大白菜雄性不育株与可育株花蕾生理生化特性比较分析

通过对大白菜雄性不育株与可育株花蕾中总糖、还原糖、蛋白质含量、游离氨基酸的种类、含量、呼吸强度的测定及过氧化物酶同工酶、酯酶同工酶、游离全蛋白质电泳分析，发现供试的大白菜６个雄性不育“两用系”，一个雄性不育系均表现为不育株较可育株的还原糖、游离氨基酸、蛋白质含量低，呼吸强度弱，过氧化物酶同工酶酶带多，雄性不育系酯酶同工酶谱带出现一条“杂种带”。还讨论了过氧化物酶同工酶、酯酶同工酶的表达与雄性不育性的关系。     

玉米穗,茎腐病禾谷镰孢菌和串珠镰孢菌同工酶分析

对沈阳、河北、吉林、北京等地区玉米穗、茎腐病禾谷镰孢菌和串珠镰孢菌进行了可溶性蛋白和同工酶聚丙烯酰胺凝胶电泳，以研究玉米穗、茎腐病同种镰孢菌是否存在寄生专化性或生理分化现象。结果表明：各地区的玉米穗、茎腐病串珠镰孢菌同源性高，亲缘关系近，相似性系数在0．86～0．98之间，菌株间差异仅表现为个体间多样性。而两种病害禾谷镰孢菌亲缘关系困地区不同而有差异，河北、吉林两地区穗、茎腐禾谷镰孢菌间均具有很近的亲缘关系，相似性系数在0．93以上；但沈阳地区禾谷镰孢菌亲缘关系相对较远，相似性系数仅为0．65～0．67。     

小麦花粉白化苗成因的研究——Ⅱ.小麦花粉愈组和花粉苗的同工酶分析

花粉白、绿苗同工酶的分析表明,花粉白苗的过氧化物酶的酶谱上,有一条相对迁移率为0.34～0.36的“特征带”。而花粉绿苗的酯酶的酶谱上,有一条与β—萘酯起作用的“特征带”。花粉愈伤组织过氧化物酶的同工酶分析表明,愈伤组织苗色的分化方向似乎在愈伤组织阶段或在此之前便决定了的,再分化的培养条件对苗色的分化不再起作用。花粉白苗叶绿素吸收光谱分析表明,小麦白苗仍会有少量叶绿素a和叶绿素b,这就排除了白苗的产生是由于愈伤组织细胞的旺盛分裂及生理上障碍致使细胞质体缺失所致。     

低锌和缺锌胁迫对不同基因型玉米的影响及机理

以两种不同基因型玉米(唐玉10号、博丰12号)为材料,用溶液培养的方法研究了低锌、缺锌和正常供锌对玉米生长发育的影响,并对两品种的SOD同工酶谱和蛋白质表达进行了分析。结果表明:一定浓度的低锌比缺锌对玉米危害更大,但不同基因型玉米对此敏感性不同,博丰12号表现出这种现象相对迟缓,而唐玉10号表现得迅速。与正常供锌相比,低锌处理时唐玉10号和博丰12号的一些SOD同工酶的活性降低,但博丰12号的一个SOD同工酶的活性明显提高,而缺锌对两品种的SOD同工酶谱影响不大。SDS-PAGE分析表明,低锌或缺锌处理时两种不同基因型玉米都有一些蛋白质组分增加或缺失。     

花生云纹斑病发生规律的初步研究

花生云纹斑病是我国花生生产上的一种新病害。经鉴定,其病原菌是一种真菌,属于半知菌亚门、球亮孢目、壳二孢属(Ascochyta sp.)。接种试验表明,该菌主要为害花生叶片,其次为害叶柄和托叶,而茎和荚果不发病;该菌不为害大豆、赤小豆、豌豆、蚕豆和菜豆。病菌以分生孢子器形态在病叶内越冬;翌年六月下旬～七月上旬从植株底部叶片开始发病,七月下旬～八月上旬形成子实体,借风雨传播进行重复侵染。病害流行与八月份降雨量有关。花生品种间存在着抗性差异。     

辽宁省农田作物根围的真菌（Ⅲ）

报道了辽宁省农田作物根围有丝分裂孢子真菌中15属的21种，其中拟小球孢壳属、分枝绿霉属、指轮枝孢属和缨霉属为中国新记录属，橄揽形拟小球孢壳、包围漆斑菌、苏拉耳分枝绿霉苏拉耳变种、安纳力结担菌、缢缩结担菌、棕色结担菌、二色指轮枝孢和帚状缨霉为中国新记录种。     

