【目的】通过研究氮素和水分添加对毛乌素沙地油蒿群落优势植物叶片性状的影响,探讨荒漠生态系统中不同功能群植物对环境变化的适应策略,为预测未来环境变化背景下荒漠植物群落的响应提供理论参考。【方法】以宁夏盐池毛乌素沙地典型植...【目的】通过研究氮素和水分添加对毛乌素沙地油蒿群落优势植物叶片性状的影响,探讨荒漠生态系统中不同功能群植物对环境变化的适应策略,为预测未来环境变化背景下荒漠植物群落的响应提供理论参考。【方法】以宁夏盐池毛乌素沙地典型植被群落油蒿群落为研究对象,通过连续2年(2015—2016)0 kg N ha^(-1) yr^(-1)(N0)60 kgN·ha^(-1) yr^(-1)(N60)氮添加;自然降水(W0),增加20%降水(W20)、增加40%降水(W40)水添加的野外控制试验,测定优势植物种油蒿和赖草叶片的比叶面积(SLA)、碳、氮、磷(C、N、P)含量。【结果】1)氮添加显著增加土壤中的无机氮含量;水添加显著增加土壤含水量;水氮交互作用对土壤无机氮含量、土壤含水量都没有显著影响。而土壤速效磷含量比较稳定,在各处理下均没有表现出显著差异。2)油蒿SLA在氮素和水分添加下显著增加,而交互作用效应不明显;赖草SLA在氮素和水分单独添加下均没有发生显著变化,而水氮交互作用对其存在显著影响。3)氮添加对油蒿和赖草叶片C含量没有显著影响,显著增加了N含量、C∶P、N∶P,降低了P含量和C∶N。水添加对赖草的C、N、P及其化学计量比无显著影响,而显著增加了油蒿叶片N和P含量,降低了C含量、C∶N、C∶P和N∶P。水氮交互作用对油蒿叶片N含量和C∶N有显著影响。【结论】氮素和水分添加对毛乌素沙地油蒿群落优势植物油蒿和赖草叶片性状均有明显影响,氮素对叶片性状的影响比水分添加的影响更为显著。2种不同功能群植物,对氮素和水分添加的响应不同,呈现出不同的适应策略,油蒿的叶片性状对氮素和水分添加的响应较为敏感,趋向于资源快速获取与利用的策略,而赖草表现出较强的稳定性,从而体现出保守的资源利用策略。在未来可能增加的氮沉降和降水情景下,油蒿群落的物种组成可能会由于2种植物不�展开更多
Nanomaterials with intense near-infrared （NIR） absorption exhibit effective photon-to-thermal energy transfer capabilities and can generate heat to ablate cancer cells, thus playing a pivotal role in photothermal ca...Nanomaterials with intense near-infrared （NIR） absorption exhibit effective photon-to-thermal energy transfer capabilities and can generate heat to ablate cancer cells, thus playing a pivotal role in photothermal cancer therapeutics. Herein, hydrophilic flower-like bismuth sulfur （Bi2S3） superstructures with uniform size and improved NIR absorption were controllably synthesized via a facile solvothermal procedure assisted by polyvinylpyrrolidone （PVP）, which could adjust the product morphology. Induced by an 808-nm laser, the as-prepared Bi2S3 nanoflowers exhibited much higher photothermal conversion efficiency （64.3%） than that of Bi2S3 nanobelts （36.5%） prepared in the absence of PVP. This can be attributed not only to the Bi2S3 nanoflower superstructures assembled by 3-dimensional crumpled-paper-like nanosheets serving as many laser-cavity mirrors with improved reflectivity and absorption of NIR light but also to the amorphous structures with a lower band gap. Thus, to achieve the same temperature increase, the concentration or laser power density could be greatly reduced when using Bi2S3 nanoflowers compared to when using Bi2S3 nanobelts, which makes them more favorable for use in therapy due to decreased toxicity. Furthermore, these Bi2S3 nanoflowers effectively achieved photothermal ablation of cancer ceils in vitro and in vivo. These results not only supported the Bi2S3 nanoflowers as a promising photothermal agent for cancer therapy but also paved an approach to exploit new agents with improved photothermal efficiency.展开更多
In recent years,the incidence of myopia has increased at an alarming rate among children and adolescents in China.The exploration of an effective prevention and control method for myopia is in urgent need.With the dev...In recent years,the incidence of myopia has increased at an alarming rate among children and adolescents in China.The exploration of an effective prevention and control method for myopia is in urgent need.With the development of information technology in the past decade,artificial intelligence with the Internet