Barcoding cells using cell-surface programmable DNA-binding domains

We develop here a novel approach to barcode large numbers of cells through cell-surface expression of programmable zinc-finger DNA-binding domains (sZFs). We show sZFs enable double-stranded DNA to sequence-specifically label living cells, and also develop a sequential tagging approach to in situ image >3 cell types using just 3 fluorophores. Finally we demonstrate their broad versatility through ability to serve as surrogate reporters and facilitate selective cell capture and targeting

On the design of clone-based haplotyping

Background: Haplotypes are important for assessing genealogy and disease susceptibility of individual genomes, but are difficult to obtain with routine sequencing approaches. Experimental haplotype reconstruction based on assembling fragments of individual chromosomes is promising, but with variable yields due to incompletely understood parameter choices. Results: We parameterize the clone-based haplotyping problem in order to provide theoretical and empirical assessments of the impact of different parameters on haplotype assembly. We confirm the intuition that long clones help link together heterozygous variants and thus improve haplotype length. Furthermore, given the length of the clones, we address how to choose the other parameters, including number of pools, clone coverage and sequencing coverage, so as to maximize haplotype length. We model the problem theoretically and show empirically the benefits of using larger clones with moderate number of pools and sequencing coverage. In particular, using 140 kb BAC clones, we construct haplotypes for a personal genome and assemble haplotypes with N50 values greater than 2.6 Mb. These assembled haplotypes are longer and at least as accurate as haplotypes of existing clone-based strategies, whether in vivo or in vitro. Conclusions: Our results provide practical guidelines for the development and design of clone-based methods to achieve long range, high-resolution and accurate haplotypes

Correction: Jang, J.; Lee, H.-N. Profitable Double-Spending Attacks. Appl. Sci. 2020, 10, 8477

There was an error in the original publication [...

Virtual metrology for copper-clad laminate manufacturing

Copper-clad laminate (CCL), the key material for printed circuit board production, is used in various electronic products; thereby, the demand for CCL is on the rise. The process of CCL manufacturing occurs in three phases: treating, lay-up, and pressing, while the process with the largest influence on quality control is the treating. For effective quality control, the treating process requires intermediate inspection for three important quality factors: treated weight, minimum viscosity, and gel time. However, a manual inspection, which present-day manufacturers perform, incurs heavy cost in terms of time and money, rendering it ineffective. This study proposes the application of virtual metrology for CCL manufacturing to predict product quality derived from processing data without a product quality inspection. The actual process data from a CCL manufacturer in Korea was collected for a duration of approximately 5 months. Based on these data, the application builds a prediction model for CCL quality by utilizing the process variables affecting the CCL quality as predictor variables. As a result, four regression algorithms and three methods of variable selection were applied to build the prediction models for virtual metrology. Prediction models were obtained with a high accuracy in specific target variables. It was also verified that quality control was influenced by not only the important predictor variables empirically recognized by process engineers in the field but also by several essential variables previously unknown to the engineers; effective quality control will be possible by focusing on these variables particularly and more efficiently instead of overall monitoring. (C) 2017 Elsevier Ltd. All rights reserved.OAIID:RECH_ACHV_DSTSH_NO:T201734262RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A004522CITE_RATE:3.195DEPT_NM:산업공학과EMAIL:[email protected]_YN:YN