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Secondary salinization caused by the overaccumulation of calcium nitrate [Ca(NO)] in soils due to excessive fertilization has become one of the major handicaps of protected vegetable production. Brassinolide, a bioactive plant steroid hormone, plays an important role in improving abiotic stress tolerance in plants. However, whether and how brassinolide (BR) can alleviate Ca(NO) stress remains elusive. Here, we investigated the effects of exogenous BR on hydroponically grown tomato ( L.) plants under Ca(NO) stress through proteomics combined with physiological studies. Proteomics analysis revealed that Ca(NO) stress affected the accumulation of proteins involved in photosynthesis, stress responses, and antioxidant defense, however, exogenous BR increased the accumulation of proteins involved in chlorophyll metabolism and altered the osmotic stress responses in tomatoes under Ca(NO) stress. Further physiological studies supported the results of proteomics and showed that the exogenous BR-induced alleviation of Ca(NO) stress was associated with the improvement of photosynthetic efficiency, levels of soluble sugars and proteins, chlorophyll contents, and antioxidant enzyme activities, leading to the reduction in the levels of reactive oxygen species and membrane lipid peroxidation, and promotion of the recovery of photosynthetic performance, energy metabolism, and plant growth under Ca(NO) stress. These results show the importance of applying BR in protected agriculture as a means for the effective management of secondary salinization.
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http://dx.doi.org/10.3389/fpls.2021.724288 | DOI Listing |
Alzheimers Dement
September 2025
Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
J Clin Med
August 2025
Salus Vitae Women's Health Clinical Center, 46004 Valencia, Spain.
Ovarian aging is characterized by a gradual decline in both reproductive and endocrine functions, ultimately culminating in the cessation of ovarian activity around the age of 50, when most women experience natural menopause. The decline begins early, as follicular attrition is initiated in utero and continues throughout childhood and reproductive life. Most follicles undergo atresia without progressing through substantial stages of growth.
View Article and Find Full Text PDFNat Commun
August 2025
Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
Pavlovian fear conditioning is a fundamental process in both health and disease. We investigate its neural correlates and sources of variability using harmonized functional magnetic resonance imaging data from 2199 individuals across nine countries, including 1888 healthy individuals and 311 with anxiety-related or depressive disorders. Using mega-analysis and normative modeling, we show that fear conditioning consistently engages brain regions within the "central autonomic-interoceptive" or "salience" network.
View Article and Find Full Text PDFAm J Drug Alcohol Abuse
August 2025
Department of Population and Community Health, College of Public Health, The University of North Texas Health Science Center, Fort Worth, TX, USA.
Exposure to (RED) is positively associated with alcohol-related outcomes among Hispanics; however, links between RED and alcohol use are poorly understood, particularly when considering RED in social media. Study aimed to (1) examine the direct and indirect associations between RED on social media and alcohol use severity (i.e.
View Article and Find Full Text PDFNat Commun
July 2025
School of Medicine, Cardiff University, Cardiff, UK.
Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer's disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MB-MDR) models.
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