Detection of hepatitis B virus markers using a biosensor based on imaging ellipsometry

A biosensor based on imaging ellipsometry (BIE) has been developed and validated in 169 patients for detecting five markers of hepatitis B virus (HBV) infection. The methodology has been established to pave the way for clinical diagnosis, including ligand screening, determination of the sensitivity, set-up of cut-off values (CoVs) and comparison with other clinical methods. A matrix assay method was established for ligand screening. The CoVs of HBV markers were derived with the help of receiver operating characteristic curves. Enzyme-linked immunosorbent assay (ELISA) was the reference method. Ligands with high bioactivity were selected and sensitivities of 1 ng/mL and 1 IU/mL for hepatitis B surface antigen (HBsAg) and surface antibody (anti-HBs) were obtained respectively. The CoVs of HBsAg, anti-HBs, hepatitis B e antigen, hepatitis B e antibody and core antibody were as follows: 15%, 18%, 15%, 20% and 15%, respectively, which were the percentages over the values of corresponding ligand controls. BIE can simultaneously detect up to five markers within 1 h with results in acceptable agreement with ELISA, and thus shows a potential for diagnosing hepatitis B with high throughput.

应用SVM预测杜鹃属（Rhododendron L.）在中国的潜在分布中心

杜鹃属(Rhododendron L.)是中国种子植物中最大的属，其现代分布和分化中心是我国西南部的横断山区和东喜马拉雅地区。我国西部、西南部的云南、四川、西藏等地共有杜鹃达450种，仅特有种就有约300种。对杜鹃属分布的深入研究是横断山区生物多样性保护不可缺少的重要部分。 由于物种分布与环境因子之间存在着紧密的联系，利用环境因子作为预测物种分布模型的变量是当前最普遍的建模思路。但是绝大多数物种分布预测模型都遇到了难以解决的“高维小样本”问题――模型在标本数据不足时无法给出合理的预测，或者模型无法处理大量的环境变量。机器学习领域的理论和实践已经证明，基于结构风险最小化原理的支持向量机(Support Vector Machine, SVM)算法非常适合“高维小样本”的分类问题。为了探索其应用在物种分布预测问题上的可能性，本文创新性的实现了基于SVM算法的物种分布预测系统。然后，本文以30个杜鹃属(Rhododendron L.)物种为检验对象，利用其标本数据和11个1km的栅格环境变量图层作为模型变量，预测其在中国的潜在分布区。本文通过全面的模型评估——专家评估，ROC (Receiver Operator Characteristic)曲线和曲线下方面积AUC (Area Under the Curve)——来比较模型的性能。试验结果表明，我们所实现的以SVM为核心的物种分布预测系统无论在计算速度还是预测效果上都远远优于当前广泛使用的GARP (Genetic Algorithm for Rule-Set Prediction)预测系统。 之后，本文进一步探讨了SVM预测系统预测效果与环境变量维数和标本点个数的关系。试验结果表明，对于只有少量标本点的物种SVM的预测结果仍然具有相当的合理性。由此可见， SVM预测系统很好的解决了以前众多模型无法克服的稀有种和标本点稀少的物种的潜在分布区模拟问题。同时本文发现大的环境维数（高维）对于物种潜在分布区的预测有着决定性的作用，因此模型处理高维问题的能力显得至关重要。 最后，我们使用中国所有可获取的杜鹃属标本数据，以及83个1km的栅格环境变量图层，对400种杜鹃属物种的潜在分布区进行预测。根据预测出来的物种潜在分布区，我们得到了中国杜鹃属物种潜在多样性分布格局，特有物种潜在多样性分布格局，濒危杜物种潜在的分布格局，各亚属物种潜在分布格局，以及不同生活型物种潜在多样性分布格局。这些分布区图不仅可以对杜鹃属起源研究提供分析验证的条件，还能为其引种、保护和新种的搜寻提供有利的空间依据。

