Regional high-resolution climate projections are crucial for many applications, such as agriculture, hydrology, and natural hazard risk assessment. Dynamical downscaling, the state-of-the-art method to produce localized future climate information, involves running a regional climate model (RCM) driven by an Earth System Model (ESM), but it is too computationally expensive to apply to large climate projection ensembles. We propose an approach combining dynamical downscaling with generative AI to reduce the cost and improve the uncertainty estimates of downscaled climate projections. In our framework, an RCM dynamically downscales ESM output to an intermediate resolution, followed by a generative diffusion model that further refines the resolution to the target scale. This approach leverages the generalizability of physics-based models and the sampling efficiency of diffusion models, enabling the downscaling of large multimodel ensembles. We evaluate our method against dynamically downscaled climate projections from the Coupled Model Intercomparison Project 6 (CMIP6) ensemble. Our results demonstrate its ability to provide more accurate uncertainty bounds on future regional climate than alternatives such as dynamical downscaling of smaller ensembles, or traditional empirical statistical downscaling methods. We also show that dynamical-generative downscaling results in significantly lower errors than popular statistical downscaling techniques, and captures more accurately the spectra, tail dependence, and multivariate correlations of meteorological fields. These characteristics make the dynamical-generative framework a flexible, accurate, and efficient way to downscale large ensembles of climate projections, currently out of reach for pure dynamical downscaling.

BACKGROUND : Colorectal polypectomy is operator dependent, with variable rates of complete resection. The currently available assessment tools do not provide specific competency-based evaluation of provider technique. We aimed to validate the Global Polypectomy Assessment Tool (GPAT), a novel competency assessment tool for colorectal polypectomy. METHODS : GPAT was derived from the ESGE Curriculum for Training in endoscopic mucosal resection in the colon. Members of the curriculum taskforce plus three invited trainees and three medical students (collectively: the assessors) anonymously assessed nine endoscopic-view only polypectomy videos. The primary end point was the correlation of the assessors' GPAT scores with a consensus-derived reference GPAT score per video. Secondary end points were the assessors' subjective impression versus their GPAT score and interobserver agreement among assessors' GPAT scores. RESULTS : 171 GPAT assessments by 19 assessors (consultant gastroenterologists [n = 10], trainee gastroenterologists [n = 4], consultant surgeons [n = 2], and medical students [n = 3]) were analyzed. Reference GPAT scores did not differ significantly from those of the assessors (73.1 % [95 %CI 64.6 %-81.6 %] vs. 69.3 % [95 %CI 64.9 %-81.2 %]; P = 0.47). There was moderate IOA in GPAT scores among gastroenterologists (intraclass correlation coefficient [ICC], 0.52 [moderate]) but not among nongastroenterologists (ICC 0.32 [poor]). GPAT correlated with assessors' subjective impression of polypectomy quality (correlation coefficient 0.98 [95 %CI 0.90-1.00]; P < 0.001). Overall assessors' qualitative usability scoring of GPAT was positive. CONCLUSIONS : GPAT allows standardized scoring of polypectomies, with moderate IOA among gastroenterologists and correlation with subjective impressions of polypectomy quality. GPAT could standardize assessment of trainee polypectomy competency offering structured feedback on performance.

