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The purpose was to explore the value of high on-treatment platelet reactivity (HTPR) in predicting long-term clinical outcomes for stroke patients. The platelet reactivity was assayed after being treated with either 75 mg clopidogrel or 100 mg aspirin daily with VerifyNow System in stroke patients. HTPR for clopidogrel was defined as PRU ≥ 208, and that for aspirin was defined as ARU ≥ 550. CYP2C19 genotyping was performed using the Sequenom MassARRAY iPLEX platform. The primary endpoint was a composite of recurrent ischemic stroke, transient ischemic attack, myocardial infarction, or ischemic vascular death. The safety endpoint was bleeding. In the clopidogrel group, among 345 patients recruited, 174 of them were categorized as HTPR. A total of 270 patients were followed up for 54 months. There was a significant association between HTPR and the primary endpoint (HRadj 2.13 [95% CI, 1.43–3.15], p

Background Monte Carlo (MC) has been widely used in fields such as biomedicine and computer graphics owing to its unique capabilities of flexibility, high-accuracy and simplicity for modeling light transport in tissues, but the applications in food and agricultural domain are limited or hindered due to the lack of knowledge on the optical properties of food products. Thanks to major breakthroughs in optical measuring and computing technologies since the year of 2000, significant advances have been made in sensing techniques for measuring tissue optical properties. Therefore, MC has witnessed great progress in food and agricultural domain over the past two decades. Scope and approach The development of MC for modeling light transport in food and agricultural products, including the principle, advanced MC methods, relevant applications, and future perspectives were reviewed. The paper is aimed at helping interested researchers to gain a better understanding of the MC technique, thus stimulating quality and safety assessment of food and agricultural products. Key findings and conclusions This paper provides an overview of the procedure of MC modeling for light transport in food and agricultural products and commonly used MC models. Advanced methods for accelerating MC simulations are then presented. Applications of MC simulations in food and agricultural products, since the year of 2000, for optimizing the design of sensing configuration and parameter, estimating tissue optical property, and assessing quality and safety are then reviewed. Finally, challenges and future perspectives for MC technique in modeling light transport are discussed.

BackgroundAmple evidence has suggested that vascular modifications are associated with aging. To expand previous understanding of age-related vascular changes, we examined the association between aging and cerebrovascular morphologies.Methods1176 participants aged 35 to 75 years recruited from Shanghai, China were included in this study. Cerebrovascular morphological features comprising arterial branch density, radius, and tortuosity were quantified using three-dimensional magnetic resonance angiography. Linear regression was used to examine the association between age and vasculature features.ResultsAge was found to be a significant predictor for cerebrovascular morphological alterations after adjusting for vascular risk factors. However, after dividing subjects into subgroups based on their age, aging was found to be significantly correlated with all three morphometric features only in the 45-54 subgroup after adjusting for the other vascular risk factors. Smoking gives rise to a more rapid age-related changes in vascular morphologies, while alcohol consumption could decelerate those age-related alterations.ConclusionsRapid alternations in all three morphological features assessed have been noticed to be associated with aging in the 45-54 subgroup, suggesting the potential importance of the 5th decade for early preservation method of vascular aging.FundingNational Natural Science Foundation of China.

The rapid development of bike-sharing systems has brought people enormous convenience during the past decade. On the other hand, high transport flexibility gives rise to problems for both users and operators. For users, dynamic distribution of shared bikes caused by uneven user demand often leads to the check in or check out service unavailable at some stations. For operators, unbalanced bike usage comes with more bike broken and growing maintenance cost. In this paper, we consider enhancing user experiences and rebalance bicycle utilization by directing users to different stations with a higher success rate of rental and return. For the first time, we devise a trip advisor that recommends bike check-in and check-out stations with joint consideration of service quality and bicycle utilization. To ensure service quality, we firstly predict the user demand of each station to obtain the success rate of rental and return in the future. Experiments indicate that the precision of our method is as much as 0.826, which has raised by 25.9 percent as compared with that of the historical average method. To rebalance bike usage, from historical data, we identify that biased bike usage is rooted from circumscribed bicycle circulation among few active stations. Therefore, with defined station activeness, we optimize the bike circulation by leading users to shift bikes between highly active stations and inactive ones. We extensively evaluate the performance of our design through real-world datasets. Evaluation results show that the percentage of frequently used bikes decreases by 33.6 percent on usage number and 28.6 percent on usage time.

