Fully automated intensity compensation for confocal microscopic images

<p>Figure S1. (A, B) Confocal immunofluorescence microscopic images showing colocalization of C1GalT1, ST3Gal1 and C2GnT-L with ER marker HSP70 in LNCaP, PC-3 and DU145 cells, in normal prostate tissues and different grades of prostatic adenocarcinoma. White boxes in each panel are enlarged and shown at the right side as green, red and merged images. All confocal images were acquired with same imaging parameters; bar, 10 μm. Figure S2. (A, B) Confocal immunofluorescence microscopic images showing colocalization of C1GalT1, ST3Gal1 and C2GnT-L with Golgi matrix protein GM130 and Golgi-specific GTPase Rab6a in PC-3 and DU145 cells. All confocal images were acquired with same imaging parameters; bar, 10 μm. Figure S3. (A, B) Confocal immunofluorescence microscopic images showing colocalization of C1GalT1 with Golgi matrix protein GM130 in PC-3 and DU145 cells treated with scramble, giantin, GM130 and GRASP65 siRNAs. (C, D) Confocal immunofluorescence microscopic images showing colocalization of C1GalT1 with giantin in PC-3 and DU145 cells treated with GM130 and GRASP65 siRNAs. The images inside the white boxes are enlarged and shown as green and red channels at the bottom of each panel. All confocal images were acquired with same imaging parameters; bar, 10 μm. Figure S4. (A) Confocal immunofluorescence microscopy shows colocalization of C2GnT-L and ST3Gal1 with giantin in DU145 cells treated with DMSO, Blebbistatin or NMIIA siRNAs. Images in the white boxes are amplified and placed at the right side of each panel. (B) Confocal immunofluorescence images of giantin in DU145 cells treated with scramble and Plk3 siRNAs followed by treatment with Blebbistatin, or Plk3, NMIIA, or NMIIA plus Plk3 siRNAs. Bar, 10 μm. Figure S5. (A, C) Confocal fluorescence images of immunostained giantin, and MAA lectin stained α2-3Sia or LEA lectin stained polylactosamine in DU145 cells treated with Blebbistatin or NMIIA siRNAs. Control cells were treated with DMSO. (B, D) Quantification of α-2,3-Sia- and polylactosamine-specific average integrated fluorescence (in a.u.) in DU145 cells shown in A and C. (E) The 8% SDS PAGE of DU145 cells plasma membrane fractions pulled-down by MAA and LEA lectins followed by capturing with Streptavidin magnet beads. (F) DAPI staining of DU145 cells treated with DMSO, Blebbistatin, or scramble or NMIIA siRNAs and then with or without treatment with 10 μM galectin-1. (G, H) The percentage apoptotic cells and caspase-3 activity in cells are presented in F. Apoptotic nuclei were counted in 30 fields each of three independent experiments and expressed as percentage apoptotic cells out of total number of cells. Caspase-3 activity is expressed as relative mean fluorescence units (RFU) from three independent experiments. Bar, 10 μm; *, p<0.001.</p>

