玉米灰斑病菌致病性分化的寄主鉴定方法

玉米灰斑病现已严重威胁中国北方玉米生产的可持续发展。本研究采用苗期鉴定、成株期鉴定、温室鉴定和田间鉴定等方法，利用玉米自交系和品种对玉米灰斑病菌致病性分化鉴定方法进行了研究。结果表明：玉米苗期和成株期均可用于玉米灰斑病的抗性鉴定，两者鉴定结果有一定的相似性，但利用成株期进行鉴定比较好，因为接种比较容易；利用自交系或品种均可进行抗性鉴定，从效果上看，利用自交系较好，因为利用成株期自交系鉴定法，不同自交系一菌株组合间病级差异较明显，变异系数为47．32％，能有效地区分不同菌株间致病性分化的程度，但是由于成株期品种鉴定法与成株期白交系鉴定法比较。变异系数差异很小，所以成株期品种鉴定法可以作为抗性鉴定的辅助方法。     

玉米大斑病菌毒素的钝化反应及其机理研究

用１８种化合物对ＨＴ－毒素进行钝化反应试验，结果表明：代森锰锌、Ｎａ２ＭｏＯ４，ＮａＮＯ，ＺｎＳＯ４，ＫＭｎＯ４，ＫＩ及４种糖可钝化毒素，使其对玉米的毒性降低；而ＣｕＳＯ４，ＮａＣｌ，ＫＣＩ２Ｏ５，Ｈ２Ｏ２可使毒素毒性增强。毒素处理玉米叶片后细胞膜透性、细胞的Ｖｃ氧化酶，ＣＡＴ，ＰＡＬ，ＰＰＯ活性都发生了一定的规律性变化，而毒素中加入Ｈ２Ｏ２和代森锰锌可抑制或加强这种变化。     

玉米弯孢菌叶斑病菌生理人化鉴定技术的研究

通过国内玉米骨干自交系和广泛种植的玉米品种的筛选,确认用自交系京１３５,７８５９９－１,Ｍｏ１７,吉４７７,Ｃ８６０５,Ｅ２８,４７８和黄早４作为玉米弯孢菌叶斑病菌生理分化的鉴别寄主,将采自中国主要玉米产区的玉米弯孢菌叶斑病菌划分成不同的若干生理分化类型。玉米弯孢菌叶斑病菌生理分化苗期温室和大棚鉴定与成株期鉴定结果一致,是该病菌生理分化适宜的鉴定方法,鉴别寄主生长条件如温、湿度和氮、磷营养也会对病菌生理分化鉴定产生影响。     

华北地区玉米小斑病菌生理小种监测

玉米小斑病是华北夏玉米区主要病害之一。虽然通过选育和推广种植优良抗病品种有效地控制了此病的危害，但玉米是异花受粉作物，容易出现混杂退化，降低抗病性，同时玉米小斑病菌又是一个复杂的群体，所以有必要进行玉米小斑病菌生理小种的监测，及时掌握其变异动态与分布，为选育和种植抗病品种及合理布局提供科学依据。为此，1991～1995年我们对华北地区玉米小斑病菌生理小种变异动态进行了监测工作。根据国内外玉米小斑病菌生理小种鉴定方法与划分标准，5年来，从保定、承德、唐山、沧州、邢台、济南等病圃及生产中主要推广的玉米品种上…     

玉米弯孢菌叶斑病研究进展

玉米弯孢菌叶斑病[Curvularialunata(Wakker)Boed．]是我国新近几年玉米上发生的重要病害。其危害性在一些地区已经超过大小斑病，成为主要玉米病害．国内外关于该病害的研究，在寄主方面主要侧重在抗病性、抗病性遗传及品种抗病性鉴定上。在我国，普遍缺少高度抗病的品种和自交系，抗病的品种和自交系的遗传背景和抗病基因也不清楚，目前主推的品种遗传基础过窄，是寻找多抗品种的主要障碍．在病原菌方面，我国以新月弯孢菌气生变种（Curvularialunata）危害玉米为主，该病菌致病性和产毒能力都很强，不同地区的病菌菌株存在致病力的差异，但其遗传背景和相互关系尚需深入研究。该病害蔓延快，危害重,应引起广泛重视．     

玉米弯孢菌叶斑病菌毒素的生物活性测定及对玉米叶片超微 …

玉米弯孢菌叶斑病（Ｃｕｒｖｕｌａｒｉａｌｕｎａｔａ）是目前玉米生产的一个主要病害,该病菌可以产生毒素,本文对玉米弯孢菌叶斑病菌产生的毒素进行了生物学测定,并观察了毒素对寄主叶片超微结构的破坏作用,证明玉米弯孢菌叶斑病菌产生的毒素是其致病因子之一,研究发现该毒素不是蛋白质。     

