ABSTRACT
The thermotolerant yeast Kluyveromyces marxianus is known for its potential in high-temperature ethanol fermentation, yet it suffers from excess acetic acid production at elevated temperatures, which hinders ethanol production. To better understand how the yeast responds to acetic acid stress during high-temperature ethanol fermentation, this study investigated its transcriptomic changes under this condition. RNA sequencing (RNA-seq) was used to identify differentially expressed genes (DEGs) and enriched gene ontology (GO) terms and pathways under acetic acid stress. The results showed that 611 genes were differentially expressed, and GO and pathway enrichment analysis revealed that acetic acid stress promoted protein catabolism but repressed protein synthesis during high-temperature fermentation. Protein-protein interaction (PPI) networks were also constructed based on the interactions between proteins coded by the DEGs. Hub genes and key modules in the PPI networks were identified, providing insight into the mechanisms of this yeast's response to acetic acid stress. The findings suggest that the decrease in ethanol production is caused by the imbalance between protein catabolism and protein synthesis. Overall, this study provides valuable insights into the mechanisms of K. marxianus's response to acetic acid stress and highlights the importance of maintaining a proper balance between protein catabolism and protein synthesis for high-temperature ethanol fermentation.
ABSTRACT
Hepatoid adenocarcinoma of the stomach (HAS) is a rare malignancy with aggressive biological behavior. This study aimed to compare the genetic landscape of HAS with liver hepatocellular carcinoma (LIHC), gastric cancer (GC), and AFP-producing GC (AFPGC) and identify clinically actionable alterations. Thirty-eight cases of HAS were collected for whole-exome sequencing. Significantly mutated genes were identified. TP53 was the most frequently mutated gene (66%). Hypoxia, TNF-α/NFκB, mitotic spindle assembly, DNA repair, and p53 signaling pathways mutated frequently. Mutagenesis mechanisms in HAS were associated with spontaneous or enzymatic deamination of 5-methylcytosine to thymine and defective homologous recombination-related DNA damage repair. However, LIHC was characteristic of exposure to aflatoxin and aristolochic acid. The copy number variants (CNVs) in HAS was significantly different compared to LIHC, GC, and AFPGC. Aggressive behavior-related CNVs were identified, including local vascular invasion, advanced stages, and adverse prognosis. In 55.26% of HAS patients there existed at least one clinically actionable alteration, including ERBB2, FGFR1, CDK4, EGFR, MET, and MDM2 amplifications and BRCA1/2 mutations. MDM2 amplification with functional TP53 was detected in 5% of HAS patients, which was proved sensitive to MDM2 inhibitors. A total of 10.53% of HAS patients harbored TMB > 10 muts/Mb. These findings improve our understanding of the genomic features of HAS and provide potential therapeutic targets.
ABSTRACT
Emotion recognition (ER) is an important part of building an intelligent human-computer interaction system and plays an important role in human-computer interaction. Often, people express their feelings through a variety of symbols, such as words and facial expressions. A business ecosystem is an economic community based on interacting organizations and individuals. Over time, they develop their capabilities and roles together and tend to develop themselves in the direction of one or more central enterprises. This paper aims to study a multimodal ER method based on attention mechanism. It analyzes the current emotional state of consumers and the development direction of enterprises through multi-modal ER of human emotions and analysis of market trends, so as to provide the most appropriate response or plan. This paper firstly describes the related methods of multimodal ER and deep learning in detail, and briefly outlines the meaning of enterprise strategy in the business ecosystem. Then, two datasets, CMU-MOSI and CMU-MOSEI, are selected to design the scheme for multimodal ER based on self-attention mechanism. Through the comparative analysis of the accuracy of single-modal and multi-modal ER, the self-attention mechanism is applied in the experiment. The experimental results show that the average recognition accuracy of happy under multimodal ER reaches 91.5%.
ABSTRACT
The technology for producing bioethanol from sweet sorghum stalks by solid-state fermentation has developed rapidly in recent years, and has many similarities with traditional Chinese liquor production. However, the product from sweet sorghum stalks was lacking in volatile flavors, and the level of harmful contents were uncertain, therefore it could not be sold as liquor. In this study, the protein, fat, and tannin in the clusters and leaves of sweet sorghum were utilized to increase the content of flavor compounds in the ethanol product through the anaerobic fermentation of Saccharomyces cerevisiae. Meanwhile, the silage fermentation method was used to extend the preservation time of the raw materials and to further enhance the flavors of Fen-flavor liquor, with ethyl acetate as the characteristic flavor. The effects of different feedstock groups on ethyl acetate, ethyl lactate, methanol, acetaldehyde, acetal, fusel oil, total acid, and total ester were evaluated by analyzing the chemical composition of different parts of sweet sorghum and determined by gas chromatograph. The effect of different fermentation periods on the volatile flavor of sweet sorghum Baijiu was evaluated. The yield of the characteristic volatile flavor was increased by the extension of the fermentation time. Sweet sorghum Baijiu with a high ester content can be used as a flavoring liquor, blended with liquor with a shorter fermentation period to prepare the finished Fen-flavor Baijiu, conforming to the Chinese national standard for sale.