<p>Figure S1. (A, B) Confocal immunofluorescence microscopic images showing colocalization of C1GalT1, ST3Gal1 and C2GnT-L with ER marker HSP70 in LNCaP, PC-3 and DU145 cells, in normal prostate tissues and different grades of prostatic adenocarcinoma. White boxes in each panel are enlarged and shown at the right side as green, red and merged images. All confocal images were acquired with same imaging parameters; bar, 10 μm. Figure S2. (A, B) Confocal immunofluorescence microscopic images showing colocalization of C1GalT1, ST3Gal1 and C2GnT-L with Golgi matrix protein GM130 and Golgi-specific GTPase Rab6a in PC-3 and DU145 cells. All confocal images were acquired with same imaging parameters; bar, 10 μm. Figure S3. (A, B) Confocal immunofluorescence microscopic images showing colocalization of C1GalT1 with Golgi matrix protein GM130 in PC-3 and DU145 cells treated with scramble, giantin, GM130 and GRASP65 siRNAs. (C, D) Confocal immunofluorescence microscopic images showing colocalization of C1GalT1 with giantin in PC-3 and DU145 cells treated with GM130 and GRASP65 siRNAs. The images inside the white boxes are enlarged and shown as green and red channels at the bottom of each panel. All confocal images were acquired with same imaging parameters; bar, 10 μm. Figure S4. (A) Confocal immunofluorescence microscopy shows colocalization of C2GnT-L and ST3Gal1 with giantin in DU145 cells treated with DMSO, Blebbistatin or NMIIA siRNAs. Images in the white boxes are amplified and placed at the right side of each panel. (B) Confocal immunofluorescence images of giantin in DU145 cells treated with scramble and Plk3 siRNAs followed by treatment with Blebbistatin, or Plk3, NMIIA, or NMIIA plus Plk3 siRNAs. Bar, 10 μm. Figure S5. (A, C) Confocal fluorescence images of immunostained giantin, and MAA lectin stained α2-3Sia or LEA lectin stained polylactosamine in DU145 cells treated with Blebbistatin or NMIIA siRNAs. Control cells were treated with DMSO. (B, D) Quantification of α-2,3-Sia- and polylactosamine-specific average integrated fluorescence (in a.u.) in DU145 cells shown in A and C. (E) The 8% SDS PAGE of DU145 cells plasma membrane fractions pulled-down by MAA and LEA lectins followed by capturing with Streptavidin magnet beads. (F) DAPI staining of DU145 cells treated with DMSO, Blebbistatin, or scramble or NMIIA siRNAs and then with or without treatment with 10 μM galectin-1. (G, H) The percentage apoptotic cells and caspase-3 activity in cells are presented in F. Apoptotic nuclei were counted in 30 fields each of three independent experiments and expressed as percentage apoptotic cells out of total number of cells. Caspase-3 activity is expressed as relative mean fluorescence units (RFU) from three independent experiments. Bar, 10 μm; *, p<0.001.</p>

Location‐only and use‐availability data: analysis methods converge

Automated tracing of neuronal processes from 3D confocal microscopy images is essential for quantitative neuroanatomy and neuronal assays. Two basic approaches are described in the literature-one based on skeletonization and another based on sequential tracing along neuronal processes. This article presents algorithms for improving the rate of detection, and the accuracy of estimating the location and process angles at branching points for the latter class of algorithms. The problem of simultaneously detecting branch points and estimating their measurements is formulated as a generalized likelihood ratio test defined on a spatial neighborhood of each candidate point, in which likelihoods were computed using a ridge detection approach. The average detection rate increased from from 37 to 86%. The average error in locating the branch points decreased from 2.6 to 2.1 voxels in 3D images. The generalized hypothesis test improves the rate of detection of branching points, and the accuracy of location estimates, enabling a more complete extraction of neuroanatomy and more accurate counting of branch points in neuronal assays. More accurate branch point morphometry is valuable for image registration and change analysis.

Maximum-entropy approach with higher moments for solving Fokker–Planck equation

A maximum entropy approach to adaptive statistical language modelling

Understanding how ensembles of neurons collectively interact will be a key step in developing a mechanistic theory of cognitive processes. Recent progress in multineuron recording and analysis techniques has generated tremendous excitement over the physiology of living neural networks. One of the key developments driving this interest is a new class of models based on the principle of maximum entropy. Maximum entropy models have been reported to account for spatial correlation structure in ensembles of neurons recorded from several different types of data. Importantly, these models require only information about the firing rates of individual neurons and their pairwise correlations. If this approach is generally applicable, it would drastically simplify the problem of understanding how neural networks behave. Given the interest in this method, several groups now have worked to extend maximum entropy models to account for temporal correlations. Here, we review how maximum entropy models have been applied to neuronal ensemble data to account for spatial and temporal correlations. We also discuss criticisms of the maximum entropy approach that argue that it is not generally applicable to larger ensembles of neurons. We conclude that future maximum entropy models will need to address three issues: temporal correlations, higher-order correlations, and larger ensemble sizes. Finally, we provide a brief list of topics for future research.

