The first phloroglucinol-triterpenoid hybrids,myrtphlotritins A-E(1-5),were rapidly recognized and isolated from two species of Myrtaceae by employing the building blocks-based molecular network(BBMN)strategy.Compound...The first phloroglucinol-triterpenoid hybrids,myrtphlotritins A-E(1-5),were rapidly recognized and isolated from two species of Myrtaceae by employing the building blocks-based molecular network(BBMN)strategy.Compounds 1-5 featured new carbon skeletons in which phloroglucinol derivatives were coupled with lupane-and dammarane-type triterpenoids through different linkage patterns.Their structures and absolute configurations were elucidated by comprehensive analysis of spectroscopic data and quantum chemical calculations.Biosynthetic pathways for compounds 1-5 were proposed on the basis of the coexisting precursors.Guided by the biogenetic pathways,the biomimetic synthesis of compound 1 was also achieved.Additionally,compounds 2,3,and 5 exhibited potent antiviral activities against herpes simplex virus type-1(HSV-1)infection,and compounds 2 and 5 displayed significant anti-inflammatory activities on RAW264.7 cells.展开更多
基于MODIS数据和改进的光能利用率模型(CASA模型)对2008—2018年伊犁河流域植被净初级生产力(NPP)进行估算,通过一元线性回归趋势分析、变异系数、Hurst指数等方法对其时空分异特征进行分析。结论如下:(1)时间特征上,伊犁河流域植被NPP...基于MODIS数据和改进的光能利用率模型(CASA模型)对2008—2018年伊犁河流域植被净初级生产力(NPP)进行估算,通过一元线性回归趋势分析、变异系数、Hurst指数等方法对其时空分异特征进行分析。结论如下:(1)时间特征上,伊犁河流域植被NPP呈现波动上升趋势,年内植被NPP呈现出“单峰型”特点,该流域四季植被NPP大小关系为:夏季>春季>秋季>冬季;(2)空间特征上,伊犁河流域植被NPP呈现东北低西南高,沿天山山脉呈环状分布,各植被类型NPP的大小为:林地(624.13 g C m^(-2)a^(-1))>耕地(575.04 g C m^(-2)a^(-1))>草地(270.57 g C m^(-2)a^(-1))>裸地(114.26 g C m^(-2)a^(-1))。该流域植被NPP在海拔、经纬度方面均呈现不同的变化特征。(3)空间稳定性上,伊犁河流域植被NPP存在明显的空间差异性,各变异程度面积比例从大到小为:稳定(44.78%)>不稳定(25.47%)>比较稳定(16.46%)>很不稳定(13.3%);(4)未来变化趋势上,伊犁河流域大部分地区植被NPP未来的变化趋势将以持续增加为主,未来变化趋势的面积比例大小为:持续增加(51.67%)>由增加变为减少(31.75%)>持续减少(9%)>由减小变为增加(7.54%)>无法预测(0.06%)。研究结果可为伊犁河流域的生态环境和社会、经济的可持续发展提供可靠的理论依据。展开更多
It is necessary to quantitatively study the relationship between climate and human factors on net primary productivity(NPP)inorder to understand the driving mechanism of NPP and prevent desertification.This study inve...It is necessary to quantitatively study the relationship between climate and human factors on net primary productivity(NPP)inorder to understand the driving mechanism of NPP and prevent desertification.This study investigated the spatial and temporal differentiation features of actual net primary productivity(ANPP)in the Ili River Basin,a transboundary river between China and Kazakhstan,as well as the proportional contributions of climate and human causes to ANPP variation.Additionally,we analyzed the pixel-scale relationship between ANPP and significant climatic parameters.ANPP in the Ili River Basin increased from 2001 to 2020 and was lower in the northeast and higher in the southwest;furthermore,it was distributed in a ring around the Tianshan Mountains.In the vegetation improvement zone,human activities were the dominant driving force,whereas in the degraded zone,climate change was the primary major driving force.The correlation coefficients of ANPP with precipitation and temperature were 0.322 and 0.098,respectively.In most areas,there was a positive relationship between vegetation change,temperature and precipitation.During 2001 to 2020,the basin’s climatic change trend was warm and humid,which promoted vegetation growth.One of the driving factors in the vegetation improvement area was moderate grazing by livestock.展开更多
The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identific...The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identification has been a research hotspot in the field of network management and security mon-itoring.However,as more network services use encryption technology,network traffic identification faces many challenges.Although classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and labor-intensive.Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples.However,in real scenarios,labeled samples are often difficult to obtain.This paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning.Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.展开更多
基金supported by the Guangdong Basic and Applied Basic Research Foundation(Nos.2020B1515120066 and 2022A1515010010)the National Natural Science Foundation of China[Nos.82293681(82293680)and 82273822]+3 种基金the Science and Technology Key Project of Guangdong Province(No.2020B1111110004)the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program(No.2017BT01Y036)the Fundamental Research Funds for the Central Universitiesthe support of K.C.Wong Education Foundation。
文摘The first phloroglucinol-triterpenoid hybrids,myrtphlotritins A-E(1-5),were rapidly recognized and isolated from two species of Myrtaceae by employing the building blocks-based molecular network(BBMN)strategy.Compounds 1-5 featured new carbon skeletons in which phloroglucinol derivatives were coupled with lupane-and dammarane-type triterpenoids through different linkage patterns.Their structures and absolute configurations were elucidated by comprehensive analysis of spectroscopic data and quantum chemical calculations.Biosynthetic pathways for compounds 1-5 were proposed on the basis of the coexisting precursors.Guided by the biogenetic pathways,the biomimetic synthesis of compound 1 was also achieved.Additionally,compounds 2,3,and 5 exhibited potent antiviral activities against herpes simplex virus type-1(HSV-1)infection,and compounds 2 and 5 displayed significant anti-inflammatory activities on RAW264.7 cells.
