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This article established a rapid detection method for 34 pesticides in vegetables by combining QuEChERS purification method with high-performance liquid chromatography triple quadrupole linear ion trap tandem mass spectrometry. The samples were extracted with acetonitrile (containing 1% acetic acid), 1.5 g sodium acetate, 4 g anhydrous magnesium sulfate, and 1 g sodium chloride. The extract was further purified using 20 mg graphitized carbon black (GCB), 300 mg primary secondary amine (PSA), 200 mg octadecylsilane chemically bonded silica (C18), and 1000 mg MgSO4. Acetonitrile-water was chosen as mobile phase. The samples were analyzed using MRM-IDA-EPI mode, and an EPI database was established. Qualitative analysis was performed based on retention time, ion pair peak ratio, and EPI database, and quantification was carried out using matrix-matched external standard method. The study results indicated that using eggplant, garlic chives, cowpea, and spinach as representative matrices, the 34 pesticides showed good linear correlation with R2≥0.99011. The detection limits ranged from 0.16 to 1.81 μg/kg, and the quantification limits ranged from 0.52 to 6.02 μg/kg. The spiked recoveries of the samples were between 73.51% and 118.77%, and the precision was in the range of 0.01% to 14.27%. Vegetable samples were tested, with a detection rate of 58.23% for thiamethoxam. This method was characterized by its rapid and effective detection, simple operation, accurate data, and high sensitivity, making it suitable for the detection of 34 pesticides in vegetables.

Abstract Background Increasing evidence shows the pivotal significance of miRNAs in the pathogenesis of osteoporosis. miR-381-3p has been identified as an inhibitor of osteogenesis. This study explored the role and mechanism of miR-381-3p in postmenopausal osteoporosis (PMOP), the most common type of osteoporosis. Methods Bilateral ovariectomy (OVX) rat model was established and miR-381-3p antagomir was administrated through the tail vein in vivo. The pathological changes in rats were assessed through the evaluation of serum bone turnover markers (BALP, PINP, and CTX-1), hematoxylin and eosin (H&E) staining, as well as the expression of osteoblast differentiation biomarkers. Moreover, isolated bone marrow mesenchymal stem cells from OVX-induced rats (OVX-BMMSCs) were utilized to explore the impact of miR-381-3p on osteoblast differentiation. In addition, the target gene and downstream pathway of miR-381-3p were further investigated both in vivo and in vitro. Results miR-381-3p expression was elevated, whereas KLF5 was suppressed in OVX rats. miR-381-3p antagomir decreased serum levels of bone turnover markers, improved trabecular separation, promoted osteoblast differentiation biomarker expression in OVX rats. ALP activity and mineralization were suppressed, and levels of osteoblast differentiation biomarkers were impeded after miR-381-3p overexpression during osteoblast differentiation of OVX-BMMSCs. While contrasting results were found after inhibition of miR-381-3p. miR-381-3p targets KLF5, negatively affecting its expression as well as its downstream Wnt/β-catenin pathway, both in vivo and in vitro. Silencing of KLF5 restored Wnt/β-catenin activation induced by miR-381-3p antagomir. Conclusion miR-381-3p aggravates PMOP by inhibiting osteogenic differentiation through targeting KLF5/Wnt/β-catenin pathway. miR-381-3p appears to be a promising candidate for therapeutic intervention in PMOP.

Abstract Background The minimally invasive approach for the treatment of displaced scapular neck or body fractures has the advantages of less trauma and minimal muscle dissection. In clinical practice, the minimally invasive approach combined with an anatomical locking plate has been used to treat scapular body fractures. In addition, we have made minor modifications to the minimally invasive approach. However, the biomechanical study about the approach combined with an anatomical locking plate in treating scapular body fractures was limited. Methods Finite element analysis (FEA) was used to conduct the biomechanical comparison between the anatomical locking plate (AP model) and reconstructive plate (RP model) in the treatment of scapular body fractures through the modified minimally invasive approach. A healthy male volunteer with no history of scapula or systemic diseases was recruited. High-resolution computed tomography images of his right scapula were obtained. Two scapula models were constructed and analyzed by the software of Mimics 21.0, Geomagic Wrap 2021, SolidWorks 2021, and ANSYS Workbench 2022, respectively. Results Through static structural analysis, in terms of equivalent von Mises stress, equivalent elastic strain, and total deformation, the AP model exhibited superior safety characteristics, enhanced flexibility, and anticipated stability compared with the RP model. This was evidenced by lower maximum stress, lower maximum strain and displacement. Conclusion The minimally invasive approach combined with an anatomical locking plate for scapular body fractures had better biomechanical stability. The study provided a biomechanical basis to guide the clinical treatment of scapular body fractures.

