Statistical Analysis of Lateral Diffusion and Multistate Kinetics in Single-Molecule Imaging

Single-molecule trajectories of molecules on the membrane of living cells have indicated the possibility that the lateral mobility of individual molecules is variable with time. Such temporal variation in mobility may indicate intrinsic kinetics of multiple molecular states. To clarify the mechanisms of signal processing on the membrane, quantitative characterizations of such temporal variations are necessary. Here we propose a method to analyze and characterize the multiple states in lateral mobility and their transition kinetics from single-molecule trajectories based on a displacement probability density function and an autocorrelation function of squared displacements. We performed our method for three cases: a molecule with a single diffusion coefficient (D), a mixture of molecules in two states with different D-values, and a molecule switching between two states with different D-values. Our analysis of numerically generated trajectories successfully distinguished the three cases and estimated the characteristic parameters for mobility and the kinetics of state transitions. This method is applicable to single-molecule tracking analysis of molecules in multiple functional states with different lateral mobility on the membrane of living cells.     

Statistical Analysis of Motor Unit Firing Patterns in a Human Skeletal Muscle

A statistical analysis of the firing pattern of single motor units in the human brachial biceps muscle is presented. Single motor unit spike trains are recorded and analyzed. The statistical treatment of these spike trains is as stochastic point processes, the theory of which is briefly discussed. Evidence is presented that motor unit spike trains may be modelled by a renewal process with an underlying gaussian probability density. Statistical independence of successive interspike intervals is shown using scatter diagrams; the hypothesis of a gaussian distribution is accepted at the 99th percentile confidence limit, chi-square test, in 90% of the units tested. A functional relationship between the mean and standard deviation is shown and discussed; its implications in obtaining sample size are presented in an appendix.The results of higher order analysis in the form of autocorrelograms and grouped interval histograms are presented. Grouped interval histograms are discussed in the context of motor unit data, and used to confirm the hypothesis that a stable probability density function does not represent a good model of the data at this level of analysis.     

Statistical Patterns in Movie Rating Behavior

Currently, users and consumers can review and rate products through online services, which provide huge databases that can be used to explore people’s preferences and unveil behavioral patterns. In this work, we investigate patterns in movie ratings, considering IMDb (the Internet Movie Database), a highly visited site worldwide, as a source. We find that the distribution of votes presents scale-free behavior over several orders of magnitude, with an exponent very close to 3/2, with exponential cutoff. It is remarkable that this pattern emerges independently of movie attributes such as average rating, age and genre, with the exception of a few genres and of high-budget films. These results point to a very general underlying mechanism for the propagation of adoption across potential audiences that is independent of the intrinsic features of a movie and that can be understood through a simple spreading model with mean-field avalanche dynamics.     

Modeling and Statistical Analysis of the Spatio-Temporal Patterns of Seasonal Influenza in Israel

Background

Seasonal influenza outbreaks are a serious burden for public health worldwide and cause morbidity to millions of people each year. In the temperate zone influenza is predominantly seasonal, with epidemics occurring every winter, but the severity of the outbreaks vary substantially between years. In this study we used a highly detailed database, which gave us both temporal and spatial information of influenza dynamics in Israel in the years 1998–2009. We use a discrete-time stochastic epidemic SIR model to find estimates and credible confidence intervals of key epidemiological parameters.

Findings

Despite the biological complexity of the disease we found that a simple SIR-type model can be fitted successfully to the seasonal influenza data. This was true at both the national levels and at the scale of single cities.The effective reproductive number R_e varies between the different years both nationally and among Israeli cities. However, we did not find differences in R_e between different Israeli cities within a year. R_e was positively correlated to the strength of the spatial synchronization in Israel. For those years in which the disease was more “infectious”, then outbreaks in different cities tended to occur with smaller time lags. Our spatial analysis demonstrates that both the timing and the strength of the outbreak within a year are highly synchronized between the Israeli cities. We extend the spatial analysis to demonstrate the existence of high synchrony between Israeli and French influenza outbreaks.

