Anatomical brain difference of subthreshold depression in young and middle-aged individuals

Background: Subthreshold depression (StD) is associated with substantial functional impairments due to depressive symptoms that do not fully meet the diagnosis of major depressive disorder (MDD). Its high incidence in the general population and debilitating symptoms has recently put it at the forefront of mood disorder research. Aim In this study we investigated common volumetric brain changes in both young and middle-aged StD patients. Methods: Two cohorts of StD patients, young and middle-aged, (n = 57) and matched controls (n = 76) underwent voxel-based morphometry (VBM). Results: VBM analysis found that: 1) compared with healthy controls, StD patients showed decreased gray matter volume (GMV) in the bilateral globus pallidus and precentral gyrus, as well as increased GMV in the left thalamus and right rostral anterior cingulate cortex/medial prefrontal cortex; 2) there is a significant association between Center for Epidemiological Studies Depression Scale scores and the bilateral globus pallidus (negative) and left thalamus (positive); 3) there is no interaction between age (young vs. middle-age) and group (StD vs. controls). Conclusions: Our findings indicate significant VBM brain changes in both young and middle-aged individuals with StD. Individuals with StD, regardless of age, may share common neural characteristics

Real-Time Localization Method of Large Pressure Vessel Leaks Based on Improved CNN and STCA of Elastic Wavefield

In this paper, a real-time leak source localization method based on convolutional neural network (CNN) of elastic wavefield images and spatio-temporal correlation analysis (STCA) is developed for the pressure vessel leakage. This method uses a single sensor array coupled to the wall to collect the elastic wave data excited by the leak source. Besides, the distance $R$ and the direction $\theta$ between the leak source and the sensor array are calculated based on CNN and STCA respectively, to finally obtain the location ( $R$ , $\theta$ ) of the leak source. In this paper, the digital twin model of the experimental platform is established, the training set is obtained by the finite element simulation, and the CNN model applied to the elastic wavefield images is studied and constructed. The experimental results show that the maximum locating error is 1.46 cm and the average locating error is about 0.56 cm within the range of a 1 m2 experimental plate based on the method proposed in this paper

Snf1p Regulates Gcn5p Transcriptional Activity by Antagonizing Spt3p

The budding yeast Gcn5p is a prototypic histone acetyltransferase controlling transcription of diverse genes. Here we show that Gcn5p is itself regulated by Snf1p and Spt3p. Snf1p likely controls Gcn5p via direct interaction. Mutating four residues in the Gcn5p catalytic domain, T203, S204, T211, and Y212 (TSTY), phenocopies snf1 null cells, including Gcn5p hypophosphorylation, hypoacetylation at the HIS3 promoter, and transcriptional defects of the HIS3 gene. However, overexpressing Snf1p suppresses the above phenotypes associated with the phosphodeficient TSTY mutant, suggesting that it is the interaction with Snf1p important for Gcn5p to activate HIS3. A likely mechanism by which Snf1p potentiates Gcn5p function is to antagonize Spt3p, because the HIS3 expression defects caused by snf1 knockout, or by the TSTY gcn5 mutations, can be suppressed by deleting SPT3. In vitro, Spt3p binds Gcn5p, but the interaction is drastically enhanced by the TSTY mutations, indicating that a stabilized Spt3p–Gcn5p interaction may be an underlying cause for the aforementioned HIS3 transcriptional defects. These results suggest that Gcn5p is a target regulated by the competing actions of Snf1p and Spt3p

Short-Term Wind Power Prediction Method Based on Combination of Meteorological Features and CatBoost

As one of the hot topics in the field of new energy, short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy. Therefore, a short-term wind power prediction method based on the combination of meteorological features and CatBoost is presented. Firstly, morgan-stone algebras and sure independence screening(MS-SIS) method is designed to filter the meteorological features, and the influence of the meteorological features on the wind power is explored. Then, a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element. Finally, a prediction method based on CatBoost network is constructed to further realize short-term wind power prediction. The National Renewable Energy Laboratory (NREL) dataset is used for experimental analysis. The results show that the short-term wind power prediction method based on the combination of meteorological features and CatBoost not only improve the prediction accuracy of short-term wind power, but also have higher calculation efficiency

Economic evaluation of energy storage integrated with wind power

Abstract Energy storage can further reduce carbon emission when integrated into the renewable generation. The integrated system can produce additional revenue compared with wind-only generation. The challenge is how much the optimal capacity of energy storage system should be installed for a renewable generation. Electricity price arbitrage was considered as an effective way to generate benefits when connecting to wind generation and grid. This wind-storage coupled system can make benefits through a time-of-use (TOU) tariff. A proportion of electricity is stored from the wind power system at off-peak time (low price), and released to the customer at peak time (high price). Thus, extra benefits are added to the wind-storage system compared with wind-only system. A Particle Swarm Optimization (PSO) algorithm based optimization model was constructed for this integrated system including constraints of state-of-charge (SOC), maximum storage and release powers etc. The proposed optimization model was to obtain the optimal capacity of energy storage system and its operation control strategy of the storage-release processes, to maximize the revenue of the coupled system considering the arbitrage. Furthermore, the energy storage can provide reserve ancillary services for the grid, which generates benefits. The benefits of energy storage system through reserve ancillary services were also calculated. A case study was analyzed with respect to yearly wind generation and electricity price profiles. The benefit compared with no energy storage scenario was calculated. The impact of the energy storage efficiency, cost and lifetime was considered. The sensitivity and optimization capacity under various conditions were calculated. An optimization capacity of energy storage system to a certain wind farm was presented, which was a significant value for the development of energy storage system to integrate into a wind farm