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Electrocatalytic water splitting has huge potential for generating hydrogen fuel. Its wide application suffers from high energy loss and sluggish reaction kinetics. The adoption of appropriate electrocatalysts is capable of reducing the overpotential and accelerating the reaction. Present research mainly focuses on adjusting electrocatalysts, but the performances are also dependent on other parameters. Therefore, the development of an efficient strategy to enhance electrocatalytic performance through integrating with other driving force, especially a renewable driving force, is of great interest. Herein, we present a photothermal-effect-driven strategy to promote the electrocatalytic hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) activities of nickel/reduced graphene oxide (denoted as Ni/RGO) bifunctional electrocatalysts. The Ni/RGO composite exhibited significant enhancement of activities after exposure to light irradiation (49 mV and 50 mV decrease of overpotential at 10 mA/cm2 for HER and OER, respectively). It was found that the improved electrocatalytic activities arose from the photothermal effect of Ni/RGO, which can efficiently facilitate the thermodynamics and kinetics of electrocatalytic reactions. Furthermore, the photothermal-effect-induced enhancement for electrocatalysis showed good stability, indicating its promising potential in practical application.

Atrial hypertrophy and fibrosis are essential pathological features of atrial fibrillation. Recently, adiponectin has become a protein of interest due to its beneficial effects on cardiovascular diseases. However, the molecular mechanism of atrial structural remodeling and signaling pathways evoked by adiponectin remain unclear. In the present study, we investigated the cardioprotective effect of globular adiponectin (gAcrp) on angiotensin II-induced atrial hypertrophy and fibrosis in neonatal Sprague-Dawley rat. To further investigate the molecular mechanisms underlying the preventive effect of gAcrp, transfection of cells with siRNA was used to suppress the mRNA expression of adiponectin receptor 1 (AdipoR1) and its downstream adaptor protein APPL1. Non-silencing-Cy-3 labelled siRNA was used to determine transfection efficiency using fluorescence microscopy. The expression of atrial natriuretic peptide and procollagen type1 α-1, hypertrophy marker and fibrosis one, respectively, was detected by real-time PCR. Furthermore, the expression of adenosine monophosphate-activated protein kinase (AMPK), phosphatidylinositol 3-kinase (PI3K) and Akt was detected by western blotting. In addition, nuclear p65 translocation activity was analyzed by EMSA supershift assay. Our results showed that AdipoR1 and the adaptor protein APPL1 mediated the protective effects of gAcrp. In addition, the function of adiponectin and phosphorylation of AMPK were prominently diminished by inhibition of PI3K. Furthermore, nuclear factor-κB (NF-κB) transcription was diminished by the specific inhibition of AMPK. Taken together, AMPK pivotally interacts with NF-κB and PI3K, mediating the cardioprotective effect of adiponectin, and may serve as a therapeutic target for preventing atrial hypertrophy and fibrosis. Our present study suggests that gAcrp could ameliorate AngII-induced cardiac hypertrophy and fibrosis in rat atrial cells, which is mediated by the activation of AMPK signaling pathways. APPL1 and AdipoR1 are the key factors involved in the downstream of gAcrp approach.

Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification.

Speech quality assessment methods are necessary for evaluating and documenting treatment outcomes of patients suffering from degraded speech due to Parkinson's disease, stroke, or other disease processes. Subjective methods of speech quality assessment are more accurate and more robust than objective methods but are time-consuming and costly. We propose a novel objective measure of speech quality assessment that builds on traditional speech processing techniques such as dynamic time warping (DTW) and the Itakura-Saito (IS) distortion measure. Initial results show that our objective measure correlates well with the more expensive subjective methods.

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Soil contamination caused by petroleum hydrocarbons has become a worldwide environmental problem. Microorganism combined with phytoremediation appears to be more effective for removal and/or degradation of petroleum hydrocarbons from impacted soils. The current study investigated the effect of inoculated with PGPR Serratia marcescens BC-3 alone or in combination with AMF Glomus intraradices on the phytoremediation of petroleum-contaminated soil. Pot experiments were conducted to analyze the effect on plant and soil for 90 days in greenhouse. The inoculation treatments showed higher plant biomass and antioxidant enzyme activities than the non inoculation control. Inoculation treatments also improved rhizosphere microbial populations in petroleum contaminated soil. The degradation rate of total petroleum hydrocarbons with PGPR and AMP co-inoculation treatment was up to 72.24 %. The results indicated that plant combined with microorganisms for remediation of petroleum hydrocarbons would be a feasible method.