Transcriptomic analysis of formic acid stress response in Saccharomyces cerevisiae.

Formic acid is a representative small molecule acid in lignocellulosic hydrolysate that can inhibit the growth of Saccharomyces cerevisiae cells during alcohol fermentation. However, the mechanism of formic acid cytotoxicity remains largely unknown. In this study, RNA-Seq technology was used to study the response of S. cerevisiae to formic acid stress at the transcriptional level. Scanning electron microscopy and Fourier transform infrared spectroscopy were conducted to observe the surface morphology of yeast cells. A total of 1504 genes were identified as being differentially expressed, with 797 upregulated and 707 downregulated genes. Transcriptomic analysis showed that most genes related to glycolysis, glycogen synthesis, protein degradation, the cell cycle, the MAPK signaling pathway, and redox regulation were significantly induced under formic acid stress and were involved in protein translation and synthesis amino acid synthesis genes were significantly suppressed. Formic acid stress can induce oxidative stress, inhibit protein biosynthesis, cause cells to undergo autophagy, and activate the intracellular metabolic pathways of energy production. The increase of glycogen and the decrease of energy consumption metabolism may be important in the adaptation of S. cerevisiae to formic acid. In addition, formic acid can also induce sexual reproduction and spore formation. This study through transcriptome analysis has preliminarily reveal the molecular response mechanism of S. cerevisiae to formic acid stress and has provided a basis for further research on methods used to improve the tolerance to cell inhibitors in lignocellulose hydrolysate.

Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World.

This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions-the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world-Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing 70% of the urban space was used to train the models, and the remaining 30% was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of 90.0% for Dhaka, 91.5% for Nairobi, 94.75% for Jakarta, 82.0% for Guangzhou city, 94.25% for Mumbai, 91.75% for Cairo, and 96.75% for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities' current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces.