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Background As an illustrative example of COVID-19 pandemic community-based participatory research (CBPR), we describe a community-academic partnership to prioritize future research most important to people experiencing high occupational exposure to COVID-19 – food service workers. Food service workers face key challenges surrounding (1) health and safety precautions, (2) stress and mental health, and (3) the long-term pandemic impact. Method Using CBPR methodologies, academic scientists partnered with community stakeholders to develop the research aims, methods, and measures, and interpret and disseminate results. We conducted a survey, three focus groups, and a rapid qualitative assessment to understand the three areas of concern and prioritize future research. Results The survey showed that food service employers mainly supported basic droplet protections (soap, hand sanitizer, gloves), rather than comprehensive airborne protections (high-quality masks, air quality monitoring, air cleaning). Food service workers faced challenging decisions surrounding isolation, quarantine, testing, masking, vaccines, and in-home transmission, described anxiety, depression, and substance use as top mental health concerns, and described long-term physical and financial concerns. Focus groups provided qualitative examples of concerns experienced by food service workers and narrowed topic prioritization. The rapid qualitative assessment identified key needs and opportunities, with help reducing in-home COVID-19 transmission identified as a top priority. COVID-19 mitigation scientists offered recommendations for reducing in-home transmission. Conclusions The COVID-19 pandemic has forced food service workers to experience complex decisions about health and safety, stress and mental health concerns, and longer-term concerns. Challenging health decisions included attempting to avoid an airborne infectious illness when employers were mainly only concerned with droplet precautions and trying to decide protocols for testing and isolation without clear guidance, free tests, or paid sick leave. Key mental health concerns were anxiety, depression, and substance use. Longer-term challenges included Long COVID, lack of mental healthcare access, and financial instability. Food service workers suggest the need for more research aimed at reducing in-home COVID-19 transmission and supporting long-term mental health, physical health, and financial concerns. This research provides an illustrative example of how to cultivate community-based partnerships to respond to immediate and critical issues affecting populations most burdened by public health crises.

Research has demonstrated that defense mechanisms, defined as automatic psychological processes that mediate internal and external stressors, are highly relevant to coping and determining the quality of life for individuals dealing with and experiencing the stressful conditions of living with chronic illness. Defense mechanisms have been associated with important psychological and physical health factors and may be modifiable with specific psychological interventions. Yet, current methods and measures assessing defensive functioning are limited in implementation in clinical and research settings. The present study aims to help fill this assessment gap by evaluating the psychometric properties of a novel measure of defensive functioning for individuals with cancer and other chronic, non-cancer health conditions. Participants completed a 70-item measure of health-related defenses comprised of seven subscales and relevant emotional functioning measures. Data from 583 adults (mean age = 59.88) with hypertension [47.5%], arthritis [36.5%], diabetes type II [28.1%], and cancer [26.2%]) were analyzed. A 32-item version of the scale was obtained following item reduction. Analyses examined criterion validity through correlations with associated emotional functioning variables as well as the internal consistency and factor structure of the scale across groups. Future research will focus on further refinement of the scale, improving reliability and confirming the factor structure of the scale.

Purpose Women with breast cancer diagnosed from mammogram screenings have a lower mortality risk than women diagnosed from symptoms. Currently, the U.S Preventive Services Task Force recommends biannual screening for women aged 50–74 years old. In this study, we aimed to identify factors associated with inadequate screening defined as “no mammogram screening within past 2 years” to guide cancer prevention and early detection efforts. Methods This study utilized area-based probabilistic sampling survey data, collected across Oregon in 2019. Dataset weights were calculated using a raking approach. Demographic and behavior information were collected with existing validated questionnaire items from national surveys. Weighted multivariable logistic regression analyses with missing-value imputations were conducted to identify factors associated with inadequate mammogram screening. Results The study included 254 women 50–74 years old without previous breast or ovarian cancer history. 19.29% of the sample reported no mammogram within two years, including 1.57% with no previous mammograms. Following unadjusted analyses, the significant factors included education, occupation status, health insurance and smoking and were therefore included into the adjusted model. In the multivariate adjusted model education remained significant while occupation status, health insurance and smoking were no longer significant. Compared to women with a college graduate degree, women with less than college graduate degree were at higher risk of inadequate screening [OR (95% CI) = 3.23 (1.54, 6.74)]. Conclusions Lack of education was significantly associated with inadequate mammogram screening even after adjusting for occupation status, health insurance and smoking, which should prompt further outreach and education.

The Segment Anything Model (SAM) enables promptable, high-quality segmentation but is often too computationally expensive for latency-critical settings. TinySAM is a lightweight, distilled SAM variant that preserves strong zero-shot mask quality, yet its \"segment-everything\" mode still requires hundreds of prompts and remains slow in practice. We first replicate TinySAM on COCO val2017 using official checkpoints, matching the reported AP within 0.03%, establishing a reliable experimental baseline. Building on this, we propose Tiny-YOLOSAM, a fast hybrid pipeline that uses a recent YOLO detector (YOLOv12) to generate box prompts for TinySAM on salient foreground objects, and supplements uncovered regions with sparse point prompts sampled only where YOLO-guided masks provide no coverage. On COCO val2017, the hybrid system substantially improves class-agnostic coverage (AR from 16.4% to 77.1%, mIoU from 19.2% to 67.8%) while reducing end-to-end runtime from 49.20s/image to 10.39s/image (4.7x) on an Apple M1 Pro CPU. These results suggest detector-guided prompting combined with targeted sparse sampling as an effective alternative to dense \"segment-everything\" prompting for practical full-scene segmentation.

