Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

[1]  N. Bonnet,et al.  Multivariate statistical methods for the analysis of microscope image series: applications in materials science , 1998 .

[2]  Olav Solgaard,et al.  An atomic force microscope tip designed to measure time-varying nanomechanical forces , 2007, Nature Nanotechnology.

[3]  Stephen Jesse,et al.  High resolution electromechanical imaging of ferroelectric materials in a liquid environment by piezoresponse force microscopy. , 2006, Physical review letters.

[4]  A. Noy.  Handbook of Molecular Force Spectroscopy , 2008 .

[5]  Tomasz Kowalewski,et al.  Scanning probe acceleration microscopy (SPAM) in fluids: mapping mechanical properties of surfaces at the nanoscale. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Stephen Jesse,et al.  Towards local electromechanical probing of cellular and biomolecular systems in a liquid environment. , 2007, Nanotechnology.

[7]  Roger Proksch,et al.  Multifrequency, repulsive-mode amplitude-modulated atomic force microscopy , 2006 .

[8]  B. Derjaguin.  Problems of adhesion , 1994 .

[9]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[10]  Sergei V. Kalinin,et al.  High-resolution imaging of proteins in human teeth by scanning probe microscopy. , 2007, Biochemical and biophysical research communications.

[11]  H. Deutsch.  Principle Component Analysis , 2004 .

[12]  Simon Haykin,et al.  Correlative Learning: A Basis for Brain and Adaptive Systems (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) , 2007 .

[13]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[14]  Noël Bonnet,et al.  Some trends in microscope image processing , 2004 .

[15]  W. Arnold,et al.  Theoretical description of the transfer of vibrations from a sample to the cantilever of an atomic force microscope , 1997 .

[16]  D. Lohr,et al.  Single-molecule recognition imaging microscopy. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Ute Rabe,et al.  Acoustic microscopy by atomic force microscopy , 1994 .

[18]  A. Malkin,et al.  In vitro high-resolution structural dynamics of single germinating bacterial spores , 2006, Proceedings of the National Academy of Sciences.

[19]  James K. Gimzewski,et al.  Applicability of AFM in cancer detection , 2009 .

[20]  H. Lang,et al.  How the doors to the nanoworld were opened , 2006, Nature nanotechnology.

[21]  C D Woodworth,et al.  Atomic force microscopy detects differences in the surface brush of normal and cancerous cells. , 2009, Nature nanotechnology.

[22]  Ricardo Garcia,et al.  Dynamic atomic force microscopy methods , 2002 .

[23]  D. Alexander,et al.  Mapping chemical and bonding information using multivariate analysis of electron energy-loss spectrum images. , 2006, Ultramicroscopy.

[24]  Stephen Jesse,et al.  Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy , 2009, Nanotechnology.

[25]  Arvind Raman,et al.  Cantilever dynamics in atomic force microscopy , 2008 .

[26]  Subra Suresh,et al.  The biomechanics toolbox: experimental approaches for living cells and biomolecules , 2003 .

[27]  J. Rao,et al.  Nanomechanical analysis of cells from cancer patients. , 2007, Nature nanotechnology.

[28]  John E. Sader,et al.  Small amplitude oscillations of a thin beam immersed in a viscous fluid near a solid surface , 2005 .

[29]  Ernst Meyer,et al.  Scanning Probe Microscopy: The Lab on a Tip , 2021 .

[30]  Alexei Gruverman,et al.  Nanoscale ferroelectrics: processing, characterization and future trends , 2006 .

[31]  Ricardo Garcia,et al.  Theory of phase spectroscopy in bimodal atomic force microscopy , 2009 .

[32]  Alexander G. Petrov,et al.  Flexoelectricity of Charged and Dipolar Bilayer Lipid Membranes Studied by Stroboscopic Interferometry , 1994 .