落叶栎树在中国的地理替代分布及其气候制约

栎属（Quercus L.）按落叶习性可自然分为落叶类型和常绿类型，我国落叶栎类共有20个种和9个变种。落叶栎是栎属中较为进化的一个类群，源于横断山区和云贵高原;除新疆外，全国各省都有落叶栎的天然分布，一些亲缘关系密切的树种之间呈现出较为明显的地理替代分布格局。本研究的目的在于：（1）应用BIOCLIM模型模拟预测落叶栎类植物的潜在分布区，分析其目前的分布格局以及下一步的发展趋势;（2）分析造成落叶栎树地理替代分布格局的主导气候因子，探讨气候因子对不同落叶栎树种地理分布格局的制约作用。 本文以16个在中国具有成片天然分布区的落叶栎树种（包括变种）为研究对象，利用已核对的标本数据以及13个栅格化环境变量图层（分辨率为1km×1km），按照分类（全国广布型、南方广布型、南方狭域型和北方狭域型）和不分类（全部16种）两种处理方式，通过BIOCLIM模型模拟得出了它们的潜在核心分布区和潜在边缘分布区。在运行模型之前，除必选的海拔高程图层外，采用了主成分分析（PCA）的方法从30个候选的气候变量图层中筛选出对相应落叶栎树种的地理分布格局有较大影响的12个图层作为输入图层。然后，本文通过比较两种处理所得模拟结果的ROC（Receiver Operator Characteristic）曲线下方面积AUC（Area Under the Curve），同时结合文献分析来推测不同落叶栎树种地理分布格局的稳定性及发展趋势。结果表明，在无人类活动干扰且种源传播不受阻碍的情况下，全国广布型和南方广布型落叶栎目前的分布格局在维持稳定的基础上有向周边地区扩展的趋势;南方狭域型和北方狭域型落叶栎的分布格局则基本保持稳定，短期内发生扩散的可能很小。 论文中计算了每个落叶栎树种所在分布范围的气候指标（共11个），以便进行下一步的研究。以蒙古栎（Q. mongolica）、辽东栎（Q. wutaishanica）与槲栎（Q. aliena）、锐齿槲栎（Q. aliena var. acuteserrata）、北京槲栎（Q. aliena var. pekingensis）这两组地理替代系列为研究对象，分别采用独立样本t检验和单因素方差分析的方法，分析了气候因子对其地理替代分布格局的主导作用。结果表明，冬季的低温、较高的气温年较差和大陆度是蒙古栎向东北替代辽东栎的主要原因;槲栎向北被北京槲栎和锐齿槲栎替代的主要原因是生长季高温和冬季高温对其分布的双重制约;除最暖月（7月）最高温外，北京槲栎的各项水热指标与另两种槲栎均存在极显著差异，对冬季低温和较大的年较差的适应可能是限制其向南分布的主要原因。 本研究最后部分的内容是对不同类型落叶栎分布区的气候参数进行的主成分分析。结果表明，生长季温度是制约落叶栎分布的最主要的气候因子;寒冷程度和冬季的低温则对其在大尺度范围的扩散有较大影响;另外，降水、年较差与大陆度对落叶栎的向北分布也起着重要的作用。

