Interpreting the dependence of mutation rates on age and time

Mutations can arise from the chance misincorporation of nucleotides during DNA replication or from DNA lesions that are not repaired correctly. We introduce a model that relates the source of mutations to their accumulation with cell divisions, providing a framework for understanding how mutation rates depend on sex, age and absolute time. We show that the accrual of mutations should track cell divisions not only when mutations are replicative in origin but also when they are non-replicative and repaired efficiently. One implication is that the higher incidence of cancer in rapidly renewing tissues, an observation ascribed to replication errors, could instead reflect exogenous or endogenous mutagens. We further find that only mutations that arise from inefficiently repaired lesions will accrue according to absolute time; thus, in the absence of selection on mutation rates, the phylogenetic "molecular clock" should not be expected to run steadily across species.Comment: 5 figures, 2 table

Analysis of post-merger integration of automobile firms

1 online resource (v, 31 p.)Includes abstract and appendix.Includes bibliographical references (p. 27-30).This paper’s objective is to determine whether merger announcements of world automobile companies would influence the stock price of acquired companies and whether the market reaction to merger announcement is good or bad. 24 acquired companies from the OTC market and 10 acquired companies come from the NYSE market are randomly chosen for this study. The time period is chosen from 1998 to 2012.The DJ (USA) index is used as market return in this paper. The Market Return Model, the Average Abnormal Return Model and the Capital Asset Pricing Model (CAPM) will be used in this paper. In summary, this study is going to prove whether the merged world automobile firms would gain or loss after merger announcement

DUA-DA: Distillation-based Unbiased Alignment for Domain Adaptive Object Detection

Though feature-alignment based Domain Adaptive Object Detection (DAOD) have achieved remarkable progress, they ignore the source bias issue, i.e. the aligned features are more favorable towards the source domain, leading to a sub-optimal adaptation. Furthermore, the presence of domain shift between the source and target domains exacerbates the problem of inconsistent classification and localization in general detection pipelines. To overcome these challenges, we propose a novel Distillation-based Unbiased Alignment (DUA) framework for DAOD, which can distill the source features towards a more balanced position via a pre-trained teacher model during the training process, alleviating the problem of source bias effectively. In addition, we design a Target-Relevant Object Localization Network (TROLN), which can mine target-related knowledge to produce two classification-free metrics (IoU and centerness). Accordingly, we implement a Domain-aware Consistency Enhancing (DCE) strategy that utilizes these two metrics to further refine classification confidences, achieving a harmonization between classification and localization in cross-domain scenarios. Extensive experiments have been conducted to manifest the effectiveness of this method, which consistently improves the strong baseline by large margins, outperforming existing alignment-based works.Comment: 10pages,5 figure

Distribution and Determinants of Correlation between PM2.5 and O3 in China Mainland: Dynamitic simil-Hu Lines

In recent years, China has made great efforts to control air pollution. During the governance process, it is found that fine particulate matter (PM2.5) and ozone (O3) change in the same trend among some areas and the opposite in others, which brings some difficulties to take measures in a planned way. Therefore, this study adopted multi-year and large-scale air quality data to explore the distribution of correlation between PM2.5 and O3, and proposed a concept called dynamic similar hu lines to replace the single fixed division in the previous research. Furthermore, this study discussed the causes of distribution patterns quantitatively with geographical detector and random forest. The causes included natural factors and anthropogenic factors. And these factors could be divided into three parts according to the characteristics of spatial distribution: broadly changing with longitude, changing with latitude, and having local characteristics. Overall, regions with relatively more densely population, higher GDP, lower altitude, higher humidity, higher atmospheric pressure, higher surface temperature, less sunshine hours and more accumulated precipitation often corresponds to positive correlation coefficient between PM2.5 and O3, no matter in which season. The parts with opposite conditions that mentioned above are essentially negative correlation coefficient. And what's more, humidity, global surface temperature, air temperature and accumulated precipitation are four decisive factors to form the distribution of correlation between PM2.5 and O3. In general, collaborative governance of atmospheric pollutants should consider particular time and space background and also be based on the local actual socio-economic situations, geography and geomorphology, climate and meteorology and other comprehensive factors.Comment: Our research group have decided to withdraw this preprin