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The digital economy has introduced new ideas and methods for the high-quality development of agriculture, becoming a driving force for enhancing agricultural production efficiency. This manuscript utilizes balanced panel data from 1,503 counties and employs the three-stage DEA-Malmquist method to measure agricultural total factor productivity (hereinafter referred to as ATFP). Fixed effect models and mediation models are used to explore the impact and mechanisms of digital rural development on ATFP. The research findings indicate that both ATFP and digital rural development are increasing, although regional disparities exist. The eastern region has higher levels of digital rural development and ATFP compared to the central and western regions. Analysis using fixed effect models and mediation models shows that digital rural development has a significant positive effect on ATFP during the sample period, with rural inclusive finance and human capital playing a mediating role. The impact of digital rural development on ATFP varies across regions. It is more prominent in main grain-producing and main grain-consuming areas, and less significant in balanced production and consumption areas. Geographically, the impact is stronger in the eastern region than in the central and western regions. When categorized by the type of agricultural production, the impact is significant in livestock areas, while relatively smaller in planting industry areas. Based on these findings, it is recommended that local areas tailor their approach to digital rural construction and formulate agricultural production policies that leverage regional strengths and address weaknesses. Additionally, promoting the development of rural inclusive finance, cultivating digital talent, improving talent introduction policies, and expanding the rural digital talent pool are suggested to build momentum for agricultural development.

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To detect redox potential evolution during the initial stage of an acute wound, a redox-sensitive SERS-active optical fiber was fabricated by integrating redox-sensitive SERS probes in a hole of an optical fiber. The redox-sensitive SERS-active optical fibers carried redox-sensitive SERS probes into the inside of a wound to sense its redox potential. The laser was transmitted to the redox-sensitive SERS probes in the body by optical fibers, and the SERS signals of the redox-sensitive SERS probes were transferred out of the body by optical fibers to indicate the redox potentials in the wound. The redox-sensitive SERS probes dynamically sensed the redox potential in vivo, and their SERS signals were collected constantly to indicate the redox potentials. The assessments in vivoand in vitroproved the responsiveness of redox-sensitive SERS-active optical fibers. The redox potential evolution during the initial stage of an acute wound with the treatments of different concentrations of glucose was detected to verify the feasibility of redox-sensitive SERS-active optical fibers to dynamically detect redox potentials in vivo. The redox-sensitive SERS-active optical fiber would be a versatile tool to explore the roles of redox potentials in living organisms.

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With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are increasing, and pixel-oriented classification has attracted much attention. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains and have been employed in HSI classification. But most methods based on GCN are hard to sufficiently exploit information of ground objects due to feature aggregation. To solve this issue, in this article, we proposed a graph-in-graph (GiG) model and a related GiG convolutional network (GiGCN) for HSI classification from a superpixel viewpoint. The GiG representation covers information inside and outside superpixels, respectively, corresponding to the local and global characteristics of ground objects. Concretely, after segmenting HSI into disjoint superpixels, each one is converted to an internal graph. Meanwhile, an external graph is constructed according to the spatial adjacent relationships among superpixels. Significantly, each node in the external graph embeds a corresponding internal graph, forming the so-called GiG structure. Then, GiGCN composed of internal and External graph convolution (EGC) is designed to extract hierarchical features and integrate them into multiple scales, improving the discriminability of GiGCN. Ensemble learning is incorporated to further boost the robustness of GiGCN. It is worth noting that we are the first to propose the GiG framework from the superpixel point and the GiGCN scheme for HSI classification. Experiment results on four benchmark datasets demonstrate that our proposed method is effective and feasible for HSI classification with limited labeled samples. For study replication, the code developed for this study is available at https://github.com/ShuGuoJ/GiGCN.git.

