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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202204957
|2 doi
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|a pubmed24n1525.xml
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|a (NLM)35945159
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
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|a Kuo, Yu-An
|e verfasserin
|4 aut
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| 245 |
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|a Massively Parallel Selection of NanoCluster Beacons
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 17.10.2022
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|a Date Revised 06.09.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2022 Wiley-VCH GmbH.
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|a NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here, a high-throughput selection method is reported that takes advantage of repurposed next-generation-sequencing chips to screen the activation fluorescence of ≈40 000 activator sequences. It is found that the nucleobases at positions 7-12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6 and 2-4 are hotspots to generate yellow-orange and red POTs, respectively. Based on these findings, a "zipper-bag" model is proposed that can explain how these hotspots facilitate the formation of distinct silver cluster chromophores and alter their chemical yields. Combining high-throughput screening with machine-learning algorithms, a pipeline is established to design bright and multicolor NCBs in silico
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|a Journal Article
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|a NanoCluster Beacons
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|a fluorescent nanomaterials
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|a high-throughput screening
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|a next-generation sequencing
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|a silver nanoclusters
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|a Nucleotides
|2 NLM
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|a Silver
|2 NLM
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|a 3M4G523W1G
|2 NLM
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|a DNA
|2 NLM
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|a 9007-49-2
|2 NLM
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| 700 |
1 |
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|a Jung, Cheulhee
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Yu-An
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kuo, Hung-Che
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Oliver S
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Nguyen, Trung D
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Rybarski, James R
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hong, Soonwoo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Yuan-I
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wylie, Dennis C
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hawkins, John A
|e verfasserin
|4 aut
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|a Walker, Jada N
|e verfasserin
|4 aut
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1 |
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|a Shields, Samuel W J
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Brodbelt, Jennifer S
|e verfasserin
|4 aut
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| 700 |
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|a Petty, Jeffrey T
|e verfasserin
|4 aut
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|a Finkelstein, Ilya J
|e verfasserin
|4 aut
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| 700 |
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|a Yeh, Hsin-Chih
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 41 vom: 01. Okt., Seite e2204957
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
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|g volume:34
|g year:2022
|g number:41
|g day:01
|g month:10
|g pages:e2204957
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|u http://dx.doi.org/10.1002/adma.202204957
|3 Volltext
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