Isolation of compound microsatellite loci in the herbaceous perennial Cirsium purpuratum (Maxim.) Matsum

The herbaceous perennial Cirsium purpuratum is a pioneer on the southeast side of Mount Fuji in Japan. For genetic analysis of reproduction in this species, we developed polymorphic compound microsatellite markers using an adaptor-ligated library method and a simpler method called the intercompound microsatellite method. The latter method was an effective method for developing compound simple sequence repeat markers. In total, 11 polymorphic, codominant microsatellite markers were developed and characterized for this species. These polymorphic markers had three to 20 alleles per locus, a range of observed heterozygosity from 0.25 to 0.90, and were considered effective for genetic analysis.     

A new screening method to identify inhibitors of the Lol (localization of lipoproteins) system, a novel antibacterial target

As the Lol system, which is involved in localization of lipoproteins, is essential for Escherichia coli growth and widely conserved among gram-negative bacteria, it is considered to be a promising target for the development of anti-gram-negative bacterial agents. However, no high-throughput screening method has so far been developed to screen for Lol system inhibitors. By combining three assay systems (anucleate cell blue assay, Lpp assay, and LolA-dependent release inhibition assay) and a drug susceptibility test, we have successfully developed a new screening method for identification of compounds that inhibit the Lol system. Using this new screening method, we screened 23,600 in-house chemical compounds and found 2 Lol system inhibitors. We therefore conclude that our new screening method can efficiently identify new antibacterial agents that target the Lol system.     

Belonging probability inverse image approach for forest fire detection

We present a method for early forest fire detection from a satellite image using the belonging probability matrix image. We have considered each satellite image matrix line as a realization of a nonstationary random process in the thermal infra‐red (TIR) spectral band and then divided each line into very small stationary and ergodic intervals to obtain an adequate mathematical model. Furthermore, the pixels of the satellite image are considered to be statistically independent; thus, any small interval of each line behaves, naturally, as a Gaussian stationary noise. In this work, we have, therefore, selected the latter as a mathematical model for modelling these intervals of a satellite image without fire, and then, we have determined the parameters of this Gaussian realization. So, when a fire occurs in this forest zone, we can use these parameters to calculate its belonging probability to the original image without fire. This probability should be very small because the fire, in any forest, can be considered as a rare event. As a consequence, we have presented a matrix image of the probability inverse of each interval for a better fire detection observation.     

A moment-based method for estimating the proportion of true null hypotheses and its application to microarray gene expression data

Due to advances in experimental technologies, it is feasible to collect measurements for a large number of variables. When these variables are simultaneously screened by a statistical test, it is necessary to consider the adjustment for multiple hypothesis testing. The false discovery rate has been proposed and widely used to address this issue. A related problem is the estimation of the proportion of true null hypotheses. The long-standing difficulty to this problem is the identifiability of the nonparametric model. In this study, we propose a moment-based method coupled with sample splitting for estimating this proportion. If the p values from the alternative hypothesis are homogeneously distributed, then the proposed method will solve the identifiability and give its optimal performances. When the p values from the alternative hypothesis are heterogeneously distributed, we propose to approximate this mixture distribution so that the identifiability can be achieved. Theoretical aspects of the approximation error are discussed. The proposed estimation method is completely nonparametric and simple with an explicit formula. Simulation studies show the favorable performances of the proposed method when it is compared to the other existing methods. Two microarray gene expression data sets are considered for applications.     

A resolution of the ascertainment sampling problem. III. Pedigrees.

When nuclear families are sampled by an ascertainment procedure whose properties are not known, biased estimates of genetic parameters will arise if an incorrect specification of the ascertainment procedure is made. Elsewhere we have put forward a resolution of this problem by introducing an ascertainment-assumption-free (AAF) method, for nuclear family data, which gives asymptotically unbiased estimators no matter what the true nature of the ascertainment process. In the present paper we extend this method to cover pedigree data. Problems that arise with pedigrees but not with families--for example, the question of which families in a pedigree are "ascertainable"--are also considered. Comparisons of numerical results for pedigrees and nuclear families are also made.     

Prediction of mass transfer coefficients in non-Newtonian fermentation media using first-principles methods

Bubble flows in non-Newtonian fluids were analyzed using first-principles methods with the aim to compute and predict mass transfer coefficients in such fermentation media. The method we used is a Direct Numerical Simulation (DNS) of the reactive multiphase flow with deformable boundaries and interfaces. With this method, we are able for the first time to calculate mass transfer coefficients in non-Newtonian liquids of different rheologies without any experimental data. In the current article, shear-thinning fluids are considered. However, the results provide the basis for further investigations, such as the study of viscoelastic fluids.     