The reconstruction of subsurface ocean temperature using sea surface observations and in situ Argo measurements is an important yet challenging task. The availability of long-term and high-resolution sea surface remote sensing, combined with advancements in deep learning technology, has opened new opportunities for studying subsurface temperature (ST) reconstruction. In this study, a novel spatial–temporal Siamese convolutional neural network (SSCNN) is proposed to improve the accuracy of ST reconstruction in the Indian Ocean. First, considering the distinctions of temperature characteristics among different sea areas, a multiscale division scheme based on the correlation coefficient of integral ST is designed for refined reconstruction modeling. Second, since ocean heat is significantly affected by solar radiation, asymmetric convolutional operation with rectangular patches and kernels is designed to capture the information characteristics in longitude and latitude directions, respectively. Third, given the temporal changes and correlations of ocean temperature, an SSCNN with shared parameters is proposed for multiview feature mining and accurate temperature structure reconstruction. The reconstructed results provide a precise depiction of the subsurface Indian Ocean dipole (sub-IOD)’s evolution, including the spatial distribution of positive and negative anomaly signals and its temporal changes. It demonstrates that the subsurface dipole index series obtained from SSCNN reconstruction is consistent with that from International Pacific Research Center (IPRC) observation, remaining within a reasonable error range. Comparative experiments indicate that the SSCNN model surpasses other existing methods in terms of higher accuracy and smaller error. Overall, this study provides a promising approach for effectively reconstructing the ST using deep learning methods and offers valuable insights for analyzing the evolution of subsurface positive dipole in Indian Ocean.

The international array for real-time geostrophic oceanography (Argo) project is committed to rapidly and precisely acquiring comprehensive 3-D data on ocean temperature and salinity, which is crucial for monitoring ocean climate change and natural phenomena. During the buoy observation, environmental factors, human mistakes, and equipment malfunctions can cause abnormalities such as density inversion and spike, and thus detecting the errors in Argo data is significant to ensure its reliability and applicability. Traditional methods mainly rely on the knowledge and judgment of marine experts, ensuring high accuracy but requiring large amounts of effort. Machine-learning methods are used for automatic Argo data error detection, while they still struggle with extracting deep and discriminative features from profiles. Recently, deep-learning methods have received increasing attention in this field, yet their effectiveness have not been widely explored, faced with challenges of imbalanced samples, joint detection, and complicated patterns. In this article, a novel vertical attention-based siamese ConvLSTM (VAS-CLSTM) network is proposed for the accurate error detection of Argo data. First, an oversampling approach with optimized deep clustering based on inheritance theory and Mahalanobis distance is designed to effectively augment the error samples. Second, a siamese convolutional long–short-term memory (ConvLSTM) network with contextual connection and spatial–temporal adjacent profile search is built to learn interactively from temperature and salinity profiles. Third, a depth-based vertical attention mechanism with grouped weights and vertical trends is proposed for adaptive modeling and flexible learning. Experimental results of North and South Atlantic datasets show that the proposed VAS-CLSTM method effectively improves the accuracy and reliability of error detection in Argo observation data.

Due to climate change, the impact of drought on field crop production is extremely important. This study focuses on the vegetation temperature condition index (VTCI), an index-based drought monitoring index that can characterize drought conditions in near real time (at ten-day intervals), and explores the applicability of different spatial and temporal data fusion schemes to it. It also proposes a field-scale VTCI fusion framework based on the Sentinel-3 VTCI calculation and the land surface temperature (LST) downscaling. First, based on analyzing the computational characteristics of VTCI, multiyear VTCI based on Sentinel data sources was obtained, which further expands the diversity of data sources for VTCI. On this basis, a combination of qualitative and quantitative methods was used to compare the applicability of two schemes: Scheme 1, based on the “blend-then-index” (BI) strategy, which first fuses normalized difference vegetation index (NDVI) and LST, and then calculated the fused VTCIs; and Scheme 2, based on the “index-then-blend” (IB) strategy, which directly fuses the VTCIs based on the calculated VTCIs. It was found that all the fused VTCIs remained highly correlated with the ten-day cumulative precipitation. Compared with the fused VTCIs obtained by Scheme 2, the VTCIs obtained by Scheme 1 were able to display more spatial details. In addition, the VTCIs of Scheme 1 were more consistent with the Sentinel-3 VTCIs, and the accuracy of field yield estimation using the fused VTCIs was higher ( $r$ of 0.58 and root-mean-square error (RMSE) of 783.27 kg/ha).

The advantages of 2D materials in alleviating the issues of short-channel effect and power dissipation in field-effect transistors (FETs) are well recognized. However, the progress of complementary integrated circuits has been stymied by the absence of high-performance (HP) and low-power (LP) p-channel transistors. Therefore, we conducted an investigation into the electronic and ballistic transport characteristics of monolayer Be2C, which features quasi-planar hexacoordinate carbons, by employing nonequilibrium Green’s function combined with density functional theory. Be2C monolayer has planar anticonventional bonds and a direct bandgap of 1.53 eV. The Ionof p-type Be2C HP FETs can achieve a remarkable 2767 μA μm–1. All of the device properties of 2D Be2C FETs can exceed the demands of the International Roadmap for Devices and Systems. The excellent properties of Be2C as a 2D p-orbital material with a high hole mobility are discussed from different aspects. Our findings thus illustrate the tremendous potential of 2D Be2C for the next generation of HP and LP electronics applications.

