On evaluation of infection probabilities for different age groups

It is well known that evaluation of parameters for any mathematical model is always very important and often a very difficult problem. This is especially true for agents-based models because as a rule the evaluation of an agent's parameters can be made only based on available information from the higher levels of a complex system. Such problems can be ill-posed or even have a non-unique solution. That is why there is no general algorithm for solving these problems and a researcher has to search for it for every specific model type.     

On statistical inference for the random set generated Cox process with set-marking

Cox point process is a process class for hierarchical modelling of systems of non-interacting points in Rd under environmental heterogeneity which is modelled through a random intensity function. In this work a class of Cox processes is suggested where the random intensity is generated by a random closed set. Such heterogeneity appears for example in forestry where silvicultural treatments like harvesting and site-preparation create geometrical patterns for tree density variation in two different phases. In this paper the second order property, important both in data analysis and in the context of spatial sampling, is derived. The usefulness of the random set generated Cox process is highly increased, if for each point it is observed whether it is included in the random set or not. This additional information is easy and economical to obtain in many cases and is hence of practical value; it leads to marks for the points. The resulting random set marked Cox process is a marked point process where the marks are intensity-dependent. The problem with set-marking is that the marks are not a representative sample from the random set. This paper derives the second order property of the random set marked Cox process and suggests a practical estimation method for area fraction and covariance of the random set and for the point densities within and outside the random set. A simulated example and a forestry example are given.     

Visualising and assessing the probable error from downscaling ecological time series data

Collecting natural data at regular, fine scales is an onerous and often costly procedure. However, there is a basic need for fine scale data when applying inductive methods such as neural networks or genetic algorithms for the development of ecological models. This paper will address the issues involved in interpolating data for use in machine learning methods by considering how to determine if a downscaling of the data is valid. The approach is based on a multi-scale estimate of errors. The resulting function has similar properties to a time series variogram; however, the comparison at different scales is based on the variance introduced by rescaling from the original sequence. This approach has a number of properties, including the ability to detect frequencies in the data below the current sampling rate, an estimate of the probable average error introduced when a sampled variable is downscaled and a method for visualising the sequences of a time series that are most susceptible to error due to sampling. The described approach is ideal for supporting the ongoing sampling of ecological data and as a tool for assessing the impact of using interpolated data for building inductive models of ecological response.     

一类双线性生态系统的无源控制

研究了一类广义双线性系统的无源控制问题,利用广义Lyapunov函数和线性矩阵不等式,给出了广义双线性系统无源且零解渐近稳定的充分条件,并在一定条件下得到存在状态反馈控制器,使得闭环系统无源且零解渐近稳定的充分条件,同时给出相应的控制器构造方法。     

Use of computational fluid dynamics as a tool for establishing process design space for mixing in a bioreactor

The concept of "design space" plays an integral part in implementation of quality by design for pharmaceutical products. ICH Q8 defines design space as "the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality. Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post-approval change process. Design space is proposed by the applicant and is subject to regulatory assessment and approval." Computational fluid dynamics (CFD) is increasingly being used as a tool for modeling of hydrodynamics and mass transfer. In this study, a laboratory-scale aerated bioreactor is modeled using CFD. Eulerian-Eulerian multiphase model is used along with dispersed k-ε turbulent model. Population balance model is incorporated to account for bubble breakage and coalescence. Multiple reference frame model is used for the rotating region. We demonstrate the usefulness of CFD modeling for evaluating the effects of typical process parameters like impeller speed, gas flow rate, and liquid height on the mass transfer coefficient (k(L)a). Design of experiments is utilized to establish a design space for the above mentioned parameters for a given permissible range of k(L)a.     

