This meta-analysis investigated the persuasive effects of temporal framing in health messaging. Our analysis included 39 message pairs from 22 studies in 20 articles ( = 4,998) that examined the effects of temporal framing (i.e. present-oriented messages vs. future-oriented messages) on attitudes, intentions, and behaviors in health contexts. We found that present-oriented messages were significantly more persuasive than future-oriented messages in terms of intentions and integrated persuasive outcomes. Effects of temporal framing on attitudes and behaviors were not statistically significant. We tested six moderators of temporal framing effects (gain vs. loss framing, temporal framing operationalization, behavior type, timing of effect assessment, age, CFC levels) but none of them was statistically significant. Implications for future temporal framing research are discussed.
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http://dx.doi.org/10.1080/10410236.2023.2175407 | DOI Listing |
Nat Biotechnol
January 2025
Department of Automation, Tsinghua University, Beijing, China.
Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network.
View Article and Find Full Text PDFSci Rep
January 2025
The Alan Turing Institute, London, UK.
Air pollution in cities, especially NO, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities.
View Article and Find Full Text PDFPurpose: To propose a domain-conditioned and temporal-guided diffusion modeling method, termed dynamic Diffusion Modeling (dDiMo), for accelerated dynamic MRI reconstruction, enabling diffusion process to characterize spatiotemporal information for time-resolved multi-coil Cartesian and non-Cartesian data.
Methods: The dDiMo framework integrates temporal information from time-resolved dimensions, allowing for the concurrent capture of intra-frame spatial features and inter-frame temporal dynamics in diffusion modeling. It employs additional spatiotemporal ($x$-$t$) and self-consistent frequency-temporal ($k$-$t$) priors to guide the diffusion process.
We present a widefield fluorescence microscope that integrates an event-based image sensor (EBIS) with a CMOS image sensor (CIS) for ultra-fast microscopy with spectral distinction capabilities. The EBIS achieves a temporal resolution of ∼10s (∼ 100,000 frames/s), while the CIS provides diffraction-limited spatial resolution. A diffractive optical element encodes spectral information into a diffractogram, which is recorded by the CIS.
View Article and Find Full Text PDFGhost holography has attracted notable applied interest in the modern quantitative imaging applications with the futuristic features of complex field recovery in the diversified imaging scenarios. However, the utilization of digital holography in ghost frame works introduces space bandwidth or time bandwidth restrictions in the implementation of the technique in applied domains. Here, we propose and demonstrate a quantitative ghost phase imaging approach with holographic ghost diffraction scheme in combination with the phase-shifting technique.
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