Microglial activation and the subsequent inflammatory response in the central nervous system play important roles in secondary damage after traumatic brain injury (TBI). High-mobility group box 1 ...(HMGB1) protein, an important mediator in late inflammatory responses, interacts with transmembrane receptor for advanced glycation end products (RAGE) and toll-like receptors (TLRs) to activate downstream signaling pathways, such as the nuclear factor (NF)-κB signaling pathway, leading to a cascade amplification of inflammatory responses, which are related to neuronal damage after TBI. Omega-3 polyunsaturated fatty acid (ω-3 PUFA) is a commonly used clinical immunonutrient, which has antioxidative and anti-inflammatory effects. However, the effects of ω-3 PUFA on HMGB1 expression and HMGB1-mediated activation of the TLR4/NF-κB signaling pathway are not clear.
The Feeney DM TBI model was adopted to induce brain injury in rats. Modified neurological severity scores, brain water content, and Nissl staining were employed to determine the neuroprotective effects of ω-3 PUFA supplementation. Assessment of microglial activation in lesioned sites and protein markers for proinflammatory, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, interferon (IFN)-γ, and HMGB1 were used to evaluate neuroinflammatory responses and anti-inflammation effects of ω-3 PUFA supplementation. Immunofluorescent staining and western blot analysis were used to detect HMGB1 nuclear translocation, secretion, and HMGB1-mediated activation of the TLR4/NF-κB signaling pathway to evaluate the effects of ω-3 PUFA supplementation and gain further insight into the mechanisms underlying the development of the neuroinflammatory response after TBI.
It was found that ω-3 PUFA supplementation inhibited TBI-induced microglial activation and expression of inflammatory factors (TNF-α, IL-1β, IL-6, and IFN-γ), reduced brain edema, decreased neuronal apoptosis, and improved neurological functions after TBI. We further demonstrated that ω-3 PUFA supplementation inhibited HMGB1 nuclear translocation and secretion and decreased expression of HMGB1 in neurons and microglia in the lesioned areas. Moreover, ω-3 PUFA supplementation inhibited microglial activation and the subsequent inflammatory response by regulating HMGB1 and the TLR4/NF-κB signaling pathway.
The results of this study suggest that microglial activation and the subsequent neuroinflammatory response as well as the related HMGB1/TLR4/NF-κB signaling pathway play essential roles in secondary injury after TBI. Furthermore, ω-3 PUFA supplementation inhibited TBI-induced microglial activation and the subsequent inflammatory response by regulating HMGB1 nuclear translocation and secretion and also HMGB1-mediated activation of the TLR4/NF-κB signaling pathway, leading to neuroprotective effects.
Achieving moldable energy storage devices will make it possible to power wearable electronics by adapting to body contours for wear comfort. Herein, we present a new approach to create hierarchically ...structured electrodes that enable supercapacitors to retain their capacity under mechanical deformation. The electrodes are made by first growing vertical graphene nanosheets (VGNs) and then depositing manganese oxide (MnO2) on ductile nickel wires. Two such electrodes are made into a moldable supercapacitor using a solid-state electrolyte containing carboxymethylcellulose and sodium sulfate. This foldable supercapacitor achieves a high areal capacitance up to 56 mF cm−2, areal energy density of 7.7 μWh cm−2, and areal power density of 5 mWh cm−2. These exceptional properties originate from the synergy between VGNs and MnO2, where the highly porous VGNs serve two critical functions: a mechanically robust platform of large surface area allowing high mass loading deposition of pseudocapacitive MnO2 and an interconnected conductive network for efficient electron/ion transport. Fiber-shaped supercapacitors made from these electrodes can be molded into different shapes by bending and twisting with little performance loss. The promising results presented in this study provide a new route for fabricating high-performance moldable energy storage devices for wearable electronics and wireless electronic skins.
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New colonies of Formosan subterranean termites are founded by monogamous pairs. During swarming season, alates (winged reproductives) leave their parental colony. After swarming, they drop to the ...ground, shed their wings, and male and female dealates find suitable nesting sites where they mate and become kings and queens of new colonies. The first generation of offspring is entirely dependent on the nutritional resources of the founder pair consisting of the fat and protein reserves of the dealates and their microbiota, which include the cellulose-digesting protozoa and diverse bacteria. Since termite kings and queens can live for decades, mate for life and colony success is linked to those initial resources, we hypothesized that gut microbiota of founders affect pair formation. To test this hypothesis, we collected pairs found in nest chambers and single male and female dealates from four swarm populations. The association of three factors (pairing status, sex of the dealates and population) with dealate weights, total protozoa, and protozoa Pseudotrichonympha grassii numbers in dealate hindguts was determined. In addition, Illumina 16S rRNA gene sequencing and the QIIME2 pipeline were used to determine the impact of those three factors on gut bacteria diversity of dealates. Here we report that pairing status was significantly affected by weight and total protozoa numbers, but not by P. grassii numbers and bacteria diversity. Weight and total protozoa numbers were higher in paired compared to single dealates. Males contained significantly higher P. grassii numbers and bacteria richness and marginally higher phylogenetic diversity despite having lower weights than females. In conclusion, this study showed that dealates with high body weight and protozoa numbers are more likely to pair and become colony founders, probably because of competitive advantage. The combined nutritional resources provided by body weight and protozoa symbionts of the parents are important for successful colony foundation and development.
