Exploring Regulation Genes Involved in the Expression of L-Amino Acid Oxidase in Pseudoalteromonas sp. Rf-1

Bacterial L-amino acid oxidase (LAAO) is believed to play important biological and ecological roles in marine niches, thus attracting increasing attention to understand the regulation mechanisms underlying its production. In this study, we investigated genes involved in LAAO production in marine bacterium Pseudoalteromonas sp. Rf-1 using transposon mutagenesis. Of more than 4,000 mutants screened, 15 mutants showed significant changes in LAAO activity. Desired transposon insertion was confirmed in 12 mutants, in which disrupted genes and corresponding functionswere identified. Analysis of LAAO activity and lao gene expression revealed that GntR family transcriptional regulator, methylase, non-ribosomal peptide synthetase, TonB-dependent heme-receptor family, Na⁺/H⁺ antiporter and related arsenite permease, N-acetyltransferase GCN5, Ketol-acid reductoisomerase and SAM-dependent methytransferase, and their coding genes may be involved in either upregulation or downregulation pathway at transcriptional, posttranscriptional, translational and/or posttranslational level. The nhaD and sdmT genes were separately complemented into the corresponding mutants with abolished LAAO-activity. The complementation of either gene can restore LAAO activity and lao gene expression, demonstrating their regulatory role in LAAO biosynthesis. This study provides, for the first time, insights into the molecular mechanisms regulating LAAO production in Pseudoalteromonas sp. Rf-1, which is important to better understand biological and ecological roles of LAAO

OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

Sentinel-1 mission provides a freely accessible opportunity for urban interpretation from synthetic aperture radar (SAR) images with specific resolution, which is of paramount importance for earth observation. In parallel, with the rapid development of advanced technologies, especially deep learning, it is urgently needed to construct a large-scale SAR dataset leading urban interpretation. This paper presents OpenSARUrban: a Sentinel-1 dataset dedicated to urban interpretation from SAR images, including a well-defined hierarchical annotation scheme, the data collection, the well-established procedures for dataset construction and organizations, the properties, visualizations, and applications of this dataset. Particularly, the OpenSARUrban provides 33358 image patches of SAR urban scene, covering 21 major cities of China, including 10 different categories, 4 kinds of formats, 2 kinds of polarization modes, and owning 5 essential properties: large-scale, diversity, specificity, reliability, and sustainability. These properties guarantee the achievable of several goals for OpenSARUrban. The first is to support urban target characterization. The second is to help develop applicable and advanced algorithms for Sentinel-1 urban target classification. The dataset visualization is implemented from the perspective of manifold to give an intuitive understanding. Besides a detailed description and visualization of the dataset, we present results of some benchmark algorithms, demonstrating that this dataset is practical and challenging. Notably, developing algorithms to enhance the classification performance on the whole dataset and considering the data imbalance are especially challenging

Comparative Genomics of Degradative Novosphingobium Strains With Special Reference to Microcystin-Degrading Novosphingobium sp. THN1

Bacteria in genus Novosphingobium associated with biodegradation of substrates are prevalent in environments such as lakes, soil, sea, wood and sediments. To better understand the characteristics linked to their wide distribution and metabolic versatility, we report the whole genome sequence of Novosphingobium sp. THN1, a microcystin-degrading strain previously isolated by Jiang et al. (2011) from cyanobacteria-blooming water samples from Lake Taihu, China. We performed a genomic comparison analysis of Novosphingobium sp. THN1 with 21 other degradative Novosphingobium strains downloaded from GenBank. Phylogenetic trees were constructed using 16S rRNA genes, core genes, protein-coding sequences, and average nucleotide identity of whole genomes. Orthologous protein analysis showed that the 22 genomes contained 674 core genes and each strain contained a high proportion of distributed genes that are shared by a subset of strains. Inspection of their genomic plasticity revealed a high number of insertion sequence elements and genomic islands that were distributed on both chromosomes and plasmids. We also compared the predicted functional profiles of the Novosphingobium protein-coding genes. The flexible genes and all protein-coding genes produced the same heatmap clusters. The COG annotations were used to generate a dendrogram correlated with the compounds degraded. Furthermore, the metabolic profiles predicted from KEGG pathways showed that the majority of genes involved in central carbon metabolism, nitrogen, phosphate, sulfate metabolism, energy metabolism and cell mobility (above 62.5%) are located on chromosomes. Whereas, a great many of genes involved in degradation pathways (21–50%) are located on plasmids. The abundance and distribution of aromatics-degradative mono- and dioxygenases varied among 22 Novosphingoibum strains. Comparative analysis of the microcystin-degrading mlr gene cluster provided evidence for horizontal acquisition of this cluster. The Novosphingobium sp. THN1 genome sequence contained all the functional genes crucial for microcystin degradation and the mlr gene cluster shared high sequence similarity (≥85%) with the sequences of other microcystin-degrading genera isolated from cyanobacteria-blooming water. Our results indicate that Novosphingobium species have high genomic and functional plasticity, rearranging their genomes according to environment variations and shaping their metabolic profiles by the substrates they are exposed to, to better adapt to their environments

