7 results on '"Katherine P. Ingram"'
Search Results
2. Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation
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Afnan Ahmed Crystal, Maria Valero, Valentina Nino, and Katherine H. Ingram
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diabetes ,food image recognition ,convolutional neural networks ,mobile vision ,glucose monitoring ,Chemical technology ,TP1-1185 - Abstract
Diabetes has emerged as a worldwide health crisis, affecting approximately 537 million adults. Maintaining blood glucose requires careful observation of diet, physical activity, and adherence to medications if necessary. Diet monitoring historically involves keeping food diaries; however, this process can be labor-intensive, and recollection of food items may introduce errors. Automated technologies such as food image recognition systems (FIRS) can make use of computer vision and mobile cameras to reduce the burden of keeping diaries and improve diet tracking. These tools provide various levels of diet analysis, and some offer further suggestions for improving the nutritional quality of meals. The current study is a systematic review of mobile computer vision-based approaches for food classification, volume estimation, and nutrient estimation. Relevant articles published over the last two decades are evaluated, and both future directions and issues related to FIRS are explored.
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- 2024
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3. Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study
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Maria Valero, Priyanka Pola, Oluwaseyi Falaiye, Katherine H Ingram, Liang Zhao, Hossain Shahriar, and Sheikh Iqbal Ahamed
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Medicine - Abstract
BackgroundDiabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer. However, this process is invasive and painful, and leads to a risk of infection. Therefore, there is an urgent need for noninvasive, inexpensive, novel platforms for continuous blood sugar monitoring. ObjectiveOur study aimed to describe a pilot test to test the accuracy of a noninvasive glucose monitoring prototype that uses laser technology based on near-infrared spectroscopy. MethodsOur system is based on Raspberry Pi, a portable camera (Raspberry Pi camera), and a visible light laser. The Raspberry Pi camera captures a set of images when a visible light laser passes through skin tissue. The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy. ResultsWhen using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement. ConclusionsOur pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population.
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- 2022
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4. Locating unregistered and unreported data for use in a social science systematic review and meta-analysis
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Joshua R. Polanin, Dorothy L. Espelage, Jennifer K. Grotpeter, Alberto Valido, Katherine M. Ingram, Cagil Torgal, America El Sheikh, and Luz E. Robinson
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Medicine - Abstract
Abstract Meta-analysts rely on the availability of data from previously conducted studies. That is, they rely on primary study authors to register their outcome data, either in a study’s text or on publicly available websites, and report the results of their work, either again in a study’s text or on publicly accessible data repositories. If a primary study author does not register data collection and similarly does not report the data collection results, the meta-analyst is at risk of failing to include the collected data. The purpose of this study is to attempt to locate one type of meta-analytic data: findings from studies that neither registered nor reported the collected outcome data. To do so, we conducted a large-scale search for potential studies and emailed an author query request to more than 600 primary study authors to ask if they had collected eligible outcome data. We received responses from 75 authors (12.3%), three of whom sent eligible findings. The results of our search confirmed our proof of concept (i.e., that authors collect data but fail to register or report it publicly), and the meta-analytic results indicated that excluding the identified studies would change some of our substantive conclusions. Cost analyses indicated, however, a high price to finding the missing studies. We end by reaffirming our calls for greater adoption of primary study pre-registration as well as data archiving in publicly available repositories.