隐马尔科夫过程在生物信息学中的应用

隐马尔科夫过程（hidden markov model,简称HMM）是20世纪70年代提出来的一种统计方法,以前主要用于语音识别。1989年Churchill将其引入计算生物学。目前,HMM是生物信息学中应用比较广泛的一种统计方法,主要用于：线性序列分析、模型分析、基因发现等方面。对HMM进行了简明扼要的描述,并对其在上述几个方面的应用作一概略介绍。     

X-inactivation is stably maintained in mouse embryos deficient for histone methyl transferase G9a

One of the two X chromosomes becomes inactivated during early development of female mammals. Recent studies demonstrate that the inactive X chromosome is rich in histone H3 methylated at Lys-9 and Lys-27, suggesting an important role for these modifications in X-inactivation. It has been shown that in the mouse Eed is required for maintenance of X-inactivation in the extraembryonic lineages. Interestingly, Eed associates with Ezh2 to form a complex possessing histone methyltransferase activity predominantly for H3 Lys-27. We previously showed that G9a is one of the histone methyltransferases specific for H3 Lys-9 and is essential for embryonic development. Here we examined X-inactivation in mouse embryos deficient for G9a. Expression of Xist, which is crucial for the initiation of X-inactivation, was properly regulated and the inactivated X chromosome was stably maintained even in the absence of G9a. These results demonstrate that G9a is not essential for X-inactivation.     

The dynamics of two viral infections in a single host population with applications to hantavirus

An SI epidemic model for a host with two viral infections circulating within the population is developed, analyzed, and numerically simulated. The model is a system of four differential equations which includes a state for susceptible individuals, two states for individuals infected with a single virus, one which is vertically transmitted and the other which is horizontally transmitted, and a fourth state for individuals infected with both viruses. A general growth function with density-dependent mortality is assumed. A special case of this model, where there is no coinfection and total cross immunity, is thoroughly analyzed. Several threshold values are defined which determine establishment of the disease and persistence at equilibrium for one or both of the infections within the host population. The model has applications to a hantavirus and an arenavirus that infect cotton rats. The hantavirus is transmitted horizontally whereas the arenavirus is transmitted vertically. It is shown through analysis and numerical simulations that both diseases can be maintained within a single host population, where individuals can be either infected with both viruses or with a single virus.     

Voltage clamp calculations for myelinated and demyelinated axons

Exact cable theory is used to calculate voltage distributions along fully myelinated axons and those with various patterns of demyelination. The model employed uses an R-C circuit for the soma, an equivalent cable for the dendrites, a myelinated axon with n internodes and a cable representing telodendria. For the case of a voltage clamp at the soma, a system of 2n + 1 equations must be solved to obtain the potential distribution and this is done for arbitrary n. An explicit calculation is performed for one internode whereas computer-generated solutions are obtained for several internodes. The relative importance of the position of a single demyelinated internode is determined. An approximate expression is given for the critical internodal length necessary for action potential generation.     

Orientational relaxation time of bottom-heavy squirmers in a semi-dilute suspension

One of the important quantities to characterize unsteady behaviour of a cell suspension is the orientational relaxation time, which is the time scale for a micro-organism to re-orientate to its preferred direction from disorientated conditions. In this paper, a swimming micro-organism is modelled as a squirming sphere with prescribed tangential surface velocity, in which the centre of mass of the sphere is displaced from the geometric centre (bottom-heaviness). The orientational relaxation time of bottom-heavy squirmers in a suspension is investigated both analytically and numerically. The three-dimensional movement of 64 identical squirmers in a fluid otherwise at rest, contained in a cube with periodic boundary conditions, is dynamically computed, for random initial positions and orientations. The effects of volume fraction of squirmers, the bottom-heaviness and the squirming mode on the relaxation time are discussed. The results for a semi-dilute suspension show that both the mean stresslet strength and the orientational relaxation time decrease from those for a dilute suspension. We also observe a stress overshoot in some cases. The mechanism for this is different from that for a visco-elastic fluid, and is explained by the change with time of the orientation of squirmers.     

Model of two noise source dependent Ornstein-Uhlenbeck processes applied to postural sway

In this study the postural control system is modeled in terms of two counteracting bio-subsystems. Their activities are described by two Ornstein-Uhlenbeck processes with different-in-magnitude noise sources. The model is constructed as a sum of these processes, where in each of them the same noise source with opposite sign of the noise coefficients was introduced. Since the friction coefficients are also different for these processes, the delay of a crossover from ballistic to diffusive motion for one of the subsystems is greater than for its counterpart. It turns out that for smaller time intervals a superdiffusive behavior is observed, whereas, counteraction of subsystems is called into play for a larger time intervals, what for investigated range of data, is exhibited as a slow, subdiffusive behavior.     

