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Kang, Seungwoo; Lee, Jinwon; Jang, Hyun‐Jun; Lee, Hyonik; Lee, Mi Kyung; Park, Souneil; Park, Taiwoo; Song, Junehwa

doi:10.1145/1378600.1378630

Cited by 152 publications

(5 citation statements)

References 23 publications

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“…이를 보완하기 위해 수 산화기를 메틸기로 치환함으로써 (methoxylation) 친수성인 quercetin의 지용성을 증가시키고, 음이온을 띠게 하여 대사 안정성과 세포내 수송율을 높일 수 있다 (Graf et al, 2005;Wilson et al, 2008). Quercetin의 다양한 기능성이 보고되고 있음에도 불구하고 이러한 천연 물질이 동물 근육의 기능성 에 미치는 연구는 시작 단계이고 (Vichi et al, 2001;Jang et al, 2008;Jung et al, 2010) 분배 추출한 후 핵산층을 제거하고, 증류수층은 다시 클로로 포름과 물 혼합액(1:1)으로 3회 분배 추출하여 이용하였다. 또한 quercetin에 포함된 수산화기의 일부를 메틸기로 치환 함으로써 지용성을 증가시켜 흡수를 증진시키고 대사안정 성이 높은 methoxylated quercetin의 준비는 Lee et al(2011) 의 방법에 따라 효소 공학적 방법을 이용하여 대장균에 과 발현시킨 후 추출한 것으로 quercetin의 7번 위치의 hydroxyl group을 methyl group으로 치환한 7-methoxy-quercetin을 이 용하였다 (Fig.…”

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Effect of Quercetin and Methoxylated Quercetin on Chicken Thigh Meat Quality during Cold Storage

Jang¹,

Ham²,

Chae³

et al. 2011

Korean Journal of Poultry Science

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This study was carried out to determine the effect of dietary supplementation of quercetin and methoxylated quercetin extracted from onions on chicken thigh meat quality during cold storage. For 35 days, 1-day-old 320 broiler chicks (Ross) were divided into 8 groups and supplemented the diet; basal diet only (CONTROL), CONTROL with antibiotics (AB), vitamin E 20 IU (VE20), vitamin E 200 IU (VE200), quercetin 20 ppm (QC20), quercetin 200 ppm (QC200), methoxylated quercetin 20 ppm (MQ20), and methoxylated quercetin 200 ppm (MQ200). After slaughtering the broilers, thighs were separated and analyzed the quality change of the meat during storage at 4℃ for 7 days. The meat quality factors such as pH, color, water holding capacity, and sensory characteristics of thigh meat were determined on the experiment day 0, 3, and 7. After slaughtering, the pH of AB, VE 20, QC 20, and MQ 200 showed no significant difference compare to that of CONTROL. However, VE 200 and QC 20 showed higher pH value than CONTROL on storage day 3. L* value of chicken thigh of MQ 20 was lower than CONTROL on storage day 0, however, no significant difference was found between CONTROL and treatments on storage day 3. Redness (a*) of chicken thigh in CONTROL was increased during storage. QC 20, QC 200, and MQ 200 significantly reduced the b* value of chicken thigh (p<0.05). Water holding capacity of VE 20 and MQ 200 was significantly higher than the CONTROL on the day 0. Also, QC 200 showed higher WHC compare to the CONTROL. In sensory evaluation, overall acceptability of chicken thigh in quercetin and methoxylated quercetin group showed no significant differences compare to that of CONTROL by storage day 3. These results suggested that the quercetin and methoxylated quercetin could be used as additives to enhance broiler thigh meat quality such as pH and WHC without adverse effect on color and sensory characteristics.

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unclassified

Effect of Quercetin and Methoxylated Quercetin on Chicken Thigh Meat Quality during Cold Storage

Jang¹,

Ham²,

Chae³

et al. 2011

Korean Journal of Poultry Science

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“…The challenge to mobile resources of executing the algorithms necessary to extract context and user activities from sensor data has been long recognized and studied. Approaches include the development of sensing algorithm optimizations such as short-circuiting sequences of processing or identifying ecient sampling rates and duty cycles for sensors and algorithm components like [44,45,46]. Work such as [47] aims to combine such optimizations along with careful usage of energy e cient hardware.…”

Section: Related Workmentioning

confidence: 99%

Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables

Bhattacharya

Lane

2016

Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM

213

116

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Deep learning has revolutionized the way sensor data are analyzed and interpreted. The accuracy gains these approaches o↵er make them attractive for the next generation of mobile, wearable and embedded sensory applications. However, state-of-the-art deep learning algorithms typically require a significant amount of device and processor resources, even just for the inference stages that are used to discriminate high-level classes from low-level data. The limited availability of memory, computation, and energy on mobile and embedded platforms thus pose a significant challenge to the adoption of these powerful learning techniques. In this paper, we propose SparseSep, a new approach that leverages the sparsification of fully connected layers and separation of convolutional kernels to reduce the resource requirements of popular deep learning algorithms. As a result, SparseSep allows large-scale DNNs and CNNs to run e ciently on mobile and embedded hardware with only minimal impact on inference accuracy. We experiment using SparseSep across a variety of common processors such as the Qualcomm Snapdragon 400, ARM Cortex M0 and M3, and Nvidia Tegra K1, and show that it allows inference for various deep models to execute more e ciently; for example, on average requiring 11.3 times less memory and running 13.3 times faster on these representative platforms.

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“…Sensing applications like CareSafe [20], BikeNet [21], CenceMe [22], NoiseTube and MobiShop [17] amongst several others use the capability of multiple sensors surrounding the human to send context feedback to mobile users. Efforts to make mobile sensing systems more energy-efficient range from low-power processors [4], sensing pipelines [6], bidirectional feedback pipelines [5] and context correlation with association rules [7]. Energy-accuracy trade-offs [23] have also been considered, with techniques such as mobile tethering with cloud gatherers and an energy-aware striper [24], multiple access links for collaborative downloading and gateways [25], and data prefetching [26,27] having been investigated.…”

Section: Related Workmentioning

confidence: 99%

“…Several mobile sensing systems have been designed with energy efficiency in mind [4,5,6,7]. Energy efficiency has also been a major concern for wireless sensor networks, where the application of in-network aggregation and compression techniques has been widely explored [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Energy-aware collaborative sensing for multiple applications in mobile cloud computing

Loomba

Shi

Jennings

et al. 2015

Sustainable Computing: Informatics and Systems

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Cited by 152 publications

References 23 publications

Effect of Quercetin and Methoxylated Quercetin on Chicken Thigh Meat Quality during Cold Storage

Effect of Quercetin and Methoxylated Quercetin on Chicken Thigh Meat Quality during Cold Storage

Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables

Energy-aware collaborative sensing for multiple applications in mobile cloud computing

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