Radiotherapy (RT) has been the standard of care for treating a multitude of cancer types. Radiation-induced gastric injury (RIGI) is a common complication of RT for thoracic and abdominal tumors. It manifests acutely as radiation gastritis or gastric ulcers, and chronically as chronic atrophic gastritis or intestinal metaplasia. In recent years, studies have shown that intracellular signals such as oxidative stress response, p38/MAPK pathway and transforming growth factor-β signaling pathway are involved in the progression of RIGI. This review also summarized the risk factors, diagnosis and treatment of this disease. However, the root of therapeutic challenges lies in the incomplete understanding of the mechanisms. Here, we also highlight the potential mechanistic, diagnostic and therapeutic directions of RIGI.

Weight reduction is essential for improving health outcomes in people with obesity and type 2 diabetes. We assessed the efficacy and safety of tirzepatide, a glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist, versus placebo, for weight management in people living with obesity and type 2 diabetes.

Two-dimensional (2D) layered metal–organic frameworks (LMOFs), an emerging type of 2D materials, have aroused significant attention recently. Although a great number of LMOFs with diverse topologies have been reported, their predictable preparation remains challenging. Inspired by the transformation of three-dimensional pillared-layer MOFs (PLMOFs) to 2D layers via solvent-assisted link exchanging, we developed herein a novel de novo synthesis strategy, i.e., in situ capping, for the rational fabrication of 2D LMOFs. During PLMOF construction, the pillars are in situ substituted by terminal ligands to seal both sides of the instantaneously generated layers, forming the targeted 2D LMOFs. Employing such a strategy, 2D LMOFs {[Zn(hsb-2)(benzoate)2]·0.5H2O}n(HSB-W11), [Zn(hsb-2)(2-benzoylbenzoate)2]n(HSB-W12), [Zn(hsb-2)(2-acetylbenzoate)2]n(HSB-W13), and {[Zn(hsb-2)(2-naphthoate)2]·DMF·H2O}n(HSB-W14) were obtained predictably by exchanging the terephthalate pillar (bdc) of HSB-W1 (a 3D PLMOF composed of bdc and 1,2-bis(4′-pyridylmethylamino)-ethane) with the capping ligand benzoate or its analogues. The strategy is also valid for other PLMOF systems with bipyridine-type ligands as pillars, X-pcu-1-Zn, for example. Furthermore, the undersized materials of HSB-W11–W14 have been prepared by the instant in situ exfoliation method. The structure, morphology, and fluorescence were tuned by changing the anchoring terminal ligands. In addition, LMOFs HSB-W11–W14 were utilized as I2adsorbents; remarkably, HSB-W13 displayed a record-high volatile iodine uptake at room temperature. The in situ capping strategy has many merits including high designability and efficiency, facile green synthesis, and atom economy, which may be widely adapted to fabricate diverse 2D LMOFs.

Hyperspectral image (HSI) classification based on deep learning is a hot research topic. The convolutional model employs a single rectangular window to interpret the sample neighborhood features, whereas effective characterization of the complex spatial structure of HSI is still an unsolved problem. In this article, we propose a structure-adaptive convolutional neural network (SACNN) for HSI classification, which efficiently exploits the intrinsic spatial geometry information. Four novel strategies are designed to construct the proposed SACNN network. First, superpixel homogeneous region (SHR) sample generation is introduced to achieve neighborhood features within the intercepted rectangular window of the superpixel. Second, online batch-wise standardization uses zero padding to unify the size of inputs in the same batch, thereby realizing parallel processing of irregular inputs. Third, structure-adaptive convolution (SConv) and structure-adaptive average pooling (SAP) are correspondingly constructed to extract deep spectral, spatial, and geometric features from the effective mapping area of superpixels, and further aggregate the information within irregular boundaries. Finally, a sample-adaptive loss weight (SLW) scheme is designed to adjust the influence of different labels on the same input. Experimental results show that the overall classification accuracy of SACNN reaches 93.11%, 90.96%, and 85.04% for 15 randomly selected training samples per class on three HSI datasets, respectively, obtaining an improvement of 0.97%–2.97% with respect to the best-compared method.

