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

Precursor Amino Acids Inhibit Polymyxin E Biosynthesis in Paenibacillus polymyxa, Probably by Affecting the Expression of Polymyxin E Biosynthesis-Associated Genes

Polymyxin E belongs to cationic polypeptide antibiotic bearing four types of direct precursor amino acids including L-2,4-diaminobutyric acid (L-Dab), L-Leu, D-Leu, and L-Thr. The objective of this study is to evaluate the effect of addition of precursor amino acids during fermentation on polymyxin E biosynthesis in Paenibacillus polymyxa. The results showed that, after 35 h fermentation, addition of direct precursor amino acids to certain concentration significantly inhibited polymyxin E production and affected the expression of genes involved in its biosynthesis. L-Dab repressed the expression of polymyxin synthetase genes pmxA and pmxE, as well as 2,4-diaminobutyrate aminotransferase gene ectB; both L-Leu and D-Leu repressed the pmxA expression. In addition, L-Thr affected the expression of not only pmxA, but also regulatory genes spo0A and abrB. As L-Dab precursor, L-Asp repressed the expression of ectB, pmxA, and pmxE. Moreover, it affected the expression of spo0A and abrB. In contrast, L-Phe, a nonprecursor amino acid, had no obvious effect on polymyxin E biosynthesis and those biosynthesis-related genes expression. Taken together, our data demonstrated that addition of precursor amino acids during fermentation will inhibit polymyxin E production probably by affecting the expression of its biosynthesis-related genes

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

Enhanced NADH Metabolism Involves Colistin-Induced Killing of <em>Bacillus subtilis</em> and <em>Paenibacillus polymyxa</em>

The commonly believed mechanism of colistin against Gram-negative bacteria is to cause cell membrane lysis, whereas the mechanism of colistin against Gram-positive bacteria is extremely fragmented. In this study, we found that colistin treatment on Bacillus subtilis WB800, Paenibacillus polymyxa C12 and Paenibacillus polymyxa ATCC842 enhances not only the activities of α-ketoglutaric dehydrogenase and malate dehydrogenase in tricarboxylic acid (TCA) cycle, but also the relative expression levels of their encoding genes. Additionally, the oxaloacetate concentration also increases. Interestingly, the analysis of the relative expression of genes specific for respiratory chain showed that colistin treatment stimulates the respiratory chain in Gram-positive bacteria. Accordingly, the NAD+/NADH ratio increases and the oxidative level is then boosted up. As a result, the intensive oxidative damages are induced in Gram-positive bacteria and cells are killed. Notably, both rotenone and oligomycin, respectively, inhibiting NADH dehydrogenase and phosphorylation on respiratory chain can downgrade oxidative stress formation, thus alleviating the colistin-induced killing of Gram-positive cells. Besides, thiourea-based scavenging for reactive oxygen species also rescues the colistin-subjected cells. These data collectively demonstrate that colistin stimulates both TCA cycle and respiratory chain in Gram-positive bacteria, leading to the enhancement of NADH metabolism and resulting in the generation of oxidative damages in Gram-positive cells. Our studies provide a better understanding of antibacterial mechanism of colistin against Gram-positive bacteria, which is important for knowledge on bacterial resistance to colistin happening via the inhibition of respiratory chain and manipulation of its production

Oxidative Stress Induced by Polymyxin E Is Involved in Rapid Killing of Paenibacillus polymyxa

Historically, the colistin has been thought to kill bacteria through membrane lysis. Here, we present an alternative mechanism that colistin induces rapid Paenibacillus polymyxa death through reactive oxygen species production. This significantly augments our understanding of the mechanism of colistin action, which is critical knowledge toward the yield development of colistin in the future

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