Existing critical point theories including metric and topological critical point theories are difficult to be applied directly to some concrete problems in particular polyhedral settings, because the notions of critical sets could be either very vague or too large. To overcome these difficulties, we develop critical point theory for nonsmooth but Lipschitzian functions defined on convex polyhedrons. This yields natural extensions of classical results in critical point theory, such as the Liusternik-Schnirelmann multiplicity theorem. More importantly, eigenvectors for some eigenvalue problems involving graph 1-Laplacian coincide with critical points of the corresponding functions on polytopes, which indicates that the critical point theory proposed in the present paper can be applied to study the nonlinear spectral graph theory.
Coinfection of hepatitis B virus (HBV) and hepatitis C virus (HCV) may result in severe liver disease and frequent progression to cirrhosis and hepatocellular carcinoma. Clinical evidence suggests that HBV replication is suppressed by replicating HCV and often rebounds after treatment with drugs against HCV. Thus, a highly efficient cell culture system permissive for HBV/HCV would facilitate investigation on the interaction and pathogenesis after coinfection. Here we reported a robust HBV/HCV coinfection cell culture model by overexpressing human sodium-taurocholate cotransporting polypeptide (NTCP), CD81 and Mir122 into HepG2 cells and investigated interactions between HBV and HCV. In this system, HepG2-NTCP/CD81/Mir122 cells not only supported robust infection and replication of HBV and HCV, but also allowed HBV/HCV coinfection in the single cell level. Our result showed cells with replicating HBV still supported HCV infection. However, HBV replication was suppressed by HCV through the inhibition of HBV core promoter and S promoter II activity, and this inhibition was attenuated by the interferon alpha (IFNα) treatment, suggesting HCV influence on HBV at transcriptional level. Coinfection of HBV/HCV in this system did not block IFN stimulated genes expression. Inhibition of HCV by direct-acting antiviral drugs restored HBV replication and expression of viral genes. Conclusions: HepG2-NTCP/CD81/Mir122 fully supports HBV/HCV coinfection, replication and interaction. This novel cell model offers a platform to advance our understanding of the molecular details of the interaction, pathogenesis and outcomes of HBV/HCV coinfection.
In this study, we designed an integrated electrochemical filtration system for catalytic activation of peroxymonosulfate (PMS) and degradation of aqueous microcontaminants. Composites of carbon nanotube (CNT) and nanoscale zero valence copper (nZVC) were developed to serve as high-performance catalysts, electrode and filtration media simultaneously. We observed both radical and nonradical reaction pathways, which collectively contributed to the degradation of model pollutants. Congo red was completely removed via a single-pass through the nZVCCNT filter (τ <2 s) at neutral pH. The rapid kinetics of Congo red degradation were maintained across a wide pH range (from 3.0–7.0), in complicated matrixes (e.g., tap water and lake water), and for the degradation of a wide array of persistent organic contaminants. The superior activity of nZVCCNT stems from the boosted redox cycles of Cu2+/Cu+ in the presence of an external electric field. The flow-through design remarkably outperformed the conventional batch system due to the convection-enhanced mass transport. Mechanism studies suggested that the carbonyl group and electrophilic oxygen of CNT served as electron donor and electron acceptor, respectively, to activate PMS to generate •OH and 1O2 via one-electron transport. The electron-deficient Cu atoms are prone to react with PMS via surface hydroxyl group to produce reactive intermediates (Cu2+-O-O-SO3−), and then 1O2 will be generated by breaking the coordination bond of the metastable intermediate. The study will provide a green strategy for the remediation of organic pollution by a highly efficient and integrated system based on catalytic oxidation, electrochemistry, and nano-filtration techniques.
