E-cadherin signal sequence disruption: a novel mechanism underlying hereditary cancer.

The aim of this study was to uncover the pathogenic relevance and the underlying molecular mechanism of a novel CDH1 variant found in a Hereditary Diffuse Gastric Cancer family (p.L13_L15del), which affects the signal peptide of E-cadherin without changing the remaining predicted sequence. We verified that p.L13_L15del cells yield low levels of E-cadherin, decreased cell adhesion and enhanced cell invasion. Further, we demonstrated that the disruption of the highly conserved hydrophobic core of the signal peptide hampers the binding of cellular components crucial for E-cadherin translation and translocation into the endoplasmic reticulum, constituting a new molecular basis for the loss of a tumour suppressor gene causative of hereditary cancer.

Blue intensity matters for cell cycle profiling in fluorescence DAPI-stained images.

In the past decades, there has been an amazing progress in the understanding of the molecular mechanisms of the cell cycle. This has been possible largely due to a better conceptualization of the cycle itself, but also as a consequence of technological advances. Herein, we propose a new fluorescence image-based framework targeted at the identification and segmentation of stained nuclei with the purpose to determine DNA content in distinct cell cycle stages. The method is based on discriminative features, such as total intensity and area, retrieved from in situ stained nuclei by fluorescence microscopy, allowing the determination of the cell cycle phase of both single and sub-population of cells. The analysis framework was built on a modified k-means clustering strategy and refined with a Gaussian mixture model classifier, which enabled the definition of highly accurate classification clusters corresponding to G1, S and G2 phases. Using the information retrieved from area and fluorescence total intensity, the modified k-means (k=3) cluster imaging framework classified 64.7% of the imaged nuclei, as being at G1 phase, 12.0% at G2 phase and 23.2% at S phase. Performance of the imaging framework was ascertained with normal murine mammary gland cells constitutively expressing the Fucci2 technology, exhibiting an overall sensitivity of 94.0%. Further, the results indicate that the imaging framework has a robust capacity to both identify a given DAPI-stained nucleus to its correct cell cycle phase, as well as to determine, with very high probability, true negatives. Importantly, this novel imaging approach is a non-disruptive method that allows an integrative and simultaneous quantitative analysis of molecular and morphological parameters, thus awarding the possibility of cell cycle profiling in cytological and histological samples.

A single stiffened nucleus alters cell dynamics and coherence in a monolayer.

Force transmission throughout a monolayer is the result of complex interactions between cells. Monolayer adaptation to force imbalances such as singular stiffened cells provides insight into the initiation of disease and fibrosis. Here, NRK-52E cells transfected with ∆50LA, which significantly stiffens the nucleus. These stiffened cells were sparsely placed in a monolayer of normal NRK-52E cells. Through morphometric analysis and temporal tracking, the impact of the singular stiffened cells shows a pivotal role in mechanoresponse of the monolayer. A method for a detailed analysis of the spatial aspect and temporal progression of the nuclear boundary was developed and used to achieve a full description of the phenotype and dynamics of the monolayers under study. Our findings reveal that cells are highly sensitive to the presence of mechanically impaired neighbors, leading to generalized loss of coordination in collective cell migration, but without seemingly affecting the potential for nuclear lamina fluctuations of neighboring cells. Reduced translocation in neighboring cells appears to be compensated by an increase in nuclear rotation and dynamic variation of shape, suggesting a "frustration" of cells and maintenance of motor activity. Interestingly, some characteristics of the behavior of these cells appear to be dependent on the distance to a ∆50LA cell, pointing to compensatory behavior in response to force transmission imbalances in a monolayer. These insights may suggest the long-range impacts of single cell defects related to tissue dysfunction.

Determining transaminase activity in bacterial libraries by time-lapse imaging.

Transaminase activity was determined by time-lapse imaging using a colourimetric reaction and image analysis. A correlation between the benzaldehyde conversion and relative luminance was determined, allowing the identification of the most promising biocatalysts, the determination of kinetic parameters, and the assessment of the effect of the substrate concentration on activity.

State-of-the-art review on deep learning in medical imaging.

