Neutralizing antibodies could potentially be used as antivirals against the coronavirus disease 2019 (COVID-19) pandemic. Here, we report isolation of four human-origin monoclonal antibodies from a ...convalescent patient, all of which display neutralization abilities. The antibodies B38 and H4 block binding between the spike glycoprotein receptor binding domain (RBD) of the virus and the cellular receptor angiotensin-converting enzyme 2 (ACE2). A competition assay indicated different epitopes on the RBD for these two antibodies, making them a potentially promising virus-targeting monoclonal antibody pair for avoiding immune escape in future clinical applications. Moreover, a therapeutic study in a mouse model validated that these antibodies can reduce virus titers in infected lungs. The RBD-B38 complex structure revealed that most residues on the epitope overlap with the RBD-ACE2 binding interface, explaining the blocking effect and neutralizing capacity. Our results highlight the promise of antibody-based therapeutics and provide a structural basis for rational vaccine design.
Performing deep neural network (DNN) inference in real time requires excessive network resources, which poses a big challenge to the resource-limited industrial Internet of things (IIoT) networks. To ...address the challenge, in this paper, we introduce an end-edge-cloud orchestration architecture, in which the inference task assignment and DNN model placement are flexibly coordinated. Specifically, the DNN models, trained and pre-stored in the cloud, are properly placed at the end and edge to perform DNN inference. To achieve efficient DNN inference, a multi-dimensional resource management problem is formulated to maximize the average inference accuracy while satisfying the strict delay requirements of inference tasks. Due to the mix-integer decision variables, it is difficult to solve the formulated problem directly. Thus, we transform the formulated problem into a Markov decision process which can be solved efficiently. Furthermore, a deep reinforcement learning based resource management scheme is proposed to make real-time optimal resource allocation decisions. Simulation results are provided to demonstrate that the proposed scheme can efficiently allocate the available spectrum, caching, and computing resources, and improve average inference accuracy by 31.4<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> compared with the deep deterministic policy gradient benchmark.
Shopkeepers will be warned and fined if they sell alcohol to minors, according to a new regulation which comes into force nationwide from January 1 next year. Tong Lihua, a well-known lawyer with the ...Beijing Legal Assistance and Research Centre for Young People, said the new regulation is an improvement because it "finally includes substantial punishment." Tong also advised alcohol sellers and parents to have the sense of responsibility to keep minors away from alcohol, as they do with cigarettes.
In this work, we report the successful synthesis of hierarchical porous carbon microrods via a one-step pyrolysis and activation treatment at different temperatures. The optimized porous carbon ...microrods have many advantages, including high specific area (2757.63 m2 g−1) with a total pore volume (1.47 cm3 g−1), high self-doping N contents, and unique porous microrod structure. As a result, the optimized porous carbon microrods show a fairly high specific capacitance of 406 F g−1 at 0.5 A g−1 (335 F g−1 at 10 A g−1) in 6 M KOH electrolyte and a good rate capability of 86.1%. Furthermore, the symmetric supercapacitor of porous carbon.
microrods with 1 M Na2SO4 electrolyte exhibits a high energy density of 26.3 W h kg−1 at a power density of 429 W kg−1. The symmetric supercapacitor also shows excellent cycle ability with a capacitance retention of 94% over 10,000 cycles. This study provides a valuable approach for preparation of electrode materials for energy storage systems by using abundant biomass.
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Aromatic polyimides have excellent thermal stability, mechanical strength and toughness, high electric insulating properties, low dielectric constants and dissipation factors, and high radiation and ...wear resistance, among other properties, and can be processed into a variety of materials, including films, fibers, carbon fiber composites, engineering plastics, foams, porous membranes, coatings, etc. Aromatic polyimide materials have found widespread use in a variety of high-tech domains, including electric insulating, microelectronics and optoelectronics, aerospace and aviation industries, and so on, due to their superior combination characteristics and variable processability. In recent years, there have been many publications on aromatic polyimide materials, including several books available to readers. In this review, the representative progress in aromatic polyimide films for electronic applications, especially in our laboratory, will be described.
Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic wavelet, ...observed data band limitation and noise, but it also requires a forward operator that characterizes physical relation between measured data and model parameters. Deep learning methods have been successfully applied to solve geophysical inversion problems recently. It can obtain results with higher resolution compared to traditional inversion methods, but its performance often not fully explored for the lack of adequate labeled data (i.e., well logs) in training process. To alleviate this problem, we propose a semi-supervised learning workflow based on generative adversarial network (GAN) for acoustic impedance inversion. The workflow contains three networks: a generator, a discriminator and a forward model. The training of the generator and discriminator are guided by well logs and constrained by unlabeled data via the forward model. The benchmark models Marmousi2, SEAM and a field data are used to demonstrate the performance of our method. Results show that impedance predicted by the presented method, due to making use of both labeled and unlabeled data, are better consistent with ground truth than that of conventional deep learning methods.
