Classical skyrmions in SU(N)/SO(N) cosets

We construct the skyrmion solutions appearing in the coset spaces SU(N)/SO(N) for N > 2 and compute their classical mass. For N = 3, the third homotopy group pi_3(SU(3)/SO(3)) = Z_4 implies the existence of two distinct solutions: the skyrmion of winding number two has spherical symmetry and is found to be the lightest non-trivial field configuration; the skyrmion and antiskyrmion of winding number plus and minus one are slightly heavier and of toroidal shape. For N >= 4, there is only one skyrmion since the third homotopy group is Z_2. It is found to have spherical symmetry and is significantly lighter than the N = 3 solutions.Comment: 14 pages, 3 figures; v2: discussion improve

Annexin-A5 assembled into two-dimensional arrays promotes cell membrane repair

Eukaryotic cells possess a universal repair machinery that ensures rapid resealing of plasma membrane disruptions. Before resealing, the torn membrane is submitted to considerable tension, which functions to expand the disruption. Here we show that annexin-A5 (AnxA5), a protein that self-assembles into two-dimensional (2D) arrays on membranes upon Ca2+ activation, promotes membrane repair. Compared with wild-type mouse perivascular cells, AnxA5-null cells exhibit a severe membrane repair defect. Membrane repair in AnxA5-null cells is rescued by addition of AnxA5, which binds exclusively to disrupted membrane areas. In contrast, an AnxA5 mutant that lacks the ability of forming 2D arrays is unable to promote membrane repair. We propose that AnxA5 participates in a previously unrecognized step of the membrane repair process: triggered by the local influx of Ca2+, AnxA5 proteins bind to torn membrane edges and form a 2D array, which prevents wound expansion and promotes membrane resealing

Trauma-related emotions and radical acceptance in dialectical behavior therapy for posttraumatic stress disorder after childhood sexual abuse

Background: Posttraumatic Stress Disorder (PTSD) related to childhood sexual abuse (CSA) is often associated with a wide range of trauma-related aversive emotions such as fear, disgust, sadness, shame, guilt, and anger. Intense experience of aversive emotions in particular has been linked to higher psychopathology in trauma survivors. Most established psychosocial treatments aim to reduce avoidance of trauma-related memories and associated emotions. Interventions based on Dialectical Behavior Therapy (DBT) also foster radical acceptance of the traumatic event. Methods: This study compares individual ratings of trauma-related emotions and radical acceptance between the start and the end of DBT for PTSD (DBT-PTSD) related to CSA. We expected a decrease in trauma-related emotions and an increase in acceptance. In addition, we tested whether therapy response according to the Clinician Administered PTSD-Scale (CAPS) for the DSM-IV was associated with changes in trauma-related emotions and acceptance. The data was collected within a randomized controlled trial testing the efficacy of DBT-PTSD, and a subsample of 23 women was included in this secondary data analysis. Results: In a multilevel model, shame, guilt, disgust, distress, and fear decreased significantly from the start to the end of the therapy whereas radical acceptance increased. Therapy response measured with the CAPS was associated with change in trauma-related emotions. Conclusions: Trauma-related emotions and radical acceptance showed significant changes from the start to the end of DBT-PTSD. Future studies with larger sample sizes and control group designs are needed to test whether these changes are due to the treatment. Trial registration: ClinicalTrials.gov, number NCT0048100

Combined use of the Consolidated Framework for Implementation Research (CFIR) and the Theoretical Domains Framework (TDF): A systematic review

Background: Over 60 implementation frameworks exist. Using multiple frameworks may help researchers to address multiple study purposes, levels, and degrees of theoretical heritage and operationalizability; however, using multiple frameworks may result in unnecessary complexity and redundancy if doing so does not address study needs. The Consolidated Framework for Implementation Research (CFIR) and the Theoretical Domains Framework (TDF) are both well-operationalized, multi-level implementation determinant frameworks derived from theory. As such, the rationale for using the frameworks in combination (i.e., CFIR + TDF) is unclear. The objective of this systematic review was to elucidate the rationale for using CFIR + TDF by (1) describing studies that have used CFIR + TDF, (2) how they used CFIR + TDF, and (2) their stated rationale for using CFIR + TDF. Methods: We undertook a systematic review to identify studies that mentioned both the CFIR and the TDF, were written in English, were peer-reviewed, and reported either a protocol or results of an empirical study in MEDLINE/PubMed, PsycInfo, Web of Science, or Google Scholar. We then abstracted data into a matrix and analyzed it qualitatively, identifying salient themes. Findings: We identified five protocols and seven completed studies that used CFIR + TDF. CFIR + TDF was applied to studies in several countries, to a range of healthcare interventions, and at multiple intervention phases; used many designs, methods, and units of analysis; and assessed a variety of outcomes. Three studies indicated that using CFIR + TDF addressed multiple study purposes. Six studies indicated that using CFIR + TDF addressed multiple conceptual levels. Four studies did not explicitly state their rationale for using CFIR + TDF. Conclusions: Differences in the purposes that authors of the CFIR (e.g., comprehensive set of implementation determinants) and the TDF (e.g., intervention development) propose help to justify the use of CFIR + TDF. Given that the CFIR and the TDF are both multi-level frameworks, the rationale that using CFIR + TDF is needed to address multiple conceptual levels may reflect potentially misleading conventional wisdom. On the other hand, using CFIR + TDF may more fully define the multi-level nature of implementation. To avoid concerns about unnecessary complexity and redundancy, scholars who use CFIR + TDF and combinations of other frameworks should specify how the frameworks contribute to their study. Trial registration: PROSPERO CRD4201502761

Characteristics of control group participants who increased their physical activity in a cluster-randomized lifestyle intervention trial

<p>Abstract</p> <p>Background</p> <p>Meaningful improvement in physical activity among control group participants in lifestyle intervention trials is not an uncommon finding, and may be partly explained by participant characteristics. This study investigated which baseline demographic, health and behavioural characteristics were predictive of successful improvement in physical activity in usual care group participants recruited into a telephone-delivered physical activity and diet intervention trial, and descriptively compared these characteristics with those that were predictive of improvement among intervention group participants.</p> <p>Methods</p> <p>Data come from the Logan Healthy Living Program, a primary care-based, cluster-randomized controlled trial of a physical activity and diet intervention. Multivariable logistic regression models examined variables predictive of an improvement of at least 60 minutes per week of moderate-to-vigorous intensity physical activity among usual care (n = 166) and intervention group (n = 175) participants.</p> <p>Results</p> <p>Baseline variables predictive of a meaningful change in physical activity were different for the usual care and intervention groups. Being retired and completing secondary school (but no further education) were predictive of physical activity improvement for usual care group participants, whereas only baseline level of physical activity was predictive of improvement for intervention group participants. Higher body mass index and being unmarried may also be predictors of physical activity improvement for usual care participants.</p> <p>Conclusion</p> <p>This is the first study to examine differences in predictors of physical activity improvement between intervention group and control group participants enrolled in a physical activity intervention trial. While further empirical research is necessary to confirm findings, results suggest that participants with certain socio-demographic characteristics may respond favourably to minimal intensity interventions akin to the treatment delivered to participants in a usual care group. In future physical activity intervention trials, it may be possible to screen participants for baseline characteristics in order to target minimal-intensity interventions to those most likely to benefit. (Australian Clinical Trials Registry, <url>http://www.anzctr.org.au/default.aspx</url>, ACTRN012607000195459)</p

Blind testing of shoreline evolution models

International audienceBeaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for tairua beach, new Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. in general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. this was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties