Quantitative FRET measurement based on spectral unmixing of donor,acceptor and spontaneous excitation‐emission spectra

The spontaneous excitation‐emission (ExEm) spectrum is introduced to the quantitative mExEm‐spFRET methodology we recently developed as a spectral unmixing component for quantitative fluorescence resonance energy transfer measurement, named as SPEES‐FRET method. The spectral fingerprints of both donor and acceptor were measured in HepG2 cells with low autofluorescence separately expressing donor and acceptor, and the spontaneous spectral fingerprint of HEK293 cells with strong autofluoresence was measured from blank cells. SPEES‐FRET was performed on improved spectrometer‐microscope system to measure the FRET efficiency (E) and concentration ratio (R _C) of acceptor to donor vales of FRET tandem plasmids in HEK293 cells, and obtained stable and consistent results with the expected values. Moreover, SPEES‐FRET always obtained stable results for the bright and dim cells coexpressing Cerulean and Venus or Cyan Fluorescent Protein (CFP)‐Bax and Yellow fluorescent protein (YFP)‐Bax, and the E values between CFP‐Bax and YFP‐Bax were 0.02 for healthy cells and 0.14 for the staurosporine (STS)‐treated apoptotic cells. Collectively, SPEES‐FRET has very strong robustness against cellular autofluorescence, and thus is applicable to quantitative evaluation on the protein‐protein interaction in living cells with strong autofluoresence.      

基于中高分辨率遥感的植被覆盖度时相变换方法

植被覆盖度是衡量地表植被状况、指示生态环境变化的一个重要指标,也是许多学科的重要参数。传统的测量方法难以获取时间连续的面状数据,且耗时、耗力,很难大范围推广。遥感估算方法虽然可以弥补传统方法的不足,但由于云覆盖等天气条件的影响,获得同一时相覆盖整个研究区的遥感影像非常困难,时相的差异必然导致研究结果产生误差。针对植被覆盖度这一重要生态参数,结合低分辨率遥感数据的时间优势和中高分辨率遥感数据的空间优势,提出一种时相变换方法,将源于中高分辨率影像的植被覆盖度变换到研究需要的时相上。首先,利用像元二分模型计算MODIS尺度的时间序列植被覆盖度,并利用已经获得的SPOT影像计算其获取时相上的植被覆盖度;其次,利用土地利用图划分植被覆盖类型,并利用MODIS数据和土地利用数据之间的空间对应关系制作MODIS像元内各类植被覆盖的面积百分比数据;再次,利用面积百分比数据提取各类植被覆盖的纯像元,结合MODIS植被覆盖度时间序列,从而提取各类植被覆盖纯像元的植被覆盖度时间序列曲线;最后利用像元分解的方法提取MODIS像元内各类植被覆盖组分的植被覆盖度的变化规律,将其应用到该组分对应位置上SPOT像元的植被覆盖度上,从而将其变换到所需要的时相上。在密云水库上游进行试验,将覆盖研究区的10景SPOT5多光谱影像计算的植被覆盖度统一变换到7月上旬,结果显示:视觉效果上明显好转,且空间上连续一致;变换前后植被覆盖度的统计量对比结果也符合植被生长规律;利用外业样点数据与对应位置的植被覆盖度变换结果进行回归分析,结果发现各植被覆盖类型的R2均在0.8左右,表明变换结果与实测值非常接近,时相变换的效果较好,从而可以很好地促进相关研究精度的提高。     

On dose cube pixel spacing pre-processing for features extraction stability in dosiomic studies

PurposeDosiomics allows to parameterize regions of interest (ROIs) and to produce quantitative dose features encoding the spatial and statistical distribution of radiotherapy dose. The stability of dosiomics features extraction on dose cube pixel spacing variation has been investigated in this study.Material and MethodsBased on 17 clinical delivered dose distributions (P_n), dataset has been generated considering all the possible combinations of four dose grid resolutions and two calculation algorithms. Each dose voxel cube has been post-processed considering 4 different dose cube pixel spacing values: 1x1x1, 2x2x2, 3x3x3 mm³ and the one equal to the planning CT. Dosiomics features extraction has been performed from four different ROIs. The stability of each extracted dosiomic feature has been analyzed in terms of coefficient of variation (CV) intraclass correlation coefficient (ICC).ResultsThe highest CV mean values were observed for PTV ROI and for the grey level size zone matrix features family. On the other hand, the lowest CV mean values have been found for RING ROI for the grey level co-occurrence matrix features family. P₃ showed the highest percentage of CV >1 (1.14%) followed by P₁₅ (0.41%), P₁ (0.29%) and P13 (0.19%). ICC analysis leads to identify features with an ICC >0.95 that could be considered stable to use in dosiomic studies when different dose cube pixel spacing are considered, especially the features in common among the seventeen plans.ConclusionConsidering the observed variability, dosiomic studies should always provide a report not only on grid resolution and algorithm dose calculation, but also on dose cube pixel spacing.     