The variations of the b coefficient in the frequency-magnitude relationship for earthquakes which occurred at Mt. Etna from 01/01/90 to 31/12/92 are analyzed; the completeness threshold for our earthquakes catalogue is M = 2.30. The b values calculated using the Maximum Entropy Principle (MEP) are compared to those obtained by conventional methods of Least Squares (LS) and Maximum Likelihood (ML). All the differences among the b values computed using these methods, and the reasons for these differences, are discussed and examined. In particular, our results show that the b values obtained by MEP are lower than the others calculated using LS and ML; this implies that, on the average, LS and ML underestimate the seismic hazard at Mt. Etna. Moreover, temporal variations of bMEP are more evident than the corresponding ones of bLS and bML; indeed, in some cases the trend of bMEP variation is opposite those of bLS and bML. A significant correlation between temporal variations of the volcanic activity and the b values is evident only if the MEP is used; this means that, if b temporal variations are analyzed in order to detect changes in the volcano dynamics and predict the eruptions, the maximum entropy approach should be preferred. Finally, the observed pattern of bMEP temporal variations with regard to the changes in the volcanic activity is consistent with the hypothesis of a compressive stress field acting on Mt. Etna.

Various seismic deconvolution operators can be determined by estimating a seismic wavelet and subsequently designing an appropriate inverse filter which converts the wavelet to a spike. Seismic wavelets and deconvolution operators must be estimated in a time adaptive sense due to the nonstationarity of the seismic trace. The wavelet estimation methods considered in this paper either use the assumption of a minimum phase wavelet and a random impulse response, or the assumption that the wavelet cepstrum is readily separable from the cepstrum of the seismic trace. The former assumption is required in using the Hilbert transform and Wiener‐Levinson wavelet estimations, while the latter assumption is used in homomorphic deconvolution. These wavelet estimates can be used in the design of multichannel Wiener and Kalman deconvolution operators. Multichannel usage of homomorphic deconvolution can also be implemented through various types of cepstral stacking.The discussion of deconvolution filter design focuses on the problems of filter length, degree of prewhitening, and nonstationarity. In designing time adaptive deconvolution filters, the autocorrelation function can be used to monitor the nonstationarity of the seismic trace. The autocorrelation function, which is used in the computation of least squares inverse filters, can be estimated in an optimum fashion by using the maximum entropy method. Differences between minimum phase Wiener deconvolution and maximum entropy deconvolution become more pronounced for shorter data gates. As a result, the maximum entropy approach is preferred for time adaptive deconvolution.In this paper, the performance of various wavelet estimators and inverse filters is discussed using real and synthetic seismic data. Discussions of homomorphic deconvolution and maximum entropy prediction error filtering are merged with descriptions of conventional approaches to deconvolution.

This paper describes an analysis of 1H-1H residual dipolar couplings (RDCs) in diphenylmethane (DPM) dissolved in a nematic liquid crystal, reported by Celebre et al. [J. Chem. Phys. 118, 6417 (2003)]. In that article, the conformational distribution function for DPM was extracted from the RDCs, using the additive potential (AP) model which is based on the molecular-field theory. The AP approach is a powerful, and frequently used, tool for analysis of the nuclear-magnetic-resonance (NMR) parameters in liquid crystals. It requires, however, a priori knowledge of the functional form of the torsional potential, which may even for a simple molecule, such as DPM, be complicated to determine. Here, we analyze the same set of the RDCs using our APME procedure, which is a hybrid model based on the AP approach and maximum entropy (ME) theory. The APME procedure does not require any assumptions about the functional form of the torsional potential and, in contrast with the ME method, is applicable to weakly ordered systems. In the investigation reported in the present study, the results from the APME analysis are in good agreement with the AP interpretation, whereas the ME approach essentially fails in the extraction of the conformational distribution function for DPM.

