The role of Bicoid cooperative binding in the patterning of sharp borders in Drosophila melanogaster

In Drosophila embryonic development, the Bicoid (Bcd) protein establishes positional information of downstream developmental genes like hunchback (hb), which has a strong anterior expression and a sharp on-off boundary in the mid-embryo. The role of Bcd cooperative binding in the positioning of the Hb pattern has been previously demonstrated. However, there are discrepancies in the reported results about the role of this mechanism in the sharp Hb border. Here, we determined the Hill coefficient (n(H)) required for Bcd to generate the sharp border of Hb in wild-type (WT) embryos. We found that an n(H) of approximately 6.3 (s.d. 1.4) and 10.8 (s.d. 4.0) is required to account for Hb sharpness at early and late cycle 14A, respectively. Additional mechanisms are possibly required because the high n(H) is likely unachievable for Bcd binding to the hb promoter. To test this idea, we determined the n(H) required to pattern the Hb profile of 15 embryos expressing an hb(14F) allele that is defective in self-activation and found n(H) to be 3.0 (s.d. 1.0). This result indicates that in WT embryos, the hb self-activation is important for Hb sharpness. Corroborating our results, we also found a progressive increase in the required value of n(H) spanning from 4.0 to 9.2 by determining this coefficient from averaged profiles of eight temporal classes at cycle 14A (T1 to T8). Our results indicate that there is a transition in the mechanisms responsible for the sharp Hb border during cycle 14A: in early stages of this cycle, Bcd cooperative binding is primarily responsible for Hb sharpness; in late cycle 14A, hb self-activation becomes the dominant mechanism.     

Distributed computing in image analysis using open source frameworks and application to image sharpness assessment of histological whole slide images

Background

Automated image analysis on virtual slides is evolving rapidly and will play an important role in the future of digital pathology. Due to the image size, the computational cost of processing whole slide images (WSIs) in full resolution is immense. Moreover, image analysis requires well focused images in high magnification.

Methods

We present a system that merges virtual microscopy techniques, open source image analysis software, and distributed parallel processing. We have integrated the parallel processing framework JPPF, so batch processing can be performed distributed and in parallel. All resulting meta data and image data are collected and merged. As an example the system is applied to the specific task of image sharpness assessment. ImageJ is an open source image editing and processing framework developed at the NIH having a large user community that contributes image processing algorithms wrapped as plug-ins in a wide field of life science applications. We developed an ImageJ plug-in that supports both basic interactive virtual microscope and batch processing functionality. For the application of sharpness inspection we employ an approach with non-overlapping tiles. Compute nodes retrieve image tiles of moderate size from the streaming server and compute the focus measure. Each tile is divided into small sub images to calculate an edge based sharpness criterion which is used for classification. The results are aggregated in a sharpness map.

Results

Based on the system we calculate a sharpness measure and classify virtual slides into one of the following categories - excellent, okay, review and defective. Generating a scaled sharpness map enables the user to evaluate sharpness of WSIs and shows overall quality at a glance thus reducing tedious assessment work.

Conclusions

Using sharpness assessment as an example, the introduced system can be used to process, analyze and parallelize analysis of whole slide images based on open source software.

     

Towards a molecular theory of the nerve membrane: Prediction of the maximum negative conductance

A model of the squid axon membrane based on the theory of absolute reaction rates generates the rapid potential dependence of the membrane properties from the statistics of a simple gate-closing mechanism. It is shown that the peak negative transient conductance, normalized to the peak inward transient current, has a maximum value which is only weakly dependent upon parameter values, and is basically a property of the proposed mechanism. Those parameters which do influence the normalized peak conductance also affect the potential at which the maximum occurs, enabling an upper limit of 0.10 ± 0.02 mV^?1 to be established. Published data are consistent with this value but more precise measurements are desirable. The same limit should be observed in all excitable tissues which depend on the postulated central mechanism. Since other models do not predict such a maximum, experimental measurements of this property can provide a stringent test of the unifying principle suggested.     

