DF capability of the helicopter-integrated single antenna.

The pursuit of superresolution (SR) with large upscaling factors, such as $8\times $ , for enhancing the spatial resolution of low-resolution (LR) remote sensing images is a persistent and challenging problem. To address this issue, we propose the progressive feature enhancement SR (PFESR) network with an $8\times $ upscaling factor. Given the limited high-frequency information provided by a single LR image, we propose an improved style transfer technology to generate auxiliary details that aid in the recovery of high-resolution (HR) images. Additionally, multiscale texture features are extracted through the visual geometry group (VGG) feature extraction (VFE) block. To efficiently fuse various features, we combine hard and soft attention mechanisms. Finally, we use a hierarchical fusion block to address the progressive fusion problem of multiple scale features. Experiments on three datasets demonstrate that our method achieves the state-of-the-art performance and exhibits good robustness in $8\times $ and higher scale SR tasks.

Deep learning has emerged as a powerful method for hyperspectral image (HSI) classification. However, a significant prerequisite for HSI classification using deep learning is enough labeled samples, which is both time-consuming and labor-intensive. Yet, labeled samples are essential for training deep learning models. This article proposes an HSI classification method based on the self-supervised learning of spectral masking (SSLSM). The method mainly includes two steps: self-supervised pretraining and fine-tuning. First, considering the rich spectral information of HSI, we propose masked spectral reconstruction as the pretext task. The unmasked data are input into the encoder and decoder sequentially, which are composed of a multilayer transformer, for feature learning of masked spectral reconstruction. Second, we use reference samples to fine-tune the network, and the encoder and decoder are innovatively cascaded for deep semantic feature extraction, which can further improve the ability of feature extraction in the downstream classification tasks. The experimental results show that, compared with other methods, the SSLSM obtains the highest classification accuracy of 96.52%, 97.03%, and 96.70% on the Indian Pines dataset, Pavia University dataset, and Yancheng Wetlands dataset, respectively. Our method can also be applied to other HSI datasets, and the codes will be available from https://github.com/CIRSM-GRoup/2023-TGRS-SSLSM.

Deep learning methods face significant challenges in practical cross-scene classification tasks of hyperspectral images (HSIs), primarily due to the difficulty of acquiring labels and the issue of inconsistent distribution caused by spectral drift. To tackle the above issues, we propose a deep dynamic adaptation network based on joint correlation alignment (DDAN-JCA) for cross-scene HSI classification. First, the dual-channel residual network (DCRN) and the attention mechanism module (AMM) are employed to extract spatial–spectral joint features from both the source domain (SD) and target domain (TD). Then, the method of correlation alignment (CORAL) is employed to minimize the marginal distribution discrepancy between two domains and further reduce the conditional distribution discrepancy of each class. Finally, a dynamic distribution adaptation strategy is used to dynamically adjust the importance of marginal distribution and conditional distribution by using a balance factor. DDAN-JCA can achieve unsupervised classification without using target labels. The performance of DDAN-JCA has been validated using three hyperspectral datasets, and the experimental results demonstrate that DDAN-JCA significantly enhances classification accuracy and exhibits greater robustness compared with state-of-the-art methods.

Change detection on heterogeneous optical and synthetic aperture radar (SAR) images is soaring and plays a crucial role in monitoring land cover changes, such as disaster emergencies and natural resource monitoring. This is commonly recognized as a promising but challenging work due to the intrinsic differences in imaging mechanisms between the optical and SAR images. Recently, deep learning (DL)-based change detection methods based on two-step processing have attracted attention, i.e., first image translation between optical and SAR images to alleviate their modality differences and then change detection based on the translated images. However, image translation itself is a trouble task for the heterogeneous optical and SAR images. The unreliable image translation results further limit the accuracy of change detection. In this article, to mitigate this problem, we propose a change detection model on domain adaptation by novelty integrating change detection and image reconstruction into a unified framework. Specifically, we first transform the optical and SAR images into an intermediate common domain for comparison. Moreover, cross reconstruction for optical and SAR images is designed to maintain the characteristics of the images and improve the performance of domain adaptation. In addition, a feedback guidance mechanism is circumspectly designed to co-optimize change detection and image reconstruction tasks. Extensive experiments were conducted on four publicly available datasets, and the results demonstrate the effectiveness of our proposed method.

