41 research outputs found

    Isolation and fine mapping of Rps6: An intermediate host resistance gene in barley to wheat stripe rust

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    A plant may be considered a nonhost of a pathogen if all known genotypes of a plant species are resistant to all known isolates of a pathogen species. However, if a small number of genotypes are susceptible to some known isolates of a pathogen species this plant maybe considered an intermediate host. Barley (Hordeum vulgare) is an intermediate host for Puccinia striiformis f. sp. tritici (Pst), the causal agent of wheat stripe rust. We wanted to understand the genetic architecture underlying resistance to Pst and to determine whether any overlap exists with resistance to the host pathogen, Puccinia striiformis f. sp. hordei (Psh). We mapped Pst resistance to chromosome 7H and show that host and intermediate host resistance is genetically uncoupled. Therefore, we designate this resistance locus Rps6. We used phenotypic and genotypic selection on F2:3 families to isolate Rps6 and fine mapped the locus to a 0.1 cM region. Anchoring of the Rps6 locus to the barley physical map placed the region on two adjacent fingerprinted contigs. Efforts are now underway to sequence the minimal tiling path and to delimit the physical region harbouring Rps6. This will facilitate additional marker development and permit identification of candidate genes in the region

    Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

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    Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

    Optimal sensor selection for video-based target tracking in a wireless sensor network

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    The use of wireless sensor networks for target tracking is an active area of research. Imaging sensors that obtain video-rate images of a scene can have a significant impact in such networks, as they can measure vital information on the identity, position, and velocity of moving targets. Since wireless networks must operate under stringent energy constraints, it is important to identify the optimal set of imagers to be used in a tracking scenario such that the network lifetime is maximized. We formulate this problem as one of maximizing the information utility gained from a set of sensors subject to a constraint on the average energy consumption in the network. We use an unscented Kalman filter framework to solve the tracking and data fusion problem with multiple imaging sensors in a computationally efficient manner, and use a lookahead algorithm to optimize the sensor selection based on the predicted trajectory of the target. Simulation results show the effectiveness of this method of sensor selection. 1

    Detection, classification, and collaborative tracking of multiple targets using video sensors

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    The study of collaborative, distributed, real-time sensor networks is an emerging research area. Such networks are expected to play an essential role in a number of applications such as, surveillance and tracking of vehicles in the battlefield of the future. This paper proposes an approach to detect and classify multiple targets, and collaboratively track their position and velocity utilizing video cameras. Arbitrarily placed cameras collaboratively perform selfcalibration and provide complete battlefield coverage. If some of the cameras are equipped with a GPS system, they are able to metrically reconstruct the scene and determine the absolute coordinates of the tracked targets. A background subtraction scheme combined with a Markov random field based approach is used to detect the target even when it becomes stationary. Targets are continuously tracked using a distributed Kalman filter approach. As the targets move the coverage is handed over to the "best" neighboring cluster of sensors. This paper demonstrates the potential for the development of distributed optical sensor networks and addresses problems and tradeoffs associated with this particular implementation

    Rate-Distortion Optimized Video Summary Generation and Transmission Over Packet Lossy Networks

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    The goal of video summarization is to select key frames from a video sequence in order to generate an optimal summary that can accommodate constraints on viewing time, storage, or bandwidth. While video summary generation without transmission considerations has been studied extensively, the problem of rate-distortion optimized summary generation and transmission in a packet-lossy network has gained little attention. We consider the transmission of summarized video over a packet-lossy network such as the Internet. We depart from traditional rate control methods by not sacrificing the image quality of each transmitted frame but instead focusing on the frames that can be dropped without seriously affecting the quality of the video sequence. We take into account the packet loss probability, and use the end-to-end distortion to optimize the video quality given constraints on the temporal rate of the summary. Different network scenarios such as when a feedback channel is not available, and when a feedback channel is available with the possibility of retransmission, are considered. In each case, we assume a strict end-to-end delay constraint such that the summarized video can be viewed in real-time. We show simulation results for each case, and also discuss the case when the feedback delay may not be constant
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