Improved Lower Bounds of DNA Tags Based on a Modified Genetic Algorithm

The well-known massively parallel sequencing method is efficient and it can obtain sequence data from multiple individual samples. In order to ensure that sequencing, replication, and oligonucleotide synthesis errors do not result in tags (or barcodes) that are unrecoverable or confused, the tag sequences should be abundant and sufficiently different. Recently, many design methods have been proposed for correcting errors in data using error-correcting codes. The existing tag sets contain small tag sequences, so we used a modified genetic algorithm to improve the lower bound of the tag sets in this study. Compared with previous research, our algorithm is effective for designing sets of DNA tags. Moreover, the GC content determined by existing methods includes an imprecise range. Thus, we improved the GC content determination method to obtain tag sets that control the GC content in a more precise range. Finally, previous studies have only considered perfect self-complementarity. Thus, we considered the crossover between different tags and introduced an improved constraint into the design of tag sets.     

Quartered neighbor method: A new distance method for density estimation

Quantitative data are essential to an appro-priate characterization of vegetation. In the past few years, considerable attention has been paid to vegetation sampling techniques. A number of methods have been developed for plant density estimations that utilize spacing distances instead of fixed-area quadrats. In this paper, we review the main distance methods for estimating density and propose a new distance method denominated the quartered neighbor method. In this method, the sampling point is considered the center, and the area around it is divided into four quadrants. The distance from the closest individual in each quadrant to its closest neighbor in the same quadrant is measured, and the average of them is the distance we need. It is actually an integration of two old distance methods, the nearest neighbor method, and the point-centered quarter method. With our new method and an old distance method (the point-centered quarter method), we calculated the average spacing distances of the Larix principis-rupprechtii population in the larch forests of the Donglingshan Mountain. Comparing the two methods with the quadrat method, we found they were almost the same in accuracy, but the precision of the new one was better. Meanwhile, it is adequate in sampling intensity and adaptable for general use in rapid ecological survey work.     

光通过散射介质聚焦的空间位相逐个单元调节方法

本研究在分析现有散射介质聚焦方法的基础上,提出一种利用单元裂解进行波前位相调制,将经过强散射介质的散射光聚焦的快速收敛方法.文中详细地描述了这一新方法的原理,并同现有逐个单元调节方法进行运行对比.单元裂解方法的物理本质,即为实现空间光调制器的调制各单元光波之间同位相干涉.本方法具有更高的信噪比,并且聚焦收敛的速度快.这一新方法为进一步开展生物成像的光学理论研究提供了新思路.     

Quartered neighbor method: A new distance method for density estimation

Quantitative data are essential to an appropriate characterization of vegetation. In the past few years, considerable attention has been paid to vegetation sampling techniques. A number of methods have been developed for plant density estimations that utilize spacing distances instead of fixed-area quadrats. In this paper, we review the main distance methods for estimating density and propose a new distance method denominated the quartered neighbor method. In this method, the sampling point is considered the center, and the area around it is divided into four quadrants. The distance from the closest individual in each quadrant to its closest neighbor in the same quadrant is measured, and the average of them is the distance we need. It is actually an integration of two old distance methods, the nearest neighbor method, and the point-centered quarter method. With our new method and an old distance method (the point-centered quarter method), we calculated the average spacing distances of the Larix principis-rupprechtii population in the larch forests of the Donglingshan Mountain. Comparing the two methods with the quadrat method, we found they were almost the same in accuracy, but the precision of the new one was better. Meanwhile, it is adequate in sampling intensity and adaptable for general use in rapid ecological survey work.     

Improved Predictions in Linear Regression Models with Stochastic Linear Constraints

In this article, we have considered two families of predictors for the simultaneous prediction of actual and average values of study variable in a linear regression model when a set of stochastic linear constraints binding the regression coefficients is available. These families arise from the method of mixed regression estimation. Performance properties of these families are analyzed when the objective is to predict values outside the sample and within the sample.     

