Search

Neurofibromatosis Type 1 is a genetic disorder characterized by the development of neurofibromas (NFs), which exhibit significant variability in size, morphology, and anatomical location. Accurate and automated segmentation of these tumors in whole-body magnetic resonance imaging (WB-MRI) is crucial to assess tumor burden and monitor disease progression. In this study, we present and analyze a fully automated pipeline for NF segmentation in fat-suppressed T2-weighted WB-MRI, consisting of three stages: anatomy segmentation, NF segmentation, and tumor candidate classification. In the first stage, we use the MRSegmentator model to generate an anatomy segmentation mask, extended with a high-risk zone for NFs. This mask is concatenated with the input image as anatomical context information for NF segmentation. The second stage employs an ensemble of 3D anisotropic anatomy-informed U-Nets to produce an NF segmentation confidence mask. In the final stage, tumor candidates are extracted from the confidence mask and classified based on radiomic features, distinguishing tumors from non-tumor regions and reducing false positives. We evaluate the proposed pipeline on three test sets representing different conditions: in-domain data (test set 1), varying imaging protocols and field strength (test set 2), and low tumor burden cases (test set 3). Experimental results show a 68% improvement in per-scan Dice Similarity Coefficient (DSC), a 21% increase in per-tumor DSC, and a two-fold improvement in F1 score for tumor detection in high tumor burden cases by integrating anatomy information. The method is integrated into the 3D Slicer platform for practical clinical use, with the code publicly accessible.

Thematic Analysis (TA) is a fundamental method in healthcare research for analyzing transcript data, but it is resource-intensive and difficult to scale for large, complex datasets. This study investigates the potential of large language models (LLMs) to augment the inductive TA process in high-stakes healthcare settings. Focusing on interview transcripts from parents of children with Anomalous Aortic Origin of a Coronary Artery (AAOCA), a rare congenital heart disease, we propose an LLM-Enhanced Thematic Analysis (LLM-TA) pipeline. Our pipeline integrates an affordable state-of-the-art LLM (GPT-4o mini), LangChain, and prompt engineering with chunking techniques to analyze nine detailed transcripts following the inductive TA framework. We evaluate the LLM-generated themes against human-generated results using thematic similarity metrics, LLM-assisted assessments, and expert reviews. Results demonstrate that our pipeline outperforms existing LLM-assisted TA methods significantly. While the pipeline alone has not yet reached human-level quality in inductive TA, it shows great potential to improve scalability, efficiency, and accuracy while reducing analyst workload when working collaboratively with domain experts. We provide practical recommendations for incorporating LLMs into high-stakes TA workflows and emphasize the importance of close collaboration with domain experts to address challenges related to real-world applicability and dataset complexity. https://github.com/jiaweixu98/LLM-TA, Comment: Accepted by GenAI for Health Workshop @ AAAI 2025, Philadelphia

This report presents the results of a study conducted by RAND researchers that investigated the implementation and effects of the Positive Emotional Development and Learning Skills (PEDALS) program in prekindergarten classrooms in Western and Central New York and Southeast Michigan. PEDALS is a social-emotional learning (SEL) program that combines the Second Step curriculum, a universal curriculum whose implementation provides SEL instruction to all children in a classroom, with two years of supports for site directors and teachers. Supports include screening children to connect them with resources as needed, coaching, collecting and using data to inform implementation, and sustainability planning. Through a collaborative process, RAND and PEDALS partners in each state conceptualized the implementation study and quasi-experimental impact study leveraging a propensity score weighting analysis. This report--the second and final report in a series on PEDALS--summarizes and expands upon implementation findings and presents the results of the quasi-experimental study. This report should be of interest to PEDALS staff in Michigan and New York, early childhood educators who are implementing PEDALS, early childhood educators interested in implementing PEDALS or prekindergarten SEL programs more generally, state and federal policymakers interested in SEL and the effectiveness of PEDALS, and researchers interested in SEL approaches. [Additional funding by the Health Foundation for Western & Central New York.]

Formal mentoring has been an effective approach to mitigate challenges facing underserved youth and contributes to observable improvements in behavior, relationships, and emotional well-being. In recent years, virtual mentoring has emerged as a promising way to expand the provision of mentoring. By engaging virtually, mentorship programs can mitigate typical challenges of travel time and distance between mentor and mentee. Nonetheless, there are also challenges associated with virtual mentoring, including the potential for miscommunication and possible difficulties developing deep connections. In 2020, the U.S. Dream Academy began developing and implementing virtual and hybrid mentoring services to explore the benefits and address the challenges of virtual mentoring. As the coronavirus disease 2019 (COVID-19) pandemic caused widespread disruption in schooling in the United States, the U.S. Dream Academy leveraged this unique opportunity to begin implementing virtual mentoring. This report describes how the U.S. Dream Academy sought to expand its mentoring program to include a virtual option. The authors summarize the development and implementation of the mentoring program and lessons learned from this experience with the goal of supporting program improvement. This report may also assist other mentoring programs that seek to adopt a similar mentoring model. [The research for this report was sponsored by U.S. Dream Academy.]