听神经元的频率调谐

频率作为声音的一个重要参数,在听敏感神经元对声音进行分析和编码过程中扮演重要角色。一般用频率调谐曲线来表示听敏感神经元的频率调谐特性,并用Qn(10,30,50)值表达频率调谐曲线的尖锐程度,Qn值越大,频率调谐曲线也越尖锐,神经元的频率调谐能力越好,对频率的分辨能力越高。从听觉外周到中枢,听敏感神经元的频率调谐逐级锐化,而这种锐化主要是由听中枢的多种抑制性神经递质的作用而产生的,其中起主要作用的是GABA能和甘氨酸能神经递质。此外,离皮层调控,双侧下丘间的联合投射以及弱噪声前掩蔽等因素也会影响听敏感神经元的频率调谐特性。     

Geomicrobial Kinetics: Extrapolating Laboratory Studies to Natural Environments

Predicting metabolic rates and population sizes of microorganisms in natural environments is a central problem in geomicrobiology. Such predictions can be made on the basis of a thermodynamically consistent rate law that accounts for both kinetic and thermodynamic controls on microbial metabolism. Application of the rate law requires kinetic and growth parameters, the values of which have been determined for pure and mixed cultures growing in laboratory reactors. However, not all parameter values derived from laboratory studies can be validly applied to the environment. This article illustrates a best-choice approach for extrapolating experimentally-derived parameter values to natural environments, using microbial sulfate reduction coupled to acetate oxidation as an example. We compiled kinetic and growth parameters determined by previous laboratory studies and evaluated their applicability to natural environments. Our results suggest that some parameters, such as rate constants and maximum growth yields, can be applied directly to the environment; others, such as half-saturation constants and specific maintenance rates, are best determined using samples recovered from the environment of interest. The best-choice parameter values were applied to simulation of acetotrophic sulfate reduction in the sediments of a freshwater lake. Our analysis shows that the best-choice approach reduces the tasks of parameter fitting and simplifies the modeling exercise. The proposed approach also ensures that parameters in use are consistent with the physiology of indigenous microorganisms, and relevant to the environment of interest.     

State estimation bias induced by optimization under uncertainty and error cost asymmetry is likely reflected in perception

It is well known from numerous studies that perception can be significantly affected by intended action in many everyday situations, indicating that perception and related decision-making is not a simple, one-way sequence, but a complex iterative cognitive process. However, the underlying functional mechanisms are yet unclear. Based on an optimality approach, a quantitative computational model of one such mechanism has been developed in this study. It is assumed in the model that significant uncertainty about task-related parameters of the environment results in parameter estimation errors and an optimal control system should minimize the cost of such errors in terms of the optimality criterion. It is demonstrated that, if the cost of a parameter estimation error is significantly asymmetrical with respect to error direction, the tendency to minimize error cost creates a systematic deviation of the optimal parameter estimate from its maximum likelihood value. Consequently, optimization of parameter estimate and optimization of control action cannot be performed separately from each other under parameter uncertainty combined with asymmetry of estimation error cost, thus making the certainty equivalence principle non-applicable under those conditions. A hypothesis that not only the action, but also perception itself is biased by the above deviation of parameter estimate is supported by ample experimental evidence. The results provide important insights into the cognitive mechanisms of interaction between sensory perception and planning an action under realistic conditions. Implications for understanding related functional mechanisms of optimal control in the CNS are discussed.     

Survival of a mixture of cells of variable linear-quadratic sensitivity to radiation

The experimentally observed survival of a heterogeneous mixture of cells, each component of which obeys a different linear-quadratic survival response to ionizing radiation, is examined. It is shown that the survival relationship for the mixed population approaches a linear-quadratic form for low doses. The linear parameter of the low-dose relationship approached is equal to the average of the distribution of values of the linear parameter (alpha(i)) of the various components of the mixture. The quadratic parameter of the low-dose relationship approached is equal to the average of the distribution of values of the quadratic parameter (beta(i)) of the various components of the mixture minus one-half the variance of the distribution of the values of alpha(i). A numerical example of the survival expected for an exponentially growing population of Chinese hamster V79 cells is presented. From this it can be appreciated that the apparent value of the alpha and beta parameters obtained by fitting an experimentally obtained survival curve will depend on the range of doses over which survival is determined. The apparent value of beta is decreased at higher doses, producing a straightening of the survival curve to approach the exponential decrease in survival commonly observed for the terminal high-dose portion of survival curves. Apparent exponential survival at high doses is not inconsistent with linear-quadratic survival and may not indicate a multitarget or other mechanism of cell killing.     

Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis

The accurate measurement of bacterial and protistan cell biomass is necessary for understanding their population and trophic dynamics in nature. Direct measurement of fluorescently stained cells is often the method of choice. The tedium of making such measurements visually on the large numbers of cells required has prompted the use of automatic image analysis for this purpose. Accurate measurements by image analysis require an accurate, reliable method of segmenting the image, that is, distinguishing the brightly fluorescing cells from a dark background. This is commonly done by visually choosing a threshold intensity value which most closely coincides with the outline of the cells as perceived by the operator. Ideally, an automated method based on the cell image characteristics should be used. Since the optical nature of edges in images of light-emitting, microscopic fluorescent objects is different from that of images generated by transmitted or reflected light, it seemed that automatic segmentation of such images may require special considerations. We tested nine automated threshold selection methods using standard fluorescent microspheres ranging in size and fluorescence intensity and fluorochrome-stained samples of cells from cultures of cyanobacteria, flagellates, and ciliates. The methods included several variations based on the maximum intensity gradient of the sphere profile (first derivative), the minimum in the second derivative of the sphere profile, the minimum of the image histogram, and the midpoint intensity. Our results indicated that thresholds determined visually and by first-derivative methods tended to overestimate the threshold, causing an underestimation of microsphere size. The method based on the minimum of the second derivative of the profile yielded the most accurate area estimates for spheres of different sizes and brightnesses and for four of the five cell types tested. A simple model of the optical properties of fluorescing objects and the video acquisition system is described which explains how the second derivative best approximates the position of the edge.     

Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis.

The accurate measurement of bacterial and protistan cell biomass is necessary for understanding their population and trophic dynamics in nature. Direct measurement of fluorescently stained cells is often the method of choice. The tedium of making such measurements visually on the large numbers of cells required has prompted the use of automatic image analysis for this purpose. Accurate measurements by image analysis require an accurate, reliable method of segmenting the image, that is, distinguishing the brightly fluorescing cells from a dark background. This is commonly done by visually choosing a threshold intensity value which most closely coincides with the outline of the cells as perceived by the operator. Ideally, an automated method based on the cell image characteristics should be used. Since the optical nature of edges in images of light-emitting, microscopic fluorescent objects is different from that of images generated by transmitted or reflected light, it seemed that automatic segmentation of such images may require special considerations. We tested nine automated threshold selection methods using standard fluorescent microspheres ranging in size and fluorescence intensity and fluorochrome-stained samples of cells from cultures of cyanobacteria, flagellates, and ciliates. The methods included several variations based on the maximum intensity gradient of the sphere profile (first derivative), the minimum in the second derivative of the sphere profile, the minimum of the image histogram, and the midpoint intensity. Our results indicated that thresholds determined visually and by first-derivative methods tended to overestimate the threshold, causing an underestimation of microsphere size. The method based on the minimum of the second derivative of the profile yielded the most accurate area estimates for spheres of different sizes and brightnesses and for four of the five cell types tested. A simple model of the optical properties of fluorescing objects and the video acquisition system is described which explains how the second derivative best approximates the position of the edge.     

基于对象特征的山东省丘陵地区多时相遥感土地覆被自动分类

对于基于像元的土地覆被分类来说,植被的分类是难点。使用多时相面向对象分类方法可以较好的解决这个问题。以山东省烟台市丘陵地区为研究区,采用Landsat TM(Landsat Thematic Mapper remotely sensed imagery)、DEM(Digital Elevation Model)、坡度、坡位、坡向等多种数据,利用基于对象特征的多时相分类方法对研究区进行土地覆盖自动分类。首先对影像进行多尺度分割并检验分割结果选取合适的分割尺度,然后分析对象的光谱、纹理、形状特征。根据各类地物的光谱特征、地理相关性、形状、空间分布等特征,明确类别之间的差异。建立决策树使用隶属度函数进行模糊分类,借助支持向量机提高分类精度。研究结果表明,通过使用多时相影像采用面向对象分类方法,相对于传统的基于像素的分类可以明显提高分类精度,尤其是解决了乔灌草的区分问题。     

A simple and fast method to determine the parameters for fuzzy c-means cluster analysis

MOTIVATION: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional datasets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to set optimal values of algorithm parameters. Wrong parameter values may either lead to the inclusion of purely random fluctuations in the results or ignore potentially important data. The optimal solution has parameter values for which the clustering does not yield any results for a purely random dataset but which detects cluster formation with maximum resolution on the edge of randomness. RESULTS: Estimation of the optimal parameter values is achieved by evaluation of the results of the clustering procedure applied to randomized datasets. In this case, the optimal value of the fuzzifier follows common rules that depend only on the main properties of the dataset. Taking the dimension of the set and the number of objects as input values instead of evaluating the entire dataset allows us to propose a functional relationship determining the fuzzifier directly. This result speaks strongly against using a predefined fuzzifier as typically done in many previous studies. Validation indices are generally used for the estimation of the optimal number of clusters. A comparison shows that the minimum distance between the centroids provides results that are at least equivalent or better than those obtained by other computationally more expensive indices.     

