Diet alters age-related remodeling of aortic collagen in mice susceptible to atherosclerosis.

Vascular cells restructure extracellular matrix in response to aging or changes in mechanical loading. Here, we characterized collagen architecture during age-related aortic remodeling in atherosclerosis-prone mice. We hypothesized that changes in collagen fiber orientation reflect an altered balance between passive and active forces acting on the arterial wall.

Transforming Growth Factor Beta3 is Required for Cardiovascular Development.

Transforming growth factor beta3 () gene mutations in patients of arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD1) and Loeys-Dietz syndrome-5 (LDS5)/Rienhoff syndrome are associated with cardiomyopathy, cardiac arrhythmia, cardiac fibrosis, cleft palate, aortic aneurysms, and valvular heart disease. Although the developing heart of embryos express , its overarching role remains unclear in cardiovascular development and disease. We used histological, immunohistochemical, and molecular analyses of fetuses and compared them to wildtype littermate controls.

Both diet and Helicobacter pylori infection contribute to atherosclerosis in pre- and postmenopausal cynomolgus monkeys.

A number of viruses and bacterial species have been implicated as contributors to atherosclerosis, potentially providing novel pathways for prevention. Epidemiological studies examining the association between Helicobacter pylori and cardiovascular disease have yielded variable results and no studies have been conducted in nonhuman primates. In this investigation, we examined the relationship between H.

Loss of Function of Phosphodiesterase 11A4 Shows that Recent and Remote Long-Term Memories Can Be Uncoupled.

Systems consolidation is a process by which memories initially require the hippocampus for recent long-term memory (LTM) but then become increasingly independent of the hippocampus and more dependent on the cortex for remote LTM. Here, we study the role of phosphodiesterase 11A4 (PDE11A4) in systems consolidation. PDE11A4, which degrades cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), is preferentially expressed in neurons of CA1, the subiculum, and the adjacently connected amygdalohippocampal region.

Axonal G3BP1 stress granule protein limits axonal mRNA translation and nerve regeneration.

Critical functions of intra-axonally synthesized proteins are thought to depend on regulated recruitment of mRNA from storage depots in axons. Here we show that axotomy of mammalian neurons induces translation of stored axonal mRNAs via regulation of the stress granule protein G3BP1, to support regeneration of peripheral nerves. G3BP1 aggregates within peripheral nerve axons in stress granule-like structures that decrease during regeneration, with a commensurate increase in phosphorylated G3BP1.

Treatment effect on ordinal functional outcome using piecewise multistate Markov model with unobservable baseline: an application to the modified Rankin scale.

In clinical trials, longitudinally assessed ordinal outcomes are commonly dichotomized and only the final measure is used for primary analysis, partly for ease of clinical interpretation. Dichotomization of the ordinal scale and failure to utilize the repeated measures can reduce statistical power. Additionally, in certain emergent settings, the same measure cannot be assessed at baseline prior to treatment.

Assessing type I error and power of multistate Markov models for panel data-A simulation study.

Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time.

Comparison of multistate Markov modeling with contemporary outcomes in a reanalysis of the NINDS tissue plasminogen activator for acute ischemic stroke treatment trial.

Historically, ordinal measures of functional outcome have been dichotomized for the primary analysis in acute stroke therapy trials. A number of alternative methods to analyze the ordinal scales have been proposed, with an emphasis on maintaining the ordinal structure as much as possible. In addition, despite the availability of longitudinal outcome data in many trials, the primary analysis consists of a single endpoint.

Multinomial probit Bayesian additive regression trees.

This article proposes multinomial probit Bayesian additive regression trees (MPBART) as a multinomial probit extension of BART - Bayesian additive regression trees. MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and multiclass classification methods.

Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment.

This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose-response data and when there are competing model classes for the dose-response function. Strategies involving a two-step approach, a model-averaging approach, a focused-inference approach, and a nonparametric approach based on a PAVA-based estimator of the dose-response function are described and compared. Attention is raised to the perils involved in data "double-dipping" and the need to adjust for the model-selection stage in the estimation procedure.

Comparison of Aortic Collagen Fiber Angle Distribution in Mouse Models of Atherosclerosis Using Second-Harmonic Generation (SHG) Microscopy.

Characterization of collagen fiber angle distribution throughout the blood vessel wall provides insight into the mechanical behavior of healthy and diseased arteries and their capacity to remodel. Atherosclerotic plaque contributes to the overall mechanical behavior, yet little is known experimentally about how collagen fiber orientation is influenced by atherogenesis. We hypothesized that atherosclerotic lesion development, and the factors contributing to lesion development, leads to a shift in collagen fiber angles within the aorta.

Sojourning with the Homogeneous Poisson Process.

In this pedagogical article, distributional properties, some surprising, pertaining to the homogeneous Poisson process (HPP), when observed over a possibly random window, are presented. Properties of the gap-time that covered the termination time and the correlations among gap-times of the observed events are obtained. Inference procedures, such as estimation and model validation, based on event occurrence data over the observation window, are also presented.

Asymptotics for a Class of Dynamic Recurrent Event Models.

Asymptotic properties, both consistency and weak convergence, of estimators arising in a general class of dynamic recurrent event models are presented. The class of models take into account the impact of interventions after each event occurrence, the impact of accumulating event occurrences, the induced informative and dependent right-censoring mechanism due to the data-accrual scheme, and the effect of covariate processes on the recurrent event occurrences. The class of models subsumes as special cases many of the recurrent event models that have been considered in biostatistics, reliability, and in the social sciences.

