ELECTRON TOMOGRAPHY OF MACROMOLECULAR ASSEMBLIES

Automatic CTF correction for single particles based upon multivariate statistical analysis of individual power spectra

Subtomogram analysis using the Volta phase plate.

Quantifying resolution limiting factors in subtomogram averaged cryo-electron tomography using simulations.

BackgroundThe importance of wildlife disease surveillance is increasing, because wild animals are playing a growing role as sources of emerging infectious disease events in humans. Syndromic surveillance methods have been developed as a complement to traditional health data analyses, to allow the early detection of unusual health events. Early detection of these events in wildlife could help to protect the health of domestic animals or humans. This paper aims to define syndromes that could be used for the syndromic surveillance of wildlife health data. Wildlife disease monitoring in France, from 1986 onward, has allowed numerous diagnostic data to be collected from wild animals found dead. The authors wanted to identify distinct pathological profiles from these historical data by a global analysis of the registered necropsy descriptions, and discuss how these profiles can be used to define syndromes. In view of the multiplicity and heterogeneity of the available information, the authors suggest constructing syndromic classes by a multivariate statistical analysis and classification procedure grouping cases that share similar pathological characteristics.ResultsA three-step procedure was applied: first, a multiple correspondence analysis was performed on necropsy data to reduce them to their principal components. Then hierarchical ascendant clustering was used to partition the data. Finally the k-means algorithm was applied to strengthen the partitioning. Nine clusters were identified: three were species- and disease-specific, three were suggestive of specific pathological conditions but not species-specific, two covered a broader pathological condition and one was miscellaneous. The clusters reflected the most distinct and most frequent disease entities on which the surveillance network focused. They could be used to define distinct syndromes characterised by specific post-mortem findings.ConclusionsThe chosen statistical clustering method was found to be a useful tool to retrospectively group cases from our database into distinct and meaningful pathological entities. Syndrome definition from post-mortem findings is potentially useful for early outbreak detection because it uses the earliest available information on disease in wildlife. Furthermore, the proposed typology allows each case to be attributed to a syndrome, thus enabling the exhaustive surveillance of health events through time series analyses.

3D Structure of a Recombinant DHPR Expressed in Mouse

An Acoustic Emission (AE) signal processing and analysis system is developed for process and condition monitoring of Dense Medium Cyclones (DMCs), key units in coal preparation plants. This paper describes the use of a combination of statistical signal processing, multivariate statistical analysis and pattern classification methods to formulate robust soft-sensor predictive models for DMC process variables and wear state based on passive contact AE signals. Power spectral density and statistical moment ‘feature’ variables are extracted from AE signals. Principal Component Analysis (PCA) is used to evaluate the sensitivity of the AE ‘features’ to process variables and wear state. Principal Component Regression (PCR) and Partial Least Squares (PLS) are used to quantitatively model process variables. Hierarchical clustering is used to qualitatively model wear state and process variables. The AE hardware, signal processing and soft-sensor modelling system can be used for process and condition monitoring of other mineral processing equipment.

As flow cytometric data becomes more complex, it becomes increasingly difficult to classify cells using conventional flow cytometry data techniques based on visual classification of the data by user-drawn regions. This paper shows some simple applications of multivariate statistical classification to classify flow cytometric data. Discriminant Function Analysis (DFA) and Logistic Regression (LR) analysis techniques were evaluated with respect to their potential utility in the problem of detecting human breast cancer cells within normal bone marrow cells. Data sets having defined properties were employed to evaluate the potential utility of these statistical classification techniques whose performance was measured by ROC analysis. Two extreme but reasonable situations are presented: (1) data where the separation of cells was obvious by visual inspection and (2) data where major overlaps in the values of the individual FCM parameters made intuitive classification improbable. Both DFA and LR analysis were able to classify the cells of each type with acceptable accuracy and yield. The excellent empirical performance of both DFA and LR techniques, suggests that they offer promising approaches for classifying multiparameter FCM data using objective rules that may represent an improvement over commonly employed ad hoc approaches.

Semper: Distortion Compensation, Selective Averaging, 3-D Reconstruction, and Transfer Function Correction in a Highly Programmable System

Single particle tomography in EMAN2.

