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An existing technique for quickly obtaining the spatial distribution data of soil organic matter (SOM) involves combining an unmanned aerial vehicle (UAV) platform and imaging spectroscopy technology; this technique is suitable for precision agricultural management and real-time monitoring. In this study, the average spectral data were first extracted via circular regions of interest (ROIs) of different radii and modelled using the measured SOM to determine the optimal extraction range for the spectral data. Partial least-squares regression (PLSR) and support vector machine (SVM) models were constructed based on the raw spectra, standard normal transform spectra, multiple scattering-corrected spectra, and first-order differential spectra. The difference index, ratio index, and normalized index were calculated and analysed for their correlation with SOM content, and the spectral indices were screened to establish the SOM prediction model. Finally, the accuracy of the model was evaluated, and we selected the optimal model to map the spatial distribution of SOM. The results of our study show that 1) the radius of the ROI significantly affects the model’s accuracy; 2) the accuracy of the nonlinear SVM model is much higher than that of the linear PLSR model; 3) the prediction model based on spectral indices is better than full-band model. This study can serve as a model for the application of UAV imaging spectroscopy technology to field-scale SOM estimations and rapid monitoring, as well as provide technical support for precision agriculture and climate change studies.

High-resolution Digital Elevation Models (DEMs) are essential for precise geographic analysis. However, obtaining high-resolution DEMs in regions with dense vegetation, complex terrain, or satellite imagery voids presents substantial challenges. This study introduces a deep learning approach using three-dimensional (3D) terrain features combined with Conditional Generative Adversarial Networks (CGANs) to reconstruct DEMs. The 3D terrain features, such as valley and ridge lines, exhibit topographic relief patterns and provide constraints for CGANs to reconstruct DEMs. Experiments conducted in the Loess Plateau of Shaanxi confirmed the performance of the proposed method, demonstrating marked improvements in the accuracy of DEM reconstruction compared to models based on two-dimensional (2D) terrain features. The elevation accuracy of the reconstructed DEMs by the proposed method is 5.30 m, which is higher than that of the 2D terrain features method (18.90 m) by 71.96 %. Meanwhile, the proposed method shows a 15.78 % and 17.64 % improvement in elevation accuracy and slope accuracy, respectively, when reconstructing a 5 m high-resolution DEM from a 30 m low-resolution DEM. The proposed method can be flexibly used for reconstructing, repairing, and filling voids in DEM data.

This study aimed to investigate Solanum lyratum Thunb. with respect to the potential ingredients with anti-inflammatory activity from its essential oil by silico study. To this regard, the essential oil of Solanum lyratum Thunb. was extracted by hydrodistillation. 25 compounds were identified by GC-MS. Using virtual screening, molecular docking and molecular dynamics simulation of the 25 identified compounds, the ones showing anti-inflammatory activity on COX-2 were identified. According to the drug-like principle and the prediction of ADEMT properties, the six compounds of Spathulenol, Cedrol, Juniper camphor, Santalol, Nootkatone and 7,9-Di-tert-butyl-1-oxaspiro[4.5]deca-6,9-diene-2,8-dione were identified and then studied for molecular docking, and based on which the top two compounds of binding free energy were studied by the molecular dynamics simulation. The molecular docking data indicated that the binding free energies of Spathulenol, Cedrol, Juniper camphor, Santalol, Nootkatone and 7,9-Di-tert-butyl-1-oxaspiro[4.5]deca-6,9-diene-2,8-dione to COX-2 protein were −5.65, −7.19, −6.35, −4.94, −5.82 and −5.14 kcal/mol, respectively. The findings showed the steady interactions of hydrogen bonds and hydrophobic bonds between both the top two compounds of binding free energy and the active site residues of COX-2 (4M11) throughout the simulation via hydrogen bonds and hydrophobic bonds. The very study shall be supportive for in vitro and in vivo studies in developing drug products using the lead bioactive ingredients for anti-inflammatory in the future.

Visceral white adipose tissues (WAT) regulate systemic lipid metabolism and inflammation. Dysfunctional WAT drive chronic inflammation and facilitate atherosclerosis. Adipose tissue-associated macrophages (ATM) are the predominant immune cells in WAT, but their heterogeneity and phenotypes are poorly defined during atherogenesis. The scavenger receptor CD36 mediates ATM crosstalk with other adipose tissue cells, driving chronic inflammation. Here, we combined the single-cell RNA sequencing technique with cell metabolic and functional assays on major WAT ATM subpopulations using a diet-induced atherosclerosis mouse model (Apoe-null). We also examined the role of CD36 using Apoe/Cd36 double-null mice. Based on transcriptomics data and differential gene expression analysis, we identified a previously undefined group of ATM displaying low viability and high lipid metabolism and labeled them as “unhealthy macrophages”. Their phenotypes suggest a subpopulation of ATM under lipid stress. We also identified lipid-associated macrophages (LAM), which were previously described in obesity. Interestingly, LAM increased 8.4-fold in Apoe/Cd36 double-null mice on an atherogenic diet, but not in Apoe-null mice. The increase in LAM was accompanied by more ATM lipid uptake, reduced adipocyte hypertrophy, and less inflammation. In conclusion, CD36 mediates a delicate balance between lipid metabolism and inflammation in visceral adipose tissues. Under atherogenic conditions, CD36 deficiency reduces inflammation and increases lipid metabolism in WAT by promoting LAM accumulation.

Digital Elevation Model (DEM) errors tend to be spatially correlated, inevitably affecting DEM-based topographic change detection. Traditional topographic change detection methods often ignore the spatial distribution of the DEM error. This paper aims to develop a workflow that considers the spatial autocorrelation of the error in topographic change detection. Firstly, the DEM of Difference (DoD) is obtained from two-period DEMs, and the Monte Carlo method is employed to evaluate the Spatially Distributed Errors (SDE) in DEMs. Secondly, DoD errors are calculated by propagation based on spatially distributed DEM errors. At the same time, its spatial distribution is quantified using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial distribution analysis and significance detection. The results in two small catchments indicate that DEM errors are spatially correlated, increasing the volume calculation errors. However, using Standard Deviation of Errors (SDE) instead of Root Mean Square Error (RMSE) can effectively reduce the sensitivity of the detection results in the significance threshold. When the significance threshold increases from 68% to 95%, the observations loss using the spatially distributed error is 4.67% −6.92% lower than that using the RMSE. The level of detection has little impact on the net topographic change and significantly influences gross erosion and deposition. In particular, the use of level of detection can effectively reduce the misclassification of erosion or deposition in stable topography areas. The proposed method can be effectively utilized in various applications like surface deformation monitoring, erosion monitoring, and sediment transport assessment.

