基于核密度估计的动物生境适宜度制图方法

生境适宜度制图能提供动物适宜生境的空间分布信息,对野生动物种群管理、保护地规划等非常重要。生境适宜度制图的关键是构建生境适宜度模型(habitat suitability model, HSM),只基于动物出现位置数据构建HSM的方法在实践中得到了非常广泛的应用。然而现有的只基于动物出现位置数据构建HSM的方法还不能很好地直接表达动物生境适宜度和环境因子之间具有生态学意义的数量关系,因此也就不能很好地体现环境因子对动物生境利用的生态学作用。 本文提出了一种基于核密度估计构建HSM的方法,在地理信息系统技术支持下,通过运用核密度估计从代表性的动物出现位置数据中估计出动物出现对各个环境因子的概率密度函数来直接表达生境适宜度与各个环境因子之间的数量关系,以体现环境因子对动物生境利用的生态学作用,在此基础上对生境适宜度与各个环境因子之间的数量关系进行综合构建了具有明确生态学意义的HSM用于动物生境适宜度制图。以美国Voyageures国家公园的白尾鹿(Odocoileus virginianus)生境适宜度制图为例,基于365个出现位置点位数据并结合积雪深度、地表覆被类型、森林边界长度和坡度等环境因子数据,开展了该方法的案例研究。通过交叉验证计算连续Boyce指数对制图结果进行评价,结果表明：基于核密度估计方法构建的HSM预测能力强,所得出的生境适宜度图经10次交叉验证,连续Boyce指数平均值为0.75,标准差为0.11,达到了较高精度。此外,由于基于核密度估计的方法能以“生境适宜度和环境因子之间具有生态学意义的数量关系”的形式来直接体现环境因子对动物生境利用的生态学作用,就模型的可解释性而言,该方法要优于现有的其他构建HSM的方法。     

数量性状的核质互作遗传效应分析:二倍体植株遗传模型

提出了能分析二倍体植株数量性状核质互作效应的遗传模型,该模型把控制数量性状总的遗传效应分为核效应、质效应和核质互作效应,以及它们分别与环境作用的效应。其中,核质互作效应可进一步分解为加性核质互作与显性核质互作。基于平衡与非平衡两种双列杂交试验设计,蒙特卡罗模拟结果表明:采用混合线性模型方法进行统计分析,可以有效地估计各项遗传效应值及其方差分量。此外,运用该模型对棉花的4个数量性状(单株铃数、衣分、2.5%跨长和麦克隆值)进行了遗传分析。     

Comparison and assessment of electron cross sections for Monte Carlo track structure codes.

The purpose of this study was to make an intercomparison and assessment of cross sections for electrons in water used in electron track structure codes. This study is intended to shed light on the extent to which the differences between the input data and physical and chemical assumptions influence the outcome in biophysical modeling of radiation effects. Ionization cross sections and spectra of secondary electrons were calculated by various theories. The analyses were carried out for water vapor cross sections, as these are more abundant and readily available. All suitable published experimental total ionization cross sections were fitted by an appropriate function and used for generation of electron tracks. Three sets of compiled data were used for comparison of total excitation cross sections and mean excitation energy. The tracks generated by a Monte Carlo track code, using various combinations of cross sections, were compared in terms of radial distributions of interactions and point kernels. The spectrum of secondary electrons emitted by the ionization process was found to be the factor that has the most influence on these quantities. A different set of cross sections for excitation and elastic scattering did not affect the electron track structure as much as did ionization cross sections. It is concluded that all codes, using different cross sections and in different phase, currently used for biophysical modeling exhibit close similarities for energy deposition in larger size targets while appreciable differences are observed in B-DNA-size targets. We recommend fitted functions to all available suitable experimental data for the total ionization and elastic cross sections. We conclude that most codes produce tracks in reasonable agreement with the macroscopic quantities such as total stopping power and total yield of strand breaks. However, we predict differences in frequencies of clustering in tracks from the different models.     

