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The heterodimeric GABA B receptor, composed of GB1 and GB2 subunits, is a metabotropic G protein-coupled receptor (GPCR) activated by the neurotransmitter GABA. GABA binds to the extracellular domain of GB1 to activate G proteins through GB2. Here we show that GABA B receptors can be activated by mechanical forces, such as traction force and shear stress, in a GABA-independent manner. This GABA-independent mechano-activation of GABA B receptor is mediated by a direct interaction between integrins and the extracellular domain of GB1, indicating that GABA B receptor and integrin form a mechano-transduction complex. Mechanistically, shear stress promotes the binding of integrin to GB1 and induces an allosteric re-arrangement of GABA B receptor transmembrane domains towards an active conformation, culminating in receptor activation. Furthermore, we demonstrate that shear stress-induced GABA B receptor activation plays a crucial role in astrocyte remodeling. These findings reveal a role of GABA B receptor in mechano-transduction, uncovering a ligand-independent activation mechanism for GPCRs. In this study, the heterodimeric GABA B receptor, a class C G protein-coupled receptor for the neurotransmitter GABA, is found to be allosterically activated by mechanical forces in a GABA independent manner through a direct interaction with integrin.

The eyeless C . elegans exhibits robust phototaxis behavior in response to short-wavelength light, particularly UV light. C . elegans senses light through LITE-1, a unique photoreceptor protein that belongs to the invertebrate taste receptor family. However, it remains unclear how LITE-1 is regulated. Here, we performed a forward genetic screen for genes that when mutated suppress LITE-1 function. One group of lite-1 suppressors are the genes required for producing the two primary antioxidants thioredoxin and glutathione, suggesting that oxidization of LITE-1 inhibits its function. Indeed, the oxidant hydrogen peroxide (H 2 O 2 ) suppresses phototaxis behavior and inhibits the photoresponse in photoreceptor neurons, whereas other sensory behaviors are relatively less vulnerable to H 2 O 2 . Conversely, antioxidants can rescue the phenotype of lite-1 suppressor mutants and promote the photoresponse. As UV light illumination generates H 2 O 2 , we propose that upon light activation of LITE-1, light-produced H 2 O 2 then deactivates LITE-1 to terminate the photoresponse, while antioxidants may promote LITE-1’s recovery from its inactive state. Our studies provide a potential mechanism by which H 2 O 2 and antioxidants act synergistically to regulate photosensation in C . elegans .

The heterodimeric GABA receptor, composed of GB1 and GB2 subunits, is a metabotropic G protein-coupled receptor (GPCR) activated by the neurotransmitter GABA. GABA binds to the extracellular domain of GB1 to activate G proteins through GB2. Here we show that GABA receptors can be activated by mechanical forces, such as traction force and shear stress, in a GABA-independent manner. This GABA-independent mechano-activation of GABA receptor is mediated by a direct interaction between integrins and the extracellular domain of GB1, indicating that GABA receptor and integrin form a mechano-transduction complex. Mechanistically, shear stress promotes the binding of integrin to GB1 and induces an allosteric re-arrangement of GABA receptor transmembrane domains towards an active conformation, culminating in receptor activation. Furthermore, we demonstrate that shear stress-induced GABA receptor activation plays a crucial role in astrocyte remodeling. These findings reveal a role of GABA receptor in mechano-transduction, uncovering a ligand-independent activation mechanism for GPCRs.

The heterodimeric GABAB receptor, composed of GB1 and GB2 subunits, is a metabotropic G protein-coupled receptor (GPCR) activated by the neurotransmitter GABA. GABA binds to the extracellular domain of GB1 to activate G proteins through GB2. Here we show that GABAB receptors can be activated by mechanical forces, such as traction force and shear stress, in a GABA-independent manner. This GABA-independent mechano-activation of GABAB receptor is mediated by a direct interaction between integrins and the extracellular domain of GB1, indicating that GABAB receptor and integrin form a novel type of mechano-transduction complex. Mechanistically, shear stress promotes the binding of integrin to GB1 and induces an allosteric re-arrangement of GABAB receptor transmembrane domains towards an active conformation, culminating in receptor activation. Furthermore, we demonstrate that shear stress-induced GABAB receptor activation plays a crucial role in astrocyte remodeling. These findings reveal a previously unrecognized function of GABAB receptor in mechano-transduction, uncovering a novel ligand-independent activation mechanism for GPCRs.

In today’s rapid development of technology, with the popularity and use of information technology networking, big data and cloud computing technology are gradually integrated into all walks of life. In this context, the financial management and control of enterprises have put forward higher requirements and brought greater challenges for enterprise finance. In the actual work, how to analyze the problems of enterprise financial management according to the actual situation of the enterprise and effectively apply big data and cloud computing technology to the work practice of enterprise financial control is a problem worth exploring.

