The superior parietal lobule (SPL) plays a pivotal role in many cognitive, perceptive and motor-related processes. This implies that a mosaic of distinct functional and structural subregions may exist in this area. Recent studies have demonstrated that the ongoing spontaneous fluctuations in the brain at rest are highly structured and, like co-activation patterns, reflect the integration of cortical locations into long-distance networks. This suggests that the internal differentiation of a complex brain region may be revealed by interaction-patterns that are reflected in different neuroimaging modalities. On the basis of this perspective, we aimed to identify a convergent functional organization of the SPL using multimodal neuroimaging approaches. The SPL was first parcellated based on its structural connections as well as on its resting-state connectivity and coactivation patterns. Then, post-hoc functional characterizations and connectivity analyses were performed for each subregion. The three types of connectivity-based parcellations consistently identified five subregions in the SPL of each hemisphere. The two anterior subregions were found to be primarily involved in action processes and in visually guided visuomotor functions, whereas the three posterior subregions were primarily associated with visual perception, spatial cognition, reasoning, working memory, and attention. This parcellation scheme for the SPL was further supported by revealing distinct connectivity patterns for each sub-region in all the employed modalities. These results thus indicate a convergent functional architecture of the SPL that can be revealed based on different types of connectivity and is reflected by different functions and interactions.
Electrochemical CO 2 reduction enables the conversion of intermittent renewable energy to value-added chemicals and fuel, presenting a promising strategy to relieve CO 2 emission and achieve clean energy storage. In this work, we developed nanosized Cu 2 O catalysts using the hydrothermal method for electrochemical CO 2 reduction to alcohols. Cu 2 O nanoparticles (NPs) of various morphologies that were enclosed with different crystal facets, named as Cu 2 O-c (cubic structure with (100) facets), Cu 2 O-o (octahedron structure with (111) facets), Cu 2 O-t (truncated octahedron structure with both (100) and ( 111) facets), and Cu 2 O-u (urchin-like structure with (100), ( 220), and ( 222) facets), were prepared by regulating the content of a polyvinyl pyrrolidone (PVP) template. The electrochemical CO 2 reduction performance of the different Cu 2 O NPs was evaluated in the CO 2 -saturated 0.5 M KHCO 3 electrolyte. The as-synthesized Cu 2 O nanostructures were capable of reducing CO 2 to produce alcohols including methanol, ethanol, and isopropanol. The alcohol selectivity of the different Cu 2 O NPs followed the order of Cu 2 O-t < Cu 2 O-u < Cu 2 O-c < Cu 2 O-o (with the total Faradaic efficiencies of alcohol products of 10.7, 25.0, 26.2, and 35.4%). The facet-dependent effects were associated with the varied concentrations of oxygen-vacancy defects, different energy barriers of CO 2 reduction, and distinct Cu−O bond lengths over the different crystal facets. The desired Cu 2 O-o catalyst exhibited good reduction activity with the highest partial current density of 0.51 mA/cm 2 for alcohols. The Faradaic efficiencies of alcohol products were 4.9% for methanol, 17.9% for ethanol, and 12.6% for isopropanol. The good electrochemical CO 2 reduction performance was also associated with the surface reconstruction of Cu 2 O, which endowed the catalyst with abundant Cu 0 and Cu + sites for promoted CO 2 activation and stabilized CO* adsorption for enhanced C−C coupling. This work will provide a new route for enhancing the alcohol selectivity of nanostructured Cu 2 O catalysts by crystal facet engineering.
Pattern classification algorithm is the crucial step in developing brain-computer interface (BCI) applications. In this paper, a hierarchical support vector machine (HSVM) algorithm is proposed to address an EEG-based four-class motor imagery classification task. Wavelet packet transform is employed to decompose raw EEG signals. Thereafter, EEG signals with effective frequency sub-bands are grouped and reconstructed. EEG feature vectors are extracted from the reconstructed EEG signals with one versus the rest common spatial patterns (OVR-CSP) and one versus one common spatial patterns (OVO-CSP). Then, a two-layer HSVM algorithm is designed for the classification of these EEG feature vectors, where "OVO" classifiers are used in the first layer and "OVR" in the second layer. A public dataset (BCI Competition IV-II-a)is employed to validate the proposed method. Fivefold cross-validation results demonstrate that the average accuracy of classification in the first layer and the second layer is 67.5 ± 17.7% and 60.3 ± 14.7%, respectively. The average accuracy of the classification is 64.4 ± 16.7% overall. These results show that the proposed method is effective for four-class motor imagery classification.
Growing evidence has shown that hepatic oval cells, also named liver progenitor cells, play an important role in the process of liver regeneration in various liver diseases. Oval cell proliferation has been reported in hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) and chronic liver disease. Studies have found expression of HBV surface and core antigens in oval cells in the livers of patients with HCC, suggesting that HBV infection of oval cells could be a mechanism of human hepatocarcinogenesis. In addition, there is evidence of multiplication of HBV in oval cell culture. However, little research has been performed to explore the role of HBV-encoded proteins in the proliferation of hepatic oval cells. Previously, we successfully transfected the HBV x (HBx) gene, one of the four genes in the HBV genome, into a rat LE/6 oval cell line. In this study, we tested whether or not the transfected HBx gene could affect oval cell proliferation in vitro. Our results show that overexpression of HBx promotes the proliferation of oval cells and increases cyclin D1 expression, assessed at both the mRNA and protein levels. We also found that HBx activated the PI-3K/Akt and MEK/ERK1/2 pathways in HBx-transfected oval cells. Furthermore, the HBx-induced increases in cyclin D1 expression and oval cell proliferation were completely abolished by treatment with either MEK inhibitor PD184352 or PI-3K inhibitor LY294002. These results demonstrated that HBx has the ability to promote oval cell proliferation in vitro, and its stimulatory effects on cell proliferation and expression of cyclin D1 depend on the activation of the MEK/ERK and PI3K/Akt signaling pathways in cultured oval cells.
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