Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs

With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation.     

“Spot and hop”: Internal referencing for surface plasmon resonance imaging using a three-dimensional microfluidic flow cell array

We have developed a novel referencing technique for surface plasmon resonance imaging systems referred to as “spot and hop.” The technique enables internal referencing for individual flow cells in a parallel processing microfluidic network. Internal referencing provides the ability to correct for nonspecific binding and instrument drift, significantly improving data quality at each region of interest. The performance of a 48-flow-cell device was demonstrated through a series of studies, including “rise and fall” time, ligand preconcentration, ligand immobilization, analyte binding, and regeneration tests. Interfacing parallel processing fluidics with imaging systems will significantly expand the throughput and applications of array-based optical biosensors while retaining high data quality.     

Detect the Hybridization of Single-Stranded DNA by Parallel Scan Spectral Surface Plasmon Resonance Imaging

We investigate the ability of a parallel scan spectral surface plasmon resonance imaging system in analyzing hybridization of single-stranded DNA (ssDNA) on microarray. The ssDNA probes are modified by a thiol group and thereby can be immobilized onto the gold film. Hybridization experiments are carried out by using both complementary and noncomplementary sequence to confirm the specificity of interaction. We also demonstrate that the data analysis is very important in reducing the noise and improving the resolution by comparing polynomial fitting method with the combination of polynomial fitting and centroid method. Finally, the results demonstrate that the parallel scan spectral surface plasmon resonance imaging system can be used for high-throughput analysis of the hybridization of the ssDNA.     

Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non‐invasive determination of these characteristics. We present an image‐processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source‐code for MATLAB and ImageJ is freely available under a permissive open‐source license. Biotechnol. Bioeng. 2014;111: 504–517. © 2013 Wiley Periodicals, Inc.     

A fast global fitting algorithm for fluorescence lifetime imaging microscopy based on image segmentation

Global fitting algorithms have been shown to improve effectively the accuracy and precision of the analysis of fluorescence lifetime imaging microscopy data. Global analysis performs better than unconstrained data fitting when prior information exists, such as the spatial invariance of the lifetimes of individual fluorescent species. The highly coupled nature of global analysis often results in a significantly slower convergence of the data fitting algorithm as compared with unconstrained analysis. Convergence speed can be greatly accelerated by providing appropriate initial guesses. Realizing that the image morphology often correlates with fluorophore distribution, a global fitting algorithm has been developed to assign initial guesses throughout an image based on a segmentation analysis. This algorithm was tested on both simulated data sets and time-domain lifetime measurements. We have successfully measured fluorophore distribution in fibroblasts stained with Hoechst and calcein. This method further allows second harmonic generation from collagen and elastin autofluorescence to be differentiated in fluorescence lifetime imaging microscopy images of ex vivo human skin. On our experimental measurement, this algorithm increased convergence speed by over two orders of magnitude and achieved significantly better fits.     

Sample Preparation and Imaging Conditions Affect mEos3.2 Photophysics in Fission Yeast Cells

Photoconvertible fluorescent proteins (PCFPs) are widely used in super-resolution microscopy and studies of cellular dynamics. However, our understanding of their photophysics is still limited, hampering their quantitative application. For example, we do not know the optimal sample preparation methods or imaging conditions to count protein molecules fused to PCFPs by single-molecule localization microscopy in live and fixed cells. We also do not know how the behavior of PCFPs in live cells compares with fixed cells. Therefore, we investigated how formaldehyde fixation influences the photophysical properties of the popular green-to-red PCFP mEos3.2 in fission yeast cells under a wide range of imaging conditions. We estimated photophysical parameters by fitting a three-state model of photoconversion and photobleaching to the time course of fluorescence signal per yeast cell expressing mEos3.2. We discovered that formaldehyde fixation makes the fluorescence signal, photoconversion rate, and photobleaching rate of mEos3.2 sensitive to the buffer conditions likely by permeabilizing the yeast cell membrane. Under some imaging conditions, the time-integrated mEos3.2 signal per yeast cell is similar in live cells and fixed cells imaged in buffer at pH 8.5 with 1 mM DTT, indicating that light chemical fixation does not destroy mEos3.2 molecules. We also discovered that 405-nm irradiation drove some red-state mEos3.2 molecules to enter an intermediate dark state, which can be converted back to the red fluorescent state by 561-nm illumination. Our findings provide a guide to quantitatively compare conditions for imaging mEos3.2-tagged molecules in yeast cells. Our imaging assay and mathematical model are easy to implement and provide a simple quantitative approach to measure the time-integrated signal and the photoconversion and photobleaching rates of fluorescent proteins in cells.     

