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Sulfide-based all-solid-state lithium–sulfur batteries (ASSLSBs) have shown promise in next-generation energy storage devices. Nevertheless, controversy surrounds the redox pathway and the mechanism of the sulfur cathode. In this study, through galvanostatic intermittent titration technique tests, we first disclose that the difference between the discharge curves at room temperature and 60 °C originates from the sluggish kinetics at RT. Furthermore, we unravel the asymmetric sulfur redox paths and show the species evolution during the charging/discharging process at 60 °C through electrochemomechanical-based differential pressure versus voltage (dP/dV) analysis and spectroscopy studies. Our findings reveal that the sulfur cathode undergoes a two-step conversion from S8to Li2S4and finally to Li2S during discharge, while a one-step process from Li2S to S8occurs during charge. Density functional theory calculations suggest that the slow diffusion of polysulfide Li2S4and/or S42–anions in the solid-state electrolyte and the high intermediate phase dissociation energy barrier are mainly responsible for the observed asymmetry in the discharge/charge curves. In this case, the sulfur cathode undergoes a thermodynamic ground state path during discharge and a kinetically driven path during charging. This work sheds light on the debated sulfur redox mechanism and contributes to high-performance design in sulfide-based ASSLSBs.

The deposition/stripping behavior of lithium metal is intriguing, and the associated formation of inactive lithium at various temperatures remains elusive, which hinders the practical application of lithium metal batteries. Here, utilizing the variable-temperature operando solid-state nuclear magnetic resonance (SS NMR) technique, we reveal the temperature effects on the lithium microstructure evolution in a carbonate-based electrolyte system. In addition, the mass spectrometry titration (MST) method is used to quantify the evolution of inactive lithium components, including dead lithium, solid electrolyte interface (SEI), and lithium hydride (LiH). Combined SS NMR and MST results show that the morphology of lithium metal is reasonably correlated to the amount of inactive Li formed. At low/ambient temperature, the lithium microstructure has a similar evolution pattern, and its poor morphology leads to a large amount of dead lithium, which dominates capacity loss; however, at high temperature large and dense lithium deposits form with less dead Li detected, and the intensified electrolyte consumption in SEI formation is the major cause for capacity loss. Our phase-field simulation results reveal that the compact lithium deposition formed at higher temperature is due to the more uniformly distributed electric field and Li+concentration. Lastly, two strategies in forming a dense Li deposit are proposed and tested that show performance-enhancing results.

Different from the ground image with uniform haze, the haze in remote sensing (RS) image has the characteristics of irregular shape and uneven concentration in hazy weather. It brings a great challenge to the application of RS image data in advanced image processing tasks. A novel dehazing network for nonuniform hazy RS images, named KFA-Net, is proposed to solve the aforementioned issues. The designed asymmetric size feature cascade (ASFC), k-means pixel attention (KPA), and FFT channel attention (FCA) in KFA-Net all show excellent effects. Compared with symmetrically linked typical Unet, ASFC can more easily extract shallow features for feature reconstruction. Furthermore, different from the commonly used pixel attention that compresses feature maps directly, KPA introduces the k-means clustering algorithm in machine learning into the attention mechanism, which facilitates network training to focus on the thick hazy region. Compared to a typical squeeze-and-excitation block, FCA uses the low-frequency region feature of the spectrogram to obtain the attention weight coefficient in the frequency domain, making network training pay more attention to the feature of the image low-frequency region. Extensive comparison experiments verify that the proposed KFA-Net has great superiority. Peak signal to noise ratio (PSNR) and structural similarity (SSIM) of KFA-Net are 31.0952% and 6.6401% higher than DCP with the highest citation in traditional dehazing methods, respectively. PSNR and SSIM of KFA-Net are 2.2049% and 0.4966% higher than the recently proposed 4KDehazing with the best performance among all comparison dehazing methods, respectively. The KFA-Net proposed in this research can greatly enhance the temporal and spatial scopes of RS image application in hazy weather conditions.

