Evaluation of afoxolaner chewables to control flea populations in naturally infested dogs in private residences in Tampa FL, USA

Citation: Dryden, M. W., Smith, V., Chwala, M., Jones, E., Crevoiserat, L., McGrady, J. C., . . . Carithers, D. (2015). Evaluation of afoxolaner chewables to control flea populations in naturally infested dogs in private residences in Tampa FL, USA. Parasites & Vectors, 8, 7. doi:10.1186/s13071-015-0897-zBackground: A study was conducted to evaluate the effectiveness of afoxolaner chewables to control flea populations in naturally infested dogs in private residences in Tampa FL, USA. Evaluations of on-animal and premises flea burdens, flea sex structure and fed-unfed premises flea populations were conducted to more accurately assess flea population dynamics in households. Methods: Thirty seven naturally flea infested dogs in 23 homes in Tampa, FL were enrolled in the study and treated with afoxolaner chewables. Chewables (NexGard (R) Chewables; Merial) were administered according to label directions by study investigators on study day 0 and once again between study days 28 and 30. Flea infestations on pets were assessed using visual area thumb counts and premises flea infestations were assessed using intermittent-light flea traps on days 0, 7, 14, 21, and once between study days 28-30, 40-45, and 54-60. Results: Within 7 days of administration of afoxolaner chewable tablets, flea counts on dogs were reduced by 99.3 %. By one month post-treatment, total flea counts on dogs were reduced by 99.9 %, with 97.3 % (36/37) of the dogs being flea free. Following the second dosing on study day 28-30, total on-dog flea burden was reduced by 100 % on days 40-45 and 54-60. On day 0, the traps collected a geometric mean of 18.2 fleas. Subsequent reductions in emerging flea populations were 97.7 and 100 % by days 28-30 and 54-60, respectively. There were 515 total fleas (Ctenocephalides felis felis) collected in the intermittent light flea traps on day 0, and 40.4 % of those fleas displayed visual evidence of having fed. Seven days after initial treatment, only 13.1 % of the fleas contained blood and by day 14 only 4.9 % of the fleas collected in traps displayed evidence of having fed. On day 0, prior to treatment, 60 % of the unfed fleas collected in intermittent-light flea traps were females, but by days 28-30, unfed males accounted for 78 % of the population. Conclusions: This in-home investigation conducted during the summer of 2014 in subtropical Tampa, FL demonstrated that afoxolaner chewables rapidly and effectively eliminated flea populations in infested dogs and homes

Real time data acquisition of commercial microwave link networks for hydrometeorological applications

The usage of data from commercial microwave link (CML) networks for scientific purposes is becoming increasingly popular, in particular for rain rate estimation. However, data acquisition and availability is still a crucial problem and limits research possibilities. To overcome this issue, we have developed an open source data acquisition system based on the Simple Network Management Protocol (SNMP). It is able to record transmitted- and received signal levels of a large number of CMLs simultaneously with a temporal resolution of up to one second. We operate this system at Ericsson Germany, acquiring data from 450 CMLs with minutely real time transfer to our data base. Our data acquisition system is not limited to a particular CML hardware model or manufacturer, though. We demonstrate this by running the same system for CMLs of a different manufacturer, operated by an alpine skiing resort in Germany. There, the data acquisition is running simultaneously for four CMLs with a temporal resolution of one second. We present an overview of our system, describe the details of the necessary SNMP requests and show results from its operational application

Real-time data acquisition of commercial microwave link networks for hydrometeorological applications

The usage of data from commercial microwave link (CML) networks for scientific purposes is becoming increasingly popular, in particular for rain rate estimation. However, data acquisition and availability is still a crucial problem and limits research possibilities. To overcome this issue, we have developed an open-source data acquisition system based on the Simple Network Management Protocol (SNMP). It is able to record transmitted and received signal levels of a large number of CMLs simultaneously with a temporal resolution of up to 1 s. We operate this system at Ericsson Germany, acquiring data from 450 CMLs with minutely real-time transfer to our database. Our data acquisition system is not limited to a particular CML hardware model or manufacturer, though. We demonstrate this by running the same system for CMLs of a different manufacturer, operated by an alpine ski resort in Germany. There, the data acquisition is running simultaneously for four CMLs with a temporal resolution of 1 s. We present an overview of our system, describe the details of the necessary SNMP requests and show results from its operational application. © Author(s) 2016