Deep learning (DL) is affecting each and every sphere of public and private lives and becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the activities of neurons in the neocortex of human brain where the thought process takes place. Therefore, like the brain, it tries to learn and recognize patterns in the form of digital images. This power is built on the depth of many layers of computing neurons backed by high power processors and graphics processing units (GPUs) easily available today. In the current scenario, we have provided detailed survey of various types of DL systems available today, and specifically, we have concentrated our efforts on current applications of DL in medical imaging. We have also focused our efforts on explaining the readers the rapid transition of technology from machine learning to DL and have tried our best in reasoning this paradigm shift. Further, a detailed analysis of complexities involved in this shift and possible benefits accrued by the users and developers.

Medical image noise reduction using the Sylvester-Lyapunov equation.

Multiplicative noise is often present in medical and biological imaging, such as magnetic resonance imaging (MRI), Ultrasound, positron emission tomography (PET), single photon emission computed tomography (SPECT), and fluorescence microscopy. Noise reduction in medical images is a difficult task in which linear filtering algorithms usually fail. Bayesian algorithms have been used with success but they are time consuming and computationally demanding. In addition, the increasing importance of the 3-D and 4-D medical image analysis in medical diagnosis procedures increases the amount of data that must be efficiently processed. This paper presents a Bayesian denoising algorithm which copes with additive white Gaussian and multiplicative noise described by Poisson and Rayleigh distributions. The algorithm is based on the maximum a posteriori (MAP) criterion, and edge preserving priors which avoid the distortion of relevant anatomical details. The main contribution of the paper is the unification of a set of Bayesian denoising algorithms for additive and multiplicative noise using a well-known mathematical framework, the Sylvester-Lyapunov equation, developed in the context of the Control theory.

Geometric compensation applied to image analysis of cell populations with morphological variability: a new role for a classical concept.

Immunofluorescence is the gold standard technique to determine the level and spatial distribution of fluorescent-tagged molecules. However, quantitative analysis of fluorescence microscopy images faces crucial challenges such as morphologic variability within cells. In this work, we developed an analytical strategy to deal with cell shape and size variability that is based on an elastic geometric alignment algorithm. Firstly, synthetic images mimicking cell populations with morphological variability were used to test and optimize the algorithm, under controlled conditions. We have computed expression profiles specifically assessing cell-cell interactions (IN profiles) and profiles focusing on the distribution of a marker throughout the intracellular space of single cells (RD profiles). To experimentally validate our analytical pipeline, we have used real images of cell cultures stained for E-cadherin, tubulin and a mitochondria dye, selected as prototypes of membrane, cytoplasmic and organelle-specific markers. The results demonstrated that our algorithm is able to generate a detailed quantitative report and a faithful representation of a large panel of molecules, distributed in distinct cellular compartments, independently of cell's morphological features. This is a simple end-user method that can be widely explored in research and diagnostic labs to unravel protein regulation mechanisms or identify protein expression patterns associated with disease.

A multiscale algorithm for three-dimensional free-hand ultrasound.

This paper presents a multiscale algorithm for the reconstruction of human anatomy from a set of ultrasound (US) images. Reconstruction is formulated in a Bayesian framework as an optimization problem with a large number of unknown variables. Human tissues are represented by the interpolation of coefficients associated to the nodes of a 3-D cubic grid. The convergence of the Bayesian method is usually slow and initialization dependent. In this paper, a multiscale approach is proposed to increase the convergence rate of the iterative process of volume estimation. A coarse estimate of the volume is first obtained using a cubic grid with a small number of nodes initialized with a constant value computed from the observed data. The volume estimate is then recursively improved by refining the grid step. Experimental results are provided to show that multiscale method achieves faster convergence rates compared with a single-scale approach. This is the key improvement toward real-time implementations. Experimental results of 3-D reconstruction of human anatomy are presented to assess the performance of the algorithm and comparisons with the single-scale method are presented.

Compensation of log-compressed images for 3-D ultrasound.

In this study, a Bayesian approach was used for 3-D reconstruction in the presence of multiplicative noise and nonlinear compression of the ultrasound (US) data. Ultrasound images are often considered as being corrupted by multiplicative noise (speckle). Several statistical models have been developed to represent the US data. However, commercial US equipment performs a nonlinear image compression that reduces the dynamic range of the US signal for visualization purposes. This operation changes the distribution of the image pixels, preventing a straightforward application of the models. In this paper, the nonlinear compression is explicitly modeled and considered in the reconstruction process, where the speckle noise present in the radio frequency (RF) US data is modeled with a Rayleigh distribution. The results obtained by considering the compression of the US data are then compared with those obtained assuming no compression. It is shown that the estimation performed using the nonlinear log-compression model leads to better results than those obtained with the Rayleigh reconstruction method. The proposed algorithm is tested with synthetic and real data and the results are discussed. The results have shown an improvement in the reconstruction results when the compression operation is included in the image formation model, leading to sharper images with enhanced anatomical details.

