School of Nursing Class of 1976

Top Row: Peggy Alford, Anne Allen, Barbara Amann, Joann Baker, Elizabeth Bando, Sara Barnes, Mary Barz, Anne Bauer, Patricia Baxter, Gwendolyn Beaudette, Nancy Bidigare, Marian Bier, Susan Blaszczyk, Jacqueline bolin, Anne Bostwick, Heather Brendel, Theresa Brisker, Rosalyn Cage, Jeanne Calhoun

Row 2: Karen Caputo, Carolyn Carpenter, Laura Lewellen, Catherine Pearson, Pamela Carr, Lisa Clark

Row 3: Sarah Cleland, Kathy Collar, Ann Connors, Marian Coppo, Martha Coppo, Debra Wirth, Kathleen Coughlin, Mayble Craig, Jennifer Crittenden, Linda Dean, Ann Dika

Row 4: Shira Doneson, Valerie Dray, Lee Duffey, Mary Dunn, Nancy Edwards, Mildred Jett, Patricia Johnson, Jan Walters, Karen Dimitroff, Debra Walton, Moira Stein, Madi Ehrenreich, Paula Elliott, Claudia Evans, Jolaync Farrell, Karen Fierke

Row 5: Debra Finch, Nadine Furlong, Susan Gamerman, Anita Gardocki, Marcia Gerpheide, Roberta Gies, Deborah Glotzhober, Marlene Golabeck, Janet Goldberg, Rene Green, Diana Greer, Susan Gross, Vivian Hall, Jill Hallead

Row 6: Sharon Hamlett, Tamara Hanson, Jane Harper, Jesusa Heilig, Steinunn Hermannsson, Susan Hicks, Karen Hillebrand, Jomatia Hoff, Michelle Howey, Holly Howieson, Sandra Hubar, Kathleen Hughes, Shirley Jvery, Laura Johnson, Susan Johnson, Shirley Jones, Judith Kellermier, Lynda Kitchen, Susan Kleinbeck

Row 7: Nanette Kotz, Kathleen Kroh, Judith Krohn, Catherine Lahti, Mary Lange, Patti Larson, Susan Leach, Rebecca Linn, Lacy Loomis, Francene Lundy, Sue Lymperis, Robyn Main, Patricia McCleary, Theresa McGowan, Elizabeth Messiter, Mary Miller, Nancy Moffatt, Catherine Munn, Karen Munson

Row 8: Virginia Newman, Laura Novak, Thomas O'Connell, Julie O'Connor, Kaathleen O'Hara, Kimberly O'Loughlin, Karen Olsen, Marcy Ouellette, Gail Park, Georgiana Parsell, Mary Patchak, Linda Pearsall, Kathleen Poage, Shelly Ponte, Thomas Parter, Marilyn Pratt, Karen Prince, Kathryn Procter, Rebecca Raymond

Row 9: Jill remter, Cheryl Ricca, Brenda Robinson, Karen Rollins, Lisa Root, Audrey Ross, Barbara Rutherford, Linda Rykwalder, Margaret Sampson, Sherril Santo, Jeanne Scheer, Kathy Schlichter, Nancy Schuman, Debra Sihtala, Michele Smit, Donna Smith, Bonnic Smrcka, Janine Speck, Elizabeth Stainsby

Row 10: Grace Steinaway, Jennifer Stinson, Sally Stone, Anne Sullivan, Barbara Tonak, Linda Towers, Cindy Tremblay, Gregory Trowbridge, Sandra Tucker, Debbie Ullrich, Lee Ann Van Houten, Pamela Waggener, Martha Walker, Michele Wenderski, Catherine West, Harriet Wilkinson, Diane Willis, Jan Winslow, Karen Wismer

Allen Steckle, University of Michigan football, 1899

(cropped from 1899 team photo)

The association between objectively measured neighborhood features and walking in middle-aged adults

