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This study examined whether supermarkets can be considered patches in the marginal value theorem (MVT) sense despite their particular features and whether they are models of human food foraging in resource-dense conditions. On the basis of the MVT, the quantitative relationship between gains in the Euro and patch residence time was modeled as an exponential growth function toward an upper asymptote, allowing the choice of an optimal strategy under diminishing returns. N = 61 participants were interviewed about their current shopping trip and contextual variables at a German supermarket and provided data to estimate relevant model parameters. A nonlinear model of the patch residence time and resulting gain based on an exponential function was fitted via nonlinear orthogonal distance regression. The results generally revealed the relationships predicted by the model, with some uncertainty regarding the estimation of the upper asymptote due to a lack of data from participants with long residence times. Despite this limitation, the data support the applicability of the MVT-based model. The results show that approaches from optimal foraging theory, such as the MVT, can be used successfully to model human shopping behavior even when participants' verbal reports are used., Competing Interests: Declarations. Competing Interests: The authors have no conflicts of interest to declare., (© 2025. The Author(s).)

Toxicological information as needed for risk assessments of chemical compounds is often sparse. Unfortunately, gathering new toxicological information experimentally often involves animal testing. Simulated alternatives, e.g., quantitative structure-activity relationship (QSAR) models, are preferred to infer the toxicity of new compounds. Aquatic toxicity data collections consist of many related tasks─each predicting the toxicity of new compounds on a given species. Since many of these tasks are inherently low-resource, i.e., involve few associated compounds, this is challenging. Meta-learning is a subfield of artificial intelligence that can lead to more accurate models by enabling the utilization of information across tasks. In our work, we benchmark various state-of-the-art meta-learning techniques for building QSAR models, focusing on knowledge sharing between species. Specifically, we employ and compare transformational machine learning, model-agnostic meta-learning, fine-tuning, and multi-task models. Our experiments show that established knowledge-sharing techniques outperform single-task approaches. We recommend the use of multi-task random forest models for aquatic toxicity modeling, which matched or exceeded the performance of other approaches and robustly produced good results in the low-resource settings we studied. This model functions on a species level, predicting toxicity for multiple species across various phyla, with flexible exposure duration and on a large chemical applicability domain.

Purpose: In Germany, Austria, and Switzerland, pretreatment radiotherapy quality control (RT-QC) for tumor bed boost (TB) in non-metastatic medulloblastoma (MB) was not mandatory but was recommended for patients enrolled in the SIOP PNET5 MB trial between 2014 and 2018. This individual case review (ICR) analysis aimed to evaluate types of deviations in the initial plan proposals and develop uniform review criteria for TB boost., Patients and Methods: A total of 78 patients were registered in this trial, of whom a subgroup of 65 patients were available for evaluation of the TB treatment plans. Dose uniformity was evaluated according to the definitions of the protocol. Additional RT-QC criteria for standardized review of target contours were elaborated and data evaluated accordingly., Results: Of 65 initial TB plan proposals, 27 (41.5%) revealed deviations of target volume delineation. Deviations according to the dose uniformity criteria were present in 14 (21.5%) TB plans. In 25 (38.5%) cases a modification of the RT plan was recommended. Rejection of the TB plans was rather related to unacceptable target volume delineation than to insufficient dose uniformity., Conclusion: In this analysis of pretreatment RT-QC, protocol deviations were present in a high proportion of initial TB plan proposals. These findings emphasize the importance of pretreatment RT-QC in clinical trials for MB. Based on these data, a proposal for RT-QC criteria for tumor bed boost in non-metastatic MB was developed., (© 2021. The Author(s).)

Purpose: Several studies have demonstrated the negative impact of radiotherapy protocol deviations on tumor control in medulloblastoma. In the SIOP PNET5 MB trial, a pretreatment radiotherapy quality control (RT-QC) program was introduced. A first analysis for patients enrolled in Germany, Switzerland and Austria with focus on types of deviations in the initial plan proposals and review criteria for modern radiation technologies was performed., Methods and Patients: Sixty-nine craniospinal irradiation (CSI) plans were available for detailed analyses. RT-QC was performed according to protocol definitions on dose uniformity. Because of the lack of definitions for high-precision 3D conformal radiotherapy within the protocol, additional criteria for RT-QC on delineation and coverage of clinical target volume (CTV) and planning target volume (PTV) were defined and evaluated., Results: Target volume (CTV/PTV) deviations occurred in 49.3% of initial CSI plan proposals (33.3% minor, 15.9% major). Dose uniformity deviations were less frequent (43.5%). Modification of the RT plan was recommended in 43.5% of CSI plans. Unacceptable RT plans were predominantly related to incorrect target delineation rather than dose uniformity. Unacceptable plans were negatively correlated to the number of enrolled patients per institution with a cutoff of 5 patients (p = 0.001)., Conclusion: This prospective pretreatment individual case review study revealed a high rate of deviations and emphasizes the strong need of pretreatment RT-QC in clinical trials for medulloblastoma. Furthermore, the experiences point out the necessity of new RT-QC criteria for high-precision CSI techniques., (© 2020. The Author(s).)

Background: The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) (PET-MRI) is a unique hybrid imaging modality mainly used in oncology and neurology. The MRI-based attenuation correction (MRAC) is crucial for correct quantification of PET data. A suitable phantom to validate quantitative results in PET-MRI is currently missing. In particular, the correction of attenuation due to bone is usually not verified by commonly available phantoms. The aim of this work was, thus, the development of such a phantom and to explore whether such a phantom might be used to validate MRACs., Method: Various materials were investigated for their attenuation and MR properties. For the substitution of bone, water-saturated gypsum plaster was used. The attenuation of 511 keV annihilation photons was regulated by addition of iodine. Adipose tissue was imitated by silicone and brain tissue by agarose gel, respectively. The practicability with respect to the comparison of MRACs was checked as follows: A small flask inserted into the phantom and a large spherical phantom (serving as a reference with negligible error in MRAC) were filled with the very same activity concentration. The activity concentration was measured and compared using clinical protocols on PET-MRI and different built-in and offline MRACs. The same measurements were carried out using PET-CT for comparison., Results: The phantom imitates the human head in sufficient detail. All tissue types including bone were detected as such so that the phantom-based comparison of the quantification accuracy of PET-MRI was possible. Quantitatively, the activity concentration in the brain, which was determined using different MRACs, showed a deviation of about 5% on average and a maximum deviation of 11% compared to the spherical phantom. For PET-CT, the deviation was 5%., Conclusions: The comparatively small error in quantification indicates that it is possible to construct a brain PET-MRI phantom that leads to MR-based attenuation-corrected images with reasonable accuracy.