BackgroundCINV is a frequent adverse response to cancer treatment. There is still much to learn about the pathophysiology and initiating event of CINV, which necessitates continued research despite decades of effort. Identifying the current foci of the complex disease and assessing the scientific impact of pertinent study are made more difficult by the abundance of publications on CINV. Therefore, our goals in this article are to evaluate developments in this field, examine patterns in research domains, and gauge the expansion of CINV research production globally.MethodsArticles about CINV published between 2012 and 2022 were found by searching the Web of Science Core Collection of Clarivate Analytics. The number of publications over time was visualized using Microsoft Office Excel 2019. CiteSpace and VOSviewer were utilized to create knowledge maps that analyzed collaborations between nations, organizations, and writers. They also presented the history of CINV research and highlighted its current areas of focus.ResultsIn this study, 846 papers in all were assessed. Most publications (237, 28.01%) came from the United States. University of Toronto was the most productive institution (34, 4.01%). With 25 articles published, or 2.96% of the total, Aapro Matti published the most. The most frequently published journal was found to be Supportive Care (158, 18.68%). “Palonosetron,” “Moderately emetogenic chemotherapy,” “5-HT3 receptor antagonist,” and “Neurokinin 1 receptor antagonists” were considered the hot topics. It can be seen that the research focus is on the drug treatment of chemotherapy-induced nausea and vomiting.ConclusionThrough bibliometric analysis, we were able to gain profound insights into CINV research for the first time. Researchers looking to uncover research frontiers and comprehend important information in this discipline may find the study’s findings useful.

Abstract Spinal cord injury (SCI) is a serious traumatic disease of the central nervous system and leads to incomplete or complete loss of the body’s autonomous motor and sensory functions, seriously endangering human health. Recently, exosomes have been proposed as important substances in cell-to-cell interactions. Mesenchymal stem cell (MSC)-derived exosomes exert good therapeutic effects and play a crucial role in neurological damage repair. However, the detailed mechanisms underlying their effects remain unknown. Herein, we found that compared to SCI rats, those subjected to umbilical cord MSC (UC-MSC)-derived exosomes injection showed an improved motor ability. Nevertheless, the transcriptome of BV2 microglia in different treatment groups indicated that the action pathway of exosomes might be the NF-κB/MAPK pathway. Additionally, exosomes from UC-MSCs could inhibit P38, JNK, ERK, and P65 phosphorylation in BV2 microglia and SCI rat tissues. Moreover, exosomes could inhibit apoptosis and inflammatory reaction and reactive oxygen species (ROS) production of BV2 microglia in vitro and in vivo. In conclusion, UC-MSCs-derived exosomes might protect SCI in rats by inhibiting inflammatory response via the NF-κB/MAPK signaling pathway, representing novel treatment targets or approaches for SCI.

The brain topology highly reflects the complex cognitive functions of the biological brain after million-years of evolution. Learning from these biological topologies is a smarter and easier way to achieve brain-like intelligence with features of efficiency, robustness, and flexibility. Here we proposed a brain topology-improved spiking neural network (BT-SNN) for efficient reinforcement learning. First, hundreds of biological topologies are generated and selected as subsets of the Allen mouse brain topology with the help of the Tanimoto hierarchical clustering algorithm, which has been widely used in analyzing key features of the brain connectome. Second, a few biological constraints are used to filter out three key topology candidates, including but not limited to the proportion of node functions (e.g., sensation, memory, and motor types) and network sparsity. Third, the network topology is integrated with the hybrid numerical solver-improved leaky-integrated and fire neurons. Fourth, the algorithm is then tuned with an evolutionary algorithm named adaptive random search instead of backpropagation to guide synaptic modifications without affecting raw key features of the topology. Fifth, under the test of four animal-survival-like RL tasks (i.e., dynamic controlling in Mujoco), the BT-SNN can achieve higher scores than not only counterpart SNN using random topology but also some classical ANNs (i.e., long-short-term memory and multi-layer perception). This result indicates that the research effort of incorporating biological topology and evolutionary learning rules has much in store for the future.

