Self‐injurious behaviour: limbic dysregulation and stress effects in an animal model

Background  Self‐injurious behaviour (SIB) is prevalent in neurodevelopmental disorders, but its expression is highly variable within, and between diagnostic categories. This raises questions about the factors that contribute to aetiology and expression of SIB. Expression of SIB is generally described in relation to social reinforcement. However, variables that predispose vulnerability have not been as clearly characterised. This study reports the aetiology and expression of self‐injury in an animal model of pemoline‐induced SIB. It describes changes in gross neuronal activity in selected brain regions after chronic treatment with pemoline, and it describes the impact that a history of social defeat stress has on the subsequent expression of SIB during pemoline treatment. Methods  Experiment 1 – Male Long‐Evans rats were injected on each of five consecutive days with pemoline or vehicle, and the expression of SIB was evaluated using a rating scale. The brains were harvested on the morning of the sixth day, and were assayed for expression of cytochrome oxidase, an index of sustained neuronal metabolic activity. Experiment 2 – Male Long‐Evans rats were exposed to a regimen of 12 daily sessions of social defeat stress or 12 daily sessions of handling (i.e. controls). Starting on the day after completion of the social defeat or handling regimen, each rat was given five daily injections of pemoline. The durations of self‐injurious oral contact and other stereotyped behaviours were monitored, and the areas of tissue injury were quantified. Results  Experiment 1 – Neuronal metabolic activity was significantly lower in a variety of limbic and limbic‐associated brain structures in the pemoline‐treated rats, when compared with activity in the same regions of vehicle‐treated controls. In addition, neuronal activity was low in the caudate–putamen, and in subfields of the hypothalamus, but did not differ between groups for a variety of other brain regions, including nucleus accumbens, substantia nigra, ventral tegmentum, thalamus, amygdala, and cortical regions. Experiment 2 – All the pemoline‐treated rats exhibited SIB, and whereas the social defeat regimen did not alter the total amount of self‐injurious oral contact or other stereotyped behaviours, it significantly increased the severity of tissue injury. Conclusions  A broad sampling of regional metabolic activity indicates that the pemoline regimen produces enduring changes that are localised to specific limbic, hypothalamic and striatal structures. The potential role of limbic function in aetiology of SIB is further supported by the finding that pemoline‐induced self‐injury is exacerbated by prior exposure to social defeat stress. Overall, the results suggest brain targets that should be investigated further, and increase our understanding of the putative role that stress plays in the pathophysiology of SIB.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91181/1/j.1365-2788.2011.01485.x.pd

Spatial Blind Source Separation in the Presence of a Drift

Multivariate measurements taken at different spatial locations occur frequently in practice. Proper analysis of such data needs to consider not only dependencies on-sight but also dependencies in and in-between variables as a function of spatial separation. Spatial Blind Source Separation (SBSS) is a recently developed unsupervised statistical tool that deals with such data by assuming that the observable data is formed by a linear latent variable model. In SBSS the latent variable is assumed to be constituted by weakly stationary random fields which are uncorrelated. Such a model is appealing as further analysis can be carried out on the marginal distributions of the latent variables, interpretations are straightforward as the model is assumed to be linear, and not all components of the latent field might be of interest which acts as a form of dimension reduction. The weakly stationarity assumption of SBSS implies that the mean of the data is constant for all sample locations, which might be too restricting in practical applications. Therefore, an adaptation of SBSS that uses scatter matrices based on differences was recently suggested in the literature. In our contribution we formalize these ideas, suggest an adapted SBSS method and show its usefulness on synthetic and real data

Sliced Inverse Regression for Spatial Data

Sliced inverse regression is one of the most popular sufficient dimension reduction methods. Originally, it was designed for independent and identically distributed data and recently extend to the case of serially and spatially dependent data. In this work we extend it to the case of spatially dependent data where the response might depend also on neighbouring covariates when the observations are taken on a grid-like structure as it is often the case in econometric spatial regression applications. We suggest guidelines on how to decide upon the dimension of the subspace of interest and also which spatial lag might be of interest when modeling the response. These guidelines are supported by a conducted simulation study

Friedrichs V. California Teachers Association, Union Security, Campaign Finance, and the First Amendment

In 2016, the Supreme Court took the case Friedrichs v. California Teachers Association that would solidify its place in society not as an impartial referee, the last refuge in a politicized world, but as a political actor itself. the case presented the Court with an amalgamation of every attack levied at agency shops in the last 40 years. Petitioners relied on the Court's approach to campaign finance jurisprudence which had declared that money is speech and invoked the muddled areas of compelled speech and the compelled subsidization of speech. After evaluating the Court's approach to union security and campaign finance it is clear the Court has used the First Amendment to elevate corporate speech while also using it in labor management to undermine the union.978035516391

Culture of Democracy: Community, Norms, and Technology in the United States

This dissertation investigates the impact of technology on community formation and the creation, adoption, and enforcement of social and democratic norms. Norms are informal codes of conduct that guide a person’s behavior as a member of a community and a democracy. As a social form of government, democracy is particularly reliant on community and the norms that arise within them. Yet, technological advancements have changed how people connect, form community, and learn vital norms like fairness, tolerance, legitimacy of the opposition, and cooperation. Thirty semi-structured interviews were conducted with people aged 22 to 81 to examine the new ways in which people are forming community and learning norms. Suggestive evidence indicates that technology makes it more difficult for people to connect their online experiences with their responsibilities and obligations as members of a community and a democracy. Generations Alpha and Beta are on track to be more technologically connected than any generation prior. These findings suggest that without significant changes to how people use technology to connect and form community, these generations are also the least likely to learn the social and democratic norms necessary to create a culture robust enough to sustain democracy

Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals

Non-stationary source separation is a well-established branch of blind source separation with many different methods. However, for none of these methods large-sample results are available. To bridge this gap, we develop large-sample theory for NSS-JD, a popular method of non-stationary source separation based on the joint diagonalization of block-wise covariance matrices. We work under an instantaneous linear mixing model for independent Gaussian non-stationary source signals together with a very general set of assumptions: besides boundedness conditions, the only assumptions we make are that the sources exhibit finite dependency and that their variance functions differ sufficiently to be asymptotically separable. The consistency of the unmixing estimator and its convergence to a limiting Gaussian distribution at the standard square root rate are shown to hold under the previous conditions. Simulation experiments are used to verify the theoretical results and to study the impact of block length on the separation