Matching Items (124)
Description
Cuticular hydrocarbons (CHCs) play a crucial role in social insect recognition systems. In this study we investigated mate choice in the red harvester ant, Pogonomyrmex barbatus. In Phoenix, this species has two lineages, J1 and J2, which look identical, but are genetically isolated. In the genetic caste determination (GCD) system workers and queens are determined by their genotype (i.e., workers develop from interlineage crosses, queens from intralineage crosses). As such, J1 and J2 lineages are dependent on each other in order for colonies to produce both workers and reproductive queens. Given their genetic isolation and interdependence, we hypothesized that the CHCs of alate males and queens are affected by lineage, and that differences in the CHC profile are used for mate recognition. We tested these hypotheses by analyzing the lineage distributions of actively mating pairs (n=65), and compared them with the overall distribution of male and female sexuals (n=180). We additionally analyzed the five most abundant CHC compounds for 20 of the actively mating P. barbatus alate male and queen pairs to determine how variable the two lineages are between each sex. We found that mating pair distributions did not significantly differ from those expected under a random mating system (�2= 1.4349, P= 0.6973), however, CHC profiles did differ between J1 and J2 lineages and sexes for the five most abundant CHC compounds. Our results show that random mating is taking place in this population, however given the differences observed in CHC profiles, mate recognition could be taking place.
ContributorsTula Del Moral Testai, Pedro Rafael (Co-author) / Cash, Elizabeth (Co-author) / Gadau, Juergen (Thesis director) / Liebig, Juergen (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
Over the last decade, the ability to track daily activity through step counting devices has undergone major changes. Advanced technologies have brought about new step counting devices and new form factors. The validity of these new devices is not fully known. The purpose of this study was to validate and compare the step counting accuracy of commercially available hip- and wrist-worn accelerometers. A total of 185 participants (18-64 years of age) were analyzed for this study, with the sample composed nearly evenly of each gender (53.5% female) and BMI classification (33% overweight, 31.9% obese). Each participant wore five devices including hip-worn Omron HJ-112 and Fitbit One, and wrist-worn Fitbit Flex, Nike Fuelband, and Jawbone UP. A range of activities (some constant among all participants, some randomly assigned) were then used to accumulate steps including walking on a hard surface for 400m, treadmill walking/running at 2mph, 3mph, and ≥5mph, walking up five flights of stairs, and walking down five flights of stairs. To validate the accuracy of each device, steps were also counted by direct observation. Results showed high concordance with directly observed steps for all devices (intraclass correlation coefficient range: 0.86 to 0.99), with hip-worn devices more accurate than wrist-worn devices. Absolute percent error values were lower among hip-worn devices and at faster walking/running speeds. Nike Fuelband consistently was the worst performing of all test devices. These results are important because as pedometers become more complex, it is important that they remain accurate throughout a variety of activities. Future directions for this research are to explore the validity of these devices in free-living settings and among younger and older populations.
ContributorsKramer, Cody Lee (Author) / Buman, Matthew (Thesis director) / Hoffner, Kristin (Committee member) / Marshall, Simon (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2014-05
Description
I tested the hypothesis that in mature colonies of the seed harvester Pogonomyrmex californicus ant species, paired pleometrotic queens would produce workers more efficiently after a massive removal of their work force than haplometrotic queens, paired pleometrotic with haplometrotic queens, and single pleometrotic queens. I suggested that the paired pleometrotic queens would have an advantage of cooperating together in reproducing more workers quicker than the other conditions to make up for the lost workers. This would demonstrate a benefit that pleometrosis has over haplometrosis for mature colonies, which would explain why pleometrosis continues for P.californicus after colony foundation. After removing all but twenty workers for every colony, I took pictures and counted the emerging brood for 52 days. Analyses showed that the paired pleometrotic queens and the haplometrotic queens both grew at an equally efficient rate and the paired pleometrotic and haplometrotic queens growing the least efficiently. However, the results were not significant and did not support the hypothesis that paired pleometrotic queens recover from worker loss more proficiently than other social systems.
