Calibration using constrained smoothing with applications to mass spectrometry data.

Linear regressions are commonly used to calibrate the signal measurements in proteomic analysis by mass spectrometry. However, with or without a monotone (e.g., log) transformation, data from such functional proteomic experiments are not necessarily linear or even monotone functions of protein (or peptide) concentration except over a very restricted range. A computationally efficient spline procedure improves upon linear regression. However, mass spectrometry data are not necessarily homoscedastic; more often the variation of measured concentrations increases disproportionately near the boundaries of the instruments measurement capability (dynamic range), that is, the upper and lower limits of quantitation. These calibration difficulties exist with other applications of mass spectrometry as well as with other broad-scale calibrations. Therefore the method proposed here uses a functional data approach to define the calibration curve and also the limits of quantitation under the two assumptions: (i) that the variance is a bounded, convex function of concentration; and (ii) that the calibration curve itself is monotone at least between the limits of quantitation, but not necessarily outside these limits. Within this paradigm, the limit of detection, where the signal is definitely present but not measurable with any accuracy, is also defined. An iterative approach draws on existing smoothing methods to account simultaneously for both restrictions and is shown to achieve the global optimal convergence rate under weak conditions. This approach can also be implemented when convexity is replaced by other (bounded) restrictions. Examples from Addona et al. (2009, Nature Biotechnology 27, 663-641) both motivate and illustrate the effectiveness of this functional data methodology when compared with the simpler linear regressions and spline techniques.

Systematic decrements in QTc between the first and second day of contiguous daily ECG recordings under controlled conditions.

BACKGROUND: Many thorough QT (TQT) studies use a baseline day and double delta analysis to account for potential diurnal variation in QTc. However, little is known about systematic changes in the QTc across contiguous days when normal volunteers are brought into a controlled inpatient environment. METHODS: Two separate crossover TQT studies included 2 days of no treatment lead-in days with ECG collection preceding periods of drug treatment . In the first study, there were two pairs of such contiguous days with 10 replicate electrocardiograms (ECGs) collected at six time points, and in the second study, there were four pairs of contiguous days with nine replicate ECGs collected at five time points. These lead-in day pairs provided the opportunity to evaluate any systematic changes across contiguous first and second days of an inpatient environment. Within-patient consistency of change across pairs of days as well as within day, diurnal variation could also be evaluated. RESULTS: Modest (4.2 ms [range 1.9-6.5 ms]) but consistent decreases (significant [P < 0.05] for all 32 comparisons) were observed (probability: ≤5.4 × 10(-16)). Although group behavior with respect to QTc was consistent, individual subjects demonstrated substantial variability across pairs of days. Evidence of diurnal variation was weak and inconsistent. Magnitude of any diurnal variation was less than magnitude of change across days. CONCLUSIONS: Subjects show a systematic decrease in QTc from first day to second day of inpatient status and do not demonstrate a significant diurnal pattern. The magnitude of this systematic change is sufficient to influence QTc study interpretation.

Variance component analysis of a multi-site study for the reproducibility of multiple reaction monitoring measurements of peptides in human plasma.

BACKGROUND: In the Addona et al. paper (Nature Biotechnology 2009), a large-scale multi-site study was performed to quantify Multiple Reaction Monitoring (MRM) measurements of proteins spiked in human plasma. The unlabeled signature peptides derived from the seven target proteins were measured at nine different concentration levels, and their isotopic counterparts were served as the internal standards. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, the sources of variation are analyzed by decomposing the variance into parts attributable to specific experimental factors: technical replicates, sites, peptides, transitions within each peptide, and higher-order interaction terms based on carefully built mixed effects models. The factors of peptides and transitions are shown to be major contributors to the variance of the measurements considering heavy (isotopic) peptides alone. For the light ((12)C) peptides alone, in addition to these factors, the factor of study*peptide also contributes significantly to the variance of the measurements. Heterogeneous peptide component models as well as influence analysis identify the outlier peptides in the study, which are then excluded from the analysis. Using a log-log scale transformation and subtracting the heavy/isotopic peptide [internal standard] measurement from the peptide measurements (i.e., taking the logarithm of the peak area ratio in the original scale establishes that), the MRM measurements are overall consistent across laboratories following the same standard operating procedures, and the variance components related to sites, transitions and higher-order interaction terms involving sites have greatly reduced impact. Thus the heavy peptides have been effective in reducing apparent inter-site variability. In addition, the estimates of intercepts and slopes of the calibration curves are calculated for the sub-studies. CONCLUSIONS/SIGNIFICANCE: The MRM measurements are overall consistent across laboratories following the same standard operating procedures, and heavy peptides can be used as an effective internal standard for reducing apparent inter-site variability. Mixed effects modeling is a valuable tool in mass spectrometry-based proteomics research.

Species-specific dibutyl phthalate fetal testis endocrine disruption correlates with inhibition of SREBP2-dependent gene expression pathways.