Conclusions

The data analysis combined with mathematical modeling provided a better understanding of the spatio-temporal and synchronization dynamics of influenza in Israel and between Israel and France. Altogether, we show that despite major differences in demography and weather conditions intra-annual influenza epidemics are tightly synchronized in both their timing and magnitude, while they may vary greatly between years. The predominance of a similar main strain of influenza, combined with population mixing serve to enhance local and global influenza synchronization within an influenza season.     

Comparing Analysis Methods in Functional Calcium Imaging of the Insect Brain

We investigate four different methods for background estimation in calcium imaging of the insect brain and evaluate their performance on six data sets consisting of data recorded from two sites in two species of moths. The calcium fluorescence decay curve outside the potential response is estimated using either a low-pass filter or constant, linear or polynomial regression, and is subsequently used to calculate the magnitude, latency and duration of the response. The magnitude and variance of the responses that are obtained by the different methods are compared, and, by computing the receiver operating characteristics of a classifier based on response magnitude, we evaluate the ability of each method to detect the stimulus type and conclude that a polynomial approximation of the background gives the overall best result.     

Fine-Scale Dissection of Functional Protein Network Organization by Statistical Network Analysis

Revealing organizational principles of biological networks is an important goal of systems biology. In this study, we sought to analyze the dynamic organizational principles within the protein interaction network by studying the characteristics of individual neighborhoods of proteins within the network based on their gene expression as well as protein-protein interaction patterns. By clustering proteins into distinct groups based on their neighborhood gene expression characteristics, we identify several significant trends in the dynamic organization of the protein interaction network. We show that proteins with distinct neighborhood gene expression characteristics are positioned in specific localities in the protein interaction network thereby playing specific roles in the dynamic network connectivity. Remarkably, our analysis reveals a neighborhood characteristic that corresponds to the most centrally located group of proteins within the network. Further, we show that the connectivity pattern displayed by this group is consistent with the notion of “rich club connectivity” in complex networks. Importantly, our findings are largely reproducible in networks constructed using independent and different datasets.     

Iterative Cross-Correlation Analysis of Resting State Functional Magnetic Resonance Imaging Data

Seed-based cross-correlation analysis (sCCA) and independent component analysis have been widely employed to extract functional networks from the resting state functional magnetic resonance imaging data. However, the results of sCCA, in terms of both connectivity strength and network topology, can be sensitive to seed selection variations. ICA avoids the potential problems due to seed selection, but choosing which component(s) to represent the network of interest could be subjective and problematic. In this study, we proposed a seed-based iterative cross-correlation analysis (siCCA) method for resting state brain network analysis. The method was applied to extract default mode network (DMN) and stable task control network (STCN) in two independent datasets acquired from normal adults. Compared with the networks obtained by traditional sCCA and ICA, the resting state networks produced by siCCA were found to be highly stable and independent on seed selection. siCCA was used to analyze DMN in first-episode major depressive disorder (MDD) patients. It was found that, in the MDD patients, the volume of DMN negatively correlated with the patients'' social disability screening schedule scores.     

核磁功能成像在前列腺癌诊断中的应用

磁共振成像是诊断早期前列腺癌及评价分期最好的影像学技术之一,然而常规MRI-T2WI在诊断中存在较低的特异性缺陷.随着核磁技术的发展,对前列腺癌的诊断发展到从定性到定量、从形态到功能的变化,本文主要就近年来的磁共振功能成像技术在前列腺癌诊断中的研究进展作一论述.     

