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Applications of mass spectrometry imaging in botanical research.
Adv Biotechnol (Singap)
February 2024
State Key Laboratory of Traditional Chinese Medicine/School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.
Mass spectrometry imaging (MSI) serves as a valuable tool enabling researchers to scrutinize various compounds, peptides, and proteins within a sample, providing detailed insights at both elemental and molecular levels. This innovative technology transforms information obtained from a mass spectrometer- encompassing ionic strength, mass-to-charge ratio, and ionized molecule coordinates-within a defined region into a pixel-based model. Consequently, it reconstructs the spatial distribution of ions, allowing for a comprehensive understanding of molecular landscapes.
View Article and Find Full Text PDFDistribution of early-branching Cyanobacteriia and the potential habitats that gave rise to the earliest oxygenic phototrophs.
mSphere
January 2025
Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA.
The evolution of oxygenic photosynthesis in the Cyanobacteria was one of the most transformative events in Earth history, eventually leading to the oxygenation of Earth's atmosphere. However, it is difficult to understand how the earliest Cyanobacteria functioned or evolved on early Earth in part because we do not understand their ecology, including the environments in which they lived. Here, we use a cutting-edge bioinformatics tool to survey nearly 500,000 metagenomes for relatives of the taxa that likely bookended the evolution of oxygenic photosynthesis to identify the modern environments in which these organisms live.
View Article and Find Full Text PDFEvaluation of machine learning techniques for real-time prediction of implanted lower limb mechanics.
Front Bioeng Biotechnol
January 2025
Center for Orthopaedic Biomechanics, University of Denver, Denver, CO, United States.
Introduction: Accurate prediction of knee biomechanics during total knee replacement (TKR) surgery is crucial for optimal outcomes. This study investigates the application of machine learning (ML) techniques for real-time prediction of knee joint mechanics.
Methods: A validated finite element (FE) model of the lower limb was used to generate a dataset of knee joint kinematics, kinetics, and contact mechanics.
SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions.
Front Artif Intell
January 2025
Department of Physics and Astronomy, The University of Alabama, Tuscaloosa, AL, United States.
Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation functions on the edges of the computational graph followed by summation on nodes. The learnable edge activation functions in the original implementation are basis spline functions (B-Spline).
View Article and Find Full Text PDFRevolutionizing load harmony in edge computing networks with probabilistic cellular automata and Markov decision processes.
Sci Rep
January 2025
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, UK.
In general, edge computing networks are based on a distributed computing environment and hence, present some difficulties to obtain an appropriate load balancing, especially under dynamic workload and limited resources. The conventional approaches of Load balancing like Round-Robin and Threshold-based load balancing fails in scalability and flexibility issues when applied to highly variable edge environments. To solve the problem of how to achieve steady-state load balance and provide dynamic adaption to edge networks, this paper proposes a new framework that using PCA and MDP.
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