Objective Operator technical skill is recognised as a critical determinant of surgical outcomes. However, no equivalent recognition for quality of endoscope tip manipulation (tip-control) exists. We aimed to create an ex-vivo snare tip soft coagulation (STSC) model to objectively quantify endoscopist tip-control. Method This prospective interventional study was conducted at Ghent University Hospital. Participants applied STSC to a training model simulating four endoscopic mucosal resection (EMR) defects on a slice of ham. Accuracy (correct/total-hits) and speed (correct-hits/s) were assessed from a video by a single-blinded rater using a web-based scoring system. Results 22 endoscopists participated. Interventional endoscopists demonstrated significantly higher accuracy (87.0%) and speed (0.184 correct-hits/s) compared with trainees (74.5%, 0.106 correct-hits/s; both p<0.001) and non-interventional consultants (77%, 0.141 correct-hits/s; p<0.001). The tip-control of trainees and non-interventional consultants was not significantly different. Endoscopists having performed >= 1000 colonoscopies, performing SMSA-4 polypectomies or >= 50 EMRs/year showed superior tip-control. Endoscopists with >5 years of endoscopic experience did not have better tip-control (accuracy 88.0%(p=0.07), speed 0.132 hits/s (p=0.36)) when compared with those with <= 4 years of experience. Conclusion This inexpensive ex vivo STSC simulation model effectively quantified endoscopic tip-control, correlating with endoscopist expertise and clinical profiles. The model could support the shift towards competency-based education, potentially improving patient outcomes. Trial registration number NCT05660317.

Pyrocumulonimbus (pyroCb) firestorms – wildfire-generated thunderstorms – can trigger rapid fire spread. However, the multi-physics nature of pyroCb has made their core mechanisms inaccessible to direct observation and previous simulation and prediction efforts. We introduce a new simulation capability with the first high-resolution, fully coupled simulations of a pyroCb, allowing us to unravel its life cycle governed by two opposing mechanisms. We show fuel moisture is an energy sink that attenuates fire intensity rather than fueling clouds, resolving a long-standing debate. Conversely, we identify the driver of rapid intensification: the Self-Amplifying Fire-Induced Recirculation (SAFIR) mechanism, where precipitation-induced downdrafts intensify the parent fire under weak winds. This work provides a new mechanistic framework for pyroCb prediction and demonstrates a transformative computational approach for previously intractable problems in environmental science.

Over the last 50 years, endoscopy technology and its clinical application has improved enormously. Endoscopy now provides a wide variety of non-invasive treatments, and it prevents upper and lower gastrointestinal cancer. Performing high-quality endoscopy requires a complex blend of cognitive, technical and non-technical skills. For an individual to acquire these skills requires high-quality training. Unfortunately, the development of training has lagged the advances in technology, resulting in unwarranted variation in the effectiveness of the technology. This chapter argues that to enable a more uniform high-quality endoscopic service, the solution is to improve endoscopy training. It describes what constitutes high-quality training, how new methods of training will improve the traditional training pathway and what can be done to transform endoscopy training.

Background:The quality of esophagogastroduodenoscopy (EGD) performed by trainees depends on their competency level and training. This study assessed whether the addition of simulation-based training (SBT) reduces the number of supervised clinical procedures needed to achieve independent procedural completion compared with conventional clinically based training (CBT) alone. Methods:EGD novices were randomized (1 : 1) to either SBT followed by CBT or CBT alone in a randomized controlled trial. The primary outcome was the number of EGD procedures required to reach independent procedural completion, defined as adequate competence to perform EGDs without direct supervision. Secondary outcomes were patient satisfaction and estimated training costs. The study was powered to include 13 participants per group, including 20% dropout (power 0.80; two-sided significance level P < 0.05). Results:26 physicians from nine departments performed 1183 EGDs. A total of 661 patient satisfaction surveys were included. The SBT group required fewer procedures to achieve independent procedural completion than the CBT only group (median 31 [95%CI 25–37] vs. 44 [95%CI 33–55]; P = 0.006). No significant differences were found in patient satisfaction or median training costs (US$2993 vs. $3150, respectively; P = 0.55). Conclusions:SBT prior to CBT reduces the number of supervised procedures required to achieve independent procedural completion without negatively affecting patient satisfaction or increasing training costs. These findings support the routine implementation of SBT when learning EGD.