Optimization of methods for immobilizing antibodies against the carcino-embryonic antigen on insoluble matrices. Equilibrium parameters of the interaction of the immobilized antibodies with the antigen

To obtain highly efficient immunosorbents for solid-phase immunoassay and affinity chromatography, methods for immobilization of antibodies against the carcino-embryonic antigen (CEA) on insoluble matrices were optimized. The immunosorbents obtained were characterized by equilibrium parameters of the reaction between immobilized anti-CEA and CEA calculated from rather a simple kinetic model. This model describes the interaction of the monovalent antigen with two independent types of binding sites. The role of some amino acid residues of anti-CEA in the interaction with CEA was investigated. The effects of immobilization density and the spacer arm length on the functional properties of the immobilized antibodies were studied. The optimal immunosorbent was used to purify 125I-CEA by immunoaffinity chromatography.     

Bayesian analysis of genetic architecture of quantitative trait using data of crosses of multiple inbred lines

Using the data of crosses of multiple of inbred lines for mapping QTL can increase QTL detecting power compared with only cross of two inbred lines. Although many fixed-effect model methods have been proposed to analyze such data, they are largely based on one-QTL model or main effect model, and the interaction effects between QTL are always neglected. However, effectively separating the interaction effects from the residual error can increase the statistical power. In this article, we both extended the novel Bayesian model selection method and Bayesian shrinkage estimation approaches to multiple inbred line crosses. With two extensions, interacting QTL are effectively detected with high solution; in addition, the posterior variances for both main effects and interaction effects are also subjected to full Bayesian estimate, which is more optimal than two step approach involved in maximum-likelihood. A series of simulation experiments have been conducted to demonstrate the performance of the methods. The computer program written in FORTRAN language is freely available on request.     

The nature of 2-locus epistatic interactions in animals: evidence from Sewall Wright's guinea pig data

Summary The nature of epistatic interactions affects covariance between relatives and the expression of heterosis in various crossbred genotypes. The investigation of these interactions for metric traits requires large data sets of a suitable type. Data from Sewall Wright's early work with guinea pigs are used to compare the goodness-of-fit of seven biological models of 2-locus interaction for the six out of eleven traits in which epistatic effects are apparent. The model equivalent to additive x additive epistasis gives the best general fit over traits, with an average transformed R² value significantly greater than that of the next best fitting model (P<0.05). This result is compatible with results from the one other study in this area, using data from mice. It is concluded that, based on results available to date, the additive x additive 2-locus model of epistatic interaction appears most suitable for reduced genetic models.     

Epistasis interaction of QTL effects as a genetic parameter influencing estimation of the genetic additive effect

Epistasis, an additive-by-additive interaction between quantitative trait loci, has been defined as a deviation from the sum of independent effects of individual genes. Epistasis between QTLs assayed in populations segregating for an entire genome has been found at a frequency close to that expected by chance alone. Recently, epistatic effects have been considered by many researchers as important for complex traits. In order to understand the genetic control of complex traits, it is necessary to clarify additive-by-additive interactions among genes. Herein we compare estimates of a parameter connected with the additive gene action calculated on the basis of two models: a model excluding epistasis and a model with additive-by-additive interaction effects. In this paper two data sets were analysed: 1) 150 barley doubled haploid lines derived from the Steptoe × Morex cross, and 2) 145 DH lines of barley obtained from the Harrington × TR306 cross. The results showed that in cases when the effect of epistasis was different from zero, the coefficient of determination was larger for the model with epistasis than for the one excluding epistasis. These results indicate that epistatic interaction plays an important role in controlling the expression of complex traits.     

