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This paper addresses the inherent limitations in traditional color modeling techniques for measuring the flame equivalence ratio (Φ), particularly focusing on the subjectivity involved in threshold settings and the challenges posed by uneven 2D color distribution. To overcome these issues, this study introduces an attention-based convolutional neural network (ACN) model, a novel approach that transcends the conventional reliance on B/G color features (Tf). The ACN model leverages adaptive feature extraction, augmented by a spatial attention mechanism, to more effectively analyze flame images. By amplifying key features, autonomously minimizing background noise, and standardizing variations in color distribution, the ACN model in this experiment achieved a prediction accuracy of 99%, with a 76% reduction in error rate compared to the original model, significantly improving the accuracy and objectivity of flame Φ measurement. This method marks a substantial development in the precision and reliability of flame analysis. [ABSTRACT FROM AUTHOR]

Objective: We aimed to test whether region‐specific factors, including spatial expression patterns of the tau‐encoding gene MAPT and regional levels of amyloid positron emission tomography (PET), enhance connectivity‐based modeling of the spatial variability in tau‐PET deposition in the Alzheimer disease (AD) spectrum. Methods: We included 685 participants (395 amyloid‐positive participants within AD spectrum and 290 amyloid‐negative controls) with tau‐PET and amyloid‐PET from 3 studies (Alzheimer's Disease Neuroimaging Initiative, 18F‐AV‐1451‐A05, and BioFINDER‐1). Resting‐state functional magnetic resonance imaging was obtained in healthy controls (n = 1,000) from the Human Connectome Project, and MAPT gene expression from the Allen Human Brain Atlas. Based on a brain‐parcellation atlas superimposed onto all modalities, we obtained region of interest (ROI)‐to‐ROI functional connectivity, ROI‐level PET values, and MAPT gene expression. In stepwise regression analyses, we tested connectivity, MAPT gene expression, and amyloid‐PET as predictors of group‐averaged and individual tau‐PET ROI values in amyloid‐positive participants. Results: Connectivity alone explained 21.8 to 39.2% (range across 3 studies) of the variance in tau‐PET ROI values averaged across amyloid‐positive participants. Stepwise addition of MAPT gene expression and amyloid‐PET increased the proportion of explained variance to 30.2 to 46.0% and 45.0 to 49.9%, respectively. Similarly, for the prediction of patient‐level tau‐PET ROI values, combining all 3 predictors significantly improved the variability explained (mean adjusted R2 range across studies = 0.118–0.148, 0.156–0.196, and 0.251–0.333 for connectivity alone, connectivity plus MAPT expression, and all 3 modalities combined, respectively). Interpretation: Across 3 study samples, combining the functional connectome and molecular properties substantially enhanced the explanatory power compared to single modalities, providing a valuable tool to explain regional susceptibility to tau deposition in AD. ANN NEUROL 2024;95:274–287 [ABSTRACT FROM AUTHOR]