Multi-detector fusion and Bayesian smoothing for tracking viral and chromatin structures.

Automatic tracking of viral and intracellular structures displayed as spots with varying sizes in fluorescence microscopy images is an important task to quantify cellular processes. We propose a novel probabilistic tracking approach for multiple particle tracking based on multi-detector and multi-scale data fusion as well as Bayesian smoothing. The approach integrates results from multiple detectors using a novel intensity-based covariance intersection method which takes into account information about the image intensities, positions, and uncertainties. The method ensures a consistent estimate of multiple fused particle detections and does not require an optimization step. Our probabilistic tracking approach performs data fusion of detections from classical and deep learning methods as well as exploits single-scale and multi-scale detections. In addition, we use Bayesian smoothing to fuse information of predictions from both past and future time points. We evaluated our approach using image data of the Particle Tracking Challenge and achieved state-of-the-art results or outperformed previous methods. Our method was also assessed on challenging live cell fluorescence microscopy image data of viral and cellular proteins expressed in hepatitis C virus-infected cells and chromatin structures in non-infected cells, acquired at different spatial-temporal resolutions. We found that the proposed approach outperforms existing methods.

Data fusion and smoothing for probabilistic tracking of viral structures in fluorescence microscopy images.

Automatic tracking of viral structures displayed as small spots in fluorescence microscopy images is an important task to determine quantitative information about cellular processes. We introduce a novel probabilistic approach for tracking multiple particles based on multi-sensor data fusion and Bayesian smoothing methods. The approach exploits multiple measurements as in a particle filter, both detection-based measurements and prediction-based measurements from a Kalman filter using probabilistic data association with elliptical sampling. Compared to previous probabilistic tracking methods, our approach exploits separate uncertainties for the detection-based and prediction-based measurements, and integrates them by a sequential multi-sensor data fusion method. In addition, information from both past and future time points is taken into account by a Bayesian smoothing method in conjunction with the covariance intersection algorithm for data fusion. Also, motion information based on displacements is used to improve correspondence finding. Our approach has been evaluated on data of the Particle Tracking Challenge and yielded state-of-the-art results or outperformed previous approaches. We also applied our approach to challenging time-lapse fluorescence microscopy data of human immunodeficiency virus type 1 and hepatitis C virus proteins acquired with different types of microscopes and spatial-temporal resolutions. It turned out, that our approach outperforms existing methods.

Globally optimal segmentation of cell nuclei in fluorescence microscopy images using shape and intensity information.

Accurate and efficient segmentation of cell nuclei in fluorescence microscopy images plays a key role in many biological studies. Besides coping with image noise and other imaging artifacts, the separation of touching and partially overlapping cell nuclei is a major challenge. To address this, we introduce a globally optimal model-based approach for cell nuclei segmentation which jointly exploits shape and intensity information. Our approach is based on implicitly parameterized shape models, and we propose single-object and multi-object schemes. In the single-object case, the used shape parameterization leads to convex energies which can be directly minimized without requiring approximation. The multi-object scheme is based on multiple collaborating shapes and has the advantage that prior detection of individual cell nuclei is not needed. This scheme performs joint segmentation and cluster splitting. We describe an energy minimization scheme which converges close to global optima and exploits convex optimization such that our approach does not depend on the initialization nor suffers from local energy minima. The proposed approach is robust and computationally efficient. In contrast, previous shape-based approaches for cell segmentation either are computationally expensive, not globally optimal, or do not jointly exploit shape and intensity information. We successfully applied our approach to fluorescence microscopy images of five different cell types and performed a quantitative comparison with previous methods.

GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation.

Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks. In this work, we propose a new deep learning method for cell segmentation, which integrates convolutional neural networks and gated recurrent neural networks over multiple image scales to exploit the strength of both types of networks. To increase the robustness of the training and improve segmentation, we introduce a novel focal loss function. We also present a distributed scheme for optimized training of the integrated neural network. We applied our proposed method to challenging data of glioblastoma cell nuclei and performed a quantitative comparison with state-of-the-art methods. Insights on how our extensions affect training and inference are also provided. Moreover, we benchmarked our method using a wide spectrum of all 22 real microscopy datasets of the Cell Tracking Challenge.

Personalized stent design for congenital heart defects using pulsatile blood flow simulations.

Stent size selection and placement are among the most challenging tasks in the treatment of pulmonary artery stenosis in congenital heart defects (CHD). Patient-specific 3D model from CT or MR improves the understanding of the patient's anatomy and information about the hemodynamics aid in patient risk assessment and treatment planning. This work presents a new approach for personalized stent design in pulmonary artery interventions combining personalized patient geometry and hemodynamic simulations. First, the stent position is initialized using a geometric approach. Second, the stent and artery expansion, including the foreshortening behavior of the stent is simulated. Two stent designs are considered, a regular stent and a Y-stent for bifurcations. Computational fluid dynamics (CFD) simulations of the blood flow in the initial and expanded artery models are performed using patient-specific boundary conditions in form of a pulsatile inflow waveform, 3-element Windkessel outflow conditions, and deformable vessel walls. The simulations have been applied to 16 patient cases with a large variability of anatomies. Finally, the simulations have been clinically validated using retrospective imaging from angiography and pressure measurements. The simulated pressure, volume flow and flow velocity values were on the same order of magnitude as the reference values obtained from clinical measurements, and the simulated stent placement showed a positive impact on the hemodynamic values. Simulation of geometric changes combined with CFD simulations offers the possibility to optimize stent type, size, and position by evaluating different configurations before the intervention, and eventually allow to test customized stent geometries and new deployment techniques in CHD.

Sub-millisecond (125)Te NMR spin-lattice relaxation times and large Knight shifts in complex tellurides: Validation of a quadratic relation across the spectrum.

(125)Te NMR spectra and spin-lattice relaxation times, T1, have been measured for several GeTe-based materials with Te excess. The spectra show inhomogeneous broadening by several thousand ppm and a systematic variation in T1 relaxation time with resonance frequency. The quadratic dependence of the spin-lattice relaxation rate, 1/T1, on the Knight shift in the Korringa relation is found to be valid over a wide range of Knight shifts. This result confirms that T1 relaxation in GeTe-based materials is mostly dominated by hyperfine interaction between nuclei and free charge carriers. In GeTe with 2.5% excess of Te, about 15% of the material exhibits a Knight shift of ≥4500ppm and a T1 of only 0.3ms, indicating a high hole concentration that could correspond to close to 50% vacancies on the Ge sublattice in this component. Our findings provide a basis for determining the charge carrier concentration and its distribution in complex thermoelectric and phase-change tellurides, which should lead to a better understanding of electronic and thermal transport properties as well as chemical bonding in these materials.

Targeting mitosis-regulating genes in cisplatin-sensitive and -resistant melanoma cells: A live-cell RNAi screen displays differential nucleus-derived phenotypes.

Chemoresistance in malignant melanoma remains an unresolved clinical issue. In the search for novel molecular targets, a live-cell high-content RNAi screen based on gene expression data was performed in cisplatin-sensitive and cisplatin-resistant MeWo melanoma cells, Mel-28 cells and a melanocyte cell line. Cells were exposed to 91 siRNAs and distinct nucleus-derived phenotypes such as cell division, cell death and migration phenotypes were detected by time-lapse microscopy over 60 h. Using this approach, cisplatin-sensitive and cisplatin-resistant melanoma cells were compared by automated image analysis and visual inspection. In cisplatin-sensitive MeWo melanoma cells, 14 genes were identified that showed distinct phenotype abnormalities after exposure to gene-specific siRNAs. In cisplatin-resistant MeWo cells, five genes were detected. Nine genes were detected whose knock-down led to differential nuclear phenotypes in cisplatin-sensitive and -resistant cells. In Mel-28 cells, nine genes were identified which induced nuclear phenotypes including all eight genes which were identified in cisplatin-resistant MeWo cells. An analogous RNAi screen on melanocytes revealed no detectable phenotype abnormalities after RNAi. Pathway analysis showed in cisplatin-sensitive MeWo cells and Mel-28 cells an enrichment of at least three genes in major mitotic pathways. We hereby show that siRNA screening may help to identify tumor-specific genes leading to phenotype abnormalities. These genes may serve as potential therapeutic targets in the treatment of melanoma.

125Te NMR chemical-shift trends in PbTe-GeTe and PbTe-SnTe alloys.

