Use of antibiotic disks to evolve drug-resistant bacteria

The methods used to generate antibiotic-resistant bacterial strains can be labour-intensive, costly, lengthy and/or prone to plate-to-plate variation. We propose a simple, inexpensive and easily replicated method to expose bacteria to a continuous gradient of antibiotic concentration, providing an environment of positive selective pressure for evolution of antibiotic-resistant strains.     

Independent component analysis of Raman spectra: Application on paraffin-embedded skin biopsies

Raman spectra provide wealthy but complex information about the chemical constituents of biological samples. Digital processing techniques are usually needed to extract the spectra of chemical constituents and their associated concentration profiles. However, spectral signatures may admit transformations from those recorded on pure constituents and these techniques require a priori knowledge of spectra to be estimated. We propose in this study to analyse paraffin-embedded skin biopsies of malignant and benign tumors dedicated to oncology researches by Raman spectroscopy and advanced signal processing methods. We show that the commonly used principal component analysis (PCA) does not give physically interpretable estimators of spectra and associated concentration profiles. Based on a linear model and taking into account the statistical properties of spectra, independent component analysis (ICA) is used to better estimate the spectra of chemical constituents. The estimators of associated concentration profiles are no longer orthogonal and have only positive values, contrary to PCA. ICA allows to model the paraffin by three Raman spectra and provides good estimators of underlying spectra of the human skin, which is of great interest in oncology since the retrieval of spectral features of different types of skin tumors is sufficient for their discrimination.     

Varying coefficients model with measurement error

Summary .   We propose a semiparametric partially varying coefficient model to study the relationship between serum creatinine concentration and the glomerular filtration rate (GFR) among kidney donors and patients with chronic kidney disease. A regression model is used to relate serum creatinine to GFR and demographic factors in which coefficient of GFR is expressed as a function of age to allow its effect to be age dependent. GFR measurements obtained from the clearance of a radioactively labeled isotope are assumed to be a surrogate for the true GFR, with the relationship between measured and true GFR expressed using an additive error model. We use locally corrected score equations to estimate parameters and coefficient functions, and propose an expected generalized cross-validation (EGCV) method to select the kernel bandwidth. The performance of the proposed methods, which avoid distributional assumptions on the true GFR and residuals, is investigated by simulation. Accounting for measurement error using the proposed model reduced apparent inconsistencies in the relationship between serum creatinine and GFR among different clinical data sets derived from kidney donor and chronic kidney disease source populations.     

Robust Gaussian Graphical Modeling Via l1 Penalization

Summary Gaussian graphical models have been widely used as an effective method for studying the conditional independency structure among genes and for constructing genetic networks. However, gene expression data typically have heavier tails or more outlying observations than the standard Gaussian distribution. Such outliers in gene expression data can lead to wrong inference on the dependency structure among the genes. We propose a l₁ penalized estimation procedure for the sparse Gaussian graphical models that is robustified against possible outliers. The likelihood function is weighted according to how the observation is deviated, where the deviation of the observation is measured based on its own likelihood. An efficient computational algorithm based on the coordinate gradient descent method is developed to obtain the minimizer of the negative penalized robustified‐likelihood, where nonzero elements of the concentration matrix represents the graphical links among the genes. After the graphical structure is obtained, we re‐estimate the positive definite concentration matrix using an iterative proportional fitting algorithm. Through simulations, we demonstrate that the proposed robust method performs much better than the graphical Lasso for the Gaussian graphical models in terms of both graph structure selection and estimation when outliers are present. We apply the robust estimation procedure to an analysis of yeast gene expression data and show that the resulting graph has better biological interpretation than that obtained from the graphical Lasso.     

Modeling the effects of a bidirectional latent predictor from multivariate questionnaire data

Researchers often measure stress using questionnaire data on the occurrence of potentially stress-inducing life events and the strength of reaction to these events, characterized as negative or positive and assigned an ordinal ranking. In studying the health effects of stress, one needs to obtain measures of an individual's negative and positive stress levels to be used as predictors. Motivated by data of this type, we propose a latent variable model, which is characterized by event-specific negative and positive reaction scores. If the positive reaction score dominates the negative reaction score for an event, then the individual's reported response to that event will be positive, with an ordinal ranking determined by the value of the score. Measures of overall positive and negative stress can be obtained by summing the reactivity scores across the events that occur for an individual. By incorporating these measures as predictors in a regression model and fitting the stress and outcome models jointly using Bayesian methods, inferences can be conducted without the need to assume known weights for the different events. We propose an MCMC algorithm for posterior computation and apply the approach to study the effects of stress on preterm delivery.     

