Femtosecond transient absorption measurements of the cis-trans isomerization of the visual pigment rhodopsin clarify the interpretation of the dynamics of the first step in vision. We present femtosecond time-resolved spectra as well as kinetic measurements at specific wavelengths between 490 and 670 nm using 10-fs probe pulses centered at 500 and 620 nm following a 35-fs pump pulse at 500 nm. The expanded spectral window beyond that available (500-570 nm) in our previous study [Schoenlein, R. W., Peteanu, L. A., Mathies, R. A. & Shank, C. V. (1991) Science 254, 412-415] provides the full differential absorption spectrum of the photoproduct as a function of delay time after photolysis. The high time-resolution data presented here contradict an alternative interpretation of the rhodopsin photochemistry offered by Callender and co-workers [Yan, M., Manor, D., Weng, G., Chao, H., Rothberg, L., Jedju, T. M., Alfano, R. R. & Callender, R. H. (1991) Proc. Natl. Acad. Sci. USA 88, 9809-9812]. Our results confirm that the red-shifted (lambda max approximately 570 nm) photo-product of the isomerization reaction is fully formed within 200 fs. Subsequent changes in the differential spectra between 200 fs and 6 ps are attributed to a combination of dynamic ground-state processes such as intramolecular vibrational energy redistribution, vibrational cooling, and conformational relaxation.
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http://dx.doi.org/10.1073/pnas.90.24.11762 | DOI Listing |
Aging Ment Health
January 2025
Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Objectives: This study aimed to investigate the impact of sensory impairments on well-being, depression symptoms, and relationship satisfaction among older adults, and to examine whether these associations vary by gender.
Method: The study analyzed a sample of 640 Danish individuals aged 60 and older. Multilevel modeling was conducted using PROC MIXED in SAS to assess the impact of sensory impairments on well-being, depression symptoms, and relationship satisfaction.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFJ Opt Soc Am A Opt Image Sci Vis
August 2024
In the pinhole point diffraction interferometer (PPDI), proper alignment between the reflection spot of the tested component and the pinhole is critical to obtain accurate interferograms. At present, adjusting for tilt error requires manual manipulation, and defocus error cannot be corrected. These limitations impede the instrumentation process of PPDI.
View Article and Find Full Text PDFNutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFSensors (Basel)
January 2025
German Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, LMU Munich, 81377 Munich, Germany.
Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy.
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