Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets

To propose the determination of the macromolecular baseline (MMBL) in clinical 1H MR spectra based on T(1) and T(2) differentiation using 2D fitting in FiTAID, a general Fitting Tool for Arrays of Interrelated Datasets.

An integrative transcranial magnetic stimulation mapping technique using non-linear curve fitting

The aim of this study is to develop a new simple method for analyzing one-dimensional transcranial magnetic stimulation (TMS) mapping studies in humans. Motor evoked potentials (MEP) were recorded from the abductor pollicis brevis (APB) muscle during stimulation at nine different positions on the scalp along a line passing through the APB hot spot and the vertex. Non-linear curve fitting according to the Levenberg-Marquardt algorithm was performed on the averaged amplitude values obtained at all points to find the best-fitting symmetrical and asymmetrical peak functions. Several peak functions could be fitted to the experimental data. Across all subjects, a symmetric, bell-shaped curve, the complementary error function (erfc) gave the best results. This function is characterized by three parameters giving its amplitude, position, and width. None of the mathematical functions tested with less or more than three parameters fitted better. The amplitude and position parameters of the erfc were highly correlated with the amplitude at the hot spot and with the location of the center of gravity of the TMS curve. In conclusion, non-linear curve fitting is an accurate method for the mathematical characterization of one-dimensional TMS curves. This is the first method that provides information on amplitude, position and width simultaneously.

Prediction of arrhythmic events after myocardial infarction based on signal-averaged electrocardiogram and ejection fraction

Trials on implantable cardioverter-defibrillators (ICD) for patients after acute myocardial infarction (AMI) have highlighted the need for risk assessment of arrhythmic events (AE). The aim of this study was to evaluate risk predictors based on a novel approach of interpreting signal-averaged electrocardiogram (SAECG) and ejection fraction (EF).

Quantitative 1H-magnetic resonance spectroscopy of human brain: Influence of composition and parameterization of the basis set in linear combination model-fitting

Localized short-echo-time (1)H-MR spectra of human brain contain contributions of many low-molecular-weight metabolites and baseline contributions of macromolecules. Two approaches to model such spectra are compared and the data acquisition sequence, optimized for reproducibility, is presented. Modeling relies on prior knowledge constraints and linear combination of metabolite spectra. Investigated was what can be gained by basis parameterization, i.e., description of basis spectra as sums of parametric lineshapes. Effects of basis composition and addition of experimentally measured macromolecular baselines were investigated also. Both fitting methods yielded quantitatively similar values, model deviations, error estimates, and reproducibility in the evaluation of 64 spectra of human gray and white matter from 40 subjects. Major advantages of parameterized basis functions are the possibilities to evaluate fitting parameters separately, to treat subgroup spectra as independent moieties, and to incorporate deviations from straightforward metabolite models. It was found that most of the 22 basis metabolites used may provide meaningful data when comparing patient cohorts. In individual spectra, sums of closely related metabolites are often more meaningful. Inclusion of a macromolecular basis component leads to relatively small, but significantly different tissue content for most metabolites. It provides a means to quantitate baseline contributions that may contain crucial clinical information.

Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.

Screening and prediction of erosive potential

The literature on the erosive potential of drinks and other products is summarised, and aspects of the conduct of screening tests as well as possible correlations of the erosive potential with various solution parameters are discussed. The solution parameters that have been suggested as important include pH, acid concentration (with respect to buffer capacity and concentration of undissociated acid), degree of saturation, calcium and phosphate concentrations, and inhibitors of erosion. Based on the available data, it is concluded that the dominant factor in erosion is pH. The effect of buffer capacity seems to be pH dependent. The degree of saturation probably has a non-linear relationship with erosion. While calcium at elevated concentrations is known to reduce erosion effectively, it is not known whether it is important at naturally occurring concentrations. Fluoride at naturally occurring concentrations is inversely correlated with erosive potential, but phosphate is probably not. Natural plant gums, notably pectin, do not inhibit erosion, so they are unlikely to interfere with the prediction of erosive potential. The non-linearity of some solution factors and interactions with pH need to be taken into account when developing multivariate models for predicting the erosive potential of different solutions. Finally, the erosive potential of solutions towards enamel and dentine might differ.