Enhanced joint synergistic lubrication combined with anti-inflammatory therapy is an effective strategy to delay the progression of early osteoarthritis (OA) but has been rarely reported. The hydration lubrication of zwitterions and inherent super-lubrication properties of the cyclic brush, as well as the enhancement of the steric stability of the cyclic topology, can effectively improve the drug loading and utilization; herein we report a pH-responsive cyclic brush zwitterionic polymer (CB) with SBMA and DMAEMA as brushes and a cyclic polymer (c-P(HEMA)) as the core template, possessing a low coefficient of friction (0.017). After loading with hydrophobic curcumin and hydrophilic loxoprofen sodium it demonstrates high drug-loading efficiency. In vitroand in vivoexperiments confirmed the triple function of the CB on superlubrication, sequence controlled release and anti-inflammatory effects demonstrated by Micro CT, histological analysis and qRT-PCR. Overall, the CB is a promising long-acting lubricating therapeutic agent, with potential for OA treatment or other diseases.

As complementary to the imaging of primaries, multiples can be used for imaging and produce additional angular illumination. These additional illumination angles can improve imaging resolution. There are several methods to calculate the angle gathers from imaging of primaries, among which the directional vector methods are matured and have been implemented in practice. For conventional directional vector algorithms using common-shot reverse time migration, usually, only one angle with the main contribution to each imaging point is collected, which is referred to as a one-shot, one-position, and one-angle mapping strategy. This strategy is appropriate for imaging of primaries; however, the principle fails in the imaging of multiples, where more than one angle exists at an imaging point even using a shot gather. Therefore, conventional directional vector approaches cannot separate different imaging angles produced by imaging of multiples. In this article, we introduce the Poynting vector method to calculate angle gathers for imaging of multiples. Based on the one-shot, one-position, and one-angle mapping strategy, we search for the peak of modulus of Poynting vectors in a time window to differentiate imaging angles at an imaging point. The ray path diagrams and angle-gather imaging conditions are used to demonstrate the improved angular illumination from imaging of multiples. Then, the arriva -time equation is derived mathematically to demonstrate wavefield arrival time differences for different imaging angles from imaging of multiples. Based on the arrival time differences, a Poynting vector algorithm is proposed to compute more than one angle at one subsurface location for imaging of multiples. Data examples are used to validate our approach.

This article studies how to improve the generalization performance and learning speed of the navigation agents trained with deep reinforcement learning (DRL). Although DRL exhibits huge potential in robot mapless navigation, DRL agents performing well in training scenarios are often found to perform poorly in unfamiliar scenarios. In this work, we propose that the representation of LiDAR readings is a key factor behind the degradation of agents’ performance and present a powerful input preprocessing (IP) approach to address this issue. As this approach uses adaptively parametric reciprocal functions to preprocess LiDAR readings, we refer to this approach as IPAPRec and its normalized version as IPAPRecN. IPAPRec/IPAPRecN can highlight important short-distance values and compress the range of less-important long-distance values in laser scans, which well address the issues induced by conventional representations of laser scans. Their high performance was validated by extensive simulation and real-world experiments. The results show that our methods can substantially improve navigation agents’ generalization performance and greatly reduce the training time compared to conventional methods.

High-entropy alloys (HEAs) represent prospective applications considering their outstanding mechanical properties. The properties in HEAs can be affected by the phase structure. Artificial neural network (ANN) is a promising machine learning approach for predicting the phases of HEAs. In this work, a deep neural network (DNN) structure using a residual network (RESNET) is proposed for the phase formation prediction of HEAs. It shows a high overall accuracy of 81.9%. Compared it with machine learning models, e.g., ANN and conventional DNN, its Micro-F1 score highlights the advantages of phase prediction of HEAs. It can remarkably prevent network degradation and improve the algorithm accuracy. It delivers a new path to develop the phase formation prediction model using deep learning, which can be of universal relevance in assisting the design of the HEAs with novel chemical compositions.