Near-realtime quantitative precipitation estimation and prediction (RealPEP)

Flash floods in small- to medium-sized catchments and intense precipitation over cities caused by severe local storms pose increasing threats to our society. For the timely prediction of such events, the value of high-resolution and high-quality QPE and corresponding forecasts cannot be overrated. Seamless predictions harmonizing nowcasting and numerical weather prediction (NWP) across forecast lead times from minutes to days would greatly help to improve the value and efficiency of warnings. Organized by the Research Unit on Near-Realtime Precipitation Estimation and Prediction (RealPEP, www2.meteo.uni-bonn.de/realpep) and supported by the Project on Seamless Integrated Forecasting System (SINFONY, www.dwd .de/DE/forschung/forschungsprogramme/sinfony_iafe/sinfony_node.html) of the German Meteorological Service (DWD), an international 3-day online conference was held from 5 to 7 October 2020, dedicated to Precipitation and Flash-Flood Predictions from Minutes to Days (https://indico .scc.kit.edu/event/883/). Most speakers agreed to have their presentations recorded, which we uploaded to YouTube for further distribution (see, e.g., on the conference homepage, https:// indico.scc.kit.edu/event/883/page/588-recorded-talks). The speakers were both invited experts in the respective research fields and researchers from the RealPEP and SINFONY projects. Talks and discussions could be followed on video stream. Interaction between the about 250 participants was enabled by entering written questions and comments via a dedicated tool, which allowed for voting and thus also ranking questions. Registered participants could enter chat rooms from where they could be moved to the speaker room for posing the questions directly to the speakers and the auditorium. On the last day of the conference podium discussions with selected speakers summarized talks and discussions and elaborated on overarching problems, ideas, and developments in the fields of quantitative precipitation estimation (QPE), quantitative precipitation nowcasting (QPN), quantitative precipitation forecasting (QPF), flash-flood prediction (FFP), and their organization into seamless prediction systems, which also constituted the topics of the five sessions during the conference. We report here in particular on the outcomes of the panel discussions

Technical note: A simple feedforward artificial neural network for high-temporal-resolution rain event detection using signal attenuation from commercial microwave links

Two simple feedforward neural networks (multilayer perceptrons – MLPs) are trained to detect rainfall events using signal attenuation from commercial microwave links (CMLs) as predictors and high-temporal-resolution reference data as the target. MLPGA is trained against nearby rain gauges, and MLPRA is trained against gauge-adjusted weather radar. Both MLPs were trained on 26 CMLs and tested on 843 CMLs, all located within 5 km of a rain gauge. Our results suggest that these MLPs outperform existing methods, effectively capturing the intermittent behaviour of rainfall. This study is the first to use both radar and rain gauges for training and testing CML rainfall detection. While previous studies have mainly focused on hourly reference data, our findings show that it is possible to classify rainy and dry time steps with a higher temporal resolution.</p

Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges

For the reconstruction and interpolation of precipitation fields, we present the application of a stochastic approach called Random Mixing. Generated fields are based on a data set consisting of rain gauge observations and path-averaged rain rates estimated using Commercial Microwave Link (CML) derived information. Precipitation fields are received as linear combination of unconditional spatial random fields, where the spatial dependence structure is described by copulas. The weights of the linear combination are optimized such that the observations and the spatial structure of the precipitation observations are reproduced. The innovation of the approach is that this strategy enables the simulation of ensembles of precipitation fields of any size. Each ensemble member is in concordance with the observed path-averaged CML derived rain rates and additionally reflects the observed rainfall variability along the CML paths. The ensemble spread allows additionally an estimation of the uncertainty of the reconstructed precipitation fields. The method is demonstrated both for a synthetic data set and a real-world data set in South Germany. While the synthetic example allows an evaluation against a known reference, the second example demonstrates the applicability for real-world observations. Generated precipitation fields of both examples reproduce the spatial precipitation pattern in good quality. A performance evaluation of Random Mixing compared to Ordinary Kriging demonstrates an improvement of the reconstruction of the observed spatial variability. Random Mixing is concluded to be a beneficial new approach for the provision of precipitation fields and ensembles of them, in particular when different measurement types are combined