苋属入侵种的可能分布区预测及相关环境因子分析

苋属（Amaranthus）约40种，世界均有分布。我国有20种，分布很广，其中外来种为17种(11种为入侵种)，危害旱田作物、果树、茶树和蔬菜。反枝苋（Amaranthus retroflexus L.）是苋属入侵种中发生频率最多、分布最广、危害最严重的杂草。本文首先基于反枝苋在世界范围内4207个实际分布点及其对应的气候、地形和土壤三类要素28个环境因子的定量关系，利用主成分分析确定了影响其分布的主要环境因子，据此估测其中心可能分布区和最大可能分布区，并与实际分布点进行比较;然后利用GARP生态位模型和地理信息系统(GIS)对影响苋属8个入侵种地理分布的环境因子进行分析并对其全球可能分布区进行预测，并根据苋属入侵种与环境因子的关系对8个苋属入侵种进行聚类分析;最后基于Receiver Operating Characteristics(ROC)分析对GARP模型及GIS模型对反枝苋全球可能分布区的预测结果进行精度检验和比较，结果表明： (1) GIS模型预测显示14个环境因子在决定反枝苋全球分布格局中起着重要作用。反枝苋中心可能分布区位于新西兰南部、澳大利亚东南部、南美洲北部少数地区、北美洲西北部及东南部部分地区、欧洲大部分地区和中国东南部。最大可能分布区位于南美洲中南部、北美洲大部分、非洲北部小部分、澳大利亚南部及北部少数区域、欧洲大部分地区和亚洲大部分地区及中国除西藏、青海、新疆、四川西部以外的地区。中心可能分布区的预测结果与实际分布点吻合较好，而最大可能分布区则过于广阔。 (2) GARP模型预测显示14个环境因子中雨日频率，极端低温，海拔这三个环境因子的影响较为重要，是苋属8个入侵种分布的主要限制因子。聚类分析表明8种苋属入侵种按欧式距离的长度可分为三类：第一类：反枝苋、凹头苋;第二类：刺苋、皱果苋、尾穗苋;第三类：绿穗苋、白苋、北美苋。 ROC分析结果显示GARP模型对反枝苋的可能分布区模拟效果（AUCGARP=0.857）好于GIS模型，其中GIS模型对反枝苋中心可能分布区的模拟效果(AUCGIS-CENTER=0.832)好于最大可能分布区(AUCGIS-MAX=0.778)。 苋属8个入侵种均有分布的地区为澳大利亚沿海地区，新西兰，中国东南沿海，欧洲西部，南美洲部分国家，美国，非洲中部。 (3)两种模型所预测的反枝苋的可能分布区有很大程度的重合性，GARP模型预测的可能分布区大于GIS模型预测出的中心可能分布区，但小于GIS模型预测出的最大可能分布区，且和实际分布点拟合程度较好。

砂粒含量对土壤水分蓄持能力影响模拟试验研究

通过人工配制不同质地土壤,测定土壤水分特征曲线,研究了土壤中砂粒含量对其水分蓄持能力的定量影响。结果表明:(1)砂粒含量对土壤水分蓄持能力有较大影响,土壤持水能力随砂粒含量增加递减,表征土壤持水能力的水分特征曲线Gardner模型参数及表征土壤饱和含水量的Van Genuchten模型参数均随砂粒含量增加逐渐减小。(2)砂粒含量对土壤比水容量有较大影响,试验土壤在任一吸力水平下的比水容量值均随其砂粒含量增加递减。(3)试验土壤饱和含水量与砂粒含量呈线性关系,田间持水量、凋萎系数与砂粒含量都呈开口向下抛物线右半段的关系。(4)试验土壤有效水、迟效水含量随砂粒含量增加递减,二者与砂粒含量均呈开口向下抛物线右半段的关系。易效水含量与砂粒含量呈开口向上抛物线关系。

黏粒质量分数对土壤水分蓄持能力影响的模拟试验

通过人工配制不同质地土壤,测定土壤水分特征参数,研究土壤中黏粒质量分数对其水分蓄持能力的定量影响。结果表明:1)黏粒质量分数对土壤水分蓄持能力有较大影响,土壤持水能力随黏粒质量分数增加而递增。2个水分特征曲线模型——Gardner模型及van Genuchten模型中,表征土壤持水能力的参数均随黏粒质量分数增加而增大。2)黏粒质量分数对土壤比水容量有较大影响,试验土壤在任一水吸力水平下的比水容量值均随其黏粒质量分数增大而增大。3)试验土壤饱和含水量、田间持水量分别与黏粒质量分数呈指数、对数正相关,凋萎系数与黏粒质量分数呈指数正相关。4)试验土壤有效水、迟效水含量随黏粒质量分数增加呈先升高后降低趋势,二者与黏粒质量分数均呈抛物线关系,最高点分别出现在黏粒质量分数为35.9%和35.8%处,易效水含量与黏粒质量分数相关性不显著。研究结果可为黄土区土壤水分蓄持机制进一步研究提供一定理论依据。