of Things technology(AIoT)is characterized by strong computing power,advanced algorithm,continuous monitoring,and accurate prediction of long-term progression.Therefore,big data and artificial intelligence technology have the potential to be applied to data mining of myopia etiology and prediction of myopia occurrence and development.More recently,there has been a growing recognition that myopia study involving AIoT needs to undergo a rigorous evaluation to demonstrate robust results.展开更多
Our knowledge on permafrost carbon(C)cycle is crucial for understanding its feedback to climate warming and developing nature-based solutions for mitigating climate change.To understand the characteristics of permafro...Our knowledge on permafrost carbon(C)cycle is crucial for understanding its feedback to climate warming and developing nature-based solutions for mitigating climate change.To understand the characteristics of permafrost C cycle on the Tibetan Plateau,the largest alpine permafrost region around the world,we summarized recent advances including the stocks and fluxes of permafrost C and their responses to thawing,and depicted permafrost C dynamics within this century.We find that this alpine permafrost region stores approximately 14.1 Pg(1 Pg=1015g)of soil organic C(SOC)in the top 3 m.Both substantial gaseous emissions and lateral C transport occur across this permafrost region.Moreover,the mobilization of frozen C is expedited by permafrost thaw,especially by the formation of thermokarst landscapes,which could release significant amounts of C into the atmosphere and surrounding water bodies.This alpine permafrost region nevertheless remains an important C sink,and its capacity to sequester C will continue to increase by 2100.For future perspectives,we would suggest developing long-term in situ observation networks of C stocks and fluxes with improved temporal and spatial coverage,and exploring the mechanisms underlying the response of ecosystem C cycle to permafrost thaw.In addition,it is essential to improve the projection of permafrost C dynamics through in-depth model-data fusion on the Tibetan Plateau.展开更多
Tropical cloud clusters(TCCs)can potentially develop into tropical cyclones(TCs),leading to significant casualties and economic losses.Accurate prediction of tropical cyclogenesis(TCG)is crucial for early warnings.Mos...Tropical cloud clusters(TCCs)can potentially develop into tropical cyclones(TCs),leading to significant casualties and economic losses.Accurate prediction of tropical cyclogenesis(TCG)is crucial for early warnings.Most traditional deep learning methods applied to TCG prediction rely on predictors from a single time point,neglect the ocean-atmosphere interactions,and exhibit low model interpretability.This study proposes the Tropical Cyclogenesis Prediction-Net(TCGP-Net)based on the Swin Transformer,which leverages convolutional operations and attention mechanisms to encode spatiotemporal features and capture the temporal evolution of predictors.This model incorporates the coupled ocean-atmosphere interactions,including multiple variables such as sea surface temperature.Additionally,causal inference and integrated gradients are employed