ABSTRACTGut microbiota plays an essential role in nonalcoholic fatty liver disease (NAFLD). However, the contribution of individual bacterial strains and their metabolites to childhood NAFLD pathogenesis remains poorly understood. Herein, the critical bacteria in children with obesity accompanied by NAFLD were identified by microbiome analysis. Bacteria abundant in the NAFLD group were systematically assessed for their lipogenic effects. The underlying mechanisms and microbial-derived metabolites in NAFLD pathogenesis were investigated using multi-omics and LC-MS/MS analysis. The roles of the crucial metabolite in NAFLD were validated in vitro and in vivo as well as in an additional cohort. The results showed that Enterococcus spp. was enriched in children with obesity and NAFLD. The patient-derived Enterococcus faecium B6 (E. faecium B6) significantly contributed to NAFLD symptoms in mice. E. faecium B6 produced a crucial bioactive metabolite, tyramine, which probably activated PPAR-γ, leading to lipid accumulation, inflammation, and fibrosis in the liver. Moreover, these findings were successfully validated in an additional cohort. This pioneering study elucidated the important functions of cultivated E. faecium B6 and its bioactive metabolite (tyramine) in exacerbating NAFLD. These findings advance the comprehensive understanding of NAFLD pathogenesis and provide new insights for the development of microbe/metabolite-based therapeutic strategies.

Path planning plays a crucial role in various domains, such as autonomous driving, robot navigation, logistics and emergency rescue. In applications such as logistics distribution and emergency rescue, the passage time cost is often a key factor. Traditional path planning algorithms prioritize the shortest or cost-optimal paths while overlooking passage time efficiency. This paper proposes an improved A* algorithm for the optimal passage time cost. First, we used the analytic hierarchy process method to establish the passage time cost based on multiple topographic and geological factors. Then, we improved the heuristic function in the A* algorithm by taking the time cost as the evaluation index. Finally, the performance of the improved A* algorithm was validated under rectangular and hexagonal grid settings and compared to that of the classical A* and Dijkstra algorithms. The experimental results show that the path passage time planned with the proposed time–cost optimal A* algorithm is reduced by 20.21% and 20.08% on average compared with that of the path shortest A* algorithm under rectangular and hexagonal grids, respectively. The proposed time–cost optimal A* algorithm saves computing time by 9.08% and 44.56% under rectangular and hexagonal grids, respectively, compared to Dijkstra. The passage time costs of the paths planned by the proposed A* algorithm under hexagonal grids are reduced by 5.99%-13.98% compared to those under rectangular grids, indicating that hexagonal grids are more suitable than rectangular grids for path planning. The proposed method reduces the response time, enhances the life-saving efficiency, and minimizes casualties and property damage when determining the fastest rescue route for emergency scenarios.

Deep Learning (DL) models can make short- and long-term predictions in just a few seconds, beyond the capabilities of traditional physical models. However, the capabilities of different DL models for meteorological element forecasting, are still yet to be comprehensively evaluated. Here, DL models were used to forecast multiple meteorological elements, including temperature (T), surface net solar radiation (SSR), soil moisture (SM), and evapotranspiration (ET). We compared the seven models in term of training performance, prediction accuracy, and the effects of parameters. We found that the training of RNN-based models (LSTM, GRU, and Bi-LSTM) was faster than others. However, with sufficient training epochs, Transformer-based models consistently achieve the lowest loss function. Among the Transformers, the Informer demonstrates the best prediction accuracy in most scenarios. Beyond the choice of DL model, the prediction performance is also influenced by the meteorological element itself. The MTGNN is comparable to Transformer-based models for T and SSR forecasting, but it does not perform as well as the Informer for SM and ET. The sliding window size and prediction time step have a slight impact on the performance differences between the models. The results can offer insights into applying DL models in meteorological element forecasting.

Abstract Gestational diabetes mellitus (GDM) is a major complication of pregnancy. GDM is associated with a higher risk of adverse pregnancy outcomes (APO). The purpose of this study was to assess the association between third-trimester platelet to lymphocyte ratio (PLR) and high-sensitivity C-reactive protein (hsCRP) concentration and the risk of APO in GDM pregnant women. This study selected 406 non-elderly gestational diabetes patients diagnosed in the Renmin Hospital of Wuhan University from May 2021 to February 2023 as the research objects. According to the presence or absence of APO, they were divided into an APO group (n = 171) and a non-APO group (n = 235). Logistic regression model to evaluate the correlation between PLR and hsCRP and APO in women with GDM; Restricted cubic spline analyses was used to explore nonlinear correlations between PLR or hsCRP and the risk of APO; ROC curve analysis of the diagnostic performance of PLR and hsCRP for APO in women with GDM. APO occurred in 171 of the 406 included participants. Compared with the non-APO group, patients in the APO group had higher PLR and hsCRP levels. The incidence of APO was positively associated with PLR and the hsCRP level in each logistic regression model (P

ABSTRACTAltered gut microbiota and metabolites are important for non-alcoholic fatty liver disease (NAFLD) in children. We aimed to comprehensively examine the effects of gut metabolites on NAFLD progression. We performed integrative metabolomics (untargeted discovery and targeted validation) analysis of non-alcoholic fatty liver (NAFL), non-alcoholic steatohepatitis (NASH), and obesity in children. Fecal samples were collected from 75 subjects in the discovery cohort (25 NAFL, 25 NASH, and 25 obese control children) and 145 subjects in an independent validation cohort (53 NAFL, 39 NASH, and 53 obese control children). Among 2,491 metabolites, untargeted metabolomics revealed a complete NAFLD metabolic map containing 318 increased and 123 decreased metabolites. Then, machine learning selected 65 important metabolites that can distinguish the severity of the NAFLD. Furthermore, precision-targeted metabolomics selected 5 novel gut metabolites from 20 typical metabolites. The functionality of candidate metabolites was validated in hepatocyte cell lines. In the end, this study annotated two novel elevated pathogenic metabolites (dodecanoic acid and creatinine) and a relationship between depleted protective gut microbiota (Butyricicoccus and Alistipes), increased inflammation (IL-1β), lipid metabolism (TG), and liver function (ALT and AST). This study demonstrates the role of novel gut metabolites (dodecanoic acid and creatinine), as the fatty acid metabolism regulator contributing to NAFLD development through its influence on inflammation and liver function.IMPORTANCEAltered gut microbiota and metabolites are a major cause of non-alcoholic fatty liver disease (NAFLD) in children. This study demonstrated a complete gut metabolic map of children with NAFLD, containing 318 increased and 123 decreased metabolites by untargeted metabolomic. Multiple validation approaches (machine learning and targeted metabolomic) selected five novel gut metabolites for targeted metabolomics, which can distinguish NAFLD status and severity. The gut microbiota (Butyricicoccus and Alistipes) and metabolites (creatinine and dodecanoic acid) were novel biomarkers associated with impaired liver function and inflammation and validated by experiments of hepatocyte cell lines. The data provide a better understanding of the importance of gut microbiota and metabolite alterations in NAFLD, which implies that the altered gut microbiota and metabolites may represent a potential target to prevent NAFLD development.

Summary: Gut microbiota is known to have a significant impact on nonalcoholic fatty liver disease (NAFLD), particularly in children with obesity. However, the specific functions of microbiota at the strain level in this population have not been fully elucidated. In this study, we successfully isolated and identified several commensal gut bacterial strains that were dominant in children with obesity and NAFLD. Among these, four novel isolates were found to have significant lipogenic effects in vitro. These strains exhibited a potential link to hepatocyte steatosis by regulating the expression of genes involved in lipid metabolism and inflammation. Moreover, a larger cohort analysis confirmed that these identified bacterial strains were enriched in the NAFLD group. The integrated analysis of these strains effectively distinguished NASH from NAFL. These four strains might serve as potential biomarkers in children with NAFLD. These findings provided new insights into the exploration of therapeutic targets for NAFLD.

