D
David A. Jackson
Researcher at King's College London
Publications - 1166
Citations - 76015
David A. Jackson is an academic researcher from King's College London. The author has contributed to research in topics: Optical fiber & Interferometry. The author has an hindex of 136, co-authored 1095 publications receiving 68352 citations. Previous affiliations of David A. Jackson include University of California, Berkeley & University of Alberta.
Papers
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Journal ArticleDOI
Cloning of sea urchin actin gene sequences for use in studying the regulation of actin gene transcription.
Glenn Merlino,Robert D. Water,John P. Chamberlain,David A. Jackson,M. Raafat El-Gewely,Lewis J. Kleinsmith +5 more
TL;DR: By using pSA38 as a hybridization probe, it has been found that the level of actin-specific RNA sequences increases dramatically during early sea urchin development.
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Interferometric fibre-optic hydrogen sensor
TL;DR: In this article, a hydrogen sensor was developed in which the sensing element is a palladium wire, mechanically attached to a monomode optical fibre, and the phase retardance is proportional to the square root of the hydrogen partial pressure.
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CD44 expression and modulation on human neuroblastoma tumours and cell lines.
TL;DR: The CD44 standard molecule was highly expressed in the majority of tumours of stages 1-3, in all stage 4s and ganglioneuromas, but only in a subset of stage 4 tumours, demonstrating a highly significant inverse relationship between MYCN amplification and CD44 expression in neuroblastoma.
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Phase shift nulling DC-field fibre-optic magnetometer
TL;DR: In this paper, a closed-loop DC-field fiber-optic magnetometer is described which uses the dependence of the AC magneto-strictive responsitivity of a metallic glass sensing element on the local DC field in order to detect low-frequency or DC changes in the total local field.
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Instrumentation for the monitoring of toxic pollutants in water resources by means of neural network analysis of absorption and fluorescence spectra
T. Kuzniz,D. Halot,A.G. Mignani,L. Ciaccheri,Kyriacos Kalli,Moshe Tur,Andreas Othonos,Constantinos Christofides,David A. Jackson +8 more
TL;DR: In this article, the concentration of several pollutants, usually present in industrial waste waters, is predicted by the neural network data processing of absorption and fluorescence measurements in the visible spectral range, and proper network training provides quantitative analysis of many pollutants with sub-ppm resolution.