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Role of Ventral Hippocampus in the Contextual Control of Avoidance
No full textAnimals react to aversive situations with a complex set of behaviors that reduce the likelihood of predation. Of course, animals have the capacity to learn from their experience; this allows them to anticipate and defend against future threats. On the one hand, Pavlovian conditioned responses, such as freezing behavior in rats, adaptively generalize to a variety of threats and environments in which they might be encountered. On the other hand, instrumental learning, such as making an active escape or avoidance response to avoid a painful event, enables animals to develop new behavioral strategies to avoid future threats. Compared to their Pavlovian counterparts, the neural and behavioral mechanisms of instrumental avoidance responses are poorly understood. In particular, whether avoidance responses exhibit a tendency to generalize across many contexts is unknown. To address this question, I assessed the context-dependence of instrumental avoidance learning in male and female rats. These studies used a two-way signaled active avoidance (SAA) task, in which animals can avoid an aversive footshock by shuttling in response to a warning signal. In the first set of experiments, I determined whether a learned avoidance response transfers to a new context and whether the contextual control of avoidance requires the hippocampus, a brain area that has been implicated in this form of learning. This work revealed that shuttle-box avoidance was context-dependent and decreased outside of the training context; inactivation of the ventral (VH), but not dorsal (DH) hippocampus, with the GABAA agonist, muscimol, eliminated the context-dependence of the response. In another set of experiments, I examined avoidance responses to the training context itself���so called inter-trial responses (ITRs). Inactivation of the VH decreased ITRs, whereas chemogenetic activation of the VH with ���designer receptors exclusively activated by designer drugs��� dramatically increased the number of ITRs. Together, these studies reveal that the VH has a role in both determining the context in which a threat-induced avoidance response is emitted and in promoting ITRs in the aversive context itself. The neural circuits by which the VH mediates these two functions of context in avoidance requires further investigation
Effects of Information on Markets and Behavioral Economics
No full textThe dissertation systematically analyzed the effect of information in various aspects of economic and social systems. Specifically, it focused on tax information on food markets, market information on strategic decisions, and social information on decision-making in economic games.
The first essay investigated the effects of sugar-sweetened beverages (SSBs) tax in the city of Berkeley and Philadelphia. We constructed a difference-in-difference (DID) model to examine the effects of the tax on the consumption and retail prices using Nielsen retail scanner data. The finding suggested that SSBs tax had no significant effect on consumption and prices in Berkeley, while the tax significantly increased the price and decreased the consumption of SSBs in Philadelphia. The opposite results in the city of Berkeley and Philadelphia suggested that geographical and social demographic characteristics would affect the effectiveness of a policy, and SSBs tax works better in large territories with diverse population.
The second essay analyzed transaction data from eBay to explore sellers��� strategies in online auctions. We focused on three selling formats on eBay: fixed-price listings, auctions, and BIN(Buy it Now)-auctions, comparing new products and outdated products for electronics. A probit model and Heckman correction procedure were applied to examine the effect of seller���s selling strategy on transaction success rate, final transaction price, and the seller���s revenue. Results indicate that fixed-price listings are preferred. Selling formats significantly influence outcomes for new products, while they show minimal effects for outdated ones. Moreover, we identify key price determinants for each listing format. The BIN price is crucial for fixed-price listings. For auctions and BIN-auctions, the duration and starting price emerge as key factors influencing transaction outcomes.
The third essay explores cooperation in social dilemmas, focusing on the impact of inequality on cooperation in public goods dilemmas. Our study evaluate cooperation in metrics of efficiency (total contribution) and equality (contribution from the wealthy). Incorporating 32 articles with 50 experiments, we found that heterogeneous endowments do not significantly affect equality, but it negatively impact efficiency, leading to decreased total contributions. Overall, heterogeneous endowments detrimentally affect cooperation. Furthermore, moderator analyses show that iteration numbers could negatively moderate the relationship between heterogeneous endowments and cooperation, indicating a greater detrimental effect on cooperation in public goods games with more iterations
John Bickham field notebook: AK23001-AK23500.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for AK23501-AK24000 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
From Remote Work to Virtual Collaboration: Toward the Design of Collaborative Virtual Reality Environments
No full textRecently the world was hit by the COVID-19 pandemic, during this time mandatory social distancing went into effect to stop the spread of the virus. Due to the social distancing mandates many schools and businesses were forced to close their doors for an undisclosed amount of time, opening the door for online and remote work. Individuals who worked on collaborative teams during this time were forced to adopt new methods of collaboration. Although teleconferencing applications such as Zoom, Google Hangouts and Microsoft Teams provided online support for collaboration the human-human social interaction was lacking. Like these teleconferencing applications Virtual Reality (VR) has taken off during these unprecedented times. Recent developments into VR have created immersive applications that allow users to meet socially and professionally in virtual environments, bringing back the human-human social interaction. This research seeks to understand how and to what extent collaborative VR environments can support interdisciplinary team collaboration. A series of five studies was conducted to first explore how collaborative teams work with each other in different environments (face-to-face vs virtual). The following studies will investigate user preferences of Virtual Characters (VCs) in immersive VR environments. A final study will observe an interdisciplinary team to compare team collaboration face-to-face, virtually and in a commercially available collaborative VR application. From these five studies this body of work will contribute a set of design guidelines to be used in the development of collaborative VR environments
