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The NLRP3 inflammasome is a critical component of the innate immune system that mediates caspase-1 activation and the secretion of proinflammatory cytokines IL-1β/IL-18 in response to microbial infection and cellular damage. However, the aberrant activation of the NLRP3 inflammasome has been linked with several inflammatory disorders, which include cryopyrin-associated periodic syndromes, Alzheimer's disease, diabetes, and atherosclerosis. The NLRP3 inflammasome is activated by diverse stimuli, and multiple molecular and cellular events, including ionic flux, mitochondrial dysfunction, and the production of reactive oxygen species, and lysosomal damage have been shown to trigger its activation. How NLRP3 responds to those signaling events and initiates the assembly of the NLRP3 inflammasome is not fully understood. In this review, we summarize our current understanding of the mechanisms of NLRP3 inflammasome activation by multiple signaling events, and its regulation by post-translational modifications and interacting partners of NLRP3.

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

Nonbiaryl N-C atropisomer is an important structural scaffold, which is present in natural products, medicines and chiral ligands. However the direct enantioselective C-H amination to access optically pure N-C atropisomer is still difficult and rare. Here we report a π-π interaction and dual H-bond concerted control strategy to develop the chiral phosphoric acids (CPAs) catalyzed direct intermolecular enantioselective C-H amination of N-aryl-2-naphthylamines with azodicarboxylates as amino sources for the construction of atroposelective naphthalene-1,2-diamines. This type of N-C atropisomers is stabilized by intramolecular hydrogen bond and the method features a broad range of substrates, high yields and ee values, providing a strategy to chirality transfer via the modification of N-C atropisomers. Atropisomers with a chiral C-N axis are useful for natural products synthesis and as ligands in asymmetric catalysis. Here, the authors reportt a π-π interaction and dual H-bond concerted control strategy in enantioselective C-H amination affording configurationally stable N-C atropisomers.

Competing and intertwined orders including inhomogeneous patterns of spin and charge are observed in many correlated electron materials, such as high-temperature superconductors. Introducing a new development of the constrained-path auxiliary-field quantum Monte Carlo method, we study the interplay between thermal and quantum fluctuations in the two-dimensional Hubbard model. We obtain an accurate and systematic characterization of the evolution of the spin and charge correlations as a function of temperatureTand how it connects to the ground state, at three representative hole doping levelsδ=1/5,1/8, and1/10. We find increasing short-range commensurate antiferromagnetic correlations asTis lowered. As the correlation length grows sufficiently large, a modulated spin-density wave (SDW) appears. Atδ=1/5andU/t=6, the SDW saturates and remains short-ranged asT→0. In contrast, atδ=1/8,1/10andU/t=8, this evolves into a ground-state stripe phase. We study the relation between spin and charge orders and find that formation of charge order appears to be driven by that of the spin order. We identify a finite-temperature phase transition below which charge ordering sets in and discuss the implications of our results for the nature of this transition.

Background Dendritic cells (DCs) are central for the initiation and regulation of innate and adaptive immunity in the tumor microenvironment. As such, many kinds of DC-targeted vaccines have been developed to improve cancer immunotherapy in numerous clinical trials. Targeted delivery of antigens and adjuvants to DCs in vivo represents an important approach for the development of DC vaccines. However, nonspecific activation of systemic DCs and the preparation of optimal immunodominant tumor antigens still represent major challenges. Methods We loaded the immunogenic cell death (ICD) inducers human neutrophil elastase (ELANE) and Hiltonol (TLR3 agonist) into α-lactalbumin (α-LA)-engineered breast cancer-derived exosomes to form an in situ DC vaccine (HELA-Exos). HELA-Exos were identified by transmission electron microscopy, nanoscale flow cytometry, and Western blot analysis. The targeting, killing, and immune activation effects of HELA-Exos were evaluated in vitro. The tumor suppressor and immune-activating effects of HELA-Exos were explored in immunocompetent mice and patient-derived organoids. Results HELA-Exos possessed a profound ability to specifically induce ICD in breast cancer cells. Adequate exposure to tumor antigens and Hiltonol following HELA-Exo-induced ICD of cancer cells activated type one conventional DCs (cDC1s) in situ and cross-primed tumor-reactive CD8 + T cell responses, leading to potent tumor inhibition in a poorly immunogenic triple negative breast cancer (TNBC) mouse xenograft model and patient-derived tumor organoids. Conclusions HELA-Exos exhibit potent antitumor activity in both a mouse model and human breast cancer organoids by promoting the activation of cDC1s in situ and thus improving the subsequent tumor-reactive CD8 + T cell responses. The strategy proposed here is promising for generating an in situ DC-primed vaccine and can be extended to various types of cancers. Graphic Abstract Scheme 1. Schematic illustration of HELA-Exos as an in situ DC-primed vaccine for breast cancer. (A) Allogenic breast cancer-derived exosomes isolated from MDA-MB-231 cells were genetically engineered to overexpress α-LA and simultaneously loaded with the ICD inducers ELANE and Hiltonol (TLR3 agonist) to generate HELA-Exos. (B) Mechanism by which HELA-Exos activate DCs in situ in a mouse xenograft model ofTNBC. HELA-Exos specifically homed to the TME and induced ICD in cancer cells, which resulted in the increased release of tumor antigens, Hiltonol, and DAMPs, as well as the uptake of dying tumor cells by cDC1s. The activated cDC1s then cross-primed tumor-reactive CD8+ T cell responses. (C) HELA-Exos activated DCs in situ in the breast cancer patient PBMC-autologous tumor organoid coculture system. Abbreviations: DCs: dendritic cells; α-LA: α-lactalbumin; HELA-Exos: Hiltonol-ELANE-α-LA-engineered exosomes; ICD: immunogenic cell death; ELANE: human neutrophil elastase; TLR3: Toll-like receptor 3; TNBC: triple-negative breast cancer; TME: tumor microenvironment; DAMPs: damage-associated molecular patterns; cDC1s: type 1 conventional dendritic cells; PBMCs: peripheral blood mononuclear cells

