Secure Display of Space-Exploration Images

Java EDR Display Interface (JEDI) is software for either local display or secure Internet distribution, to authorized clients, of image data acquired from cameras aboard spacecraft engaged in exploration of remote planets. ( EDR signifies experimental data record, which, in effect, signifies image data.) Processed at NASA s Multimission Image Processing Laboratory (MIPL), the data can be from either near-realtime processing streams or stored files. JEDI uses the Java Advanced Imaging application program interface, plus input/output packages that are parts of the Video Image Communication and Retrieval software of the MIPL, to display images. JEDI can be run as either a standalone application program or within a Web browser as a servlet with an applet front end. In either operating mode, JEDI communicates using the HTTP(s) protocol(s). In the Web-browser case, the user must provide a password to gain access. For each user and/or image data type, there is a configuration file, called a "personality file," containing parameters that control the layout of the displays and the information to be included in them. Once JEDI has accepted the user s password, it processes the requested EDR (provided that user is authorized to receive the specific EDR) to create a display according to the user s personality file

Preferences across the Menstrual Cycle for Masculinity and Symmetry in Photographs of Male Faces and Bodies

Background: Previous studies have shown that women increase their preference for masculinity during the fertile phase of the menstrual cycle. Evidence for a similar preference shift for symmetry is equivocal. These studies have required participants to choose between subtle variations in computer-generated stimuli, and preferences for more natural stimuli have not been investigated. Methodology/Principal Findings: Our study employed photographs of individual males to investigate women’s preferences for face and body masculinity and symmetry across the menstrual cycle. We collected attractiveness ratings from 25 normally cycling women at high- and low-fertility days of the menstrual cycle. Attractiveness ratings made by these women were correlated with independent ratings of masculinity and symmetry provided by different sets of raters. We found no evidence for any cyclic shift in female preferences. Correlations between attractiveness and masculinity, and attractiveness and symmetry did not differ significantly between high- and low-fertility test sessions. Furthermore, there was no significant difference between high- and low-fertility ratings of attractiveness. Conclusions: These results suggest that a menstrual cycle shift in visual preferences for masculinity and symmetry may be too subtle to influence responses to real faces and bodies, and subsequent mate-choice decisions

Does Genetic Diversity Predict Health in Humans?

Genetic diversity, especially at genes important for immune functioning within the Major Histocompatibility Complex (MHC), has been associated with fitness-related traits, including disease resistance, in many species. Recently, genetic diversity has been associated with mate preferences in humans. Here we asked whether these preferences are adaptive in terms of obtaining healthier mates. We investigated whether genetic diversity (heterozygosity and standardized mean d2) at MHC and nonMHC microsatellite loci, predicted health in 153 individuals. Individuals with greater allelic diversity (d2) at nonMHC loci and at one MHC locus, linked to HLA-DRB1, reported fewer symptoms over a four-month period than individuals with lower d2. In contrast, there were no associations between MHC or nonMHC heterozygosity and health. NonMHC-d2 has previously been found to predict male preferences for female faces. Thus, the current findings suggest that nonMHC diversity may play a role in both natural and sexual selection acting on human populations

Conceptualising and mapping coupled estuary, coast and inner shelf sediment systems

Whilst understanding and predicting the effects of coastal change are primarily modelling problems, it is essential that we have appropriate conceptual frameworks for (1) the formalisation of existing knowledge; (2) the formulation of relevant scientific questions and management issues; (3) the implementation and deployment of predictive models; and (4) meaningful engagement involvement of stakeholders. Important progress continues to be made on the modelling front, but our conceptual frameworks have not evolved at a similar pace. Accordingly, this paper presents a new approach that re-engages with formal systems analysis and provides a mesoscale geomorphological context within which the coastal management challenges of the 21st century can be more effectively addressed. Coastal and Estuarine System Mapping (CESM) is founded on an ontology of landforms and human interventions that is partly inspired by the coastal tract concept and its temporal hierarchy of sediment sharing systems, but places greater emphasis on a hierarchy of spatial scales. This extends from coastal regions, through landform complexes, to landforms, the morphological adjustment of which is constrained by diverse forms of human intervention. Crucially, CESM integrates open coastal environments with estuaries and relevant portions of the inner shelf that have previously been treated separately.In contrast to the simple nesting of littoral cells that has hitherto framed shoreline management planning, CESM charts a complex web of interactions, of which a sub-set of mass transfer pathways defines the sediment budget, and a multitude of human interventions constrains natural landform behaviour. Conducted within a geospatial framework, CESM constitutes a form of knowledge formalisation in which disparate sources of information (published research, imagery, mapping, raw data etc.) are generalised into usable knowledge. The resulting system maps provide a framework for the development and application of predictive models and a repository for the outputs they generate (not least, flux estimates for the major sediment system pathways). They also permit comparative analyses of the relative abundance of landforms and the multi-scale interactions between them. Finally, they articulate scientific understanding of the structure and function of complex geomorphological systems in a way that is transparent and accessible to diverse stakeholder audiences. As our models of mesoscale landform evolution increase in sophistication, CESM provides a platform for a more participatory approach to their application to coastal and estuarine management

Progress on “Changing coastlines: data assimilation for morphodynamic prediction and predictability”

The task of assessing the likelihood and extent of coastal flooding is hampered by the lack of detailed information on near-shore bathymetry. This is required as an input for coastal inundation models, and in some cases the variability in the bathymetry can impact the prediction of those areas likely to be affected by flooding in a storm. The constant monitoring and data collection that would be required to characterise the near-shore bathymetry over large coastal areas is impractical, leaving the option of running morphodynamic models to predict the likely bathymetry at any given time. However, if the models are inaccurate the errors may be significant if incorrect bathymetry is used to predict possible flood risks. This project is assessing the use of data assimilation techniques to improve the predictions from a simple model, by rigorously incorporating observations of the bathymetry into the model, to bring the model closer to the actual situation. Currently we are concentrating on Morecambe Bay as a primary study site, as it has a highly dynamic inter-tidal zone, with changes in the course of channels in this zone impacting the likely locations of flooding from storms. We are working with SAR images, LiDAR, and swath bathymetry to give us the observations over a 2.5 year period running from May 2003 – November 2005. We have a LiDAR image of the entire inter-tidal zone for November 2005 to use as validation data. We have implemented a 3D-Var data assimilation scheme, to investigate the improvements in performance of the data assimilation compared to the previous scheme which was based on the optimal interpolation method. We are currently evaluating these different data assimilation techniques, using 22 SAR data observations. We will also include the LiDAR data and swath bathymetry to improve the observational coverage, and investigate the impact of different types of observation on the predictive ability of the model. We are also assessing the ability of the data assimilation scheme to recover the correct bathymetry after storm events, which can dramatically change the bathymetry in a short period of time