Cinnamon and motherwort are traditional Chinese medicines and are often combined to treat benign prostatic hyperplasia; however, the specific therapeutic mechanisms involved remain unclear. Therefore, in this study, we applied a network pharmacology approach to investigate the potential mechanisms of action of the drug pair cinnamon and motherwort (PCM) for the treatment of benign prostatic hyperplasia. Relevant targets for the use of PCM to treat benign prostatic hyperplasia were obtained through databases. Protein–protein interactions were then identified by the STRING database and core targets were screened. Enrichment analysis was conducted through the Metascape platform. Finally, molecular docking experiments were carried out to evaluate the affinity between the target proteins and ligands of PCM. We identified 22 active ingredients in PCM, 315 corresponding targets and 130 effective targets of PCM for the treatment of benign prostatic hyperplasia. These targets were related to the PI3K-Akt, MAPK, FoxO, TNF, and IL-17 signaling pathways. Network pharmacology was used to identify the effective components and action targets of PCM. We also identified potential mechanisms of action for PCM in the treatment of benign prostatic hyperplasia. Our results provide a foundation for expanding the clinical application of PCM and provide new ideas and directions for further research on the mechanisms of action of PCM and its components for the treatment of benign prostatic hyperplasia.

ABSTRACTMitochondrial fission depends on dynamin-related protein 1 (Drp1) guanosine triphosphatase activity. Although there is some association between Drp1 and gastric cancer, the detailed mechanism remains largely unknown. In this study, the elevation of Drp1 was observed in human gastric carcinoma specimens including gastric mixed adenocarcinoma tissues, gastric intestinal-type adenocarcinoma tissues, and human gastric cancer cells compared to normal control, but not in diffuse gastric adenocarcinoma tissues. Gastric cancer patients with high Drp1 harbored advanced pathological stages and poor progression-free survival probability compared to those with low Drp1. Mdivi-1-mediated inactivation of Drp1 robustly inhibited cell viability and tumor growth but conversely induced cell apoptotic events in vitroand in vivo. Based on the Encyclopedia of RNA Interactomes Starbase, L22 ribosomal protein (RPL22) was recognized as the potential downstream oncogene of Drp1. Clinically, the significant correlation of Drp1 and RPL22 was also verified. Mechanistically, Drp1 inactivation did not affect the accumulation of RPL22 in gastric carcinoma. However, the intracellular distribution of RPL22 had an endonuclear location in Drp1-inactivated tumors. Of note, Drp1 inactivation notably reduced the expression of cytoplasmic RPL22 and increased its nuclear level in gastric cancer cells. Collectively, Drp1 had high levels in human gastric carcinoma specimens and could serve as a potential diagnostic and prognostic biomarker in gastric carcinoma. The Drp1 inactivation-mediated anti-proliferative and pro-apoptosis effects on gastric cancer were possibly associated with nuclear import of RPL22. This knowledge may provide new therapeutic tools for treating gastric carcinoma via targeting mitochondria-related ribosome pathway.

In two-dimensional (2D) magnetic systems, significant magnetic anisotropy is required to protect magnetic ordering against thermal fluctuation. In this paper, we explored the effect of molecular adsorption on the magnetic anisotropy and intralayer Dzyaloshinskii–Moriya interaction (DMI) of monolayer MnPS3, combining the first-principles calculation and theoretical analysis. We find that molecular adsorption can break the spatial inversion symmetry in a 2D magnet, and results in a significant DMI, which is rare in pristine 2D magnets. For example, in an MPS–NO system, the magnitude of the asymmetric DMI vector increases 9 times, and the magnetocrystalline anisotropy increases 600 times compared with the pristine MPS monolayer. It is found the DMI mainly comes from the structural deformation after adsorption, whereas the increase of magnetocrystalline anisotropy mainly originates from a new ‘bridge’ super-exchange interaction between Mn ions and NO gas molecules. The calculated Mn–NO–Mn ‘bridge’ super-exchange coupling strength is much higher than the Mn–S–Mn coupling strength. Our findings offer a new strategy to increase the magnetic anisotropy and induce chiral magnetic structures in 2D magnets.

Volatile organic compounds (VOCs) and Hg adversely affect the health of communities in coal-based economies in countries that depend on coal-fired power plants. In this study, biochar with incorporated Mn–Fe oxide composite was synthesized by co-ball milling of metal ions and biomass, followed by in situ pyrolysis carbonization and oxidation. The scanning electron microscopy (SEM) result shows that the generated metal oxides were incorporated into the biochar matrix rather than being physically deposited. By avoiding wet impregnation in the MnFe loading process, abundant functional groups were preserved on the surface of the materials. MnFe oxide homogeneity and dispersion on biochar were effectively promoted by mechanochemical synthesis for 4 h and showed high Mn and Fe oxidation states and a large amount of Lewis acid of 56.47 mol/g. Effectively promoted by these properties, the Mn–Fe oxides/biochar composite efficiently purified Hg0and VOC by adsorption and catalytic oxidation from simulated coal-fired flue gas. The mutual inhibitory effect between Hg0and o-xylene is pounced at high reaction temperatures when catalytic oxidation dominated. The mechanochemical strategy endowed the Mn–Fe oxides/biochar composite with strong magnetic properties, showing a saturation magnetization of 24.33 emu/g, which enabled 100% separation of the material from coal-fired fly ash.

