Low-Background gamma counting at the Kimballton Underground Research Facility

The next generation of low-background physics experiments will require the use of materials with unprecedented radio-purity. A gamma-counting facility at the Kimballton Underground Research Facility (KURF) has been commissioned to perform initial screening of materials for radioactivity primarily from nuclides in the 238U and 232Th decay chains, 40K and cosmic-ray induced isotopes. The facility consists of two commercial low-background high purity germanium (HPGe) detectors. A continuum background reduction better than a factor of 10 was achieved by going underground. This paper describes the facility, detector systems, analysis techniques and selected assay results.Comment: 7 pages, 7 figures. Submitted to NIM

The MGDO software library for data analysis in Ge neutrinoless double-beta decay experiments

The GERDA and Majorana experiments will search for neutrinoless double-beta decay of germanium-76 using isotopically enriched high-purity germanium detectors. Although the experiments differ in conceptual design, they share many aspects in common, and in particular will employ similar data analysis techniques. The collaborations are jointly developing a C++ software library, MGDO, which contains a set of data objects and interfaces to encapsulate, store and manage physical quantities of interest, such as waveforms and high-purity germanium detector geometries. These data objects define a common format for persistent data, whether it is generated by Monte Carlo simulations or an experimental apparatus, to reduce code duplication and to ease the exchange of information between detector systems. MGDO also includes general-purpose analysis tools that can be used for the processing of measured or simulated digital signals. The MGDO design is based on the Object-Oriented programming paradigm and is very flexible, allowing for easy extension and customization of the components. The tools provided by the MGDO libraries are used by both GERDA and Majorana.Comment: 4 pages, 1 figure, proceedings for TAUP201

Changing Hydrozoan Bauplans by Silencing Hox-Like Genes

Regulatory genes of the Antp class have been a major factor for the invention and radiation of animal bauplans. One of the most diverse animal phyla are the Cnidaria, which are close to the root of metazoan life and which often appear in two distinct generations and a remarkable variety of body forms. Hox-like genes have been known to be involved in axial patterning in the Cnidaria and have been suspected to play roles in the genetic control of many of the observed bauplan changes. Unfortunately RNAi mediated gene silencing studies have not been satisfactory for marine invertebrate organisms thus far. No direct evidence supporting Hox-like gene induced bauplan changes in cnidarians have been documented as of yet. Herein, we report a protocol for RNAi transfection of marine invertebrates and demonstrate that knock downs of Hox-like genes in Cnidaria create substantial bauplan alterations, including the formation of multiple oral poles (“heads”) by Cnox-2 and Cnox-3 inhibition, deformation of the main body axis by Cnox-5 inhibition and duplication of tentacles by Cnox-1 inhibition. All phenotypes observed in the course of the RNAi studies were identical to those obtained by morpholino antisense oligo experiments and are reminiscent of macroevolutionary bauplan changes. The reported protocol will allow routine RNAi studies in marine invertebrates to be established

Knowledge Extraction and Prediction from Behavior Science Randomized Controlled Trials: A Case Study in Smoking Cessation

Due to the fast pace at which randomized controlled trials are published in the health domain, researchers, consultants and policymakers would benefit from more automatic ways to process them by both extracting relevant information and automating the meta-analysis processes. In this paper, we present a novel methodology based on natural language processing and reasoning models to 1) extract relevant information from RCTs and 2) predict potential outcome values on novel scenarios, given the extracted knowledge, in the domain of behavior change for smoking cessation

Ontologies relevant to behaviour change interventions: a method for their development.

Background: Behaviour and behaviour change are integral to many aspects of wellbeing and sustainability. However, reporting behaviour change interventions accurately and synthesising evidence about effective interventions is hindered by lacking a shared, scientific terminology to describe intervention characteristics. Ontologies are standardised frameworks that provide controlled vocabularies to help unify and connect scientific fields. To date, there is no published guidance on the specific methods required to develop ontologies relevant to behaviour change. We report the creation and refinement of a method for developing ontologies that make up the Behaviour Change Intervention Ontology (BCIO). Aims: (1) To describe the development method of the BCIO and explain its rationale; (2) To provide guidance on implementing the activities within the development method. Method and results: The method for developing ontologies relevant to behaviour change interventions was constructed by considering principles of good practice in ontology development and identifying key activities required to follow those principles. The method's details were refined through application to developing two ontologies. The resulting ontology development method involved: (1) defining the ontology's scope; (2) identifying key entities; (3) refining the ontology through an iterative process of literature annotation, discussion and revision; (4) expert stakeholder review; (5) testing inter-rater reliability; (6) specifying relationships between entities, and; (7) disseminating and maintaining the ontology. Guidance is provided for conducting relevant activities for each step.  Conclusions: We have developed a detailed method for creating ontologies relevant to behaviour change interventions, together with practical guidance for each step, reflecting principles of good practice in ontology development. The most novel aspects of the method are the use of formal mechanisms for literature annotation and expert stakeholder review to develop and improve the ontology content. We suggest the mnemonic SELAR3, representing the method's first six steps as Scope, Entities, Literature Annotation, Review, Reliability, Relationships

