Gotfredsenvogel4838

Botrytis cinerea, a fungal pathogen that causes gray mold, is damaging more than 200 plant species, and especially tomato. Photosystem II (PSII) responses in tomato (Solanum lycopersicum L.) leaves to Botrytis cinerea spore suspension application were evaluated by chlorophyll fluorescence imaging analysis. Hydrogen peroxide (H2O2) that was detected 30 min after Botrytis application with an increasing trend up to 240 min, is possibly convening tolerance against B. cinerea at short-time exposure, but when increasing at relative longer exposure, is becoming a damaging molecule. In accordance, an enhanced photosystem II (PSII) functionality was observed 30 min after application of B. cinerea, with a higher fraction of absorbed light energy to be directed to photochemistry (ΦPSΙΙ). The concomitant increase in the photoprotective mechanism of non-photochemical quenching of photosynthesis (NPQ) resulted in a significant decrease in the dissipated non-regulated energy (ΦNO), indicating a possible decreased singlet oxygen (1O2) formation, thus specifying a modified reactive oxygen species (ROS) homeostasis. Therefore, 30 min after application of Botrytis spore suspension, before any visual symptoms appeared, defense response mechanisms were triggered, with PSII photochemistry to be adjusted by NPQ in a such way that PSII functionality to be enhanced, but being fully inhibited at the application spot and the adjacent area, after longer exposure (240 min). Hence, the response of tomato PSII to B. cinerea, indicates a hormetic temporal response in terms of "stress defense response" and "toxicity", expanding the features of hormesis to biotic factors also. The enhanced PSII functionality 30 min after Botrytis application can possible be related with the need of an increased sugar production that is associated with a stronger plant defense potential through the induction of defense genes.The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object's range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (U-LPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. STF-083010 molecular weight The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others.Remdesivir has been approved for treatment of COVID-19 and shortens the time to recovery in hospitalized patients. Drug transporters removing remdesivir from the circulation may reduce efficacy of treatment by lowering its plasma levels. Information on the interaction of remdesivir with drug transporters is limited. We therefore assessed remdesivir as substrate and inhibitor of the clinically relevant hepatic drug uptake transporters organic anion transporting poly-peptide (OATP)-1B1 (SLCO1B1), its common genetic variants OATP1B1*1b, OATP1B1*5, OATP1B1*15, as well as OATP1B3 (SLCO1B3), OATP2B1 (SLCO2B1) and organic cation transporter (OCT)-1 (SLC22A1). Previously established transporter-overexpressing cells were used to measure (i) cellular remdesivir uptake and (ii) cellular uptake of transporter probe substrates in the presence of remdesivir. There was a high remdesivir uptake into vector-transfected control cells. Moderate, but statistically significant higher uptake was detected only for OATP1B1-, OATP1B1*1b and OATP1B1*15-expressing cells when compared with control cells at 5 µM.