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Background: Asian American (AA) community leaders, Native Hawaiian/Pacific Islander (NH/PI) community leaders, and allies in the United States Pacific Northwest expressed concern that there are families and children from AA communities and NH/PI communities who experience and witness acts of xenophobia and racism. This can cause racial trauma. The long-time practice of aggregating AA and NH/PI data contributes to erasure and makes it challenging to advance health equity, such as allocating resources. According to AAPI Data's long-awaited report in June 2022, there are over 24 million AAs and 1.6 million NHs/PIs in the United States, growing by 40% and 30%, respectively, between 2010 and 2020. Philanthropic investments have not kept up with this substantive increase. The National Academies of Sciences, Engineering, and Medicine emphasized the need for effective partnerships to advance the health and well-being of individuals and communities in antiracism and system-level research.
Objective: The aim of this community-based participatory research qualitative description study was to identify perceptions and experiences regarding racial discrimination, race-based stress, and racial trauma; intergenerational healing and resiliency; and sharing the body with science from key informants of an academic and community partnership to inform antiracism coalition work. This partnership includes academic researchers and community leaders from community-based organizations and a health care organization serving immigrant and marginalized communities, including AAs and NHs/PIs in the United States Pacific Northwest.
Methods: In total, 10 key informants joined 1 of 2 participatory group discussions via videoconference for 2 hours in 2022. We used a semistructured and open-ended group interview guide. A qualitative participatory group-level assessment was conducted with the key informants and transcribed. Interpretations and meanings of the main points and the main themes were reflected upon, clarified, and verified with the key informants in real time. The field note-based data transcripts were manually coded using conventional content analysis. Reflexivity was used.
Results: There were 6 main themes: prejudice plus power in racism definition and working in solidarity to counter lateral oppression/false sense of security, microaggression as multilayers, "not assimilationist by nature" and responding differently to white superiority, intergenerational- and identity-related trauma, what is healing among People of Color and through a lens of resiliency and intergenerational connection and knowledge, and mistrust and fear in the research and health care systems surrounding intentions of the body.
Conclusions: The themes highlight the importance of internal and intergenerational healing from racial trauma and the need for solidarity among communities of color to combat white supremacy and colonization. This work was foundational in an ongoing effort to dismantle racism and uplift the community voice through a cross-sector academic and community partnership to inform antiracism coalition work.
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http://dx.doi.org/10.2196/43150 | DOI Listing |
Neural Netw
September 2025
School of Electronic Science and Engineering, Nanjing University, China. Electronic address:
The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and medical imaging, where precise segmentation is crucial. However, SAM is vulnerable to adversarial attacks that can significantly impair its functionality through minor input perturbations. Traditional techniques, such as FGSM and PGD, are often ineffective in segmentation tasks due to their reliance on global perturbations that overlook spatial nuances.
View Article and Find Full Text PDFNeural Netw
September 2025
organization=Chongqing Key Laboratory of Computer Network and Communication Technology, School of Computer Science and Technology (National Exemplary Software School), Chongqing University of Posts and Telecommunications, city=Chongqing, postcode=400065, country=China. Electronic address: tianh519@1
Image deblurring and compression-artifact removal are both ill-posed inverse problems in low-level vision tasks. So far, although numerous image deblurring and compression-artifact removal methods have been proposed respectively, the research for explicit handling blur and compression-artifact coexisting degradation image (BCDI) is rare. In the BCDI, image contents will be damaged more seriously, especially for edges and texture details.
View Article and Find Full Text PDFNeural Netw
September 2025
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:
Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.