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A potential for higher-order phenomenological strain gradient plasticity to predict reliable response under non-proportional loading.

ABSTRACT: We propose a plastic potential for higher-order (HO) phenomenological strain gradient plasticity (SGP), predicting reliable size-dependent response for general loading histories. By constructing the free energy density as a sum of quadratic plastic strain gradient contributions that each transitions into linear terms at different threshold values, we show that we can predict the expected micron-scale behaviour, including increase of strain hardening and strengthening-like behaviour with diminishing size. Furthermore, the anomalous behaviour predicted by most HO theories under non-proportional loading is avoided. Though we demonstrate our findings on the basis of Gurtin (Gurtin 2004 J. Mech. Phys. Solids 52, 2545-2568, doi:10.1016/j.jmps.2003.11.002) distortion gradient plasticity, adopting Nye's dislocation density tensor as primal HO variable, we expect our results to hold qualitatively for any HO SGP theory, including crystal plasticity.

SUBMITTER: Panteghini A

PROVIDER: S-EPMC6784394 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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A potential for higher-order phenomenological strain gradient plasticity to predict reliable response under non-proportional loading.

Panteghini Andrea A Bardella Lorenzo L Niordson Christian F CF

Proceedings. Mathematical, physical, and engineering sciences 20190911 2229

We propose a plastic potential for higher-order (HO) phenomenological strain gradient plasticity (SGP), predicting reliable size-dependent response for general loading histories. By constructing the free energy density as a sum of quadratic plastic strain gradient contributions that each transitions into linear terms at different threshold values, we show that we can predict the expected micron-scale behaviour, including increase of strain hardening and strengthening-like behaviour with diminish ...[more]

PMID: 31611720

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Project description:It is increasingly being recognized that physical interactions between distant chromosomal loci influence the expressivity of the genome. Thus, multiple enhancers may converge on a single gene promoter 1,2 and a single enhancer can stochastically loop to multiple promoters 3. Regulatory elements from neighboring domains or from other chromosomes may interact to generate chromatin structures poised for transcription4-8 or repression 9-11. However, a broader perspective on the formation and function of such networks remains largely unexplored. Using the high-resolution 4C (Circular Chromosome Conformation Capture) analysis, we identify here a genome-wide physical network between genomically imprinted domains impinging H19 imprinting control region (ICR). Sequences interacting with the H19 ICR constitutively map at or within most of known imprinted domains both in mouse embryonic stem cells and in vitro-derived embryoid bodies, despite distinct differences in chromatin environments. Moreover, all-to-all 3D DNA FISH analyses revealed that imprinted domains from 7 different chromosomes co-localize with each other in pair-wise patterns, highlighting the dynamic nature of these interactions. The observed interaction network is dependent on CTCF binding sites within the maternally inherited H19 ICR allele and delays reprogramming of epigenetic features at imprinted domains during male germline development. As other imprinted regions can interact with each other in an H19 ICR-dependent manner, the H19 ICR emerges as an organizer of chromosomal networks by specifically recognizing and reconfiguring imprinted domains. We propose that an ancient key regulatory region, such as the H19 ICR, gradually impacted epigenetic modifications via long-range interactions in the germline to facilitate the recruitment of imprinted genes and their formation in clusters during mammalian radiation. Furthermore, the perspective of epigenetic lesions de-stabilizing such chromosomal networks may advance our understanding of how quantitative traits influence human diseases, such as cancer.

Dataset Information

A potential for higher-order phenomenological strain gradient plasticity to predict reliable response under non-proportional loading.

Publications

A potential for higher-order phenomenological strain gradient plasticity to predict reliable response under non-proportional loading.

Similar Datasets

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