Abstract The cleavage and functionalization of C–S bonds have become a rapidly growing field for the design or discovery of new transformations. However, it is usually difficult to achieve in a direct and selective fashion due to the intrinsic inertness and catalyst-poisonous character. Herein, for the first time, we report a novel and efficient protocol that enables direct oxidative cleavage and cyanation of organosulfur compounds by heterogeneous nonprecious-metal Co-N-C catalyst comprising graphene encapsulated Co nanoparticles and Co-Nx sites using oxygen as environmentally benign oxidant and ammonia as nitrogen source. A wide variety of thiols, sulfides, sulfoxides, sulfones, sulfonamides, and sulfonyl chlorides are viable in this reaction, enabling access to diverse nitriles under cyanide-free conditions. Moreover, modifying the reaction conditions also allows for the cleavage and amidation of organosulfur compounds to deliver amides. This protocol features excellent functional group tolerance, facile scalability, cost-effective and recyclable catalyst, and broad substrate scope. Characterization and mechanistic studies reveal that the remarkable effectiveness of the synergistic catalysis of Co nanoparticles and Co-Nx sites is crucial for achieving outstanding catalytic performance.

Sepsis-induced cardiomyopathy (SIC) is characterized by high mortality and poor outcomes. This study aimed to explore the relationship between testosterone and soluble ST2 (sST2) and all-cause mortality in patients with SIC. Clinical data from SIC patients at Renmin Hospital of Wuhan University from January 2021 and March 2023 were reviewed. Serum testosterone and sST2 were measured at admission. Kaplan–Meier analysis and receiver operative characteristic curve (ROC) were used to estimate the predictive values of testosterone and sST2 on 28 days and 90 days mortality of SIC. A total of 327 male subjects with SIC were enrolled in this study. During the 28 days and 90 days follow-up, 87 (26.6%) and 103 deaths (31.5%) occurred, respectively. Kaplan–Meier analysis showed significantly higher 28 days and 90 days survival in patients with higher testosterone and decreased sST2 levels (p

ABSTRACTHigh-precision digital elevation models (DEMs) are the basic data for constructing digital cities. With the acceleration of urbanization, the topography of urban plots is constantly transformed by human activities, so that surface morphology shows the characteristics of diversification and discontinuity. Existing modelling methods focus on the expression of continuous terrain. Constructing high-precision DEM in urban plots is still challenging. A block-based modelling method that considers the morphological characteristics of urban plot elements was proposed. The Jinzhai County urban area was selected as the research area. The elements of urban plots were classified into six types, in which the boundaries and elevation information were extracted with real sense 3D models, Digital Orthophoto Maps (DOMs) and dense matching point cloud data. The DEMs of the 6 types of elements were generated separately with different methods, that are fused to complete the DEM of urban plots. The DEM obtained using the method in this manuscript was consistent with the reality in terms of topographic relief within each element and clarified the boundary of each element. The accuracy assessment showed that the RMSE results of roads, slopes, other terrains and natural terrains are approximately 0.05 m, which meets the elevation accuracy requirements of the 1:1000 large-scale mapping.

Green economic growth is the main direction of China’s future economic development, while energy efficiency improvement—an important prerequisite for promoting the nation’s sustainable development—is a necessary way to guarantee its economic transformation and development. It is thus of great practical significance to study the relationship between energy efficiency improvement and green economic development. On the basis of analyzing the mechanism and how energy efficiency improvement influences green economic growth, this paper measures energy efficiency with the SBM model and constructs a system of green economic growth index, and verifies the relationship between energy efficiency improvement and green economic growth with an econometric model empirical analysis. It is found that, on the whole, the improvement of energy efficiency at this stage in China inhibits green economic growth. However, with the improvement of energy efficiency level, the inhibition effect gradually weakens, showing a non-linear trajectory of “inhibition–inhibition weakening”. At present, China’s energy rebound effect is still on the rise in the short term, and green economic development is restrained to a certain extent. The current expansion of the industrial sector will exacerbate resource consumption and pollutant emissions, while technological innovation and urbanization levels will alleviate the current energy rebound tension. The paper concludes with recommendations from the perspectives of the government, R&D institutions and personnel, the power sector, and urbanization.

Summary: An unprecedented efficient protocol is developed for the oxidative cleavage of C≡C bonds in alkynes to produce structure-diverse esters using heterogeneous cobalt nanoparticles as catalyst with molecular oxygen as the oxidant. A diverse set of mono- and multisubstituted aromatic and aliphatic alkynes can be effectively cleaved and converted into the corresponding esters. Characterization analysis and control experiments indicate high surface area and pore volume, as well as nanostructured nitrogen-doped graphene-layer coated cobalt nanoparticles are possibly responsible for excellent catalytic activity. Mechanistic studies reveal that ketones derived from alkynes under oxidative conditions are formed as intermediates, which subsequently are converted to esters through a tandem sequential process. The catalyst can be recycled up to five times without significant loss of activity.

Lane-level road maps are crucial for urban traffic management, autonomous driving, and vehicle navigations. Optical remote sensing image suffers from trees and buildings occlusion for lane-level road mapping due to the top-down view. While street view images (SVIs) have been used for road detection, however, most of the previous articles focused on extracting road in image space. The reconstruction of lane-level road maps with measurability in geographic space remains challenging. Hence, this article proposed an operational framework for extracting and reconstructing lane-level road maps from urban open access data. First, a sample strategy was used to collect SVIs based on OpenStreetMap (OSM) road central lines. Then, a deep-learning-based method was adopted to identify lanes accurately, and road width was extracted based on design knowledge and OSM information. Finally, the lane-level road map was reconstructed by integrating the lane and its width information. The proposed framework achieves the transformation from image space to geographic space. The case study shows that 82.43% of the roadway is accurately reconstructed in lane-level. The difference between the reconstructed width of the roadway and the reference true value is within the m-level and the RMSE is 0.32 m. The proposed method is cost-effective and accurate-acceptable for acquiring lane-level road datasets in cities.

Highlights A three-tiered thermal interface materials was proposed with the through-plane thermal conductivity up to176 W m−1 K−1 and contact thermal resistance as low as 4–6 K mm2 W−1 (double sides). The liquid metal acts as a buffer layer to connect vertically aligned graphene with the rough heater/heat sink, improving effective contact thermal conductance by more than an order of magnitude.

Abstract Urban road DEM is not only an important basic geographic information data of the city, but also an important element to describe and express the urban topography, and it is an indispensable part of the construction of the smart digital city, urban planning and urban surface process simulation. Previous methods for constructing urban road DEMs do not sufficiently consider the actual morphological characteristics of urban roads, and morphological distortion is evident in the expression of urban roads, seriously affecting the application of urban rainfall flood simulation and urban pipe network design. In response to these problems, this study proposed a considering morphological characteristics fine (CMCF) method of urban road DEM construction, selected a typical urban road area in the Jianye District of Nanjing City in China as the study area, used the 1:500 digital line graphic as data source, hierarchized roads in accordance with different morphological characteristics from the perspective of DEM construction, constructed the corresponding DEMs, and finally merged all levels of road DEMs to produce a complete high-precision urban road DEM. Results showed that the DEM constructed using the CMCF method not only exhibited higher elevation accuracy than the urban road DEM constructed using previous methods, i.e., inverse distance weighting (IDW) and triangulated irregular network (TIN) methods, with a mean error and a root-mean-square error of 0.015 and 0.060, respectively, but it can also accurately express the spatial element composition form and road morphological characteristics of urban roads, avoiding the distorted expression of road morphological characteristics. This study can provide a reference for a new DEM construction method and data support for smart digital city construction and urban surface simulation.

