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A New Tile-Based Method for Constructing Single-Line Drawings
No full textWe present a new tile-based method for constructing single-line drawings. Our method involves positioning decorated square tiles on a rectangular board. We use a set of eight tiles. Each one has a light beige background, and seven of the eight are adorned with black line segments that connect one or more midpoints of certain sides of the tile to one or more midpoints of other sides. When we place copies of these tiles on the board, we follow one rule: we must arrange them in such a way that their black line segments join together to form a single line that enters the board on the left and leaves it on the right. Our goal is to construct a single line that can be easily traced by hand or eye when viewed from up close yet also resembles a recognizable target image when viewed from a distance
Bringing Autoethnography to Undergraduates: An Interdisciplinary Course-Cluster and Lab at Oberlin College
No full textThe label “autoethnography” has been applied to a wide range of knowledge-producing practices, from what might be considered “normal” science to narrative-driven writing to performance. These debates highlight some of the most fundamental tensions about legitimate ways of knowing/knowledge production in the contemporary world. Further, one strength of autoethnography as a method lies in situating personal experience within broader political, social, and cultural events, which can create new opportunities in academia for voices often silenced. With these elements of autoethnography in mind, and in response to the COVID-19 pandemic, the authors founded an interdisciplinary autoethnography course cluster and lab at Oberlin College and Conservatory. In this essay, we describe the course cluster, lab, and successes and challenges of each. We also discuss the strategies and innovations of introducing undergraduate students to autoethnography. We hope that our model will be instructive for colleagues with similar goals at their institutions. Through the cross-course workshops and collaborative exercises of the autoethnography lab, our students had the opportunity to use autoethnography not just to analyze their communities but also to build a community of practice
Library Perspectives, Issue 69, Spring 2023
No full textThis issue includes items about the Metcalf Scholars, 2023 Commencement, Percussion Music by Women, and staff retirements.https://digitalcommons.oberlin.edu/perspectives/1128/thumbnail.jp
New algorithm for isometric embedding of black hole horizons
No full textIsometric Embedding is a classic problem in differential geometry and general relativity that involves constructing a surface in Euclidean space described by a metric tensor. The results from this problem have a long history for visualization, but are also relevant for calculating quantities like black hole mass and energy. Unfortunately, in general scenarios, this problem requires a solver capable of handling a system of strongly nonlinear and nonstandard PDEs, for which there is no generally established algorithm. We have explored a radically new approach to the embedding problem, applying it to a variety of specific test cases and confirming that the results converge as expected and agree with results obtained analytically and by other algorithms. This presentation describes this novel algorithm and results of a finite-difference-based C++ code that we have written to implement and test it
Activity-based protein profiling of rhomboid intramembrane proteases
No full textRhomboid intramembrane proteases (RIPs) are a subclass of serine proteases responsible for cleaving polypeptide chains in other proteins. These enzymes have been associated with various metabolic, neurodegenerative, and parasitic diseases including type II diabetes and Alzhheimer’s disease. However, for many RIPs, no substrates have been reported, and our knowledge of the physiological pathways of these enzymes is still relatively limited. A valuable alternative tool to study these enzymes is activity-based protein profiling (ABPP), which utilizes small molecule probes to monitor protein activity. My current project is focusing on synthesizing a library of potential activity-based probes, including β-lactams, benzoxazinones and isocoumarins, and testing for their ability to engage bacterial (GlpG) and mammalian RIPs (RHBDL1, RHBDL2, RHBDL3, RHBDL4, and PARL). We are using the azide-alkyne Huisgen cycloaddition reaction with a functionalized rhodamine as a means to visualize labeling of these proteins. Using these methods, we have already observed probe engagement of GlpG, RHBDL2, and RHBDL3 with some of our initial structures. These promising initial results could provide a useful foundation for understanding the physiological roles of human RIPs and could inspire the development of molecules that could alter the activity of these enzymes
Neural Tracing of 3D Images with Deep Learning Models
No full textThe reconstruction of 3D microscope images is vital to understand the 3D morphology of neurons and glial cells, a critical step in brain anatomy and function research. Manual and digital neural tracing has been used for investigating neurodegeneration, modulating animal behavior, and mapping out complex neural circuits. We propose a deep learning model and methods that can be further refined relative to the biological question at hand, for the analysis of morphologies, tracing, and reconstruction of neurons and glial cells. The model we propose will utilize a convolutional neural network with transformers to trace 3D images, which will then be used to extract relevant features from the images to classify different types of neurons as well as different sub-compartments of neuron and glial cells. Using datasets of annotated neuron images, Zhou and colleagues achieved average accuracy of 98% with their open software tool-box, DeepNeuron, whereas the current leading model, authored by Li and Shen only has an average accuracy of 96%. As we aim to standardize across pooled datasets and correct annotations, our model has the potential to significantly improve the efficiency and accuracy of neural classification and offer deeper insights into neural connectivity and neurodegeneration