The Six Word Memoir as Teaching Tool

This exercise takes place on the final day of class in my Business Communication course. The semester has been devoted, largely, to the idea that the fewer words used in business communication, the better. I use Coco Chanel\u27s quote about accessories- Never add, always remove -as a guideline for composing and editing both writing and speech. The goal is to get students to realize that, in almost all business communication, less is more, especially in today\u27s business world where much communication takes place electronically. But students also come to realize that less is more work , and that concision and brevity are more difficult to come by than long, effusive communication

Seeing the Results of a Mutation With a Vertex Weighted Hierarchical Graph

We represent the protein structure of scTIM with a graph-theoretic model. We construct a hierarchical graph with three layers - a top level, a midlevel and a bottom level. The top level graph is a representation of the protein in which its vertices each represent a substructure of the protein. In turn, each substructure of the protein is represented by a graph whose vertices are amino acids. Finally, each amino acid is represented as a graph where the vertices are atoms. We use this representation to model the effects of a mutation on the protein. Methods: There are 19 vertices (substructures) in the top level graph and thus there are 19 distinct graphs at the midlevel. The vertices of each of the 19 graphs at the midlevel represent amino acids. Each amino acid is represented by a graph where the vertices are atoms in the residue structure. All edges are determined by proximity in the protein\u27s 3D structure. The vertices in the bottom level are labelled by the corresponding molecular mass of the atom that it represents. We use graph-theoretic measures that incorporate vertex weights to assign graph based attributes to the amino acid graphs. The attributes of the corresponding amino acids are used as vertex weights for the substructure graphs at the midlevel. Graph-theoretic measures based on vertex weighted graphs are subsequently calculated for each of the midlevel graphs. Finally, the vertices of the top level graph are weighted with attributes of the corresponding substructure graph in the midlevel. Results: We can visualize which mutations are more influential than others by using properties such as vertex size to correspond with an increase or decrease in a graph-theoretic measure. Global graph-theoretic measures such as the number of triangles or the number of spanning trees can change as the result. Hence this method provides a way to visualize these global changes resulting from a small, seemingly inconsequential local change. Conclusions: This modelling method provides a novel approach to the visualization of protein structures and the consequences of amino acid deletions, insertions or substitutions and provides a new way to gain insight on the consequences of diseases caused by genetic mutations

Rediscovery of Cicindela scabrosa floridana Cartwright (Coleoptera: Cicindelidae) and its elevation to species level

First discovered in 1934 and described as a variety of Cicindela abdominalis Fabricius (Coleoptera: Cicindelidae), the form floridana, to our knowledge, has not been recollected until we discovered it in 2007, south of the presumed type locality. From our examination of the type specimen, eight paratypes and 40 specimens from the new locality and additional study, we reinterpreted its status to be a full species. This interpretation is based on distinctive and consistent differences from the closely related Cicindelidia scabrosa (Schaupp). These differences include morphology (maculation, color and elytral microsculpture), distribution, habitat, and seasonality. We present here a more detailed description of this species within the genus Cicindelidia Rivalier, following Rivalier and Wiesner becoming Cicindelidia floridana (Cartwright) new combination

Polycystic Ovarian Syndrome

This is an educational poster outlining the topic of Poly Cystic Ovarian Syndrome (PCOS). This is an endocrine disorder that affects women in their childbearing years which can result in fertility issues, obesity, insulin resistance, and the development of cysts on the ovaries. The poster covers the signs and symptoms, a case study, statistics, etiology, implications for nursing, and treatment. The main objective of this poster is to discuss the pathophysiology of PCOS and its significance to the affected population

Rediscovery and status of Cylindera (s. str.) lunalonga (Schaupp, 1884) (Coleoptera: Carabidae: Cicindelinae) in the San Joaquin Valley of California with a comparison to a Sierra Nevada population