Femtosecond filament-induced breakdown spectroscopy (FIBS) is an efficient approach in remote and in situ detection of a variety of trace elements, but it was recently discovered that the FIBS of ...water is strongly dependent on the large-bandgap semiconductor property of water, making the FIBS signals sensitive to laser ionization mechanisms. Here, we show that the sensitivity of the FIBS technique in monitoring metal elements in water can be efficiently improved by using chirped femtosecond laser pulses, but an asymmetric enhancement of the FIBS intensity is observed for the negatively and positively chirped pulses. We attribute the asymmetric enhancement to their different ionization rates of water, in which the energy of the photons participating in the ionization process in the front part of the negatively chirped pulse is higher than that in the positively chirped pulse. By optimizing the pulse chirp, we show that the limit of detection of the FIBS technique for metal elements in water, e.g., aluminum, can reach to the sub-ppm level, which is about one order of magnitude better than that by the transform-limited pulse. We further examine the FIBS spectra of several representative water samples including commercial mineral water, tap water, and lake water taken from two different environmental zones, i.e., a national park and a downtown business district (Changchun, China), from which remarkably different concentrations of Ca, Na, and K elements of these samples are obtained. Our results provide a possibility of using FIBS for direct and fast metal elemental analysis of water in different field environments.
Software-defined networking (SDN) has become one of the critical technologies for data center networks, as it can improve network performance from a global perspective using artificial intelligence ...algorithms. Due to the strong decision-making and generalization ability, deep reinforcement learning (DRL) has been used in SDN intelligent routing and scheduling mechanisms. However, traditional deep reinforcement learning algorithms present the problems of slow convergence rate and instability, resulting in poor network quality of service (QoS) for an extended period before convergence. Aiming at the above problems, we propose an automatic QoS architecture based on multistep DRL (AQMDRL) to optimize the QoS performance of SDN. AQMDRL uses a multistep approach to solve the overestimation and underestimation problems of the deep deterministic policy gradient (DDPG) algorithm. The multistep approach uses the maximum value of the n-step action currently estimated by the neural network instead of the one-step Q-value function, as it reduces the possibility of positive error generated by the Q-value function and can effectively improve convergence stability. In addition, we adapt a prioritized experience sampling based on SumTree binary trees to improve the convergence rate of the multistep DDPG algorithm. Our experiments show that the AQMDRL we proposed significantly improves the convergence performance and effectively reduces the network transmission delay of SDN over existing DRL algorithms.
Dust storms are one of the most frequent meteorological disasters in China, endangering agricultural production, transportation, air quality, and the safety of people’s lives and property. Against ...the backdrop of climate change, Mongolia’s contribution to China’s dust cannot be ignored in recent years. In this study, we used the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), along with dynamic dust sources and the HYSPLIT model, to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023. The results show that the frequency of dust storms in 2023 was the highest observed in the past decade. Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China. Specifically, Mongolia contributed more than 42% of dust, while the Taklimakan Desert accounted for 26%. A cold high-pressure center, a cold front, and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China, where they affected most parts of the region. Moreover, two machine learning methods the XGBoost algorithm and the Synthetic Minority Oversampling Technique (SMOTE) were used to forecast the dust storms in March 2023, based on ground observations and WRF-Chem simulations over East Asia. XGBoost-SMOTE performed well in predicting hourly PM
10
concentrations in China in March 2023, with a mean absolute error of 33.8 µg m
−3
and RMSE of 54.2 µg m
−3
.
Abstract
Anthropogenic dust (AD), as a crucial component of particulate matter, is defined as dust emitted through modifying or disturbing soil particles directly or indirectly associated with human ...activities in urban areas, croplands, pasturelands and dry lakes. The sources, characteristics, and impacts of AD remain poorly studied, in contrast to the large body of research on natural dust (ND). This review summarizes scientific findings published since the 1990s regarding the emissions, physical-chemical characteristics, and spatio-temporal distributions of AD from the micro to the global scale. AD accounts for 5%–60% of the global dust loading, with notable spread in existing estimates. Compared with ND, AD has more complex and variable compositions and physical-chemical properties. Influenced by human disturbances, AD exhibits small particle sizes, easily accessible critical friction velocity, and large emissions. Further research should improve the observations and simulations to investigate the complex interactions among AD, climate change, and human health.
Optical transport networks (OTNs) are widely used in backbone- and metro-area transmission networks to increase network transmission capacity. In the OTN, it is particularly crucial to rationally ...allocate routes and maximize network capacities. By employing deep reinforcement learning (DRL)- and software-defined networking (SDN)-based solutions, the capacity of optical networks can be effectively increased. However, because most DRL-based routing optimization methods have low sample usage and difficulty in coping with sudden network connectivity changes, converging in software-defined OTN scenarios is challenging. Additionally, the generalization ability of these methods is weak. This paper proposes an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN) method for optical network routing optimization to address this problem. To effectively explore the environment and improve agent performance, the multiple EMDQN agents select actions based on the highest upper-confidence bounds. Furthermore, the EMDQN agent captures the network’s spatial feature information using a message passing neural network (MPNN)-based DRL policy network, which enables the DRL agent to have generalization capability. The experimental results show that the EMDQN algorithm proposed in this paper performs better in terms of convergence. EMDQN effectively improves the throughput rate and link utilization of optical networks and has better generalization capabilities.
Ni(O)-catalyzed regio-and diastereodivergent4+2annulation of biphenylenes with α,β unsaturated ketones is described.This solvent-controlled diastereodivergent reaction integrates C-C bond cleavage of ...biphenylene and C=C double bond insertion selectivity,offering a mild approach to all possible di-astereoisomers of 9,10-dihydrophenanthrene derivatives from the same starting materials.
Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and ...control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.
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![Diastereodivergent [4 + 2] ...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/1001-8417/small "Diastereodivergent [4 + 2] annulation of biphenylenes with enones via nickel(0)-catalyzed C C bond activation")