ContributorsFernandez, Marisa Raquel (Author) / Fewell, Jennifer (Thesis director) / Gadau, Juergen (Committee member) / Haney, Brian (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Department of Psychology (Contributor)
Created2014-05
Description
Wolbachia is a genus of obligately intracellular bacterial endosymbionts of arthropods and nematodes, infecting up to 66% of all such species. In order to ensure its transmission, it may modify host reproduction by inducing one of four phenotypes: cytoplasmic incompatibility, feminization of genetic males, killing of male embryos, and induction of thelytokous parthenogenesis. This investigation was a characterization of the so-far unexamined Wolbachia infection of Pogonomyrmex ants. Five main questions were addressed: whether Wolbachia infection rates vary between North and South America, whether infection rates are dependent on host range, whether Wolbachia affects the caste determination of P. barbatus, whether infection rates in Pogonomyrmex are similar to those of other ants, and whether Wolbachia phylogeny parallels the phylogeny of its Pogonomyrmex hosts. Using PCR amplification of the wsp, ftsZ, and gatB loci, Wolbachia infections were detected in four of fifteen Pogonomyrmex species (26.7%), providing the first known evidence of Wolbachia infection in this genus. All infected species were from South America, specifically Argentina. Therefore, Wolbachia has no role in the caste determination of the North American species P. barbatus. Additionally, while it appears that the incidence of Wolbachia in Pogonomyrmex may be limited to South America, host range did not correlate with infection status. The incidence of Wolbachia in Pogonomyrmex as a whole was similar to that of invasive Solenopsis and Linepithema species, but not to Wasmannia auropunctata or Anoplolepis gracilipes, which retain Wolbachia infection in non-native locations. This suggests that there may be a parallel in Wolbachia infection spread in certain short-term models of species colonization and long-term models of genus radiation. Finally, there was no congruity between host and parasite phylogeny according to maximum likelihood analyses, necessarily due to horizontal transfer of Wolbachia between hosts and lateral gene transfer between Wolbachia strains within hosts.
ContributorsHarris, Alexandre Marm (Author) / Gadau, Juergen (Thesis director) / Martin, Thomas (Committee member) / Helmkampf, Martin Erik (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.
ContributorsLi, Vincent (Author) / Turaga, Pavan (Thesis director) / Buman, Matthew (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
As life expectancy increases worldwide, age related diseases are becoming greater health concerns. One of the most prevalent age-related diseases in the United States is dementia, with Alzheimer’s disease (AD) being the most common form, accounting for 60-80% of cases. Genetics plays a large role in a person’s risk of developing AD. Familial AD, which makes up less than 1% of all AD cases, is caused by autosomal dominant gene mutations and has almost 100% penetrance. Genetic risk factors are believed to make up about 49%-79% of the risk in sporadic cases. Many different genetic risk factors for both familial and sporadic AD have been identified, but there is still much work to be done in the field of AD, especially in non-Caucasian populations. This review summarizes the three major genes responsible for familial AD, namely APP, PSEN1 and PSEN2. Also discussed are seven identified genetic risk factors for sporadic AD, single nucleotide polymorphisms in the APOE, ABCA7, NEDD9, CASS4, PTK2B, CLU, and PICALM genes. An overview of the main function of the proteins associated with the genes is given, along with the supposed connection to AD pathology.
ContributorsRichey, Alexandra Emmeline (Author) / Brafman, David (Thesis director) / Raman, Sreedevi (Committee member) / School of International Letters and Cultures (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Background
The purpose of this study is to determine the feasibility of three widely used wearable sensors in research settings for 24 h monitoring of sleep, sedentary, and active behaviors in middle-aged women.
Methods
Participants were 21 inactive, overweight (M Body Mass Index (BMI) = 29.27 ± 7.43) women, 30 to 64 years (M = 45.31 ± 9.67). Women were instructed to wear each sensor on the non-dominant hip (ActiGraph GT3X+), wrist (GENEActiv), or upper arm (BodyMedia SenseWear Mini) for 24 h/day and record daily wake and bed times for one week over the course of three consecutive weeks. Women received feedback about their daily physical activity and sleep behaviors. Feasibility (i.e., acceptability and demand) was measured using surveys, interviews, and wear time.
Results
Women felt the GENEActiv (94.7 %) and SenseWear Mini (90.0 %) were easier to wear and preferred the placement (68.4, 80 % respectively) as compared to the ActiGraph (42.9, 47.6 % respectively). Mean wear time on valid days was similar across sensors (ActiGraph: M = 918.8 ± 115.0 min; GENEActiv: M = 949.3 ± 86.6; SenseWear: M = 928.0 ± 101.8) and well above other studies using wake time only protocols. Informational feedback was the biggest motivator, while appearance, comfort, and inconvenience were the biggest barriers to wearing sensors. Wear time was valid on 93.9 % (ActiGraph), 100 % (GENEActiv), and 95.2 % (SenseWear) of eligible days. 61.9, 95.2, and 71.4 % of participants had seven valid days of data for the ActiGraph, GENEActiv, and SenseWear, respectively.
Conclusion
Twenty-four hour monitoring over seven consecutive days is a feasible approach in middle-aged women. Researchers should consider participant acceptability and demand, in addition to validity and reliability, when choosing a wearable sensor. More research is needed across populations and study designs.
The purpose of this study is to determine the feasibility of three widely used wearable sensors in research settings for 24 h monitoring of sleep, sedentary, and active behaviors in middle-aged women.
