Promoting and maintaining changes in smoking behaviour for patients following discharge from a smokefree mental health inpatient stay: Development of a complex intervention using the Behaviour Change Wheel.

Evidence suggests that smokers can successfully quit, remain abstinent or reduce smoking during a smokefree mental health inpatient stay, provided behavioural/pharmacological support are offered. However, few evidence-based strategies to prevent the return to pre-hospital smoking behaviours post-discharge exist. We report the development of an intervention designed to support smoking-related behaviour change following discharge from a smokefree mental health stay. We followed the Behaviour Change Wheel (BCW) intervention development process. The target behaviour was supporting patients to change their smoking behaviours following discharge from a smokefree mental health stay. Using systematic reviews, we identified the barriers/enablers, classified according to the Theoretical Domains Framework (TDF). Potential intervention functions to address key influences were identified by consulting the BCW and Behaviour Change Technique (BCT) taxonomy. Another systematic review identified effectiveness of BCTs in this context. Stakeholder consultations were conducted to prioritise/refine intervention content. Barriers/enablers to supporting smoking cessation were identified within the domains of environmental context and resources (lack of staff time); knowledge (ill-informed interactions about smoking); social influences, and intentions (lack of intention to deliver support). Potential strategies to address these influences included goal setting, problem solving, feedback, social support, and information on health consequences. A strategy for operationalising these techniques into intervention components was agreed: pre-discharge evaluation sessions, personalised resource folder, tailored behavioural and text message support post-discharge, and a peer interaction group, delivered by a trained mental health worker. The intervention includes targeted resources to support smoking-related behaviour change in patients following discharge from a smokefree mental health setting. Using the BCW and TDF supported a theoretically and empirically informed process to define and develop a tailored intervention that acknowledges barriers and enablers to supporting smoking cessation in mental health settings. The result is a novel complex theory- and evidence-based intervention that will be formally tested in a randomised controlled feasibility study

The Maia detector array and x-ray fluorescence imaging system: Locating rare precious metal phases in complex samples

X-ray fluorescence images acquired using the Maia large solid-angle detector array and integrated real-time processor on the X-ray Fluorescence Microscopy (XFM) beamline at the Australian Synchrotron capture fine detail in complex natural samples with images beyond 100M pixels. Quantitative methods permit real-time display of deconvoluted element images and for the acquisition of large area XFM images and 3D datasets for fluorescence tomography and chemical state (XANES) imaging. This paper outlines the Maia system and analytical methods and describes the use of the large detector array, with a wide range of X-ray take-off angles, to provide sensitivity to the depth of features, which is used to provide an imaging depth contrast and to determine the depth of rare precious metal particles in complex geological samples. © 2013 SPIE

The Impact of Domestic Energy Efficiency Retrofit Schemes on Householder Attitudes and Behaviours

Retrofitting existing housing stock to improve energy efficiency is often required to meet climate mitigation, public health and fuel poverty targets. Increasing uptake and effectiveness of retrofit schemes requires understanding of their impacts on householder attitudes and behaviours. This paper reports results of a survey of 500 Kirklees householders in the UK, where the Kirklees Warm Zone scheme took place. This was a local government led city-scale domestic retrofit programme that installed energy efficiency measures at no charge in over 50,000 houses. The results highlight key design features of the scheme, socio-economic and attitudinal factors that affected take-up of energy efficiency measures and impacts on behaviour and energy use after adoption. The results emphasise the role that positive feedback plays in reinforcing pro-environmental attitudes and behaviours of participants and in addressing concerns of non-participants. Our findings have implications for the design and operation of future domestic energy efficiency retrofit schemes

