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Parkinson’s disease (PD), the second most common age-associated neurodegenerative disorder, is characterized by the loss of dopaminergic (DA) neurons and the presence of α-synuclein-containing aggregates in the substantia nigra pars compacta (SNpc). Chronic neuroinflammation is one of the hallmarks of PD pathophysiology. Post-mortem analyses of human PD patients and experimental animal studies indicate that activation of glial cells and increases in pro-inflammatory factor levels are common features of the PD brain. Chronic release of pro-inflammatory cytokines by activated astrocytes and microglia leads to the exacerbation of DA neuron degeneration in the SNpc. Besides, peripheral immune system is also implicated in the pathogenesis of PD. Infiltration and accumulation of immune cells from the periphery are detected in and around the affected brain regions of PD patients. Moreover, inflammatory processes have been suggested as promising interventional targets for PD and even other neurodegenerative diseases. A better understanding of the role of inflammation in PD will provide new insights into the pathological processes and help to establish effective therapeutic strategies. In this review, we will summarize recent progresses in the neuroimmune aspects of PD and highlight the potential therapeutic interventions targeting neuroinflammation.

Carbon-based fibres hold promise for preparing multifunctional fabrics with electrical conductivity, thermal conductivity, permeability, flexibility and lightweight. However, these fabrics are of limited performance mainly because of the weak interaction between fibres. Here we report non-woven graphene fibre fabrics composed of randomly oriented and interfused graphene fibres with strong interfibre bonding. The all-graphene fabrics obtained through a wet-fusing assembly approach are porous and lightweight, showing high in-plane electrical conductivity up to ∼2.8 × 10 4 S m −1 and prominent thermal conductivity of ∼301.5 W m −1 K −1 . Given the low density (0.22 g cm −3 ), their specific electrical and thermal conductivities set new records for carbon-based papers/fabrics and even surpass those of individual graphene fibres. The as-prepared fabrics are further used as ultrafast responding electrothermal heaters and durable oil-adsorbing felts, demonstrating their great potential as high-performance and multifunctional fabrics in real-world applications. Carbon-based fibres are at the core of electrically conductive multifunctional fabrics, yet improving the weak interaction between fibres remains a challenge. Here, the authors demonstrate an assembly method where graphene fibres are fused at junctions with record specific electrical and thermal conductivity.

Discrete event systems (DESs) are powerful abstract representations for large human-made physical systems in a wide variety of industries. Safety control issues on DESs have been extensively studied based on the logical specifications of the systems in various literature. However, when facing the DESs under uncertain environment which brings into the implicit specifications, the classical supervisory control approach may not be capable of achieving the performance. So in this research, we propose a new approach for optimal control of DESs under uncertain environment based on supervisory control theory (SCT) and reinforcement learning (RL). Firstly, we use SCT to gather deliberative planning algorithms with the aim to safe control. Then we convert the supervised system to Markov Decision Process simulation environments that is suitable for optimal algorithm training. Furthermore, a SCT-based RL algorithm is designed to maximize performance of the system based on the probabilistic attributes of the state transitions. Finally, a case study on the autonomous navigation task of a delivery robot is provided to corroborate the proposed method by multiple simulation experiments. The result shows the proposed approach owning 8.27 % performance improvement compared with the non-intelligent methods. This research will contribute to further studying the optimal control of human-made physical systems in a wide variety of industries.