Methods
Participants were 21 inactive, overweight (M Body Mass Index (BMI) = 29.27 ± 7.43) women, 30 to 64 years (M = 45.31 ± 9.67). Women were instructed to wear each sensor on the non-dominant hip (ActiGraph GT3X+), wrist (GENEActiv), or upper arm (BodyMedia SenseWear Mini) for 24 h/day and record daily wake and bed times for one week over the course of three consecutive weeks. Women received feedback about their daily physical activity and sleep behaviors. Feasibility (i.e., acceptability and demand) was measured using surveys, interviews, and wear time.
Results
Women felt the GENEActiv (94.7 %) and SenseWear Mini (90.0 %) were easier to wear and preferred the placement (68.4, 80 % respectively) as compared to the ActiGraph (42.9, 47.6 % respectively). Mean wear time on valid days was similar across sensors (ActiGraph: M = 918.8 ± 115.0 min; GENEActiv: M = 949.3 ± 86.6; SenseWear: M = 928.0 ± 101.8) and well above other studies using wake time only protocols. Informational feedback was the biggest motivator, while appearance, comfort, and inconvenience were the biggest barriers to wearing sensors. Wear time was valid on 93.9 % (ActiGraph), 100 % (GENEActiv), and 95.2 % (SenseWear) of eligible days. 61.9, 95.2, and 71.4 % of participants had seven valid days of data for the ActiGraph, GENEActiv, and SenseWear, respectively.
Conclusion
Twenty-four hour monitoring over seven consecutive days is a feasible approach in middle-aged women. Researchers should consider participant acceptability and demand, in addition to validity and reliability, when choosing a wearable sensor. More research is needed across populations and study designs.
ContributorsHuberty, Jennifer (Author) / Ehlers, Diane (Author) / Kurka, Jonathan (Author) / Ainsworth, Barbara (Author) / Buman, Matthew (Author) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2015-07-30
Validity of the Rapid Eating Assessment for Patients for assessing dietary patterns in NCAA athletes
Description
Background
Athletes may be at risk for developing adverse health outcomes due to poor eating behaviors during college. Due to the complex nature of the diet, it is difficult to include or exclude individual food items and specific food groups from the diet. Eating behaviors may better characterize the complex interactions between individual food items and specific food groups. The purpose was to examine the Rapid Eating Assessment for Patients survey (REAP) as a valid tool for analyzing eating behaviors of NCAA Division-I male and female athletes using pattern identification. Also, to investigate the relationships between derived eating behavior patterns and body mass index (BMI) and waist circumference (WC) while stratifying by sex and aesthetic nature of the sport.
Methods
Two independent samples of male (n = 86; n = 139) and female (n = 64; n = 102) collegiate athletes completed the REAP in June-August 2011 (n = 150) and June-August 2012 (n = 241). Principal component analysis (PCA) determined possible factors using wave-1 athletes. Exploratory (EFA) and confirmatory factor analyses (CFA) determined factors accounting for error and confirmed model fit in wave-2 athletes. Wave-2 athletes' BMI and WC were recorded during a physical exam and sport participation determined classification in aesthetic and non-aesthetic sport. Mean differences in eating behavior pattern score were explored. Regression models examined interactions between pattern scores, participation in aesthetic or non-aesthetic sport, and BMI and waist circumference controlling for age and race.
Results
A 5-factor PCA solution accounting for 60.3% of sample variance determined fourteen questions for EFA and CFA. A confirmed solution revealed patterns of Desserts, Healthy food, Meats, High-fat food, and Dairy. Pattern score (mean ± SE) differences were found, as non-aesthetic sport males had a higher (better) Dessert score than aesthetic sport males (2.16 ± 0.07 vs. 1.93 ± 0.11). Female aesthetic athletes had a higher score compared to non-aesthetic female athletes for the Dessert (2.11 ± 0.11 vs. 1.88 ± 0.08), Meat (1.95 ± 0.10 vs. 1.72 ± 0.07), High-fat food (1.70 ± 0.08 vs. 1.46 ± 0.06), and Dairy (1.70 ± 0.11 vs. 1.43 ± 0.07) patterns.
Conclusions
REAP is a construct valid tool to assess dietary patterns in college athletes. In light of varying dietary patterns, college athletes should be evaluated for healthful and unhealthful eating behaviors.
Athletes may be at risk for developing adverse health outcomes due to poor eating behaviors during college. Due to the complex nature of the diet, it is difficult to include or exclude individual food items and specific food groups from the diet. Eating behaviors may better characterize the complex interactions between individual food items and specific food groups. The purpose was to examine the Rapid Eating Assessment for Patients survey (REAP) as a valid tool for analyzing eating behaviors of NCAA Division-I male and female athletes using pattern identification. Also, to investigate the relationships between derived eating behavior patterns and body mass index (BMI) and waist circumference (WC) while stratifying by sex and aesthetic nature of the sport.
