Dallas College

BACKGROUND CONTEXT Leg length discrepancy (LLD) is common and present in up to 90% of the general population. Even discrepancies of less than 10 mm have been associated with lower back pain and lumbar radiculopathy. LLD may alter gait and posture, increasing mechanical stress on lumbar structures and potentially contributing to degeneration and spinal stenosis. PURPOSE To determine whether a relationship exists between leg length discrepancy and the development of lumbar spinal stenosis. STUDY DESIGN/SETTING Cadaveric study utilizing specimens from the Hamann-Todd Osteologic Collection in Cleveland, OH. PATIENT SAMPLE A total of 350 randomly selected cadaveric lumbar spines and corresponding lower extremities (femurs and tibias) were analyzed. The cohort included 284 men and 66 women, aged 17 to 87 years. Demographic variables included age, sex, and race at death. OUTCOME MEASURES Lumbar spinal canal area (L1–S1) and leg length discrepancy (LLD). METHODS Canal area at each lumbar level was calculated using a validated geometric formula and confirmed with computerized measurements. Total canal area was defined as the sum across L1–S1 levels. Leg length was determined by summing the maximum lengths of the femur and tibia on each side. LLD was calculated as the absolute difference between sides. Linear regression analysis evaluated the relationship between LLD and total canal area, adjusting for age, sex, and race. RESULTS In subjects older than 35 years, LLD demonstrated a significant negative association with total canal area (p < 0.01). For every 10 mm increase in LLD, total canal area decreased by approximately 1.1 cm2. No significant association was observed in subjects younger than 35 years (p > 0.05), likely due to minimal degenerative changes. Additionally, no association was found when LLD was less than 5 mm (p > 0.05). CONCLUSIONS Leg length discrepancy is associated with decreased lumbar canal area in individuals over 35 years of age. Discrepancies greater than 5 mm appear to contribute to lumbar degenerative changes and spinal stenosis, whereas smaller discrepancies do not demonstrate a significant effect. FDA Device/Drug Status This abstract does not discuss or include any applicable devices or drugs.

This qualitative study explores how experiences during a writing retreat in Ghana, West Africa, helped reframe self-love. First, I define "(Re)membering," then present Endarkened Feminist Epistemology and nkwaethnography as the framework and methodology that ground it. I draw upon "(Re)membering" as a lens through which I discovered misconceptions of self-love and recognized the "seduction [to] forget" wholeness. A thematic analysis of journal data, videos, and photos precedes prose, poetry, and original paintings, one of which employs visual grammar principles. The original art offers symbolic illustrations of the ongoing work (re)membering requires. Findings center (re)membering truths about my identity, my people, my community of women, and my professional calling. This work adds to the growing scholarship about the significant role African ascendants' memories play in framing, sustaining, and modeling self-love.

Interoperability with JavaScript is one of the highlights of ReScript that makes it well-suited for web development. It allows ReScript programs to use the huge ecosystem of JavaScript libraries that many web developers are already familiar with. Interoperability also makes it easy to convert parts of an existing JavaScript code base into ReScript while maintaining compatibility with existing code, whether for experimentation purposes or as part of a wider migration effort.

Anomaly detection is an interdisciplinary research area which attracts substantial attention both in statistics and in machine learning due to its critical role in a wide range of diverse applications, from cybersecurity to health monitoring. Most recently, spatio-temporal modeling through contrastive graph learning emerges as a promising alternative in scenarios, where there exists limited or even non-existent records of labeled anomalies and the data exhibit a sophisticated nonlinear dependence structure. However, prevailing graph contrastive learning methods are based on restrictive assumptions regarding selection of anomalousness thresholds, which limits their scope of usage. Motivated by threat detection in cyber-physical systems, we develop a new approach to anomaly detection in spatio-temporal data by fuzing the notion of self-supervised graph contrastive learning and extreme value theory. Our key idea is to bolster the anomaly detection performance of neural networks by leveraging the complementary insight on the intrinsic multi-scale data organization which contrastive learning can provide. We then show how our ideas at the interface of extreme value theory and graph contrastive learning can be combined to deliver more systematic and reliable detection of anomalies. We illustrate the utility of the new approach to detecting anomalies in cyber-physical Internet of Things, water distribution and military computer network systems.