The climatic parameters within greenhouse facilities, such as temperature, humidity, and light, exert significant influence on the growth and yield of crops, particularly seedlings. Therefore, it is crucial to establish an accurate predictive model to monitor and adjust the greenhouse microclimate for optimizing the greenhouse environment to the fullest extent. To precisely forecast the greenhouse microclimate and assess the suitability of nursery environments, this study focuses on greenhouse environmental factors. This study leveraged open-source APIs to acquire meteorological data, integrated a model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), and utilized the sparrow search algorithm to optimize model parameters, consequently developing a time series greenhouse microclimate prediction model. Furthermore, Squeeze-and-Excitation (SE) Networks were employed to enhance the model’s attention mechanism, enabling more accurate predictions of environmental factors within the greenhouse. The predictive results indicated that the optimized model achieved high precision in forecasting the greenhouse microclimate, with average errors of 0.540 °C, 0.936%, and 1.586 W/m² for temperature, humidity, and solar radiation, respectively. The coefficients of determination (${\mathrm{R}}^2$) reached 0.940, 0.951, and 0.936 for temperature, humidity, and solar radiation, respectively. In comparison to individual CNN or LSTM models, as well as the back-propagation (BP) neural network, the proposed model demonstrates a significant improvement in predictive accuracy. Moreover, this research was applied to the greenhouse nursery environment, demonstrating that the proposed model significantly enhanced the efficiency of greenhouse seedling cultivation and the quality of seedlings. Our study provided an effective approach for optimizing greenhouse environmental control and nursery environment suitability, contributing significantly to achieving sustainable and efficient agricultural production.

Recent advances in graph neural networks (GNNs) have enhanced multimodal recommendation systems’ ability to process complex user–item interactions. However, current approaches face two key limitations: they rely on static similarity metrics for product relationship graphs and they struggle to effectively fuse information across modalities. We propose MR-CSAF, a novel multimodal recommendation algorithm using cross-self-attention fusion. Building on FREEDOM, our approach introduces an adaptive modality selector that dynamically weights each modality’s contribution to product similarity, enabling more accurate product relationship graphs and optimized modality representations. We employ a cross-self-attention mechanism to facilitate both inter- and intra-modal information transfer, while using graph convolution to incorporate updated features into item and product modal representations. Experimental results on three public datasets demonstrate MR-CSAF outperforms eight baseline methods, validating its effectiveness in providing personalized recommendations, advancing the field of personalized recommendation in complex multimodal environments.

The Yunling cattle breed, a three-breed crossbreed, which comprises 50% Brahman cattle, 25% Murray Grey cattle and 25% Yunnan Yellow cattle, has several advantageous traits, including rapid growth, superior meat quality, ability to improve tolerance in hot and humid climates, tick resistance and rough feed. It can be rightfully stated that Yunling cattle serve as vital genetic repositories of the local Yunnan cattle. Gaining insights into the genetic information of Yunling cattle plays a significant role in the formulation of sound breeding strategies for this breed, safeguarding genetic resources and mitigating the risks associated with inbreeding depression. In this study, we constructed the Yunling cattle standard reference genome and aligned the whole genomes of 129 Yunling cattle individuals to the constructed reference genome to estimate the current genetic status of Yunling cattle in Yunnan Province, China. The average alignment rate and the average percentage of properly paired are both 99.72%. The average nucleotide diversity in Yunling cattle is 0.000166, which indicates a lower level of diversity. Population structure analysis classified Yunling cattle into two subgroups. Inbreeding analysis revealed that inbreeding events did occur in the Yunling cattle, which may have contributed to the low genetic diversity observed. This study presents a comprehensive assessment of the genetic structure and diversity among the Yunling cattle and provides a theoretical foundation for the preservation and exploitation of these precious germplasm resources.

Copy number variation (CNV) serves as a crucial contributor to genetic diversity, exerting a profound influence on phenotypic diversity, traits of economic significance, and the evolutionary trajectory of livestock species. This study aimed to dissect the genome-wide CNV landscape of the Nanyang cattle line (Nanyang, Pinnan, and Xianan cattle) to identify functionally relevant CNVs associated with key economic traits and breed differentiation. In this study, 27 resequencing datasets were utilized to analyze the genome-wide distribution of CNVs in three breeds of Nanyang cattle (Nanyang cattle, Pinnan cattle, and Xianan cattle) based on the latest reference genome ARS-UCD2.0. This study identified a total of 97,564 CNVs, and after merging CNVs with overlapping genomic positions, we obtained 10,349 CNV regions (CNVRs), accounting for 1.48% of the reference genome. Functional enrichment analysis showed that CNVR genes were mainly involved in organ development, neural regulation, immune regulation, and metabolism. In addition, 131 CNVRs overlapped with 81 quantitative trait loci (QTLs), such as growth and carcass QTL, multiple birth QTL, tenderness score QTL, and antal follicle number QTL. Additionally, AOX1, KRT72, and ZBTB7C were found to overlap with body weight QTLs. Furthermore, a selective sweep analysis of CNVR revealed that numerous genes (KIF26A, SPINT4, OR5W1, etc.) exhibited divergent copy numbers between breeds. Conclusively, this study facilitates comprehension of the genetic characteristics of the Nanyang cattle line at the CNV level and furnishes valuable information for the advancement of the Nanyang cattle line breeding system.

Machine learning has attracted much attention in the field of genomic prediction due to its powerful predictive capabilities, yet the lack of an explanatory nature in modeling decisions remains a major challenge. In this study, we propose a novel machine learning method, ExAutoGP, which aims to improve the accuracy of genomic prediction and enhance the transparency of the model by combining automated machine learning (AutoML) with SHapley Additive exPlanations (SHAP). To evaluate ExAutoGP’s effectiveness, we designed a comparative experiment consisting of a simulated dataset and two real animal datasets. For each dataset, we applied ExAutoGP and five baseline models—Genomic Best Linear Unbiased Prediction (GBLUP), BayesB, Support Vector Regression (SVR), Kernel Ridge Regression (KRR), and Random Forest (RF). All models were trained and evaluated using five repeated five-fold cross-validation, and their performance was assessed based on both predictive accuracy and computational efficiency. The results show that ExAutoGP exhibits robust and excellent prediction performance on all datasets. In addition, the SHAP method not only effectively reveals the decision-making process of ExAutoGP and enhances its interpretability, but also identifies genetic markers closely related to the traits. This study demonstrates the strong potential of AutoML in genomic prediction, while the introduction of SHAP provides actionable biological insights. The synergy of high prediction accuracy and interpretability offers new perspectives for optimizing genomic selection strategies in livestock and poultry breeding.