It has been suggested that indoor exposure to ozone oxidation products contributes materially to the apparent associations between outdoor ozone concentration and morbidity and mortality. Our current understanding of indoor ozone chemistry derives mainly from studies with test surfaces under controlled conditions. Little is known about the overall impact of ozone chemistry on air composition in dynamically changing indoor residential environments. The results presented here reflect a quantitative characterization of overall indoor ozone chemistry in a normally occupied home. Findings reveal a strong influence of off-body skin lipids on indoor ozone chemistry. Being able to elucidate indoor air pollutants derived from ozone chemistry facilitates the investigation of causal links between outdoor ozone concentrations and adverse health effects.Outdoor ozone transported indoors initiates oxidative chemistry, forming volatile organic products. The influence of ozone chemistry on indoor air composition has not been directly quantified in normally occupied residences. Here, we explore indoor ozone chemistry in a house in California with two adult inhabitants. We utilize space- and time-resolved measurements of ozone and volatile organic compounds (VOCs) acquired over an 8-wk summer campaign. Despite overall low indoor ozone concentrations (mean value of 4.3 ppb) and a relatively low indoor ozone decay constant (1.3 h−1), we identified multiple VOCs exhibiting clear contributions from ozone-initiated chemistry indoors. These chemicals include 6-methyl-5-hepten-2-one (6-MHO), 4-oxopentanal (4-OPA), nonenal, and C8-C12 saturated aldehydes, which are among the commonly reported products from laboratory studies of ozone interactions with indoor surfaces and with human skin lipids. These VOCs together accounted for ≥12% molecular yield with respect to house-wide consumed ozone, with the highest net product yield for nonanal (≥3.5%), followed by 6-MHO (2.7%) and 4-OPA (2.6%). Although 6-MHO and 4-OPA are prominent ozonolysis products of skin lipids (specifically squalene), ozone reaction with the body envelopes of the two occupants in this house are insufficient to explain the observed yields. Relatedly, we observed that ozone-driven chemistry continued to produce 6-MHO and 4-OPA even after the occupants had been away from the house for 5 d. These observations provide evidence that skin lipids transferred to indoor surfaces made substantial contributions to ozone reactivity in the studied house.All study data are included in the article and supporting information.
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry. The most efficient way to train deep SNNs is through ANN-SNN conversion. However, the conversion usually suffers from accuracy loss and long inference time, which impede the practical application of SNN. In this paper, we theoretically analyze ANN-SNN conversion and derive sufficient conditions of the optimal conversion. To better correlate ANN-SNN and get greater accuracy, we propose Rate Norm Layer to replace the ReLU activation function in source ANN training, enabling direct conversion from a trained ANN to an SNN. Moreover, we propose an optimal fit curve to quantify the fit between the activation value of source ANN and the actual firing rate of target SNN. We show that the inference time can be reduced by optimizing the upper bound of the fit curve in the revised ANN to achieve fast inference. Our theory can explain the existing work on fast reasoning and get better results. The experimental results show that the proposed method achieves near loss-less conversion with VGG-16, PreActResNet-18, and deeper structures. Moreover, it can reach 8.6× faster reasoning performance under 0.265× energy consumption of the typical method. The code is available at https://github.com/DingJianhao/OptSNNConvertion-RNL-RIL.
China purchases around 66% of the soy that is traded internationally. This strains the global food supply and contributes to greenhouse gas emissions. Here we show that optimizing the maize and soy production of China can improve its self-sufficiency and also alleviate adverse environmental effects. Using data from more than 1,800 counties in China, we estimate the area-weighted yield potential (Ypot) and yield gaps, setting the attainable yield (Yatt) as the yield achieved by the top 10% of producers per county. We also map out county-by-county acreage allocation and calculate the attainable production capacity according to a set of sustainability criteria. Under optimized conditions, China would be able to produce all the maize and 45% of the soy needed by 2035—while reducing nitrogen fertilizer use by 26%, reactive nitrogen loss by 28% and greenhouse gas emissions by 19%—with the same acreage as 2017, our reference year.
The Noise-Shaping (NS) SAR is an attractive new ADC architecture that emerged in the last decade. It combines the advantages of the SAR and the DSM architectures. NS SAR shows excellent potential for high efficiency and low cost, and is highly suited to process scaling. This paper gives an overview of the history of NS-SAR, reviews the fundamentals challenges, and summarizes the latest developments, including advanced loop filtering techniques, DAC mismatch mitigation, kT/C mitigation, and bandwidth boosting. A comprehensive comparison of the state-of-the-art NS-SAR ADCs is provided, and conclusions are derived.