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No reflow manifests coronary microvascular injury caused by continuous severe myocardial ischemia and reperfusion. Microvascular obstruction (MVO) has emerged as one fundamental mechanism of no reflow. However, the underlying pathophysiology remains incompletely defined. Herein, we explore the contribution of high mobility group box 1 (HMGB1), derived mainly from platelet microparticles exacerbating MVO in no reflow.44 STEMI patients undergoing successful primary percutaneous coronary intervention (PCI) were included in our study. Plasma HMGB1 levels in both the peripheral artery (PA) and infarct-related coronary artery (IRA) were measured by ELISA. Flow cytometry and confocal microscopy assessed the level of HMGB1HMGB1 levels were significantly higher in the IRA in no-reflow patients. The levels of HMGB1Our results illustrate a novel mechanism by which HMGB1, derived mainly from PMPs, plays a crucial role in the pathogenesis of no-reflow, revealing a novel therapeutic target.

Lignin-derived aromatics can be efficiently demethylated with a high yield in an acidic lithium bromide solution under moderate conditions.

An alcoholysis-hydrogenation integrated continuous process for the production of 1,4-PDO from industrialized FOL.

Confined interfacial transformation of MOFs was realized via the regulation of ion–ligand concentrations, which enables spontaneous control over the morphology and composition for efficient oxygen evolution reaction.

H-Beta zeolites were employed for hexose decarburization to furfural. Excellent performance was achieved over high-Si H-Beta zeolite due to its high B/(B + L) ratio. The synergetic mechanism between *BEA channel and framework-Al is decisive.

Objective:The aim of this study was to investigate the changes in Chinese residents’ psychological state and its influencing factors after the Wuhan shutdown on January 23, 2020.Methods:Two surveys were conducted on February 1-5 and February 20-24, separately, using an online self-administrated questionnaire among 3145 and 3814 participants, respectively. Subjective indicators of daily-life changes include level of attention, risk of infection, impact of daily life, self-perceived health status, and mental health help-seeking. Individual scores on changes in anxiety, depression, and stress were generated by 6-item, 4-item, and 3-item questions. A multivariate regression model was fitted in each survey, separately and combined.Results:A total of 6959 residents participated in the study, with 32.78% male and 67.22% female, people living in Wuhan and other cities in Hubei Province accounted for 25.22% and 18.85%, respectively. One week after the Wuhan shutdown, their anxiety, depression, and stress had all increased. Compared with the first survey, the changes in the scores of anxiety, depression, and stress in the second survey were decreased (β = −1.220, −0.798, and −0.623, all P < 0.001). The level of attention, risk of infection, and self-perceived health status tended to be positively associated with the changes in the scores of anxiety, depression, and stress.Conclusions:The study showed that the lives and psychological conditions of residents had undergone negative changes after the Wuhan shutdown, but the measures taken during this period were effective. These results may provide guidance for public health policies in other countries and regions.

In the in situ visualization of large-scale simulation, if using the traditional sort-last parallel rendering method, the performance cannot be fully improved due to the heavy communication cost in the final stage: compositing of images. In order to resolve this problem, in this paper, we proposed a composition-free parallel volume rendering method. This is achieved by our proposed hybrid parallelization based on the image space partition and octree-based data space partition. Firstly, in the data distribution stage, an octree structure decoupled from volume representation was proposed, which only stored the indicts of sub-blocks, the adjacent sub-blocks, and the space coordinate information of its bounding box. Before rendering, to avoid the compositing stage, a ray-data intersection list was created for each pixel, which sorted by visibility order and identified NON-EMPTY sub-blocks along the ray. Then in the rendering stage, to improve the efficiency of rendering, a ray-data intersection lists-based rendering method was proposed. Finally, we only need to align the partial images to form the final output image. No compositing process is necessary in our parallel volume rendering method. In order to demonstrate the efficiency of our approach, two existing volume rendering methods have been carried out to compare with our composition-free method.

Recently, extensive studies have focused on analyzing aerodynamic performance due to its important impact on aircraft design. Most of these works compute the aerodynamic coefficient of the airfoil through computational fluid dynamics (CFD) simulation, which is too time-consuming. To reduce the computational time required, some intelligence-based methods have been presented. However, these methods also suffer from certain issues. First, most of them directly implement existing machine learning methods used to predict the aerodynamic coefficient without adding any improvements. Second, some methods convert the airfoil shape and aerodynamic curves into images, which may lead to curve distortion and the introduction of noise. Third, some methods learn the relationship between the airfoil shape and aerodynamic coefficients but ignore the influence of initial inflow conditions. Accordingly, to address these issues, we propose an intelligent method for predicting the pressure coefficients (Cp) of airfoil based on a conditional generative adversarial network (cGAN). More specifically, we first present a two-step data augmentation strategy designed to expand the original airfoil dataset. Subsequently, we design a novel cGAN-based neural network to predict the Cp curve. To the best of our knowledge, this is the first work to apply generative adversarial network (GAN) to aerodynamic coefficient prediction. Moreover, we design a new loss function to train our network. Extensive experimental results demonstrate that the Cp curve predicted by our method is very close to that generated via CFD simulation. More importantly, our method achieves a speedup close to 1000x compared with CFD simulation.

With increasing concern for reducing CO

Device-free passive wireless indoor localization has attracted great interest due to the widespread deployment of Wi-Fi devices and the rapid growth in demand for location-based services. In this paper, we propose a novel device-free passive wireless localization system with a weighted transferable discriminative dimensionality reduction method (termed TLLOC). It utilizes the channel state information (CSI) extracted from a single link to estimate the location of the target, neither requiring the target to wear any electronic equipment nor deploying a large number of APs and Monitor Devices. To cope with the problem of reduced localization accuracy caused by the unpredictable nature of CSI over time that ignored by most previous CSI-based localization works, a novel weighted transferable discriminative dimensionality reduction (termed WTR) method combining transfer learning and dimensionality reduction is proposed. The WTR method constructs a low-dimensional latent space, which can simultaneously improve the discrimination of training samples and narrow the distribution divergence between the training samples and the test samples, further enhancing the performance of our system. Experimental results are presented to confirm that TLLOC can effectively improve localization accuracy while saving a great amount of the calibration cost, compared with the other existing methods in a representative indoor environment.

Inadequate availability of organs and tissues for transplantation is a major problem. While offering an alternative, development of engineered tissue constructs containing a functional vascular network that allows delivery of nutrients and especially O2 remains a challenge. Using native vasculature, especially microvasculature, of a tissue or an organ offers potential to overcome this shortcoming. Decellularized animal organs retain the extracellular matrix (ECM) scaffold and native vascular networks but are challenging to source. A low cost, more readily available, and consistent source of tissues and organs is required. We investigated a plant scaffold model that would mimic input (arterial)-output (venous) flow by grafting two Aptenia cordifolia leaves together with opposite facing petioles. The abaxial epidermis of the top leaf and the adaxial epidermis of the bottom leaf were removed; wounds were soaked for 20 s in 0.1-mg L−1 naphthaleneacetic acid (NAA) and 1-mg L−1 benzylaminopurine (BAP) before horizontally appressing the wounded side of each leaf together. Leaves were decellularized 2 to 4 wk after grafting, lyophilized, and stored at room temperature. Prior to use, grafts were sterilized by ethylene oxide and rehydrated. Structural connections between the leaves were visualized histologically in thin sections using hematoxylin and eosin (H&E) and toluidine blue stains. Ponceau Red dye and red blood cells were perfused into the grafted leaves through one petiole of one leaf after decellularization to observe flow from input petiole into the first and then the second of the grafted leaves prior to exiting via the output petiole. Grafts were recellularized with the green fluorescent protein (GFP) expressing human breast cancer line MDA-MB231 and cell attachment and morphology observed 7 d post cell seeding. Results suggest that these grafted leaves provided an input-output cell-compatible vascularized scaffold as a possible bioscaffold for engineering tissues for transplantation.

To rationally design efficient and cost-effective electrocatalysts, a simple but efficient strategy has been developed to directly anchor prussian blue analogue (PBA) nanocubes on cobalt hydroxide nanoplates (PBA@Co(OH)2 ) via the in-situ interfacial precipitation process. Subsequently, the thermal treatment in the presence of sodium hydrogen phosphite enabled the successful transition into metal phosphides with the hierarchical cube-on-plate structure. When used as electrocatalytsts, the obtained bimetal phosphides exhibited good bifunctional electrocatalytic activities for hydrogen and oxygen evolution reactions with good long-term stability. Thus, an enhanced performance for overall water splitting can be achieved, which could be ascribed to the hierarchical structure and favorable composition of as-prepared bimetal phosphide for rapid electron and mass transfer. The present study demonstrates a favorable approach to modulate the composition and structure of metal phosphide for enhancing the electrocatalytic ability toward water splitting.

Since vortex is an important flow structure and has significant influence on numerous industrial applications, vortex extraction is always a research hotspot in flow visualization. This paper presents a novel vortex extraction method by employing a machine learning clustering algorithm to identify and locate vortical structures in complex flow fields. Specifically, the proposed approach firstly chooses an objective, physically based metric that describes the vortex-like behavior of intricate flow and then normalizes the metric for applying on different flow fields. After that, it performs the clustering on normalized metric to automatically determine vortex regions. Our method requires relatively few flow variables as inputs, making it suitable for vortex extraction in large-scale datasets. Moreover, this approach detects all vortices in the flow simultaneously, thereby showing great potential for automated vortex tracking. Extensive experimental results demonstrate the efficiency and accuracy of our proposed method in comparison with existing approaches.

