Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps

A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, automatic updating of road data is imperative to maintain the high quality of current basic geographic information. However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases.To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road.Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results. Further, to fully utilize the limited amount of label information and to enhance the prediction accuracy on unlabeled images, we utilized the mean teacher framework as the basic semi-supervised learning framework and introduced Regional Contrast (ReCo) in our work to improve the model capacity for distinguishing between the characteristics of roads and background elements.We applied our method to two datasets. Our model can effectively improve the performance of a model with fewer labels. Overall, the proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.Comment: 22 pages, 8 figure

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

Optimal Codon Identities in Bacteria: Implications from the Conflicting Results of Two Different Methods

A correlation method was recently adopted to identify selection-favored ‘optimal’ codons from 675 bacterial genomes. Surprisingly, the identities of these optimal codons were found to track the bacterial GC content, leading to a conclusion that selection would generally shape the codon usages to the same direction as the overall mutation does. Raising several concerns, here we report a thorough comparative study on 203 well-selected bacterial species, which strongly suggest that the previous conclusion is likely an illusion. Firstly, the previous study did not preclude species that are suffering weak or no selection pressures on their codon usages. For these species, as showed in this study, the optimal codon identities are prone to be incorrect and follow GC content. Secondly, the previous study only adopted the correlation method, without considering another method to test the reliability of inferred optimal codons. Actually by definition, optimal codons can also be identified by simply comparing codon usages between high- and low-expression genes. After using both methods to identify optimal codons for the selected species, we obtained highly conflicting results, suggesting at least one method is misleading. Further we found a critical problem of correlation method at the step of calculating gene bias level. Due to a failure of accurately defining the background mutation, the problem would result in wrong optimal codon identities. In other words, partial mutational effects on codon choices were mistakenly regarded as selective influences, leading to incorrect and biased optimal codon identities. Finally, considering the translational dynamics, optimal codons identified by comparison method can be well-explained by tRNA compositions, whereas optimal codons identified by correlation method can not be. For all above reasons, we conclude that real optimal codons actually do not track the genomic GC content, and correlation method is misleading in identifying optimal codons and better be avoided

Microbiology, ecology, and application of the nitrite-dependent anaerobic methane oxidation process

Nitrite-dependent anaerobic methane oxidation (n-damo), which couples the anaerobic oxidation of methane to denitrification, is a recently discovered process mediated by “Candidatus Methylomirabilis oxyfera.” M. oxyfera is affiliated with the “NC10” phylum, a phylum having no members in pure culture. Based on the isotopic labeling experiments, it is hypothesized that M. oxyfera has an unusual intra-aerobic pathway for the production of oxygen via the dismutation of nitric oxide into dinitrogen gas and oxygen. In addition, the bacterial species has a unique ultrastructure that is distinct from that of other previously described microorganisms. M. oxyfera-like sequences have been recovered from different natural habitats, suggesting that the n-damo process potentially contributes to global carbon and nitrogen cycles. The n-damo process is a process that can reduce the greenhouse effect, as methane is more effective in heat-trapping than carbon dioxide. The n-damo process, which uses methane instead of organic matter to drive denitrification, is also an economical nitrogen removal process because methane is a relatively inexpensive electron donor. This mini-review summarizes the peculiar microbiology of M. oxyfera and discusses the potential ecological importance and engineering application of the n-damo process

Molecular Mechanisms of Hepatocellular Carcinoma Related to Aflatoxins: An Update

Hepatocellular carcinoma (hepatocarcinoma) is a major type of primary liver cancer and one of the most frequent human malignant neoplasms. Aflatoxins are I-type chemical carcinogen for hepatocarcinoma. Increasing evidence has shown that hepatocarcinoma induced by aflatoxins is the result of interaction between aflatoxins and hereditary factor. Aflatoxins can induce DNA damage including DNA strand break, adducts formation, oxidative DNA damage, and gene mutation and determine which susceptible individuals feature cancer. Inheritance such as alterations may result in the activation of proto-oncogenes and the inactivation of tumor suppressor genes and determine individual susceptibility to cancer. Interaction between aflatoxins and genetic susceptible factors commonly involve in almost all pathologic sequence of hepatocarcinoma: chronic liver injury, cirrhosis, atypical hyperplastic nodules, and hepatocarcinoma of early stages. In this review, we discuss the biogenesis, toxification, and epidemiology of aflatoxins and signal pathways of aflatoxin-induced hepatocarcinoma. We also discuss the roles of some important genes related to cell apoptosis, DNA repair, drug metabolism, and tumor metastasis in hepatocarcinogenesis related to aflatoxins

Abnormal Global Brain Functional Connectivity in Primary Insomnia Patients: A Resting-State Functional MRI Study

