The OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS): Spectral Maps of the Asteroid Bennu

The OSIRIS-REx Visible and Infrared Spectrometer (OVIRS) is a point spectrometer covering the spectral range of 0.4 to 4.3 microns (25,000-2300 cm-1). Its primary purpose is to map the surface composition of the asteroid Bennu, the target asteroid of the OSIRIS-REx asteroid sample return mission. The information it returns will help guide the selection of the sample site. It will also provide global context for the sample and high spatial resolution spectra that can be related to spatially unresolved terrestrial observations of asteroids. It is a compact, low-mass (17.8 kg), power efficient (8.8 W average), and robust instrument with the sensitivity needed to detect a 5% spectral absorption feature on a very dark surface (3% reflectance) in the inner solar system (0.89-1.35 AU). It, in combination with the other instruments on the OSIRIS-REx Mission, will provide an unprecedented view of an asteroid's surface.Comment: 14 figures, 3 tables, Space Science Reviews, submitte

Detectors for the James Webb Space Telescope Near-Infrared Spectrograph I: Readout Mode, Noise Model, and Calibration Considerations

We describe how the James Webb Space Telescope (JWST) Near-Infrared Spectrograph's (NIRSpec's) detectors will be read out, and present a model of how noise scales with the number of multiple non-destructive reads sampling-up-the-ramp. We believe that this noise model, which is validated using real and simulated test data, is applicable to most astronomical near-infrared instruments. We describe some non-ideal behaviors that have been observed in engineering grade NIRSpec detectors, and demonstrate that they are unlikely to affect NIRSpec sensitivity, operations, or calibration. These include a HAWAII-2RG reset anomaly and random telegraph noise (RTN). Using real test data, we show that the reset anomaly is: (1) very nearly noiseless and (2) can be easily calibrated out. Likewise, we show that large-amplitude RTN affects only a small and fixed population of pixels. It can therefore be tracked using standard pixel operability maps.Comment: 55 pages, 10 figure

JWST Near-Infrared Detectors: Latest Test Results

The James Webb Space Telescope, an infrared-optimized space telescope being developed by NASA for launch in 2013, will utilize cutting-edge detector technology in its investigation of fundamental questions in astrophysics. JWST's near infrared spectrograph, NIRSpec utilizes two 2048 x 2048 HdCdTe arrays with Sidecar ASIC readout electronics developed by Teledyne to provide spectral coverage from 0.6 microns to 5 microns. We present recent test and calibration results for the NIRSpec flight arrays as well as data processing routines for noise reduction and cosmic ray rejection

Detector Arrays for the James Webb Space Telescope Near-Infrared Spectrograph

The James Webb Space Telescope's (JWST) Near Infrared Spectrograph (NIRSpec) incorporates two 5 micron cutoff (lambda(sub co) = 5 microns) 2048x2048 pixel Teledyne HgCdTe HAWAII-2RG sensor chip assemblies. These detector arrays, and the two Teledyne SIDECAR application specific integrated circuits that control them, are operated in space at T approx. 37 K. In this article, we provide a brief introduction to NIRSpec, its detector subsystem (DS), detector readout in the space radiation environment, and present a snapshot of the developmental status of the NIRSpec DS as integration and testing of the engineering test unit begins

James Webb Space Telescope Near-Infrared Spectrograph: Dark Performance of the First Flight Candidate Detector Arrays

The James Webb Space Telescope (JWST) Near Infrared Spectrograph (NIRSpec) incorporates two 5 micron cutoff (lambda(sub co) = 5 micron) 2048x2048 pixel Teledyne HgCdTe HAWAII-2RG sensor chip assemblies. These detector arrays, and the two Teledyne SIDECAR application specific integrated circuits that control them, are operated in space at T approx. 37 K. This article focuses on the measured performance of the first flight-candidate, and near-flight candidate, detector arrays. These are the first flight-packaged detector arrays that meet NIRSpec's challenging 6 e(-) rms total noise requirement

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Detectors for the James Webb Space Telescope Near-Infrared Spectrograph I: Readout Mode, Noise Model, and Calibration Considerations

We describe how the James Webb Space Telescope (JWST) Near-Infrared Spectrograph's (NIRSpec's) detectors will be read out, and present a model of how noise scales with the number of multiple non-destructive reads sampling-up-the-ramp. We believe that this noise model, which is validated using real and simulated test data, is applicable to most astronomical near-infrared instruments. We describe some non-ideal behaviors that have been observed in engineering grade NIRSpec detectors, and demonstrate that they are unlikely to affect NIRSpec sensitivity, operations, or calibration. These include a HAWAII-2RG reset anomaly and random telegraph noise (RTN). Using real test data, we show that the reset anomaly is: (1) very nearly noiseless and (2) can be easily calibrated out. Likewise, we show that RTN affects only a small and fixed population of pixels. It can therefore be tracked using standard pixel operability maps

Amyloid Plaques Beyond Aβ: A Survey of the Diverse Modulators of Amyloid Aggregation

Aggregation of the amyloid-β (Aβ) peptide is strongly correlated with Alzheimer’s disease (AD). Recent research has improved our understanding of the kinetics of amyloid fibril assembly and revealed new details regarding different stages in plaque formation. Presently, interest is turning toward studying this process in a holistic context, focusing on cellular components which interact with the Aβ peptide at various junctures during aggregation, from monomer to cross-β amyloid fibrils. However, even in isolation, a multitude of factors including protein purity, pH, salt content, and agitation affect Aβ fibril formation and deposition, often producing complicated and conflicting results. The failure of numerous inhibitors in clinical trials for AD suggests that a detailed examination of the complex interactions that occur during plaque formation, including binding of carbohydrates, lipids, nucleic acids, and metal ions, is important for understanding the diversity of manifestations of the disease. Unraveling how a variety of key macromolecular modulators interact with the Aβ peptide and change its aggregation properties may provide opportunities for developing therapies. Since no protein acts in isolation, the interplay of these diverse molecules may differentiate disease onset, progression, and severity, and thus are worth careful consideration

Reduced clinical and postmortem measures of cardiac pathology in subjects with advanced Alzheimer's Disease

Background. Epidemiological studies indicate a statistical linkage between atherosclerotic vascular disease (ATH) and Alzheimer\u27s disease (AD). Autopsy studies of cardiac disease in AD have been few and inconclusive. In this report, clinical and gross anatomic measures of cardiac disease were compared in deceased human subjects with and without AD. Methods. Clinically documented cardiovascular conditions from AD (n = 35) and elderly non-demented control subjects (n = 22) were obtained by review of medical records. Coronary artery stenosis and other gross anatomical measures, including heart weight, ventricular wall thickness, valvular circumferences, valvular calcifications and myocardial infarct number and volume were determined at autopsy. Results. Compared to non-demented age-similar control subjects, those with AD had significantly fewer total diagnosed clinical conditions (2.91 vs 4.18), decreased coronary artery stenosis (70.8 vs 74.8%), heart weight (402 vs 489 g for males; 319 vs 412 g for females) and valvular circumferences. Carriage of the Apolipoprotein E-ε4 allele did not influence the degree of coronary stenosis. Group differences in heart weight remained significant after adjustment for age, gender, body mass index and apolipoprotein E genotype while differences in coronary artery stenosis were significantly associated with body mass index alone. Conclusions. The results are in agreement with an emerging understanding that, while midlife risk factors for ATH increase the risk for the later development of AD, once dementia begins, both risk factors and manifest disease diminish, possibly due to progressive weight loss with increasing dementia as well as disease involvement of the brain\u27s vasomotor centers. © 2011 Beach et al; licensee BioMed Central Ltd

Design strategies to improve patient motivation during robot-aided rehabilitation

BACKGROUND: Motivation is an important factor in rehabilitation and frequently used as a determinant of rehabilitation outcome. Several factors can influence patient motivation and so improve exercise adherence. This paper presents the design of two robot devices for use in the rehabilitation of upper limb movements, that can motivate patients during the execution of the assigned motor tasks by enhancing the gaming aspects of rehabilitation. In addition, a regular review of the obtained performance can reinforce in patients' minds the importance of exercising and encourage them to continue, so improving their motivation and consequently adherence to the program. In view of this, we also developed an evaluation metric that could characterize the rate of improvement and quantify the changes in the obtained performance. METHODS: Two groups (G1, n = 8 and G2, n = 12) of patients with chronic stroke were enrolled in a 3-week rehabilitation program including standard physical therapy (45 min. daily) plus treatment by means of robot devices (40 min., twice daily) respectively for wrist (G1) and elbow-shoulder movements (G2). Both groups were evaluated by means of standard clinical assessment scales and the new robot measured evaluation metric. Patients' motivation was assessed in 9/12 G2 patients by means of the Intrinsic Motivation Inventory (IMI) questionnaire. RESULTS: Both groups reduced their motor deficit and showed a significant improvement in clinical scales and the robot measured parameters. The IMI assessed in G2 patients showed high scores for interest, usefulness and importance subscales and low values for tension and pain subscales. CONCLUSION: Thanks to the design features of the two robot devices the therapist could easily adapt training to the individual by selecting different difficulty levels of the motor task tailored to each patient's disability. The gaming aspects incorporated in the two rehabilitation robots helped maintain patients' interest high during execution of the assigned tasks by providing feedback on performance. The evaluation metric gave a precise measure of patients' performance and thus provides a tool to help therapists promote patient motivation and hence adherence to the training program