Double-stranded RNA (dsRNA)-dependent protein kinase R (PKR) activation via autophosphorylation is the central cellular response to stress that promotes cell death or apoptosis. However, the key factors and mechanisms behind the simultaneous activation of pro-survival signaling pathways remain unknown. We have discovered a novel regulatory mechanism for the maintenance of cellular homeostasis that relies on the phosphorylation interplay between sphingosine kinase 1 (SPHK1) and PKR during exogenous stress. We identified SPHK1 as a previously unrecognized PKR substrate. Phosphorylated SPHK1, a central kinase, mediates the activation of PKR-induced pro-survival pathways by the S1P/S1PR1/MAPKs/IKKa signal axis, and antagonizes PKR-mediated endoplasmic reticulum (ER) stress signal transduction under stress conditions. Otherwise, phosphorylated SPHK1 also acts as the negative feedback factor, preferentially binding to the latent form of PKR at the C-terminal kinase motif, inhibiting the homodimerization of PKR, suppressing PKR autophosphorylation, and reducing the signaling strength for cell death and apoptosis. Our results suggest that the balance of the activation levels between PKR and SPHK1, a probable hallmark of homeostasis maintenance, determines cell fate during cellular stress response.

The rapid analysis of fuel properties is important for the utilization of solid biomass due to its great variation in feedstock. Laser-induced breakdown spectroscopy (LIBS) technology combined with quantitative analysis models can be used for this analysis. Most existing prediction models used in LIBS for fuel property analysis are linear methods, such as the partial least squares (PLS) model, which fail to reflect the non-linear relationships between the LIBS spectrum and the fuel property index being predicted. In the present work, LIBS data combined with a kernel partial least squares (KPLS) method are used to analyze the gross calorific value, and the volatile matter, ash and fixed carbon content of the solid biomass fuel. Quantitative analysis performance of the KPLS model was compared to that of the widely used PLS method, with the results showing some improvements. The KPLS model was further improved using three data normalization methods (i.e., C internal standardization, total intensity standardization and standard normal variate). The best quantitative analysis results of the volatile matter and ash content were obtained when the KPLS model was combined with C internal standardization, with root mean square errors of prediction (RMSEP) of 1.365% and 0.290%, and average standard deviations (ASD) of 0.277% and 0.080%, respectively. The best quantitative analysis results of the gross calorific value and fixed carbon content were obtained when using KPLS without normalization. The RMSEP and ASD of the gross calorific value and fixed carbon content were 0.198 MJ kg−1and 0.746%, and 0.070 MJ kg−1and 0.111% respectively.

The unburned carbon in fly ash is an important indicator of the coal-fired boiler combustion efficiency. Laser-induced breakdown spectroscopy (LIBS) has been applied for measurement of unburned carbon for its fast response. Traditionally, the unburned carbon content determined by the loss-on-ignition (LOI) method was used as the reference value of the LIBS calibration model. However, the reference value of unburned carbon content would not correspond to the LIBS spectral information because the result measured by the LOI method represents not only unburned carbon but also decomposition of some mineral phases in fly ash during ignition. In this paper, different values of unburned carbon content determined by the loss-on-ignition method and elemental analysis technique, respectively, were compared as the reference of LIBS calibration model. The analysis results showed that there is a better correspondence between the total carbon content determined by elemental analysis technique and the LIBS spectral information on fly ash. Compared with the reference value determined by the LOI method, the regression coefficient (R2) of the calibration model by the reference value determined by elemental analysis technique improved from 0.980 to 0.992, and the root-mean-square error of prediction decreased from 0.702% to 0.111%. This research demonstrates that the total carbon content is more suitable to be used as reference value to establish a calibration model of unburned carbon content in fly ash by LIBS analysis.

Bacillus subtilisis widely used in aquaculture as a probiotic. However, few studies have been conducted to examine the effect of B. subtilison liver lipid metabolism. A total of 135 healthy grass carp (50.24 ± 1.38 g) were randomly divided into three groups: control (Con), high-fat diet (HF), and high-fat diet + B. subtilis(HF + B. subtilis), and fed for 8 weeks. The results showed that compared with the HF group, the weight gain rate (WGR) significantly increased (P < 0.05) and the hepatic lipid content, serum low-density lipoprotein cholesterol (LDL-C), and aspartate aminotransferase (AST) decreased in the group supplemented with B. subtilis(P < 0.05). Moreover, the hepatic mRNA expression of fatty acid synthase (FAS) was significantly down-regulated and the carnitine palmitoyl transferases (CPT1α1a) were up-regulated in the HF + B. subtilisgroup compared to the HF group (P < 0.05), respectively. Additionally, in the HF + B. subtilisgroup, glutathione (GSH) significantly increased (P < 0.05), while hydrogen peroxide (H2O2) and malondialdehyde (MDA) contents significantly decreased compared to the HF group (P < 0.05). B. subtilismay reduce the hepatic lipid content by inhibiting its synthesis and promoting β-oxidation of fatty acids. B. subtilismay also alleviate dyslipidaemia and prevent oxidative damage in the liver caused by the high-fat diet of grass carp. Hence, dietary supplementation with B. subtilisshows promise as a therapeutic or preventive tool against fatty liver disease.

