Do Enallagma exsulans from Streams and Lakes Show Patterns of Divergence?

Divergent selection across heterogenous environments could lead to adaptive divergence in populations resulting in potential local adaption. These populations have phenotypic differences that are fitness related and make native individuals more fit than non-native individuals. My research focuses on a species of damselfly, Enallagma exsulans, to explore local adaptation and morphological differences as a result of divergent selection or plasticity. My first study explored potential local adaptation of wild caught stream and lake E. exsulans using a reciprocal transplant design, a classic approach for this objective. The stream and lake sites chosen were on a small spatial scale allowing for potential gene flow among populations, a process that could hinder local adaptation. In the second part of my research, I reared stream and lake E. exsulans in a common garden and transplanted them into stream and lake environments. I expected to find that native individuals had higher fitness, measured as growth rates, than non-native individuals indicating local adaptation. Unfortunately, I was unable to collect any results due to a storm damaging my experimental set-up. There are still important questions about local adaptation occurring at small spatial scales with potential for gene flow, and if plasticity is another mechanism for coping with changing environments. In the next part of my study, I used individuals raised in a common garden environment for a small scale mesocosm reciprocal transplant replicating the field study. All larvae lost body mass, no matter the origin of the individual or the condition under which it was tested. I also completed geometric morphometric analyses of wild caught individuals from both stream and lake environments and common garden reared individuals to determine if morphological differences are the result of divergent selection between populations. In wild-caught individuals, I found significant differences in body and lamellae shape between lake and stream populations suggesting divergent selection. In common garden individuals, I did not detect significant differences, suggesting morphological divergence is not genetically based. Last, I completed behavioral assays with common garden individuals placing larvae into stream and lake conditions and scoring behavior, but no results were significant between lake and stream populations

Socioscientific Issues-Based Instruction: The Messier Side of (Leading) Science Teaching

The present case centers on a socioscientific issues-based lesson taught by a preservice teacher (PST) in an AP Biology class. The PST designed and delivered a lesson on disease transmission and ways to avoid infection with connections to the COVID-19 pandemic mask mandates and vaccine reticence. The Principal received several emails from parents (positive and negative), citing the incorporation of political issues and critical race theory into the science lesson. With this framing, the case depicts how the Principal, PST, university supervisor, and cooperating teacher navigate the situation. The case highlights the role of school leader as instructional leader. In particular, to interact with teachers and other stakeholders about content and pedagogy, leaders must develop leadership content knowledge (LCK). The present case offers school leaders an opportunity to build LCK around the Nature of Science and socioscientific issues, while exploring how they might address challenges to curriculum and pedagogy

Learning Progression of Students’ Reasoning about Life Cycles

This study explored elementary students’ reasoning about the life cycles of various organisms, including insects and amphibians. The study took place in a private school in Lebanon with 24 fifth-grade students. Students participated in a life cycle unit with pre and post-written assessments about what they learned and interviews to help determine their reasoning about life cycles. Using our findings, we suggest a learning progression (LP) approach to guide students over time in their learning about life cycles and their importance for species persistence within an ecosystem. Two LPs were developed from this study: Reasoning about the cyclic nature of life and comparison of life cycle stages. Overall, students improved their understanding of the cyclical nature of life, but comparing organisms’ structures, stages, and life cycles proved to be more challenging. These LPs have direct implications for elementary instruction about life cycles, organisms, and species

Dr. Bobbie Bailey & Family Performance Center Anniversary Celebration

The School of Music is proud to welcome back to campus several of our esteemed alumni for a special recital as part of the Bailey Performance Center 10th anniversary celebration! The School of Music celebrates the opening of the Bailey Performance Center with featured performances by the KSU Wind Ensemble Brass and Percussion, Symphony Orchestra, Chamber Singers, University Chorale and Chamber Singers Alumni Choir, along with pianist Robert Henry, soprano Jana Young, and more!https://digitalcommons.kennesaw.edu/musicprograms/1969/thumbnail.jp

Data Science and Machine Learning in Education

The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit greatly from materials widely available materials for use in education, training and workforce development. They are also contributing to these materials and providing software to DS/ML-related fields. Increasingly, physics departments are offering courses at the intersection of DS, ML and physics, often using curricula developed by HEP researchers and involving open software and data used in HEP. In this white paper, we explore synergies between HEP research and DS/ML education, discuss opportunities and challenges at this intersection, and propose community activities that will be mutually beneficial.Comment: Contribution to Snowmass 202

