Senior Capstone Lecture Recital: Hannah Howard, viola

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Arts in Music. Miss Howard studies viola with Allyson Fleck.https://digitalcommons.kennesaw.edu/musicprograms/1231/thumbnail.jp

Senior Recital: Katie Baumgarten, viola

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Performance. Ms. Baumgarten studies viola with Cathy Lynn.https://digitalcommons.kennesaw.edu/musicprograms/1126/thumbnail.jp

Norepinephrine and Corticosterone in the neoCLOM Animal Model of Obsessive Compulsive Disorder: Effects of Treatment and Sex

This study examined a novel animal model of OCD, the neoCLOM model, in which rats are treated twice daily from postnatal Days 9-16 with 15 mg/kg of the serotonin-norepinephrine uptake inhibitor clomipramine. Results showed there was an effect of neonatal TREATMENT on levels of norepinephrine (NE) measured from micropunches of post-mortem brain tissue using High Performance Liquid Chromatography. Compared to control males, neoCLOM males had higher levels of NE in the amygdala and the lateral thalamus. Compared to control females, neoCLOM females had higher levels of NE in the motor cortex, orbitofrontal cortex, and the hypothalamus. There was also an effect of SEX. Versus control males, control females had higher levels of NE in the lateral thalamus, ventral striatum, and anterior cingulate cortex. Conversely, levels of NE in the hypothalamus were lower in the control females versus males. Compared to neoCLOM males, neoCLOM females had higher levels of NE in the prefrontal cortex and the motor cortex. SEX (but not treatment) had a significant effect on corticosterone levels (rat analog of cortisol) in post-mortem trunk blood. The current finding that the elevation of NE evidenced in OCD was mirrored by increased levels of NE in brain structures of the neoCLOM rats adds support for the validity of this new animal model.https://orb.binghamton.edu/research_days_posters_spring2020/1075/thumbnail.jp

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Scaling Up Local Food Sourcing: a Multi-Campus Farm to College Pilot (2015)

Presented at the Northeast Campus Sustainability Consortium Conference

Scaling Up Local Food Purchases

Presented at the State of NY Sustainability Conference

Collective Impact: Results from a Multi-Campus Farm to College Pilot Program

Presented at Association for the Advancement of Sustainability in Higher Education (AASHE) Conference and State of NY Sustainability Conference

Scaling Up Local Food Sourcing: a Multi-Campus Farm to College Pilot

Presented at Association for the Advancement of Sustainability in Higher Education (AASHE) Conference and State of NY Sustainability Conference