Car-following (CF) models are an appealing research area because they fundamentally describe the longitudinal interactions of vehicles and contribute substantially to an understanding of traffic flow. In this study, by combining numerous deep neural network structures to mimic the memory, attention and prediction (MAP) mechanisms of real drivers, a deep learning-based car following model named MAP is built. The proposed MAP learned CF behaviour from real-world datasets. Experiments on predicting future driving behaviour show that the MAP model with memory, attention and prediction mechanisms outperforms models without those mechanisms. Several analysis are conducted to understand the MAP model, and quantitatively provides some explanations of how MAP achieves the CF behaviour. A simulation is also presented and explicitly analysed. The results show that the proposed approach can produce space-time diagrams similar to real traffic. The analysis also shows that the supervised learning models generate the most likely, rather than the best, CF behaviour.

ABSTRACTThe detailed pathogenesis of endometriosis remains largely unclear despite decades of research. Recent studies have demonstrated that miRNAs plays an important role in endometriosis. The expression of miR-142-3p was decreased in ectopic endometrial tissues, while KLF9 and VEGFA expression levels were increased. Overexpression of miR-142-3p or knockdown of KLF9 significantly suppressed CRL-7566 cell proliferation and metastasis, induced cell apoptosis, and decreased both cell autophagy and vascularization. Additionally, KLF9 was confirmed to be a direct target of miR-142-3p and to directly bind to the promoter of the VEGFAgene, regulating its expression. Finally, intraperitoneal injection of miR-142-3p lentivirus significantly attenuated ectopic endometriotic lesions in vivo.miR-142-3p directly targeted KLF9, regulated VEGFA expression, and was protective against the growth of ectopic endometriotic lesions. Therefore, the miR-142-3p/KLF9/VEGFA signalling pathway may be a potential target in endometriosis treatment.

Background: This study aimed to validate the efficacy the multiplication of neutrophils and monocytes (MNM) and a novel dynamic nomogram for predicting in-hospital death in patients with aneurysmal subarachnoid hemorrhage (aSAH). Methods: Retrospective study was done on 986 patients with endovascular coiling for aSAH. Independent risk factors associated with in-hospital death were identified using both univariate and multivariate logistic regression analysis. In the development cohort, a dynamic nomogram of in-hospital deaths was introduced and made available online as a straightforward calculator. To predict the in-hospital death from the external validation cohort by nomogram, calibration analysis, decision curve analysis, and receiver operating characteristic analysis were carried out. Results: 72/687 patients (10.5%) in the development cohort and 31/299 patients (10.4%) in the validation cohort died. MNM was linked to in-hospital death in univariate and multivariate regression studies. In the development cohort, a unique nomogram demonstrated a high prediction ability for in-hospital death. According to the calibration curves, the nomogram has a reliable degree of consistency and calibration. With threshold probabilities between 10% and 90%, the nomogram’s net benefit was superior to the basic model. The MNM and nomogram also exhibited good predictive values for in-hospital death in the validation cohort. Conclusions: MNM is a novel predictor of in-hospital mortality in patients with aSAH. For aSAH patients, a dynamic nomogram is a useful technique for predicting in-hospital death.

