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Abstract Meeting the power demand from the transmission system operator is an important objective for power dispatch, which introduces a power supply-demand equality constraint coupling all the wind turbines among the wind farm into the optimization problem. For a large-scale wind farm, processing the global equality constraint in a centralized or distributed framework is time-consuming and computationally complex. Here we considered the fast and localized execution issue of the power optimal dispatch problems. A completely decentralized dynamic system was designed to optimize power flow while satisfying the electricity supply constraints. A voltage optimization problem with the global power constraints was decoupled into local wind turbine controllers based on the node-dependence nature, which is an inherent characteristic of wind farms and was fitted to the power sensitivity matrix in this paper. The local optimization problem was solved iteratively using the gradient projection method, and the system converged linearly to the equilibrium point. The simulations for the case studies performed in Simulink demonstrate that the proposed method achieves a near-global optimal performance using only local measurements.

Abstract Myeloid-derived growth factor (MYDGF) is a novel secreted protein with potent antiapoptotic and tissue-repairing properties that is present in nearly 140 human tissues and cell lines, with the highest abundance in the oral epithelium and skin. Initially, MYDGF was found in bone marrow-derived monocytes and macrophages for cardioprotection and repair after myocardial infarction. Subsequent studies have shown that MYDGF plays an important role in other cardiovascular diseases (e.g., atherosclerosis and heart failure), metabolic disorders, renal disease, autoimmune/inflammatory disorders, and cancers. Although the underlying mechanisms have not been fully explored, the role of MYDGF in health and disease may involve cell apoptosis and proliferation, tissue repair and regeneration, anti-inflammation, and glycolipid metabolism regulation. In this review, we summarize the current progress in understanding the role of MYDGF in health and disease, focusing on its structure, function and mechanisms. The graphical abstract shows the current role of MYDGF in different organs and diseases (Fig. 1).

Lithium‐excess cation‐disordered rocksalt materials are a promising class of transition metal‐based cathodes that exhibit high specific capacity and energy density. The exceptional performance is achieved through participation of anionic redox in addition to cationic redox reactions in the electrochemistry. However, anionic redox reactions accompanied by oxygen evolution, accelerated electrolyte breakdown, and structural evolution lead to voltage hysteresis and low initial Coulombic efficiency. Herein, an Al2O3 layer with varying thickness has been coated onto a high‐entropy disordered rocksalt oxyfluoride cathode through atomic layer deposition to enhance battery performance. The results indicate that the utilization of a uniform Al2O3 coating improves the capacity retention and rate capability of the cathode, with the performance being strongly dependent on the layer thickness. Further investigation into cathode–electrolyte interfacial reactions reveals that the thin protecting Al2O3 coating can reduce the decomposition of electrolyte on the cathode surface but cannot prevent bulk phase degradation during prolonged cycling. These findings highlight the need for optimized coating design on the disordered rocksalt cathode to improve battery performance.

Inspecting sewage outfall into rivers is significant to the precise management of the ecological environment because they are the last gate for pollutants to enter the river. Unmanned Aerial Vehicles (UAVs) have the characteristics of maneuverability and high-resolution images and have been used as an important means to inspect sewage outfalls. UAVs are widely used in daily sewage outfall inspections, but relying on manual interpretation lacks the corresponding low-altitude sewage outfall images dataset. Meanwhile, because of the sparse spatial distribution of sewage outfalls, problems like less labeled sample data, complex background types, and weak objects are also prominent. In order to promote the inspection of sewage outfalls, this paper proposes a low-attitude sewage outfall object detection dataset, namely UAV-SOD, and an attention-guided feature enhancement network, namely AFENet. The UAV-SOD dataset features high resolution, complex backgrounds, and diverse objects. Some of the outfall objects are limited by multi-scale, single-colored, and weak feature responses, leading to low detection accuracy. To localize these objects effectively, AFENet first uses the global context block (GCB) to jointly explore valuable global and local information, and then the region of interest (RoI) attention module (RAM) is used to explore the relationships between RoI features. Experimental results show that the proposed method improves detection performance on the proposed UAV-SOD dataset than representative state-of-the-art two-stage object detection methods.

Abstract Ultrasound is an acoustic wave which can noninvasively penetrate the skull to deep brain regions, enabling neuromodulation. However, conventional ultrasound’s spatial resolution is diffraction-limited and low-precision. Here, we report acoustic nanobubble-mediated ultrasound stimulation capable of localizing ultrasound’s effects to only the desired brain region in male mice. By varying the delivery site of nanobubbles, ultrasound could activate specific regions of the mouse motor cortex, evoking EMG signaling and limb movement, and could also, separately, activate one of two nearby deep brain regions to elicit distinct behaviors (freezing or rotation). Sonicated neurons displayed reversible, low-latency calcium responses and increased c-Fos expression in the sub-millimeter-scale region with nanobubbles present. Ultrasound stimulation of the relevant region also modified depression-like behavior in a mouse model. We also provide evidence of a role for mechanosensitive ion channels. Altogether, our treatment scheme allows spatially-targetable, repeatable and temporally-precise activation of deep brain circuits for neuromodulation without needing genetic modification.

Abstract Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However, traffic data generated daily are vast and complex, involving dynamic and intricate changes in the traffic road network and traffic flow. Therefore, real-time and accurate prediction of traffic flow is a challenging task that requires modeling the intricate spatial–temporal dynamics of traffic data. In this paper, we propose a novel approach for traffic flow prediction, based on a Multi-Scale Residual Graph Convolution Network with hierarchical attention. First, we design a novel encoder–decoder with multi-independent channels to capture traffic flow information from different time scales and diverse temporal dependencies. Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial–temporal dependencies and accurately predict traffic flow. We evaluate the proposed approach on multiple benchmark datasets, and the experimental results demonstrate its superior performance compared to state-of-the-art approaches in terms of various metrics.

Abstract Green Infrastructure (GI) has garnered increasing attention from various regions due to its potential to mitigate urban heat island (UHI), which has been exacerbated by global climate change. This study focuses on the central area of Fuzhou city, one of the “furnace” cities, and aims to explore the correlation between the GI pattern and land surface temperature (LST) in the spring and autumn seasons. The research adopts a multiscale approach, starting from the urban scale and using urban geographic spatial characteristics, multispectral remote sensing data, and morphological spatial pattern analysis (MSPA). Significant MSPA elements were tested and combined with LST to conduct a geographic weighted regression (GWR) experiment. The findings reveal that the UHI in the central area of Fuzhou city has a spatial characteristic of “high temperature in the middle and low temperature around,” which is coupled with a “central scattered and peripheral concentrated” distribution of GI. This suggests that remote sensing data can effectively be utilised for UHI inversion. Additionally, the study finds that the complexity of GI, whether from the perspective of the overall GI pattern or the classification study based on the proportion of the core area, has an impact on the alleviation of UHI in both seasons. In conclusion, this study underscores the importance of a reasonable layout of urban green infrastructure for mitigating UHI.