During the MaxEnt 2002 workshop in Moscow, Idaho, Tony Vignaux asked again a few simple questions about using Maximum Entropy or Bayesian approaches for the famous Dice problems which have been analyzed many times through this workshop and also in other places. Here, there is another analysis of these problems. I hope that, this paper will answer a few questions of Tony and other participants of the workshop on the situations where we can use Maximum Entropy or Bayesian approaches or even the cases where we can actually use both of them.

In this paper, a probabilistic technique for compensation of intensity loss in confocal microscopy images is presented. For single-colour-labelled specimen, confocal microscopy images are modelled as a mixture of two Gaussian probability distribution functions, one representing the background and another corresponding to the foreground. Images are segmented into foreground and background by applying Expectation Maximization algorithm to the mixture. Final intensity compensation is carried out by scaling and shifting the original intensities with the help of parameters estimated for the foreground. Since foreground is separated to calculate the compensation parameters, the method is effective even when image structure changes from frame to frame. As intensity decay function is not used, complexity associated with estimation of the intensity decay function parameters is eliminated. In addition, images can be compensated out of order, as only information from the reference image is required for the compensation of any image. These properties make our method an ideal tool for intensity compensation of confocal microscopy images that suffer intensity loss due to absorption/scattering of light as well as photobleaching and the image can change structure from optical/temporal section-to-section due to changes in the depth of specimen or due to a live specimen. The proposed method was tested with a number of confocal microscopy image stacks and results are presented to demonstrate the effectiveness of the method.

When analyzing acoustical data, the goal often is to find source strength and characteristics of the environment. Instead of finding point estimates, a more informative approach is to find probability distributions from either a Bayesian approach or a maximum entropy approach. The Bayesian approach relies on prior distributions and an estimate of the noise covariance, while the maximum entropy approach relies on an estimate of a “temperature” derived from analogies with statistical mechanics. Different representations of the temperature are appropriate in different situations. The best choice is related to confidence in the model being used and if the model captures all the relevant physics. Examples of how the choice of temperature impacts the posterior distributions are shown using a toy model and a real-world application. Specifically, the maximum entropy approach is applied to models of transiting ship noise to obtain posterior distributions of source level and porosity of the sediment layer. These examples show that the selection of temperature significantly impacts the posterior distributions. [Work supported by the Office of Naval Research.]

Typically, the lexicon models used in statistical machine translation systems do not include any kind of linguistic or contextual information, which often leads to problems in performing a correct word sense disambiguation. One way to deal with this problem within the statistical framework is to use maximum entropy methods. In this paper, we present how to use this type of information within a statistical machine translation system. We show that it is possible to significantly decrease training and test corpus perplexity of the translation models. In addition, we perform a rescoring of N-Best lists using our maximum entropy model and thereby yield an improvement in translation quality. Experimental results are presented on the so-called Verbmobil Task.

A new maximum entropy approach has been applied to analyse three types of transient transport experiments. For sawtooth propagation experiments in the ASDEX Upgrade and ECRH power modulation and power-switching experiments in the Wendelstein 7-AS Stellarator, either the time evolution of the temperature perturbation or the phase and amplitude of the modulated temperature perturbation are used as non-linear constraints to the profile to be fitted. Simultaneously, the constraints given by the equilibrium temperature profile for steady-state power balance are fitted. In the maximum entropy formulation, the flattest profile consistent with the constraints is found. It was found that determined from sawtooth propagation was greater than the power balance value by a factor of five in the ASDEX Upgrade. From power modulation experiments, employing the measurements of four modulation frequencies simultaneously, the power deposition profile as well as the profile could be determined. A comparison of the predictions of a time-independent model and a power-dependent model is made. The power-switching experiments show that the profile must change within a millisecond to a new value consistent with the power balance value at the new input power. Neither power deposition broadening due to suprathermal electrons nor temperature or temperature gradient dependences of can explain this observation.