Mechanisms for carbon stock driving and scenario modeling in typical mountainous watersheds of northeastern China.

Watershed ecosystems play a pivotal role in maintaining the global carbon cycle and reducing global warming by serving as vital carbon reservoirs for sustainable ecosystem management. In this study, we based on the "quantity-mechanism-scenario" frameworks, integrate the MCE-CA-Markov and InVEST models to evaluate the spatiotemporal variations of carbon stocks in mid- to high-latitude alpine watersheds in China under historical and future climate scenarios. Additionally, the study employs the Geographic Detector model to explore the driving mechanisms influencing the carbon storage capacity of watershed ecosystems. The results showed that the carbon stock of the watershed increased by about 15.9 Tg from 1980 to 2020. Fractional Vegetation Cover (FVC), Digital Elevation Model (DEM), and Mean Annual Temperature (MAT) had the strongest explanatory power for carbon stocks. Under different climate scenarios, it was found that the SSP2-4.5 scenario had a significant rise in carbon stock from 2020 to 2050, roughly 24.1 Tg. This increase was primarily observed in the southeastern region of the watersheds, with forest and grassland effectively protected. Conversely, according to the SSP5-8.5 scenario, the carbon stock would decrease by about 50.53 Tg with the expansion of cultivated and construction land in the watershed's southwest part. Therefore, given the vulnerability of mid- to high-latitude mountain watersheds, global warming trends continue to pose a greater threat to carbon sequestration in watersheds. Our findings carry important implications for tackling potential ecological threats in mid- to high-latitude watersheds in the Northern Hemisphere and assisting policymakers in creating carbon sequestration plans, as well as for reducing climate change.

Progress on CRISPR/Cas9 system in the genetic improvement of livestock and poultry.

CRISPR/Cas9 gene editing technology, as a highly efficient genome editing method, has been extensively employed in the realm of animal husbandry for genetic improvement. With its remarkable efficiency and precision, this technology has revolutionized the field of animal husbandry. Currently, CRISPR/Cas9-based gene knockout, gene knock-in and gene modification techniques are widely employed to achieve precise enhancements in crucial production traits of livestock and poultry species. In this review, we summarize the operational principle and development history of CRISPR/Cas9 technology. Additionally, we highlight the research advancements utilizing this technology in muscle growth and development, fiber growth, milk quality composition, disease resistance breeding, and animal welfare within the livestock and poultry sectors. Our aim is to provide a more comprehensive understanding of the application of CRISPR/Cas9 technology in gene editing for livestock and poultry.

Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks.

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.

HTFSMMA: Higher-Order Topological Guided Small Molecule-MicroRNA Associations Prediction.

Small molecules (SMs) play a pivotal role in regulating microRNAs (miRNAs). Existing prediction methods for associations between SM-miRNA have overlooked crucial aspects: the incorporation of local topological features between nodes, which represent either SMs or miRNAs, and the effective fusion of node features with topological features. This study introduces a novel approach, termed high-order topological features for SM-miRNA association prediction (HTFSMMA), which specifically addresses these limitations. Initially, an association graph is formed by integrating SM-miRNA association data, SM similarity, and miRNA similarity. Subsequently, we focus on the local information of links and propose target neighborhood graph convolutional network for extracting local topological features. Then, HTFSMMA employs graph attention networks to amalgamate these local features, thereby establishing a platform for the acquisition of high-order features through random walks. Finally, the extracted features are integrated into the multilayer perceptron to derive the association prediction scores. To demonstrate the performance of HTFSMMA, we conducted comprehensive evaluations including five-fold cross-validation, leave-one-out cross-validation (LOOCV), SM-fixed local LOOCV, and miRNA-fixed local LOOCV. The area under receiver operating characteristic curve values were 0.9958 ± 0.0024 (0.8722 ± 0.0021), 0.9986 (0.9504), 0.9974 (0.9111), and 0.9977 (0.9074), respectively. Our findings demonstrate the superior performance of HTFSMMA over existing approaches. In addition, three case studies and the DeLong test have confirmed the effectiveness of the proposed method. These results collectively underscore the significance of HTFSMMA in facilitating the inference of associations between SMs and miRNAs.