基于复合叶片特征的计算机植物识别方法

该文探讨如何根据植物的叶片特征,利用图像处理和机器学习的方法对植物进行分类。鉴于现有的叶片分类系统多采用单一的特征,如几何和纹理等,仅能在小规模数据库上得到较好的结果。然而,随着样本种类的增多,单一特征在不同种类叶片之间的相似性非常明显,致使分类正确率降低。该研究使用多种复合特征,并提出了原创的预处理方法以及宽度、叶缘频率特征,较传统的几何特征更为详尽。研究结果显示,复合特征可以有效避免算法过拟合问题,使之适用于更大的数据库。通过提取21类植物的叶片宽度、颜色、叶缘和纹理共292维特征,对1 915张数字图像进行了分类,正确率达到93%,并分析了各类特征对分类结果的影响。研究结果表明,在不影响分类正确率前提下,可将特征减少到约100维。     

大麦G－带变动性以及与染色粒的一致性分析

以大麦为材料,进行了Ｇ－带带纹变动性分析以及Ｇ－带与减数分裂粗线期染色粒的比较。有丝分裂早中期至中期两个不同时期Ｇ－带带纹总数减少３６％,染色体组的绝对长度缩短２５％,带数减少的幅度大于染色体长度缩短的幅度。染色体组中每条染色体之间带纹减少的比例不尽相同,变幅在０．２９－０．５０之间。不同染色体绝对长度减少的幅度在０．２７－３．７０μ之间。从早中期至中期,每单位染色体绝对长度带数具相对减少的趋势。选择第６染色体进行的比较显示,减数分裂的染色粒与有丝分裂的Ｇ－带带纹之间具有很好的对应关系,Ｇ－带带纹和染色粒间的数目、位置和着色程度上都具一致性。对Ｇ－带性质和植物中Ｇ－带的应用等问题进行了讨论。     

Physiological model using diffuse reflectance spectroscopy for nonmelanoma skin cancer diagnosis

Diffuse reflectance spectroscopy (DRS) is a noninvasive, fast, and low‐cost technology with potential to assist cancer diagnosis. The goal of this study was to test the capability of our physiological model, a computational Monte Carlo lookup table inverse model, for nonmelanoma skin cancer diagnosis. We applied this model on a clinical DRS dataset to extract scattering parameters, blood volume fraction, oxygen saturation and vessel radius. We found that the model was able to capture physiological information relevant to skin cancer. We used the extracted parameters to classify (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]) vs actinic keratosis (AK) and (BCC, SCC, AK) vs normal. The area under the receiver operating characteristic curve achieved by the classifiers trained on the parameters extracted using the physiological model is comparable to that of classifiers trained on features extracted via Principal Component Analysis. Our findings suggest that DRS can reveal physiologic characteristics of skin and this physiologic model offers greater flexibility for diagnosing skin cancer than a pure statistical analysis. Physiological parameters extracted from diffuse reflectance spectra data for nonmelanoma skin cancer diagnosis.     

中国东北产6种藓类植物的染色体观察

苔藓植物细胞学研究始于 2 0世纪 [1 ] 。我国有关这方面的研究自 1 988年开始 ,据 1 998年周云龙统计 ,国内学者共报道了 94种苔藓植物的染色体数目 ,其中苔类有 1 6科、1 9属、2 3种 ,5种为首次报道 ;藓类有 2 1科、37属、71种 ,2 7种为首次报道[2 ] 。这些研究为苔藓植物的系统学、生态学、遗传学等提供了直接或间接的资料和证据。1 　材料和方法将采集的苔藓植物培养在室内北窗台上。取生长旺盛的茎尖用对二氯苯饱和水溶液预处理 5～ 7h,卡诺固定液 (纯酒精∶冰醋酸 =3∶ 1 )固定 ,95%酒精∶盐酸 ( 1∶ 1 )离析1 0～ 2 0 min后剥离茎尖…     

Structural analysis based on state-space modeling.

A new method has been developed to compute the probability that each amino acid in a protein sequence is in a particular secondary structural element. Each of these probabilities is computed using the entire sequence and a set of predefined structural class models. This set of structural classes is patterned after Jane Richardson''s taxonomy for the domains of globular proteins. For each structural class considered, a mathematical model is constructed to represent constraints on the pattern of secondary structural elements characteristic of that class. These are stochastic models having discrete state spaces (referred to as hidden Markov models by researchers in signal processing and automatic speech recognition). Each model is a mathematical generator of amino acid sequences; the sequence under consideration is modeled as having been generated by one model in the set of candidates. The probability that each model generated the given sequence is computed using a filtering algorithm. The protein is then classified as belonging to the structural class having the most probable model. The secondary structure of the sequence is then analyzed using a "smoothing" algorithm that is optimal for that structural class model. For each residue position in the sequence, the smoother computes the probability that the residue is contained within each of the defined secondary structural elements of the model. This method has two important advantages: (1) the probability of each residue being in each of the modeled secondary structural elements is computed using the totality of the amino acid sequence, and (2) these probabilities are consistent with prior knowledge of realizable domain folds as encoded in each model. As an example of the method''s utility, we present its application to flavodoxin, a prototypical alpha/beta protein having a central beta-sheet, and to thioredoxin, which belongs to a similar structural class but shares no significant sequence similarity.     

