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High‐performance biosensors play a crucial role in elucidating the intricate spatiotemporal regulatory roles and dynamics of membrane phospholipids. However, enhancing the sensitivity and imaging performance remains a significant challenge. Here, optogenetic‐based strategies are presented to optimize phospholipid biosensors. These strategies involves presequestering unbound biosensors in the cell nucleus and regulating their cytosolic levels with blue light to minimize background signal interference in phospholipid detection, particularly under conditions of high expression levels of biosensor. Furthermore, optically controlled phase separation and the SunTag system are employed to generate punctate probes for substrate detection, thereby amplifying biosensor signals and enhancing visualization of the detection process. These improved phospholipid biosensors hold great potential for enhancing the understanding of the spatiotemporal dynamics and regulatory roles of membrane lipids in live cells and the methodological insights in this study might be valuable for developing other high‐performance biosensors. [ABSTRACT FROM AUTHOR]

Tetrabromobisphenol A (TBBPA) is a kind of widely used brominated flame retardant (BFR), which is proven to be harmful to ecological systems and public health. It is very important to remove TBBPA from the environment. In our study, a magnetic composite named Fe3O4/GO/ZIF-67 was synthesized by a coprecipitation method and applied in the highly efficient adsorption of TBBPA from water. Static adsorption experiments demonstrated that the adsorption capacity could reach 232 mg·g−1 within 120 min, which is much higher than those reported in the other literature. The experimental results show that the adsorption of TBBPA on Fe3O4/GO/ZIF-67 followed Langmuir and pseudo-second-order kinetic adsorption models. The main mechanisms for these adsorptions were identified as hydrogen bonds between OH groups in TBBPA and COOHs of Fe3O4/GO/ZIF-67, and π-π stacking between Fe3O4/GO/ZIF-67 and TBBPA. This study provides a method with great promise for the design and synthesis of better adsorbents for the removal of TBBPA from the water environment. [ABSTRACT FROM AUTHOR]

Background: Cuproptosis is a novel copper-dependent mode of cell death that has recently been discovered. The relationship between Cuproptosis-related ncRNAs and breast cancer subtypes, however, remains to be studied. Methods: The aim of this study was to construct a breast cancer subtype prediction model associated with Cuproptosis. This model could be used to determine the subtype of breast cancer patients. To achieve this aim, 21 Cuproptosis-related genes were obtained from published articles and correlation analysis was performed with ncRNAs differentially expressed in breast cancer. Random forest algorithms were subsequently utilized to select important ncRNAs and build breast cancer subtype prediction models. Results: A total of 94 ncRNAs significantly associated with Cuproptosis were obtained and the top five essential features were chosen to build a predictive model. These five biomarkers were differentially expressed in the five breast cancer subtypes and were closely associated with immune infiltration, RNA modification, and angiogenesis. Conclusion: The random forest model constructed based on Cuproptosis-related ncRNAs was able to accurately predict breast cancer subtypes, providing a new direction for the study of clinical therapeutic targets. [ABSTRACT FROM AUTHOR]

Lane detection, a crucial component of autonomous driving systems, is in charge of precise lane location to ensure that cars navigate lanes appropriately. However, in challenging conditions like shadows and extreme lighting, lanes may become obstructed or blurred, posing a significant challenge to the lane-detection task as the model struggles to extract sufficient visual information from the image. The current anchor-based lane-detection network detects lanes in complex scenes by mapping anchors to images to extract features and calculating the relationship between each anchor and other anchors for feature fusion. However, it is insufficient for anchors to extract subtle features from images, and there is no guarantee that the information carried by each anchor is valid. Therefore, this study proposes the adaptive cross-scale ROI fusion network (ACSNet) to fully extract the features in the image so that the anchor carries more useful information. ACSNet selects important anchors in an adaptive manner and fuses these important anchors with the original anchors across scales. Through this feature extraction method, the features of different field-of-view ranges under complex road surfaces can be learned, and diversified features can be integrated to ensure that lanes can be well detected under complex road surfaces such as shadows and extreme lighting. Furthermore, due to the slender structure of lane lines, there are relatively few useful features in the images. Therefore, this study also proposes a Three-dimensional Coordinate Attention Mechanism (TDCA) to enhance image features. The Three-dimensional Coordinate Attention Mechanism extensively explores relationships among features in the row, column, and spatial dimensions. It calculates feature weights for each of these dimensions and ultimately performs element-wise multiplication with the entire feature map. Experimental results demonstrate that our network achieves excellent performance on the existing public datasets, CULane and Tusimple. [ABSTRACT FROM AUTHOR]

Location information on Twitter plays a critical role in emergency detection, event recommendation, and disaster warning. However, only a limited amount of Twitter data are geotagged. Previous research has presented various models for inferring location based on text, social relations, and contextual data, yielding highly promising results. Nonetheless, these existing methods have certain limitations that need to be addressed. Firstly, most of the existing methods overlook the role of local celebrities (well-known users in the local community) as indicators of location within the social network. Secondly, they fail to consider the associations between words in tweets, resulting in insufficiently rich features extracted from the tweets. We propose a multi-view-based location inference model called MVGeo to overcome these limitations. In the network view, our approach employs the Gaussian Mixture Model (GMM) to identify and retain local celebrities, thereby strengthening user location associations. In the tweet view, we construct a heterogeneous graph based on the co-occurrence relationship between words in tweets and the user's mentioned relationship with the words. This allows us to fully leverage the local correlation between words and the global correlation to extract tweet features more comprehensively. Finally, we employ a modified multi-layer graph convolutional network, called Gate-GCN, to fuse the network and tweet information. This expansion of the feature space enables us to extract sample features from multiple perspectives. To demonstrate the effectiveness of MVGeo, we conduct exhaustive experimental evaluations on a publicly available dataset and compare its performance against several state-of-the-art benchmark models. The results confirm the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach that subtly combines the transformer with grasping CNN to achieve more optimal grasps in complex real-life situations. The approach comprises two unique designs that effectively improve grasp precision in complex scenes. The first essential design uses self-attention mechanisms to capture contextual information from RGB images, boosting contrast between key object features and their surroundings. We precisely adjust internal parameters to balance accuracy and computing costs. The second crucial design involves building a feature fusion bridge that processes all one-dimensional sequence features at once to create an intuitive visual perception for the detection stage, ensuring a seamless combination of the transformer block and CNN. These designs eliminate noise features in complex backgrounds and emphasize graspable object features, providing valuable semantic data to the subsequent grasping CNN to achieve appropriate grasping. We evaluated the approach on the Cornell and VMRD datasets. According to the experimental results, our method achieves better performance than the original grasping CNN in single-object and multi-object scenarios, exhibiting 97.7% and 72.2% accuracy on the Cornell and VMRD grasp datasets using RGB, respectively. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis aims to understand people's attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article provides a new model based on a multimodal sentiment analysis framework based on a recurrent neural network with a complex attention mechanism. First, after the raw data is preprocessed, the numerical feature representation is obtained using feature extraction. Next, the numerical features are input into the recurrent neural network, and the output results are multimodally fused using a complex attention mechanism layer. The objective of the complex attention mechanism is to leverage enhanced non-linearity to more effectively capture the inter-modal correlations, thereby improving the performance of multimodal sentiment analysis. Finally, the processed results are fed into the classification layer and the sentiment output is obtained using the classification layer. This process can effectively capture the semantic information and contextual relationship of the input sequence and fuse different pieces of modal information. Our model was tested on the CMU-MOSEI datasets, achieving an accuracy of 82.04%. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) are plastic fragments with particle sizes smaller than 5 mm that have potentially harmful effects on ecosystems and human health. The soil environment is not only the source but also the sink of MPs. Thus, it is necessary to fully understand the pollution and distribution of MPs in soils. In this study, Qinghai Province, northeast of the Qinghai–Tibet Plateau, was selected as the research area, and 22 soil samples were collected and analyzed to study the levels and distribution characteristics of MPs in grassland soils. MPs were obtained from the soils by using density separation, and a laser confocal micro Raman spectrometer was used for MP identification. The results showed that MPs were detected in all of the soil samples. The total abundances of MPs ranged from 1125 to 1329 items/kg, with a mean abundance of 1202 items/kg. Various types, shapes, sizes, and colors of MPs were observed. Polyethylene terephthalate (PET) was the dominant polymer in all the grassland soil samples. The size range of 10–50 μm accounted for 50% of all identified MPs. Pellets were the dominant MP shape, and colored MPs accounted for 64% of all MPs. The results revealed the presence of large quantities of MPs in the grassland soils of remote areas as well. This study can act as a reference for further studies of MPs in terrestrial systems. At the end of the paper, the prospects and suggestions for pollution control by soil MPs are given. [ABSTRACT FROM AUTHOR]

