Systemic analysis of microarray data sets towards identifying genes regulating root development

Nitrogen and water are essential for plant growth and development. In this study, we designed experiments to produce gene expression data of poplar roots under nitrogen starvation and water deprivation conditions. We found low concentration of nitrogen led first to increased root elongation followed by lateral root proliferation and eventually increased root biomass. To identify genes regulating root growth and development under nitrogen starvation and water deprivation, we designed a series of data analysis procedures, through which, we have successfully identified biologically important genes. Differentially Expressed Genes (DEGs) analysis identified the genes that are differentially expressed under nitrogen starvation or drought. Protein domain enrichment analysis identified enriched themes (in same domains) that are highly interactive during the treatment. Gene Ontology (GO) enrichment analysis allowed us to identify biological process changed during nitrogen starvation. Based on the above analyses, we examined the local Gene Regulatory Network (GRN) and identified a number of transcription factors. After testing, one of them is a high hierarchically ranked transcription factor that affects root growth under nitrogen starvation. It is very tedious and time-consuming to analyze gene expression data. To avoid doing analysis manually, we attempt to automate a computational pipeline that now can be used for identification of DEGs and protein domain analysis in a single run. It is implemented in scripts of Perl and R.

Assessing Decision Making Skill in Complex and Dynamic Environments Using Representative and Simulated Tasks

Traditional decision making research has often focused on one's ability to choose from a set of prefixed options, ignoring the process by which decision makers generate courses of action (i.e., options) in-situ (Klein, 1993). In complex and dynamic domains, this option generation process is particularly critical to understanding how successful decisions are made (Zsambok & Klein, 1997). When generating response options for oneself to pursue (i.e., during the intervention-phase of decision making) previous research has supported quick and intuitive heuristics, such as the Take-The-First heuristic (TTF; Johnson & Raab, 2003). When generating predictive options for others in the environment (i.e., during the assessment-phase of decision making), previous research has supported the situational-model-building process described by Long Term Working Memory theory (LTWM; see Ward, Ericsson, & Williams, 2013). In the first three experiments, the claims of TTF and LTWM are tested during assessment- and intervention-phase tasks in soccer. To test what other environmental constraints may dictate the use of these cognitive mechanisms, the claims of these models are also tested in the presence and absence of time pressure. In addition to understanding the option generation process, it is important that researchers in complex and dynamic domains also develop tools that can be used by `real-world' professionals. For this reason, three more experiments were conducted to evaluate the effectiveness of a new online assessment of perceptual-cognitive skill in soccer. This test differentiated between skill groups and predicted performance on a previously established test and predicted option generation behavior. The test also outperformed domain-general cognitive tests, but not a domain-specific knowledge test when predicting skill group membership. Implications for theory and training, and future directions for the development of applied tools are discussed.