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.

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.

PSPLINE: Stata module providing a penalized spline scatterplot smoother based on linear mixed model technology

Pspline uses xtmixed to fit a penalized spline regression and plots the smoothed function. Additional covariates can be specified to adjust the smooth and plot partial residuals.

RRREG: Stata module to estimate linear probability model for randomized response data

rrreg fits a linear probability model for randomized response data

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.

A continuous-time MILP model for short-term scheduling of make-and-pack production processes

In process industries, make-and-pack production is used to produce food and beverages, chemicals, and metal products, among others. This type of production process allows the fabrication of a wide range of products in relatively small amounts using the same equipment. In this article, we consider a real-world production process (cf. Honkomp et al. 2000. The curse of reality – why process scheduling optimization problems are diffcult in practice. Computers & Chemical Engineering, 24, 323–328.) comprising sequence-dependent changeover times, multipurpose storage units with limited capacities, quarantine times, batch splitting, partial equipment connectivity, and transfer times. The planning problem consists of computing a production schedule such that a given demand of packed products is fulfilled, all technological constraints are satisfied, and the production makespan is minimised. None of the models in the literature covers all of the technological constraints that occur in such make-and-pack production processes. To close this gap, we develop an efficient mixed-integer linear programming model that is based on a continuous time domain and general-precedence variables. We propose novel types of symmetry-breaking constraints and a preprocessing procedure to improve the model performance. In an experimental analysis, we show that small- and moderate-sized instances can be solved to optimality within short CPU times.

Using state variables to model the response of tumour cells to radiation and heat: a novel multi-hit-repair approach

In order to overcome the limitations of the linear-quadratic model and include synergistic effects of heat and radiation, a novel radiobiological model is proposed. The model is based on a chain of cell populations which are characterized by the number of radiation induced damages (hits). Cells can shift downward along the chain by collecting hits and upward by a repair process. The repair process is governed by a repair probability which depends upon state variables used for a simplistic description of the impact of heat and radiation upon repair proteins. Based on the parameters used, populations up to 4-5 hits are relevant for the calculation of the survival. The model describes intuitively the mathematical behaviour of apoptotic and nonapoptotic cell death. Linear-quadratic-linear behaviour of the logarithmic cell survival, fractionation, and (with one exception) the dose rate dependencies are described correctly. The model covers the time gap dependence of the synergistic cell killing due to combined application of heat and radiation, but further validation of the proposed approach based on experimental data is needed. However, the model offers a work bench for testing different biological concepts of damage induction, repair, and statistical approaches for calculating the variables of state.

Counterbalancing effects of competition for resources and facilitation against grazing in alpine snowbed communities

Alpine snowbeds are habitats where the major limiting factors for plant growth are herbivory and a small time window for growth due to late snowmelt. Despite these limitations, snowbed vegetation usually forms a dense carpet of palatable plants due to favourable abiotic conditions for plant growth within the short growing season. These environmental characteristics make snowbeds particularly interesting to study the interplay of facilitation and competition. We hypothesised an interplay between resource competition and facilitation against herbivory. Further, we investigated whether these predicted neighbour effects were species-specific and/or dependent on ontogeny, and whether the balance of positive and negative plant–plant interactions shifted along a snowmelt gradient. We determined the neighbour effects by means of neighbour removal experiments along the snowmelt gradient, and linear mixed model analyses. The results showed that the effects of neighbour removal were weak but generally consistent among species and snowmelt dates, and depended on whether biomass production or survival was considered. Higher total biomass and increased fruiting in removal plots indicated that plants competed for nutrients, water, and light, thereby supporting the hypothesis of prevailing competition for resources in snowbeds. However, the presence of neighbours reduced herbivory and thereby also facilitated survival. For plant growth the facilitative effects against herbivores in snowbeds counterbalanced competition for resources, leading to a weak negative net effect. Overall the neighbour effects were not species-specific and did not change with snowmelt date. Our finding of counterbalancing effects of competition and facilitation within a plant community is of special theoretical value for species distribution models and can explain the success of models that give primary importance to abiotic factors and tend to overlook interrelations between biotic and abiotic effects on plants.