Fetal rat phthalate exposure produces a spectrum of male reproductive tract malformations downstream of reduced Leydig cell testosterone production, but the molecular mechanism of phthalate perturbation of Leydig cell function is not well understood. By bioinformatically examining fetal testis expression microarray data sets from susceptible (rat) and resistant (mouse) species after dibutyl phthalate (DBP) exposure, we identified decreased expression of several metabolic pathways in both species. However, lipid metabolism pathways transcriptionally regulated by sterol regulatory element-binding protein (SREBP) were inhibited in the rat but induced in the mouse, and this differential species response corresponded with repression of the steroidogenic pathway. In rats exposed to 100 or 500 mg/kg DBP from gestational days (GD) 16 to 20, a correlation was observed between GD20 testis steroidogenic inhibition and reductions of testis cholesterol synthesis endpoints including testis total cholesterol levels, Srebf2 gene expression, and cholesterol synthesis pathway gene expression. SREBP2 expression was detected in all fetal rat testis cells but was highest in Leydig cells. Quantification of SREBP2 immunostaining showed that 500 mg/kg DBP exposure significantly reduced SREBP2 expression in rat fetal Leydig cells but not in seminiferous cords. By Western analysis, total rat testis SREBP2 levels were not altered by DBP exposure. Together, these data suggest that phthalate-induced inhibition of fetal testis steroidogenesis is closely associated with reduced activity of several lipid metabolism pathways and SREBP2-dependent cholesterologenesis in Leydig cells.

Dedicated breast computed tomography: volume image denoising via a partial-diffusion equation based technique.

Dedicated breast computed tomography (CT) imaging possesses the potential for improved lesion detection over conventional mammograms, especially for women with dense breasts. The breast CT images are acquired with a glandular dose comparable to that of standard two-view mammography for a single breast. Due to dose constraints, the reconstructed volume has a non-negligible quantum noise when thin section CT slices are visualized. It is thus desirable to reduce noise in the reconstructed breast volume without loss of spatial resolution. In this study, partial diffusion equation (PDE) based denoising techniques specifically for breast CT were applied at different steps along the reconstruction process and it was found that denoising performed better when applied to the projection data rather than reconstructed data. Simulation results from the contrast detail phantom show that the PDE technique outperforms Wiener denoising as well as adaptive trimmed mean filter. The PDE technique increases its performance advantage relative to Wiener techniques when the photon fluence is reduced. With the PDE technique, the sensitivity for lesion detection using the contrast detail phantom drops by less than 7% when the dose is cut down to 40% of the two-view mammography. For subjective evaluation, the PDE technique was applied to two human subject breast data sets acquired on a prototype breast CT system. The denoised images had appealing visual characteristics with much lower noise levels and improved tissue textures while maintaining sharpness of the original reconstructed volume.

Neutron-stimulated emission computed tomography of a multi-element phantom.

This paper describes the implementation of neutron-stimulated emission computed tomography (NSECT) for non-invasive imaging and reconstruction of a multi-element phantom. The experimental apparatus and process for acquisition of multi-spectral projection data are described along with the reconstruction algorithm and images of the two elements in the phantom. Independent tomographic reconstruction of each element of the multi-element phantom was performed successfully. This reconstruction result is the first of its kind and provides encouraging proof of concept for proposed subsequent spectroscopic tomography of biological samples using NSECT.

Computer-aided detection of amorphous calcifications.

OBJECTIVE: Computer-aided detection (CAD) systems have been used successfully to detect malignant calcifications on mammography, with sensitivities ranging from 86% to 99%. Amorphous calcifications are a subset of small indistinct calcifications of intermediate concern that have a 20% likelihood of being malignant and that are frequently overlooked on mammography. The purpose of our study was to determine the sensitivity of one commercially available CAD system for detecting amorphous calcifications. MATERIALS AND METHODS: A commercially available CAD system evaluated mammograms of 82 patients with 85 mammographically detected and histologically sampled groups of amorphous calcifications (21 malignant, 14 high risk, and 50 benign). The sensitivity of the system for detecting the calcifications on at least one image of the two-view mammographic examination (case sensitivity) and on each individual mammographic image (image sensitivity) was determined. Findings were correlated with results from large core needle biopsy or surgical excision in each case. RESULTS: The CAD system detected amorphous calcifications in 43 of 85 cases (case sensitivity, 51%) and in 59 of 146 mammographic images (image sensitivity, 40%). The case sensitivities by histologic outcome were 57% for malignant calcifications, 29% for high-risk calcifications, and 54% for benign calcifications. An average of 2.0 false-positive marks were displayed per case. CONCLUSION: The CAD sensitivity for malignant amorphous calcifications is markedly lower than previously reported for all malignant calcifications. Breast imaging radiologists who use CAD systems should continue to search diligently for these difficult-to-detect lesions.

High-resolution determination of soft tissue deformations using MRI and first-order texture correlation.

Mechanical factors such as deformation and strain are thought to play important roles in the maintenance, repair, and degeneration of soft tissues. Determination of soft tissue static deformation has traditionally only been possible at a tissue's surface, utilizing external markers or instrumentation. Texture correlation is a displacement field measurement technique which relies on unique image patterns within a pair of digital images to track displacement. The technique has recently been applied to MR images, indicating the possibility of high-resolution displacement and strain field determination within the mid-substance of soft tissues. However, the utility of MR texture correlation analysis may vary amongst tissue types depending on their underlying structure, composition, and contrast mechanism, which give rise to variations in texture with MRI. In this study, we investigate the utility of a texture correlation algorithm with first-order displacement mapping terms for use with MR images, and suggest a novel index of image "roughness" as a way to decrease errors associated with the use of texture correlation for intra-tissue strain measurement with MRI. We find that a first-order algorithm can significantly reduce strain measurement error, and that an image "roughness" index correlates with displacement measurement error for a variety of imaging conditions and tissue types.