Targeting hyperarousal: Mantram Repetition Program for PTSD in US veterans.

Background: Hyperarousal appears to play an important role in the development and maintenance of posttraumatic stress disorder (PTSD) symptoms, but current evidence-based treatments appear to address this symptom type less effectively than the other symptom clusters. The Mantram Repetition Program (MRP) is a meditation-based intervention that has previously been shown to improve symptoms of posttraumatic stress disorder (PTSD) and may be especially helpful for hyperarousal. If MRP is an effective tool for decreasing this often treatment-resistant symptom cluster, it may become an important clinical tool. Objective: The goal of this secondary analysis was to examine the effect of the MRP on hyperarousal and other PTSD symptom clusters and to examine hyperarousal as a mediator of treatment response. Method: Secondary analyses were conducted on data from a randomized controlled trial in which Veterans with PTSD (n = 173) were assigned to the MRP or a non-specific psychotherapy control and assessed pre-treatment, post-treatment and 8 weeks after treatment completion. The impact of the interventions on PTSD symptom clusters was examined, and time-lagged hierarchical linear modelling was applied to examine alternative mediation models. Results: All PTSD symptom clusters improved in both treatments. MRP led to greater reductions in hyperarousal at post-treatment (Hedge's g = 0.57) and follow-up (Hedge's g = 0.52), and in numbing at post-treatment (Hedge's g = 0.47). Hyperarousal mediated reductions in the composite of the other PTSD symptom clusters. Although the reverse model was significant as well, the effect was weaker in this direction. Conclusion: Interventions focused on the management of hyperarousal may play an important role in recovery from PTSD. The MRP appears efficacious in reducing hyperarousal, and thereby impacting other PTSD symptom clusters, as one pathway to facilitating recovery

Triangulating Horizontal Inequality: Towards Improved Conflict Analysis.

Does economic inequality cause civil war? Deviating from individualist measures of inequality such as the Gini coefficient, recent studies have found a statistical link between group-level inequalities and conflict onset. Yet, this connection remains controversial, not least because of the difficulties associated with conceptualizing and measuring group-level differences in development. In an effort to overcome weaknesses afflicting specific methods of measurement, we introduce a new composite indicator that exploits the strengths of three sources of data. The first step of our method combines geocoded data from the G-Econ project with night lights emissions data from satellites. In a second step, we bring together the combined spatial values with survey estimates in order to arrive at an improved measure of group-level inequality that is both more accurate and robust than any one of the component measures. We evaluate the effect of the combined indicator and its components on the onset of civil violence. As expected, the combined index yields stronger results as more information becomes available, thus confirming the initial hypothesis that horizontal economic inequality does drive conflict in the case of groups that are relatively poorer compared to the country average. Furthermore, these findings appear to be considerably more robust than those relying on a single data source.Swiss National Science FoundationAlexander von Humboldt Foundatio

The influence of DACCIWA radiosonde data on the quality of ECMWF analyses and forecasts over southern West Africa

During the DACCIWA (Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa) field campaign ∼900 radiosondes were launched from 12 stations in southern West Africa from 15 June to 31 July 2016. Subsequently, data-denial experiments were conducted using the Integrated Forecasting System of the European Centre for Medium-range Weather Forecasts (ECMWF) to assess the radiosondes\u27 impact on the quality of analyses and forecasts. As observational reference, satellite-based estimates of rainfall and outgoing long-wave radiation (OLR) as well as the radiosonde measurements themselves are used. With regard to the analyses, the additional observations show positive impacts onwinds throughout the troposphere and lower stratosphere, while large lower-tropospheric cold and dry biases are hardly reduced. Nonetheless, downstream, that is farther inland from the radiosonde stations,we find a significant increase (decrease) in low-level night-time temperatures (monsoon winds) when incorporating the DACCIWA observations, suggesting a possible linkage via weaker cold air advection fromthe Gulf of Guinea. The associated lower relative humidity leads to reduced cloud cover in the DACCIWA analysis. Closer to the coast and over Benin and Togo, DACCIWA observations increase low-level specific humidity and precipitable water, possibly due to changes in advection and vertical mixing. During daytime, differences between the two analyses are generally smaller at low levels. With regard to the forecasts, the impact of the additional observations is lost after a day or less. Moderate improvements occur in low-level wind and temperature but also in rainfall over the downstream Sahel, while impacts on OLR are ambiguous. The changes in precipitation appear to also affect high-level cloud cover and the tropical easterly jet. The overall rather small observation impact suggests that model and data assimilation deficits are the main limiting factors for better forecasts inWest Africa. The new observations and physical understanding from DACCIWA can hopefully contribute to reducing these issues