SMC-M1 Connectivity and Motor Sequence Learning: A TMS Study
No full textWe experience various types of motor learning throughout our lives. As children, we learned to walk unconsciously. As we grow older, we continue to learn skills like sports or musical instruments. In this study, the primary motor cortex (M1) and the supplementary motor complex (SMC), which are brain regions involved in motor sequence learning but where many aspects still remain unclear, were mainly investigated, and the connectivity of these two regions was measured using the paired-pulse transcranial magnetic stimulation (ppTMS) technique. In Experiment I, sixty-three right-handed undergraduate students participated. Conditioning stimulus (CS) was administered at SMC (i.e., 4 cm anterior to Cz), and test stimulus (TS) was applied at M1. As a result, consistent with the previous ppTMS studies, a facilitatory influence was observed between SMC and M1. In Experiment II, fifty-one right-handed undergraduate students participated. After measuring baseline SMC-M1 connectivity in the same way as in Experiment I, individuals practiced one of three motor sequential tasks (i.e., implicit, explicit, or random sequence task). After practice, ppTMS as post-training stimulation was administered in the same way as the baseline stimulation to investigate the changes in the connectivity between SMC and M1. As a result, it was found that the facilitatory influence decreased after the explicit sequence task that involved motor chunking. This may be because SMC plays a role in motor chunking. In Experiment III, instead of a motor task, intermittent theta-burst stimulation (iTBS) was administered at SMC between two ppTMS sessions, and the connectivity changes were investigated. Similar to the influence of the explicit sequence task in Experiment II, the facilitatory influence decreased in the iTBS group. Although the mechanisms are different from each other, iTBS appears to induce post-synaptic plasticity in the cortico-basal ganglia-thalamic network. After post-stimulation of ppTMS, participants performed the explicit sequence task used in Experiment II twice (i.e., practice and retention test) to reveal the effect of iTBS on motor sequence learning. In the iTBS group, the offline improvement was disturbed at concatenation points compared to the Rest group. In the future, follow-up research that dissociates motor chunking conditions using neuroimaging techniques seems necessary
Fetal and Infant Mortality Review (FIMR) Programs and Infant Mortality Outcomes
No full textInfant mortality is defined as the death of a baby before his or her first birthday. The United States had the highest infant mortality rate (IMR) of any high-income country at 5.4 deaths per 1,000 live births in 2020. Having a substantial number of poor birth outcomes in a community is a multi-faceted problem and often requires an interdisciplinary approach to understand the challenges impacting fetal and/or infant death. One such coordinated approach to address this problem is the Fetal & Infant Mortality Review (FIMR) program.
Despite the proposed function of FIMR programs to combat high infant mortality (IM), there is no published evidence linking the existence of FIMR programs to improved IM outcomes. Existing published research has been useful in clarifying what FIMR is in general terms.
In Chapter 2, a scoping literature review provides a focused approach to determining the relationship between FIMRs and intended outcomes of reduced IM by reviewing the literature for existing studies on FIMR and IM outcomes. Out of 97 screened articles, 12 were empirical articles on FIMR programs, and 7 articles included an evaluation of a FIMR program or process and were included in the review.
In Chapter 3, a quantitative study assesses differences in IMR in communities with an existing FIMR program examining IMR pre- and post FIMR implementation. Results demonstrated a decrease in IMR in communities post-FIMR implementation thus showing that there appears to be an association between FIMR programs and IM outcomes.
In Chapter 4, a qualitative study evaluates the FIMR process, how it works, and what makes it effective or ineffective. Eleven people participated in the virtual one-on-one interviews. A phenomenological approach was used, and findings revealed reported benefits of the FIMR program as well as areas for needed improvement.
This research concluded that more evaluative studies are needed for assessing FIMR program outcomes specific to IM, not solely its processes; and a nationally standardized approach for the operation of FIMRs is needed with room to tailor to specific communities. Findings need to be disseminated widely and used as an opportunity for greater accountability and improvements to FIMR programs
Atomic Boson Sampling in a Bose-Einstein Condensed Gas
No full textWe propose a multi-qubit Bose-Einstein-condensate (BEC) trap as a platform for studies of quantum statistical phenomena in many-body interacting systems. In particular, it could facilitate testing atomic boson sampling of the excited-state occupations and its quantum advantage over classical computing in a full, controllable and clear way. Contrary to a linear interferometer enabling Gaussian boson sampling of non-interacting non-equilibrium photons, the BEC trap platform pertains to an interacting equilibrium many-body system of atoms with established Bose���Einstein condensate. We discuss a basic model and the main features of such a multi-qubit BEC trap.
