Pub Date : 2024-08-21DOI: 10.1016/j.xinn.2024.100686
Ji Dai
{"title":"Pursue the nature of science: Advocate for a better research environment","authors":"Ji Dai","doi":"10.1016/j.xinn.2024.100686","DOIUrl":"https://doi.org/10.1016/j.xinn.2024.100686","url":null,"abstract":"","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"404 1","pages":""},"PeriodicalIF":32.1,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1016/j.xinn.2024.100687
Fan Wang, Xinglin Jiang, Yuchen Liu, Ge Zhang, Yao Zhang, Yongming Jin, Sujuan Shi, Xiao Men, Lijuan Liu, Lei Wang, Weihong Liao, Xiaona Chen, Guoqiang Chen, Haobao Liu, Manzoor Ahmad, Chunxiang Fu, Qian Wang, Haibo Zhang, Sang Yup Lee
Energy crops play a vital role in meeting future energy and chemical demands while addressing climate change. However, the idealization of low-carbon workflows and careful consideration of cost-benefit equations are crucial for their more sustainable implementation. Here, we propose tobacco as a promising energy crop because of its exceptional water solubility, mainly attributed to a high proportion of water-soluble carbohydrates and nitrogen, less lignocellulose, and the presence of acids. We then designed a strategy that maximizes biomass conversion into bio-based products while minimizing energy and material inputs. By autoclaving tobacco leaves in water, we obtained a nutrient-rich medium capable of supporting the growth of microorganisms and the production of bioproducts without the need for extensive pretreatment, hydrolysis, or additional supplements. Additionally, cultivating tobacco on barren lands can generate sufficient biomass to produce approximately 573 billion gallons of ethanol per year. This approach also leads to a reduction of greenhouse gas emissions by approximately 76% compared to traditional corn stover during biorefinery processes. Therefore, our study presents a novel and direct strategy that could significantly contribute to the goal of reducing carbon emissions and global sustainable development compared to traditional methods.
{"title":"Tobacco as a promising crop for low-carbon biorefinery","authors":"Fan Wang, Xinglin Jiang, Yuchen Liu, Ge Zhang, Yao Zhang, Yongming Jin, Sujuan Shi, Xiao Men, Lijuan Liu, Lei Wang, Weihong Liao, Xiaona Chen, Guoqiang Chen, Haobao Liu, Manzoor Ahmad, Chunxiang Fu, Qian Wang, Haibo Zhang, Sang Yup Lee","doi":"10.1016/j.xinn.2024.100687","DOIUrl":"https://doi.org/10.1016/j.xinn.2024.100687","url":null,"abstract":"Energy crops play a vital role in meeting future energy and chemical demands while addressing climate change. However, the idealization of low-carbon workflows and careful consideration of cost-benefit equations are crucial for their more sustainable implementation. Here, we propose tobacco as a promising energy crop because of its exceptional water solubility, mainly attributed to a high proportion of water-soluble carbohydrates and nitrogen, less lignocellulose, and the presence of acids. We then designed a strategy that maximizes biomass conversion into bio-based products while minimizing energy and material inputs. By autoclaving tobacco leaves in water, we obtained a nutrient-rich medium capable of supporting the growth of microorganisms and the production of bioproducts without the need for extensive pretreatment, hydrolysis, or additional supplements. Additionally, cultivating tobacco on barren lands can generate sufficient biomass to produce approximately 573 billion gallons of ethanol per year. This approach also leads to a reduction of greenhouse gas emissions by approximately 76% compared to traditional corn stover during biorefinery processes. Therefore, our study presents a novel and direct strategy that could significantly contribute to the goal of reducing carbon emissions and global sustainable development compared to traditional methods.","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"19 1","pages":""},"PeriodicalIF":32.1,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1016/j.xinn.2024.100685
Yi Shi
{"title":"Drug development in the AI era: AlphaFold 3 is coming!","authors":"Yi Shi","doi":"10.1016/j.xinn.2024.100685","DOIUrl":"https://doi.org/10.1016/j.xinn.2024.100685","url":null,"abstract":"","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"4 1","pages":""},"PeriodicalIF":32.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1016/j.xinn.2024.100681