Spatial resolution is a crucial indicator for measuring the quality of hyperspectral imaging (HSI) and obtaining high-resolution (HR) hyperspectral images without any auxiliary information has become increasingly challenging. One promising approach is to use deep-learning (DL) techniques to reconstruct HR hyperspectral images from low-resolution (LR) images, namely super-resolution (SR). While convolutional neural networks are commonly used for hyperspectral image SR (HSI-SR), they often lead to unavoidable performance degradation due to the lack of long-range dependence learning ability. In this article, we propose a dual self-attention Swin transformer SR (DSSTSR) network that utilizes the ability of the shifted windows (Swin) transformer in the spatial representation of both global and local features and learns spectral sequence information from adjacent bands of HSI. Additionally, DSSTSR incorporates an image denoising module using the wavelet transformation method to mitigate the impact of stripe noise on HSI-SR. Our extensive experiments using publicly close-range datasets demonstrate that DSSTSR outperforms other state-of-art HSI-SR methods in terms of three image quality metrics. Furthermore, we applied DSSTSR to the SR of satellite hyperspectral images and achieved improved classification results. Compared to its competitors, DSSTSR exhibits superior performance in enhancing spatial resolution while preserving spectral information. These results suggest that the DSSTSR network has great potential for standardization in remote-sensing image processing and practical applications.

The effects of tirzepatide, a glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist, on weight reduction after successful intensive lifestyle intervention are unknown. This double-blind, placebo-controlled trial randomized (1:1) adults with body mass index ≥30 or ≥27 kg/m2and at least one obesity-related complication (excluding diabetes), who achieved ≥5.0% weight reduction after a 12-week intensive lifestyle intervention, to tirzepatide maximum tolerated dose (10 or 15 mg) or placebo once weekly for 72 weeks (n= 579). The treatment regimen estimand assessed effects regardless of treatment adherence in the intention-to-treat population. The coprimary endpoint of additional mean per cent weight change from randomization to week 72 was met with changes of −18.4% (standard error (s.e.) 0.7) with tirzepatide and 2.5% (s.e. 1.0) with placebo (estimated treatment difference −20.8 percentage points (95% confidence interval (CI) −23.2%, −18.5%; P< 0.001). The coprimary endpoint of the percentage of participants achieving additional weight reduction ≥5% was met with 87.5% (s.e. 2.2) with tirzepatide and 16.5% (s.e. 3.0) with placebo achieving this threshold (odds ratio 34.6%; 95% CI 19.2%, 62.6%; P< 0.001). The most common adverse events with tirzepatide were gastrointestinal, with most being mild to moderate in severity. Tirzepatide provided substantial additional reduction in body weight in participants who had achieved ≥5.0% weight reduction with intensive lifestyle intervention. ClinicalTrials.gov registration: NCT04657016.

Argo observations are spatially sparse and temporally uneven, whereas satellites can provide high-resolution and continuous observations at the sea surface. The reconstruction of subsurface thermohaline structure using multisource remote sensing data is thus of great significance for investigating the ocean interior dynamics. Aiming at the existing problems of temporal feature extraction and nonlinear relationship fitting, this article proposes a multivariate temporal self-attention network (MTSAN) to effectively reconstruct the subsurface temperature anomaly (STA) and subsurface salinity anomaly (SSA) in the Pacific Ocean. The model integrates multisource remote sensing data, including sea surface temperature (SST) and sea surface salinity (SSS), wind speed, absolute dynamic topography (ADT), and significant wave height (SWH). In order to better extract the complex small- and medium-scale signals, a two-branch asymmetric residual module based on dilation causal convolution is designed to enhance the representation ability. Moreover, zonal weighted loss function with comprehensive indicators is proposed, in order to minimize the real error of grids and raise the accuracy of self-attention network. MTSAN reconstructs the STA and SSA during the El Ni $\tilde {\mathrm {n}}\text{o}$ event, and the results show that it has good performance for spatial distribution, vertical variation, and temporal extension. The overall $R^{2}$ and RMSE of STA are 0.536 and 0.241 °C, respectively, and the overall $R^{2}$ and RMSE of SSA are 0.645 and 0.037 psu, respectively. In addition, the results of comparison experiments illustrate the superiority of MTSAN over other machine learning and deep learning-based methods. Overall, we provide a new temporal self-attention approach to accurately reconstruct the 3-D thermohaline structure using high-resolution quasi-real-time satellite observations.