The pathogenesis of diabetes mellitus is characterized by insulin resistance and islet β-cell dysfunction. Up to now, the focus of diabetes treatment has been to control blood glucose to prevent diabetic complications. There is an urgent need to develop a therapeutic approach to restore the mass and function of β-cells. Although exogenous islet cell transplantation has been used to help patients control blood glucose, it is costly and has very narrow application scenario. So far, small molecules have been reported to stimulate β-cell proliferation and expand β-cell mass, increasing insulin secretion. Dual-specificity tyrosine-regulated kinase 1A (DYRK1A) inhibitors can induce human β-cell proliferation in vitro and in vivo, and show great potential in the field of diabetes therapeutics. From this perspective, we elaborated on the mechanism by which DYRK1A inhibitors regulate the proliferation of pancreatic β-cells, and summarized several effective natural DYRK1A inhibitors, hoping to provide clues for subsequent structural optimization and drug development in the future. [ABSTRACT FROM AUTHOR]

The distribution and evolution of city size are critical for town layout optimization. Based on the most recent classification standards and census data for 2010 and 2020, this paper aims to explore China's city size distribution above the prefecture level. Using the rank-size law, Kernel density estimation, Spatial Gini coefficient, and Markov transition matrix, the newest city size distribution characteristics and spatial evolution patterns in China are shown from national and regional viewpoints. Our main findings are as follows: (1) Over the period from 2010 to 2020, China's city size distribution follows the rank-size law but deviates from Zipf's ideal. The distribution of city size is centralized in general. (2) China's city-size hierarchy exhibits a good "olive" structure, with fewer megacities but larger populations. The growth rate of small and medium-sized cities is higher than the number of medium-sized cities. (3) China's cities have grown greatly in size, with more than a third of them expanding. Over the last decade, high-ranking cities have become the primary driver of change. (4) There are disparities in city size between regions. A diminishing trend can be seen in three key economic zones. [ABSTRACT FROM AUTHOR]

Obesity is a chronic metabolic disease caused by an imbalance between energy intake and expenditure during a long period and is characterized by adipose tissue disfunction and hepatic steatosis. The aim of this study was to investigate the effect of 4-methylesculetin (4-ME), a coumarin derivative, upon adipose microenvironment and hepatic steatosis in mice induced by a high-fat diet (HFD), and to explore potential mechanisms of its beneficial effect on metabolic disorders. HFD-fed mice displayed visceral obesity, insulin resistance, and hepatic lipid accumulation, which was remarkably ameliorated by 4-ME treatment. Meanwhile, 4-ME ameliorated adipocyte hypertrophy, macrophage infiltration, hypoxia, and fibrosis in epididymal adipose tissue, thus improving the adipose tissue microenvironment. Furthermore, 4-ME reversed the increase in CD36, PPAR-γ, SREBP-1, and FASN, and the decrease in CPT-1A, PPAR-α, and Nrf2 translocation into the nucleus in livers of HFD mice and in FFA-incubated hepatocytes. Moreover, the beneficial effects of 4-ME upon lipid deposition and the expression of proteins related to lipid metabolism in FFA-induced LO2 cells were abolished by ML385, a specific Nrf2 inhibitor, indicating that Nrf2 is necessary for 4-ME to reduce hepatic lipid deposition. These findings suggested that 4-ME might be a potential lead compound candidate for preventing obesity and MAFLD. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the correlation between satisfaction with rural residents' income and mental health well-being in the context of industrial structure upgrading. Most of the studies on residents' subjective well-being from the perspective of relative income or income inequality have started from the happiness of rural residents and the satisfaction of rural residents' life, and few scholars have focused on the psychological health of rural residents. Subjective well-being is significantly related to external and internal goals in desire. Life satisfaction is significantly and positively correlated with external and internal goals, as well as the six dimensions of desire, except for social identity; positive emotions are significantly and positively correlated with internal goals; negative emotions are only negatively correlated with self-acceptance, and there is a significant positive correlation between income level and desire. In vertical income, there is also a process of judging whether the expected income is achieved. If the expected income growth level is achieved, the income satisfaction will increase. Desire mediates the effect of income level on subjective well-being. Income level influences subjective well-being through internal goals; income level influences life satisfaction and positive emotions through external goals. The relationship between income inequality and mental health is influenced by the characteristics of the population, with women and middle-aged people being the most negatively affected. This relationship is also influenced by income level, with the effect of income inequality on mental health showing a negative effect in the lower and middle-income groups but a positive effect in the higher income groups. Income inequality affects residents' mental health through the mediating effects of a sense of social justice, life stress, and trust in government. Inequality in household wealth can exacerbate the negative effects of income inequality on mental health. [ABSTRACT FROM AUTHOR]

