Air pollution and adverse health effects: Assessing exposure windows and sensitivity to modeling choices

Air pollution is one of the leading environmental problems of the 21st century, and the rise of global urbanization has increasingly exacerbated air pollution’s public health impact. Existing epidemiologic studies have tackled the relationship between air pollution and health from a variety of perspectives, but many areas of research remain lacking, including studies originating from developing countries, the assessment of exposure windows and sensitivity of modeling choices, and a better understanding of the climate change impacts on air pollution and health. In this dissertation, I address all of the issues mentioned above. Chapter 1 formally introduces the topics of this dissertation and summarizes background information on several major air pollutants. It then provides an overview of existing research on air pollution epidemiology and describes key knowledge gaps. In Chapter 2, we conduct a systematic review of the scientific literature for data on fine particulate matter (PM2.5) in China, where historical PM2.5 data are not widely available prior to 2013. Using the 574 PM2.5 measurements we identified from the literature, we detected differences in PM2.5 levels across both geographic and economic regions of China. In Chapter 3, we investigate the associations between short- and intermediate-term exposure of nitrogen dioxide (NO2) and mortality in 42 counties in China from 2013 to 2015, and find evidence of significant associations up to seven days prior to exposure. In Chapter 4, we investigate the association between PM2.5 and hospitalizations in New York State using five separate exposure datasets from 2002 to 2012. We find that despite some fluctuations in effect estimates, the majority of models yielded consistently significantly harmful associations. In Chapter 5, we utilize a global chemistry-climate model to project ozone levels in 2050 under a variety of emissions scenarios and quantify the mortality impact associated with changes in ozone concentrations between 2015 and 2050 according to each scenario. We find that under climate change alone and adherence to current legislation, ozone-related deaths would increase. However, under a best-case scenario of maximum technologically feasible reductions in emissions, over 300,000 premature deaths related to ozone can be avoided. Finally, Chapter 6 summarizes the findings of this dissertation and discusses potential directions in future research. While much work remains to be done, this dissertation work takes an important step forward in closing existing knowledge gaps in the field of air pollution epidemiology. More importantly, we hope that our work sends a strong public health message on the importance of continuing research on air pollution and health

Can digitalization improve the equality and equity of food environment? Evidence from greengrocers in central Shanghai

IntroductionOnline food shopping has a profound impact on people’s food acquisition behavior, the current study aims to understand how online food shopping may affect the accessibility of the local food environment and further influence the health equity among different populations.MethodsTaking 8512 traditional and online greengrocers in central Shanghai as an example, this paper uses Gini coefficient, location quotient and spatial clustering method to compare the equality and equity of food environment between physical and digital food outlets.ResultsIt finds that spatial equality is more significantly improved as a result of online food stores than are population equality and social equity of the food environment; older populations are not disadvantaged in terms of healthy food access but lower-income people are; the impact of online stores varies for different regions and different types of stores; depot-based stores have the most positive impact on health equity.DiscussionPolicy implications are discussed to promote the environmental justice of healthy food accessibility

An evaluation of learning analytics to identify exploratory dialogue in online discussions

Social learning analytics are concerned with the process of knowledge construction as learners build knowledge together in their social and cultural environments. One of the most important tools employed during this process is language. In this paper we take exploratory dialogue, a joint form of co-reasoning, to be an external indicator that learning is taking place. Using techniques developed within the field of computational linguistics, we build on previous work using cue phrases to identify exploratory dialogue within online discussion. Automatic detection of this type of dialogue is framed as a binary classification task that labels each contribution to an online discussion as exploratory or non-exploratory. We describe the development of a self-training framework that employs discourse features and topical features for classification by integrating both cue-phrase matching and k-nearest neighbour classification. Experiments with a corpus constructed from the archive of a two-day online conference show that our proposed framework outperforms other approaches. A classifier developed using the self-training framework is able to make useful distinctions between the learning dialogue taking place at different times within an online conference as well as between the contributions of individual participants