We describe boson sampling of interacting atoms from the noncondensed fraction of Bose-Einstein-condensed gas confined in a box trap with periodic boundary conditions. We explicitly show increasing apparent complexity of sampling probability patterns with changing the observational basis of excited atom states from the eigen-squeeze modes to more and more involved unitary mixtures of them. We calculate the characteristic function and statistics of atom numbers via newly found hafnian master theorem. Using Bloch-Messiah reduction, we find that interatomic interactions give rise to two equally important entities ��� eigen-squeeze modes and eigen-energy quasiparticles ��� whose interplay with sampling atom states determines quantum statistics of the BEC gas. We infer that two necessary ingredients of computational ���P-hardness, squeezing and interference, are self-generated in the BEC gas and, contrary to Gaussian boson sampling in linear interferometers, external sources of squeezed bosons are not required for observation of quantum advantage manifestations.
The focus of the dissertation is on the origin of the computational ���P-hard complexity and quantum advantage of atomic boson sampling due to interference and squeezing of the sampled atom states via their interplay with the eigen-squeeze modes and eigen-energy quasiparticles
Accelerating Finite Element Analysis Using a Multi-Fidelity Computational Scheme for Nuclear Applications
No full textNext-generation microreactors are currently being designed to be operated terrestrial and ex-traterrestrial for remote surface power production. These systems will provide an alternative source of carbon-free energy that is versatile and can be utilized for various applications. This recent de-sire to design and build next-generation nuclear systems requires high-fidelity analysis to ensure the proposed design can operate safely and as intended. Traditionally, this can be achieved by obtaining a combination of experimental and numerical results, however it has become difficult and expensive to perform integral experiments. Therefore, high-fidelity numerical results have become heavily relied upon to provide the required analysis, specifically finite-element based codes. This reliance on numerical codes has presented its own set of issues as it can take millions of CPU hours to gather the required results for a given design. Therefore, a novel computational scheme is pro-posed to accelerate transient finite element analysis of these next-generation nuclear systems. A discrepancy function between a low and high-fidelity model is approximated and used to actively correct the low-fidelity solution. By exploiting the computational cheap low-fidelity solution and a few snapshots in time of the high-fidelity solution, an approximated discrepancy function can be found to correct the low-fidelity model. This approach aims to produce a solution that is close to the full-order high-fidelity model while requiring a smaller computational cost. The idea was decided to be implemented to work alongside the Abaqus finite element software, and utilized to model a series of transient events for two conceptual reactor designs
Methods for Large-Scale Inference with Application to Genomics Data
No full textGenomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction. The first project shows that the traditional approach is sub-optimal and proposes a new two-dimensional false discovery rate control framework (2dFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2dFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control is performed based on conditional independence test statistics in the remaining features. 2dFDR+ provides (asymptotically) valid inference from samples in settings where the conditional distribution of the genomic variables given the covariate of interest and the confounders is arbitrary and completely unknown. In genome-wide epigenetic studies, exposures (e.g., Single Nucleotide Polymorphisms) affect outcomes (e.g., gene expression) through intermediate variables such as DNA methylation. Mediation analysis offers a way to study these intermediate variables and identify the presence or absence of causal mediation effects. Testing for mediation effects leads to a composite null hypothesis. Existing methods like Sobel���s test or the Max-P test are often underpowered because 1) statistical inference is often conducted based on distributions determined under a subset of the null, and 2) they are not designed to shoulder the multiple testing burden. To tackle these issues, we introduce a technique called MLFDR (Mediation Analysis using Local False Discovery Rates) for high dimensional mediation analysis, which uses the local false discovery rates based on the coefficients of the structural equation model specifying the mediation relationship to construct a rejection region. We have shown theoretically as well as through simulation studies that in the high-dimensional setting, the new method of identifying the mediating variables controls the false discovery rate asymptotically and performs better with respect to power than several existing methods
An Empirical Investigation of Afghanistan���s Organizational Culture
No full textThe purpose of this study was to examine Afghanistan culture using Geert Hofstede's Value Survey Module (VSM-2013). This research aimed to uncover and interpret the VSM profiles for Afghanistan, particularly focusing on differences across gender, ethnicities, languages, and religions in relation to Hofstede���s six cultural dimensions: power distance (PD), individualism���collectivism (IC), masculinity���femininity (MF), uncertainty avoidance (UA), long-term���short-term orientation (LSO), and indulgence���restraint (IR). Survey data were collected from 2,071 students across 15 universities in five provinces ���Kabul, Kandahar, Herat, Balkh, and Nangarhar. After ensuring the reliability and validity of the data, the study employed two main analytical techniques: Multivariate Analysis of Variance (MANOVA) to explore cultural variances across groups (e.g., gender, ethnicity, language, and religion) and Hofstede���s Classic VSM-2013 technique to compute VSM indices for Afghanistan as well as those groups. The results revealed insightful distinctions and similarities in cultural dimensions among Afghan men and women, as well as across various ethnic, linguistic, and religious groups. The study's findings are particularly valuable for addressing the need for empirical evidence on Afghanistan���s national culture. Understanding these cultural contexts is critical for the effective management of human resources in Afghanistan