Selection on parental performance opposes selection for larger body mass in a wild population of blue tits

There is abundant evidence in many taxa for positive directional selection on body size, and yet little evidence for microevolutionary change. In many species, variation in body size is partly determined by the actions of parents, so a proposed explanation for stasis is the presence of a negative genetic correlation between direct and parental effects. Consequently, selecting genes for increased body size would result in a correlated decline in parental effects, reducing body size in the following generation. We show that these arguments implicitly assume that parental care is cost free, and that including a cost alters the predicted genetic architectures needed to explain stasis. Using a large cross‐fostered population of blue tits, we estimate direct selection on parental effects for body mass, and show it is negative. Negative selection is consistent with a cost to parental care, mainly acting through a reduction in current fecundity rather than survival. Under these conditions, evolutionary stasis is possible for moderately negative genetic correlations between direct and parental effects. This is in contrast to the implausibly extreme correlations needed when care is assumed to be cost‐free. Thus, we highlight the importance of accounting correctly for complete selection acting on traits across generations.     

Prediction of signal peptides using scaled window

Cells use a ZIP code system to sort newly synthesized proteins and deliver them wherever they are needed: into different internal compartments called organelles or even out of the cell altogether. One of the most essential features of the ZIP code system is the signal sequence or “address tag,” which is originally present in the N-terminal part of the protein and is trimmed away by the time it is secreted. Owing to the importance of signal peptides for understanding the molecular mechanisms of genetic diseases, reprogramming cells for gene therapy, and constructing new drugs for correcting a specific defect, it is highly desirable to develop a fast and accurate method to identify the signal peptides. In this paper, a scaled window model is proposed. Based on such a model as well as Markov chain theory, a new algorithm is formulated for predicting the signal peptides. Test results for the 1939 secretory proteins and 1440 non-secretary proteins have indicated that the new algorithm is particularly successful in the overall success rate, and hence can serve as a complementary tool to the existing algorithms for signal peptide prediction.     

Towards plant pangenomics

As an increasing number of genome sequences become available for a wide range of species, there is a growing understanding that the genome of a single individual is insufficient to represent the gene diversity within a whole species. Many studies examine the sequence diversity within genes, and this allelic variation is an important source of phenotypic variation which can be selected for by man or nature. However, the significant gene presence/absence variation that has been observed within species and the impact of this variation on traits is only now being studied in detail. The sum of the genes for a species is termed the pangenome, and the determination and characterization of the pangenome is a requirement to understand variation within a species. In this review, we explore the current progress in pangenomics as well as methods and approaches for the characterization of pangenomes for a wide range of plant species.     

"基因电脑克隆"软件SiClone的并行优化研究与实现

生物信息学中,发现、鉴别新基因是承上启下的一步,它既承接了过往如“基因组测序”的工作,又是未来“后基因时代”研究的基石．“基因电脑克隆”是利用计算手段发现、鉴别新基因的方法,SiClone软件实现了“基因电脑克隆”功能．本文对SiClone软件操作的数据库提出并行处理方案,并详述了基于MPI(message passing interface)平台实现的并行优化版本PSiClone．根据已得到的EST数据库,展示了软件并行版PSiClone的运行性能,试验数据库EST序列条数仅仅是NCBI(The National Center for Biotechnology Information)dbEST庞大数据库的很小部分,这也暗示我们软件的并行工作对于大数据库的比较和运算将更有应用前景．     

Do grasslands act as a perpetual sink for carbon?

It is increasingly commonly suggested that grasslands are a perpetual sink for carbon, and that just maintaining grasslands will yield a net carbon sink. I examine the evidence for this from repeated soil surveys, long term grassland experiments and simple mass balance calculations. I conclude that it is untenable that grasslands act as a perpetual carbon sink, and the most likely explanation for observed grassland carbon sinks over short periods is legacy effects of land use and land management prior to the beginning of flux measurement periods. Simply having grassland does not result is a carbon sink, but judicious management or previously poorly managed grasslands can increase the sink capacity. Given that grasslands are a large store of carbon, and that it is easier and faster for soils to lose carbon that it is for them to gain carbon, it is an important management target to maintain these stocks.     