Infected diabetic wound (DW) presents a prolonged and challenging healing process within the field of regenerative medicine. The effectiveness of conventional drug therapies is hindered by their limited ability to reach deep tissues and promote adequate wound healing rates. Therefore, there is an imperative to develop drug delivery systems that can penetrate deep tissues while exhibiting multifunctional properties to expedite wound healing. In this study, w e devised a soluble microneedle (MN) patch made of γ‐PGA, featuring multiple arrays, which w as loaded with core‐shell structured nanoparticles (NPs) known as Ag@MSN@CeO2, to enhance the healing of infected DWs. The NP comprises a cerium dioxide (CeO2) core with anti‐inflammatory and antioxidant properties, a mesoporous silica NP (MSN) shell with angiogenic characteristics, and an outermost layer doped with Ag to combat bacterial infections. W e demonstrated that the MN platform loaded with Ag@MSN@CeO2successfully penetrated deep tissues for effective drug delivery. These MN tips induced the formation of multiple regenerative sites at various points, leading to antibacterial, reactive oxygen species‐lowering, macrophage ecological niche‐regulating, vascular regeneration‐promoting, and collagen deposition‐promoting effects, thus significantly expediting the healing process of infected DWs. Considering these findings, the multifunctional MN@Ag@MSN@CeO2patch exhibits substantial potential for clinical applications in the treatment of infected DW. A soluble microneedle patch made of γ‐PGA, loaded with core‐shell structured nanoparticles (Ag@MSN@CeO2), is developed to treat infected diabetic wounds. This patch effectively penetrates deep tissues, delivering antibacterial, anti‐inflammatory, and antioxidant properties while promoting vascular regeneration and collagen deposition, significantly enhancing wound healing. The multifunctional patch shows promise for clinical applications.

Radon is a naturally occurring radioactive gas and considered as a serious carcinogen to humans. Continuous radioactive decay of this gas emits high-energy alpha particles. Long-term radon exposure induces oxidative stress and inflammatory response, which results in chronic lung diseases. However, biological effects after radon exposure in other organs have been rarely reported. As the outermost organ of the human body, the skin suffers from environmental damage to agents such as air pollution. Epidemiological studies indicated that areas with high level of radon had a high incidence of skin cancer. However, whether radon exposure induces skin damage has not been reported yet. In this study, we established a radon-exposed mouse model and found that radon exposure affected the structure of skin tissues, which was manifested by inflammatory cell infiltration and skin atrophy. Using proteomic approach, we found 45 preferentially expressed proteins in 60 Working Level Months (WLM) group and 314 preferentially expressed proteins in 120 WLM group from radon-exposed skin tissues. Through microRNA (miRNA) sequencing profiling analysis, 57 dysregulated miRNAs were screened between the control and radon-treated mouse skin. By integrating the dysregulated proteins and miRNAs, radon-induced fatty acid synthase (FASN) was investigated in greater detail. Results showed that FASN was regulated by miR-206-3p and miR-378a-3p and involved in the pathogenesis of radon-induced skin damage. Overexpression of FASN inhibited the proliferation, and induced in WS1 cells. Our present findings illustrate the molecular change during radon-induced skin damage and the potential role of FASN during this process.

Proneural (PN) to mesenchymal (MES) transition (PMT) is a crucial phenotypic shift in glioblastoma stem cells (GSCs). However, the mechanisms driving this process remain poorly understood. Here, we report that Fos-like antigen 1 (FOSL1), a component of AP1 transcription factor complexes, is a key player in regulating PMT. FOSL1 is predominantly expressed in the MES subtype, but not PN subtype, of GSCs. Knocking down FOSL1 expression in MES GSCs leads to the loss of MES features and tumor-initiating ability, whereas ectopic expression of FOSL1 in PN GSCs is able to induce PMT and maintain MES features. Moreover, FOSL1 facilitates ionizing radiation (IR)-induced PMT and radioresistance of PN GSCs. Inhibition of FOSL1 enhances the anti-tumor effects of IR by preventing IR-induced PMT. Mechanistically, we find that FOSL1 promotes UBC9-dependent CYLD SUMOylation, thereby inducing K63-linked polyubiquitination of major nuclear factor κB (NF-κB) intermediaries and subsequent NF-κB activation, which results in PMT induction in GSCs. Our study underscores the importance of FOSL1 in the regulation of PMT and suggests that therapeutic targeting of FOSL1 holds promise to attenuate molecular subtype switching in patients with glioblastomas.

