The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa.Oceans cover approximately 71% of the Earth's surface and harbour most of the phylum diversity of the animal kingdom. Understanding marine biodiversity and its evolution remains a major challenge. The Pacific oyster C. gigas (Thunberg, 1793) is a marine bivalve belonging to the phylum Mollusca, which contains the largest number of described marine animal species 1 . Molluscs have vital roles in the functioning of marine, freshwater and terrestrial ecosystems, and have had major effects on humans, primarily as food sources but also as sources of dyes, decorative pearls and shells, vectors of parasites, and biofouling or destructive agents. Many molluscs are important fishery and aquaculture species, as well as models for studying neurobiology, biomineralization, ocean acidification and adaptation to coastal environments under climate change 2,3 . As the most speciose member of the Lophotrochozoa, phylum Mollusca is central to our understanding of the biology and evolution of this superphylum of protostomes.As sessile marine animals living in estuarine and intertidal regions, oysters must cope with harsh and dynamically changing environments. Abiotic factors such as temperature and salinity fluctuate wildly, and toxic metals and desiccation also pose serious challenges. Filter-feeding oysters face tremendous exposure to microbial pathogens. Oysters do have a notable physical line of defence against predation and desiccation in the formation of thick calcified shells, a key evolutionary innovation making molluscs a successful group. However, acidification of the world's oceans by uptake of anthropogenic carbon dioxide poses a potentially serious threat to this ancient adaptation 4 . Understanding biomineralization and molluscan shell formation is, thus, a major area of interest 5 . Crassostrea gigas is also an interesting model for developmental biology owing to its mosaic development with typical molluscan stages, including trochophore and veliger larvae and metamorphosis.A complete genome sequence of C. gigas would enable a more th...

Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. However, features used in most Siamese tracking approaches can only discriminate foreground from the non-semantic backgrounds. The semantic backgrounds are always considered as distractors, which hinders the robustness of Siamese trackers. In this paper, we focus on learning distractor-aware Siamese networks for accurate and long-term tracking. To this end, features used in traditional Siamese trackers are analyzed at first. We observe that the imbalanced distribution of training data makes the learned features less discriminative. During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors. During inference, a novel distractor-aware module is designed to perform incremental learning, which can effectively transfer the general embedding to the current video domain. In addition, we extend the proposed approach for long-term tracking by introducing a simple yet effective local-to-global search region strategy. Extensive experiments on benchmarks show that our approach significantly outperforms the state-of-thearts, yielding 9.6% relative gain in VOT2016 dataset and 35.9% relative gain in UAV20L dataset. The proposed tracker can perform at 160 FPS on short-term benchmarks and 110 FPS on long-term benchmarks. The code is available at https://github.com/foolwood/DaSiamRPN.

Objectives: We aimed to (1) assess parental hesitancy about category A (Expanded Program on Immunization (EPI)) and B (non-EPI) vaccines, (2) assess parental willingness for COVID-19 and influenza vaccinations, and (3) explore the association of vaccination hesitancy of parents and healthcare workers (HCWs). Methods: The study was performed in Wuxi, eastern China between 21 September 2020 and 17 October 2020. Parents of children aged <18 years and HCWs were recruited from the selected immunization clinics. Vaccine hesitancy was assessed using the Strategic Advisory Group of Experts (SAGE) vaccine hesitancy survey (VHS) by summing the total score for 10 items (maximum 50 points). Results: A total of 3009 parents and 86 HCWs were included in the analysis. The category A VHS scores were significantly higher than the category B VHS scores (p = 0.000). Overall, 59.3% and 52.4% of parents reported willingness to avail COVID-19 and influenza vaccination for their children, respectively; 51.2% of the HCWs wanted to be vaccinated against COVID-19. Parental category B VHS scores were associated with HCW category B VHS scores (r = 0.928, p = 0.008). Conclusions: In China, parents are more hesitant about category B vaccines than category A vaccines. More than 40% of parents showed hesitancy and a refusal to use COVID-19 and influenza vaccines.

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China’s success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method. Because of their relatively large uncertainties, three model parameters were considered: the interfacial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.Key words: conditional nonlinear optimal perturbation, Kuroshio large meander, predictability, model parameters Citation: Wang, Q., M. Mu, and H. A. Dijkstra, 2012: Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander.