Roles of ß-adrenoceptor Subtypes and Therapeutics in Human Cardiovascular Disease: Heart Failure, Tachyarrhythmias and Other Cardiovascular Disorders.

ß-Adrenoceptors (ß-ARs) provide an important therapeutic target for the treatment of cardiovascular disease. Three ß-ARs, ß1-AR, ß2-AR, ß3-AR are localized to the human heart. Activation of ß1-AR and ß2-ARs increases heart rate, force of contraction (inotropy) and consequently cardiac output to meet physiological demand. However, in disease, chronic over-activation of ß1-AR is responsible for the progression of disease (e.g. heart failure) mediated by pathological hypertrophy, adverse remodelling and premature cell death. Furthermore, activation of ß1-AR is critical in the pathogenesis of cardiac arrhythmias while activation of ß2-AR directly influences blood pressure haemostasis. There is an increasing awareness of the contribution of ß2-AR in cardiovascular disease, particularly arrhythmia generation. All ß-blockers used therapeutically to treat cardiovascular disease block ß1-AR with variable blockade of ß2-AR depending on relative affinity for ß1-AR vs ß2-AR. Since the introduction of ß-blockers into clinical practice in 1965, ß-blockers with different properties have been trialled, used and evaluated, leading to better understanding of their therapeutic effects and tolerability in various cardiovascular conditions. ß-Blockers with the property of intrinsic sympathomimetic activity (ISA), i.e. ß-blockers that also activate the receptor, were used in the past for post-treatment of myocardial infarction and had limited use in heart failure. The ß-blocker carvedilol continues to intrigue due to numerous properties that differentiate it from other ß-blockers and is used successfully in the treatment of heart failure. The discovery of ß3-AR in human heart created interest in the role of ß3-AR in heart failure but has not resulted in therapeutics at this stage.

Subgrouping with Chain Graphical VAR Models.

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

Systematic review of the associations between prenatal sleep behaviours and components of energy balance for regulating weight gain.

This systematic review aimed to examine the magnitude and direction of the associations between prenatal sleep behaviours (i.e. nighttime sleep duration, sleep quality, night awakenings and daytime nap duration) and eating behaviours, physical activity and gestational weight gain. A systematic search was conducted using Medline/PubMed, PsychINFO, CINAHL Complete, ProQuest Dissertations and Thesis A&I, and Web of Science to identify studies with at least one sleep measure, and either eating behaviours, physical activity and/or gestational weight gain. In summary, 11 studies met the review criteria and generated 11 total effect size across 10,900 participants. The majority of the studies were conducted after 2010, which highlights the infancy of this research. Overall, the strengths of the effect size were small: sleep-gestational weight gain (effect size = 0.29), sleep-eating behaviours (effect size = 0.13) and sleep-physical activity (effect size = 0.13). The only effect size that emerged as significant was for the pooled sleep behaviours-physical activity association; good sleep behaviours were positively associated with higher levels of physical activity. These findings summarize and provide insight on how sleep behaviours are related to prenatal gestational weight gain, eating behaviours and physical activity by identifying the strength and direction of the associations that have been previously unknown. Results support the rationale for future longitudinal and randomized control trials to examine the effects of sleep behaviours on gestational weight gain, eating behaviours and physical activity over the course of pregnancy.

Evaluating Discrete Time Methods for Subgrouping Continuous Processes.

Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework-such as the vector autoregression (VAR)-have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system's dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.

Describing and Controlling Multivariate Nonlinear Dynamics: A Boolean Network Approach.

We introduce a discrete-time dynamical system method, the Boolean network method, that may be useful for modeling, studying, and controlling nonlinear dynamics in multivariate systems, particularly when binary time-series are available. We introduce the method in three steps: inference of the temporal relations as Boolean functions, extraction of attractors and assignment of desirability based on domain knowledge, and design of network control to direct a psychological system toward a desired attractor. To demonstrate how the Boolean network can describe and prescribe control for emotion regulation dynamics, we applied this method to data from a study of how children use bidding to an adult and/or distraction to regulate their anger during a frustrating task (N = 120, T = 480 seconds). Network control strategies were designed to move the child into attractors where anger is OFF. The sample shows heterogeneous emotion regulation dynamics across children in 22 distinct Boolean networks, and heterogeneous control strategies regarding which behavior to perturb and how to perturb it. The Boolean network method provides a novel method to describe nonlinear dynamics in multivariate psychological systems and is a method with potential to eventually inform the design of interventions that can guide those systems toward desired goals.

