Illinois Mathematics and Science Academy
Illinois Mathematics and Science Academy: DigitalCommons@IMSANot a member yet
9795 research outputs found
Sort by
2025 Euler\u27s Method for Early Calculus Connections
Get PDFEuler’s Method is typically discussed in the context of differential equations. At our school, we present it early in calculus to provide additional perspective on some crucial concepts. Our session will explain how we use notions from Euler’s Method to help explain the Fundamental Theorem of Calculus and various differentiation rules. We hope to see you there
Strengthening Experiential Learning at Your School
Get PDFExperiential learning has become increasingly pervasive across education and is particularly relevant to STEM education. This session will discuss foundational theories and best practices for experiential learning and provide strategies and approaches to scale and enhance experiential learning opportunities in and across your schools. It will be relevant for all grade levels and applicable to educators of all levels of experience with experiential learning
Analysing the Mechanisms of Aging in the Human Brain Through Single Cell Isolation and Multi-omic Analysis
No full textAging in the brain refers to the gradual loss of physiological and biochemical functions, leading to cognitive decline and neurodegeneration. Aging is a natural process, but its progression can be accelerated by primary causes such as genetic predisposition, inflammation, and protein misfolding, and secondary influences like poor lifestyle choices, chronic stress, toxins, pollutants, and neurodegenerative diseases. Genomic instability, a byproduct of aging, is triggered by both primary and secondary causes. In order to investigate the cellular consequences of such instability, we performed single-cell isolation of different human brain samples of different age groups and conducted multi-omics analyses. Our experiments identified different types of neuronal cell types and states affected or involved by aging, with astrocytes standing out among the chromatin clusters. Biochemical pathway analysis of astrocytes and other cells involved, such as oligodendrocytes, intratelencephalic neurons, and microglia cells, revealed age-related biochemical changes, including both upregulated and downregulated pathways. We found the MYC, and transcription regulator is an upstream-activated regulator to many adverse age-related effects, such as RNA repression and neuron apoptosis. These findings contribute to medical research, molecular biology, and bioinformatics, with the potential to guide the creation of treatments to counteract cognitive decline and neurodegenerative disease
Detecting Expert Users in Stack Exchange Using Machine Learning Presenter(s)
No full textOnline question-answering platforms, such as StackExchange, have grown rapidly in recent years, making it necessary to identify the credibility of users and the information they share online to maintain trust within these communities. This issue can be addressed through accurate expert detection methods to determine whether or not users are experts in a certain field. For our study, we conducted analyses on a dataset consisting of various posts and comments written by over 10,000 StackExchange users to identify which classification techniques can most accurately distinguish between the written contributions of experts and non-experts. After comparing 12 different methods, we discovered that transformer-based embeddings, ensemble learning, and Naive Bayes models achieved higher f1-scores. We conducted an ablation study of these three approaches and found that using Gemini embeddings helped maintain a high detection rate even when the class imbalance became more skewed to reality. Although expert detection remains a challenging task, our study provides promising results for accurately identifying the expertise of StackExchange users. Future analysis could include metadata (e.g. users’ voting behavior or whose posts they comment on) along with their written contributions or utilizing transfer learning to test our model performance on other online platforms, like Reddit or Quora
Efficacy of Tomivosertib (MNK1/2 Inhibitor) in Mitigating RDEB Mice Pain
