Foundation to Promote Scholarship and Teaching 2013-2014 Awards

Proposal abstracts of 2013-2014 award recipients in a wide range of disciplinary areas

Patterns and rules for sensitivity and elasticity in population projection matrices

Sensitivity and elasticity analysis of population projection matrices (PPMs) are established tools in the analysis of structured populations, allowing comparison of the contributions made by different demographic rates to population growth. In some commonly used structures of PPM, however, there are mathematically inevitable patterns in the relative sensitivity and elasticity of certain demographic rates. We take a simulation approach to investigate these mathematical constraints for a range of PPM models. Our results challenge some previously proposed constraints on sensitivity and elasticity. We also identify constraints beyond those which have already been proven mathematically, and promote them as candidates for future mathematical proof. A general theme among these rules is that changes to the demographic rates of older or larger individuals have less impact on population growth than do equivalent changes among younger or smaller individuals. However, the validity of these rules in each case depends on the choice between sensitivity and elasticity, the growth rate of the population and the PPM structure used. If the structured population conforms perfectly to the assumptions of the PPM used to model it, the rules we describe represent biological reality, allowing us to prioritise management strategies in the absence of detailed demographic data. Conversely, if the model is a poor fit to the population (specifically; if demographic rates within stages are heterogeneous) such analyses could lead to inappropriate management prescriptions. Our results emphasise the importance of choosing a structured population model which fits the demographics of the population

ETV eMedia Update May 2013

This monthly newsletter from the State Department of Education Office contains valuable curriculum insights, K-12 and professional development resources, and relevant programming information airing on SCETV

Machine learning and computational methods to identify molecular and clinical markers for complex diseases – case studies in cancer and obesity

In biomedical research, applied machine learning and bioinformatics are the essential disciplines heavily involved in translating data-driven findings into medical practice. This task is especially accomplished by developing computational tools and algorithms assisting in detection and clarification of underlying causes of the diseases. The continuous advancements in high-throughput technologies coupled with the recently promoted data sharing policies have contributed to presence of a massive wealth of data with remarkable potential to improve human health care. In concordance with this massive boost in data production, innovative data analysis tools and methods are required to meet the growing demand. The data analyzed by bioinformaticians and computational biology experts can be broadly divided into molecular and conventional clinical data categories. The aim of this thesis was to develop novel statistical and machine learning tools and to incorporate the existing state-of-the-art methods to analyze bio-clinical data with medical applications. The findings of the studies demonstrate the impact of computational approaches in clinical decision making by improving patients risk stratification and prediction of disease outcomes. This thesis is comprised of five studies explaining method development for 1) genomic data, 2) conventional clinical data and 3) integration of genomic and clinical data. With genomic data, the main focus is detection of differentially expressed genes as the most common task in transcriptome profiling projects. In addition to reviewing available differential expression tools, a data-adaptive statistical method called Reproducibility Optimized Test Statistic (ROTS) is proposed for detecting differential expression in RNA-sequencing studies. In order to prove the efficacy of ROTS in real biomedical applications, the method is used to identify prognostic markers in clear cell renal cell carcinoma (ccRCC). In addition to previously known markers, novel genes with potential prognostic and therapeutic role in ccRCC are detected. For conventional clinical data, ensemble based predictive models are developed to provide clinical decision support in treatment of patients with metastatic castration resistant prostate cancer (mCRPC). The proposed predictive models cover treatment and survival stratification tasks for both trial-based and realworld patient cohorts. Finally, genomic and conventional clinical data are integrated to demonstrate the importance of inclusion of genomic data in predictive ability of clinical models. Again, utilizing ensemble-based learners, a novel model is proposed to predict adulthood obesity using both genetic and social-environmental factors. Overall, the ultimate objective of this work is to demonstrate the importance of clinical bioinformatics and machine learning for bio-clinical marker discovery in complex disease with high heterogeneity. In case of cancer, the interpretability of clinical models strongly depends on predictive markers with high reproducibility supported by validation data. The discovery of these markers would increase chance of early detection and improve prognosis assessment and treatment choice

Bibliography

Bibliography of publications by Bruce Kessler

2022 SDSU Data Science Symposium Program

https://openprairie.sdstate.edu/ds_symposium_programs/1003/thumbnail.jp

Critical Analysis of Problems Encountered in Incorporating Indigenous Knowledge in Science Teaching by Primary School Teachers in Zimbabwe

In Zimbabwe the need to incorporate indigenous knowledge in science education to reflect local cultural settings cannot be overemphasized. Current policies on science are situated in Western cultural definitions, thus marginalizing indigenous knowledge, which is misconceived as irrational and illogical. This study used qualitative research methods. Ten teachers were purposively selected and interviewed to gain their insights into problems faced in incorporating indigenous knowledge into science teaching. The study found that the problems were attitudinal, institutional, and systemic. Teachers were found to be conservative “gatekeepers” who exhibited negative attitudes toward indigenous science and supported maintaining the teaching of Western science. The study suggests reforming and transforming science curriculum, policymaking, and teacher education to promote cross-cultural science in Zimbabwean primary schools

Synthetic Biology, Artificial Intelligence, and Quantum Computing

We envisage a world where genetic engineering, artificial intelligence (AI), and quantum computing (QC) will coalesce to bring about a forced speciation of the Homo sapiens. A forced speciation will drastically reduce the emergence time for a new species to a few years compared to Nature’s hundreds of millennia. In this chapter, we explain the basic concepts that would allow a forced speciation of the Homo sapiens to occur and its consequences on life on Earth thereafter. Accelerating speciation mediated by Homo sapiens via domestication, gene splicing, and gene drive mechanisms is now scientifically well understood. Synthetic biology can advance speciation far more rapidly using a combination of clustered regularly interspaced short palindromic repeats (CRISPR) technology, advanced computing technologies, and knowledge creation using AI. The day is perhaps not far off when Homo sapiens itself will initiate its own speciation once it advances synthetic biology to a level where it can safely modify the brain to temper emotion and enhance rational thinking as a means of competing against AI-embedded machines guided by quantum algorithms