Classes of Multiple Decision Functions Strongly Controlling FWER and FDR.

Two general classes of multiple decision functions, where each member of the first class strongly controls the family-wise error rate (FWER), while each member of the second class strongly controls the false discovery rate (FDR), are described. These classes offer the possibility that optimal multiple decision functions with respect to a pre-specified Type II error criterion, such as the missed discovery rate (MDR), could be found which control the FWER or FDR Type I error rates. The gain in MDR of the associated FDR-controlling procedure relative to the well-known Benjamini-Hochberg (BH) procedure is demonstrated via a modest simulation study with gamma-distributed component data.

Compound -value statistics for multiple testing procedures.

Many multiple testing procedures make use of the -values from the individual pairs of hypothesis tests, and are valid if the -value statistics are independent and uniformly distributed under the null hypotheses. However, it has recently been shown that these types of multiple testing procedures are inefficient since such -values do not depend upon all of the available data. This paper provides tools for constructing -value statistics, which are those that depend upon all of the available data, but still satisfy the conditions of independence and uniformity under the null hypotheses.

Nonparametric estimation with recurrent competing risks data.

Nonparametric estimators of component and system life distributions are developed and presented for situations where recurrent competing risks data from series systems are available. The use of recurrences of components' failures leads to improved efficiencies in statistical inference, thereby leading to resource-efficient experimental or study designs or improved inferences about the distributions governing the event times. Finite and asymptotic properties of the estimators are obtained through simulation studies and analytically.

Information-theoretic model-averaged benchmark dose analysis in environmental risk assessment.

An important objective in environmental risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a pre-specified Benchmark Response (BMR) in a dose-response experiment. In such settings, representations of the risk are traditionally based on a specified parametric model. It is a well-known concern, however, that existing parametric estimation techniques are sensitive to the form employed for modeling the dose response.

The Impact of Model Uncertainty on Benchmark Dose Estimation.

We study the popular benchmark dose (BMD) approach for estimation of low exposure levels in toxicological risk assessment, focusing on dose-response experiments with quantal data. In such settings, representations of the risk are traditionally based on a specified, parametric, dose-response model. It is a well-known concern, however, that uncertainty can exist in specification and selection of the model.

Bayes multiple decision functions.

This paper deals with the problem of simultaneously making many () binary decisions based on one realization of a random data matrix . is typically large and will usually have rows associated with each of the decisions to make, but for each row the data may be low dimensional. Such problems arise in many practical areas such as the biological and medical sciences, where the available dataset is from microarrays or other high-throughput technology and with the goal being to decide which among of many genes are relevant with respect to some phenotype of interest; in the engineering and reliability sciences; in astronomy; in education; and in business.

Parametric Estimation in a Recurrent Competing Risks Model.

A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the competing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails.

Characterization of the bipartite degron that regulates ubiquitin-independent degradation of thymidylate synthase.

TS (thymidylate synthase) is a key enzyme in the de novo biosynthesis of dTMP, and is indispensable for DNA replication. Previous studies have shown that intracellular degradation of the human enzyme [hTS (human thymidylate synthase)] is mediated by the 26S proteasome, and occurs in a ubiquitin-independent manner. Degradation of hTS is governed by a degron that is located at the polypeptide's N-terminus that is capable of promoting the destabilization of heterologous proteins to which it is attached.

Semiparametric Estimation with Recurrent Event Data under Informative Monitoring.

Consider a study where the times of occurrences of a recurrent event for n units are monitored. For the ith unit, T(i1), T(i2), …, denote the successive event interoccurrence times and this unit is monitored over a random period [0, τ(i)] with τ(i) independent of the T(ij)s. Over this monitoring period, [Formula: see text] is the random number of event occurrences.

POWER-ENHANCED MULTIPLE DECISION FUNCTIONS CONTROLLING FAMILY-WISE ERROR AND FALSE DISCOVERY RATES.

Improved procedures, in terms of smaller missed discovery rates (MDR), for performing multiple hypotheses testing with weak and strong control of the family-wise error rate (FWER) or the false discovery rate (FDR) are developed and studied. The improvement over existing procedures such as the Šidák procedure for FWER control and the Benjamini-Hochberg (BH) procedure for FDR control is achieved by exploiting possible differences in the powers of the individual tests. Results signal the need to take into account the powers of the individual tests and to have multiple hypotheses decision functions which are not limited to simply using the individual -values, as is the case, for example, with the Šidák, Bonferroni, or BH procedures.

Randomised -values and nonparametric procedures in multiple testing.

The validity of many multiple hypothesis testing procedures for false discovery rate (FDR) control relies on the assumption that -value statistics are uniformly distributed under the null hypotheses. However, this assumption fails if the test statistics have discrete distributions or if the distributional model for the observables is misspecified. A stochastic process framework is introduced that, with the aid of a uniform variate, admits -value statistics to satisfy the uniformity condition even when test statistics have discrete distributions.

Recurrent events and the exploding Cox model.

Counting process models have played an important role in survival and event history analysis for more than 30 years. Nevertheless, almost all models that are being used have a very simple structure. Analyzing recurrent events invites the application of more complex models with dynamic covariates.