to validate the effectiveness of the predictors and provide an interpretability analysis of the model's decision-making process.The model is trained using GridSat satellite data and ERA5 reanalysis datasets.Experimental results demonstrate that TCGP-Net achieves high accuracy and stability,with a detection rate of 97.9%and a false alarm rate of 2.2%for predicting TCG 24 hours in advance,significantly outperforming existing models.This indicates that TCGP-Net is a reliable tool for tropical cyclogenesis prediction.展开更多
文摘【目的】通过研究氮素和水分添加对毛乌素沙地油蒿群落优势植物叶片性状的影响,探讨荒漠生态系统中不同功能群植物对环境变化的适应策略,为预测未来环境变化背景下荒漠植物群落的响应提供理论参考。【方法】以宁夏盐池毛乌素沙地典型植被群落油蒿群落为研究对象,通过连续2年(2015—2016)0 kg N ha^(-1) yr^(-1)(N0)60 kgN·ha^(-1) yr^(-1)(N60)氮添加;自然降水(W0),增加20%降水(W20)、增加40%降水(W40)水添加的野外控制试验,测定优势植物种油蒿和赖草叶片的比叶面积(SLA)、碳、氮、磷(C、N、P)含量。【结果】1)氮添加显著增加土壤中的无机氮含量;水添加显著增加土壤含水量;水氮交互作用对土壤无机氮含量、土壤含水量都没有显著影响。而土壤速效磷含量比较稳定,在各处理下均没有表现出显著差异。2)油蒿SLA在氮素和水分添加下显著增加,而交互作用效应不明显;赖草SLA在氮素和水分单独添加下均没有发生显著变化,而水氮交互作用对其存在显著影响。3)氮添加对油蒿和赖草叶片C含量没有显著影响,显著增加了N含量、C∶P、N∶P,降低了P含量和C∶N。水添加对赖草的C、N、P及其化学计量比无显著影响,而显著增加了油蒿叶片N和P含量,降低了C含量、C∶N、C∶P和N∶P。水氮交互作用对油蒿叶片N含量和C∶N有显著影响。【结论】氮素和水分添加对毛乌素沙地油蒿群落优势植物油蒿和赖草叶片性状均有明显影响,氮素对叶片性状的影响比水分添加的影响更为显著。2种不同功能群植物,对氮素和水分添加的响应不同,呈现出不同的适应策略,油蒿的叶片性状对氮素和水分添加的响应较为敏感,趋向于资源快速获取与利用的策略,而赖草表现出较强的稳定性,从而体现出保守的资源利用策略。在未来可能增加的氮沉降和降水情景下,油蒿群落的物种组成可能会由于2种植物不�
基金We thank the financial support of the National Natural Science Foundation of China （Nos. 21171035 and 51472049）, the Key Grant Project of the Chinese Ministry of Education （No. 313015）, the PhD Programs Foundation of the Ministry of Education of China （No. 20130075120001） and the National High-tech R＆D Program of China （No. 2013AA031903）.
文摘Nanomaterials with intense near-infrared （NIR） absorption exhibit effective photon-to-thermal energy transfer capabilities and can generate heat to ablate cancer cells, thus playing a pivotal role in photothermal cancer therapeutics. Herein, hydrophilic flower-like bismuth sulfur （Bi2S3） superstructures with uniform size and improved NIR absorption were controllably synthesized via a facile solvothermal procedure assisted by polyvinylpyrrolidone （PVP）, which could adjust the product morphology. Induced by an 808-nm laser, the as-prepared Bi2S3 nanoflowers exhibited much higher photothermal conversion efficiency （64.3%） than that of Bi2S3 nanobelts （36.5%） prepared in the absence of PVP. This can be attributed not only to the Bi2S3 nanoflower superstructures assembled by 3-dimensional crumpled-paper-like nanosheets serving as many laser-cavity mirrors with improved reflectivity and absorption of NIR light but also to the amorphous structures with a lower band gap. Thus, to achieve the same temperature increase, the concentration or laser power density could be greatly reduced when using Bi2S3 nanoflowers compared to when using Bi2S3 nanobelts, which makes them more favorable for use in therapy due to decreased toxicity. Furthermore, these Bi2S3 nanoflowers effectively achieved photothermal ablation of cancer ceils in vitro and in vivo. These results not only supported the Bi2S3 nanoflowers as a promising photothermal agent for cancer therapy but also paved an approach to exploit new agents with improved photothermal efficiency.