In this study, we developed a conceptual model for studying the adoption of electronic business (e-business or EB) at the firm level, incorporating six adoption facilitators and inhibitors, based on the technology–organization–environment theoretical framework. Survey data from 3100 businesses and 7500 consumers in eight European countries were used to test the proposed adoption model. We conducted confirmatory factor analysis to assess the reliability and validity of constructs. To examine whether adoption patterns differ across different e-business environments, we divided the full sample into high EB-intensity and low EB-intensity countries. After controlling for variations of industry and country effects, the fitted logit models demonstrated four findings: (1) Technology competence, firm scope and size, consumer readiness , and competitive pressure are significant adoption drivers, while lack of trading partner readiness is a significant adoption inhibitor. (2) As EB-intensity increases, two environmental factors – consumer readiness and lack of trading partner readiness – become less important, while competitive pressure remains significant. (3) In high EB-intensity countries, e-business is no longer a phenomenon dominated by large firms; as more and more firms engage in e-business, network effect works to the advantage of small firms. (4) Firms are more cautious in adopting e-business in high EB-intensity countries – it seems to suggest that the more informed firms are less aggressive in adopting e-business, a somehow surprising result. Explanations and implications are offered.

Grounded in the technology-organization-environment (TOE) framework, we develop a research model for assessing the value of e-business at the firm level. Based on this framework, we formulate six hypotheses and identify six factors (technology readiness, firm size, global scope, financial resources, competition intensity, and regulatory environment) that may affect value creation of e-business. Survey data from 612 firms across 10 countries in the financial services industry were collected and used to test the theoretical model. To examine how e-business value is influenced by economic environments, we compare two subsamples from developed and developing countries. Based on structural equation modeling, our empirical analysis demonstrates several key findings: (1) Within the TOE framework, technology readiness emerges as the strongest factor for e-business value, while financial resources, global scope, and regulatory environment also significantly contribute to e-business value. (2) Firm size is negatively related to e-business value, suggesting that structural inertia associated with large firms tends to retard e-business value. (3) Competitive pressure often drives firms to adopt e-business, but e-business value is associated more with internal organizational resources (e.g., technological readiness) than with external pressure to adopt. (4) While financial resources are an important factor in developing countries, technological capabilities become far more important in developed countries. This suggests that as firms move into deeper stages of e-business transformation, the key determinant of e-business value shifts from monetary spending to higher dimensions of organizational capabilities. (5) Government regulation plays a much more important role in developing countries than in developed countries. These findings indicate the usefulness of the proposed research model and theoretical framework for studying e-business value. They also provide insights for both business managers and policy-makers.

Feature lifting has emerged as a crucial component in 3D scene understanding, enabling the attachment of rich image feature descriptors (e.g., DINO, CLIP) onto splat-based 3D representations. The core challenge lies in optimally assigning rich general attributes to 3D primitives while addressing the inconsistency issues from multi-view images. We present a unified, kernel- and feature-agnostic formulation of the feature lifting problem as a sparse linear inverse problem, which can be solved efficiently in closed form. Our approach admits a provable upper bound on the global optimal error under convex losses for delivering high quality lifted features. To address inconsistencies and noise in multi-view observations, we introduce two complementary regularization strategies to stabilize the solution and enhance semantic fidelity. Tikhonov Guidance enforces numerical stability through soft diagonal dominance, while Post-Lifting Aggregation filters noisy inputs via feature clustering. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on open-vocabulary 3D segmentation benchmarks, outperforming training-based, grouping-based, and heuristic-forward baselines while producing lifted features in minutes. Our \\textbf{code} is available in the \\href{https://github.com/saliteta/splat-distiller/tree/main}{\\textcolor{blue}{GitHub}}. We provide additional \\href{https://splat-distiller.pages.dev/}{\\textcolor{blue}{website}} for more visualization, as well as the \\href{https://www.youtube.com/watch?v=CH-G5hbvArM}{\\textcolor{blue}{video}}.

Feature lifting has emerged as a crucial component in 3D scene understanding, enabling the attachment of rich image feature descriptors (e.g., DINO, CLIP) onto splat-based 3D representations. The core challenge lies in optimally assigning rich general attributes to 3D primitives while addressing the inconsistency issues from multi-view images. We present a unified, kernel- and feature-agnostic formulation of the feature lifting problem as a sparse linear inverse problem, which can be solved efficiently in closed form. Our approach admits a provable upper bound on the global optimal error under convex losses for delivering high quality lifted features. To address inconsistencies and noise in multi-view observations, we introduce two complementary regularization strategies to stabilize the solution and enhance semantic fidelity. Tikhonov Guidance enforces numerical stability through soft diagonal dominance, while Post-Lifting Aggregation filters noisy inputs via feature clustering. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on open-vocabulary 3D segmentation benchmarks, outperforming training-based, grouping-based, and heuristic-forward baselines while producing lifted features in minutes. Our \\textbf{code} is available in the \\href{https://github.com/saliteta/splat-distiller/tree/main}{\\textcolor{blue}{GitHub}}. We provide additional \\href{https://splat-distiller.pages.dev/}{\\textcolor{blue}{website}} for more visualization, as well as the \\href{https://www.youtube.com/watch?v=CH-G5hbvArM}{\\textcolor{blue}{video}}.

The term ＂lineage reprogram- ming＂ is typically used to describe the conversion of one differentiated somatic cell type into another without transit through a pluripotent inter- mediate. Two recent reports in Nature demonstrate that such a conversion can be achieved in the heart in situ, and suggest a novel, regenerative ap- proach for the development of cardiac therapeutics.

Re \"Harvard Rates Asian-Americans as Less Likable, Plaintiffs Claim\" (front page, June 16):