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Cortical folds encode the architecture of human cognition, yet the mechanisms that transform the smooth fetal cortex into its convoluted geometry remain elusive. Biophysical modeling enables mechanistic insight into cortical morphogenesis, but existing models often lack anatomical realism and fail to capture key hallmarks and morphometrics of dynamic cortical folding in the developing human brain. Here, we introduce a whole-brain developmental framework that integrates region-specific, data-driven growth laws with anatomically realistic cortical geometry to enable biologically interpretable modeling of cortical morphogenesis during gestation. Growth fields derived from large-scale prenatal magnetic resonance imaging data capture spatiotemporal variations in cortical expansion and thickness across parcellated regions. Incorporating heterogeneous growth yields folding patterns that match key anatomical landmarks and quantitative morphometrics from human imaging. Systematic perturbations of geometry and growth attributes delineate control parameters that produce realistic morphological variability and replicate clinically atypical brain phenotypes consistent with lissencephaly, pachygyria, and polymicrogyria. This framework provides a quantitative foundation for elucidating the mechanisms of typical and atypical fetal brain development.
Large Language Models (LLMs) offer new avenues to simulate online communities and social media. Potential applications range from testing the design of content recommendation algorithms to estimating the effects of content policies and interventions. However, the validity of using LLMs to simulate conversations between various users remains largely untested. We evaluated whether LLMs can convincingly mimic human group conversations on social media. We collected authentic human conversations from Reddit and generated artificial conversations on the same topic with two LLMs: Llama 3 70B and GPT-4o. When presented side-by-side to study participants, LLM-generated conversations were mistaken for human-created content 39% of the time. In particular, when evaluating conversations generated by Llama 3, participants correctly identified them as AI-generated only 56% of the time, barely better than random chance. Our study demonstrates that LLMs can generate social media conversations sufficiently realistic to deceive humans when reading them, highlighting both a promising potential for social simulation and a warning message about the potential misuse of LLMs to generate new inauthentic social media content.
Emotional experience and the regulation thereof are typically studied using picture inventories or short films to induce and modify affective states. These approaches, however, lack ecological validity due to their passive and receptive nature. Recent innovations in virtual reality and mobile neurophysiological technologies have enabled researchers to study the behavioral and neural correlates of more ecologically valid emotional responses. In this preregistered study, 58 healthy participants were randomly assigned to either use cognitive reappraisal (intervention) or to immerse themselves in their senses and surroundings (control) while walking across a wooden plank suspended 80 stories above the ground in virtual reality. We measured subjective fear ratings, salivary alpha amylase and cortisol levels, as well as frontal brain asymmetries, captured using mobile electroencephalography (EEG). Across both conditions, we found decisive evidence of increased subjective fear and salivary alpha amylase, a marker of sympathetic activation. However, we found no increase in cortisol levels following the task suggesting that subjective fear alone is not sufficient to trigger a cortisol response. In contrast to our hypotheses, the reappraisal group did not show any difference compared to the control group for neither emotional, endocrine nor neural measures. On the one hand, our findings may suggest that reappraisal might not be a suitable strategy to regulate realistic and intensely frightening situations. On the other hand, further analyses also indicated that the control group may have also regulated their emotions due to increased mindfulness of their inner states and their environment. Future studies are needed to confirm these observations and ascertain the efficacy of cognitive reappraisal on fear in realistic settings.
