Corticotropin-releasing factor inhibits maternal aggression in mice

Lactating females that fiercely protect offspring exhibit decreased fear and anxiety. The authors tested whether decreased corticotropin-releasing factor (CRF), an activator of fear and anxiety, plays a functional role in maternal aggression. Intracerebroventricular (icv) injections of CRF (1.0 and 0.2 microg, but not 0.02 microg) significantly inhibited maternal aggression but not other maternal behaviors. The CRF antagonist D-Phe-CRF(12-41) had no effect. Maternal aggression and icv CRF (0.2 microg) induced Fos in 11 of the same regions, including the lateral and medial septum, the bed nucleus of the stria terminalis, the medial and central amygdala, the periaqueductal gray, the dorsal raphe, and the locus coeruleus. These findings suggest that decreased CRF is necessary for maternal aggression and may act by altering brain activity in response to an intruder.

Comparing methods of analyzing fMRI statistical parametric maps

Approaches for the analysis of statistical parametric maps (SPMs) can be crudely grouped into three main categories in which different philosophies are applied to delineate activated regions. These being type I error control thresholding, false discovery rate (FDR) control thresholding and posterior probability thresholding. To better understand the properties of these main approaches, we carried out a simulation study to compare the approaches as they would be used on real data sets. Using default settings, we find that posterior probability thresholding is the most powerful approach, and type I error control thresholding provides the lowest levels of type I error. False discovery rate control thresholding performs in between the other approaches for both these criteria, although for some parameter settings this approach can approximate the performance of posterior probability thresholding. Based on these results, we discuss the relative merits of the three approaches in an attempt to decide upon an optimal approach. We conclude that viewing the problem of delineating areas of activation as a classification problem provides a highly interpretable framework for comparing the methods. Within this framework, we highlight the role of the loss function, which explicitly penalizes the types of errors that may occur in a given analysis.

Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer

MOTIVATION

It is a common practice in cancer microarray experiments that a normal tissue is collected from the same individual from whom the tumor tissue was taken. The indirect design is usually adopted for the experiment that uses a common reference RNA hybridized both to normal and tumor tissues. However, it is often the case that the test material is not large enough for the experimenter to extract enough RNA to conduct the microarray experiment. Hence, collecting n cases does not necessarily end up with a matched pair sample of size n. Instead we usually have a matched pair sample of size n1, and two independent samples of sizes n2 and n3, respectively, for 'reference versus normal tissue only' and 'reference versus tumor tissue only' hybridizations (n=n1 + n2 + n3). Standard statistical methods need to be modified and new statistical procedures are developed for analyzing this mixed dataset.

RESULTS

We propose a new test statistic, t3, as a means of combining all the information in the mixed dataset for detecting differentially expressed (DE) genes between normal and tumor tissues. We employed the extended receiver operating characteristic approach to the mixed dataset. We devised a measure of disagreement between a RT-PCR experiment and a microarray experiment. Hotelling's T2 statistic is employed to detect a set of DE genes and its prediction rate is compared with the prediction rate of a univariate procedure. We observe that Hotelling's T2 statistic detects DE genes more efficiently than a univariate procedure and that further research is warranted on the formal test procedure using Hotelling's T2 statistic.

CONTACT

bskim@yonsei.ac.kr.

Population history and natural selection shape patterns of genetic variation in 132 genes

Identifying regions of the human genome that have been targets of natural selection will provide important insights into human evolutionary history and may facilitate the identification of complex disease genes. Although the signature that natural selection imparts on DNA sequence variation is difficult to disentangle from the effects of neutral processes such as population demographic history, selective and demographic forces can be distinguished by analyzing multiple loci dispersed throughout the genome. We studied the molecular evolution of 132 genes by comprehensively resequencing them in 24 African-Americans and 23 European-Americans. We developed a rigorous computational approach for taking into account multiple hypothesis tests and demographic history and found that while many apparent selective events can instead be explained by demography, there is also strong evidence for positive or balancing selection at eight genes in the European-American population, but none in the African-American population. Our results suggest that the migration of modern humans out of Africa into new environments was accompanied by genetic adaptations to emergent selective forces. In addition, a region containing four contiguous genes on Chromosome 7 showed striking evidence of a recent selective sweep in European-Americans. More generally, our results have important implications for mapping genes underlying complex human diseases.

An analysis of 132 human genes suggests that the migration of modern humans out of Africa into new environments was accompanied by genetic adaptations to emergent selective forces

Mauve: multiple alignment of conserved genomic sequence with rearrangements

As genomes evolve, they undergo large-scale evolutionary processes that present a challenge to sequence comparison not posed by short sequences. Recombination causes frequent genome rearrangements, horizontal transfer introduces new sequences into bacterial chromosomes, and deletions remove segments of the genome. Consequently, each genome is a mosaic of unique lineage-specific segments, regions shared with a subset of other genomes and segments conserved among all the genomes under consideration. Furthermore, the linear order of these segments may be shuffled among genomes. We present methods for identification and alignment of conserved genomic DNA in the presence of rearrangements and horizontal transfer. Our methods have been implemented in a software package called Mauve. Mauve has been applied to align nine enterobacterial genomes and to determine global rearrangement structure in three mammalian genomes. We have evaluated the quality of Mauve alignments and drawn comparison to other methods through extensive simulations of genome evolution.

