Selecting RNA aptamers for synthetic biology: investigating magnesium dependence and predicting binding affinity.

The ability to generate RNA aptamers for synthetic biology using in vitro selection depends on the informational complexity (IC) needed to specify functional structures that bind target ligands with desired affinities in physiological concentrations of magnesium. We investigate how selection for high-affinity aptamers is constrained by chemical properties of the ligand and the need to bind in low magnesium. We select two sets of RNA aptamers that bind planar ligands with dissociation constants (K(d)s) ranging from 65 nM to 100 microM in physiological buffer conditions. Aptamers selected to bind the non-proteinogenic amino acid, p-amino phenylalanine (pAF), are larger and more informationally complex (i.e., rarer in a pool of random sequences) than aptamers selected to bind a larger fluorescent dye, tetramethylrhodamine (TMR). Interestingly, tighter binding aptamers show less dependence on magnesium than weaker-binding aptamers. Thus, selection for high-affinity binding may automatically lead to structures that are functional in physiological conditions (1-2.5 mM Mg(2+)). We hypothesize that selection for high-affinity binding in physiological conditions is primarily constrained by ligand characteristics such as molecular weight (MW) and the number of rotatable bonds. We suggest that it may be possible to estimate aptamer-ligand affinities and predict whether a particular aptamer-based design goal is achievable before performing the selection

Design Index for Deep Neural Networks

AbstractIn this paper, we propose a Deep Neural Networks (DNN) Design Index which would aid a DNN designer during the designing phase of DNNs. We study the designing aspect of DNNs from model-specific and data-specific perspectives with focus on three performance metrics: training time, training error and, validation error. We use a simple example to illustrate the significance of the DNN design index. To validate it, we calculate the design indices for four benchmark problems. This is an elementary work aimed at setting a direction for creating design indices pertaining to deep learning

Academic cross-pollination: The role of disciplinary affiliation in research collaboration

Academic collaboration is critical to knowledge production, especially as teams dominate scientific endeavors. Typical predictors of collaboration include individual characteristics such as academic rank or institution, and network characteristics such as a central position in a publication network. The role of disciplinary affiliation in the initiation of an academic collaboration between two investigators deserves more attention. Here, we examine the influence of disciplinary patterns on collaboration formation with control of known predictors using an inferential network model. The study group included all researchers in the Institute of Clinical and Translational Sciences (ICTS) at Washington University in St. Louis. Longitudinal data were collected on co-authorships in grants and publications before and after ICTS establishment. Exponential-family random graph models were used to build the network models. The results show that disciplinary affiliation independently predicted collaboration in grant and publication networks, particularly in the later years. Overall collaboration increased in the post-ICTS networks, with cross-discipline ties occurring more often than within-discipline ties in grants, but not publications. This research may inform better evaluation models of university-based collaboration, and offer a roadmap to improve cross-disciplinary collaboration with discipline-informed network interventions

Teaching Faulkner

Teaching Faulkner I: The Bear / Robert W. Hamblin and James B. Carothers. Yerby AuditoriumTeaching Faulkner II: Faulkner\u27s Dirt / Charles A. Pee

Teaching Faulkner

Teaching Faulkner I / James B. Carothers and Robert W. Hamblin. Yerby AuditoriumTeaching Faulkner II / Arlie Herron and Charles A. Peek. Barnard Observator

Implementation of the Exercise is Medicine (EIM)™ Referral System at Slippery Rock University

Please view abstract in the attached PDF file

Legislative strengthening meets party support in international assistance: a closer relationship?

Recent reports recommend that international efforts to help strengthen legislatures in emerging democracies should work more closely with support for building stronger political parties and competitive party systems. This article locates the recommendations within international assistance more generally and reviews the arguments. It explores problems that must be addressed if the recommendations are to be implemented effectively. The article argues that an alternative, issue-based approach to strengthening legislatures and closer links with civil society could gain more traction. However, that is directed more centrally at promoting good governance for the purpose of furthering development than at democratisation goals sought by party aid and legislative strengtheners in the democracy assistance industry