A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters.

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.

Compromised right ventricular contractility in an ovine model of heart transplantation following 24 h donor brain stem death.

BACKGROUND: Heart failure is an inexorably progressive disease with a high mortality, for which heart transplantation (HTx) remains the gold standard treatment. Currently, donor hearts are primarily derived from patients following brain stem death (BSD). BSD causes activation of the sympathetic nervous system, increases endothelin levels, and triggers significant inflammation that together with potential myocardial injury associated with the transplant procedure, may affect contractility of the donor heart. We examined peri-transplant myocardial catecholamine sensitivity and cardiac contractility post-BSD and transplantation in a clinically relevant ovine model. METHODS: Donor sheep underwent BSD (BSD, n = 5) or sham (no BSD) procedures (SHAM, n = 4) and were monitored for 24h prior to heart procurement. Orthotopic HTx was performed on a separate group of donor animals following 24h of BSD (BSD-Tx, n = 6) or SHAM injury (SH-Tx, n = 5). The healthy recipient heart was used as a control (HC, n = 11). A cumulative concentration-effect curve to (-)-noradrenaline (NA) was established using left (LV) and right ventricular (RV) trabeculae to determine ß1-adrenoceptor mediated potency (-logEC50 [(-)-noradrenaline] M) and maximal contractility (Emax). RESULTS: Our data showed reduced basal and maximal (-)-noradrenaline induced contractility of the RV (but not LV) following BSD as well as HTx, regardless of whether the donor heart was exposed to BSD or SHAM. The potency of (-)-noradrenaline was lower in left and right ventricles for BSD-Tx and SH-Tx compared to HC. CONCLUSION: These studies show that the combination of BSD and transplantation are likely to impair contractility of the donor heart, particularly for the RV. For the donor heart, this contractile dysfunction appears to be independent of changes to ß1-adrenoceptor sensitivity. However, altered ß1-adrenoceptor signalling is likely to be involved in post-HTx contractile dysfunction.

Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory.

Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting patterns at the symptom level. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.

A Tribute to the Mind, Methodology and Mentoring of Wayne Velicer.

Wayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin State University in Oshkosh, and his PhD in Quantitative Psychology from Purdue led him to a fruitful and far-reaching career. He was honored several times as a high-impact author, was a renowned scholar in quantitative and health psychology, and had more than 300 scholarly publications and 54,000+ citations of his work, advancing the arenas of quantitative methodology and behavioral health. In his methodological work, Velicer sought out ways to measure, synthesize, categorize, and assess people and constructs across behaviors and time, largely through principal components analysis, time series, and cluster analysis. Further, he and several colleagues developed a method called Testing Theory-based Quantitative Predictions, successfully applied to predicting outcomes and effect sizes in smoking cessation, diet behavior, and sun protection, with the potential for wider applications. With $60,000,000 in external funding, Velicer also helped engage a large cadre of students and other colleagues to study methodological models for a myriad of health behaviors in a widely applied Transtheoretical Model of Change. Unwittingly, he has engendered indelible memories and gratitude to all who crossed his path. Although Wayne Velicer left this world on October 15, 2017 after battling an aggressive cancer, he is still very present among us.

Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models.

The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches-GIMME, uSEM, and LASSO gVAR-in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three approaches dynamic networks provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches' respective strengths and limitations.

Calmodulin inhibition of human RyR2 channels requires phosphorylation of RyR2-S2808 or RyR2-S2814.

