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s Background Many neurological diseases involve neuroinflammation, during which overproduction of cytokines by immune cells, especially microglia, can aggregate neuronal death. Ferroptosis is a recently discovered cell metabolism-related form of cell death and RSL3 is a well-known inducer of cell ferroptosis. Here, we aimed to investigate the effects of RSL3 in neuroinflammation and sensitivity of different type of microglia and macrophage to ferroptosis. Methods Here, we used quantitative RT-PCR analysis and ELISA analysis to analyze the production of proinflammatory cytokine production of microglia and macrophages after lipopolysaccharides (LPS) stimulation. We used CCK8, LDH, and flow cytometry analysis to evaluate the sensitivity of different microglia and macrophages to RSL3-induced ferroptosis. Western blot was used to test the activation of inflammatory signaling pathway and knockdown efficiency. SiRNA-mediated interference was conducted to knockdown GPX4 or Nrf2 in BV2 microglia. Intraperitoneal injection of LPS was performed to evaluate systemic inflammation and neuroinflammation severity in in vivo conditions. Results We found that ferroptosis inducer RSL3 inhibited lipopolysaccharides (LPS)-induced inflammation of microglia and peritoneal macrophages (PMs) in a cell ferroptosis-independent manner, whereas cell ferroptosis-conditioned medium significantly triggered inflammation of microglia and PMs. Different type of microglia and macrophages showed varied sensitivity to RSL3-induced ferroptosis. Mechanistically, RSL3 induced Nrf2 protein expression to inhibit RNA Polymerase II recruitment to transcription start site of proinflammatory cytokine genes to repress cytokine transcription, and protect cells from ferroptosis. Furthermore, simultaneously injection of RSL3 and Fer-1 ameliorated LPS-induced neuroinflammation in in vivo conditions. Conclusions These data revealed the proinflammatory role of ferroptosis in microglia and macrophages, identified RSL3 as a novel inhibitor of LPS-induced inflammation, and uncovered the molecular regulation of microglia and macrophage sensitivity to ferroptosis. Thus, targeting ferroptosis in diseases by using RSL3 should consider both the pro-ferroptosis effect and the anti-inflammation effect to achieve optimal outcome.

Physical reservoirs holding intrinsic nonlinearity, high dimensionality, and memory effects have attracted considerable interest regarding solving complex tasks efficiently. Particularly, spintronic and strain-mediated electronic physical reservoirs are appealing due to their high speed, multi-parameter fusion and low power consumption. Here, we experimentally realize a skyrmion-enhanced strain-mediated physical reservoir in a multiferroic heterostructure of Pt/Co/Gd multilayers on (001)-oriented 0.7PbMg 1/3 Nb 2/3 O 3 −0.3PbTiO 3 (PMN-PT). The enhancement is coming from the fusion of magnetic skyrmions and electro resistivity tuned by strain simultaneously. The functionality of the strain-mediated RC system is successfully achieved via a sequential waveform classification task with the recognition rate of 99.3% for the last waveform, and a Mackey-Glass time series prediction task with normalized root mean square error (NRMSE) of 0.2 for a 20-step prediction. Our work lays the foundations for low-power neuromorphic computing systems with magneto-electro-ferroelastic tunability, representing a further step towards developing future strain-mediated spintronic applications. An energy-efficient physical reservoir is crucial for reservoir computing (RC). Here the authors demonstrate an all-electric skyrmion-enhanced strain-mediated physical RC system and achieve a benchmark chaotic time series prediction.

Cross-institutional collaboration in privacy-sensitive domains such as healthcare and finance requires machine learning frameworks that balance model utility, privacy protection, and communication efficiency. Federated learning (FL) enables decentralized model training without direct data sharing, yet existing approaches inadequately address vulnerabilities in Trusted Execution Environments (TEEs), which are increasingly adopted to safeguard local computations. TEE side-channel attacks (e.g., cache-timing leaks, speculative execution exploits) can expose sensitive gradient information even when cryptographic defenses are deployed. Furthermore, traditional FL methods treat privacy and communication as independent objectives, leading to suboptimal tradeoffs when both constraints are active. This paper proposes Confidential Computing-Aware Projected Gradient Descent (CC-PGD), a constrained multi-objective optimization framework that jointly minimizes model loss, privacy leakage risk (incorporating TEE vulnerability modeling), and communication overhead. We formulate privacy risk as a combination of gradient entropy and a binary indicator function for TEE exploit susceptibility, while communication cost accounts for model size and network latency. We prove that CC-PGD achieves convergence under non-convex objectives with Lipschitz-continuous gradients. Experiments on MNIST and CIFAR-10 under IID and non-IID data partitioning demonstrate that CC-PGD reduces privacy leakage by 23–31% and communication cost by 18–27% compared to baselines (FedAvg, DP-FL, FedProx), while maintaining competitive accuracy (within 2% of centralized training). Our work provides the first optimization framework explicitly accounting for TEE side-channel risks in federated learning, with theoretical guarantees and empirical validation.

Stretchable neuromorphic optoelectronics present tantalizing opportunities for intelligent vision applications that necessitate high spatial resolution and multimodal interaction. Existing neuromorphic devices are either stretchable but not reconcilable with multifunctionality, or discrete but with low-end neurological function and limited flexibility. Herein, we propose a defect-tunable viscoelastic perovskite film that is assembled into strain-insensitive quasi-continuous microsphere morphologies for intrinsically stretchable neuromorphic vision-adaptive transistors. The resulting device achieves trichromatic photoadaptation and a rapid adaptive speed (<150 s) beyond human eyes (3 ~ 30 min) even under 100% mechanical strain. When acted as an artificial synapse, the device can operate at an ultra-low energy consumption (15 aJ) (far below the human brain of 1 ~ 10 fJ) with a high paired-pulse facilitation index of 270% (one of the best figures of merit in stretchable synaptic phototransistors). Furthermore, adaptive optical imaging is achieved by the strain-insensitive perovskite films, accelerating the implementation of next-generation neuromorphic vision systems. Wearable neuromorphic optoelectronics require stretchable photosensitive materials and multifunctional integration. Here, authors develop intrinsically stretchable neuromorphic vision-adaptive transistors for skin-like neuromorphic vision systems.

Transformer-based methodologies in object detection have recently piqued considerable interest and have produced impressive results. DETR, an end-to-end object detection framework, ingeniously integrates the Transformer architecture, traditionally used in NLP, into computer vision for sequence-to-sequence prediction. Its enhanced variant, DINO, featuring improved denoising anchor boxes, has showcased remarkable performance on the COCO val2017 dataset. However, it often encounters challenges when applied to scenarios involving small object detection. Thus, we propose an innovative method for feature enhancement tailored to recursive prediction tasks, with a particular emphasis on augmenting small object detection performance. It primarily involves three enhancements: refining the backbone to favor feature maps that are more sensitive to small targets, incrementally augmenting the number of queries for small objects, and advancing the loss function for better performance. Specifically, The study incorporated the Switchable Atrous Convolution (SAC) mechanism, which features adaptable dilated convolutions, to increment the receptive field and thus elevate the innate feature extraction capabilities of the primary network concerning diminutive objects. Subsequently, a Recursive Small Object Prediction (RSP) module was designed to enhance the feature extraction of the prediction head for more precise network operations. Finally, the loss function was augmented with the Normalized Wasserstein Distance (NWD) metric, tailoring the loss function to suit small object detection better. The efficacy of the proposed model is empirically confirmed via testing on the VISDRONE2019 dataset. The comprehensive array of experiments indicates that our proposed model outperforms the extant DINO model in terms of average precision (AP) small object detection.

