A Framework for Supervision for Mindfulness-Based Teachers:a Space for Embodied Mutual Inquiry

Over recent decades, there has been an exponential growth in mindfulness-based interventions (MBIs). To disseminate MBIs with fidelity, care needs to be taken with the training and supervision of MBI teachers. A wealth of literature exists describing the process and practice of supervision in a range of clinical approaches, but, as of yet, little consideration has been given to how this can best be applied to the supervision of MBI teachers. This paper articulates a framework for supervision of MBI teachers. It was informed by the following: the experience of eight experienced mindfulness-based supervisors, the literature and understandings from MBIs, and by the authors’ experience of training and supervision. It sets out the nature and distinctive features of mindfulness-based supervision (MBS), representing this complex, multilayered process through a series of circles that denote its essence, form, content and process. This paper aims to be a basis for further dialogue on MBS, providing a foundation to increase the availability of competent supervision so that MBIs can expand without compromising integrity and efficacy

Transitions of cardio-metabolic risk factors in the Americas between 1980 and 2014

Describing the prevalence and trends of cardiometabolic risk factors that are associated with non-communicable diseases (NCDs) is crucial for monitoring progress, planning prevention, and providing evidence to support policy efforts. We aimed to analyse the transition in body-mass index (BMI), obesity, blood pressure, raised blood pressure, and diabetes in the Americas, between 1980 and 2014

Search for the Chiral Magnetic Effect in Au+Au collisions at sNN=27\sqrt{s_{_{\rm{NN}}}}=27 GeV with the STAR forward Event Plane Detectors

A decisive experimental test of the Chiral Magnetic Effect (CME) is considered one of the major scientific goals at the Relativistic Heavy-Ion Collider (RHIC) towards understanding the nontrivial topological fluctuations of the Quantum Chromodynamics vacuum. In heavy-ion collisions, the CME is expected to result in a charge separation phenomenon across the reaction plane, whose strength could be strongly energy dependent. The previous CME searches have been focused on top RHIC energy collisions. In this Letter, we present a low energy search for the CME in Au+Au collisions at $\sqrt{s_{_{\rm{NN}}}}=27$ GeV. We measure elliptic flow scaled charge-dependent correlators relative to the event planes that are defined at both mid-rapidity $|\eta|<1.0$ and at forward rapidity $2.1 < |\eta|<5.1$. We compare the results based on the directed flow plane ($\Psi_1$) at forward rapidity and the elliptic flow plane ($\Psi_2$) at both central and forward rapidity. The CME scenario is expected to result in a larger correlation relative to $\Psi_1$ than to $\Psi_2$, while a flow driven background scenario would lead to a consistent result for both event planes[1,2]. In 10-50\% centrality, results using three different event planes are found to be consistent within experimental uncertainties, suggesting a flow driven background scenario dominating the measurement. We obtain an upper limit on the deviation from a flow driven background scenario at the 95\% confidence level. This work opens up a possible road map towards future CME search with the high statistics data from the RHIC Beam Energy Scan Phase-II.Comment: main: 8 pages, 5 figures; supplementary material: 2 pages, 1 figur

Search for resonances in the mass distribution of jet pairs with one or two jets identified as b-jets in proton–proton collisions at √s=13TeV with the ATLAS detector

Searches for high-mass resonances in the dijet invariant mass spectrum with one or two jets identi-fied as b-jets are performed using an integrated luminosity of 3.2fb−1of proton–proton collisions with a centre-of-mass energy of √s=13TeVrecorded by the ATLAS detector at the Large Hadron Collider. Noevidence of anomalous phenomena is observed in the data, which are used to exclude, at 95%credibility level, excited b∗quarks with masses from 1.1TeVto 2.1TeVand leptophobic Z bosons with masses from 1.1TeVto 1.5TeV. Contributions of a Gaussian signal shape with effective cross sections ranging from approximately 0.4 to 0.001pb are also excluded in the mass range 1.5–5.0TeV

Defining the risk of SARS-CoV-2 variants on immune protection

The global emergence of many severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants jeopardizes the protective antiviral immunity induced after infection or vaccination. To address the public health threat caused by the increasing SARS-CoV-2 genomic diversity, the National Institute of Allergy and Infectious Diseases within the National Institutes of Health established the SARS-CoV-2 Assessment of Viral Evolution (SAVE) programme. This effort was designed to provide a real-time risk assessment of SARS-CoV-2 variants that could potentially affect the transmission, virulence, and resistance to infection- and vaccine-induced immunity. The SAVE programme is a critical data-generating component of the US Government SARS-CoV-2 Interagency Group to assess implications of SARS-CoV-2 variants on diagnostics, vaccines and therapeutics, and for communicating public health risk. Here we describe the coordinated approach used to identify and curate data about emerging variants, their impact on immunity and effects on vaccine protection using animal models. We report the development of reagents, methodologies, models and notable findings facilitated by this collaborative approach and identify future challenges. This programme is a template for the response to rapidly evolving pathogens with pandemic potential by monitoring viral evolution in the human population to identify variants that could reduce the effectiveness of countermeasures

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work