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The use of polyurethane (PU)-modified asphalt for paving purposes has recently gained increasing interest in both academia and industry. This study aimed to characterise the engineering performance of an asphalt mixture modified with one-component PU and explore its adhesive mechanism. To achieve these objectives, PU-modified bitumen and asphalt mixtures with two different PU contents (10% and 30%) were prepared. Various laboratory material property tests, such as rotational viscosity test of the binder, Marshall test, indirect tensile strength (ITS) test, Hamburg wheel-tracking test, moisture susceptibility and indirect tensile fatigue test of the PU-modified mixture, were carried out. Fourier transform infrared spectroscopy was carried out to explore the adhesive mechanism between PU-modified binders and aggregates. The results indicated that the overall performance of the asphalt mixture increased significantly with the PU prepolymer, with increased Marshall stability, ITS and resistance to moisture damage, rutting and fatigue damage. It was found that the PU prepolymer reacted with hydroxyl groups on the surface of the aggregate, which contributed to the improved performance of the asphalt mixture. The findings of this study may facilitate further practical applications of bituminous materials modified with one-component PU in the pavement industry.

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To meet the demand for energy and biomass, T lymphocytes (T cells) activated to proliferation and clonal expansion, require uptake and metabolism of glucose (Gluc) and the amino acid (AA) glutamine (Gln). Whereas exogenous Gln is converted to glutamate (Glu) by glutaminase (GLS), Gln is also synthesized from the endogenous pool of AA through Glu and activity of glutamine synthase (GS). Most of this knowledge comes from studies on cell cultures under ambient oxygen conditions (normoxia, 21% O2). However, in vivo, antigen induced T-cell activation often occurs under moderately hypoxic (1–4% O2) conditions and at various levels of exogenous nutrients. Here, CD4+ T cells were stimulated for 72 h with antibodies targeting the CD3 and CD28 markers at normoxia and hypoxia (1% O2). This was done in the presence and absence of the GLS and GS inhibitors, Bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl) ethyl sulfide (BPTES) and methionine sulfoximine (MSO) and at various combinations of exogenous Gluc, Gln and pyruvate (Pyr) for the last 12 h of stimulation. We found that T-cell proliferation, viability and levels of endogenous AA were significantly influenced by the availability of exogenous Gln, Gluc and Pyr as well as inhibition of GLS and GS. Moreover, inhibition of GLS and GS and levels of oxygen differentially influenced oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). Finally, BPTES-dependent down-regulation of ECAR was associated with reduced hexokinase (HK) activity at both normoxia and hypoxia. Our results demonstrate that Gln availability and metabolism is rate-limiting for CD4+ T-cell activity.

BACKGROUND: Inadequate scaffolding performance hinders the clinical application of the biodegradable zinc alloy stents. OBJECTIVE: In this study we propose a novel stent with the tenon-and-mortise structure to improve its scaffolding performance. METHODS: 3D models of stents were established in Pro/E. Based on the biodegradable zinc alloy material and two numerical simulation experiments were performed in ABAQUS. Firstly, the novel stent could be compressed to a small-closed ring by a crimp shell and can form a tenon-and-mortise structure after expanded by a balloon. Finally, 0.35 MPa was applied to the crimp shell to test the scaffolding performance of the novel stent and meanwhile compare it with an ordinary stent. RESULTS: Results showed that the novel stent decreased the recoiling ratio by 70.7% compared with the ordinary stent, indicating the novel structure improved the scaffolding performance of the biodegradable zinc alloy stent. CONCLUSION: This study proposes a novel design that is expected to improve the scaffolding performance of biodegradable stents.

In this study, a pH/reduction dual responsive carrier containing 42nt-nucleic acid HApt based on mesoporous silica nanoparticles (MSNs) was designed. Two kinds of low molecular weight oligomeric hyaluronic acid (HA) were used to graft onto MSN for better drug encapsulation. Crosslinked MSN-HA

Reunderstanding the faradaic reaction mechanism at the electrode/electrolyte interface from the specific adsorption of solvation structures towards advanced aqueous Zn–Mn batteries.