Complex tellurides, such as doped PbTe, GeTe, and their alloys, are among the best thermoelectric materials. Knowledge of the change in (125)Te NMR chemical shift due to bonding to dopant or "solute" atoms is useful for determination of phase composition, peak assignment, and analysis of local bonding. We have measured the (125)Te NMR chemical shifts in PbTe-based alloys, Pb1-xGexTe and Pb1-xSnxTe, which have a rocksalt-like structure, and analyzed their trends. For low x, several peaks are resolved in the 22-kHz MAS (125)Te NMR spectra. A simple linear trend in chemical shifts with the number of Pb neighbors is observed. No evidence of a proposed ferroelectric displacement of Ge atoms in a cubic PbTe matrix is detected at low Ge concentrations. The observed chemical shift trends are compared with the results of DFT calculations, which confirm the linear dependence on the composition of the first-neighbor shell. The data enable determination of the composition of various phases in multiphase telluride materials. They also provide estimates of the (125)Te chemical shifts of GeTe and SnTe (+970 and +400±150 ppm, respectively, from PbTe), which are otherwise difficult to access due to Knight shifts of many hundreds of ppm in neat GeTe and SnTe.

Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information.

We introduce a Python-based program that utilizes the large database of (13)C and (15)N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D (13)C-(13)C, (15)N-(13)C, or 3D (15)N-(13)C-(13)C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D (13)C-(13)C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the Cα and Cß chemical shifts, the highest-ranked PLUQ assignments were 40-60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO-Cα-Cß or N-Cα-Cß), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.

Spectral editing of two-dimensional magic-angle-spinning solid-state NMR spectra for protein resonance assignment and structure determination.

Several techniques for spectral editing of 2D (13)C-(13)C correlation NMR of proteins are introduced. They greatly reduce the spectral overlap for five common amino acid types, thus simplifying spectral assignment and conformational analysis. The carboxyl (COO) signals of glutamate and aspartate are selected by suppressing the overlapping amide N-CO peaks through (13)C-(15)N dipolar dephasing. The sidechain methine (CH) signals of valine, lecuine, and isoleucine are separated from the overlapping methylene (CH(2)) signals of long-chain amino acids using a multiple-quantum dipolar transfer technique. Both the COO and CH selection methods take advantage of improved dipolar dephasing by asymmetric rotational-echo double resonance (REDOR), where every other π-pulse is shifted from the center of a rotor period t(r) by about 0.15 t(r). This asymmetry produces a deeper minimum in the REDOR dephasing curve and enables complete suppression of the undesired signals of immobile segments. Residual signals of mobile sidechains are positively identified by dynamics editing using recoupled (13)C-(1)H dipolar dephasing. In all three experiments, the signals of carbons within a three-bond distance from the selected carbons are detected in the second spectral dimension via (13)C spin exchange. The efficiencies of these spectral editing techniques range from 60 % for the COO and dynamic selection experiments to 25 % for the CH selection experiment, and are demonstrated on well-characterized model proteins GB1 and ubiquitin.

Abundant and stable char residues in soils: implications for soil fertility and carbon sequestration.

Large-scale soil application of biochar may enhance soil fertility, increasing crop production for the growing human population, while also sequestering atmospheric carbon. But reaching these beneficial outcomes requires an understanding of the relationships among biochar's structure, stability, and contribution to soil fertility. Using quantitative (13)C nuclear magnetic resonance (NMR) spectroscopy, we show that Terra Preta soils (fertile anthropogenic dark earths in Amazonia that were enriched with char >800 years ago) consist predominantly of char residues composed of ~6 fused aromatic rings substituted by COO(-) groups that significantly increase the soils' cation-exchange capacity and thus the retention of plant nutrients. We also show that highly productive, grassland-derived soils in the U.S. (Mollisols) contain char (generated by presettlement fires) that is structurally comparable to char in the Terra Preta soils and much more abundant than previously thought (~40-50% of organic C). Our findings indicate that these oxidized char residues represent a particularly stable, abundant, and fertility-enhancing form of soil organic matter.

Pressurisation leads to better cement penetration into the glenoid bone: a cadaveric study.

The aim of this study was to compare a third-generation cementing procedure for glenoid components with a new technique for cement pressurisation. In 20 pairs of scapulae, 20 keeled and 20 pegged glenoid components were implanted using either a third-generation cementing technique (group 1) or a new pressuriser (group 2). Cement penetration was measured by three-dimensional (3D) analysis of micro-CT scans. The mean 3D depth of penetration of the cement was significantly greater in group 2 (p < 0.001). The mean thickness of the cement mantle for keeled glenoids was 2.50 mm (2.0 to 3.3) in group 1 and 5.18 mm (4.4 to 6.1) in group 2, and for pegged glenoids it was 1.72 mm (0.9 to 2.3) in group 1 and 5.63 mm (3.6 to 6.4) in group 2. A cement mantle < 2 mm was detected less frequently in group 2 (p < 0.001). Using the cement pressuriser the proportion of cement mantles < 2 mm was significantly reduced compared with the third-generation cementing technique.

