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Abstract A hallmark of glioma cells, particularly glioblastoma multiforme (GBM) cells, is their resistance to apoptosis. Accumulating evidences has demonstrated that CARD16, a caspase recruitment domain (CARD) only protein, enhances both anti-apoptotic and tumorigenic properties. Nevertheless, there is a limited understanding of the expression and functional role of CARD16 in glioma. This study seeks to investigate, through in silico analysis and clinical specimens, the role of CARD16 as a potential tumor promoter in glioma. Functional assays and molecular studies revealed that CARD16 promotes tumorigenesis and suppresses apoptosis in glioma cells. Moreover, knockdown of CARD16 enhances the expression of the FOXO1/TRAIL axis in GBM cells. Additionally, FOXO1 downregulation in CARD16 knockdown GBM cells restores proliferation and reduces apoptosis. Further investigation demonstrated that elevated P21 expression inhibits CDK2-mediated FOXO1 phosphorylation and ubiquitination in CARD16-knockdown GBM cells. Collectively, these findings suggest that CARD16 is a tumor-promoting molecular in glioma via downregulating FOXO1/TRAIL axis, and suppressing TRAIL-induced apoptosis. The CARD16 gene presents significant potential for prognostic prediction and advances in innovative apoptotic therapeutics.

Abstract Background Patient-reported outcome (PRO) is a distinct and indispensable dimension of clinical characteristics and recent advances have made remote PRO measurement possible. Sex difference in PRO of Parkinson’s disease (PD) is hardly extensively researched. Methods A smartphone-based self-management platform, offering remote PRO measurement for PD patients, has been developed. A total of 1828 PD patients, including 1001 male patients and 827 female patients, were enrolled and completed their PRO submission through this platform. Results Sex differences in PROs have been identified. The female group had a significantly lower height, weight, and body mass index (BMI) than the male group (P

Abstract A new Parkinson’s disease (PD) subtyping model has been recently proposed based on the initial location of α-synuclein inclusions, which divides PD patients into the brain-first subtype and the body-first subtype. Premotor RBD has proven to be a predictive marker of the body-first subtype. We found compared to PD patients without possible RBD (PDpRBD–, representing the brain-first subtype), PD patients with possible premotor RBD (PDpRBD+, representing the body-first subtype) had lower Movement Disorders Society Unified Parkinson’s Disease Rating Scale part III (MDS UPDRS-III) score (p = 0.022) at baseline but presented a faster progression rate (p = 0.009) in MDS UPDRS-III score longitudinally. The above finding indicates the body-first subtype exhibited a faster disease progression in motor impairments compared to the brain-first subtype and further validates the proposed subtyping model.

Abstract Exciton binding energy (Eb) has been regarded as a critical parameter in charge separation during photovoltaic conversion. Minimizing the Eb of the photovoltaic materials can facilitate the exciton dissociation in low‐driving force organic solar cells (OSCs) and thus improve the power conversion efficiency (PCE); nevertheless, diminishing the Eb with deliberate design principles remains a significant challenge. Herein, bulky side chain as steric hindrance structure was inserted into Y‐series acceptors to minimize the Eb by modulating the intra‐ and intermolecular interaction. Theoretical and experimental results indicate that steric hindrance‐induced optimal intra‐ and intermolecular interaction can enhance molecular polarizability, promote electronic orbital overlap between molecules, and facilitate delocalized charge transfer pathways, thereby resulting in a low Eb. The conspicuously reduced Eb obtained in Y‐ChC5 with pinpoint steric hindrance modulation can minimize the detrimental effects on exciton dissociation in low‐driving‐force OSCs, achieving a remarkable PCE of 19.1% with over 95% internal quantum efficiency. Our study provides a new molecular design rationale to reduce the Eb.

Abstract China’s extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of China’s planted forest area and its carbon storage remain uncaptured. Here we reveal such changes in China’s planted forests from 1990 to 2020 using satellite and field data. Results show a doubling of planted forest area, a trend that intensified post-2000. These changes lead to China’s planted forest carbon storage increasing from 675.6 ± 12.5 Tg C in 1990 to 1,873.1 ± 16.2 Tg C in 2020, with an average rate of ~ 40 Tg C yr−1. The area expansion of planted forests contributed ~ 53% (637.2 ± 5.4 Tg C) of the total above increased carbon storage in planted forests compared with planted forest growth. This proactive policy-driven expansion of planted forests has catalyzed a swift increase in carbon storage, aligning with China’s Carbon Neutrality Target for 2060.

The safety and reliability of cables are directly linked to the safe operation of bridges as crucial load-bearing components. The accuracy and efficiency of current methods are still insufficient to segment and quantitatively characterize surface defects on cables. This paper proposes a novel and efficient method for the refined segmentation and quantitative characterization of bridge cable surface defects based on an improved you only look at coefficients++ (YOLACT++) model. For defect segmentation, several enhancements have been made to the YOLACT++ model, including incorporating the convolutional block attention module (CBAM), optimizing the anchor box generation mechanism, and introducing the smoother Mish activation function, which enhances both the accuracy and speed of defect detection. For quantitative characterization, the method adopts surface correction algorithms, pixel statistics, and crack skeleton extraction, resulting in a more accurate representation of defect areas and the length and width of cracks. Compared to the baseline model, the optimized model achieves a 3.58 % improvement in mean average precision (mAP) and an inference speed of 25.74 frames per second (FPS). The results show that the error is within 10 % compared with the manually measured area, which offers a more objective and comprehensive foundation for cable safety assessment.

The deformation of long-span suspension bridges in multiple loads is an important indictor to reflect their operation state. However, the correlation between multiple loads and structural deformation is difficult to quantify. Therefore, this study proposes an explainable machine learning model for the load-deformation correlation in long-span suspension bridges using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Firstly, the structural health monitoring system for a suspension bridge was used to construct the dataset for the training and testing of XGBoost model. Herein, temperature, wind and vehicle loads were used as the input variables, while midspan deflections and expansion joint displacements were treated as outputs. Subsequently, the hyperparameters of XGBoost model were optimized using grid search and 5-fold cross-validation to ensure its prediction performance. Then, the prediction results were compared with other four machine learning methods (i.e., linear regression, artificial neural networks, gradient boosted decision trees and CatBoost). Finally, the correlation between different loads and displacement responses were explained by the SHAP method to identify the contribution of the loads on deformation. The results show that the XGBoost model has the highest prediction accuracy. Compared to vehicle and wind loads, temperature significantly affects the deformation of long-span suspension bridges during daily operation. The effects of temperature and wind on bridge deformation are independent, and there is no significant interaction between these two factors.

Accurate prediction of the state-of-charge (SOC) of battery energy storage system (BESS) is critical for its safety and lifespan in electric vehicles. To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction, this paper introduces a novel multi-scale fusion (MSF) model based on gated recurrent unit (GRU), which is specifically designed for complex multi-step SOC prediction in practical BESSs. Pearson correlation analysis is first employed to identify SOC-related parameters. These parameters are then input into a multi-layer GRU for point-wise feature extraction. Concurrently, the parameters undergo patching before entering a dual-stage multi-layer GRU, thus enabling the model to capture nuanced information across varying time intervals. Ultimately, by means of adaptive weight fusion and a fully connected network, multi-step SOC predictions are rendered. Following extensive validation over multiple days, it is illustrated that the proposed model achieves an absolute error of less than 1.5% in real-time SOC prediction.