Rechargeable Li-CO2 batteries (LCBs) are considered as a promising candidate for the next generation energy storage system, but still be impeded by the lack of high-performance cathode catalyst and poor understanding for the complicated reaction mechanism. In the present work, we demonstrate that the catalytic capability of cathode catalyst of LCBs can be remarkably enhanced from the accelerated reaction kinetics using a p-type substrate as adsorption/desorption promoter for refined reaction route. A carbon-free atomic Ir-Te cathode catalyst with Ir atomic cluster is uniformly super-assembled on the surface of Te nanowires forming an amorphous surface layer (ca. 3 nm) to maximize the catalytic capability of active Ir sites and provide a refined reaction pathway due to synergistic effect of Ir active sites and Te substrate. The adsorption ability of Li2C2O4 during discharging and the desorption ability of CO2 species during charging of the p-type Te substrate could both promote the catalytic reaction kinetics and optimize the reaction pathways on Ir active sites. Finally, a large specific capacity of 13,247.1 mAh g−1 and an excellent high rate cyclability with stably over 350 and 200 cycles at the current density of 1000 and 2000 mA g−1 are achieved. This contribution provides a rational design strategy for high performance cathode catalyst, and intrinsic insight towards the understanding the reaction mechanisms of LCBs.

Recent years have witnessed the prominent advancements of deep learning (DL) in the arsenal of prognostics and health management. However, the prognostic uncertainty problem extensively existed in industrial devices is not addressed by most DL approaches. This article formulates a novel Bayesian Deep Learning (BDL) framework to characterize the prognostic uncertainties. A distinguished advantage of the framework is its capacity of capturing the comprehensive effects of two critical uncertainties: 1) epistemic uncertainty, accounting for the uncertainty in the model, and 2) aleatoric uncertainty, representing the impact of random disturbance, such as measurement errors. The former arises from the variability of the model weights, and the latter is characterized by selected lifetime distributions. We integrate both uncertainties by defining BDL as priors of lifetime parameters. A sequential Bayesian boosting algorithm is executed to improve the estimation accuracy and compress the credible intervals. The superior prediction performance of our framework is validated by a real-world dataset collected from hydraulic mechanisms of circuit breakers.

Purpose A common surgical methods for the treatment of coronary heart disease is coronary artery bypass grafting ( CABG ). The most important concern is the graft patency. When restenosis or occlusion occurs on the graft, the blood supply to the downstream will be reduced, and serious myocardial ischemia will occur again. Hemodynamics are the key factors affecting the graft patency. If the hemodynamic results of the graft under different surgical methods can be known before coronary artery bypass grafting, it can help doctors to choose the optimal operation method. Methods In this study, the modeling and simulation method of multi-scaled model of coronary had been designed. The 3D model of the bypass surgery region is constructed by the 3D reconstruction of the patient's medical imaging and the virtual bypass surgery. The individuation lumped parameter model is constructed by the basic physiological information of the patients. Finally, the two models are connected by a special boundary surface coupling algorithm to construct a 0D-3D coupled multi-scaled model that can be used to calculate the hemodynamic environment. Through calculation, the flow waveform, wall shear stress(WSS), oscillating shear index(OSI) and other hemodynamic parameters in the graft under different bypass methods were compared, and the optimal operation method with the best hemodynamic environment was selected. Results The method was used to calculate two clinical cases, and the effectiveness of the method was proved by the comparison of calculated graft flow and real graft flow, and the comparison of hemodynamic environment and graft outcomes after one year. Conclusion This method can realize preoperative evaluation of the hemodynamic environment of the graft under different bypass methods, and then select the optimal operation method for the patients and improve the graft patency after operation.

