In situ and in-transit analysis of cosmological simulations.

Modern cosmological simulations have reached the trillion-element scale, rendering data storage and subsequent analysis formidable tasks. To address this circumstance, we present a new MPI-parallel approach for analysis of simulation data while the simulation runs, as an alternative to the traditional workflow consisting of periodically saving large data sets to disk for subsequent 'offline' analysis. We demonstrate this approach in the compressible gasdynamics/N-body code Nyx, a hybrid MPI + OpenMP code based on the BoxLib framework, used for large-scale cosmological simulations. We have enabled on-the-fly workflows in two different ways: one is a straightforward approach consisting of all MPI processes periodically halting the main simulation and analyzing each component of data that they own ('in situ'). The other consists of partitioning processes into disjoint MPI groups, with one performing the simulation and periodically sending data to the other 'sidecar' group, which post-processes it while the simulation continues ('in-transit'). The two groups execute their tasks asynchronously, stopping only to synchronize when a new set of simulation data needs to be analyzed. For both the in situ and in-transit approaches, we experiment with two different analysis suites with distinct performance behavior: one which finds dark matter halos in the simulation using merge trees to calculate the mass contained within iso-density contours, and another which calculates probability distribution functions and power spectra of various fields in the simulation. Both are common analysis tasks for cosmology, and both result in summary statistics significantly smaller than the original data set. We study the behavior of each type of analysis in each workflow in order to determine the optimal configuration for the different data analysis algorithms.

HIV and incarceration: prisons and detention.

The high prevalence of HIV infection among prisoners and pre-trial detainees, combined with overcrowding and sub-standard living conditions sometimes amounting to inhuman or degrading treatment in violation of international law, make prisons and other detention centres a high risk environment for the transmission of HIV. Ultimately, this contributes to HIV epidemics in the communities to which prisoners return upon their release. We reviewed the evidence regarding HIV prevalence, risk behaviours and transmission in prisons. We also reviewed evidence of the effectiveness of interventions and approaches to reduce the risk behaviours and, consequently, HIV transmission in prisons. A large number of studies report high levels of risk behaviour in prisons, and HIV transmission has been documented. There is a large body of evidence from countries around the world of what prison systems can do to prevent HIV transmission. In particular, condom distribution programmes, accompanied by measures to prevent the occurrence of rape and other forms of non-consensual sex, needle and syringe programmes and opioid substitution therapies, have proven effective at reducing HIV risk behaviours in a wide range of prison environments without resulting in negative consequences for the health of prison staff or prisoners.The introduction of these programmes in prisons is therefore warranted as part of comprehensive programmes to address HIV in prisons, including HIV education, voluntary HIV testing and counselling, and provision of antiretroviral treatment for HIV-positive prisoners. In addition, however, action to reduce overcrowding and improve conditions in detention is urgently needed.

Interactive Exploration and Analysis of Large-Scale Simulations Using Topology-Based Data Segmentation.

Large-scale simulations are increasingly being used to study complex scientific and engineering phenomena. As a result, advanced visualization and data analysis are also becoming an integral part of the scientific process. Often, a key step in extracting insight from these large simulations involves the definition, extraction, and evaluation of features in the space and time coordinates of the solution. However, in many applications, these features involve a range of parameters and decisions that will affect the quality and direction of the analysis. Examples include particular level sets of a specific scalar field, or local inequalities between derived quantities. A critical step in the analysis is to understand how these arbitrary parameters/decisions impact the statistical properties of the features, since such a characterization will help to evaluate the conclusions of the analysis as a whole. We present a new topological framework that in a single-pass extracts and encodes entire families of possible features definitions as well as their statistical properties. For each time step we construct a hierarchical merge tree a highly compact, yet flexible feature representation. While this data structure is more than two orders of magnitude smaller than the raw simulation data it allows us to extract a set of features for any given parameter selection in a postprocessing step. Furthermore, we augment the trees with additional attributes making it possible to gather a large number of useful global, local, as well as conditional statistic that would otherwise be extremely difficult to compile. We also use this representation to create tracking graphs that describe the temporal evolution of the features over time. Our system provides a linked-view interface to explore the time-evolution of the graph interactively alongside the segmentation, thus making it possible to perform extensive data analysis in a very efficient manner. We demonstrate our framework by extracting and analyzing burning cells from a large-scale turbulent combustion simulation. In particular, we show how the statistical analysis enabled by our techniques provides new insight into the combustion process.

Risk behaviours and healthcare needs of homeless drug users in Saint Lucia and Trinidad.

In the Caribbean AIDS has become the leading cause of death among those aged 15-45. The homeless are at high risk due to the nexus of mobility, psychiatric disorders and substance use, particularly crack. Seventy-four homeless, out-of-treatment drug users were recruited in Saint Lucia and Trinidad to assess risk behaviour and healthcare needs. Information was collected in several domains: medical, mental health, substance use, sexual risk, and barriers to care. Ninety-five percent of the sample reported crack cocaine use within the previous 30 days. Approximately 35% of the sample had ever been diagnosed with a sexually transmitted disease and 25% of the sample reported being HIV+. Approximately 40% had a history of trading sex for crack or money. Due to the range of needs in this population, interventions should be multimodal. A focus on the individual, community, and structural levels is warranted to improve chances of program effectiveness.