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200 - Topographical Individualized Neuromarkers in Children With and Without Attention-Deficit/ Hyperactivity Disorder
Full text linkThe Topographical Individualized Neuromarkers (TIN) project uses an innovative approach to analyze topographical patterns of brain function to discover biomarkers for mental health. In previous studies, neurotypically developing children show a distinct nonlinear connectivity pattern from the Anterior Cingulate Cortex (ACC) to the insula, with a maximum at the mid-dorsal ACC, compared to those at risk for anxiety who show a decreased, more linear pattern within these regions (Taber-Thomas et al., 2016). In the current study, we focus on the topographical functional connectivity patterns in children with attention-deficit/ hyperactivity disorder (ADHD) across the limbic system, a composition of different neural networks working together to process and control emotions, memory, and behavior (Catani et al., 2013). We analyzed a large, publicly available fMRI dataset (ADHD-200 Preprocessed; Bellec et al., 2017), with a total of 677 participants: 241 ADHD (gender: 189M/ 52F) and 436 typically developing (TD; gender: 232M / 204F). We also examined the topographical pattern in typically developing adults sourced from Neurosynth. We expect that the fMRI functional connectivity in participants with ADHD will show higher connectivity in the dorsal anterior cingulate, a network that regulates emotions, compared to typically developing participants. This approach is still very new and is an exploratory analysis that could help us further discover connectivity patterns along specific brain regions among other neurodivergent disorders. Our findings aim to enhance understanding of ADHD’s neural mechanisms and encourage further biomarker research
211 - Evaluating the Differences in Ecosystem Services Provided by Native and Non-Native SUNY Geneseo Trees
Full text linkMany of the trees planted on SUNY Geneseo’s campus are for aesthetic purposes, but also provide many benefits to campus. The trees help to avoid stormwater runoff, remove air pollutants, sequester carbon dioxide, and have the ability to host lepidoptera species. iTree Design is a platform that enables investigators to estimate ecosystem services provided by trees on SUNY Geneseo campus, based on their species and size - diameter at breast height (DBH). It also estimates future ecosystem services. In part of an ongoing research project, ten native species and ten non-native species planted on SUNY Geneseo’s campus were measured and identified to be analyzed in iTree. Their total benefits, including ability to avoid stormwater runoff, remove air pollution, sequester carbon dioxide, and host ability for lepidoptera, were calculated for 25 years from present and adjusted to be presented as the unit per centimeter DBH for an accurate comparison of their ecosystem benefits. Many of the native trees, such as the red maple (Acer rubrum), sequesters more carbon dioxide (around 126.6748466 kg/cm DBH in 25 years) compared to the non-native trees, such as the paperbark maple (Acer griseum) (sequesters around 42.43243243 kg/cm DBH in 25 years). The differences in these values for additional native and non-native trees will be assessed. Ecosystem service evaluations can provide additional criteria for evaluating tree species for campus plantings
292 - Our Veteran and Military-Affiliated Students and What We Can Do For Them
Full text linkVeterans and military-affiliated students have made massive sacrifices in the name of service and deserve the best Geneseo has to offer. Services for veterans and military-affiliated students on campus are lacking or outright unavailable. Veterans and military-affiliated students are often unaware of each other\u27s presence on campus, in addition, veterans undergo a significantly different college experience than most who attend directly following high school. We intend to bridge the gap between academia and veteran/military-affiliated students by connecting them to each other and providing new and improved resources to enhance their academic experience and push them to success
Claude AI and Literature Reviews: An Experiment in Utility and Ethical Use
No full textGenerative large-language models (LLMs) such as ChatGPT and Claude have sparked debate throughout higher education about their potential and threats to the current pedagogical models for research. This paper scans the literature on the benefits and drawbacks of using LLMs in teaching and research, and compares and contrasts two literature reviews on the subject, one written by a human author and one produced by Claude. The comparison explores areas where the generative AI excels, such as quickly summarizing points of agreement across sources, as well as its limitations, like struggling with synthesis, providing incomplete citations, and hallucinating false information. While Claude was able to identify key themes and sources, the areas where it struggles show that without human intervention (providing context and analysis), the tool cannot produce a literature review that would stand on its own. However, the experiment demonstrates potential for these models to augment and accelerate research workflows when leveraged responsibly alongside human scholars
