JoVo

top 10 articles of the decade (in no particular order)

note that these are the articles that i care about most, insofar as their impact on my intended future research. they are all related to either statistics or neuroscience, mostly with regard to data collection technologies. interestingly, they are all tools that enable us to ask more questions, not answers about any particular questions.  anybody have any other articles that i’m missing?

1) brainbow

Nature. 2007 Nov 1;450(7166):56-62. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Livet J, Weissman TA, Kang H, Draft RW, Lu J, Bennis RA, Sanes JR, Lichtman JW.

Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA.

Detailed analysis of neuronal network architecture requires the development of new methods. Here we present strategies to visualize synaptic circuits by genetically labelling neurons with multiple, distinct colours. In Brainbow transgenes, Cre/lox recombination is used to create a stochastic choice of expression between three or more fluorescent proteins (XFPs). Integration of tandem Brainbow copies in transgenic mice yielded combinatorial XFP expression, and thus many colours, thereby providing a way to distinguish adjacent neurons and visualize other cellular interactions. As a demonstration, we reconstructed hundreds of neighbouring axons and multiple synaptic contacts in one small volume of a cerebellar lobe exhibiting approximately 90 colours. The expression in some lines also allowed us to map glial territories and follow glial cells and neurons over time in vivo. The ability of the Brainbow system to label uniquely many individual cells within a population may facilitate the analysis of neuronal circuitry on a large scale.

2) STED/PALM/STORM

2a) Nature Biotechnology 21, 1347 - 1355 (2003) Toward fluorescence nanoscopy Stefan W Hell

Abstract For more than a century, the resolution of focusing light microscopy has been limited by diffraction to 180 nm in the focal plane and to 500 nm along the optic axis. Recently, microscopes have been reported that provide three- to sevenfold improved axial resolution in live cells. Moreover, a family of concepts has emerged that overcomes the diffraction barrier altogether. Its first exponent, stimulated emission depletion microscopy, has so far displayed a resolution down to 28 nm. Relying on saturated optical transitions, these concepts are limited only by the attainable saturation level. As strong saturation should be feasible at low light intensities, nanoscale imaging with focused light may be closer than ever.

2b) Nature Methods - 3, 793 - 796 (2006) Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM) Michael J Rust1, 5, Mark Bates2, 5 & Xiaowei Zhuang1, 3, 4

We have developed a high-resolution fluorescence microscopy method based on high-accuracy localization of photoswitchable fluorophores. In each imaging cycle, only a fraction of the fluorophores were turned on, allowing their positions to be determined with nanometer accuracy. The fluorophore positions obtained from a series of imaging cycles were used to reconstruct the overall image. We demonstrated an imaging resolution of 20 nm. This technique can, in principle, reach molecular-scale resolution.

2c) Science 15 September 2006: Vol. 313. no. 5793, pp. 1642 - 1645 Imaging Intracellular Fluorescent Proteins at Nanometer Resolution Eric Betzig,1,2* George H. Patterson,3 Rachid Sougrat,3 O. Wolf Lindwasser,3 Scott Olenych,4Juan S. Bonifacino,3 Michael W. Davidson,4 Jennifer Lippincott-Schwartz,3 Harald F. Hess5*

We introduce a method for optically imaging intracellular proteins at nanometer spatial resolution. Numerous sparse subsets of photoactivatable fluorescent protein molecules were activated, localized (to 2 to 25 nanometers), and then bleached. The aggregate position information from all subsets was then assembled into a superresolution image. We used this method—termed photoactivated localization microscopy—to image specific target proteins in thin sections of lysosomes and mitochondria; in fixed whole cells, we imaged vinculin at focal adhesions, actin within a lamellipodium, and the distribution of the retroviral protein Gag at the plasma membrane.

3) array tomography

Neuron. 2007 Jul 5;55(1):25-36. Array tomography: a new tool for imaging the molecular architecture and ultrastructure of neural circuits. Micheva KD, Smith SJ.

