Cotton fibers are routinely harvested from cotton plants (in planta), and their end-use qualities depend on their development stages. Cotton fibers are also cultured in controlled laboratory environments, so that cotton researchers can investigate many aspects of experimental protocols in cotton breeding programs at reduced expenses. In this work, attenuated total reflection Fourier transform infrared (ATR FT-IR) spectra of cotton fibers grown in planta and in culture were collected to explore the potential of FT-IR technique as a simple, rapid, and direct method for characterizing the fiber development. Complementary to visual inspection of spectral variations, principal component analysis (PCA) of ATR FT-IR spectra revealed the occurrence of phase transition from primary to secondary cell wall synthesis and also the difference of starting the phase transition between two types of fibers. Like PCA observation, three simple algorithms were capable of monitoring the secondary cell wall formation effectively. Interestingly and uniquely, simple algorithms were able to detect the subtle discrepancies in fibers older than 25 days post-anthesis, which was not apparent from PCA results. The observation indicated the feasibility of FT-IR technique in rapid, routine, nondestructive, and direct assessment of fiber development for cotton physiology and breeding applications.
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http://dx.doi.org/10.1366/15-07876 | DOI Listing |
Mar Environ Res
January 2025
College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai, China.
Highly migratory pelagic sharks have the potential to serve as carriers of particle contamination in a vast three-dimensional space. We investigate the occurrence, abundance and characteristics of plastic and non-plastic particles in the scroll intestine of the blue shark (Prionace glauca), one of the most abundant pelagic shark species worldwide. We detected both plastic and non-plastic particles in all sections of the intestine, with the posterior region exhibiting the highest concentration.
View Article and Find Full Text PDFGenes (Basel)
December 2024
Agricultural College, Shihezi University, Shihezi 832003, China.
Background: The gene family of myelomatosis (MYC), serving as a transcription factor in the jasmonate (JA) signaling pathway, displays a significant level of conservation across diverse animal and plant species. Cotton is the most widely used plant for fiber production. Nevertheless, there is a paucity of literature reporting on the members of MYCs and how they respond to biotic stresses in cotton.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
The Collaborative Innovation Center for Eco-Friendly and Fire-Safety Polymeric Materials (MoE), College of Chemistry, Sichuan University, Chengdu 610064, China.
The development of bio-based flame retardants has garnered significant attention, however, significant challenges remain in achieving efficient flame retardancy and eco-friendly preparation methods. Herein, we propose a facile, atomic-efficient, and eco-friendly strategy for synthesizing a trinity chitosan-based flame retardant, phosphite-protonated chitosan (PCS). The chemical structure was systematically analyzed and the impact of varying degrees of protonation on the dissolution behavior and rheological properties were investigated.
View Article and Find Full Text PDFPlant Biotechnol J
January 2025
College of Life Sciences, Shaanxi Normal University, Xi'an, China.
Theor Appl Genet
January 2025
State Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.
Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic architecture of complex traits in other crops, its application in cotton has been limited. Here, we applied five machine learning models-AdaBoost, Gradient Boosting Regressor, LightGBM, Random Forest, and XGBoost-to identify loci associated with fiber quality and flowering days in cotton.
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