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- 2020
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5. Family Violence, Sibling, and Peer Aggression During Adolescence: Associations With Behavioral Health Outcomes
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Katherine M. Ingram, Dorothy L. Espelage, Jordan P. Davis, and Gabriel J. Merrin
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bullying ,substance (drug) abuse ,peer deviance ,childhood trauma and adversity ,adverse child experiences ,aggressive behavior ,Psychiatry ,RC435-571 - Abstract
Bullying and sibling aggression can appear as similar behavior, though the latter is comparatively understudied. Aligned with the Theory of Intergenerational Transmission of Violence, research suggests that exposure to family violence increases an individual's risk for perpetrating violence in their own future relationships. Additionally, Problem Behavior Theory suggests that engaging in one problem behavior (e.g., bullying) increases the likelihood of engaging in other problem behavior (e.g., substance use). In Phase 1, this study of middle school students from the U.S. examined how exposure to family violence predicted membership in latent classes of bullying and sibling aggression perpetration (N = 894, sampled from four middle schools). In Phase 2, we used mixture modeling to understand how latent classes of family violence, sibling aggression, and bullying predict future substance use, mental health outcomes, and deviance behavior later in high school. Results yielded four profiles of peer and sibling aggression: high all, high sibling aggression, high peer aggression, and low all aggression. Youth who reported witnessing more family violence at home were significantly more likely to fall into the sibling aggression only and high all classes, compared to the low all class. Phase 2 results also yielded four classes: a high all class, a sibling aggression and family violence class, a peer aggression class, and a low all class. Individuals in the high all class were more likely to experience several unfavorable outcomes (substance use, depression, delinquency) compared to other classes. This study provides evidence for pathways from witnessing violence, to perpetrating aggression across multiple contexts, to developing other deleterious mental and behavioral health outcomes. These findings highlight the negative impact family violence can have on child development, providing support for a cross-contextual approach for programming aimed at developing relationships skills.
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- 2020
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6. Small mammals exhibit limited spatiotemporal structure in Sierra Nevada forests
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Peter A. Stine, Jaya R. Smith, Seth W. Bigelow, Dirk H. Van Vuren, Ryan D. Burnett, Brett R. Jesmer, Michael L. Johnson, James A. Wilson, Katherine P. Ingram, Robin J. Innes, and Douglas A. Kelt
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Ecology ,Forest management ,Logging ,Species diversity ,Geography ,Habitat ,Disturbance (ecology) ,Genetics ,Secondary forest ,Animal Science and Zoology ,Species richness ,Transect ,Ecology, Evolution, Behavior and Systematics ,Nature and Landscape Conservation - Abstract
Forests in the Sierra Nevada, similar to those across the continent, have been substantially altered by logging, fire exclusion, and other human activities. Current forest management emphasizes maintenance or restoration of resiliency in the face of contemporary disturbance factors that include wildfire, climate change, continued urbanization, and invasive species. We evaluated responses of small mammals to forest management by monitoring a series of 12 replicate trapping grids in compositionally homogeneous forest over 8 years, and implemented 2 levels of canopy thinning. Livetrapping efforts (119,712 trap-nights) yielded 15,613 captures of 2,305 individuals of 13 species, and although forest structure was significantly influenced by canopy treatments, small mammal numbers and assemblage composition were not. To better understand this we assessed habitat associations of small mammals at 599 census points on 75 transects established in a stratified random manner throughout Plumas National Forest....
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- 2013
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7. Farmer Perceptions and Behaviors Related to Wildlife and On-Farm Conservation Actions
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Sara M. Kross, Meredith T. Niles, Rachael Long, and Katherine P. Ingram
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0106 biological sciences ,Ecology ,business.industry ,Agroforestry ,Environmental resource management ,Wildlife ,Land management ,Pest control ,04 agricultural and veterinary sciences ,010603 evolutionary biology ,01 natural sciences ,Ecosystem services ,Outreach ,Geography ,Habitat ,Agriculture ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,business ,Ecology, Evolution, Behavior and Systematics ,Nature and Landscape Conservation ,Wildlife conservation - Abstract
Policy makers are increasingly encouraging farmers to protect or enhance habitat on their farms for wildlife conservation. However, a lack of knowledge of farmers’ opinions toward wildlife can lead to poor integration of conservation measures. We surveyed farmers to assess their perceptions of ecosystem services and disservices from perching birds, raptors, and bats—three taxa commonly targeted by conservation measures. The majority of farmers thought that perching birds and bats were beneficial for insect pest control and that raptors were beneficial for vertebrate pest control; however, fruit farmers viewed perching birds more negatively than did farmers growing other crops. Farmers using organic methods viewed all three wildlife groups more positively than conventional farmers. Farmer perception toward each wildlife group predicted their action to either attract or deter those taxa, suggesting the need to focus research and outreach on the effects of wildlife on farms for conservation programs to positively influence farmer perceptions.
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- 2017
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