Background: The rst case of a corona virus 2019 (COVID-19) infection in a Sri Lankan was reported on March 11, 2020. The situation in Sri Lanka changed with the rapid increase of personnel contracting COVID-19 in a Naval base camp that housed more than 4000 people. This provided a unique opportunity to study the effectiveness of hydroxychloroquine (HCQ) for post-exposure prophylaxis (PEP), while taking stringent, non-pharmacologic, public health measures to prevent spread. Our aim is to study the effectiveness and safety of HCQ for PEP among naval personnel with exposure to COVID-19 positive patients. Methods/design: This is a placebo-controlled, randomized, clinical trial carried out in the Naval base camp and quarantine centers of the Sri Lanka Navy, Ministry of Defense, Sri Lanka. Navy personnel who are exposed to a patient with con rmed COVID-19 infection but test negative for the virus on reverse, real-time polymerase chain reaction (rRT-PCR) at recruitment will be randomized, 200 to each arm, to receive HCQ or placebo, and monitored for the development of symptoms or rRT-PCR positivity for severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) virus for 14 days. Discussion: This trial will provide high-quality evidence of the effectiveness and safety of HCQ as PEP for COVID-19. The study design is unique due to the circumstances of the outbreak in a con ned area among otherwise healthy adults, at a relatively early stage of its spread.

The JUNO experiment locates in Jinji town, Kaiping city, Jiangmen city, Guangdong province. The geographic location is east longitude 112 • 31'05' and North latitude 22 • 07'05'. The experimental site is 43 km to the southwest of the Kaiping city, a county-level city in the prefecture-level city Jiangmen in Guangdong province. There are five big cities, Guangzhou, Hong Kong, Macau, Shenzhen, and Zhuhai, all in ∼200 km drive distance, as shown in figure 3.

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DNA methylation is extensively reprogrammed during the early phases of mammalian development, yet genomic targets of this process are largely unknown. We optimized methylated DNA immunoprecipitation for low numbers of cells and profiled DNA methylation during early development of the mouse embryonic lineage in vivo. We observed a major epigenetic switch during implantation at the transition from the blastocyst to the postimplantation epiblast. During this period, DNA methylation is primarily targeted to repress the germline expression program. DNA methylation in the epiblast is also targeted to promoters of lineage-specific genes such as hematopoietic genes, which are subsequently demethylated during terminal differentiation. De novo methylation during early embryogenesis is catalyzed by Dnmt3b, and absence of DNA methylation leads to ectopic gene activation in the embryo. Finally, we identify nonimprinted genes that inherit promoter DNA methylation from parental gametes, suggesting that escape of post-fertilization DNA methylation reprogramming is prevalent in the mouse genome.

We have observed that Au nanorods (NRs) have distinct effects on cell viability via killing cancer cells while posing negligible impact on normal cells and mesenchymal stem cells. Obvious differences in cellular uptake, intracellular trafficking, and susceptibility of lysosome to Au NRs by different types of cells resulted in selective accumulation of Au NRs in the mitochondria of cancer cells. Their long-term retention decreased mitochondrial membrane potential and increased reactive oxygen species level that enhances the likelihood of cell death. These findings thus provide guidance for the design of organelle-targeted nanomaterials in tumor therapy.

Abstract. Previous studies on multi-instance learning typically treated instances in the bags as independently and identically distributed. The instances in a bag, however, are rarely independent in real tasks, and a better performance can be expected if the instances are treated in an non-i.i.d. way that exploits relations among instances. In this paper, we propose two simple yet effective methods. In the first method, we explicitly map every bag to an undirected graph and design a graph kernel for distinguishing the positive and negative bags. In the second method, we implicitly construct graphs by deriving affinity matrices and propose an efficient graph kernel considering the clique information. The effectiveness of the proposed methods are validated by experiments.

Determination of the neutrino mass hierarchy in a reactor neutrino experiment at the medium baseline is discussed. Observation of the interference effects between the Ám 2 31 and Ám 2 32 oscillations enables a relative measurement independent of the knowledge of the absolute mass-squared difference. With a 20 kton liquid scintillator detector of the 3%= ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi E ðMeVÞ p energy resolution, the Daya Bay II experiment at a baseline of $50 km from reactors of total thermal power 36 GW can determine the mass hierarchy at a confidence level of Á 2 MH $ ð10 Ä 12Þ (3 Ä 3:5) in six years after taking into account the real spatial distribution of reactor cores. We show that the unknown residual energy nonlinearity of the liquid scintillator detector has limited impact on the sensitivity due to the self-calibration of small oscillation peaks. Furthermore, an extra increase of Á 2 MH ' 4ð9Þ can be obtained, by including the precise measurement of the effective mass-squared difference Ám 2 of expected relative error 1.5% (1%) from ongoing long-baseline muon neutrino disappearance experiments. The sensitivities from the interference and from absolute measurements can be cross-checked. When combining these two, the mass hierarchy can be determined at a confidence level of Á 2 MH $ ð15 Ä 20Þ (4) in six years.