Statistical Significance of Precisely Repeated Intracellular Synaptic Patterns

Can neuronal networks produce patterns of activity with millisecond accuracy? It may seem unlikely, considering the probabilistic nature of synaptic transmission. However, some theories of brain function predict that such precision is feasible and can emerge from the non-linearity of the action potential generation in circuits of connected neurons. Several studies have presented evidence for and against this hypothesis. Our earlier work supported the precision hypothesis, based on results demonstrating that precise patterns of synaptic inputs could be found in intracellular recordings from neurons in brain slices and in vivo. To test this hypothesis, we devised a method for finding precise repeats of activity and compared repeats found in the data to those found in surrogate datasets made by shuffling the original data. Because more repeats were found in the original data than in the surrogate data sets, we argued that repeats were not due to chance occurrence. Mokeichev et al. (2007) challenged these conclusions, arguing that the generation of surrogate data was insufficiently rigorous. We have now reanalyzed our previous data with the methods introduced from Mokeichev et al. (2007). Our reanalysis reveals that repeats are statistically significant, thus supporting our earlier conclusions, while also supporting many conclusions that Mokeichev et al. (2007) drew from their recent in vivo recordings. Moreover, we also show that the conditions under which the membrane potential is recorded contributes significantly to the ability to detect repeats and may explain conflicting results. In conclusion, our reevaluation resolves the methodological contradictions between Ikegaya et al. (2004) and Mokeichev et al. (2007), but demonstrates the validity of our previous conclusion that spontaneous network activity is non-randomly organized.     

Convex-Envelope Based Automated Quantitative Approach to Multi-Voxel 1H-MRS Applied to Brain Tumor Analysis

Background

Magnetic Resonance Spectroscopy (MRS) can measure in vivo brain tissue metabolism that exhibits unique biochemical characteristics in brain tumors. For clinical application, an efficient and versatile quantification method of MRS would be an important tool for medical research, particularly for exploring the scientific problem of tumor monitoring. The objective of our study is to propose an automated MRS quantitative approach and assess the feasibility of this approach for glioma grading, prognosis and boundary detection.

Methods

An automated quantitative approach based on a convex envelope (AQoCE) is proposed in this paper, including preprocessing, convex-envelope based baseline fitting, bias correction, sectional baseline removal, and peak detection, in a total of 5 steps. Some metabolic ratios acquired by this quantification are selected for statistical analysis. An independent sample t-test and the Kruskal-Wallis test are used for distinguishing low-grade gliomas (LGG) and high-grade gliomas (HGG) and for detecting the tumor, peritumoral and contralateral areas, respectively. Seventy-eight cases of pre-operative brain gliomas with pathological reports are included in this study.

Results

Cho/NAA, Cho/Cr and Lip-Lac/Cr (LL/Cr) calculated by AQoCE in the tumor area differ significantly between LGG and HGG, with p≤0.005. Using logistic regression combining Cho/NAA, Cho/Cr and LL/Cr to generate a ROC curve, AQoCE achieves a sensitivity of 92.9%, a specificity of 72.2%, and an area under ROC curve (AUC) of 0.860. Moreover, both Cho/NAA and Cho/Cr in the AQoCE approach show a significant difference (p≤0.019) between tumoral, peritumoral, and contralateral areas. The comparison between the results of AQoCE and Siemens MRS processing software are also discussed in this paper.

Conclusions

The AQoCE approach is an automated method of residual water removal and metabolite quantification. It can be applied to multi-voxel ¹H-MRS for evaluating brain glioma grading and demonstrating characteristics of brain glioma metabolism. It can also detect infiltration in the peritumoral area. Under the limited clinical data used, AQoCE is significantly more versatile and efficient compared to the reference approach of Siemens.     

Functional Lung Imaging during HFV in Preterm Rabbits

Although high frequency ventilation (HFV) is an effective mode of ventilation, there is limited information available in regard to lung dynamics during HFV. To improve the knowledge of lung function during HFV we have developed a novel lung imaging and analysis technique. The technique can determine complex lung motion information in vivo with a temporal resolution capable of observing HFV dynamics. Using high-speed synchrotron based phase contrast X-ray imaging and cross-correlation analysis, this method is capable of recording data in more than 60 independent regions across a preterm rabbit lung in excess of 300 frames per second (fps). This technique is utilised to determine regional intra-breath lung mechanics of preterm rabbit pups during HFV. Whilst ventilated at fixed pressures, each animal was ventilated at frequencies of 1, 3, 5 and 10 Hz. A 50% decrease in delivered tidal volume was measured at 10 Hz compared to 1 Hz, yet at the higher frequency a 500% increase in minute activity was measured. Additionally, HFV induced greater homogeneity of lung expansion activity suggesting this ventilation strategy potentially minimizes tissue damage and improves gas mixing. The development of this technique permits greater insight and further research into lung mechanics and may have implications for the improvement of ventilation strategies used to support severe pulmonary trauma and disease.     

Wastewater-Based Epidemiology of Stimulant Drugs: Functional Data Analysis Compared to Traditional Statistical Methods

Background

Wastewater-based epidemiology (WBE) is a new methodology for estimating the drug load in a population. Simple summary statistics and specification tests have typically been used to analyze WBE data, comparing differences between weekday and weekend loads. Such standard statistical methods may, however, overlook important nuanced information in the data. In this study, we apply functional data analysis (FDA) to WBE data and compare the results to those obtained from more traditional summary measures.

Methods

We analysed temporal WBE data from 42 European cities, using sewage samples collected daily for one week in March 2013. For each city, the main temporal features of two selected drugs were extracted using functional principal component (FPC) analysis, along with simpler measures such as the area under the curve (AUC). The individual cities’ scores on each of the temporal FPCs were then used as outcome variables in multiple linear regression analysis with various city and country characteristics as predictors. The results were compared to those of functional analysis of variance (FANOVA).

Results

The three first FPCs explained more than 99% of the temporal variation. The first component (FPC1) represented the level of the drug load, while the second and third temporal components represented the level and the timing of a weekend peak. AUC was highly correlated with FPC1, but other temporal characteristic were not captured by the simple summary measures. FANOVA was less flexible than the FPCA-based regression, and even showed concordance results. Geographical location was the main predictor for the general level of the drug load.

Conclusion

FDA of WBE data extracts more detailed information about drug load patterns during the week which are not identified by more traditional statistical methods. Results also suggest that regression based on FPC results is a valuable addition to FANOVA for estimating associations between temporal patterns and covariate information.     

Functional Imaging of Mitochondria in Saponin-permeabilized Mice Muscle Fibers

Confocal laser-scanning and digital fluorescence imaging microscopy were used to quantify the mitochondrial autofluorescence changes of NAD(P)H and flavoproteins in unfixed saponin-permeabilized myofibers from mice quadriceps muscle tissue. Addition of mitochondrial substrates, ADP, or cyanide led to redox state changes of the mitochondrial NAD system. These changes were detected by ratio imaging of the autofluorescence intensities of fluorescent flavoproteins and NAD(P)H, showing inverse fluorescence behavior. The flavoprotein signal was colocalized with the potentiometric mitochondria-specific dye dimethylaminostyryl pyridyl methyl iodide (DASPMI), or with MitoTracker™ Green FM, a constitutive marker for mitochondria. Within individual myofibers we detected topological mitochondrial subsets with distinct flavoprotein autofluorescence levels, equally responding to induced rate changes of the oxidative phosphorylation. The flavoprotein autofluorescence levels of these subsets differed by a factor of four. This heterogeneity was substantiated by flow-cytometric analysis of flavoprotein and DASPMI fluorescence changes of individual mitochondria isolated from mice skeletal muscle. Our data provide direct evidence that mitochondria in single myofibers are distinct subsets at the level of an intrinsic fluorescent marker of the mitochondrial NAD–redox system. Under the present experimental conditions these subsets show similar functional responses.     

