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Stress, Physiological

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Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants.

Mathematical biosciences
It has been found that each non-coding RNA (ncRNA) can act not only through its target gene, but also interact with each other to act on biological traits, and this interaction is more common. Many studies focus mainly on the analysis of microRNA(miR...

Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease...

De novo assembly of Agave sisalana transcriptome in response to drought stress provides insight into the tolerance mechanisms.

Scientific reports
Agave, monocotyledonous succulent plants, is endemic to arid regions of North America, exhibiting exceptional tolerance to their xeric environments. They employ various strategies to overcome environmental constraints, such as crassulacean acid metab...

Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.

Molecular omics
Abiotic stress exposure of plants induces metabolic reprogramming which is tightly regulated by signalling cascades connecting transcriptional with translational and metabolic regulation. Complexity of such interconnected metabolic networks impedes t...

Deep Analysis of Mitochondria and Cell Health Using Machine Learning.

Scientific reports
There is a critical need for better analytical methods to study mitochondria in normal and diseased states. Mitochondrial image analysis is typically done on still images using slow manual methods or automated methods of limited types of features. Mi...

Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.

Trends in plant science
Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. These tools are increasingly being use...

Effect of abiotic and biotic stress factors analysis using machine learning methods in zebrafish.

Comparative biochemistry and physiology. Part D, Genomics & proteomics
In order to understand the mechanisms underlying stress responses, meta-analysis of transcriptome is made to identify differentially expressed genes (DEGs) and their biological, molecular and cellular mechanisms in response to stressors. The present ...

Support vector machines for explaining physiological stress response in Wood mice (Apodemus sylvaticus).

Scientific reports
Physiological stress response is a crucial adaptive mechanism for prey species survival. This paper aims to identify the main environmental and/or individual factors better explaining the stress response in Wood mice, Apodemus sylvaticus. We analyzed...

Association between 4-day vaginal temperature assessment during the dry period and performance in the subsequent lactation of dairy cows during the warm season.

Journal of animal science
The objective of the study was to investigate the relationships between vaginal temperature during the dry-period and health, milk production, and reproduction in the subsequent lactation of cows during the warm season. A total of 105 nonlactating Ho...

Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

Scientific reports
Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study develop...