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Traffic signs are pivotal components of traffic management, ensuring the regulation and safety of road traffic. However, existing detection methods often suffer from low accuracy and poor real-time performance in dynamic road environments. This paper reviews traditional traffic sign detection methods and introduces an enhanced detection algorithm (YOLO-BS) based on YOLOv8 (You Only Look Once version 8). This algorithm addresses the challenges of complex backgrounds and small-sized detection targets in traffic sign images. A small object detection layer was incorporated into the YOLOv8 framework to enrich feature extraction. Additionally, a bidirectional feature pyramid network (BiFPN) was integrated into the detection framework to enhance the handling of multi-scale objects and improve the performance in detecting small objects. Experiments were conducted on the TT100K dataset to evaluate key metrics such as model size, recall, mean average precision (mAP), and frames per second (FPS), demonstrating that YOLO-BS surpasses current mainstream models with mAP50 of 90.1% and FPS of 78. Future work will refine YOLO-BS to explore broader applications within intelligent transportation systems.

Reducing needs for heating and cooling from fossil energy is one of the biggest challenges, which demand accounts for almost half of global energy consumption, consequently resulting in complicated climatic and environmental issues. Herein, we demonstrate a high-performance, intelligently auto-switched and zero-energy dual-mode radiative thermal management device. By perceiving temperature to spontaneously modulate electromagnetic characteristics itself, the device achieves ~859.8 W m −2 of average heating power (∼91% of solar-thermal conversion efficiency) in cold and ~126.0 W m −2 of average cooling power in hot, without any external energy consumption during the whole process. Such a scalable, cost-effective device could realize two-way temperature control around comfortable temperature zone of human living. A practical demonstration shows that the temperature fluctuation is reduced by ~21 K, compared with copper plate. Numerical prediction indicates that this real zero-energy dual-mode thermal management device has a huge potential for year-round energy saving around the world and provides a feasible solution to realize the goal of Net Zero Carbon 2050. Real-world practical utilization of zero-energy thermal management systems often requires adaptability to dynamic weather. Here, authors demonstrate a zero-energy, self-adapting, dual-mode radiative thermal management device, capable of switching between heating and cooling based on the ambient temperature.

Dysregulation of the Notch signaling pathway, which is highly conserved across species, can drive aberrant epigenetic modification, transcription, and translation. Defective gene regulation caused by dysregulated Notch signaling often affects networks controlling oncogenesis and tumor progression. Meanwhile, Notch signaling can modulate immune cells involved in anti- or pro-tumor responses and tumor immunogenicity. A comprehensive understanding of these processes can help with designing new drugs that target Notch signaling, thereby enhancing the effects of cancer immunotherapy. Here, we provide an up-to-date and comprehensive overview of how Notch signaling intrinsically regulates immune cells and how alterations in Notch signaling in tumor cells or stromal cells extrinsically regulate immune responses in the tumor microenvironment (TME). We also discuss the potential role of Notch signaling in tumor immunity mediated by gut microbiota. Finally, we propose strategies for targeting Notch signaling in cancer immunotherapy. These include oncolytic virotherapy combined with inhibition of Notch signaling, nanoparticles (NPs) loaded with Notch signaling regulators to specifically target tumor-associated macrophages (TAMs) to repolarize their functions and remodel the TME, combining specific and efficient inhibitors or activators of Notch signaling with immune checkpoint blockers (ICBs) for synergistic anti-tumor therapy, and implementing a customized and effective synNotch circuit system to enhance safety of chimeric antigen receptor (CAR) immune cells. Collectively, this review aims to summarize how Notch signaling intrinsically and extrinsically shapes immune responses to improve immunotherapy.

The development of energy storage devices that can endure large and complex deformations is central to emerging wearable electronics. Hydrogels made from conducting polymers give rise to a promising integration of high conductivity and versatility in processing. However, the emergence of conducting polymer hydrogels with a desirable network structure cannot be readily achieved using conventional polymerization methods. Here we present a cryopolymerization strategy for preparing an intrinsically stretchable, compressible and bendable anisotropic polyvinyl alcohol/polyaniline hydrogel with a complete recovery of 100% stretching strain, 50% compressing strain and fully bending. Due to its high mechanical strength, superelastic properties and bi-continuous phase structure, the as-obtained anisotropic polyvinyl alcohol/polyaniline hydrogel can work as a stretching/compressing/bending electrode, maintaining its stable output under complex deformations for an all-solid-state supercapacitor. In particular, it achieves an extremely high energy density of 27.5 W h kg −1 , which is among that of state-of-the-art stretchable supercapacitors. Energy storage devices that can endure large and complex deformations are central to the development of wearable electronics. Here the authors present a cryopolymerization strategy for preparing an anisotropic polyvinyl alcohol/polyaniline hydrogel for flexible supercapacitor electrodes.