Abstract Accurate traffic flow prediction information can help traffic managers and drivers make more rational decisions and choices. To make an effective and accurate traffic flow prediction, we need to consider not only the spatio-temporal dependencies between data, but also the temporal correlation between data. However, most existing methods only consider temporal continuity and ignore temporal correlation. In this paper, we propose a multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation (LSTSC). In the model, we employed attention mechanisms to capture the spatio-temporal correlations of the sequences, and the model based on multiple decision forms demonstrated higher accuracy and reliability. The superiority of the model is demonstrated on two datasets, PeMS08 and PeMSD7(M), particularly for long-term predictions.

Aiming at the problem of low degree of exploration and research of geothermal geological conditions in the Shifugou area, which seriously restricts the development and utilization of geothermal resources, on the basis of available data, the geothermal geological conditions and the characteristics of the thermal reservoir in the Lanzhou faulted basin are summarized. Combined with the exploration results of the controlled source audio frequency magnetotelluric (CSAMT) method, the potential of geothermal resources development in the Shifogou area in the southern part of the basin is inferred. The results show that (1) the geothermal geological conditions in the Lanzhou faulted basin are obviously different. Taking the buried faults of Shengouqiao (F5) and Leitai River (F8) as the boundary, respectively, the faulted basin can be divided into the western Xigu district, central Qilihe district and eastern Chengguan-Shifogou district. (2) The geothermal temperature increases from west to east, and increases vertically from the depth of 2000 to 2500 m, inferring that this is the basic depth of geothermal exploitation in the Lanzhou faulted basin. (3) In the southern section of the eastern part may exist layered-conductive geothermal resources and fault-zone geothermal resources, it is inferred that there may exist “combined type” of layered and banded geothermal water in the Shifogou district, which is located in the F2 fracture zone or the intersection of F2 fracture zone and F8 fault zone. It is suggested that the exploration and production well depth of geothermal water in the faulted-zone thermal reservoir should be considered as 800−1100 m, and that the exploration and production well depth of the “combined” layered thermal reservoir should be considered as 1500−2000 m. The wellhead geothermal water temperature in this area is expected to reach 50−60 °C, the single well yield can reach 500−1000 m3/d, and the quality of the geothermal water can reach the standard for medical bathing. The results can provide a basis for geothermal resources exploration in Lanzhou.

Urban landscape patterns significantly impact land surface temperature (LST) and the urban heat island (UHI) effect. This study employs the boosted regression tree (BRT) model and variance partitioning analysis to examine the contributions and relationships of two-dimensional and three-dimensional building and vegetation patterns to LST, and their marginal effects at different heights. The results show that the dominant indicators affecting LST differ between buildings and vegetation, with three-dimensional building features being slightly more important than two-dimensional features (percentage of landscape of buildings) and two-dimensional vegetation features (three-dimensional green index) having a greater impact than three-dimensional features. When both buildings and vegetation are considered, building patterns still have significant explanatory power. Building height differences influence each indicator’s contribution and marginal effects on LST, with lower-height areas seeing a joint dominance of buildings and vegetation on LST changes, and higher-height areas showing greater impact from vegetation indicators. Increasing the percentage of landscape of vegetation (PLAND_V) provides the best cooling effect in lower-building-height areas, but in higher-building-height areas, the cooling effect weakens, requiring additional vegetation indicators to assist in cooling.

Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment.

The thermal effect mainly includes boundary temperature stratification and the local thermal effect. The combined effect of these factors on flow and dispersion in a bi-dimensional canyon was investigated by the RANS and LES methods to evaluate their performance. The results, including the flow field, turbulent kinetic energy, temperature, heat flux, pollutant concentration and fluxes, were compared with the data from wind tunnel experiments. The comparison results showed that the RANS method severely overestimated the impact of windward heating on the flow in the canyon because of the lack of simulated flow separation ability and the limitation of the Boussinesq model, leading to an incorrect flow field and an incorrect temperature and concentration. In contrast, LES performed better mainly because of its ability to simulate flow separation. LES regenerated the right vortexes, flow field and low wind velocity. LES slightly overestimates the overall temperature in the canyon because heat exchange is eliminated in LES but difficult to avoid in the experiment. The difference in the air exchange rate at the roof level between the LES and wind tunnel data was no more than 5%, and the pollutant concentration distribution of the LES was almost the same as that of the experiments. This work emphasizes that the RANS method has limited ability to simulate flow and dispersion when the thermal effect is considered even at a reduced-scale, while LES can simulate the combined effects of incoming flow temperature stratification and local thermal effects. It is therefore suggested that if computing resources are limited and the temperature difference is not large, a steady-state calculation RANS can be used. Otherwise, LES must be performed.

Abstract Background Early identification of intravenous immunoglobulin (IVIG) resistance contributes to better management of Kawasaki disease (KD). This study aims to establish an effective prediction model for IVIG resistance in the Chinese population. Methods A total of 658 eligible patients diagnosed with KD were enrolled in this study, with 461 in the training cohort and 197 in the validation cohort. The demographic characteristics and potential risk factors were compared between IVIG-responsive and resistant groups. Predictors were selected by the Akaike information criterion. The nomogram’s performance was evaluated by calibration curve, decision curve analysis, and operating characteristic curve. Results White blood cell counts (WBC), neutrophil-lymphocyte ratio (N/L ratio), hematocrit (HCT), albumin (ALB), total bilirubin (TBIL), lactate dehydrogenase (LDH), and creatinine (Cr) were detected as predictors of IVIG resistance. A predictive nomogram incorporating these predictors was constructed using the training cohort. The calibration curve and decision curve analysis showed good discrimination and calibration of the proposed nomogram in both training and validation sets, and the area under the receiver operating characteristic curve (AUROC) in both sets was 75.8% and 74.2%, respectively. Conclusion This study identified WBC, N/L ratio, HCT, ALB, TBIL, LDH, and Cr as predictors for IVIG resistance in patients with KD. The proposed novel nomogram with a high level of accuracy and reliability may benefit clinical decision-making upon treatment initiation.

Abstract Gear power-honing is mainly applied to finish small and medium-sized automotive gears, especially in new energy vehicles. The distinctive curved surface texture greatly improves the noise emission and service life of honed gears. The surface texture for honed gear considering the motion path and geometrical shape of abrasive particles has not been investigated. In this paper, the kinematics of the gear honing process is analyzed, and the machining marks produced by the abrasive particles of honing wheel scratching abrasive particles against the workpiece gear are calculated. The tooth surface roughness is modeled considering abrasive particle shapes and material plastic pile-ups. This results in a mathematical model that characterizes the structure of the tooth surface and the orientation of the machining marks. Experiments were used to verify the model, with a maximum relative error of less than 10% when abrasive particles are spherical. Based on this model, the effects of process parameters on the speeds of discrete points on the tooth flank, orientations of machining marks and roughness are discussed. The results show that the shaft angle between the workpiece gear and the honing wheel and the speed of the honing wheel is the main process parameters affecting the surface texture. This research proposes a surface texture model for honed gear, which can provide a theoretical basis for optimizing process parameters for gear power-honing.

