Quantifying Ant Populations to Measure Biodiversity in Morehead, KY

To effectively conduct conservation efforts, we can use biodiversity to assess the condition of our environment. Biodiversity has been commonly defined as the variety and variability among living organisms within an area. When our ecosystems are at their best, they clean water, purify air, maintain soil, regulate climate, recycle nutrients, and provide food. Everything within an ecosystem is interdependent, so biodiversity is an important factor and indicator of environmental health. Indicators help us to measure and monitor pressures or threats in land and water use, habitat loss or invasive species, the state of species and ecosystems, the conservation response, and the benefits to people. Many different organisms have been used to assess biodiversity, such as plants, mammals, birds, butterflies, beetles, etc. Ants are a great candidate for biodiversity research, as they are found in many types of habitats, are diverse, extremely numerous, fulfill a variety of ecological roles, are sensitive to environmental change, and are conveniently easy to collect. Our most used method of collection is sorting through leaf litter. We collected leaf litter from three sites in Rowan County: Eagle Lake, Stony Cove, and Rodburn Hollow. We used Berlese funnels to extract the specimens from the litter, organized, identified, and counted them in order to analyze the biodiversity. Over the past three years we have collected almost 7,000 ants, including 18 genera. We plan to use the Shannon and Simpson indices to better evaluate alpha and beta diversity among our three study sites using ants.https://scholarworks.moreheadstate.edu/celebration_posters_2022/1030/thumbnail.jp

Human Umbilical Cord Therapy Improves Long-Term Behavioral Outcomes Following Neonatal Hypoxic Ischemic Brain Injury

Background: Hypoxic ischemic (HI) insult in term babies at labor or birth can cause long-term neurodevelopmental disorders, including cerebral palsy (CP). The current standard treatment for term infants with hypoxic ischemic encephalopathy (HIE) is hypothermia. Because hypothermia is only partially effective, novel therapies are required to improve outcomes further. Human umbilical cord blood cells (UCB) are a rich source of stem and progenitor cells making them a potential treatment for neonatal HI brain injury. Recent clinical trials have shown that UCB therapy is a safe and efficacious treatment for confirmed cerebral palsy. In this study, we assessed whether early administration of UCB to the neonate could improve long-term behavioral outcomes and promote brain repair following neonatal HI brain injury.Methods: HI brain injury was induced in postnatal day (PND) 7 rat pups via permanent ligation of the left carotid artery, followed by a 90 min hypoxic challenge. UCB was administered intraperitoneally on PND 8. Behavioral tests, including negative geotaxis, forelimb preference and open field test, were performed on PND 14, 30, and 50, following brains were collected for assessment of neuropathology.Results: Neonatal HI resulted in decreased brain weight, cerebral tissue loss and apoptosis in the somatosensory cortex, as well as compromised behavioral outcomes. UCB administration following HI improved short and long-term behavioral outcomes but did not reduce long-term histological evidence of brain injury compared to HI alone. In addition, UCB following HI increased microglia activation in the somatosensory cortex compared to HI alone.Conclusion: Administration of a single dose of UCB cells 24 h after HI injury improves behavior, however, a single dose of cells does not modulate pathological evidence of long-term brain injury