Objectives: Endoscopy teaching practice is variable, which inevitably affects the training provided. There is only one Train the Paediatric Colonoscopy Trainer (TPCT) course in the UK. Informal feedback has been positive, but its practical value has never been formally assessed. We aim to assess the practical value of the TPCT course and how attendees perceive their teaching practice compared to nonattendees.Methods: A questionnaire based on the TPCT course learning aims and objectives was distributed to two groups of consultant paediatric gastroenterologists who teach colonoscopy in the UK; those who had attended the course (participants) and those who had not (controls).Results: The 41 completed responses were received. Overall, responses indicated participants of the TPCT course rated their confidence and knowledge in teaching practices as higher than controls (4.27 vs. 3.56 p = < 0.001). There was a statistically significant difference in all areas: set (4.21 vs. 3.71 p = 0.011), dialogue (4.29 vs. 3.55 p = < 0.001) and closure (4.37 vs. 3.6 p = < 0.001) with those who attended the TPCT course giving higher ratings. There was evidence of increased understanding of key concepts such as using standardised language, conscious competence, dual task interference and performance enhancing feedback.Conclusion: Attending a TPCT course results in a higher perceived level of knowledge in fundamental teaching principles and confidence in colonoscopy teaching skills.

Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decisions. We introduce GenFocal, an AI framework that generates statistically accurate, fine-scale weather from coarse climate projections, without requiring paired simulated and observed events during training. GenFocal synthesizes complex and long-lived hazards, such as heat waves and tropical cyclones, even when they are not well represented in the coarse climate projections. It also samples high-impact, rare events more accurately than leading methods. By translating large-scale climate projections into actionable, localized information, GenFocal provides a powerful new paradigm to improve climate adaptation and resilience strategies.

MASPEX (Mass Spectrometer for Planetary Exploration/Europa) is a mass spectrometer flying aboard the Europa Clipper mission to identify gases in the Europa exosphere. As a mass spectrometer looking for trace species, the instrument is highly sensitive to outgassing contamination. A single monolayer of a species of interest collected on MASPEX may impact the science measurement. MASPEX has a one-time deployable vacuum sealed door to protect it from outgassing contamination. The initial plan was to deploy the door after Jupiter Orbit Insertion (JOI), however it was proposed that the door open earlier in cruise to accommodate concerns about the door deployment mechanism operating at cold temperatures. As part of the assessment to determine if the aperture cover door could be opened early, the Contamination Control Engineering group at the NASA Jet Propulsion Laboratory was tasked with assessing the contamination levels expected to make it into the MASPEX inlet. The main contamination environments for MASPEX during cruise and JOI are outgassing and propellant droplets. Outgassing follows three transport mechanisms to deposit on sensitive surfaces; direct line of sight, reflected, and return flux. The JPL Contamination Control team evaluated the contamination environments and mechanisms and found that the primary vector for contamination to reach MASPEX on Europa Clipper is reflected flux. This presentation discusses the modeling approach used to predict outgassing deposition on MASPEX resulting from opening the aperture cover door earlier in cruise. The focus is on the impact of the thermal environment on spacecraft outgassing, the extended duration over which contamination accumulation can occur, and the orientation of the solar array inducing a reflective path for outgassing.

Aims The JNET classification can be used to predict large non-pedunculated colorectal polyp (LNPCP) histology and presence/depth of submucosal invasion (SMI). However, Japanese endoscopist accuracies>84% have not been replicated amongst Western experts; for example, in a recent study of European experts JNET accuracy was 55%. Furthermore, European guidelines suggest both low-grade dysplasia (LGD) and high-grade dysplasia (HGD) can be treated using endoscopic mucosal resection, thus there is no clinical consequence to interpreting HGD as JNET 2A. We aimed to compare JNET with CRIS (Colorectal Regular-Irregular Score) for LNPCP histology prediction amongst Western experts using both the original JNET interpretation and a clinically relevant approach.

In this video, Javed Butler, MD, Jonathan Rich, MD, Rachel Pessah-Pollack, MD, and John E. Anderson, MD, summarize the key points of the enhanced publication "Role of SGLT2 Inhibitors in the Management of Heart Failure With and Without Type 2 Diabetes." The panel then delves deeper into some of the topics raised.