Spatial agglomeration, as a phenomenon of commodity trading markets, reflects regional economic development in China. This study explores the spatial agglomeration of commodity trading markets and analyzes its influencing factors. Based on the panel data of 30 provinces in China from 2010 to 2019, this article first calculated the location quotient of the transaction volume of commodity trading markets and analyzed their temporal and spatial trends. Finally, a spatial econometric model was used to conduct an empirical examination of the influencing factors determining the spatial agglomeration of commodity trading markets. The results show that the agglomeration pattern of China's commodity trading markets has changed significantly from 2010 to 2019. In terms of geographic variations, we discovered that the eastern region has a higher degree of commodity trading market concentration than the central and western regions. In terms of influencing factors, this study found that the level of economic development, the degree of openness, and the development of private industrial enterprises still positively affect the spatial agglomeration of commodity trading markets. However, the level of social consumption has no significant impact. Based on these findings, this article puts forward relevant policy recommendations to promote the further development of China's commodity exchange market. [ABSTRACT FROM AUTHOR]

In this paper, an in-depth study and analysis of the ecological economy of fine agriculture are carried out using image detection methods of smart sensor networks. The analog signal output from the wireless sensor network is filtered and thresholder to convert into a digital signal to complete the sensor monitoring data preprocessing for digital information analysis. In this paper, with the objectives of good environmental adaptability, low power consumption, low cost, and standardization, the key technologies of wireless sensor networks for fine agriculture are studied, including network structure, networking method, node positioning method, data fusion method, rapid energy self-sufficiency, and energy-saving strategy, and the performance evaluation method of wireless sensor network system, IoT-oriented middleware design method, generic node software and hardware design method, and typical application system. Firstly, a convolutional layer is used instead of a fully connected layer, which makes the network more flexible in terms of input image requirements and enables the calculation of the target rice region. Not only will many complex operations be generated, but it will also limit the generalization ability of the model. Then, by introducing a flexible connection layer based on unit and optimizing the loss function of the network, a crop convolutional neural network (Crop-Net) is finally proposed for training and testing rice images at different growth stages to improve the detection accuracy. In this paper, a network quality of service goal-driven evaluation strategy and evaluation method for agricultural wireless sensor network systems is designed to provide a reference for the establishment of industry standards for wireless sensor network systems for fine agriculture. [ABSTRACT FROM AUTHOR]

The metabolic syndrome has become one of the major public health challenges in the world, and adjusting glucose and lipid levels to their normal values is crucial for treating the metabolic syndrome. Olfactory receptors (ORs) expressed in extra‐nasal tissues participate in diverse biological processes, including the regulation of glucose and lipid metabolism. Ectopic ORs can regulate a variety of metabolic events including insulin secretion, glucagon secretion, fatty acid oxidation, lipogenesis and thermogenesis. Understanding the physiological function and deciphering the olfactory recognition code by suitable ligands make ectopic ORs potential targets for the treatment of the metabolic syndrome. In this review, we delineate the roles and mechanisms of ectopic ORs in the regulation of glucose and lipid metabolism, summarize the corresponding natural ligands, and discuss existing problems and the therapeutic potential of targeting ORs in the metabolic syndrome. [ABSTRACT FROM AUTHOR]