No full textRecessive dystrophic epidermolysis bullosa (RDEB) is a skin disorder caused by pathogenic variants in the COL7A1 gene, resulting in a deficiency of functional collagen VII, which anchors the epidermis to the dermis. Without collagen VII, the skin is fragile and blisters easily, leading to severe pain, chronic wounds, and infection risk. However, effective, non-opioid pain treatments remain limited, highlighting the need for alternatives. MNK1/2 kinases are part of a known pain pathway that regulates pain-related protein translation. This study investigates the role of MNK1/2 signaling in RDEB pain and the effect of tomivosertib, an MNK1/2 inhibitor, on pain-related behaviors and gene expression in an RDEB mouse model with hypomorphic Col7a1 pathogenic variants. RDEB mice were treated with tomivosertib or DMSO control for two to four weeks. Pain behaviors (paw nibbling, grooming, grimacing) were assessed using behavioral assays, and gene expression (Bdnf, Eif4e) was quantified by RT-qPCR, which measures mRNA levels, along with immunostaining to assess MNK pathway activation. Tomivosertib reduces pain-related gene expression and alleviates pain behaviors in RDEB mice without significantly affecting itch. MNK inhibition decreased Bdnf expression in dorsal root ganglia, supporting MNK1/2 inhibition as a potential intervention for RDEB pain
Investigating the Role of Refractory Periods in Neuronal Network Dynamics Using SNNAP and MATLAB
No full textThe refractory period plays a crucial role in shaping neuronal excitability and network dynamics. This study examines how absolute and relative refractory periods influence spike timing in a three-neuron network simulated in SNNAP (Simulator for Neural Networks and Action Potentials) and more complex neural networks. The network consists of a Hodgkin-Huxley (HH) neuron exciting gi_6 and gi_7 integrate-and-fire neurons through weighted synapses. Unexpectedly, post-synaptic neurons fired during the hyperpolarization phase of the presynaptic neuron rather than at the peak of the action potential. By adjusting synaptic conductance (g_syn) and threshold reset parameters, we analyzed how refractory dynamics affect spike timing and oscillatory behavior. Further analysis extends to MATLAB simulations, where we model single neurons and more detailed networks using Hodgkin-Huxley and integrate-andfire equations, as well as making more accurate models. By systematically varying membrane capacitance (Cm), synaptic delay, and threshold adaptation, we examine how refractory periods influence neural synchronization. These results provide insights into how refractory periods regulate network excitability and oscillatory activity. Future directions include studying adaptive refractory mechanisms and synaptic plasticity to explore how neural circuits dynamically adjust their firing properties over time
Fake News Classification in 2024 News Articles
No full textStrong machine learning models for identifying fake news have been developed due to the spread of false information in digital news outlets. Using a labeled dataset, this study investigates how well different classification and embedding strategies can differentiate between fake and authentic news. We compare deep learning designs like convolutional neural networks (CNNs) and transformers with conventional machine learning classifiers like logistic regression, support vector machines, and random forests. In order to evaluate the effects of word embedding techniques on classification performance, we also examine Word2Vec, TF-IDF, and BERT embeddings. According to our findings, transformer-based models—in particular, refined BERT variants— perform better than conventional methods in terms of precision and recall, making better use of contextual semantics. However, lightweight models utilizing TF-IDF with logistic regression provide competitive performance with significantly lower computational costs
Electrochemical Characterization of (R-,R′-bpy)Os(Cl)2(=O)2 Complexes Presenter
No full textThis project involved the 1-step synthesis and characterization of osmium complexes following the chemical formula, (R-,R′-bpy)Os(Cl)2(=O)2, where R represents a variety of carbon-based groups such as tert-butyl (–C(CH3)3), methoxy (–OCH3), and trifluoromethyl (–C(CF3)3). These osmium complexes are a marked improvement over osmium tetroxide, which is volatile and extremely hazardous. Furthermore, previous studies of these complexes were limited to stoichiometric use with harsh oxidants, whereas we propose catalysis using only water and green energy inputs (e.g. electricity). Herein, we evaluated the ability of these complexes to oxidize thioethers (70-80% yield), via the input of a mild, applied current or potential. Specifically, these complexes were observed to undergo an initial activation step: 1) reduction followed by 2) geometric reorganization. Subsequent reoxidation of this species formed the active complex for catalysis. To inform these claims, we used a combination of electrochemistry (cyclic voltammetry, chronoamperometry), spectroelectrochemistry (ultraviolet-visible), and spectroscopy (electron-paramagnetic resonance, nuclear magnetic resonance) techniques. Additionally, further understanding of the complex was achieved through computational modeling, which was used to explain the initial activation step involving chloride dissociation and/or halide exchange