玉米弯孢菌叶斑病菌毒素的生物活性测定及对玉米叶片超微结构的影响

玉米弯孢菌叶斑病（Curvularialunata）是目前玉米生产上的一个主要病害,该病菌可以产生毒素。本文对玉米弯孢菌叶斑病菌产生的毒素进行了生物学测定,并观察了毒素对寄主叶片超微结构的破坏作用。证明玉米弯孢菌叶斑病菌产生的毒素是其致病因子之一,研究发现该毒素不是蛋白质。     

玉米弯孢菌叶斑病抗性机制的初步研究

玉米弯孢菌叶斑病菌［Curvularialunata（Wakker）Boed．］侵染不同抗性品种后,寄主叶片防御酶系和PR蛋白活性的变化及叶片汁液对病菌孢子萌发有影响,研究发现：PAL,PO,SOD和β-1,3-葡聚精酶在对弯孢菌叶斑病的抗性中起到一定的作用,尤其是PR蛋白β-1,3-葡聚糖酶活性在抗性机制中发挥更为重要的作用。木质素可能是在病菌侵染初期发挥抗侵入的作用。抗病品种叶片汁液可能含有抑菌物质,病菌侵染有利于刺激这种物质抑菌作用的提高,在抗病品种中更为明显。     

玉米灰斑病菌培养性状观察

研究表明，玉米灰斑病菌的不同菌株在玉米叶煎汁固体和液体培养基上菌落形态不同，但多数菌株菌落为黑色、有白色絮状菌丝、液体培养的孢子球为黑色具绒毛。不同菌株在同一培养基上或同一菌株在两种不同培养基上的生长速度均有显著的差异，说明灰斑病菌的分化或多态性在菌丝生长速率方面有明显的反映。首次在自然条件下分离到灰斑病菌的白化菌株（20-46）。白化菌株在菌落性状和生长速率上均与其他菌株差异显著。白化菌株的发现证明玉米灰斑病菌存在遗传变异的潜能。     

不同诱抗剂诱导玉米对灰斑病的抗性及其与PAL的关系

用水杨酸、磷酸氢二钾、灰斑病菌波液和草酸作诱抗剂，研究了玉米对灰斑病的诱导抗病作用，同时测定了挑战接种前后的ＰＡＬ酶活性的变化。结果表明：上述４种诱抗剂均有不同程度的诱导抗病性，其中水杨酸诱抗效果最好，病菌增减滤液和磷酸氢二钾次之，草酸诱抗效果最差。诱导后挑战接种，ＰＡＬ酶活性变化都有增加，且酶活性曲线波形变化较大。     

Estimation and comparison of acid detergent lignin and acetyl bromide lignin in fallen leaves using near-infrared spectroscopy

Several analytical methods have been developed to measure the lignin content corresponding to different plant species and different regions. The sulphuric acid method is commonly used for objective determinations of lignin content using near-infrared spectroscopy. Lignin is a complex polymer of lignin units. The types and ratios of lignin units vary among taxonomic classes of plants. To compare the lignin content as determined by different methods of chemical analysis, fallen leaves of different species were analysed using both the acid detergent and acetyl bromide procedures. Near-infrared reflectance spectra were obtained for each sample of dried ground leaves, and stepwise multiple linear regression analyses were performed to compare the amounts of lignin determined using acid detergent and acetyl bromide. In monocotyledonous herbaceous plants, the lignin content determined by acetyl bromide was more than twice that determined by acid detergent. Despite the difference in the values, regression analysis provided acceptable results for both lignin preparations. Although the acid detergent procedure has generally been regarded as accurate for lignin determination, our results suggest that caution is required in the selection of the method of chemical analysis when using near-infrared spectroscopy to estimate the lignin content of different taxonomic classes of plants.     

An evaluation of imaging spectrometry for estimating forest canopy chemistry

Abstract

High spectral resolution Airborne Imaging Spectrometer (AIS) data were acquired over 20 well-studied Wisconsin forest sites to evaluate the potential of remote sensing for estimating forest canopy chemistry. Intensive nutrient cycling research in these forests demonstrates that canopy lignin content is strongly related to measured annual nitrogen mineralization at the undisturbed sites and may serve as an accurate index for nitrogen cycling rates. Ground measurements were made of foliar biomass and canopy nitrogen and lignin content, the latter within two weeks of the AIS overflight. The spectral data were transformed using derivative techniques modified from laboratory spectroscopy. Stepwise regression assisted in determining combinations of wavelengths most highly correlated with canopy chemistry and biomass. Strong correlations between AIS data and total canopy lignin content in deciduous forests and canopy lignin concentration (total lignin/biomass) in both deciduous and coniferous stands indicate that imaging spectrometry can be used to estimate canopy lignin content and, from that, the spatial distribution of annual nitrogen mineralization rates.     

