青少年发病的成人型糖尿病(MODY)是一组青年起病的常染色体显性遗传病.目前,MODY按致病基因至少可分为13型:MODY1(肝细胞核因子4α,HNF4α),MODY2(葡萄糖激酶,GCK),MODY3(肝细胞核因子1α,HNF1α),MODY4(胰岛素启动因子1,IPF1),MODY5(肝细胞核因子1β,HNF1β),MODY6(神经元分化因子1,NEUROD1),MODY7(kruppel样因子11,KLF11),MODY8 (羧基酯脂肪酶,CEL),MODY9(成对盒基因4,PAX4),MODY10(胰岛素基因),MODY11(B淋巴细胞激酶,BLK),MODY12(ATP结合C家族8因子,ABCC8)和MODY13(内向整流性钾离子通道J家族11因子,KCNJ11).其发病机制、临床表现和治疗方法各有不同.

Maturity-onset diabetes of the young (MODY) is a young-onset diabetes mellitus with a autosomal-dominant mode of transmission.At least thirteen subtypes with distinct genetic aetiologies have been reported:MODY1 (hepatocyte nuclear factor 4α,HNF4α),MODY2 (glucokinase,GCK),MODY3 (hepatocyte nuclear factor 1α,HNF1α),MODY4 (insulin promoter factor 1,IPF1),MODY5 (hepatocyte nuclear factor 1β,HNF1β),MODY6 (neurogenic differentiation factor 1,NEUROD 1),MODY7 (kruppel-like factor 11,KLF11),MODY8 (carboxyl-ester lipase,CEL),MODY9 (paired-homeodomain transcription factor,PAX4),MODY10 (insulin gene),MODY1 1 (B-lymphocyte kinase,BLK),MODY1 2 (ATP-binding cassette,sub-family C member 8,ABCC8,and MODY13 (potassium inwardly-rectifying channel,subfamily J,member 11,KCNJ11).The etiopathogenisis,clinic phenotype and treatment of them are different.

目的寻找最能真实反映南方地区植被变化状况的遥感数据，并将其用于评价土地覆被变化及其带来的影响．方法通过分析韩江流域2001—2006年间AVHRR、SPOT-VGT和MODIS 3种归一化植被指数（ NDVI）遥感数据，比较它们对不同植被响应特征的异同，并采用线性回归方法分析它们的相关关系．结果和结论3种NDVI遥感空间分布总体格局大体一致，且MODIS和SPOT-VGT的NDVI空间分布吻合良好．MODIS传感器波段宽度窄、空间分辨率高，对地物分辨能力高，NDVI在流域内的变化范围大，数值分布分散．3种NDVI的季节变化步调基本一致，幅度相当．MODIS NDVI最能精确反映地面植被覆盖的变化，而AVHRR NDVI反映的流域地面植被变化情况与实际不相符．3种NDVI反映的各种植被季节变化情况基本一致，MODIS NDVI与SPOT-VGT NDVI的相似度更高． MODIS NDVI能够明显区分农作物、郁闭灌木林和草地的季节变化与其他植被的不同，比SPOT-VGT NDVI 和AVHRR NDVI能更好地反映地面植被的多样性和植被覆盖的变化．在全流域和各种植被类型上，3种NDVI两两间都表现出一定的线性关系，其中MODIS NDVI与SPOT-VGT NDVI间的线性关系最强．由MODIS NDVI与AVHRR NDVI的线性回归关系预测的韩江流域2000年的NDVI与实测值吻合良好，为MODIS NDVI时间序列向历史年份拓展提供了思路．

[Objective]To select an optimum time series in remote sensing data for evaluating land cover changes and their impacts on southern China .[Method] Three types of remote sensing NDVI data from AVHRR, SPOT-VGT and MODIS were analysed to compare their similarities and differences in different vegetation types over Hanjiang River basin from 2001 to 2006, and their correlations were analysed using linear regression method .[Result and conclusion] These data had generally similar distribution patterns in space .MODIS and SPOT-VGT NDVI matched well .MODIS could recognize objects more clearly on the earth surface due to its spectrally narrow sensors with high spatial resolution .In MODIS NDVI , val-ues varied within a wide range , therefore more vegetation types could be detected .Three types of NDVI changed seasonally similar to the same amplitude .MODIS NDVI reflected vegetations seasonally change more accurately.In AVHRR NDVI, some vegetation types change did not match with the ac

