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The spatiotemporal patterns and shifts of net ecosystem productivity (NEP) play a pivotal role in ecological conservation and addressing climate change. For example, by quantifying the NEP information within ecosystems, we can achieve the protection and restoration of natural ecological balance. Monitoring the changes in NEP enables a more profound understanding and prediction of ecosystem alterations caused by global warming, thereby providing a scientific basis for formulating policies aimed at mitigating and adapting to climate change. The accurate prediction of NEP sheds light on the ecosystem's response to climatic variations and aids in formulating targeted carbon sequestration policies. While traditional ecological process models provide a comprehensive approach to predicting NEP, they often require extensive experimental and empirical data, increasing research costs. In contrast, machine-learning models offer a cost-effective alternative for NEP prediction; however, the delicate balance in algorithm selection and hyperparameter tuning is frequently overlooked. In our quest for the optimal prediction model, we examined a combination of four mainstream machine-learning algorithms with four hyperparameter-optimization techniques. Our analysis identified that the backpropagation neural network combined with Bayesian optimization yielded the best performance, with an R2 of 0.68 and an MSE of 1.43. Additionally, deep-learning models showcased promising potential in NEP prediction. Selecting appropriate algorithms and executing precise hyperparameter-optimization strategies are crucial for enhancing the accuracy of NEP predictions. This approach not only improves model performance but also provides us with new tools for a deeper understanding of and response to ecosystem changes induced by climate change. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) played essential roles in nearly every biological process and disease. Many algorithms were developed to distinguish lncRNAs from mRNAs in transcriptomic data and facilitated discoveries of more than 600 000 of lncRNAs. However, only a tiny fraction (<1%) of lncRNA transcripts (~4000) were further validated by low-throughput experiments (EVlncRNAs). Given the cost and labor-intensive nature of experimental validations, it is necessary to develop computational tools to prioritize those potentially functional lncRNAs because many lncRNAs from high-throughput sequencing (HTlncRNAs) could be resulted from transcriptional noises. Here, we employed deep learning algorithms to separate EVlncRNAs from HTlncRNAs and mRNAs. For overcoming the challenge of small datasets, we employed a three-layer deep-learning neural network (DNN) with a K-mer feature as the input and a small convolutional neural network (CNN) with one-hot encoding as the input. Three separate models were trained for human (h), mouse (m) and plant (p), respectively. The final concatenated models (EVlncRNA-Dpred (h), EVlncRNA-Dpred (m) and EVlncRNA-Dpred (p)) provided substantial improvement over a previous model based on support-vector-machines (EVlncRNA-pred). For example, EVlncRNA-Dpred (h) achieved 0.896 for the area under receiver-operating characteristic curve, compared with 0.582 given by sequence-based EVlncRNA-pred model. The models developed here should be useful for screening lncRNA transcripts for experimental validations. EVlncRNA-Dpred is available as a web server at https://www.sdklab-biophysics-dzu.net/EVlncRNA-Dpred/index.html , and the data and source code can be freely available along with the web server. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the role of microRNA (miR)-130a in the pathogenesis of myocardial hypoxia/reoxygenation (H/R) injury. Primary rat cardiomyocytes were cultured and subjected to H/R treatment. Reverse transcription-quantitative polymerase chain reaction was performed to detect the levels of miR-130a, western blot analysis was used to determine the expression of various proteins, and CCK-8 assay was performed to determine cell viability. In addition, flow cytometry was used to assess apoptosis. The cell viability was significantly decreased and the apoptosis rate was significantly increased in H/R-treated primary cardiomyocytes, and the expression level of miR-130a was also elevated in these model cells. Transfection with miR-130a inhibitor significantly elevated the cell viability and reduced the apoptosis rate in H/R-treated cardiomyocytes. Bioinformatics analysis indicated that autophagy-related gene 14 (ATG14) is the target for miR-130a, which was confirmed by dual-luciferase reporter assay and western blot analysis. When the H/R model cells were co-transfected with miR-130a inhibitor and small interfering RNA against ATG14, the cell viability was significantly reduced and the apoptosis rate was significantly elevated, compared with that of cells transfected with miR-130a inhibitor alone. miR-130a inhibitor transfection significantly elevated the levels of ATG14 and phosphorylated (p-)Beclin 1, increased the LC3II/LC3I ratio, and decreased the expression levels of P62 and cleaved caspase-3, while the co-transfection of miR-130a inhibitor and siR-ATG14 attenuated these effects in H/R-induced primary cardiomyocytes. These results indicate that miR-130a is involved in H/R-induced injuries in primary cardiomyocytes, and that the inhibition of miR-130a increases the levels of ATG14 and p-Beclin 1, thereby increasing autophagy and inhibiting apoptosis in these cells.