KING (Kinemage,Next Generation): A versatile interactive molecular and scientific visualization program

Proper visualization of scientific data is important for understanding spatial relationships. Particularly in the field of structural biology, where researchers seek to gain an understanding of the structure and function of biological macromolecules, it is important to have access to visualization programs which are fast, flexible, and customizable. We present KiNG, a Java program for visualizing scientific data, with a focus on macromolecular visualization. KiNG uses the kinemage graphics format, which is tuned for macromolecular structures, but is also ideal for many other kinds of spatially embedded information. KiNG is written in cross‐platform, open‐source Java code, and can be extended by end users through simple or elaborate “plug‐in” modules. Here, we present three such applications of KiNG to problems in structural biology (protein backbone rebuilding), bioinformatics of high‐dimensional data (e.g., protein sidechain chi angles), and classroom education (molecular illustration). KiNG is a mature platform for rapidly creating and capitalizing on scientific visualizations. As a research tool, it is invaluable as a test bed for new methods of visualizing scientific data and information. It is also a powerful presentation tool, whether for structure browsing, teaching, direct 3D display on the web, or as a method for creating pictures and videos for publications. KiNG is freely available for download at http://kinemage.biochem.duke.edu .     

Doubly robust estimation in missing data and causal inference models

The goal of this article is to construct doubly robust (DR) estimators in ignorable missing data and causal inference models. In a missing data model, an estimator is DR if it remains consistent when either (but not necessarily both) a model for the missingness mechanism or a model for the distribution of the complete data is correctly specified. Because with observational data one can never be sure that either a missingness model or a complete data model is correct, perhaps the best that can be hoped for is to find a DR estimator. DR estimators, in contrast to standard likelihood-based or (nonaugmented) inverse probability-weighted estimators, give the analyst two chances, instead of only one, to make a valid inference. In a causal inference model, an estimator is DR if it remains consistent when either a model for the treatment assignment mechanism or a model for the distribution of the counterfactual data is correctly specified. Because with observational data one can never be sure that a model for the treatment assignment mechanism or a model for the counterfactual data is correct, inference based on DR estimators should improve upon previous approaches. Indeed, we present the results of simulation studies which demonstrate that the finite sample performance of DR estimators is as impressive as theory would predict. The proposed method is applied to a cardiovascular clinical trial.     

Defining life from death: Problems with the somatic integration definition of life

To determine when the life of a human organism begins, Mark T. Brown has developed the somatic integration definition of life. Derived from diagnostic criteria for human death, Brown’s account requires the presence of a life-regulation internal control system for an entity to be considered a living organism. According to Brown, the earliest point at which a developing human could satisfy this requirement is at the beginning of the fetal stage, and so the embryo is not regarded as a living human organism. This, Brown claims, has significant bioethical implications for both abortion and embryo experimentation. Here, we dispute the cogency of Brown’s derivation. Diagnostic criteria for death are used to determine when an organism irreversibly ceases functioning as an integrated whole, and may vary significantly depending on how developed the organism is. Brown’s definition is derived from a specific definition of death applicable to postnatal human beings, which is insufficient for generating a general definition for human organismal life. We have also examined the bioethical implications of Brown’s view, and have concluded that they are not as significant as he believes. Whether the embryo is classified as a human organism is of peripheral interest—a far more morally relevant question is whether the embryo is a biological individual with an identity that is capable of persisting during development.     

Binary regression: Total gain in positive and negative predictive values

Models that predict disease incidence or disease recurrence are attractive for clinicians as well as for patients. The usefulness of a risk prediction model is linked to the two questions whether the observed outcome is confirmed by the prediction and whether the risk prediction is accurate in predicting the future outcome, respectively. The first phrasing of the question is linked to considering sensitivity and specificity and the latter to the positive and negative predictive values. We present the measures of standardized total gain in positive and negative predictive values dealing with the performance or accuracy of the prediction model for a binary outcome. Both measures provide a useful tool for assessing the performance or accuracy of a set of predictor variables for the prediction of a binary outcome. This concept is a tool for evaluating the optimal prediction model in future research.