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Eating supplementing involving methionine mitigates oxidative strain inside broilers beneath high stocking thickness.
Cognitive workload, as measured by the NASA-Task Load Index, was significantly higher in the Nino® than in participants' manual wheelchairs. Findings from qualitative interviews suggest that the Nino® is unlikely to be suitable as a functional replacement of an individual's manual wheelchair. Most participants felt unsafe during braking. Other perceptions included that the Nino may be a good alternative for use as a recreational outdoor mobility device, a powered mobility option to help prevent upper extremity overuse injuries, have a positive impact on social interactions, but that a high degree of focus was required during use. In addition to needing to address safety, usability, and functional concerns, the data suggests a clinical focus on training individuals to use these new devices may be necessary for effective community use.Augmented reality (AR) may be a useful technique to overcome issues of conventionally used navigation systems supporting medical needle insertions, like increased mental workload and complicated hand-eye coordination. GSK429286A mw Previous research primarily focused on the development of AR navigation systems designed for specific displaying devices, but differences between employed methods have not been investigated before. To this end, a user study involving a needle insertion task was conducted comparing different AR display techniques with a monitor-based approach as baseline condition for the visualization of navigation information. A video see-through stationary display, an optical see-through head-mounted display and a spatial AR projector-camera-system were investigated in this comparison. Results suggest advantages of using projected navigation information in terms of lower task completion time, lower angular deviation and affirmative subjective participant feedback. Techniques requiring the intermediate view on screens, i.e. the stationary display and the baseline condition, showed less favorable results. Thus, benefits of providing AR navigation information compared to a conventionally used method could be identified. Significant objective measures results, as well as an identification of advantages and disadvantages of individual display techniques contribute to the development and design of improved needle navigation systems.Video person re-identification (video Re-ID) plays an important role in surveillance video analysis and has gained increasing attention recently. However, existing supervised methods require vast labeled identities across cameras, resulting in poor scalability in practical applications. Although some unsupervised approaches have been exploited for video Re-ID, they are still in their infancy due to the complex nature of learning discriminative features on unlabelled data. In this paper, we focus on one-shot video Re-ID and present an iterative local-global collaboration learning approach to learning robust and discriminative person representations. Specifically, it jointly considers the global video information and local frame sequence information to better capture the diverse appearance of the person for feature learning and pseudo-label estimation. Moreover, as the cross-entropy loss may induce the model to focus on identity-irrelevant factors, we introduce the variational information bottleneck as a regularization term to train the model together. It can help filter undesirable information and characterize subtle differences among persons. Since accuracy cannot always be guaranteed for pseudo-labels, we adopt a dynamic selection strategy to select part of pseudo-labeled data with higher confidence to update the training set and re-train the learning model. During training, our method iteratively executes the feature learning, pseudo-label estimation, and dynamic sample selection until all the unlabeled data have been seen. Extensive experiments on two public datasets, i.e., DukeMTMC-VideoReID and MARS, have verified the superiority of our model to several cutting-edge competitors.Recently, super-harmonic ultrasound imaging technology has caused much attention due to its capability of distinguishing microvessels from the tissues surrounding them. However, the fabrication of a dual-frequency confocal transducer is still a challenge. In this work, 270- [Formula see text] PMN-PT single crystal 1-3 composite and 28- [Formula see text] PVDF thick film, acting as transmission layer and receiving layer, respectively, are integrated in a novel co-focusing structure. To realize delicate wave propagation control, microwave transmission line theory is introduced to design such structure. Two acoustic filter layers, 13- [Formula see text] copper layer and 39- [Formula see text] Epoxy 301 layer, are indispensable and should be added between two piezoelectric layers. Therefore, an acoustic issue can be overcome via an electrical method and the successful achievement of a dual-frequency (5 MHz/30 MHz) ultrasound transducer with a confocal distance of 8 mm can be realized. The super-harmonic ultrasound imaging experiment is conducted using this kind of device. The 3-D image of 110- [Formula see text]-diameter phantom tube injected with microbubbles can be obtained. These promising results demonstrate that this novel dual-frequency (5 MHz/30 MHz) confocal ultrasound transducer is potentially usable for microvascular medical imaging application in the future.Brain surface analysis is essential to neuroscience, however, the complex geometry of the brain cortex hinders computational methods for this task. The difficulty arises from a discrepancy between 3D imaging data, which is represented in ---Euclidean space---, and the ---non-Euclidean--- geometry of the highly-convoluted brain surface. Recent advances in machine learning have enabled the use of neural networks for non-Euclidean spaces. These facilitate the learning of surface data, yet pooling strategies often remain constrained to a single fixed-graph. This paper proposes a new learnable graph pooling method for processing multiple surface-valued data to output subject-based information. The proposed method innovates by learning an intrinsic aggregation of graph nodes based on graph spectral embedding. We illustrate the advantages of our approach with in-depth experiments on two large-scale benchmark datasets. The ablation study in the paper illustrates the impact of various factors affecting our learnable pooling method.
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