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Budovsky, I.,Abidin, A.R.B.Z.,Yan, A.Y.K.,Liu, LingXiang,Rustagi, V.K.,Govil, A.K.,Klonz, M.,Wei, Yih-cheng,Early, M.D.,Sasaki, H.,Fujiki, H.,Kumtawee, C.,Charoensook, A.,Kwon, Sung-Won,Son, N.A.,Supr IEEE 2005 IEEE transactions on instrumentation and measureme Vol.54 No.2
This paper presents preliminary results of the APMP.EM-K6a key comparison of AC-DC transfer conducted during 2000 to 2003. The AC-DC voltage transfer difference of the travelling standard was measured at 3 V and selected frequencies from 1 kHz to 1 MHz that included those of the CCEM-K6a key comparison. Also discussed are the methods used to compare thermal voltage converters with different input connectors, and to link the results to the parent Consultative Committee on Electricity and Magnetism (CCEM) comparison.
A. M. Ali,L. Y. Mooi,K. Yih Yih,A. W. Norhanom,K. Mat Saleh,N. H. Lajis,A. M. Yazid,F. B. H. Ahmad,U. Prasad 한국생약학회 2000 Natural Product Sciences Vol.6 No.3
The extracts of Carica papaya (flower), Barringtonia macrostachya (leaves), Coleus tuberosus (tuber), Mangifera indica (fruit skin) and Eugenia polyantha (leaves) showed strong in vitro anti-tumor promoting activity when assayed using Raji cells (Mooi et al., 1999). The anti-tumor promoting activity of the crude extracts was further analyzed by immunoblotting analysis of Raji cells carrying Epstein-Barr virus genome. The expression of early antigens diffuse (EA-D) and early antigens restricted (EA-R) was determined by performing western blotting of treated Raji cells with human sera of nasopharyngeal carcinoma patients. All the plant extracts were shown to be able to suppress both EA-D and EA-R.
Combined in silico modeling and metabolomics analysis to characterize fed‐batch CHO cell culture
Selvarasu, Suresh,Ho, Ying Swan,Chong, William P. K.,Wong, Niki S. C.,Yusufi, Faraaz N. K.,Lee, Yih Yean,Yap, Miranda G. S.,Lee, Dong‐,Yup Wiley Subscription Services, Inc., A Wiley Company 2012 Biotechnology and bioengineering Vol.109 No.6
<P><B>Abstract</B></P><P>The increasing demand for recombinant therapeutic proteins highlights the need to constantly improve the efficiency and yield of these biopharmaceutical products from mammalian cells, which is fully achievable only through proper understanding of cellular functioning. Towards this end, the current study exploited a combined metabolomics and in silico modeling approach to gain a deeper insight into the cellular mechanisms of Chinese hamster ovary (CHO) fed‐batch cultures. Initially, extracellular and intracellular metabolite profiling analysis shortlisted key metabolites associated with cell growth limitation within the energy, glutathione, and glycerophospholipid pathways that have distinct changes at the exponential‐stationary transition phase of the cultures. In addition, biomass compositional analysis newly revealed different amino acid content in the CHO cells from other mammalian cells, indicating the significance of accurate protein composition data in metabolite balancing across required nutrient assimilation, metabolic utilization, and cell growth. Subsequent in silico modeling of CHO cells characterized internal metabolic behaviors attaining physiological changes during growth and non‐growth phases, thereby allowing us to explore relevant pathways to growth limitation and identify major growth‐limiting factors including the oxidative stress and depletion of lipid metabolites. Such key information on growth‐related mechanisms derived from the current approach can potentially guide the development of new strategies to enhance CHO culture performance. Biotechnol. Bioeng. 2012; 109:1415–1429. © 2012 Wiley Periodicals, Inc.</P>
Smart Self-Checkout Carts Based on Deep Learning for Shopping Activity Recognition
Hong-Chuan Chi,Muhammad Atif Sarwar,Yousef-Awwad Daraghmi,Kuan-Wen Liu,Tsi-Ui ?k,Yih-Lang Li 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
Fast and reliable communication plays a major role in the success of smart shopping applications. In a ”Just Walk Out” shopping scenario, a video camera is installed on the cart to monitor shopping activities and transmit images to the cloud for processing so that items in the cart can be tracked and checked out. This paper proposes a prototype of a smart shopping cart based on image-based action recognition. Firstly, deep learning networks such as Faster R-CNN, YOLOv2, and YOLOv2-Tiny are utilized to analyze the content of each video frame. Frames are classified into three classes: No Hand, Empty Hand, and Holding Items. The classification accuracy based on Faster RCNN, YOLOv2, or YOLOv2-Tiny is between 93.0% and 90.3%, and the processing speed of the three networks can be up to 5 fps, 39 fps, and 50 fps, respectively. Secondly, based on the sequence of frame classes, the timeline is divided into No Hand intervals, Empty Hand intervals, and Holding Items intervals. The accuracy of action recognition is 96%, and the time error is 0.119s on average. Finally, we categorize the events into four cases: No Change, placing, Removing, and Swapping. Even including the correctness of the item recognition, the accuracy of shopping event detection is 97.9%, which is higher than the minimal requirement to deploy such a system in a smart shopping environment. A demo of the system and a link to download the data set used in the paper are in Smart Shopping Cart Prototype or found at this URL: https://hackmd.io/abEiC83rQoqxz7zpL4Kh2w.
FOXM1 mediates Dox resistance in breast cancer by enhancing DNA repair
Park, Yun-Yong,Jung, Sung Yun,Jennings, Nicholas B,Rodriguez-Aguayo, Cristian,Peng, Guang,Lee, Se-Ran,Kim, Sang Bae,Kim, Kyounghyun,Leem, Sun-Hee,Lin, Shiaw-Yih,Lopez-Berestein, Gabriel,Sood, Anil K,L Oxford University Press 2012 Carcinogenesis Vol.33 No.10
<B>Abstract</B><P>Transcription factors are direct effectors of altered signaling pathways in cancer and frequently determine clinical outcomes in cancer patients. To uncover new transcription factors that would determine clinical outcomes in breast cancer, we systematically analyzed gene expression data from breast cancer patients. Our results revealed that Forkhead box protein M1 (FOXM1) is the top-ranked survival-associated transcription factor in patients with triple-negative breast cancer. Surprisingly, silencing FOXM1 expression led breast cancer cells to become more sensitive to doxorubicin (Dox). We found that FOXM1-dependent resistance to Dox is mediated by regulating DNA repair genes. We further demonstrated that NFκB1 interacts with FOXM1 in the presence of Dox to protect breast cancer cells from DNA damage. Finally, silencing FOXM1 expression in breast cancer cells in a mouse xenograft model significantly sensitized the cells to Dox. Our systematic approaches identified an unexpected role of FOXM1 in Dox resistance by regulating DNA repair genes, and our findings provide mechanistic insights into how FOXM1 mediates resistance to Dox and evidence that FOXM1 may be a promising therapeutic target for sensitizing breast cancer cells to Dox.</P>