Many biological functions depend critically upon fine details of tissue molecular architecture that have resisted exploration by existing imaging techniques. This is particularly true for nervous system tissues, where information processing function depends on intricate circuit and synaptic architectures. Here, we describe a new imaging method, called array tomography, which combines and extends superlative features of modern optical fluorescence and electron microscopy methods. Based on methods for constructing and repeatedly staining and imaging ordered arrays of ultrathin (50-200 nm), resin-embedded serial sections on glass microscope slides, array tomography allows for quantitative, high-resolution, large-field volumetric imaging of large numbers of antigens, fluorescent proteins, and ultrastructure in individual tissue specimens. Compared to confocal microscopy, array tomography offers the advantage of better spatial resolution, in particular along the z axis, as well as depth-independent immunofluorescent staining. The application of array tomography can reveal important but previously unseen features of brain molecular architecture.

4) random forests

Random Forests. Journal Machine Learning. Leo Breiman

Abstract Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

5) high-throughput electron microscopy

5a) PLoS Biol. 2004 Nov;2(11):e329. Epub 2004 Oct 19. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. Denk W, Horstmann H.

Three-dimensional (3D) structural information on many length scales is of central importance in biological research. Excellent methods exist to obtain structures of molecules at atomic, organelles at electron microscopic, and tissue at light-microscopic resolution. A gap exists, however, when 3D tissue structure needs to be reconstructed over hundreds of micrometers with a resolution sufficient to follow the thinnest cellular processes and to identify small organelles such as synaptic vesicles. Such 3D data are, however, essential to understand cellular networks that, particularly in the nervous system, need to be completely reconstructed throughout a substantial spatial volume. Here we demonstrate that datasets meeting these requirements can be obtained by automated block-face imaging combined with serial sectioning inside the chamber of a scanning electron microscope. Backscattering contrast is used to visualize the heavy-metal staining of tissue prepared using techniques that are routine for transmission electron microscopy. Low-vacuum (20-60 Pa H(2)O) conditions prevent charging of the uncoated block face. The resolution is sufficient to trace even the thinnest axons and to identify synapses. Stacks of several hundred sections, 50-70 nm thick, have been obtained at a lateral position jitter of typically under 10 nm. This opens the possibility of automatically obtaining the electron-microscope-level 3D datasets needed to completely reconstruct the connectivity of neuronal circuits.

5b) Automating the Collection of Ultrathin Serial Sections for Large Volume TEM Reconstructions K. J. Hayworth*, N. Kasthuri**, R. Schalek***, J. W. Lichtman**

TEM serial section reconstructions have proven invaluable for mapping the complex neural circuitry of tiny invertebrate animals such as C. Elegans [1], as well as small pieces of the vertebrate nervous system. If such reconstructions could be applied to larger volumes of neural tissue (on the order of many cubic millimeters) they could provide answers to many lingering questions of vertebrate neuroanatomy. Unfortunately, such reconstructions are currently limited to several thousand sections and correspondingly small tissue volumes due, in part, to the manual nature of the ultramicrotomy and tissue collection process [2]. Here we introduce a new type of ultramicrotome we are developing, an Automatic Tape-collecting Lathe Ultramicrotome (ATLUM). The ATLUM is designed to automate not only the sectioning process, but also the collection of ultrathin sections from the knife’s waterboat (currently an exclusively manual process). The ATLUM’s mechanism collects ultrathin sections onto a specially prepared copper tape that can subsequently be pattern- etched to generate TEM slot grids with the collected tissue already attached and ready for imaging.

6) in vivo 2p calcium with dyes

Proc Natl Acad Sci U S A. 2003 Jun 10;100(12):7319-24. Epub 2003 May 30. In vivo two-photon calcium imaging of neuronal networks. Stosiek C, Garaschuk O, Holthoff K, Konnerth A.