Inorganic halide perovskites have attracted enormous interest in recent years owing to their remarkable properties for next-generation optoelectronics. Yet their basic optical properties have been rarely explored, which failed to meet the demand of accelerating the progress of this emerging field. In this work, temperature dependent reflection spectroscopy and ellipsometry were carried out on a CsPbBr3bulk perovskite single crystal. Two discernible optical transitions at around 2.4 and 3.4 eV with opposite temperature dependence are observed, indicating the complexity of the carrier-phonon scattering process in perovskites. The intrinsic transition energy, exciton–phonon interaction, exciton polariton and strong oscillator strength (∼1.3 × 1030rad2/s2) of the three-dimensional Wannier–Mott excitons are revealed. We have acquired by ellipsometry measurement the wavelength dependent dielectric constants and the complex refractive indices that are vital for informed optical designs to achieve device performance optimization. Furthermore, the essential and elemental optical properties such as absorption coefficient, absorption cross-section, optical conductivity, and dispersion relation of CsPbBr3are obtained. These parameters give insights into both fundamental physics and practical applications of CsPbBr3perovskites. This work unveils the photophysics of inorganic perovskites as well as offers approaches to identify the optical transitions and extract essential physical parameters, which can be utilized to explore other perovskite materials and nanostructures.

Injecting spins from ferromagnetic metals into semiconductors efficiently is a crucial step towards the seamless integration of charge- and spin-information processing in a single device1,2. However, efficient spin injection into semiconductors has remained an elusive challenge even after almost three decades of major scientific effort3–5, due to, for example, the extremely low injection efficiencies originating from impedance mismatch1,2,5,6, or technological challenges originating from stability and the costs of the approaches7–12. We show here that, by utilizing the strongly out-of-equilibrium nature of subpicosecond spin-current pulses, we can obtain a massive spin transfer even across a bare ferromagnet/semiconductor interface. We demonstrate this by producing ultrashort spin-polarized current pulses in Co and injecting them into monolayer MoS2, a two-dimensional semiconductor. The MoS2layer acts both as the receiver of the spin injection and as a selective converter of the spin current into a charge current, whose terahertz emission is then measured. Strikingly, we measure a giant spin current, orders of magnitude larger than typical injected spin-current densities using currently available techniques. Our result demonstrates that technologically relevant spin currents do not require the very strong excitations typically associated with femtosecond lasers. Rather, they can be driven by ultralow-intensity laser pulses, finally enabling ultrashort spin-current pulses to be a technologically viable information carrier for terahertz spintronics.

The signaling data of cellular phones, as a passively generated, real-time, wide-coverage, and low-cost data source, have been widely used in recent studies to understand human activity and model urban travel demand. However, in contrast with the Global Positioning System (GPS) data, cellular phone signaling data are sparsely distributed in time and space, which makes travel-mode inference a challenge. Recent studies presented methods of deriving users’ home and work locations, origin-destination trips, and other activities. Very few provided a complete and feasible framework for travel-mode derivation with effective validation methods. This paper provides a real-time travel-mode derivation framework using signaling data and a web-based mapping service. A trip-chain model is proposed to detect individual activity patterns and derive the trips of mobile phone users. Then, the travel mode of each trip is identified by a Fuzzy K-Means model, which is trained and validated by the point-to-point travel time from a web-based mapping service. Finally, the travel-mode shares are aggregated and scaled to the whole population of the study area. The framework is demonstrated using cellular signaling data from 1.9 million users in Shanghai, China for seven days, and citywide point-to-point travel times from a web-based mapping service for three of those seven days. Comparing the modeled travel-mode shares with travel survey data and transportation hub statistics demonstrates the plausibility and efficiency of using a large data source (mobile-trace data and web-based mapping) to accurately assess the travel modes of people in a big city using the proposed framework.

Low-rank tensor completion methods have been advanced recently for modeling sparsely observed data with a multimode structure. However, low-rank priors may fail to interpret the model factors of general tensor objects. The most common method to address this drawback is to use regularizations together with the low-rank priors. However, due to the complex nature and diverse characteristics of real-world multiway data, the use of a single or a few regularizations remains far from efficient, and there are limited systematic experimental reports on the advantages of these regularizations for tensor completion. To fill these gaps, we propose a modified CP tensor factorization framework that fuses the $l_{2}$ norm constraint, sparseness ($l_{1}$ norm), manifold, and smooth information simultaneously. The factorization problem is addressed through a combination of Nesterov’s optimal gradient descent method and block coordinate descent. Here, we construct a smooth approximation to the $l_{1}$ norm and TV norm regularizations, and then, the tensor factor is updated using the projected gradient method, where the step size is determined by the Lipschitz constant. Extensive experiments on simulation data, visual data completion, intelligent transportation systems, and GPS data of user involvement are conducted, and the efficiency of our method is confirmed by the results. Moreover, the obtained results reveal the characteristics of these commonly used regularizations for tensor completion in a certain sense and give experimental guidance concerning how to use them. [ABSTRACT FROM AUTHOR]