Background and objective: The balance between neural oscillations provides valuable insights into the organisation of neural oscillations related to brain states, which may play important roles in dystonia. We aim to investigate the relationship of the balance in the globus pallidus internus (GPi) with the dystonic severity under different muscular contraction conditions. Methods: Twenty-one patients with dystonia were recruited. All of them underwent bilateral GPi implantation, and local field potentials (LFPs) from the GPi were recorded via simultaneous surface electromyography. The power spectral ratio between neural oscillations was computed as the measure of neural balance. This ratio was calculated under high and low dystonic muscular contraction conditions, and its correlation with the dystonic severity was assessed using clinical scores. Results: The power spectral of the pallidal LFPs peaked in the theta and alpha bands. Within participant comparison showed that the power spectral of the theta oscillations significantly increased during high muscle contraction compared with that during low contraction. The power spectral ratios between the theta and alpha, theta and low beta, and theta and high gamma oscillations were significantly higher during high contraction than during low contraction. The total score and motor score were associated with the power spectral ratio between the low and high beta oscillations, which was correlated with the dystonic severity both during high and low contractions. The power spectral ratios between the low beta and low gamma and between the low beta and high gamma oscillations showed a significantly positive correlation with the total score during both high and low contractions; a correlation with the motor scale score was found only during high contraction. Meanwhile, the power spectral ratio between the theta and alpha oscillations during low contraction showed a significantly negative correlation with the total score. The power spectral ratios between the alpha and high beta, alpha and low gamma, and alpha and high gamma oscillations were significantly correlated with the dystonic severity only during low contraction. Conclusion: The balance between neural oscillations, as quantified by the power ratio between specific frequency bands, differed between the high and low muscular contraction conditions and was correlated with the dystonic severity. The balance between the low and high beta oscillations was correlated with the dystonic severity during both conditions, making this parameter a new possible biomarker for closed-loop deep brain stimulation in patients with dystonia.

Anthracite-associated graphite is an important graphite resource with a wide range of applications besides being used as a fuel. This paper introduces a method for evaluating the graphite equivalent evaluation of anthracite-associated graphite. A series of graphite-anthracite standard samples with known graphite content were prepared, and their Raman spectra were obtained using a Raman spectrometer. By employing peak-fitting analysis to decipher the peak spectrum information of the D peak and G peak, trends in the peak position, peak intensity ratio, half-width, and peak area of the D peak and G peak in standard samples with different graphite contents were obtained. Subsequently, a standard curve and fitting equation were established using the peak area data. The goodness of fit for the equation (R2) was 0.9984. Then the equation was used to evaluate 100 natural anthracite-associated graphite samples with unknown graphite content, obtaining a corresponding graphite equivalent evaluation.

The sediment transport process in watersheds is an important research component of geomorphology and surface dynamics. Previous work has inferred the spatial distribution of the sediment transport rate (STR) by the flow direction algorithm and measured topographic variation; however, the simple application of the flow direction algorithm contributes to mass non-conservation during a simulation. This study designs an improved flow direction algorithm for a sediment transport process simulation by judging the mass conservation situation in the simulation process. The specific implementation is to evaluate the existence of negative values for the STR; if they exist, the negative values of the STR are reset to stop the propagation of the negative values downstream. Experiments are conducted to improve the classical D8, MFD–se, and MFD–md flow algorithms in this paper, and the experimental results show that the method in this paper can effectively improve the simulation effect of STR. The STR simulations of the three models, D8, MFD–se, and MFD–md, improved by 1.26%, 4.17%, and 4.54%, respectively. Moreover, the MFD–se model is more suitable for the simulation of the STR when comparing the three models. The improved flow algorithm can be used to simulate the STR, sediment content, and pollutant migration in watersheds, providing a new method for the fine-grained characterization of surface processes in watersheds.

Land use and land cover (LULC) change is a pattern of alteration of the Earth’s land surface cover by human society and have a significant impact on the terrestrial carbon cycle. Optimizing the distribution of LULC is critical for the redistribution of land resources, the management of carbon storage in terrestrial ecosystems, and global climate change. We integrated the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model to simulate and assess future LULC and ecosystem carbon storage in the Nanjing metropolitan circle in 2030 under four scenarios: natural development (ND), economic development (ED), ecological protection (EP), and collaborative development (CD). The results showed that (1) LULC and carbon storage distribution were spatially heterogenous in the Nanjing metropolitan circle for the different scenarios, with elevation, nighttime lights, and population being the main driving factors of LULC changes; (2) the Nanjing metropolitan circle will experience a carbon increase of 0.50 Tg by 2030 under the EP scenario and losses of 1.74, 3.56, and 0.48 Tg under the ND, ED, and CD scenarios, respectively; and (3) the CD scenario is the most suitable for the development of the Nanjing metropolitan circle because it balances ED and EP. Overall, this study reveals the effects of different development scenarios on LULC and ecosystem carbon storage, and can provide a reference for policymakers and stakeholders to determine the development patterns of metropolitan areas under a dual carbon target orientation.

Abstract Studies have confirmed that olanzapine, the mainstay treatment for schizophrenia, triggers metabolic diseases, including non-alcoholic fatty liver disease (NAFLD). However, the etiology of olanzapine-induced NAFLD is poorly understood. Proprotein convertase subtilisin kexin type 9 (PCSK9) is involved in NAFLD pathogenesis, and metformin can significantly decrease circulating PCSK9. The purpose of this study was to investigate the role of PCSK9 and explore the therapeutic effect of metformin for olanzapine-associated NAFLD. Olanzapine significantly upregulated PCSK9 and promoted lipid accumulation in mouse livers and HepG2 and AML12 cells. Metformin ameliorated these pathological alterations. PCSK9 upstream regulator liver X receptor α (LXRα) was significantly upregulated in olanzapine-induced NAFLD. LXRα antagonist treatment and LXRα overexpression resulted in a decrease and increase of PCSK9, respectively. Hepatic lipogenesis-associated genes FAS and SCD1 were significantly upregulated in olanzapine-induced NAFLD mice and HepG2 cells overexpressing PCSK9, and genes related to lipid β-oxidation (SCAD and PPARα) were downregulated, while metformin reversed these changes. In addition, we found that LXRα overexpression compromised the effect of metformin on PCSK9 levels and intracellular lipid droplet formation. Taken together, our findings suggest that olanzapine enhances hepatic PCSK9 expression by upregulating LXRα, thereby increasing FAS and SCD1 expression as well as decreasing SCAD and PPARα, and promoting lipid accumulation, and, subsequently, NAFLD, which is ameliorated by metformin.