基金The Science and Technology Planning Projects of Guangdong Province(Grant No.2018B010109008)National Key R&D Program of China(Grant No.2018YFC0116500).
文摘In recent years,the incidence of myopia has increased at an alarming rate among children and adolescents in China.The exploration of an effective prevention and control method for myopia is in urgent need.With the development of information technology in the past decade,artificial intelligence with the Internet of Things technology(AIoT)is characterized by strong computing power,advanced algorithm,continuous monitoring,and accurate prediction of long-term progression.Therefore,big data and artificial intelligence technology have the potential to be applied to data mining of myopia etiology and prediction of myopia occurrence and development.More recently,there has been a growing recognition that myopia study involving AIoT needs to undergo a rigorous evaluation to demonstrate robust results.
基金supported by the National Natural Science Foundation of China(32241034,32425004,42141006)the CAS Project for Young Scientists in Basic Research(YSBR-037)+2 种基金the National Key Research and Development Program of China(2022YFF0801903)the New Cornerstone Science Foundation through the XPLORER PRIZEsupported by the Spanish Government grants PID2022-140808NB-I00 and TED2021-132627 B-I00 funded by MCIN,AEI/10.13039/501100011033 European Union Next Generation EU/PRTR。
文摘Our knowledge on permafrost carbon(C)cycle is crucial for understanding its feedback to climate warming and developing nature-based solutions for mitigating climate change.To understand the characteristics of permafrost C cycle on the Tibetan Plateau,the largest alpine permafrost region around the world,we summarized recent advances including the stocks and fluxes of permafrost C and their responses to thawing,and depicted permafrost C dynamics within this century.We find that this alpine permafrost region stores approximately 14.1 Pg(1 Pg=1015g)of soil organic C(SOC)in the top 3 m.Both substantial gaseous emissions and lateral C transport occur across this permafrost region.Moreover,the mobilization of frozen C is expedited by permafrost thaw,especially by the formation of thermokarst landscapes,which could release significant amounts of C into the atmosphere and surrounding water bodies.This alpine permafrost region nevertheless remains an important C sink,and its capacity to sequester C will continue to increase by 2100.For future perspectives,we would suggest developing long-term in situ observation networks of C stocks and fluxes with improved temporal and spatial coverage,and exploring the mechanisms underlying the response of ecosystem C cycle to permafrost thaw.In addition,it is essential to improve the projection of permafrost C dynamics through in-depth model-data fusion on the Tibetan Plateau.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2142211,42075141&42341202)the National Key Research and Development Program of China(Grant No.2020YFA0608000)+1 种基金the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100)the Fundamental Research Funds for the Central Universities。
文摘Tropical cloud clusters(TCCs)can potentially develop into tropical cyclones(TCs),leading to significant casualties and economic losses.Accurate prediction of tropical cyclogenesis(TCG)is crucial for early warnings.Most traditional deep learning methods applied to TCG prediction rely on predictors from a single time point,neglect the ocean-atmosphere interactions,and exhibit low model interpretability.This study proposes the Tropical Cyclogenesis Prediction-Net(TCGP-Net)based on the Swin Transformer,which leverages convolutional operations and attention mechanisms to encode spatiotemporal features and capture the temporal evolution of predictors.This model incorporates the coupled ocean-atmosphere interactions,including multiple variables such as sea surface temperature.Additionally,causal inference and integrated gradients are employed to validate the effectiveness of the predictors and provide an interpretability analysis of the model's decision-making process.The model is trained using GridSat satellite data and ERA5 reanalysis datasets.Experimental results demonstrate that TCGP-Net achieves high accuracy and stability,with a detection rate of 97.9%and a false alarm rate of 2.2%for predicting TCG 24 hours in advance,significantly outperforming existing models.This indicates that TCGP-Net is a reliable tool for tropical cyclogenesis prediction.