为了分析卫星MODIS影像数据计算水体浊度的精度,探索利用卫星遥感数据监测水体水质的方法,以武汉东湖为研究区域,布置了水体浊度监测点。利用光谱分析法,将MODIS影像的光谱数据转化为浊度数据,并与地面准同步悬浮物浓度实测数据进行比较分析,建立了适合东湖水体的悬浮物浓度遥感定量估算模型。模型估算结果表明,根据MODIS影像数据所得的水体浊度数据精度满足相关要求,MODIS数据应用于湖泊水体悬浮物浓度监测具有可行性。

In order to analyze the accuracy of water turbidity calculated by satellite MODIS data and explore the water quality monitoring method by using satellite remote data, water turbidity monitoring points were arranged in the research area of Donghu Lake. By spectrum analysis, the spectrum data of MODIS images are converted to turbidity data and compared with the quasi-synchronously measured suspended matter concentration in Donghu Lake, and the quantitative estimation model of suspended matter concentration appropriate to Donghu Lake is established. The model estimation results show that the water turbidity accura-cy obtained by MODIS image data can meet the correlative requirements, and it is feasible for the application of MODIS data in monitoring the suspended matter concentration of Donghu Lake.

以印度洋北部海域为研究区域，分别从地理位置、温度曲线和反演精度3个方面对AQUA卫星上的MODIS和AMSR-E反演的海表温度特征进行了对比分析。其主要特征表现为MODIS SST与AMSR-E SST之间的差异随纬度变化较为明显；在近岸区域，AMSR-E SST无法获得准确的海表面温度；MODIS SST与AMSR-E SST之间的差异随温度而不同；在本次研究中，AMSR-E SST反演精度总体优于MODIS SST。本次研究结果对利用热红外遥感和被动微波遥感进行海洋表面温度的定量反演具有重要的参考价值。

In this paper, the northern Indian Ocean served as the study area. Characteristics of sea surface temperature retrieved from MODIS and AMSR-E sensor aboard AQUA satellite were analyzed from three aspects, namely, the location,the temperature curve and the inversion accuracy. The main characteristics showed that the difference of MODIS SST and the AMSR-E SST changed with the latitude obviously; AMSR-E SST failed to offer accurate sea surface temperature in the coastal region; the diversity of MODIS SST and AMSR-E SST changed with the temperature differences. In this paper, the retrieval accuracy of AMSR-E SST was often better than that of MODIS SST. The result of this study had important reference to the sea surface temperature quantitative inversion using the thermal infrared and passive microwave remote sensing technology.

采用最大似然法、马氏距离法、光谱角填图法、支持向量机法、神经网络法和最小距离法6种分类方法，对辽宁省2010年3-12月MODIS NDVI数据，用该数据做主成分分析的前3个主成分数据、前5个主成分数据和2010年6-10月MODIS NDVI 数据等4类数据进行土地利用分类研究。结果表明：6种分类方法中最大似然法、马氏距离法和最小距离法3种方法较适合对MODIS NDVI数据进行信息提取，其总体分类精度分别达82.63％、80.29％、79.17％，乔木林类型信息提取精度分别达81.91％、78.54％、80.02％；3种对原始数据进行变换的方法中6-10月数据效果较好，其总体分类精度最高达82.63％，乔木林信息提取的最高精度达78.54％。

The land-use classification in Liaoning Province was conducted with MODIS NDVI data between March and December in 2010 (by taking the top 3 principal components of MODIS NDVI data,and the top 5 prin-cipal components of MODIS NDVI data of this time duration individually),and with MODIS NDVI data from June to October in 2010 by means of six kinds of classification methods,i.e.,the Maximum Likelihood Method,the Mahalanobis Distance Method,the Spectral Angle Mapping Method,the Support Vector Machines Method,the Neural Network Method and the Minimum Distance Method.The classification results showed that the Maximum Likelihood Method,the Mahalanobis Distance Method and the Minimum Distance Method were comparatively more suitable for MODIS NDVI data information extraction,whose overall classification accuracy was 82.63%,80.29%and 79.17% individually,and whose information extraction precision of arbor forests reached 81.91%,78.54%and 80.02%,respectively.In terms of growing phases,the best resu