Coastal wetland ecosystems around the world are facing serious challenges due to rapid economic development, climate change, and sea level rise. These factors have a great influence on the tidal creek network and vegetation ecosystem. Accordingly, based on long-term time-series remote sensing images, the evolution characteristics of tidal creeks and vegetation in silting muddy flats on the Yellow Sea were analyzed, and different quantitative methods were used to describe the relationship between tidal creeks and vegetation. The results showed that the total number of tidal creeks exhibited a downward trend, while the number of small creeks increased over time. The total length of the tidal creeks decreased at a rate of 16.1 km per year during the study period. The length of three-grade tidal creeks, the patch area ratio of Suaeda salsa, and a low vegetation coverage had a great influence on the average return flow length (LOP). LOP was negatively correlated with the patch area ratio of S. salsa, but positively correlated with the reclamation area, both significantly. With the increase in the patch area ratio of S. salsa, the total length of the tidal creeks increased by 12.95 km, and LOP decreased by 35.35 m.

The topographic skeleton is the primary expression and intuitive understanding of topographic relief. This study integrated a topographic skeleton into deep learning for terrain reconstruction. Firstly, a topographic skeleton, such as valley, ridge, and gully lines, was extracted from a global digital elevation model (GDEM) and Google Earth Image (GEI). Then, the Conditional Generative Adversarial Network (CGAN) was used to learn the elevation sequence information between the topographic skeleton and high-precision 5 m DEMs. Thirdly, different combinations of topographic skeletons extracted from 5 m, 12.5 m, and 30 m DEMs and a 1 m GEI were compared for reconstructing 5 m DEMs. The results show the following: (1) from the perspective of the visual effect, the 5 m DEMs generated with the three combinations (5 m DEM + 1 m GEI, 12.5 m DEM + 1 m GEI, and 30 m DEM + 1 m GEI) were all similar to the original 5 m DEM (reference data), which provides a markedly increased level of terrain detail information when compared to the traditional interpolation methods; (2) from the perspective of elevation accuracy, the 5 m DEMs reconstructed by the three combinations have a high correlation (>0.9) with the reference data, while the vertical accuracy of the 12.5 m DEM + 1 m GEI combination is obviously higher than that of the 30 m DEM + 1 m GEI combination; and (3) from the perspective of topographic factors, the distribution trends of the reconstructed 5 m DEMs are all close to the reference data in terms of the extracted slope and aspect. This study enhances the quality of open-source DEMs and introduces innovative ideas for producing high-precision DEMs. Among the three combinations, we recommend the 12.5 m DEM + 1 m GEI combination for DEM reconstruction due to its relative high accuracy and open access. In regions where a field survey of high-precision DEMs is difficult, open-source DEMs combined with GEI can be used in high-precision DEM reconstruction.

UAV-SfM photogrammetry is widely used in remote sensing and geoscience communities. Scholars have tried to optimize UAV-SfM for terrain modeling based on analysis of error statistics like root mean squared error (RMSE), mean error (ME), and standard deviation (STD). However, the errors of terrain modeling tend to be spatially distributed. Although the error statistic can represent the magnitude of errors, revealing spatial structures of errors is still challenging. The “best practice” of UAV-SfM is lacking in research communities from the perspective of spatial structure of errors. Thus, this study designed various UAV-SfM photogrammetric scenarios and investigated the effects of image collection strategies and GCPs on terrain modeling. The error maps of different photogrammetric scenarios were calculated and quantitatively analyzed by ME, STD, and Moran’s I. The results show that: (1) A high camera inclination (20–40°) enhances UAV-SfM photogrammetry. This not only decreases the magnitude of errors, but also mitigates its spatial correlation (Moran’s I). Supplementing convergent images is valuable for reducing errors in a nadir camera block, but it is unnecessary when the image block is with a high camera angle. (2) Flying height increases the magnitude of errors (ME and STD) but does not affect the spatial structure (Moran’s I). By contrast, the camera angle is more important than the flying height for improving the spatial structure of errors. (3) A small number of GCPs rapidly reduce the magnitude of errors (ME and STD), and a further increase in GCPs has a marginal effect. However, the structure of errors (Moran’s I) can be further improved with increasing GCPs. (4) With the same number, the distribution of GCPs is critical for UAV-SfM photogrammetry. The edge distribution should be first considered, followed by the even distribution. The research findings contribute to understanding how different image collection scenarios and GCPs can influence subsequent terrain modeling accuracy, precision, and spatial structure of errors. The latter (spatial structure of errors) should be routinely assessed in evaluations of the quality of UAV-SfM photogrammetry.

As a noninvasive behavioral intervention, the retrieval-extinction (R-E) procedure has drawn much research attention for its capacity to target the reconsolidation of maladaptive memories. However, later research findings suggest that the cause and consequence of R-E may be more complicated than previously suggested. For example, the R-E procedure could increase an animal's motivation for drug-seeking under certain circumstances, and the reversed extinction-retrieval (E-R) procedure could also suppress the drug memory. Two possible mechanisms underlying the R-E procedure have been proposed: the reconsolidation-update and extinction-facilitation hypotheses. To elucidate the paradoxical prior findings and examine these two hypotheses, we systematically examined the efficacy of the extinction (E), R-E, and E-R procedures in mice's low-dose versus high-dose cocaine-induced conditioned place preference (CPP) memory. We showed that the dose of cocaine is a crucial determinant of the efficacy of the three behavioral interventions. The E procedure exerted a long-lasting suppression of the low-dose cocaine CPP memory, while the R-E procedure induced more memory defects than the E and E-R procedures in its long-term suppression of the high-dose cocaine CPP memory. It warrants further investigation of whether the R-E procedure's underlying neurochemical and molecular mechanisms differ from the E and E-R procedures.

Abstract Background High serum Lipoprotein(a) (Lp(a)) level and Apolipoprotein B/Apolipoprotein AΙ (ApoB/ApoA-Ι) ratio are risk factors for cardiovascular disease and kidney disease and have been found to be correlated with the prevalence and prognosis of various kidney diseases. However, it is not clear whether the serum Lp(a) level and ApoB/ApoA-Ι ratio pre-PCI are correlated with the prevalence of contrast-induced acute kidney injury (CI-AKI). Methods A total of 931 participants undergoing emergency PCI from July 2018 to July 2020 were included. According to whether the serum creatinine concentration was higher than the baseline concentration (by ≥25% or ≥ 0.5 mg/dL) 48–72 h after contrast exposure, these participants were divided into a CI-AKI group (n = 174) and a non-CI-AKI group (n = 757). Serum Lp(a), ApoA-Ι and ApoB concentration were detected in the patients when they were admitted to hospital, and the ApoB/ApoA-Ι ratio was calculated. Logistic regression and restricted cubic spline analyses were used to explore the correlation between the Lp(a) concentration or the ApoB/ApoA-Ι ratio and the risk of CI-AKI. Results Among the 931 participants undergoing emergency PCI, 174 (18.69%) participants developed CI-AKI. Compared with the non-CI-AKI group, the Lp(a) level and ApoB/ApoA-Ι ratio pre-PCI in the CI-AKI group were significantly higher (P