Comparison of the quick Gram stain method to the B&M modified and favor methods

The Gram stain is an established method for bacterial identification, but the time needed to carry out this stain is 2-3 min. We attempted to shorten this time and stained a total of 70 clinical specimens isolated from using the Bartholomew & Mittwer (B&M) modified or Favor methods with a 3 s duration for washing and staining steps. Results were plotted and analyzed using a Hue Saturation Intensity (HSI) model. The range based on a plot of the two methods with the HSI model was presented as a reference interval. Our results indicated that 100% (35/35) of strains were Gram positive and 97.1% (34/35) were Gram negative for the quick B&M modified method. In the quick Favor method, 80.0% (28/35) were Gram positive and 68.6% (24/35) of strains were Gram negative. We propose that the quick B&M modified method is equivalent to the standard Gram staining method and is superior to the quick Favor method.     

Automated phylogenetic detection of recombination using a genetic algorithm

The evolution of homologous sequences affected by recombination or gene conversion cannot be adequately explained by a single phylogenetic tree. Many tree-based methods for sequence analysis, for example, those used for detecting sites evolving nonneutrally, have been shown to fail if such phylogenetic incongruity is ignored. However, it may be possible to propose several phylogenies that can correctly model the evolution of nonrecombinant fragments. We propose a model-based framework that uses a genetic algorithm to search a multiple-sequence alignment for putative recombination break points, quantifies the level of support for their locations, and identifies sequences or clades involved in putative recombination events. The software implementation can be run quickly and efficiently in a distributed computing environment, and various components of the methods can be chosen for computational expediency or statistical rigor. We evaluate the performance of the new method on simulated alignments and on an array of published benchmark data sets. Finally, we demonstrate that prescreening alignments with our method allows one to analyze recombinant sequences for positive selection.     

Detecting pulsatile hormone secretions using nonlinear mixed effects partial spline models

Neuroendocrine ensembles communicate with their remote and proximal target cells via an intermittent pattern of chemical signaling. The identification of episodic releases of hormonal pulse signals constitutes a major emphasis of endocrine investigation. Estimating the number, temporal locations, secretion rate, and elimination rate from hormone concentration measurements is of critical importance in endocrinology. In this article, we propose a new flexible statistical method for pulse detection based on nonlinear mixed effects partial spline models. We model pulsatile secretions using biophysical models and investigate biological variation between pulses using random effects. Pooling information from different pulses provides more efficient and stable estimation for parameters of interest. We combine all nuisance parameters including a nonconstant basal secretion rate and biological variations into a baseline function that is modeled nonparametrically using smoothing splines. We develop model selection and parameter estimation methods for the general nonlinear mixed effects partial spline models and an R package for pulse detection and estimation. We evaluate performance and the benefit of shrinkage by simulations and apply our methods to data from a medical experiment.     

Hormone-induced calcium oscillations in liver cells can be explained by a simple one pool model

Hormone-induced oscillations of the free intracellular calcium concentration are thought to be relevant for frequency encoding of hormone signals. In liver cells, such Ca2+ oscillations occur in response to stimulation by hormones acting via phosphoinositide breakdown. This observation may be explained by cooperative, positive feedback of Ca2+ on its own release from one inositol 1,4,5-trisphosphate-sensitive pool, obviating oscillations of inositol 1,4,5-trisphosphate. The kinetic rate laws of the associated model have a mathematical structure reminiscent of the Brusselator, a hypothetical chemical model involving a rather improbable trimolecular reaction step, thus giving a realistic biological interpretation to this hallmark of dissipative structures. We propose that calmodulin is involved in mediating this cooperativity and positive feedback, as suggested by the presented experiments. For one, hormone-induced calcium oscillations can be inhibited by the (nonphenothiazine) calmodulin antagonists calmidazolium or CGS 9343 B. Alternatively, in cells overstimulated by hormone, as characterized by a non-oscillatory elevated Ca2+ concentration, these antagonists could again restore sustained calcium oscillations. The experimental observations, including modulation of the oscillations by extracellular calcium, were in qualitative agreement with the predictions of our mathematical model.     

Asymptotic Behavior of a Chemostat Model with Constant Recycle Sludge Concentration

In this work, we study a several species aerobic chemostat model with constant recycle sludge concentration in continuous culture. We reduce the number of parameters by considering a dimensionless model. First, the existence of a global positive uniform attractor for the model with different removal rates is proved using the theory of dissipative dynamical systems. Hence, we investigate the asymptotic behavior of the model under small perturbations using methods of singular perturbation theory and we prove that, in the case of two species in competition, the unique equilibrium which is positive is globally asymptotically stable. Finally, we establish the link between the open problem of the chemostat with different removal rates and monotone functional responses, and our model when two species compete on the same nutrient. We give some numerical simulations to illustrate the results.     

Polarity Related Influence Maximization in Signed Social Networks

Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust) between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust) between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM) problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC) model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P) diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.     