Large area and highly aligned polymer semiconductor sub-microwires were fabricated using the coaxial focused electrohydrodynamic jet printing technology. As indicated by the results, the sub-microwire arrays have smooth morphology, well reproducibility and controllable with a width of ~110 nm. Analysis shows that the molecular chains inside the sub-microwires mainly exhibited edge-on arrangement and the π-stacking direction (010) of the majority of crystals is parallel to the long axis of the sub-microwires. Sub-microwires based organic field effect transistors showed high mobility with an average of 1.9 cm 2 V −1 s −1 , approximately 5 times higher than that of thin film based organic field effect transistors. In addition, the number of sub-microwires can be conveniently controlled by the printing technique, which can subsequently concisely control the performance of organic field effect transistors. This work demonstrates that sub-microwires fabricated by the coaxial focused electrohydrodynamic jet printing technology create an alternative path for the applications of high-performance organic flexible device. Here, the authors fabricate large area and highly aligned polymer semiconductor sub-microwires arrays via coaxial focused electrohydrodynamic jet printing technology, achieving high on/off ratio and average mobility that is 5x higher than that of thin film based organic field effect transistors.

Synthetic aperture radar (SAR) sensor often produces a shadow in pairs with the target due to its slant-viewing imaging. As a result, shadows in SAR images can provide critical discriminative features for classifiers, such as target contours and relative positions. However, shadows possess unique properties that differ from targets, such as low intensity and sensitivity to depression angles, making it challenging to extract depth features from shadows directly using convolutional neural networks (CNN). In this paper, we propose a new SAR image-classification framework to utilize target and shadow information comprehensively. First, we design a SAR image segmentation method to extract target regions and shadow masks. Second, based on SAR projection geometry, we propose a data-augmentation method to compensate for the geometric distortion of shadows due to differences in depression angles. Finally, we introduce a feature-enhancement module (FEM) based on depthwise separable convolution (DSC) and convolutional block attention module (CBAM), enabling deep networks to fuse target and shadow features adaptively. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that when only using target and shadow information, the published deep-learning models can still achieve state-of-the-art performance after embedding the FEM.

With the prevalence of cardiovascular disease, it is imperative that medical monitoring and treatment become more instantaneous and comfortable for patients. Recently, wearable and implantable optoelectronic devices can be seamlessly integrated into human body to enable physiological monitoring and treatment in an imperceptible and spatiotemporally unconstrained manner, opening countless possibilities for the intelligent healthcare paradigm. To achieve biointegrated cardiac healthcare, researchers have focused on novel strategies for the construction of flexible/stretchable optoelectronic devices and systems. Here, we overview the progress of biointegrated flexible and stretchable optoelectronics for wearable and implantable cardiac healthcare devices. Firstly, the device design is addressed, including the mechanical design, interface adhesion, and encapsulation strategies. Next, the practical applications of optoelectronic devices for cardiac physiological monitoring, cardiac optogenetics, and nongenetic stimulation are presented. Finally, an outlook on biointegrated flexible and stretchable optoelectronic devices and systems for intelligent cardiac healthcare is discussed.

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries’ GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning.

Electrohydrodynamic (EHD) printing technique, which deposits micro/nanostructures through high electric force, has recently attracted significant research interest owing to their fascinating characteristics in high resolution (<1 μm), wide material applicability (ink viscosity 1–10 000 cps), tunable printing modes (electrospray, electrospinning, and EHD jet printing), and compatibility with flexible/wearable applications. Since the laboratory level of the EHD printed electronics' resolution and efficiency is gradually approaching the commercial application level, an urgent need for developing EHD technique from laboratory into industrialization have been put forward. Herein, we first discuss the EHD printing technique, including the ink design, droplet formation, and key technologies for promoting printing efficiency/accuracy. Then we summarize the recent progress of EHD printing in fabrication of displays, organic field‐effect transistors (OFETs), transparent electrodes, and sensors and actuators. Finally, a brief summary and the outlook for future research effort are presented. Electrohydrodynamic (EHD) printing presents unique advantages of ultra‐high resolution (<1 μm), wide ink compatibility (e.g., perovskite, quantum dots, 2D materials), and powerful multi‐mode‐tunability. To promote its practical applications in new materials patterning and industrial manufacturing, this review highlights the EHD based ink design, droplet formation, key technologies for promoting efficiency/accuracy, and wide applications in electronics fabrication.