Can the Study of Natural Vegetation Succession Assist in the Control of Soil Erosion on Abandoned Croplands on the Loess Plateau,China?

In the Loess Plateau, China, arable cultivation of slope lands is common and associated with serious soil erosion. Planting trees or grass may control erosion, but planted species may consume more soil water and can threaten long‐term ecosystem sustainability. Natural vegetation succession is an alternative ecological solution to restore degraded land, but there is a time cost, given that the establishment of natural vegetation, adequate to prevent soil erosion, is a longer process than planting. The aims of this study were to identify the environmental factors controlling the type of vegetation established on abandoned cropland and to identify candidate species that might be sown soon after abandonment to accelerate vegetation succession and establishment of natural vegetation to prevent soil erosion. A field survey of thirty‐three 2 × 2–m plots was carried out in July 2003, recording age since abandonment, vegetation cover, and frequency of species together with major environmental and soil variables. Data were analyzed using correspondence analysis, classification tree analysis, and species response curves. Four vegetation types were identified and the data analysis confirmed the importance of time since abandonment, total P, and soil water in controlling the type of vegetation established. Among the dominant species in the three late‐successional vegetation types, the most appropriate candidates for accelerating and directing vegetation succession were King Ranch bluestem (Bothriochloa ischaemum) and Lespedeza davurica (Leguminosae). These species possess combinations of the following characteristics: tolerance of low water and nutrient availability, fibrous root system and strong lateral vegetative spread, and a persistent seed bank.     

Studies on the Mutagenic Effect of the Chromosome by 5 MeV Electron

In this experiment, electron and y-ray-radiation were used to irradiate dry seeds of Vicia faba. Experimental results show that electron just as affective as γ-ray has significant mutagenic effect on cells. In the range of the dose, the frequency of chromosomal aberrations on the root tip cells, there are singnificantly dose-effect. But the relationship between the dose and frequency of aberrations is close to straight line and redplication. An analysis from the type of chromosomal aberrations by electron also leads to different results, in which there are more bridges and rings by electron than by γ-ray. Therefore, electron possibly have stronger two hit aberrations, and the frequency of aberration increased in proporti on with the square of dose.     

国产12种乌头属和18种翠雀属植物的细胞学研究

研究了12种乌头属Aconitum L．和18种翠雀属Delphinium L．植物的染色体。在12种乌头属植物中,除粗花乌头A．crassiflorum为四倍体(2n=4x=32)外,其他种类都为二倍体(2n=2x＝16),中甸乌头 A.piepunense中有B染色体存在,牛扁亚属Aconitum subgen．Lycoctonum的二倍体植物与乌头亚属Aconitum subgen．Aconitum 植物的染色体在大小和形态上有明显区别;所有18种翠雀属植物都为二倍体(2n＝2x=16),其染色体在大小和形态上极为相似,但与乌头亚属的染色体易于区别。翠雀属植物的核型不对称性程度明显高于乌头属植物,因此从染色体证据来看,翠雀属要比乌头属进化。     

广西百色和永乐盆地的始新世猪类化石──兼论早期猪类的分类和演化(英文)

描述了广西百色及永乐盆地发现的中始新世最晚期一晚始新世的猪类化石。这批已知最早的猪超科化石,大大丰富了亚洲地区的早期猪类,对了解全球猪类的早期演化有着重要价值。广西的材料被确定为两个新属新种：萨氏始新猪Ｅｏｃｅｎｃｈｏｅｒｕｓ ｓａｖａｇｅｉ和广西华夏猪Ｈｕａｘｉａｃｈｏｅｒｕｓ ｇｕａｎｇｘｉｅｎｓｉｓ,一个新种：粗壮暹罗猪Ｓｉａｍｏｃｈｏｅｒｕｓ　ｖｉｒｉｏｓｕｓ和一个未定属种：Ｔａｙａｓｓｕｉｄａｅ　ｇｅｎ．ｅｔ　ｓｐ．ｉｎｄｅｔ。加上已发表的单尖旅猪Ｏｄｉｏｃｈｏｅｒｕｓ　ｕｎｉｃｏｎｕｓ,百色、永乐盆地共计发现了５种始新世猪类化石。新属Ｅｏｃｅｎｃｈｏｅｒｕｓ虽然臼齿结构简单,与原始的古猪类（ｐａｉａｅｏｃｈｏｅｒｉｄ）接近,但因其Ｍ３具有一个明显的跟座而被归入猪科。早期猪科动物非常原始的臼齿形式证明,猪科动物的起源并不晚于古猪科（Ｐａｌａｅｏｃｈｏｅｒｉｄａｅ）和西科（Ｔａｙａｓｓｕｉｄａｅ）。旧大陆的古猪类长期以来被认为与新大陆的西类（ｔａｙａｓｓｕｉｄ）更为接近,多数学者将其归入西科。笔者认为两者共有的近祖特征,并不能支持它们更近的亲缘关系,而且古猪类缺乏典型西类的近裔特征,因而支持将Ｐａｌａ?     

VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.