Search Results (703)

Background/Objectives: Biorelevant in vitro dissolution testing is used increasingly to predict complex mechanisms in the gastrointestinal (GI) tract that determine oral bioavailability. However, the limited use of non-compendial systems is driven by the lack of widely accepted, standardized validation frameworks. This ongoing gap continues to restrict their adoption relative to United States Pharmacopeia (USP) apparatus. While the physiological relevance and biopredictive capabilities of the tiny-TIM and TIM-1 in vitro GI models have been demonstrated in previous studies, their inter-laboratory reproducibility has not been systematically established. Therefore, this international ring study evaluates the reproducibility of in vitro simulations of GI transit and absorption of paracetamol in fasted- and fed-state conditions in tiny-TIM and TIM-1. Methods: Three laboratories used TIM-1 and five used tiny-TIM to simulate oral administration of a 500 mg paracetamol solution to a healthy adult. Paracetamol solution was selected as a well-characterized and widely available BCS I compound to minimize formulation and solubility effects and focus on system performance, enabling the generation of a generic validation dataset for the reproducibility of TIM experiments. Results: Paracetamol bioaccessibility profiles were repeatable and reproducible (all pairwise f₂ > 50). Maximum differences in total bioaccessible paracetamol were 0.9% (TIM-1) and 2.8% (tiny-TIM) within laboratories and 3.4 and 5.9% between laboratories. Inter-lab variability at individual time points remained <4.0% (fasted) and 5.2% (fed). Both TIM models produced biopredictive metrics, correctly predicting no food effect on total paracetamol bioaccessibility and capturing delayed t_max. Gastric and intestinal environments showed repeatable pH, temperature, and GI transit characteristics, with fluctuations across transit stages that mirrored reported in vivo patterns. Conclusions: These results demonstrate that TIM systems can reproducibly simulate gastrointestinal conditions across laboratories and generate consistent measurements of drug product performance, despite the complexity of the dynamic processes involved. While this evaluation involving a single BCS I drug solution should not be directly extrapolated to experiments with poorly soluble compounds or different formulations, it supports the use of TIM systems as robust in vitro models in drug product development. This study provides a standardized, inter-laboratory, baseline performance dataset to support regulatory submissions incorporating TIM data and enable more confident interpretation of TIM experiments. Full article

To address the challenges faced by heterogeneous Unmanned Aerial Vehicle (UAV) systems in complex task allocation, including over-reliance on centralized scheduling, training deadlock, inadequate capture of temporal collaboration, and unstable training under sparse reward conditions, this paper proposes a distributed task allocation algorithm based on reinforcement learning. The algorithm adopts a decentralized decision-making architecture, which enables the autonomous formation of UAV collaborative groups without the need for a global scheduling center. A cascaded submission timeout mechanism is introduced to prevent training deadlock; the combination of Long Short-Term Memory (LSTM) and attention mechanism is employed to accurately model temporal correlations and collaborative dependencies; and the Proximal Policy Optimization (PPO) algorithm is leveraged to optimize the training stability under sparse reward conditions. Experimental results demonstrate that the proposed algorithm achieves a 100% task success rate in scenarios of different scales, and its key metrics, including makespan, time cost and waiting time, are significantly superior to those of mainstream baseline methods such as the Genetic Algorithm (GA) and the Hungarian Algorithm (HA). Moreover, the algorithm still maintains excellent robustness under the conditions of UAV failures, parameter variations, and dynamic task perturbations. This method supports zero-shot generalization for any number of UAVs and tasks and provides an efficient and reliable solution for the real-time collaborative scheduling of heterogeneous UAV systems. Full article

Background/Objectives: The high risk of CNS dissemination poses a significant challenge in the management of primary vitreoretinal lymphoma (PVRL). We evaluated the clinical value of our institutional protocol for PVRL, which combines targeted vitreous sampling with routine central nervous system (CNS) surveillance using magnetic resonance imaging (MRI) every 4–6 months. Methods: We retrospectively reviewed 34 consecutive patients who underwent vitreous biopsies at Niigata University Hospital between January 2010 and December 2021; 12 patients were initially diagnosed with PVRL without CNS involvement. The protocol mandates submission of both undiluted vitreous samples and the entire vitreous cassette contents, including perfusion fluid, for cytologic evaluation. Patients with PVRL underwent MRI surveillance every 4–6 months. Results: Among 12 patients with PVRL, vitreous cytology classified as Class IV or higher demonstrated a positivity rate of 75% (9/12) using undiluted samples alone, which increased to 92% (11/12) when cassette contents were included. Ancillary test results revealed an interleukin (IL)-10/IL-6 ratio > 1 in 75% (9/12) and immunoglobulin heavy chain gene rearrangement in 92% (11/12). Extraocular relapse occurred in 92% of patients (11/12), including 10 cases of CNS involvement and one systemic relapse, with a mean time to CNS progression of 11.8 months. The 5-year overall survival was 58%. Conclusions: Comprehensive vitreous sampling incorporating perfusion fluid may improve cytologic detection in PVRL within a single-center setting. Routine MRI surveillance facilitates early detection of CNS relapse in patients with PVRL; however, a survival benefit cannot be established from this retrospective analysis. Full article

Bovine brucellosis is an endemic zoonotic disease in South Africa with significant consequences for livestock productivity and public health. Although routine laboratory surveillance data from the Rose Bengal Test (RBT) are widely collected, they are seldom used to investigate temporal and spatial patterns of disease detection. This study aimed to examine temporal, seasonal, and spatial predictors of RBT positivity for bovine brucellosis in Mpumalanga Province, South Africa. A retrospective observational study was conducted using routine laboratory records from the Mpumalanga Provincial Veterinary Laboratory between January 2021 and December 2024. The dataset included all bovine serum samples with complete information on testing date, municipality, and RBT results. Laboratory submissions were recorded as batches, defined as groups of serum samples submitted together to the laboratory as part of a single surveillance or investigation event. The primary outcome was batch-level RBT positivity, defined as the presence of at least one RBT-positive serum sample within a submission batch. Temporal (year of testing), seasonal (season of submission), and spatial (local municipality area) variables were evaluated as predictors of RBT positivity using logistic regression models. Mixed-effects logistic regression accounted for the clustering of submissions within municipalities. A total of 568 submission batches comprising 67,974 serum samples were analysed, of which 6182 tested positive, yielding an overall positivity of 9.1%. RBT positivity increased significantly in 2023 compared with 2021 (AOR = 2.47; 95% CI: 2.27–2.68). Seasonal variation was observed, with higher odds of positivity in spring (AOR = 1.80; 95% CI: 1.65–1.97) and lower odds in autumn and winter relative to summer. Mixed-effects modelling indicated significant residual spatial heterogeneity in RBT positivity across municipalities. Routine laboratory surveillance data can provide valuable epidemiological insights into the temporal, seasonal, and spatial dynamics of bovine brucellosis detection and support risk-based surveillance strategies in endemic livestock systems. Full article

Generative AI enables rapid visualization of sustainable urban design scenarios, yet the question of whether these outputs encode sustainability as operable spatial logic, rather than merely depicting it as a visual impression, remains underexplored. This study proposes a two-level assessment framework that scores the same sustainability dimensions at both the visual-representation level and the spatial-logic level, treating the systematic decoupling between the two as a form of visual greenwashing: system-induced representational distortion rather than deliberate misrepresentation. Using AI-workflow reports from two site-based urban design studios (47 students, 12 teams, 36 coded scenes), the framework integrates rubric-based scoring with qualitative process tracing of breakdown–repair logs. Results show that image-level scores consistently outperform logic-level scores across all five dimensions, with the gap most severe in mobility hierarchy and walkability and smallest in green/blue infrastructure. Case analysis reveals that breakdowns arise from failures in program encoding, urban-scale coherence, functional-boundary demarcation, and relational-condition matching, and that students deploy multi-stage repair pipelines, including prompt restructuring, tool switching, reference injection, and external-source compositing, to re-inject collapsed spatial logic. These findings reframe AI-assisted urban design as repair-centered workmanship rather than automated production. The study proposes three guardrails to prevent visual sustainability from substituting for spatial-logic sustainability: image–logic paired submission, design audit trail formalization, and gap-based red-flag review. Full article