Summary: The cleavage and functionalization of carbon-carbon (C–C) bonds has emerged as a powerful tool for preparing value-added chemicals. In this protocol, we describe the preparation of amorphous manganese oxide and its application as a heterogeneous catalyst in the direct synthesis of amides via successive cleavage and amidation of C–C bonds in alcohols. Furthermore, we describe how a slight modification of reaction conditions allows for the cleavage and cyanation of alcohols to access sterically hindered nitriles.For complete details on the use and execution of this protocol, please refer to He et al. (2022). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: Na/K-ATPase (NKA), besides its ion transporter function, is a signal transducer by regulating Src family kinases (SFK). The signaling NKA contributes to oxidized LDL-induced macrophage foam cell formation and interacts with TLR4. However, its role in lipopolysaccharides (LPS)-induced signaling and glycolytic switch in macrophages remains unclear. Using peritoneal macrophages from NKA α1 haploinsufficient mice (NKA α1+/−), we found that NKA α1 haploinsufficiency led to enhanced LPS-stimulated NF-κB pathway, ROS signaling, and pro-inflammatory cytokines. Intraperitoneal injection of LPS resulted in more severe lung inflammation and injury with lower survival rate in NKA α1+/− mice. Additionally, LPS induced a higher extent of the metabolic switch from oxidative phosphorylation to glycolysis. Mechanistically, NKA α1 interacted with TLR4 and Lyn. The presence of NKA α1 in this complex attenuated Lyn activation by LPS, which subsequently restricted the downstream ROS and NF-κB signaling. In conclusion, we demonstrated that NKA α1 suppresses LPS-induced macrophage pro-inflammatory signaling through Lyn.

Long-term use of olanzapine, an antipsychotic drug, induces hypertriglyceridemia, resulting in a higher risk of cardiovascular disease. However, the effects and underlying mechanisms of short-term use of olanzapine on circulating triglyceride levels remain poorly understood. Here, the role of apolipoprotein A5 (apoA5), a regulator of triglyceride metabolism, was investigated in olanzapine-induced hypertriglyceridemia. Our multi-center clinical study recruited 36 schizophrenia patients who received short-term (8 weeks) of olanzapine. Besides, female C57BL/6J mice were treated with olanzapine (3 mg/kg/day versus 6 mg/kg/day) for 6 weeks. We demonstrated that short-term use of olanzapine increased plasma triglyceride and decreased plasma apoA5 levels in the patients and mice, with a negative correlation between the two factors. However, no obesity was observed in the patients and mice. Interestingly, olanzapine increased hepatic apoA5 protein in the mice, without significant changes in hepatic Apoa5 mRNA. Consistently, in vitro studies indicated that olanzapine increased medium triglyceride levels and decreased medium apoA5 levels in a dose-dependent manner in human HepG2 cells and primary mouse hepatocytes. Whereas the olanzapine treatment increased hepatic apoA5 protein in vitro, without effects on hepatic APOA5 mRNA. Of note, olanzapine increased the co-localization between apoA5 protein and accumulated lipid droplets in hepatocytes, as opposed to at the hepatocellular plasma membrane, in mouse liver as demonstrated by fluorescence staining. Therefore, our study indicated that short-term use of olanzapine induced hypertriglyceridemia due to defects of sorting and secretion of hepatic apoA5.

The removal of low vegetation is still challenging in UAV photogrammetry. According to the different topographic features expressed by point-cloud data at different scales, a vegetation-filtering method based on multiscale elevation-variation coefficients is proposed for terrain modeling. First, virtual grids are constructed at different scales, and the average elevation values of the corresponding point clouds are obtained. Second, the amount of elevation change at any two scales in each virtual grid is calculated to obtain the difference in surface characteristics (degree of elevation change) at the corresponding two scales. Third, the elevation variation coefficient of the virtual grid that corresponds to the largest elevation variation degree is calculated, and threshold segmentation is performed based on the relation that the elevation variation coefficients of vegetated regions are much larger than those of terrain regions. Finally, the optimal calculation neighborhood radius of the elevation variation coefficients is analyzed, and the optimal segmentation threshold is discussed. The experimental results show that the multiscale coefficients of elevation variation method can accurately remove vegetation points and reserve ground points in low- and densely vegetated areas. The type I error, type II error, and total error in the study areas range from 1.93 to 9.20%, 5.83 to 5.84%, and 2.28 to 7.68%, respectively. The total error of the proposed method is 2.43–2.54% lower than that of the CSF, TIN, and PMF algorithms in the study areas. This study provides a foundation for the rapid establishment of high-precision DEMs based on UAV photogrammetry.

Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (ML) methods, including HRNet, MobileNet, Xception, and Swin Transformer, within the improved DeepLabv3+ architecture, Segformer and random forest (RF). The results showed that the Segformer based on the combination of the Transformer architecture encoder and the lightweight multilayer perceptron (MLP) decoder achieved an overall accuracy (OA) value of 91.06%, a mean F1 Score (mF1) value of 89.26% and a mean Intersection over Union (mIoU) value of 80.70%. The Segformer outperformed other DL methods by combining the results of multiple evaluation metrics. Except for Swin Transformer, which was slightly lower than RF in OA, all DL methods significantly outperformed RF methods in accuracy for the main mapping objects, with mIoU improving by about 13.5~26%. The predicted images of paddy rice and winter wheat from the Segformer were characterized by high mapping accuracy, clear field edges, distinct detail features and a low false classification rate. Consequently, DL is an efficient option for fast and accurate mapping of paddy rice and winter wheat based on RS imagery.

With the increase in heat power density in modern integrating electronics, thermal interface materials (TIM) that can efficiently fill the gaps between the heat source and heat sinks and enhance heat dissipation are urgently needed owing to their high thermal conductivity and excellent mechanical durability. Among all the emerged TIMs, graphene-based TIMs have attracted increasing attention because of the ultrahigh intrinsic thermal conductivity of graphene nanosheets. Despite extensive efforts, developing high-performance graphene-based papers with high through-plane thermal conductivity remains challenging despite their high in-plane thermal conductivity. In this study, a novel strategy for enhancing the through-plane thermal conductivity of graphene papers by in situ depositing AgNWs on graphene sheets (IGAP) was proposed, which could boost the through-plane thermal conductivity of the graphene paper up to 7.48 W m−1 K−1 under packaging conditions. In the TIM performance test under actual and simulated operating conditions, our IGAP exhibits strongly enhanced heat dissipation performance compared to the commercial thermal pads. We envision that our IGAP as a TIM has great potential for boosting the development of next-generation integrating circuit electronics.

BackgroundIt is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.ObjectiveThis study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.MethodThe cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.ResultsWith respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.ConclusionA deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is currently the major cause of chronic liver disease globally. Bile acids (BAs) have emerged as relevant signaling molecules that are associated with NAFLD development. This study was aimed to examine the association of serum total bile acids (TBAs) with NAFLD in a large population of Chinese subjects. Methods This cross sectional study recruited 152,336 participants from the Second Xiangya Hospital, China. NAFLD was diagnosed based on the presence of hepatic steatosis on ultrasonography, without significant alcohol consumption and other known causes for chronic liver disease. A multivariate logistic regression model was used to test for the association of serum TBAs with NAFLD, adjusting for conventional risk factors of NAFLD. Results A total of 27.4% of the participants had NAFLD. Patients with NAFLD had slightly higher TBA levels than those without, 3.4 vs. 3.0 μmol/L (p

Abstract Apolipoprotein C3 (apoC3) and apolipoprotein A5 (apoA5), encoded by APOA1/C3/A4/A5 gene cluster, are two critical regulators of plasma triglyceride (TG) metabolism. Deficiency of apoC3 or apoA5 led to significant decreased or increased plasma TG levels, respectively. Recent studies indicated apoC3 and apoA5 also played roles in plasma remnant cholesterol, high density lipoprotein (HDL) and hepatic TG metabolisms. Moreover, large scale population genetic studies indicated that loss of function mutations in APOC3 and APOA5 gene conferred decreased and increased risk of coronary artery disease (CAD), respectively. This manuscript mainly reviewed existing evidences suggesting the opposite role of apoC3 and apoA5 in lipid metabolism and CAD risk, and discussed the potential correlation between these two apolipoproteins.

