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Lysine Oxidation by LOXL2 in Single Strands Versus Triple Helices: Implications for Collagen-Related Diseases
Item type: Journal Article
Poller L.M.; Fiala T.; Wennemers H. (2026)
Collagen cross-linking is essential for the structural integrity of the extracellular matrix. In several pathological conditions, the triple-helical core of collagen is disrupted, and proline-rich single strands occur. Lysyl oxidases (LOXs), enzymes that initiate cross-linking by catalyzing the oxidative deamination of lysine to allysine residues, are upregulated in disease. Surprisingly little is known about the selectivity of LOXs. LOX-initiated cross-linking is believed to occur exclusively in the unstructured terminal domains of collagen, the telopeptides. But is this really the case? Herein, we show, with a combination of tailored chemical probes, enzymatic assays, CD and NMR spectroscopic analyses, that LOXL2 oxidizes Lys in proline-rich single strands but not in triple helices. A comparison of Lys-containing collagen model peptides (CMPs) with Lys-containing telopeptides revealed comparable or higher oxidation rates in proline-rich segments. The data suggest that LOXL2-initiated cross-linking occurs in pathologically disrupted triple-helical domains, offering insights into aberrant extracellular matrix stiffening.
Authoritarian diversity and its limits: regime subtypes and climate change adaptation readiness
Item type: Journal Article
Ko J. (2026)
This study examines whether institutional differences among authoritarian regime subtypes–single-party, monarchical, military, and personalist–are associated with variation in national climate change adaptation readiness. Drawing on comparative authoritarianism, it expects single-party and monarchical regimes to be better positioned than military and personalist systems to sustain the longer-term commitments associated with adaptation readiness. Using cross-national panel data on 73 authoritarian regimes from 1995 to 2010 and estimating two-way random-effects and System GMM models, the analysis does not identify statistically significant differences across regime types. These null findings suggest that the institutional mechanisms highlighted in the theory may be difficult to detect in this domain using available cross-national indicators. One possible explanation is that adaptation readiness is a low-visibility form of collective provision whose benefits are often delayed and realized through avoided losses. Another is that existing indicators may under-capture governance processes relevant to adaptation capacity.
The Gaps in the Multiplication Table
Item type: Journal Article
Kowalski E.; Kuperberg V. (2026)
We find the explicit list of gaps between successive elements of the (Formula presented.) multiplication table for any positive integer N.
LLM-Driven Architectural Geometry Generation: From Prompt to Parametric Model
Item type: Conference Paper
Liu M.; Tai H.C.; Shen C.; et al. (2026)
Traditional architectural geometry design is a critical yet labour-intensive stage in the design process. The emergence of generative artificial intelligence (GenAI) models, such as diffusion models, has enabled rapid explorations of architectural conceptual images and 3D massing. But their limited semantic understanding, low editability, and lack of interactive refinement constrain the professional use. Large language models (LLMs), with their capacity for semantic reasoning, multimodal integration, and procedural script generation, offer new opportunities to address these limitations. This study investigates how LLMs can support early-stage geometry generation through an experiment with 27 graduate students, producing over 270 design alternatives for an office building in Beijing. Three key findings emerged: 1) Compared to diffusion-based platforms, LLMs can generate 3D models with clearer geometric boundaries, higher editability, multi-round refinement, and stronger downstream integration. 2) LLMs can uniquely generate parametric models that yield multiple design alternatives, demonstrating an ability to encode design logic and thereby offering a higher level of controllability than conventional 3D meshes. 3) We identified effective strategies to improve efficiency, including explicitly articulating design logic, providing reference models or images, and facilitating cross-model collaboration. Our findings establish LLMs as powerful co-creative partners, advancing the potential of AI-assisted architectural design.
From Robotic Scanning to XR-Guided Assembly: End-to-End Digital Workflow for the Automated Reuse of Concrete Waste: Integrating Sensing, Computation, and Immersive Technologies for Material Reuse.
Item type: Conference Paper
Önalan B.; Mitropoulou I.; Triantafyllidis E.; et al. (2026)
Reusing geometrically non-standard concrete elements to reduce reliance on raw materials in new construction remains a complex and time-intensive process, limiting its cost-effective adoption. Computational design and digital fabrication tools offer a promising pathway to automate and scale this process, enabling more efficient and systematic reuse of non-standard concrete elements. This study integrates robotic 3D scanning, algorithmic design methods, and extended reality (XR) guidance into an automated digital workflow for concrete reuse. The proposed workflow consists of three stages: (1) robotic LiDAR scanning and mesh processing to capture and rationalize material stock; (2) algorithmic nesting of non-standard geometries using packing strategies; and (3) XR-based spatiotemporal guidance for precise on-site assembly. The workflow was demonstrated through physical prototypes in two workshops in which participants constructed wall segments using concrete waste, enabling the development, demonstration, and validation of the workflow. Results show high placement accuracy with 2.3% average vertical deviation, reduced manual scanning effort through 2-minute automated element capture, and successful assembly completion enabling non-expert participants to construct a 50×50cm segment in 15 minutes after brief orientation. This study provides a replicable framework to embed circularity into design and construction workflows, advancing the practical adoption of automated reuse strategies in architecture and construction.