Calmodulin (CaM) is a Ca-binding protein that binds to, and can directly inhibit cardiac ryanodine receptor calcium release channels (RyR2). Animal studies have shown that RyR2 hyperphosphorylation reduces CaM binding to RyR2 in failing hearts, but data are lacking on how CaM regulates human RyR2 and how this regulation is affected by RyR2 phosphorylation. Physiological concentrations of CaM (100 nM) inhibited the diastolic activity of RyR2 isolated from failing human hearts by ~50% but had no effect on RyR2 from healthy human hearts. Using FRET between donor-FKBP12.6 and acceptor-CaM bound to RyR2, we determined that CaM binds to RyR2 from healthy human heart with a Kd = 121 ±â€¯14 nM. Ex-vivo phosphorylation/dephosphorylation experiments suggested that the divergent CaM regulation of healthy and failing human RyR2 was caused by differences in RyR2 phosphorylation by protein kinase A and Ca-CaM-dependent kinase II. Ca2+-spark measurements in murine cardiomyocytes harbouring RyR2 phosphomimetic or phosphoablated mutants at S2814 and S2808 suggest that phosphorylation of residues corresponding to either human RyR2-S2808 or S2814 is both necessary and sufficient for RyR2 regulation by CaM. Our results challenge the current concept that CaM universally functions as a canonical inhibitor of RyR2 across species. Rather, CaM's biological action on human RyR2 appears to be more nuanced, with inhibitory activity only on phosphorylated RyR2 channels, which occurs during exercise or in patients with heart failure.

Evaluating the organizational structure and specificity of network topology within the face processing system.

There is increasing appreciation that network-level interactions among regions produce components of face processing previously ascribed to individual regions. Our goals were to use an exhaustive data-driven approach to derive and quantify the topology of directed functional connections within a priori defined nodes of the face processing network and evaluate whether the topology is category-specific. Young adults were scanned with fMRI as they viewed movies of faces, objects, and scenes. We employed GIMME to model effective connectivity among core and extended face processing regions, which allowed us to evaluate all possible directional connections, under each viewing condition (face, object, place). During face processing, we observed directional connections from the right posterior superior temporal sulcus to both the right occipital face area and right fusiform face area (FFA), which does not reflect the topology reported in prior studies. We observed connectivity between core and extended regions during face processing, but this limited to a feed-forward connection from the FFA to the amygdala. Finally, the topology of connections was unique to face processing. These findings suggest that the pattern of directed functional connections within the face processing network, particularly in the right core regions, may not be as hierarchical and feed-forward as described previously. Our findings support the notion that topologies of network connections are specialized, emergent, and dynamically responsive to task demands.

Granger Causality Testing with Intensive Longitudinal Data.

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

The Impact of Variation in Twin Relatedness on Estimates of Heritability and Environmental Influences.

By taking advantage of the natural variation in genetic relatedness among identical (monozygotic: MZ) and fraternal (dizygotic: DZ) twins, twin studies are able to estimate genetic and environmental contributions to complex human behaviors. Recently concerns have been raised about the accuracy of twin studies in light of findings of genetic and epigenetic changes in twins. One of the concerns raised is that MZ twins are not 100% genetically and epigenetically similar because they show variations in their genomes and epigenomes leading to inaccurate estimates of heritability. This article presents findings from a simulation study that examined the degree of bias in estimates of heritability and environmentality when the genetic and epigenetic similarity of MZ twins differs from 1.00 and when the genetic and epigenetic similarity of DZ twins differs from 0.50. The findings suggest that in the standard biometric model when MZ or DZ twin similarity differs from 1.00 or 0.50, respectively, the variance that should be attributed to genetic influences is instead attributed to nonshared environmental influences, thus deflating the estimates of genetic influences and inflating the estimates of nonshared environmental influences. Although estimates of genetic and nonshared environmental influences from the standard biometric model were found to deviate from "true" values, the bias was usually smaller than 10% points indicating that the interpretations of findings from previous twin studies are mostly correct.

Silencing of Dok-7 in Adult Rat Muscle Increases Susceptibility to Passive Transfer Myasthenia Gravis.