Abstract The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RGB (red, green, and blue) image recognition technology is widely used in the study of food appearance evaluation. In addition, the hyperspectral image has outstanding performance in predicting the nutrient content of samples. However, there are few reports on the research of classification models based on the fusion data of these two sources of images. We collected RGB and hyperspectral images at four different storage times of VS. The ENVI software was adopted to extract the hyperspectral information, and the RGB images were reconstructed based on the downsampling technology. Then, the one-dimensional hyperspectral data was transformed into a two-dimensional space, which allows it to be overlaid and concatenated with the RGB image data in the channel direction, thereby generating fused data. Compared with four commonly used machine learning models, the deep learning model ResNet18 has higher classification accuracy and computational efficiency. Based on the above results, a novel classification model named ResNet-R &H, which is based on the residual networks (ResNet) structure and incorporates the fusion data of RGB and hyperspectral images, was proposed. The ResNet-R &H can achieve a testing accuracy of 97.6%, which demonstrates a significant enhancement of 4.0% and 7.2% compared to the distinct utilization of hyperspectral data and RGB data, respectively. Overall, this research is significant in providing a unique, efficient, and more accurate classification approach in evaluating the freshness of vegetable soybean. The method proposed in this study can provide a theoretical reference for classifying the freshness of fruits and vegetables to improve classification accuracy and reduce human error and variability.

Over the last decade, a number of techniques for individual tree segmentation has been developed for terrestrial laser scanning data. The superpoint segmentation algorithm based on point cloud has been widely used in individual tree segmentation because of its high efficiency and numerous geometric features. However, this algorithm is generally developed for specific tree species and forest types, limiting its universality and performance for different forest types. To handle this problem, a new method based on the topology of tree branches for individual tree segmentation was proposed. Focusing on the general topological structure of trees, the proposed method iteratively assigns each branch based on its directivity to its upper branch at the superpoint level. The proposed method was tested compared with the original superpoint method and an ecological method in six sample plots with different forest conditions. In such plots, the proposed method achieved anticipated performance with an average accuracy of 40% improvements compared with the other two methods, especially in complicated forest conditions. Experimental results also showed an improved average accuracy of 70% compared with the original superpoint method at the point level. This proposed method can effectively expand the universality of the superpoint method to further advance ecological and forest research.

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

Abstract Increasing drought frequency and severity in a warming climate threaten forest ecosystems with widespread tree deaths. Canopy structure is important in regulating tree mortality during drought, but how it functions remains controversial. Here, we show that the interplay between tree size and forest structure explains drought-induced tree mortality during the 2012-2016 California drought. Through an analysis of over one million trees, we find that tree mortality rate follows a “negative-positive-negative” piecewise relationship with tree height, and maintains a consistent negative relationship with neighborhood canopy structure (a measure of tree competition). Trees overshadowed by tall neighboring trees experienced lower mortality, likely due to reduced exposure to solar radiation load and lower water demand from evapotranspiration. Our findings demonstrate the significance of neighborhood canopy structure in influencing tree mortality and suggest that re-establishing heterogeneity in canopy structure could improve drought resiliency. Our study also indicates the potential of advances in remote-sensing technologies for silvicultural design, supporting the transition to multi-benefit forest management.

BackgroundThe relationship between the optical flow ratio (OFR) and clinical outcomes in patients with coronary artery disease (CAD) after percutaneous coronary stent implantation (PCI) remains unknown.ObjectiveTo examine the correlation between post-PCI OFR and clinical outcomes in patients with CAD following PCI.MethodsPatients who underwent optical coherence tomography (OCT) guided PCI at Guangdong Provincial People's Hospital were retrospectively and continuously enrolled. Clinical data, post-PCI OCT characteristics, and OFR measurements were collected and analyzed to identify predictors of target vessel failure (TVF) after PCI.ResultsAmong 354 enrolled patients, 26 suffered TVF during a median follow-up of 484 (IQR: 400–774) days. Post-PCI OFR was significantly lower in the TVF group than in the non-TVF group (0.89 vs. 0.93; P = 0.001). In multivariable Cox regression analysis, post-PCI OFR (HR per 0.1 increase: 0.60; 95% CI: 0.41–0.89; P = 0.011), large stent edge dissection (HR: 3.85; 95% CI: 1.51–9.84; P = 0.005) and thin-cap fibroatheroma (TCFA) (HR: 2.95; 95% CI: 1.19–7.35; P = 0.020) in the non-stented segment were independently associated with TVF. In addition, the inclusion of post-PCI OFR to baseline characteristics and post-PCI OCT findings improved the predictive power of the model to distinguish subsequent TVF after PCI (0.838 vs. 0.796; P = 0.028).ConclusionThe post-PCI OFR serves as an independent determinant of risk for TVF in individuals with CAD after PCI. The inclusion of post-PCI OFR assessments, alongside baseline characteristics and post-PCI OCT findings, substantially enhances the capacity to differentiate the subsequent manifestation of TVF in CAD patients following PCI.

Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth, and severe anthropogenic-induced grassland ecosystem function declines have been reported recently. Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies. Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate (e.g., light, water, and wind), and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions. However, the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated. Here, we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China. Aboveground net primary productivity (ANPP) and resilience, two indicators of grassland ecosystem functions, were measured from field data and satellite remote sensing data. Terrestrial laser scanning data were collected to measure canopy structure (represented by mean height and canopy cover). Our results showed that plant species diversity was positively correlated to canopy structural traits, and negatively correlated to human activity intensity. Canopy structure was a significant indicator for ANPP and resilience, but their correlations were inconsistent under different human activity intensity levels. Compared to plant species diversity, canopy structural traits were better indicators for grassland ecosystem functions, especially for ANPP. Through structure equation modeling analyses, we found that plant species diversity did not have a direct influence on ANPP under human disturbances. Instead, it had a strong indirect effect on ANPP by altering canopy structural traits. As to resilience, plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover. This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances, which should be included in future grassland monitoring and management.

Reinforced concrete slabs are widely used in building structures due to their economic, durable, and aesthetic advantages. The determination of their ultimate strength often hinges on punching shear strength. Presently, methods such as closed hoops, steel bending, and fiber reinforcement are employed to enhance punching shear strength, with fiber reinforcement gaining popularity due to its ease of implementation and efficacy in improving concrete durability. This study introduces a novel approach employing six machine learning algorithms rooted in decision trees and decision tree-based ensemble learning to predict punching shear strength in steel fiber-reinforced concrete slabs. To overcome experimental data limitations, a data augmentation approach based on the Gaussian mixture model is employed. The validation of the data augmentation is conducted through “synthetic training—real testing” and “real training—real testing”. Additionally, the best machine learning model is analyzed for explainability using Shapley Additive exPlanation (SHAP). Results demonstrate that the proposed data augmentation method effectively captures the original data distribution, enhancing the robustness and accuracy of the machine learning model. Moreover, SHAP provides better insights into the features influencing punching shear strength. Thus, the proposed data enhancement model offers a reliable approach for modeling small experimental datasets in structural engineering.

