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Two distinct methods of propagation for basic nuclear data uncertainties to large scale systems will be presented and compared. The “Total Monte Carlo” method is using a statistical ensemble of nuclear data libraries randomly generated by means of a Monte Carlo approach with the TALYS system. These libraries are then directly used in a large number of reactor calculations (for instance with MCNP) after which the exact probability distribution for the reactor parameter is obtained. The second method makes use of available covariance files and can be done in a single reactor calculation (by using the perturbation method). In this exercise, both methods are using consistent sets of data files, which implies that covariance files used in the second method are directly obtained from the randomly generated nuclear data libraries from the first method. This is a unique and straightforward comparison allowing to directly apprehend advantages and drawbacks of each method. Comparisons for different reactions and criticality-safety benchmarks from 19F to actinides will be presented. We can thus conclude whether current methods for using covariance data are good enough or not.

Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current shortage of both general and specialized radiologists, there is a large impetus to use artificial intelligence to alleviate the burden of interpreting these complex imaging studies while simultaneously using the images to extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs) that utilize both the image and the corresponding textual radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. To overcome these shortcomings for abdominal CT interpretation, we introduce Merlin - a 3D VLM that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining without requiring additional manual annotations. We train Merlin using a high-quality clinical dataset of paired CT scans (6+ million images from 15,331 CTs), EHR diagnosis codes (1.8+ million codes), and radiology reports (6+ million tokens) for training. We comprehensively evaluate Merlin on 6 task types and 752 individual tasks. The non-adapted (off-the-shelf) tasks include zero-shot findings classification (31 findings), phenotype classification (692 phenotypes), and zero-shot cross-modal retrieval (image to findings and image to impressions), while model adapted tasks include 5-year chronic disease prediction (6 diseases), radiology report generation, and 3D semantic segmentation (20 organs). We perform internal validation on a test set of 5,137 CTs, and external validation on 7,000 clinical CTs and on two public CT datasets (VerSe, TotalSegmentator). Beyond these clinically-relevant evaluations, we assess the efficacy of various network architectures and training strategies to depict that Merlin has favorable performance to existing task-specific baselines. We derive data scaling laws to empirically assess training data needs for requisite downstream task performance. Furthermore, unlike conventional VLMs that require hundreds of GPUs for training, we perform all training on a single GPU. This computationally efficient design can help democratize foundation model training, especially for health systems with compute constraints. We plan to release our trained models, code, and dataset, pending manual removal of all protected health information.

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3-20× compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies.

Introduction: Epidemiological transition to NCDs is a challenge for fragile health systems in the Caribbean. The Congregations Taking Action against NCDs (CONTACT) Study intervention proposes that trained health advocates (HAs) from places of worship (PoWs), supervised by nurses at nearby primary healthcare centres (PHCs), could facilitate access to primary care among vulnerable communities. Drawing on participatory and systems thinking, we explored the capacity of local PHCs in three Caribbean countries to support this intervention., Methods: Communities in Jamaica (rural, urban), Guyana (rural) and Dominica (Indigenous Kalinago Territory) were selected for CONTACT because of their differing socio-economic, cultural, religious and health system contexts. Through mixed-method concept mapping, we co-developed a list of perceived actionable priorities (possible intervention points ranked highly for feasibility and importance) with 48 policy actors, healthcare practitioners and civic society representatives. Guided in part by the concept mapping findings, we assessed the readiness of 12 purposefully selected PHCs for the intervention, using a staff questionnaire and an observation checklist to identify enablers and constrainers., Results: Concept mapping illustrated stakeholder optimism for the intervention, but revealed perceptions of inadequate primary healthcare service capacity, resources and staff training to support implementation. Readiness assessments of PHCs identified potential enablers and constrainers that were consistent with concept mapping results. Staff support was evident. Constraints included under-staffing, which could hinder supervision of HAs; and inadequate essential NCD medicines, training in NCDs and financial and policy support for embedding community interventions. Despite a history of socio-political disadvantage, the most enabling context was found in the Kalinago Territory, where ongoing community engagement activities could support joint development of programmes between churches and PHCs., Conclusion: Multi-sectoral stakeholder consultation and direct PHC assessments revealed viability of the proposed POW-PHC partnership for NCD prevention and control. However, structural and policy support will be key for implementing change., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gobin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor-patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The current study examined ethnic representation and stereotypes in textbooks from two core secondary school subjects: maths and Dutch. We examined all 25 hard-copy textbooks used in first-year secondary schools in the Netherlands in 2019, and coded characters' ethnic background, competence-related activities, and occupational status. Ethnicity was identifiable for 8897 characters. Results indicate that characters of colour were underrepresented in the text and images in the textbooks compared to Dutch population statistics. Additionally, subtle stereotypical patterns were found in which characters of colour were less competent and lower in occupational status than White characters. These findings suggest that implicit biases influence which groups are featured and how they are portrayed. To help all students to reach their full potential and develop an inclusive worldview, we recommend publishers use publicly available tools (e.g. random name generators) to make their textbooks more inclusive., Competing Interests: No potential conflict of interest was reported by the authors., (© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Emerging research from the United States indicates that people with an East Asian background experience COVID-19-related racial discrimination. There is some (although not consistent) evidence that these discrimination experiences can in turn have psychological and behavioral consequences, such as strengthening one's ethnic identity and influencing parents' ethnic-racial socialization practices. The current study presents a unique natural experiment examining self-reported perceived discrimination experiences, ethnic identity, and ethnic-racial socialization among 80 Chinese immigrant mothers in the Netherlands before and after the COVID-19 outbreak (39 mothers recruited before the start of the COVID-19 pandemic and 41 during). The main findings from our exploratory analyses indicated an impact of the pandemic with higher (subtle) discrimination and stronger ethnic identity among Chinese immigrant mothers living in the Netherlands, highlighting how personal experiences related to intergroup processes have changed as a result of the COVID-19 crisis in the European context., Competing Interests: The authors report there are no competing interests to declare., (© 2022 The Authors.)

