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Classification of wood species using computer-related technology is not only significant in the modernization of wood industrialization, but also makes it easier for nonwood professionals to identify different wood types accurately and avoid being deceived. Presently, most wood classification methods use a single feature to describe the wood, which is restrictive and provides incomplete information. In this study, macroscopic transverse section images and spectral curves of wood were used as the research objects, and 50 species of wood were classified. A wood species identification method based on the fusion of spectral features and texture features is proposed, which has the advantages of convenient data collection, fast identification speed, high identification accuracy, and anti-noise interference. First, a digital camera and a spectrometer were used to acquire the image and spectral curve from the wood transverse sections. The acquired spectral and texture features of the wood transverse sections were extracted with the fractal method and the local binary pattern theory method, respectively, and both extracted features were fused using the canonical correlation analysis feature fusion method. The fused features were then classified using a support vector machine classifier. The experimental results demonstrated that the classification accuracy of the texture and spectral features alone was 91.96% and 92.67%, respectively, whereas that of the fused features was 99.16% in the "leave-one-out" cross-validation. The wood classification method outlined in this paper has higher classification accuracy than existing mainstream methods. In addition, even after adding noise to the image and spectrum, it was observed that the classification accuracy did not decrease significantly, which indicates that the method described in this paper achieves excellent classification even in the presence of noise interference. [ABSTRACT FROM AUTHOR]

In this study, a method based on fuzzy gray level aura matrix (FGLAM) textural feature and spectral feature fusion is proposed to improve the accuracy of wood species classification. The experimental dataset is acquired by two sensors. The visible images of wood samples are acquired using a RGB image acquisition platform, and the spectral curves are acquired using a USB2000-VIS-NIR spectrometer having a spectral band of 350–1100 nm. Firstly, the textural features are computed by using FGLAM for gray level wood sample images. The original spectral curve is smoothed and corrected to remove noise. Secondly, the obtained textural features and spectral features are processed in extreme learning machine to complete the decision-level fusion and obtain the classification results. The experimental results for 15 wood species show that the classification accuracy of the textural feature by using FGLAM alone can reach to 90%, which is better than that acquired using the traditional gray level co-occurrence matrix, local binary pattern, and the mainstream improved basic gray level aura matrix. Moreover, the classification accuracy based on the FGLAM textural feature and spectral feature fusion proposed in this paper can reach to 100%. Finally, for 8 similar wood species, the recognition accuracy of the present fusion scheme reaches to 98.75%, which is also better than those state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Valproate exposure is associated with increased risks of autism spectrum disorder. To date, the mechanistic details of disturbance of melatonin receptor subtype 1 (MTNR1A) internalization upon valproate exposure remain elusive. By expressing epitope-tagged receptors (MTNR1A-EGFP) in HEK-293 and Neuro-2a cells, we recorded the dynamic changes of MTNR1A intracellular trafficking after melatonin treatment. Using time-lapse confocal microscopy, we showed in living cells that valproic acid interfered with the internalization kinetics of MTNR1A in the presence of melatonin. This attenuating effect was associated with a decrease in the phosphorylation of PKA (Thr197) and ERK (Thr202/Tyr204). VPA treatment did not alter the whole-cell currents of cells with or without melatonin. Furthermore, fluorescence resonance energy transfer imaging data demonstrated that valproic acid reduced the melatonin-initiated association between YFP-labeled β-arrestin 2 and CFP-labeled MTNR1A. Together, we suggest that valproic acid influences MTNR1A intracellular trafficking and signaling in a β-arrestin 2-dependent manner. [ABSTRACT FROM AUTHOR]

Septic encephalopathy (SE) is a critical factor determining sepsis mortality. Vascular inflammation is known to be involved in SE, but the molecular events that lead to the development of encephalopathy remain unclear. Using time-lapse in vivo two-photon laser scanning microscopy, we provide the first direct evidence that cecal ligation and puncture in septic mice induces microglial trafficking to sites adjacent to leukocyte adhesion on inflamed cerebral microvessels. Our data further demonstrate that septic injury increased the chemokine CXCL1 level in brain endothelial cells by activating endothelial P2RX7 and eventually enhanced the binding of Mac-1 (CD11b/CD18)-expressing leukocytes to endothelial ICAM-1. In turn, leukocyte adhesion upregulated endothelial CX3CL1, thereby triggering microglia trafficking to the injured site. The sepsis-induced increase in endothelial CX3CL1 was abolished in CD18 hypomorphic mutant mice. Inhibition of the P2RX7 pathway not only decreased endothelial ICAM-1 expression and leukocyte adhesion but also prevented microglia overactivation, reduced brain injury, and consequently doubled the early survival of septic mice. These results demonstrate the role of the P2RX7 pathway in linking neurovascular inflammation to brain damage in vivo and provide a rationale for targeting endothelial P2RX7 for neurovascular protection during SE. [ABSTRACT FROM AUTHOR]

Vascular dementia (VaD) is a complex disorder caused by reduced blood flow in the brain. However, there is no effective pharmacological treatment option available until now. Here, we reported that low-dose levamlodipine besylate could reverse the cognitive impairment in VaD mice model of right unilateral common carotid arteries occlusion (rUCCAO). Oral administration of levamlodipine besylate (0.1 mg/kg) could reduce the latency to find the hidden platform in the MWM test as compared to the vehicle group. Furthermore, vehicle-treated mice revealed reduced phospho-CaMKII (Thr286) levels in the hippocampus, which can be partially restored by levamlodipine besylate (0.1 mg/kg and 0.5 mg/kg) treatment. No significant outcome on microglia and astrocytes were observed following levamlodipine besylate treatment. This data reveal novel findings of the therapeutic potential of low-dose levamlodipine besylate that could considerably enhance the cognitive function in VaD mice. [ABSTRACT FROM AUTHOR]