•A 3D printed scaffold/hydrogel composite was fabricated to provide the topological cue and compliance cue at the same time for cell guidance•The larger pore size and bigger directed fiber number of 3D scaffold lead to a more uniform cell orientation (topotaxis)•The cells in collagen can still sense the scaffold topological cue and migrate along it (durotaxis)

The molecular orientation of thermally activated delayed fluorescence (TADF) emitters in vapor-deposited organic semiconductor films significantly affects the light out-coupling efficiency and the photoelectric performance of organic light-emitting diodes. Herein, the vacuum deposition of indolocarbazole-isomer derivatives, as highly emissive emitters for TADF-OLEDs, was simulated by use of all atomistic molecular dynamics (MD) simulations and quantum mechanical analysis. It is shown from the results that the molecular orientation in vacuum deposition is determined by the intermolecular π–π stacking. There are the strong van der Waals interactions (mainly dispersion) between emitters and substrate due to the intermolecular π–π stacking. The van der Waals forces lead to the formation of an energetically stable molecular conformation, which makes emitters as stable as possible at the horizontal orientation relative to substrate. The compact π–π stacking of emitters on the substrate is responsible for emitter–substrate weak interactions. Furthermore, theoretical optical properties of indolocarbazole-isomer derivatives are in good accordance with experiments. The present work provides a theoretical perspective for understanding molecular orientation in vacuum deposition.

ABSTRACTIn this study, we isolated eight phenylethanoid glycosides from Paraboea martiniifor the first time, and evaluated the mechanism underlying their neuroprotective effects against H2O2-induced injury in PC12 cells. The MTS method was utilized to screen the phenylethanoid glycosides for protective ability. Next, qRT-PCR and western blotting analysis were used to detect the transcription levels of HO-1 and GCLC, which are regulated by Nrf2. The inhibitor ZnPP was used to analyze the involvement of Nrf2 in HO-1 expression. Analyses showed that caleolarioside B, paraboside B, and paraboside II also upregulated the expression of HO-1, but showed no obvious effect on GCLC. Pretreatment with ZnPP significantly reduced the neuroprotective effects. Thus, phenylethanoid glycosides isolated from P. martiniiprotected PC12 cells from H2O2-induced damage by upregulating HO-1. The results provided evidence that P. martiniimight be a potential therapeutic agent for the treatment of neurodegenerative diseases.

ABSTRACTCoastlines are important basic geographic elements and mapping their spatial and attribute changes can help monitor, model and manage coastal zones. Traditional studies focused on the accuracy of extraction methods and the evolution characteristics of coastlines. Thanks to the advances in remote sensing for earth observations, recent coastline extraction studies can reveal detailed ocean-land interaction changes. In this review, we aim to identify key milestones in coastline extraction using remote sensing by associating the emergence of major research topics with the occurrence of multiple application fields, multiple data sources, and multiple algorithms. Specifically, we define coastlines that can be applied to different application fields, summarize the characteristics of coastline products, and analyze the principles, advantages and disadvantages of methods. On this basis, we discussed the development direction and the challenges involved. This study provides practical insights that can be incorporated into the future development of coastline extraction approaches using remote sensing technologies.

Recent investigations have revealed that some transition-metal dichalcogenides (TMDs), such as MoS2and WS2, are excellent candidates for high-efficiency photocatalysts for water splitting. However, the high recombination rate of photogenerated carriers greatly hinders their practical application. A promising solution involves developing novel TMDs-based van der Waals (vdW) heterostructures with type-II band alignment. In this study, we used first-principles calculations to design two new heterostructures—MoS2/BSe and WS2/BSe—as potential photocatalysts and investigated their structures, stabilities, and electronic and optical properties. We found that both MoS2/BSe and WS2/BSe vdW heterostructures are stable and possess inherent type-II band alignment, which significantly suppresses the recombination of photogenerated carriers. Furthermore, their band edges straddle the redox potential of water, making them suitable for use as photocatalysts in water splitting. They also possess significant built-in electric fields, relatively high carrier mobilities, and excellent abilities to absorb sunlight. Our theoretical findings should shed light on the design of novel TMD-based photocatalysts for water splitting and provide useful guidelines for future experiments.

