IntroductionThis study investigated the effects of recurrent urinary tract infections (rUTI) and the impact of prophylaxis on rUTI and patients’ quality of life (QoL).MethodsAltogether, 575 patients affected by rUTI were included in a 6-month observational study. QoL was assessed using the Hospital Anxiety and Depression (HAD) and the Leicester scales. Statistical analyses were performed using SAS® Version 8.2 software (SAS Institute Inc., Cary, NC, USA). The significance level was set at 5%. Spearman correlation was used to assess the degree of correlation between infectious episodes and HAD and Leicester scores. For each parameter, the comparison between Day 0 and Day 180 was performed using Wilcoxon signed-rank test for quantitative data.ResultsIn total, 61.9% of patients suffering from rUTI exhibited some degree of depression at baseline (Day 0). Alternative oral non-antimicrobial prophylactic treatment for rUTI [Escherichia coli lyophilized bacterial lysate (OM-89)] was administered to 94.4% of patients (1 capsule a day for 90 days), followed by a 3-month treatment-free period. At the end of the study (Day 180), the mean number of UTI decreased by 59.3% (P ≤ 0.0001), the total HAD score decreased by 32.1% (P ≤ 0.0001), and the mean Leicester score decreased by 44.0% (P ≤ 0.0001) from baseline. There was a correlation trend between the reduction in the numbers of UTI at the end of the study compared to the 6 months prior to study entry and the reduction in the anxiety, depression, total HAD scores, activity, feeling, and total Leicester scores registered from Day 0 to Day 180, suggesting a lessening of emotional problems, and social and functional handicaps with decreasing UTI incidence.ConclusionsThis study showed that rUTI had a negative impact on patients’ QoL and that effective alternative prophylaxis significantly improved their QoL.Electronic supplementary materialThe online version of this article (doi:10.1007/s40121-014-0054-6) contains supplementary material, which is available to authorized users.
For autonomous driving, moving objects like vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of the car. Typically, they are detected by motion segmentation of dense optical flow augmented by a CNN based object detector for capturing semantics. In this paper, our aim is to jointly model motion and appearance cues in a single convolutional network. We propose a novel two-stream architecture for joint learning of object detection and motion segmentation. We designed three different flavors of our network to establish systematic comparison. It is shown that the joint training of tasks significantly improves accuracy compared to training them independently. Although motion segmentation has relatively fewer data than vehicle detection. The shared fusion encoder benefits from the joint training to learn a generalized representation. We created our own publicly available dataset (KITTI MOD) by extending KITTI object detection to obtain static/moving annotations on the vehicles. We compared against MPNet as a baseline, which is the current state of the art for CNN-based motion detection. It is shown that the proposed two-stream architecture improves the mAP score by 21.5% in KITTI MOD. We also evaluated our algorithm on the non-automotive DAVIS dataset and obtained accuracy close to the state-of-the-art performance. The proposed network runs at 8 fps on a Titan X GPU using a basic VGG16 encoder.
Object detection and classification in 3D is a key task in Automated Driving (AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding environment, while the task of 3D object bounding box detection in real time remains a strong algorithmic challenge. In this paper, we build on the success of the oneshot regression meta-architecture in the 2D perspective image space and extend it to generate oriented 3D object bounding boxes from LiDAR point cloud. Our main contribution is in extending the loss function of YOLO v2 to include the yaw angle, the 3D box center in Cartesian coordinates and the height of the box as a direct regression problem. This formulation enables real-time performance, which is essential for automated driving. Our results are showing promising figures on KITTI benchmark, achieving real-time performance (40 fps) on Titan X GPU.
In this paper, we propose to use hardware performance counters (HPC) to detect malicious program modifications at load time (static) and at runtime (dynamic). HPC have been used for program characterization and testing, system testing and performance evaluation, and as side channels. We propose to use HPCs for static and dynamic integrity checking of programs.. The main advantage of HPC-based integrity checking is that it is almost free in terms of hardware cost; HPCs are built into almost all processors. The runtime performance overhead is minimal because we use the operating system for integrity checking, which is called anyway for process scheduling and other interrupts. Our preliminary results confirm that HPC very efficiently detect program modifications with very low cost.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.