Viaduct structures, which are girders supported on piers, are essential for high speed railway (HSR) lines to ensure uninterrupted flow in densely populated regions. Owing to dynamic amplification of deformations and forces, which can be severe under resonant conditions, and the need to limit deck acceleration for structural safety, passenger comfort and rolling stock performance, dynamic analysis is imperative in the design process. Current design standards provide little guidance regarding dynamic analysis approaches, and the research literature indicates that the options are Euler-Bernoulli beam modal analysis, Kirchhoff-Love plate theory-based Generalised Beam Theory (GBT) modal solutions, and finite element (FE) based time domain approaches. This paper investigates the applicability of the simple beam modal solutions on four representative viaduct sections under three typical axle load sets, operating between speeds of 200 to 350 km per hour. First, eigenanalysis solutions using GBT and a solid FE model are compared, indicating excellent agreement under an idealised simply supported boundary condition with torsional restraint, but significant deviations when the FE boundary is modified to represent realistic bearing-type restraints. These observations propagate to dynamic responses under high speed train loading, which generally show good agreement for the analytical boundary condition and differences under realistic boundary condition modelling. Although analytical predictions are typically conservative, the FE predictions are at times significantly higher due to a shift in resonant speed. The results indicate that simplified analytical solutions can be utilised with caution and should be conducted at all applicable speeds to capture the maximum plausible resonant response. The paper concludes with a discussion on its limitations and future research needs.
Advances in cell design have improved lithium-metal battery (LMB) cycle life, but few studies assess performance under discharge profiles representative of real-world use. These profiles, which are often overlooked due to the complexity and risk of misinterpretation, can hinder accurate analysis or even prevent publication. Realistic discharge profiles include high currents during acceleration, current reversal during regenerative braking, and low steady currents at cruising speed. This work examines LMB performance using localized high-concentration electrolytes (LHCEs) under dynamic cycling, focusing on acceleration and regeneration pulses. These profiles bridge practical usage and controlled conditions for reproducible trends. Single-layer pouch cells are tested with LHCEs of lithium bis(fluorosulfonyl)imide (LiFSI), 1,2-dimethoxyethane (DME), with either 1,1,2,2-tetrafluoroethyl 2,2,3,3-tetrafluoropropyl ether (TTE) or bis(2,2,2-trifluoroethyl) ether (BTFE). The inclusion of pulsing dramatically alters the failure of the cells and increases cell-to-cell variability. Increasing the ionic conductivity and electrolyte volume decreases cell-to-cell performance variability. Cells with LHCE-BTFE exhibit more consistent cycling capacity behavior and fewer performance metric fluctuations, such as a rise of polarization or peak cell pressure, under non-uniform cycling compared to LHCE-TTE. These findings suggest that rapid transition from benchtop testing to real-world deployment for LMBs will require the inclusion of more realistic cycling conditions.
Artificial intelligence (AI) has emerged as a tool to augment plastic and reconstructive surgery (PRS) education. AI-generated videos (AIVs) are entirely or partially created using machine learning to generate frames, scenes, or sequences depicting either fictional or realistic footage. Deepfakes are AI-generated audiovisual content in which a real person's identity or likeness, such as their face, voice, or expressions are altered or synthesized to convincingly make it appear as though they said or did something they did not.While legitimate concerns exist regarding their authenticity, AIVs and deepfakes can disseminate information using a realistic human avatar with minimal time constraints. In this study, we demonstrate the ability of AI-simulated videos to deliver educational content using a board-certified PRS surgeon who trained the AI as the avatar. By leveraging AIV technology, this study highlights a new approach to patient education with implications for virtual consultation and content creation in PRS. This method also addresses the evolving role of information sharing and marketing in PRS while focusing on maintaining professional standards and ethical integrity.
The illegal trade of rhinoceros horns continues to threaten wildlife populations and undermine conservation efforts worldwide. This study explores the feasibility of using existing radiation portal monitors to detect rhinoceros horns implanted with radioactive sources during international sea transportation. By leveraging the Gamma Detector Response and Analysis Software, a series of simulated pass-bys were conducted under controlled variables, including fill capacities, material densities, and 60Co source strengths. The models incorporated a 6.10-m (20-ft) shipping container with a keratin horn configuration, simulating realistic concealment scenarios. Background environments were modeled after Denver, CO, to reflect elevated natural radiation conditions. Detection performance was assessed using theoretical probability of detection, incorporating International Atomic Energy Agency Nuclear Security Series No. 1 standards for operational consistency. Results show that increased cargo capacity and denser shielding materials, such as aluminum and stainless steel, reduce probability of detection, though such solid-block shielding is unlikely in real-world cargo. Shadow shielding from empty or lightly filled containers enhanced detection capability by reducing background noise. Furthermore, modeling scenarios with multiple horns improved probability of detection, aligning with seizure patterns observed in sea transport trafficking cases. While the study does not determine the minimum detectable activity due to law enforcement sensitivities, it demonstrates the plausibility of radiation portal monitor systems identifying radiologically tagged horns under realistic shipping conditions. The findings support the concept of radiation tagging as a cost-effective, scalable strategy to deter trafficking, increase seizure rates, and reinforce international conservation efforts without disrupting trade flow or requiring infrastructure overhauls.