Quantising opinions for political tweets analysis

There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues. We analyzed tweet messages crawled during the eight weeks leading to the UK General Election in May 2010 and found that activities at Twitter is not necessarily a good predictor of popularity of political parties. We then proceed to propose a statistical model for sentiment detection with side information such as emoticons and hash tags implying tweet polarities being incorporated. Our results show that sentiment analysis based on a simple keyword matching against a sentiment lexicon or a supervised classifier trained with distant supervision does not correlate well with the actual election results. However, using our proposed statistical model for sentiment analysis, we were able to map the public opinion in Twitter with the actual offline sentiment in real world

Transparent Object Depth Completion

The perception of transparent objects for grasp and manipulation remains a major challenge, because existing robotic grasp methods which heavily rely on depth maps are not suitable for transparent objects due to their unique visual properties. These properties lead to gaps and inaccuracies in the depth maps of the transparent objects captured by depth sensors. To address this issue, we propose an end-to-end network for transparent object depth completion that combines the strengths of single-view RGB-D based depth completion and multi-view depth estimation. Moreover, we introduce a depth refinement module based on confidence estimation to fuse predicted depth maps from single-view and multi-view modules, which further refines the restored depth map. The extensive experiments on the ClearPose and TransCG datasets demonstrate that our method achieves superior accuracy and robustness in complex scenarios with significant occlusion compared to the state-of-the-art methods

Enhanced specific immune responses by CpG DNA in mice immunized with recombinant hepatitis B surface antigen and HB vaccine

<p>Abstract</p> <p>Background</p> <p>Hepatitis B vaccine adjuvant, alum, is generally used for vaccination although it does not stimulate Th1 immunity and 10% of the population has low or no antibody response. Efforts have been continued to find more efficient vaccine adjuvants for better antibody response as well as stimulation of Th1 immunity.</p> <p>Methods</p> <p>CpG DNA was used as an adjuvant for recombinant HBsAg to immunize 6- to 8-week-old female BALB/c mice with or without alum for different dosages. The production of HBsAb, CD80 and CD86 from dendritic cells, and cytokines IL-10, IL12, etc., were analyzed and compared for the performance of immunization.</p> <p>Results</p> <p>5-20 μg CpG DNA had the best co-stimulation effect of HBsAb serum conversion for mice vaccinated with recombinant expressed HBsAg. The mice vaccinated with recombinant 20 μg CpG DNA and regular vaccine (containing alum adjuvant) had the highest concentration of antibody production. IL-12b, IL-12a and IL10 mRNA reached to the peak level between 3 and 6 hours after the CpG DNA induction in splenocytes. The expression levels of CD80 and CD86 leucocyte surface molecules were increased with 20 μg CpG DNA alone or with 20 μg CpG DNA and 4 μg HBsAg.</p> <p>Conclusions</p> <p>Our results confirmed the adjuvant effect of CpG DNA for HBsAg in the mouse model. The increase of IL10 and IL12 production suggested the involvement of Th1 cell activation. The activation of CD80 and CD86 molecules by CpG-ODN might be part of the mechanism of T/B cells coordination and the enhancement of recombinant HBsAg induced immune response.</p

Common biological processes and mutual crosstalk mechanisms between cardiovascular disease and cancer

Cancer and cardiovascular disease (CVD) are leading causes of mortality and thus represent major health challenges worldwide. Clinical data suggest that cancer patients have an increased likelihood of developing cardiovascular disease, while epidemiologic studies have shown that patients with cardiovascular disease are also more likely to develop cancer. These observations underscore the increasing importance of studies exploring the mechanisms underlying the interaction between the two diseases. We review their common physiological processes and potential pathophysiological links. We explore the effects of chronic inflammation, oxidative stress, and disorders of fatty acid metabolism in CVD and cancer, and also provide insights into how cancer and its treatments affect heart health, as well as present recent advances in reverse cardio-oncology using a new classification approach