Geological remote sensing interpretation (GRSI), which aims to recognize multiple geological elements based on their characteristics on satellite remote sensing images, is vital in large-scale regional lithological mapping. However, due to the influence of long-term geological movements, the spatial distribution of geological elements (such as lithology, glaciers, and soils) on the image is often complex and highly fragmented. In addition, the characteristics of high inter-class similarity and severe homogenization make the annotation of geological element samples require significant cost and expert knowledge. These lead to insufficient interpretation accuracy and limited annotation samples in GRSI. To alleviate the dependence of labeled samples and promote the performance of GRSI, we propose an adversarial semi-supervised segmentation network with object-context and global-attention (AdvSemi-OCGNet) for the GRSI, which achieves effective segmentation results in the case of limited labeled samples. Under the architecture of adversarial learning, a proposed baseline network OCGNet and a full convolution discriminator (FCD) are integrated to conduct semi-supervised segmentation. OCGNet, as the generator, aims to confuse FCD by predicting probability maps. Then FCD selects trustworthy regions with high-confidence of unlabeled sample to generate pseudo-labels for semi-supervised segmentation of OCGNet. Iterative adversarial learning is employed to simulate the process of expert interpretation of geological elements through continuous discrimination and correction. Finally, a fully connected conditional random field eliminates holes and isolated areas of segmented results caused by misclassification. Two study areas are selected, which include various geological elements such as multiple lithologies, soils, rock glaciers, and surface water. Numerous experiments have revealed the superiority of the proposed model in GRSI with limited annotation samples.

Though high entropy oxides have been explored as possible conversion-type negative electrodes for Li-ion batteries, the roles of the different elements remain unclear. Here the authors determine the behavior of each element during electrochemical cycling and connect it to the nanoscale structure.

Abstract Cell-in-cell structures (CICs) refer to a type of unique structure with one or more cells within another one, whose biological outcomes are poorly understood. The present study aims to investigate the effects of CICs formation on tumor progression. Using genetically marked hepatocellular cancer cell lines, we explored the possibility that tumor cells might acquire genetic information and malignant phenotypes from parental cells undergoing CICs formation. The present study showed that the derivatives, isolated from CICs formed between two subpopulations by flow cytometry sorting, were found to inherit aggressive features from the parental cells, manifested with increased abilities in both proliferation and invasiveness. Consistently, the CICs clones expressed a lower level of E-cadherin and a higher level of Vimentin, ZEB-1, Fibronectin, MMP9, MMP2 and Snail as compared with the parental cells, indicating epithelial-mesenchymal transition. Remarkably, the new derivatives exhibited significantly enhanced tumorigenicity in the xenograft mouse models. Moreover, whole exome sequencing analysis identified a group of potential genes which were involved in CIC-mediated genetic transfer. These results are consistent with a role of genetic transfer by CICs formation in genomic instability and malignancy of tumor cells, which suggest that the formation of CICs may promote genetic transfer and gain of malignancy during tumor progression.

Road extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation. However, accurate road extraction is a challenging task due to factors such as high road density, building occlusion, and complex traffic environments. In this study, a Spatial Attention Swin Transformer (SASwin Transformer) architecture is proposed to create a robust encoder capable of extracting roads from remote sensing imagery. In this architecture, we have developed a spatial self-attention (SSA) module that captures efficient and rich spatial information through spatial self-attention to reconstruct the feature map. Following this, the module performs residual connections with the input, which helps reduce interference from unrelated regions. Additionally, we designed a Spatial MLP (SMLP) module to aggregate spatial feature information from multiple branches while simultaneously reducing computational complexity. Two public road datasets, the Massachusetts dataset and the DeepGlobe dataset, were used for extensive experiments. The results show that our proposed model has an improved overall performance compared to several state-of-the-art algorithms. In particular, on the two datasets, our model outperforms D-LinkNet with an increase in Intersection over Union (IoU) metrics of 1.88% and 1.84%, respectively.

The greening trend in consumer markets and the marketization and financialization of carbon emission rights have begun to revitalize carbon assets. However, solitary efforts and the spillover of environmental protection effects still hamper enterprises’ enthusiasm for carbon emission reduction. To tackle this challenge, two vertical cooperation mechanisms, cost cooperation and alliance cooperation, are proposed. The mathematical models and solutions are developed for both of the two mechanisms, and their values and applicability are explored, respectively. In addition, the impact of fluctuations in consumer markets, financial markets, and carbon markets on cooperation is examined. The results show that both cooperation models effectively motivate enterprises to enhance carbon reduction and boost market demand. However, cost cooperation may result in inflated product prices and even weaken the profitability of the supply chain. In contrast, alliance cooperation can enhance product price performance and effectively increase supply chain profits. Concerning environmental performance, the initial market is better suited for alliance cooperation, whereas cost cooperation fits the mid-to-late market. The higher financing costs of the financial market and the trading price of the carbon market will strengthen the applicability of cost cooperation. This study offers managerial insights for collaborative decision-making in the context of a multi-market cross-section.

The rapid urbanization in China has had a profound impact on religious practices of rural communities, but few study has explored the relevant dynamics. This study provides an overview of urbanization’s impact on rural religious spaces, and compares the changes of religious spaces before and after urbanization. It aims at improving the understanding of religion in urbanized communities. Phenomenological methods were employed to analyze our transcriptions of 16 interviews which were conducted in the religious spaces of 11 urbanized communities in Jiangsu, China. We also investigated the worship circle mentioned by the participants through field surveys and map analysis. Based on these, four similar themes were sorted out, namely, worship circle and environment, aging and feminizing agents, ritual and secular life, as well as religious well-being. And the results showed that: (1) passive change of worship circles would bring new sense of belonging and identity to elderly residents; (2) the new religious spaces contained a lot of local knowledge and traditional characteristics, which were evolving continuously with sites sacredness maintenance and secularization function expansion; (3) female agents were of great importance to religious spaces preservation.

【Objective】The study aims to improve the utilization rate of waste Xinhui citrus flesh and explore the technical feasibility of producing bacterial cellulose (BC).【Method】Taking Xinhui citrus juice as raw material and Kombucha as fermentation strain, the study investigated the effects of such factors as enzymatic pretreatment, citric acid addition, fermentation time and pH on the production rate of BC. Meanwhile, the study also explored the water content and rehydration rate of BC membrane, and the characteristics of infrared absorption spectrum, X-ray diffraction and scanning electron microscopy.【Result】The average values of Brix and pH of fresh citrus juice were 10.9% and 3.9, respectively. The results of single factor test showed that the production rate of BC failed to be improved by adding citric acid or adopting enzymatic pretreatment. The highest BC production rate could be obtained by using original citrus juice as raw material and adjusting pH to 6.0 for 15 days. Besides, the fiber diameter of BC membrane was between 80 nm and 400 nm, its crystallinity was above 80%, and it could be used to produce biological fiber material with good mechanical properties. Enzymatic pretreatmen had a great impact on the microstructure of BC membrane. The BC membrane produced by original citrus juice was mainly composed of fine fibers. However, under the influence of enzymatic pretreatment, the microstructures of BC membrane was spherical, square or in a composite state of prismatic fiber and fine fiber.【Conclusion】BC produced from Xinhui citrus juice showed good rehydration and high crystallinity, and the maximum production rate reached 2.25 g/L in single factor test. Therefore, Xinhui citrus juice can be used as raw material for bacterial cellulose production.

