Genomic value prediction for quantitative traits under the epistatic model

Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL). The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects) and marker pairs (epistatic effects) to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait) for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive) effects were used for prediction. When the interaction (epistatic) effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding

Enhancing Job Recommendation through LLM-based Generative Adversarial Networks

Recommending suitable jobs to users is a critical task in online recruitment platforms, as it can enhance users' satisfaction and the platforms' profitability. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness. With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful capabilities of text processing and reasoning, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for the models in generating high-quality resumes. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.Comment: 13 pages, 6 figures, 3 table

Identification of QTL underlying vitamin E contents in soybean seed among multiple environments

Vitamin E (VE) in soybean seed has value for foods, medicines, cosmetics, and animal husbandry. Selection for higher VE contents in seeds along with agronomic traits was an important goal for many soybean breeders. In order to map the loci controlling the VE content, F5-derived F6 recombinant inbred lines (RILs) were advanced through single-seed-descent (SSD) to generate a population including 144 RILs. The population was derived from a cross between ‘OAC Bayfield’, a soybean cultivar with high VE content, and ‘Hefeng 25’, a soybean cultivar with low VE content. A total of 107 polymorphic simple sequence repeat markers were used to construct a genetic linkage map. Seed VE contents were analyzed by high performance liquid chromatography for multiple years and locations (Harbin in 2007 and 2008, Hulan in 2008 and Suihua in 2008). Four QTL associated with α-Toc (on four linkage groups, LGs), eight QTL associated with γ-Toc (on eight LGs), four QTL associated with δ-Toc (on four LGs) and five QTL associated with total VE (on four LGs) were identified. A major QTL was detected by marker Satt376 on linkage group C2 and associated with α-Toc (0.0012 > P > 0.0001, 5.0% < R2 < 17.0%, 25.1 < α-Toc < 30.1 μg g−1), total VE (P < 0.0001, 7.0% < R2 < 10.0%, 118.2 < total VE < 478.3 μg g−1). A second QTL detected by marker Satt286 on LG C2 was associated with γ-Toc (0.0003 > P > 0.0001, 6.0% < R2 < 13.0%, 141.5 < γ-Toc < 342.4 μg g−1) and total VE (P < 0.0001, 2.0% < R2 < 9.0%, 353.9 < total VE < 404.0 μg g−1). Another major QTL was detected by marker Satt266 on LG D1b that was associated with α-Toc (0.0002 > P > 0.0001, 4.0% < R2 < 6.0%, 27.7 < α-Toc < 43.7 μg g−1) and γ-Toc (0.0032 > P > 0.0001, 3.0% < R2 < 10.0%, 69.7 < γ-Toc < 345.7 μg g−1). Since beneficial alleles were all from ‘OAC Bayfield’, it was concluded that these three QTL would have great potential value for marker assisted selection for high VE content

Secure Outsourcing of Matrix Determinant Computation under the Malicious Cloud

Computing the determinant of large matrix is a time-consuming task, which is appearing more and more widely in science and engineering problems in the era of big data. Fortunately, cloud computing can provide large storage and computation resources, and thus, act as an ideal platform to complete computation outsourced from resource-constrained devices. However, cloud computing also causes security issues. For example, the curious cloud may spy on user privacy through outsourced data. The malicious cloud violating computing scripts, as well as cloud hardware failure, will lead to incorrect results. Therefore, we propose a secure outsourcing algorithm to compute the determinant of large matrix under the malicious cloud mode in this paper. The algorithm protects the privacy of the original matrix by applying row/column permutation and other transformations to the matrix. To resist malicious cheating on the computation tasks, a new verification method is utilized in our algorithm. Unlike previous algorithms that require multiple rounds of verification, our verification requires only one round without trading off the cheating detectability, which greatly reduces the local computation burden. Both theoretical and experimental analysis demonstrate that our algorithm achieves a better efficiency on local users than previous ones on various dimensions of matrices, without sacrificing the security requirements in terms of privacy protection and cheating detectability

Increased nitrogen use efficiency via amino acid remobilization from source to sink organs in Brassica napus

Nitrogen (N) is an essential plant growth nutrient whose coordinated distribution from source to sink organs is crucial for seed development and overall crop yield. We compared high and low N use efficiency (NUE) Brassica napus (rapeseed) genotypes. Metabonomics and transcriptomics revealed that leaf senescence induced by N deficiency promoted amino acid allocation from older to younger leaves in the high-NUE genotype at the vegetative growth stage. Efficient source to sink remobilization of amino acids elevated the numbers of branches and pods per plant under a N-deficiency treatment during the reproductive stage. A 15N tracer experiment confirmed that more amino acids were partitioned into seeds from the silique wall during the pod stage in the high-NUE genotype, owing mainly to variation in genes involved in organic N transport and metabolism. We suggest that the greater amino acid source-to-sink allocation efficiency during various growth stages in the high-NUE genotype resulted in higher yield and NUE under N deficiency. These findings support the hypothesis that strong amino acid remobilization in rapeseed leads to high yield, NUE, and harvest index

Metabolic Profiling of Dendrobium officinale in Response to Precursors and Methyl Jasmonate

Alkaloids are the main active ingredients in the medicinal plant Dendrobium officinale. Based on the published genomic and transcriptomic data, a proposed terpenoid indole alkaloid (TIA) biosynthesis pathway may be present in D. officinale. In this study, protocorm-like bodies (PLBs) with a high-yielding production of alkaloids were obtained by the optimization of tryptophan, secologanin and methyl jasmonate (MeJA) treatment. The results showed that the total alkaloid content was 2.05 times greater than that of the control group when the PLBs were fed with 9 µM tryptophan, 6 µM secologanin and 100 µM MeJA after 36 days. HPLC analysis showed that strictosidine synthase (STR) activity also increased in the treated plants. A total of 78 metabolites were identified using gas chromatography-mass spectrometry (GC-MS) in combination with liquid chromatography-mass spectrometry (LC-MS) methods; 29 differential metabolites were identified according to the multivariate statistical analysis. Among them, carapanaubine, a kind of TIA, exhibited dramatically increased levels. In addition, a possible underlying process of the metabolic flux from related metabolism to the TIA biosynthetic pathway was enhanced. These results provide a comprehensive view of the metabolic changes related to alkaloid biosynthesis, especially TIA biosynthesis, in response to tryptophan, secologanin and MeJA treatment