Computational challenges in deriving dairy cows' action patterns from accelerometer data

We describe an attempt to build a computational model for deriving dairy cows' action patterns automatically from accelerometer data

Comparative analysis of COVID-19 vaccine responses and third booster dose-induced neutralizing antibodies against Delta and Omicron variants

Vaccination shows efficacy in protecting from COVID-19, but regime and dosing optimization is still ongoing. Here the authors show that BNT162b2, mRNA-1273, or their combination with ChAdOx1 induces similar antibody responses, and those receiving three doses of BNT162b2 induce neutralizing antibodies against the Omicron variant.Two COVID-19 mRNA (of BNT162b2, mRNA-1273) and two adenovirus vector vaccines (ChAdOx1 and Janssen) are licensed in Europe, but optimization of regime and dosing is still ongoing. Here we show in health care workers (n = 328) that two doses of BNT162b2, mRNA-1273, or a combination of ChAdOx1 adenovirus vector and mRNA vaccines administrated with a long 12-week dose interval induce equally high levels of anti-SARS-CoV-2 spike antibodies and neutralizing antibodies against D614 and Delta variant. By contrast, two doses of BNT162b2 with a short 3-week interval induce 2-3-fold lower titers of neutralizing antibodies than those from the 12-week interval, yet a third BNT162b2 or mRNA-1273 booster dose increases the antibody levels 4-fold compared to the levels after the second dose, as well as induces neutralizing antibody against Omicron BA.1 variant. Our data thus indicates that a third COVID-19 mRNA vaccine may induce cross-protective neutralizing antibodies against multiple variants.</p

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Abstract-The use of neural networks is popular in various building applications such as prediction of heating load, ventilation rate and indoor temperature. Significant is, that only few papers deal with indoor carbon dioxide (CO 2 ) prediction which is a very good indicator of indoor air quality (IAQ). In this study, a data-driven modelling method based on multilayer perceptron network for indoor air carbon dioxide in an apartment building is developed. Temperature and humidity measurements are used as input variables to the network. Motivation for this study derives from the following issues. First, measuring carbon dioxide is expensive and sensors power consumptions is high and secondly, this leads to short operating times of battery-powered sensors. The results show that predicting CO 2 concentration based on relative humidity and temperature measurements, is difficult. Therefore, more additional information is needed