甘蔗生物量育种的ADGE遗传分析

对甘蔗11个亲本品种及不完全双列杂交(NCdesignⅡ)遗传设计的30个组合的F1代实生苗生物量进行加性-显型-随机环境效应模型(ADGE)分析。结果表明:甘蔗的生物量性状遗传主要是由基因的加性、显性及加性×环境互作效应共同决定的,但基因的加性效应作用较大;甘蔗杂交亲本对其后代表型的遗传作用主要为母本的遗传效应影响;甘蔗生物量性状都具有较高的广义遗传率(h2B)和狭义遗传率(h2N),且h2B>h2N,说明了对甘蔗生物量性状在选育种早期阶段的选择效果好;通过对亲本的加性基因随机效应分析的综合,较优良的甘蔗亲本有粤糖72/426、粤糖79/177、粤糖85/177、ROC24和ROC25;根据杂交组合显性随机效应分析,认为粤糖72/426×ROC16、粤糖79/177×ROC24、粤糖79/177×ROC23及粤糖80/101×ROC22是较优良的高生物量甘蔗杂交组合,可以应用于甘蔗的高生物量育种。     

Bayesian mapping of genotype x expression interactions in quantitative and qualitative traits

A novel Bayesian gene mapping method, which can simultaneously utilize both molecular marker and gene expression data, is introduced. The approach enables a quantitative or qualitative phenotype to be expressed as a linear combination of the marker genotypes, gene expression levels, and possible genotype x gene expression interactions. The interaction data, given as marker-gene pairs, contains possible in cis and in trans effects obtained from earlier allelic expression studies, genetical genomics studies, biological hypotheses, or known pathways. The method is presented for an inbred line cross design and can be easily generalized to handle other types of populations and designs. The model selection is based on the use of effect-specific variance components combined with Jeffreys' non-informative prior--the method operates by adaptively shrinking marker, expression, and interaction effects toward zero so that non-negligible effects are expected to occur only at very few positions. The estimation of the model parameters and the handling of missing genotype or expression data is performed via Markov chain Monte Carlo sampling. The potential of the method including heritability estimation is presented using simulated examples and novel summary statistics. The method is also applied to a real yeast data set with known pathways.     

Identification of peptide‐binding sites within BSA using rapid,laser‐induced covalent cross‐linking combined with high‐performance mass spectrometry

We are developing a rapid, time‐resolved method using laser‐activated cross‐linking to capture protein‐peptide interactions as a means to interrogate the interaction of serum proteins as delivery systems for peptides and other molecules. A model system was established to investigate the interactions between bovine serum albumin (BSA) and 2 peptides, the tridecapeptide budding‐yeast mating pheromone (α‐factor) and the decapeptide human gonadotropin‐releasing hormone (GnRH). Cross‐linking of α‐factor, using a biotinylated, photoactivatable p‐benzoyl‐L‐phenylalanine (Bpa)–modified analog, was energy‐dependent and achieved within seconds of laser irradiation. Protein blotting with an avidin probe was used to detect biotinylated species in the BSA‐peptide complex. The cross‐linked complex was trypsinized and then interrogated with nano‐LC–MS/MS to identify the peptide cross‐links. Cross‐linking was greatly facilitated by Bpa in the peptide, but some cross‐linking occurred at higher laser powers and high concentrations of a non‐Bpa–modified α‐factor. This was supported by experiments using GnRH, a peptide with sequence homology to α‐factor, which was likewise found to be cross‐linked to BSA by laser irradiation. Analysis of peptides in the mass spectra showed that the binding site for both α‐factor and GnRH was in the BSA pocket defined previously as the site for fatty acid binding. This model system validates the use of laser‐activation to facilitate cross‐linking of Bpa‐containing molecules to proteins. The rapid cross‐linking procedure and high performance of MS/MS to identify cross‐links provides a method to interrogate protein‐peptide interactions in a living cell in a time‐resolved manner.     

A force field for naproxen

A united-atom potential model for naproxen suitable for molecular dynamics (MD) simulation has been developed. The charge distribution is approximated by point charges obtained from ab initio calculations using the CHELPG method. Also the intramolecular interactions such as bond and angle vibration, and the torsion potential are obtained from ab initio calculations. The dispersive interaction contribution is taken from the literature. By MD simulation using a naproxen film in slap geometry, the temperature dependence of the density, surface tension and self-diffusion coefficient as well as the melting temperature for the developed potential model are obtained.     

