Early detection of Varicella-Zoster Virus (VZV)-specific T-cells before seroconversion in primary varicella infection: case report

Here we report the case of a 54-year old, immunocompetent German patient with primary varicella whose Varicella-Zoster Virus (VZV)-specific T-cell responses could be detected early in infection and before the onset of seroconversion. This case demonstrates that the detection of VZV-specific T-cells may under certain circumstances support the diagnosis of a primary varicella infection, as for example in cases of atypical or subclinical varicella or in the absence of detectable VZV DNA in plasma

X-ray dark-field radiography for in situ gout diagnosis by means of an ex vivo animal study

Gout is the most common form of inflammatory arthritis, caused by the deposition of monosodium urate (MSU) crystals in peripheral joints and tissue. Detection of MSU crystals is essential for definitive diagnosis, however the gold standard is an invasive process which is rarely utilized. In fact, most patients are diagnosed or even misdiagnosed based on manifested clinical signs, as indicated by the unchanged premature mortality among gout patients over the past decade, although effective treatment is now available. An alternative, non-invasive approach for the detection of MSU crystals is X-ray dark-field radiography. In our work, we demonstrate that dark-field X-ray radiography can detect naturally developed gout in animals with high diagnostic sensitivity and specificity based on the in situ measurement of MSU crystals. With the results of this study as a potential basis for further research, we believe that X-ray dark-field radiography has the potential to substantially improve gout diagnostics

Numerical analysis of ice loads on Taraldsvikfossen dam

Static ice loads (ice actions) are a key design parameter for dams in cold climates. Since 2012,ice stresses have been measured at Taraldsvikfossen reservoir located in Narvik, Norway. Similar to earlier observations in Canada, it became evident from the first three years of data that various effects resulted in stresses, including thermal expansion and water level fluctuations, and that the relative dominance of the processes varied between seasons. A numerical model, using the commercially available finite element software LS-DYNA, is presented for the prediction of the stress field in an ice sheet due to temperature changes and water fluctuations as a function of time, under a variety of conditions. The finite element model accounts for variable temperature and properties through the thickness, an elastic foundation representation of the underlying water, nonlinear constitutive behavior of the ice, temperature dependent mechanical properties, flexibility of resisting structures. For verification of the numerical model, results from simulation are compared with measured temperatures and stresses at Taraldsvikfossen reservoir.publishedVersio

Numerical analysis of ice loads on Taraldsvikfossen dam

Static ice loads (ice actions) are a key design parameter for dams in cold climates. Since 2012,ice stresses have been measured at Taraldsvikfossen reservoir located in Narvik, Norway. Similar to earlier observations in Canada, it became evident from the first three years of data that various effects resulted in stresses, including thermal expansion and water level fluctuations, and that the relative dominance of the processes varied between seasons. A numerical model, using the commercially available finite element software LS-DYNA, is presented for the prediction of the stress field in an ice sheet due to temperature changes and water fluctuations as a function of time, under a variety of conditions. The finite element model accounts for variable temperature and properties through the thickness, an elastic foundation representation of the underlying water, nonlinear constitutive behavior of the ice, temperature dependent mechanical properties, flexibility of resisting structures. For verification of the numerical model, results from simulation are compared with measured temperatures and stresses at Taraldsvikfossen reservoir

Detection of Colchicum autumnale in drone images, using a machine-learning approach

Colchicum autumnale are toxic autumn-blooming flowering plants, which often grow on extensive meadows and pastures. Thus, they pose a threat to farm animals especially in hay and silage. Intensive grassland management or the use of herbicides could reduce these weeds but environment protection requirements often prohibit these measures. For this reason, a non-chemical site- or plant-specific weed control is sought, which aims only at a small area around the C. autumnale and with low impact on the surrounding flora and fauna. For this purpose, however, the exact locations of the plants must be known. In the present paper, a procedure to locate blooming C. autumnale in high-resolution drone images in the visible light range is presented. This approach relies on convolutional neural networks to detect the flower positions. The training data, which is based on hand-labeled images, is further enhanced through image augmentation. The quality of the detection was evaluated in particular for grassland sites which were not included in the training to get an estimate for how well the detector works on previously unseen sites. In this case, 88.6% of the flowers in the test dataset were detected, which makes it suitable, e.g., for applications where the training is performed by the manufacturer of an automatic treatment tool and where the practitioners apply it to their previously unseen grassland sites