基于修正的亚像元模型的植被覆盖度估算

植被覆盖度是陆地生态过程模型、气象和气候模型的一项重要参数.通过消除植被类型分类精度以及遥感影像噪声带来的误差,结合实际测量值确定了归一化植被指数(NDVI)的最大值和最小值,修正了亚像元模型,并通过计算北京市植被覆盖度对模型进行了验证. 结果表明: 修正后模型的模拟值与实测值非常接近,尤其是对植被类型一致但密度有不同变化的草本植被, 但对乔木植被覆盖度的估算误差相对较大,这可能与遥感影像分辨率、植被破碎度及采用的混合像元模型有关.     

Zebrafish leopard gene as a component of the putative reaction-diffusion system

It has been suggested, on a theoretical basis, that a reaction-diffusion (RD) mechanism underlies pigment pattern formation in animals, but as yet, there is no molecular evidence for the putative mechanism. Mutations in the zebrafish gene, leopard, change the pattern from stripes to spots. Interestingly each allele gives a characteristic pattern, which varies in spot size, density and connectivity. That mutations in a single gene can generate such a variety of patterns can be understood using a RD model. All the pattern variations of leopard mutants can be generated in a simulation by changing a parameter value that corresponds to the reaction kinetics in a putative RD system. Substituting an intermediate value of the parameter makes the patterns similar to the heterozygous fish. These results suggest that the leopard gene product is a component of the putative RD mechanism.     

Equilibrium Frequency of Endosymbionts in Multiple Infections Based on the Balance between Vertical Transmission and Cytoplasmic Incompatibility

Cytoplasmic incompatibility (CI)-inducing endosymbiotic bacteria, such as Wolbachia and Cardinium, have been well studied through field data and validations on the basis of numerical simulations. However, the analytically derived equilibrium frequency of multiple infections has not yet been determined, although the equilibrium for cases of single infection has been reported. In this study, we considered the difference equation for endosymbionts using three parameters: the probability of the failure of vertical transmission (), CI strength (), and the level of host inbreeding (). To analyze this model, we particularly focused on , i.e., the frequency of host individuals completely infected with all -bacterial strains in the population. , at the equilibrium state, was analytically calculated in the cases where and is any arbitrary value. We found that can be described using two parameters: and , which is identical to . has a larger value in a system with a smaller . In addition, determines the maximum number of strains that infect a single host. Our results revealed the following: i) three parameters can be reduced to a single parameter, i.e., and ii) the threshold of the maximum number of infections is defined by , which prevents additional invasions by endosymbionts.     

S-system-based analysis of the robust properties common to many biochemical network models

Robustness is a key feature to characterize the adaptation of organisms to changes in their internal and external environments. A broad range of kinetic or dynamic models of biochemical systems have been developed. Robustness analyses are attractive for exploring some common properties of many biochemical models. To reveal such features, we transform different types of mathematical equations into a standard or intelligible formula and use the multiple parameter sensitivity (MPS) to identify some factors critically responsible for the total robustness to many perturbations. The MPS would be determined by the top quarter of the highly sensitive parameters rather than the single parameter with the maximum sensitivity. The MPS did not show any correlation to the network size. The MPS is closely related to the standard deviation of the sensitivity profile. A decrease in the standard deviation enhanced the total robustness, which shows the hallmark of distributed robustness that many factors (pathways) involve the total robustness.     

Segmentation scale parameter influence on the accuracy of detecting illegal landfills on satellite imagery. A case study for Novo Sarajevo

Illegal waste dumping has been widely regarded as one of the biggest source of environmental damage. Illegal landfills are a prevailing problem existing in a large number of countries. To control and better manage illegal landfills, it is necessary to know the current locations and contents of illegal landfills. This could increase efficiency in illegal landfill management and preserve the biodiversity and ecological balance. Remote sensing methods have been proven extremely effective in detecting potential illegal landfill sites. This paper investigates the relationship between the segmentation scale parameter and the detection accuracy of illegal landfill sites in urban areas that are not covered by vegetation or buried in the ground. The research showed that there is an optimal scale parameter (SP = 20) value for the used satellite image Pléiades 1B and area of interest (Novo Sarajevo municipality). The scale parameter's stated value gives maximum Kappa values and Overall accuracy coefficients for detected illegal landfills on the satellite image.     