Surveys for adult Cylindera (s. str.) lunalonga (Schaupp) (Coleoptera: Carabidae: Cicindelinae) were conducted between 2001 and 2011 at over 80 sites throughout the species’ historic range in the San Joaquin Valley and Sierra Nevada Mountains of California. Previously considered extirpated from the Valley, these surveys resulted in finding adults at 18 sites, several with large populations (>50 individuals). As suggested by historic records, our studies confirmed that the Valley populations of Cy. lunalonga occur in what were historically wetland sites, but are now lowland agricultural croplands. Adults were always associated with wet, muddy soil within and along the edges of irrigation ditches. A comparison of morphology, behavior, habitat, and conservation is made with the only known extant Sierra Nevada population

A Predictive Model for Secondary RNA Structure Using Graph Theory and a Neural Network

Background: Determining the secondary structure of RNA from the primary structure is a challenging computational problem. A number of algorithms have been developed to predict the secondary structure from the primary structure. It is agreed that there is still room for improvement in each of these approaches. In this work we build a predictive model for secondary RNA structure using a graph-theoretic tree representation of secondary RNA structure. We model the bonding of two RNA secondary structures to form a larger secondary structure with a graph operation we call merge. We consider all combinatorial possibilities using all possible tree inputs, both those that are RNA-like in structure and those that are not. The resulting data from each tree merge operation is represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not, based on the merge data vector. The network estimates the probability of a tree being RNA-like.Results: The network correctly assigned a high probability of RNA-likeness to trees previously identified as RNA-like and a low probability of RNA-likeness to those classified as not RNA-like. We then used the neural network to predict the RNA-likeness of the unclassified trees.Conclusions: There are a number of secondary RNA structure prediction algorithms available online. These programs are based on finding the secondary structure with the lowest total free energy. In this work, we create a predictive tool for secondary RNA structures using graph-theoretic values as input for a neural network. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel and is an entirely different approach to the prediction of secondary RNA structures. Our method correctly predicted trees to be RNA-like or not RNA-like for all known cases. In addition, our results convey a measure of likelihood that a tree is RNA-like or not RNA-like. Given that the majority of secondary RNA folding algorithms return more than one possible outcome, our method provides a means of determining the best or most likely structures among all of the possible outcomes

My Path to Advanced Practice

https://scholarworks.umt.edu/grad_portfolios/1323/thumbnail.jp

Libraries from Libraries Approach to the Synthesis of Arylidene Oxindoles

A libraries from libraries combinatorial chemistry approach was employed to synthesize fluorinated derivatives of both oxindoles and isatins as potential pharmaceuticals or targeting agents for imaging purposes related to cancer or Alzheimer\u27s disease. Synthesis for these fluorinated derivatives are described by routes involving, either: a) N-alkylation of 5-substituted isatins followed by Wolff-Kishner reduction to the corresponding oxindoles and final Knoevenagel condensation with aryl aldehydes, or; b) Wolff-Kishner reduction of the isatins followed by condensation and finishing with the N-alkylation of the aldol products. In specific cases, a click reaction followed the N-alkylation of the aldol products to form the isatin 1,2,3-triazole which could be utilized to perform radiochemistry with a [18F]-radiolabel for the imaging of cancer. The strategy for the synthesis of such potential inhibitors was guided by SAR studies of peptide based inhibitors, as well as small-molecule inhibitors based upon the isatins scaffold. Previously, it was shown increasing functionality by adding 3 points of variability with the incorporation of an electron-withdrawing group such as a chlorine atom at the C-5 position allowed for increased potency of the oxindole derived inhibitors. Herein, a library of arylidene oxindoles was synthesized utilizing 3 points of variability with the incorporation of the electron-withdrawing group fluorine. Furthermore, a novel alternative synthesis was established for the creation of arylidene oxindoles which allowed for increased functionality through the incorporation of N-propargyl inhibitors. Finally, the ability to create N-propargyl compounds lead to the synthesis of isatin 1,2,3-triazoles was also explored for the possibility as potential imaging agents for cancer