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In this paper, considering the far-field seismic input, an accelerogram recorded in the bedrock at Wuquan Mountain in Lanzhou city during the 2008 Wenchuan Ms8.0 earthquake was selected, and numerical dynamic analyses were conducted. The one-dimensional equivalent linear method was implemented to estimate the ground motion effects in the loess regions. Thereafter, slope topographic effects on ground motion were studied by applying the dynamic finite-element method. The results revealed the relationship between the PGA amplification coefficients and the soil layer thickness, which confirmed that the dynamic response of the sites had obvious nonlinear characteristics. The results also showed that there was an obvious difference in the dynamic magnification factor between the short-period and long-period structures. Moreover, it was found that the amplification coefficient of the observation point at the free surface was greater than the point inside the soil at the same depth, which mainly occurred in the upper slope. Through this study, the quantitative assessment of ground motion effects in loess regions can be approximately estimated, and the amplification mechanism of the far-field ground motion mechanism can be further explained. In addition to the refraction and reflection theory of seismic waves, the resonance phenomenon may help explain the slope topographic effect through spectrum analysis.

Nocardioides, a genus belonging to Actinomycetes, can endure various low-nutrient conditions. It can degrade pollutants using multiple organic materials such as carbon and nitrogen sources. The characteristics and applications of Nocardioides are described in detail in this review, with emphasis on the degradation of several hard-to-degrade pollutants by using Nocardioides, including aromatic compounds, hydrocarbons, haloalkanes, nitrogen heterocycles, and polymeric polyesters. Nocardioides has unique advantages when it comes to hard-to-degrade pollutants. Compared to other strains, Nocardioides has a significantly higher degradation rate and requires less time to break down substances. This review can be a theoretical basis for developing Nocardioides as a microbial agent with significant commercial and application potential.

The identification of superior hybrids from candidate populations is a central goal in plant breeding, particularly for commercial applications and large-scale cultivation. In this study, several promising training set optimization methods in genomic selection (GS) are evaluated and extended to construct predictive models for the identification of top-performing genotypes in hybrid populations. The methods investigated include: (i) a ridge regression-based approach, MSPE ( v 2 ) Ridge , (ii) a generalized coefficient of determination-based method, CD mean ( v 2 ) , and (iii) an A-optimality-like ranking strategy, GV average . To assess predictive performance in identifying genotypes with the highest true breeding values (TBVs), three evaluation metrics were developed. Since TBVs are latent quantities derived from models, simulation experiments based on real genotype data from wheat ( Triticum aestivum L.), maize ( Zea mays ), and rice ( Oryza sativa L.) were carried out to assess the proposed methods. Results demonstrated that GV average not only achieved substantial computational efficiency but also generally generated highly informative training sets across a broad range of sizes. However, when constructing small training sets, GV average occasionally failed to maintain adequate genomic diversity. In such cases, CD mean ( v 2 ) is recommended as a more reliable alternative. Overall, the proposed framework provides a flexible and effective approach to optimizing training sets for hybrid breeding, thereby enhancing the accuracy of genomic prediction in practical breeding programs.

Key messageA new optimality criterion is proposed to determine a training set for genomic selection, which is derived from Pearson’s correlation between GEBVs and phenotypic values of a test set. R functions are provided to generate the optimal training set.For a specified test set, we develop a highly efficient algorithm to determine an optimal subset from a large candidate set in which the individuals have been genotyped but not phenotyped yet. The chosen subset serves as a training set to be phenotyped, and then a genomic selection (GS) model is built based on its phenotype and genotype data. In this study, we consider the additive effects whole-genome regression model and adopt ridge regression estimation for marker effects in the GS model. The resulting GS model is then employed to predict genomic estimated breeding values (GEBVs) for the individuals of the test set, which have been genotyped only. We propose a new optimality criterion to determine the required training set, which is derived directly from Pearson’s correlation between GEBVs and phenotypic values of the test set. Pearson’s correlation is the standard measure for prediction accuracy of a GS model. Our proposed methods can be applied to data with the varying degree of population structure. All the R functions for implementing our training set determination algorithms are available from the R package TSDFGS. The algorithms are illustrated with two datasets which have strong (rice genome dataset) and mild (wheat genome dataset) population structures. Our methods are shown to be advantageous over existing ones, mainly because they fully use the genomic relationship between the test set and the training set by taking into account both the variance and bias for predicting GEBVs.

