Publications by authors named "Yisha Lu"

In this paper, we introduce an efficient method for identifying fractional dynamic systems using extended sparse regression and cross-validation techniques. The former identifies equations that fit the data with varying candidate functions, while the latter determines the optimal equation with the fewest terms yet ensuring accuracy. The identified optimal equation is expected to share the same dynamic properties as the original fractional system.

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Global warming threatens aquatic systems and organisms. Many studies have focused on the vulnerability and stress responses of aquaculture organisms to future thermal conditions. However, it may be of more practical significance to reveal their acclimation potential and mechanisms.

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Aquaculture is one of the world's fastest-growing and most traded food industries, but it is under the threat of climate-related risks represented by global warming, marine heatwave (MHW) events, ocean acidification, and deoxygenation. For the sustainable development of aquaculture, selective breeding may be a viable method to obtain aquatic economic species with greater tolerance to environmental stressors. In this study, we estimated the heritability of heat tolerance trait of Pacific abalone , performed genome-wide association studies (GWAS) analysis for heat tolerance to detect single nucleotide polymorphisms (SNPs) and candidate genes, and assessed the potential of genomic selection (GS) in the breeding of abalone industry.

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Background: Transcriptome sequencing is an effective tool to reveal the essential genes and pathways underlying countless biotic and abiotic stress adaptation mechanisms. Although severely challenged by diverse environmental conditions, the Pacific abalone Haliotis discus hannai remains a high-value aquaculture mollusk and a Chinese predominantly cultured abalone species. Salinity is one of such environmental factors whose fluctuation could significantly affect the abalone's cellular and molecular immune responses and result in high mortality and reduced growth rate during prolonged exposure.

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Feed efficiency (FE) is critical to the economic and environmental benefits of aquaculture. Both the intestines and intestinal microbiota play a key role in energy acquisition and influence FE. In the current research, intestinal microbiota, metabolome, and key digestive enzyme activities were compared between abalones with high [Residual feed intake (RFI) = -0.

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This paper proposes a non-convex penalty regression method to identify governing equations of nonlinear dynamical systems from noisy state measurements. The idea to connect the non-convex penalty function instead of the l - norm with least squares is due to the fact that the l - norm excessively penalizes large coefficients and may incur estimation bias. The purpose of this work is to improve the accuracy and robustness in regression tasks.

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Background: Heterosis has been exploited for decades in different animals and crops due to it resulting in dramatic increases in yield and adaptability. Hybridization is a classical breeding method that can effectively improve the genetic characteristics of organisms through heterosis. Abalone has become an increasingly economically important aquaculture resource with high commercial value.

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Synopsis of recent research by authors named "Yisha Lu"

  • - Yisha Lu's research primarily focuses on the identification and optimization of dynamic systems and the enhancement of thermal tolerance in aquatic organisms, particularly abalone, in response to climate change.
  • - Key findings include the development of methods using extended sparse regression for identifying fractional dynamic systems, and insights into genetic and metabolic adaptations in abalone to cope with thermal stress and low salinity conditions.
  • - Lu's studies emphasize the importance of genomic selection and transcriptome analysis in improving aquaculture practices and sustainability, showcasing the potential for genetic approaches to enhance resilience against environmental challenges.