To investigate the protective effect of bexarotene in radiation-induced skin injury and elucidate underlying mechanism.

This study aimed to purify and characterize a novel antibacterial substance produced by Lactiplantibacillus plantarumCS3 and explored its antibacterial mechanism. Antimicrobial substances were isolated and purified by ultrafiltration, extraction, and gel filtration chromatography. The purified substance was identified by LC/MS as the WRWLRKL heptapeptide (1057.29 Da). The minimum inhibitory concentrations of the crude extract of antimicrobial peptides (CS3–F2) against Staphylococcus hominisand Staphylococcus capitiswere 1.25 mg/ml and 1 mg/ml, respectively. The crude extract directly affected the cell wall and membrane, inhibited biofilm formation, caused leakage of cell contents, and directly bound to genomic DNA, affecting the respiratory metabolism of spoilage bacteria, blocking normal binding of enzymes and receptors, and ultimately leaded to cell death. Furthermore, the addition of L. plantarumCS3 could improve the quality and flavor characteristics of soy sauce.

Global warming has intensified the intensity of compound drought‐hot extremes (CDHEs), posing more severe impacts on human societies and ecosystems than individual extremes. The vulnerability of global terrestrial ecosystems under CDHEs, along with its key influencing factors, remains poorly understood. Based on multiple remote sensing data, we construct a Vine Copula model to appraise vegetation vulnerability under CDHEs, and attribute it to climatic and biotic factors for five different vegetation types. High vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. The drier the climate, the higher will be the vulnerability. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability of various vegetation types, such that ecosystems with richer biodiversity and higher biomass have lower vulnerability to CDHEs. The findings deepen understanding of terrestrial ecosystem response to CDHEs. Drought and hot‐related extremes often coincide or follow one another, known as compound drought‐hot extremes (CDHEs), adversely affecting various vegetation processes. Thus, investigating the response relationship between vegetation dynamics and CDHEs is critical for maintaining the sustainable development of ecosystems under the background of climate warming. High ecosystem vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. Climatic factors (precipitation, arid index, temperature, and radiation) dominate the vulnerability of all vegetation types. The drier the climate, the higher will be the vulnerability of vegetation to CDHEs. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability. The results provide us with essential insights for managing and adapting terrestrial ecosystems against climate change and are of interest to a wide range of audiences, including but not limited to ecologists, hydrologists, and climatologists. A framework is developed to estimate the vegetation vulnerability in response to compound drought‐hot extremes and attribute it to various factorsVegetation vulnerability is higher for grasses and shrubs than for evergreen and deciduous forestsVegetation vulnerability is both affected by climate and biotic factors, but climate factors are more dominant A framework is developed to estimate the vegetation vulnerability in response to compound drought‐hot extremes and attribute it to various factors Vegetation vulnerability is higher for grasses and shrubs than for evergreen and deciduous forests Vegetation vulnerability is both affected by climate and biotic factors, but climate factors are more dominant

In the paper, we endeavor to study the asynchronous and resilient dissipative filtering issue for T–S fuzzy Markov jump singularly perturbed systems (MJSPSs) involving the dynamic event-triggered transmission protocol, partly known conditional probabilities, and deception attacks. A novel dynamic event-triggered transmission protocol is firstly offered to further decrease the data transmission percentage over the communication channels. To better depict the practical communication situations, the mutually independent random variables that subject to the Bernoulli distribution are adopted to formulate the occurrence of deception attacks. And an asynchronous and resilient filter is properly constructed, which yields that the filtering error systems are converted into the T–S fuzzy MJSPSs with time-varying delays. Then, the criteria of stochastically stable for the filtering error systems are presented concerning a group of linear matrix inequalities (LMIs). By solving the LMIs, the nonsynchronous and resilient dissipative filter, the weighting matrix of dynamic event-triggered transmission protocol can be concurrently determined. Eventually, an example is provided to verify the validity of the developed theoretical results. Further, for the prescribed dissipative performance, state trajectoires, filter trajectories, and filter errors are presented for MJSPSs involving deception attacks under different transmission protocols, which illustrate that both of them almost have the same decay rates. However, for the prescribed dissipative performance, the transmission rates are so different for two transmission protocols that the dynamic event-triggered transmission protocol is much lower than that of the static one.