Xin Fan, Lei Chen, Min Chen, Na Zhang, Hong Chang, Mingjie He, Zhenghao Shen, Lanyue Zhang, Hao Ding, Yuyan Xie, Yemei Huang, Weixin Ke, Meng Xiao, Xuelei Zang, Heping Xu, Wenxia Fang, Shaojie Li, Cunwei Cao, Yingchun Xu, Shiguang Shan, Wenjuan Wu, Changbin Chen, Xinying Xue, Linqi Wang
Strains from the species complex (CGSC) have caused the Pacific Northwest cryptococcosis outbreak, the largest cluster of life-threatening fungal infections in otherwise healthy human hosts known to date. In this study, we utilized a pan-phenome-based method to assess the fitness outcomes of CGSC strains under 31 stress conditions, providing a comprehensive overview of 2,821 phenotype-strain associations within this pathogenic clade. Phenotypic clustering analysis revealed a strong correlation between distinct types of stress phenotypes in a subset of CGSC strains, suggesting that shared determinants coordinate their adaptations to various stresses. Notably, a specific group of strains, including the outbreak isolates, exhibited a remarkable ability to adapt to all three of the most commonly used antifungal drugs for treating cryptococcosis (amphotericin B, 5-fluorocytosine, and fluconazole). By integrating pan-genomic and pan-transcriptomic analyses, we identified previously unrecognized genes that play crucial roles in conferring multidrug resistance in an outbreak strain with high multidrug adaptation. From these genes, we identified biomarkers that enable the accurate prediction of highly multidrug-adapted CGSC strains, achieving maximum accuracy and area under the curve (AUC) of 0.79 and 0.86, respectively, using machine learning algorithms. Overall, we developed a pan-omic approach to identify cryptococcal multidrug resistance determinants and predict highly multidrug-adapted CGSC strains that may pose significant clinical concern.
{"title":"Pan-omics-based characterization and prediction of highly multidrug-adapted strains from an outbreak fungal species complex","authors":"Xin Fan, Lei Chen, Min Chen, Na Zhang, Hong Chang, Mingjie He, Zhenghao Shen, Lanyue Zhang, Hao Ding, Yuyan Xie, Yemei Huang, Weixin Ke, Meng Xiao, Xuelei Zang, Heping Xu, Wenxia Fang, Shaojie Li, Cunwei Cao, Yingchun Xu, Shiguang Shan, Wenjuan Wu, Changbin Chen, Xinying Xue, Linqi Wang","doi":"10.1016/j.xinn.2024.100681","DOIUrl":"https://doi.org/10.1016/j.xinn.2024.100681","url":null,"abstract":"Strains from the species complex (CGSC) have caused the Pacific Northwest cryptococcosis outbreak, the largest cluster of life-threatening fungal infections in otherwise healthy human hosts known to date. In this study, we utilized a pan-phenome-based method to assess the fitness outcomes of CGSC strains under 31 stress conditions, providing a comprehensive overview of 2,821 phenotype-strain associations within this pathogenic clade. Phenotypic clustering analysis revealed a strong correlation between distinct types of stress phenotypes in a subset of CGSC strains, suggesting that shared determinants coordinate their adaptations to various stresses. Notably, a specific group of strains, including the outbreak isolates, exhibited a remarkable ability to adapt to all three of the most commonly used antifungal drugs for treating cryptococcosis (amphotericin B, 5-fluorocytosine, and fluconazole). By integrating pan-genomic and pan-transcriptomic analyses, we identified previously unrecognized genes that play crucial roles in conferring multidrug resistance in an outbreak strain with high multidrug adaptation. From these genes, we identified biomarkers that enable the accurate prediction of highly multidrug-adapted CGSC strains, achieving maximum accuracy and area under the curve (AUC) of 0.79 and 0.86, respectively, using machine learning algorithms. Overall, we developed a pan-omic approach to identify cryptococcal multidrug resistance determinants and predict highly multidrug-adapted CGSC strains that may pose significant clinical concern.","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"18 1","pages":""},"PeriodicalIF":32.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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