Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery

Controlling gene expression in mycobacteria with anhydrotetracycline and Tet repressor

Gene expression systems that allow the regulation of bacterial genes during an infection are valuable molecular tools but are lacking for mycobacterial pathogens. We report the development of mycobacterial gene regulation systems that allow controlling gene expression in fast and slow-growing mycobacteria, including Mycobacterium tuberculosis, using anhydrotetracycline (ATc) as inducer. The systems are based on the Escherichia coli Tn10-derived tet regulatory system and consist of a strong tet operator (tetO)-containing mycobacterial promoter, expression cassettes for the repressor TetR and the chemical inducer ATc. These systems allow gene regulation over two orders of magnitude in Mycobacterium smegmatis and M.tuberculosis. TetR-controlled gene expression was inducer concentration-dependent and maximal with ATc concentrations at least 10- and 20-fold below the minimal inhibitory concentration for M.smegmatis and M.tuberculosis, respectively. Using the essential mycobacterial gene ftsZ, we showed that these expression systems can be used to construct conditional knockouts and to analyze the function of essential mycobacterial genes. Finally, we demonstrated that these systems allow gene regulation in M.tuberculosis within the macrophage phagosome

Transcriptomic analysis reveals ethylene signal transduction genes involved in pistil development of pumpkin

Development of female flowers is an important process that directly affects the yield of Cucubits. Little information is available on the sex determination and development of female flowers in pumpkin, a typical monoecious plant. In the present study, we used aborted and normal pistils of pumpkin for RNA-Seq analysis and determined the differentially expressed genes (DEGs) to gain insights into the molecular mechanism underlying pistil development in pumpkin. A total of 3,817 DEGs were identified, among which 1,341 were upregulated and 2,476 were downregulated. The results of transcriptome analysis were confirmed by real-time quantitative RT-PCR. KEGG enrichment analysis showed that the DEGs were significantly enriched in plant hormone signal transduction and phenylpropanoid biosynthesis pathway. Eighty-four DEGs were enriched in the plant hormone signal transduction pathway, which accounted for 12.54% of the significant DEGs, and most of them were annotated as predicted ethylene responsive or insensitive transcription factor genes. Furthermore, the expression levels of four ethylene signal transduction genes in different flower structures (female calyx, pistil, male calyx, stamen, leaf, and ovary) were investigated. The ethyleneresponsive DNA binding factor, ERDBF3, and ethylene responsive transcription factor, ERTF10, showed the highest expression in pistils and the lowest expression in stamens, and their expression levels were 78- and 162-times more than that in stamens, respectively. These results suggest that plant hormone signal transduction genes, especially ethylene signal transduction genes, play an important role in the development of pistils in pumpkin. Our study provides a theoretical basis for further understanding of the mechanism of regulation of ethylene signal transduction genes in pistil development and sex determination in pumpkin

Simulated Microgravity Compromises Mouse Oocyte Maturation by Disrupting Meiotic Spindle Organization and Inducing Cytoplasmic Blebbing

In the present study, we discovered that mouse oocyte maturation was inhibited by simulated microgravity via disturbing spindle organization. We cultured mouse oocytes under microgravity condition simulated by NASA's rotary cell culture system, examined the maturation rate and observed the spindle morphology (organization of cytoskeleton) during the mouse oocytes meiotic maturation. While the rate of germinal vesicle breakdown did not differ between 1 g gravity and simulated microgravity, rate of oocyte maturation decreased significantly in simulated microgravity. The rate of maturation was 8.94% in simulated microgravity and was 73.0% in 1 g gravity. The results show that the maturation of mouse oocytes in vitro was inhibited by the simulated microgravity. The spindle morphology observation shows that the microtubules and chromosomes can not form a complete spindle during oocyte meiotic maturation under simulated microgravity. And the disorder of γ-tubulin may partially result in disorganization of microtubules under simulated microgravity. These observations suggest that the meiotic spindle organization is gravity dependent. Although the spindle organization was disrupted by simulated microgravity, the function and organization of microfilaments were not pronouncedly affected by simulated microgravity. And we found that simulated microgravity induced oocytes cytoplasmic blebbing via an unknown mechanism. Transmission electron microscope detection showed that the components of the blebs were identified with the cytoplasm. Collectively, these results indicated that the simulated microgravity inhibits mouse oocyte maturation via disturbing spindle organization and inducing cytoplasmic blebbing

