Advancing imaging technologies for patients with spinal pain : with a focus on whiplash injury

Background: Radiological observations of soft-tissue changes that may relate to clinical symptoms in patients with traumatic and non-traumatic spinal disorders are highly controversial. Studies are often of poor quality and findings are inconsistent. A plethora of evidence suggests some pathoanatomical findings from traditional imaging applications are common in asymptomatic participants across the life span, which further questions the diagnostic, prognostic, and theranostic value of traditional imaging. Although we do not dispute the limited evidence for the clinical importance of most imaging findings, we contend that the disparate findings across studies may in part be due to limitations in the approaches used in assessment and analysis of imaging findings. Purpose: This clinical commentary aimed to (1) briefly detail available imaging guidelines, (2) detail research-based evidence around the clinical use of findings from advanced, but available, imaging applications (eg, fat and water magnetic resonance imaging and magnetization transfer imaging), and (3) introduce how evolving imaging technologies may improve our mechanistic understanding of pain and disability, leading to improved treatments and outcomes. Study Design/Setting: A non-systematic review of the literature is carried out. Methods: A narrative summary (including studies from the authors' own work in whiplash injuries) of the available literature is provided. Results: An emerging body of evidence suggests that the combination of existing imaging sequences or the use of developing imaging technologies in tandem with a good clinical assessment of modifiable risk factors may provide important diagnostic information toward the exploration and development of more informed and effective treatment options for some patients with traumatic neck pain. Conclusions: Advancing imaging technologies may help to explain the seemingly disconnected spectrum of biopsychosocial signs and symptoms of traumatic neck pain

A Strategic Approach to Agricultural Research Program Planning in Sub-Saharan Africa

Recent studies have shown that agricultural research can have high payoffs in Africa, but impact depends on how well technology fits with evolving needs and capacity in the agricultural sector and the rest of the economy. Structural adjustment policies (e.g., market liberalization, currency devaluation) and political change are transforming user demands for new technology and the economic environment in which technology must perform. The challenge is how to design agricultural research as a strategic input to promote broad-based economic growth, structural transformation, and food security in the increasingly market-driven, but fragile, economies of Africa.Food Security, Food Policy, Agricultural Research, Research and Development/Tech Change/Emerging Technologies, Downloads May 2008-July 2009: 44, Q18,

A Strategic Approach to Agricultural Research Program Planning in Sub-Saharan Africa

Research and Development/Tech Change/Emerging Technologies, Downloads May 2008-July 2009: 13,

M‐BLANK: a program for the fitting of X‐ray fluorescence spectra

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148382/1/jsy2rv5095.pd

Manually defining regions of interest when quantifying paravertebral muscles fatty infiltration from axial magnetic resonance imaging : a proposed method for the lumbar spine with anatomical cross-reference

Background: There is increasing interest in paravertebral muscle composition as a potential prognostic and diagnostic element in lumbar spine health. As a consequence, it is becoming popular to use magnetic resonance imaging (MRI) to examine muscle volume and fatty infiltration in lumbar paravertebral muscles to assess both age-related change and their clinical relevance in low back pain (LBP). A variety of imaging methods exist for both measuring key variables (fat, muscle) and for defining regions of interest, making pooled comparisons between studies difficult and rendering post-production analysis of MRIs confusing. We therefore propose and define a method as an option for use as a standardized MRI procedure for measuring lumbar paravertebral muscle composition, and to stimulate discussion towards establishing consensus for the analysis of skeletal muscle composition amongst clinician researchers. Method: In this descriptive methodological study we explain our method by providing an examination of regional lumbar morphology, followed by a detailed description of the proposed technique. Identification of paravertebral muscles and vertebral anatomy includes axial E12 sheet-plastinates from cadaveric material, combined with a series of axial MRIs that encompass sequencing commonly used for investigations of muscle quality (fat-water DIXON, T1-, and T2-weighted) to illustrate regional morphology; these images are shown for L1 and L4 levels to highlight differences in regional morphology. The method for defining regions of interest (ROI) for multifidus (MF), and erector spinae (ES) is then described. Results: Our method for defining ROIs for lumbar paravertebral muscles on axial MRIs is outlined and discussed in relation to existing literature. The method provides a foundation for standardising the quantification of muscle quality that particularly centres on examining fatty infiltration and composition. We provide recommendations relating to imaging parameters that should additionally inform a priori decisions when planning studies examining lumbar muscle tissues with MRI. Conclusions: We intend this method to provide a platform towards developing and delivering meaningful comparisons between MRI data on lumbar paravertebral muscle quality

Airborne Measurements of Gravity Wave Breaking at the Tropopause

2000 FLORIDA AVE NW, WASHINGTON, DC, 2000

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihoodbased classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrencebased texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach

An Overview of Measurement Comparisons from the INTEX-B/MILAGRO Airborne Field Campaign

As part of the NASA's INTEX-B mission, the NASA DC-8 and NSF C-130 conducted three wing-tip to wing-tip comparison flights. The intercomparison flights sampled a variety of atmospheric conditions (polluted urban, non-polluted, marine boundary layer, clean and polluted free troposphere). These comparisons form a basis to establish data consistency, but also should also be viewed as a continuation of efforts aiming to better understand and reduce measurement differences as identified in earlier field intercomparison exercises. This paper provides a comprehensive overview of 140 intercomparisons of data collected as well as a record of the measurement consistency demonstrated during INTEX-B. It is the primary goal to provide necessary information for the future research to determine if the observations from different INTEX-B platforms/instrument are consistent within the PI reported uncertainties and used in integrated analysis. This paper may also contribute to the formulation strategy for future instrument developments. For interpretation and most effective use of these results, the reader is strongly urged to consult with the instrument principle investigator

The Einstein@Home Search for Radio Pulsars and PSR J2007+2722

Einstein@Home aggregates the computer power of hundreds of thousands of volunteers from 193 countries, to search for new neutron stars using data from electromagnetic and gravitational-wave detectors. This paper presents a detailed description of the search for new radio pulsars using Pulsar ALFA survey data from the Arecibo Observatory. The enormous computing power allows this search to cover a new region of parameter space; it can detect pulsars in binary systems with orbital periods as short as 11 minutes. We also describe the first Einstein@Home discovery, the 40.8 Hz isolated pulsar PSR J2007+2722, and provide a full timing model. PSR J2007+2722\u27s pulse profile is remarkably wide with emission over almost the entire spin period. This neutron star is most likely a disrupted recycled pulsar, about as old as its characteristic spin-down age of 404 Myr. However, there is a small chance that it was born recently, with a low magnetic field. If so, upper limits on the X-ray flux suggest but cannot prove that PSR J2007+2722 is at least ~100 kyr old. In the future, we expect that the massive computing power provided by volunteers should enable many additional radio pulsar discoveries