Parkinson's disease is a rapidly growing neurodegenerative disorder with various motor and non-motor symptoms, affecting millions of people worldwide. These symptoms demonstrate significant medication-related fluctuations and inter-patient variability, highlighting the need for personalized management. Objective longitudinal symptom monitoring through wearable sensors and machine learning can support individualized care. However, to date, most approaches have been tested in lab-constrained environments. This study aims to develop a modular pipeline to automatically detect three cardinal Parkinson's disease motor symptoms, tremor, bradykinesia, and levodopa-induced dyskinesia in more realistic scenarios. The proposed approach was evaluated on three datasets: the Levodopa Response Study and two newly introduced ALAMEDA datasets, containing tri-axial wrist accelerometer data collected with commercial wearable devices during clinical assessments and activities of daily living. For each symptom, separate context-agnostic models were developed using 92 hand-crafted features. Multiple segmentation window lengths and preprocessing techniques, including resampling and dimensionality reduction, alongside various machine learning models, including logistic regression, k-nearest neighbor, multilayer perceptron, support vector machine, decision tree, AdaBoost, and random forest, were explored. Statistical significance between configurations was assessed with the Wilcoxon signed-rank test. Model interpretability was investigated using Shapley additive explanations to identify highly influential predictors and assess their physiological relevance. In the Levodopa Response Study dataset, tremor, bradykinesia, and dyskinesia detection reached 0.664, 0.636, and 0.443 area under the precision-recall curve, respectively, demonstrating scalability in high-complexity settings and revealing physiologically meaningful patterns. When evaluated on the ALAMEDA datasets, tremor and dyskinesia detection achieved 0.879 and 0.648 area under the precision-recall curve, highlighting strong model and feature generalizability. Across symptoms, longer segmentation windows and random forest classifiers performed better, while synthetic oversampling and principal component analysis showed limited impact. Automated Parkinson's disease symptom detection is feasible in more realistic, free-living conditions, with only a slight performance decrease despite substantially increased complexity. With carefully selected features and pipeline components, the objective, unobtrusive monitoring of motor symptoms can support personalized, evidence-based treatment suggestions, eventually improving patients' quality of life. Not applicable.
Panoramic radiography (PR) is the one of the most widely prescribed diagnostic imaging modalities in dentistry. Achieving clinical-level automated interpretation of PR is critical for improving diagnostic efficiency, reducing radiologist workload, and ensuring interpretive consistency. Artificial intelligence (AI) has demonstrated significant utility and promise in PR interpretation, improving diagnostic efficiency, reducing radiologists' workload, and enhancing interpretive consistency. However, developing AI-based PR interpretation methods remains hindered by persistent challenges of data scarcity, privacy constraints, and annotation imbalance. Facing these limitations, the aim of this study is to develop a disease-guided, anatomy-controllable generative model, named PRGen (for "panoramic radiograph generation"), designed to mitigate challenges related to data scarcity, privacy constraints, and annotation imbalance. PRGen is developed using 50,127 paired text-image samples and overcomes the uncontrollability of existing generative models, synthesizing both realistic PRs and paired masks from textual disease descriptions, enabling precise control of dental anatomy through simple or detailed sketches, and effectively mitigating barriers to large-scale, well-annotated dataset construction. We comprehensively evaluated the quality and clinical utility of PRGen-generated PRs across held-out internal test sets, publicly available datasets, and assessments by 10 radiologists. Incorporating PRGen-synthesized images into training led to a 47.59% improvement in Dice score for segmentation and a 11.53% increase in the area under the curve. Upon expert review, more than 82% of synthesized PRs were judged to faithfully reflect the described diseases while maintaining clinically realistic anatomical structures and radiographic appearances. External multicenter validation confirmed robust generalizability, with an average Dice improvement of 25.58% for segmentation and consistent gains in diagnostic tasks. Collectively, these results demonstrate that PRGen enables the generation of high-quality, mask-annotated PRs, thereby reducing the reliance on manual annotations and supporting more reliable and automated analysis of PRs, ultimately facilitating both model development and clinical translation.
