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Spin-Orbit Coupling and Admixture Coefficients in SA-CASSCF and MS-CASPT2, and Triplet Excitation Yield Simulated via Trajectory Surface Hopping and Calibrated SA-CASSCF in 1,2-Dioxetane Derivatives.
J Phys Chem A
January 2025
Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.
The energy gaps, spin-orbit coupling (SOC), and admixture coefficients over a series of the configurations are evaluated by the SA-CASSCF/6-31G, SA-CASSCF/6-31G*, SA-CASSCF/ANO-RCC-VDZP, and MS-CASPT2/ANO-RCC-VDZP to reveal the extent of the inaccuracy of the SA-CASSCF. By comparing the mean absolute errors for the energy gaps and the admixture coefficient magnitudes (ACMs) measured between the SA-CASSCF/6-31G, SA-CASSCF/6-31G*, or SA-CASSCF/ANO-RCC-VDZP and the MS-CASPT2/ANO-RCC-VDZP, the SA-CASSCF/6-31G is selected as the electronic structure method in the nonadiabatic molecular dynamics simulation. The major components of the ACMs of the SA-CASSCF/6-31G and MS-CASPT2/ANO-RCC-VDZP are identified and compared; we find that the ACMs are underestimated by the SA-CASSCF/6-31G, which is verified by the reasonable triplet quantum yield simulated by the trajectory surface hopping and the calibrated SA-CASSCF/6-31G.
View Article and Find Full Text PDFChallenges and compromises: Predicting unbound antibody structures with deep learning.
Curr Opin Struct Biol
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Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, United Kingdom.
Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and improving development pipelines. Prediction of unbound antibodies is challenging, specifically modelling of the CDRH3 loop, where inaccuracies are potentially worse due to a bias in structural data towards antibody-antigen complexes. This class imbalance provides a challenge for deep learning models trained on this data, potentially limiting generalisation to unbound forms.
View Article and Find Full Text PDFLow-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography.
Sensors (Basel)
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Department of Biomedical and Robotics Engineering, Incheon National University, Incheon 22012, Republic of Korea.
With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities. However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame rates and missing data, leading to inaccuracies in HR measurements. This study addresses these issues by introducing low-complexity timing correction methods, including linear, cubic, and filter interpolation, to improve HR estimation from rPPG signals under conditions of irregular sampling and data loss.
View Article and Find Full Text PDFThe Intra-Examiner Variability in and Accuracy of Traditional Manual Diagnostics of Benign Paroxysmal Positional Vertigo: A Prospective Observational Cohort Study.
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Balance & Dizziness Center, Department of Otorhinolaryngology, Head & Neck Surgery and Audiology, Aalborg University Hospital, 9000 Aalborg, Denmark.
Accurate head positioning is essential for diagnostics of benign paroxysmal positional vertigo (BPPV). This study aimed to quantify the head angles and angular velocities during traditional manual BPPV diagnostics in patients with positional vertigo. : A prospective, observational cohort study was conducted at a tertiary university hospital outpatient clinic.
View Article and Find Full Text PDFAre Suggested Hiking Times Accurate? A Validation of Hiking Time Estimations for Preventive Measures in Mountains.
Medicina (Kaunas)
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Sports and Exercise Medicine Division, Department of Medicine, University of Padova, Via Giustiniani 2, 35128 Padova, Italy.
: Accurate hiking time estimate is crucial for outdoor activity planning, especially in mountainous terrains. Traditional mountain signage and online platforms provide generalized hiking time recommendations, often lacking personalization. This study aims to evaluate the variability in hiking time estimates from different methods and assess the potential of a novel algorithm, MOVE, to enhance accuracy and safety.
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