Aviation safety incident reports are largely composed of unstructured narrative text, creating significant barriers to systematic causal analysis and proactive risk management. This study introduces an automated framework that employs Large Language Models (LLMs) to construct the Aviation Safety Hazard Causal Knowledge Graph (ASH-CKG). By combining few-shot learning with a hybrid relation normalization strategy, the framework reduced around 3000 relation variants to 185 canonical expressions (a 93.71% reduction in diversity), ensuring the structural rigor required for safety analysis. To facilitate dynamic, decision-oriented querying of the static graph, we developed a Retrieval-Augmented Generation (RAG) with Path-pruning and Chain-of-Thought (RAG-P-CoT-QA) module, which optimizes multi-hop subgraph retrieval and generates logically coherent, contextually relevant responses. Quantitative evaluations show that the proposed framework achieves competitive answer relevance while improving semantic similarity and evidence grounding over the Vanilla KG-QA baseline. Crucially, this framework empowers safety managers to transition from basic information retrieval to proactive decision support, offering a data-driven foundation for uncovering latent risk interactions and refining safety mitigation strategies within Safety Management Systems (SMS).
Advances in understanding the molecular landscape of gastroesophageal junction (GEJ) adenocarcinoma underscore the need for biomarker-driven treatment approaches. The Society of Surgical Oncology (SSO) Gastrointestinal (GI) Disease Site Working Group has developed practice guidelines for the biomarker-based management of nonmetastatic GEJ adenocarcinoma, with specific integration of microsatellite instability (MSI) status. Although multimodal therapy has improved outcomes, treatment strategies for GEJ adenocarcinoma remain largely stage-based rather than biomarker-directed. With emerging evidence supporting the prognostic and predictive roles of MSI-high (MSI-H), human epidermal growth factor receptor 2 (HER2), and other novel targets, there is an urgent need for clear guidance on how to incorporate biomarkers into the management of resectable disease. These guidelines provide evidence-based recommendations to support precision oncology in GEJ cancer care.
Combinations of additive manufacturing methods such as inkjet printing and various 3D printing techniques have led to advances in customizable electronic devices by reducing the need for the complex assembly of individually fabricated components. Such combinations are particularly attractive for the fabrication of electrochemical sensing platforms, where the sizes and spatial configurations of electrodes enable (or limit) the sensitivity of the sensors. However, to realize the potential of the combined fabrication methods, the parameters of each printing technique must be mutually compatible. For instance, electrochemical sensing assemblies consisting of inkjet-printed metals on 3D-printed plastics require post-processing steps that convert nanoparticular metals in precursor ink to conformal, mechanically stable, conductive films with negligible alteration to the underlying plastic substrate. While traditional sintering techniques convert inks to conductive metal films, the use of high temperatures are not compatible with structures printed via fused deposition modeling, for instance. In this proof-of-concept study, commercial gold inks were inkjet-printed onto 3D-printed poly(lactic acid) (PLA) substrates. Optimization of the printing parameters and IR sintering resulted in stable, electrochemically active working electrodes. Surface characterization via scanning electron microscopy confirmed the formation of a homogeneous coating, and X-ray photoelectron spectroscopy data revealed the presence of gold on the surface. Electrical characterization via a four-point probe demonstrated a low sheet resistance, indicating the suitability of these electrodes for electrochemical measurements. Electrochemical data revealed that these gold electrodes are electrochemically active, and the diffusion-limited behavior of a redox probe was observed as expected. Additionally, these electrodes were tested for detecting lead in standard solutions, showing a linear response to lead concentrations, which indicates their potential in sensing applications. However, the detection range is significantly higher than the EPA-approved limit for lead concentrations in water, suggesting that electrode sensitivity requires further improvement. These findings have implications for fabricating inkjet-printed gold electrodes as sensors while highlighting the need for additional modifications to meet regulatory detection limits for heavy metal analysis.
Metabolic dysfunction and alcohol-associated steatotic liver disease (MetALD) has emerged as a new subtype of steatotic liver disease, and presents a substantial challenge in clinical trial design due to a lack of data and consensus on endpoint selection. This review proposes patient selection, stratification, and optimization in Phase II and III clinical trial designs for MetALD and provides reasonable stage-specific clinical trial endpoints. It provides a comprehensive review of data on non-invasive tests (NITs) and trial endpoints in metabolic dysfunction-associated steatotic liver disease and alcohol-associated liver disease, and extrapolates these data to MetALD. The design of effective MetALD trials necessitates rigorous stratification by fibrosis stage and drinking patterns. In pre-cirrhotic MetALD, NITs, including vibration-controlled transient elastography, magnetic resonance elastography, and magnetic resonance imaging-proton density fat fraction, are reliable surrogate endpoints, with each modality-specific cut-off set to meet clinical improvement targets. For patients with compensated or decompensated cirrhosis, the emphasis shifts to hard clinical outcomes, such as transplant-free survival, overall survival, and prevention of major adverse liver events in phase III clinical trials. Additionally, integrating alcohol-reduction endpoints and comprehensive cardiometabolic endpoints is essential to capture the full impact of interventions in clinical trials. The MetALD clinical trials require strategic design, the adoption of the NITs shift concept, and stage- and phase-specific endpoints.
Prescribed fire is an important management tool that affects the availability and quality of resources for arthropods in temperate deciduous forests. We quantified occupancy of wood cavities by arthropods within burned and unburned portions of a temperate deciduous forest in the eastern United States. We placed 400 wood blocks (4.0 & times; 4.5 & times; 25 cm) on the forest floor at each site. Each block had either one 10-mm-diameter cavity or two 5-mm-diameter cavities drilled 8 cm deep into its ends. We collected the contents of the cavities over a 5-month period immediately following the controlled burn and again 2 years post-burn. Arthropod occupancy was higher in the burned site (37% +/- 4%) versus the unburned site (17% +/- 3%) immediately following the prescribed fire, but converged on similar frequency after 2 years (26% +/- 3% and 28% +/- 3%, respectively). Ants, spiders and the cockroach Parcoblatta pennsylvanica (De Geer, 1773) were the most common cavity occupants. There was a tendency for ants and spiders to inhabit either small (5 mm) or large (10 mm) cavities, respectively; however, cavity diameter and body size were not correlated for these groups. Bees and wasps collectively occupied both diameters with similar frequency. The results suggest that prescribed fire increases arthropod occupancy of wood cavities in the short term-possibly by reducing the availability of natural cavities-but these effects disappear after 2 years of forest succession. Understanding the effects of prescribed fire on the ecology of forest arthropods is essential for biodiversity conservation.
