Matching Items (124)
Description
In vitro rearing is an important and useful tool for honey bee (Apis mellifera L.) studies. However, it often results in intercastes between queens and workers, which are normally are not seen in hive-reared bees, except when larvae older than three days are grafted for queen rearing. Morphological classification (queen versus worker or intercastes) of bees produced by this method can be subjective and generally depends on size differences. Here, we propose an alternative method for caste classification of female honey bees reared in vitro, based on weight at emergence, ovariole number, spermatheca size and size and shape, and features of the head, mandible and basitarsus. Morphological measurements were made with both traditional morphometric and geometric morphometrics techniques. The classifications were performed by principal component analysis, using naturally developed queens and workers as controls. First, the analysis included all the characters. Subsequently, a new analysis was made without the information about ovariole number and spermatheca size. Geometric morphometrics was less dependent on ovariole number and spermatheca information for caste and intercaste identification. This is useful, since acquiring information concerning these reproductive structures requires time-consuming dissection and they are not accessible when abdomens have been removed for molecular assays or in dried specimens. Additionally, geometric morphometrics divided intercastes into more discrete phenotype subsets. We conclude that morphometric geometrics are superior to traditional morphometrics techniques for identification and classification of honey bee castes and intermediates.
ContributorsDe Souza, Daiana A. (Author) / Wang, Ying (Author) / Kaftanoglu, Osman (Author) / De Jong, David (Author) / Amdam, Gro (Author) / Goncalves, Lionel S. (Author) / Francoy, Tiago M. (Author) / College of Liberal Arts and Sciences (Contributor)
Created2015-04-20
Description
Breast cancer is the leading cause of cancer-related deaths of women in the united states. Traditionally, Breast cancer is predominantly treated by a combination of surgery, chemotherapy, and radiation therapy. However, due to the significant negative side effects associated with these traditional treatments, there has been substantial efforts to develop alternative therapies to treat cancer. One such alternative therapy is a peptide-based therapeutic cancer vaccine. Therapeutic cancer vaccines enhance an individual's immune response to a specific tumor. They are capable of doing this through artificial activation of tumor specific CTLs (Cytotoxic T Lymphocytes). However, in order to artificially activate tumor specific CTLs, a patient must be treated with immunogenic epitopes derived from their specific cancer type. We have identified that the tumor associated antigen, TPD52, is an ideal target for a therapeutic cancer vaccine. This designation was due to the overexpression of TPD52 in a variety of different cancer types. In order to start the development of a therapeutic cancer vaccine for TPD52-related cancers, we have devised a two-step strategy. First, we plan to create a list of potential TPD52 epitopes by using epitope binding and processing prediction tools. Second, we plan to attempt to experimentally identify MHC class I TPD52 epitopes in vitro. We identified 942 potential 9 and 10 amino acid epitopes for the HLAs A1, A2, A3, A11, A24, B07, B27, B35, B44. These epitopes were predicted by using a combination of 3 binding prediction tools and 2 processing prediction tools. From these 942 potential epitopes, we selected the top 50 epitopes ranked by a combination of binding and processing scores. Due to the promiscuity of some predicted epitopes for multiple HLAs, we ordered 38 synthetic epitopes from the list of the top 50 epitope. We also performed a frequency analysis of the TPD52 protein sequence and identified 3 high volume regions of high epitope production. After the epitope predictions were completed, we proceeded to attempt to experimentally detected presented TPD52 epitopes. First, we successful transduced parental K562 cells with TPD52. After transduction, we started the optimization process for the immunoprecipitation protocol. The optimization of the immunoprecipitation protocol proved to be more difficult than originally believed and was the main reason that we were unable to progress past the transduction of the parental cells. However, we believe that we have identified the issues and will be able to complete the experiment in the coming months.
ContributorsWilson, Eric Andrew (Author) / Anderson, Karen (Thesis director) / Borges, Chad (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.
ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Major Depressive Disorder (MDD) is a common mental disorder that can affect individuals at nearly every stage of life. Women are especially vulnerable to MDD in part, from ovarian hormone level fluctuations. In this thesis, I focused on MDD using a rat model in middle-age to explore potential sex differences in response to a corticosterone (CORT) – induced depressive-like state. Estradiol (E2), a naturally occurring steroid sex hormone in humans and rats, is implicated in mood changes, which is especially prominent during the menopause transition. CORT, a stress hormone, was used to create a depressive-like state in middle-aged female (F) and male (M) rats with their gonads surgically removed. This produced the following independent treatment groups: Sex (F, M), CORT (vehicle = V ml/kg, C 40mg/kg), E2 (V 0.1 ml, E 0.3µg/0.1ml). CORT and E2 injections were injected daily, s.c) for 7 days before behavioral testing began and continued throughout the study when behavior was assessed. For my honor’s thesis, I focused on the social interaction test and elevated plus maze to investigate whether CORT enhanced social avoidance and anxiety, and whether E2 mitigated the CORT effects. In the social interaction test, three new behaviors were assessed (interacting, grooming, and immobility) to better understand exploratory and anxiety profiles of the rats, and these behaviors were quantified over two 5-minute periods in the 10-minute trial. These new quantifications showed that for the female rats, C+E and V+V enhanced the interaction with the novel rat significantly more than an inanimate object, which was not observed in the females given CORT only or E2 only. The males in all conditions showed a significant preference for side with the novel rat compared to the object, however no treatment differences were observed. In both sexes, the overall time spent interacting decreased in the second five minutes of quantification compared to the first five minutes. No effects were observed with grooming or immobility, in part from the high variability across rats. For EPM, female rats treated with CORT and E2 exhibited a lower anxiety index than compared to female rats given CORT only, indicating that E2 mitigated the depressive-like effects of CORT. Males showed no CORT or E2 effects. The result in part supported my hypothesis, as the CORT-treated females exhibited reduced socialization and E2 improved socialization in CORT-treated females, as this was seen in the F-C-E group. Interestingly, CORT failed to produce a depressive-like effect in males in both behavioral tests, which was an unexpected outcome. These results suggest that administration of E2 with CORT mitigated the depressive-like state created by CORT in female rats, however failed to produce these outcomes in males. The outcome of this work will give us insight into the potential mechanisms that may contribute to sex differences with MDD.
ContributorsSladkova, Sara (Author) / Conrad, Cheryl (Thesis director) / Amdam, Gro (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Life Sciences (Contributor)
Created2024-05