Technical aspects of fast magic-angle turning NMR for dilute spin-1/2 nuclei with broad spectra.

For obtaining sideband-free spectra of high-Z spin-1/2 nuclei with large (>1000 ppm) chemical-shift anisotropies and broad isotropic-shift dispersion, we recently identified Gan's modified five-pulse magic-angle turning (MAT) experiment as the best available broadband pulse sequence, and adapted it to fast magic-angle spinning. Here, we discuss technical aspects such as pulse timings that compensate for off-resonance effects and are suitable for large CSAs over a range of 1.8γB(1); methods to minimize the duration of z-periods by cyclic decrementation; shearing without digitization artifacts, by sharing between channels (points); and maximizing the sensitivity by echo-matched full-Gaussian filtering. The method is demonstrated on a model sample of mixed amino acids and its large bandwidth is highlighted by comparison with the multiple-π-pulse PASS technique. Applications to various tellurides are shown; these include GeTe, Sb(2)Te(3) and Ag(0.53)Pb(18)Sb(1.2)Te(20), with spectra spanning up to 190 kHz, at 22 kHz MAS. We have also determined the (125)Te chemical shift anisotropies from the intensities of the spinning sidebands resolved by isotropic-shift separation.

Strongly bound citrate stabilizes the apatite nanocrystals in bone.

Nanocrystals of apatitic calcium phosphate impart the organic-inorganic nanocomposite in bone with favorable mechanical properties. So far, the factors preventing crystal growth beyond the favorable thickness of ca. 3 nm have not been identified. Here we show that the apatite surfaces are studded with strongly bound citrate molecules, whose signals have been identified unambiguously by multinuclear magnetic resonance (NMR) analysis. NMR reveals that bound citrate accounts for 5.5 wt% of the organic matter in bone and covers apatite at a density of about 1 molecule per (2 nm)(2), with its three carboxylate groups at distances of 0.3 to 0.45 nm from the apatite surface. Bound citrate is highly conserved, being found in fish, avian, and mammalian bone, which indicates its critical role in interfering with crystal thickening and stabilizing the apatite nanocrystals in bone.

Geometric alignment of 2D gel electrophoresis images.

OBJECTIVES: 2D gel electrophoresis (2-DE) is the method of choice for analyzing protein expression in the field of proteomics, for example, comparing a reference with a test population. However, due to complex physical and chemical processes the locations of proteins generally vary in different 2-DE images. To cope with these variations, accurate geometric alignment of 2-DE images is important. METHODS: We introduce a new elastic registration approach for 2-DE images, which is based on an analytic solution of the Navier equation using Gaussian elastic body splines (GEBS). With this approach cross-effects in elastic deformations can be handled, which is important for the registration of 2-DE images. In addition, landmark correspondences can be included to aid the registration in regions which are difficult to register using intensity information alone. RESULTS: We have successfully applied our approach to register 2-DE gel images of different levels of complexity. In each case, gel images from a reference group are compared with a test group. To analyze the performance of our approach, we have carried out a quantitative evaluation of the registration results. Moreover, we have performed an experimental comparison with a previous elastic registration scheme. CONCLUSIONS: From the results we found that our approach is well-suited for the registration of 2-DE gel images of different levels of complexity and it turned out that the approach is superior to a previous hybrid scheme. Moreover, our approach is well-suited in a fully automatic setting and the performance can further be improved when landmark correspondences are available.

Effects of L-spin longitudinal quadrupolar relaxation in S[L] heteronuclear recoupling and S-spin magic-angle spinning NMR.