This study focuses on the prediction of concrete cover separation (CCS) in reinforced concrete beams strengthened by fiber-reinforced polymer (FRP) in flexure. First, machine learning models were constructed based on linear regression, support vector regression, BP neural networks, decision trees, random forests, and XGBoost algorithms. Secondly, the most suitable model for predicting CCS was identified based on the evaluation metrics and compared with the codes and the researcher’s model. Finally, a parametric study based on SHapley Additive exPlanations (SHAP) was carried out, and the following conclusions were obtained: XGBoost is best-suited for the prediction of CCS and codes, and researchers’ model accuracy needs to be improved and suffers from over or conservative estimation. The contributions of the concrete to the shear force and the yield strength of the reinforcement are the most important parameters for the CCS, where the shear force at the onset of CCS is approximately proportional to the contribution of the concrete to the shear force and approximately inversely proportional to the yield strength of the reinforcement.

Abstract Glial fibrillary acidic protein (GFAP) has been suggested as a biomarker for reactive astrogliosis. We measured the levels of plasma GFAP by Simoa in 60 patients with PD with normal cognition, 63 with mild cognitive impairment (PD-MCI), 24 with dementia (PDD) and 15 healthy controls. A subgroup of patients with PD-MCI (n = 31) was followed up for 4.1 ± 2.3 years. Compared with healthy controls, plasma GFAP levels were elevated in patients with PDD (adjusted P

Plate end (PE) debonding and intermediate crack (IC) debonding are the main failure modes of fiber reinforced polymer (FRP) strengthened reinforced concrete (RC) beams in flexure. Different failure modes exhibit different failure characteristics. Therefore, accurately identifying the failure mode is of great significance in selecting methods to prevent the debonding of the strengthened beams. This study first established a primary indicator system through literature research. Then one hundred and eighty-eight FRP-strengthened RC beams containing PE and IC debonding were collected from the published literature of forty-eight researchers. After that, correlation analysis and grey correlation analysis were used to study the data of two failure modes. Finally, the indicator system for the prediction of failure modes was established. After the indicator system was established, six machine learning algorithms, including K-nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF), back propagation neural network (BPNN), logistic regression (LR), and support vector machine (SVM), were used to build the prediction model of failure modes. The evaluation of the models shows that the coefficient of variation of the accuracy of the decision tree is merely 5.4%, which has the best robustness; the average accuracy of the random forest for the two failure modes reaches 93% and 98%, which has the highest precision.

Objective: The goal of this study was to examine the prognostic performance of optical flow ratio (OFR) among patients with coronary artery disease (CAD) after percutaneous coronary intervention (PCI). Methods: We recruited patients with CAD undergoing optical coherence tomography (OCT)-directed PCI between January 2019 and June 2021 for our single-center, hospital-based, retrospective cohort investigation. We assessed the link between post-PCI OFR and major adverse cardiovascular events (MACE) via multivariate Cox regression analysis. Results: Receiver operating characteristic analysis revealed that the best post-PCI OFR threshold for MACE was 0.91, and introduction of OFR into the baseline profile and OCT results markedly enhanced MACE identification after PCI. On the basis of survival curves, patients with OFR ≤0.91 (P < 0.001) and thin-cap fibroatheroma (TCFA) (P = 0.007) exhibited higher MACE incidence, and myocardial infarction (MI) incidence was considerably greater among patients with OFR ≤0.91 (P < 0.001), compared with OFR >0.91. Multivariate Cox regression analysis suggested that OFR ≤0.91 (hazard ratio [HR]: 3.60; 95% confidence interval [CI]: 1.24–10.44; P = 0.019), and TCFA (HR: 3.63; 95% CI: 1.42–9.20; P = 0.007) were independent risk factors for MACE, and OFR ≤0.91 was independently associated with MI (HR: 14.64; 95% CI: 3.27–65.54; P < 0.001). Conclusion: OFR after PCI is an independent MACE bio-indicator among patients with CAD. Adding OFR to post-PCI OCT results may potentially enhance MACE prediction.

Abstract Commercial buildings are consuming an increasing amount of energy, and accurate load demand forecasting is critical for the reliable operation of power systems and the efficient use of resources. Therefore, in this paper, a short‐term commercial load forecasting model based on tunicate swarm algorithm (TSA) combined with an extreme learning machine (ELM) under peak‐valley features is proposed as a research case for a shopping mall in Romania. This paper's overall structure is divided into two steps. In the first step, the 24‐h day is divided into six periods by analyzing the daily load characteristics of the training set, and the peak and valley loads are obtained. The ELM optimized by TSA (TSA‐ELM) algorithm is then used to forecast the peak and valley values of the test set one day ahead. In the second step, the actual load, peak, and valley for the previous week of historical load are chosen using the maximum information coefficient (MIC). Following that, the MIC ≥ 0.8 features are added to the TSA‐ELM to achieve short‐term commercial electricity load forecasting. The results show that the PV (Peak & Valley)‐TSA‐ELM model proposed in this paper has higher prediction accuracy compared with other models. Taking ELM as an example, compared with the traditional ELM, the mean absolute error, root mean square error, and mean absolute percentage error of PV‐TSA‐ELM are reduced by 20.59%, 20.13%, and 19.19% on average in the three commercial data sets. The proposed model is validated with an industrial data set, and ideal results are obtained, which verifies the effectiveness and superiority of the proposed method.