Continuous data assimilation for global numerical weather prediction

A new configuration of the European Centre for Medium-Range Weather Forecasts (ECMWF) incremental 4D-Var data assimilation (DA) system is introduced which builds upon the quasi-continuous DA concept proposed in the mid-1990s. Rather than working with a fixed set of observations, the new 4D-Var configuration exploits the near-continuous stream of incoming observations by introducing recently arrived observations at each outer loop iteration of the assimilation. This allows the analysis to benefit from more recent observations. Additionally, by decoupling the start time of the DA calculations from the observational data cut-off time, real-time forecasting applications can benefit from more expensive analysis configurations that previously could not have been considered. In this work we present results of a systematic comparison of the performance of a Continuous DA system against that of two more traditional baseline 4D-Var configurations. We show that the quality of the analysis produced by the new, more continuous configuration is comparable to that of a conventional baseline that has access to all of the observations in each of the outer loops, which is a configuration not feasible in real-time operational numerical weather prediction. For real-time forecasting applications, the Continuous DA framework allows configurations which clearly outperform the best available affordable non-continuous configuration. Continuous DA became operational at ECMWF in June 2019 and led to significant 2 to 3% reductions in medium-range forecast root mean square errors, which is roughly equivalent to 2-3 hr of additional predictive skill.Peer reviewe

Improving the condition number of estimated covariance matrices

High dimensional error covariance matrices and their inverses are used to weight the contribution of observation and background information in data assimilation procedures. As observation error covariance matrices are often obtained by sampling methods, estimates are often degenerate or ill-conditioned, making it impossible to invert an observation error covariance matrix without the use of techniques to reduce its condition number. In this paper we present new theory for two existing methods that can be used to ‘recondition’ any covariance matrix: ridge regression, and the minimum eigenvalue method. We compare these methods with multiplicative variance inflation, which cannot alter the condition number of a matrix, but is often used to account for neglected correlation information. We investigate the impact of reconditioning on variances and correlations of a general covariance matrix in both a theoretical and practical setting. Improved theoretical understanding provides guidance to users regarding method selection, and choice of target condition number. The new theory shows that, for the same target condition number, both methods increase variances compared to the original matrix, with larger increases for ridge regression than the minimum eigenvalue method. We prove that the ridge regression method strictly decreases the absolute value of off-diagonal correlations. Theoretical comparison of the impact of reconditioning and multiplicative variance inflation on the data assimilation objective function shows that variance inflation alters information across all scales uniformly, whereas reconditioning has a larger effect on scales corresponding to smaller eigenvalues. We then consider two examples: a general correlation function, and an observation error covariance matrix arising from interchannel correlations. The minimum eigenvalue method results in smaller overall changes to the correlation matrix than ridge regression, but can increase off-diagonal correlations. Data assimilation experiments reveal that reconditioning corrects spurious noise in the analysis but underestimates the true signal compared to multiplicative variance inflation

First Order Transition in the Ginzburg-Landau Model

The d-dimensional complex Ginzburg-Landau (GL) model is solved according to a variational method by separating phase and amplitude. The GL transition becomes first order for high superfluid density because of effects of phase fluctuations. We discuss its origin with various arguments showing that, in particular for d = 3, the validity of our approach lies precisely in the first order domain.Comment: 4 pages including 2 figure

Phase Transitions Driven by Vortices in 2D Superfluids and Superconductors: From Kosterlitz-Thouless to 1st Order

The Landau-Ginzburg-Wilson hamiltonian is studied for different values of the parameter $\lambda$ which multiplies the quartic term (it turns out that this is equivalent to consider different values of the coherence length $\xi$ in units of the lattice spacing $a$). It is observed that amplitude fluctuations can change dramatically the nature of the phase transition: for small values of $\lambda$ ($\xi/a > 0.7$), instead of the smooth Kosterlitz-Thouless transition there is a {\em first order} transition with a discontinuous jump in the vortex density $v$ and a larger non-universal drop in the helicity modulus. In particular, for $\lambda$ sufficiently small ($\xi/a \cong 1$), the density of bound pairs of vortex-antivortex below $T_c$ is so low that, $v$ drops to zero almost for all temperature $T<Tc$.Comment: 8 pages, 5 .eps figure

Integrating Data on Ethnicity, Geography, and Conflict

This article introduces the new Family of Ethnic Power Relations (EPR) data sets, version 2014, which is the latest in a series of data sets on ethnicity that have stimulated civil war research in the past decade. The EPR Family provides data on ethnic groups’ access to state power, their settlement patterns, links to rebel organizations, transborder ethnic kin relations, and intraethnic cleavages. The new 2014 version does not only extend the data set’s temporal coverage from 2009 to 2013, but it also offers several new features, such as a new measure of regional autonomy that is independent of national-level executive power and a new data set component coding intraethnic identities and cleavages. Moreover, for the first time, detailed documentation of the EPR data is provided through the EPR Atlas. This article presents these novelties in detail and compares the EPR Family 2014 to the most relevant alternative data sets on ethnicity