There are three kinds of membrane potentials: the surface potentials, resulting from the accumulation of charges at the membrane surfaces; the transmembrane potential, determined by imbalance of ...charge in the aqueous solutions; and the dipole potential, a membrane-internal potential from the dipolar components of the phospholipids and interface water. The absolute value of the dipole potential has been very difficult to measure, although its value has been estimated to be in the range of 200-1,000 mV from ion translocation rates (determined by the planar lipid bilayer method), the surface potential of lipid monolayers (determined by the lipid monolayer method), molecular-dynamics calculations, and electron scattering using cryoelectron microscopy (cryo-EM). Spectroscopy methods have also been used to monitor the dipole potential changes on the basis of the observed fluorescence changes of voltage-sensitive probes. The dipole potential accounts for the much larger permeability of a bare phospholipid membrane to anions than cations and affects the conformation and function of membrane proteins.
RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a ...time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed.
We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments.
RSeQC is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://code.google.com/p/rseqc/.
This paper explores the phenomenon of conflict in tourism development in rural China. Four cases were selected and analyzed as part of this exploration. The study identified eight major conflict ...issues: land expropriation, ticket revenue distribution, vending rights, tourism management rights, house demolition, house building, entry restrictions, and village elections. The conflict evolution process indicates that these issues are dynamic and connected rather than static and isolated. Local government was found to be the most important conflicting party for local people due to its authority and economic interests in tourism development. In addition, an often-ignored conflicting party, villagers' committees, was found to have limitations in maintaining local people's interests. The findings of this study shed light on this complicated and sensitive tourism conflict phenomenon in rural China. A couple of practical implications for local authorities and UNESCO are outlined at the end of the paper.
•Eight major conflict issues were identified.•Conflict issues are dynamic and connected rather than static and isolated.•Local government was found to be the most important conflicting party with local people.•Villagers’ committees are an often-ignored party in conflicts during tourism development.•Villagers’ committees were found to have limitations in maintaining local people’s interests.
•A method was developed to achieve desired liposome densities in cryo-EM images of liposomes even at extremely low liposome concentrations (e.g. in nanomolar range).•The new method enables cryo-EM ...data collection for membrane proteins reconstituted into liposomes which could not be concentrated at a concentration high enough for standard cryo-EM sample preparation.•The saturation of carbon regions (i.e. monolayer coverage) determines whether liposomes go into holes for cryo-EM images, while the accumulation of liposomes in carbon regions (i.e. multilayer adsorption) determines how many liposomes go into holes.•The new method is also applicable to prepare cryo-samples of other biological samples.
Liposomes are widely used as delivery systems in pharmaceutical, cosmetics and food industries, as well as a system for structural and functional study of membrane proteins. To accurately characterize liposomes, cryo-Electron Microscopy (cryo-EM) has been employed as it is the most precise and direct method to determine liposome lamellarity, size, shape and ultrastructure. However, its use is limited by the number of liposomes that can be trapped in the thin layer of ice that spans holes in the perforated carbon film on EM grids. We report a long-incubation method for increasing the density of liposomes in holes. By increasing the incubation time, high liposome density was achieved even with extremely dilute (in the nanomolar range) liposome solutions. This long-incubation method has been successfully employed to study the structure of an ion channel reconstituted into liposomes.
Convolutional neural networks (CNNs) have exhibited extraordinary achievements in hyperspectral image (HSI) classification due to their detailed representation of features. However, the improvement ...of classification accuracy often leads to an evident increase in the complexity of the model, which makes it challenging for the model with the state-of-the-art performance to be applied in the actual scene. Considering MobileNetV3 as a lightweight feature extractor, this article proposes a model suitable for HSI classification based on MobileNetV3. To decrease the problem of massive redundant calculations in the existing spatial attention module, this article proposes a more concise and efficient spatial attention module based on the visual feature maps experiment. Besides, multiclass focal-loss is applied to solve the problem that the difficulty of classification varies for each sample. The experimental results demonstrate that in the case of using very few training sets, the proposed model can tremendously reduce the number of calculations and parameters while maintaining high accuracy.
In this letter, the supervised classification algorithm support vector machines is extended to map both pure pixels and mixed pixels using hyperspectral data. The margins between the hyperplanes ...formed by the pixels on the class boundaries are recognized as mixed region, and the space beyond this region is related to pure pixels. In this way, each endmember is modeled by a set of training samples instead of a single (representative) spectrum to accommodate the variations within the relative pure pixels due to system noise. Unmixing outputs generate an integrated soft- and hard-classification map. The better performance comparing with conventional spectral unmixing method was demonstrated using hyperspectral data sets.
Reference genome assemblies are subject to change and refinement from time to time. Generally, researchers need to convert the results that have been analyzed according to old assemblies to newer ...versions, or vice versa, to facilitate meta-analysis, direct comparison, data integration and visualization. Several useful conversion tools can convert genome interval files in browser extensible data or general feature format, but none have the functionality to convert files in sequence alignment map or BigWig format. This is a significant gap in computational genomics tools, as these formats are the ones most widely used for representing high-throughput sequencing data, such as RNA-seq, chromatin immunoprecipitation sequencing, DNA-seq, etc.
Here we developed CrossMap, a versatile and efficient tool for converting genome coordinates between assemblies. CrossMap supports most of the commonly used file formats, including BAM, sequence alignment map, Wiggle, BigWig, browser extensible data, general feature format, gene transfer format and variant call format.
CrossMap is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://crossmap.sourceforge.net/.
Convolutional neural networks (CNNs) have outstanding advantages in the classification of remote sensing scenes. Deep CNN models with better classification performance typically have high complexity, ...whereas shallow CNN models with low complexity rarely achieve good classification performance for remote sensing images with complex spatial structures. In this article, we proposed a new lightweight CNN classification method based on branch feature fusion (LCNN-BFF) for remote sensing scene classification. In contrast to a conventional single linear convolution structure, the proposed model had a bilinear feature extraction structure. The BFF method was utilized to fuse the feature information extracted from the two branches, which improved the classification accuracy. In addition, combining depthwise separable convolution and conventional convolution to extract image features greatly reduced the complexity of the model on the premise of ensuring the accuracy of classification. We tested the method on four standard datasets. The experimental results showed that, compared with recent classification methods, the number of weight parameters of the proposed method only accounted for less than 5% of the other methods; however, the classification accuracy was equivalent to or even superior to certain high-performance classification methods.
Graph-based classification algorithms have gained increasing attention in semi-supervised classification. Nevertheless, the graph cannot fully represent the inherent spatial distribution of the data. ...In this paper, a new classification methodology based on the spatial-spectral Label Propagation is proposed for semi-supervised classification of hyperspectral imagery. The spatial information was used in two aspects: on the one hand, the spatial features extracted by a 2-D Gabor filter were stacked with spectral features; on the other hand, the width of the Gaussian function, which was used to construct graph, was determined with an adaptive method. Subsequently, the unlabeled samples from the spatial neighbors of the labeled samples were selected and the spatial graph was constructed based on spatial smoothness. Finally, labels were propagated from labeled samples to unlabeled samples with spatial-spectral graph to update the training set for a basic classifier (e.g., Support Vector Machine, SVM). Experiments on four hyperspectral datasets show that the proposed Spatial-Spectral Label Propagation based on the SVM (SS-LPSVM) can effectively represent the spatial information in the framework of semi-supervised learning and consistently produces greater classification accuracy than the standard SVM, the Laplacian Support Vector Machine (LapSVM), Transductive Support Vector Machine (TSVM) and the Spatial-Contextual Semi-Supervised Support Vector Machine (SCS3VM).
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