Geographical information system parallelization for spatial big data processing: a review

With the increasing interest in large-scale, high-resolution and real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS is not efficient enough to handle the required loads due to limited computational capabilities.Various attempts have been made to adopt high performance computation techniques from different applications, such as designs of advanced architectures, strategies of data partition and direct parallelization method of spatial analysis algorithm, to address such challenges. This paper surveys the current state of parallel GIS with respect to parallel GIS architectures, parallel processing strategies, and relevant topics. We present the general evolution of the GIS architecture which includes main two parallel GIS architectures based on high performance computing cluster and Hadoop cluster. Then we summarize the current spatial data partition strategies, key methods to realize parallel GIS in the view of data decomposition and progress of the special parallel GIS algorithms. We use the parallel processing of GRASS as a case study. We also identify key problems and future potential research directions of parallel GIS.     

Information content of volume holographic imaging.

We discuss the general properties of optical imaging in three-dimensional object domains from an information-theoretic standpoint, expressing system performance in terms of imaging mutual information (IMI). We use IMI to characterize volume holographic imaging, a new optical imaging method with depth discrimination capability. The performance of this holographic method in imaging a simple discrete, axial fluorescent object is compared with that of two alternatives: a pinhole-based system and a combined system that uses both a pinhole and a hologram in parallel.     

Flexible fitting to cryo-electron microscopy maps with coarse-grained elastic network models

Cryo-electron microscopy has become an important tool for protein structure determination in recent decades. Since proteins may exist in multiple conformational states, combining high resolution X-ray or NMR structures with cryo-electron microscopy maps is a useful approach to obtain proteins in different functional states. Flexible fitting methods used in cryo-electron microscopy aim to obtain an unknown protein conformation from a high resolution structure and a cryo-electron microscopy map. Since all-atom flexible fitting is computationally expensive, many efficient flexible fitting algorithms that utilize coarse-grained elastic network models have been proposed. In this study, we investigated performance of three coarse-grained elastic network model-based flexible fitting methods (EMFF, iModFit, NMFF) using 25 protein pairs at four resolutions. This study shows that the application of coarse-grained elastic network models to flexible fitting of cryo-electron microscopy maps can provide fast and fruitful models of various conformational states of proteins.     

Accurate flexible fitting of high-resolution protein structures into cryo-electron microscopy maps using coarse-grained pseudo-energy minimization

Cryo-electron microscopy (cryo-EM) has been widely used to explore conformational states of large biomolecular assemblies. The detailed interpretation of cryo-EM data requires the flexible fitting of a known high-resolution protein structure into a low-resolution cryo-EM map. To this end, we have developed what we believe is a new method based on a two-bead-per-residue protein representation, and a modified form of the elastic network model that allows large-scale conformational changes while maintaining pseudobonds and secondary structures. Our method minimizes a pseudo-energy which linearly combines various terms of the modified elastic network model energy with a cryo-EM-fitting score and a collision energy that penalizes steric collisions. Unlike previous flexible fitting efforts using the lowest few normal modes, our method effectively utilizes all normal modes so that both global and local structural changes can be fully modeled. We have validated our method for a diverse set of 10 pairs of protein structures using simulated cryo-EM maps with a range of resolutions and in the absence/presence of random noise. We have shown that our method is both accurate and efficient compared with alternative techniques, and its performance is robust to the addition of random noise. Our method is also shown to be useful for the flexible fitting of three experimental cryo-EM maps.     

Spectral analysis methods for the robust measurement of the flexural rigidity of biopolymers

The mechanical properties of biopolymers can be determined from a statistical analysis of the ensemble of shapes they exhibit when subjected to thermal forces. In practice, extracting information from fluorescence microscopy images can be challenging due to low signal/noise ratios and other artifacts. To address these issues, we develop a suite of tools for image processing and spectral data analysis that is based on a biopolymer contour representation expressed in a spectral basis of orthogonal polynomials. We determine biopolymer shape and stiffness using global fitting routines that optimize a utility function measuring the amount of fluorescence intensity overlapped by such contours. This approach allows for filtering of high-frequency noise and interpolation over sporadic gaps in fluorescence. We use benchmarking to demonstrate the validity of our methods, by analyzing an ensemble of simulated images generated using a simulated biopolymer with known stiffness and subjected to various types of image noise. We then use these methods to determine the persistence lengths of taxol-stabilized microtubules. We find that single microtubules are well described by the wormlike chain polymer model, and that ensembles of chemically identical microtubules show significant heterogeneity in bending stiffness, which cannot be attributed to sampling or fitting errors. We expect these approaches to be useful in the study of biopolymer mechanics and the effects of associated regulatory molecules.     

Flexible fitting of high-resolution x-ray structures into cryoelectron microscopy maps using biased molecular dynamics simulations

A methodology for flexible fitting of all-atom high-resolution structures into low-resolution cryoelectron microscopy (cryo-EM) maps is presented. Flexibility of the modeled structure is simulated by classical molecular dynamics and an additional effective potential is introduced to enhance the fitting process. The additional potential is proportional to the correlation coefficient between the experimental cryo-EM map and a synthetic map generated for an all-atom structure being fitted to the map. The additional forces are calculated as a gradient of the correlation coefficient. During the molecular dynamics simulations under the additional forces, the molecule undergoes a conformational transition that maximizes the correlation coefficient, which results in a high-accuracy fit of all-atom structure into a cryo-EM map. Using five test proteins that exhibit structural rearrangement during their biological activity, we demonstrate performance of our method. We also test our method on the experimental cryo-EM of elongation factor G and show that the model obtained is comparable to previous studies. In addition, we show that overfitting can be avoided by assessing the quality of the fitted model in terms of correlation coefficient and secondary structure preservation.     

High-resolution electron microscopy and electron tomography: resolution versus precision

The performance of high-resolution electron microscopy and electron tomography is usually discussed in terms of two-point resolution, expressing the possibility of perceiving separately two image points of an object. However, the concept resolution obtains another meaning if one uses prior knowledge about the object and the imaging procedure in the form of a parametric model describing the expectations of the observations. The unknown parameters, such as the positions of the components in an object, can be measured quantitatively by fitting this model to the observations. Due to the statistical nature of the experiment, the resulting solutions for the positions of the components and therefore for the distance between the components will never be exact. An alternative to resolution is then the precision with which the distance can be measured. In the present paper, it is shown that the precision depends on the size of the components, the distance between the components, the resolution of the instrument, and the number of electron counts. For electron tomography, it also depends on the orientation of the object with respect to the rotation axis.     

A neuron-inspired computational architecture for spatiotemporal visual processing

In this article, we present a neurologically motivated computational architecture for visual information processing. The computational architecture’s focus lies in multiple strategies: hierarchical processing, parallel and concurrent processing, and modularity. The architecture is modular and expandable in both hardware and software, so that it can also cope with multisensory integrations – making it an ideal tool for validating and applying computational neuroscience models in real time under real-world conditions. We apply our architecture in real time to validate a long-standing biologically inspired visual object recognition model, HMAX. In this context, the overall aim is to supply a humanoid robot with the ability to perceive and understand its environment with a focus on the active aspect of real-time spatiotemporal visual processing. We show that our approach is capable of simulating information processing in the visual cortex in real time and that our entropy-adaptive modification of HMAX has a higher efficiency and classification performance than the standard model (up to \(\sim \!+6\,\% \) ).     

Robust Bayesian Fluorescence Lifetime Estimation,Decay Model Selection and Instrument Response Determination for Low-Intensity FLIM Imaging

We present novel Bayesian methods for the analysis of exponential decay data that exploit the evidence carried by every detected decay event and enables robust extension to advanced processing. Our algorithms are presented in the context of fluorescence lifetime imaging microscopy (FLIM) and particular attention has been paid to model the time-domain system (based on time-correlated single photon counting) with unprecedented accuracy. We present estimates of decay parameters for mono- and bi-exponential systems, offering up to a factor of two improvement in accuracy compared to previous popular techniques. Results of the analysis of synthetic and experimental data are presented, and areas where the superior precision of our techniques can be exploited in Förster Resonance Energy Transfer (FRET) experiments are described. Furthermore, we demonstrate two advanced processing methods: decay model selection to choose between differing models such as mono- and bi-exponential, and the simultaneous estimation of instrument and decay parameters.     

A Microfluidic Platform for Correlative Live-Cell and Super-Resolution Microscopy

Recently, super-resolution microscopy methods such as stochastic optical reconstruction microscopy (STORM) have enabled visualization of subcellular structures below the optical resolution limit. Due to the poor temporal resolution, however, these methods have mostly been used to image fixed cells or dynamic processes that evolve on slow time-scales. In particular, fast dynamic processes and their relationship to the underlying ultrastructure or nanoscale protein organization cannot be discerned. To overcome this limitation, we have recently developed a correlative and sequential imaging method that combines live-cell and super-resolution microscopy. This approach adds dynamic background to ultrastructural images providing a new dimension to the interpretation of super-resolution data. However, currently, it suffers from the need to carry out tedious steps of sample preparation manually. To alleviate this problem, we implemented a simple and versatile microfluidic platform that streamlines the sample preparation steps in between live-cell and super-resolution imaging. The platform is based on a microfluidic chip with parallel, miniaturized imaging chambers and an automated fluid-injection device, which delivers a precise amount of a specified reagent to the selected imaging chamber at a specific time within the experiment. We demonstrate that this system can be used for live-cell imaging, automated fixation, and immunostaining of adherent mammalian cells in situ followed by STORM imaging. We further demonstrate an application by correlating mitochondrial dynamics, morphology, and nanoscale mitochondrial protein distribution in live and super-resolution images.     

