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Substrate-specific responses to mixing conditions in high-solids enzymatic hydrolysis: Insights from microcrystalline cellulose and dilute-acid pretreated corncob.
Int J Biol Macromol
January 2025
Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest, Resources, College of Chemical Engineering, Nanjing Forestry University, Nanjing 210037, PR China. Electronic address:
This study investigates the mixing effects on the enzymatic hydrolysis of microcrystalline cellulose (MCC) and dilute-acid pretreated corncob substrates under high-solid conditions. Enzymatic hydrolysis experiments were conducted to assess cellulose conversion rates under varying mixing conditions (0, 50, 150, and 250 rpm) and solids loadings (5 %, 15 %, 25 %, and 35 %, w/v), and distinct physicochemical properties of the substrates were characterized. Additionally, the role of mixing conditions and solid loadings on cellulose hydrolysis kinetics and enzyme adsorption on both substrates and lignin were elucidated.
View Article and Find Full Text PDFSimultaneous Emergent Phenomena: Leadership and Team Synchrony.
Nonlinear Dynamics Psychol Life Sci
January 2025
Marquette University, Milwaukee, WI.
Emergent phenomena exhibit interesting dynamics when considered individually. The present article examines two emergent processes that could be occurring simultaneously in an intense team interaction: the emergence of leaders and the emergence of autonomic synchrony within teams making dynamic decisions. In the framework of panarchy theory and related studies on complex systems, autonomic synchrony would be a fast dynamic that is shaped or controlled by leadership emergence, which is a slower dynamic.
View Article and Find Full Text PDFIncorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI.
Med Image Anal
November 2024
Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge lies in accurately estimating model parameters from the acquired data due to the inherently low signal-to-noise ratio (SNR) of the signal measurements and the complexity of solving the ill-posed inverse problem. Conventional model fitting approaches treat individual voxels as independent.
View Article and Find Full Text PDFUltrahigh concentration exfoliation and aqueous dispersion of few-layer graphene by excluded volume effect.
Nat Commun
December 2024
Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria, Australia.
Colloidal properties of nanoparticles are intricately linked to their morphology. Traditionally, achieving high-concentration dispersions of two-dimensional (2D) nanosheets has proven challenging as they tend to agglomerate or re-stack under increased surface contact and Van der Waals attraction. Here, we unveil an excluded volume effect enabled by 2D morphology, which can be coupled with electrostatic repulsion to synthesize high-concentration aqueous graphene dispersions.
View Article and Find Full Text PDFApplications of Deep Neural Networks with Fractal Structure and Attention Blocks for 2D and 3D Brain Tumor Segmentation.
J Stat Theory Pract
September 2024
Statistics Online Computational Resource, University of Michigan, 426 North Ingalls Str, Ann Arbor, Michigan 48109-2003.
In this paper, we propose a novel deep neural network (DNN) architecture with fractal structure and attention blocks. The new method is tested to identify and segment 2D and 3D brain tumor masks in normal and pathological neuroimaging data. To circumvent the problem of limited 3D volumetric datasets with raw and ground truth tumor masks, we utilized data augmentation using affine transformations to significantly expand the training data prior to estimating the network model parameters.
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