Purpose: To explore the role of the neighborhood environment in supporting walking Design: Cross sectional study of 10,286 residents of 200 neighborhoods. Participants were selected using a stratified two-stage cluster design. Data were collected by mail survey (68.5% response rate). Setting: The Brisbane City Local Government Area, Australia, 2007. Subjects: Brisbane residents aged 40 to 65 years. Measures Environmental: street connectivity, residential density, hilliness, tree coverage, bikeways, and street lights within a one kilometer circular buffer from each resident’s home; and network distance to nearest river or coast, public transport, shop, and park. Walking: minutes in the previous week categorized as < 30 minutes, ≥ 30 < 90 minutes, ≥ 90 < 150 minutes, ≥ 150 < 300 minutes, and ≥ 300 minutes. Analysis: The association between each neighborhood characteristic and walking was examined using multilevel multinomial logistic regression and the model parameters were estimated using Markov chain Monte Carlo simulation. Results: After adjustment for individual factors, the likelihood of walking for more than 300 minutes (relative to <30 minutes) was highest in areas with the most connectivity (OR=1.93, 99% CI 1.32-2.80), the greatest residential density (OR=1.47, 99% CI 1.02-2.12), the least tree coverage (OR=1.69, 99% CI 1.13-2.51), the most bikeways (OR=1.60, 99% CI 1.16-2.21), and the most street lights (OR=1.50, 99% CI 1.07-2.11). The likelihood of walking for more than 300 minutes was also higher among those who lived closest to a river or the coast (OR=2.06, 99% CI 1.41-3.02). Conclusion: The likelihood of meeting (and exceeding) physical activity recommendations on the basis of walking was higher in neighborhoods with greater street connectivity and residential density, more street lights and bikeways, closer proximity to waterways, and less tree coverage. Interventions targeting these neighborhood characteristics may lead to improved environmental quality as well as lower rates of overweight and obesity and associated chromic disease.

The Large Allen Cognitive Level Screen as an indicator for medication adherence among adults accessing community mental health services

Background: Medication remains the cornerstone treatment for mental illness. Cognition is one of the strongest predictors of non-adherence. The aim of this preliminary investigation was to examine the association between the Large Allen Cognitive Level Screen (LACLS) and medication adherence among a small sample of mental health service users to determine whether the LACLS has potential as a screening tool for capacity to manage medication regimens. Method: Demographic and clinical information was collected from a small sample of people who had recently accessed community mental health services. Participants then completed the LACLS and the Medication Adherence Rating Scale (MARS) at a single time point. The strength of association between the LACLS and MARS was examined using Spearman rank-order correlation. Results: A strong positive correlation between the LACLS and medication adherence (r = 0.71, p = 0.01) was evident. No participants reported the use of medication aids despite evidence of impaired cognitive functioning. Conclusion: This investigation has provided the first empirical evidence indicating that the LACLS may have utility as a screening instrument for capacity to manage medication adherence among this population. While promising, this finding should be interpreted with caveats given its preliminary nature.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. 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Host rather than graft origin of Matrigel-induced adipose tissue in the murine tissue-engineering chamber

We have recently shown that Matrigel-filled chambers containing fibroblast growth factor-2 (FGF2) and placed around an epigastric pedicle in the mouse were highly adipogenic. Contact of this construct with pre-existing tissue or a free adipose graft was required. To further investigate the mechanisms underpinning formation of new adipose tissue, we seeded these chambers with human adipose biopsies and human adipose-derived cell populations in severe combined immunodeficient mice and assessed the origin of the resultant adipose tissue after 6 weeks using species-specific probes. The tissues were negative for human-specific vimentin labeling, suggesting that the fat originates from the murine host rather than the human graft. This was supported by the strong presence of mouse-specific Cot-1 deoxyribonucleic acid labeling, and the absence of human Cot-1 labeling in the new fat. Even chambers seeded with FGF2/Matrigel containing cultured human stromal-vascular fraction (SVF) labeled strongly only for human vimentin in cells that did not have a mature adipocyte phenotype; the newly formed fat tissue was negative for human vimentin. These findings indicate that grafts placed in the chamber have an inductive function for neo-adipogenesis, rather than supplying adipocyte-precursor cells to generate the new fat tissue, and preliminary observations implicate the SVF in producing inductive factors. This surprising finding opens the door for refinement of current adipose tissue-engineering approaches.