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Visual adaptation allows organisms to effectively analyze visual information in varying light conditions by autonomously adjusting photosensitivity, which is essential for the visual system to perform accurate perception in complex environments. In order to realistically implement the functionality of the visual system, the exploration of bioinspired electronics with adaptive capability is highly desired. Herein, a self‐powered optoelectronic memristor based on ZnO/WOx heterojunction is developed, which can exhibit the visual adaptation functions of desensitization and Weber's law. These functions are achieved through the coupling of the photovoltaic effect with electron trapping in the space charge region of the heterojunction. Furthermore, a bioinspired visual adaptive neuron has been constructed, comprising an optoelectronic memristor and a NbOx‐based threshold switching memory, capable of directly converting constant light stimuli into dynamic spike trains. Finally, the adaptive image preprocessing is realized, which promotes the improvement of the object recognition accuracy during the overexposed image recognition process. This study offers a novel approach to developing biologically plausible visual adaptation, fostering the future progress of dynamic neuromorphic visual systems. The self‐powered optoelectronic memristor based on ZnO/WOx heterojunction is developed, exhibiting the visual adaptation functions of desensitization and Weber's law. A bioinspired visual adaptive neuron is constructed, capable of converting light stimuli into dynamic spike trains. The adaptive image preprocessing is realized, which promotes the improvement of the object recognition accuracy during the overexposed image recognition process.

Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency‐shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WOx‐based memristive synapse. The WOx memristor presents both the volatile mode (M1) and semi‐nonvolatile mode (M2), which are capable of implementing the high‐pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency‐shift information processing for velocity detection is emulated in the WOx memristor based auditory system for the first time, which relies on a scheme of triplet spike‐timing‐dependent‐plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing. A auditory sensory system with motion perception is demonstrated by using a WOx‐based memristive synapse. Due to the coexistence of the volatile mode and semi‐nonvolatile mode in the Ar‐plasma‐treated (APT) WOx memristor, the functions of azimuth detection and velocity detection are realized via implementing the high‐pass filtering and processing the spike trains with frequency shift in both modes, respectively.

Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency‐shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WO x ‐based memristive synapse. The WO x memristor presents both the volatile mode (M1) and semi‐nonvolatile mode (M2), which are capable of implementing the high‐pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency‐shift information processing for velocity detection is emulated in the WO x memristor based auditory system for the first time, which relies on a scheme of triplet spike‐timing‐dependent‐plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing.

Patients with major depressive disorder (MDD) exhibit concurrent deficits in both sensory and higher-order cognitive processing. Connectome studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this gradient structure is disrupted in patients with MDD and how this disruption associates with gene expression profiles and treatment outcome remain unknown. Using a large cohort of resting-state fMRI data from 2227 participants (1148 MDD patients and 1079 healthy controls) recruited at nine sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth, postmortem gene expression, and an 8-week antidepressant treatment (20 MDD patients) data to assess the meta-analytic cognitive functions, transcriptional profiles, and treatment outcomes related to MDD gradient alterations, respectively. Relative to the controls, MDD patients exhibited global topographic alterations in the principal primary-to-transmodal gradient, including reduced explanation ratio, gradient range, and gradient variation (Cohen’s d = 0.16–0.21), and focal alterations mainly in the primary and transmodal systems (d = 0.18–0.25). These gradient alterations were significantly correlated with meta-analytic terms involving sensory processing and higher-order cognition. The transcriptional profiles explained 53.9% variance of the altered gradient pattern, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. The baseline gradient maps of patients significantly predicted symptomatic improvement after treatment. These results highlight the connectome gradient dysfunction in MDD and its linkage with gene expression profiles and clinical management, providing insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

