Sexual reproduction is prevalent in eukaryotic organisms and plays a critical role in the evolution of new traits and in the generation of genetic diversity.Environmental factors often have a direct impact on the occu...Sexual reproduction is prevalent in eukaryotic organisms and plays a critical role in the evolution of new traits and in the generation of genetic diversity.Environmental factors often have a direct impact on the occurrence and frequency of sexual reproduction in fungi.The regulatory effects of atmospheric relative humidity(RH)on sexual reproduction and pathogenesis in plant fungal pathogens and in soil fungi have been extensively investigated.However,the knowledge of how RH regulates the lifecycles of human fungal pathogens is limited.In this study,we report that low atmospheric RH promotes the development of mating projections and same-sex(homothallic)mating in the human fungal pathogen Candida albicans.Low RH causes water loss in C.albicans cells,which results in osmotic stress and the generation of intracellular reactive oxygen species(ROS)and trehalose.The water transporting aquaporin Aqy1,and the G-protein coupled receptor Gpr1 function as cell surface sensors of changes in atmospheric humidity.Perturbation of the trehalose metabolic pathway by inactivating trehalose synthase or trehalase promotes same-sex mating in C.albicans by increasing osmotic or ROS stresses,respectively.Intracellular trehalose and ROS signal the Hog1-osmotic and Hsf1-Hsp90 signaling pathways to regulate the mating response.We,therefore,propose that the cell surface sensors Aqy1 and Gpr1,intracellular trehalose and ROS,and the Hog1-osmotic and Hsf1-Hsp90 signaling pathways function coordinately to regulate sexual mating in response to low atmospheric RH conditions in C.albicans.展开更多
Accurate retrieval of atmospheric relative humidity(RH)profiles is essential for improving our understanding of atmospheric thermodynamics and climate change.Nevertheless,it remains challenging,as traditional models r...Accurate retrieval of atmospheric relative humidity(RH)profiles is essential for improving our understanding of atmospheric thermodynamics and climate change.Nevertheless,it remains challenging,as traditional models rely exclusively on vertical brightness temperature(BT)observations.Here,we present a novel retrieval algorithm called AngleNet,a groundbreaking deep-learning model that leverages multi-angle BT observation from ground-based microwave radiometers(MWRs).The innovative“multi-angle-aware”module in AngleNet effectively exploits previously underutilized oblique scanning angle data by accurately capturing these nonlinear relationships between BT and RH profiles,and precisely characterizes its vertical fine structure.Based on the 7-year(2018-2024)in situ measurements from Beijing,Nanjing,and Shanghai,validation results reveal that AngleNet achieves substantial improvements,with an average R^(2) of 0.71 and a root mean square error(RMSE)of 10.39%,surpassing conventional models such as LGBM(light gradient boosting machine)and RF(random forest)by over 10% in both metrics,and demonstrating a remarkable 41% increase in R^(2) and a 10% reduction in RMSE compared to the previous BRNN method(batch normalization and robust neural network).Moreover,additional independent validation results demonstrate that AngleNet exhibits excellent stability and retrieval accuracy during periods without radiosonde measurements.Feature analysis and evaluations of the“multi-angle-aware”module indicate that optimal RH retrieval performance is achieved by combining zenith-angle BTs with oblique angles at 30°and 19.2°.AngleNet breakthrough performance is especially notable in consistently capturing complex RH profile features,which are critical for accurate numerical weather forecasting and climate monitoring.展开更多
基金supported by the National Key Research and Development Program of China(2021YFC2300400)the National Natural Science Foundation of China(31930005 and 32170194)+2 种基金Shanghai Municipal Science and Technology Major Project(HS2021SHZX001)supported by the National Institutes of Health(NIH)National Institute of General Medical Sciences(NIGMS)award R35GM124594by the Kamangar family in the form of an endowed chair to C.J.N.
文摘Sexual reproduction is prevalent in eukaryotic organisms and plays a critical role in the evolution of new traits and in the generation of genetic diversity.Environmental factors often have a direct impact on the occurrence and frequency of sexual reproduction in fungi.The regulatory effects of atmospheric relative humidity(RH)on sexual reproduction and pathogenesis in plant fungal pathogens and in soil fungi have been extensively investigated.However,the knowledge of how RH regulates the lifecycles of human fungal pathogens is limited.In this study,we report that low atmospheric RH promotes the development of mating projections and same-sex(homothallic)mating in the human fungal pathogen Candida albicans.Low RH causes water loss in C.albicans cells,which results in osmotic stress and the generation of intracellular reactive oxygen species(ROS)and trehalose.The water transporting aquaporin Aqy1,and the G-protein coupled receptor Gpr1 function as cell surface sensors of changes in atmospheric humidity.Perturbation of the trehalose metabolic pathway by inactivating trehalose synthase or trehalase promotes same-sex mating in C.albicans by increasing osmotic or ROS stresses,respectively.Intracellular trehalose and ROS signal the Hog1-osmotic and Hsf1-Hsp90 signaling pathways to regulate the mating response.We,therefore,propose that the cell surface sensors Aqy1 and Gpr1,intracellular trehalose and ROS,and the Hog1-osmotic and Hsf1-Hsp90 signaling pathways function coordinately to regulate sexual mating in response to low atmospheric RH conditions in C.albicans.
基金supported by the National Nature Science Foundation of China(42030606).
文摘Accurate retrieval of atmospheric relative humidity(RH)profiles is essential for improving our understanding of atmospheric thermodynamics and climate change.Nevertheless,it remains challenging,as traditional models rely exclusively on vertical brightness temperature(BT)observations.Here,we present a novel retrieval algorithm called AngleNet,a groundbreaking deep-learning model that leverages multi-angle BT observation from ground-based microwave radiometers(MWRs).The innovative“multi-angle-aware”module in AngleNet effectively exploits previously underutilized oblique scanning angle data by accurately capturing these nonlinear relationships between BT and RH profiles,and precisely characterizes its vertical fine structure.Based on the 7-year(2018-2024)in situ measurements from Beijing,Nanjing,and Shanghai,validation results reveal that AngleNet achieves substantial improvements,with an average R^(2) of 0.71 and a root mean square error(RMSE)of 10.39%,surpassing conventional models such as LGBM(light gradient boosting machine)and RF(random forest)by over 10% in both metrics,and demonstrating a remarkable 41% increase in R^(2) and a 10% reduction in RMSE compared to the previous BRNN method(batch normalization and robust neural network).Moreover,additional independent validation results demonstrate that AngleNet exhibits excellent stability and retrieval accuracy during periods without radiosonde measurements.Feature analysis and evaluations of the“multi-angle-aware”module indicate that optimal RH retrieval performance is achieved by combining zenith-angle BTs with oblique angles at 30°and 19.2°.AngleNet breakthrough performance is especially notable in consistently capturing complex RH profile features,which are critical for accurate numerical weather forecasting and climate monitoring.