棕壤微生物和几种酶活性的磁致效应研究

试验通过室内模拟方法,研究了不同磁场强度处理棕壤后对土壤微生物数量和几种酶活性的影响。结果表明磁场对微生物数量有影响,但因磁场强度和微生物种类的不同而异。100、500mT场强对细菌表现为抑制作用,但对放线菌、真菌表现为激活作用;300mT场强对各类群土壤微生物均表现为正效应。经磁场处理后的棕壤,过氧化氢酶、过氧化物酶和三种磷酸酶活性均有不同程度的提高;随着培养时间的延长,磁致效应也发生动态变化。在磁场处理后的1～7d,其细菌、放线菌、真菌的磁致效应均显著,并出现最大值,而后随培养时间的延长逐渐变小,直至培养结束,磁场对其已无显著影响。而土壤中几种酶的磁致效应持续时间较长。     

一种酶标板培养孔颜色特征提取的图像检测方法

针对现阶段酶标(ELISA)板培养孔菌体生长情况的筛选判断检测方法存在效率低、自动化程度低等问题,提出了采用机器视觉算法对酶标板培养孔菌液颜色识别的菌体生长检测方法。对酶标板孔菌体在不同生长时期、菌液浑浊度不同产生的颜色差异进行调查研究,在OpenCV平台对摄像头采集的酶标板图片信息进行预处理和边缘检测,寻找酶标板的最小包围矩形确定目标区域,选取培养孔孔位区域进行颜色计算获取判断依据。考虑到摄像头在垂直方向拍摄时酶标板四周的孔位区域会发生变形,提出了在不同孔位区域选取合适区域计算颜色特征的试验方案,可有效减小培养孔颜色特征提取的误差,为实现在微生物培养中完成菌体的自动化筛选奠定了基础。     

农用抗生素TS99产生菌YH9407的初步研究

从辽宁省丹东地区的土壤样品中分离到一株新的杀真菌链霉菌YH9407,其产生的次级代谢产物对多种植物病原真菌和细菌有较强的抑制作用。该菌株在各种合成培养基上生长良好 ,气生菌丝和孢子堆为灰白色 ,基内菌丝一般为浅黄色 ,有的可产生黄色可溶性色素 ;孢子丝柔曲或直立 ,直径为0.17～0.22μm,孢子呈球形或卵圆形 ,直径为(0.6～0.8)μm×(0.8～1.2)μm ;通过与杀真菌链霉菌已知种在形态特征、培养特征和生理生化特性方面的比较 ,YH9407为杀真菌链霉菌的一个新菌株 ,暂命名为StreptomycesfungicidicusNo.9407     

低温对番茄叶片POD活性及其同工酶的影响

在(13±1)℃/(7土1)℃的低温条件下研究了不同处理时间对番茄苗期和坐果期POD活性及其同工酶的影响。结果表明随处理时间延长,番茄叶片中的POD活性增高,苗期在处理6d后增加幅度较大,坐果期POD活性低于苗期。低温处理苗期耐低温品系6d时出现一条Rf=a625的新酶带,而不耐低温品系处理10d后才出现;低温处理坐果期番茄叶片无新酶带的出现,只表现为活性的增强。苗期Rr=0．625,0.6375;坐果期Rr=0.6,0．625的同工酶谱带与番茄的耐低沮性有关。番茄苗期与坐果期对低温的敏感程度近似相同,但坐果期比苗期耐低温能力弱。     

Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery

Hyperspectral remote sensing provides great potential to monitor and study biodiversity of tropical forests through species identification and mapping. In this study, five species were selected to examine crown-level spectral variation within and between species using HYperspectral Digital Collection Experiment (HYDICE) data collected over La Selva, Costa Rica. Spectral angle was used to evaluate the spectral variation in reflectance, first derivative and wavelet-transformed spectral domains. Results indicated that intra-crown spectral variation does not always follow a normal distribution and can vary from crown to crown, therefore presenting challenges to statistically define the spectral variation within species using conventional classification approaches that assume normal distributions. Although derivative analysis has been used extensively in hyperspectral remote sensing of vegetation, our results suggest that it might not be optimal for species identification in tropical forestry using airborne hyperspectral data. The wavelet-transformed spectra, however, were useful for the identification of tree species. The wavelet coefficients at coarse spectral scales and the wavelet energy feature are more capable of reducing variation within crowns/species and capturing spectral differences between species. The implications of this examination of intra- and inter-specific variability at crown-level were: (1) the wavelet transform is a robust tool for the identification of tree species using hyperspectral data because it can provide a systematic view of the spectra at multiple scales; and (2) it may be impractical to identify every species using only hyperspectral data, given that spectral similarity may exist between species and that within-crown/species variability may be influenced by many factors.     