The Sentinel-3 Ocean and Land Colour Instrument (OLCI) is designed for water monitoring. Its 21-spectral bands serve as the basis for the precise retrieval of water quality parameters. However, its coarse resolution restricts the depiction of the spatial distribution of water quality parameters in small inland water bodies. Spatial–spectral fusion is a common method to address the inherent constraints between the spatial and spectral resolutions of sensors. Central to the popular methods is the deep learning-based method. Nonetheless, deep-learning-based models still face challenges in fusing Sentinel-2 Multi-Spectral Instrument (MSI) and Sentinel-3 OLCI data. Here, we propose a Multi-Scale-Attention-based Unsupervised Generative Adversarial Network (MSA-UGAN), which effectively integrates OLCI’s spectral advantage and MSI’s spatial resolution. Quantitative evaluation was conducted against five benchmark methods, including traditional approaches (GS, SFIM, MTF-GLP) and deep learning models (SRCNN, UCGAN). The results show that MSA-UGAN achieves the best overall performance: QNR (0.9709) and SSIM (0.9087) are the highest, while SAM (1.1331), spatial distortion (DS = 0.0389), and spectral distortion (Dλ = 0.0252) are the lowest. This shows that MSA-UGAN can better preserve the spatial details of S2 MSI and the spectral features of S3 OLCI data. Moreover, ERGAS (2.2734) also performs excellently in the comparative experiments. The experiment of Chlorophyll-a inversion using the fused image in Chen Lake revealed a spatial gradient ranging from 3.25 to 19.33 µg/L, with the highest concentrations in the southwestern nearshore waters, likely associated with aquaculture. These results jointly indicate that MSA-UGAN can generate high-spatial-resolution multispectral images, and the fused images can be effectively utilized for water quality monitoring, thereby providing essential data support for the precision management and scientific decision-making regarding inland lakes.

Background: Afatinib, a second-generation tyrosine kinase inhibitor (TKI), exerts its radiosensitive effects in nasopharyngeal carcinoma (NPC). However, the detailed mechanism of afatinib-mediated sensitivity to radiation is still obscure in NPC. Methods: Quantitative phosphorylated proteomics and bioinformatics analysis were performed to illustrate the global phosphoprotein changes. The activity of the CD44-Stat3 axis and Epithelial-Mesenchymal Transition (EMT)-linked markers were evaluated by Western blotting. Wound healing and transwell assays were used to determine the levels of cell migration upon afatinib combined IR treatment. Cell proliferation was tested by CCK-8 assay. A pharmacological agonist by IL-6 was applied to activate Stat3. The xenograft mouse model was treated with afatinib, radiation or a combination of afatinib and radiation to detect the radiosensitivity of afatinib in vivo. Results: In the present study, we discovered that afatinib triggered global protein phosphorylation alterations in NPC cells. Further, bioinformatics analysis indicated that afatinib inhibited the CD44-Stat3 signaling and subsequent EMT process. Moreover, functional assays demonstrated that afatinib combined radiation treatment remarkably impeded cell viability, migration, EMT process and CD44-Stat3 activity in vitro and in vivo. In addition, pharmacological stimulation of Stat3 rescued radiosensitivity and biological functions induced by afatinib in NPC cells. This suggested that afatinib reversed the EMT process by blocking the activity of the CD44-Stat3 axis. Conclusion: Collectively, this work identifies the molecular mechanism of afatinib as a radiation sensitizer, thus providing a potentially useful combination treatment and drug target for NPC radiosensitization. Our findings describe a new function of afatinib in radiosensitivity and cancer treatment.

Previous studies about probabilistic back analysis for shear strength parameters of landslides generally adopted a fixed slip surface. This setting may lead to unreliable results due to the uncertainty of slip surface location speculated by limited observations. Based on Bayes’ theorem, this paper proposes a probabilistic framework for the back analysis of landslides considering slip surface uncertainty. The posterior distributions of shear strength parameters in Bayesian inference are solved by Markov chain Monte Carlo simulation method. To improve computational efficiency, a response surface function based on extreme learning machine is constructed to approximate the relationship between shear strength parameters and the corresponding factor of safety and critical slip surface. A synthetic slope, for which the actual shear strength parameters and slip surface are known, is used to compare the proposed and traditional methods. The effects of measurement error of slip surface and prior distribution of shear strength parameters on probabilistic back analysis results are also investigated. Results show that the shear strength parameters obtained from traditional probabilistic back analyses neglecting slip surface uncertainty significantly deviate from actual values, and are greatly affected by prior mean of shear strength parameters. The proposed method performs better than traditional method and is less affected by the prior distributions of shear strength parameters, and the smaller the measurement error of slip surface, the higher the Bayesian back analysis accuracy. A practical landslide is applied to further verify the effectiveness of the proposed method.

