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  • Article
    Campos Antoniêto AC, Ramos Pedersoli W, Dos Santos Castro L, da Silva Santos R, Cruz AH, Nogueira KM, Silva-Rocha R, Rossi A, Silva RN.
    PLoS One. 2017;12(1):e0169796.
    Microorganisms play a vital role in bioethanol production whose usage as fuel energy is increasing worldwide. The filamentous fungus Neurospora crassa synthesize and secrete the major enzymes involved in plant cell wall deconstruction. The production of cellulases and hemicellulases is known to be affected by the environmental pH; however, the regulatory mechanisms of this process are still poorly understood. In this study, we investigated the role of the pH regulator PAC-3 in N. crassa during their growth on sugarcane bagasse at different pH conditions. Our data indicate that secretion of cellulolytic enzymes is reduced in the mutant Δpac-3 at alkaline pH, whereas xylanases are positively regulated by PAC-3 in acidic (pH 5.0), neutral (pH 7.0), and alkaline (pH 10.0) medium. Gene expression profiles, evaluated by real-time qPCR, revealed that genes encoding cellulases and hemicellulases are also subject to PAC-3 control. Moreover, deletion of pac-3 affects the expression of transcription factor-encoding genes. Together, the results suggest that the regulation of holocellulase genes by PAC-3 can occur as directly as in indirect manner. Our study helps improve the understanding of holocellulolytic performance in response to PAC-3 and should thereby contribute to the better use of N. crassa in the biotechnology industry.
    Digital Access Access Options
  • Article
    Han F, Zhang X, Yu J, Xu S, Zhou G, Li S.
    Sci Total Environ. 2024 Feb 25;913:169796.
    The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.
    Digital Access Access Options
  • Journal
    Digital Access ClinicalKey v. 1-, 2023-