Purpose A phase correction method for high-resolution multi-shot (MSH) diffusion weighted imaging (DWI) is proposed. The efficacy and generalization capability of the method were validated on both healthy volunteers and patients. Theory and methods Conventionally, inter-shot phase variations for MSH echo-planar imaging (EPI) DWI are corrected by model-based algorithms. However, many acquisition imperfections are hard to measure accurately for conventional model-based methods, making the phase estimation and artifacts suppression unreliable. We propose a deep learning multiplexed sensitivity-encoding (DL-MUSE) framework to improve the phase estimations based on convolutional neural network (CNN) reconstruction. Aliasing-free single-shot (SSH) DW images, which have been used routinely in clinical settings, were used for training before the aliasing correction of MSH-DWI images. A dual-channel U-net comprising multiple convolutional layers was used for the phase estimation of MSH-EPI. The network was trained on a dataset containing 30 healthy volunteers and tested on another dataset of 52 healthy subjects and 15 patients with lesions or tumors with different shot numbers (4, 6 and 8). To further validate the generalization capability of our network, we acquired a dataset with different numbers of shots, TEs, partial Fourier factors, resolutions, ETLs, FOVs, coil numbers, and image orientations from two sites. We also compared the reconstruction performance of our proposed method with that of the conventional MUSE and SSH-EPI qualitatively and quantitatively. Results Our results show that DL-MUSE is capable of correcting inter-shot phase errors with high and robust performance. Compared to conventional model-based MUSE, our method, by applying deep learning-based phase corrections, showed reduced distortion, noise level, and signal loss in high b-value DWIs. The improvements of image quality become more evident as the shot number increases from 4 to 8, especially in those central regions of the images, where g-factor artifacts are severe. Furthermore, the proposed method could provide the information about the orientation of the white matter with better consistency and achieve finer fibers delineation compared to the SSH-EPI method. Besides, the experiments on volunteers and patients from two different sites demonstrated the generalizability of our proposed method preliminarily. Conclusion A deep learning-based reconstruction algorithm for MSH-EPI images, which helps improve image quality greatly, was proposed. Results from healthy volunteers and tumor patients demonstrated the feasibility and generalization performances of our method for high-resolution MSH-EPI DWI, which can be used for routine clinical applications as well as neuroimaging research.

Dynamically localizing users in online social networks is challenging because people seldom post location-related microblogs due to privacy concern. To increase inference accuracy, a promising approach is to leverage microblogs from friends. However, it is difficult because microblogs from friends may not be synchronized or informative. To tackle these challenges, we propose a system consisting two steps. Firstly, "co-location" friends are detected and used to infer the statistical locations of users. Secondly, users' dynamic locations are determined by considering both statistical locations and POI(point of interest) names in microblogs. Experiments based on real world dataset demonstrates that our approach outperforms previous studies.

This paper describes a general modeling and control approach for steering wheel variable rate liquid fertilizer applicator. An adaptive numerical modeling approach for describing the system input-output dynamics is proposed, and an optimal control that accounts for the control hardware limits is developed. Field tests have demonstrated the effectiveness of the theoretical development.

This paper describes a general modeling and control approach for automating various agricultural machines for precision farming applications. An adaptive numerical modeling approach for describing the system input–output dynamics is proposed, and an optimal control that accounts for the control hardware limits is developed. Field tests have demonstrated the effectiveness of the theoretical development.