Abstract Hybrid cycloalkyl‐alkyl side chains are considered a unique composite side‐chain system for the construction of novel organic semiconductor materials. However, there is a lack of fundamental understanding of the variations in the single‐crystal structures as well as the optoelectronic and energetic properties generated by the introduction of hybrid side chains in electron acceptors. Herein, symmetric/asymmetric acceptors (Y‐C10ch and A‐C10ch) bearing bilateral and unilateral 10‐cyclohexyldecyl are designed, synthesized, and compared with the symmetric acceptor 2,2′‐((2Z,2′Z)‐((12,13‐bis(2‐butyloctyl)‐3,9 bis(ethylhexyl)‐12,13‐dihydro‐[1,2,5]thiadiazolo[3,4‐e]thieno[2″,3″′:4′,5′]thieno[2′,3′:4,5] pyrrolo[3,2‐g]thieno[2′,3′:4,5]thieno[3,2‐b]indole‐2,10‐ diyl)bis(methanylylidene))bis(5,6‐difluoro‐3‐oxo‐2,3‐dihydro‐1H‐indene‐2,1‐diylidene))dimalononitrile (L8‐BO). The stepwise introduction of 10‐cyclohexyldecyl side chains decreases the optical bandgap, deepens the energy level, and enables the acceptor molecules to pack closely in a regular manner. Crystallographic analysis demonstrates that the 10‐cyclohexyldecyl chain endows the acceptor with a more planar skeleton and enforces more compact 3D network packing, resulting in an active layer with higher domain purity. Moreover, the 10‐cyclohexyldecyl chain affects the donor/acceptor interfacial energetics and accelerates exciton dissociation, enabling a power conversion efficiency (PCE) of >18% in the 2,2′‐((2Z,2′Z)‐((12,13‐bis(2‐ethylhexyl)‐3,9‐diundecyl12,13‐dihydro‐[1,2,5]thiadiazolo[3,4‐e]thieno[2″,3″′:4′,5′]thieno[2′,3′:4,5]pyrrolo[3,2‐g]thieno[2′,3′:4,5]thieno[3,2‐b]indole‐2,10‐diyl)bis(methanylylidene))bis(5,6‐difluoro‐3‐oxo‐2,3‐dihydro‐1H‐indene‐2,1‐diylidene))dimalononitrile (Y6) (PM6):A‐C10ch‐based organic solar cells (OSCs). Importantly, the incorporation of Y‐C10ch as the third component of the PM6:L8‐BO blend results in a higher PCE of 19.1%. The superior molecular packing behavior of the 10‐cyclohexyldecyl side chain is highlighted here for the fabrication of high‐performance OSCs.

Abstract With urbanization, municipal solid waste (MSW) transfer station as an important link in the process of refuse collection and transportation is closer to residents’ living areas. At present, studies on airborne antibiotic resistance genes (ARGs) generated during MSW management are still few and not comprehensive enough. In this study, metagenomics and high-throughput sequencing methods were used to analyze the composition and content of bioaerosol and airborne ARGs in the waste reception hall and the top vent of three MSW transfer stations. 265 ARG subtypes were detected. The main subtypes were bacA, multidrug_transporter, mexW, sul2 and macB, and the main types were multidrug resistance genes and bacitracin resistance genes. Different microbes and ARGs showed diverse release characteristics, but in general, the concentration of bacteria and ARGs at the top vent of the transfer station was higher than that in the waste reception hall, and fungi were more sensitive than bacteria when passing through the odor treatment system connecting between the top vent and the waste reception hall. For ARGs, daily intake of a worker was calculated to be 1.08 × 1010–2.79 × 1010 copies d− 1 and environmental release was 2.88 × 109–9.49 × 109 copies m− 3. Therefore, control measures for airborne ARGs and pathogenic microorganisms are urgently needed to ensure the health of workers and surrounding residents.

In recent years, the application of artificial intelligence-based methods to engineering problems has received consistent praise for their high predictive accuracy. This paper utilizes a BP neural network to predict the strength of steel–concrete composite beam shear connectors with high-strength friction-grip bolts (HSFGBs). These connectors are widely used in bridge and building construction due to their superior strength and stiffness compared to traditional beams. A validated finite element model was used to predict the strength of HSFGB shear connectors. A reliable database was created by analyzing 208 models with different characteristics for machine learning modeling. Previous studies have identified issues with result variation and overestimation or underestimation of shear connection strength. Among the machine learning methods evaluated, the backpropagation neural network model performed the best. It achieved a goodness of fit of over 93% in both the training and testing sets, with a low coefficient of variation of 6.50%. Concrete strength, bolt diameter, and bolt tensile strength were found to be important variables influencing the strength of shear connectors. Other variables showed a proportional or inverse relationship with compressive strength, except for concrete strength and bolt pretension. This study presents an accurate machine learning approach for predicting the strength of HSFGB shear connectors in steel–concrete composite beams. The study offers valuable insights into the effects of various variables on the performance of shear connection strength, providing support for structural design and analysis.

Passion fruit is a tropical liana of the Passiflora family that is commonly planted throughout the world due to its abundance of nutrients and industrial value. Researchers are committed to exploring the relationship between phenotype and genotype to promote the improvement of passion fruit varieties. However, the traditional manual phenotyping methods have shortcomings in accuracy, objectivity, and measurement efficiency when obtaining large quantities of personal data on passion fruit, especially internal organization data. This study selected samples of passion fruit from three widely grown cultivars, which differed significantly in fruit shape, size, and other morphological traits. A Micro-CT system was developed to perform fully automated nondestructive imaging of the samples to obtain 3D models of passion fruit. A designed label generation method and segmentation method based on U-Net model were used to distinguish different tissues in the samples. Finally, fourteen traits, including fruit volume, surface area, length and width, sarcocarp volume, pericarp thickness, and traits of fruit type, were automatically calculated. The experimental results show that the segmentation accuracy of the deep learning model reaches more than 0.95. Compared with the manual measurements, the mean absolute percentage error of the fruit width and length measurements by the Micro-CT system was 1.94% and 2.89%, respectively, and the squares of the correlation coefficients were 0.96 and 0.93. It shows that the measurement accuracy of external traits of passion fruit is comparable to manual operations, and the measurement of internal traits is more reliable because of the nondestructive characteristics of our method. According to the statistical data of the whole samples, the Pearson analysis method was used, and the results indicated specific correlations among fourteen phenotypic traits of passion fruit. At the same time, the results of the principal component analysis illustrated that the comprehensive quality of passion fruit could be scored using this method, which will help to screen for high-quality passion fruit samples with large sizes and high sarcocarp content. The results of this study will firstly provide a nondestructive method for more accurate and efficient automatic acquisition of comprehensive phenotypic traits of passion fruit and have the potential to be extended to more fruit crops. The preliminary study of the correlation between the characteristics of passion fruit can also provide a particular reference value for molecular breeding and comprehensive quality evaluation.

ObjectiveAlthough the seasonal variation of motor and non-motor symptoms in Parkinson's disease (PD) has been reported, the association between seasonal change and quality of life in patients with Parkinson's disease remains to be explored.MethodsWe recruited 1,036 patients with PD in this cross-sectional retrospective study. The patients were divided into four groups based on their date of assessment, according to the classical four seasons: group 1: March to May (n = 241); group 2: June to August (n = 259); group 3: September to November(n = 273); group 4: December to February (n = 263). The 39-item Parkinson's Disease Questionnaire (PDQ-39) and other clinical evaluation scales for motor and non-motor symptoms were administered. The determinants of the quality of life (QoL) were analyzed by multiple stepwise regression analyses.ResultsA significant difference in PDQ-39 was found between group 1 (spring months) and group 3 (autumn months) after correction (p = 0.002). The Unified Parkinson's Disease Rating Scale part III (UPDRS-III) score was higher in group 1 (spring months) than in group 3 (the autumn months) (p = 0.033). The most severe determinant of QoL was the UPDRS-III score in group 1 and the Geriatric Depression Scale (GDS) score in groups 2, 3, and 4.InterpretationThe current study reported seasonal fluctuation of QoL in patients with PD, with higher scores during the spring months and lower scores in the autumn months. Since the determinants for QoL also vary by season, clinicians might need to focus on specific factors across seasons before initiating therapy.

