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How agricultural ecosystems adapt to climate change is one of the most important issues facing agronomists at the turn of the century. Understanding agricultural ecosystem responses requires assessing the relative shift in climatic constraints on crop production at regional scales such as the temperate zone. In this work we propose an approach to modeling the growth, development and yield of Triticum durum Desf. under the climatic conditions of north-eastern Poland. The model implements 13 non-measurable parameters, including climate conditions, agronomic factors, physiological processes, biophysical parameters, yield components and biological yield (latent variables), which are described by 33 measurable predictors as well as grain and straw yield (manifest variables). The agronomic factors latent variable was correlated with nitrogen fertilization and sowing density, and biological yield was correlated with grain yield and straw yield. An analysis of the model parameters revealed that a one unit increase in agronomic factors increased biological yield by 0.575. In turn, biological yield was most effectively determined by climate conditions (score of 60-62) and biophysical parameters (score of 60-67) in the 2nd node detectable stage and at the end of heading. The modeled configuration of latent and manifest variables was responsible for less than 70% of potential biological yield, which indicates that the growth and development of durum wheat in north-eastern Europe can be further optimized to achieve high and stable yields. The proposed model accounts for local climate conditions and physiological processes in plants, and it can be implemented to optimize agronomic practices in the cultivation of durum wheat and, consequently, to expand the area under T. durum to regions with a temperate climate.
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http://dx.doi.org/10.1038/s41598-021-01273-8 | DOI Listing |
Bioinformatics
September 2025
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Motivation: RNA velocity has become a powerful tool for uncovering transcriptional dynamics in snapshot single-cell data. However, current RNA velocity approaches often assume constant transcriptional rates and treat genes independently with gene-specific times, which may introduce biases and deviate from biological realities. Here, we present InterVelo, a novel deep learning framework that simultaneously learns cellular pseudotime and RNA velocity.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Medicine, The Red Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Background: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy.
Methods: This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD.
Cell Rep
September 2025
Division of Molecular Neuroimmunology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan. Electronic address:
Microglia, the resident macrophages in the central nervous system (CNS), have been intensively studied using rodent genetic models, including the Cre-loxP system. Among them are tamoxifen (TAM)-inducible CX3C chemokine receptor 1 (Cx3cr1)-Cre mouse lines (Cx3cr1), which have enabled in-depth analyses of the biological features and functions of myeloid cells, including microglia. Occasionally, these Cx3cr1 tools have yielded conflicting biological outcomes, the underlying mechanism of which remains unclear.
View Article and Find Full Text PDFMetab Brain Dis
September 2025
Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei, 430022, China.
Major depression disorder (MDD) is a mental condition that significantly threatens both physical and psychological health. This study aimed to discern variances in plasma metabolic profiles between MDD sufferers and healthy counterparts. Additionally, we tracked the hospitalization journey of MDD patients to investigate the normalization of metabolic irregularities through conventional treatment in the form of self-control.
View Article and Find Full Text PDFNaturwissenschaften
September 2025
Colorado Water Center, Colorado State University, Fort Collins, CO, 80523, USA.
Drought stress is the most vulnerable abiotic factor affecting plant growth and yield. The use of silicic acid as seed priming treatment is emerging as an effective approach to regulate maize plants susceptibility to water stress. The study was formulated for investigating the effect of silicic acid seed priming treatment in modulating the oxidative defense and key physio-biochemical attributes of maize plants under drought stress conditions.
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