Untrustworthy randomised controlled trials (RCTs) and other study types are an increasingly recognised problem in health and medical research. Assessing and responding to this problem is essential for ensuring the reliability of scientific evidence informing clinical practice. We consider theory, methods and tools for assessing research integrity and the trustworthiness of both aggregate and individual-level data from peer-reviewed, published RCTs. We review approaches to the assessment of trustworthiness based on published checklists, the statistical analysis of aggregate and individual participant data, and the use of AI tools. We found checklists that examine the trustworthiness of data considering questions about the timeframe, authors, governance, and plausibility of a published, peer-reviewed paper. These checks of authenticity are complemented by statistical techniques that identify unusual patterns in aggregate or individual data, including new procedures for simulating data to determine if any of the possible distributions are realistic. The unique character of RCTs provides additional opportunities for checks, specifically for the baseline data. The approaches presented offer a series of novel, quantitative tools for assessing research integrity and data trustworthiness across published RCTs. These techniques can detect instances of data duplication, questionable research practices, and check if any aspects of the study or results are unrealistic. They work best when employed beyond isolated, single-trial analyses and, when embedded in AI platforms, should scale to meet the volume of corrupted papers already published and the stream of fake research from paper mills.
Generative artificial intelligence has shown great promise in structure-based drug design (SBDD), yet existing models often suffer from a fundamental crisis of "structural hallucinations", generating molecules with high binding scores that violate basic chemical principles or physical plausibility. Here we present DrugRPG, a physicochemically-grounded 3D molecule generation framework that bridges this gap by integrating deep-learned chemical priors with fundamental physical laws. DrugRPG introduces a cross-dimensional representation alignment objective, distilling knowledge from a chemical foundation model pre-trained on 600 million molecular similarities to ensure the generation of valid topologies and realistic pharmacophoric patterns. Simultaneously, a differentiable physics-guided sampling strategy, inspired by the Lennard-Jones potential, is applied during the reverse diffusion phase to dynamically mitigate steric clashes and enforce Van der Waals compatibility. Comprehensive benchmarking demonstrates that DrugRPG reduces severe steric clashes by 65.4% compared to the state-of-the-art baseline while maintaining competitive structural self-consistency. Crucially, DrugRPG achieves a 28.6% higher success rate in generating developable candidates that satisfy multi-objective criteria, including potency, stability, and synthetic feasibility. By merging chemical heuristics with physical laws, DrugRPG effectively addresses the hallucination crisis, shifting generative SBDD from scoring-oriented optimization toward high-fidelity, realistic lead discovery.
