•Multi-kernel extreme learning machine based method is proposed for EEG classification.•Supplementary information from different kernels are integrated for better accuracy.•Extensive experimental ...comparison confirms superiority of the proposed method.
One of the most important issues for the development of a motor-imagery based brain-computer interface (BCI) is how to design a powerful classifier with strong generalization capability. Extreme learning machine (ELM) has recently proven to be comparable or more efficient than support vector machine for many pattern recognition problems. In this study, we propose a multi-kernel ELM (MKELM)-based method for motor imagery electroencephalogram (EEG) classification. The kernel extension of ELM provides an elegant way to circumvent calculation of the hidden layer outputs and inherently encode it in a kernel matrix. We investigate effects of two different kernel functions (i.e., Gaussian kernel and polynomial kernel) on the performance of kernel ELM. The MKELM method is subsequently developed by integrating these two types of kernels with a multi-kernel learning strategy, which can effectively explore the supplementary information from multiple nonlinear feature spaces for more robust classification of EEG. An extensive experimental comparison with two public EEG datasets indicates that the MKELM method gives higher classification accuracy than those of the other competing algorithms. The experimental results confirm that superiority of the proposed MKELM-based method for accurate classification of EEG associated with motor imagery in BCI applications. Our method also provides a promising and generalized solution to investigate the complex and nonlinear information for various applications in the fields of expert and intelligent systems.
The oxides and active metals at the interface synergistically activate reactants and thus promote the reaction, but the interface structure often changes dynamically during the reaction. In the ...conventional supported catalysts, the metals at the interface have been extensively studied, while the structural evolution of oxides is often overlooked due to the interference of the bulk phase signal. In this work, CeO2−CuO inverse catalysts are designed to reveal the dynamic structure evolution of CeO2 in the CeO2−CuO system during the water gas shift (WGS) reaction by in situ Raman, in situ XRD, quasi in situ XPS, and near ambient pressure XPS (NAP‐XPS). CeO2 is partially concealed in the CuO phase in the un‐pretreated catalyst and gradually exposed to the surface, forming an inverse CeOx/Cu structure during the reducing process. This structure exhibits a high catalytic activity in the WGS reaction and remains durable under the reductive conditions. When the inverse CeOx/Cu structure is exposed to the non‐redox conditions, the reconfiguration of the reduced oxide is observed which is caused by the oxygen migration of CeO2. This work explores the structure evolution of CeO2 in CeO2−CuO inverse catalyst under different conditions by in situ characterization technique and provides a reference for monitoring the dynamic changes of oxide structure.
The structural evolution of CeO2 in CeO2−CuO catalyst was captured by in situ technique. Under reductive conditions, CeO2 was exposed to the catalyst surface to form an inverse CeOx/Cu interface with a high WGS activity. After the removing of the reductive condition, CeO2 in the catalyst will undergo a surface reconstruction, which is manifested as oxygen migration and oxidation of Cu.
Environmental ultrafine particulate matter (PM) is capable of inducing airway injury, while the detailed molecular mechanisms remain largely unclear. Here, we demonstrate pivotal roles of autophagy ...in regulation of inflammation and mucus hyperproduction induced by PM containing environmentally persistent free radicals in human bronchial epithelial (HBE) cells and in mouse airways. PM was endocytosed by HBE cells and simultaneously triggered autophagosomes, which then engulfed the invading particles to form amphisomes and subsequent autolysosomes. Genetic blockage of autophagy markedly reduced PM-induced expression of inflammatory cytokines, e.g. IL8 and IL6, and MUC5AC in HBE cells. Mice with impaired autophagy due to knockdown of autophagy-related gene Becn1 or Lc3b displayed significantly reduced airway inflammation and mucus hyperproduction in response to PM exposure in vivo. Interference of the autophagic flux by lysosomal inhibition resulted in accumulated autophagosomes/amphisomes, and intriguingly, this process significantly aggravated the IL8 production through NFKB1, and markedly attenuated MUC5AC expression via activator protein 1. These data indicate that autophagy is required for PM-induced airway epithelial injury, and that inhibition of autophagy exerts therapeutic benefits for PM-induced airway inflammation and mucus hyperproduction, although they are differentially orchestrated by the autophagic flux.
Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we ...perform a genome-scale screen to measure gRNA activity for two highly specific SpCas9 variants (eSpCas9(1.1) and SpCas9-HF1) and wild-type SpCas9 (WT-SpCas9) in human cells, and obtain indel rates of over 50,000 gRNAs for each nuclease, covering ~20,000 genes. We evaluate the contribution of 1,031 features to gRNA activity and develope models for activity prediction. Our data reveals that a combination of RNN with important biological features outperforms other models for activity prediction. We further demonstrate that our model outperforms other popular gRNA design tools. Finally, we develop an online design tool DeepHF for the three Cas9 nucleases. The database, as well as the designer tool, is freely accessible via a web server, http://www.DeepHF.com/ .