Objective: Obesity-related adipose tissue dysfunction has been linked to the development of insulin resistance, type 2 diabetes, and cardiovascular disease. Impaired calcium homeostasis is associated with altered adipose tissue metabolism; however, the molecular mechanisms that link disrupted calcium signaling to metabolic regulation are largely unknown. Here, we investigated the contribution of a calcium-sensing enzyme, calcium/calmodulin-dependent protein kinase II (CAMK2), to adipocyte function, obesity-associated insulin resistance, and glucose intolerance. Methods: To determine the impact of adipocyte CAMK2 deficiency on metabolic regulation, we generated a conditional knockout mouse model and acutely deleted CAMK2 in mature adipocytes. We further used in vitro differentiated adipocytes to dissect the mechanisms by which CAMK2 regulates adipocyte function. Results: CAMK2 activity was increased in obese adipose tissue, and depletion of adipocyte CAMK2 in adult mice improved glucose intolerance and insulin resistance without an effect on body weight. Mechanistically, we found that activation of CAMK2 disrupted adipocyte insulin signaling and lowered the amount of insulin receptor. Further, our results revealed that CAMK2 contributed to adipocyte lipolysis, tumor necrosis factor alpha (TNFα)–induced inflammation, and insulin resistance. Conclusions: These results identify a new link between adipocyte CAMK2 activity, metabolic regulation, and whole-body glucose homeostasis.

The antipsychotic drug olanzapine was reported to induce nonalcoholic fatty liver disease (NAFLD), whereas the underlying mechanism remains incompletely understood. This study investigated whether apolipoprotein A5 (apoA5) and sortilin, two interactive factors involved in NAFLD pathogenesis, are implicated in olanzapine-induced NAFLD. In our study, at week 8, olanzapine treatment successfully induced hepatic steatosis in female C57 BL/6 J mice, which was independent of body weight gain. Likewise, olanzapine effectively mediated hepatocyte steatosis in HepG2 cells characterized by substantially elevated intracellular lipid droplets. Increased plasma triglyceride concentration and decreased plasma apoA5 levels were observed in mice treated with 8-week olanzapine. Surprisingly, olanzapine markedly enhanced hepatic apoA5 protein levels in mice, without a significant effect on rodent hepatic ApoA5 mRNA. Our in vitro study showed that olanzapine reduced apoA5 protein levels in the medium and enhanced apoA5 protein levels in hepatocytes, whereas this drug exerted no effect on hepatocyte APOA5 mRNA. By transfecting APOA5 siRNA into HepG2 cells, it was demonstrated that APOA5 knockdown effectively reversed olanzapine-induced hepatocyte steatosis in vitro. In addition, olanzapine drastically increased sortilin mRNA and protein levels in vivo and in vitro. Interestingly, SORT1 knockdown reduced intracellular apoA5 protein levels and increased medium apoA5 protein levels in vitro, without affecting intracellular APOA5 mRNA levels. Furthermore, SORT1 knockdown greatly ameliorated hepatocyte steatosis in vitro. This study provides the first evidence that sortilin inhibits the hepatic apoA5 secretion that is attributable to olanzapine-induced NAFLD, which provides new insight into effective strategies against NAFLD for patients with schizophrenia administered olanzapine.

Background: Non-alcoholic fatty liver disease (NAFLD) is not uncommon in non-obese subjects, referred to as non-obese NAFLD. It is not fully determined whether non-obese NAFLD is associated with increased risks of type 2 diabetes (T2D) and coronary artery disease (CAD) in Chinese. This study aimed to examine the association between NAFLD and risks of T2D and CAD in a non-obese Chinese population.Methods: The present cohort study included two stages. In the first cross-sectional study, 16,093 non-obese subjects with a body max index (BMI) < 25.0 kg/m2 were enrolled from The Second Xiangya Hospital, China, from 2011 to 2014. Hepatic steatosis was evaluated by ultrasonography examination. Logistic regression analyses were used to examine the association of non-obese NAFLD with T2D and CAD at baseline. In the subsequent 5-year follow-up study, 12,649 subjects free of T2D and CAD at baseline were included, and the incidence of T2D and CAD were observed. Cox proportional hazard regression analyses were performed to determine the risk of incident T2D and CAD with NAFLD.Results: At baseline, the prevalence of NAFLD, T2D and CAD were 10.7% (1,717/16,093), 3.3% (529/16,093) and 0.7% (113/16,093), respectively. The univariate logistic regression analyses showed NAFLD associated with both T2D and CAD. Moreover, in a multivariate logistic regression model, NAFLD remained independently associated with T2D (OR: 2.7, 95% CI: 2.2–3.3, p < 0.001). However, no significant association was found between NAFLD and CAD by the multivariate logistic regression analyses (OR: 1.1, 95% CI: 0.6–1.8, p = 0.854). During a 5-year follow-up period, 177 (1.4%) patients developed T2D, and 134 (1.1%) developed CAD, respectively. In univariate Cox regression models, NAFLD associated with both T2D and CAD. Moreover, the multivariate Cox regression analysis revealed that NAFLD independently associated with an increased risk of T2D (HR: 2.3, 95% CI: 1.7–3.2, p < 0.001). However, the association between NAFLD and incident CAD was lost in the multivariate Cox regression analysis (HR = 1.5, 95% CI: 1.0–2.4, p = 0.059).Conclusions: NAFLD was an independent risk factor for T2D in non-obese subjects. However, no significant association was observed between non-obese NAFLD and incident CAD after adjusting other traditional cardiovascular risk factors, suggesting these factors might mediate the increased incidence of CAD in non-obese NAFLD patients.

Background: Circular RNAs (circRNAs) are a class of endogenous RNAs that are involved in osteosarcoma progression. Hsa_circ_0010220 (circ_0010220) is a circRNA generated by gene Rho Guanine Nucleotide Exchange Factor 10 Like (ARHGEF10L) and is upregulated in osteosarcoma, but its functional role in osteosarcoma is limited studied. This study aimed to illustrate the regulatory mechanism underlying circ_0010220 in osteosarcoma. Methods: 51 paired tumor and normal tissues were obtained from osteosarcoma patients. circ_0010220, microRNA (miR)-198 and Syntaxin 6 (STX6) abundances were examined by quantitative reverse transcription polymerase chain reaction and western blot. Cell proliferation, cell cycle, apoptosis, migration and invasion were analyzed via Cell Counting Kits-8 (CCK-8), colony formation, flow cytometry and transwell analyses. Target relationship was verified via dual-luciferase reporter analysis, RNA immunoprecipitation and pull-down. The in vivo function was analyzed using a xenograft model. Results: Circ_0010220 was elevated in osteosarcoma tissues and cells, and was related to the lower survival rate of osteosarcoma patients. Circ_0010220 knockdown inhibited cell proliferation, migration and invasion, but induced cell cycle arrest and apoptosis in vitro. Besides, circ_0010220 silence curbed the growth of xenograft osteosarcoma tumors in vivo. Mechanistic research revealed that miR-198 is a target of circ_0010220, and directly targets STX6. Moreover, circ_0010220 upregulated the expression of STX6 by sponging miR-198 to regulate cell proliferation, migration, invasion, cell cycle, and apoptosis. Conclusion: Circ_0010220 contributes to osteosarcoma progression through mediating miR-198/STX6 axis, which might be a novel therapeutic target for osteosarcoma therapy.