Graphene has an extremely high in-plane strength yet considerable out-of-plane softness. High crystalline order of graphene assemblies is desired to utilize their in-plane properties, however, challenged by the easy formation of chaotic wrinkles for the intrinsic softness. Here, we find an intercalation modulated plasticization phenomenon, present a continuous plasticization stretching method to regulate spontaneous wrinkles of graphene sheets into crystalline orders, and fabricate continuous graphene papers with a high Hermans’ order of 0.93. The crystalline graphene paper exhibits superior mechanical (tensile strength of 1.1 GPa, stiffness of 62.8 GPa) and conductive properties (electrical conductivity of 1.1 × 10 5  S m −1 , thermal conductivity of 109.11 W m −1 K −1 ). We extend the ultrastrong graphene papers to the realistic laminated composites and achieve high strength combining with attractive conductive and electromagnetic shielding performance. The intercalation modulated plasticity is revealed as a vital state of graphene assemblies, contributing to their industrial processing as metals and plastics. Strong but flexible graphene tends to wrinkle, which compromises some properties. Here the authors report a solid plasticization method to prepare continuous graphene papers with high crystalline order, achieving high strength, stiffness, electrical and thermal conductivities.

This paper employs an innovative event study methodology to demonstrate the impact of climate change on the NASDAQ index from the unique perspective of extreme weather events. This is achieved through the application of the event study methodology to a total of 526 biological, climatic, geological, hydrological, and meteorological disasters of climate change occurring in the U.S. during the period of 2000–2019. The results of the study demonstrate that: ① it can be generally observed that the five dimensions of climate change have a significant impact on stock returns. ② Empirical evidence indicates that the impact of different climate change dimensions on the return rate of stocks from NASDAQ stocks varies. In contrast, the biological and hydrological dimensions have a significantly negative impact on the return rate of stocks from the NASDAQ index, while the climate dimension has a significantly positive impact on the return rate of stocks from the NASDAQ index. ③ From the perspective of time, the impact of the five dimensions of climate change on the stock yield exhibits certain non-linear characteristics. This can be observed in the phenomenon of shock reversal, which occurs before and after the event.

Carbon aerogels demonstrate wide applications for their ultralow density, rich porosity, and multifunctionalities. Their compressive elasticity has been achieved by different carbons. However, reversibly high stretchability of neat carbon aerogels is still a great challenge owing to their extremely dilute brittle interconnections and poorly ductile cells. Here we report highly stretchable neat carbon aerogels with a retractable 200% elongation through hierarchical synergistic assembly. The hierarchical buckled structures and synergistic reinforcement between graphene and carbon nanotubes enable a temperature-invariable, recoverable stretching elasticity with small energy dissipation (~0.1, 100% strain) and high fatigue resistance more than 10 6 cycles. The ultralight carbon aerogels with both stretchability and compressibility were designed as strain sensors for logic identification of sophisticated shape conversions. Our methodology paves the way to highly stretchable carbon and neat inorganic materials with extensive applications in aerospace, smart robots, and wearable devices. Improved compressive elasticity was lately demonstrated for carbon aerogels but the problem of reversible stretchability remained a challenge. Here the authors use a hierarchical structure design and synergistic effects between carbon nanotubes and graphene to achieve high stretchability in carbon aerogels.

Purpose This epidemiological study leverages data from the Global Burden of Disease (GBD) database spanning from 1990 to 2021 to analyze the global burden of oral cancer. The research aims to provide a comprehensive assessment of the age-standardized incidence rate (ASIR), age-standardized mortality rate (ASDR), and disability-adjusted life years (DALYs) for oral cancer, examining trends over three decades. Methods The study used age standardized rate (ASRs) as an indicator of oral cancer epidemiological data. Trend analysis uses estimated annual percentage change (EAPC) to track changes in oral cancer indicators. Results The study identifies a global increase in oral cancer incidence, mortality, and DALYs. From 1990 to 2021, the global incidence rate increased significantly from 3.26 (95% UI 3.14–3.41) to 5.34 (95% UI 4.94–5.70), the global mortality rate rose from 1.83 (95% UI 1.73–1.92) to 2.64 (95% UI 2.42–2.84), and the global estimate of DALYs increased from 55.05 (95% UI 52.38–57.97) to 74.44 (95% UI 67.50–80.44). High-risk regions include Palau and certain areas in Asia. Middle SDI regions show the most significant growth, while economically underdeveloped areas like parts of Africa show less significant trends. Conclusion The research underscores the need for heightened awareness, surveillance, and prevention efforts, especially in regions with high oral cancer incidence. Policymakers are urged to implement screening programs and public health education to combat the disease.