Methods
Two independent samples of male (n = 86; n = 139) and female (n = 64; n = 102) collegiate athletes completed the REAP in June-August 2011 (n = 150) and June-August 2012 (n = 241). Principal component analysis (PCA) determined possible factors using wave-1 athletes. Exploratory (EFA) and confirmatory factor analyses (CFA) determined factors accounting for error and confirmed model fit in wave-2 athletes. Wave-2 athletes' BMI and WC were recorded during a physical exam and sport participation determined classification in aesthetic and non-aesthetic sport. Mean differences in eating behavior pattern score were explored. Regression models examined interactions between pattern scores, participation in aesthetic or non-aesthetic sport, and BMI and waist circumference controlling for age and race.
Results
A 5-factor PCA solution accounting for 60.3% of sample variance determined fourteen questions for EFA and CFA. A confirmed solution revealed patterns of Desserts, Healthy food, Meats, High-fat food, and Dairy. Pattern score (mean ± SE) differences were found, as non-aesthetic sport males had a higher (better) Dessert score than aesthetic sport males (2.16 ± 0.07 vs. 1.93 ± 0.11). Female aesthetic athletes had a higher score compared to non-aesthetic female athletes for the Dessert (2.11 ± 0.11 vs. 1.88 ± 0.08), Meat (1.95 ± 0.10 vs. 1.72 ± 0.07), High-fat food (1.70 ± 0.08 vs. 1.46 ± 0.06), and Dairy (1.70 ± 0.11 vs. 1.43 ± 0.07) patterns.
Conclusions
REAP is a construct valid tool to assess dietary patterns in college athletes. In light of varying dietary patterns, college athletes should be evaluated for healthful and unhealthful eating behaviors.
ContributorsKurka, Jonathan (Author) / Buman, Matthew (Author) / Ainsworth, Barbara (Author) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2014-08-15
Description
Cell viability is an important assessment in cell culture to characterize the health of the cell population and confirm if cells are alive. Morphology or end-line assays are used to determine cell viability of entire populations. Intracellular pO2 levels is indicative of cell health and metabolism that can be used as a factor to asses cell viability in an in-line assay. Siloxane based pO2 sensing nanoprobes present a modality to visualize intracellular pO2. Using fluorescent lifetime imaging microscopy (FLIM), pO2 levels can be mapped intracellular as a highly functional in-line assay for cell viability. FLIM is an imaging modality that reconstructs an image based of its fluorescent lifetime. Nanoprobes were synthesized in different manufacturing/storage conditions. The nanoprobes for both long- and short-term storage were characterized in a cell free environment testing for changes in fluorescent intensity, average and maximum nanoprobe diameter. The nanoprobes were validated in two different culture systems, 2D and microcarrier culture systems, for human derived neural progenitor cells (NPCs) and neurons. Long- and short-term storage nanoprobes were used to label different neuronal based culture systems to asses labeling efficiency through fluorescent microscopy and flow cytometry. NPCs and neurons in each culture system was tested to see if nanoprobe labeling effected cellular phenotype for traits such as: cell proliferation, gene expression, and calcium imaging. Long-term and short-term storage nanoprobes were successfully validated for both NPCs and neurons in all culture systems. Assessments of the pO2 sensing nanoprobes will be further developed to create a highly functional and efficient in-line test for cell viability.
ContributorsLeyasi, Salma (Author) / Brafman, David (Thesis director) / Kodibagkar, Vikram (Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Introduction: There is currently a lack of industry-wide gold standardization in accelerometer study
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear
time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout
detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three
experimental values each being tested. The main experimental variable Sleepwindow controls the
amount of time before and after a determined bout of sleep that is searched for additional sedentary
time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep
diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed
(TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both.
Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-,
and 30-minute window, a 15-minute window incurred the least error and most agreement to
comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary
to expectation, corrections for snooze, latency, and both did not substantially improve agreement to
PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both,
suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and
wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a
sleep window variable yields substantial variety in results. A 15-minute window appears best at
determining START while a 5-minute window appears best for END and TIB. Further investigation on
the optimal window length per demographic and condition is required.
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear
time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout
detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three
experimental values each being tested. The main experimental variable Sleepwindow controls the
amount of time before and after a determined bout of sleep that is searched for additional sedentary
time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep
diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed
(TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both.
Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-,
and 30-minute window, a 15-minute window incurred the least error and most agreement to
comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary
to expectation, corrections for snooze, latency, and both did not substantially improve agreement to
PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both,
suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and
wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a
sleep window variable yields substantial variety in results. A 15-minute window appears best at
determining START while a 5-minute window appears best for END and TIB. Further investigation on
the optimal window length per demographic and condition is required.
ContributorsMartin, Logan Rhett (Author) / Buman, Matthew (Thesis director) / Toledo, Meynard John (Committee member) / Kurka, Jonathan (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12