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Detection of circular permutations by Protein Language Models.
Comput Struct Biotechnol J
December 2024
School of Bioengineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250300, China.
Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods face challenges: sequence-based approaches struggle with detecting distant homologs, while structure-based approaches are limited by the need for structure generation and often treat proteins as rigid bodies. Protein Language Model-based alignment tools have shown advantages in utilizing sequence information to overcome the challenges of detecting distant homologs without requiring structural input.
View Article and Find Full Text PDFA paradigmatic approach to the topological measure of babesiosis drugs and estimating physical properties via QSPR analysis.
Heliyon
January 2025
College of Since and Art, Department of Mathematics, King Khalid University, Mahayil, Saudi Arabia.
New developments in the field of chemical graph theory have made it easier to comprehend how chemical structures relate to the graphs that underlie them on a more profound level using the ideas of classical graph theory. Chemical graphs can be effectively probed with the help of quantitative structure-property relationship (QSPR) analysis. In order to statistically correlate physical attributes.
View Article and Find Full Text PDFLWheatNet: a lightweight convolutional neural network with mixed attention mechanism for wheat seed classification.
Front Plant Sci
January 2025
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
Introduction: With the advent of technologies such as deep learning in agriculture, a novel approach to classifying wheat seed varieties has emerged. However, some existing deep learning models encounter challenges, including long processing times, high computational demands, and low classification accuracy when analyzing wheat seed images, which can hinder their ability to meet real-time requirements.
Methods: To address these challenges, we propose a lightweight wheat seed classification model called LWheatNet.
Evaluating Federal Policies Using Bayesian Time Series Models: Estimating the Causal Impact of the Hospital Readmissions Reduction Program.
Health Serv Outcomes Res Methodol
October 2023
University of Florence, DiSIA and Florence Center for Data Science, and European University Institute, Department of Economics, Florence, Italy.
Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and its effects over the targeted population can manifest at any time period afterwards. In the presence of data measured over time, Bayesian time series models have been used to impute what would have happened after the policy was initiated, had the policy not taken place, in order to estimate causal effects. However, the considerations regarding the definition of the target estimands, the underlying assumptions, the plausibility of such assumptions, and the choice of an appropriate model have not been thoroughly investigated.
View Article and Find Full Text PDFTheory for ultrafast energy transfer in photosynthesis.
Phys Chem Chem Phys
January 2025
Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China.
Photosynthesis has attracted much attention due to its extremely high energy transfer efficiency in the primary light capture stage. Numerical simulations based on energy transfer theory are helpful in exploring the physical mechanisms behind the dynamics of energy transfer. This article starts from the process of photosynthesis and explains the specific contents of two classical energy transfer theories, namely coherent and incoherent theories.
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