Sodium‐ion batteries (SIBs) as the next generation of sustainable energy technologies have received widespread investigations for large‐scale energy storage systems (EESs) and smart grids due to the ...huge natural abundance and low cost of sodium. Although the great efforts are made in exploring layered transition metal oxide cathode for SIBs, their performances have reached the bottleneck for further practical application. Nowadays, anionic redox in layered transition metal oxides has emerged as a new paradigm to increase the energy density of rechargeable batteries. Based on this point, in this review, the development history of anionic redox reaction is attempted to systematically summarize and provide an in‐depth discussion on the anionic redox mechanism. Particularly, the major challenges of anionic redox and the corresponding available strategies toward triggering and stabilizing anionic redox are proposed. Subsequently, several types of sodium layered oxide cathodes are classified and comparatively discussed according to Na‐rich or Na‐deficient materials. A large amount of progressive characterization techniques of anionic oxygen redox is also summarized. Finally, an overview of the existing prospective and the future development directions of sodium layered transition oxide with anionic redox reaction are analyzed and suggested.
Anionic redox reaction in sodium layered transition oxides is considered as a promising tactics to increase the energy density of rechargeable batteries. Herein, a comprehensive review of anionic redox reaction is summarized in terms of evolution history, fundamental mechanism, major challenges, available strategies, classification of sodium layered oxide cathodes, progressive characterization techniques, and prospective trend.
Excessive activation of the coagulation system leads to life-threatening disseminated intravascular coagulation (DIC). Here, we examined the mechanisms underlying the activation of coagulation by ...lipopolysaccharide (LPS), the major cell-wall component of Gram-negative bacteria. We found that caspase-11, a cytosolic LPS receptor, activated the coagulation cascade. Caspase-11 enhanced the activation of tissue factor (TF), an initiator of coagulation, through triggering the formation of gasdermin D (GSDMD) pores and subsequent phosphatidylserine exposure, in a manner independent of cell death. GSDMD pores mediated calcium influx, which induced phosphatidylserine exposure through transmembrane protein 16F, a calcium-dependent phospholipid scramblase. Deletion of Casp11, ablation of Gsdmd, or neutralization of phosphatidylserine or TF prevented LPS-induced DIC. In septic patients, plasma concentrations of interleukin (IL)-1α and IL-1β, biomarkers of GSDMD activation, correlated with phosphatidylserine exposure in peripheral leukocytes and DIC scores. Our findings mechanistically link immune recognition of LPS to coagulation, with implications for the treatment of DIC.
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•Deletion of caspase-11 prevents disseminated intravascular coagulation (DIC) in sepsis•Deletion of GSDMD prevents caspase-11- and TF-mediated DIC in endotoxemia•GSDMD deficiency inhibits endotoxin-induced TF activation by reducing PS exposure•Activation of GSDMD by caspase-11 triggers Ca2+-dependent PS exposure through TMEM16F
Excessive activation of the coagulation system by endotoxin leads to life-threatening disseminated intravascular coagulation (DIC). Yang, Cheng et al. reveal that caspase-11, a cytosolic LPS receptor, activates the coagulation cascade by enhancing the activation of tissue factor, an initiator of coagulation, through triggering the formation of gasdermin (GASMD) pores and subsequent phosphatidylserine exposure, in a manner independent of cell death.
In this paper, we aim to make the best joint decision of device selection and computing and spectrum resource allocation for optimizing federated learning (FL) performance in distributed industrial ...Internet of Things (IIoT) networks. To implement efficient FL over geographically dispersed data, we introduce a three-layer collaborative FL architecture to support deep neural network (DNN) training. Specifically, using the data dispersed in IIoT devices, the industrial gateways locally train the DNN model and the local models can be aggregated by their associated edge servers every FL epoch or by a cloud server every a few FL epochs for obtaining the global model. To optimally select participating devices and allocate computing and spectrum resources for training and transmitting the model parameters, we formulate a stochastic optimization problem with the objective of minimizing FL evaluating loss while satisfying delay and long-term energy consumption requirements. Since the objective function of the FL evaluating loss is implicit and the energy consumption is temporally correlated, it is difficult to solve the problem via traditional optimization methods. Thus, we propose a " Reinforcement on Federated " (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem. Specifically, the RoF scheme is executed decentralizedly at edge servers, which can cooperatively make the optimal device selection and resource allocation decisions. Moreover, a device refinement subroutine is embedded into the RoF scheme to accelerate convergence while effectively saving the on-device energy. Simulation results demonstrate that the RoF scheme can facilitate efficient FL and achieve better performance compared with state-of-the-art benchmarks.
Cadmium (Cd) is highly toxic to most organisms, but some rare plant species can hyperaccumulate Cd in aboveground tissues without suffering from toxicity. The mechanism underlying Cd detoxification ...by hyperaccumulators is interesting but unclear.
Here, the heavy metal ATPase 3 (SpHMA3) gene responsible for Cd detoxification was isolated from the Cd/zinc (Zn) hyperaccumulator Sedum plumbizincicola. RNA interference (RNAi)-mediated silencing and overexpression of SpHMA3 were induced to investigate its physiological functions in S. plumbizincicola and a nonhyperaccumulating ecotype of Sedum alfredii.
Heterologous expression of SpHMA3 in Saccharomyces cerevisiae showed Cd-specific transport activity. SpHMA3 was highly expressed in the shoots and the protein was localized to the tonoplast. The SpHMA3-RNAi lines were hypersensitive to Cd but not to Zn, with the growth of shoots and young leaves being severely inhibited by Cd. Overexpressing SpHMA3 in the nonhyperaccumulating ecotype of S. alfredii greatly increased its tolerance to and accumulation of Cd, but not Zn.
These results indicate that elevated expression of the tonoplast-localized SpHMA3 in the shoots plays an essential role in Cd detoxification, which contributes to the maintenance of the normal growth of young leaves of S. plumbizincicola in Cd-contaminated soils.
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