近红外光谱信号的优化处理

用实验室常规方法测定竹材样品的木质素含量,漫反射方式采集样品的近红外光谱信号,偏最小二乘法(PLS)和完全交互验证方式以建立毛竹木质素含量的定量分析模型。研究主成分数、建模谱区、求导和平滑预处理技术对定量分析模型的影响。结果表明,预处理技术压缩和恢复的近红外光谱信号效果良好,提高了模型的预测能力,优化近红外定量分析模型有重要参考价值。     

利用统计和物理模型反演植被叶片生化组分

通过比较统计模型、物理模型以及两者的联合模型在反演鲜叶片生化组分含量时的效果，结果表明，对于叶绿素知水份含量。物理模型的反演效果较好，对于蛋白质、纤维素 木质素含量，统计模型的反演效果相对较好，由物理模型改造得到的三种联合模型，能在一定程度上提高物理模型反演蛋白质、纤维素 木质素含量的精度，但和利用统计模型良演这两种生化组分的结果比较，改进作用并不明显。对于不同样本组叶片生化组分含量，不同模型反演效果均存在一定差异，统计模型存在的差异相对较大。     

Spectral characteristics of lignin and soluble phenolics in the near infrared- a comparative study

Prediction of canopy chemical compounds including lignin based on reflectance or absorbance in the near infrared has been demonstrated using hyperspectral data. Little attention has been paid to the fact that many types of plant phenolics have chemical structures similar to lignin and may have similar spectral responses in the near infrared. To address this problem we have analysed near infrared spectral signatures of dry powder standards of lignin and tannin, dissolved lignin, tannin and other soluble phenolics, lignin and soluble phenolics extracted from Norway spruce needles, as well as wooden blocks containing varying amounts of tannin. Our results provide solid evidence that the spectral signatures of lignin and tannin are very similar and that spectral features and wavelengths used previously for lignin determination are in fact likely to be due to tannin and lignin reflectance features. This similarity may greatly affect near infrared assessments of canopy lignin concentrations and impact analyses for which these predictions are used. Additionally, certain near infrared reflectance properties associated with increasing levels of foliar damage may be due to increased levels of tannins in affected mesophyll cells. We have also identified the existence of a threshold lignin concentration below which spectrometry cannot precisely detect lignin.     

An assessment of canopy chemistry with AVIRIS—a case study in the Landes Forest,South-west France

The capability of airborne (AVIRIS) and laboratory spectrometry was investigated for assessing the chemical composition of foliar elements of a pine forest (The Landes, SW France). Simultaneously with AVIRIS acquisition, an atmospheric profile was carried out, and the forest vegetation was sampled for chemical analyses and laboratory spectral measurements. Predictive relations between concentrations of nitrogen (r=97 per cent), lignin (r = 89 per cent) and cellulose ( = 83 per cent) and reflectances of pre-treated pine needles were determined through stepwise regression analyses. A methodology was designed to assess their extrapolation to remotely acquired spectrometric data: (1) geometric and atmospheric corrections, (2) registration within a biophysical data base (e.g. LAI, biomass), and (3) comparative statistical analysis of laboratory and airborne spectrometric information. The application of laboratory derived relationships led to relatively large correlations for nitrogen (74 per cent) and cellulose (79 per cent); poorer results were obtained for lignin (55 per cent). The use of atmospherically corrected reflectances led to slightly worse correlations: nitrogen ( 73 per cent), cellulose (78 per cent) and lignin (44 per cent). It was attempted to improve these results while taking into account the influence of the canopy structure and total quantity of chemical compounds. (1) Slightly poorer results were obtained when chemical concentrations were weighted with local biomass and LAI values. (2) Predictive equations based on laboratory measurements were applied to reflectances of pine needles that were computed through the inversion of two reflectance models. This last approach improved correlations for lignin ( 74 per cent). No improvement was observed for nitrogen ( 70 per cent) and cellulose ( 69 per cent). Finally, in order to provide suitable information to GIS based ecosystem models chemical concentrations were tentatively mapped.     