Building code waiver assessments in Singapore remain largely discretionary, relying on case officers’ subjective judgement with limited decision-support tooling. This study presents the first machine learning framework for predicting building code waiver outcomes, trained on 197 historically decided cases from the Building and Construction Authority (BCA) across five waiver categories: barrier-free accessibility (n = 45), ventilation (n = 61), staircase design (n = 37), safety provisions (n = 30), and structural modifications (n = 24), spanning 2021 to 2023. Fourteen engineered features, including documentation completeness, technical justification quality, and compliance history, were extracted through domain-expert annotation. Four models were evaluated: L2-regularised logistic regression, random forest, gradient boosting (XGBoost 2.0.1), and a weighted ensemble. The ensemble achieved the highest predictive accuracy of 83.7% (95% CI: 79.2–88.1%) with an area under the receiver operating characteristic curve (AUC) of 0.891 (95% CI: 0.854–0.928), significantly outperforming all individual models (McNemar’s test, p < 0.05). SHAP analysis revealed that documentation completeness and technical justification quality collectively account for 55% of prediction variance. A companion five-by-five risk assessment matrix, combining predicted rejection probability with consequence severity, stratified cases into actionable risk tiers correlating with observed approval rates ranging from 90.3% (very low risk) to 10.0% (very high risk; Spearman rho = −0.71, p < 0.001). Performance varied across waiver categories: ventilation waivers achieved the highest balanced accuracy (87.1%) while safety waivers proved most challenging (balanced accuracy 64.3%, sensitivity 40.0%). The framework offers a transparent, data-driven decision-support complement to regulatory judgement, learning patterns from historically decided applications within the 2021–2023 BCA context, and demonstrates feasibility for integration into Singapore’s Corenet X digital building submission platform. These five waiver categories serve as domain stratification variables. The machine learning target variable is the binary regulatory outcome: Approved (46.2% of cases) or Rejected (53.8%). Full article

The objective of this study was to evaluate relationships between antimicrobial use (AMU) and antimicrobial resistance (AMR) in isolates from farmed Atlantic salmon that could represent finfish pathogens in the British Columbia (BC) aquaculture industry using historical surveillance data. Antimicrobial susceptibility data were obtained for 1040 bacterial isolates from farmed Atlantic salmon submissions to the BC Animal Health Centre for 2007–2018. Antimicrobial use data were provided by the BC Ministry of Agriculture and Food from feed mill prescriptions for BC farmed Atlantic salmon for 2007–2018. Multivariable logistic regression models for all bacterial isolates with a random intercept for species were developed to determine associations with outcomes of resistance to trimethoprim-sulfadiazine (SXT), oxytetracycline (OXY), and florfenicol (FLOR). Resistance to SXT, FLOR, and/or OXY were all significantly associated with each resistance outcome in their respective models. Only the SXT resistance model was significantly associated with AMU, specifically potentiated sulfonamide use, but use was not significantly associated with AMR for any other resistance outcome. The results of this study contribute to the rapidly growing and increasingly pertinent body of literature on AMU and AMR in the unique marine aquaculture environment. Future research at the farm level linking pen-specific AMU to AMR outcomes will provide more understanding of selection pressure for AMR at the local level and provide more guidance for antimicrobial stewardship in finfish aquaculture. Full article

Injection well placement and rate allocation are among the most decisive factors in determining the efficiency and bankability of CCS projects. However, optimizing these parameters is notoriously complex: even a small number of injection wells leads to a virtually infinite set of injection scenarios, while traditional optimization techniques typically require thousands of high-fidelity reservoir simulations. For project developers, this computational burden can stall critical Final Investment Decisions (FID). The approach proposed here addresses this bottleneck by using a Design of Experiments (DoE) framework combined with nonlinear surrogate modeling, which efficiently maps the relationship between injection rates and storage performance, to identify near-optimal solutions with a minimal number of simulations. We show that our method achieves up to 97% of the initially targeted CO₂ sequestration with as few as 15 simulations, demonstrating a step-change reduction in time and cost. From a business standpoint, CCS operators can de-risk projects earlier, accelerate FID timelines, and evaluate multiple site configurations in parallel while minimizing computational overhead. Rather than waiting weeks or months for exhaustive optimization, decision-makers can gain timely, reliable insights that directly support capacity commitments, regulatory submissions, and ultimately revenue realization. Full article

