Toxicity prediction using multi-disciplinary data integration and novel computational approaches

Current predictive tools used for human health assessment of potential chemical hazards rely primarily on either chemical structural information (i.e., cheminformatics) or bioassay data (i.e., bioinformatics). Emerging data sources such as chemical libraries, high throughput assays and health databases offer new possibilities for evaluating chemical toxicity as an integrated system and overcome the limited predictivity of current fragmented efforts; yet, few studies have combined the new data streams. This dissertation tested the hypothesis that integrative computational toxicology approaches drawing upon diverse data sources would improve the prediction and interpretation of chemically induced diseases. First, chemical structures and toxicogenomics data were used to predict hepatotoxicity. Compared with conventional cheminformatics or toxicogenomics models, interpretation was enriched by the chemical and biological insights even though prediction accuracy did not improve. This motivated the second project that developed a novel integrative method, chemical-biological read-across (CBRA), that led to predictive and interpretable models amenable to visualization. CBRA was consistently among the most accurate models on four chemical-biological data sets. It highlighted chemical and biological features for interpretation and the visualizations aided transparency. Third, we developed an integrative workflow that interfaced cheminformatics prediction with pharmacoepidemiology validation using a case study of Stevens Johnson Syndrome (SJS), an adverse drug reaction (ADR) of major public health concern. Cheminformatics models first predicted potential SJS inducers and non-inducers, prioritizing them for subsequent pharmacoepidemiology evaluation, which then confirmed that predicted non-inducers were statistically associated with fewer SJS occurrences. By combining cheminformatics' ability to predict SJS as soon as drug structures are known, and pharmacoepidemiology's statistical rigor, we have provided a universal scheme for more effective study of SJS and other ADRs. Overall, this work demonstrated that integrative approaches could deliver more predictive and interpretable models. These models can then reliably prioritize high risk chemicals for further testing, allowing optimization of testing resources. A broader implication of this research is the growing role we envision for integrative methods that will take advantage of the various emerging data sources.Doctor of Philosoph

ACHIEVEMENT GOAL ORIENTATION AND MATH ANXIETY AMONG UNIMAS UNDERGRADUATE STUDENTS

The purpose of present study was to investigate the relationship between achievement goal orientation and math anxiety. Different types of achievement goals (mastery goal, performance approach goal and performance avoidance goal) were explored in this study. Besides, the difference in math anxiety between gender was also explored. Questionnaire was chosen as our instrument to collect data and the sample of present study involved a total number of 400 UNIMAS undergraduate students. The questionnaire used in this study included Achievement Goal Questionnaire Revised (ACQ-R) and Math Anxiety Rating Scale (MARS). Quantitative and cross-sectional design was used in this study. Independent t-test was used to compare the difference in math anxiety between male and female and the Pearson’s correlation was used to determine the relationship between the achievement goal orientation and math anxiety. Present finding showed that in math anxiety between gender. Moreover, present finding proved that mastery goal had positive relationship with math anxiety and performance approach goal had negative relationship with math anxiety. Moreover, it also found that performance avoidance goal was not correlated with math anxiety

Assessing normality for data with different sample sizes using SAS, Minitab and R

Different statistical packages may produce different results of statistical analysis such as normality test. One of possible sources of the difference is the computational approach. This study tries to explore results of normality tests based on different statistical packages. Empirical data with varied sample sizes were conducted. It was found that SAS, Minitab, and R produced different conclusion in normality test. Meanwhile, sample size also has effect on the test of normality where larger sample size tends to produce different conclusion of normality. But, all the three statistical packages produced similar results of normality test for AD and KS tests

Globalizing the Boardroom among Family-Controlled Companies on Bursa Malaysia: The effects of corporate governance on firm performance

This paper aimed to determine the research gap between corporate governance and its effects on firm performance among family-controlled listed companies on Bursa Malaysia with a globalized boardroom after implementing MCCG 2012. The study focused on family-controlled companies listed on Bursa Malaysia from 2013 to 2018. The sample size includes 240 firm-year observations. Panel data analysis (fixed and random effect) model and Hausman tests were used. Results from panel data analysis (Eviews) found no significant effects between corporate governance and firm performance of family-controlled companies listed on Bursa Malaysia with a globalized boardroom. Keywords: Corporate Governance; Firm Performance; Family-Controlled Companies; Globalized Boardroom eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under the responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians), and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI8.391

Chemistry-wide association studies (CWAS) to determine joint toxicity effects of co-occurring chemical features

Individual structural alerts often fail to accurately predict chemical toxicity as they tend to overlook the moderating effects of other co-occurring alerts. Features are said to have statistical interaction effects when one changes or modulates the effect of another on the target property. Here we introduce Chemistry-Wide Association Study (CWAS; by analogy with GWAS in genomics) to systematically elicit the individual and interaction effects of chemical features on the target property

Integrative Approaches for Predicting In Vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-Term Biological Assays

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity

How digitalisation can enable industrial symbiosis practices : a case study

Industrial Symbiosis (IS) encourages a collaborative approach aiming at recovering, reprocessing and reusing non-labour resources and it is a promising solution for mitigating the rising cost of non-labour resource. Introducing IS is a knowledge intensive process and researchers have developed various information and communication (ICT) tools to support the process. However, the use of these tools in the actual industrial practice has not been adequately investigated yet. This study investigates the role that ICT tools play in facilitating the process of creating IS through a case study of International Synergies – the company which facilitated the world’s first national-level IS programme (i.e. NISP UK). Results suggest that the role of digitalisation can increase practitioners’ productivity mainly through data analytics

Consumer personality, privacy concerns and usage of location-based services (LBS)

This paper examines the effects of the Big Five personality traits on concern for information privacy (CFIP) and the effects of the formulated concern for information privacy towards perceived risk, which in turn determine location-based services (LBS) usage intention. Data for this research was collected from 291 users and non-users of LBS. Result from Pearson correlation analysis indicated significant relationships exist between: (1) extraversion, and openness with collection; (2) extraversion, conscientiousness, and openness with improper access; (3) extraversion, conscientiousness, and openness with errors; (4) agreeableness, neuroticism, and openness with secondary use. All four dimensions of CFIP are found to have a significant direct relationship with perceived risk of using LBS. Implications for research and practice for location-based service providers are discussed