Myasthenia gravis (MG) is an autoimmune disease mediated by autoantibodies that target proteins at the neuromuscular junction, primarily the acetylcholine receptor (AChR) and the muscle-specific kinase. Because downstream of kinase 7 (Dok-7) is essential for the full activation of muscle-specific kinase and consequently for dense clustering of AChRs, we hypothesized that reduced levels of Dok-7 increase the susceptibility to passive transfer MG. To test this hypothesis, Dok-7 expression was reduced by transfecting shRNA-coding plasmids into the tibialis anterior muscle of adult rats by in vivo electroporation. Subclinical MG was subsequently induced with a low dose of anti-AChR monoclonal antibody 35. Neuromuscular transmission was significantly impaired in Dok-7-siRNA-electroporated legs compared with the contralateral control legs, which correlated with a reduction of AChR protein levels at the neuromuscular junction (approximately 25%) in Dok-7-siRNA-electroporated muscles, compared with contralateral control muscles. These results suggest that a reduced expression of Dok-7 may play a role in the susceptibility to passive transfer MG, by rendering AChR clusters less resistant to the autoantibody attack.

IgG4 autoantibodies against muscle-specific kinase undergo Fab-arm exchange in myasthenia gravis patients.

Autoimmunity mediated by IgG4 subclass autoantibodies is an expanding field of research. Due to their structural characteristics a key feature of IgG4 antibodies is the ability to exchange Fab-arms with other, unrelated, IgG4 molecules, making the IgG4 molecule potentially monovalent for the specific antigen. However, whether those disease-associated antigen-specific IgG4 are mono- or divalent for their antigens is unknown. Myasthenia gravis (MG) with antibodies to muscle specific kinase (MuSK-MG) is a well-recognized disease in which the predominant pathogenic IgG4 antibody binds to extracellular epitopes on MuSK at the neuromuscular junction; this inhibits a pathway that clusters the acetylcholine (neurotransmitter) receptors and leads to failure of neuromuscular transmission. In vitro Fab-arm exchange-inducing conditions were applied to MuSK antibodies in sera, purified IgG4 and IgG1-3 sub-fractions. Solid-phase cross-linking assays were established to determine the extent of pre-existing and inducible Fab-arm exchange. Functional effects of the resulting populations of IgG4 antibodies were determined by measuring inhibition of agrin-induced AChR clustering in C2C12 cells. To confirm the results, κ/κ, λ/λ and hybrid κ/λ IgG4s were isolated and tested for MuSK antibodies. At least fifty percent of patients had IgG4, but not IgG1-3, MuSK antibodies that could undergo Fab-arm exchange in vitro under reducing conditions. Also MuSK antibodies were found in vivo that were divalent (monospecific for MuSK). Fab-arm exchange with normal human IgG4 did not prevent the inhibitory effect of serum derived MuSK antibodies on AChR clustering in C2C12 mouse myotubes. The results suggest that a considerable proportion of MuSK IgG4 could already be Fab-arm exchanged in vivo. This was confirmed by isolating endogenous IgG4 MuSK antibodies containing both κ and λ light chains, i.e. hybrid IgG4 molecules. These new findings demonstrate that Fab-arm exchanged antibodies are pathogenic.

Real-time visual feedback of COM and COP motion properties differentially modifies postural control structures.

The experiment was setup to investigate the control of human quiet standing through the manipulation of augmented visual information feedback of selective properties of the motion of two primary variables in postural control: center of pressure (COP) and center of mass (COM). Five properties of feedback information were contrasted to a no feedback dual-task (watching a movie) control condition to determine the impact of visual real-time feedback on the coordination of the joint motions in postural control in both static and dynamic one-leg standing postures. The feedback information included 2D COP or COM position and macro variables derived from the COP and COM motions, namely virtual time-to-contact (VTC) and the COP-COM coupling. The findings in the static condition showed that the VTC and COP-COM coupling feedback conditions decreased postural motion more than the 2D COP or COM positional information. These variables also induced larger sway amplitudes in the dynamic condition showing a more progressive search strategy in exploring the stability limits. Canonical correlation analysis (CCA) found that COP-COM coupling contributed less than the other feedback variables to the redundancy of the system reflected in the common variance between joint motions and properties of sway motion. The COP-COM coupling had the lowest weighting of the motion properties to redundancy under the feedback conditions but overall the qualitative pattern of the joint motion structures was preserved within the respective static and dynamic balance conditions.

Equivalent Dynamic Models.

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions.

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.