Abstract This paper explores the wake of a wind turbine yaw and its influence on the output power of the downstream wind turbine. A wind turbine model (hub height and diameter of 1.2 m and 1.0 m, respectively) was used in the experiments, and the yaw angle of the upstream wind turbine was successively varied through values of 0°, 15°, and 30°. The wind speed and the intensity of turbulence in the wake region were measured using a hot‐wire anemometer. The measuring positions were changed to 3, 5, and 7 rotor diameters behind the upstream wind turbine. In addition, the output power of the downstream wind turbine model was measured using the rotational speed torque meter. The results show that, in the case of large‐angle yaw of the upstream wind turbine, the recovery of the wind speed in the wake area was greatly improved, and up to 30% recovery was achieved. The power output of the downstream wind turbine was also significantly increased. Under the yaw conditions of 15° and 30° for the upstream wind turbine, the average output power of the downstream wind turbine increased by 17.6% and 21.6%, respectively.

Abstract Objective New subtyping classification systems of Parkinson’s disease (PD) have been proposed for phenotyping patients into three different subtypes: mild motor‐predominant (PD‐MMP), intermediate (PD‐IM) and diffuse malignant (PD‐DM). The quality of life (QoL) underlying the novel PD clinical subtypes is unknown. This study aimed explore the feasibility of the classification in Chinese PD patients and to investigate the potential heterogeneous determinants of QoL among the three subtypes. Methods 298 PD patients were enrolled, including 129 PD‐MMP patients, 121 PD‐IM patients and 48 PD‐DM patients. All patients completed the QoL assessment, clinical evaluations and neuropsychological tests. Univariate linear analysis and multiple stepwise regression analysis were performed to identify determinants of QoL. Results Compared to PD‐MMP patients, PD‐IM and PD‐DM patients had more impaired QoL. The Geriatric Depression Rating Scale (GDS) score, Non‐Motor Symptoms Questionnaire (NMSQ) score, Unified Parkinson’s Disease Rating Scale part III (UPDRS‐III) score and Epworth Sleepiness Score (ESS) were independent contributors to QoL in PD‐MMP patients. The GDS score, ESS and sniffin’ sticks screening 12 test score were independent contributors to QoL in PD‐IM patients. The GDS score and Mini Mental State Examination score were independent contributors to QoL in PD‐DM patients. Interpretation The new novel subtyping classification is feasible for Chinese PD patients. Although depression was the most crucial determinant for QoL in PD‐MMP, PD‐IM and PD‐DM patients, the other contributors of QoL in the three subtypes were heterogeneous. These findings may prompt clinicians to target specific factors for improving QoL depending on PD subtypes.

Human-scale greenery has been heavily featured in the development of planning-related theories and research. Daily exposure to street greenery is proportionately greater than exposure to parks. Several case studies have estimated the green view index (GVI), which quantifies daily exposure to street-side greenery, at the city-scale. However, the street-side greenery distribution is uneven. The relationship between the GVI and the surrounding environment has rarely been discussed. Here, we investigate the differentiation of street greenery from the physical features of a street and examine how the surrounding environment affects the physical appearance of eye-level greenery. In this work, we analyzed the GVI using the internet data crawling approach and obtained 12,232 panoramic street view images from Baidu Map for the urban zones of Hangzhou, China. A range of 17 surrounding environmental characteristics are integrated with multisource geographic data to analyze their relationship with the GVI at the microscale. Spatial econometric models are explored to provide insights into the underlying mechanisms associated with the GVI. The results showed that the surrounding environment exhibited a strong role in the physical appearance of eye-level greenness. There are two pathways through which surrounding environmental characteristics (the nature of land use and the enclosure of the street) affect the distribution of GVI. Four factors proved to be important: scenic protection policies and ecological renewal projects, vertical and horizontal green characteristics of different land uses, and facade designs dominated by enclosed exterior walls or railing walls. Interesting approaches are presented to rationally implement activities related to these factors. The urban green design process should be facilitated in terms of the trade-off between greenery and optimum land use. These findings can be useful in drafting appropriate policies and increasing eye-level greenery in cities.

It is unclear how the length of a repetitive DNA tract determines the onset and progression of repeat expansion diseases, but the dynamics of secondary DNA structures formed by repeat sequences are believed to play an important role. It was recently shown that three-way DNA junctions containing slip-out hairpins of CAG or CTG repeats and contiguous triplet repeats in the adjacent duplex displayed single-molecule FRET (smFRET) dynamics that were ascribed to both local conformational motions and longer-range branch migration. Here we explore these so-called ''mobile'' slip-out structures through a detailed kinetic analysis of smFRET trajectories and coarse-grained modeling. Despite the apparent structural simplicity, with six FRET states resolvable, most smFRET states displayed biexponential dwell-time distributions, attributed to structural heterogeneity and overlapping FRET states. Coarse-grained modeling for a (GAC)10 repeat slip-out included trajectories that corresponded to a complete round of branch migration; the structured free energy landscape between slippage events supports the dynamical complexity observed by smFRET. A hairpin slip-out with 40 CAG repeats, which is above the repeat length required for disease in several triplet repeat disorders, displayed smFRET dwell times that were on average double those of 3WJs with 10 repeats. The rate of secondary-structure rearrangement via branch migration, relative to particular DNA processing pathways, may be an important factor in the expansion of triplet repeat expansion diseases.

mRNA products are therapies that are regulated from the post-transcriptional, pre-translational stage of a gene and act upstream of protein synthesis. Compared with traditional small molecule drugs and antibody drugs, mRNA drugs had the advantages of simple design, short development cycle, strong target specificity, wide therapeutic field, and long-lasting effect. mRNA drugs were now widely used in the treatment of genetic diseases, tumors, and viral infections, and are expected to become the third major class of drugs after small molecule drugs and antibody drugs. The delivery system technology was the key to ensuring the efficacy and safety of mRNA drugs, which plays an important role in protecting RNA structure, enhancing targeting ability, reducing the dose of drug delivery, and reducing toxic side effects. Lipid nanoparticles (LNP) were the most common delivery system for mRNA drugs. In recent years, mRNA drugs have seen rapid development, with the number of drugs on the market increasing each year. The success of commercializing mRNA vaccines has driven a wave of nucleic acid drug development. mRNA drugs were clinically used in genetic diseases, oncology, and infectious diseases worldwide, while domestic mRNA clinical development was focused on COVID-19 vaccines, with more scope for future indication expansion.

Membrane computing is a branch of natural computing which has been extended to solve various optimization problems. A hybrid mode membrane-computing-based algorithm (HMMCA) is proposed in this paper to solve complex unconstrained optimization problems with continuous variables. The algorithmic framework of HMMCA translates from its distributed cell-like membrane structure and communication rule. A non-deterministic evolutionary programming method and two computational rules are applied to enhance the computational performance. In a numerical simulation, 12 benchmark test functions with different variables are used to verify the algorithmic performance. The test results and comparison with three other algorithms illustrate its effectiveness and superiority. Moreover, a case study on a proton exchange membrane fuel cell (PEMFC) system parameter optimization problem is applied to validate its practicability. The results of the simulation and comparison with seven other algorithms demonstrate its practicability.

Abstract Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Methods A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. Results Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. Conclusion We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.

Product innovation integrates technology, knowledge, management practices, and market innovation, making it essential to gain a competitive advantage. Effective management of dynamic knowledge, which is the foundation of and driving force for product innovation, is a powerful tool that allows a firm to successfully innovate, adapt to environmental changes, and improve its competitiveness. In the “nanosecond age,” unlearning and learning in an organization is crucial to a firm’s ability to promptly update its organizational knowledge and maintain innovation vitality. Based on the dynamic knowledge management perspective, this study integrates and constructs a theoretical model with environmental dynamism as the moderating variable, discusses the impact of organizational unlearning on product innovation performance, and empirically analyzes 208 valid questionnaires in the Yangtze River Delta using the multiple regression method. The results show that organizational unlearning shares a positive relationship with dynamic capabilities and product innovation performance. Dynamic capability is positively related to product innovation performance and has a partial mediating effect on the relationship between organizational unlearning and product innovation performance. Environmental dynamism shares a positive moderating effect on the relationship between organizational unlearning and product innovation performance. This study deepens the existing research on the factors that influence product innovation performance, which may help firms improve their dynamic knowledge management and product innovation performance.

Abstract Vegetation community complexity is a critical factor influencing terrestrial ecosystem stability. China, the country leading the world in vegetation greening resulting from human activities, has experienced dramatic changes in vegetation community composition during the past 30 years. However, how China's vegetation community complexity varies spatially and temporally remains unclear. Here, we examined the spatial pattern of China's vegetation community complexity and its temporal changes from the 1980s to 2015 using two vegetation maps of China as well as more than half a million field samples. Spatially, China's vegetation community complexity distribution is primarily dominated by elevation, although temperature and precipitation can be locally more influential than elevation when they become the factors limiting plant growth. Temporally, China's vegetation community complexity shows a significant decreasing trend during the past 30 years, despite the observed vegetation greening trend. Prevailing climate warming across China exhibits a significant negative correlation with the decrease in vegetation community complexity, but this correlation varies with biogeographical regions. The intensity of human activities have an overall negative influence on vegetation community complexity, but vegetation conservation and restoration efforts can have a positive effect on maintaining vegetation composition complexity, informing the critical role of vegetation management policies in achieving the sustainable development goal.

Since the outbreak of Coronavirus Disease 2019 (COVID-19), the Chinese government has taken a number of measures to effectively control the pandemic. By the end of 2021, China achieved a full vaccination rate higher than 85%. The Chinese Plan provides an important model for the global fight against COVID-19. Internet search reflects the public's attention toward and potential demand for a particular thing. Research on the spatiotemporal characteristics of online attention to vaccines can determine the spatiotemporal distribution of vaccine demand in China and provides a basis for global public health policy making. This study analyzes the spatiotemporal characteristics of online attention to vaccines and their influencing factors in 31 provinces/municipalities in mainland China with Baidu Index as the data source by using geographic concentration index, coefficient of variation, GeoDetector, and other methods. The following findings are presented. First, online attention to vaccines showed an overall upward trend in China since 2011, especially after 2016. Significant seasonal differences and an unbalanced monthly distribution were observed. Second, there was an obvious geographical imbalance in online attention to vaccines among the provinces/municipalities, generally exhibiting a spatial pattern of “high in the east and low in the west.” Low aggregation and obvious spatial dispersion among the provinces/municipalities were also observed. The geographic distribution of hot and cold spots of online attention to vaccines has clear boundaries. The hot spots are mainly distributed in the central-eastern provinces and the cold spots are in the western provinces. Third, the spatiotemporal differences in online attention to vaccines are the combined result of socioeconomic level, socio-demographic characteristics, and disease control level.

ABSTRACT Multidrug resistance (MDR) in Enterobacteriaceae including resistance to quinolones is rising worldwide. The plasmid-mediated quinolone resistance (PMQR) gene qnrS is prevalent in Enterobacteriaceae. However, the qnrS gene is rarely found in Enterobacter hormaechei (E. hormaechei). Here, we reported one multidrug resistant E. hormaechei strain M1 carrying the qnrS1 and blaTEM-1 genes. This study was to analyze the characteristics of MDR E. hormaechei strain M1. The E. hormaechei strain M1 was identified as Enterobacter cloacae complex by biochemical assay and 16S rRNA sequencing. The whole genome was sequenced by the Oxford Nanopore method. Taxonomy of the E. hormaechei was based on multilocus sequence typing (MLST). The qnrS with the other antibiotic resistance genes were coexisted on IncF plasmid (pM1). Besides, the virulence factors associated with pathogenicity were also located on pM1. The qnrS1 gene was located between insertion element IS2A (upstream) and transposition element ISKra4 (downstream). The comparison result of IncF plasmids revealed that they had a common plasmid backbone. Susceptibility experiment revealed that the E. hormaechei M1 showed extensive resistance to the clinical antimicrobials. The conjugation transfer was performed by filter membrane incubation method. The competition and plasmid stability assays suggested the host bacteria carrying qnrS had an energy burden. As far as we know, this is the first report that E. hormaechei carrying qnrS was isolated from chicken feed. The chicken feed and poultry products could serve as a vehicle for these MDR bacteria, which could transfer between animals and humans through the food chain. We need to pay close attention to the epidemiology of E. hormaechei and prevent their further dissemination. IMPORTANCE Enterobacter hormaechei is an opportunistic pathogen. It can cause infections in humans and animals. Plasmid-mediated quinolone resistance (PMQR) gene qnrS can be transferred intergenus, which is leading to increase the quinolone resistance levels in Enterobacteriaceae. Chicken feed could serve as a vehicle for the MDR E. hormaechei. Therefore, antibiotic-resistance genes (ARGs) might be transferred to the intestinal flora after entering the gastrointestinal tract with the feed. Furthermore, antibiotic-resistant bacteria (ARB) were also excreted into environment with feces, posing a huge threat to public health. This requires us to monitor the ARB and antibiotic-resistant plasmids in the feed. Here, we demonstrated the characteristics of one MDR E. hormaechei isolate from chicken feed. The plasmid carrying the qnrS gene is a conjugative plasmid with transferability. The presence of plasmid carrying antibiotic-resistance genes requires the maintenance of antibiotic pressure. In addition, the E. hormaechei M1 belonged to new sequence type (ST). These data show the MDR E. hormaechei M1 is a novel strain that requires our further research.

This paper focuses on the compressive strength of Glass fiber reinforced polymer (GFRP)-confined reinforced concrete columns. Data from 114 sets of GFRP-confined reinforced concrete columns were collected to evaluate the researchers’ and proposed model. A data-driven machine learning model was used to model the compressive strength of the GFRP-confined reinforced concrete columns and investigate the importance and sensitivity of the parameters affecting the compressive strength. The results show that the researchers’ model facilitates the study of the compressive strength of confined columns but suffers from a large coefficient of variation and too high or conservative estimation of compressive strength. The back propagation (BP) neural network has the best accuracy and robustness in predicting the compressive strength of the confined columns, with the coefficient of variation of only 14.22%, and the goodness of fit for both the training and testing sets above 0.9. The parameters that have an enormous influence on compressive strength are the concrete strength and FRP thickness, and all the parameters, except the fracture strain of FRP, are positively or inversely related to the compressive strength.

Heavy metal contamination in wheat not only endangers human health, but also causes crop quality degradation, leads to economic losses and affects social stability. Therefore, this paper proposes a Pyraformer-based model to predict the safety risk level of Chinese wheat contaminated with heavy metals. First, based on the heavy metal sampling data of wheat and the dietary consumption data of residents, a wheat risk level dataset was constructed using the risk evaluation method; a data-driven approach was used to classify the dataset into risk levels using the K-Means++ clustering algorithm; and, finally, on the constructed dataset, Pyraformer was used to predict the risk assessment indicator and, thus, the risk level. In this paper, the proposed model was compared to the constructed dataset, and for the dataset with the lowest risk level, the precision and recall of this model still reached more than 90%, which was 25.38–4.15% and 18.42–5.26% higher, respectively. The model proposed in this paper provides a technical means for hierarchical management and early warning of heavy metal contamination of wheat in China, and also provides a scientific basis for dynamic monitoring and integrated prevention of heavy metal contamination of wheat in farmland.

Tropical forests are biologically diverse and structurally complex ecosystems that can store a large quantity of carbon and support a great variety of plant and animal species. However, tropical forest structure can vary dramatically within seemingly homogeneous landscapes due to subtle changes in topography, soil fertility, species composition and past disturbances. Although numerous studies have reported the effects of field-based stand structure attributes on aboveground biomass (AGB) in tropical forests, the relative effects and contributions of UAV LiDAR-based canopy structure and ground-based stand structural attributes in shaping AGB remain unclear. Here, we hypothesize that mean top-of-canopy height (TCH) enhances AGB directly and indirectly via species richness and horizontal stand structural attributes, but these positive relationships are stronger at a larger spatial scale. We used a combined approach of field inventory and LiDAR-based remote sensing to explore how stand structural attributes (stem abundance, size variation and TCH) and tree species richness affect AGB along an elevational gradient in tropical forests at two spatial scales, i.e., 20 m × 20 m (small scale), and 50 m × 50 m (large scale) in southwest China. Specifically, we used structural equation models to test the proposed hypothesis. We found that TCH, stem size variation and abundance were strongly positively associated with AGB at both spatial scales, in addition to which increasing TCH led to greater AGB indirectly through increased stem size variation. Species richness had negative to negligible influences on AGB, but species richness increased with increasing stem abundance at both spatial scales. Our results suggest that light capture and use, modulated by stand structure, are key to promoting high AGB stocks in tropical forests. Thus, we argue that both horizontal and vertical stand structures are important for shaping AGB, but the relative contributions vary across spatial scales in tropical forests. Importantly, our results highlight the importance of including vertical forest stand attributes for predicting AGB and carbon sequestration that underpins human wellbeing.

Aiming to detect concrete defects, we propose a new identification method based on stochastic configuration networks. The presented model has been trained by time-domain and frequency-domain features which are extracted from filtering and decomposing ultrasonic detection signals. This method was applied to ultrasonic detection data collected from 5 mm, 7 mm, and 9 mm penetrating holes in C30 class concrete. In particular, wavelet packet transform (WPT) was then used to decompose the detected signals, thus the information in different frequency bands can be obtained. Based on the data from the fundamental frequency nodes of the detection signals, we calculated the means, standard deviations, kurtosis coefficients, skewness coefficients, and energy ratios to characterize the detection signals. We also analyzed their typical statistical features to assess the complexity of identifying these signals. Finally, we used the stochastic configuration networks (SCNs) algorithm to embed four-fold cross-validation for constructing the recognition model. Based upon the experimental results, the performance of the presented model has been validated and compared with the genetic algorithm based on BP neural network model, where the comparison shows that the SCNs algorithm has superior generalization abilities, better fitting abilities, and higher recognition accuracy for recognizing defect signals. In addition, the test and analysis results show that the proposed method is feasible and effective in detecting concrete hole defects.

DNA three-way junctions are branched structures formed during replication, repair, and recombination, and are involved in models of repeat expansion. Here the authors use single-molecule Förster resonance energy transfer to reveal the dynamics of DNA three-way junctions containing slip-outs composed of CAG or CTG repeats.

Digital technologies have played a significant role in the defense against the COVID-19 pandemic. This development raises the question of whether digital technologies have helped Chinese exports recover quickly and even grow. To answer this question, we study monthly data on Chinese exports to 40 countries/regions from January 2019 to June 2020 and covering 97 product categories. The study takes the COVID-19 outbreak as a natural experiment and treats digital trade products as the treatment group. Using a generalized difference-in-differences (DID) approach, we empirically investigate how this major global public health crisis and digital trade have influenced Chinese exports. Our empirical analysis reveals that the COVID-19 pandemic has inhibited China's export trade overall, digital trade has significantly promoted trade, and the supply mechanism has played a significant role in promoting the recovery of exports. Heterogeneity tests on destination countries/regions reveal that digital trade has significantly promoted exports to countries/regions with different income levels, with a more significant effect on low-risk destinations than on high-risk destinations. The sector heterogeneity test demonstrates that digital trade has enhanced the export recovery of sectors dealing in necessities for pandemic prevention. Other robustness tests, including parallel trend and placebo tests, support the above conclusions. Finally, we extend the research conclusions and discuss their implication for health economics and the practice of fighting COVID-19.

Abstract Background Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms. Results In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R2 was greater than 0.80), and biomass estimation at leaf group level was the most precise (R2 = 0.97, RMSE = 2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well. Conclusion We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices.

Airborne laser scanning (ALS) data is one of the most commonly used data for terrain products generation. Filtering ground points is a prerequisite step for ALS data processing. Traditional filtering methods mainly use handcrafted features or predefined classification rules with preprocessing/post-processing operations to filter ground points iteratively, which is empirical and cumbersome. Deep learning provides a new approach to solve classification and segmentation problems because of its ability to self-learn features, which has been favored in many fields, particularly remote sensing. In this article, we proposed a point-based fully convolutional neural network (PFCN) which directly consumed points with only geometric information and extracted both point-wise and tile-wise features to classify each point. The network was trained with 37449157 points from 14 sites and evaluated on 6 sites in various forested environments. Additionally, the method was compared with five widely used filtering methods and one of the best point-based deep learning methods (PointNet++). Results showed that the PFCN achieved the best results in terms of mean omission error (T1 = 1.10%), total error (Te = 1.73%), and Kappa coefficient (93.88%), but ranked second for the root mean square error of the digital Terrain model caused by the worst commission error. Additionally, our method was on par with or even better than PointNet++ in accuracy. Moreover, the method consumes one-third of the computational resource and one-seventh of the training time. We believe that PFCN is a simple and flexible method that can be widely applied for ground point filtering.