Children often show a positive ingroup bias in altruistic behaviors such as sharing. Insight in factors related to ethnic bias in sharing can help towards understanding the origins of inequality in the distribution of resources in society. The present study examined the effect of priming secure attachment (versus positive affect) and multiculturalism (versus color-evasiveness) on ingroup bias in dominant ethnic group children's altruistic sharing. One hundred twenty-five White Dutch children (45 % boys, 55 % girls) between 7 and 11 years old (M age  = 8.47, SD age  = 0.87) participated in a Dictator game after being primed. The Dictator game was played against three same-gender children with different ethnic backgrounds (White, Black, Middle Eastern). Results support the idea that priming secure attachment and multiculturalism can decrease ingroup bias in dominant ethnic group children's altruistic sharing, although the effects do not strengthen each other and are effective in situations with different trade-offs and interaction partners. Future research is needed to disentangle the effectiveness of secure attachment and multiculturalism messages in different sharing situations and with interaction partners with different ethnic backgrounds. Results from the present study provide starting points from which to further examine which messages potentially positively impact children's interethnic relations., Competing Interests: Declaration of competing interest None., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

A prerequisite to anti-racist socialization in families is acknowledging ethnic-racial (power) differences, also known as color-consciousness. In a sample of 138 White Dutch families from the urban Western region of the Netherlands with children aged 6-10 years (53% girls), observations and questionnaires on maternal color-consciousness and measures of children's attitudes toward Black and Middle-Eastern ethnic-racial outgroups were collected in 2018-2019. Variable-centered analyses showed that maternal color-conscious socialization practices were related to less negative child outgroup attitudes only. Person-centered analysis revealed a cluster of families with higher maternal color-consciousness and less prejudiced child attitudes, and a cluster with the opposite pattern. The mixed results emphasize the importance of multiple methods and approaches in advancing scholarship on anti-racism in the family context., (© 2022 The Authors. Child Development published by Wiley Periodicals LLC on behalf of Society for Research in Child Development.)

A functional demonstration of bit-interleaving TDM downstream protocol for passive optical networks (Bi-PON) is reported. The proposed protocol presents a significant reduction in dynamic power consumption in the customer premise equipment over the conventional TDM protocol. It allows to select the relevant bits of all aggregated incoming data immediately after clock and data recovery (CDR) and, hence, allows subsequent hardware to run at much lower user rate. Comparison of experimental results of FPGA-based implementations of Bi-PON and XG-PON shows that more than 30x energy-savings in protocol processing is achievable.

This paper presents a high performance DC-coupled 10 Gbit/s APD-based optical burst-mode receiver (BM-RX) for symmetric 10G-GPONs. The BM-RX can handle upstream bursts with a loud/soft range wider than 24.0 dB, and the measured sensitivity is -30.0 dBm at BER = 10(-3) after a start of burst settling time no longer than 51 ns. This paper also investigates, for the first time, the inter-burst guard time required for a 10 Gbit/s BM-RX with auto-reset generation, while the PON operates upstream at a high raw BER and is using FEC., (© 2011 Optical Society of America)