Formal mentoring has been an effective approach to mitigate challenges facing underserved youth and contributes to observable improvements in behavior, relationships, and emotional well-being (Rhodes, 2008). Mentored youth appear to gain improvements in peer and parent relationships and school performance, while also engaging in lower levels of substance and alcohol use relative to youth who are not mentored (Raposa et al., 2019). Studies have found mentoring effective in preventing psychosocial problems, such as delinquent behavior (DuBois and Karcher, 2013). Despite the evidence on the benefits of formal mentoring, an estimated 9 million young people are growing up in under-resourced environments without the support of formal mentors (Bruce and Bridgeland, 2014). In recent years, virtual mentoring has emerged as a promising way to expand the provision of mentoring (Andersen and Wellen, 2023). By engaging virtually, mentorship programs can mitigate the typical challenges of travel time and distance between mentor and mentee (Ensher, Heun, and Blanchard, 2003) and may even facilitate the development of rapport and trust (Ensher and Murphy, 1997; Miller and Griffiths, 2005). In 2020, the U.S. Dream Academy (hereafter referred to as "Dream"), a longtime innovator in developing out-of-school-time programs and formal mentoring for young people, expanded its mentoring program to include a virtual option in six Learning Centers across the country. RAND researchers collaborated with Dream to evaluate the development and implementation of the mentoring program and the supplemental services offered. This brief illustrates how flexible programming, including the use of in-person, hybrid, and virtual mentoring approaches, allowed for the adaptation of supports to meet the specific needs of youth and families in their communities.

The broad band resonant spin echo interferometer, CANISIUS, is presented. CANISIUS is built in a versatile way, such that it can be operated in both a continuous broad band beam or a pulsed Time of Flight beam. This versatility also extends to the modes available to the instrument, such as Neutron Resonant Spin Echo, Spin Echo (Modulated) Small Angle Neutron Scattering and coherent averaging to produce structured wavefunctions for scattering. The instrument may also be used as an interferometer, to probe fundamental questions in quantum mechanics. In this paper we detail both the continuous and Time of Flight options of the instruments. In addition we demonstrate the applicability of our interferometer to ultra small angle scattering in a white beam. Finally we demonstrate a new spin echo interferometry tool, which uses incomplete recombination of the two path states to generate composite wavefunctions with special structure. In particular we show that this method produces neutron wavefunctions that exist in a superposition of two quantum mechanical OAM modes, l =+1 or -1 We illustrate that just as this method can be used to generate certain structured waves, it may also be used to characterize the structure of the input wavefunction.

In this report, the authors describe how implementation of restorative practices in educational settings and a juvenile diversion program run by the National Conflict Resolution Center (NCRC) in San Diego County are working to stem the school-to-prison pipeline. The authors focus on how NCRC is serving as an intermediary organization for youth-focused programs, signs of success, opportunities to improve, and insights for other communities.

Though early childhood educators have a long history of emphasizing learning through play to foster the development of the whole child, including social-emotional competencies, the proliferation of formal social-emotional learning (SEL) curricula first seen in kindergarten to 12th grade has expanded to prekindergarten. This report presents initial implementation findings of the Positive Emotional Development and Learning Skills (PEDALS) program, an SEL program in prekindergarten classrooms in Western and Central New York and Southeast Michigan. PEDALS is a two-year program that combines the Second Step Early Learning SEL curriculum with comprehensive supports for teachers and site directors. Findings indicate that educators perceived the Second Step curriculum, PEDALS training, and PEDALS coaching support very favorably; strongly believed in supporting children's social-emotional development; and had a strong sense of self-efficacy for doing so. Observations indicated that educators were highly skilled in implementing Second Step lessons; however, teachers used strategies less consistently when children were having a difficult time or experiencing interpersonal conflict. Educators identified PEDALS resources for developing children's social-emotional competencies as the factors that provided the most implementation support; meanwhile, they identified some contextual factors, such as children's challenging behavior and lack of planning time, as the factors that most hindered implementation. Preliminary findings suggest that many preconditions for effective SEL instruction were met. This implementation study is a part of a larger evaluation of PEDALS that also will estimate the effect of the program on children's SEL and executive functioning skills. A second report will be published at the study's conclusion. [Additional funding from the Health Foundation for Western & Central New York.]

The U.S. Department of Health and Human Services, Administration for Children and Families, Office of Child Care, requires states to use information about the provider cost of child care to inform the setting of payment rates under the Child Care Development Fund (CCDF) program. This requirement is consistent with the growing recognition that payment rates for early childhood care and education (ECCE) through CCDF are often too low to fully reimburse providers for their costs. Drawing from a 2020 cost-of-care study for Oklahoma that was conducted by RAND researchers, this updated report will meet the CCDF requirement for Oklahoma to have a current narrow cost analysis to inform subsidy rate setting. This report also provides an opportunity to understand how ECCE provider costs may have changed since the onset of the coronavirus disease 2019 (COVID-19) pandemic, which took place just as the data collection for the 2020 study was completed. The authors adopted a two-pronged approach for this study: (1) collecting program and expenditure data from a sample of licensed providers, purposively selected to capture variation in key provider features that affect program structure and, therefore, per-child cost; and (2) developing a cost model (or calculator) with parameters informed by the information gathered from the sampled providers. The results from this updated report, with cost of care estimates for 2023, can continue to help policymakers in Oklahoma improve the access to and quality of ECCE. [This research was sponsored by the Oklahoma Partnership for School Readiness Foundation.]

