Quantitative Phenotype Analysis to Identify, Validate and Compare Rat Disease Models

Introduction The laboratory rat has been widely used as an animal model in biomedical research. There are many strains exhibiting a wide variety of phenotypes. Capturing these phenotypes in a centralized database provides researchers with an easy method for choosing the appropriate strains for their studies. Current resources such as NBRP and PhysGen provided some preliminary work in rat phenotype databases. However, there are drawbacks in both projects: (1) small number of animals (6 rats) used by NBRP; (2) NBRP project is a one-time effort for each strain; (3) PhysGen web interface only enables queries within a single study – data comparison and integration not possible; (4) PhysGen lacks a data standardization process so that the measurement method, experimental condition, and age of rats used are hidden. Therefore, there is a need for a better data integration and visualization method in order to provide users with more insights about phenotype differences across rat strains. The Rat Genome Database (RGD) PhenoMiner tool has provided the first step in this effort by standardizing and integrating data from individual studies as well as NBRP and PhysGen. Methods Our work involved the following key steps: (1) we developed a meta-analysis pipeline to automatically integrate data from heterogeneous sources and to produce expected ranges (standardized phenotype ranges) for different strains, and different phenotypes under different experimental conditions; (2) we created tools to visualize expected ranges for individual strains and strain groups; (3) we clustered substrains into different sub-populations according to phenotype correlations. Results We developed a meta-analysis pipeline and an interactive web interface that summarizes and visualizes expected ranges produced from the meta-analysis pipeline. Automation of the pipeline allows for updates as additional data becomes available. The interactive web interface provides the researchers with a platform for identifying and validating expected ranges for a variety of quantitative phenotypes. In addition, we performed a preliminary cluster analysis that enables researchers to examine similarities of strains, substrains, and different sex or age groups of strains on a multi-dimensional scale by using multiple phenotype features. Conclusion The data resources and the data mining and visualization tools will promote an understanding of rat disease models, guide researchers to choose optimal strains for their research needs, and encourage data sharing from different research hubs. Such resources also help to promote research reproducibility. Data produced and interactive platforms created in this project will continue to provide a valuable resource for Translational Research efforts

Essays on corporate governance

This thesis revolves around the topic of corporate governance. Chapter 1 provides an overview of the research topics. Chapter 2 examines the relationship between CEOs’ generalist and specialist managerial skills and future stock price crash risk. Using nearly 20,000 firm-year observations across North American firms from 1995 to 2015, we find weak evidence that generalist CEOs are positively associated with future stock price crash risk. We conjecture that this may be the case because generalist CEOs frequently change jobs and are less engaged with their current position. Chapter 3 studies the importance of effective board governance and presents some empirical evidence that board monitoring quality is negatively associated with future stock price crash risk. The empirical tests use more than 3,000 firm-year observations from North American firms from 2009 to 2020. The chapter includes some CEO characteristics that can potentially change the politics and dynamics of the board and thus affect the monitoring quality over time. The conclusion remains with the inclusion of these variables and finds that boards that offer high monitoring quality can help prevent extreme consequences. The research provides an alternative view of the motivation of directors and offers suggestions on how to improve board governance. Chapter 4 summarizes the conclusions of the study

Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation

Background: The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model. We combined natural language processing (NLP) and semantic network analysis in our model generation pipeline. Methods: As a use case of our pipeline, we utilized data from an open source imaging case repository, Radiopaedia.org, to generate a knowledge model that represents the contents of medical imaging reports. We extracted entities and relationships using the Stanford part-of-speech parser and the “Subject:Relationship:Object” syntactic data schema. The identified noun phrases were tagged with the Unified Medical Language System (UMLS) semantic types. An evaluation was done on a dataset comprised of 83 image notes from four data sources. Results: A semantic type network was built based on the co-occurrence of 135 UMLS semantic types in 23,410 medical image reports. By regrouping the semantic types and generalizing the semantic network, we created a knowledge model that contains 14 semantic categories. Our knowledge model was able to cover 98% of the content in the evaluation corpus and revealed 97% of the relationships. Machine annotation achieved a precision of 87%, recall of 79%, and F-score of 82%. Conclusion: The results indicated that our pipeline was able to produce a comprehensive content-based knowledge model that could represent context from various sources in the same domain

Opinion Dynamics in Two-Step Process: Message Sources, Opinion Leaders and Normal Agents