Learning Physical Scattering Patterns from POLSAR Images By Using Complex-Valued CNN

Full-polarimetric synthetic aperture radar (SAR) images have the ability to provide physical patterns of the earth observation, no more than geometric information. In order to learn physical patterns from non-full-polarimetric SAR images, a complex-valued CNN is leveraged to learn a model containing physical parameters. The parameters are learned from the original complex scattering matrix of full-polarimetric SAR images and they can be adopted to extract physical patterns from non-full-polarimetric SAR images. Cloude and Pottier’s H-α division, as the annotation principle, is computed by way of coherence matrix. We perform experiments on (German Aerospace Center) DLR’s full-polarimetric, airborne F-SAR data, demonstrating that extracting physical patterns from non-full-polarimetric images is feasible. The comparative results illustrate that: 1) The best physical categoric patterns can be extracted from HV and VH polarimetric images in general, while performance from HH and VV polarimetric images are limited; 2) Cross-polarimetric SAR images have greater ability for surface and volume scattering, while co-polarimetric ones are better for multiple scattering extraction

Contrastive-Regulated CNN in the Complex Domain: A Method to Learn Physical Scattering Signatures From Flexible PolSAR Images

Single- and dual-polarimetric synthetic aperture radar (SAR) images provide very limited capabilities to interpret physical radar signatures. For generality and simplicity, we call single-polarimetric, dual-polarimetric, and fully polarimetric SAR (PolSAR) images flexible PolSAR images. In order to sufficiently extract physical scattering signatures from this kind of data and explore the potentials of different polarization modes on this task, this paper proposes a contrastive-regulated convolutional neural network (CNN) in the complex domain, attempting to learn a physically interpretable deep learning model directly from the original backscattered data. To achieve a better deep model containing physically interpretable parameters, the objective cost is compared to and selected from several commonly used loss functions in the complex form. The required ground-truth labels are generated automatically according to Cloude and Pottier's H-alpha division plane, which significantly reduces intensive labor cost and transfers this method to an unsupervised learning mechanism. The boundaries between different scattering signatures, however, sometimes show an erroneous separation. With the aim of aggregating intra-class instances and alienating inter-class instances, meanwhile, a complex-valued contrastive regularization term is computed mathematically and is added to the objective cost by a tradeoff factor. Moreover, data augmentation is applied to relieve the side effects caused by data imbalance. Finally, we performed experiments on German Aerospace Center's (DLR)'s L-band, high-resolution (HR), and airborne F-SAR data. Our results demonstrate the possibility of extracting physical scattering signatures from flexible PolSAR images. Physically interpretable potentials of SAR images with different polarization modes are analyzed, and we conclude with physical signature identification

Convolutional neural Network for SAR Image Classification at Patch Level

Convolutional Neural Network (CNN) has attracted much at- tention for feature learning and image classification, mostly related to close range photography. As a benchmark work, we trained a relatively large CNN to classify SAR image patches into five different categories, where the image patches tiled and annotated from a typical TerraSAR-X spotlight scene of Wuhan, China. The neural network designed in this paper consists of seven layers, including one input layer, two convolutional layers where each followed by a max-pooling layer, as well as two fully-connected layers with a final five-class softmax. Using the toolkit caffe, we achieved the training and testing accuracy of 85:7% and 85:6% respectively, which is considerably better than the traditional feature extraction and classification based SVM method and shows great potential of CNN used for SAR image interpretation. In order to accelerate the training process, a very efficient GPU implementation was employed

Correlation between the detected H₂O₂ concentration and the diameters of Prussian blue holes.

<p>The distribution can perfectly be fitted with the exponential equation y = 0.673×^5.611, where x is the diameter of blue hole and y the H₂O₂ concentration. The inset showed that in the range of 0.5 mM≤H₂O₂≤20 mM, the change in diameter of blue hole was a function of logarithm of H₂O₂ concentration with linear fits under the equation y = 1.772x–1.890, where x is the diameter of blue hole and y the logarithm of H₂O₂ concentration.</p