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Janus kinase 1 drives endoplasmic reticulum stress-induced transcriptional reprogramming in astrocytes

Neurological and neurodegenerative diseases are heterogenous and devastating diseases with limited therapeutic options and no cures. The broad, long-term goal of this project was to elucidate therapeutic targets for neurodegenerative conditions that attenuate damaging inflammation while leaving the beneficial immune response intact and avoiding broad immunosuppression. Inflammation and the accumulation of misfolded proteins are associated with a wide variety of neurological diseases. Here, we have examined how the accumulation of misfolded proteins shapes inflammatory signaling in the glial cell population astrocytes. Astrocytes are the most populous cell in the central nervous system (CNS) and provide physical and trophic support to the CNS. Proper astrocyte function is paramount for a healthy brain. Recent evidence indicates endoplasmic reticulum (ER) stress and inflammation are linked. ER stress occurs when the protein folding capacity of the cell is overwhelmed, resulting in the initiation of the unfolded protein response (UPR) to regain homeostasis. However, unresolved UPR activation leads to cell death and aberrant inflammation. Further, astrocytes are relatively resistant to ER stress-induced cell death. We have found that UPR activation in astrocytes activates JAK1-dependent inflammatory gene expression. Canonical JAK1 signaling is initiated by ligand binding of a cytokine receptor that results in Signal Transducers and Activators of Transcription (STAT)-dependent inflammatory gene expression. Using RNA sequencing of primary murine astrocytes, we have demonstrated that JAK1 regulates approximately 10% of ER stress-induced gene expression. However, we found JAK1 initiates different gene expression based on the activating stimulus. In response to ER stress, JAK1 regulates a distinct subset of gene expression that we hypothesize does not rely on JAK1-dependent phosphorylation of STATs. Instead, we have described a noncanonical role for JAK1 in response to ER stress that utilizes the transcription factor activating transcription factor (ATF) 4. ATF4 is expressed in response to ER stress and other types of cell stress. We demonstrate here that JAK1 and ATF4 coimmunoprecipitate, suggesting a physical interaction between these two proteins. Further, we showed via ChIP-seq that JAK1 is required for ATF4 to bind transcription start sites in promoter regions. Here, we have demonstrated a mechanism by which JAK1 regulates ER stress-induced gene expression in astrocytes in a noncanonical mechanism. Future directions of this project will focus on understanding the physiological consequences of this pathway in vivo in models of neuroinflammation

An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records.

The goal of this study is to develop and validate a lightweight, interpretable machine learning (ML) classifier to identify opioid overdoses in emergency medical services (EMS) records. We conducted a comparative assessment of three feature engineering approaches designed for use with unstructured narrative data. Opioid overdose annotations were provided by two harm reduction paramedics and two supporting annotators trained to reliably match expert annotations. Candidate feature engineering techniques included term frequency-inverse document frequency (TF-IDF), a highly performant approach to concept vectorization, and a custom approach based on the count of empirically-identified keywords. Each feature set was trained using four model architectures: generalized linear model (GLM), Naïve Bayes, neural network, and Extreme Gradient Boost (XGBoost). Ensembles of trained models were also evaluated. The custom feature models were also assessed for variable importance to aid interpretation. Models trained using TF-IDF feature engineering ranged from AUROC = 0.59 (95% CI: 0.53-0.66) for the Naïve Bayes to AUROC = 0.76 (95% CI: 0.71-0.81) for the neural network. Models trained using concept vectorization features ranged from AUROC = 0.83 (95% 0.78-0.88)for the Naïve Bayes to AUROC = 0.89 (95% CI: 0.85-0.94) for the ensemble. Models trained using custom features were the most performant, with benchmarks ranging from AUROC = 0.92 (95% CI: 0.88-0.95) with the GLM to 0.93 (95% CI: 0.90-0.96) for the ensemble. The custom features model achieved positive predictive values (PPV) ranging for 80 to 100%, which represent substantial improvements over previously published EMS encounter opioid overdose classifiers. The application of this approach to county EMS data can productively inform local and targeted harm reduction initiatives