According to mass media theory, the dissemination of messages and the evolution of opinions in social networks follow a two-step process. First, opinion leaders receive the message from the message sources, and then they transmit their opinions to normal agents. However, most opinion models only consider the evolution of opinions within a single network, which fails to capture the two-step process accurately. To address this limitation, we propose a unified framework called the Two-Step Model, which analyzes the communication process among message sources, opinion leaders, and normal agents. In this study, we examine the steady-state opinions and stability of the Two-Step Model. Our findings reveal that several factors, such as message distribution, initial opinion, level of stubbornness, and preference coefficient, influence the sample mean and variance of steady-state opinions. Notably, normal agents' opinions tend to be influenced by opinion leaders in the two-step process. We also conduct numerical and social experiments to validate the accuracy of the Two-Step Model, which outperforms other models on average. Our results provide valuable insights into the factors that shape social opinions and can guide the development of effective strategies for opinion guidance in social networks

Modeling Information Acquisition and Social Learning Dynamics: A Rational Inattention Perspective

Social learning, a fundamental process through which individuals shape their beliefs and perspectives via observation and interaction with others, is critical for the development of our society and the functioning of social governance. Prior works on social learning usually assume that the initial beliefs are given and focus on the update rule. With the recent proliferation of online social networks, there is an avalanche amount of information, which may significantly influence users' initial beliefs. In this paper, we use the rational inattention theory to model how agents acquire information to form initial beliefs and assess its influence on their adjustments in beliefs. Furthermore, we analyze the dynamic evolution of belief distribution among agents. Simulations and social experiments are conducted to validate our proposed model and analyze the impact of model parameters on belief dynamics.Comment: 10 pages, 6 figures, submitted to ICASSP 202

An empirical study of touch-based authentication methods on smartwatches

The emergence of smartwatches poses new challenges to information security. Although there are mature touch-based authentication methods for smartphones, the effectiveness of using these methods on smartwatches is still unclear. We conducted a user study (n=16) to evaluate how authentication methods (PIN and Pattern), UIs (Square and Circular), and display sizes (38mm and 42mm) affect authentication accuracy, speed, and security. Circular UIs are tailored to smartwatches with fewer UI elements. Results show that 1) PIN is more accurate and secure than Pattern; 2) Pattern is much faster than PIN; 3) Square UIs are more secure but less accurate than Circular UIs; 4) display size does not affect accuracy or speed, but security; 5) Square PIN is the most secure method of all. The study also reveals a security concern that participants' favorite method is not the best in any of the measures. We finally discuss implications for future touch-based smartwatch authentication design.Comment: ISWC '17, Proceedings of the 2017 ACM International Symposium on Wearable Computers, 122-125, ACM New York, NY, US

Associations of plasma very-long-chain SFA and the metabolic syndrome in adults

Plasma levels of very-long-chain SFA (VLCSFA) are associated with the metabolic syndrome (MetS). However, the associations may vary by different biological activities of individual VLCSFA or population characteristics. We aimed to examine the associations of VLCSFA and MetS risk in Chinese adults. Totally, 2008 Chinese population aged 35–59 years were recruited and followed up from 2010 to 2012. Baseline MetS status and plasma fatty acids data were available for 1729 individuals without serious diseases. Among 899 initially metabolically healthy individuals, we identified 212 incident MetS during the follow-up. Logistic regression analysis was used to estimate OR and 95 % CI. Cross-sectionally, each VLCSFA was inversely associated with MetS risk; comparing with the lowest quartile, the multivariate-adjusted OR for the highest quartile were 0·18 (95 % CI 0·13, 0·25) for C20 : 0, 0·26 (95 % CI 0·18, 0·35) for C22 : 0, 0·19 (95 % CI 0·13, 0·26) for C24 : 0 and 0·16 (0·11, 0·22) for total VLCSFA (all Pfor trend<0·001). The associations remained significant after further adjusting for C16 : 0, C18 : 0, C18 : 3n-3, C22 : 6n-3, n-6 PUFA and MUFA, respectively. Based on follow-up data, C20 : 0 or C22 : 0 was also inversely associated with incident MetS risk. Among the five individual MetS components, higher levels of VLCSFA were most strongly inversely associated with elevated TAG (≥1·7 mmol/l). Plasma levels of VLCSFA were significantly and inversely associated with MetS risk and individual MetS components, especially TAG. Further studies are warranted to confirm the findings and explore underlying mechanisms