Coping personality type and environmental enrichment affect aggression at weaning in pigs

This study investigated the effects of different environmental treatments and personality types on aggression at mixing of newly weaned domestic piglets. From birth to weaning, 16 litters were housed with their dams in either barren (B) or larger, substrate-enriched (E) environments. At 15 days old, piglets were classified as 'high' (HR) or low resistant' (LR) in a manual restraint test (backtest), which is thought to identify proactive (HR) and reactive (LR) stress coping strategies that may reflect different personality types. At 30 days old, 128 piglets were weaned, relocated and mixed into 32 pens comprising two HR and two LR unfamiliar pigs, balanced for sex and weaning weight. Eight B and eight E groups changed environmental condition whereas the others remained in the same type of environment. Number and duration of fights. fight outcomes and unilateral fighting were scored for 5 h post-mixing and skin lesions were counted before and 5 h, 1 day and 2 days after mixing. On the day following weaning, fighting and also exploratory and oral manipulative behaviours were measured for 6 h. Generalized Linear Mixed Model analyses suggested interactions between pre-weaning environment, post-weaning environment and personality type. Overall, pre-weaning E pigs had longer fights at weaning and mixing (P=0.01) and fought for longer on the next day (P=0.02) than pre-weaning B pigs, and inflicted more skin lesions (P=0.02). Post-weaning enrichment did not affect fighting at mixing but reduced the time spent fighting the next day (P=0.03). Personality had subtle and environment-dependent effects on fighting, and influenced the "structure" rather than the amount of aggressive behaviour. HR pigs, for instance, bullied (i.e. chased surrendering pigs) more often (P=0.009) and their fighting behaviour was less affected by their relative body weight than that of LR pigs. Post-weaning E pigs showed relatively higher levels of exploratory behaviour (P=0.02) and less oral manipulative behaviour (P=0.04) than post-weaning B pigs. In particular, switching from a good quality environment (E) to a worse quality one (B) at weaning decreased exploratory behaviour on the next day, especially for LR pigs, who also tended to fight with and orally manipulate their pen mates more in that condition, and seemed to be more affected by a deterioration of the environment. Overall, pre-weaning enrichment increased aggression after weaning whereas post-weaning enrichment reduced it, and personality type related to some aspects of fighting behaviour. (C) 2011 Elsevier B.V. All rights reserved.

Genuine cross-correlations: Which surrogate based measure reproduces analytical results best?

The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.

Clinical and molecular characterization of the potential CF disease modifier syntaxin 1A

Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator gene (CFTR). Disease severity in CF varies greatly, and sibling studies strongly indicate that genes other than CFTR modify disease outcome. Syntaxin 1A (STX1A) has been reported as a negative regulator of CFTR and other ion channels. We hypothesized that STX1A variants act as a CF modifier by influencing the remaining function of mutated CFTR. We identified STX1A variants by genomic resequencing patients from the Bernese CF Patient Data Registry and applied linear mixed model analysis to establish genotype-phenotype correlations, revealing STX1A rs4363087 (c.467-38A>G) to significantly influence lung function. The same STX1A risk allele was recognized in the European CF Twin and Sibling Study (P=0.0027), demonstrating that the genotype-phenotype association of STX1A to CF disease severity is robust enough to allow replication in two independent CF populations. rs4363087 is in linkage disequilibrium to the exonic variant rs2228607 (c.204C>T). Considering that neither rs4363087 nor rs2228607 changes the amino-acid sequence of STX1A, we investigated their effects on mRNA level. We show that rs2228607 reinforces aberrant splicing of STX1A mRNA, leading to nonsense-mediated mRNA decay. In conclusion, we demonstrate the clinical relevance of STX1A variants in CF, and evidence the functional relevance of STX1A variant rs2228607 at molecular level. Our findings show that genes interacting with CFTR can modify CF disease progression.European Journal of Human Genetics advance online publication, 10 April 2013; doi:10.1038/ejhg.2013.57.