The CCP4 suite: integrative software for macromolecular crystallography

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.Jon Agirre is a Royal Society University Research Fellow (UF160039 and URF\R\221006). Mihaela Atanasova is funded by the UK Engineering and Physical Sciences Research Council (EPSRC; EP/R513386/1). Haroldas Bagdonas is funded by The Royal Society (RGF/R1/181006). Jose´ Javier Burgos-Ma´rmol and Daniel J. Rigden are supported by the BBSRC (BB/S007105/1). Robbie P. Joosten is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871037 (iNEXTDiscovery) and by CCP4. This work was supported by the Medical Research Council as part of United Kingdom Research and Innovation, also known as UK Research and Innovation: MRC file reference No. MC_UP_A025_1012 to Garib N. Murshudov, which also funded Keitaro Yamashita, Paul Emsley and Fei Long. Robert A. Nicholls is funded by the BBSRC (BB/S007083/1). Soon Wen Hoh is funded by the BBSRC (BB/T012935/1). Kevin D. Cowtan and Paul S. Bond are funded in part by the BBSRC (BB/S005099/1). John Berrisford and Sameer Velankar thank the European Molecular Biology Laboratory–European Bioinformatics Institute, who supported this work. Andrea Thorn was supported in the development of AUSPEX by the German Federal Ministry of Education and Research (05K19WWA and 05K22GU5) and by Deutsche Forschungsgemeinschaft (TH2135/2-1). Petr Kolenko and Martin Maly´ are funded by the MEYS CR (CZ.02.1.01/0.0/0.0/16_019/0000778). Martin Maly´ is funded by the Czech Academy of Sciences (86652036) and CCP4/STFC (521862101). Anastassis Perrakis acknowledges funding from iNEXT (grant No. 653706), iNEXT-Discovery (grant No. 871037), West-Life (grant No. 675858) and EOSC-Life (grant No. 824087) funded by the Horizon 2020 program of the European Commission. Robbie P. Joosten has been the recipient of a Veni grant (722.011.011) and a Vidi grant (723.013.003) from the Netherlands Organization for Scientific Research (NWO). Maarten L. Hekkelman, Robbie P. Joosten and Anastassis Perrakis thank the Research High Performance Computing facility of the Netherlands Cancer Institute for providing and maintaining computation resources and acknowledge the institutional grant from the Dutch Cancer Society and the Dutch Ministry of Health, Welfare and Sport. Tarik R. Drevon is funded by the BBSRC (BB/S007040/1). Randy J. Read is supported by a Principal Research Fellowship from the Wellcome Trust (grant 209407/Z/17/Z). Atlanta G. Cook is supported by a Wellcome Trust SRF (200898) and a Wellcome Centre for Cell Biology core grant (203149). Isabel Uso´n acknowledges support from STFC-UK/CCP4: ‘Agreement for the integration of methods into the CCP4 software distribution, ARCIMBOLDO_LOW’ and Spanish MICINN/AEI/FEDER/UE (PID2021-128751NB-I00). Pavol Skubak and Navraj Pannu were funded by the NWO Applied Sciences and Engineering Domain and CCP4 (grant Nos. 13337 and 16219). Bernhard Lohkamp was supported by the Ro¨ntgen A˚ ngstro¨m Cluster (grant 349-2013-597). Nicholas Pearce is currently funded by the SciLifeLab and Wallenberg Data Driven Life Science Program (grant KAW 2020.0239) and has previously been funded by a Veni Fellowship (VI.Veni.192.143) from the Dutch Research Council (NWO), a Long-term EMBO fellowship (ALTF 609-2017) and EPSRC grant EP/G037280/1. David M. Lawson received funding from BBSRC Institute Strategic Programme Grants (BB/P012523/1 and BB/P012574/1). Lucrezia Catapano is the recipient of an STFC/CCP4-funded PhD studentship (Agreement No: 7920 S2 2020 007).Peer reviewe

The CCP4 suite : integrative software for macromolecular crystallography

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Quantified, whole section trace element mapping of carbonaceous chondrites by Synchrotron X-ray Fluorescence Microscopy: 1. CV meteorites

We present the application of a new synchrotron-based technique for rapid mapping of trace element distributions across large areas of the CV3 meteorites Allende and Vigarano. This technique utilizes the Australian Synchrotron X-ray Fluorescence Microscopy (XFM) beam line with its custom designed and built X-ray detector array called Maia. XFM with Maia allows data to be collected using a 2 µm spot size at very low dwell times (~0.1–0.5 ms), resulting in maps of entire thin sections in ~5 h. Maia is an energy dispersive detector system with a large collection solid-angle, which allows full spectral acquisition and high sensitivity. Hence, there is no need to constrain the elements of interest a priori.We collected whole section maps (~2 cm × 1 cm) from 3 thick sections of Allende and a single map (2 cm × 1.5 cm) from a thick section of Vigarano. Our experimental conditions provide data for elements with 20 ? Z ? 40 (K-shell, Ca through Zr) and the L-emissions of Os, Ir, Pt, Au, and Pb. We illustrate the unique capabilities of this technique by presenting observations across myriad length scales, from the centimeter-scale down to the detection of sub-micrometer particles within these objects. Our initial results show the potential of this technique to help decipher spatial and textural variations in trace element chemistry between CAIs, chondrules, matrix, and other chondritic components. We also illustrate how these datasets can be applied to understanding both nebular and parent-body processes within meteorites