Application of remote sensing to state and regional problems

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Application of remote sensing to state and regional problems

The primary purpose of the remote sensing applications program is for various members of the university community to participate in activities that improve the effective communication between the scientific community engaged in remote sensing research and development and the potential users of modern remote sensing technology. Activities of this program are assisting the State of Mississippi in recognizing and solving its environmental, resource and socio-economic problems through inventory, analysis, and monitoring by appropriate remote sensing systems. Objectives, accomplishments, and current status of the following individual projects are reported: (1) bark beetle project; (2) state park location planning; and (3) waste source location and stream channel geometry monitoring

Vitamin E, γ-tocopherol, diminishes ex vivo basophil response to dust mite allergen

Epidemiologic studies suggest that dietary vitamin E is a candidate intervention for atopic disease. We used in vitro and ex vivo exposures to test the hypothesis that the most common dietary isoform of vitamin E, γ-tocopherol (γT), could suppress FcεRI-mediated basophil activation

Predicting the Onset of Cavitation in Automotive Torque Converters—Part II: A Generalized Model

The objective of this investigation was to develop a dimensionless model for predicting the onset of cavitation in torque converters applicable to general converter designs. Dimensional analysis was applied to test results from a matrix of torque converters that ranged from populations comprised of strict geometric similitude to those with more relaxed similarities onto inclusion of all the torque converters tested. Stator torque thresholds at the onset of cavitation for the stall operating condition were experimentally determined with a dynamometer test cell using nearfield acoustical measurements. Cavitation torques, design parameters, and operating conditions were resolved into a set of dimensionless quantities for use in the development of dimensionless empirical models. A systematic relaxation of the fundamental principle of dimensional analysis, geometric similitude, was undertaken to present empirical models applicable to torque converter designs of increasingly diverse design parameters. A stepwise linear regression technique coupled with response surface methodology was utilized to produce an empirical model capable of predicting stator torque at the onset of cavitation with less than 7% error for general automotive torque converter designs

Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence

Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches. We have pursued different approaches within this pipeline structure. First, as a demonstration of data-driven discovery, the team has developed a technique for discovery of repeated subcircuits, or motifs. These were incorporated into a neural architecture search approach to evolve network architectures. Second, we have conducted analysis of the heading direction circuit in the fruit fly, which performs fusion of visual and angular velocity features, to explore augmenting existing computational models with new insight. Our team discovered a novel pattern of connectivity, implemented a new model, and demonstrated sensor fusion on a robotic platform. Third, the team analyzed circuitry for memory formation in the fruit fly connectome, enabling the design of a novel generative replay approach. Finally, the team has begun analysis of connectivity in mammalian cortex to explore potential improvements to transformer networks. These constraints increased network robustness on the most challenging examples in the CIFAR-10-C computer vision robustness benchmark task, while reducing learnable attention parameters by over an order of magnitude. Taken together, these results demonstrate multiple potential approaches to utilize insight from neural systems for developing robust and efficient machine learning techniques.Comment: 11 pages, 4 figure

The use of 3D surface scanning for the measurement and assessment of the human foot

<p>Abstract</p> <p>Background</p> <p>A number of surface scanning systems with the ability to quickly and easily obtain 3D digital representations of the foot are now commercially available. This review aims to present a summary of the reported use of these technologies in footwear development, the design of customised orthotics, and investigations for other ergonomic purposes related to the foot.</p> <p>Methods</p> <p>The PubMed and ScienceDirect databases were searched. Reference lists and experts in the field were also consulted to identify additional articles. Studies in English which had 3D surface scanning of the foot as an integral element of their protocol were included in the review.</p> <p>Results</p> <p>Thirty-eight articles meeting the search criteria were included. Advantages and disadvantages of using 3D surface scanning systems are highlighted. A meta-analysis of studies using scanners to investigate the changes in foot dimensions during varying levels of weight bearing was carried out.</p> <p>Conclusions</p> <p>Modern 3D surface scanning systems can obtain accurate and repeatable digital representations of the foot shape and have been successfully used in medical, ergonomic and footwear development applications. The increasing affordability of these systems presents opportunities for researchers investigating the foot and for manufacturers of foot related apparel and devices, particularly those interested in producing items that are customised to the individual. Suggestions are made for future areas of research and for the standardization of the protocols used to produce foot scans.</p

MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures

<p>Abstract</p> <p>Background</p> <p>One-dimensional ¹H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.</p> <p>Results</p> <p>We introduce a web server application, called MetaboHunter, which can be used for automatic assignment of ¹H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter's metabolite identification methods.</p> <p>Conclusions</p> <p>MetaboHunter is a freely accessible, easy to use and user friendly ¹H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries.</p> <p>Availability</p> <p><url>http://www.nrcbioinformatics.ca/metabohunter/</url></p

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data

The importance of the exposome and allostatic load in the planetary health paradigm

In 1980, Jonas Salk (1914-1995) encouraged professionals in anthropology and related disciplines to consider the interconnections between "planetary health," sociocultural changes associated with technological advances, and the biology of human health. The concept of planetary health emphasizes that human health is intricately connected to the health of natural systems within the Earth's biosphere; experts in physiological anthropology have illuminated some of the mechanisms by which experiences in natural environments (or the built environment) can promote or detract from health. For example, shinrin-yoku and related research (which first emerged from Japan in the 1990s) helped set in motion international studies that have since examined physiological responses to time spent in natural and/or urban environments. However, in order to advance such findings into planetary health discourse, it will be necessary to further understand how these biological responses (inflammation and the collective of allostatic load) are connected to psychological constructs such as nature relatedness, and pro-social/environmental attitudes and behaviors. The exposome refers to total environmental exposures-detrimental and beneficial-that can help predict biological responses of the organism to environment over time. Advances in "omics" techniques-metagenomics, proteomics, metabolomics-and systems biology are allowing researchers to gain unprecedented insight into the physiological ramifications of human behavior. Objective markers of stress physiology and microbiome research may help illuminate the personal, public, and planetary health consequences of "extinction of experience." At the same time, planetary health as an emerging multidisciplinary concept will be strengthened by input from the perspectives of physiological anthropology.Peer reviewe

SARS-CoV-2 seroprevalence among a southern U.S. population indicates limited asymptomatic spread under physical distancing measures

Characterizing the asymptomatic spread of SARS-CoV-2 is important for understanding the COVID-19 pandemic. This study was aimed at determining asymptomatic spread of SARS-CoV-2 in a suburban, Southern U.S. population during a period of state restrictions and physical distancing mandates. This is one of the first published seroprevalence studies from North Carolina and included multicenter, primary care, and emergency care facilities serving a low-density, suburban and rural population since description of the North Carolina state index case introducing the SARS-CoV-2 respiratory pathogen to this population. To estimate point seroprevalence of SARS-CoV-2 among asymptomatic individuals over time, two cohort studies were examined. The first cohort study, named ScreenNC, was comprised of outpatient clinics, and the second cohort study, named ScreenNC2, was comprised of inpatients unrelated to COVID-19. Asymptomatic infection by SARS-CoV-2 (with no clinical symptoms) was examined using an Emergency Use Authorization (EUA)-approved antibody test (Abbott) for the presence of SARS-CoV-2 IgG. This assay as performed under CLIA had a reported specificity/sensitivity of 100%/99.6%. ScreenNC identified 24 out of 2,973 (0.8%) positive individuals among asymptomatic participants accessing health care during 28 April to 19 June 2020, which was increasing over time. A separate cohort, ScreenNC2, sampled from 3 March to 4 June 2020, identified 10 out of 1,449 (0.7%) positive participants. IMPORTANCE This study suggests limited but accelerating asymptomatic spread of SARS-CoV-2. Asymptomatic infections, like symptomatic infections, disproportionately affected vulnerable communities in this population, and seroprevalence was higher in African American participants than in White participants. The low, overall prevalence may reflect the success of shelter-in-place mandates at the time this study was performed and of maintaining effective physical distancing practices among suburban populations. Under these public health measures and aggressive case finding, outbreak clusters did not spread into the general population