STUDY ON THE DRIVING GAZE SHIFT CHARACTERISTICS OF VISION INTERESTING AREA ON MOUNTAINOUS ROAD

Mountainous road landscape is the main source of driving information. The characteristics of two-lane mountainous road result in real-time dynamic changes in the driver's vision interesting areas. In order to explore the dynamic gaze characteristics, a driving experiment is conducted, and the gaze data of 10 drivers are collected. Markov chain is used to analyze the change process of gaze. The results show that: (1) when the current gaze point is in the straight front area, different road landscape has no significant impact on the gaze shift probability; (2) when the current gaze point is in the near left area, next gaze will expand the search scope to obtain much more driving information; (3) when the current gaze point is in the near right area, there is a high probability that the driver's next gaze will return to the front area; (4) when the current gaze point is in the far right area, the gaze will move back and forth between the near right and the far right areas; (5) when the current gaze point is in the far left area, there is a high probability that the gaze will remain in current area; (6) the main source of traffic information obtained by the driver in mountainous road landscape is the straight front area in the vision field, and the gaze point constantly shifts between the far ahead and the near ahead. The research results can provide technical reference for the construction of landscape in mountainous two-lane road

MedDM:LLM-executable clinical guidance tree for clinical decision-making

It is becoming increasingly emphasis on the importance of LLM participating in clinical diagnosis decision-making. However, the low specialization refers to that current medical LLMs can not provide specific medical advice, which are more like a medical Q\&A. And there is no suitable clinical guidance tree data set that can be used directly with LLM. To address this issue, we first propose LLM-executavle clinical guidance tree(CGT), which can be directly used by large language models, and construct medical diagnostic decision-making dataset (MedDM), from flowcharts in clinical practice guidelines. We propose an approach to screen flowcharts from medical literature, followed by their identification and conversion into standardized diagnostic decision trees. Constructed a knowledge base with 1202 decision trees, which came from 5000 medical literature and covered 12 hospital departments, including internal medicine, surgery, psychiatry, and over 500 diseases.Moreover, we propose a method for reasoning on LLM-executable CGT and a Patient-LLM multi-turn dialogue framework

The art of defense: letting networks fool the attacker

Some deep neural networks are invariant to some input transformations, such as Pointnet is permutation invariant to the input point cloud. In this paper, we demonstrated this property could be powerful in defense of gradient-based attacks. Specifically, we apply random input transformation which is invariant to the networks we want to defend. Extensive experiments demonstrate that the proposed scheme defeats various gradient-based attackers in the targeted attack setting, and breaking the attack accuracy into nearly zero. Our code is available at: {\footnotesize{\url{https://github.com/cuge1995/IT-Defense}}}

Metabolic crosstalk: molecular links between glycogen and lipid metabolism in obesity.

Glycogen and lipids are major storage forms of energy that are tightly regulated by hormones and metabolic signals. We demonstrate that feeding mice a high-fat diet (HFD) increases hepatic glycogen due to increased expression of the glycogenic scaffolding protein PTG/R5. PTG promoter activity was increased and glycogen levels were augmented in mice and cells after activation of the mechanistic target of rapamycin complex 1 (mTORC1) and its downstream target SREBP1. Deletion of the PTG gene in mice prevented HFD-induced hepatic glycogen accumulation. Of note, PTG deletion also blocked hepatic steatosis in HFD-fed mice and reduced the expression of numerous lipogenic genes. Additionally, PTG deletion reduced fasting glucose and insulin levels in obese mice while improving insulin sensitivity, a result of reduced hepatic glucose output. This metabolic crosstalk was due to decreased mTORC1 and SREBP activity in PTG knockout mice or knockdown cells, suggesting a positive feedback loop in which once accumulated, glycogen stimulates the mTORC1/SREBP1 pathway to shift energy storage to lipogenesis. Together, these data reveal a previously unappreciated broad role for glycogen in the control of energy homeostasis

Ginsenoside Rh1 Improves the Effect of Dexamethasone on Autoantibodies Production and Lymphoproliferation in MRL/lpr Mice

Ginsenoside Rh1 is able to upregulate glucocorticoid receptor (GR) level, suggesting Rh1 may improve glucocorticoid efficacy in hormone-dependent diseases. Therefore, we investigated whether Rh1 could enhance the effect of dexamethasone (Dex) in the treatment of MRL/lpr mice. MRL/lpr mice were treated with vehicle, Dex, Rh1, or Dex + Rh1 for 4 weeks. Dex significantly reduced the proteinuria and anti-dsDNA and anti-ANA autoantibodies. The levels of proteinuria and anti-dsDNA and anti-ANA autoantibodies were further decreased in Dex + Rh1 group. Dex, Rh1, or Dex + Rh1 did not alter the proportion of CD4+ splenic lymphocytes, whereas the proportion of CD8+ splenic lymphocytes was significantly increased in Dex and Dex + Rh1 groups. Dex + Rh1 significantly decreased the ratio of CD4+/CD8+ splenic lymphocytes compared with control. Con A-induced CD4+ splenic lymphocytes proliferation was increased in Dex-treated mice and was inhibited in Dex + Rh1-treated mice. Th1 cytokine IFN-γ mRNA was suppressed and Th2 cytokine IL-4 mRNA was increased by Dex. The effect of Dex on IFN-γ and IL-4 mRNA was enhanced by Rh1. In conclusion, our data suggest that Rh1 may enhance the effect of Dex in the treatment of MRL/lpr mice through regulating CD4+ T cells activation and Th1/Th2 balance