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Northern snakehead, Channa argus (C. argus), is an important economic and ecological fish species. The wild population of the species was sharply declined in the last decade. A high-quality reference genome could lay a solid foundation for the genetic and conservation studies for C. argus. In this work, we report a chromosomal genome assembly with PacBio and Hi-C technology using the albino northern snakehead, a color variety of C. argus. A 644.1-Mb genome with 24 chromosomes was obtained with a contig and scaffold N50 of 11.78 and 27.8 Mb, respectively. We inferred that C. argus diverged from A. testudineus around 85.6 million years ago. 514 expanded gene families and 214 positively selected genes were identified in the C. argus genome. The chromosome-level genome provides a valuable high-quality genomic resource for population, as well as genetic and evolutionary studies for C. argus and other species in Channidae.

Ocean tides in polar regions play an important role in the study of sea ice dynamics and floating ice shelves. The accuracy of existing ocean tide models in shallow waters and polar seas is much lower than that in open deep oceans. This study summarized the advances of state-of-the-art global tide models in the Antarctic Ocean, the construction of tide models around Circum-Antarctica, and five typical regions: Antarctic Peninsula, Ross Sea, Filchner–Ronne Ice Shelf (FRIS), Weddell Sea, and Amery Ice Shelf (AIS). The accuracy of FES 2014, TPXO9, EOT20, CATS 2008, and regional tide models in the Antarctic Ocean and typical areas was evaluated using tidal records and satellite altimetry data. EOT20 and ANTPEN04.01 models have higher accuracy in the Antarctic Peninsula, and the root sum square (RSS) values are 8.29 and 7.46 cm, respectively. TPXO9 has the highest accuracy in the Weddell Sea and FRIS and AIS and RSS values are 18.33 and 12.77 cm, respectively. TPXO9 and RIS_Optimal models have higher accuracy in the Ross Sea and Ross Ice Shelf (RIS), RSS values are 5.62 and 6.21 cm, respectively. The accuracy of FES 2014, TPXO9, CATS 2008, and the regional tide model in the Drake Passage, Kerguelen Islands, and Adelie–Mertz was evaluated using satellite altimetry data. The RSS values are less than 4 cm. By using the altimetry data at Sentinel-3A single-satellite crossovers in terms of the STD of the SLA, the comparison of the STDs show that FES2014 is the best.

Iceberg detection is of great significance for marine environmental monitoring and safe sailing of vessels. It is an important part of the construction of the Arctic channel and the exploitation of the Arctic. Iceberg detection using synthetic aperture radar (SAR) images has unique advantages. Many machine learning algorithms can be used in the recognition of icebergs in SAR images. In order to maximize the performance of machine learning algorithms, it is necessary to evaluate different machine learning algorithms and their matching feature and feature standardization methods, so as to select the optimal iceberg detection process method. Therefore, based on Sentinel-1A SAR image, this paper uses a variety of machine learning methods, a variety of feature combinations and a variety of feature standardization methods for iceberg detection, and compares the performance differences of each process method. Machine learning algorithms include Bayes classifier (Bayes), back propagation neural network (BPNN), linear discriminant analysis (LDA), random forest (RF) and support vector machine (SVM); feature standardization methods include Min-max standardization, Z-score standardization and log function standardization; data sets are comprised of 969 iceberg and non-iceberg samples with 12 SAR image features, located mainly on the east coast of Greenland. The classification result is measured by the area under the receiver operating characteristic (ROC) curve (AUC). The results show that the AUC value of RF with the best configuration is the highest, reaching 0.945, which is 0.09 higher than worst Bayes. In terms of detection rate, under the case of 80% iceberg recall rate, the non-iceberg recall rate of RF is 92.6%, which is the best, 1.4% higher than the second BPNN, 2.6% higher than the worst Bayes; under the case of 90% iceberg recall rate, the non-iceberg recall rate of BPNN is 87.4%, 0.8% higher than the second RF and 2.7% higher than the worst Bayes. The above results show that it is very important to select the best machine learning algorithm, the best features and feature standardization method for iceberg detection.

Marine vertical datum system construction is the basic work of marine surveying. In the 2009-2012, China has preliminarily constructed the transformation and unification of different height/depth datum model in the China Sea of 80 nautical mile. In recent years, this model has been extended to the South China Sea, the western Pacific and the eastern Indian Ocean, and then a seamless vertical datum model in the south and north pole area to the whole world gradually will be constructed in the near future, this is the foundation of supporting the digital ocean construction in China. This paper mainly discusses the research status of marine vertical datum construction which had been carried out in major coastal countries or regions, analyzes the main work and approaches and key technologies in the process of the marine vertical datum system building, and then expounds the achievements and existing problems in the practice of our country marine vertical datum building.

My thesis includes three papers on contingent claims valuation of corporate securities using structural models of credit risk. Our study focuses on structural models and their applications in estimating damages in security class actions, option pricing and warrant pricing. Securities class actions typically involve some misrepresentation by a firm that overstates its true value. In securities class actions econometric models are used to assess damages to shareholders. However, studies on measuring damages for debt-holders are limited. My first paper uses a modified Merton framework to measure the impact of misrepresentation on the value of other components (e.g., debt, warrants) of a firm’s capital structure. Using structural models and leveraging the relationship between equity and firm value, we use observable eq- uity information to determine firm value and hence the effect of misrepresentation on value of other securities in the capital structure. We investigate various capital structures and show that misrepresentation can have a significant impact on the value of all components in the capital structure. We find that the misrepresentation impact on debt value depends on firm leverage and debt seniority and not on the warrant dilution factor. Generally, the debt for higher-leverage firms is more sensitive to the misrepresentation impact than for lower-leverage firms and junior debt is more affected by fraud than senior debt. The impact on warrant value is determined by warrant moneyness (stock price), with the dilution factor having no effect. My second paper extends the study in my first paper into the First Passage Time (FPT) framework, which is capable of modeling firms with complex debt structures. Our findings have important consequences for damages assessment and allocation of settlement awards in securities class actions. In some jurisdictions damages awarded are net of any hedge or risk- limitation transaction. Since corporate securities such as bonds and stocks are often held in portfolios for hedging purposes, measuring the effect of misrepresentation on all of the firm’s issuances is essential to accurately computed damages awards. In addition to our main findings, we explicitly discuss bankruptcy costs for the First Passage Time model. Furthermore, we are able to reduce a system of two non-linear equations, used to connect the unobservable firm value and firm value volatility to observable equity value and equity volatility, into one equation. This technique improves the ability to solve the non-linear system. My third paper studies option and warrant pricing under the structural framework (both Merton and FPT frameworks). We study the calibration of structural frameworks using a mar- ket implied volatility skew. We show that the model implied volatility skew under FPT frame- work is much more flexible than that under the Merton framework. Moreover, we extend the FPT structural framework to include warrants into the firm’s capital structure. Using historical market data, we show the pricing model (for both options and warrants) under FPT framework significantly outperforms the pricing models under Merton framework.