LC-MS/MS method for simultaneous analysis of uracil, 5,6-dihydrouracil, 5-fluorouracil and 5-fluoro-5,6-dihydrouracil in human plasma for therapeutic drug monitoring and toxicity prediction in cancer patients

The chemotherapeutic drug 5-fluorouracil (5-FU) is widely used for treating solid tumors. Response to 5-FU treatment is variable with 10-30% of patients experiencing serious toxicity partly explained by reduced activity of dihydropyrimidine dehydrogenase (DPD). DPD converts endogenous uracil (U) into 5,6-dihydrouracil (UH(2) ), and analogously, 5-FU into 5-fluoro-5,6-dihydrouracil (5-FUH(2) ). Combined quantification of U and UH(2) with 5-FU and 5-FUH(2) may provide a pre-therapeutic assessment of DPD activity and further guide drug dosing during therapy. Here, we report the development of a liquid chromatography-tandem mass spectrometry assay for simultaneous quantification of U, UH(2) , 5-FU and 5-FUH(2) in human plasma. Samples were prepared by liquid-liquid extraction with 10:1 ethyl acetate-2-propanol (v/v). The evaporated samples were reconstituted in 0.1% formic acid and 10 μL aliquots were injected into the HPLC system. Analyte separation was achieved on an Atlantis dC(18) column with a mobile phase consisting of 1.0 mm ammonium acetate, 0.5 mm formic acid and 3.3% methanol. Positively ionized analytes were detected by multiple reaction monitoring. The analytical response was linear in the range 0.01-10 μm for U, 0.1-10 μm for UH(2) , 0.1-75 μm for 5-FU and 0.75-75 μm for 5-FUH(2) , covering the expected concentration ranges in plasma. The method was validated following the FDA guidelines and applied to clinical samples obtained from ten 5-FU-treated colorectal cancer patients. The present method merges the analysis of 5-FU pharmacokinetics and DPD activity into a single assay representing a valuable tool to improve the efficacy and safety of 5-FU-based chemotherapy.

Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients

To derive tests for randomness, nonlinear-independence, and stationarity, we combine surrogates with a nonlinear prediction error, a nonlinear interdependence measure, and linear variability measures, respectively. We apply these tests to intracranial electroencephalographic recordings (EEG) from patients suffering from pharmacoresistant focal-onset epilepsy. These recordings had been performed prior to and independent from our study as part of the epilepsy diagnostics. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control. This allowed us to define two distinct sets of signals: One set of signals recorded from brain areas where the first ictal EEG signal changes were detected as judged by expert visual inspection ("focal signals") and one set of signals recorded from brain areas that were not involved at seizure onset ("nonfocal signals"). We find more rejections for both the randomness and the nonlinear-independence test for focal versus nonfocal signals. In contrast more rejections of the stationarity test are found for nonfocal signals. Furthermore, while for nonfocal signals the rejection of the stationarity test increases the rejection probability of the randomness and nonlinear-independence test substantially, we find a much weaker influence for the focal signals. In consequence, the contrast between the focal and nonfocal signals obtained from the randomness and nonlinear-independence test is further enhanced when we exclude signals for which the stationarity test is rejected. To study the dependence between the randomness and nonlinear-independence test we include only focal signals for which the stationarity test is not rejected. We show that the rejection of these two tests correlates across signals. The rejection of either test is, however, neither necessary nor sufficient for the rejection of the other test. Thus, our results suggest that EEG signals from epileptogenic brain areas are less random, more nonlinear-dependent, and more stationary compared to signals recorded from nonepileptogenic brain areas. We provide the data, source code, and detailed results in the public domain.

A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.

