Post-traumatic stress disorder (PTSD) is a psychiatric disorder that afflicts many individuals. However, its molecular and cellular mechanisms remain largely unexplored. Here, we found PTSD ...susceptible mice exhibited significant up-regulation of alpha-Ca2+/calmodulin-dependent kinase II (αCaMKII) in the lateral amygdala (LA). Consistently, increasing αCaMKII in the LA not only caused PTSD-like behaviors such as impaired fear extinction and anxiety-like behaviors, but also attenuated N-methyl-D-aspartate receptor (NMDAR)-dependent long-term depression (LTD) at thalamo-lateral amygdala (T-LA) synapses, and reduced GluA1-Ser845/Ser831 dephosphorylation and a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) internalization. Suppressing the elevated αCaMKII to normal levels completely rescued both PTSD-like behaviors and the impairments in LTD, GluA1-Ser845/Ser831 dephosphorylation, and AMPAR internalization. Intriguingly, deficits in GluA1-Ser845/Ser831 dephosphorylation and AMPAR internalization were detected not only after impaired fear extinction, but also after attenuated LTD. Our results suggest that αCaMKII in the LA may be a potential molecular determinant of PTSD. We further demonstrate for the first time that GluA1-Ser845/Ser831 dephosphorylation and AMPAR internalization are molecular links between fear extinction and LTD.
•Methylprednisolone (MP) shows promise in alleviating lung injury in sepsis.•MP regulates the miR-151-5p/USP38 pathway in acute lung injury.•MP enhances pulmonary function and reduces inflammation in ...ALI.•miR-151-5p levels decrease after MP treatment, targeting USP38.•Inhibition of miR-151-5p or overexpression of USP38 enhances MP's effects.
Acute lung injury (ALI) is manifested by increased blood vessel permeability within the lungs and subsequent impairment of alveolar gas exchange. Methylprednisolone (MP) is commonly used as a treatment for ALI to reduce inflammation, yet its molecular mechanism remains unclear. This study aims to explore the underlying mechanisms of MP on ALI in a model induced by lipopolysaccharide (LPS).
The proliferation, viability, apoptosis, and miR-151-5p expression of alveolar type II epithelial cells (AECII) were detected using the cell EdU assay, Annexin V/PI Apoptosis Kit, counting kit-8 (CCK-8) assay, and RT-qPCR. Western blot analysis was used to detect the Usp38 protein level. IL-6 and TNF-α were measured by ELISA. The combination of miR-151-5p and USP38 was determined by chromatin immunoprecipitation (ChIP)-PCR and dual-luciferase reporter assay.
MP greatly improved pulmonary function in vivo, reduced inflammation, and promoted the proliferation of the alveolar type II epithelial cells (AECII) in vitro. By comparing the alterations of microRNAs in lung tissues between MP treatment and control groups, we found that miR-151-5p exhibited a significant increase after LPS-treated AECII, but decreased after MP treatment. Confirmed by a luciferase reporter assay, USP38, identified as a downstream target of miR-151-5p, was found to increase after MP administration. Inhibition of miR-151-5p or overexpression of USP38 in AECII significantly improved the anti-inflammatory, anti-apoptotic, and proliferation-promotive effects of MP.
In summary, our data demonstrated that MP alleviates the inflammation and apoptosis of AECII induced by LPS, and promotes the proliferation of AECII partially via miR-151-5p suppression and subsequent USP38 activation.
In this paper, Independent Component Analysis (ICA) is presented as an efficient face feature extraction method. In a task such as face recognition, important information may be contained in the ...high-order relationship among pixels. ICA is sensitive to high-order statistic in the data and finds not-necessarily orthogonal bases, so it may better identify and reconstruct high-dimensional face image data than Principle Component Analysis (PCA). ICA algorithms are time-consuming and sometimes converge difficultly. A modified FastICA algorithm is developed in this paper, which only need to compute Jacobian Matrix one time in once iteration and achieves the corresponding effect of FastICA. Finally a genetic algorithm is introduced to select optimal independent components (ICs). The experiment results show that modified FastICA algorithm quickens convergence and genetic algorithm optimizes recognition performance. ICA based features extraction method is robust to variations and promising for face recognition.