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

This study aimed to identify genetic evaluation models (GEM) to accurately select cattle for milk production when only limited data are available. It is based on a data set from the Pakistani Sahiwal progeny testing programme which includes records from five government herds, each consisting of 100 to 350 animals, with lactation records dating back to 1968. Different types of GEM were compared, namely: (1) multivariate v. repeatability model when using the first three lactations, (2) an animal v. a sire model, (3) different fixed effects models to account for effects such as herd, year and season; and (4) fitting a model with genetic parameters fixed v. estimating the genetic parameters as part of the model fitting process. Two methods were used for the comparison of models. The first method used simulated data based on the Pakistani progeny testing system and compared estimated breeding values with true breeding values. The second method used cross-validation to determine the best model in subsets of actual Australian herd-recorded data. Subsets were chosen to reflect the Pakistani data in terms of herd size and number of herds. Based on the simulation and the cross-validation method, the multivariate animal model using fixed genetic parameters was generally the superior GEM, but problems arise in determining suitable values for fixing the parameters. Using mean square error of prediction, the best fixed effects structure could not be conclusively determined. The simulation method indicated the simplest fixed effects structure to be superior whereas in contrast, the cross-validation method on actual data concluded that the most complex one was the best. In conclusion it is difficult to propose a universally best GEM that can be used in any data set of this size. However, some general recommendations are that it is more appropriate to estimate the genetic parameters when evaluating for selection purposes, the animal model was superior to the sire model and that in the Pakistani situation the repeatability model is more suitable than a multivariate.     

Buoyant and potentiometric titrations of synthetic polpeptides. II. Five copolypeptides and two nonionizable homopolypeptides in CsCl solutions.

The buoyant density titrations of five ionizable copolypeptides in concentrated CsCl solutions have been determined. The results are used to formulate models for predicting the buoyant density titration behavior of copolypeptides and proteins using the previously reported homopolypeptide buoyant density titration curves. It was determined for these copolypeptides that the best predictive model must include not only the buoyant densities of the constituent amino acid residues and the relative composition, but also hydration and salt binding. Hydrations determined for the homopolypeptides are used in the copolypeptide predictive model. The hydrations of the neutral homopolypeptides were readily calculable since their buoyant densities are thermodynamically defined in terms of their partial specific volumes and hydrations. For the case of a charged macromolecule, an expression for the buoyant density as a function of the number and nature of the bound ions, its partial specific volume, and its relative hydration has also been available for some time. This heretofore intuitive relationship is now derived from thermodynamic principles and allows calculations of hydrations to charged macromolecules which bind either cations, anions, or both. The potentiometric titrations of three of the five copolypeptides in concentrated CsCl solutions were determined in order to study the effect of residue interaction and solvation effects on their ionization behavior. The potentiometric results are also combined directly with the buoyant density titration results to determine the correlation of the buoyant density with the degree of ionization. As in the cases of poly(Glu) and poly(His), the buoyant density of the copolypeptides changed linearily with the degree of ionization. The buoyant density titrations of two nonionizable homopolypeptides, poly(Gly) and poly(Ala), were determined in concentrated CsCl solutions. The buoyant density was found to increase with increasing pH, despite the fact that side chains do not contain ionizable groups. This is the first evidence from homopolypeptide or copolypeptide data that buoyant density changes can be observed from effects other than side-chain ionizations.     