Co encapsulated in N-doped carbon nanotubes (Co-NCNT) catalysts are of high interest as bifunctional electrocatalyst material for both efficient oxygen evolution and reduction (OER/ORR) in applications of rechargeable metal-air batteries. Up to now, the role played by the functional metal species in OER/ORR is still insufficiently understood. The main focus of our research is to shed light on the mechanistic role of the Co species that serve as active sites in the bi-functional Co-NCNT catalysts. It is found that S700 exhibits an outstanding OER/ORR activity. We thus hypothesize that CoII and CoIII clusters predominately function as active sites in the OER and ORR processes, respectively. Furthermore, OER/ORR activity for Co-NCNT catalyst primarily correlates to eg occupation. A near-unity occupancy of the eg orbital of S700 is revealed to be the cause for the maximum intrinsic OER/ORR activity, which provides guidelines for the design of highly active catalysts.

The conversion of carbon dioxide into valuable organic compounds is a highly promising approach to address the energy issues and environmental problems (e.g., global warming). Herein, we presents a facile and efficient method to prepare highly dense and well-dispersed SnO2 nanocrystals on 1D N-doped carbon nanowires as advanced catalysts for the efficient electroreduction of CO2 to formate. The ultrasmall SnO2 coated on the N-doped carbon nanowires (SnO2@N-CNW) has been synthesized via the simple hydrothermal treatment coupled with a pyrolysis process. The unique structure enables to expose the active tin oxide and also provides the facile pathways for rapid transfer of electron and electrolyte along with the highly porous carbon foam composed with interconnected carbon nanowires. Therefore, SnO2@N-CNW electrocatalyst exhibits good durability and high selectivity for formate formation with a Faradaic efficiency of ca. 90%. This work demonstrates a simple method to rationally design high-dense tin oxide nanocrystals on the conductive carbon support as advanced catalysts for CO2 electroreduction.

Vortex identification methods have been extensively studied in recent years due to their importance in understanding the potential physical mechanism of the flow field. Although demonstrating great success in various scenarios, these methods cannot achieve a compromise between computational speed and accuracy, which restricts their usage in large-scale applications. In specific, local methods provide results rapidly with pool accuracies. By contrast, global methods can obtain reliable results by consuming much more time. To take the advantages of both local and global methods, several methods based on convolutional neural networks are proposed. These methods use local patches around each point and the labels obtained by global methods to train the network. They convert the vortex identification tasks into binary classification problems. In this manner, these methods detect vortices rapidly and robustly. By revisiting these methods, we observe two drawbacks that limit their performance: (i) the large number of parameters and (ii) high computational complexity. To address these issues, we provide a rapid vortex identification method by using a fully convolutional segmentation network in this work. Specifically, we discard the fully connected layers to decrease the number of parameters and design a segmentation network to reduce computational complexity. Intensive experimental results show that the accuracy and recall performance of our method are comparable with those of the global methods. Moreover, the time consumption of our method is less than that of all other methods.

Exploring cheap and efficient bifunctional electrocatalysts is highly desirable but challenging for sustainable production of hydrogen via electrocatalytic water splitting. Herein, a versatile strategy was demonstrated to controllably synthesize a family of metal ion-chelated tannic acid coatings on diverse metal hydroxide nanoarchitectures via the interfacial coordination assembly process. Typically, the interfacial coordination of tannic acid with a range of metal ions (e.g., Fe3+, Co2+, and Ni2+) enables the in situ formation of metal–ligand networks on nickel hydroxide nanowalls and other hierarchical substrates. Importantly, the obtained hierarchical coordination complex showed that its bifunctional electrocatalytic activity toward the oxygen evolution reaction and hydrogen evolution reaction is highly dependent on the chelated metal ions. The coordination coating of tannic acid with iron ions on nickel hydroxide nanowalls significantly enhanced the bifunctional electrocatalytic activity and achieved maximal activity toward overall water splitting with good long-term durability. This interfacial coordination assembly coating strategy is facile yet efficient, opening a new avenue for rational modulation of the surface properties of metal (hydro)oxides for promising applications.

Abstract: Cysteine (Cys) is a semi-essential nutrient amino acid that plays an important role in cells through endogenous production and various transport systems. Intracellular Cys can be used as a precursor of protein synthesis to maintain cell homeostasis and to generate sulfur-containing substances, including glutathione (GSH), hydrogen sulfide (H2S), and taurine. There have been quite a few reports that Cys is related to tumor occurrence and development, and its level is closely related to tumor proliferation, invasion, and metastasis. Moreover, it helps in maintaining the tumor redox balance and increasing drug resistance. This review aims to summarize the production and metabolism of Cys and its role in tumors, with special emphasis on the potential therapeutic value of Cys in tumors to improve the quality of life of cancer patients.