662 research outputs found

    Gonococcal osteomyelitis in a pediatric patient with disseminated gonococcal infection: Implications for antimicrobial management

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    © 2020 The Authors We report a case of a female teenager with gonococcal septic arthritis of the right shoulder that also caused osteomyelitis of the humeral head. Infection with Neisseria gonorrhoeae is a frequently diagnosed sexually transmitted infection in the sexually active teenage population and disseminated gonococcal infection (DGI) is the most common systemic manifestation of acute gonorrhea. DGI commonly involves acute arthritis, tenosynovitis and dermatitis with less common complications of endocarditis, hepatitis and meningitis. In contrast, osteomyelitis has only rarely been reported as a result of gonococcal infection. Clinicians need to be aware of this unusual manifestation of DGI as a prolonged duration of antimicrobial treatment may be needed to assure complete resolution of this infection

    Tools for Assessing Climate Impacts on Fish and Wildlife

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    Climate change is already affecting many fish and wildlife populations. Managing these populations requires an understanding of the nature, magnitude, and distribution of current and future climate impacts. Scientists and managers have at their disposal a wide array of models for projecting climate impacts that can be used to build such an understanding. Here, we provide a broad overview of the types of models available for forecasting the effects of climate change on key processes that affect fish and wildlife habitat (hydrology, fire, and vegetation), as well as on individual species distributions and populations. We present a framework for how climate-impacts modeling can be used to address management concerns, providing examples of model-based assessments of climate impacts on salmon populations in the Pacific Northwest, fire regimes in the boreal region of Canada, prairies and savannas in the Willamette Valley-Puget Sound Trough-Georgia Basin ecoregion, and marten Martes americana populations in the northeastern United States and southeastern Canada. We also highlight some key limitations of these models and discuss how such limitations should be managed. We conclude with a general discussion of how these models can be integrated into fish and wildlife management

    Relativistic calculations of the K-K charge transfer and K-vacancy production probabilities in low-energy ion-atom collisions

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    The previously developed technique for evaluation of charge-transfer and electron-excitation processes in low-energy heavy-ion collisions [I.I. Tupitsyn et al., Phys. Rev. A 82, 042701(2010)] is extended to collisions of ions with neutral atoms. The method employs the active electron approximation, in which only the active electron participates in the charge transfer and excitation processes while the passive electrons provide the screening DFT potential. The time-dependent Dirac wave function of the active electron is represented as a linear combination of atomic-like Dirac-Fock-Sturm orbitals, localized at the ions (atoms). The screening DFT potential is calculated using the overlapping densities of each ions (atoms), derived from the atomic orbitals of the passive electrons. The atomic orbitals are generated by solving numerically the one-center Dirac-Fock and Dirac-Fock-Sturm equations by means of a finite-difference approach with the potential taken as the sum of the exact reference ion (atom) Dirac-Fock potential and of the Coulomb potential from the other ion within the monopole approximation. The method developed is used to calculate the K-K charge transfer and K-vacancy production probabilties for the Ne(1s22s22p6)(1s^2 2s^2 2p^6) -- F8+(1s)^{8+}(1s) collisions at the F8+(1s)^{8+}(1s) projectile energies 130 keV/u and 230 keV/u. The obtained results are compared with experimental data and other theoretical calculations. The K-K charge transfer and K-vacancy production probabilities are also calculated for the Xe -- Xe53+(1s)^{53+}(1s) collision.Comment: 16 pages, 4 figure

    Counterfeit Detection and Prevention in Additive Manufacturing Based on Unique Identification of Optical Fingerprints of Printed Structures

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    Printed Electronics (PE) based on additive manufacturing has a rapidly growing market. Due to large feature sizes and reduced complexity of PE applications compared to silicon counterparts, they are more prone to counterfeiting. Common solutions to detect counterfeiting insert watermarks or extract unique fingerprints based on (irreproducible) process variations of valid components. Commonly, such fingerprints have been extracted through electrical methods, similar to those of physically unclonable functions (PUFs). Hence, they introduce overhead to the production resulting in additional costs. While such costs may be negligible for application domains targeted by silicon-based technologies, they are detrimental to the ultra-low-cost PE applications. In this paper, we propose an optical unique identification, by extracting fingerprints from the optically visible variations of printed inks in the PE components. The images can be obtained from optical cameras, such as cell phones, thanks to large feature sizes of PE, by trusted parties, such as an end user wanting to verify the authenticity of a particular product. Since this approach does not require any additional circuitry, the fingerprint production cost consists of merely acquisition, processing and saving an image of the circuit components, matching the requirements of ultra-low-cost applications of PE. To further decrease the storage costs for the unique fingerprints, we utilize image downscaling resulting in a compression rate between 83– 188× , while preserving the reliability and uniqueness of the fingerprints. The proposed fingerprint extraction methodology is applied to four datasets and the results show that the optical variation printed inks is suitable to prevent counterfeiting in PE
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