Accurate quantification of grassland structural and functional traits is the foundation for grassland management and restoration. Light detection and ranging (lidar), especially the unmanned aerial vehicle (UAV) lidar, has been recognized as an accurate and effective technique for local to regional-scale vegetation structural and functional traits estimation. However, in grassland ecosystems, it is more likely to be influenced by UAV lidar information loss caused by dense vegetation canopies. In this study, we investigated how UAV lidar information loss may occur and how it may influence the estimation accuracy of grassland structural and functional traits by comparing it with terrestrial laser scanning (TLS) and field measurements in a meadow steppe of northern China. Five structural traits (i.e., mean vegetation height, maximum vegetation height, standard deviation of vegetation height, canopy cover, and canopy volume) and one functional trait (i.e., aboveground biomass) were estimated from the UAV lidar data and TLS data for evaluation. The results showed that TLS-derived structural and functional traits had a much higher accuracy than UAV lidar-derived traits. By comparing with TLS data, we found that UAV lidar data had a much more prevailing information loss at canopy tops than at canopy bottoms. The average height loss of UAV lidar at canopy tops reached over 0.30 m, and the average relative height loss reached over 49%, comparing to a value of 0.03 m and 6% at canopy bottoms. Maximum vegetation height, standard deviation, and the distance from the UAV lidar system to the ground were the three most influential factors on UAV lidar information loss at canopy tops, indicating the commonly seen sharp canopy tops of grasslands were prone to be missed by the UAV lidar system. UAV lidar information loss at canopy tops had a much stronger influence on the estimation accuracy of grassland structural and functional traits than that at canopy bottoms. With the decrease of information loss at canopy tops, UAV lidar can be used to extract grassland structural and functional traits with a comparable accuracy to TLS. Among the five grassland traits, aboveground biomass was the least influenced by UAV lidar information loss. This study is a very first evaluation on the UAV lidar information loss in grassland ecosystems and its influence on grassland structural and functional trait estimation, which can provide guidance for UAV lidar data collection and processing in future grassland applications.

This study aimed to determine the effect of enrofloxacin (ENR) on the transfer of the plasmid-mediated quinolone resistance (PMQR) gene qnrS from opportunistic pathogen Escherichia coli (E2) to Salmonella Enteritidis (SE211) and to analyze the resistance characteristics of SE211-qnrS isolates. The plasmid carrying qnrS gene of E2 was sequenced by Oxford Nanopore technology. The plasmid carrying qnrS gene belonged to incompatibility group IncY. In vitro, the transfer experiment of IncY plasmid was performed by the liquid medium conjugation method. The conjugation transfer frequency of the IncY plasmid was 0.008 ± 0.0006 in the absence of ENR, 0.012 ± 0.003 in 1/32 MICENR, 0.01 ± 0.008 in 1/8 MICENR, and 0.03 ± 0.015 (Mean±SD) in 1/2 MICENR, respectively. After inoculation of E. coli E2 and SE211, chickens were treated with different doses of ENR (3.03, 10, and 50 mg/kg b.w.) for 7 days consecutively. To screen the SE211-qnrS strains from intestinal tract of chickens, the resistance genes and susceptibility of isolates were identified. The amount of E. coli E2 and the copy number of qnrS gene in the chicken intestinal tract were determined by colony counting and qPCR, respectively. In vivo, more SE211-qnrS strains were isolated from the treated group compared with the untreated group. SE211-qnrS strains not only obtained IncY plasmid, but also showed similar resistance phenotype as E2. In conclusion, ENR treatment can promote the spread of a IncY-resistance plasmid carrying the qnrS fluoroquinolone-resistance gene in Escherichia coli and the development of drug-resistant bacteria.

Canopy structural complexity is a critical emergent forest attribute, and light detection and ranging (lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree level. However, the current lidar-based estimation method is highly sensitive to data characteristics, and its scalability from individual trees to forest stands remains unclear. This study proposed an improved method to estimate fractal dimension from lidar data by considering Shannon entropy, and evaluated its scalability from individual trees to forest stands through mathematical derivations. Moreover, a total of 280 forest stand scenes simulated from the terrestrial lidar data of 115 trees spanning large variability in canopy structural complexity were used to evaluate the robustness of the proposed method and the scalability of fractal dimension. The results show that the proposed method can significantly improve the robustness of lidar-derived fractal dimensions. Both mathematical derivations and experimental analyses demonstrate that the fractal dimension of a forest stand is equal to that of the tree with the largest fractal dimension in it, manifesting its nonscalability from individual trees to forest stands. The nonscalability of fractal dimension reveals its limited capability in canopy structural complexity quantification and indicates that the power-law scaling theory of a forest stand underlying fractal geometry is determined by its dominant tree instead of the entire community. Nevertheless, we believe that fractal dimension is still a useful indicator of canopy structural complexity at the individual tree level and might be used along with other stand-level indexes to reflect the “tree-to-stand” correlation of canopy structural complexity.

Reinforced concrete (RC) beams strengthened with fiber reinforced polymers (FRPs) are structurally complex and prone to plate-end (PE) debonding. In this study, considering the extremely complicated nonlinear relationship between the PE debonding and the parameters, machine learning algorithms, namely, linear regression, ridge regression, decision tree, random forest, and neural network improved by sparrow search algorithm, are established to predict the PE debonding of RC beams strengthened with FRP. The results of reliability evaluation and parameter analysis reveal that ACI, CNR, fib-1, fib-2, and TR55-2 are a little conservative; AS and TR55-1 have the problem of overestimating the shear force; the accuracy and robustness of the SSA-BP model developed in this paper are good; the stirrup reinforcement has the greatest effect on PE debonding; and each parameter shows a complex nonlinear relationship with the shear force when PE debonding occurs.

Film cooling is a major cooling technique used in modern gas turbines and air engines. The geometry of film-cooling holes is the fundamental aspect affecting the cooling performance. In this paper, a new cooling configuration called the double-expansion film-cooling hole has been put forward, which yields better performance than the widely used shaped holes and is easy to manufacture. The double-expansion holes at inclination angles of α=30∘, 45∘, and 60∘ are optimized using the genetic algorithm and the Kriging surrogate model, which is trained by CFD data randomly sampled using the Latin hypercube method. The numerically optimized double-expansion holes at different inclination angles were experimentally evaluated and compared with the optimized single-expansion laid-back fan-shaped holes, and the optimized double-expansion hole at α=30∘ was manually modified based on experiment results. Compared with the optimal single-expansion holes, the area-averaged cooling effectiveness of the double-expansion holes was increased by 34.5% at α=30∘, by 27.8% at α=45∘, and basically the same at α=60∘, showing the benefit of the double-expansion concept. The loss mechanism of film cooling was also analyzed in the perspective of the entropy generation rate, showing the optimal double-expansion holes have 21% less loss compared to a baseline narrow single-expansion hole. It was also found that CFD sometimes predicts a different trend from the experiment in optimization, and the experimental validation is necessary.