Agricultural heritage is a fundamental aspect of cultural heritage, as it is of significant cultural, economic, and biological value. The objective of this study is to reveal the spatial distribution and change of the Chinese Agricultural Heritage Site (CAHS) by means of spatial analysis. The results show that: (1) The distribution of 532 CAHSs (7 types) is patchy, and the horticulture system is gaining the quantitative advantage (55.26%), (2) The distribution of CAHSs shows the spatial aggregation of “dense in the east and sparse in the west.” The areas of Beijing, Shandong, Zhejiang, and their surroundings form the densest belt region of the heritages, and 44.17% of the CAHSs are distributed in this belt, (3) Single factor analysis shows that culture, population, soil, and elevation are the most critical driving forces that influence the distribution (all explanatory power > 0.20). Two factors interaction analysis confirms that the distribution is influenced by the enhancement of both natural and human factors. Of these, a high explanatory power (>0.70) is related to the matching between culture factors, population factors, and geographic factors (i.e., elevation, soil, and river). This study has the potential to provide scientific evidence for CAHS assessment, protection, and sustainable development.

BackgroundWe explored the prognostic and immunogenic characteristics of iron pendant disease regulators in colon cancer to provide a scientific basis for the prediction of tumor prognosis-related markers and potential immunotherapeutic drug targets.MethodsRNA sequencing and matched complete clinical information of colon cancer (COAD) were retrieved from the UCSC Xena database, and genomic and transcriptomic data of colon cancer from the TCGA database were downloaded. Then univariate and multifactorial Cox regression were used to process these data. The prognostic factors were analyzed by single-factor and multi-factor Cox regression, followed by Kaplan-Meier survival curves with the aid of R software “survival” package. Then we use FireBrowse online analysis tool to analyze the expression variation of all cancer genes, and draw a histogram according to the influencing factors to predict the 1, 3, and 5 year survival rates of patients.ResultsThe results show that age, tumor stage and iron death score were significantly correlated with prognosis (p

High Mountain Asia (HMA) is characterized by some of the most complex and rugged terrain conditions in the world. However, high resolution terrain data are not easy to quickly acquire from the area due to difficulties in accessing the region. In this study, we trained a modified super-resolution residual network (MSRResNet) to develop super-resolution (SR) digital elevation models (DEMs) in the HMA areas by using freely available DEM data from the HMA region and limited high resolution (HR) DEMs from other areas to train the model. In this network, a new loss function was constructed that considered the terrain parameters of slope and curvature to constrain the network learning and convergence. The proposed method was applied to and validated by data from the Hengduan Mountains in the southeastern part of HMA, which is a world-famous longitudinal belt of mountains and canyons. A comparative analysis between the current and existing methods (i.e., SRGAN and Bicubic interpolation) was conducted to assess the effectiveness of the proposed approach. The experimental results were also investigated and evaluated by visual inspection and analysis of the terrain parameters. The results demonstrate that the proposed MSRResNet super-resolution process can achieve highly accurate terrain data by downscaling DEMs in HMA. This SR process also outperforms the other comparable methods. Compared to the Bicubic interpolation method, the RMSE and MAE accuracy are improved by 32.17% and 33.97%, and compared to the SRGAN method, the RMSE and MAE accuracy are improved by 39.15% and 32.47%. The HR DEM generated by the new method is more conducive to improving the accuracy of extracted terrain features, such as stream networks. It is promising to apply this model on other areas on Earth or even other planets with terrain similar to that of HMA.

Summary: The precise sequence control of polymer chain is an important research topic of polymer chemistry. Although some methods such as iterative synthesis and supramolecular polymerization have been developed to fabricate sequence-controllable polymer, it is still a great challenge to consecutively prepare multiple supramolecular polymers with different sequence structures. In this work, through the reasonable utilization of assembly motifs, we integrated multiple host-guest recognitions and metal coordination interactions to prepare different sequence-controlled supramolecular polymers by a multistep assembly strategy. This research provides inspiration for the design and preparation of supramolecular polymers with different sequence structures.

Summary: Microglia are the brain’s resident immune cells, performing surveillance to promote homeostasis and healthy functioning. While microglial chemical signaling is well-studied, mechanical cues regulating their function are less well-understood. Here, we investigate the role of the mechanosensitive ion channel Piezo1 in microglia migration, pro-inflammatory cytokine production, and stiffness sensing. In Piezo1 knockout transgenic mice, we demonstrated the functional expression of Piezo1 in microglia and identified genes whose expression was consequently affected. Functional assays revealed that Piezo1 deficiency in microglia enhanced migration toward amyloid β-protein, and decreased levels of pro-inflammatory cytokines produced upon stimulation by lipopolysaccharide, both in vitro and in vivo. The phenomenon could be mimicked or reversed chemically using a Piezo1-specific agonist or antagonist. Finally, we also showed that Piezo1 mediated the effect of substrate stiffness-induced migration and cytokine expression. Altogether, we show that Piezo1 is an important molecular mediator for microglia, its activation modulating microglial migration and immune responses.

Capital constraints hinder enterprises' carbon reduction efforts and affect the sustainability of the supply chain. To alleviate this limitation, the core enterprise considers offering two financial-based carbon reduction incentive mechanisms: cost-sharing mechanism (CS) and preferential financing mechanism (PF). In a supply chain with the dual sensitivity of market demand to price and carbon reduction, we model each incentive mechanism, discussing their impact, value, and selection strategies. The results show that neither party under CS pursues an excessively high share ratio. Only a below-threshold sharing ratio can promote the supplier's carbon reduction behavior and improve efficiency for both parties. Conversely, PF has a stable incentive effect on the supplier's carbon reduction behavior and can effectively increase the retailer's profits. However, a reasonable carbon reduction standard is needed to attract the supplier. In addition, as market demand becomes more sensitive to carbon reduction, the feasible range of CS narrows and that of PF expands. We compare players' preferences of PF and CS and find a Pareto region in which all players prefer PF to CS. Finally, we test the robustness of our findings by an extending model. Our study provides guidance for supply chain decisions facing dual pressures of financial constraints and carbon reduction.

