499 research outputs found

    Network community detection via iterative edge removal in a flocking-like system

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    We present a network community-detection technique based on properties that emerge from a nature-inspired system of aligning particles. Initially, each vertex is assigned a random-direction unit vector. A nonlinear dynamic law is established so that neighboring vertices try to become aligned with each other. After some time, the system stops and edges that connect the least-aligned pairs of vertices are removed. Then the evolution starts over without the removed edges, and after enough number of removal rounds, each community becomes a connected component. The proposed approach is evaluated using widely-accepted benchmarks and real-world networks. Experimental results reveal that the method is robust and excels on a wide variety of networks. Moreover, for large sparse networks, the edge-removal process runs in quasilinear time, which enables application in large-scale networks

    Efficient Machine Learning Force Field for Large-Scale Molecular Simulations of Organic Systems

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    To address the computational challenges of ab initio molecular dynamics and the accuracy limitations of empirical force fields, the introduction of machine learning force fields has proven effective in various systems including metals and inorganic materials. However, in large-scale organic systems, the application of machine learning force fields is often hindered by impediments such as the complexity of long-range intermolecular interactions and molecular conformations, as well as the instability in long-time molecular simulations. Therefore, we propose a universal multiscale higher-order equivariant model combined with active learning techniques, efficiently capturing the complex long-range intermolecular interactions and molecular conformations. Compared to existing equivariant models, our model achieves the highest predictive accuracy, and magnitude-level improvements in computational speed and memory efficiency. In addition, a bond length stretching method is designed to improve the stability of long-time molecular simulations. Utilizing only 901 samples from a dataset with 120 atoms, our model successfully extends high precision to systems with hundreds of thousands of atoms. These achievements guarantee high predictive accuracy, fast simulation speed, minimal memory consumption, and robust simulation stability, satisfying the requirements for high-precision and long-time molecular simulations in large-scale organic systems.Comment: 20 pages, 7 figures, Machine learning force field mode

    Endogenous Sulfur Dioxide: A New Member of Gasotransmitter Family in the Cardiovascular System

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    Sulfur dioxide (SO2) was previously regarded as a toxic gas in atmospheric pollutants. But it has been found to be endogenously generated from metabolism of sulfur-containing amino acids in mammals through transamination by aspartate aminotransferase (AAT). SO2 could be produced in cardiovascular tissues catalyzed by its synthase AAT. In recent years, studies revealed that SO2 had physiological effects on the cardiovascular system, including vasorelaxation and cardiac function regulation. In addition, the pathophysiological effects of SO2 were also determined. For example, SO2 ameliorated systemic hypertension and pulmonary hypertension, prevented the development of atherosclerosis, and protected against myocardial ischemia-reperfusion (I/R) injury and isoproterenol-induced myocardial injury. These findings suggested that endogenous SO2 was a novel gasotransmitter in the cardiovascular system and provided a new therapy target for cardiovascular diseases.National Natural Science Foundation of China [81400311, 31440052, 91439110]SCI(E)[email protected]

    Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

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    Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate

    Hypaconitine confers protection on ketamine-induced neuronal injury in neonatal rat brain via a mechanism involving PI3K/Akt/Bcl-2 pathway

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    Purpose: To investigate the neuroprotective effect of hypaconitine against ketamine-induced neuronal injury in the brains of neonatal rats, and the underlying mechanism of action. Methods: Seven day-old Sprague-Dawley pups weighing 15.0 to 20.0 g (mean weight = 17.5 ± 2.5 g), and aged 7 days were used for this study. The pups were sacrificed, and their forebrains isolated and used to prepare cell suspensions. The isolated cells were treated with ketamine (100 µM) or varied concentrations of hypaconitine (0.1 – 2 µM) or LY294002 (10 µM). The cells were trypsinized and cultured at 37 °C in 10 % fetal bovine serum (FBS) supplemented Dulbecco's modified Eagle's medium (DMEM) in a humidified incubator containing 5 % CO2. Cell viability was determined using MTT assay, while TUNEL assay was used to determine the extent of apoptosis in the cells. The expressions of pAkt, Bcl-2 and caspase-3 were determined using Western blotting. Results: There were only few viable cells in the ketamine-treated group, and cell viability was significantly and dose-dependently increased in hypaconitine-treated groups (p < 0.05). The extent of apoptosis was significantly higher in ketamine-treated cells than in control cells, but treatment with hypaconitine significantly reduced the number of apoptotic cells (p < 0.05). However, in the presence of LY294002 (a PI3K-specific inhibitor), the effect of hypaconitine on neuronal cell apoptosis was significantly reversed (p < 0.05). The expressions of p-Akt and Bcl-2 were significantly down-regulated while the expression of caspase-3 was significantly upregulated in ketamine-treated neuronal cells, when compared with control group (p < 0.05). However, in cells treated with hypaconitine, the expressions of p-Akt and Bcl-2 were significantly upregulated, while the expression of caspase-3 was significantly down-regulated (p < 0.05). Treatment of neuronal cells with hypaconitine in the presence of LY294002 significantly reversed the effect of hypaconitine on the expressions of p-Akt, Bcl-2 and caspase-3 (p < 0.05). Conclusion: These results suggest that hypaconitine ameliorates ketamine-induced neuronal injury in neonatal rats via a mechanism involving the PI3K/Akt/Bcl-2 pathway

    Equipments for Crop Protection:Standardization Development in China

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     The history of standardization for crop protection equipments was reviewed to analyze the trends of standards preparation in this paper. The currently active standards were firstly reviewed by their attributes to present the general state of art. The trends of standard preparation, through which the overall development of crop protection equipments are reflected, were interpreted by descriptive items. Finally the future development was predicted as suggestions for decision-making in policy constitution

    Hydrogen Sulfide Inhibits L-Type Calcium Currents Depending upon the Protein Sulfhydryl State in Rat Cardiomyocytes

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    Hydrogen sulfide (H2S) is a novel gasotransmitter that inhibits L-type calcium currents (I Ca, L). However, the underlying molecular mechanisms are unclear. In particular, the targeting site in the L-type calcium channel where H2S functions remains unknown. The study was designed to investigate if the sulfhydryl group could be the possible targeting site in the L-type calcium channel in rat cardiomyocytes. Cardiac function was measured in isolated perfused rat hearts. The L-type calcium currents were recorded by using a whole cell voltage clamp technique on the isolated cardiomyocytes. The L-type calcium channel containing free sulfhydryl groups in H9C2 cells were measured by using Western blot. The results showed that sodium hydrosulfide (NaHS, an H2S donor) produced a negative inotropic effect on cardiac function, which could be partly inhibited by the oxidant sulfhydryl modifier diamide (DM). H2S donor inhibited the peak amplitude of I Ca, L in a concentration-dependent manner. However, dithiothreitol (DTT), a reducing sulfhydryl modifier markedly reversed the H2S donor-induced inhibition of I Ca, L in cardiomyocytes. In contrast, in the presence of DM, H2S donor could not alter cardiac function and L type calcium currents. After the isolated rat heart or the cardiomyocytes were treated with DTT, NaHS could markedly alter cardiac function and L-type calcium currents in cardiomyocytes. Furthermore, NaHS could decrease the functional free sulfhydryl group in the L-type Ca2+ channel, which could be reversed by thiol reductant, either DTT or reduced glutathione. Therefore, our results suggest that H2S might inhibit L-type calcium currents depending on the sulfhydryl group in rat cardiomyocytes
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