An evaluation of thematic mapper simulator data for the geobotanical discrimination of rock types in Southwest Oregon

Rock type identification may be assisted by the use of remote sensing of associated vegetation, particularly in areas of dense vegetative cover where surface materials are not imaged directly by the sensor. The geobotanical discrimination of ultramafic parent materials was investigated and analytical techniques for lithologic mapping and mineral exploration were developed. The utility of remotely sensed data to discriminate vegetation types associated with ultramafic parent materials in a study area in southwest Oregon were evaluated. A number of specific objectives were identified, which include: (1) establishment of the association between vegetation and rock types; (2) examination of the spectral separability of vegetation types associated with rock types; (3) determination of the contribution of each TMS band for discriminating vegetation associated with rock types and (4) comparison of analytical techniques for spectrally classifying vegetation

Boltzmann Collision Term

We derive the Boltzmann equation for scalar fields using the Schwinger-Keldysh formalism. The focus lies on the derivation of the collision term. We show that the relevant self-energy diagrams have a factorization property. The collision term assumes the Boltzmann-like form of scattering probability times statistical factors for those self-energy diagrams which correspond to tree level scattering processes. Our proof covers scattering processes with any number of external particles, which come from self-energy diagrams with any number of loops.Comment: 17 pages, 4 figure

Magnetometer suitable for Earth field measurement based on transient atomic response

We describe the development of a simple atomic magnetometer using $^{87}$Rb vapor suitable for Earth magnetic field monitoring. The magnetometer is based on time-domain determination of the transient precession frequency of the atomic alignment around the measured field. A sensitivity of 1.5 nT/$\sqrt{Hz}$ is demonstrated on the measurement of the Earth magnetic field in the laboratory. We discuss the different parameters determining the magnetometer precision and accuracy and predict a sensitivity of 30 pT/$\sqrt{Hz}$Comment: 6 pages, 5 figure

Genome sequences of 15 Gardnerella vaginalis strains isolated from the vaginas of women with and without bacterial vaginosis

Gardnerella vaginalis is a predominant species in bacterial vaginosis, a dysbiosis of the vagina that is associated with adverse health outcomes, including preterm birth. Here, we present the draft genome sequences of 15 Gardnerella vaginalis strains (now available through BEI Resources) isolated from women with and without bacterial vaginosis

Adrenal insufficiency is a contraindication for omalizumab therapy in mast cell activation disease: Risk for serum sickness

Omalizumab is an effective therapeutic humanized murine IgE antibody in many cases of primary systemic mast cell activation disease (MCAD). The present study should enable the clinician to recognize when treatment of MCAD with omalizumab is contraindicated because of the potential risk of severe serum sickness and to report our successful therapeutic strategy for such adverse event (AE). Our clinical observations, a review of the literature including the event reports in the FDA AE Reporting System, the European Medicines Agency Eudra-Vigilance databases (preferred search terms: omalizumab, Xolair®, and serum sickness) and information from the manufacturer\u27s Novartis database were used. Omalizumab therapy may be more likely to cause serum sickness than previously thought. In patients with regular adrenal function, serum sickness can occur after 3 to 10 days which resolves after the antigen and circulating immune complexes are cleared. If the symptoms do not resolve within a week, injection of 20 to 40 mg of prednisolone on two consecutive days could be given. However, in MCAD patients whose adrenal cortical function is completely suppressed by exogenous glucocorticoid therapy, there is a high risk that serum sickness will be masked by the MCAD and evolve in a severe form with pronounced damage of organs and tissues, potentially leading to death. Therefore, before the application of the first omalizumab dose, it is important to ensure that the function of the adrenal cortex is not significantly limited so that any occurring type III allergy can be self-limiting

Development and analysis of the Software Implemented Fault-Tolerance (SIFT) computer

SIFT (Software Implemented Fault Tolerance) is an experimental, fault-tolerant computer system designed to meet the extreme reliability requirements for safety-critical functions in advanced aircraft. Errors are masked by performing a majority voting operation over the results of identical computations, and faulty processors are removed from service by reassigning computations to the nonfaulty processors. This scheme has been implemented in a special architecture using a set of standard Bendix BDX930 processors, augmented by a special asynchronous-broadcast communication interface that provides direct, processor to processor communication among all processors. Fault isolation is accomplished in hardware; all other fault-tolerance functions, together with scheduling and synchronization are implemented exclusively by executive system software. The system reliability is predicted by a Markov model. Mathematical consistency of the system software with respect to the reliability model has been partially verified, using recently developed tools for machine-aided proof of program correctness

Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study

Download PDFPDF Download PDF + Supplemental DataPDF + Supplementary Material Epidemiology/Health services research Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case–control study http://orcid.org/0000-0002-9905-4855Nikki L B Freeman1, Rashmi Muthukkumar2, Ruth S Weinstock3, M Victor Wickerhauser4, http://orcid.org/0000-0003-2701-101XAnna R Kahkoska5,6 Correspondence to Dr Nikki L B Freeman; [email protected] Abstract Introduction Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures. Research design and methods Data from a case–control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics. Results Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score. Conclusions Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics