New therapeutic directions in type II diabetes and its complications: mitochondrial dynamics

As important organelles of energetic and metabolism, changes in the dynamic state of mitochondria affect the homeostasis of cellular metabolism. Mitochondrial dynamics include mitochondrial fusion and mitochondrial fission. The former is coordinated by mitofusin-1 (Mfn1), mitofusin-2 (Mfn2), and optic atrophy 1 (Opa1), and the latter is mediated by dynamin related protein 1 (Drp1), mitochondrial fission 1 (Fis1) and mitochondrial fission factor (MFF). Mitochondrial fusion and fission are generally in dynamic balance and this balance is important to preserve the proper mitochondrial morphology, function and distribution. Diabetic conditions lead to disturbances in mitochondrial dynamics, which in return causes a series of abnormalities in metabolism, including decreased bioenergy production, excessive production of reactive oxygen species (ROS), defective mitophagy and apoptosis, which are ultimately closely linked to multiple chronic complications of diabetes. Multiple researches have shown that the incidence of diabetic complications is connected with increased mitochondrial fission, for example, there is an excessive mitochondrial fission and impaired mitochondrial fusion in diabetic cardiomyocytes, and that the development of cardiac dysfunction induced by diabetes can be attenuated by inhibiting mitochondrial fission. Therefore, targeting the restoration of mitochondrial dynamics would be a promising therapeutic target within type II diabetes (T2D) and its complications. The molecular approaches to mitochondrial dynamics, their impairment in the context of T2D and its complications, and pharmacological approaches targeting mitochondrial dynamics are discussed in this review and promise benefits for the therapy of T2D and its comorbidities

Ultra-small topological spin textures with size of 1.3nm at above room temperature in Fe78Si9B13 amorphous alloy

Topologically protected spin textures, such as skyrmions1,2 and vortices3,4, are robust against perturbations, serving as the building blocks for a range of topological devices5-9. In order to implement these topological devices, it is necessary to find ultra-small topological spin textures at room temperature, because small size implies the higher topological charge density, stronger signal of topological transport10,11 and the higher memory density or integration for topological quantum devices5-9. However, finding ultra-small topological spin textures at high temperatures is still a great challenge up to now. Here we find ultra-small topological spin textures in Fe78Si9B13 amorphous alloy. We measured a large topological Hall effect (THE) up to above room temperature, indicating the existence of highly densed and ultra-small topological spin textures in the samples. Further measurements by small-angle neutron scattering (SANS) reveal that the average size of ultra-small magnetic texture is around 1.3nm. Our Monte Carlo simulations show that such ultra-small spin texture is topologically equivalent to skyrmions, which originate from competing frustration and Dzyaloshinskii-Moriya interaction12,13 coming from amorphous structure14-17. Taking a single topological spin texture as one bit and ignoring the distance between them, we evaluated the ideal memory density of Fe78Si9B13, which reaches up to 4.44*104 gigabits (43.4 TB) per in2 and is 2 times of the value of GdRu2Si218 at 5K. More important, such high memory density can be obtained at above room temperature, which is 4 orders of magnitude larger than the value of other materials at the same temperature. These findings provide a unique candidate for magnetic memory devices with ultra-high density.Comment: 26 pages, 4 figure

Research on Adaptive Intelligent Decision Algorithm in Industry 4.0 Digital Economy and Management Transformation Based on Clustering Algorithm

How to transform the business model of enterprises and make it more in line with the new requirements of industrial upgrading is an important part of the government\u27s supply-side reform in the era of Industry 4.0. For the current research on the good management transformation of digital economy in the fourth Industrial revolution, linear function and four commonly used nonlinear function models are first selected to test the good management transformation of digital economy in different technologies, and the key success factors of the strategic transformation from traditional industry to Industry 4.0 business model are explored based on fuzzy hierarchical analysis. Logistic and Gompertz models were used to judge the life cycle stage of the hot technologies of the fourth Industrial revolution, and based on their development trend clustering, the hot technology groups of the fourth Industrial revolution were explored. Secondly, k-means clustering algorithm based on the optimal class center perturbation is proposed, and simulation experiments are carried out. A clustering algorithm based on k-means algorithm is designed, and the moving mode of k-means in the algorithm is changed, and a disturbance strategy is added to strengthen the adaptive intelligent decision-making ability of the algorithm. K-means clustering algorithm based on the optimal class center disturbance is proposed, and simulation experiments are conducted. At the same time, the values of step size factors in the algorithm are compared experimentally. Finally, it explores the function mechanism of digital economy, management transformation and management transformation in the operation of supply chain node enterprises. By analyzing the current situation and problems of digital operation of logistics and supply chain enterprises, it constructs the digital operation system of supply chain enterprises, and proposes the path of digital transformation and upgrading of supply chain enterprises, so as to promote the rapid development of supply chain node enterprises

New Teaching Model of Professional Big Data Courses in Universities Based on an Outcome-Oriented Educational Concept

Most traditional Big Data courses focus on the cultivation of students’ professional theoretical knowledge but neglect the training of students’ programming skills. To test the effectiveness of students’ programming learning, the study constructed a classification algorithm based on the TCNN model from the perspective of program identification for identifying programs written by students on their own. To improve the convergence speed and to retain more data features of the programs, the study used the Softplus-Relu combined activation function. To avoid overfitting the model, a dropout strategy was also introduced to optimize the existing TCNN model. The experimental results show that the TCNN model with the Softplus-Relu activation function converges faster, and the classification accuracy obtained is higher than 95%. The loss values and classification accuracies obtained with the TCNN-Dropout model are better than those of the GIST-KNN algorithm. The former has a loss value close to 0 and an accuracy rate of 98.9%. Thus, this indicates that the improved TCNN model proposed in the study has advantages in the identification procedure and can be used as a teaching aid for the training of big data professionals