Typhoon cloud image prediction based on enhanced multi-scale deep neural network

Typhoons threaten individuals’ lives and property. The accurate prediction of typhoon activity is crucial for reducing those threats and for risk assessment. Satellite images are widely used in typhoon research because of their wide coverage, timeliness, and relatively convenient acquisition. They are also important data sources for typhoon cloud image prediction. Studies on typhoon cloud image prediction have rarely used multi-scale features, which cause significant information loss and lead to fuzzy predictions with insufficient detail. Therefore, we developed an enhanced multi-scale deep neural network (EMSN) to predict a 3-hour-advance typhoon cloud image, which has two parts: a feature enhancement module and a feature encode-decode module. The inputs of the EMSN were eight consecutive images, and a feature enhancement module was applied to extract features from the historical inputs. To consider that the images of different time steps had different contributions to the output result, we used channel attention in this module to enhance important features. Because of the spatially correlated and spatially heterogeneous information at different scales, the feature encode-decode module used ConvLSTMs to capture spatiotemporal features at different scales. In addition, to reduce information loss during downsampling, skip connections were implemented to maintain more low-level information. To verify the effectiveness and applicability of our proposed EMSN, we compared various algorithms and explored the strengths and limitations of the model. The experimental results demonstrated that the EMSN efficiently and accurately predicted typhoon cloud images with higher quality than in the literature

Genome-wide identification and analysis of heterotic loci in three maize hybrids

Heterosis, or hybrid vigour, is a predominant phenomenon in plant genetics, serving as the basis of crop hybrid breeding, but the causative loci and genes underlying heterosis remain unclear in many crops. Here, we present a large-scale genetic analysis using 5360 offsprings from three elite maize hybrids, which identifies 628 loci underlying 19 yield-related traits with relatively high mapping resolutions. Heterotic pattern investigations of the 628 loci show that numerous loci, mostly with complete–incomplete dominance (the major one) or overdominance effects (the secondary one) for heterozygous genotypes and nearly equal proportion of advantageous alleles from both parental lines, are the major causes of strong heterosis in these hybrids. Follow-up studies for 17 heterotic loci in an independent experiment using 2225 F2 individuals suggest most heterotic effects are roughly stable between environments with a small variation. Candidate gene analysis for one major heterotic locus (ub3) in maize implies that there may exist some common genes contributing to crop heterosis. These results provide a community resource for genetics studies in maize and new implications for heterosis in plants

Establishment of the Comprehensive Shape Similarity Model for Complex Polygon Entity by Using Bending Mutilevel Chord Complex Function

A method about shape similarity measurement of complex holed objects is proposed in this paper. The method extracts features including centroid distance, multilevel chord length, bending degree and concavity-convexity of a geometric object, to construct complex functions based on multilevel bending degree and radius. The complex functions are capable of describing geometry shape from entirety to part. The similarity between geometric objects can be measured by the shape descriptor which is based on the fast Fourier transform of the complex functions. Meanwhile, the matching degree of each scene of complex holed polygons can be got by scene completeness and shape similarity model. And using the feature of multi-level can accomplish the shape similarity measurement among complex geometric objects. Experimenting on geometric objects of different space complexity, the results match human's perceive and show that this method is simple with precision

GSA-SiamNet: A Siamese Network with Gradient-Based Spatial Attention for Pan-Sharpening of Multi-Spectral Images

Pan-sharpening is a fusion process that combines a low-spatial resolution, multi-spectral image that has rich spectral characteristics with a high-spatial resolution panchromatic (PAN) image that lacks spectral characteristics. Most previous learning-based approaches rely on the scale-shift assumption, which may not be applicable in the full-resolution domain. To solve this issue, we regard pan-sharpening as a multi-task problem and propose a Siamese network with Gradient-based Spatial Attention (GSA-SiamNet). GSA-SiamNet consists of four modules: a two-stream feature extraction module, a feature fusion module, a gradient-based spatial attention (GSA) module, and a progressive up-sampling module. In the GSA module, we use Laplacian and Sobel operators to extract gradient information from PAN images. Spatial attention factors, learned from the gradient prior, are multiplied during the feature fusion, up-sampling, and reconstruction stages. These factors help to keep high-frequency information on the feature map as well as suppress redundant information. We also design a multi-resolution loss function that guides the training process under the constraints of both reduced- and full-resolution domains. The experimental results on WorldView-3 satellite images obtained in Moscow and San Juan demonstrate that our proposed GSA-SiamNet is superior to traditional and other deep learning-based methods

Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method

Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications

Profiling the interactome of protein kinase C ζ by proteomics and bioinformatics

Abstract Background Protein kinase C ζ (PKCζ), an isoform of the atypical protein kinase C, is a pivotal regulator in cancer. However, the molecular and cellular mechanisms whereby PKCζ regulates tumorigenesis and metastasis are still not fully understood. In this study, proteomics and bioinformatics analyses were performed to establish a protein-protein interaction (PPI) network associated with PKCζ, laying a stepping stone to further understand the diverse biological roles of PKCζ. Methods Protein complexes associated with PKCζ were purified by co-immunoprecipitation from breast cancer cell MDA-MB-231 and identified by LC-MS/MS. Two biological replicates and two technical replicates were analyzed. The observed proteins were filtered using the CRAPome database to eliminate the potential false positives. The proteomics identification results were combined with PPI database search to construct the interactome network. Gene ontology (GO) and pathway analysis were performed by PANTHER database and DAVID. Next, the interaction between PKCζ and protein phosphatase 2 catalytic subunit alpha (PPP2CA) was validated by co-immunoprecipitation, Western blotting and immunofluorescence. Furthermore, the TCGA database and the COSMIC database were used to analyze the expressions of these two proteins in clinical samples. Results The PKCζ centered PPI network containing 178 nodes and 1225 connections was built. Network analysis showed that the identified proteins were significantly associated with several key signaling pathways regulating cancer related cellular processes. Conclusions Through combining the proteomics and bioinformatics analyses, a PKCζ centered PPI network was constructed, providing a more complete picture regarding the biological roles of PKCζ in both cancer regulation and other aspects of cellular biology

Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement

International audienceSuper-resolution (SR) is able to improve the spatial resolution of remote sensing images, which is critical for many practical applications such as fine urban monitoring. In this paper, a new single-image SR method, deep gradient-aware network with image-specific enhancement (DGANet-ISE) was proposed to improve the spatial resolution of remote sensing images. First, DGANet was proposed to model the complex relationship between low-and high-resolution images. A new gradient-aware loss was designed in the training phase to preserve more gradient details in super-resolved remote sensing images. Then, the ISE approach was proposed in the testing phase to further improve the SR performance. By using the specific features of each test image, ISE can further boost the generalization capability and adaptability of our method on inexperienced datasets. Finally, three datasets were used to verify the effectiveness of our method. The results indicate that DGANet-ISE outperforms the other 14 methods in the remote sensing image SR, and the cross-database test results demonstrate that our method exhibits satisfactory generalization performance in adapting to new data

Enhanced Mechanochemiluminescence from End-Functionalized Polyurethanes with Multiple Hydrogen Bonds

The chemiluminescent mechanophore, 1,2-dioxetane (Ad), is incorporated into the backbone of a polyurethane-based prepolymer, which is further end-capped with dimerizable strong hydrogen bonding units, ureidopyrimidinone and pyrimidinedione (UPy, DHB-2) or hydrogen bonding free unit (EtOH). Mechanical, optomechanical measurements, and small-angle/wide-angle X-ray scattering (SAXS/WAXS) analyses of these end-functionalized polyurethanes have demonstrated that the difference in the strength of hydrogen bonding interactions led to different degrees of chain orientation in the bulk, and consequently, different levels of mechano-activation of Ad with distinguishable mechanochemiluminescence intensity. This study not only offers a straightforward way to enhance the mechanochemiluminescence of Ad containing polymers by tailoring the supramolecular interactions between different macromers but also deepens our understanding of the correlations between chain orientation behavior, magnitudes of hydrogen bonding interactions, and the activation of mechanophores

Unveiling the Role of Defects on Oxygen Activation and Photodegradation of Organic Pollutants

10.1021/acs.est.8b03558ENVIRONMENTAL SCIENCE & TECHNOLOGY522313879-1388

Effect of Anoxic Atmosphere on the Physicochemical and Pelletization Properties of Pinus massoniana Sawdust during Storage

The 34-day anoxic storage of Pinus massoniana sawdust (PS) in a sealed constant temperature and humidity chambers was carried out to simulate the limited-oxygen storage process inside piles at industrial scale. The effects of anoxic storage on feedstock’s properties and pelletization process were investigated with respect to elemental composition, dry matter loss, thermogravimetric characteristics, energy consumption, pellets’ density, and microbial communities, etc. After anoxic storage, the microbial community of PS samples was altered, such as the fungi content (Clonostachys, Strelitziana, and Orbilia, etc.), resulting the elemental composition of PS was altered. Thus, the cellulose and ash content of the stored PS were increased, while the hemicellulose, volatile, and fixed carbon were decreased. The energy consumption was increased 7.85–21.98% with the increase in anoxic storage temperature and with the additive of fresh soil collected from PS field in storage process. The single pellet density was altered slightly. Meanwhile, the moisture uptake of PS pellets was decreased. After anoxic storage, the combustion behavior of the stored PS became more stable. The results can be applied directly to guide the development of commercial PS storage and pelletization process currently under development in Asia, Europe and North America