牧草茎秆生物力学数据库的研究与开发

为了进一步培育优良品种、开发草产品深加工技术,选用北方典型多年生豆科牧草紫花苜蓿、小冠花和禾本科扁穗冰草、无芒雀麦收获期底部茎秆为研究对象,在500 N微机控制电子万能试验机上试验研究了主要力学性能,并测定了茎秆在不同水分状态下纤维素、木质素、蛋白质等主要化学成分含量,观测了茎秆的微观组织结构,得到了各显微指标数据.该系统针对测试所得的数据进行了整合处理,利用数据库技术,结合统计分析软件Matlab进行统计分析,得出了牧草茎秆的力学特性、化学特性和显微特性,以它们之间的生物力学相关性,并以各种图表的形式表达出来.研究表明,研制多功能豆禾牧草收割机设计采用切割方向与茎秆轴线倾角为30°的滑切,最省力省功;禾本科牧草茎秆最大抗弯强度是豆科牧草的3～4倍,抗弯强度小的牧草收割时不宜采用传统的切割方式与切割刀具;茎秆的机械强度和韧性低,会更有利于机械化收获.     

Fuzzy neural networks for estimation and fuzzy controller design: simulation study for a pulp batch digester

A structural implementation of a fuzzy inference system through connectionist network based on MLP with logical neurons connected through binary and numerical weights is considered. The resulting fuzzy neural network is trained using classical backpropagation to learn the rules of inference of a fuzzy system, by adjustment of the numerical weights. For controller design, training is carried out off line in a closed loop simulation. Rules for the fuzzy logic controller are extracted from the network by interpreting the consequence weights as measure of confidence of the underlying rule. The framework is used in a simulation study for estimation and control of a pulp batch digester. The controlled variable, the Kappa number, a measure of lignin content in the pulp, which is not measurable is estimated through temperature and liquor concentration using the fuzzy neural network. On the other hand a fuzzy neural network is trained to control the Kappa number and rules are extracted from the trained network to construct a fuzzy logic controller.     

Hyperspectral indices for characterizing upland peat composition

The erosion of blanket peat is a major environmental issue in the UK. Maps of erosion extent and peat composition, especially humification and moisture content, would aid our understanding of the erosion process and provide information for management decisions. HyMap images, acquired as part of the SAR and Hyperspectral Airborne Campaign (SHAC), were used to test candidate indices of peat composition for eroded blanket peat in the southern Pennines. Peat physical properties, including moisture content and degree of humification (measured as transmission), were derived in the laboratory and related to the remotely sensed data. Strong correlations were found between HyMap SWIR reflectance and transmission, but other peat physical properties were not significantly correlated. Spectral indices were calculated to express the depth of cellulose, lignin and water absorption features. Strong positive correlations were found between transmission and an adjusted cellulose absorption index (CAI), r 0.71, and the gradient of its shoulders between 2020 and 2200?nm, r 0.89. Other indices also performed well. Normalized indices performed better because they allowed for differences in brightness. Higher moisture content in poorly humified peats may have reinforced the effect of deeper ligno-celluloic absorptions, but further sampling is required to test this. The results suggest the potential for hyperspectral remote sensing to provide information on surface peat composition across large areas.     

DBN wavelet transform denoising method in soybean straw composition based on near-infrared rapid detection

In Hei Longjiang Province of China, ten million tons of soybean straws are produced every year, most of which are burned or discarded. The comprehensive utilization rate is less than 5 %. However, as a kind of high-quality raw material resources, the soybean straw is the main material when producing fuel ethanol and biodiesel. The traditional chemical detection technology was always applied to test the quality of straws’ internal components, which needs long time and high cost. Therefore, this research adopted near-infrared spectroscopy technology to establish models to achieve rapid detection of straw’s internal components. One hundred and fifty-two samples of straw were collected within Heilongjiang Province, and the main ingredients, lignin and hemicellulose, were chosen as the research objects. K-stone method is used to divide into calibration and validation sets. The correlation coefficient is adopted to select and validate characteristic bands. After removal of the abnormal sample by concentration of residual method, the emphasis is put on the effect of removal of spectrum noise by applying the wavelet transform. The experimental results are that the model has good stability under the full spectrum band 4000–12,000 cm^?1. After the removal of 3 lignin and 3 hemicellulose samples, respectively, the R ² of calibration set of lignin and hemicellulose models increased significantly, from 0.5836436 and 0.4994598 to 0.6994097 and 0.6943559, respectively. However, after the 5-layer decomposition of DB2 wavelet, the R ² increased to 0.8075574 and 0.8214309 separately. The validation set’s root mean square error prediction (RMSEP) fell from 0.7738772 and 0.3069899 to 0.5979685 and 0.2761462. After the conversion, the relative standard deviations (RSD) are 1.92 and 1.86 %, respectively. Unknown samples can be real-time predicted in short times after building the model, and the prediction process needs only 1960s. The experiments show that it is feasible to employ near-infrared spectroscopy method for rapid detection of straw components, which provides a new method to detect the straw ingredients rapidly in the future.