The passenger transportation hub plays a crucial role in the urban transportation system. Analyzing transportation hub related travel demand is necessary to support urban transportation planning and management. However, it is difficult to use the traditional travel survey methods to study travel demand because tracking passenger travel trajectories is a near impossible task. The location information from the cellular system provides a feasible way to solve the problem. This paper concentrates on applying mobile phone data to study passenger travel demand related to the Hongqiao transportation hub in Shanghai, China. First, a method is introduced to collect passenger travel information related to the hub from mobile phone data. Then, travel demand indexes are presented to characterize the travel demand in a visual way. Finally, transportation corridors, which connect the hub and other urban areas, are identified to analyze the distribution of travel demand more thoroughly. The results illustrate that the passenger travel demand shows an obvious tide pattern in the city area with the Hongqiao transportation hub as the center. Moreover, there are two identified transportation corridors which reveal the major distribution directions of the passengers, that is, the city center and the Zizhu industrial development zone. The approach in this study testifies that mobile phone data has great potential for transportation planning and management related to transportation hubs.

Non-structural protein 1 (NS1) of the influenza virus plays a crucial role in modulating the host immune response and facilitating virus replication. The formation of a homodimer or an oligomer is necessary for NS1 to exert its function efficiently. In the present study, the NS1 protein from the A/Shantou/602/06(H3N2) virus (herein abbreviated as NS32) was found to interact with NS1 from A/Shantou/169/06(H1N1), A/Chicken/Guangdong/1/05(H5N1) and A/Quail/Hong Kong/G1/97(H9N2) (abbreviated as NS11, NS51 and NS92, respectively) viruses, although NS32 shares 17.4%–20.9% sequence diversity with NS11, NS51 and NS92. This indicates that the heterologous interactions between NS1 proteins from different influenza A virus subtypes/ strains may be a common event during co-infection.

In this research, we have established a high-throughput screening (HTS) platform based on the influenza A virus (IAV) vRNA promoter. Using this HTS platform, we selected 35 medicinal plants out of 83 examples of traditional Chinese medicine and found that 7 examples had not been reported. After examining many previous reports, we found that Vaccinium angustifoliumAit., Vitis viniferaL, and Cinnamomum cassiaPresl had a common active compound, procyanidin, and then determined the anti-IAV effect of procyanidin and explored its mechanism of action. With a plaque inhibition assay and a time-of-addition experiment, we found that procyanidin could inhibit the IAV replication at several stages of the life cycle. In the Western blot and EGFP-LC3 localization assays, we found that procyanidin could inhibit the accumulation of LC3II and the dot-like aggregation of EGFP-LC3. In the RT-PCR and Western blot assays, we found procyanidin could inhibit the expression of Atg7, Atg5, and Atg12. Finally, by the bimolecular fluorescence complementation–fluorescence resonance energy transfer and co-immunoprecipitation assays, we found that procyanidin could inhibit the formation of the Atg5-Atg12/Atg16 heterotrimer and the dissociation of the beclin1/bcl2 heterodimer. In conclusion, we have established an HTS platform and identified procyanidin as a novel and promising anti-IAV agent.

Focuses on the production of freshwater crayfish in China. Estimation of freshwater crayfish production in China; Production of native freshwater crayfish species; Foreign species that were introduced in China.

Tetrahydroxanthone dimers are fungal products, among which secalonic acid D (1) is one of the most studied compounds because of its potent biological activity. Because the biosynthetic gene cluster of 1has been previously identified, we sought to heterologously produce 1in Aspergillus oryzaeby expressing the relevant biosynthetic genes. However, our initial attempt of the total biosynthesis of 1failed; instead, it produced four isomers of 1due to the activity of an endogenous enzyme of A. oryzae. Subsequent overexpression of the Baeyer–Villiger monooxygenase, AacuH, which competes with the endogenous enzyme, altered the product profile and successfully generated 1. Characterization of the key biosynthetic enzymes revealed the surprising substrate promiscuity of the dimerizing enzyme, AacuE, and indicated that efficient synthesis of 1requires highly selective preparation of the tetrahydroxanthone monomer, which is apparently controlled by AacuH. This study facilitates engineered biosynthesis of tetrahydroxanthone dimers both in a selective and divergent manner.