Forecasting traffic flow is significant for intelligent transportation systems (ITS), such as urban road planning, traffic control, traffic planning, and many more. A flow prediction model aims at forecasting the traffic flow of future time slices at certain regions by learning the historical traffic flow data and environmental information. However, due to the complicated traffic network topology and the dynamicity of traffic patterns in the real world, it is difficult to capture the multi-level spatial dependencies (e.g. global and local impacts to the traffic) and temporal dependencies (e.g. long-term and short-term impacts to the traffic). In this paper, we propose a Multi-step Coupled Graph Convolution Neural network (MCGCN) with temporal attention to capture the spatial and temporal dependencies of different levels in a traffic network, simultaneously, to predict traffic flow. First, a Multi-step Coupled Graph Convolution module (MCGC) is designed to learn the representation of a traffic network by coupling learning the relationship matrices, to capture the different levels’ information of a traffic network. Then, the traffic network information extracted by MCGC is fed into a Multi-step Coupled Graph Gated Recurrent Unit (MCGRU) module to realize the fusion of traffic network information and temporal features. Finally, a Multi-step Coupled Graph Attention mechanism (MCGCAtt) is used to extract the temporal information of historical time steps to predict the future traffic flow. The experiments are conducted on the NYCTaxi and NYCBike datasets, and the evaluation results demonstrate that our proposed model performs better than the nine compared methods.

In this paper, a new method for pesticide detection based on porous silicon microcavities was proposed. First, the aptamer of acetamiprid was fixed in the functionalized porous silicon microcavity. Then, the complementary strand of the aptamer modified with graphene quantum dots was hybridized with the aptamer, and the effective refractive index of the porous silicon microcavity increased. When acetamiprid was added, it bound to the aptamer due to its stronger specificity. The aptamer separated from the complementary strand of the coupled graphene quantum dots and bound to acetamiprid, resulting in a decrease in the effective refractive index, and its central wavelength of reflection spectrum was blue shifted, and the blueshift increased with the increase of acetamiprid concentration. The relationship between the change in central wavelength and the concentration of acetamiprid was analysed, and the detection limit of acetamiprid was 151 nm.

Abstract Multi‐tap and multi‐capacitors type arc suppression coils are commonly used in distribution systems. Due to their low compensation accuracy and limited arc suppression capability, it is hardly to avoid personal safety accidents and wildfires absolutely. In this paper, a novel single‐phase grounding fault voltage full compensation topology based on antiphase transformer is proposed, which is composed by an antiphase transformer, a phase‐selection switch and a multi‐tap single‐phase voltage regulator. The proposed topology can control the neutral point voltage, thus cause the fault voltage being zero. By this way, the lack of arc suppression coil is overcome. The novel compensation topology features low costs, high precision of fault voltage compensation. The principle of the novel compensation topology is explained. The mathematical expressions of residual voltage and current are derived in detail. Finally, the simulation model and experimental platform were built. The results show that the proposed novel compensation topology can effectively make up for the shortcomings of traditional arc suppression coils when it is used with them.

In the digital economy era, as a new production factor, digital technology can break through the information blocking constraints on farmers’ behavior, become an important driving force affecting farmers’ low-carbon production behavior, and provide new opportunities for agricultural green low-carbon transformation and the realization of carbon emission peak and carbon neutrality goals. Based on the field survey data of 571 farm households in Jiangsu Province, China, this paper employs the Ordered Probit model and the mediating effect test method, and takes risk cognition as the mediating variable to empirically analyze the impact of digital technology use on farmers’ low-carbon production behavior and its path. The study results show that environmental risk cognition, health risk cognition, agricultural product safety risk cognition, and pesticide residue risk cognition have a significant positive impact on farmers’ low-carbon production behavior. Study findings also reveal that, on one side, the use of digital technology has a direct effect on the farmers’ low-carbon production behavior, on the other side, it indirectly affects the farmers’ low-carbon production behavior by affecting their risk cognition, that is, risk cognition plays a partial mediation role in this relationship. Furthermore, the results indicate that among the control variables, joint cooperative membership, food security knowledge, agricultural income, technical guidance, and following instructions have a significant and positive impact on farmers’ low-carbon production behavior. Based on study findings, the variable “number of the labor force” has a negative and significant impact on farmers’ low-carbon production behavior. Based on study findings, to effectively realize carbon emission peak and carbon neutrality goals and promote sustainable and high-quality agriculture development, agricultural policy makers should pay attention to the role of digital technology to actively promote low-carbon production behavior.

Parvimonas micra is an anaerobic Gram-positive coccus frequently found in the oral cavity and gastrointestinal tract, but rarely in the lung. Therefore, pneumonia caused by P. micra is also rare. Although there are some reports of P. micra related pneumonia due to aspiration or blood-borne infection with definite remote infection source, there are no reported cases of hematogenous P. micra pneumonia in healthy adults lacking a remote source of infection. Herein, we described the intact disease of P. micra-related pneumonia mimicking hematogenous Staphylococcus aureus pneumonia in terms of chest imagery and diagnosed via metagenomic next-generation sequencing (mNGS). Interestingly, there was no clear remote pathogenic source identified in the patient. Microbiome analysis revealed dysbiosis of the oral flora possibly related to poor oral hygiene and a long history of smoking. The patient was treated with moxifloxacin for 3 months. Ultimately, computed tomography (CT) of the chest showed total resolution of the lung lesion. Clinicians need to update the etiology of community-acquired pneumonia. When antibiotic therapy is not effective, pathogen examination becomes very important. New methods of pathogen detection such as mNGS should be employed to this end. For the treatment of P. micra pneumonia, no standardized course of treatment was reported. Imaging absorption of lung infections may provide a more objective guidance for the duration of antibiotics in P. micra pneumonia.

Gastric cancer is one of the most common malignant cancers globally. Chemotherapy resistance remains a major obstacle in the treatment of gastric cancer, and the molecular mechanisms underlying drug resistance are still not well understood. We previously reported that Zipper interacting protein kinase (ZIPK), also known as death‐associated protein kinase3, exerts an oncogenic effect on gastric cancer via activation of Akt/NF‐κB signaling and promotion of stemness. Here, we explored the roles of ZIPK in cisplatin resistance. We report that ZIPK enhances cell proliferation and invasion and reduces the antitumor activity of cisplatin in gastric cancer. In addition, our western blot data suggest that ZIPK activated the IL‐6/STAT3 signaling pathway. Furthermore, ZIPK increased the expression of IL‐6 and multidrug‐resistance genes. Using the STAT3 inhibitor stattic to block the IL‐6/STAT3 signaling pathway strongly increased the sensitivity of ZIPK‐expressed cells to cisplatin. In conclusion, ZIPK may play a role in cisplatin resistance through activation of the IL‐6/ STAT3 signaling pathway. Inhibition of STAT3 in gastric cancer overexpressing ZIPK might have potential to improve the efficacy of cisplatin.