Spectroscopic classification of tropical forest species using radiative transfer modeling

Leaf spectroscopy may be useful for tropical species discrimination, but few studies have provided an understanding of the spectral separability of species or how leaf spectroscopy scales to the canopy level relevant to mapping. Here we report on a study to classify humid tropical forest canopy species using field-measured leaf optical properties with leaf and canopy radiative transfer models. The experimental dataset included 188 canopy species collected in humid tropical forests of Hawaii. The leaf optical model PROSPECT-5 was used to simulate the leaf spectra of each species, which was used to train a classifier based on Linear Discriminant Analysis, and a canopy radiative transfer model 4SAIL2 to scale leaf measurements to the canopy level. The relationship linking classification accuracy at the leaf level to biodiversity showed an asymptotic trend reaching a maximum error of 47% when applied to the entire 188 species experimental dataset, and 56% when a simulated dataset showing amplified within-species spectral variability was used, suggesting uniqueness of the spectral signature for a significant proportion of species under study. The maximum error in canopy-level species classification was higher than leaf-level classification: 55% when canopy structure was held constant, and 64% with varying and unknown canopy structure. However, when classifying fewer species at a time, errors dropped considerably; for example, 20 species can be classified to 82-88% accuracy. These results highlight the potential of imaging spectroscopy to provide species discrimination in high-diversity, humid tropical forests.     

Using remote sensing to assess biodiversity

This review paper evaluates the potential of remote sensing for assessing species diversity, an increasingly urgent task. Existing studies of species distribution patterns using remote sensing can be essentially categorized into three types. The first involves direct mapping of individual plants or associations of single species in relatively large, spatially contiguous units. The second technique involves habitat mapping using remotely sensed data, and predictions of species distribution based on habitat requirements. Finally, establishment of direct relationships between spectral radiance values recorded from remote sensors and species distribution patterns recorded from field observations may assist in assessing species diversity. Direct mapping is applicable over smaller extents, for detailed information on the distribution of certain canopy tree species or associations. Estimations of relationships between spectral values and species distributions may be useful for the limited purpose of indicating areas with higher levels of species diversity, and can be applied over spatial extents of hundreds of square kilometres. Habitat maps appear most capable of providing information on the distributions of large numbers of species in a wider variety of habitat types. This is strongly limited by variation in species composition, and best applied over limited spatial extents of tens of square kilometres.     

用于函数优化的自适应类种子保留遗传算法

类种子保留遗传算法可以较好地处理维持进化种群多样性和保留重要个体的矛盾,但尚无有效方法确定其类控制参数.本文提出一种类控制参数随进化进程自适应变化的策略,其思想是:在进化前期类控制参数较大,将进化种群分成数目较少的粗类;随着进化的进行类控制参数自适应减小,将进化种群分成数目较多的细类.另外,个体的自适应变异充分利用了个体当前状态、本类种子和种群最优种子的信息.将该算法应用于5个基准数值函数优化问题,计算结果验证了本文算法在找到多个极值点的前提下有效地减少了计算量、提高了进化效率.     

Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data

Tree species identification is important for a variety of natural resource management and monitoring activities including riparian buffer characterization, wildfire risk assessment, biodiversity monitoring, and wildlife habitat assessment. Intensity data recorded for each laser point in a LIDAR system is related to the spectral reflectance of the target material and thus may be useful for differentiating materials and ultimately tree species. The aim of this study is to test if LIDAR intensity data can be used to differentiate tree species. Leaf-off and leaf-on LIDAR data were obtained in the Washington Park Arboretum, Seattle, Washington, USA. Field work was conducted to measure tree locations, tree species and heights, crown base heights, and crown diameters of individual trees for eight broadleaved species and seven coniferous species. LIDAR points from individual trees were identified using the field-measured tree location. Points from adjacent trees within a crown were excluded using a procedure to separate crown overlap. Mean intensity values of laser returns within individual tree crowns were compared between species. We found that the intensity values for different species were related not only to reflective properties of the vegetation, but also to a presence or absence of foliage and the arrangement of foliage and branches within individual tree crowns. The classification results for broadleaved and coniferous species using linear discriminant function with a cross validation suggests that the classification rate was higher using leaf-off data (83.4%) than using leaf-on data (73.1%), with highest (90.6%) when combining these two LIDAR data sets. The result also indicates that different ranges of intensity values between two LIDAR datasets didn't affect the result of discriminant functions. Overall results indicate that some species and species groups can be differentiated using LIDAR intensity data and implies the potential of combining two LIDAR datasets for one study.     

