Increased Atmospheric CO2 to over 400 ppm has prompted global climate irregularities. Reducing the released CO2 from biotechnological processes could remediate these phenomena. In this study, we sought to find a solution to reduce the amount of CO2 in the process of growth and reproduction by preventing the conversion of formic acid into CO2. The (bio)chemical conversion of formic acid to CO2 is a key reaction. Therefore, we compared the growth of BL21, being a subfamily of K12, alongside two strains in which two different genes related to the formate metabolism were deleted, in complex and simple media. Experimental results were entirely consistent with metabolic predictions. Subsequently, the knockout bacteria grew more efficiently than BL21. Interestingly, TsFDH, a formate dehydrogenase with the tendency of converting CO2 to formate, increased the growth of all strains compared with cells without the TsFDH. Most mutants grew in a simple medium containing glycerol, which showed that glycerol is the preferred carbon source compared to glucose for the growth of E. coli. These results explain the reasons for the inconsistency of predictions in previous metabolic models that declared glycerol as a suitable carbon source for the growth of E. coli but failed to achieve it in practice. To conduct a more mechanistic evaluation of our observations, RNA sequencing data analysis was conducted on an E. coli RNA-seq dataset. The gene expression correlation outcome revealed the increased expression levels of several genes related to protein biosynthesis and glycerol degradation as a possible explanation of our observations.

Urban traffic flow prediction using data-driven models can play an important role in route planning and preventing congestion on highways. These methods utilize data collected from traffic recording stations at different timestamps to predict the future status of traffic. Hence, data collection, transmission, storage, and extraction techniques can have a significant impact on the performance of the traffic flow model. On the other hand, a comprehensive database can provide the opportunity for using complex, yet reliable predictive models such as deep learning methods. However, most of these methods have difficulties in handling missing values and outliers. This study focuses on hybrid deep neural networks to predict traffic flow in the California Freeway Performance Measurement System (PeMS) with missing values. The proposed networks are based on a combination of recurrent neural networks (RNNs) to consider the temporal dependencies in the data recorded in each station and convolutional neural networks (CNNs) to take the spatial correlations in the adjacent stations into account. Various architecture configurations with series and parallel connections are considered based on RNNs and CNNs, and several prevalent data imputation techniques are used to examine the robustness of the hybrid networks to missing values. A comprehensive analysis performed on two different datasets from PeMS indicates that the proposed series-parallel hybrid network with the mean imputation technique achieves the lowest error in predicting the traffic flow and is robust to missing values up until 21% missing ratio in both complete and incomplete training data scenarios when applied to an incomplete test data.