Two-photon calcium imaging is a powerful means for monitoring the activity of distinct neurons in brain tissue in vivo. In the mammalian brain, such imaging studies have been restricted largely to calcium recordings from neurons that were individually dye-loaded through microelectrodes. Previous attempts to use membrane-permeant forms of fluorometric calcium indicators to load populations of neurons have yielded satisfactory results only in cell cultures or in slices of immature brain tissue. Here we introduce a versatile approach for loading membrane-permeant fluorescent indicator dyes in large populations of cells. We established a pressure ejection-based local dye delivery protocol that can be used for a large spectrum of membrane-permeant indicator dyes, including calcium green-1 acetoxymethyl (AM) ester, Fura-2 AM, Fluo-4 AM, and Indo-1 AM. We applied this dye-loading protocol successfully in mouse brain tissue at any developmental stage from newborn to adult in vivo and in vitro. In vivo two-photon Ca2+ recordings, obtained by imaging through the intact skull, indicated that whisker deflection-evoked Ca2+ transients occur in a subset of layer 2/3 neurons of the barrel cortex. Thus, our results demonstrate the suitability of this technique for real-time analyses of intact neuronal circuits with the resolution of individual cells.

7) generalized linear models for neural encoding and decoding

7a) J Neurophysiol 93: 1074-1089, 2005. First published September 8, 2004; A Point Process Framework for Relating Neural Spiking Activity to Spiking History, Neural Ensemble, and Extrinsic Covariate Effects

Wilson Truccolo1, Uri T. Eden2,3, Matthew R. Fellows1, John P. Donoghue1 and Emery N. Brown2,3

Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron’s spiking probability to three typical covariates: the neuron’s own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron’s spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.

7b) Network. 2004 Nov;15(4):243-62. Maximum likelihood estimation of cascade point-process neural encoding models. Paninski L.

Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage. We analyze the estimation of one such model for which this nonlinear step is implemented by a known parametric function; the assumption that this function is known speeds the estimation process considerably. We investigate the shape of the likelihood function for this type of model, give a simple condition on the nonlinearity ensuring that no non-global local maxima exist in the likelihood-leading, in turn, to efficient algorithms for the computation of the maximum likelihood estimator-and discuss the implications for the form of the allowed nonlinearities. Finally, we note some interesting connections between the likelihood-based estimators and the classical spike-triggered average estimator, discuss some useful extensions of the basic model structure, and provide two novel applications to physiological data.

8) compressed sensing

8a) DONOHO David L. IEEE transactions on information theory Suppose x is an unknown vector in Rm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements n can be dramatically smaller than the size m. Thus, certain natural classes of images with m pixels need only n = O(m1/4 log5/2(m)) nonadaptive nonpixel samples for faithful recovery, as opposed to the usual m pixel samples. More specifically, suppose x has a sparse representation in some orthonormal basis (e.g., wavelet, Fourier) or tight frame (e.g., curvelet, Gabor)-so the coefficients belong to an łp ball for 0 < p < 1. The N most important coefficients in that expansion allow reconstruction with ł2 error O(N1/2-1/P). It is possible to design n = O(N log(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients. Moreover, a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing. The nonadaptive measurements have the character of “random” linear combinations of basis/frame elements. Our results use the notions of optimal recovery, of n-widths, and information-based complexity. We estimate the Gel’fand n-widths of łp balls in high-dimensional Euclidean space in the case 0 < p < 1, and give a criterion identifying near-optimal subspaces for Gel’fand n-widths. We show that “most” subspaces are near-optimal, and show that convex optimization (Basis Pursuit) is a near-optimal way to extract information derived from these near-optimal subspaces.

8b) Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

Candès, Emmanuel J. and Tao, Terence IEEE Transactions on Information Theory, 52 (12). pp. 5406-5425. ISSN 0018-9448 Suppose we are given a vector f in a class F ⊂ ℝN, e.g., a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to recover f to within precision ε in the Euclidean (ℓ2) metric? This paper shows that if the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program. More precisely, suppose that the nth largest entry of the vector |f| (or of its coefficients in a fixed basis) obeys |f|(n) ≤ R n^(1-p), where R > 0 and p > 0. Suppose that we take measurements yk = {f,Xk}, k = 1, …,K, where the Xk are N-dimensional Gaussian vectors with independent standard normal entries. Then for each f obeying the decay estimate above for some 0 < p < 1 and with overwhelming probability, our reconstruction f#, defined as the solution to the constraints yk = 〈f#, Xk〉 with minimal ℓ1 norm, obeys [equation]. There is a sense in which this result is optimal; it is generally impossible to obtain a higher accuracy from any set of K measurements whatsoever. The methodology extends to various other random measurement ensembles; for example, we show that similar results hold if one observes a few randomly sampled Fourier coefficients of f. In fact, the results are quite general and require only two hypotheses on the measurement ensemble which are detailed.

9) Latent Dirichlet Allocation David M. Blei, Andrew Y. Ng, Michael I. Jordan; JMLR 3(Jan):993-1022, 2003.

We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.

10) High angular resolution diffusion imaging

Magn Reson Med. 2002 Oct;48(4):577-82. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ.

Magnetic resonance (MR) diffusion tensor imaging (DTI) can resolve the white matter fiber orientation within a voxel provided that the fibers are strongly aligned. However, a given voxel may contain a distribution of fiber orientations due to, for example, intravoxel fiber crossing. The present study sought to test whether a geodesic, high b-value diffusion gradient sampling scheme could resolve multiple fiber orientations within a single voxel. In regions of fiber crossing the diffusion signal exhibited multiple local maxima/minima as a function of diffusion gradient orientation, indicating the presence of multiple intravoxel fiber orientations. The multimodality of the observed diffusion signal precluded the standard tensor reconstruction, so instead the diffusion signal was modeled as arising from a discrete mixture of Gaussian diffusion processes in slow exchange, and the underlying mixture of tensors was solved for using a gradient descent scheme. The multitensor reconstruction resolved multiple intravoxel fiber populations corresponding to known fiber anatomy.

wanna ecobikeride.org with me?

i’m going on a bike ride on may 16th, in the morning? i’d love to have some company….

http://www.ecobikeride.org/

if you must know, the ride is actually a fund raiser for a local, sustainable farm in baltimore.  they are awesome.  the ride will be totally fun.there is a whole weekend extravaganza too, if you wanna play for the whole weekend.
hope to see you soon….
love, j

ps - i you don’t wanna ride that day, you can always just come and play ;)

spring olympics

i think they “should” start having the spring olympics. all the competitions for the spring olympics, however, would be contributions to the world, not sports.  categories could include fields within science, politics, education, art, etc.  each country would nominate their top “candidates” for each of these categories.  the actual “event” would look more like the olympic award ceremony, as opposed to a competition.  medals for gold, silver, and copper would be given.  medals would be award to both men and women in each category, and maybe also non-gendered individuals, if so desired.

what is the purpose of these spring olympics?  well, currently, children have many people to look up to, as role models.  however, most of them are famous by virtue of looking good, or performing some skill.  not that these are not very admirable and impressive traits, but i think it might also be nice for role models to exist by virtue of their contribution to society, not merely their physique or performance talent.  these people would not be competing intentionally, but rather, by simply following their passions.  children from every country in the world would therefore have a role model in every discipline, both male and female.  the awards would be given based on largest contribution to the field in the preceding year.  perhaps these awards would incentivize children to devote their energies to these disciplines as well as sports and performance arts, instead of only sports and performance arts.

thoughts?

Looking for a Mobile Web & App Developer (contract)

I’m currently working with a customer who is looking for: a) a mobile version of their web site b) a downloadable mobile app (ideally HTML based for cross platform functionality) We’re looking for developer(s) interested in a contract project. Any recommendations for developer(s) with experience in either or both that you’d like to throw some work to? Thanks! Ben

testing