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Among ongoing eforts to broaden participation in K–12 computer science (CS) education, the Advanced Placement (AP) Computer Science Principles (CSP) course receives a lot of attention. While prior research has shown increased participation among some his- torically underrepresented groups, little is known about how the course serves students with disabilities. This study examines participation patterns of students with dis- abilities in CSP courses across 230 public schools in Pennsylvania during the 2022–2023 academic year. Drawing on statewide enroll- ment data from 306 CSP courses, we conducted a series of statistical analyses to investigate relationships between student participation and school-level capacity factors identifed by the Capacity, Ac- cess, Participation, and Experience (CAPE) framework, including teacher experience, school funding, and locale. Findings show that many factors have a small, but statistically signifcant infuence. However, CSP courses labeled as AP were associated with signif- cantly lower participation rates among students with disabilities, compared to CSP courses without the designation. These fndings suggest that course labeling and underlying assumptions about aca- demic rigor may unintentionally limit opportunities for students with disabilities. 

Parsons problems have become a mainstay of computer science education. They are heavily used among students, especially in K-12 and provide a small puzzle-like experience for students to practice their skills. Today, while prior work has explored com- plex issues with accessibility and block languages in general, the 2024 changes to accessibility regulations by the U.S. Department of Justice includes new rules around mobile platforms. These rules are ill-defned and in need of evaluation. In this work, we make several contributions. First, we conducted an evaluation of existing blocks with respect to their regulatory compliance and discuss a new blocks technology that we developed that meets these new mobile guidelines. Second, we conducted three empirical studies using Parsons problems to evaluate the usability of the technology with teachers of the visually impaired (n = 32), high-school students with diverse disabilities (n = 28), and high-school students with blindness or low vision (n = 13). 

Dielectrophoresis (DEP) is a label-free electrokinetic method for selectively trapping polarizable particles using non-uniform electric fields. While co-planar electrode systems are common, their inherent DEP force distribution limits throughput. This study presents a computationally efficient framework for modeling two-dimensional DEP-based particle trapping in ordered arrays of conductive cylinders. These cylinders are modeled at a range of sizes, from micrometers to nanometers, to represent microfluidic systems consisting of conductive pillars, nanofibers, etc. Analytical solutions for fluid flow and electric potential were derived using eigenfunction expansions and collocation, then used in a particle tracking model that includes hydrodynamic drag, Brownian motion, and multipolar DEP forces. Although focused on conductive arrays, this framework is extensible to other configurations. This work provides a foundation for future work in the design of high-throughput DEP systems. Both dimensionless and dimensional analyses were performed across a wide range of particle sizes (30 nm to 3 μm), voltages (10 mV to 100 V), and array geometries. No specific optimal cylinder size was found; instead, optimal performance arises from a balance between DEP force distribution and flow through the cylinder array gap. Diamond-oriented arrays exhibited enhanced trapping under moderate dielectrophoretic velocity-to-fluid velocity ratios (up to 39% greater), while square arrays performed better under low-field and large-cylinder conditions (up to 40% greater). 

Abstract Continuous time Markov chains are commonly used as models for the stochastic behavior of chemical reaction networks. More precisely, these Stochastic Chemical Reaction Networks (SCRNs) are frequently used to gain a mechanistic understanding of how chemical reaction rate parameters impact the stochastic behavior of these systems. One property of interest is mean first passage times (MFPTs) between states. However, deriving explicit formulas for MFPTs can be highly complex. In order to address this problem, we first introduce the concept of$$coclique\, level\, structure$$and develop theorems to determine whether certain SCRNs have this feature by studying associated graphs. Additionally, we develop an algorithm to identify, under specific assumptions, all possible coclique level structures associated with a given SCRN. Finally, we demonstrate how the presence of such a structure in a SCRN allows us to derive closed form formulas for both upper and lower bounds for the MFPTs. Our methods can be applied to SCRNs taking values in a generic finite state space and can also be applied to models with non-mass-action kinetics. We illustrate our results with examples from the biological areas of epigenetics, neurobiology and ecology. 

World-scale augmented reality (AR) applications need a ubiquitous 6DoF localization backend to anchor content to the real world consistently across devices. Large organizations such as Google and Niantic are 3D scanning outdoor public spaces in order to build their own Visual Positioning Systems (VPS). These centralized VPS solutions fail to meet the needs of many future AR applications---they do not cover private indoor spaces because of privacy concerns, regulations, and the labor bottleneck of updating and maintaining 3D scans. In this paper, we present OpenFLAME a federated VPS backend that allows independent organizations to 3D scan and maintain a separate VPS service for their own spaces. This enables access control of indoor 3D scans, distributed maintenance of the VPS backend, and encourages larger coverage. Sharding of VPS services introduces several unique challenges---coherency of localization results across spaces, quality control of VPS services, selection of the right VPS service for a location, and many others. We introduce the concept of federated image-based localization and provide reference solutions for managing and merging data across maps without sharing private data. 

Human cytochrome P450 (P450) 27A1 catalyzes the hydroxylation of cholesterol and vitamin D derivatives. P450 27A1 is localized in the mitochondria and is reduced by its redox partner protein adrenodoxin twice for each catalytic cycle. The reliance on adrenodoxin is conserved across all human mitochondrial P450 enzymes. This study examines the adrenodoxin interaction with P450 27A1 and draws comparisons with studies of other P450 enzymes to determine if differences exist. The P450-adrenodoxin complex structure was examined by chemical crosslinking and analyzed by mass spectrometry. The effect of adrenodoxin concentration on P450 27A1 function was assessed by studying effects on steady state enzyme kinetics parameters and equilibrium substrate binding. The results suggest that adrenodoxin binds to P450 27A1 at a proximal site like other P450 enzymes but differs in the specific residues involved. Furthermore, the presence of adrenodoxin and/or substrate decreases the number of interprotein and intraprotein crosslinks observed, indicating that these components change the conformation of the P450 enzyme. Increased adrenodoxin concentration causes the P450 and vitamin D3 kcat value to increase, the kcat/Km value to decrease, and the substrate Kd to remain constant. These results suggest adrenodoxin alters enzyme efficiency beyond electron transfer without affecting substrate loading. The adrenodoxin effects on P450 27A1 kinetics and equilibrium constants differ from those of other human mitochondrial P450 enzymes. In total, these structural and functional studies suggest that while the general adrenodoxin binding site and function is conserved across P450 enzymes, the details and additional effects of this interaction vary.