This article considers the trajectory tracking control for unmanned surface vessels with unknown time-variant external disturbances and input saturation. The strategy mainly consists of event-triggered reset sub-controller and nonlinear disturbance observer–based compensation sub-controller. To reduce network transmissions, and in the meanwhile, guarantee the desirable closed-loop behavior, the event-triggered reset control is proposed where the reset law and the event-triggered mechanism are designed separately. Both of static and dynamic event-triggered reset controllers are designed. Their corresponding stability is demonstrated using Lyapunov stability theory. Finally, numerical simulation results are presented to demonstrate the effectiveness and robustness of the proposed trajectory tracking control strategy.

The purpose of this study was to investigate the mechanism of PEF treatment on the regulation of pine nut peptide (VNAVLH) induced immune activity of RAW 264.7 based on C-terminal modification by molecular dynamics (MD). The results showed that PEF treatment can reduce the overall stability of pine nut peptides, enhance the flexibility of hydrophobic and positively charged residues in pine nut peptides, and make their residue structures more flexible. Active sites verification experiments showed that PEF treatment could improve the immune activity of histidine only when it was at C-terminal. Western blot and immunofluorescence analysis showed that PEF-treated VNAVLH could activate NF-κB pathway by promoting the nuclear translocation of NF-κB/p65 and the expression of IKKβ, and activate MAPK pathway by promoting the phosphorylation of JNK, ERK, and p38 in RAW 264.7. Furthermore, toll-like receptor 4 (TLR4), as the main receptor for VNAVLH to exert immune activity, exhibited a more prominent peptide-binding capacity and had more hydrogen-bonding sites to PEF-treated peptide compared to toll-like receptor 2 (TLR2). Therefore, enhanced immunoreactivity of VNAVLH may be due to the formation of a more stable complex with TLR4 after PEF treatment, which activates the MAPK/NF-κB signaling pathway causing an intracellular cascade response.

Recent extremely heavy precipitation has led to substantial economic losses and affected millions of residences in the Lancang‐Mekong River Basin (LMRB). This study analyzed the spatial‐temporal characteristics of the annual maximum precipitation (R1X) of the LMRB and identified the moisture sources and pathways conducive to R1Xs using a Lagrangian back trajectory model. Results show that India Ocean and Bay of Bengal (IO/BOB), local evapotranspiration, and West Pacific Ocean and East China (WP/EC) are the three main moisture transport pathways of the R1Xs in LMRB, contributing 68.3%, 20.4% and 11.3% of the trajectories, respectively. R1Xs in the downstream eastern area are affected by tropical cyclones bringing large amounts of moisture from the WP/EC. As tropical cyclones shifted northward under climate change impact, more extreme precipitation occurred over the LMRB due to moisture coming from WP/EC, but those from the IO/BOB had decreased because of the slowdown of flows across the Equator. Recent extremely heavy precipitation has led to more frequent floods, storm surges, and other natural hazards in the Lancang‐Mekong River Basin, resulting in substantial economic losses and affecting millions of residences. This study used annual maximum precipitation to represent the extreme precipitation and analyzed its spatial‐temporal characteristics and the moisture sources and pathways. Results show that the extreme precipitation of the upstream region mainly occurred in July, while that of the downstream region mainly occurred in August‐September. The moisture pathways of the historical extreme precipitation were identified using a physical‐based model, and are classified into three clusters using a machine‐learning model. West Pacific Ocean and East China, local evapotranspiration, and Indian Ocean and Bay of Bengal (IO/BOB) are the three moisture transport pathways with contributions of 68.3%, 20.4%, and 11.3% to the total pathways. The tropical cyclones bring large amounts of moisture and mainly affect R1Xs in the downstream eastern area. Tropical cyclones shifted northward under climate change impact, and more extreme precipitation occurred over the LMRB due to moisture coming from the West Pacific Ocean and East China, but those from the IO and BOB had decreased because of the slowdown of flows across the Equator. The timing of the annual maximum precipitation of the Lancang‐Mekong River Basin (LMRB) varies from July to SeptemberThe extreme precipitation of the LMRB mainly received moisture from the Indian Ocean to the West Pacific OceanTropical cyclones will bring more extreme precipitation to the LMRB under climate change The timing of the annual maximum precipitation of the Lancang‐Mekong River Basin (LMRB) varies from July to September The extreme precipitation of the LMRB mainly received moisture from the Indian Ocean to the West Pacific Ocean Tropical cyclones will bring more extreme precipitation to the LMRB under climate change