两种土壤水分特征曲线间接推求方法对黄土的适应性评价

土壤水分特征曲线(SWCC)是模拟土壤水分运动和溶质运移的一个重要参数,利用土壤的基本物理性质来间接推求SWCC的方法已经成为当今土壤物理学领域的研究热点。为了比较两种SWCC间接推求方法——Arya-Paris物理经验方法(简称AP方法)和Tyler-Wheatcraft分形几何方法(简称TW方法)对黄土的适应性,该文分析了黄土高原296组土壤颗粒分布、容重和水分特征曲线等资料,利用简化的Fredlund(Fred3P)模型模拟得到连续的土壤颗粒分布曲线,然后应用AP和TW方法预测出相应吸力下的土壤含水量。研究结果表明,对于黄土性土壤,AP和TW两种方法的预测结果均达到了一定的精度,相比较而言AP方法的预测效果明显优于TW方法,且受质地影响小。

土壤结构改良剂影响下的土壤水分有效性研究

以聚丙烯酰胺(PAM)与磷石膏(PG)为土壤结构改良剂,利用离心机法,测定土壤水分特征曲线,从分析土壤的吸水能力和持水能力的角度出发,研究土壤结构改良剂对土壤水分有效性的影响。研究结果表明,土壤的吸水能力、持水能力与释水能力均表现出与用量密切相关;在使用土壤结构改良剂的情况下,仍然可用van Genuchten方程很准确的模拟土壤吸力与含水率之间的关系,即可作为使用土壤结构改良剂后的土壤水分特征曲线的模拟表达式;在试验的用量范围内,土壤结构改良剂的使用不会影响植物对水分的吸收和利用。

推求土壤水分运动参数的简单入渗法──Ⅱ.实验验证

预报土壤中水分流动需要的土壤导水特性可通过观测水平土柱的入渗过程来确定，这一观测过程的分析是基于对Richards方程求积分解。土壤水分特征曲线中的参数由观测的水平立柱的特征湿润长度和吸力来确定，非饱和土壤导水率由已确定的特征曲线中的参数和测定的饱和导水率导出。供试土壤有三种，它们的质地从砂壤到粘壤。由这种方法所确定的这三种土壤的水分特征曲线与实测的特征曲线符合良好，所确定的砂壤的非他和导水率与实测值的比较令人满意。利用数值法和积分法分别计算了土壤含水量剖面，计算结果吻合良好，说明了这种方法的合理性。

推求土壤水分运动参数的简单入渗法 I.理论分析

预报非饱和土壤水分运动必须首先获得土壤水分运动参数。土壤水分运动参数包括土壤水分特征曲线和导水率。本文使用积分方法求解了一维水平非饱和土壤水分运动问题，根据其解建立了推求非饱和土壤水分运动参数的简单入渗法，用以推求ｖａｎ Ｇｅｎｕｃｈｔｅｎ特征曲线模型中的参数α和ｎ。α和ｎ是根据湿润区的特征长度、吸渗率和土壤的饱和导水率（ｋｓ）来确定的，而非饱和导水率可由α、ｎ和Ｋｓ确定。这一新的简单入渗法是基于Ｒｉｃｈａｒｄｓ方程和土壤导水特征的闭合型方程。简单入渗法提供了利用瞬态水流方法来确定土壤水分特征曲线而替代通常的平衡法。简单入渗法是一个全新的、简捷的确定土壤导水特性的方法。

