Abstract Writing and Implementing Successful NSF S-STEM ProposalsIn this paper, the authors provide specific guidelines on the approaches they used to write andimplement successful NSF S-STEM proposals. The NSF S-STEM program provides up to$600,000 in scholarship funding for academically talented, FAFSA-eligible students. Currently,there are 1,176 active S-STEM grants in the United States [1], with 80-100 additional awardsexpected following the August, 2014 submission deadline.At the authors’ university, three different faculty members have been successful in obtaining S-STEM funding over a period of four years. In 2011, the Department of Engineering obtained$599,894 to support a program that expands engineering in the state, particularly among the ruralpopulation in one region of the state. In 2012, the Department of Biology was provided with$599,945 to fund Biology students who are first-generation college students. In 2014, a facultymember in the Department of Chemistry, working with faculty from the Department of Physics,secured $620,833 to fund students majoring in chemistry or physics.This paper will provide useful information regarding the development and revising of these threesuccessful S-STEM proposals. It will also examine the impact these programs are having at thisinstitution and offer advice and lessons learned regarding the proposal process and theimplementation of S-STEM programs.For all three proposals, there were a number of commonalities that the authors believecontributed to the success of the proposals. Among these was a tie to STEM education andrelated literature that indicates how different types of activities and involvement impact studentsand their retention. For example, the work of Besterfield-Sacre et al. [2] indicates that anincrease in confidence and improvement in communication skills can result from experiencesthat requires students to communicate with other students. Additionally, MacGuire and Halpin[3] note that, in reference to the work of Tinto [4], “once at the university, the quality of theindividual’s interactions with others has a strong impact on persistence (p. 6)”. Related to thetypes of activities the authors included in their proposals, “vicarious experiences”, such asshadowing and observing, and “verbal persuasion”, such as the encouragement of faculty andother adults, have been shown to serve as significant contributors in the enhancement of self-efficacy [5].Additional information about the university and the characteristics it has that make it a candidatefor S-STEM awards will be shared. It is not possible to include details here without revealingthe school, so that information will only appear in the final paper. Other aspects of the paperinclude: learning communities, retention figures for the three programs, summarized GPA datafor the S-STEM participants, cohort bonding activities, and lessons learned.REFERENCES[1] NSF Scholarships in Science, Technology, Engineering, and Mathematics: Active Awards,http://www.nsf.gov/awards/award_visualization.jsp?org=NSF&pims_id=5257&ProgEleCode=1536&RestrictActive=on&BooleanElement=true&BooleanRef=true&from=fund, downloaded on10/14.2014.[2] Besterfield-Sacre, M. Amaya, N.Y., Shuman, L.J., Atman, C.J., Porter, R.L. (1998).Understanding student confidence as it relates to first year achievement. 28th Annual Frontiersin Education v(1) 258-263.[3] MacGuire, S. & Halpin, G. (1995). Factors related to persistence in engineering: results of aqualitative study. Presentation at Mid-South Research Association Meeting, Biloxi, MS.[4] Tinto, V. (1993). Leaving college. 2nd edition, Chicago: University of Chicago Press.[5] Ponton, M.K., Edmister, J.H., Ukeiley, L.S., Seiner, J.M. (2001). Understanding the role ofself-efficacy in engineering education". Journal of Engineering Education 90(2), 247-251.
Pharmacogenomics, toxicogenomics, and small RNA expression analysis are three of the most active research topics in the biological, biomedical, pharmaceutical, and toxicological fields. All of these studies are based on gene expression analysis, which requires reference genes to reduce the variations derived from different amounts of starting materials and different efficiencies of RNA extraction and cDNA synthesis. Thus, accurate normalization to one or several constitutively expressed reference genes is a prerequisite to valid gene expression studies. Although selection of reliable reference genes has been conducted in previous studies in several animals and plants, no research has been focused on pharmacological targets, and very few studies have had a toxicological context. More interestingly, no studies have been performed to identify reference genes for small RNA analysis although small RNA, particularly microRNA (miRNA)-related research is currently one of the fastest-moving topics. In this study, using MCF-7 breast cancer cells as a model, we employed quantitative real-time PCR (qRT-PCR), one of the most reliable methods for gene expression analysis in many research fields, to evaluate and to determine the most reliable reference genes for pharmacogenomics and toxicogenomics studies as well as for small RNA expression analysis. We tested the transcriptional expression of five protein-coding genes as well as five non-coding genes in MCF-7 cells treated with five different pharmaceuticals or toxicants [paclitaxel (PTX), gossypol (GOS), methyl jasmonate (JAS), L-nicotine (NIC), and melamine (mela)] and analyzed the stability of the selected reference genes by four different methods: geNorm, NormFinder, BestKeeper, and the comparative ΔCt method. According to our analysis, a protein-coding gene, hTBCA and four non-coding genes, hRNU44, hRNU48, hU6, and hRNU47, appear to be the most reliable reference genes for the five chemical treatments. Similar results were also obtained in dose-response and time-course assays with gossypol (GOS) treatment. Our results demonstrated that traditionally used reference genes, such as 18s RNA, β-actin, and GAPDH, are not reliable reference genes for pharmacogenomics and toxicogenomics studies. In contrast, hTBCA and small RNAs are more stable during drug treatment, and they are better reference genes for pharmacogenomics and toxicogenomics studies. To widely use these genes as reference genes, these results should be corroborated by studies with other human cell lines and additional drugs classes and hormonal treatments.
Introduction Roles of miRNAs in human disease and miRNAs as a novel biomarker for cancer and disease diagnostics miRNAs as new targets for gene therapy Concluding remarks and future perspectives Abstract microRNAs (miRNAs) are a new class of non‐protein‐coding small RNAs, which regulate the expression of more than 30% protein‐coding genes. The unique expression profiles of different miRNAs in different types of cancers and at different stages in one cancer type suggest that miRNAs can function as novel biomarkers for disease diagnostics and may present a new strategy for miRNA gene therapy. Anti‐miRNAs and antisense oligonucleotides (ASO) have been employed to inhibit specific miRNA expression in vitro and in vivo for investigational and clinical purposes. Although miRNA‐based diagnostics and gene therapy are still in their infancy, their huge potentials will meet our need for future disease diagnostics and gene therapy. High efficient delivery of miRNAs into targeted sites, designing accurate anti‐miRNA/ASOs, and related biosafety issues are three major challenges in this field.
