Gas Plume Species Identification in LWIR Hyperspectral Imagery by Regression Analyses

The goal of this research was to develop an algorithm for identifying the constituent gases in stack releases. At the heart of the algorithm is a stepwise linear regression technique that only includes a basis vector in the model if it contributes significantly to the fit. This significance is calculated by an F-statistic. Issues such as atmospheric compensation, gas absorption and emission, background modeling, and fitting a linear regression to a non-linear radiance model were addressed in order to generate the matrix of basis vectors. Synthetic imagery generated by the DIRISG model were used as test cases. Results show that the ability to correctly identify a gas diminishes as a function of decreasing concentration path-length of the plume. Results drawn from pixels near the stack are more likely to give an accurate identification of the gas present in the plume

Gas Plume Species Identification in Airborne LWIR Imagery Using Constrained Stepwise Regression Analyses

Identification of constituent gases in effluent plumes is performed using linear least-squares regression techniques. Airborne thermal hyperspectral imagery is used for this study. Synthetic imagery is employed as the test-case for algorithm development. Synthetic images are generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. The use of synthetic data provides a direct measure of the success of the algorithm through the comparison with truth map outputs. In image test-cases, plumes emanating from factory stacks will have been identified using a separate detection algorithm. The gas identification algorithm being developed in this work is performed only on pixels having been determined to contain the plume. Constrained stepwise linear regression is used in this study. Results indicate that the ability of the algorithm to correctly identify plume gases is directly related to the concentration of the gas. Previous concerns that the algorithm is hindered by spectral overlap were eliminated through the use of constraints on the regression

Gas Plume Species Identification by Regression Analyses

Identification of constituent gases in effluent plumes is performed using linear least-squares regression techniques. Overhead thermal hyperspectral imagery is used for this study. Synthetic imagery is employed as the test-case for algorithm development. Synthetic images are generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. The use of synthetic data provides a direct measure of the success of the algorithm through the comparison with truth map outputs. In image test-cases, plumes emanating from factory stacks will have been identified using a separate detection algorithm. The gas identification algorithm being developed in this work will then be used only on pixels having been determined to contain the plume. Stepwise linear regression is considered in this study. Stepwise regression is attractive for this application as only those gases truly in the plume will be present in the final model. Preliminary results from the study show that stepwise regression is successful at correctly identifying the gases present in a plume. Analysis of the results indicates that the spectral overlap of absorption features in different gas species leads to false identifications

Detection of Gaseous Plumes using Basis Vectors

Detecting and identifying weak gaseous plumes using thermal imaging data is complicated by many factors. There are several methods currently being used to detect plumes. They can be grouped into two categories: those that use a chemical spectral library and those that don't. The approaches that use chemical libraries include physics-based least squares methods (matched filter). They are “optimal” only if the plume chemical is actually in the search library but risk missing chemicals not in the library. The methods that don't use a chemical spectral library are based on a statistical or data analytical transformation applied to the data. These include principle components, independent components, entropy, Fourier transform, and others. These methods do not explicitly take advantage of the physics of the signal formulation process and therefore don't exploit all available information in the data. This paper describes generalized least squares detection using gas spectra, presents a new detection method using basis vectors, and compares detection images resulting from applying both methods to synthetic hyperspectral data

Developing consensus on core outcome sets of domains for acute, the transition from acute to chronic, recurrent/episodic, and chronic pain: results of the INTEGRATE-pain Delphi process

This is the final version. Available on open access from Elsevier via the DOI in this recordData sharing statement: Individual participant data that was collected throughout the research process from Delphi participants is not available to others. De-identified participant data was aggregated for analysis and presented in an anonymized format through tables in the article and supplement. All other research data is unavailable.Background: Pain is the leading cause of disability worldwide among adults and effective treatment options remain elusive. Data harmonization efforts, such as through core outcome sets (COS), could improve care by highlighting cross-cutting pain mechanisms and treatments. Existing pain-related COS often focus on specific conditions, which can hamper data harmonization across various pain states. Methods: Our objective was to develop four overarching COS of domains/subdomains (i.e., what to measure) that transcend pain conditions within different pain categories. We hosted a meeting to assess the need for these four COS in pain research and clinical practice. Potential COS domains/subdomains were identified via a systematic literature review (SLR), meeting attendees, and Delphi participants. We conducted an online, three step Delphi process to reach a consensus on domains to be included in the four final COS. Survey respondents were identified from the SLR and pain-related social networks, including multidisciplinary health care professionals, researchers, and people with lived experience (PWLE) of pain. Advisory boards consisting of COS experts and PWLE provided advice throughout the process. Findings: Domains in final COS were generally related to aspects of pain, quality of life, and physical function/activity limitations, with some differences among pain categories. This effort was the first to generate four separate, overarching COS to encourage international data harmonization within and across different pain categories. Interpretation: The adoption of the COS in research and clinical practice will facilitate comparisons and data integration around the world and across pain studies to optimize resources, expedite therapeutic discovery, and improve pain care. Funding: Innovative Medicines Initiative 2 Join Undertaking; European Union Horizon 2020 research innovation program, European Federation of Pharmaceutical Industries and Associations (EFPIA) provided funding for IMI-PainCare. RDT acknowledges grants from Esteve and TEVA.European Union Horizon 202

Neurophysiology of Skin Thermal Sensations

Undoubtedly, adjusting our thermoregulatory behavior represents the most effective mechanism to maintain thermal homeostasis and ensure survival in the diverse thermal environments that we face on this planet. Remarkably, our thermal behavior is entirely dependent on the ability to detect variations in our internal (i.e., body) and external environment, via sensing changes in skin temperature and wetness. In the past 30 years, we have seen a significant expansion of our understanding of the molecular, neuroanatomical, and neurophysiological mechanisms that allow humans to sense temperature and humidity. The discovery of temperature‐activated ion channels which gate the generation of action potentials in thermosensitive neurons, along with the characterization of the spino‐thalamo‐cortical thermosensory pathway, and the development of neural models for the perception of skin wetness, are only some of the recent advances which have provided incredible insights on how biophysical changes in skin temperature and wetness are transduced into those neural signals which constitute the physiological substrate of skin thermal and wetness sensations. Understanding how afferent thermal inputs are integrated and how these contribute to behavioral and autonomic thermoregulatory responses under normal brain function is critical to determine how these mechanisms are disrupted in those neurological conditions, which see the concurrent presence of afferent thermosensory abnormalities and efferent thermoregulatory dysfunctions. Furthermore, advancing the knowledge on skin thermal and wetness sensations is crucial to support the development of neuroprosthetics. In light of the aforementioned text, this review will focus on the peripheral and central neurophysiological mechanisms underpinning skin thermal and wetness sensations in humans. © 2016 American Physiological Society. Compr Physiol 6:1279‐1294, 2016

A Simplified Three Dimensional Model of the VIIRS On-board Calibration System for Visualization and Anomaly Investigation

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the Suomi NPP satellite is a polar-orbiting imager that acquires Earth imagery in 22 spectral bands. Its mechanical design features a rotating telescope that scans the Earth, along with several onboard calibration sources, in sequence. These sources include a blackbody held at a known temperature and a diffuser of known reflectivity that redirects incident solar light of known irradiance. Upon activation of VIIRS in orbit an intensive effort was begun to characterize the performance of the instrument and validate the calibration of the Earth scene data. During the course of this analysis it was critical to have an intuitive understanding of the design of the instrument, as many variables in the calibration equation are dependent on geometry. It is difficult to visualize the precise alignment of the individual components solely using the position and orientation angles reported in the instrument description documents, particularly when there are inconsistencies across multiple sources. A scale model is preferred to allow analysts to visualize and interact with the instrument from various viewpoints. To accomplish this, a three-dimensional model of VIIRS was constructed using the software package Blender. The size and orientation of individual components of the instrument were taken from source documents provided by the instrument vendor, with any disparities between documents being resolved by the VIIRS Sensor Data Record Team. The model has enabled calibration scientists to see the precise location and orientation of the blackbody and solar diffuser, which has helped resolve any anomalous behavior observed from these calibration devices. Visualization has also helped validate solar incidence angle and lunar observation predictions, which are helpful for identifying future calibration events that may warrant specific analysis. A discussion of the model and its creation, as well as instances where it was used to assist in instrument anomaly resolution, will be presented

Designing a Human-in-the-Loop Process for Maintaining Optimal Calibration of GOES-R ABI Visible Channels

The Advanced Baseline Imager (ABI) onboard NOAA\u27s Geostationary Operational Environmental Satellite-R (GOES-R) series provides high-quality, radiometrically-calibrated Earth-observing imaging data. Operational calibration of the six visible/near-infrared (VNIR) spectral channels is maintained through periodic observations of the on-board solar diffuser. Currently, the ground processing algorithm automatically updates the calibration coefficients for the VNIR channels after each solar diffuser observation. These automatic updates are working well, but it has been recognized that having a human-in-the-loop is preferred to optimize the long-term quality of the L1b products. In this talk, we describe the process involved in modifying the ground processing to incorporate this change. Beyond being a simple code change, the overall process involves coordinating the efforts of several teams to ensure this transitions from a research-level idea to a reliable, sustainable operational activity. Additionally, we will show examples of how this method has been used to positively impact L1b products

An intergenerational partnership between a college and congregate housing facility: How it works, what it means

Purpose: We describe the goals, development, operation, and outcomes of an intergenerational programmatic relationship between a private comprehensive college and a congregate facility that houses both independent-living apartments and assisted living for older adults. Design and Methods: Activities are based on a communal-developmental model that promotes learning with as opposed to doing for. We identify key components involved in implementing such a model and provide examples of the activities that constitute the programmatic relationship. We also identify program implementation challenges and discuss outcomes. Results: Faculty and students report that partnership activities provide excellent opportunities for increasing the understanding of aging and older adults. Residents report programs provide social, recreational, and educational benefits. Implications: Programmatic partnerships between colleges and residential facilities for older adults provide many benefits for students and residents. They require shared responsibility, deliberate and creative planning, and ongoing coordination

Evidence That Msh1p Plays Multiple Roles in Mitochondrial Base Excision Repair

Mitochondrial DNA is thought to be especially prone to oxidative damage by reactive oxygen species generated through electron transport during cellular respiration. This damage is mitigated primarily by the base excision repair (BER) pathway, one of the few DNA repair pathways with confirmed activity on mitochondrial DNA. Through genetic epistasis analysis of the yeast Saccharomyces cerevisiae, we examined the genetic interaction between each of the BER proteins previously shown to localize to the mitochondria. In addition, we describe a series of genetic interactions between BER components and the MutS homolog MSH1, a respiration-essential gene. We show that, in addition to their variable effects on mitochondrial function, mutant msh1 alleles conferring partial function interact genetically at different points in mitochondrial BER. In addition to this separation of function, we also found that the role of Msh1p in BER is unlikely to be involved in the avoidance of large-scale deletions and rearrangements