Streetscape is an important factor that forms the image of a city. The visual value and preference in streetscape images need to be measured by examining the interaction between places and visitors in the real environment. The study applied a method to recognize and evaluate urban streetscape from the perspective of tour bus passengers, and proved its effectiveness in streetscape visual attraction research. In this study, a visitor-employed photography (VEP) survey was conducted along 32 blocks on the No. 1 urban tour bus (with an average speed of 28 km/h) in Suzhou, China; the visual attributes and significance of urban streetscapes were recognized through empirical research on tour bus visitors’ on-site experience. The researcher cultivated the emotional motivations and block distribution captured by 30 respondents through analyzing the types of photos the respondents liked and disliked. The results of this study show that, in general, positive streetscape types tend to be of regional landscape, with cultural and readable emotional motivations. Negative streetscape types tend to be of modern architecture and construction sites, with unreadable and unattractive emotional motivations. The block cluster analysis, based on the average frequency/km of positive and negative streetscape photos, shows that the four block unit clusters were perceived by visitors with deviation between the land use image and the planned land use. The bus VEP method revealed that the visual attractiveness and preferences of streetscape were highly context-specific, rather than for performance.

Abstract Background The role of surgery for the primary tumor in breast cancer patients with bone metastases (BM) remains unclear. The purpose of this study was to determine the impact of surgery for the primary tumor in breast cancer patients with BM and to develop prognostic nomograms to predict the overall survival (OS) of breast cancer patients with BM. Methods A total of 3956 breast cancer patients with BM from the Surveillance, Epidemiology, and End Results database between 2010 and 2016 were included. Propensity score matching (PSM) was used to eliminate the bias between the surgery and non-surgery groups. The Kaplan-Meier analysis and the log-rank test were performed to compare the OS between two groups. Cox proportional risk regression models were used to identify independent prognostic factors. Two nomograms were constructed for predicting the OS of patients in the surgery and non-surgery groups, respectively. In addition, calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the performance of nomograms. Result The survival analysis showed that the surgery of the primary tumor significantly improved the OS for breast cancer patients with BM. Based on independent prognostic factors, separate nomograms were constructed for the surgery and non-surgery groups. The calibration and ROC curves of these nomograms indicated that both two models have high predictive accuracy, with the area under the curve values ≥0.700 on both the training and validation cohorts. Moreover, DCA showed that nomograms have strong clinical utility. Based on the results of the X-tile analysis, all patients were classified in the low-risk-of-death subgroup had a better prognosis. Conclusion The surgery of the primary tumor may provide survival benefits for breast cancer patients with BM. Furthermore, these prognostic nomograms we constructed may be used as a tool to accurately assess the long-term prognosis of patients and help clinicians to develop individualized treatment strategies.

In this paper, based on the gray value change of reflected light image of porous silicon (PSi) Bragg mirror, a fast and simple biological detection method is proposed. In this method, CdSe/ZnS quantum dots (QDs) are used as markers for refractive index amplification, and digital image method is used for detection. The detection light has the same wavelength as the lowest reflectivity of the edge of the Bragg mirror, and the reflected light radiated on the surface of the Bragg mirror is received by the detector. The theoretical simulation results show that the intensity of the reflected light increases with the increase of the refractive index caused by the biological reaction. According to the experimental results, the average gray value variation increases with the increase of the target DNA concentration and becomes a linear relationship in a certain range. Based on this method, the DNA detection limit is 20.74 pM. The method is low-cost, has a short detection time and can be used in the detection of biosensor microarray.

To further improve the sensitivity of porous silicon-assembled microcavity biosensors, the detection of porous silicon assembled microcavity by angle spectrum method is also researched. This contribution uses CdSe/ZnS quantum dot-labelled probe DNA to amplify the refractive index and detect it by the angle spectrum method. The first layer is a microcavity layer, and the next is porous silicon with a Bragg structure. After functionalization, different concentrations of target DNA were coupled to construct quantum dot-labelled probe DNA to bind specifically to the target DNA. With the Bragg device based on quartz glass forming an assembled microcavity structure, using a He-Ne laser as the detection light and collimation beam, the angle spectrum method is used to detect different concentrations of target DNA before and after biological reaction with quantum dot-labelled probe DNA and reflect the angle of minimum light intensity. The experimental results show that with increasing target DNA concentration, probe DNA concentration and angle, the detection limit is 13.25 pM. The angle-spectrum method has been successfully used to detect the assembled porous silicon microcavity. The angle-spectrum method has the advantages of spectrometer free and low cost.

Historical earth observation (EO) data have played an important role in long-term scientific and environmental monitoring. The effective organization of large-scale and long-term remote-sensing data to achieve efficient retrieval and access has become one of the important issues. However, inherent big data characteristics, such as a large scale, and asymmetrical temporal and spatial distributions, have caused problems with the efficiency of data retrieval and access. Therefore, this study proposes an efficient data organization method for use in a cloud-computing environment that has two aims. First, it addresses the problem of low retrieval efficiency. An asymmetrical index model for the image metadata is constructed that is based on a unified spatio-temporal grid coding; a prepartitioning mechanism under the HBase architecture is established to realize the uniform storage of the metadata with an asymmetrical spatiotemporal distribution and to avoid retrieval efficiency bottlenecks caused by a load imbalance. Second, it addresses low access efficiency. By dividing the remote-sensing image into tiles, a unified spatio-temporal code is established for each tile, and a consistent hash operation is performed; tiles with similar hash values are stored in the same or adjacent Hadoop Distributed File System nodes. In this way, tiles with temporal or spatial correlations can be gathered and stored, and lots of disk seeks can be avoided during retrieval, thereby greatly improving the data access efficiency. Comparative experiments showed that the data organization method can effectively improve the retrieval and access efficiencies of large-scale and long time-series remote-sensing data in a cloud-computing environment.

In recent years, with the widespread use of location-aware handheld devices and the development of wireless networks, trajectory data have shown a trend of rapid growth in data volume and coverage, which has led to the prosperous development of location-based services (LBS). Spatio-temporal range query, as the basis of many services, remains a challenge in supporting efficient analysis and calculation of data, especially when large volumes of trajectory data have been accumulated. We propose a Hilbert-GeoSOT spatio-temporal meshing and coding method called HGST to improve the efficiency of spatio-temporal range queries on massive trajectory data. First, the method uses Hilbert to encode the grids obtained based on the GeoSOT space division model, and then constructs a unified time division standard to generate the space–time location identification of trajectory data. Second, this paper builds a novel spatio-temporal index to organize trajectory data, and designs an adaptive spatio-temporal scaling and coding method based on HGST to improve the query performance on indexed records. Finally, we implement a prototype system based on HBase and Spark, and develop a Spark-based algorithm to accelerate the spatio-temporal range query for huge trajectory data. Extensive experiments on a real taxi trajectory dataset demonstrate that HGST improves query efficiency levels by approximately 14.77% and 34.93% compared with GeoSOT-ST and GeoMesa at various spatial scales, respectively, and has better scalability under different data volumes.