Background Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis. Methods Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k -nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model. Results A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models. Conclusion The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

Assembling metal-organic framework (MOF)-based particles is an emerging approach for creating colloidal superstructures and hierarchical functional materials. However, realization of this goal requires strategies that not only regulate particle interactions but also harness the anisotropic morphologies and functions of various frameworks. Here, by exploiting depletion interaction induced by ionic amphiphiles, we show the assembly of a broad range of low-dimensional MOF colloidal superstructures, including 1D straight chains, alternating or bundled chains, 2D films of hexagonal, square, centered rectangular, and snowflake-like architectures, and quasi-3D supercrystals. With well-defined polyhedral shapes, the MOF particles are mutually oriented upon assembly, producing super-frameworks with hierarchically coordinated crystallinity and micropores. We demonstrate this advantage by creating functional MOF films with optical anisotropy, in our cases, birefringence and anisotropic fluorescence. Given the variety of MOFs available, our technique should allow access to advanced materials for sensing, optics, and photonics. Colloidal self-assembly is a powerful strategy for designing materials, and MOFs offer wide structural and functional diversity. Here, authors present the self-assembly of MOF microcrystals using depletion interactions to form low-dimensional MOF colloidal superstructures with anisotropic properties.

The assembly of active and self-propelled particles is an emerging strategy to create dynamic materials otherwise impossible. However, control of the complex particle interactions remains challenging. Here, we show that various dynamic interactions of active patchy particles can be orchestrated by tuning the particle size, shape, composition, etc. This capability is manifested in establishing dynamic colloidal bonds that are highly selective and directional, which greatly expands the spectrum of colloidal structures and dynamics by assembly. For example, we demonstrate the formation of colloidal molecules with tunable bond angles and orientations. They exhibit controllable propulsion, steering, reconfiguration as well as other dynamic behaviors that collectively reflect the bond properties. The working principle is further extended to the co-assembly of synthetic particles with biological entities including living cells, giving rise to hybrid colloidal molecules of various types, for example, a colloidal carrousel structure. Our strategy should enable active systems to perform sophisticated tasks in future such as selective cell treatment. The assembly of active and self-propelled particles is an emerging strategy to create dynamic materials otherwise impossible. Here, the authors show the assembly of active colloidal molecules with a wide spectrum of new structures and dynamics, conferred to them by highly selective and directional interactions.

Immunotherapy has become a robust and routine treatment strategy for patients with cancer; however, there are efficacy and safety issues that should be resolved. Natural killer (NK) cells are important innate immune cells that have attracted increasing attention owing to their major histocompatibility complex-independent immunosurveillance ability. These cells provide the first-line defense against carcinogenesis and are closely related to cancer development. However, NK cells are functionally suppressed owing to multiple immunosuppressive factors in the tumor microenvironment; thus, releasing the suppressed state of NK cells is an emergent project and a promising solution for immunotherapy. As a result, many clinical trials of NK cell therapy alone or in combination with other agents are currently underway. This review describes the current status of NK cell therapy for cancer treatment based on the effector function and releasing the inhibited state of NK cells in the cancer microenvironment.

Collecting ground truth labels for hyperspectral image classification is difficult and time-consuming. Without an adequate number of training samples, hyperspectral image (HSI) classification is a challenging problem. Using generative adversarial networks (GANs) is a promising technique for solving this problem because GANs can learn features from both labeled and unlabeled samples. The cost functions widely used in current GAN methods are suitable for 2D nature images. Compared with natural images, HSIs have a simpler one-dimensional structure that facilitates image generation. Motivated by the one-dimensional spectral features of HSIs, we propose a novel semisupervised algorithm for HSI classification by introducing spectral angle distance (SAD) as a loss function and employing multilayer feature fusion. Since the differences between spectra can be quickly calculated using the spectral angle distance, the convergence speed of the GAN can be improved, and the samples generated by the generator model in the GAN are closer to the real spectrum. Once the entire GAN model has been trained, the discriminator can extract multiscale features of labeled and unlabeled samples. The classifier is then trained for HSI classification using the multilayer features extracted from a few labeled samples by the discriminator. The proposed method was validated on four hyperspectral datasets: Pavia University, Indiana Pines, Salinas, and Tianshan. The experimental results show that the proposed model provides very promising results compared with other related state-of-the-art methods.

DNA-coated colloids hold great promise for self-assembly of programmed heterogeneous microstructures, provided they not only bind when cooled below their melting temperature, but also rearrange so that aggregated particles can anneal into the structure that minimizes the free energy. Unfortunately, DNA-coated colloids generally collide and stick forming kinetically arrested random aggregates when the thickness of the DNA coating is much smaller than the particles. Here we report DNA-coated colloids that can rearrange and anneal, thus enabling the growth of large colloidal crystals from a wide range of micrometre-sized DNA-coated colloids for the first time. The kinetics of aggregation, crystallization and defect formation are followed in real time. The crystallization rate exhibits the familiar maximum for intermediate temperature quenches observed in metallic alloys, but over a temperature range smaller by two orders of magnitude, owing to the highly temperature-sensitive diffusion between aggregated DNA-coated colloids. DNA-coated colloids have failed to achieve their promise of programmable self-assembly because they stick to each other like Velcro. Here Wang et al. overcome this problem by making clickable smooth colloids that are coated with short single-stranded DNA at high density.