Control of in situ 3D bioprinting of hydrogel without toxic crosslinker is ideal for tissue regeneration by reinforcing and homogeneously distributing biocompatible reinforcing agent during fabrication of large area and complex tissue engineering scaffolds. In this study, homogeneous mixing, and simultaneous 3D bioprinting of a multicomponent bioink based on alginate (AL)-chitosan (CH), and kaolin was obtained by an advanced pen-type extruder to ensure structural and biological homogeneity during the large area tissue reconstruction. The static, dynamic and cyclic mechanical properties as well as in situ self-standing printability significantly improved with the kaolin concentration for AL-CH bioink-printed samples due to polymer-kaolin nanoclay hydrogen bonding and cross-linking with less amount of calcium ions. The Biowork pen ensures better mixing effectiveness for the kaolin-dispersed AL-CH hydrogels (evident from computational fluid dynamics study, aluminosilicate nanoclay mapping and 3D printing of complex multilayered structures) than the conventional mixing process. Two different cell lines (osteoblast and fibroblast) introduced during large area multilayered 3D bioprinting have confirmed the suitability of such multicomponent bioinks for in vitro even tissue regeneration. The effect of kaolin to promote uniform growth and proliferation of the cells throughout the bioprinted gel matrix is more significant for this advanced pen-type extruder processed samples.
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http://dx.doi.org/10.1016/j.carbpol.2023.121046 | DOI Listing |
Viruses
December 2024
Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611, USA.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been detected in multiple animal species, including white-tailed deer (WTD), raising concerns about zoonotic transmission, particularly in environments with frequent human interactions. To understand how human exposure influences SARS-CoV-2 infection in WTD, we compared infection and exposure prevalence between farmed and free-ranging deer populations in Florida. We also examined the timing and viral variants in WTD relative to those in Florida's human population.
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November 2024
Key Laboratory of Green Prevention and Control of Tropical Plant Diseases and Pests (Ministry of Education), School of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China.
Patchouli is a valuable medicinal herb and cash crop in China, but viral infections cause significant yield losses. This study identified six viruses in patchouli transcriptome data, including the first-ever detection of East Asian Passiflora Virus (EAPV) in patchouli. RT-PCR validated three viruses from diseased patchouli plants in Haikou, China: telosma tosaic virus (TelMV), broad bean wilt virus-2 (BBWV-2), and pogostemom alphacytorhabdovirus 1 (PogACRV1_Pog).
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December 2024
Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA.
Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at the forefront of multi-modal fusion research and are used extensively to represent information in all-day all-weather applications.
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December 2024
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.
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December 2024
Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA.
Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process.
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