基于DenseNet的无人机光学图像树种分类研究

利用无人机航拍获得光学影像数据,结合深度学习理论,建立树种识别模型,以期为大规模树种识别提供一种新的方式。首先以福建安溪县为例,采用无人机获取20 m及40 m高度的航拍影像。其次,以树种为对象,对航拍影像进行分割,获得12种树种影像。最后,结合深度学习理论,采用DenseNet卷积神经网络建立树种识别模型,探讨不同航拍高度以及不同网络深度对树种识别的影响。结果表明:不同航拍高度的树种识别模型,其分类精度均达80%以上,最高精度为87.54%。从航拍影像解析度分析,随着航拍影像解析度的下降,模型识别精度呈现下降趋势,以20 m航拍影像数据建构的树种识别模型,其分类精度高于40 m模型;从模型网络深度分析,随着模型网络层数的增加,模型分类精度出现下降现象,DenseNet121模型分类精度高于DenseNet169模型分类精度。综上所述,基于无人机航拍影像,结合深度卷积神经网络,提出了新的树种识别方式,并以安溪县森林树种识别为例证明了该分类框架的有效性。     

基于DenseNet的无人机光学图像树种分类研究

利用无人机航拍获得光学影像数据，结合深度学习理论，建立树种识别模型，以期为大规模树种识别提供一种新的方式。首先以福建安溪县为例，采用无人机获取20 m及40 m高度的航拍影像。其次，以树种为对象，对航拍影像进行分割，获得12种树种影像。最后，结合深度学习理论，采用DenseNet卷积神经网络建立树种识别模型，探讨不同航拍高度以及不同网络深度对树种识别的影响。结果表明:不同航拍高度的树种识别模型，其分类精度均达80%以上，最高精度为87.54%。从航拍影像解析度分析，随着航拍影像解析度的下降，模型识别精度呈现下降趋势，以20 m航拍影像数据建构的树种识别模型，其分类精度高于40 m模型；从模型网络深度分析，随着模型网络层数的增加，模型分类精度出现下降现象，DenseNet121模型分类精度高于DenseNet169模型分类精度。综上所述，基于无人机航拍影像，结合深度卷积神经网络，提出了新的树种识别方式，并以安溪县森林树种识别为例证明了该分类框架的有效性。     

Persistence and permanence of two linked donor-recipient systems

The question of the detritivore mediated co-existence of two competing donor species is treated. It is shown that two donor-recipient systems linked by interspecific competition become permanent if and only if the competing donor species are in coexistence. The introduction of two detritivores cannot stabilize the system in the case of bistability or dominance of competing donor species and in this case one of the donor species becomes extinct.     

复杂模糊分类系统的协同进化设计方法

提出一种基于协同进化算法的复杂模糊分类系统的设计方法．该方法由以下3步组成:1)利用Simba算法进行特征变量选择;2)采用模糊聚类算法辨识初始的模糊模型;3)利用协同进化算法对所获得的初始模糊模型进行结构和参数的优化．协同进化算法由三类种群组成;规则数种群,规则前件种群和隶属函数种群;其适应度函数同时考虑模型的精确性和解释性,采用三类种群合作计算的策略．利用该方法对多个典型问题进行分类,仿真结果验证了方法的有效性．     

Modeling temperature and species fluctuations in turbulent,reacting flow

Assumed Gaussian and β probability density functions (PDFs) for temperature are used with series expansions of the reaction-rate coefficients to compute the mean reaction-rate coefficients in a turbulent, reacting flow. The series-expansion/assumed PDF approach does not require any numerical integration, which substantially reduces computational cost with little loss of accuracy. An assumed multivariate β PDF for species is investigated for use in modeling the interaction of species fluctuations and chemical combustion. The multivariate β PDF is initially evaluated through a parametric study. Results of the parametric study indicate that species fluctuations can increase or decrease the magnitude of the species production term, depending on the type of reaction. The models are then tested on a two-dimensional high-speed turbulent reacting hydrogen-air mixing layer. For the conditions tested the numerical simulations indicate that the net effect of species fluctuations is to reduce the mean species production rate.