Hyperatlas: A New Framework for Image Federation

Hyperatlas is an open standard intended to facilitate the large-scale federation of image-based data. The subject of hyperatlas is the space of sphere-to-plane projection mappings (the FITS-WCS information), and the standard consists of coherent collections of these on which data can be resampled and thereby federated with other image data. We hope for a distributed effort that will produce a multi-faceted image atlas of the sky, made by federating many different surveys at different wavelengths and different times. We expect that hyperatlas-compliant imagery will be published and discovered through an International Virtual Observatory Alliance (IVOA) registry, and that grid-based services will emerge for the required resampling and mosaicking.Comment: Published in ADASS XIII proceeding

Atlasmaker: A Grid-based Implementation of the Hyperatlas

The Atlasmaker project is using Grid technology, in combination with NVO interoperability, to create new knowledge resources in astronomy. The product is a multi-faceted, multi-dimensional, scientifically trusted image atlas of the sky, made by federating many different surveys at different wavelengths, times, resolutions, polarizations, etc. The Atlasmaker software does resampling and mosaicking of image collections, and is well-suited to operate with the Hyperatlas standard. Requests can be satisfied via on-demand computations or by accessing a data cache. Computed data is stored in a distributed virtual file system, such as the Storage Resource Broker (SRB). We expect these atlases to be a new and powerful paradigm for knowledge extraction in astronomy, as well as a magnificent way to build educational resources. The system is being incorporated into the data analysis pipeline of the Palomar-Quest synoptic survey, and is being used to generate all-sky atlases from the 2MASS, SDSS, and DPOSS surveys for joint object detection.Comment: Published in the Proceedings of ADASS XI

Actigraphy, Sleep Diaries and Polysomnography in College Students with Insomnia

Thesis written by a student in the UNT Honors College discussing the use of actigraphy on college students to measure the number of awakenings, wake after sleep onset, total sleep time, and sleep efficiency

A Deterioration in Hearing is Associated With Functional and Cognitive Impairments, Difficulty With Communication and Greater Health Instability

Objectives: To examine the relationship between hearing deterioration and several health-related outcomes among home care clients in Ontario. Design: Longitudinal analysis was completed for clients with at least two comprehensive assessments. Hearing status, based on a single item, ranged from zero (no impairment) to three (highly impaired). Hearing deterioration was defined as at least a 1-point decline between subsequent assessments. Results: Seven percent experienced a 1-point deterioration in hearing and roughly 1% had a 2/3-point decline. After adjusting for other covariates, increasing age (odds ratio = 1.94; 95% confidence intervals [CIs] = [1.45, 2.61]) and a diagnosis of Alzheimer\u27s disease (1.37; CI = [1.04, 1.80]) and other dementias (1.32; CI = [1.07, 1.63]) increased the risk of a 2/3-point deterioration. Conclusion: These findings can assist home care professionals and policy makers in creating and refining interventions to meet the needs of older adults with hearing difficulties

Mutation of the co-chaperone Tsc1 in bladder cancer diminishes Hsp90 acetylation and reduces drug sensitivity and selectivity

The molecular chaperone Heat shock protein 90 (Hsp90) is essential for the folding, stability, and activity of several drivers of oncogenesis. Hsp90 inhibitors are currently under clinical evaluation for cancer treatment, however their efficacy is limited by lack of biomarkers to optimize patient selection. We have recently identified the tumor suppressor tuberous sclerosis complex 1 (Tsc1) as a new co-chaperone of Hsp90 that affects Hsp90 binding to its inhibitors. Highly variable mutations of TSC1 have been previously identified in bladder cancer and correlate with sensitivity to the Hsp90 inhibitors. Here we showed loss of TSC1 leads to hypoacetylation of Hsp90-K407/K419 and subsequent decreased binding to the Hsp90 inhibitor ganetespib. Pharmacologic inhibition of histone deacetylases (HDACs) restores acetylation of Hsp90 and sensitizes Tsc1-mutant bladder cancer cells to ganetespib, resulting in apoptosis. Our findings suggest that TSC1 status may predict response to Hsp90 inhibitors in patients with bladder cancer, and co-targeting HDACs can sensitize tumors with Tsc1 mutations to Hsp90 inhibitors

Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere

Choice of resident costimulatory molecule can influence cell fate in human naïve CD4+ T cell differentiation

With antigen stimulation, naïve CD4+ T cells differentiate to several effector or memory cell populations, and cytokines contribute to differentiation outcome. Several proteins on these cells receive costimulatory signals, but a systematic comparison of their differential effects on naïve T cell differentiation has not been conducted. Two costimulatory proteins, CD28 and ICAM-1, resident on human naïve CD4+ T cells were compared for participation in differentiation. Under controlled conditions, and with no added cytokines, costimulation through either CD3+CD28 or CD3+ICAM-1 induced differentiation to T effector and T memory cells. In contrast, costimulation through CD3+ICAM-1 induced differentiation to Treg cells whereas costimulation through CD3+CD28 did not

The role of low-level image features in the affective categorization of rapidly presented scenes

It remains unclear how the visual system is able to extract affective content from complex scenes even with extremely brief (\u3c 100 millisecond) exposures. One possibility, suggested by findings in machine vision, is that low-level features such as unlocalized, two-dimensional (2-D) Fourier spectra can be diagnostic of scene content. To determine whether Fourier image amplitude carries any information about the affective quality of scenes, we first validated the existence of image category differences through a support vector machine (SVM) model that was able to discriminate our intact aversive and neutral images with ~ 70% accuracy using amplitude-only features as inputs. This model allowed us to confirm that scenes belonging to different affective categories could be mathematically distinguished on the basis of amplitude spectra alone. The next question is whether these same features are also exploited by the human visual system. Subsequently, we tested observers’ rapid classification of affective and neutral naturalistic scenes, presented briefly (~33.3 ms) and backward masked with synthetic textures. We tested categorization accuracy across three distinct experimental conditions, using: (i) original images, (ii) images having their amplitude spectra swapped within a single affective image category (e.g., an aversive image whose amplitude spectrum has been swapped with another aversive image) or (iii) images having their amplitude spectra swapped between affective categories (e.g., an aversive image containing the amplitude spectrum of a neutral image). Despite its discriminative potential, the human visual system does not seem to use Fourier amplitude differences as the chief strategy for affectively categorizing scenes at a glance. The contribution of image amplitude to affective categorization is largely dependent on interactions with the phase spectrum, although it is impossible to completely rule out a residual role for unlocalized 2-D amplitude measures

Trajectory Design Considerations for Exploration Mission 1

Exploration Mission 1 (EM-1) will be the first mission to send an uncrewed Orion Multi-Purpose Crew Vehicle (MPCV) to cislunar space in the fall of 2019. EM-1 was originally conceived as a lunar free-return mission, but was later changed to a Distant Retrograde Orbit (DRO) mission as a precursor to the Asteroid Redirect Mission. To understand the required mission performance (i.e., propellant requirement), a series of trajectory optimization runs was conducted using JSC's Copernicus spacecraft trajectory optimization tool. In order for the runs to be done in a timely manner, it was necessary to employ a parallelization approach on a computing cluster using a new trajectory scan tool written in Python. Details of the scan tool are provided and how it is used to perform the scans and post-process the results. Initially, a scan of daily due east launched EM-1 DRO missions in 2018 was made. Valid mission opportunities are ones that do not exceed the useable propellant available to perform the required burns. The initial scan data showed the propellant and delta-V performance patterns for each launch period. As questions were raised from different subsystems (e.g., power, thermal, communications, flight operations, etc.), the mission parameters or data that were of interest to them were added to the scan output data file. The additional data includes: (1) local launch and landing times in relation to sunrise and sunset, (2) length of eclipse periods during the in-space portion of the mission, (3) Earth line of sight from cislunar space, (4) Deep Space Network field of view looking towards cislunar space, and (5) variation of the downrange distance from Earth entry interface to splashdown. Mission design trades can also be performed based on the information that the additional data shows. For example, if the landing is in darkness, but the recovery operations team desires a landing in daylight, then an analysis is performed to determine how to change the mission design to meet this request. Also, subsystems request feasibility of alternate or contingency mission designs, such as adding an Orion main engine checkout burn or Orion completing all of its burns using only its auxiliary thrusters. This paper examines and presents the evolving trade studies that incorporate subsystem feedback and demonstrate the feasibility of these constrained mission trajectory designs and contingencies