Supervisory control theory (SCT) is widely used as safeguard mechanism with control of discrete event systems (DESs). In complex continuous systems, in order to avoid system’s behavior violating specifications, the supervised control problem of these systems is quite different. Continuous state and action spaces of high dimension make languages of automaton no longer suitable for describing the information of specifications which remains challenging on control of real physical systems. Reinforcement learning (RL) automatically learns complex decisions through trial and error, but it requires the design of precise reward functions combined with domain knowledge. For complex scenarios where the reward function cannot be achieved or is only with sparse rewards, we proposed a novel supervised optimal control framework based on trajectory imitation (TI) and reinforcement learning (RL) in this paper. Firstly, behavior cloning (BC) is adopted to pre-train the policy model based on a small number of human demonstrations. Secondly, a generative adversarial imitation learning (GAIL) method is carried out to obtain the implicit characteristics of demonstration data. Furthermore, after the primary and implicit features are extracted by the above steps, a Demo-based RL algorithm is designed by adding the demonstration data to the RL replay buffer with augmented loss function to enhance the system performance to its maximum potential. Finally, the proposed method is validated through multiple simulation experiments on object relocation and tool using task of dexterous multifingered hands. In handling the more complex tool using task, the proposed approach achieves a 19.7% decrease in convergence time as opposed to the latest method. And the proposed method for the two tasks results in policies that display natural movements and shows higher robustness compared with the baseline model.

As a new generation of green media and functional materials, ionic liquids (ILs) have been extensively investigated in scientific and industrial communities, which have found numerous ap-plications in polymeric materials. On the one hand, much of the research has determined that ILs can be applied to modify polymers which use nanofillers such as carbon black, silica, graphene oxide, multi-walled carbon nanotubes, etc., toward the fabrication of high-performance polymer composites. On the other hand, ILs were extensively reported to be utilized to fabricate polymeric materials with improved thermal stability, thermal and electrical conductivity, etc. Despite substantial progress in these areas, summary and discussion of state-of-the-art functionalities and underlying mechanisms of ILs are still inadequate. In this review, a comprehensive introduction of various fillers modified by ILs precedes a systematic summary of the multifunctional applications of ILs in polymeric materials, emphasizing the effect on vulcanization, thermal stability, electrical and thermal conductivity, selective permeability, electromagnetic shielding, piezoresistive sensitivity and electrochemical activity. Overall, this review in this area is intended to provide a fundamental understanding of ILs within a polymer context based on advantages and disadvantages, to help researchers expand ideas on the promising applications of ILs in polymer fabrication with enormous potential.

Macroscopic fibres assembled from two-dimensional (2D) nanosheets are new and impressing type of fibre materials besides those from one-dimensional (1D) polymers, such as graphene fibres. However, the preparation and property-enhancing technologies of these fibres follow those from 1D polymers by improving the orientation along the fibre axis, leading to non-optimized microstructures and low integrated performances. Here, we show a concept of bidirectionally promoting the assembly order, making graphene fibres achieve synergistically improved mechanical and thermal properties. Concentric arrangement of graphene oxide sheets in the cross-section and alignment along fibre axis are realized by multiple shear-flow fields, which bidirectionally promotes the sheet-order of graphene sheets in solid fibres, generates densified and crystalline graphitic structures, and produces graphene fibres with ultrahigh modulus (901 GPa) and thermal conductivity (1660 W m −1 K −1 ). We believe that the concept would enhance both scientific and technological cognition of the assembly process of 2D nanosheets. Aligned 2D assembled fibres have been developed by drawing like 1D polymers, yet with disorders in cross-section. Here, the authors bidirectionally promote assembly order of graphene fibres, achieving high modulus and highly thermal conductivity.