小兴安岭针叶树种在不同尺度上对环境因子的敏感性分析

小兴安岭地区是我国重要的林区之一,预测该地区针叶树种的分布,在不同尺度上查找针叶树种分布最敏感的环境因子,是不同层次的林业部门制定森林恢复和植树造林方针的重要科学依据。该文以坡度、坡向、综合地形指数、海拔、坡位指数、年平均温度和年平均降水量作为环境因子,利用Logistic回归模型对红松(Pinus koraien-sis)、兴安落叶松(Larix gmelinii)、冷杉(Abies nephrolepis)、红皮云杉(Picea koraiensis)、鱼鳞云杉(P.jezoensis)和樟子松(Pinus sylvestris var.mongolica)的分布进行了预测。并且采用相对运行特征(Relative operating characteristic,ROC),对模型进行了精度评价。其取值范围为0～1,如果ROC小于0.7,认为模型具有低精度;如果大于0.7且小于0.9,则模型具有较好的模拟精度;如果大于0.9,认为模型具有很高的预测精度。对每个树种的模型验证表明只有冷杉的ROC大于80%,红松、兴安落叶松和云杉的ROC在70%～80%之间,而樟子松的为67.9%。之后,把预测模型应用到丰林保护区,揭示局域尺度上树种分布最敏感的环境因子。经过树种分布预测图与环境因子之间的相关分析发现,在区域尺度(整个研究区)上,红松、冷杉、云杉和樟子松对年降水量最为敏感,而兴安落叶松对坡度最敏感。在局域尺度(丰林保护区)上,红松分布对坡度最敏感,冷杉和云杉对海拔最敏感,兴安落叶松对坡位最敏感。在不同尺度上,树种最敏感的环境因子的转移,引起了在不同尺度上树种分布类型的变化。红松在区域尺度上聚集分布(ROC=78.6%),而在局域尺度上其聚集程度有所减弱(ROC=74.4%),红松的分布范围增加。在区域尺度上,云杉和冷杉聚集分布,但在局域尺度上,它们的分布接近随机分布类型(ROC<60%),它们在丰林保护区内分布面积较大。与以上3个树种相反,兴安落叶松的ROC从71.7%增加到了82.0%,在区域尺度上聚集分布的兴安落叶松,在局域尺度上更加聚集,其分布范围局限于某个特定环境(谷底)。总的来说,在区域尺度上,多数树种分布对气候因子最为敏感,在局域尺度上,对地理因子最为敏感。不同树种对不同环境因子的敏感性,揭示了树种空间分布格局和分异规律。

河流-三角洲储层剩余油分布模式及预测

Aim at the variousness and complexity of the spatial distribution of Remaining Oil in the fluvial and delta facies reservoir in paper. For example, in the La-Sa-Xing oilfield of Daqing, based on the research of the control factor and formation mechanization of block, single layer, interlayer and micromechanism, synthesizing the theories and methods of geology, well logging, reservoir engineering, artificial intelligence, physical simulation test , and computer multidisciplinary; Fully utilizing the material of geology, well logging, core well, dynamic monitor of oil and water well, and experimental analysis, from macro to micro, from quality to quantity, from indoor to workplace, we predicted the potentiality and distribution according to the four levels of Block, single layer, interlayer and micromechanism, and comprehensively summarized the different distribution pattern of remaining oil in the fluvial and delta facies reservoir This paper puts forward an efficient method to predict the remaining recoverable reserves by using the water flooding characteristic curve differential method and neutral network; for the first time utilizes multilevel fuzzy comprehensive judgment method and expert neutral network technology to predict the remaining oil distribution in the single layer? comprehensively takes advantage of reservoir flowing unit, indoor physical simulation test, inspection well core analysis and well-logging watered-out layer interpretation to efficiently predict the distribution of remaining oil; makes use of core analysis of different periods and indoor water driving oil test to study the micro distribution of remaining oil and the parameters varying law of reservoir substance properties, rock properties, wetting properties. Based on above, the remaining oil distribution predicting software is developed, which contains four levels of block, single layer, interlayer and micromechanism. This achievement has been used inLa-Sa-Xing oil field of Daqing and good results have been received.