Hyperspectral image (HSI) classification is a significant foundation for remote sensing image analysis, widely used in biology, aerospace, and other applications. Convolution neural networks (CNNs) and attention mechanisms have shown outstanding ability in HSI classification and have been widely studied in recent years. However, the existing CNN-based and attention mechanism-based methods cannot fully use spatial–spectral information, which is not conducive to further improving HSI classification accuracy. This paper proposes a new spatial–spectral Transformer network with multi-scale convolution (SS-TMNet), which can effectively extract local and global spatial–spectral information. SS-TMNet includes two key modules, i.e., multi-scale 3D convolution projection module (MSCP) and spatial–spectral attention module (SSAM). The MSCP uses multi-scale 3D convolutions with different depths to extract the fused spatial–spectral features. The spatial–spectral attention module includes three branches: height spatial attention, width spatial attention, and spectral attention, which can extract the fusion information of spatial and spectral features. The proposed SS-TMNet was tested on three widely used HSI datasets: Pavia University, IndianPines, and Houston2013. The experimental results show that the proposed SS-TMNet is superior to the existing methods.

Curvularia trifolii is an important pathogenic fungus that causes leaf spot disease in strawberry and other crops. Increased resistance in pathogenic fungi against chemical fungicides necessitates the search for biological alternatives to control plant fungal diseases. The present study aimed to perform transcriptome and metabolome analysis of C. trifolii fungi. We evaluated the potential of an alkaloid, namely alpha (α)-solanine, to inhibit the growth of Curvularia under in vitro conditions. Furthermore, transcriptomic and metabolomic analysis of treated C. trifolii was performed to identify the differential genes and metabolites. Results revealed that treatment with α-solanine resulted in the poor growth and development of fungal spores. The transcriptome analysis revealed that 1413 genes were differentially expressed (DEGs), among which 340 unigenes were up-regulated, 100 unigenes were down-regulated, and the rest were unaffected in treated samples. Gene ontology analysis revealed that the majority of the genes were related to oxidative stress in the fungus. Additionally, using ultra-high performance liquid chromatography-tandem mass spectrometry, we identified 455 metabolites, among which the majority of metabolites were related to lipid biosynthesis. The high number of genes related to lipid biosynthesis and reactive oxygen species revealed that α-solanine causes oxidative stress in Curvularia, leading to growth inhibition, and can be potentially used as an alternative to chemical fungicides.

The spatial–temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. Due to the complex spatial correlation between different roads and the dynamic trend of time patterns, traditional forecasting methods still have limitations in obtaining spatial–temporal correlation, which makes it difficult to extract more valid information. In order to improve the accuracy of the forecasting, this paper proposes a multi-scale temporal dual graph convolution network for traffic flow prediction (MD-GCN). Firstly, we propose a gated temporal convolution based on a channel attention and inception structure to extract multi-scale temporal dependence. Then, aiming at the complexity of the traffic spatial structure, we develop a dual graph convolution module including the graph sampling and aggregation submodule (GraphSAGE) and the mix-hop propagation graph convolution submodule (MGCN) to extract the local correlation and global correlation between neighbor nodes. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods.

Background/purposes: Treatment of Staphylococcus aureus infections is challenging owing to widespread multidrug resistance. There is now considerable interest in the potential of combination therapies. Although linezolid/fosfomycin combination appears to be a promising treatment option based on in vitro data, further preclinical work is needed. In this study, the Galleria mellonella system was employed to study the in vivo efficacy of this combination in order to determine whether it should be explored further for the treatment of S. aureus infections. Methods: The antimicrobial activity of linezolid and fosfomycin alone and in combination was assessed versus four S. aureus. Synergy studies were performed using the microtitre plate chequerboard assay and time-kill methodology. The in vivo activity of linezolid/fosfomycin combination was assessed using a G. mellonella larvae model. Results: The combination of linezolid and fosfomycin was synergistic and bacteriostatic against four tested strains. Treatment of G. mellonella larvae infected with lethal doses of S. aureus resulted in significantly enhanced survival rates when low-dose of combination has no significant differences with high-dose combination (P > 0.05), G. mellonella hemolymph burden of S. aureus suggest that combination therapy with rapid and sustained bacteriostatic activity compared monotherapy. Conclusion: This work indicated that linezolid combination with fosfomycin has synergistic effect against S. aureus in vitro and in an experimental G. mellonella model, and it suggests that high-dose of linezolid and fosfomycin may not necessary.

AimsIn garlic cultivation, long-time monoculture has resulted in continuous-cropping obstacles. However, the cause has not been studied to date.MethodsWe analyzed soils from garlic fields in Pengzhou, China, to determine continuous-cropping obstacle related changes in soil physicochemical properties and enzyme activities, and in the diversity and composition of bacterial and fungal communities. Furthermore, we examined the relationships between soil properties and the bacterial and fungal communities.ResultsThe soil pH and the soil catalase, urease, invertase, and polyphenol oxidase activities were lower in the cropping obstacle soil than in the healthy control soil. The richness and diversity of the bacteria were lower in the cropping obstacle soil than in the control. The bacterial and fungal communities in the cropping obstacle soil were clearly different from those in the control soil. The differences in bacterial communities between the cropping obstacle soil and the control soil were associated with differences in pH and available potassium content. The taxa with higher relative abundances in the cropping obstacle soils included potential plant pathogens and the taxa with lower relative abundances included potential plant growth promoters.ConclusionThe enrichment of plant pathogens and the depletion of plant growth promoting fungi may have contributed to the poor growth of garlic in the cropping obstacle soil. The enzyme activity and microbial community differences were associated with acidification that was likely an important factor in the deterioration of the soil ecological environment and the garlic cropping obstacle. The results provide information to guide agricultural practices in cultivating garlic.

Homoharringtonine (HHT) exhibits an anti-inflammatory activity. The potential protective effects and mechanisms of HHT on dextran sulfate sodium- (DSS-) induced colitis were investigated. DSS-induced colitis mice were intraperitoneally injected with HHT. Body weight, colon length, disease activity index (DAI), and histopathological change were examined. The relative contents of interleukin- (IL-) 1β, tumor necrosis factor- (TNF-) α, IL-6, and the chemokine (C-C motif) ligand 2 (CCL2) in the colon tissues and HHT-treated RAW264.7 cells were detected with the enzyme-linked immunosorbent assay. In the meantime, the levels of p-p65 and p-IκBα were detected by Western blot. The proportion of macrophages (CD11b+F4/80+) in the colon tissues was detected by flow cytometry. HHT alleviated DSS-induced colitis with downregulated TNF-α, IL-1β, IL-6, and CCL2 expression; reduced activation of nuclear factor-kappa B (NF-κB) signaling; and diminished proportion of recruited macrophages in colon tissues. It was further testified that HHT inhibited lipopolysaccharide-induced macrophage activation with reduced activation of NF-κB signaling. In addition, HHT inhibited the M1 polarization of both human and mouse macrophages, while HHT did not affect the differentiation of human CD4 T cells into Th17, Th1, or Treg cells and did not affect the proliferation and migration of human colon epithelial cells. In summary, HHT attenuates DSS-induced colitis by inhibiting macrophage-associated NF-κB activation and M1 polarization, which could be an option for the treatment of ulcerative colitis.

