A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China.

Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring its stable and healthy development. To ensure the accuracy and reliability of the predictions in practical applications, it is important to evaluate the model's robustness. In this paper, we built models with different parameters to predict carbon trading prices, and proposed models with high accuracy and robustness. The accuracy of the models was assessed using traditional survey indicators. The robustness of the CNN-LSTM model was compared to that of the LSTM model using Z-scores. The CNN-LSTM model with the best prediction performance was compared to a single LSTM model, resulting in a 9% reduction in MSE and a 0.0133 shortening of the Z-score range. Furthermore, the CNN-LSTM model achieved a level of accuracy comparable to other popular models such as CEEMDAN, Boosting, and GRU. It also demonstrated a training speed improvement of at least 40% compared to the aforementioned methods. These results suggest that the CNN-LSTM enhances model resilience. Moreover, the practicality of using Z-score to evaluate model robustness is confirmed.

Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models.

Accurate assessment of greenhouse gas emissions from wastewater treatment plants is crucial for mitigating climate change. N2O is a potent greenhouse gas that is emitted from wastewater treatment plants during the biological denitrification process. In this study, we developed and evaluated deep learning models for predicting N2O emissions from a WWTP in Switzerland. Six key parameters were selected to obtain the optimal LSTM model by adjusting experimental parameter conditions. The optimal parameter condition was achieved with 150 neurons, the tanh activation function, the RMSprop optimization algorithm, a learning rate of 0.001, no dropout regularization, and a batch size of 128. Under the same conditions, we compared the performance of recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. We found that LSTM models outperformed RNN models in predicting N2O emissions. The optimal LSTM model achieved a 36% improvement in mean absolute error (MAE), a 19% improvement in root mean squared error (RMSE), and a 6.92% improvement in R2 score compared to the RNN model. Additionally, LSTM models demonstrated better resilience to sudden changes in the target sequence, exhibiting a 9.54% higher percentage of explained variance compared to RNNs. These results highlight the potential of LSTM models for accurate and robust prediction of N2O emissions from wastewater treatment plants, contributing to effective greenhouse gas mitigation strategies.

CEBPγ facilitates lamellipodia formation and cancer cell migration through CERS6 upregulation.

Ceramide synthase 6 (CERS6) promotes lung cancer metastasis by stimulating cancer cell migration. To examine the underlying mechanisms, we performed luciferase analysis of the CERS6 promoter region and identified the Y-box as a cis-acting element. As a parallel analysis of database records for 149 non-small-cell lung cancer (NSCLC) cancer patients, we screened for trans-acting factors with an expression level showing a correlation with CERS6 expression. Among the candidates noted, silencing of either CCAAT enhancer-binding protein γ (CEBPγ) or Y-box binding protein 1 (YBX1) reduced the CERS6 expression level. Following knockdown, CEBPγ and YBX1 were found to be independently associated with reductions in ceramide-dependent lamellipodia formation as well as migration activity, while only CEBPγ may have induced CERS6 expression through specific binding to the Y-box. The mRNA expression levels of CERS6, CEBPγ, and YBX1 were positively correlated with adenocarcinoma invasiveness. YBX1 expression was observed in all 20 examined clinical lung cancer specimens, while 6 of those showed a staining pattern similar to that of CERS6. The present findings suggest promotion of lung cancer migration by possible involvement of the transcription factors CEBPγ and YBX1.

CERS6 required for cell migration and metastasis in lung cancer.

Sphingolipids constitute a class of bio-reactive molecules that transmit signals and exhibit a variety of physical properties in various cell types, though their functions in cancer pathogenesis have yet to be elucidated. Analyses of gene expression profiles of clinical specimens and a panel of cell lines revealed that the ceramide synthase gene CERS6 was overexpressed in non-small-cell lung cancer (NSCLC) tissues, while elevated expression was shown to be associated with poor prognosis and lymph node metastasis. NSCLC profile and in vitro luciferase analysis results suggested that CERS6 overexpression is promoted, at least in part, by reduced miR-101 expression. Under a reduced CERS6 expression condition, the ceramide profile became altered, which was determined to be associated with decreased cell migration and invasion activities in vitro. Furthermore, CERS6 knockdown suppressed RAC1-positive lamellipodia/ruffling formation and attenuated lung metastasis efficiency in mice, while forced expression of CERS6 resulted in an opposite phenotype in examined cell lines. Based on these findings, we consider that ceramide synthesis by CERS6 has important roles in lung cancer migration and metastasis.

A large-scale comparison of computational models on the residue flexibility for NMR-derived proteins.

As an alternative to X-ray crystallography, nuclear magnetic resonance (NMR) has also emerged as the method of choice for studying both protein structure and dynamics in solution. However, little work using computational models such as Gaussian network model (GNM) and machine learning approaches has focused on NMR-derived proteins to predict the residue flexibility, which is represented by the root mean square deviation (RMSD) with respect to the average structure. We provide a large-scale comparison of computational models, including GNM, parameter-free GNM and several linear regression models using local solvent exposures as inputs, based on a dataset of 1609 protein chains whose structures were resolved by NMR. The result again confirmed that the correlation of GNM outputs with raw RMSD values was better than that using B-factors of X-ray data. Nevertheless, it was also concluded that the parameter-free GNM and the solvent exposure based linear regression models performed worse than GNM when predicting RMSD, contrary to results using X-ray data. The discrepancy of residue flexibility prediction between NMR and X-ray data is likely attributable to a combination of their physical and methodological differences.