Community structure is a common property that exists in social networks. Community structure analysis is important for understanding network structure and analyzing the network characteristics. ...Recently community detecting methods are reported continually, but within community the structure is still complex. This paper proposed a method applied to classify community by using Laplacian spectrum feature, and defined the distance measure between the features extracted from difference community. As experiments, this paper studied three complex network models: the random graph of Erdös-Rényi, the small world of Watts and Strogatz and the scale-free graph, and classified them based on Laplacian spectrum feature successfully. The result shows the Laplacian spectrum feature and similarity measure are effective for classification.
This paper is focusing on the neural network based classifier design of modulation types for communication signals. A tree-structured neural network is proposed which could make correct ...identification among 13 modulation types by the use of comprehensive features, including power spectral features, cyclic spectral features and high-order cumulant features. The tree-structured neural network is a self-organizing, hierarchical classifier implementing a sequential linear strategy and requiring no statistical analysis of the features. The design procedure is discussed and simulation results are presented. Experiments show that these types of modulation can be recognized under low SNR in AWGN, and this method also works well for frequency modulations and some amplitude-phase modulation in multipath environment.
This paper proposes an efficient independent component analysis (ICA) based modulation feature extraction method applied in digital modulation identification. In modulation identification, important ...information may be contained in the high-order relationship among sampling points. ICA is sensitive to high-order statistic in the data and finds not-necessarily orthogonal bases, so it may better identify and reconstruct high-dimensional communication signal data than traditional time and frequency domain features. ICA algorithms are time-consuming and sometimes converge difficultly. So a modified FastICA algorithm is developed in this paper, which only need to computer Jacobian Matrix once time in one iteration and achieves the correspondent effect of FastICA. After obtaining all independent components, a genetic algorithm is introduced to select optimal independent components (ICs). The experiment results show that modified FastICA algorithm fast convergence speed and genetic algorithm optimize recognition performance. ICA based features extraction method is innovative and promising for digital modulation identification.
In this paper, a novel automatic modulation recognition method is proposed based on unsupervised learning neural networks, which adopts self-organizing map (SOM) algorithm. Firstly the basic ...structure of the SOM neural networks is described, and then the traditional training algorithm of SOM is improved in terms of learning efficiency and classification performance. Practical communication signal is made use of testing the method performance. Experiment results illustrate that SOM neural networks based digital modulation identification method has higher recognition rate than the method based on back-propagation neural networks. The method proposed in this paper provides a new approach for communication signal modulation identification.
This paper presents a practical speaker recognition system based on multi-classifier structure. Multi-classifier structure overcomes the shortcomings of single classifier, such as low recognition ...rate, narrow application field and critical demand of environment. Additionally, multi-classifier provides a novel way of improvement of system performance. The involved classifiers include ANN (artificial neural networks), GMM (Gaussian mixed model), sub-band classifiers, etc. The input features of classifiers contain MFCC (mel frequency cepstrum coefficient), LPCC (linear prediction cepstrum coefficient). Multi-classifier confusion adopts CFM (classification figure of merit) principle as object function. In practical application, the recognition rate of the system achieves 94% in environment of super short wave (SNR 15db)
China pushed for the unification of markets for natural resources and emissions, wherein initial allocation mechanisms have a significant impact on how well the market functions. To identify how the ...market works well, this paper builds an integrated framework, comprised of permits initialization and reallocation processes. Additionally, we simulate variables by 2030 through scenario analysis and machine learning methods. At the unified energy-carbon market, GDP could increase by more than 49%, while the total energy consumption and the total carbon emission increase by around 410 million tons and 711 million tons, respectively, in China. This illustrates how joint trading markets favor economic growth and emission reduction simultaneously. Besides, most of China's provinces obtain improved energy consumption and carbon emission intensities, which proves the effectiveness and importance of the unified market. However, two rebound effects are found, one is that the increased investment is not always promoting positive effects, and the other is that provinces with higher GDPs have less incentive to cut their energy use. Further, the accumulated per capita initialization type favors energy conservation, while per GDP initialization benefits economic growth and carbon reduction. It's needed that simultaneously constructing an economic and efficient incentive mechanism while effectively playing up the government's role.
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•Energy and carbon joint markets have economic and environmental co-benefits.•A novel framework is designed enabling the exchange of energy and carbon permits.•The influence of socio-economic and climate change scenarios in the future is considered.•The influence of initial permits allocation modes on market performance is identified.•Promotion measures are needed especially for provinces with higher economic outputs.
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