INTRODUCTION: The development of integrated energy systems (IES) is of paramount significance in addressing climate change and other challenges. Ensuring the rapid and accurate calculation of energy flow states is crucial for their efficient operation. However, the difference in response time of various heterogeneous energy flows in IES will lead to the inaccuracy of the steady-state model. OBJECTIVES: This paper proposes a model for multi-stage multi-energy flow IES of electricity, gas, and heat based on heterogeneous energy flow characteristics. Methods: IES was divided into fast variable networks and slow variable networks, and a multi-energy flow multi-stage model was established. Suitable models were matched for different subnets at different stages to improve the calculation accuracy. RESULTS: Selected a practical Electrical-Gas-Heat IES as a case study for simulation. Through case studies, the effectiveness and accuracy of the proposed method are demonstrated. CONCLUSION: The multi-stage model proposed in this paper can improve the accuracy of multi-energy flow in IES.

Abstract In recent years, China's automobile industry has undergone rapid development, creating new opportunities for the auto loan industry. Currently, auto financing companies are actively seeking to expand their cooperation with banks. Therefore, improving the approval rate and scale of joint loan business is of significant practical importance. In this paper, we propose a Stacking‐based financial institution risk approval model and select the optimal stacking model by comparing its performance with other models. Additionally, we construct a bank approval model using deep learning techniques on a biased data set, with feature extraction performed using convolution neural networks (CNN) and feature‐based counterfactual augmentation used for balanced sampling. Finally, we optimize the model of the prediction of auto finance companies by selecting the optimal coefficients of loss function based on the features and results of the bank approval model. The proposed approach leads to an approximately 6% increase in the joint loan approval rate on the actual data set, as demonstrated by experimental results.

IntroductionSpinal cord injury (SCI) is a profoundly disabling and devastating neurological condition, significantly impacting patients’ quality of life. It imposes unbearable psychological and economic pressure on both patients and their families, as well as placing a heavy burden on society.MethodsIn this study, we integrated datasets GSE5296 and GSE47681 as training groups, analyzed gene variances between sham group and SCI group mice, and conducted Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis based on the differentially expressed genes. Subsequently, we performed Weighted Gene Correlation Network Analysis (WGCNA) and Lasso regression analyses.ResultsWe identified four characteristic disease genes: Icam1, Ch25h, Plaur and Tm4sf1. We examined the relationship between SCI and immune cells, and validated the expression of the identified disease-related genes in SCI rats using PCR and immunohistochemistry experiments.DiscussionIn conclusion, we have identified and verified four genes related to SCI: Icam1, Ch25h, Plaur and Tm4sf1, which could offer insights for SCI treatment.

Structural vibration has always been a major concern in the engineering field. A dynamic vibration absorber in the form of contacts with adjustable stiffness (CDVA) offers effective vibration suppression and can improve conventional dynamic vibration absorbers with high sensitivity to frequency deviation and difficulty in adjusting the frequency. In this research, first, based on the theoretical model of the contact between a rubber ball and an inner cone, the feasibility of changing the axial contact state to change the structure’s natural frequency was verified using an ANSYS simulation. A theoretical model of the static contact stiffness between the ball and the inner cone was constructed using Hertzian contact theory and Hooke’s law, and a theoretical model of the cubic nonlinear elastic restoring force was used to characterize the stiffness properties of the rubber ball during compressive rebound. The steady-state frequency response equations of the main vibration structure were derived using the averaging method in conjunction with the two-degree-of-freedom dynamics model, and the stability of the solutions to the frequency response equations was obtained in conjunction with the stability determination criterion. Then, the impact of the CDVA’s design parameters on the nonlinear dynamic response of the primary vibration structure was simulated and analyzed. The resulting findings can serve as guidance for designing dynamic vibration absorber parameters. Based on the principles of ball-inner cone contact, a dynamic vibration absorber structure was proposed. A design test was conducted to verify the correctness of the contact stiffness model, and an experimental study was carried out to investigate the law of change in the dynamic stiffness and damping of the principle structure of CDVA under dynamic excitation conditions. Finally, the vibration test platform of the solidly supported beam structure was constructed, and vibration suppression tests of the CDVA in different compression states were conducted to investigate the tunability and feasibility of CDVA vibration suppression. The results showed that the dynamic vibration absorber had good vibration absorption characteristics and could be used for single-mode vibration suppression of multimodal main structures.

A computational spectrometer is a novel form of spectrometer powerful for portable in situ applications. In the encoding part of the computational spectrometer, filters with highly non-correlated properties are requisite for compressed sensing, which poses severe challenges for optical design and fabrication. In the reconstruction part of the computational spectrometer, conventional iterative reconstruction algorithms are featured with limited efficiency and accuracy, which hinders their application for real-time in situ measurements. This study proposes a neural network computational spectrometer trained by a small dataset with high-correlation optical filters. We aim to change the paradigm by which the accuracy of neural network computational spectrometers depends heavily on the amount of training data and the non-correlation property of optical filters. First, we propose a presumption about a distribution law for the common large training dataset, in which a unique widespread distribution law is shown when calculating the spectrum correlation. Based on that, we extract the original dataset according to the distribution probability and form a small training dataset. Then a fully connected neural network architecture is constructed to perform the reconstruction. After that, a group of thin film filters are introduced to work as the encoding layer. Then the neural network is trained by a small dataset under high-correlation filters and applied in simulation. Finally, the experiment is carried out and the result indicates that the neural network enabled by a small training dataset has performed very well with the thin film filters. This study may provide a reference for computational spectrometers based on high-correlation optical filters.