Tensegrity mechanisms are lighter weight, lower inertial force and higher strength to weight ratio compared with conventional ones. This paper proposes a four-DOF automatic device for polishing the surface of a workspiece, in which a passive tensegrity-based parallel mechanism is connected to an industrial robot arm through an adjustable spring-loaded prismatic joint. The device consists of moving and base platforms that are connected by three compliant spring legs and one rigid central leg. A major advantage of this device is that attitude of polishing tool mounted on the moving platform can be changed automatically to fit the polished workspiece surface without any sensors, due to the three compliant spring legs. The kinematics, statics and stiffness problems of the mechanism are considered and solved. Finally, an experiment is executed on the surface of a discarded car shell, to verify the reliability of the polishing device within ranges of some parameters. [ABSTRACT FROM AUTHOR]

Previous studies presented various beneficial effects of mogrosides extract from Siraitia grosvenorii, which has been included in the list of Medicine Food Homology Species in China. Mogroside V (MV) is one of the main ingredients in mogrosides extract; however, whether and how MV improves impaired lipid metabolism in the liver remains to be elucidated. Herein, we investigated the therapeutic effects of mogroside V upon hepatic steatosis in vivo and in vitro and explored the underlying mechanisms. The results showed that MV significantly ameliorated hepatic steatosis in high-fat diet- (HFD-) fed mice. Furthermore, the increased protein expression of PPAR-γ, SREBP-1, and FASN and mRNA expression of pparg, srebp1, scd1, and fasn in the liver in HFD-fed mice, which contribute to de novo lipogenesis, were dose-dependently reversed by MV treatment. Meanwhile, MV counteracted the suppressed expression of PPAR-α and CPT-1A and mRNA expression of atgl, hsl, ppara, and cpt1a, thus increasing lipolysis and fatty acid oxidation. In addition, in free fatty acids- (FFAs-) incubated LO2 cells MV downregulated de novo lipogenesis and upregulated lipolysis and fatty acid oxidation, thereby attenuating lipid accumulation, which was significantly abrogated by treatment with Compound C, an inhibitor of AMP-activated protein kinase (AMPK). Taken together, these results suggested that MV exerted a pronounced effect upon improving hepatic steatosis through regulating the disequilibrium of lipid metabolism in the liver via an AMPK-dependent pathway, providing a potential lead compound candidate for preventing nonalcoholic fatty liver disease. [ABSTRACT FROM AUTHOR]

Carbonaceous substances are fundamental organic nutrients for microbial metabolism and catabolism in natural habitats. Microbial abilities to sense, accumulate, and utilize organic carbonaceous substances in the complex nutrient environment are important for their growth and ecological functions. Bacterial chemotaxis is an effective mechanism for microbial utilization of carbonaceous substances under nutrient depletion conditions. Although bacterial accumulation and utilization to individual carbonaceous substance in long-term cultivation has been well studied, their selective affinity of mixed carbonaceous substances remains to be investigated, primarily because of technical limitations of conventional methods. Herein, we applied Raman microspectroscopy to identify chemotaxis-driven affinity and accumulation of four organic carbonaceous substances (glucose, succinate, acetate, and salicylate) by three bacterial strains (Acinetobacter baylyi , Pseudomonas fluorescence , and Escherichia coli). A. baylyi exhibited strong binding affinity toward glucose and succinate, whereas P. fluorescence and E. coli were preferentially responsive to glucose and acetate. For the first time, bacterial transient selectivity of carbonaceous substances was studied via interrogating Raman spectral alterations. Post-exposure to carbonaceous-substance mixtures, the three bacterial strains showed distinct selective behaviors. Stronger selective affinity enhanced the chemotaxis-related signal transduction in A. baylyi cells, whereas the carbonaceous substance signal transduction in E. coli was decreased by higher selective affinity. In P. fluorescence , there was no specific effect of selective affinity on signal transduction. Our study suggests that Raman microspectroscopy can successfully investigate and distinguish different scenarios of bacterial competitive and transient unitization of organic carbonaceous substances. [ABSTRACT FROM AUTHOR]