Coevolution Based Adaptive Monte Carlo Localization (CEAMCL)

An adaptive Monte Carlo localization algorithm based on coevolution mechanism of ecological species is proposed. Samples are clustered into species, each of which represents a hypothesis of the robot's pose. Since the coevolution between the species ensures that the multiple distinct hypotheses can be tracked stably, the problem of premature convergence when using MCL in highly symmetric environments can be solved. And the sample size can be adjusted adaptively over time according to the uncertainty of the robot's pose by using the population growth model. In addition, by using the crossover and mutation operators in evolutionary computation, intra-species evolution can drive the samples move towards the regions where the desired posterior density is large. So a small size of samples can represent the desired density well enough to make precise localization. The new algorithm is termed coevolution based adaptive Monte Carlo localization (CEAMCL). Experiments have been carried out to prove the efficiency of the new localization algorithm.

ESTs, cDNA microarrays, and gene expression profiling: tools for dissecting plant physiology and development

Gene expression profiling holds tremendous promise for dissecting the regulatory mechanisms and transcriptional networks that underlie biological processes. Here we provide details of approaches used by others and ourselves for gene expression profiling in plants with emphasis on cDNA microarrays and discussion of both experimental design and downstream analysis. We focus on methods and techniques emphasizing fabrication of cDNA microarrays, fluorescent labeling, cDNA hybridization, experimental design, and data processing. We include specific examples that demonstrate how this technology can be used to further our understanding of plant physiology and development (specifically fruit development and ripening) and for comparative genomics by comparing transcriptome activity in tomato and pepper fruit.

Behavioral and autonomic dynamics during contextual fear conditioning in mice

Aversive conditioning to contextual stimulation was performed in mice implanted with ECG transmitters to investigate heart rate (HR) and behavioral responses during contextual retention. The dynamics of HR were analyzed by advanced nonlinear techniques to uncover central neuroautonomic outflow inferred from its sympathetic (SNS) and parasympathetic (PNS) projection onto the sinus node of the heart. Mice experienced a single foot shock (US, unconditioned stimulus) either immediately (USi) or late (USl) after placement in the conditioning context. Contextual memory was tested 24 h after training by reexposure to the conditioning context for 32 min. Only mice that experienced the USl exhibited a pronounced and sustained behavioral suppression (immobility) indicative of conditioned contextual fear. In contrast, HR was initially close to its maximal physiological limit (approximately 800 bpm) in all groups, and recovery towards baseline levels was sluggish, the most pronounced delay observed in the USl group. The results demonstrate that behavioral immobility was associated with maximum activation of autonomic system output in response to contextual reexposure. However, advanced complexity analysis of the variability of HR revealed uniform or stereotyped dynamical properties that were interpreted to reflect a generalized state of anticipatory emotional arousal experienced during reexposure to contextual stimuli. It is concluded that the dynamics of HR is a highly sensitive index of the autonomic nervous system response and emotional state elicited by sensory stimulation of an unfamiliar environment.

Stochastic multitype epidemics in a community of households: estimation and form of optimal vaccination schemes

This paper treats a stochastic model for an SIR (susceptible-->infective-->removed) multitype household epidemic. The community is assumed to be closed, individuals are of different types and each individual belongs to a household. Previously obtained probabilistic and inferential results for the model are used to derive the optimal vaccination scheme. By this is meant the scheme that vaccinates the fewest among all vaccination schemes that reduce the threshold parameter below 1. This is done for the situation where all model parameters are known and also for the case where parameters are estimated from an outbreak in the community prior to vaccination. It is shown that the algorithm which chooses vaccines sequentially, at each step selecting the individual which reduces the threshold parameter the most, is not in general an optimal scheme. As a consequence, explicit characterisation of the optimal scheme is only possible in certain special cases. Two different types of vaccine responses, leaky and all-or-nothing, are considered and compared for the problems mentioned above. The methods are illustrated with some numerical examples.

