Aleksandra RADENOVIC

Dr. Aleksandra RADENOVIC

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Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 November 2020 in Nature
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GMGuilherme Migliato Marega
Guilherme Migliato Marega
YZYanfei Zhao
Yanfei Zhao
AAAhmet Avsar
Ahmet Avsar
ZWZhenyu Wang
Zhenyu Wang
MTMukesh Tripathi
Mukesh Tripathi
AKAndras Kis
Andras Kis

30 July 2019 in Nature Reviews Materials
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MMMichal Macha
Michal Macha
SMSanjin Marion
Sanjin Marion
VNVishal V. R. Nandigana
Vishal V. R. Nandigana
ARAleksandra RADENOVIC
Aleksandra RADENOVIC

26 August 2019 in Nature Methods
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ADA. Descloux
A. Descloux
KGK. S. Grußmayer
K. S. Grußmayer
ARAleksandra RADENOVIC
Aleksandra RADENOVIC

26 July 2011 in PLOS ONE
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PAPaolo Annibale
Paolo Annibale
SVStefano Vanni
Stefano Vanni
Marco ScarselliMarco Scarselli
Marco Scarselli
URUrsula Rothlisberger
Ursula Rothlisberger
ARAleksandra RADENOVIC
Aleksandra RADENOVIC
In this work we discuss how to use photophysical information for improved quantitative measurements using Photo Activated Localization Microscopy (PALM) imaging. We introduce a method that reliably estimates the number of photoblinking molecules present in a biological sample and gives a robust way to quantify proteins at the single-cell level from PALM images. We apply this method to determine the amount of β2 adrenergic receptor, a prototypical G Protein Coupled Receptor, expressed on the plasma membrane of HeLa cells.

12 June 2011 in Nature Methods
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PAPaolo Annibale
Paolo Annibale
SVStefano Vanni
Stefano Vanni
Marco ScarselliMarco Scarselli
Marco Scarselli
URUrsula Rothlisberger
Ursula Rothlisberger
ARAleksandra RADENOVIC
Aleksandra RADENOVIC

19 June 2012 in Nanoscale
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CRC. Raillon
C. Raillon
PGP. Granjon
P. Granjon
MGM. Graf
M. Graf
LSL. J. Steinbock
L. J. Steinbock
ARAleksandra RADENOVIC
Aleksandra RADENOVIC
We have developed a method to analyze in detail, translocation events providing a novel and flexible tool for data analysis of nanopore experiments. Our program, called OpenNanopore, is based on the cumulative sums algorithm (CUSUM algorithm). This algorithm is an abrupt change detection algorithm that provides fitting of current blockages, allowing the user to easily identify the different levels in each event. Our method detects events using adaptive thresholds that adapt to low-frequency variations in the baseline. After event identification, our method uses the CUSUM algorithm to fit the levels inside every event and automatically extracts their time and amplitude information. This facilitates the statistical analysis of an event population with a given number of levels. The obtained information improves the interpretation of interactions between the molecule and nanopore. Since our program does not require any prior information about the analyzed molecules, novel molecule–nanopore interactions can be characterized. In addition our program is very fast and stable. With the progress in fabrication and control of the translocation speed, in the near future, our program could be useful in identification of the different bases of DNA.