Uncertainty quantification is crucial to decision-making. A prominent example is probabilistic forecasting in numerical weather prediction. The dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts by running physics-based simulations under different conditions, which is a computationally costly process. We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data. The learned models are highly scalable with respect to high-performance computing accelerators and can sample thousands of realistic weather forecasts at low cost. When designed to emulate operational ensemble forecasts, the generated ones are similar to physics-based ensembles in statistical properties and predictive skill. When designed to correct biases present in the operational forecasting system, the generated ensembles show improved probabilistic forecast metrics. They are more reliable and forecast probabilities of extreme weather events more accurately. While we focus on weather forecasting, this methodology may enable creating large climate projection ensembles for climate risk assessment.

Background. Wildfire research uses ensemble methods to analyze fire behaviors and assess uncertainties. Nonetheless, current research methods are either confined to simple models or complex simulations with limits. Modern computing tools could allow for efficient, high-fidelity ensemble simulations. Aims. This study proposes a high-fidelity ensemble wildfire simulation framework for studying wildfire behavior, ML tasks, fire-risk assessment, and uncertainty analysis. Methods. In this research, we present a simulation framework that integrates the Swirl-Fire large-eddy simulation tool for wildfire predictions with the Vizier optimization platform for automated run-time management of ensemble simulations and large-scale batch processing. All simulations are executed on tensor-processing units to enhance computational efficiency. Key results. A dataset of 117 simulations is created, each with 1.35 billion mesh points. The simulations are compared to existing experimental data and show good agreement in terms of fire rate of spread. Computations are done for fire acceleration, mean rate of spread, and fireline intensity. Conclusions. Strong coupling between these 2 parameters are observed for the fire spread and intermittency. A critical Froude number that delineates fires from plume-driven to convection-driven is identified and confirmed with literature observations. Implications. The ensemble simulation framework is efficient in facilitating parametric wildfire studies.

NASA’s Europa Clipper mission aims to conduct detailed reconnaissance of Jupiter’s icy moon Europa and to investigate whether the moon could harbor conditions suitable for life. Europa Clipper carries with it the Plasma Instrument for Magnetic Sounding, or PIMS, which will study the density, temperature, and flow of plasma near Europa. The instrument plays a key role in determining Europa’s ice shell thickness, ocean depth, and conductivity. There is both an Upper PIMS instrument and a Lower PIMS instrument.Radiation induced outgassing testing led by the JPL Contamination Control group indicates that a high level of outgassing is expected from Clipper’s Stamet Coated Kapton blankets in the Jovian radiation environment. A recent change to the Europa Clipper design has led to the Launch Vehicle Adapter (LVA) remaining with Clipper throughout the mission lifetime. This change introduces both a direct contamination source and a contamination reflection point for radiation induced outgassed contamination from Clipper’s thermal blankets to deposit on the Lower PIMS instrument. Free molecular flow analysis performed by the JPL Contamination group showed the expected molecular contamination deposition level on Lower PIMS drastically increases with inclusion of the LVA on Clipper, pushing the deposition on Lower PIMS over the requirement provided by the instrument. The contamination exceedance could significantly impact the science return from the PIMS instrument.A working group was formed with JPL Contamination Control, Mechanical, Materials and Processes, Thermal, and Systems teams to develop mitigations. The primary approach investigated was the implementation of a contamination shield on PIMS to block contamination from transporting to PIMS Lower. Other mitigations investigated include a contamination shield to block contamination from reaching the LVA and blanketing key locations on the spacecraft with a metallic MLI that outgasses less under radiation. The PIMS team was consulted to ensure the approaches did not cause harm to PIMS and to provide final review of the proposed solution. In the end CC analysis showed that with a PIMS shield implemented the flight system will meet PIMS’s End of Life (EOL) requirement for molecular contamination deposition. This approach shows a method by which Contamination Control identifies a contamination concern with a late-breaking spacecraft configuration change and functions with a multi-disciplinary working group to address and mitigate the concern.

