Conectar
by Zhihao Zheng, Jianguang Zhao, Jingjing Fan
Uav target detection is a key technology in low altitude security, disaster relief and other fields. However, in practical application scenarios, there are many complex and highly uncertain factors, such as extreme weather changes, large scale and span of the target, complex background interference, motion ambiguity, etc., which makes accurate and real-time UAV target detection still a great challenge. In order to reduce the interference of these situations in real detection scenes and improve the accuracy of UAV detection, a Global Edge Information Enhance (GEIE)module is proposed in this paper, which enables edge information to be fused into features extracted at various scales. It can improve the attention of the network to the edge information of the object. In addition, special weather conditions can greatly reduce the detection accuracy of the target, this paper proposes a Multiscale Edge Feature Enhance(MEFE) module to extract features from different scales and highlight edge information, which can improve the model’s perception of multi-scale features. Finally, we propose a Lightweight layered Shared Convolutional BN(LLSCB) Detection Head based on LSCD, so that the detection heads share the convolutional layer, and the BN is calculated independently, which improves the detection accuracy and reduces the number of parameters. A high performance YOLO detector (YOLO-GML) based on YOLO11 model is proposed. Experimental results show that Compared with YOLO11s, YOLO-GML can improve AP50 by 2.3% to 73.6% on the challenging UAV detection dataset HazyDet, achieving a better balance between accuracy and inference efficiency compared to the most advanced detection algorithms. YOLO-GML also showed good performance improvement in the SODA-A and VisDrone-2019 datasets, demonstrating the generalization of the model.by James A. Votava, Jing Fan, Brian W. Parks
The lactating mammary gland strongly induces de novo lipogenesis (DNL) to support the synthesis of fatty acids, triglycerides, and cholesterol found within milk. In monogastric species, glucose is a major substrate utilized for DNL within the lactating mammary gland and must be efficiently taken up and processed to supply cytosolic acetyl-CoA for DNL. Along with the enzymes of the DNL pathway, the glycolytic enzyme, Aldolase C (Aldoc), is transcriptionally upregulated and is highly expressed during lactation in the mammary gland, suggesting a role for Aldoc in lactation. Aldoc is also a transcriptional target of the sterol regulatory element binding proteins 1 and 2 (Srebp1 and Srebp2), which transcriptionally regulate enzymes within the DNL pathway and has recently been shown to regulate plasma cholesterol and triglycerides. Here, we investigate the role of Aldoc in lactation, by utilizing a whole-body Aldoc knockout mouse. Our results demonstrate that Aldoc has a significant impact on lactation, whereby pups nursing from Aldoc-/- dams have reduced body weight. Biochemical analysis of milk identified that milk from Aldoc-/- dams have significantly higher galactose, lower lactose, and cholesterol content. Mass spectrometry analysis of milk lipids from Aldoc-/- dams revealed significantly lower quantities of medium and long chain fatty acid containing triglycerides, which has direct implications on lactation as these are the predominant triglycerides synthesized from glucose in human mammary gland. Overall, our results provide functional evidence for the contribution of Aldoc in mammary gland lactose and lipid synthesis during lactation.The disturbance of the inflammatory microenvironment is a frequent pathological trait of diabetic wounds, contributing to the emergence of numerous chronic illnesses. This is crucial in both the development and recovery of wounds caused by diabetes. This study aims to perform a bibliometric analysis of research on the inflammatory microenvironment within the domain of diabetic wounds (DW) over the past 10 years. The objective is to map out the current global research landscape, pinpoint the most significant areas of study and offer guidance for future research avenues. Our research involved querying the Web of Science Core Collection (WoSCC) database for all pertinent studies on the inflammatory microenvironment in diabetic wounds (DW). We utilized bibliometric tools such as CiteSpace, VOSviewer and R (version 4.3.1) to identify and highlight the most impactful studies in the field. The study encompassed a review of 1454 articles published from 2014 to 2023, highlighting China and the United States as pivotal nations in the research of the inflammatory microenvironment in diabetic wounds (DW). Within this sphere, the University of Michigan and Harvard University in the United States, along with Shanghai Jiaotong University in China, emerged as the most prolific institutions. WANG Y from China was identified as the most productive author, while KUNKEL SL from the United States received the most citations. The research primarily focuses on topics such as wound healing, repair processes, angiogenesis, oxidative stress and macrophage activity. Additionally, “macrophage” and “delivery” were pinpointed as the leading subjects with promising research potential in this area. Research on the inflammatory microenvironment of diabetic wounds is rapidly advancing through active international collaboration. The study of new mechanisms related to the inflammatory microenvironment and the development of novel materials for repair based on this microenvironment represent emerging fields of future research, particularly in terms of translational applications. This may offer guidance and novel perspectives for further research in the area of the diabetic wound inflammatory microenvironment.
Diabetic foot ulcer (DFU), a common intractable chronic complication of diabetes mellitus (DM), has a prevalence of up to 25%, with more than 17% of the affected patients at risk of amputation or even death. Vascular risk factors, including vascular stenosis or occlusion, dyslipidemia, impaired neurosensory and motor function, and skin infection caused by trauma, all increase the risk of DFU in patients with diabetes. Therefore, diabetic foot is not a single pathogenesis. Preclinical studies have contributed greatly to the pathogenesis determination and efficacy evaluation of DFU. Many therapeutic tools are currently being investigated using DFU animal models for effective clinical translation. However, preclinical animal models that completely mimic the pathogenesis of DFU remain unexplored. Therefore, in this review, the preparation methods and evaluation criteria of DFU animal models with three major pathological mechanisms: neuropathy, angiopathy and DFU infection were discussed in detail. And the advantages and disadvantages of various DFU animal models for clinical sign simulation. Furthermore, the current status of vitro models of DFU and some preclinical studies have been transformed into clinical treatment programs, such as medical dressings, growth factor therapy, 3D bioprinting and pre-vascularization, Traditional Chinese Medicine treatment. However, because of the complexity of the pathological mechanism of DFU, the clinical transformation of DFU model still faces many challenges. We need to further optimize the existing preclinical studies of DFU to provide an effective animal platform for the future study of pathophysiology and clinical treatment of DFU.
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