Prediction of isobaric heat capacities of room temperature ionic liquids

Ionic liquids (ILs) have many potential applications that require knowledge of a variety of physical and thermodynamic properties. While these properties can often be determined experimentally, this is impossible for novel, yet to be synthesised ILs; thus, property prediction from first principles is essential to unlock new developments in the rational design of ILs. The isobaric heat capacity (CP ) is an important thermodynamic property that quantifies the amount of heat needed to increase the temperature of a material and is thus of great importance in engineering applications involving the design of heat-transfer systems. From a theoretical viewpoint, the heat capacity is a fundamental quantity that expresses the temperature dependence of enthalpy and entropy. Several models for the prediction of CP have been developed to date; however, these are often trained on limited data sets and published model performance is largely dependent on the judicious choice of the testing data. Moreover, popular techniques such as group contribution methods (GCMs) cannot always be applied to structurally novel ILs and quantitative structure property relationships (QSPRs) are highly dependent on the diversity of training data. In this work, predictive models for CP have been developed using linear and nonlinear machine-learning methods. A large data set of 2463 temperature-dependent CP values, spanning 208 ILs, was obtained from the ILThermo database. Molecular volumes, features based on the electrostatic potential (ESP) and other molecular descriptors were calculated for each cation and anion in the data set. Following this, several multiple linear regression models were developed, for which Lasso regression was used reduce the number of features, where necessary. The models were developed using a methodology that attempts to reduce the dependency of the results on the identity of the specific species in the training set. The complexity of these models was gradually increased from a simple volume-based model (inspired by the success of the Volume Based Thermodynamics (VBT) approach of Glasser and Jenkins [L. Glasser and H. D. B. Jenkins, Chem. Soc. Rev., 2005, 34, 866], which was applied to ionic liquids and augmented by Krossing and co-workers [W. Beichel et al., J. Mol. Liq., 2014, 192, 3]), to the addition of electrostatic potential surface areas and finally including the General Interaction Properties Functions (GIPFs) of Murray and Politzer [J. S. Murray et al., J. Mol. Struct. (THEOCHEM), 1994, 307, 55], which are statistically well-defined quantities derived from ESP data, and a Feed Forward Neural Network (FFNN) was developed using the most effective of the aforementioned feature sets. In addition to reporting test-set errors, an external data set was carefully compiled, containing ILs with components (either the cation or anion) not present in the training data, and structurally distinct. This was done to assess the general applicability and flexibility of the final models, and to allow for a fair comparison of model performance. Of the linear models developed, that using interacting features consisting of molecular volumes and GIPFs produced the lowest errors; this is likely due to the ability of the interaction features to describe intermolecular interactions between cations and anions. Consequently, molecular volumes and GIPFs features were also used to develop a nonlinear FFNN. Finally, the linear interacting GIPFs model and FFNN also produced the lowest errors of 3.2 1.5% and 3.8 2.4%, respectively, when applied to the external data set

CSBI and SCS

Sexual compulsivity describes poorly controlled sexual thoughts, fantasies, urges, and behavior. The purpose of the current study was to examine and compare, utilizing a non-clinical sample, the relative psychometric properties of two existing scales used to assess sexual compulsivity, the Sexual Compulsivity Scale and the Compulsive Sexual Behavior Inventory. Participants were 334 male undergraduate students ranging in age from 18 to 42 years (M =19.54, SD = 2.16) enrolled in Introductory Psychology courses at a mid-sized Midwestern university. Zero-order correlation analyses were conducted to identify which sexual behaviors and constructs associated with sexuality were significantly related to scores on the CSBI and the SCS. Examination of the differential patterns of sexuality relations suggests the CSBI and the SCS may measure different aspects of compulsivity. Step-wise regression analyses indicated that the use of drugs and alcohol to gain compliance from a sexual partner, fantasies of impersonal sex, and sexual anxiety were significant predictors for both the CSBI and the SCS. On the CSBI, the final predictor that accounted for a significant increase in variance accounted for was expressing anger, while on the SCS additional variance was accounted for by sexual preoccupation. Implications, limitations, and future directions are discussed.Thesis (M.A.)Department of Psychological Scienc

Examining the Potential for Gender Bias in the Prediction of Symptom Validity Test Failure by MMPI-2 Symptom Validity Scale Scores

Using a sample of individuals undergoing medico-legal evaluations (690 men, 519 women), the present study extended past research on potential gender biases for scores of the Symptom Validity (FBS) scale of the Minnesota Multiphasic Personality Inventory-

Differentiating PTSD symptomatology with the MMPI-S_RF (Restructured Dorm) in a forensic disability sample

The current study was designed to explore models of assessing various forms of Post-Traumatic Stress Disorder (PTSD) symptomatology that incorporate both broad and more narrowly focused affective markers. We used broader markers of demoralization, negative activation, positive activation, and aberrant experiences to predict global PTSD scores, whereas more narrowly focused markers of positive and negative affect were used to differentiate between PTSD symptom clusters. A disability sample consisting of 347 individuals undergoing medico-legal psychological evaluations was used for this study. All participants completed symptom measures of PTSD and the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) (from which MMPI-2-RF scores were derived). The results indicated that demoralization was the best individual predictor of PTSD globally, and that more narrowly focused MMPI-2-RF Specific Problems scales provided a differential prediction of PTSD symptom clusters. Theoretical and practical implications of these findings are discussed within contemporary frameworks of internalizing personality and psychopathology