文摘基于MODIS数据和改进的光能利用率模型(CASA模型)对2008—2018年伊犁河流域植被净初级生产力(NPP)进行估算,通过一元线性回归趋势分析、变异系数、Hurst指数等方法对其时空分异特征进行分析。结论如下:(1)时间特征上,伊犁河流域植被NPP呈现波动上升趋势,年内植被NPP呈现出“单峰型”特点,该流域四季植被NPP大小关系为:夏季>春季>秋季>冬季;(2)空间特征上,伊犁河流域植被NPP呈现东北低西南高,沿天山山脉呈环状分布,各植被类型NPP的大小为:林地(624.13 g C m^(-2)a^(-1))>耕地(575.04 g C m^(-2)a^(-1))>草地(270.57 g C m^(-2)a^(-1))>裸地(114.26 g C m^(-2)a^(-1))。该流域植被NPP在海拔、经纬度方面均呈现不同的变化特征。(3)空间稳定性上,伊犁河流域植被NPP存在明显的空间差异性,各变异程度面积比例从大到小为:稳定(44.78%)>不稳定(25.47%)>比较稳定(16.46%)>很不稳定(13.3%);(4)未来变化趋势上,伊犁河流域大部分地区植被NPP未来的变化趋势将以持续增加为主,未来变化趋势的面积比例大小为:持续增加(51.67%)>由增加变为减少(31.75%)>持续减少(9%)>由减小变为增加(7.54%)>无法预测(0.06%)。研究结果可为伊犁河流域的生态环境和社会、经济的可持续发展提供可靠的理论依据。
文摘背景结直肠癌发病率、死亡率高,早诊早治十分重要。但常规检查难以鉴别诊断直径≥20 mm的进展期腺瘤,造成了患者经济和心理的双重负担。目的探讨^(18)F-FDG PET/CT鉴别直径≥20 mm结直肠癌癌前病变与结直肠腺癌的诊断效能及相关影响因素。方法回顾性分析2010年1月-2020年1月于解放军总医院第一医学中心明确诊断为结直肠癌癌前病变或结直肠腺癌曾术前行^(18)F-FDG PET/CT检查,并于内镜下切除或行外科手术治疗患者的临床资料,将其显像方式及半定量法评估PET/CT摄取特点(SUV_(max))与术后病理组织学特点进行比较。结果研究共纳入402例患者,病理学分类:管状腺瘤16例,绒毛状腺瘤61例,结直肠早癌68例,结直肠腺癌257例。^(18)F-FDG PET/CT检查中22例病变未见显像;^(18)F-FDG PET/CT对不同病理类型检出率:管状腺瘤组62.5%,绒毛状腺瘤组83.61%,结直肠早癌组95.59%,结直肠癌组98.83%。结直肠腺癌病灶直径显著大于管状腺瘤、绒毛状腺瘤、结直肠早癌[(47.34±20.75) mm vs (22.50±3.54) mm、(29.51±13.09) mm、(30.97±13.63) mm,P<0.001]。不同病理分组间肿瘤SUV_(max)差异有统计学意义(P<0.05),其中管状腺瘤组的SUV_(max)显著低于绒毛状腺瘤组和结直肠腺癌组(7.84±3.90 vs 14.51±8.91、14.99±7.60,P<0.05),结直肠早癌组与结直肠癌组SUV_(max)有统计学差异(11.85±7.03 vs 14.99±7.60,P=0.003)。多元回归分析结果显示,病变直径、病变的病理性质与SUV_(max)密切相关。管状腺瘤的SUV_(max)相对其他类型更低,腺癌的SUV_(max)相对其他类型更高,但未发现绒毛状腺瘤与其他病理类型的SUV_(max)有统计学差异。结论 ^(18)F-FDG PET/CT在直径≥20 mm结直肠病变中可以鉴别管状腺瘤和结直肠腺癌,但鉴别结直肠绒毛状腺瘤与其他肿瘤性病灶的价值有限。
基金Under the auspices of the Key Laboratory of Xinjiang Science and Technology Department(No.2022D04009)National Social Science Foundation of China’s Major Program(No.17ZDA064)。
文摘It is necessary to quantitatively study the relationship between climate and human factors on net primary productivity(NPP)inorder to understand the driving mechanism of NPP and prevent desertification.This study investigated the spatial and temporal differentiation features of actual net primary productivity(ANPP)in the Ili River Basin,a transboundary river between China and Kazakhstan,as well as the proportional contributions of climate and human causes to ANPP variation.Additionally,we analyzed the pixel-scale relationship between ANPP and significant climatic parameters.ANPP in the Ili River Basin increased from 2001 to 2020 and was lower in the northeast and higher in the southwest;furthermore,it was distributed in a ring around the Tianshan Mountains.In the vegetation improvement zone,human activities were the dominant driving force,whereas in the degraded zone,climate change was the primary major driving force.The correlation coefficients of ANPP with precipitation and temperature were 0.322 and 0.098,respectively.In most areas,there was a positive relationship between vegetation change,temperature and precipitation.During 2001 to 2020,the basin’s climatic change trend was warm and humid,which promoted vegetation growth.One of the driving factors in the vegetation improvement area was moderate grazing by livestock.
基金This work is supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.under Grant No.J2020068.
文摘The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identification has been a research hotspot in the field of network management and security mon-itoring.However,as more network services use encryption technology,network traffic identification faces many challenges.Although classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and labor-intensive.Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples.However,in real scenarios,labeled samples are often difficult to obtain.This paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning.Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.