Universitair Ziekenhuis Brussel, Belgium; Universitair Ziekenhuis Gent, Belgium; Universiteit Gent Faculteit Geneeskunde en Gezondheidswetenschappen, Belgium; Gloucestershire Health and Care NHS Foundation Trust, United Kingdom; Cheltenham General Hospital, United Kingdom; AZ Delta vzw, Belgium.

Universitair Ziekenhuis Gent, Belgium; Universiteit Gent Faculteit Geneeskunde en Gezondheidswetenschappen, Belgium; Cheltenham General Hospital, United Kingdom; AZ Delta vzw, Belgium.

IntroductionJoint Advisory Group (JAG) certification in endoscopy is awarded when trainees attain minimum competency standards for independent practice. A national evidence-based review was undertaken to update standards for training and certification in flexible sigmoidoscopy (FS).MethodsA modified Delphi process was conducted between 2019 and 2020 with multisociety representation from experts and trainees. Following literature review and Grading of Recommendations, Assessment, Development and Evaluations appraisal, recommendation statements on FS training and certification were formulated and subjected to anonymous voting to obtain consensus. Accepted statements were peer-reviewed by national stakeholders for incorporation into the JAG FS certification pathway.ResultsIn total, 41 recommendation statements were generated under the domains of: definition of competence (13), acquisition of competence (17), assessment of competence (7) and postcertification support (4). The consensus process led to revised criteria for colonoscopy certification, comprising: (A) achieving key performance indicators defined within British Society of Gastroenterology standards (ie, rectal retroversion >90%, polyp retrieval rate >90%, patient comfort <10% with moderate-severe discomfort); (B) minimum procedure count ≥175; (C) performing 15+ procedures over the preceding 3 months; (D) attendance of the JAG Basic Skills in Lower gastrointestinal Endoscopy course; (E) satisfying requirements for formative direct observation of procedural skill (DOPS) and direct observation of polypectomy skill (SMSA level 1); (F) evidence of reflective practice as documented on the JAG Endoscopy Training System reflection tool and (G) successful performance in summative DOPS.ConclusionThe UK standards for training and certification in FS have been updated to support training, uphold standards in FS and polypectomy, and provide support to the newly independent practitioner.

Uncertainty quantification is crucial to decision-making. A prominent example is probabilistic forecasting in numerical weather prediction. The dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts. This is done by running many physics-based simulations under different conditions, which is a computationally costly process. We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data. The learned models are highly scalable with respect to high-performance computing accelerators and can sample hundreds to tens of thousands of realistic weather forecasts at low cost. When designed to emulate operational ensemble forecasts, the generated ones are similar to physics-based ensembles in important statistical properties and predictive skill. When designed to correct biases present in the operational forecasting system, the generated ensembles show improved probabilistic forecast metrics. They are more reliable and forecast probabilities of extreme weather events more accurately. While this work demonstrates the utility of the methodology by focusing on weather forecasting, the generative artificial intelligence methodology can be extended for uncertainty quantification in climate modeling, where we believe the generation of very large ensembles of climate projections will play an increasingly important role in climate risk assessment.

Aims No objective tool to measure the quality of endoscope tip manipulation (tip-control) exists. This study aimed to develop and validate a score for tip-control in an ex-vivo setting.

We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differential equations (SDE) for stochastic modeling. The ideal LES models the LES flow by treating each full-order trajectory as a random realization of the underlying dynamics, as such, the effect of small-scales is marginalized to obtain the deterministic evolution of the LES state. However, ideal LES is analytically intractable. In our work, we use a latent neural SDE to model the evolution of the stochastic process and an encoder-decoder pair for transforming between the latent space and the desired ideal flow field. This stands in sharp contrast to other types of neural parameterization of closure models where each trajectory is treated as a deterministic realization of the dynamics. We show the effectiveness of our approach (niLES - neural ideal LES) on a challenging chaotic dynamical system: Kolmogorov flow at a Reynolds number of 20,000. Compared to competing methods, our method can handle non-uniform geometries using unstructured meshes seamlessly. In particular, niLES leads to trajectories with more accurate statistics and enhances stability, particularly for long-horizon rollouts.