Thermochromic metal–organic frameworks (MOFs) are promising functional materials for a wide range of applications due to their ability to exhibit color variation under external temperature stimuli, yet the development of them with high cyclability and efficient regeneration processes remains challenging. Here, presented is a rare example of an ultrastable Ni(II)-MOF exhibiting an unprecedented reversible four-step color change between two complementary colors in a wide temperature range, which could be repeated for at least 500 cycles without losing crystallinity and thermochromic performance. Notably, the regeneration can be achieved within 1 min by simply letting the crystals cool naturally in the air, facilitated by the unique nature of the channels’ inner surface. The reversible thermochromic behavior is owing to a series of reversible crystal structure changes with temperature, including the stepwise dehydration/rehydration process, and structural changes. This work facilitates the future development of more MOF-based reversible thermochromic materials with excellent performance and improved practical applicability.

ABSTRACTIncreasingly attention is being given to immune molecules in pancreatic cancer. The purpose of this study was to understand the potential clinical application of immune-regulated genes (IRGs) in the stratification of prognosis and to facilitate the development of personalized prognostic information for pancreatic cancer patients. We systematically used public data to comprehensively analyze immune-regulated gene pair (IRGP) expression profiles and clinical data. In our study, IRGP signature was identified to predict the overall survival (OS) of pancreatic cancer patients. We suggested that immune genes are enriched in different risk groups. In the high-risk group, M1 macrophages and resting NK cells were significantly enriched, while the percentages of naïve B cells, resting dendritic cells, CD8 T cells and regulatory T cells (Tregs) were significantly higher in the low-risk group, and we verified these results with immunohistochemical experiments. Gene ontology (GO) analysis confirmed that the IRGP index (IRGPI) signature genes in the cohort were mostly party to sensory perception of a chemical stimulus and the adaptive immune response. The identification of these pathways provides a basis for studying the molecular mechanisms of IRGPI signaling to predict the prognosis of pancreatic cancer. Our study effectively constructed a robust IRGP signature with prognostic value for pancreatic cancer, presenting a conceivable method for deciding on a preoperative treatment.

At present, mainstream neuromorphic hardware is based on artificial synapses; however, an engram, instead of a synapse, has recently been confirmed as the basic unit of memory, which verifies the engram theory proposed by Richard Semon in 1904. Here, we demonstrate an artificial engram device based on a nanoimprinted curable resin. The variation in the relative diffraction efficiency based on the asymmetric reversible topological change of the nanoimprinted resin enables the device to meet all the requirements for artificial engrams, including synaptic plasticity, long memory storage time, asymmetric memorizing–forgetting behaviour and measurable changes and responses. On this basis, we demonstrate the concept of realizing memory formation, memory manipulation and implantation, and memory consolidation using our artificial engram device in comparison with its biological counterpart.

The development of new nanocarriers with desired degradability and targeted ability is of great significance for efficient drug delivery. In this work, a monodisperse hollow structured MnO2(H-MnO2) with a mesoporous shell is prepared and functionalized for efficient targeted drug delivery. The highly monodisperse H-MnO2with a uniform morphology was obtained by in situgrowing MnO2on solid silica nanoparticles and subsequently removing the silica core. Then, the H-MnO2is successively modified with polyethylene glycol and targeted molecule folate (FA). The resultant H-MnO2-FA shows excellent colloidal stability and high drug-loading content (∼58.2%) of the antitumor drug doxorubicin hydrochloride (DOX). The H-MnO2-FA possesses acid-responsive T1-weighted magnetic resonance imaging ability. The pH-dependent biodegradation behavior of H-MnO2-FA is directly observed in vitro and confirmed by in vivoimaging, which is expected to favor the potential clearance of this hollow structured nanocarrier and eliminate its long-term toxicity. In addition, the DOX-loaded H-MnO2-FA also demonstrates excellent cancer cell-killing effect and tumor inhibition efficacy.