Beta-Endorphin and Oxytocin in Patients with Alcohol Use Disorder and Comorbid Depression

Background: The neuropeptides β-endorphin and oxytocin are released into the bloodstream as hormones from the pituitary gland but also have an important function as neuroregulators in the forebrain. The blood levels of both polypeptides have been shown to reflect depressive symptoms. β-Endorphin, in particular, is also involved in abstinence from alcohol. Methods: The serum levels of β-endorphin and oxytocin were measured during the early withdrawal phase in patients with alcohol use disorder (AUD) with (N = 35) or without (N = 45) depressive comorbidity and compared with those in healthy volunteers (N = 23). In addition to comparing the groups, the study examined whether serum levels correlated with various psychometric measures of dependence, depression and aggression, as well as with clinical characteristics of dependence. Results: Both serum levels of beta-endorphin and oxytocin were significantly lower in patients than those in healthy controls (p = 0.011 for β-endorphin and p = 0.005 for oxytocin, Kruskal–Wallis test). In patients with depressive comorbidity, the significance was greatest (p = 0.005 for β-endorphin and p = 0.004 for oxytocin, U-test). There was no correlation with clinical or psychometric parameters (p > 0.05, Spearman test), but beta-endorphin levels did correlate significantly with physical aggression (p = 0.026, Spearman test). Conclusions: Serum levels of β-endorphin and oxytocin are lower in patients with AUD, particularly in those with depressive comorbidity. β-Endorphin levels correlated with physical aggression according to the Buss–Durkee (BDHI) estimates.

Alternative Measures of Dependence for Cyclic Behaviour Identification in the Signal with Impulsive Noise—Application to the Local Damage Detection

The local damage detection procedures in rotating machinery are based on the analysis of the impulsiveness and/or the periodicity of disturbances corresponding to the failure. Recent findings related to non-Gaussian vibration signals showed some drawbacks of the classical methods. If the signal is noisy and it is strongly non-Gaussian (heavy-tailed), searching for impulsive behvaior is pointless as both informative and non-informative components are transients. The classical dependence measure (autocorrelation) is not suitable for non-Gaussian signals. Thus, there is a need for new methods for hidden periodicity detection. In this paper, an attempt will be made to use alternative measures of dependence used in time series analysis that are less known in the condition monitoring (CM) community. They are proposed as alternatives for the classical autocovariance function used in the cyclostationary analysis. The methodology of the auto-similarity map calculation is presented as well as a procedure for a “quality” or “informativeness” assessment of the map is proposed. In the most complex case, the most resistant to heavy-tailed noise turned out the proposed techniques based on Kendall, Spearman and Quadrant autocorrelations. Whereas in the case of the local fault disturbed by the Gaussian noise, the most efficient proved to be a commonly-known approach based on Pearson autocorrelation. The ideas proposed in the paper are supported by simulation signals and real vibrations from heavy-duty machines.

The power to gaslight

Gaslighting is a dysfunctional pattern of relating that destabilizes an individual’s sense of reality. Adopting a communication perspective, this study examined the relationship between gaslighting and interpersonal power dynamics. Participants ( N = 298) recalled a disagreement with a dating partner and provided measures of dependence power and their experience of gaslighting. Results were counter-theoretical, suggesting a curvilinear relationship between gaslighting and power. Specifically, individuals with low and high levels of power reported greater gaslighting than those with moderate power. Results suggest that tacit control attempts differ from overt control attempts in how they (re)produce relational hierarchy. The study underscores the importance of understanding relationship contexts in studying dysfunctional interpersonal dynamics.

Determining the dependence structure of multivariate extremes

Summary In multivariate extreme value analysis, the nature of the extremal dependence between variables should be considered when selecting appropriate statistical models. Interest often lies in determining which subsets of variables can take their largest values simultaneously while the others are of smaller order. Our approach to this problem exploits hidden regular variation properties on a collection of nonstandard cones, and provides a new set of indices that reveal aspects of the extremal dependence structure not available through existing measures of dependence. We derive theoretical properties of these indices, demonstrate their utility through a series of examples, and develop methods of inference that also estimate the proportion of extremal mass associated with each cone. We apply the methods to river flows in the U.K., estimating the probabilities of different subsets of sites being large simultaneously.