Introduction To investigate the presence and distribution of hepatitis B virus (HBV) in the corneas of hepatitis B virus surface antigen (HBsAg) seropositive donors in different preservation media and times. Materials and Methods Eight corneas were acquired from seven HBsAg seropositive donors. Four corneas were preserved in storage media for less than two weeks (group A), two corneas were put in glycerin for a month (group B), and two corneas were put in glycerin for a year (group C). Two corneas were acquired from two HBsAg seronegative donors and set as a control group. One was put into bottles with storage media for a week. Another was kept in glycerin for a year. Results Immunofluorescent staining was used to identify and localize HBsAg. With immunofluorescence staining, HBV surface antigens were observed in the epithelium and/or endothelium layer in three out of eight corneas of HBsAg seropositive donors. Conclusion The results show that HBsAg can be detected in the epithelium and/orendothelium layer of the cornea in HBsAg seropositive donors, regardless of preservation media or duration.

To explore the potential application of plant essential oil in the postharvest preservation of fruits, the inclusion complex (IC) of Litsea cubeba essential oil (LCEO) with hydroxypropyl-β-cyclodextrin (HPβCD), prepared by the saturated aqueous solution method, was studied. LCEO/HPβCD-IC was characterized by fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), particle size distribution, and thermogravimetric-differential scanning calorimetry (TG-DSC) analysis. The formation of LCEO/HPβCD-IC was confirmed, and the volume average particle diameter was 24.376 µm. Due to the inclusion of HPβCD, the volatility of LCEO was significantly reduced and the thermal stability was significantly improved. In addition, the antifungal activities of the LCEO ICs were compared, and LCEO/HPβCD-IC was more effective against the citrus postharvest pathogens (P. italicum and G. citri-aurantii). The effects of the LCEO ICs on the postharvest quality of Shatang mandarin were studied. Compared with the control group (CK) and LCEO/βCD-IC group, the LCEO/HPβCD-IC group showed a significant delay in the decrease of good fruit rate, hardness, total soluble solids (TSSs), and Vitamin C (Vc) content, with a lower weight loss rate of Shatang mandarin. Therefore, LCEO/HPβCD-IC is expected to be used as a green preservative for the storage and preservation of citrus fruits. PRACTICAL APPLICATION: In this study, LCEO was encapsulated in HPβCD by the saturated aqueous solution method and the prepared inclusion complex was characterized. The effects of LCEO/HPβCD-IC and LCEO/βCD-IC on postharvest preservation of Shatang mandarin were compared. This work offers valuable insights into the postharvest preservation of citrus fruit by essential oil inclusion complexes.

Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and chemometric methods were utilized to analyze changes in volatile organic compounds (VOCs) of yellow-fleshed peach by 1-methylcyclopropylene (1-MCP) treatment and modified atmosphere packaging (MAP). Meanwhile, the storage quality of yellow-fleshed peach at room temperature (25°C) was also studied. Yellow-fleshed peach was treated by four methods, namely, nanomaterial packaging (NA), 1-MCP fumigation and nanomaterial packaging (1-MCP-NA), polyethylene (PE) packaging, and 1-MCP fumigation with polyethylene packaging (1-MCP-PE). Changes in the decay rate, firmness, browning index, soluble solid content, and titratable acid of the fruit were then measured at room temperature at 1, 4 and 10 days of storage. Thirty-two VOCs, including terpenoids, alcohols, esters, aldehydes, and ketones, were identified. Seventeen VOCs were found to be significant with predictive variable important in the projection (VIP) 1 and p 0.05 by analysis of variance and orthogonal projection to latent structure discriminant analysis (OPLS-DA). After 10 days of storage, the browning index of 1-MCP-NA group was 51.7%, which was lower than the control fruit (PE, 76.7%). Compared with other three treatments, 1-MCP-NA showed the better ability to delay and inhibit decreases in ester and aldehyde contents, and the ethanol content was lowest in the samples treated by 1-MCP-NA during storage. Differences among treatment groups were distinguished by principal component analysis (PCA) and hierarchical clustering heat map. The results showed that 1-MCP-NA could well maintain the quality and flavor stability of yellow-fleshed peach, and it had a good application prospect in the postharvest preservation of yellow-fleshed peach. PRACTICAL APPLICATION: In this study, 1-methylcyclopropylene (1-MCP) combined with nanomaterial (NA) packaging (1-MCP-NA) proved to have a better fresh preservation effect. 1-MCP-NA showed better ability to delay and inhibit decreases in ester and aldehyde contents by HS-GC-IMS technique. It provided a new strategy for postharvest storage of yellow-fleshed peaches.