Using enhanced sampling and structural restraints to refine atomic structures into low-resolution electron microscopy maps

For a variety of problems in structural biology, low-resolution maps generated by electron microscopy imaging are often interpreted with the help of various flexible-fitting computational algorithms. In this work, we systematically analyze the quality of final models of various proteins obtained via molecular dynamics flexible fitting (MDFF) by varying the map-resolution, strength of structural restraints, and the steering forces. We find that MDFF can be extended to understand conformational changes in lower-resolution maps if larger structural restraints and lower steering forces are used to prevent overfitting. We further show that the capabilities of MDFF can be extended by combining it with an enhanced conformational sampling method, temperature-accelerated molecular dynamics (TAMD). Specifically, either TAMD can be used to generate better starting configurations for MDFF fitting or TAMD-assisted MDFF (TAMDFF) can be performed to accelerate conformational search in atomistic simulations.     

Nonlinear‐optical stain‐free stereoimaging of astrocytes and gliovascular interfaces

Methods of nonlinear optics provide a vast arsenal of tools for label‐free brain imaging, offering a unique combination of chemical specificity, the ability to detect fine morphological features, and an unprecedentedly high, subdiffraction spatial resolution. While these techniques provide a rapidly growing platform for the microscopy of neurons and fine intraneural structures, optical imaging of astroglia still largely relies on filament‐protein‐antibody staining, subject to limitations and difficulties especially severe in live‐brain studies. Once viewed as an ancillary, inert brain scaffold, astroglia are being promoted, as a part of an ongoing paradigm shift in neurosciences, into the role of a key active agent of intercellular communication and information processing, playing a significant role in brain functioning under normal and pathological conditions. Here, we show that methods of nonlinear optics provide a unique resource to address long‐standing challenges in label‐free astroglia imaging. We demonstrate that, with a suitable beam‐focusing geometry and careful driver‐pulse compression, microscopy of second‐harmonic generation (SHG) can enable a high‐resolution label‐free imaging of fibrillar structures of astrocytes, most notably astrocyte processes and their endfeet. SHG microscopy of astrocytes is integrated in our approach with nonlinear‐optical imaging of red blood cells based on third‐harmonic generation (THG) enhanced by a three‐photon resonance with the Soret band of hemoglobin. With astroglia and red blood cells providing two physically distinct imaging contrasts in SHG and THG channels, a parallel detection of the second and third harmonics enables a high‐contrast, high‐resolution, stain‐free stereoimaging of gliovascular interfaces in the central nervous system. Transverse scans of the second and third harmonics are shown to resolve an ultrafine texture of blood‐vessel walls and astrocyte‐process endfeet on gliovascular interfaces with a spatial resolution within 1 μm at focusing depths up to 20 μm inside a brain.     

Correlative 3D imaging of whole mammalian cells with light and electron microscopy

We report methodological advances that extend the current capabilities of ion-abrasion scanning electron microscopy (IA-SEM), also known as focused ion beam scanning electron microscopy, a newly emerging technology for high resolution imaging of large biological specimens in 3D. We establish protocols that enable the routine generation of 3D image stacks of entire plastic-embedded mammalian cells by IA-SEM at resolutions of ∼10–20 nm at high contrast and with minimal artifacts from the focused ion beam. We build on these advances by describing a detailed approach for carrying out correlative live confocal microscopy and IA-SEM on the same cells. Finally, we demonstrate that by combining correlative imaging with newly developed tools for automated image processing, small 100 nm-sized entities such as HIV-1 or gold beads can be localized in SEM image stacks of whole mammalian cells. We anticipate that these methods will add to the arsenal of tools available for investigating mechanisms underlying host-pathogen interactions, and more generally, the 3D subcellular architecture of mammalian cells and tissues.     

冷冻聚焦离子束-扫描电镜成像技术研究进展

冷冻聚焦离子束-扫描电镜成像(Cryo-FIB-SEM)是一种新兴的成像检测技术,在原位进行冷冻聚焦离子束切割和冷冻扫描电镜成像,为研究天然含水状态下生物样品内部未被破坏的原始结构打开了一扇窗口。近年来,该技术在生命科学领域的应用研究取得了一系列重要进展。该文对其在冷冻体积连续成像、冷冻光电关联成像、冷冻透射扫描成像、冷冻含水切片制备监控及冷冻扫描图像处理等方面的研究进展进行综述,并展望了该技术在大体积生物样品三维原位成像研究领域的前沿性发展趋势,以期推动Cryo-FIB-SEM技术在生物样品三维结构研究中的应用。