This study evaluated how prostatic levels of antioxidants relate to plasma levels and self-reported usual dietary intake. Definition of these relations may aid in interpreting studies of antioxidant exposure and prostate cancer risk. Between July 1996 and April 1997, plasma and prostatic tissue levels of tocopherols, carotenoids, and retinol were measured in 47 men undergoing radical prostatectomy or transurethral prostatectomy at Loyola University Medical Center, Maywood, Illinois, and an affiliate hospital. Dietary intake was measured by using a 122-item version of the Block Health Habits and History Questionnaire, and correlations were assessed with Pearson's coefficients. Prostatic levels of tocopherols and carotenoids (but not retinol) were significantly correlated with plasma levels (r= 0.31-0.56, p < 0.05-0.0001); the strongest correlations were associated with lycopene, beta-carotene, and gamma-tocopherol (0.56, 0.54, and 0.52, respectively; p < 0.0001). Relative concentrations of tocopherols and carotenoids in prostate tissue were proportionate to those in plasma. No correlation between prostatic levels and reported dietary intake was observed (r = -0.09 to 0.16, p < not significant). Adjustment for energy intake, body mass index, and serum lipids did not impact these relations. These results suggest that plasma levels of tocopherols and carotenoids better reflect prostatic exposure than self-reported usual dietary intake.
We present DEBAR, a scalable and high-performance de-duplication storage system for backup and archiving, to overcome the throughput and scalability limitations of the state-of-the-art data de-duplication schemes, including the Data Domain De-duplication File System (DDFS). DEBAR uses a two-phase de-duplication scheme (TPDS) that exploits memory cache and disk index properties to judiciously turn the notoriously random and small disk I/Os of fingerprint lookups and updates into large sequential disk I/Os, hence achieving a very high de-duplication throughput. The salient feature of this approach is that both the system backup and archiving capacity and the de-duplication performance can be dynamically and cost-effectively scaled up on demand; it hence not only significantly improves the throughput of a single de-duplication server but also is conducive to distributed implementation and thus applicable to largescale and distributed storage systems.
While deep learning models have contributed to the advancement of sensor-based Human Activity Recognition (HAR), it usually requires large amounts of annotated sensor data to extract robust features. To alleviate the limitations of data annotation, contrastive learning has been applied to sensor-based HAR. One of the essential factors of contrastive learning is data augmentation, significantly impacting the performance of pre-training. However, current popular augmentation methods do not achieve competitive performance in contrastive learning for sensor-based HAR. Motivated by this issue, we propose a new sensor data augmentation method by resampling, which introduces variable domain information and simulates realistic activity data by varying the sampling frequency to maximize the coverage of the sampling space.The resampling augmentation method was evaluated in supervised learning and contrastive learning (SimCLRHAR and MoCoHAR). In the experiment, we use four datasets, UCI-HAR, MotionSense, USC-HAD, and MobiAct, using the mean F1score as the evaluation metric for downstream tasks. The experiment results show that the resampling data augmentation outperforms all state-of-the-art augmentation methods in supervised learning and contrastive learning with a small amount of labeled data. The results also demonstrate that not all data augmentation methods have positive effects in contrastive learning frameworks.
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