The impact of cross-immunity, mutation and stochastic extinction on pathogen diversity

We examine the dynamics of antigenically diverse infectious agents using a mathematical model describing the transmission dynamics of arbitrary numbers of pathogen strains, interacting via cross-immunity, and in the presence of mutations generating new strains and stochastic extinctions of existing ones. Equilibrium dynamics fall into three classes depending on cross-immunity, transmissibility and host population size: systems where global extinction is likely, stable single-strain persistence, and multiple-strain persistence with stable diversity. Where multi-strain dynamics are stable, a diversity threshold region separates a low-prevalence, low-diversity region of parameter space from a high-diversity, high-prevalence region. The location of the threshold region is determined by the reproduction number of the pathogen and the intensity of cross-immunity, with the sharpness of the transition being determined by the manner in which immunity accrues with repeated infections. Host population size and cross-immunity are found to be the most decisive factors in determining pathogen diversity. While the model framework developed is simplified, we show that it can capture essential aspects of the complex evolutionary dynamics of pathogens such as influenza.     

Towards a cognitive niche: divergent foraging strategies resulting from limited cognitive ability of foraging herbivores in a spatially complex environment.

A model was developed to explain one mechanism whereby differential optimal foraging strategies can occur between species as a result of selection for competition avoidance. This is the primary requirement for niche differentiation to evolve without a difference in the underlying foraging ability or morphology. The model used an individual-based patch choice mechanism, whereby herbivores move from patch to patch seeking food with the highest nutrient intake characteristics. The choice of patch was governed by a parameter, mu, which determined to what extent information in the landscape at different distances from the herbivore was used by it to make foraging decisions. A genetic algorithm was used to optimise the value, mu, in a complex landscape. The value of mu quickly converged to a single value with stabilising selection occurring when there was only a single species foraging. When there was a competing species with a fixed value of mu, the value of mu evolved to be above or below the mean for the single species mean depending on whether the value of mu for the competitor was below, or above the single-species mean, respectively. This was indicative of niche segregation. However mu tended to vary unstably over time when allowed to vary simultaneously in both species, although there was evidence for interaction between the two values. These results indicate that there can be a competitive advantage in choosing a cognitive strategy that is complementary to that used by other species.     

Impact of nonignorable coarsening on Bayesian inference

The coarse data model of Heitjan and Rubin (1991) generalizes the missing data model of Rubin (1976) to cover other forms of incompleteness such as censoring and grouping. The model has 2 components: an ideal data model describing the distribution of the quantity of interest and a coarsening mechanism that describes a distribution over degrees of coarsening given the ideal data. The coarsening mechanism is said to be nonignorable when the degree of coarsening depends on an incompletely observed ideal outcome, in which case failure to properly account for it can spoil inferences. A theme in recent research is to measure sensitivity to nonignorability by evaluating the effect of a small departure from ignorability on the maximum likelihood estimate (MLE) of a parameter of the ideal data model. One such construct is the "index of local sensitivity to nonignorability" (ISNI) (Troxel and others, 2004), which is the derivative of the MLE with respect to a nonignorability parameter evaluated at the ignorable model. In this paper, we adapt ISNI to Bayesian modeling by instead defining it as the derivative of the posterior expectation. We propose the application of ISNI as a first step in judging the robustness of a Bayesian analysis to nonignorable coarsening. We derive formulas for a range of models and apply the method to evaluate sensitivity to nonignorable coarsening in 2 real data examples, one involving missing CD4 counts in an HIV trial and the other involving potentially informatively censored relapse times in a leukemia trial.     

Stimulus features relevant to the perception of sharpness and mechanically evoked cutaneous pain

Mechanical probes of various sizes and shapes were used to determine thresholds for the perception of pressure, sharpness, and pain on the human finger. As force increased, perception changed from dull pressure to sharp pressure to sharp pain. With the smallest probe (0.01 mm2), sharpness threshold was very close to pressure threshold. As probe size increased, sharpness and pain threshold expressed in terms of force) increased in proportion to probe circumference (not probe area), whereas pressure threshold increased relatively little. Pain and sharpness thresholds also increased as probe angle became obtuse. There was a statistically significant increase in both thresholds with a probe angle change of 15 degrees. Thus, both size and shape are necessary to describe a mechanical stimulus adequately, and pressure (force/area) is not a sufficient metric for pain studies. Thresholds varied at different skin sites on the finger. The dorsal surface had lower thresholds than the volar surface, but the difference between the two areas was not always statistically significant. The compliance of the skin (e.g., the amount of indentation produced by a given force) exhibited no relation to sharpness or pain threshold, whether considered within subjects at various skin sites, or across subjects at the same skin site. Comparison of the perceptual thresholds with the thresholds for nociceptors determined in electrophysiological studies indicates that the sensation of nonpainful sharpness is likely to be mediated by nociceptors. Furthermore, considerably more than threshold activation of nociceptors is necessary for normal pain perception.