Periodic breathing during ascent to extreme altitude quantified by spectral analysis of the respiratory volume signal

High altitude periodic breathing (PB) shares some common pathophysiologic aspects with sleep apnea, Cheyne-Stokes respiration and PB in heart failure patients. Methods that allow quantifying instabilities of respiratory control provide valuable insights in physiologic mechanisms and help to identify therapeutic targets. Under the hypothesis that high altitude PB appears even during physical activity and can be identified in comparison to visual analysis in conditions of low SNR, this study aims to identify PB by characterizing the respiratory pattern through the respiratory volume signal. A number of spectral parameters are extracted from the power spectral density (PSD) of the volume signal, derived from respiratory inductive plethysmography and evaluated through a linear discriminant analysis. A dataset of 34 healthy mountaineers ascending to Mt. Muztagh Ata, China (7,546 m) visually labeled as PB and non periodic breathing (nPB) is analyzed. All climbing periods within all the ascents are considered (total climbing periods: 371 nPB and 40 PB). The best crossvalidated result classifying PB and nPB is obtained with Pm (power of the modulation frequency band) and R (ratio between modulation and respiration power) with an accuracy of 80.3% and area under the receiver operating characteristic curve of 84.5%. Comparing the subjects from 1(st) and 2(nd) ascents (at the same altitudes but the latter more acclimatized) the effect of acclimatization is evaluated. SaO(2) and periodic breathing cycles significantly increased with acclimatization (p-value < 0.05). Higher Pm and higher respiratory frequencies are observed at lower SaO(2), through a significant negative correlation (p-value < 0.01). Higher Pm is observed at climbing periods visually labeled as PB with > 5 periodic breathing cycles through a significant positive correlation (p-value < 0.01). Our data demonstrate that quantification of the respiratory volume signal using spectral analysis is suitable to identify effects of hypobaric hypoxia on control of breathing.

Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.

Direct MR arthrography at 1.5 and 3.0 T: signal dependence on gadolinium and iodine concentrations--phantom study

PURPOSE: To prospectively quantify in vitro the influence of gadopentetate dimeglumine and ioversol on the magnetic resonance (MR) imaging signal observed with a variety of musculoskeletal pulse sequences to predict optimum gadolinium concentrations for direct MR arthrography at 1.5 and 3.0 T. MATERIALS AND METHODS: In an in vitro study, T1 and T2 relaxation times of three dilution series of gadopentetate dimeglumine (concentration, 0-20.0 mmol gadolinium per liter) at ioversol concentrations with iodine concentration of 0, 236.4, and 1182 mmol iodine per liter (corresponding to 0, 30, and 150 mg of iodine per milliliter) were measured at 1.5 and 3.0 T. The relaxation rate dependence on concentrations of gadolinium and iodine was analytically modeled, and continuous profiles of signal versus gadolinium concentration were calculated for 10 pulse sequences used in current musculoskeletal imaging. After fitting to experimental discrete profiles, maximum signal-to-noise ratio (SNR), gadolinium concentration with maximum SNR, and range of gadolinium concentration with 90% of maximum SNR were derived. The overall influence of field strength and iodine concentration on these parameters was assessed by using t tests. The deviation of simulated from experimental signal-response profiles was assessed with the autocorrelation of the residuals. RESULTS: The model reproduced relaxation rates of 0.37-38.24 sec(-1), with a mean error of 4.5%. Calculated SNR profiles matched the discrete experimental profiles, with autocorrelation of the residuals divided by the mean of less than 5.0. Admixture of ioversol consistently reduced T1 and T2, narrowed optimum gadolinium concentration ranges (P = .004-.006), and reduced maximum SNR (P < .001 to not significant). Optimum gadolinium concentration was 0.7-3.4 mmol/L at both field strengths. At 3.0 T, maximum SNR was up to 75% higher than at 1.5 T. CONCLUSION: Admixture of ioversol to gadopentetate dimeglumine solutions results in a consistent additional relaxation enhancement, which can be analytically modeled to allow a near-quantitative a priori optimized match of contrast media concentrations and imaging protocol for a broad variety of pulse sequences.

Development and validation of energy expenditure prediction models based on GT3X accelerometer data in 5- to 9-year-old children

Background: Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children. Methods: Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model. Results: All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches. Conclusions: The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.

Learning by the Dendritic Prediction of Somatic Spiking

Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.