Background: Resting-state functional magnetic resonance imaging (fMRI) studies have uncovered the disruptions of functional brain networks in primary insomnia (PI) patients. However, the etiology and pathogenesis underlying this disorder remains ambiguous, and the insomnia related symptoms are influenced by a complex network organization in the brain. The purpose of this study was to explore the abnormal intrinsic functional hubs in PI patients using a voxel-wise degree centrality (DC) analysis and seed-based functional connectivity (FC) approach.Methods: A total of 26 PI patients and 28 healthy controls were enrolled, and they underwent resting-state fMRI. Degree centrality was measured across the whole brain, and group differences in DC were compared. The peak points, which significantly altered DC between the two groups, were defined as the seed regions and were further used to calculate FC of the whole brain. Later, correlation analyses were performed between the changes in brain function and clinical features.Results: Primary insomnia patients showed DC values lower than healthy controls in the left inferior frontal gyrus (IFG) and middle temporal gyrus (MTG) and showed a higher DC value in the right precuneus. The seed-based analyses demonstrated decreased FC between the left MTG and the left posterior cingulate cortex (PCC), and decreased FC was observed between the right precuneus and the right lateral occipital cortex. Reduced DC in the left IFG and decreased FC in the left PCC were positively correlated with the Pittsburgh sleep quality index and the insomnia severity index.Conclusions: This study revealed that PI patients exhibited abnormal intrinsic functional hubs in the left IFG, MTG, and the right precuneus, as well as abnormal seed-based FC in these hubs. These results contribute to better understanding of how brain function influences the symptoms of PI

Identification and characterization of microRNAs in Clonorchis sinensis of human health significance

<p>Abstract</p> <p>Background</p> <p><it>Clonorchis sinensis </it>is a zoonotic parasite causing clonorchiasis-associated human disease such as biliary calculi, cholecystitis, liver cirrhosis, and it is currently classified as carcinogenic to humans for cholangiocarcinoma. MicroRNAs (miRNAs) are non-coding, regulating small RNA molecules which are essential for the complex life cycles of parasites and are involved in parasitic infections. To identify and characterize miRNAs expressed in adult <it>C. sinensis </it>residing chronically in the biliary tract, we developed an integrative approach combining deep sequencing and bioinformatic predictions with stem-loop real-time PCR analysis.</p> <p>Results</p> <p>Here we report the use of this approach to identify and clone 6 new and 62,512 conserved <it>C. sinensis </it>miRNAs which belonged to 284 families. There was strong bias on families, family members and sequence nucleotides in <it>C. sinensis</it>. Uracil was the dominant nucleotide, particularly at positions 1, 14 and 22, which were located approximately at the beginning, middle and end of conserved miRNAs. There was no significant "seed region" at the first and ninth positions which were commonly found in human, animals and plants. Categorization of conserved miRNAs indicated that miRNAs of <it>C. sinensis </it>were still innovated and concentrated along three branches of the phylogenetic tree leading to bilaterians, insects and coelomates. There were two miRNA strategies in <it>C. sinensis </it>for its parasitic life: keeping a large category of miRNA families of different animals and keeping stringent conserved seed regions with high active innovation in other places of miRNAs mainly in the middle and the end, which were perfect for the parasite to perform its complex life style and for host changes.</p> <p>Conclusions</p> <p>The present study represented the first large scale characterization of <it>C. sinensis </it>miRNAs, which have implications for understanding the complex biology of this zoonotic parasite, as well as miRNA studies of other related species such as <it>Opisthorchis viverrini </it>and <it>Opisthorchis felineus </it>of human and animal health significance.</p

Prevalence of Clonorchis sinensis infection in dogs and cats in subtropical southern China

<p>Abstract</p> <p>Background</p> <p>Clonorchiasis, caused by <it>Clonorchis sinensis</it>, is one of the major parasitic zoonoses in China, particularly in China's southern Guangdong province where the prevalence of <it>C. sinensis </it>infection in humans is high. However, little is known of the prevalence of <it>C. sinensis </it>infection in its reservoir hosts dogs and cats. Hence, the prevalence of <it>C. sinensis </it>infection in dogs and cats was investigated in Guangdong province, China between October 2006 and March 2008.</p> <p>Results</p> <p>A total of 503 dogs and 194 cats from 13 administrative regions in Guangdong province were examined by post-mortem examination. The worms were examined, counted, and identified to species according to existing keys and descriptions. The average prevalences of <it>C. sinensis </it>infection in dogs and cats were 20.5% and 41.8%, respectively. The infection intensities in dogs were usually light, but in cats the infection intensities were more serious. The prevalences were higher in some of the cities located in the Pearl River Delta region which is the most important endemic area in Guangdong province, but the prevalences were relatively lower in seaside cities.</p> <p>Conclusions</p> <p>The present investigation revealed a high prevalence of <it>C. sinensis </it>infection in its reservoir hosts dogs and cats in China's subtropical Guangdong province, which provides relevant "base-line" data for conducting control strategies and measures against clonorchiasis in this region.</p