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ref: -2019 tags: super-resolution microscopy fluorescent protein molecules date: 05-28-2019 16:02 gmt revision:3 [2] [1] [0] [head]

PMID-30997987 Chemistry of Photosensitive Fluorophores for Single-Molecule Localization Microscopy

  • Excellent review of all the photo-convertable, photo-switchable, and more complex (photo-oxidation or reddening) of both proteins and small molecule fluorophore.
    • E.g. PA-GFP is one of the best -- good photoactivation quantum yield, good N ~ 300
    • Other small molecules, like Alexa Fluor 647 have a photon yield > 6700, which can be increased with triplet quenchers and antioxidants.
  • Describes the chemical mechanism of the various photo switching -- review is targeted at (bio)chemists interested in getting into imaging.
  • Emphasize that critical figures of merit are photoactivation quantum yield Φ pa\Phi_{pa} and N, overall photon yield before photobleaching.
  • See also Colorado lecture

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ref: -2011 tags: Andrew Ng high level unsupervised autoencoders date: 03-15-2019 06:09 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

Building High-level Features Using Large Scale Unsupervised Learning

  • Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, Andrew Y. Ng
  • Input data 10M random 200x200 frames from youtube. Each video contributes only one frame.
  • Used local receptive fields, to reduce the communication requirements. 1000 computers, 16 cores each, 3 days.
  • "Strongly influenced by" Olshausen & Field {1448} -- but this is limited to a shallow architecture.
  • Lee et al 2008 show that stacked RBMs can model simple functions of the cortex.
  • Lee et al 2009 show that convolutonal DBN trained on faces can learn a face detector.
  • Their architecture: sparse deep autoencoder with
    • Local receptive fields: each feature of the autoencoder can connect to only a small region of the lower layer (e.g. non-convolutional)
      • Purely linear layer.
      • More biologically plausible & allows the learning of more invariances other than translational invariances (Le et al 2010).
      • No weight sharing means the network is extra large == 1 billion weights.
        • Still, the human visual cortex is about a million times larger in neurons and synapses.
    • L2 pooling (Hyvarinen et al 2009) which allows the learning of invariant features.
      • E.g. this is the square root of the sum of the squares of its inputs. Square root nonlinearity.
    • Local contrast normalization -- subtractive and divisive (Jarrett et al 2009)
  • Encoding weights W 1W_1 and deconding weights W 2W_2 are adjusted to minimize the reconstruction error, penalized by 0.1 * the sparse pooling layer activation. Latter term encourages the network to find invariances.
  • minimize(W 1,W 2) minimize(W_1, W_2) i=1 m(||W 2W 1 Tx (i)x (i)|| 2 2+λ j=1 kε+H j(W 1 Tx (i)) 2) \sum_{i=1}^m {({ ||W_2 W_1^T x^{(i)} - x^{(i)} ||^2_2 + \lambda \sum_{j=1}^k{ \sqrt{\epsilon + H_j(W_1^T x^{(i)})^2}} })}
    • H jH_j are the weights to the j-th pooling element, λ=0.1\lambda = 0.1 ; m examples; k pooling units.
    • This is also known as reconstruction Topographic Independent Component Analysis.
    • Weights are updated through asynchronous SGD.
    • Minibatch size 100.
    • Note deeper autoencoders don't fare consistently better.

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ref: -0 tags: superresolution imaging scanning lens nanoscale date: 02-04-2019 20:34 gmt revision:1 [0] [head]

PMID-27934860 Scanning superlens microscopy for non-invasive large field-of-view visible light nanoscale imaging

  • Recently, the diffraction barrier has been surpassed by simply introducing dielectrics with a micro-scale spherical configuration when using conventional optical microscopes by transforming evanescent waves into propagating waves. 18,19,20,21,22,23,24,25,26,27,28,29,30
  • The resolution of this superlens-based microscopy has been decreased to ∼50 nm (ref. 26) from an initial resolution of ∼200 nm (ref. 21).
  • This method can be further enhanced to ∼25 nm when coupled with a scanning laser confocal microscope 31.
  • It has achieved fast development in biological applications, as the sub-diffraction-limited resolution of high-index liquid-immersed microspheres has now been demonstrated23,32, enabling its application in the aqueous environment required to maintain biological activity.
  • Microlens is a 57 um diameter BaTiO3 microsphere, resolution of lambda / 6.3 under partial and inclined illumination
  • Microshpere is in contact with the surface during imaging, by gluing it to the cantilever tip of an AFM.
  • Get an image with the microsphere-lens, which improves imaging performance by ~ 200x. (with a loss in quality, naturally).

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ref: -0 tags: super resolution imaging PALM STORM fluorescence date: 09-21-2016 05:57 gmt revision:0 [head]

PMID-23900251 Parallel super-resolution imaging

  • Christopher J Rowlands, Elijah Y S Yew, and Peter T C So
  • Though this is a brief Nature intro article, I found it to be more usefully clear than the wikipedia articles on super-resolution techniques.
  • STORM and PALM seek to stochastically switch fluorophores between emission and dark states, and are parallel but stochastic; STED and RESOLFT use high-intensity donut beams to stimulate emission (STED) or photobleach (RESOLFT) fluorophores outside of an arbitrarily-small location.
    • All need gaussian-fitting to estimate emitter location from the point-spread function.
  • This article comments on a clever way of making 1e5 donuts for parallel (as opposed to rastered) STED / RESOLFT.
  • I doubt stetting up a STED microscope is at all easy; to get these resolutions, everything must be still to a few nm!

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ref: bookmark-0 tags: memory supermemo leraning psychology Hermann Ebbinghaus date: 05-08-2008 15:25 gmt revision:0 [head]

http://www.wired.com/medtech/health/magazine/16-05/ff_wozniak -- wonderful article, well written. Leaves you with a sense of Piotr Wozniak (SuperMemo's inventor) crazy, slightly surreal, impassioned, purposeful, but self-regressive (and hence fundamentally stationary) life.

  • Quote: SuperMemo was like a genie that granted Wozniak a wish: unprecedented power to remember. But the value of what he remembered depended crucially on what he studied, and what he studied depended on his goals, and the selection of his goals rested upon the efficient acquisition of knowledge, in a regressive function that propelled him relentlessly along the path he had chosen.
  • http://www.wired.com/images/article/magazine/1605/ff_wozniak_graph_f.jpg
  • Quote: This should lead to radically improved intelligence and creativity. The only cost: turning your back on every convention of social life.

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ref: engineering notes-0 tags: homopolar generator motor superconducting magnet date: 03-09-2007 14:39 gmt revision:0 [head]


  • the magnets are energized in 'opposite directions - forcing the field lines to go normal to the rotar.
  • still need brushes - perhaps there is no way to avoid them in a homopolar generator.

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ref: bookmark-0 tags: neuroanatomy pulvinar thalamus superior colliculus image gray brainstem date: 0-0-2007 0:0 revision:0 [head]

http://en.wikipedia.org/wiki/Image:Gray719.png --great, very useful!

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ref: bookmark-0 tags: neural_networks machine_learning matlab toolbox supervised_learning PCA perceptron SOM EM date: 0-0-2006 0:0 revision:0 [head]

http://www.ncrg.aston.ac.uk/netlab/index.php n.b. kinda old. (or does that just mean well established?)