D2L (Canada)

Diabetic foot infections (DFIs) are a serious complication of diabetes, leading to high rates of hospital admission, amputation, and early death-particularly in persons living in resource-limited communities. Although the updated International Working Group on the Diabetic Foot guidelines in 2024 provide well-laid-out strategies for managing DFIs, these tend to be better suited for use in well-resourced centers. This case-based review discusses and applies these guidelines for an approach to DFI in real-world scenarios. It focuses on supporting health care providers working in community and resource-limited settings. The review explores how to recognize and diagnose DFI, including the use of imaging modalities, and how to distinguish soft tissue infection from bone infection or noninfectious entities. Management strategies are detailed regarding antibiotic considerations and approaches, including in resource-limited settings, and when other interventions or surgical referral may be important. Practical clinical cases are used to illustrate key points, emphasizing how care may be adapted based on the context. The authors also consider health equity aspects by identifying barriers that may be faced by patients who are from racialized backgrounds and live in resource-limited settings. The review aims to equip frontline wound clinicians with a pragmatic approach to managing DFIs that is grounded in up-to-date evidence framed in a real-world context.JOURNAL/aswca/04.03/00129334-202604000-00004/figure1/v/2026-03-10T151712Z/r/image-jpeg GENERAL PURPOSE: To provide clinicians with an evidence-based approach to the diagnosis and management of diabetic foot infections (DFIs). TARGET AUDIENCE: This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and registered nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES: After participating in this educational activity, the participant will:Analyze evidence-based clinical and laboratory findings to accurately diagnose (DFIs) and diabetic foot osteomyelitis.Evaluate patient-specific clinical data to select evidence-based management strategies for patients with (DFIs) and diabetic foot osteomyelitis.

Artist-drawn sketches only loosely conform to analytical models of perspective projection; the deviation of human-drawn perspective from analytical perspective models is persistent and well documented, but has yet to be algorithmically replicated. We encode this deviation between human and analytic perspectives as a continuous function in 3D space and develop a method to learn it. We seek deviation functions that (i) mimic artist deviation on our training data; (ii) generalize to other shapes; (iii) are consistent across different views of the same shape; and (iv) produce outputs that appear human-drawn. The natural data for learning this deviation is pairs of artist sketches of 3D shapes and best-matching analytical camera views of the same shapes. However, a core challenge in learning perspective deviation is the heterogeneity of human drawing choices, combined with relative data paucity (the datasets we rely on have only a few dozen training pairs). We sidestep this challenge by learning perspective deviation from an individual pair of an artist sketch of a 3D shape and the contours of the same shape rendered from a best-matching analytical camera view. We first match contours of the depicted shape to artist strokes, then learn a spatially continuous local perspective deviation function that modifies the camera perspective projecting the contours to their corresponding strokes. This function retains key geometric properties that artists strive to preserve when depicting 3D content, thus satisfying (i) and (iv) above. We generalize our method to alternative shapes and views (ii,iii) via a self-augmentation approach that algorithmically generates training data for nearby views, and enforces spatial smoothness and consistency across all views. We compare our results to potential alternatives, demonstrating the superiority of the proposed approach. Code and models will be released upon acceptance.

Usability is widely recognized as a problem in the context of the administration of access control systems. We seek to relate the notion of declarative semantics, a recurring theme in research in access control, with usability. We adopt the concrete context of POSIX ACLs and the traditional interface for it that comprises two utilities getfacl and setfacl whose natural semantics is operational. We have designed and implemented an alternate interface that we call askfacl whose natural semantics is declarative. We discuss our design of askfacl. We then discuss a human-subject usability study that we have designed and conducted that compares the two interfaces. Our results measurably demonstrate the goodness of declarative semantics in access control.

We propose a novel design of a Student Success System (S3), a holistic analytical system for identifying and treating at-risk students. S3 synthesizes several strands of risk analytics: the use of predictive models to identify academically at-risk students, the creation of data visualizations for reaching diagnostic insights, and the application of a case-based approach for managing interventions. Such a system poses numerous design, implementation, and research challenges. In this paper we discuss a core research challenge for designing early warning systems such as S3. We then propose our approach for meeting that challenge. A practical implementation of an student risk early warning system, utilizing predictive models, must meet two design criteria: a) the methodology for generating predictive models must be flexible to allow generalization from one context to another; b) the underlying mechanism of prediction should be easily interpretable by practitioners whose end goal is to design meaningful interventions on behalf of students. Our proposed solution applies an ensemble method for predictive modeling using a strategy of decomposition. Decomposition provides a flexible technique for generating and generalizing predictive models across different contexts. Decomposition into interpretable semantic units, when coupled with data visualizations and case management tools, allows practitioners, such as instructors and advisors, to build a bridge between prediction and intervention.

In distributed data mining, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a Hierarchically-distributed Peer-to-Peer (HP2PC) architecture and clustering algorithm. The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Within a certain level of the hierarchy, peers cooperate within their respective neighborhoods to perform P2P clustering. Using this model, we can partition the clustering problem in a modular way across neighborhoods, solve each part individually using a distributed K-means variant, then successively combine clusterings up the hierarchy where increasingly more global solutions are computed. In addition, for document clustering applications, we summarize the distributed document clusters using a distributed keyphrase extraction algorithm, thus providing interpretation of the clusters. Results show decent speedup, reaching 165 times faster than centralized clustering for a 250-node simulated network, with comparable clustering quality to the centralized approach. We also provide comparison to the P2P K-means algorithm and show that HP2PC accuracy is better for typical hierarchy heights. Results for distributed cluster summarization match those of their centralized counterparts with up to 88% accuracy.