Gynaecological cancers, including ovarian, cervical, and endometrial malignancies, remain major causes of cancer-related morbidity and mortality because of tumour heterogeneity, recurrence, and therapeutic resistance. Epigenetic dysregulation, involving aberrant DNA methylation, altered histone modifications, dysregulated non-coding RNAs, and N⁶-methyladenosine (m⁶A) RNA remodelling, contributes to these processes. Direct evidence that natural compounds modulate m⁶A machinery in gynaecological cancers remains absent and is considered a knowledge gap. We synthesised evidence on epigenetic alterations in gynaecological cancers and critically evaluated dietary and plant-derived compounds as candidate epigenetic modulators, focusing on preclinical mechanistic studies, pharmacokinetic data, selected early-phase clinical studies, and computational approaches to compound discovery and biomarker stratification. Natural agents, including curcumin, epigallocatechin-3-gallate, sulforaphane, berberine, resveratrol, genistein, diindolylmethane, quercetin, capsaicin, and butyrate, have been reported, mainly in preclinical models, to modulate DNA methyltransferases, histone deacetylases, microRNA networks, tumour suppressor gene expression, and chemosensitivity. However, translation is limited by poor bioavailability, pharmacokinetic variability, insufficient tumour-tissue target-engagement data, weak potency compared with approved epigenetic drugs, limited patient-derived model validation, and a lack of biomarker-driven trials. Compound-specific evidence remains uneven, with stronger support for selected chemosensitising mechanisms than for direct clinical epigenetic efficacy. Natural compounds are mechanistically plausible but clinically under-validated adjunctive epigenetic modulators. Future development requires standardised formulations, improved delivery systems, tumour-tissue pharmacodynamic validation, multi-omics profiling, patient stratification, and biomarker-guided clinical trials to define their realistic role in precision gynaecological oncology. The proposed translational framework may support rational prioritisation of candidates for future preclinical and clinical testing.
Statistical calibration of the results in adult age-at-death estimation is a fundamental yet challenging task in forensic anthropology and bioarchaeology. This study conducts a large-scale Bayesian validation of two probabilistic models for adult dental age-at-death estimation: the established FIDBv2, using root dentin translucency and periodontal retraction, and a novel simplified counterpart, FIDBv3, relying solely on translucency. Analysis of 8,595 single-rooted teeth from a global sample of 3,801 individuals demonstrates that FIDBv3 consistently outperforms its predecessor, reducing overall mean inaccuracy by 22.52% and increasing total sample accuracy by 8.11 percentage points. This superior performance across diverse populations indicates that periodontal retraction introduces more statistical noise than a reliable biological signal. The strategic exclusion of this variable, combined with the inclusion of a logarithmic transformation that better models the non-linear trajectory of translucency development, yields a more biologically realistic and universally applicable method. This simplification also streamlines analysis, while deployment as a free, open-access web platform democratizes global access to this robust, probability-based estimation. Despite persistent challenges at the extremes of the lifespan, FIDBv3 establishes a new standard for transparent, reliable and practical dental age-at-death estimation in forensic and archaeological contexts.
In Fourier-domain optical coherence tomography (FD-OCT) imaging, saturation of the data acquisition chain leads to loss of information by clipping the spectral interferograms. When reconstructed, affected depth profiles are degraded by bright axial lines or periodical patterns which mask the underlying information and thereby affect clinical utility. In this work, we propose an approach to faithfully recover the distorted information from saturated FD-OCT scans based on physical insights. For this purpose, genesis and appearance of saturation artifacts are analyzed, revealing a wavelength-dependency within individual interferograms. We propose to use this knowledge in a dual manner: Firstly, we present a simulation model that can dynamically generate realistically saturated interferograms from clean counterparts. This physics-based model allows training neural networks for artifact removal solely on synthesized image pairs. Secondly, to make the wavelength-dependency of artifacts directly accessible, we propose a multi-input, single-output (MISO) network framework. In addition to full B-scans, MISO receives images reconstructed from various spectral sub-windows. Our experiments confirm successful generalization of networks trained on simulated training data to real-world artifact removal in ophthalmic anterior segment imaging. This includes reflexes of various origin in diagnostic or intra-surgical imaging, such as different tissue structures or surgical instruments. Furthermore, a comparison of the proposed multi-input network to single-input baselines reveals consistent performance and reliability gains on both swept-source and spectral domain datasets.
Metal-polymer hybrid structures are increasingly required for lightweight and high-performance applications; however, conventional joining approaches based on chemical surface treatments suffer from poor reproducibility and limited design flexibility. In this study, a process-informed structural interlocking strategy using laser powder bed fusion (LPBF)-fabricated lattice architectures is proposed for injection molded direct joining. Unlike conventional approaches that primarily focus on geometric design, this study incorporates injection molding-induced flow and pressure conditions into the evaluation of lattice performance. Three lattice configurations were designed and fabricated using AlSi10Mg, and their mechanical responses were experimentally assessed through injection molding and tensile testing. In parallel, injection molding simulations were conducted to analyze the pressure distribution and structural deformation of lattice inserts under process-representative conditions. The results show that all lattice configurations achieved stable bonding, while differences in load-bearing capacity were observed depending on lattice geometry. In particular, tensile strength decreased by up to 23.6% with increasing lattice complexity, despite maintained interfacial bonding. Simulation results suggested that lattice density influences both flow behavior and structural response, leading to a trade-off between injection pressure and deformation. The lower-density lattice exhibited lower simulated injection pressure during filling, potentially facilitating polymer penetration, whereas the higher-density lattice exhibited improved structural resistance, accompanied by an increase in von Mises stress of up to 143.3%. This study demonstrates that lattice performance in metal-polymer joining cannot be evaluated solely based on structural design, but should be considered in conjunction with processing conditions. The proposed approach provides a process-structure integrated evaluation framework for LPBF-based lattice interlocking under realistic injection molding environments.
Optoretinography (ORG) enables non-invasive measurement of stimulus-evoked photoreceptor deformation using phase-sensitive optical coherence tomography (OCT). In this study, we evaluated how lateral resolution, acquisition speed, and spatial and inter-trial averaging influence velocity-based phase ORG measured with a raster-scanning spectral-domain OCT platform configured to resemble clinically realistic hardware. Seven healthy eyes were imaged while varying beam diameter (1.6-4.8 mm), B-scan rate (400-800 Hz), and averaging strategies. The characteristic biphasic response, a rapid contraction followed by slower elongation, was consistently observed across all conditions. Increasing NA improved structural cone visibility but did not proportionally enhance averaged ORG metrics, while moderate NA often yielded more stable signals. Higher acquisition speeds sharpened the measured contraction dynamics but reduced the signal-to-noise ratio due to increased phase noise. Both spatial and trial averaging substantially improved SNR. These findings demonstrate that repeatable phase-based ORGs can be achieved without cellular resolution or ultrahigh scan rates, and provide practical guidance for implementing proto-clinical ORG on conventional OCT systems.
Surgical simulation is increasingly central to structured training in minimally invasive and robotic surgery. Biotissue models are emerging as potential tools for psychomotor skill acquisition. A systematic review and meta-analysis identified studies evaluating biotissue or 3D models for psychomotor training in abdominal surgery. Thirty-one studies including 574 learners were analysed. Repeated practice significantly reduced operative time (-7.5 min, p < 0.0001) and improved Objective Struscutred Assesment of Tecnhincal Skills (OSATS) scores (+4.41 points, p < 0.0001). Experts completed biotissue tasks 50.18 min faster than novices (p < 0.0001), supporting construct validity. Biotissue models demonstrate construct validity and improve psychomotor performance. Their integration into structured curricula may represent a realistic and cost-effective alternative for laparoscopic and robotic surgical training.
Significant effort has been put into understanding the role of abscisic acid (ABA) on stomatal response to water stress. However, the parametrization of ABA controls in current models of stomatal conductance ([Formula: see text]) remains challenging. Here, we synthesized current literature that quantifies ABA relation with [Formula: see text], water potential (Ψ), or both, across various experimental settings. We compiled a total of 242 datasets covering 60 plant species, with 193 observation sets linking [Formula: see text] and ABA and 138 observation sets linking Ψ and ABA. Ψ was measured in either the leaf, stem, or root, as reported in the corresponding studies. We examined different fitting functions to approximate the relation between Ψ and ABA, and [Formula: see text] and ABA. Statistical correlations were used to determine the best fit of [Formula: see text] vs ABA and Ψ vs ABA relationships, and to exclude datasets with poor correlations. No statistically significant differences were found between stomatal sensitivity to ABA (β, unitless) and biome type or the source of ABA (Kruskal-Wallis, P > 0.05). Statistical differences were found between β and experimental conditions (Kruskal-Wallis, P < 0.05), likely due to observational uncertainties. The overall distribution of β in all classifications suggests a typical sensitivity range (β) of 0.21 - 21.59 (median 1.87). These findings can be used to guide the development and evaluation of mechanistic models of [Formula: see text], providing realistic parameterizations of the links between ABA and stomatal closure.