Major depressive disorder (MDD) is a neurasthenic disease, which is the second-largest burden of disease globally. Increasing studies have revealed that depression is associated with abnormalities in gut microbiota and metabolites. Several species of bacteria have been classified as psychobiotics, which confer mental health benefits through interactions with commensal gut microbiota. Therefore, it is essential to identify new psychobiotics and elucidate their mechanisms in the treatment of depression. This study aims to evaluate the antidepressant effect of Akkermansia muciniphila (AKK) in a mouse model of depression induced by chronic restraint stress (CRS). C57BL/6 male mice were divided into three groups: mice subjected to CRS, mice not subjected to CRS, and mice treated with AKK for 3 weeks. Behavioral tests were performed, and hormone, neurotransmitter, and brain-derived neurotrophic factor (BDNF) levels were measured. Cecal microbiota was analyzed using 16S rRNA gene sequencing, and serum metabolites were detected using untargeted metabolomics. In addition, correlations between altered gut microbiota and metabolites with significant variations in serum associated with AKK ameliorating depression were analyzed using Pearson’s correlation coefficient. The results revealed that AKK significantly ameliorated depressive-like behavior and restored abnormal variations in depression-related molecular (corticosterone, dopamine, and BDNF). Moreover, AKK altered chronic stress–induced gut microbial abnormalities. Untargeted metabolomics analysis revealed 23 potential biomarkers in serum that could be associated with the mechanisms underlying CRS-induced depression and the therapeutic effects of AKK. Pearson’s correlation coefficient analysis revealed that AKK predominantly upregulated β-alanyl-3-methyl-l-histidine and edaravone to relieve depression. Furthermore, β-alanyl-3-methyl-l-histidine and edaravone exhibited the antidepressant phenotype in mice subjected to CRS. In conclusion, the study demonstrated that AKK ameliorates chronic stress–induced depressive symptoms in mice by regulating gut microbiota and metabolites.Key points• AKK reduces depressive-like behaviors induced by chronic stress.• AKK regulates the gut microbial structure and metabolomics of serum under the chronic stress.• Antidepressant effect of AKK correlates with the increase of β-alanyl-3-methyl-l-histidine and edaravone.

Cross-sectional studies suggest that insomnia symptoms are associated with falls in later life. This longitudinal study examines the independent and interactive effects of the extent of insomnia symptoms (i.e., multiple co-existing insomnia symptoms) and sleep medications on fall risk over a 2-year follow-up among community-dwelling older adults. Using data from the Health and Retirement Study (2006-2014, N = 6882, Mage = 74.5 years ± 6.6 years), we calculated the extent of insomnia symptoms (range = 0-4) participants reported (i.e., trouble falling asleep, waking up during the night, waking up too early, and not feeling rested). At each wave, participants reported recent sleep medications use and falls since the last wave, and were evaluated for balance and walking speed. A greater burden of insomnia symptoms and using physician-recommended sleep medications at baseline independently predicted falling after adjusting for known risk factors of falling. The effects of insomnia symptoms on fall risk differed by sleep medications use. The extent of insomnia symptoms exhibited a positive, dose-response relation with risk of falling among those not using sleep medications. Older adults using physician-recommended sleep medications exhibited a consistently higher fall risk irrespective of the extent of insomnia symptoms. The number of insomnia symptoms predicts 2-year fall risk in older adults. Taking physician-recommended sleep medications increases the risks for falling in older adults, irrespective of the presence of insomnia symptoms. Future efforts should be directed toward treating insomnia symptoms, and managing and selecting sleep medications effectively to decrease the risk of falling in older adults.