The interaction of platelet factor 4 with heparins of different chain length

The interaction of platelet factor 4 with heparin of varying chain lengths has been investigated by labelling the heparin with fluorescein isothiocyanate and monitoring the change in anisotropy of fluorescence when the protein is added to a solution of the polysaccharide. The shape of the titration curve depends on the Mr of the heparin and chains of Mr greater than 10 000 showed a definite break when the concentration of polysaccharide and protein became equimolar. Evidence is presented to show that most of the fluorescein label is linked to residual serines on the heparin. Similar break-points were observed if total fluorescence or light-scattering was used to monitor the interaction. Unlabelled heparin was used for the latter method. These results together with those obtained in buffer of high ionic strength lead us to propose a model where the heparin is wrapped around the tetrameric protein.     

Techniques for temporal detection of neural sensitivity to external stimulation

We propose a simple measure of neural sensitivity for characterizing stimulus coding. Sensitivity is defined as the fraction of neurons that show positive responses to n stimuli out of a total of N. To determine a positive response, we propose two methods: Fisherian statistical testing and a data-driven Bayesian approach to determine the response probability of a neuron. The latter is non-parametric, data-driven, and captures a lower bound for the probability of neural responses to sensory stimulation. Both methods are compared with a standard test that assumes normal probability distributions. We applied the sensitivity estimation based on the proposed method to experimental data recorded from the mushroom body (MB) of locusts. We show that there is a broad range of sensitivity that the MB response sweeps during odor stimulation. The neurons are initially tuned to specific odors, but tend to demonstrate a generalist behavior towards the end of the stimulus period, meaning that the emphasis shifts from discrimination to feature learning.     

非生物因子对河蚬重金属富集量的影响

对影响长江口滨岸带河蚬体内重金属富集量的4个主要非生物因子依次进行分析。结果表明,较高温度有助于河蚬对Cu、Zn、Cr、Ni的吸收;盐度对河蚬的重金属累积量影响比较复杂,仅在秋季表现出一定相关性,春季盐度影响不大;河蚬体内Cu含量与沉积物粒径存在显著正相关,Zn与沉积物中有机质含量存在一定正相关,Pb与沉积物中有机质含量存在着负相关;河蚬体内重金属元素两两间的相关性明显,多种元素间的相互作用机理值得进一步探讨。     

Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling

Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood as a function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response. Population PK/PD studies aim to determine the sources of variability in the observed concentrations/responses across groups of individuals. In this article, we consider the joint modeling of PK/PD data. The natural approach is to specify a joint model in which the concentration and response data are simultaneously modeled. Unfortunately, this approach may not be optimal if, due to sparsity of concentration data, an overly simple PK model is specified. As an alternative, we propose an errors-in-variables approach in which the observed-concentration data are assumed to be measured with error without reference to a specific PK model. We give an example of an analysis of PK/PD data obtained following administration of an anticoagulant drug. The study was originally carried out in order to make dosage recommendations. The prior for the distribution of the true concentrations, which may incorporate an individual's covariate information, is derived as a predictive distribution from an earlier study. The errors-in-variables approach is compared with the joint modeling approach and more naive methods in which the observed concentrations, or the separately modeled concentrations, are substituted into the response model. Throughout, a Bayesian approach is taken with implementation via Markov chain Monte Carlo methods.     

Gene selection for oligonucleotide array: an approach using PM probe level data

MOTIVATION: Analysis of oligonucleotide array data, especially to select genes of interest, is a highly challenging task because of the large volume of information and various experimental factors. Moreover, interaction effect (i.e. expression changes depend on probe effects) complicates the analysis because current methods often use an additive model to analyze data. We propose an approach to address these issues with the aim of producing a more reliable selection of differentially expressed genes. The approach uses the rank for normalization, employs the percentile-range to measure expression variation, and applies various filters to monitor expression changes. RESULTS: We compare our approach with MAS and Dchip models. A data set from an angiogenesis study is used for illustration. Results show that our approach performs better than other methods either in identification of the positive control gene or in PCR confirmatory tests. In addition, the invariant set of genes in our approach provides an efficient way for normalization.     

On Circuit Functionality in Boolean Networks

It has been proved, for several classes of continuous and discrete dynamical systems, that the presence of a positive (resp. negative) circuit in the interaction graph of a system is a necessary condition for the presence of multiple stable states (resp. a cyclic attractor). A positive (resp. negative) circuit is said to be functional when it “generates” several stable states (resp. a cyclic attractor). However, there are no definite mathematical frameworks translating the underlying meaning of “generates.” Focusing on Boolean networks, we recall and propose some definitions concerning the notion of functionality along with associated mathematical results.     

The role of the nucleocytoplasmic ratio in development regulation of the early mouse embryo

The hypothesis suggesting that the blastocoele is able to form only at a definite nucleocytoplasmic ratio was tested. We compared the development of preimplantation mouse embryos under different conditions. The results demonstrated that the start of cavitation is not dependent on the number of cell divisions. Thus, a definite nucleocytoplasmic ratio is not required for blastocoele formation to start. Our studies on embryos with microsurgically altered cytoplasm content provided evidence for the following biological clock mechanism: a change in the cell program of morphogenesis needs definite concentration of the products of a previous genetic program.