Human-driven land-use change has significantly altered forest ecosystems and carbon dynamics in mountainous regions. This study aims to quantify land cover transitions and associated forest carbon stocks changes in Bhutan. It also seeks to support the development of a national measurement, reporting, and verification system. Using Landsat-based satellite imagery and object-based image classification techniques, we assessed forest cover transitions, stand structure variations, and forest type changes across temporal intervals. The analysis revealed a consistent increase in agricultural and built-up areas. It also showed a concomitant decline in coniferous forest cover. In particular, agricultural land increased by approximately 0.77 million ha, while coniferous forest decreased by approximately 0.19 million ha over the study period. These changes were driven by both climatic shifts and socio-economic factors. Approximately 57% of Bhutan’s population depends on agriculture. Correspondingly, forest carbon stocks declined from approximately 570 million tC in 1995 to 405 million tC in 2017. This decline was largely attributed to coniferous forest loss and climate-induced mortality. Bhutan has made significant preparations for the implementation of the Warsaw REDD+ framework under the United Nations Framework Convention on Climate Change. These preparations include the establishment of a forest reference emission level for submission. However, challenges remain in detecting small-scale land use changes. Additional challenges include addressing spectral misclassification in mountainous regions. Our study provides a scientific baseline to support national forest monitoring and carbon accounting systems. It also offers policy-relevant insights for achieving Bhutan’s nationally determined contributions and enhancing its carbon sink potential. Full article

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

Background: In 2024, Reddit, an emerging social media platform, saw a 50% increase in monthly users to nearly 100 million. Reddit has also emerged as a significant space for discussions about health conditions, including epilepsy, which affects about 50 million people globally. Purpose: This study aims to explore trends in the volume, timing, themes, emotional tone, and sentiment of posts on the r/Epilepsy subreddit from 1 December 2023 to 31 December 2024. Methods: We collected 25,222 original English-language posts from r/Epilepsy using Reddit’s Application Programming Interface (API). Data extraction was restricted to English-language submissions to ensure compatibility with sentiment and thematic analyses. We analyzed post volume and timing using chi-square tests and Poisson regression. Emotional tone was measured using TextBlob (version 0.19.0), while compound sentiment scores were calculated via VADER (Valence Aware Dictionary and Sentiment Reasoner) (NLTK version 3.9.1). A Pearson correlation assessed agreement between sentiment and emotional tone, with statistical significance set at p < 0.05. Thematic analysis was conducted using a KMeans clustering algorithm (scikit-learn version 1.6.1) to identify recurring discussion topics. Results: Total monthly posts steadily increased, with the highest number (2175) in December 2024. Peak posts in descending order were in December 2024, August 2024, and November 2024. Posts were not evenly distributed across the week, with a significant peak on Mondays (χ² = 86.75, p < 0.001) and Poisson regression confirming higher activity early in the week (p = 0.001). Emotional tones fluctuated, with positive sentiments in January and October 2024, and negative sentiments in March and August 2024. KMeans clustering identified five main themes: treatment experiences, community engagement, personal experiences, solidarity, and subreddit gratitude. Manual validation of a random subset of posts demonstrated moderate concordance between automated sentiment classification and human ratings. Conclusions: This study highlights temporal patterns, sentiment dynamics, and thematic structure in online discussions on epilepsy. Social media may offer valuable, real-time insights into patient-centered concerns and community engagement, which can inform healthcare professionals and advocacy groups in supporting individuals affected by epilepsy. Future studies may compare trends of epilepsy discussions across various social media platforms, such as X and Instagram, to further understand online patient experiences. Full article