The interplay between thermal and quantum fluctuations controls the competition between phases of matter in strongly correlated electron systems. We study finite-temperature properties of the strongly coupled two-dimensional doped Hubbard model using the minimally entangled typical thermal states method on width-four cylinders. We discover that a phase characterized by commensurate short-range antiferromagnetic correlations and no charge ordering occurs at temperatures above the half-filled stripe phase extending to zero temperature. The transition from the antiferromagnetic phase to the stripe phase takes place at temperatureT/t≈0.05and is accompanied by a steplike feature of the specific heat. We find the single-particle gap to be smallest close to the nodal point atk=(π/2,π/2)and detect a maximum in the magnetic susceptibility. These features bear a strong resemblance to the pseudogap phase of high-temperature cuprate superconductors. The simulations are verified using a variety of different unbiased numerical methods in the three limiting cases of zero temperature, small lattice sizes, and half filling. Moreover, we compare to and confirm previous determinantal quantum Monte Carlo results on incommensurate spin-density waves at finite doping and temperature.

Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities.

The concerns about reducing carbon emissions and dealing with climate change have led to a surge in interest and development of new energy Vehicles (NEVs). These vehicles, which include electric vehicles (EVs) and hybrid electric vehicles (HEVs), are crucial in the transition towards sustainable transportation. This review paper provides an in-depth analysis of the current situation and advancements in NEV technologies, highlighting significant improvements in battery technology, power electronics, and charging infrastructure. It also examines various Energy Management System (EMS) optimization strategies, including rule-based, optimization-based, and learning-based approaches, and their impact on vehicle performance and economic viability. The paper discusses the environmental and economic benefits of advanced EMS technologies, such as reducing emissions and operational costs and enhancing the longevity of key components. Additionally, the paper explores future directions for NEV development, emphasizing the importance of government policies, technological innovations, and research priorities to overcome existing challenges and promote widespread adoption. This paper aims to analyze the sustainable growth and integration of NEVs in the automotive industry.

Lake water temperature (LWT) is an important indicator of physical processes within a lake, but traditional process‐based and data‐driven models are limited in their ability to estimate long‐term changes in LWT because of simplified physical laws, insufficient onsite measurements and high computational demands. To overcome these limitations, this study proposes a hybrid multi‐parameter scientific knowledge‐guided neural network (MP‐KgNN) for solving 1‐D lake temperature governing equation trained using both simulations of the WRF‐Lake model and onsite LWT measurements based on a novel training framework called physics‐informed deep learning (PIDL) framework and simulates the thermodynamics in a large deep reservoir located in eastern China from 1960 to 2021. The results revealed that the MP‐KgNN can estimate the dynamic changes in LWT with satisfactory accuracy (mean absolute error [MAE] = 1.14 K, root mean square error [RMSE] = 1.49 K). Moreover, it outperformed the pre‐trained MP‐KgNN trained with only the WRF‐Lake model (MAE = 2.43 K, RMSE = 2.77 K), which indicates its successful prediction of the thermal structure of the lake. The prediction derived by MP‐KgNN showed an increasing trend (0.04 K decade−1) of LWT in the Lake Qiandaohu. Specifically, the LWT was experienced to increase at a rate of 0.10 K decade−1 near the lake surface. These changes resulted in an extension and deepening of lake thermal stratification, as indicated by a 0.58 m increase in metalimnion thickness and a 20.46 kJ increase in Schmidt stability. The proposed MP‐KgNN is expected to become a powerful tool for estimating long‐term variations in the thermodynamics of lake ecosystems.