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Costs, benefits and redundant mechanisms of adaption to chronic low-dose stress in yeast.

ABSTRACT: All organisms live in changeable, stressful environments. It has been reported that exposure to low-dose stresses or poisons can improve fitness. However, examining the effects of chronic low-dose chemical exposure is challenging. To address this issue we used temperature sensitive mutations affecting the yeast cell division cycle to induce low-dose stress for 40 generation times, or more. We examined cdc13-1 mutants, defective in telomere function, and cdc15-2 mutants, defective in mitotic kinase activity. We found that each stress induced similar adaptive responses. Stress-exposed cells became resistant to higher levels of stress but less fit, in comparison with unstressed cells, in conditions of low stress. The costs and benefits of adaptation to chronic stress were reversible. In the cdc13-1 context we tested the effects of Rad9, a central player in the response to telomere defects, Exo1, a nuclease that degrades defective telomeres, and Msn2 and Msn4, 2 transcription factors that contribute to the environmental stress response. We also observed, as expected, that Rad9 and Exo1 modulated the response of cells to stress. In addition we observed that adaptation to stress could still occur in these contexts, with associated costs and benefits. We conclude that functionally redundant cellular networks control the adaptive responses to low dose chronic stress. Our data suggests that if organisms adapt to low dose stress it is helpful if stress continues or increases but harmful should stress levels reduce.

SUBMITTER: Markiewicz-Potoczny M

PROVIDER: S-EPMC5053569 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Costs, benefits and redundant mechanisms of adaption to chronic low-dose stress in yeast.

Markiewicz-Potoczny Marta M Lydall David D

Cell cycle (Georgetown, Tex.) 20160811 20

All organisms live in changeable, stressful environments. It has been reported that exposure to low-dose stresses or poisons can improve fitness. However, examining the effects of chronic low-dose chemical exposure is challenging. To address this issue we used temperature sensitive mutations affecting the yeast cell division cycle to induce low-dose stress for 40 generation times, or more. We examined cdc13-1 mutants, defective in telomere function, and cdc15-2 mutants, defective in mitotic kina ...[more]

PMID: 27628486

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Project description:BackgroundDeep learning has been successfully applied to low-dose CT (LDCT) denoising. But the training of the model is very dependent on an appropriate loss function. Existing denoising models often use per-pixel loss, including mean abs error (MAE) and mean square error (MSE). This ignores the difference in denoising difficulty between different regions of the CT images and leads to the loss of large texture information in the generated image.PurposeIn this paper, we propose a new hybrid loss function that adapts to the noise in different regions of CT images to balance the denoising difficulty and preserve texture details, thus acquiring CT images with high-quality diagnostic value using LDCT images, providing strong support for condition diagnosis.MethodsWe propose a hybrid loss function consisting of weighted patch loss (WPLoss) and high-frequency information loss (HFLoss). To enhance the model's denoising ability of the local areas which are difficult to denoise, we improve the MAE to obtain WPLoss. After the generated image and the target image are divided into several patches, the loss weight of each patch is adaptively and dynamically adjusted according to its loss ratio. In addition, considering that texture details are contained in the high-frequency information of the image, we use HFLoss to calculate the difference between CT images in the high-frequency information part.ResultsOur hybrid loss function improves the denoising performance of several models in the experiment, and obtains a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Moreover, through visual inspection of the generated results of the comparison experiment, the proposed hybrid function can effectively suppress noise and retain image details.ConclusionsWe propose a hybrid loss function for LDCT image denoising, which has good interpretation properties and can improve the denoising performance of existing models. And the validation results of multiple models using different datasets show that it has good generalization ability. By using this loss function, high-quality CT images with low radiation are achieved, which can avoid the hazards caused by radiation and ensure the disease diagnosis for patients.

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Costs, benefits and redundant mechanisms of adaption to chronic low-dose stress in yeast.

Publications

Costs, benefits and redundant mechanisms of adaption to chronic low-dose stress in yeast.

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