In experiments on S-L heteronuclear spin systems with evolution of the S-spin magnetization under the influence of a quadrupolar nucleus (L-spin), effects of longitudinal quadrupolar (T(1Q)) relaxation of the L-spin coherence on the sub-millisecond time scale have been documented and explored, and methods for minimizing their effect have been demonstrated. The longitudinal relaxation results in heteronuclear dephasing even in the reference signal S(0) of S[L] REDOR, REAPDOR, RIDER, or SPIDER experiments, due to T(1Q)-relaxation of the transiently generated S(y)L(z) coherence, reducing or even eliminating the observable dephasing DeltaS. Pulse sequences for measuring an improved reference signal S(00) with minimal heteronuclear recoupling but the same number of pulses as for S(0) and S have been demonstrated. From the observed intensity DeltaS(0)=S(00)-S(0) and the SPIDER signal DeltaS/S(0), T(1Q) can be estimated. Accelerated decays analogous to the dipolar S(0) curves will occur in T(2) measurements for J-coupled S-L spin pairs. Even in the absence of recoupling pulses, fast T(1Q) relaxation of the unobserved nucleus shortens the transverse relaxation time T(2S,MAS) of the observed nucleus, in particular at low spinning frequencies, due to unavoidable heteronuclear dipolar evolution during a rotation period. The observed spinning-frequency dependence of T(2S,MAS) matches the theoretical prediction and may be used to estimate T(1Q). The effects are demonstrated on several (13)C[(14)N] spin systems, including an arginine derivative, the natural N-acetylated polysaccharide chitin, and a model peptide, (POG)(10).

Deterministic and probabilistic approaches for tracking virus particles in time-lapse fluorescence microscopy image sequences.

Modern developments in time-lapse fluorescence microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore spatial-temporal relationships. In this work, we developed deterministic and probabilistic approaches for multiple virus tracking in multi-channel fluorescence microscopy images. The deterministic approaches follow a traditional two-step paradigm comprising particle localization based on either the spot-enhancing filter or 2D Gaussian fitting, as well as motion correspondence based on a global nearest neighbor scheme. Our probabilistic approaches are based on particle filters. We describe approaches based on a mixture of particle filters and based on independent particle filters. For the latter, we have developed a penalization strategy that prevents the problem of filter coalescence (merging) in cases where objects lie in close proximity. A quantitative comparison based on synthetic image sequences is carried out to evaluate the performance of our approaches. In total, eight different tracking approaches have been evaluated. We have also applied these approaches to real microscopy images of HIV-1 particles and have compared the tracking results with ground truth obtained from manual tracking. It turns out that the probabilistic approaches based on independent particle filters are superior to the deterministic schemes as well as to the approaches based on a mixture of particle filters.

Shape normalization of 3D cell nuclei using elastic spherical mapping.

Topological analysis of cells and subcellular structures on the basis of image data, is one of the major trends in modern quantitative biology. However, due to the dynamic nature of cell biology, the optical appearance of different cells or even time-series of the same cell is undergoing substantial variations in shape and texture, which makes a comparison of shapes and distances across different cells a nontrivial task. In the absence of canonical invariances, a natural approach to the normalization of cells consists of spherical mapping, enabling the analysis of targeted regions in terms of canonical spherical coordinates, that is, radial distances and angles. In this work, we present a physically-based approach to spherical mapping, which has been applied for topological analysis of multichannel confocal laser scanning microscopy images of human fibroblast nuclei. Our experimental results demonstrate that spherical mapping of entire nuclear domains can automatically be obtained by inverting affine and elastic transformations, performed on a spherical finite element template mesh.

Nonrigid registration of 3-d multichannel microscopy images of cell nuclei.

We present an intensity-based nonrigid registration approach for the normalization of 3-D multichannel microscopy images of cell nuclei. A main problem with cell nuclei images is that the intensity structure of different nuclei differs very much; thus, an intensity-based registration scheme cannot be used directly. Instead, we first perform a segmentation of the images from the cell nucleus channel, smooth the resulting images by a Gaussian filter, and then apply an intensity-based registration algorithm. The obtained transformation is applied to the images from the nucleus channel as well as to the images from the other channels. To improve the convergence rate of the algorithm, we propose an adaptive step length optimization scheme and also employ a multiresolution scheme. Our approach has been successfully applied using 2-D cell-like synthetic images, 3-D phantom images as well as 3-D multichannel microscopy images representing different chromosome territories and gene regions. We also describe an extension of our approach, which is applied for the registration of 3D + t (4-D) image series of moving cell nuclei.

Automated classification of mitotic phenotypes of human cells using fluorescent proteins.

High-throughput screens of the gene function provide rapidly increasing amounts of data. In particular, the analysis of image data acquired in genome-wide cell phenotype screens constitutes a substantial bottleneck in the evaluation process and motivates the development of automated image analysis tools for large-scale experiments. In this chapter, we present a computational scheme to process multicell time-lapse images as they are produced in high-throughput screens. We describe an approach to automatically segment and classify cell nuclei into different mitotic phenotypes. This enables automated identification of cell cultures that show an abnormal mitotic behavior. Our scheme proves high classification accuracy, suggesting a promising future for automating the evaluation of high-throughput experiments.