Large-scale plasmid DNA processing: evidence that cell harvesting and storage methods affect yield of supercoiled plasmid DNA

2008 ◽  
Vol 51 (1) ◽  
pp. 43 ◽  
Author(s):  
Simyee Kong ◽  
Cassandra F. Rock ◽  
Andrew Booth ◽  
Nicholas Willoughby ◽  
Ronan D. O'Kennedy ◽  
...  



BioTechniques ◽  
2009 ◽  
Vol 47 (1) ◽  
pp. 633-635 ◽  
Author(s):  
Marita C. Barth ◽  
Debra A. Dederich ◽  
Peter C. Dedon


2021 ◽  
Vol 64 (6) ◽  
pp. 107-116
Author(s):  
Yakun Sophia Shao ◽  
Jason Cemons ◽  
Rangharajan Venkatesan ◽  
Brian Zimmer ◽  
Matthew Fojtik ◽  
...  

Package-level integration using multi-chip-modules (MCMs) is a promising approach for building large-scale systems. Compared to a large monolithic die, an MCM combines many smaller chiplets into a larger system, substantially reducing fabrication and design costs. Current MCMs typically only contain a handful of coarse-grained large chiplets due to the high area, performance, and energy overheads associated with inter-chiplet communication. This work investigates and quantifies the costs and benefits of using MCMs with finegrained chiplets for deep learning inference, an application domain with large compute and on-chip storage requirements. To evaluate the approach, we architected, implemented, fabricated, and tested Simba, a 36-chiplet prototype MCM system for deep-learning inference. Each chiplet achieves 4 TOPS peak performance, and the 36-chiplet MCM package achieves up to 128 TOPS and up to 6.1 TOPS/W. The MCM is configurable to support a flexible mapping of DNN layers to the distributed compute and storage units. To mitigate inter-chiplet communication overheads, we introduce three tiling optimizations that improve data locality. These optimizations achieve up to 16% speedup compared to the baseline layer mapping. Our evaluation shows that Simba can process 1988 images/s running ResNet-50 with a batch size of one, delivering an inference latency of 0.50 ms.



Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1568
Author(s):  
Rebecca R. Milczarek ◽  
Carl W. Olsen ◽  
Ivana Sedej

Watermelon (Citrullus lanatus) juice is known for its refreshing flavor, but its high perishability limits its availability throughout the year. Watermelon juice concentrate has extended shelf-life and lower transportation and storage costs, but the conventional thermal evaporation process for concentrating juice degrades the nutritional components and sensory quality of the product. Thus, in this work, a large-scale, non-thermal forward osmosis (FO) process was used to concentrate fresh watermelon juice up to 65°Brix. The FO concentrate was compared to thermal concentrate and fresh juices, and to commercially available refrigerated watermelon juices, in terms of lycopene and citrulline content, total soluble phenolics, antioxidant activity, and sensory properties. The FO concentrate had statistically similar (p < 0.05) levels of all the nutrients of interest except antioxidant activity, when compared to the thermal concentrate. The reconstituted FO concentrate maintained the same antioxidant activity as the raw source juice, which was 45% higher than that of the reconstituted thermal concentrate. Sensory results showed that reconstituted FO concentrate resulted in highly liked juice, and it outperformed the reconstituted thermal concentrate in the sensory hedonic rating. This work demonstrates the possibility to produce a high-quality watermelon juice concentrate by forward osmosis.



2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chen Zhang ◽  
Bin Hu ◽  
Yucong Suo ◽  
Zhiqiang Zou ◽  
Yimu Ji

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.



FEBS Letters ◽  
1983 ◽  
Vol 153 (1) ◽  
pp. 60-64 ◽  
Author(s):  
Ujjala DasGupta ◽  
Sudhamoy Ghosh


2020 ◽  
Vol 6 ◽  
pp. 1597-1603
Author(s):  
Lei Liu ◽  
Tomonobu Senjyu ◽  
Takeyoshi Kato ◽  
Abdul Motin Howlader ◽  
Paras Mandal ◽  
...  


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