FADD regulates adipose inflammation, adipogenesis, and adipocyte survival.

Adipose tissue, aside from adipocytes, comprises various abundant immune cells. The accumulation of low-grade chronic inflammation in adipose tissue serves as a primary cause and hallmark of insulin resistance. In this study, we investigate the physiological roles of FADD in adipose tissue inflammation, adipogenesis, and adipocyte survival. High levels of Fadd mRNA were observed in mitochondrial-rich organs, particularly brown adipose tissue. To explore its metabolic functions, we generated global Fadd knockout mice, resulting in embryonic lethality, while heterozygous knockout (Fadd+/-) mice did not show any significant changes in body weight or composition. However, Fadd+/- mice exhibited reduced respiratory exchange ratio (RER) and serum cholesterol levels, along with heightened global and adipose inflammatory responses. Furthermore, AT masses and expression levels of adipogenic and lipogenic genes were decreased in Fadd+/- mice. In cellular studies, Fadd inhibition disrupted adipogenic differentiation and suppressed the expression of adipogenic and lipogenic genes in cultured adipocytes. Additionally, Fadd overexpression caused adipocyte death in vitro with decreased RIPK1 and RIPK3 expression, while Fadd inhibition downregulated RIPK3 in iWAT in vivo. These findings collectively underscore the indispensable role of FADD in adipose inflammation, adipogenesis, and adipocyte survival.

Enhanced spontaneous radiation of quantum dots based on modulated anapole states in dielectric metamaterial.

Dielectric nanostructures exhibit low-loss electrical and magnetic resonance, making them ideal for quantum information processing. In this study, the periodic double-groove silicon nanodisk (DGSND) is used to support the anapole state. Based on the distribution properties of the electromagnetic field in anapole states, the anapoles are manipulated by cutting the dielectric metamaterial. Quantum dots (QDs) are used to stimulate the anapole and control the amplification of the photoluminescence signal within the QDs. By opening symmetrical holes in the long axis of the nanodisk in the dielectric metamaterial, the current distribution of Mie resonance can be adjusted. As a result, the toroidal dipole moment is altered, leading to an enhanced electric field (E-field) and Purcell factor. When the dielectric metamaterial is deposited on the Ag substrate separated by the silicon dioxide (SiO2) layer, the structure exhibits ultra-narrow perfect absorption with even higher E-field and Purcell factor enhancement compared to silicon (Si) nanodisks.

Optimized Design of EdgeBoard Intelligent Vehicle Based on PP-YOLOE.

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system's ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles' operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model's performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms.

Lactate Conversion by Lactate Dehydrogenase B Is Involved in Beige Adipocyte Differentiation and Thermogenesis in Mice.

Adipose tissue (AT) is the primary reservoir of lipid, the major thermogenesis organ during cold exposure, and an important site for lactate production. However, the utilization of lactate as a metabolic substrate by adipocytes, as well as its potential involvement in the regulation of adipocyte thermogenesis, remain unappreciated. In vitro experiments using primary stromal vascular fraction preadipocytes isolated from mouse inguinal white adipose tissue (iWAT) revealed that lactate dehydrogenase B (LDHB), the key glycolytic enzyme that catalyzes the conversion of lactate to pyruvate, is upregulated during adipocyte differentiation, downregulated upon chronic cold stimulation, and regained after prolonged cold exposure. In addition, the global knockout of Ldhb significantly reduced the masses of iWAT and epididymal WAT (eWAT) and impeded the utilization of iWAT during cold exposure. In addition, Ldhb loss of function impaired the mitochondrial function of iWAT under cold conditions. Together, these findings uncover the involvement of LDHB in adipocyte differentiation and thermogenesis.

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network.

Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent's attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the "feature store & return" module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.

Hepatic Acat2 overexpression promotes systemic cholesterol metabolism and adipose lipid metabolism in mice.

AIMS/HYPOTHESIS: Acetyl coenzyme A acetyltransferase (ACAT), also known as acetoacetyl-CoA thiolase, catalyses the formation of acetoacetyl-CoA from acetyl-CoA and forms part of the isoprenoid biosynthesis pathway. Thus, ACAT plays a central role in cholesterol metabolism in a variety of cells. Here, we aimed to assess the effect of hepatic Acat2 overexpression on cholesterol metabolism and systemic energy metabolism. METHODS: We generated liver-targeted adeno-associated virus 9 (AAV9) to achieve hepatic Acat2 overexpression in mice. Mice were injected with AAV9 through the tail vein and subjected to morphological, physiological (body composition, indirect calorimetry, treadmill, GTT, blood biochemistry, cardiac ultrasonography and ECG), histochemical, gene expression and metabolomic analysis under normal diet or feeding with high-fat diet to investigate the role of ACAT2 in the liver. RESULTS: Hepatic Acat2 overexpression reduced body weight and total fat mass, elevated the metabolic rate, improved glucose tolerance and lowered the serum cholesterol level of mice. In addition, the overexpression of Acat2 inhibited fatty acid, glucose and ketone metabolic pathways but promoted cholesterol metabolism and changed the bile acid pool and composition of the liver. Hepatic Acat2 overexpression also decreased the size of white adipocytes and promoted lipid metabolism in white adipose tissue. Furthermore, hepatic Acat2 overexpression protected mice from high-fat-diet-induced weight gain and metabolic defects CONCLUSIONS/INTERPRETATION: Our study identifies an essential role for ACAT2 in cholesterol metabolism and systemic energy expenditure and provides key insights into the metabolic benefits of hepatic Acat2 overexpression. Thus, adenoviral Acat2 overexpression in the liver may be a potential therapeutic tool in the treatment of obesity and hypercholesterolaemia.

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting.

Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.