Putative DHHC-Cysteine-Rich Domain S-Acyltransferase in Plants

Protein S-acyltransferases (PATs) containing Asp-His-His-Cys within a Cys-rich domain (DHHC-CRD) are polytopic transmembrane proteins that are found in eukaryotic cells and mediate the S-acylation of target proteins. S-acylation is an important secondary and reversible modification that regulates the membrane association, trafficking and function of target proteins. However, little is known about the characteristics of PATs in plants. Here, we identified 804 PATs from 31 species with complete genomes. The analysis of the phylogenetic relationships suggested that all of the PATs fell into 8 groups. In addition, we analysed the phylogeny, genomic organization, chromosome localisation and expression pattern of PATs in Arabidopsis, Oryza sative, Zea mays and Glycine max. The microarray data revealed that PATs genes were expressed in different tissues and during different life stages. The preferential expression of the ZmPATs in specific tissues and the response of Zea mays to treatments with phytohormones and abiotic stress demonstrated that the PATs play roles in plant growth and development as well as in stress responses. Our data provide a useful reference for the identification and functional analysis of the members of this protein family

An overview of hybrid systems consisting of synchronous condensers and battery energy storage system to support power grid

As wind farms are integrated into power systems, synchronous condensers (SCs) could play an important role in support of reactive power, inertia, short-circuit current, and working as a voltage source. In practical projects and various places, the deployment of SC has verified its functions, and the stability of the power system has been improved. Battery energy storage system (BESS), as a relatively mature energy storage technology, can bring frequency support to the power system. Therefore, it also retains the advantages of power electronics, providing reactive power and a fast response to the power system. In this paper, we review the development of both techniques and discuss the future feasibility of hybrid systems. In addition to analyzing the existing hybrid systems of SC and BESS in parallel, we also propose a novel type of hybrid system, which may be one of the development directions of hybrid systems and renewable energy power systems

Uncertainty Ordinal Multi-Instance Learning for Breast Cancer Diagnosis

Ordinal multi-instance learning (OMIL) deals with the weak supervision scenario wherein instances in each training bag are not only multi-class but also have rank order relationships between classes, such as breast cancer, which has become one of the most frequent diseases in women. Most of the existing work has generally been to classify the region of interest (mass or microcalcification) on the mammogram as either benign or malignant, while ignoring the normal mammogram classification. Early screening for breast disease is particularly important for further diagnosis. Since early benign lesion areas on a mammogram are very similar to normal tissue, three classifications of mammograms for the improved screening of early benign lesions are necessary. In OMIL, an expert will only label the set of instances (bag), instead of labeling every instance. When labeling efforts are focused on the class of bags, ordinal classes of the instance inside the bag are not labeled. However, recent work on ordinal multi-instance has used the traditional support vector machine to solve the multi-classification problem without utilizing the ordinal information regarding the instances in the bag. In this paper, we propose a method that explicitly models the ordinal class information for bags and instances in bags. Specifically, we specify a key instance from the bag as a positive instance of bags, and design ordinal minimum uncertainty loss to iteratively optimize the selected key instances from the bags. The extensive experimental results clearly prove the effectiveness of the proposed ordinal instance-learning approach, which achieves 52.021% accuracy, 61.471% sensitivity, 47.206% specificity, 57.895% precision, and an 59.629% F1 score on a DDSM dataset

Deep Extreme Learning Machine and Its Application in EEG Classification

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed