U.S. Climate Resilience Toolkit Road Test: Bridging the Data-Practice Divide A summary report by Antioch University New England Center for Climate Resilience and Community Preparedness April 2015

Antioch University’s Center for Climate Preparedness and Community Resilience developed an online Facilitated Community of Practice model (FCoP) to convene 29 end-user decision-makers, working with 25 Eastern United States coastal communities, to “road test” the U.S. Climate Resilience Toolkit. FCoP participants (e.g., planners, emergency preparedness and municipal administration personnel, natural resource specialists) represented communities from Norfolk, VA, to Rockland, ME. The project was designed to provide constructive feedback to federal agencies to inform the usability of the toolkit for local decision makers and planners. The project also was intended to contribute to two broader outcomes: 1. building resilience in Eastern coastal communities; and 2. piloting a replicable model for networking and building the capacity of decision-makers in all regions of the U.S. for the impacts of a changing climate. The FCoP included online exercises comprising the following: an introduction to the toolkit; research questions each participant developed regarding resilience challenges in their coastal communities; and discussions through synchronous and asynchronous forums. An exit survey measured participants’ satisfaction using the toolkit, content integrity, usability, and interactive influence with the toolkit developers. Key findings included the priority theoretical and applied climate resilience interests pertinent to coastal communities and the importance of peer-to-peer learning and networking, using an online FCoP, to strengthen capacity for climate resilience. Keywords: climate change resilience, climate change adaptation, coastal flood risk, capacity building, online decision support, evaluation, local governmen

Internet-based psychoeducation for bipolar disorder: a qualitative analysis of feasibility, acceptability and impact

<p>Background: In a recent exploratory randomised trial we found that a novel, internet-based psychoeducation programme for bipolar disorder (Beating Bipolar) was relatively easy to deliver and had a modest effect on psychological quality of life. We sought to explore the experiences of participants with respect to feasibility, acceptability and impact of Beating Bipolar.</p> <p>Methods: Participants were invited to take part in a semi-structured interview. Thematic analysis techniques were employed; to explore and describe participants’ experiences, the data were analysed for emerging themes which were identified and coded.</p> <p>Results: The programme was feasible to deliver and acceptable to participants where they felt comfortable using a computer. It was found to impact upon insight into illness, health behaviour, personal routines and positive attitudes towards medication. Many participants regarded the programme as likely to be most beneficial for those recently diagnosed.</p> <p>Conclusions: An online psychoeducation package for bipolar disorder, such as Beating Bipolar, is feasible and acceptable to patients, has a positive impact on self-management behaviours and may be particularly suited to early intervention. Alternative (non-internet) formats should also be made available to patients.</p&gt

CX3CR1 Polymorphisms are associated with atopy but not asthma in German children

Chemokines and their receptors are involved in many aspects of immunity. Chemokine CX3CL1, acting via its receptor CX3CR1, regulates monocyte migration and macrophage differentiation as well as T cell-dependent inflammation. Two common, nonsynonymous polymorphisms in CX3CR1 have previously been shown to alter the function of the CX3CL1/CX3CR1 pathway and were suggested to modify the risk for asthma. Using matrix-assisted laser desorption/ionization time-of-flight technology, we genotyped polymorphisms Val249Ile and Thr280Met in a cross-sectional population of German children from Munich (n = 1,159) and Dresden ( n = 1,940). For 249Ile an odds ratio of 0.77 (95% confidence interval 0.63-0.96; p = 0.017) and for 280Met an odds ratio of 0.71 ( 95% confidence interval 0.56-0.89; p = 0.004) were found with atopy in Dresden but not in Munich. Neither polymorphism was associated with asthma. Thus, amino acid changes in CX3CR1 may influence the development of atopy but not asthma in German children. Potentially, other factors such as environmental effects may modify the role of CX3CR1 polymorphisms. Copyright (c) 2007 S. Karger AG, Basel

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Aromatase expression is increased in BRCA1 mutation carriers

<p>Abstract</p> <p>Background</p> <p>Until recently, the molecular mechanisms explaining increased incidence of ovarian and breast cancers in carriers of <it>BRCA1 </it>gene mutations had not been clearly understood. Of significance is the finding that BRCA1 negatively regulates aromatase expression <it>in vitro</it>. Our objective was to characterise aromatase gene <it>(CYP19A1) </it>and its promoter expression in breast adipose and ovarian tissue in <it>BRCA1 </it>mutation carriers and unaffected controls.</p> <p>Methods</p> <p>We measured aromatase transcripts, total and promoter-specific (PII, PI.3, PI.4) in prophylactic oophorectomy or mastectomy, therapeutic mastectomy, ovarian and breast tissue from unaffected women.</p> <p>Results</p> <p>We demonstrate that the lack of functional BRCA1 protein correlates to higher aromatase levels in 85% of <it>BRCA1 </it>mutation carriers. This increase is mediated by aberrant transcriptional regulation of aromatase; in breast adipose by increases in promoter II/I.3 and I.4-specific transcripts; and in the ovary with elevation in promoter I.3 and II-specific transcripts.</p> <p>Conclusion</p> <p>Understanding the link between BRCA1 and aromatase is significant in terms of understanding why carcinogenesis is restricted to estrogen-producing tissues in <it>BRCA1 </it>mutation carriers.</p

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

Undifferentiated febrile illnesses in South Sudan: a case series from Operation TRENTON from June to August 2017

Undifferentiated febrile illnesses present diagnostic and treatment challenges in the Firm Base, let alone in the deployed austere environment. We report a series of 14 cases from Operation TRENTON in South Sudan in 2017 that coincided with the rainy season, increased insect numbers and a Relief in Place. The majority of patients had headaches, myalgia, arthralgia and back pain, as well as leucopenia and thrombocytopenia. No diagnoses could be made in theatre, despite a sophisticated deployed laboratory being available, and further testing in the UK, including next-generation sequencing, was unable to establish an aetiology. Such illnesses are very likely to present in tropical environments, where increasing numbers of military personnel are being deployed, and clinicians must be aware of the non-specific presentation and treatment, as well as the availability of Military Infection Reachback services to assist in the management of these cases

The credibility challenge for global fluvial flood risk analysis

Quantifying flood hazard is an essential component of resilience planning, emergency response, and mitigation, including insurance. Traditionally undertaken at catchment and national scales, recently, efforts have intensified to estimate flood risk globally to better allow consistent and equitable decision making. Global flood hazard models are now a practical reality, thanks to improvements in numerical algorithms, global datasets, computing power, and coupled modelling frameworks. Outputs of these models are vital for consistent quantification of global flood risk and in projecting the impacts of climate change. However, the urgency of these tasks means that outputs are being used as soon as they are made available and before such methods have been adequately tested. To address this, we compare multi-probability flood hazard maps for Africa from six global models and show wide variation in their flood hazard, economic loss and exposed population estimates, which has serious implications for model credibility. While there is around 30-40% agreement in flood extent, our results show that even at continental scales, there are significant differences in hazard magnitude and spatial pattern between models, notably in deltas, arid/semi-arid zones and wetlands. This study is an important step towards a better understanding of modelling global flood hazard, which is urgently required for both current risk and climate change projections