An analytical framework for estimating aquatic species density from environmental DNA

Environmental DNA (eDNA) analysis of water samples is on the brink of becoming a standard monitoring method for aquatic species. This method has improved detection rates over conventional survey methods and thus has demonstrated effectiveness for estimation of site occupancy and species distribution. The frontier of eDNA applications, however, is to infer species density. Building upon previous studies, we present and assess a modeling approach that aims at inferring animal density from eDNA. The modeling combines eDNA and animal count data from a subset of sites to estimate species density (and associated uncertainties) at other sites where only eDNA data are available. As a proof of concept, we first perform a cross‐validation study using experimental data on carp in mesocosms. In these data, fish densities are known without error, which allows us to test the performance of the method with known data. We then evaluate the model using field data from a study on a stream salamander species to assess the potential of this method to work in natural settings, where density can never be known with absolute certainty. Two alternative distributions (Normal and Negative Binomial) to model variability in eDNA concentration data are assessed. Assessment based on the proof of concept data (carp) revealed that the Negative Binomial model provided much more accurate estimates than the model based on a Normal distribution, likely because eDNA data tend to be overdispersed. Greater imprecision was found when we applied the method to the field data, but the Negative Binomial model still provided useful density estimates. We call for further model development in this direction, as well as further research targeted at sampling design optimization. It will be important to assess these approaches on a broad range of study systems.     

Genotype-environmental interaction in the activity and preening of Drosophila melanogaster

Summary Techniques, recently developed to analyze genotype-environmental interaction in plants, are used to study the behaviour of two inbred lines of Drosophila melanogaster and their F ₁s. The locomotor activity and preening of the hybrids altered far less with age and between two different test conditions than did the behaviour of their homozygous parents. In one apparatus, age affected the additive genetical component leading to heterosis for high activity and, in the other condition, a maternal effect on activity was age-dependent. Preening varied far less with age but, like activity, showed dominance for low inter-individual variability and differences between replicates, due entirely to the unstable performance of one inbred line.In the light of additional evidence on larval-adult survival and on adult viability, it is suggested that a negative correlation exists between viability and responsiveness to many types of environmental variation. Therefore information may be gained by analyzing inter-individual variability, instead of the customary practice of rescaling data to make the variances homogeneous.     

Plant genetic identity of foundation tree species and their hybrids affects a litter-dwelling generalist predator

The effects of plant genetics on predators, especially those not living on the plant itself, are rarely studied and poorly understood. Therefore, we investigated the effect of plant hybridization and genotype on litter-dwelling spiders. Using an 18-year-old cottonwood common garden, we recorded agelenid sheet-web density associated with the litter layers of replicated genotypes of three tree cross types: Populus fremontii, Populus angustifolia, and their F₁ hybrids. We surveyed 118 trees for agelenid litter webs at two distances from the trees (0–100 and 100–200 cm from trunk) and measured litter depth as a potential mechanism of web density patterns. Five major results emerged: web density within a 1-m radius of P. angustifolia was approximately three times higher than within a 1-m radius of P. fremontii, with F₁ hybrids having intermediate densities; web density responded to P. angustifolia and F₁ hybrid genotypes as indicated by a significant genotype × distance interaction, with some genotypes exhibiting a strong decline in web density with distance, while others did not; P. angustifolia litter layers were deeper than those of P. fremontii at both distance classes, and litter depth among P. angustifolia genotypes differed up to 300 %; cross type and genotype influenced web density via their effects on litter depth, and these effects were influenced by distance; web density was more sensitive to the effects of tree cross type than genotype. By influencing generalist predators, plant hybridization and genotype may indirectly impact trophic interactions such as intraguild predation, possibly affecting trophic cascades and ecosystem processes.     

Testing for predator dependence in predator-prey dynamics: a non-parametric approach

The functional response is a key element in all predator-prey interactions. Although functional responses are traditionally modelled as being a function of prey density only, evidence is accumulating that predator density also has an important effect. However, much of the evidence comes from artificial experimental arenas under conditions not necessarily representative of the natural system, and neglecting the temporal dynamics of the organism (in particular the effects of prey depletion on the estimated functional response). Here we present a method that removes these limitations by reconstructing the functional response non-parametrically from predator-prey time-series data. This method is applied to data on a protozoan predator-prey interaction, and we obtain significant evidence of predator dependence in the functional response. A crucial element in this analysis is to include time-lags in the prey and predator reproduction rates, and we show that these delays improve the fit of the model significantly. Finally, we compare the non-parametrically reconstructed functional response to parametric forms, and suggest that a modified version of the Hassell-Varley predator interference model provides a simple and flexible function for theoretical investigation and applied modelling.     