VARSKIN is becoming more commonplace in nuclear medicine departments to calculate skin dose from droplet or surface contamination due to its accessibility and ease of use. VARSKIN uses simplified dose kernels, which give dose estimates quickly; however, the disadvantage is that some accuracy may be compromised. The aim of this article was to independently compare the VARSKIN (v2.1) skin dose module against Geant4 for typical skin contamination scenarios. Realistic skin contamination scenarios were modeled in Geant4 including droplet and surface contamination. Three droplet volumes (10, 30, and 50 μL) were considered as well as an infinitely thin disk source (A = 1 cm2) to model surface contamination. Four basal depths (70, 140, 220, and 370 µm) were considered in combination with two glove layers defined by 0.1 mm and 0.2 mm of natural rubber (ρ = 0.92 g cm-3) as well as an absent glove layer. The Geant4 models were then replicated as closely as possible using VARSKIN (v2.1) skin dose module. Forty-four (44) radionuclides were considered giving a total of 2,112 direct comparisons of instantaneous skin dose rates between VARSKIN (v2.1) and Geant4. Most data (86%) were within ±50% (relative) difference between VARSKIN and Geant4 with a median difference of only +4.0% for all data. However, the 95% confidence interval was wide at -29% to +210%, showing a large positive skew. This skewness is predominantly caused by large relative differences observed for photon emitting radionuclides including 57Co, 51Cr, 67Ga, 123I, 125I, 129I and 111In. However, the differences were low in absolute terms for these radionuclides. Despite large relative differences between VARSKIN and Geant4 for certain radionuclides and geometries, the differences were low in absolute terms. Overall, we conclude that VARSKIN (v2.1) is a reliable tool for skin dose assessment for the radionuclides and geometries studied in this article.
Pathogen cross-contamination during food production is primarily controlled through environmental sanitation. However, sanitizer efficacy is often studied in bench-scale experiments that poorly approximate the fluid dynamics of sanitization and limit our understanding of commercial sanitization efficacy. This study paired computational fluid dynamics estimates of shear stress with experimental measurements of Listeria innocua reduction on stainless steel following treatment with 100 ppm hypochlorite sanitizer. At the pilot scale, sanitizer spray manually applied by researchers achieved a 2.6 ± 0.4 log CFU/surface reduction; however, microbial reduction from manual operation of sanitizer spray equipment differed significantly between researchers (P < 0.05). Microbial reduction varied by location following stationary, bench-scale spray application of sanitizer for 3 s. The greatest reduction was at the point of sanitizer spray impingement (7.5 ± 0.5 log CFU/surface) and directly adjacent to the impingement point (6.4 ± 0.7 log CFU/surface), where shear stress was the highest. Significantly less microbial reduction (0.4 ± 0.1 log CFU/surface) occurred where shear stress was lowest in the fluid film of sanitizer running down from the impingement point (P < 0.05). Static submersion of inoculated coupons in sanitizer for 3 s resulted in a log reduction of 2.3 ± 0.1 log CFU/surface. Discrepancies between bench-scale spraying, pilot-scale spraying, and submerged coupons demonstrate the need for sanitizer efficacy testing under realistic conditions to better estimate the risk reduction achieved through sanitation programs.IMPORTANCESanitation is critical for controlling pathogen cross-contamination during food production. These findings highlight the limitations of traditional approaches to sanitizer efficacy testing, not because they are invalid, but because they do not reflect the level of microbial reduction typically achieved in application. We demonstrate that these differences in outcomes are attributable to fluid dynamics and exposure, which are not well approximated in submerged coupon experiments. Accurate estimation of microbial reduction from sanitizer application is needed to guide food safety policy decisions. For example, overestimation of the risk reduction conferred by sanitizer treatment may result in food safety policies that neglect other sources of microbial reduction within sanitation programs.