The intelligent identification of coal gangue on industrial conveyor belts is a crucial technology for the precise sorting of coal gangue. To address the issues in coal gangue detection algorithms, such as high false negative rates, complex network structures, and substantial model weights, an optimized coal gangue detection algorithm based on YOLOv5s is proposed. In the backbone network, a feature refinement module is employed for feature extraction, enhancing the capability to extract features for coal and gangue. The improved BIFPN structure is employed as the feature pyramid, augmenting the model’s capability for cross-scale feature fusion. In the prediction layer, the ESIOU is utilized as the bounding box regression loss function to rectify the misalignment issue between predicted and actual box angles. This approach expedites the convergence speed of the network while concurrently enhancing the accuracy of coal gangue detection. Channel pruning is implemented on the network to diminish model computational complexity and weight, consequently augmenting detection speed. The experimental results demonstrate that the refined YOLOv5s coal gangue detection algorithm outperforms the original YOLOv5s algorithm, achieving a notable accuracy enhancement of 2.2% to reach 93.8%. Concurrently, a substantial reduction in model weight by 38.8% is observed, resulting in a notable 56.2% increase in inference speed. These advancements meet the detection requirements for scenarios involving mixed coal gangue.

The original purpose of resilience design for traditional architectures is to be coordinated with and adapted to the natural environment. The natural environmental resilience of traditional dwellings refers to the ability of the dwellings to maintain the residents’ comfort, safety, and health in the face of natural environmental challenges including various disasters. In the process of designing traditional dwellings, the wisdom contributed to improving living environment conditions is of significant reference value. Therefore, this paper reviews the main literature on the resilience of traditional dwellings to the natural environment in Fujian of China. A topical review framework is proposed to cover the resilience performance of traditional dwellings in Fujian under various natural environmental conditions. Specifically, it is divided into two aspects: internal comfort resilience and external disaster-resistance resilience, based on which researchers in related fields can establish a clearer classification of resilience research in their future studies. In terms of internal comfort resilience, this paper focuses on relevant perspectives such as humidity, temperature, brightness, noise, etc. In terms of external disaster-resistance resilience, this paper summarizes the adaptability of traditional dwellings in the face of disasters triggered by natural hazards such as fires, floods, earthquakes, and typhoons. Based on this framework, this paper reviews the current research status, discusses the limitations and shortcomings in this research area, and proposes corresponding prospects for future research.

The spaceborne solar observation system is crucial for the study of space phenomena such as solar flares, which requires high tracking accuracy. This study presents a coupling model that integrates mechanical, electrical, and control models to investigate the structural flexibility effect on the micro-vibration response. We established a rigid–flexible model using mechanical parts. We considered the influence of flexible features while studying the dynamic responses in its operation. The state-space equations of the system showed that modal frequency, damping, and modal participation factors played significant roles. We derived transfer functions using the Laplace transform of the coupling models to better understand this mechanism, and Simulink models were thereby established. We simulated the acceleration responses of the rigid–flexible and rigid models under angle tracking modes, and the results showed significant differences. We also simulated the acceleration responses of the models under various control frequencies, and the optimal control frequency was thus obtained. Finally, we performed experiments, and the results indicated that the rigid–flexible model could better predict the motion and acceleration responses for the spaceborne solar observation system. This study provides valuable information for understanding the role of flexible features in space performance high-tracking accuracy instruments and for micro-vibration suppression research.

At present, autonomous driving decision-making solutions take few elements into account while ignoring the unpredictable nature of driving behavior, which makes it challenging to manage complicated traffic situations. To this end, we present a decision-making architecture in this paper that enhances the existing reinforcement learning methodology by combining the bootstrapped technique and the random prior network (RPN). The RPN can give each learner a neural network with unique weights to avoid the contingency created by the artificially built prior functions, while the Bootstrapped technique can balance out the exploration and exploitation. The ego vehicle was trained by three algorithms and verified in random environments to evaluate the effectiveness of our method. The results show that our algorithm outperformed the current reinforcement learning algorithms. [ABSTRACT FROM AUTHOR]