Owing to the unfavorable wetting and density difference between graphene and copper, it remains challenging to achieve homogeneous dispersion of graphene for utilizing the unique nature of graphene in copper matrix composites. Here, we design an in-situ process to fabricate graphene-like carbon (GLC) reinforcing copper matrix composites: GLC can be directly fabricated on commercial copper particles using modified PECVD method followed by vacuum hot pressing, which is high-efficiency and can be massively produced for graphene reinforced metal matrix composites in industrial level. After hot pressing, the GLC with ultralow content (170 to 350 ppm) can be uniformly dispersed and tightly embedded within the copper matrix. A remarkable thermal conductivity enhancement efficiency of 85% and enhanced thermal conductivity (439 W m−1 K−1), accompanied by the higher wear resistance, can be obtained in our GLC reinforced copper matrix composites. In actual arc ablation performance measurement, the breakdown strength and relative arc ablation resistance of GLC/Cu composites can be significantly improved by 106.5% and 33.3% than pure copper, respectively, demonstrating GLC/Cu composites a promising candidate for application as high voltage electrical contacts.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a common hepatic disease with an increasing prevalence but an unclear aetiology. This study aimed to investigate the functional implications of microRNA-122 (miR-122) in the pathogenesis of NAFLD and the possible molecular mechanisms. Methods Both in vitro and in vivo models of NAFLD were generated by treating HepG2 and Huh-7 cells with free fatty acids (FFA) and by feeding mice a high-fat diet (HFD), respectively. HE and Oil Red O staining were used to examine liver tissue morphology and lipid deposition, respectively. Immunohistochemical (IHC) staining was used to examine Sirt1 expression in liver tissues. qRT-PCR and Western blotting were employed to measure the expression of miR-122, Sirt1, and proteins involved in lipogenesis and the AMPK pathway. Enzyme-linked immunosorbent assay (ELISA) was used to quantify triglyceride (TG) levels in HepG2 and Huh-7 cells and in liver tissues. The interaction between miR-122 and the Sirt1 gene was further examined by a dual luciferase reporter assay and RNA-immunoprecipitation (RIP). Results NAFLD hepatic tissues and FFA-treated HepG2 and Huh-7 cells presented excess lipid production and TG secretion, accompanied by miR-122 upregulation, Sirt1 downregulation, and potentiated lipogenesis-related genes. miR-122 suppressed Sirt1 expression via binding to its 3′-untranslated region (UTR). Knockdown of miR-122 effectively mitigated excessive lipid production and suppressed the expression of lipogenic genes in FFA-treated HepG2 and Huh-7 cells via upregulating Sirt1. Furthermore, miR-122 knockdown activated the LKB1/AMPK signalling pathway. Conclusion The inhibition of miR-122 protects hepatocytes from lipid metabolic disorders such as NAFLD and suppresses lipogenesis via elevating Sirt1 and activating the AMPK pathway. These data support miR-122 as a promising biomarker and drug target for NAFLD.

Abstract Background There is a mutual interaction between inflammation and endocrine disorders in the development of coronary artery disease (CAD). Growth differentiation factor-15 (GDF-15) is associated with CAD, and the effects of testosterone on CAD as reported in literature have been considered as anti-atherosclerotic. The present study aimed to examine the possible association between serum GDF-15 and testosterone in male CAD patients. Methods GDF-15 and testosterone concentrations were determined in blood samples of 426 male patients with CAD and 220 male controls. Serum concentrations of hs-CRP, and other baseline characteristics were also measured. Results Serum levels of GDF-15 were higher in CAD patients when compared to controls, and testosterone concentrations were lower (p

Agricultural terraces, as typically artificial landforms, have maintained an important role throughout human history. Terrace ridges, which form the main constructive part of terraces, face the greatest risk from gully or gravity erosion. Mapping terrace ridges can benefit agricultural production management and soil-and-water conservation. This study presents a contour-directional detection method that automatically maps terrace ridges by considering the image features and topographic features of terraces. First, the terrace areas were extracted via object-based image analysis. Second, the Canny edge detector was employed to extract all edges from the image, and a digital elevation model (DEM) was used to generate the contour direction. The contour direction and edge image were then overlaid to implement the contour-directional detection method, which detects ridges along the contour direction. Finally, those candidate terrace ridges which lengths exceed an appropriate threshold were identified as terrace ridges. Five study areas located in the Loess Plateau of China were used to validate the proposed method. The results showed that the correctness, completeness, and quality ranged from 82.57% to 94.92%, from 82.51% to 88.39%, and from 70.28% to 84.40%, respectively. However, the proposed method is widely available and can be easily applied by using open global images and DEMs, such as Google Earth images and ASTER GDEM.

Abstract Background The contributions of inflammation, triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) to the residual risk of cardiovascular events have not been determined in a large cohort of Chinese population before. This study was aimed to investigate the association of serum levels of high sensitive C reactive protein (hs-CRP), TG and HDL-C with the residual risk of cardiovascular events in patients with stable coronary artery disease (CAD). Methods We enrolled 4090 patients with stable CAD from 13 hospitals in China. All participants received optimal medical treatment (OMT) for stable CAD suggested by guidelines and were followed. The endpoint measures were the first occurrence of a major adverse cardiovascular event (MACE), defined as cardiovascular death, non-fatal myocardial infarction, non-fatal stroke or unplanned coronary revascularization. Cox proportional regression analysis was conducted to identify independent predictors of MACE. Results We found that hs-CRP and HDL-C levels were associated with coronary lesion severity at baseline (both p

Abstract Graphene is usually embedded into polymer matrices for the development of thermally conductive composites, preferably forming an interconnected and anisotropic framework. Currently, the directional self‐assembly of exfoliated graphene sheets is demonstrated to be the most effective way to synthesize anisotropic graphene frameworks. However, achieving a thermal conductivity enhancement (TCE) over 1500% with per 1 vol% graphene content in polymer matrices remains challenging, due to the high junction thermal resistance between the adjacent graphene sheets within the self‐assembled graphene framework. Here, a multiscale structural modulation strategy for obtaining highly ordered structure of graphene framework and simultaneously reducing the junction thermal resistance is demonstrated. The resultant anisotropic framework contributes to the polymer composites with a record‐high thermal conductivity of 56.8–62.4 W m−1 K−1 at the graphene loading of ≈13.3 vol%, giving an ultrahigh TCE per 1 vol% graphene over 2400%. Furthermore, thermal energy management applications of the composites as phase change materials for solar‐thermal energy conversion and as thermal interface materials for electronic device cooling are demonstrated. The finding provides valuable guidance for designing high‐performance thermally conductive composites and raises their possibility for practical use in thermal energy storage and thermal management of electronics.