花岗岩风化土微观结构及其工程地质特性研究

Saprolite is the residual soil resulted from completely weathered or highly weathered granite and with corestones of parent rock. It is widely distributed in Hong Kong. Slope instability usually happens in this layer of residual soil and thus it is very important to study the engineering geological properties of Saprolite. Due to the relic granitic texture, the deformation and strength characteristics of Saprolite are very different from normal residual soils. In order to investigate the effects of the special microstructure on soil deformation and strength, a series of physical, chemical and mechanical tests were conducted on Saprolite at Kowloon, Hong Kong. The tests include chemical analysis, particle size analysis, mineral composition analysis, mercury injection, consolidation test, direct shear test, triaxial shear test, optical analysis, SEM & TEM analysis, and triaxial shear tests under real-time CT monitoring.Based on the testing results, intensity and degree of weathering were classified, factors affecting and controlling the deformation and strength of Saprolite were identified, and the interaction between those factors were analyzed.The major parameters describing soil microstructure were introduced mainly based on optical thin section analysis results. These parameters are of importance and physical meaning to describe particle shape, particle size distribution (PSD), and for numerical modeling of soil microstructure. A few parameters to depict particle geometry were proposed or improved. These parameters can be used to regenerate the particle shape and its distribution. Fractal dimension of particle shape was proposed to describe irregularity of particle shapes and capacity of space filling quantitatively. And the effect of fractal dimension of particle shape on soil strength was analyzed. At the same time, structural coefficient - a combined parameter which can quantify the overall microstructure of rock or soil was introduced to study Saprolite and the results are very positive. The study emphasized on the fractal characteristics of PSD and pore structure by applying fractal theory and method. With the results from thin section analysis and mercury injection, it was shown that at least two fractal dimensions Dfl(DB) and Df2 (Dw), exist for both PSD and pore structure. The reasons and physical meanings behind multi-fractal dimensions were analyzed. The fractal dimensions were used to calculate the formation depth and weathering rate of granite at Kowloon. As practical applications, correlations and mathematical models for fractal dimensions and engineering properties of soil were established. The correlation between fractal dimensions and mechanical properties of soil shows that the internal friction angle is mainly governed by Dfl 9 corresponding to coarse grain components, while the cohesion depends on Df2 , corresponding to fine grain components. The correlations between the fractal dimension, friction angle and cohesion are positive linear.Fractal models of PSD and pore size distribution were derived theoretically. Fragmentation mechanism of grains was also analyzed from the viewpoint of fractal. A simple function was derived to define the theoretical relationship between the water characteristic curve (WCC) and fractal dimension, based on a number of classical WCC models. This relationship provides a new analytical tool and research method for hydraulic properties in porous media and solute transportation. It also endues fractal dimensions with new physical meanings and facilitates applications of fractal dimensions in water retention characteristics, ground water movement, and environmental engineering.Based on the conclusions from the fractal characteristics of Saprolite, size effect on strength was expressed by fractal dimension. This function is in complete agreement with classical Weibull model and a simple function was derived to represent the relationship between them.In this thesis, the phenomenon of multi-fractal dimensions was theoretically analyzed and verified with WCC and saprolite PSD results, it was then concluded that multi-fractal can describe the characteristics of one object more accurately, compared to single fractal dimension. The multi-fractal of saprolite reflects its structural heterogeneity and changeable stress environment during the evolution history.

Capacitance-voltage characteristic as a trace of the exciton evolvement from spatially direct to indirect in quantum wells

We have investigated the photo-excited capacitance-voltage (C-V) characteristics as well as the photoluminescence spectra under different biases of a wide quantum well (QW) embedded in an n(+)-i-n(+) double-barrier structure. The pronounced peak feature at zero bias in the C-V spectrum observed upon illumination is regarded as a kind of quantum capacitance related to the quantum confined Stark effect, originating from the spatial separation of the photo-generated electron and hole gas in the QW. This fact is further demonstrated through the comparison between the C-V curve with the PL intensity versus applied voltage relationship under the same excitation. The results may provide us with a more direct and sensitive means in the detection of the separation and accumulation of both types of free carriers-electrons and holes-in low-dimensional semiconductor structures, especially in a new type of optical memory cell.