Pueraria montana var. lobata ( P. lobata ) is both a medicinal herb with significant pharmacological values and a food ingredient that can replace grains, but it still faces challenges in quality consistency and suitable cultivation. This study aims to systematically analyze its potential suitable habitats across China and evaluate the effect of environment on its growth and quality. By integrating distribution data from 926 sample points and 33 environmental variables, MaxEnt model and ArcGIS software were employed to predict the potential suitable habitats of P. lobata , and investigate distribution change at the provincial level. Chemical and correlation analysis were used to determine the total flavonoid content and explore the relationship with environmental variables. Key influencing variables were Bio12 (annual precipitation, 35.4%), Bio14 (driest month precipitation, 24%), and Bio06 (coldest month minimum temperature, 18.2%). P. lobata from Hubei and Jiangxi provinces exhibited higher flavonoid content than that in other high-suitable provinces, which showed a strong positive correlation with latitude and a significant negative correlation with January mean temperature. Under future climate scenarios, the suitable habitats of P. lobata showed northward expansion due to global warming. These findings offer a theoretical foundation for sustainable development and high-quality demand under changing climatic conditions.

Background Banana Fusarium wilt caused by Fusarium oxysporum f. sp. cubense is a soil-borne fungal disease. Especially, tropical Race 4 ( Foc TR4) can infect almost Cavendish subgroup and has a fatal threat to banana industry. Use of antagonistic microbes to manage soil-borne pathogen is viewed as a promising strategy. Results Strain XZ11-1 isolated from tropical rainforest has the production ability of high siderophore. By the analysis of physiological and biochemical profiles, construction of phylogenetic tree, and comparative results from the NR database, strain XZ11-1 was identified as Trichoderma virens . A relative content of 79.45% siderophores was produced in the optimized fermentation solution, including hydroxamate and carboxylate-type siderophores. Siderophores were key for inhibiting the growth of Foc TR4 by competing for environmental iron. Similarly, T. virens XZ11-1 also had antagonistic activities against 10 phytopathogenic fungi. Pot experiments demonstrated that T. virens XZ11-1 could colonize in the root system of banana plants. The symbiotic interaction not only improve plant resistance to Foc TR4, but also enhance iron absorption of roots to promote plant growth by secreting siderophores. Conclusions T. virens XZ11-1 with the high-yield siderophores was isolated and identified. The strain could effectively inhibit the infection of Foc TR4 in banana roots and promote plant growth. It is a promising biocontrol agent for controlling fungal disease.

The illegal wildlife trade (IWT) poses a significant global challenge that threatens biodiversity and ecosystem balance. This study addresses these complexities by proposing the Integrated Ecological Intervention Optimization Model (IEIOM). The model integrates three core metrics—habitat area, crime rate, and quantity of IWT—while incorporating multidimensional analysis and predictive modeling across ecological, social, and economic dimensions. To enhance predictive accuracy, we employed nonlinear regression, grey prediction, and autoregressive models. These predictive insights, combined with empirical data, were integrated into a multi-index intervention optimization framework using a sum-of-sines function. A simulated annealing algorithm was subsequently applied to achieve global optimization. Results indicate that the proposed IEIOM outperforms the traditional entropy weight method by providing a more dynamic, data-driven weight allocation. The optimal weights prioritized crime suppression (50%), habitat protection (28%), and trade regulation (22%), underscoring the critical roles of law enforcement and environmental preservation. Sensitivity analysis further demonstrated that technological innovation, community collaboration, and public awareness are pivotal to successful interventions. Overall, the IEIOM provides a robust decision-support tool for policymakers, enabling effective resource allocation to combat IWT and contributing to long-term sustainable development.