For the great progress in panel display devices, it is necessary to have a thorough understanding of white organic light-emitting diodes (WOLEDs). In this work, we confirmed a reasonable mechanism of the white light emission with dual emission peaks in the studied compound; the mechanism is that the white emission could be formed with the 0–0 peak and the low-energy emission peak, where the latter is constituted by the high-frequency normal modes with larger Huang–Rhys factors. Moreover, in order to systematically understand the work performance in different surrounding environments, the emission spectra in solution, crystal phase, and amorphous phase were simulated. In view of the fact that the maximum emission wavelength of this complex corresponds to the 0–0 peak, the red-shifted emission from the solution to the crystal phase in experiment could be attributed to the smaller energy gap of the d−π* transition and the lower reorganization energy of the ground state (λgs) in the crystal phase. Additionally, our calculations indicate that the smaller energy gap of the π–π* transition and the smaller λgsin the amorphous phase would cause the red-shifted emission from the crystal phase to the amorphous phase, where the smaller energy gap plays the primary role. In the meantime, it is found that vibronic couplings play a predominant role in photoluminescence efficiency changes caused by environmental effects. Our computational study will provide valuable information to the rational design of optical applications with white emission, which is meaningful to the future development of WOLEDs.

Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disorder characterized by irreversible chronic inflammation and airflow obstruction. It affects more than 64 million patients worldwide and it is predicted to become the third cause of death in the industrialized world by 2030. Currently available therapies are not able to block disease progression and to reduce mortality, underlying the need for a better understanding of COPD pathophysiological mechanisms to identify new molecular therapeutic targets. Recent studies demonstrated that phosphoinositide 3-kinase (PI3K) signaling is prominently activated in COPD and correlates with an increased susceptibility of patients to lung infections. PI3Ks have thus emerged as promising alternative drug targets for COPD and a wide array of pan-isoform and isoform-selective inhibitors have been tested in preclinical models and are currently being evaluated in clinical studies. Here, we summarize the recent knowledge on the involvement of PI3K enzymes in the pathophysiology of COPD, and we discuss the most recent results arising from the preclinical as well as the clinical testing of PI3K inhibitors as novel therapeutics for COPD.

Non-CamelliaTea, as an integral part of Chinese tea culture for several centuries, is important to prevent chronic metabolic diseases. However, it was not systematically studied until academician Pei-gen Xiao defined it. Hereby, Non-CamelliaTea was reviewed systematically in definition, classification, traditional functions, chemical compositions, and pharmacology.

In the process of ReelWell reverse circulation directional drilling, the drilling fluid returns through the inner drill pipe, and the debris particles continuously eroded and wear the drill pipe in the inclined section, which easily causes the failure of the inner drill pipe erosion. Analysis, through the Fluent software to simulate the flow field in the inner drill pipe, to study the impact of different drilling fluid displacement, different mechanical drilling speed, different cuttings particle size on the drill pipe erosion. The results show that: (1) The larger the drilling fluid displacement, the higher the return velocity of the drilling fluid, the greater the number of cuttings, the larger the maximum erosion rate of the curved pipe section, and the larger the erosion area; (2) The higher the drilling rate High, the higher the concentration of cuttings particles, the higher the probability of particles hitting the pipe wall, the greater the maximum erosion rate of the curved pipe section, and the larger the erosion area. When the mechanical drilling rate is higher than 10m/h, the maximum erosion rate increases significantly; (3) the smaller the cuttings particle size, the larger the number of cuttings particles, and the greater the maximum erosion rate of the curved pipe section. It is recommended that the drilling fluid displacement and the mechanical drilling rate should not be too large during the reverse circulation drilling process. At the same time, the cuttings particle size should be controlled, the drilling tool management should be strengthened, and the reasonable use standard after the drill pipe erosion is established. The research results have a certain guiding significance for revealing the failure of the inner drill pipe erosion in ReelWell reverse circulation directional drilling and the scientific use of the wear drill pipe.