Foreword

Fund investments are very popular in Sweden. However, we have the impression that despite this popularity, the average fund investor in Sweden does not pay much attention to the importance and possible link of fund’s asset composition features (e.g. Asset class, Holdings, and Geo-exposure) to fund’s performance. Instead, S/he relies on factors such as fees, risk levels, historical performance, etc. in her/his investment decisions. Similarly, academic studies mainly focus on attributes such as funds fees, size, and manager’s skill to explain fund’s performance. Thus there are limited premier academic studies on the relationship between fund’s performance and its asset composition features. The main purpose of this study is to investigate possible causal relationship between the performances of funds with their assets composition features. We study the whole population of 346 Swedish listed mutual funds older than five years for the period 2009-2013. The results of the study provides the investors and analysts with additional decision-making and investment-analysis tools to assist them in making more informed judgment on funds and their expected returns. The results are also useful for fund managers to improve their strategies by refining the combinations of their funds’ asset composition attributes in order to improve the absolute risk-adjusted performance of their funds. Our research philosophy has been based on positivism and objectivism along with functionalist paradigm and we have applied deductive approach to test the theories. We have used quantitative method and collected the funds’ data from public business databases and chosen Jensen’s alpha and Treynor ratio as funds’ risk-adjusted performance measures. We performed Correlation tests and Regression with robust techniques on our data to answer the research question from three aspects, namely asset class (equity, bond, and mixed assets); geo-exposures (Sweden, Global, Europe, and Nordic) and Top-ten holdings’ measures (asset concentration and Treynor of each fund’s passive top-ten sub-portfolio). We conclude that correlations between funds’ risk-adjusted performance and assets composition features are likely to exist. Stronger correlations are observed between the explanatory measures and fund’s relative risk-adjusted performance (fund’s Treynor) as compared to fund’s absolute risk adjusted performance (fund’s Jensen’s alpha). Asset concentration in top-ten holdings and bond asset class are more likely to be in casual relationship with fund’s risk-adjusted performance, whereas Treynor ratio of top-ten holdings’ passive sub-portfolio as well as fund’s geo-exposure do not seem to have strong explanatory power for funds’ absolute performance

Engineering a Molecular Missile for Pancreatic Cancer Detection: Vector Design

Pancreatic cancer, though rare, is expected to be the second-leading cause of cancer-related death by 2030 (Rahib et al., 2014). CA 19-9 is currently the most widely used biomarker for pancreatic cancer detection, but detection of CA 19-9 relies on the use of monoclonal antibodies, a technology that entails an expensive, lengthy, and ethically problematic manufacturing process. This paper presents the design of a DNA vector that can be used to program E. coli to produce a “molecular missile” that targets CA 19-9. Through the site-specific incorporation of an unnatural amino acid (L-DOPA), this peptide can be engineered to bind to a pancreatic cancer biomarker with strength comparable to a monoclonal antibody. By targeting a sugar molecule, this synthetic antibody will expand the potential diagnostic and therapeutic applications of its cost-effective, stable, and ethical modular design

Quantifying the health burden misclassification from the use of different PM2.5 exposure tier models: A case study of London

Exposure to PM2.5 has been associated with increased mortality in urban areas. Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here we quantify the misclassification that occurs when using different exposure approaches to predict the mortality burden of a population using London as a case study. We develop a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM2.5) in four major microenvironments (MEs) (dwellings, aboveground transportation, London Underground (LU) and outdoors)in the Greater London Area (GLA), in 2017. We demonstrate that differences exist between five different exposure Tier-models with incrementally increasing complexity, moving from static to more dynamic approaches. BenMap-CE, the open source software developed by the U.S. Environmental Protection Agency, is used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimate the change in mortality on a fine resolution. Our results showed that using the outdoor concentration as a surrogate for the total population exposure but ignoring the different exposure concentration that occurs indoors and the time spent in transit, would lead to a misclassification of 1,174 predicted mortalities in GLA. Indoor exposure to PM2.5 is the largest contributor to total population exposure, accounting for 80% of total mortality, followed by the London Underground which contributes 15%, albeit the average percentage of time spent there by Londoners is only 0.4%. We generally confirmed that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification in health burden assessments