The pseudotemporal bootstrap for predicting glaucoma from cross-sectional visual field data

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma, a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration, including visual field (VF) test, retinal image, and frequent intraocular pressure measurements. Like the progression of many biological and medical processes, VF progression is inherently temporal in nature. However, many datasets associated with the study of such processes are often cross sectional and the time dimension is not measured due to the expensive nature of such studies. In this paper, we address this issue by developing a method to build artificial time series, which we call pseudo time series from cross-sectional data. This involves building trajectories through all of the data that can then, in turn, be used to build temporal models for forecasting (which would otherwise be impossible without longitudinal data). Glaucoma, like many diseases, is a family of conditions and it is, therefore, likely that there will be a number of key trajectories that are important in understanding the disease. In order to deal with such situations, we extend the idea of pseudo time series by using resampling techniques to build multiple sequences prior to model building. This approach naturally handles outliers and multiple possible disease trajectories. We demonstrate some key properties of our approach on synthetic data and present very promising results on VF data for predicting glaucoma

Differential thermal analysis of lunar soil simulant

Differential thermal analysis of a lunar soil simulant, 'Minnesota Lunar Simulant-1' (MLS-1) was performed. The MLS-1 was tested in as-received form (in glass form) and with another silica. The silica addition was seen to depress nucleation events which lead to a better glass former

A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data

Dynamics from diffraction

A model-independent approach for the extraction of detailed lattice dynamical information from neutron powder diffraction data is described. The technique is based on a statistical analysis of atomistic configurations generated using reverse Monte Carlo structural refinement. Phonon dispersion curves extracted in this way are shown to reproduce many of the important features found in those determined independently using neutron triple-axis spectroscopy. The extent to which diffraction data are sensitive to lattice dynamics is explored in a range of materials. The prospect that such detailed dynamical information might be accessible using comparatively facile experiments such as neutron powder diffraction is incredibly valuable when studying systems for which established spectroscopic methods are prohibitive or inappropriate

Final data reduction and analysis of the AS and E OSO-4 grazing incidence X-ray telescope experiment

Final data analysis of grazing incidence of solar X ray telescope experiment of OSO- 4 satellit

The Impact of Community Based Adventure Therapy on Stress and Coping Skills in Adults.

Stress and coping skills are among the most essential components of the mental health counseling field. The use of coping skills (e.g., meditation, physical activities, appropriate uses of leisure) has been identified as an effective strategy for stress management. Adventure therapy has emerged as a modality that can positively augment other therapeutic approaches by improving coping skills and assisting clients in managing stress. As with all therapies, a positive working alliance has been found to be important toward achieving clinical outcomes. This study explored how adventure therapy enhanced learned coping strategies for stress and improved therapeutic alliance. Outcomes from this exploratory research highlighted the potential of adventure therapy to decrease stress, increase coping skills, and build therapeutic rapport with the therapist

The Environment of ``E+A'' Galaxies

The violent star formation history of ``E+A'' galaxies and their detection almost exclusively in distant clusters is frequently used to link them to the ``Butcher-Oemler effect'' and to argue that cluster environment influences galaxy evolution. From 11113 spectra in the Las Campanas Redshift Survey, we have obtained a unique sample of 21 nearby ``E+A" galaxies. Surprisingly, a large fraction (about 75%) of these ``E+A''s lie in the field. Therefore, interactions with the cluster environment, in the form of the ICM or cluster potential, are not essential for ``E+A'' formation. If one mechanism is responsible for ``E+A''s, their existence in the field and the tidal features in at least 5 of the 21 argue that galaxy-galaxy interactions and mergers are that mechanism. The most likely environments for such interactions are poor groups, which have lower velocity dispersions than clusters and higher galaxy densities than the field. In hierarchical models, groups fall into clusters in greater numbers at intermediate redshifts than they do today. Thus, the Butcher-Oemler effect may reflect the typical evolution of galaxies in groups and in the field rather than the influence of clusters on star formation in galaxies. This abstract is abridged.Comment: 39 uuencoded, compressed pages (except Fig 1), complete preprint at ftp://ociw.edu/pub/aiz/eplusa.ps, ApJ, submitte

The scale of homogeneity in the Las Campanas Redshift Survey

We analyse the Las Campanas Redshift Survey using the integrated conditional density (or density of neighbors) in volume-limited subsamples up to unprecedented scales (200 Mpc/$h$) in order to determine without ambiguity the behavior of the density field. We find that the survey is well described by a fractal up to 20-30 Mpc/$h$, but flattens toward homogeneity at larger scales. Although the data are still insufficient to establish with high significance the expected homogeneous behavior, and therefore to rule out a fractal trend to larger scales, a fit with a CDM-like spectrum with high normalization well represents the data.Comment: 8 pages, 3 figures, accepted on Ap.J. Letter