The focus of this work is to study nanofibers in three different polymers: polyvinyl alcohol (PVA), polypropylene (PP) and polyethylene (PE). The nanofibers were isolated from a soybean source by ...combining chemical and mechanical treatments. Isolated nanofibers were shown to have diameter between 50 and 100
nm and the length in micrometer scale which results in very high aspect ratio. The mechanical properties demonstrated an increase in tensile strength from 21
MPa of PVA/UNF5 (untreated-fiber (5
wt%) reinforced PVA) and 65
MPa of pure PVA to 103
MPa of PVA/SBN5 (nanofiber (5
wt%) reinforced PVA). The increased stiffness of PVA/SBN5 nanocomposites was also very promising; it was 6.2
GPa compared to 2.3
GPa of pure PVA and 1.5
GPa of PVA/UNF5. In solid phase melt-mixing, nanofiber was directly incorporated into the polymer matrix using a Brabender. The nanofiber addition significantly changed the stress–strain behavior of the composites: modulus and stress were increased with coated nanofibers by ethylene–acrylic oligomer emulsion as a dispersant; however, elongation was reduced. The dynamic mechanical analysis showed the addition of the soybean nanofiber (SBN) improved the thermal properties for PVA and how the addition of different contents of SBN influenced the tan
δ peak and storage modulus of PVA.
Airborne microorganisms (AM), vital components of particulate matters (PM), are widespread in the atmosphere. Since some AM have pathogenicity, they can lead to a wide range of diseases in human and ...other organisms, meanwhile, some AM act as cloud condensation nuclei and ice nuclei which let them can affect the climate. The inherent characteristics of AM play critical roles in many aspects which, in turn, can decide microbial traits. The uncertain factors bring various influences on AM, which make it difficult to elaborate effect trends as whole. Because of the potential roles of AM in environment and potent effects of factors on AM, detailed knowledge of them is of primary significance. This review highlights the issues of composition and characteristics of AM with size-distribution, species diversity, variation and so on, and summarizes the main factors which affect airborne microbial features. This general information is a knowledge base for further thorough researches of AM and relevant aspects. Besides, current knowledge gaps and new perspectives are offered to roundly understand the impacts and application of AM in nature and human health.
•Airborne microorganisms have different composition in diverse environment.•These microorganisms own many important characteristics which can influence human and nature.•There are many factors affecting airborne microbial features with different effects.
Based on infrastructure and computing power provided by cloud computing service providers, more and more colleges and universities have developed information-based educational resources and ...implemented information-based educational applications in a virtualized environment. After accessing cloud computing services, universities do not need to spend a lot of money to purchase commercial software licenses, because cloud computing can provide a large number of commonly used application software. However, there are few cases about these educational information software resources applied to actual teaching and combined with specific courses, which makes the information software resources disconnected from the courses and reduces the teaching efficiency and effectiveness of the classroom. Based on aforementioned background, this study is aimed at the existing teaching mode in colleges and universities, to conduct research on the specific cloud computing application and practice in the teaching of colleges and universities. With rapid development of information technology, the education informatization will certainly transit from the computer-aided education to the education centered on calculation, data and service, of which possibility and technical support can be provided by the development of cloud computing. With the continuous development and popularization of cloud computing technology, its advantages of low cost, convenience and security will surely attract more colleges and universities to carry out daily education on cloud platform.
Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-Feature Pyramid Network, a ...novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a corresponding decoder followed by a region proposal network. Encoder extracts and fuses multi-scale voxel information in a bottom-up manner, whereas decoder fuses multiple feature maps from various scales by Feature Pyramid Network in a top-down way. Extensive experiments show that the proposed method has better performance on extracting features from point data and demonstrates its superiority over some baselines on the challenging KITTI-3D benchmark, obtaining good performance on both speed and accuracy in real-world scenarios.
In this work, the kinetics study on the reaction between CO2 and tertiary amine catalyzed by zinc(II)‐1,4,8,11‐tetraazacyclotetradecane complexes (CM) and zinc(II)‐1,4,7,10‐tetraazacyclododecane ...complexes (CN) was carried out in a stopped‐flow device. The effects of the catalyst concentration, type of tertiary amines, and temperature on the reaction rate (ν) and catalytic activity (φ) were studied. It was found that the catalyst concentration, tertiary amine with higher pKa, and temperature had positive effects on ν. ν in N‐methyl diethanolamine solution with 10.0 mol m−3 CM and CN were 16.62 and 26.05 folds than the uncatalyzed ν at 298 K, respectively. φ increased with increasing catalyst concentration, decreasing temperature and tertiary amine's pKa. In addition, the kinetics behavior of tertiary amine‐CM/CN‐CO2 systems conformed to the Michaelis–Menten model. The activation energies in catalytic systems were 4%–15% lower than that in the non‐catalytic systems.
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