Statistical equivalent of the classical TDT for quantitative traits and multivariate phenotypes

Clinical end-point traits are usually governed by quantitative precursors. Hence, there is active research interest in developing statistical methods for association mapping of quantitative traits. Unlike population-based tests for association, family-based tests for transmission disequilibrium are protected against population stratification. In this study, we propose a logistic regression model to test the association for quantitative traits based on a trio design. We show that the method can be viewed as a direct extension of the classical transmission diequilibrium test for binary traits to quantitative traits. We evaluate the performance of our method using extensive simulations and compare it with an existing method, family-based association test. We found that the two methods yield comparable powers if all families are considered. However, unlike FBAT, which yields an inflated rate of false positives when noninformative trios with all three individuals’ heterozygous are removed, our method maintains the correct size without compromising too much on power. We show that our method can be easily modified to incorporate multivariate phenotypes. Here, we applied this method to analyse a quantitative endophenotype associated with alcoholism.     

A participatory counting method to monitor populations of large mammals in non-protected areas: a case study of bicycle counts in the Zambezi Valley, Zimbabwe

The sustainable use of wildlife resources within community-based programmes is considered as a valuable option to enhance development and conservation objectives. The application of such a concept in communal lands requires the strong support of local communities through their active involvement in wildlife management. Precise and regular information on wildlife abundance is also essential for effective conservation. In this paper, we present an innovative participatory monitoring method based on bicycle counts developed within the framework of an integrated conservation and development project in the Zambezi Valley, Zimbabwe. Results from the 1999 count of population density and structure of common species as well as diversity of large to medium size species are presented. We demonstrate the efficiency of this method, which allowed a high census intensity with efficient animal detection, and hence appeared appropriate for use in a non-protected area. The method gave high levels of precision for the density estimates obtained (10–30% cv) and is therefore useful as a repeatable monitoring tool. In addition, this method is inexpensive to run and is easy to implement for local people. We emphasise the technical and financial autonomy offered by the bicycle counts for communities to monitor their wildlife resources, and we discuss the contribution of this method to the wildlife management process.     

Prediction of functional phosphorylation sites by incorporating evolutionary information

Protein phosphorylation is a ubiquitous protein post-translational modification, which plays an important role in cellular signaling systems underlying various physiological and pathological processes. Current in silico methods mainly focused on the prediction of phosphorylation sites, but rare methods considered whether a phosphorylation site is functional or not. Since functional phosphorylation sites are more valuable for further experimental research and a proportion of phosphorylation sites have no direct functional effects, the prediction of functional phosphorylation sites is quite necessary for this research area. Previous studies have shown that functional phosphorylation sites are more conserved than non-functional phosphorylation sites in evolution. Thus, in our method, we developed a web server by integrating existing phosphorylation site prediction methods, as well as both absolute and relative evolutionary conservation scores to predict the most likely functional phosphorylation sites. Using our method, we predicted the most likely functional sites of the human, rat and mouse proteomes and built a database for the predicted sites. By the analysis of overall prediction results, we demonstrated that protein phosphorylation plays an important role in all the enriched KEGG pathways. By the analysis of protein-specific prediction results, we demonstrated the usefulness of our method for individual protein studies. Our method would help to characterize the most likely functional phosphorylation sites for further studies in this research area.     

An extended multi-objective mixed integer programming for water resources management through possibility theory

In this study, we develop an extended multi-objective mixed integer programming (EMOMIP) approach for water resources management under uncertainty, in which the parameters are fuzzy random variables while the decision variables are interval variables. Furthermore, some alternatives are considered to retrieve the difference between the quantities of promised water-allocation targets and the actual allocated water. Then, the proposed EMOMIP for the problem is solved by a new method using fuzzy random chance-constrained programming based on the idea of possibility theory. This method can satisfy both optimistic and pessimistic decision makers simultaneously. Finally, a real example is given to explain the proposed method.     

Pseudo-likelihood analysis of codon substitution models with neighbor-dependent rates.

Recently, Markov processes for the evolution of coding DNA with neighbor dependence in the instantaneous substitution rates have been considered. The neighbor dependency makes the models analytically intractable, and previously Markov chain Monte Carlo methods have been used for statistical inference. Using a pseudo-likelihood idea, we introduce in this paper an approximative estimation method which is fast to compute. The pseudo-likelihood estimates are shown to be very accurate, and from analyzing 348 human-mouse coding sequences we conclude that the incorporation of a CpG effect improves the fit of the model considerably.     