Interrelation of peri-operative morbidity and ASA class assignment in patients undergoing gynaecological surgery

OBJECTIVE: The aim of this study was to estimate intra- and post-operative risk using the American Society of Anaesthesiologists (ASA) classification which is an important predictor of an intervention and of the entire operating programme. STUDY DESIGN: In this retrospective study, 4435 consecutive patients undergoing elective and emergency surgery at the Gynaecological Clinic of the University Hospital of Zurich were included. The ASA classification for pre-operative risk assessment was determined by an anaesthesiologist after a thorough physical examination. We observed several pre-, intra- and post-operative parameters, such as age, body-mass-index, duration of anaesthesia, duration of surgery, blood loss, duration of post-operative stay, complicated post-operative course, morbidity and mortality. The investigation of different risk factors was achieved by a multiple linear regression model for log-transformed duration of hospitalisation. RESULTS: Age and obesity were responsible for a higher ASA classification. ASA grade correlates with the duration of anaesthesia and the duration of the surgery itself. There was a significant difference in blood loss between ASA grades I (113+/-195 ml) and III (222+/-470 ml) and between classes II (176+/-432 ml) and III. The duration of post-operative hospitalisation could also be correlated with ASA class. ASA class I=1.7+/-3.0 days, ASA class II=3.6+/-4.3 days, ASA class III=6.8+/-8.2 days, and ASA class IV=6.2+/-3.9 days. The mean post-operative in-hospital stay was 2.5+/-4.0 days without complications, and 8.7+/-6.7 days with post-operative complications. Multiple linear regression model showed that not only the ASA classification contained an important information for the duration of hospitalisation. Parameters such as age, class of diagnosis, post-operative complications, etc. also have an influence on the duration of hospitalisation. CONCLUSION: This study shows that the ASA classification can be used as a good and early available predictor for the planning of an intervention in gynaecological surgery. The ASA classification helps the surgeon to assess the peri-operative risk profile of which important information can be derived for the planning of the operation programme.

Long-term gas exchange characteristics as marker of deterioration in patients with cystic fibrosis

Background and Aim In patients with cystic fibrosis (CF) the architecture of the developing lungs and the ventilation of lung units are progressively affected, influencing intrapulmonary gas mixing and gas exchange. We examined the long-term course of blood gas measurements in relation to characteristics of lung function and the influence of different CFTR genotype upon this process. Methods Serial annual measurements of PaO2 and PaCO2 assessed in relation to lung function, providing functional residual capacity (FRCpleth), lung clearance index (LCI), trapped gas (VTG), airway resistance (sReff), and forced expiratory indices (FEV1, FEF50), were collected in 178 children (88 males; 90 females) with CF, over an age range of 5 to 18 years. Linear mixed model analysis and binary logistic regression analysis were used to define predominant lung function parameters influencing oxygenation and carbon dioxide elimination. Results PaO2 decreased linearly from age 5 to 18 years, and was mainly associated with FRCpleth, (p < 0.0001), FEV1 (p < 0.001), FEF50 (p < 0.002), and LCI (p < 0.002), indicating that oxygenation was associated with the degree of pulmonary hyperinflation, ventilation inhomogeneities and impeded airway function. PaCO2 showed a transitory phase of low PaCO2 values, mainly during the age range of 5 to 12 years. Both PaO2 and PaCO2 presented with different progression slopes within specific CFTR genotypes. Conclusion In the long-term evaluation of gas exchange characteristics, an association with different lung function patterns was found and was closely related to specific genotypes. Early examination of blood gases may reveal hypocarbia, presumably reflecting compensatory mechanisms to improve oxygenation.