Functional Imaging of Rel Expression in Inflammatory Processes Using Bioluminescence Imaging System in Transgenic Mice

c-Rel plays important roles in many inflammatory diseases. Revealing the dynamic expression of c-Rel in disease processes in vivo is critical for understanding c-Rel functions and for developing anti-inflammatory drugs. In this paper, a transgenic mouse line, B6-Tg(c-Rel-luc)^Mlit, which incorporated the transgene firefly luciferase driven by a 14.5-kb fragment containing mouse c-Rel gene Rel promoter, was generated to monitor Rel expression in vivo. Luciferase expression could be tracked in living mice by the method of bioluminescence imaging in a variety of inflammatory processes, including LPS induced sepsis and EAE disease model. The luciferase expression in transgenic mice was comparable to the endogenous Rel expression and could be suppressed by administration of anti-inflammatory drug dexamethasone or aspirin. These results indicate that the B6-Tg(c-Rel-luc)^Mlit mouse is a valuable animal model to study Rel expression in physiological and pathological processes, and the effects of various drug treatments in vivo.     

Statistical Analysis Reveals Co-Expression Patterns of Many Pairs of Genes in Yeast Are Jointly Regulated by Interacting Loci

Expression quantitative trait loci (eQTL) studies have generated large amounts of data in different organisms. The analyses of these data have led to many novel findings and biological insights on expression regulations. However, the role of epistasis in the joint regulation of multiple genes has not been explored. This is largely due to the computational complexity involved when multiple traits are simultaneously considered against multiple markers if an exhaustive search strategy is adopted. In this article, we propose a computationally feasible approach to identify pairs of chromosomal regions that interact to regulate co-expression patterns of pairs of genes. Our approach is built on a bivariate model whose covariance matrix depends on the joint genotypes at the candidate loci. We also propose a filtering process to reduce the computational burden. When we applied our method to a yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 225 and 224 modules, with each module consisting of two genes and two eQTLs where the two eQTLs epistatically regulate the co-expression patterns of the two genes. We found that many of these modules have biological interpretations. Under the glucose condition, ribosome biogenesis was co-regulated with the signaling and carbohydrate catabolic processes, whereas silencing and aging related genes were co-regulated under the ethanol condition with the eQTLs containing genes involved in oxidative stress response process.     

Functional Gene Hybridization Patterns of Terrestrial Ammonia-Oxidizing Bacteria

Abstract The biochemical pathway and genetics of autotrophic ammonia oxidation have been studied almost exclusively in Nitrosomonas europaea. Terrestrial autotrophic ammonia-oxidizing bacteria (AAOs), however, comprise two distinct phylogenetic groups in the beta-Proteobacteria, the Nitrosomonas and Nitrosospira groups. Hybridization patterns were used to assess the potential of functional probes in non-PCR-based molecular analysis of natural AAO populations and their activity. The objective of this study was to obtain an overview of functional gene homologies by hybridizing probes derived from N. europaea gene sequences ranging in size from 0.45 to 4.5 kb, and labeled with 32P to Southern blots containing genomic DNA from four Nitrosospira representatives. Probes were specific for genes encoding ammonia monooxygenase (amoA and amoB), hydroxylamine oxidoreductase (hao), and cytochrome c-554 (hcy). These probes produced hybridization signals, at low stringency (30 degreesC), with DNA from each of the four representatives; signals at higher stringency (42 degreesC) were greatly reduced or absent. The hybridization signals at low stringency ranged from 20 to 76% of the total signal obtained with N. europaea DNA. These results indicate that all four functional genes in the ammonia oxidation pathway have diverged between the Nitrosomonas and Nitrosospira groups. The hao probe produced the most consistent hybridization intensities among the Nitrosospira representatives, suggesting that hao sequences would provide the best probes for non-PCR-based molecular analysis of terrestrial AAOs. Since N. europaea can also denitrify, an additional objective was to hybridize genomic DNA from AAOs with probes for Pseudomonas genes involved in denitrification. These probes were specific for genes encoding heme-type dissimilatory nitrite reductase (dNir), Cu-type dNir, and nitrous oxide reductase (nosz). No hybridization signals were observed from probes for the heme-type dNir or nosz, but Nitrosospira sp. NpAV and Nitrosolobus sp. 24-C hybridized, under low-stringency conditions, with the Cu-type dNir probe. These results indicate that AAOs may also differ in their mechanisms and capacities for denitrification.