Covariation of GC content and the silent site substitution rate in rodents: implications for methodology and for the evolution of isochores

Many attempts to test selectionist and neutralist models employ estimates of synonymous (Ks) and non-synonymous (Ka) substitution rates of orthologous genes. For example, a stronger Ka-Ks correlation than expected under neutrality has been argued to indicate a role for selection and the absence of a Ks-GC4 correlation has been argued to be inconsistent with neutral models for isochore evolution. However, both of these results, we have shown previously, are sensitive to the method by which Ka and Ks are estimated. Using a maximum likelihood (ML) estimator (GY94) we found a positive correlation between Ks and GC4 and only a weak correlation between Ka and Ks, lower than expected under neutral expectations. This ML method is computationally slow. Recently, a new ad hoc approximation of this ML method has been provided (YN00). This is effectively an extension of Li's protocol but that also allows for codon usage bias. This method is computationally near-instantaneous and therefore potentially of great utility for analysis of large datasets. Here we ask whether this method might have such applicability. To this end we ask whether it too recovers the two unusual results. We report that when the ML and earlier ad hoc methods disagree, YN00 recovers the results described by the ML methods, i.e. a positive correlation between GC4 and Ks and only a weak correlation between Ks and Ka. If the ML method can be trusted, then YN00 can also be considered an adequately reliable method for analysis of large datasets. Assuming this to be so we also analyze further the patterns. We show, for example, that the positive correlation between GC4 and Ks is probably in part a mutational bias, there being more methyl induced CpG-->TpG mutations in GC rich regions. As regards the evolution of isochores, it seems inappropriate to use the claimed lack of a correlation between GC and Ks as definitive evidence either against or for any model. If the positive correlation is real then, we argue, this is hard to reconcile with the biased gene conversion model for isochore formation as this predicts a negative correlation.     

Site interdependence attributed to tertiary structure in amino acid sequence evolution

Standard likelihood-based frameworks in phylogenetics consider the process of evolution of a sequence site by site. Assuming that sites evolve independently greatly simplifies the required calculations. However, this simplification is known to be incorrect in many cases. Here, a computational method that allows for general dependence between sites of a sequence is investigated. Using this method, measures acting as sequence fitness proxies can be considered over a phylogenetic tree. In this work, a set of statistically derived amino acid pairwise potentials, developed in the context of protein threading, is used to account for what we call the structural fitness of a sequence. We describe a model combining statistical potentials with an empirical amino acid substitution matrix. We propose such a combination as a useful way of capturing the complexity of protein evolution. Finally, we outline features of the model using three datasets and show the approach's sensitivity to different tree topologies.     

Parameter-free testing of the shape of a probability distribution

The Kolmogorov-Smirnov test determines the consistency of empirical data with a particular probability distribution. Often, parameters in the distribution are unknown, and have to be estimated from the data. In this case, the Kolmogorov-Smirnov test depends on the form of the particular probability distribution under consideration, even when the estimated parameter-values are used within the distribution. In the present work, we address a less specific problem: to determine the consistency of data with a given functional form of a probability distribution (for example the normal distribution), without enquiring into values of unknown parameters in the distribution. For a wide class of distributions, we present a direct method for determining whether empirical data are consistent with a given functional form of the probability distribution. This utilizes a transformation of the data. If the data are from the class of distributions considered here, the transformation leads to an empirical distribution with no unknown parameters, and hence is susceptible to a standard Kolmogorov-Smirnov test. We give some general analytical results for some of the distributions from the class of distributions considered here. The significance level and power of the tests introduced in this work are estimated from simulations. Some biological applications of the method are given.     

A comparison of results obtained with two methods for assessing yield stability

Summary A major objective of the CIMMYT Maize Program is to develop open-pollinated varieties of maize (Zea mays L.) that are well adapted to a wide range of environments. To achieve this breeding goal, it is essential that the program use a stability technique that will identify high-yielding, stable genotypes accurately in international trials conducted under different environmental conditions. The objective of this study was to compare a spatial method with a modified conventional regression analysis method to determine the yield stability of 27 CIMMYT maize varieties evaluated at 37 locations. The methods also were compared on the basis of their consistency in assessing the stability of varieties when certain locations were omitted, and when subsets of varieties were analyzed. The varieties found to be stable by the spatial method with all sites included in the analysis were also stable (1) when the lowest and highest yielding sites were excluded from the analyses, and (2) when the varieties were considered, along with others, as a subset of the original group of materials. Stability parameters determined by regression analysis, however, varied for some varieties when (1) extreme sites were excluded, and (2) a subset of entries was considered in isolation. Because the spatial method was more consistent in identifying high-yielding stable varieties, it was considered the more useful of the two methods.