Abstract Background Bone marrow mesenchymal stem cell (BMSC) transplantation represents a promising therapeutic strategy for ischemic heart disease. However, its effects are hampered by the poor viability of transplanted cells and the hostile microenvironment of the ischemic region. Insulin-like growth factor-1 (IGF-1) is an important paracrine growth factor of BMSC and plays an important role in the properties of BMSC. Here, we investigated whether overexpressing IGF-1 could enhance the BMSC viability, migration, anti-apoptosis, and protective effects of cardiomyocytes, and explore the underlying mechanisms’ focus on the role of the AKT/secreted frizzled-related protein 2 (SFRP2)/β-catenin pathway. Methods We constructed BMSCs overexpressing insulin-like growth factor-1 (BMSCs-IGF-1) or empty vector (BMSCs-NC) using lentivirus, and evaluated cell survival, proliferation, and migration under normoxic and hypoxic conditions. Co-culture of rat cardiomyoblasts with BMSCs was performed to explore the paracrine effect of BMSCs-IGF-1 for rescuing cardiomyoblasts under hypoxia. Transplantation of BMSCs in acute myocardial infarction rats was used to explore the effect of BMSCs-IGF-1 therapy. Results BMSCs-IGF-1 exhibited a higher cell proliferation rate, migration capacity, and stemness, and were more resistant to apoptosis under hypoxia. Overexpression of IGF-1 upregulated the expression of total and nuclear β-catenin via the AKT-secreted frizzled-related protein 2 (SFRP2) pathway, which enhanced cell survival. Inhibition of AKT or SFRP2 knockdown by siRNA significantly antagonized the effect of IGF-1 and decreased the expression of β-catenin. The expression of β-catenin target genes, including cyclin D1 and c-Myc, were accordingly decreased. Moreover, BMSCs-IGF-1 could rescue cardiomyoblasts from hypoxia-induced apoptosis and preserve cell viability under hypoxia. Transplantation of BMSCs-IGF-1 into myocardial infarction rats greatly reduced infarct volume than BMSCs-NC, with significantly greater expression of SFRP2 and β-catenin. Conclusions These results suggest that in BMSCs overexpressing IGF-1, SFRP2 is an important mediator for the enhancement of stem cell viability via activating, rather than antagonizing, the Wnt/β-catenin pathway.

Event extraction is an important research direction in the field of natural language processing (NLP) applications including information retrieval (IR). Traditional event extraction is realized with two methods: the pipeline and the joint extraction methods. The pipeline method determines the event by triggering word recognition to further implement event extraction and is prone to error cascading. The joint extraction method applies deep learning to implement the completion of the trigger word and the argument role classification task. Most studies with the joint extraction method adopt the CNN or RNN network structure. However, in the case of event extraction, deeper understanding of complex contexts is required. Existing studies do not make full use of syntactic relations. This paper proposes a novel event extraction model, which is built upon a Tree-LSTM network and a Bi-GRU network and carries syntactically related information. It is illustrated that this method simultaneously uses Tree-LSTM and Bi-GRU to obtain a representation of the candidate event sentence and identify the event type, which results in a better performance compared to the ones that use chain structured LSTM, CNN or only Tree-LSTM. Finally, the hidden state of each node is used in Tree-LSTM to predict a label for candidate arguments and identify/classify all arguments of an event. Lab results show that the proposed event extraction model achieves competitive results compared to previous works.

The development of the mobile Internet and the success of deep learning in many applications have driven the need to deploy and apply deep learning models on mobile devices under the condition of limited resources. Long Short-Term Memory (LSTM), as a special scheme in deep learning, can learn long-distance dependencies hidden in time series. However, the high computational complexity of LSTM-related structures and the need for a large number of resources for training have become obstacles to their deployment on mobile devices. In order to reduce the resource requirements and computational costs of LSTMs, we use pruning strategies to preserve important connections during the training phase. After training, we reduce the complexity of LSTMs network by sharing weight strategy.Based on these strategies, we propose a sparse connected LSTM with a sharing weight (SCLSTM) model. The experimental results on the real data sets show that SCLSTM with 0.88% neural connections can obtain prediction capabilities comparable to densely connected LSTM. Moreover, SCLSTM can solve the problem of overfitting to some extent. The results of experiments demonstrate that SCLSTM can perform better than the-state-of-arts algorithm on mobile devices of limited resources.

Abstract Background To explore the key factors affecting prescription practices of essential medicines in Chinese county hospital. National essential medicine policy (NEMP) plays important roles in health care system, especially in developing countries. As a fundamental component in the Chinese health system reform, NEMP was implemented in primary health care institutions during the first stage of reform. As it is rolled out, priority usage and zero-mark-up policy of essential medicines are to be applied in every government-run healthcare institution. However, the intention and influence factors of physicians on priority selecting essential medicine remains unclear. Methods Based on the theory of planned behavior, a cross-sectional questionnaire survey was conducted to analyze physicians’ intention, attitude, subjective norms (SNs) and perceived behavioral control (PBC) on prescrictions and their actual behavior on selection of essential medicines. Results Two hundred eighty-two physicians participated in the structural questionnaire interview. The final structural equation model reflected influencing factors affecting physicians’ prescribing behavior (χ 2/df = 1.32, GFI = 0.99, IFI = 0.99). Structural equation model analysis showed that attitude, other influencers and institutional environment, and PBC significantly affected behavioral intention. However, the control extent of cognition behavior of physicians prescribing had no significant positive effect on the priority usage of essential medicines. Conclusion Investigation results demonstrate physicians are unaware of NEMP design and implementation plans. To help enhance rational use of essential medicines we suggest educating physicians on the value of NEMP, and integrating the drug shortage report into the essential medicine (EM) bidding system seamless communication with pharmaceutical manufacturers’ credit information system.

Abstract To assess the pharmacokinetic parameters of vancomycin in Chinese critically ill pediatric patients, children treated with vancomycin, hospitalized in the intensive care unit were included. Samples to determine peak and trough serum concentrations were obtained on the third day of treatment. Half‐life was significantly longer in neonates and showed a decreasing trend in infants and children. In patients aged ≥1 month, AUC24/MIC ≥400 was achieved in 31.8% at the dose of 40 mg/kg/d, and in 48.7% at the dose of 60 mg/kg/d with an assumed MIC of 1 mg/L. Augmented renal clearance (ARC) was present in 27.3% of children, which was associated with higher vancomycin clearance and lower AUC values. A good correlation was observed between trough concentration and AUC24, and the trough concentration that correlated with AUC24 of 400 were varied according to the dosage regimens, 8.42 mg/L for 6‐hintervals, and 6.63 mg/L for 8‐h intervals. To conclude, vancomycin trough concentration that related to the AUC24 of 400 was much lower in critically ill children than that in adults. The dosage of 60 mg/kg/day did not enough for producing AUC24 in the range of 400–600 mg h/L in critically ill children, especially in those with ARC.