Despite the significant contributions of graduate students and postdoctoral scholars to scientific research, the environments in which they work and learn often offer inconsistent mentorship and professional development opportunities and only intermittently address their mental, financial, and social well-being. To examine mentorship, well-being, and professional development and the challenge of translating existing mentorship theory into effective interventions, The Roundtable on Mentorship, Well-being, and Professional Development convened a workshop on November 1 and 2, 2023. This publication summarizes the presentation and discussion of the workshop.

The coronavirus disease 2019 (COVID-19) pandemic continues to have profound effects on U.S. society. However, one group that is often forgotten in the public health debate and that is especially vulnerable to the spread of the virus and its adverse consequences is the 1.2 million incarcerated adults in U.S. federal and state prisons. The COVID-19 pandemic has had a negative impact on rehabilitative programs, including education programs, that are provided in state prison systems. State and federal correctional systems implemented a variety of policies to prevent or contain the spread of COVID-19 within this population. As part of this response, many correctional systems ceased or substantially cut back on programming starting in 2020; this included shutting down ongoing education and workforce training programs, as well as other programs and activities, and preventing instructors and other staff from entering prison facilities. This report presents the authors' findings on how COVID-19 has affected prison education programs within state correctional systems from 2020 through 2023. The overall goal was to collect critical information to help inform educators, colleges, corrections officials, and policymakers about the magnitude of the pandemic's overall impact and its effects on access to education programs.

Rising rates of individuals with mental illness in the United States have policymakers, education officials, and medical professionals worried about the need for additional support for struggling college students--and the ability of higher education institutions to provide it. Many colleges have designed and implemented a variety of interventions to ensure that mental health issues do not interfere with students' abilities to persist and succeed in college. In many cases, colleges have begun to integrate mental health supports into wider efforts to proactively connect students with relevant supports to help them address academic and nonacademic challenges. However, some colleges--particularly community colleges--continue to face challenges, such as insufficient resources and capacity to treat mental illness, that prevent them from adequately addressing students' mental health needs. The authors of this report present findings from a descriptive study of ten community colleges in Texas that are working to address student mental health at their institutions. They document the strategies and supports that those colleges have implemented to support student mental health, how the colleges are working to integrate these approaches into the organizational fabric of the colleges, and key challenges to supporting student mental health in a community college setting. They offer a set of recommendations for decisionmakers who are interested in addressing student mental health on community college campuses.

Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher-level computation, existing computational frameworks require the integration of large-scale networked logic gates, which places demanding requirements on the fabrication of materials counterparts and the propagation of signals. Inspired by cellular automata, we propose a novel computational framework based on multistable origami metamaterials by incorporating reservoir computing, which can accomplish high-level computation tasks without the need to construct a logic gate network. This approach thus eleimates the demanding requirements for fabrication of materials and signal propagation when constructing large-scale networks for high-level computation in conventional mechano-logic. Using the multistable stacked Miura-origami metamaterial as a validation platform, digit recognition is successfully implemented through experiments by a single actuator. Moreover, complex tasks, such as handwriting recognition and 5-bit memory tasks, are also shown to be feasible with the new computation framework. Our research represents a significant advancement in developing a new generation of intelligent materials with advanced computational capabilities. With continued research and development, these materials could have a transformative impact on a wide range of fields, from computational science to material mechano-intelligence technology and beyond., Comment: 24 pages, 7 figures

Transitions of multistability in structures have been exploited for various functions and applications, such as spectral gap tuning, impact energy trapping, and wave steering. However, a fundamental and comprehensive understanding of the transitions, either quasi-static or dynamic transitions, has not yet been acquired, especially in terms of the sequence predictability and tailoring mechanisms. This research, utilizing the stacked Miura-ori-variant (SMOV) structure that has exceptional multistability and shape reconfigurability as a platform, uncovers the deep knowledge of quasi-static and dynamic transitions, and pioneers the corresponding versatile formation and tuning of mechanical logic gates. Through theoretical, numerical, and experimental means, discriminative and deterministic quasi-static transition sequences, including reversible and irreversible ones, are uncovered, where they constitute a transition map that is editable upon adjusting the design parameters. Via applying dynamic excitations and tailoring the excitation conditions, reversible transitions between all stable configurations become attainable, generating a fully-connected transition map. Benefiting from the nonlinearity of the quasi-static and dynamic transitions, basic and compound mechanical logic gates are achieved. The versatility of the scheme is demonstrated by employing a single SMOV structure to realize different complex logic operations without increasing structural complexity, showing its superior computing power and inspiring the avenue for efficient physical intelligence., Comment: 26 pages, 5 figures, 8 supplementary figures, 2 supplementary tables