Identifying Resilience in HIV-Negative Sexual Minority Men with Syndemic Conditions

HIV/AIDS is a major public health concern for sexual minority men, especially men with risk factors for the virus. Most HIV prevention programs target relatively few behaviors, such as increasing individual condom use (Coates, Richter, & Caceres, 2008) through an exclusive focus on reducing high-risk behaviors (Herrick et al., 2011). Some researchers have posited whether more effective interventions, based on identifying and bolstering strengths of sexual minority men, should be developed. To that end, I conducted (a) a systematic review and (b) a qualitative study to serve as foundational steps in identifying resilience resources in samples of high risk, HIV-negative, sexual minority men. Both research inquiries examined samples of HIV-negative sexual minority men who endorsed at least one syndemic condition—empirically supported psychosocial risk factors identified as significantly increasing risk for HIV—including childhood sexual abuse, partner abuse, substance abuse, or mental health problems. In the systematic review, I identified 34 distinct resilience resources, including identity descriptors, behaviors related directly and indirectly to sex, cognitions, emotions, and relationships. I utilized these results to develop a qualitative interview guide. Results from interviews with 13 sexual minority men buttressed findings from the review: that resilience resources occurred at multiple ecosystem levels. More work is needed on ecosystem frameworks in HIV prevention to address the comprehensive issues influencing HIV infection. In addition, one hypothesis I generated from the interviews is that psychosocial risk factors for HIV may trigger stress-related growth for a certain subset of sexual minority men, leading to development of factors that decrease their HIV risk

Complete Genome Sequence of Aneurinibacillus migulanus E1, a Gramicidin S- and d-Phenylalanyl-l-Propyl Diketopiperazine-Deficient Mutant

ACKNOWLEDGMENTS This work was supported by the European Union’s Seventh Framework Programme grant 245268 (ISEFOR; to L.B.). Further support came from the SwissBOL project (the Swiss Federal Office for the Environment, to L.B.) and the Sciex-Scientific Exchange Programme NMS.CH (to L.L. and L.B.).Peer reviewedPublisher PD

Koch’s Postulates: An Interventionist Perspective

We argue that Koch’s postulates are best understood within an interventionist account of causation, in the sense described in Woodward (2003). We show how this treatment helps to resolve interpretive puzzles associated with Koch’s work and how it clarifies the different roles the postulates play in providing useful, yet not universal criteria for disease causation. Our paper is an effort at rational reconstruction; we attempt to show how Koch’s postulates and reasoning make sense and are normatively justified within an interventionist framework and more difficult to understand within alternative frameworks for thinking about causation

Automated Detection of Vehicles with Machine Learning

Considering the significant volume of data generated by sensor systems and network hardware which is required to be analysed and intepreted by security analysts, the potential for human error is significant. This error can lead to consequent harm for some systems in the event of an adverse event not being detected. In this paper we compare two machine learning algorithms that can assist in supporting the security function effectively and present results that can be used to select the best algorithm for a specific domain. It is suggested that a naive Bayesian classiifer (NBC) and an artificial neural network (ANN) are most likely the best candidate algorithms for the proposed application. It was found that NBC was faster and more accurate than the ANN for the given data set. Future research will look to repeat this process for cyber security specific applications, and also examine teh GPGPU optimisations to the machine learning algorithms

On the density-potential mapping in time-dependent density functional theory

The key questions of uniqueness and existence in time-dependent density functional theory are usually formulated only for potentials and densities that are analytic in time. Simple examples, standard in quantum mechanics, lead however to non-analyticities. We reformulate these questions in terms of a non-linear Schr\"odinger equation with a potential that depends non-locally on the wavefunction.Comment: 8 pages, 2 figure

Automated detection of vehicles with machine learning

Considering the significant volume of data generated by sensor systems and network hardware which is required to be analysed and interpreted by security analysts, the potential for human error is significant. This error can lead to consequent harm for some systems in the event of an adverse event not being detected. In this paper we compare two machine learning algorithms that can assist in supporting the security function effectively and present results that can be used to select the best algorithm for a specific domain. It is suggested that a naïve Bayesian classifier (NBC) and an artificial neural network (ANN) are most likely the best candidate algorithms for the proposed application. It was found that the NBC was faster and more accurate than the ANN for the given data set. Future research will look to repeat this process for cyber security specific applications, and also examine GPGPU optimisations to the machine learning algorithms.