A parental selection approach based on genomic prediction has been developed to help plant breeders identify a set of superior parental lines from a candidate population before conducting field trials. A classical parental selection approach based on genomic prediction usually involves truncation selection, i.e., selecting the top fraction of accessions on the basis of their genomic estimated breeding values (GEBVs). However, truncation selection inevitably results in the loss of genomic diversity during the breeding process. To preserve genomic diversity, the selection of closely related accessions should be avoided during parental selection. We thus propose a new index to quantify the genomic diversity for a set of candidate accessions, and analyze two real rice ( Oryza sativa L.) genome datasets to compare several selection strategies. Our results showed that the pure truncation selection strategy produced the best starting breeding value but the least genomic diversity in the base population, leading to less genetic gain. On the other hand, strategies that considered only genomic diversity resulted in greater genomic diversity but less favorable starting breeding values, leading to more genetic gain but unsatisfactorily performing recombination inbred lines (RILs) in progeny populations. Among all strategies investigated in this study, compromised strategies, which considered both GEBVs and genomic diversity, produced the best or second-best performing RILs mainly because these strategies balance the starting breeding value with the maintenance of genomic diversity.

A set of superior parental lines is imperative for the development of high-performing inbred lines in any biparental crossing program for crops. The main objectives of this study are to (a) develop a genomic prediction approach to identify superior parental lines for multi-trait selection, and (b) generate a software package for users to execute the proposed approach before conducting field experiments. According to different breeding goals of the target traits, a novel selection index integrating information from genomic-estimated breeding values (GEBVs) of candidate accessions was proposed to evaluate the composite performance of simulated progeny populations. Two rice ( Oryza sativa L.) genome datasets were analyzed to illustrate the potential applications of the proposed approach. One dataset applied to the parental selection for producing inbred lines with satisfactory performance in primary and secondary traits simultaneously. The other one applied to demonstrate the application of producing inbred lines with high adaptability to different environments. Overall, the results showed that incorporating GEBV and genomic diversity into a selection strategy based on the proposed selection index could assist in selecting superior parents to meet the desired breeding goals and increasing long-term genetic gain. An R package, called IPLGP, was generated to facilitate the widespread application of the approach.

Key message Approaches for constructing training sets in genomic selection are proposed to efficiently identify top-performing genotypes from a breeding population. Identifying superior genotypes from a candidate population is a key objective in plant breeding programs. This study evaluates various methods for the training set optimization in genomic selection, with the goal of enhancing efficiency in discovering top-performing genotypes from a breeding population. Additionally, two approaches, inspired by classical optimal design criteria, are proposed to expand the search space for the best genotypes and compared with methods focusing on maximizing accuracy in breeding value prediction. Evaluation metrics such as normalized discounted cumulative gain, Spearman’s rank correlation, and Pearson’s correlation are employed to assess performance in both simulation studies and real trait analyses. Overall, for candidate populations lacking a strong subpopulation structure, a ridge regression-based method, referred to as MSPE Ridge , is recommended. For candidate populations with a strong subpopulation structure, a heuristic-based version of generalized coefficient of determination CD mean ( v 2 ) and a D-optimality-like method that maximizes overall genomic variation ( GV overall ) are preferred approaches for the primary objective of plant breeding. For populations with a large number of candidates, a proposed ranking method ( GV average ) can first be used to down-scale the candidate population, after which a heuristic-based method is employed to identify the best genotypes. Notably, the proposed CD mean ( v 2 ) has been verified to be equivalent to the original version, known as CD mean , but its implementation is much more computationally efficient.

Key messageA practical approach is developed to determine a cost-effective optimal training set for selective phenotyping in a genomic prediction study. An R function is provided to facilitate the application of the approach.Genomic prediction (GP) is a statistical method used to select quantitative traits in animal or plant breeding. For this purpose, a statistical prediction model is first built that uses phenotypic and genotypic data in a training set. The trained model is then used to predict genomic estimated breeding values (GEBVs) for individuals within a breeding population. Setting the sample size of the training set usually takes into account time and space constraints that are inevitable in an agricultural experiment. However, the determination of the sample size remains an unresolved issue for a GP study. By applying the logistic growth curve to identify prediction accuracy for the GEBVs and the training set size, a practical approach was developed to determine a cost-effective optimal training set for a given genome dataset with known genotypic data. Three real genome datasets were used to illustrate the proposed approach. An R function is provided to facilitate widespread application of this approach to sample size determination, which can help breeders to identify a set of genotypes with an economical sample size for selective phenotyping.