Ensuring the consistency of recurring ETL processes is a critical challenge in large-scale financial analytics, where upstream data changes—such as variable redefinitions, unit conversions (e.g., from days past due to number of overdue installments or currency changes), or erroneous submissions following source system updates—can silently degrade model reliability. These risks are amplified in automated modeling environments, where dozens of models are retrained monthly for each financial institution and the number of serviced institutions is expected to grow. This study presents an automated statistical monitoring framework for continuous quality assurance of monthly ETL outputs used in model development. The approach quantifies drift between a reference dataset and successive data deliveries using descriptive univariate and bivariate statistics combined with a normalized Canberra-based drift score, aggregated into interpretable variable-level stability measures. Sensitivity is evaluated through controlled noisification experiments with increasing Gaussian perturbations, demonstrating a monotonic decline in stability scores and consistent directional shifts in complementary metrics such as the Gini coefficient and Kolmogorov–Smirnov statistic. The results show that the framework effectively detects both subtle and large-scale distributional changes, providing a scalable, interpretable, and reproducible monitoring diagnostics suitable for fully automated financial data pipelines, with flexibility for extension. Full article

This study examined differences in finishing techniques and positional contexts across three temporal windows in the Ultimate Fighting Championship (2003–2004, 2013–2014, and 2023–2024), revealing differences consistent with a shift from greater diversity to a specialized and systematized model. Analysis of 906 finalized bouts demonstrated a marked concentration of submission finishes, with rear naked choke increasing from 15.8% to 46.8% (p ≤ 0.001), while back control was the dominant positional context (45.5%, p ≤ 0.001). In striking-based finishes, punches maintained prevalence, evolving from 77.4% (2003–2004) to 86.1% (2023–2024, p ≤ 0.001), whereas kicks declined from 20.5% to 11.3% (p ≤ 0.001). Submissions increased from 37.0% to 52.0% of all finalized bouts (p ≤ 0.001). These findings indicate a growing emphasis on specific finishing techniques, with modern mixed martial arts demonstrating increased distributional concentration in the methods used to finalize bouts. The increased frequency of certain techniques (e.g., rear naked choke and punches) among finalized fights may reflect strategic preferences, training priorities, or rule-driven changes in technique effectiveness, but cannot be interpreted as evidence of inherent technical superiority without additional data on success rates or efficiency metrics. Our data suggest that contemporary fighters have developed more direct and systematized approaches to finishing fights, reflecting the evolution of training methodologies and competitive strategies. Full article

This article underscores sites of Black and Indigenous ecological failure to draw attention to the limits of figuring ideal subjects on the “ecological field” as stewards of, laborers on, and ultimately masters of, the earth. I consider depictions of errant ecological comportment to render other kinds of orientations—boredom, distraction, orgasmic submission, grief—plausible and necessary for developing and honing an ecological ethic. What is often rendered implausible or undesirable might also contain the potential to stave off the impulse to reproduce humanisms that require mastery over the earth. To better pursue failure or an inability to achieve appropriate attunement with the ecological, I focus on a Black fat femme falling from a tree and an Anishinaabeg youth lying on the ground and looking up at a tree. These errant bodies function as sites of friction that trouble old and new materialisms that continue to shape ecological thought and subjectivity. Full article

Antimicrobial use (AMU) in finfish aquaculture production raises concerns about the link between AMU and the development of antimicrobial resistance (AMR) in bacteria found in aquatic organisms and potential transmission to humans and the environment. The objective of this study was to describe the antimicrobial susceptibilities of a historical collection of bacterial isolates from diagnostic submissions from farmed finfish in British Columbia (BC), Canada. Antimicrobial susceptibility data were obtained from the BC Ministry of Agriculture via submissions to the Animal Health Centre for 2007 to 2018 for florfenicol (FLOR), oxytetracycline (OXY), trimethoprim-sulfadiazine (SXT), and triple-sulfa compound (TRI). There were 1237 unique isolates from all finfish species (68 unique bacterial species), of which 1042 were from Atlantic salmon. For all fish species, the most common bacterial species isolated were Aeromonas salmonicida (n = 174), Aliivibrio wodanis (n = 84), and Yersinia ruckeri (n = 79). Resistance was detected to most antimicrobials tested, but levels were generally low. Resistance to FLOR was only detected in A. salmonicida. Low annual isolate numbers precluded genera-specific annual comparisons for all pathogens. Multi-drug resistance was detected, but at low levels. These results provide an important baseline for antimicrobial susceptibility data from bacterial isolates that may cause disease in finfish aquaculture in BC, Canada that will support future Canadian AMR surveillance in farmed aquaculture. Full article