基于TM NDVI的武功山山地草甸植被覆盖度时空变化研究

以江西省武功山山地草甸为研究区,基于4期TM(Thematic Mapper,专题测图仪)卫星遥感影像,提取NDVI(Normalized Difference Vegetation Index,归一化植被指数),采用像元二分模型,运用ENVI 5.1和Arc GIS 10.0软件计算得到武功山山地草甸的植被覆盖度分布格局及动态变化。研究结果表明:(1)研究期间山地草甸面积减少了9.72%,呈递减趋势。20年来随着武功山风景区成立—旅游业发展—山地草甸生态修复,山地草甸植被覆盖度增加和减少交替,总体呈上升趋势;(2)山地草甸植被覆盖度呈现东南高西北低的空间分布特征。低覆盖度草甸区集中在武功山山脉的西北侧坡面的崖壁和部分山脊线上,而高覆盖度草甸区多分布在武功山山脉的东南坡面;(3)研究区山地草甸退化与改善并存,山地草甸最北端和白鹤峰-九龙山区域的东南坡、南坡低海拔处植被总体呈退化特征;发云界南部的东坡植被总体呈现改善特征。研究期间山地草甸退化面积比改善面积多出1.78%。(4)山地草甸植被覆盖度的分布格局和地形因子存在较高的相关性(P0.05):植被覆盖度随着坡向的变化而呈规律性的变化,总体上山地草甸植被覆盖度的分布为阳坡平坡阴坡;植被覆盖度先是随着坡度的上升而升高,在坡度15°—25°时达到峰值,然后随坡度的上升而下降,在45°—90°最低;植被覆盖度随海拔升高呈波浪式下降,1000—1200m最高,在主峰山顶海拔1800—1918.3m最低。遥感解译检验结果证明采用此方法对大面积山地草甸覆盖度分布及变化进行反演可行而准确;在后续研究中将采用不同季相的多期影像数据提取NDVI对研究区植被覆盖度进行长期监测,以便更准确可靠地分析山地草甸演化过程和趋势。     

基于多时相Landsat5/8影像的岷江汶川-都江堰段植被覆盖动态监测

植被覆盖度是衡量地表植被状况和指示生态环境变化的一个重要指标。基于像元二分模型,利用Landsat5/8遥感影像和DEM数据,对岷江汶川-都江堰段植被覆盖动态变化进行了监测,并结合高程、坡度和坡向数据,分析了汶川地震前后植被受损与恢复的空间动态格局变化。研究表明:植被覆盖总体良好,大部分区域的植被覆盖度均在中、高度以上,空间格局上呈现由汶川县东部、都江堰市西北部的龙门山区向两侧减少的总体趋势;地震造成植被受损面积达63808.7 hm~2,且集中分布于海拔567—4331 m、坡度26—51°的范围以及东坡、北坡、南坡和西坡;震后5a,植被恢复面积17786.47 hm~2,主要分布在海拔576—2180 m与3256—3793 m、坡度小于9°和26—51°以及东坡、东南坡和和南坡;高程和坡度对植被损毁与恢复的影响明显高于坡向。     

Fractal genome sequences

The existence of fractal sets of DNA sequences have long been suspected on the basis of statistical analyses of genome data. In this article we identify for the first time explicitly the GA-sequences as a class of fractal genomic sequences that are easy to recognize and to extract, and are scattered densely throughout the chromosomes of a large number of genomes from different species and kingdoms including the human genome. Their existence and their fractality may have significant consequences for our understanding of the origin and evolution of genomes. Furthermore, as universal and natural markers they may be used to chart and explore the non-coding regions.     

基于稀疏编码和SCG BPNN的鳞翅目昆虫图像识别

【目的】为了给林业、 农业或植物检疫等行业人员提供一种方便快捷的昆虫种类识别方法, 本文提出了一种新颖的鳞翅目昆虫图像自动识别方法。【方法】首先通过预处理对采集的昆虫标本图像去除背景, 分割出双翅, 并对翅图像的位置进行校正。然后把校正后的翅面分割成多个超像素, 用每个超像素的l, a, b颜色及x, y坐标平均值作为其特征数据。接下来用稀疏编码(SC)算法训练码本、 生成编码并汇集成特征向量训练量化共轭梯度反向传播神经网络（SCG BPNN）, 并用得到的BPNN进行分类识别。【结果】该方法对包含576个样本的昆虫图像的数据库进行了测试, 取得了高于99%的识别正确率, 并有理想的时间性能、 鲁棒性及稳定性。【结论】实验结果证明了本文方法在识别鳞翅目昆虫图像上的有效性。     

基于地-空遥感耦合的冬小麦叶片氮积累量估算

利用不同冬小麦生态区同步的SPOT-5多光谱遥感影像、地面光谱数据和植株取样数据,提出一种基于波谱响应函数拟合和混合像元分解的纯净像元光谱提取方法,并对比分析了纯净像元光谱、模拟像元光谱和实测像元光谱与冬小麦叶片氮积累量（LNA）的定量关系.结果表明： 模拟像元光谱对叶片氮积累量的反演效果较好,纯净像元光谱反演效果次之,实测像元光谱最差;但基于模拟像元光谱的LNA监测模型不能直接外推至空间尺度.模型检验结果表明,基于纯净像元光谱的LNA监测模型在2个小麦生态区均具有较好的精度和稳定性,该方法综合利用了地-空遥感的优点,可以推广应用到其他不同空间分辨率和光谱分辨率的遥感数据,从而为区域性冬小麦氮素营养状况的遥感监测提供技术依据.     

稻-鸭农作系统对稻田生物种群的影响

通过田间试验,研究了稻-鸭农作系统对稻田有关生物种群的影响.结果表明：稻鸭共育对稻田相关昆虫、杂草、病原菌等有害生物的发生和危害及天敌数量具有较大影响.与不养鸭相比,稻鸭共育后12 d和42 d, 水稻基部害虫飞虱及叶蝉的数量平均减少64.8%和78.5%;稻鸭共育后15 d和45 d,稻田杂草平均减少67.7%和98.1%;水稻分蘖高峰期和齐穗期的纹枯病病情指数分别降低了40.4%和62.0%.稻鸭共育还增加了稻田害虫天敌蜘蛛的数量,抑制了水稻害虫的危害.