Viral Diversity and Its Relationship With Environmental Factors at the Surface and Deep Sea of Prydz Bay, Antarctica

A viral metagenomic analysis of five surface and two bottom water (878 meters below surface, mbs, and 3,357 mbs) samples from Prydz Bay, was conducted during February–March 2015. The results demonstrated that most of the DNA viruses were dsDNA viruses (79.73–94.06%, except at PBI1, 37.51%). Of these, Caudovirales (Siphoviridae, Myoviridae, and Podoviridae) phages were most abundant in surface seawater (67.67–71.99%), while nucleocytoplasmic large DNA viruses (NCLDVs) (Phycodnaviridae, Mimiviridae, and Pandoraviridae accounted for >30% of dsDNA viruses) were most abundant in the bottom water (3,357 mbs). Of the ssDNA viruses, Microviridae was the dominant family in PBI2, PBI3, PBOs, and PBI4b (57.09–87.55%), while Inoviridae (58.16%) was the dominant family in PBI1. Cellulophaga phages (phi38:1 and phi10:1) and Flavobacterium phage 11b, infecting the possible host strains affiliated with the family Flavobacteriaceae of the phylum Bacteroidetes, were abundant in surface water dsDNA viromes. The long contig (PBI2_1_C) from the viral metagenomes were most similar to the genome architectures of Cellulophaga phage phi10:1 and Flavobacterium phage 11b from the Arctic Ocean. Comparative analysis showed that the surface viral community of Prydz Bay could be clearly separated from other marine and freshwater environments. The deep sea viral community was similar to the deep sea viral metagenome at A Long-term Oligotrophic Habitat Assessment Station (ALOHA, at 22°45′N, 158°00′W). The multivariable analysis indicated that nutrients probably played an important role in shaping the local viral community structure. This study revealed the preliminary characteristics of the viral community in Prydz Bay, from both the surface and the deep sea. It provided evidence of the relationships between the virome and the environment in Prydz Bay and provided the first data from the deep sea viral community of the Southern Ocean

Long memory in systematic risk of international securitized real estate

Master'sMASTER OF SCIENCE (ESTATE MANAGEMENT

Cross-market dynamics in property stock markets: Some international evidence

10.1108/14635780510575094Journal of Property Investment and Finance23155-7

A Multiobjective Integer Linear Programming Model for the Cross-Track Line Planning Problem in the Chinese High-Speed Railway Network

In China, cross-track high-speed trains (CTHSTs) play an important role in railway passenger transportation, with an increasing number of cross-track passengers sourced from the expansion of high-speed railway (HSR) network. The CTHST generally has long travel times, so running CTHSTs is not beneficial for train rescheduling work and plan’s periodicity in the periodic operation context. Thus, the main challenge in cross-track line planning is looking for a symmetry point between passenger transportation and disadvantages of running CTHSTs, which are two conflicting aspects. In this study, we developed a multiobjective integer programming model to produce a balanced cross-track line plan by combining individual-track high-speed trains (ITHSTs) into CTHSTs, which is a discrete optimization problem. This strikes a balance among four goals: the periodicity of the line plan, CTHST quantity, CTHST mileage, and CTHST stops in the context of periodic operation, while satisfying the constraints of passenger demand and the number of available ITHSTs. Numerical experiments are conducted based on a real-world network and optimal solutions were quickly obtained. We analyzed impacts of each goal and parameter on the result and influencing factors of computation. Comparisons with existing methods and real-life plans were also presented to show improvements made by proposed model

Local Deep Descriptor for Remote Sensing Image Feature Matching

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network

Genome Sequencing Provides Novel Insights into Mudflat Burrowing Adaptations in Eel Goby <i>Taenioides</i> sp. (Teleost: Amblyopinae)

Amblyopinae is one of the lineage of bony fish that preserves amphibious traits living in tidal mudflat habitats. In contrast to other active amphibious fish, Amblyopinae species adopt a seemly more passive lifestyle by living in deep burrows of mudflat to circumvent the typical negative effects associated with terrestriality. However, little is known about the genetic origin of these mudflat deep-burrowing adaptations in Amblyopinae. Here we sequenced the first genome of Amblyopinae species, Taenioides sp., to elucidate their mudflat deep-burrowing adaptations. Our results revealed an assembled genome size of 774.06 Mb with 23 pseudochromosomes anchored, which predicted 22,399 protein-coding genes. Phylogenetic analyses indicated that Taenioides sp. diverged from the active amphibious fish of mudskipper approximately 28.3 Ma ago. In addition, 185 and 977 putative gene families were identified to be under expansion, contraction and 172 genes were undergone positive selection in Taenioides sp., respectively. Enrichment categories of top candidate genes under significant expansion and selection were mainly associated with hematopoiesis or angiogenesis, DNA repairs and the immune response, possibly suggesting their involvement in the adaptation to the hypoxia and diverse pathogens typically observed in mudflat burrowing environments. Some carbohydrate/lipid metabolism, and insulin signaling genes were also remarkably alterated, illustrating physiological remolding associated with nutrient-limited subterranean environments. Interestingly, several genes related to visual perception (e.g., crystallins) have undergone apparent gene losses, pointing to their role in the small vestigial eyes development in Taenioides sp. Our work provide valuable resources for understanding the molecular mechanisms underlying mudflat deep-burrowing adaptations in Amblyopinae, as well as in other tidal burrowing teleosts

Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss

Data-driven methods have achieved notable performance on intent detection, which is a task to comprehend user queries. Nonetheless, they are controversial for over-confident predictions. In some scenarios, users do not only care about the accuracy but also the confidence of model. Unfortunately, mainstream neural networks are poorly calibrated, with a large gap between accuracy and confidence. To handle this problem defined as confidence calibration, we propose a model using the hyperspherical space and rebalanced accuracy-uncertainty loss. Specifically, we project the label vector onto hyperspherical space uniformly to generate a dense label representation matrix, which mitigates over-confident predictions due to overfitting sparse one-hot label matrix. Besides, we rebalance samples of different accuracy and uncertainty to better guide model training. Experiments on the open datasets verify that our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric

Metal‐Driven Autoantifriction Function of Artificial Hip Joint

Abstract The service life of an artificial hip joint is limited to 10–15 years, which is not ideal for young patients. To extend the lifespan of these prostheses, the coefficient of friction and wear resistance of metallic femoral heads must be improved. In this study, a Cu‐doped titanium nitride (TiNX–Cu) film with “autoantifriction” properties is deposited on a CoCrMo alloy via magnetron sputtering. When delivered in a protein‐containing lubricating medium, the Cu in TiNX–Cu quickly and consistently binds to the protein molecules in the microenvironment, resulting in the formation of a stable protein layer. The proteins adsorbed on the TiNX–Cu surface decompose into hydrocarbon fragments owing to the shear stress between the Al2O3/TiNX–Cu tribopair. The synergistic effect of the catalysis of Cu and shear stress between the Al2O3/TiNX–Cu tribopair transforms these fragments into graphite‐like carbon tribofilms with an antifriction property. These tribofilms can simultaneously reduce the friction coefficient of the Al2O3/TiNX–Cu tribopair and enhance the wear resistance of the TiNX–Cu film. Based on these findings, it is believed that the autoantifriction film can drive the generation of antifriction tribofilms for lubricating and increasing the wear resistance of prosthetic devices, thereby prolonging their lifespan