Estimation of the tissue composition of the tumour mass in neuroblastoma using segmented CT images

Neuroblastoma is the most common extra-cranial, solid, malignant tumour in children. Advances in radiology have made possible the detection and staging of the disease. Nevertheless, there is no method available at present that can go beyond detection and qualitative analysis, towards quantitative assessment of the tissue composition of the primary tumour mass in neuroblastoma. Such quantitative analysis could provide important information and serve as a decision-support tool to the radiologist and the oncologist, result in better treatment and follow-up and even lead to the avoidance of delayed surgery. The problem investigated was the improvement of the analysis of the primary tumour mass, in patients with neuroblastoma, using X-ray computed tomography (CT) images. A methodology was proposed for the estimation of the tissue content of the mass: it comprised a Gaussian mixture model for estimation, from segmented CT images, of the tissue composition of the primary tumour. To demonstrate the potential of the method, the results are presented of its application to ten CT examinations of four patients. The method provides quantitative information, and it was observed that the tumour in one of the patients reduced from 523 cm3 to 81 cm3 in volume, with an increase in calcification from about 20% to about 88% of the tumour volume, in response to chemotherapy over a period of five months. Results indicate that the proposed technique may be of considerable value in assessing the response to therapy of patients with neuroblastoma.

Shared Frailty Models for Recurrent Events and a Terminal Event

There has been an increasing interest in the analysis of recurrent event data (Cook and Lawless, 2002, Statistical Methods in Medical Research 11, 141-166). In many situations, a terminating event such as death can happen during the follow-up period to preclude further occurrence of the recurrent events. Furthermore, the death time may be dependent on the recurrent event history. In this article we consider frailty proportional hazards models for the recurrent and terminal event processes. The dependence is modeled by conditioning on a shared frailty that is included in both hazard functions. Covariate effects can be taken into account in the model as well. Maximum likelihood estimation and inference are carried out through a Monte Carlo EM algorithm with Metropolis-Hastings sampler in the E-step. An analysis of hospitalization and death data for waitlisted dialysis patients is presented to illustrate the proposed methods. Methods to check the validity of the proposed model are also demonstrated. This model avoids the difficulties encountered in alternative approaches which attempt to specify a dependent joint distribution with marginal proportional hazards and yields an estimate of the degree of dependence.

A Bayesian Approach to DNA Sequence Segmentation

Many deoxyribonucleic acid (DNA) sequences display compositional heterogeneity in the form of segments of similar structure. This article describes a Bayesian method that identifies such segments by using a Markov chain governed by a hidden Markov model. Markov chain Monte Carlo (MCMC) techniques are employed to compute all posterior quantities of interest and, in particular, allow inferences to be made regarding the number of segment types and the order of Markov dependence in the DNA sequence. The method is applied to the segmentation of the bacteriophage lambda genome, a common benchmark sequence used for the comparison of statistical segmentation algorithms.

Penalized Partial Likelihood for Frailties and Smoothing Splines in Time to First Insemination Models for Dairy Cows

In many epidemiological studies time to event data are clustered and the physiological relationship between (time-dependent) covariates and the log hazard is often not linear as assumed in the Cox model. Introducing frailties in the Cox model can account for the clustering of the data and smoothing splines can be used to describe nonlinear relations. These two extensions of the Cox model are introduced jointly and it is shown how penalized partial likelihood techniques can be used to fit the extended model. We demonstrate the need for such a model to study the relation between the physiological covariates milk ureum and protein concentration and the log hazard of first insemination in dairy cows, with the farms as clusters.

Maximum Likelihood Analysis of a General Latent Variable Model with Hierarchically Mixed Data

A general two-level latent variable model is developed to provide a comprehensive framework for model comparison of various submodels. Nonlinear relationships among the latent variables in the structural equations at both levels, as well as the effects of fixed covariates in the measurement and structural equations at both levels, can be analyzed within the framework. Moreover, the methodology can be applied to hierarchically mixed continuous, dichotomous, and polytomous data. A Monte Carlo EM algorithm is implemented to produce the maximum likelihood estimate. The E-step is completed by approximating the conditional expectations through observations that are simulated by Markov chain Monte Carlo methods, while the M-step is completed by conditional maximization. A procedure is proposed for computing the complicated observed-data log likelihood and the BIC for model comparison. The methods are illustrated by using a real data set.

Statistical considerations for the design and analysis of the ELISpot assay in HIV-1 vaccine trials

Effector T lymphocyte responses are considered critical for controlling human immunodeficiency virus type-1 (HIV-1) infection. The enzyme-linked immunospot (ELISpot) assay has emerged as a primary means of assessing HIV-specific T cell responses, and the development of objective methods that distinguish positive and negative ELISpot responses while properly controlling the rate of false positives is critical. In this paper, we consider several statistical methods that are helpful in defining such a positive criterion. Simulation results under a variety of scenarios suggest that a permutation-based criterion using a resampling adjustment for multiple comparisons yields the desired false positive rate while remaining competitive with other potential criteria in terms of sensitivity. These results also provide guidance on the effect of the number of experimental and negative control replicate wells on assay sensitivity. Application of different potential positive criteria using ELISpot assay results from IFN-gamma-secreting T cells of HIV-1 seropositive and seronegative donors confirmed several of the results obtained under simulation. Our findings support the application of statistically-based positive criteria such as the permutation-based resampling approach in assessing HIV vaccine-induced T cell responses. Moreover, the proposed methods have potential utility in related HIV immunopathogenesis studies and in non-HIV clinical vaccine trials.