Development of an Intervention Setting Ontology for behaviour change: Specifying where interventions take place

Background: Contextual factors such as an intervention's setting are key to understanding how interventions to change behaviour have their effects and patterns of generalisation across contexts. The intervention's setting is not consistently reported in published reports of evaluations. Using ontologies to specify and classify intervention setting characteristics enables clear and reproducible reporting, thus aiding replication, implementation and evidence synthesis. This paper reports the development of a Setting Ontology for behaviour change interventions as part of a Behaviour Change Intervention Ontology, currently being developed in the Wellcome Trust funded Human Behaviour-Change Project. Methods: The Intervention Setting Ontology was developed following methods for ontology development used in the Human Behaviour-Change Project: 1) Defining the ontology's scope, 2) Identifying key entities by reviewing existing classification systems (top-down) and 100 published behaviour change intervention reports (bottom-up), 3) Refining the preliminary ontology by literature annotation of 100 reports, 4) Stakeholder reviewing by 23 behavioural science and public health experts to refine the ontology, 5) Assessing inter-rater reliability of using the ontology by two annotators familiar with the ontology and two annotators unfamiliar with it, 6) Specifying ontological relationships between setting entities and 7) Making the Intervention Setting Ontology machine-readable using Web Ontology Language (OWL) and publishing online. Results: The Intervention Setting Ontology consists of 72 entities structured hierarchically with two upper-level classes: Physical setting including Geographic location, Attribute of location (including Area social and economic condition, Population and resource density sub-levels) and Intervention site (including Facility, Transportation and Outdoor environment sub-levels), as well as Social setting. Inter-rater reliability was found to be 0.73 (good) for those familiar with the ontology and 0.61 (acceptable) for those unfamiliar with it. Conclusion: The Intervention Setting Ontology can be used to code information from diverse sources, annotate the setting characteristics of existing intervention evaluation reports and guide future reporting

False positive diagnosis of malignancy in a case of cryptogenic organising pneumonia presenting as a pulmonary mass with mediastinal nodes detected on fluorodeoxyglucose-positron emission tomography: a case report

<p>Abstract</p> <p>Introduction</p> <p>We report the case of a patient with positive findings on a lung emission tomography/computed tomography (PET/CT) scan, with possible contra lateral mediastinal involvement, which strongly suggested an inoperable lung carcinoma. The lung mass proved to be a cryptogenic organising pneumonia. While the latter has previously been shown to be PET/CT positive, mediastinal involvement simulating malignant spread has not been previously reported.</p> <p>Case presentation</p> <p>A 50-year-old Caucasian woman presented with a history of unproductive cough and was found to have a mass in the right upper lobe as shown on chest X-ray and a computed tomography scan. A subsequent PET/CT scan showed strong uptake in the right upper lobe (maximum standard uptake values (SUVmax) 9.6) with increased uptake in the adjacent mediastinum and contralateral mediastinal nodes. Surgical resection and mediastinoscopy revealed cryptogenic organising pneumonia, with enlarged reactive mediastinal lymph nodes.</p> <p>Conclusion</p> <p>The case illustrates the limits of PET/CT scanning as a diagnostic tool, and emphasizes the importance of obtaining histological confirmation of malignant diseases whenever possible.</p

The Human Behaviour-Change Project: Harnessing the power of Artificial Intelligence and Machine Learning for evidence synthesis and interpretation

Background Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?’. Methods The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility. Discussion The HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.The project is funded by a Wellcome Trust collaborative award [The Human Behaviour-Change Project: Building the science of behaviour change for complex intervention development’, 201,524/Z/16/Z]. During the preparation of the manuscript RW’s salary was funded by Cancer Research UK