A 3-D ultrasound-based framework to characterize the echo morphology of carotid plaques.

Carotid atherosclerosis is the main cause of brain stroke, which is the most common life-threatening neurological disease. Nearly all methods aiming at assessing the risk of plaque rupture are based on its characterization from 2-D ultrasound images, which depends on plaque geometry, degree of stenosis, and echo morphology (intensity and texture). The computation of these indicators is, however, usually affected by inaccuracy and subjectivity associated with data acquisition and operator-dependent image selection. To circumvent these limitations, a novel and simple method based on 3-D freehand ultrasound is proposed that does not require any expensive equipment except the common scanner. This method comprises the 3-D reconstruction of carotids and plaques to provide clinically meaningful parameters not available in 2-D ultrasound imaging, namely diagnostic views not usually accessible via conventional techniques and local 3-D characterization of plaque echo morphology. The labeling procedure, based on graph cuts, allows us to identify, locate, and quantify potentially vulnerable foci within the plaque. Validation of the characterization method was made with synthetic data. Results of plaque characterization with real data are encouraging and consistent with the results from conventional methods and after inspection of surgically removed plaques.

Neural physiological modeling towards a hemodynamic response function for fMRI.

The BOLD signal provided by the functional MRI medical modality measures the ratio of oxy- to deoxyhaemoglobin at each location inside the brain. The detection of activated regions upon the application of an external stimulus, e.g., visual or auditive, is based on the comparison of the mentioned ratios of a rest condition (pre-stimulus) and of a stimulated condition (post-stimulus). Therefore, an accurate knowledge of the impulse response of the BOLD signal to neural stimulus in a given region is needed to design robust detectors that discriminate, with a high level of confidence activated from non activated regions. Usually, in the literature, the hemodynamic response has been modeled by known functions, e.g., gamma functions, fitting them, or not, to the experimental data. In this paper we present a different approach based on the physiologic behavior of the vascular and neural tissues. Here, a linear model based on reasonable physiological assumptions about oxygen consumption and vasodilatation processes are used to design a linear model from which a transfer function is derived. The estimation of the model parameters is performed by using the minimum square error (MSE) by forcing the adjustment of the stimulus response to the observations. Experimental results using real data have shown that the proposed model successfully explains the observations allowing to achieve small values for the fitting error.

An unified framework for Bayesian denoising for several medical and biological imaging modalities.

Multiplicative noise is often present in several medical and biological imaging modalities, such as MRI, Ultrasound, PET/SPECT and Fluorescence Microscopy. Noise removal and preserving the details is not a trivial task. Bayesian algorithms have been used to tackle this problem. They succeed to accomplish this task, however they lead to a computational burden as we increase the image dimensionality. Therefore, a significant effort has been made to accomplish this tradeoff, i.e., to develop fast and reliable algorithms to remove noise without distorting relevant clinical information. This paper provides a new unified framework for Bayesian denoising of images corrupted with additive and multiplicative multiplicative noise. This allows to deal with additive white Gaussian and multiplicative noise described by Poisson and Rayleigh distributions respectively. The proposed algorithm is based on the maximum a posteriori (MAP) criterion, and an edge preserving priors are used to avoid the distortion of the relevant image details. The denoising task is performed by an iterative scheme based on Sylvester/Lyapunov equation. This approach allows to use fast and efficient algorithms described in the literature to solve the Sylvester/Lyapunov equation developed in the context of the Control theory. Experimental results with synthetic and real data testify the performance of the proposed technique, and competitive results are achieved when comparing to the of the state-of-the-art methods.

A method for the dynamic analysis of the heart using a Lyapounov based denoising algorithm.

Heart tracking in ultrasound sequences is a difficult task due to speckle noise, low SNR and lack of contrast. Therefore it is usually difficult to obtain robust estimates of the heart cavities since feature detectors produce a large number of outliers. This paper presents an algorithm which combines two main operations: i) a novel denoising algorithm based on the Lyapounov equation and ii) a robust tracker, recently proposed by the authors, based on a model of the outlier features. Experimental results are provided, showing that the proposed algorithm is computationally efficient and leads to accurate estimates of the left ventricle during the cardiac cycle.