A General Framework for Formal Tests of Interaction after Exhaustive Search Methods with Applications to MDR and MDR-PDT

The initial presentation of multifactor dimensionality reduction (MDR) featured cross-validation to mitigate over-fitting, computationally efficient searches of the epistatic model space, and variable construction with constructive induction to alleviate the curse of dimensionality. However, the method was unable to differentiate association signals arising from true interactions from those due to independent main effects at individual loci. This issue leads to problems in inference and interpretability for the results from MDR and the family-based compliment the MDR-pedigree disequilibrium test (PDT). A suggestion from previous work was to fit regression models post hoc to specifically evaluate the null hypothesis of no interaction for MDR or MDR-PDT models. We demonstrate with simulation that fitting a regression model on the same data as that analyzed by MDR or MDR-PDT is not a valid test of interaction. This is likely to be true for any other procedure that searches for models, and then performs an uncorrected test for interaction. We also show with simulation that when strong main effects are present and the null hypothesis of no interaction is true, that MDR and MDR-PDT reject at far greater than the nominal rate. We also provide a valid regression-based permutation test procedure that specifically tests the null hypothesis of no interaction, and does not reject the null when only main effects are present. The regression-based permutation test implemented here conducts a valid test of interaction after a search for multilocus models, and can be applied to any method that conducts a search to find a multilocus model representing an interaction.     

Quantum dots as nano plug-in's for efficient NADH resonance energy routing

The routing of fluorescent signals from NADH to quantum dots (QDs) has been a subject of extensive research for FRET based applications. In the present study, the spectral cross talk of NAD(+)/NADH with QDs was used to monitor the reaction of NAD(+)-dependent dehydrogenase enzyme. CdTe QD may undergo dipolar interaction with NADH as a result of broad spectral absorption due to multiple excitonic states resulting from quantum confinement effects. Thus, non-radiative energy transfer can take place from NADH to CdTe QD enhancing QDs fluorescence. Energy routing assay of NADH-QD was applied for detection of formaldehyde as a model analyte in the range 1000-0.01 ng/mL by the proposed technique. We observed proportionate quenching of CdTe QD fluorescence by NAD(+) and enhancement in the presence of NADH formed by various concentrations of enzyme (0.028-0.4 U). Hence, it was possible to detect formaldehyde in the range 1000-0.01 ng/mL with a limit of detection (LOD) at 0.01 ng/mL and regression coefficient R(2)=0.9982. Therefore, a unique optical sensor was developed for the detection of the formaldehyde in sensitive level based on the above mechanism. This method can be used to follow the activity of NAD(+)-dependent enzymes and detection of dehydrogenases in general.     

Biological effectiveness of high-energy protons: target fragmentation.

High-energy protons traversing tissue produce local sources of high-linear-energy-transfer (LET) ions through nuclear fragmentation. We examine the contribution of these target fragments to the biological effectiveness of high-energy protons using the cellular track model. The effects of secondary ions are treated in terms of the production collision density using energy-dependent parameters from a high-energy fragmentation model. Calculations for mammalian cell cultures show that at high dose, at which intertrack effects become important, protons deliver damage similar to that produced by gamma rays, and with fragmentation the relative biological effectiveness (RBE) of protons increases moderately from unity. At low dose, where sublethal damage is unimportant, the contribution from target fragments dominates, causing the proton effectiveness to be very different from that of gamma rays with a strongly fluence-dependent RBE. At high energies, the nuclear fragmentation cross sections become independent of energy. This leads to a plateau in the proton single-particle-action cross section, below 1 keV/micron, since the target fragments dominate.