MXene based materials are theoretical predicted as one of the most promising cathode materials for lithium-oxygen batteries due to their unique electronic structure, large surface area and lattice matching with Li2O2, but still be impeded by the lack of high electrocatalytic performance, inhomogeneous surface condition and poor understanding for reaction kinetics and mechanism during ORR/OER process. In the present work, we fabricated the Ti2C MXene decorated with -O and -F multifunctional groups. DFT calculations revealed that the Ti2CO2 surface provided a high efficient single electron reaction pathway for the adsorption-nucleation-decomposition process of Li2O2 acting as the main catalytic sites compared to the Ti2CF2 surface, meanwhile, the bare Ti2C surface degraded the catalytic capability of Ti2C MXene due to the strong chemical binding with Li2O2. Further, the inhomogeneous surface condition was supposed to promote the polarized nucleation and growth of discharge products to form the porous structure from spatial-direction accumulated nanoflakes, which can provide an efficient pathway for mass transfer. As a result, the T2C MXene cathode exhibited excellent performance in terms of superior capacity and long cycle stability. The present work provides intrinsic insights toward the catalytic mechanism and reaction kinetics of Ti2C MXene decorated with multifunctional groups, which contributes to the rational design of high performance MXene based materials in Li-O2 batteries.

Operations & maintenance (O&M) of wind turbines (both onshore and offshore) are heavily affected by weather conditions, particularly wind conditions. Current O&M models focused mainly on negative impacts of wind conditions on turbine reliability and maintenance, while ignoring potential maintenance opportunities emerging from dynamic wind velocities. This article addresses this issue by constructing a novel weather-centered O&M framework, integrating wind impacts on: (a) energy production, and (b) maintenance plans. Both the positive (maintenance opportunities) and negative impacts (maintenance delays) of wind conditions are quantified in the framework. Accordingly, a weather-centered opportunistic maintenance policy is developed to enable a flexible maintenance resource allocation. The maintenance model is formulated, and analytical properties regarding optimal maintenance ages are discussed. Furthermore, the net revenue of wind turbines is evaluated using the performance-based contracting (PBC), which captures dynamics of both power generation and operational costs. Experimental results demonstrate the superior performance of our framework in revenue improvement, particularly when facing high failure risks and production losses.

Urine excretory proteins are among the most commonly used biomarkers in body fluids. Computational identification of urine excretory proteins can provide very useful information for identifying targeted disease biomarkers in urine by linking transcriptome or proteomics data. There are few methods based on conventional machine learning algorithms for predicting urine excretory proteins, and most of these methods strongly depend on the extraction of features from urine excretory proteins. An end-to-end model for urine excretory protein prediction, called DeepUEP, is presented using deep neural networks relying on only amino acid sequence information. The model achieves good performance and outperforms existing methods on training and testing sets. By comparing known urinary protein biomarkers with the results of the model, we find that the model can achieve a true-positive rate of over 80% for urinary protein biomarkers that have been detected in more than one study. We also combine our model with transcriptome and proteomics data from lung cancer patients to predict the potential urinary protein biomarkers of lung cancer. A web server is developed for the prediction of urine excretory proteins, and it can be accessed at the following URL: http://www.csbg-jlu.info/DeepUEP/. We believe that our prediction model and web server are useful for biomedical researchers who are interested in identifying urinary protein biomarkers, especially for candidate proteins in transcriptome or proteomics analyses of diseased tissues.