High-precision bladder cancer diagnosis method: 2D Raman spectrum figures based on maintenance technology combined with automatic weighted feature fusion network

Image formation in phase-contrast electron microscopy is governed by the contrast transfer function (CTF). The key parameter to tune the CTF is the defocus. A precise and unbiased estimate of the defocus is essential to determine the forward model and interpret high resolution images in cryo-TEM. We present an algorithm based on the weak-phase approximation for determination of the defocus and astigmatism of the objective lens from recorded images of an amorphous sample. The algorithm identifies Thon rings in the power spectrum density (PSD) and uses them to estimate astigmatism and defocus together with their uncertainties. For the astigmatism estimation we use a transformation to polar coordinates to fit ellipses to the Thon rings. Averaging the PSD over these ellipses reduces CTF estimation to a 1D problem and enhances the signal-to-noise ratio.

The hemodynamic differences between chronic mitral (MR: n = 30) and chronic aortic regurgitation (AR: n = 37) at various functional stages were investigated by echocardiography and compared with the findings in healthy subjects (n = 20). The ordinary statistical analysis revealed that in MR myocardial contractility and afterload to the left ventricle (LV) well preserved even at the stage of decompensation, while in AR the contractility deteriorated and the afterload was elevated even at the asymptomatic stage. By the multivariate statistical analysis, the functional differences and characteristics of the 2 diseases were clarified with 2 statistic factors derived from 6 hemodynamic parameters (LV end-diastole and end-systolic dimensions, mean velocity of circumferential fiber shortening, LV mid-systolic stress, LV mass, and left atrial dimension). In the differentiation between the 2 diseases by the multivariate analysis, left ventricular size tended to be smaller, contractility was preserved and loading of the left atrium was increased in MR more than in AR. Furthermore, a quantitative system which could explain the functional state by these echocardiographic parameters was derived from this multivariate analysis. The functional states was found to be strongly affected by LV end-systolic dimension and left atrial dimension. This quantitative method of determining the functional state, along with an accurate assessment of cardiac symptoms, provides a useful guideline for the clinical evaluation of the 2 diseases.

Liquid crystal (LC) concentrations in stratified holographic polymer-dispersed liquid crystal (PDLC) films have been quantified from depth profiles obtained by time-of-flight secondary ion mass spectrometry (SIMS). The volatile nature of the LCs was hindered during SIMS analysis by capping the PDLC samples with poly(vinyl alcohol) and cooling to cryogenic temperatures. It remains difficult to gain quantitative results from SIMS analysis due to matrix effects yielding complex SIMS fingerprint spectra. With the multivariate statistical method discriminant function analysis the LC contents in the stratified holographic PDLC films were quantified. The concentration of the LC-rich layers was determined to be 32.9 ± 3.4 wt % LC, and for the polymer-rich layers it was 28.8 ± 2.7 wt % LC. The low concentration difference was supported by imaging analysis and modeling results.

We utilized an NMR-based metabolomic approach to profile the metabolites in Drosophila melanogaster that cycle with a daily rhythm. 1H 1D and 2D NMR experiments were performed on whole-body extracts sampled from flies that experienced strong time cues in the form of both light and temperature cycles. Multivariate and univariate statistical analysis was used to identify those metabolites whose concentrations oscillate diurnally. We compared metabolite levels at two time points twelve hours apart, one close to the end of the day and the other close to the end of the night, and identified metabolites that differed significantly in their relative concentrations. We were able to identify 14 such metabolites whose concentrations differed significantly between the two time points. The concentrations of metabolites such as sterols, fatty acids, amino acids such as leucine, valine, isoleucine, alanine and lysine as well as other metabolites such as creatine, glucose, AMP and NAD were higher close to the end of the night, whereas the levels of lactic acid, and a few amino acids such as histidine and tryptophan were higher close to the end of the day. We compared signal intensities across 12 equally spaced time points for these 14 metabolites, in order to profile the changes in their levels across the day, since the NMR metabolite peak intensity is directly proportional to its molar concentration. Through this report we establish NMR-based metabolomics combined with multivariate statistical analysis as a useful method for future studies on the interactions between circadian clocks and metabolic processes.

The classification of a set of objects into predefined homogenous groups is a problem with major practical interest in many fields. Over the past two decades several non-parametric approaches have been developed to address the classification problem, originating from several scientific fields. This paper is focused on the rough sets approach and the investigation of its performance as opposed to traditional multivariate statistical classification procedures, namely the linear discriminant analysis, the quadratic discriminant analysis and the logit analysis. For this purpose an extensive Monte Carlo simulation is conducted to examine the performance of these methods under different data conditions.