Trade relations between China and its neighboring countries in Northeast Asia and Southeast Asia have exhibited varying trends of change. Notably, Japan and South Korea have clearly demonstrated a clear shift towards “distancing themselves from China and approaching the United State” in their trade practices. This development trend is in sharp contrast to the prevailing “dual structure” theory that describes the separation of politics and economy in East Asia. As the strategic competition between China and the United States intensifies, Japan and South Korea, as allies of the United States, are increasingly succumbing to American coercion and intensifying efforts to decouple trade with China. The reason why the United States can achieve this goal is twofold: firstly, in the field of security, by strengthening the China threat or regional tensions, it forces Japan and South Korea to increase military spending, forming a confrontational situation and thus compressing the space for cooperation between China, Japan, and South Korea; the second is in the field of economy and trade, using value chains and domestic market opportunities to coerce Japanese and Korean companies to invest in the United States and reduce trade in strategic industries with China. The above-mentioned active decoupling of the United States, Japan, and South Korea from China can be attributed to an offensive strategic decoupling. As long as the United States does not change its positioning as China’s biggest strategic competitor, the economic and trade relations between Japan and South Korea with China will continue to distance further.

Classification of network traffic is the essential step for many network researches. However, with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identification approaches has been greatly diminished in recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model, which combines ensemble learning paradigm with co-training techniques. Compared to previous approaches, most of which only employed single classifier, multiple classifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings: limited flow accuracy rate, weak adaptability and huge demand of labeled training set. In this paper, statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set, then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.

Despite decades of theoretical research, the nature of the self-driven collective motion remains indigestible and controversial, while the phase transition process of its dynamic is a major research issue. Recent methods propose to infer the phase transition process from various artificially extracted features using machine learning. In this thesis, we propose a new order parameter by using machine learning to quantify the synchronization degree of the self-driven collective system from the perspective of the number of clusters. Furthermore, we construct a powerful model based on the graph network to determine the long-term evolution of the self-driven collective system from the initial position of the particles, without any manual features. Results show that this method has strong predictive power, and is suitable for various noises. Our method can provide reference for the research of other physical systems with local interactions.

As we all know, the development of social innovation is inseparable from science and technology. Nowadays, the emergence of Big Data(BD) has a significant impact on society. In this data age, the traditional Teaching Mode(TM) of financial management can not meet the needs of the times, and the teaching of financial management needs to be reformed and innovated. In this context, this paper proposes the financial management professional innovation research based on BD. In order to adapt to the trend of the development of the times, this paper proposes an accurate TM of financial management based on BD. In teaching, we use BD technology to accurately mine students’ learning needs and learning characteristics, and give targeted teaching plans through data analysis and decision-making. In order to verify the effectiveness of the method proposed in this paper, we conducted a control experiment. In the experiment, we selected two classes of students with little difference in financial management major in our school as the experimental objects to carry out the research. After the experiment, we investigated the performance and satisfaction of the two classes of students, and evaluated the teaching scheme and teaching effect of the two classes. The results show that after the experiment, only 40% of the students in the control class use the traditional TM, while 63.33% of the students in the class use the TM proposed in this paper, which is far higher than the control class. It can be seen that the financial management precision TM based on BD proposed in this paper is effective and has great advantages.

The study combines field observations and modeling to assess the impact of artificial snowmaking on the Dagu Glacier Landscape No.17, with a focus on long-term changes in glacier thickness under varying snowmaking durations (5, 10, and 20 years) and intensities (low: 0.1 m d −1 , medium: 0.15 m d −1 , and high: 0.2 m d −1 ). The finds indicate that the DGL17 glacier has undergone an average annual thickness reduction of 2.5 m from 2021 to 2024, with its terminus retreating by approximately 4 m. Projections suggest that the glacier may completely disappear within the next four years. The study evaluates different snowmaking regimes under three climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), finding that high snowmaking delay the extinction of the glacier, particularly under high supply modes. Implementing a 30-day snowmaking cycle over a period of 5 to 10 years extends glacier survival by an additional 5 to 10 years. Furthermore, a 60-day snowmaking cycle with high snow supply could prolong the glacier’s lifespan and may increase its peak thickness by up to 50 m after 20 years of artificial snowmaking. Additionally, a positive correlation between snowmaking costs and conservation benefits suggests that the high supply, 60-day snowmaking model could enable the glacier’s survival until mid-century, thereby yielding economic returns from tourism. This study offers valuable insights into glacier management, especially in regions where tourism is a key economic driver.