A general linear relaxometry model of R1 using imaging data.

PURPOSE: The longitudinal relaxation rate (R1 ) measured in vivo depends on the local microstructural properties of the tissue, such as macromolecular, iron, and water content. Here, we use whole brain multiparametric in vivo data and a general linear relaxometry model to describe the dependence of R1 on these components. We explore a) the validity of having a single fixed set of model coefficients for the whole brain and b) the stability of the model coefficients in a large cohort. METHODS: Maps of magnetization transfer (MT) and effective transverse relaxation rate (R2 *) were used as surrogates for macromolecular and iron content, respectively. Spatial variations in these parameters reflected variations in underlying tissue microstructure. A linear model was applied to the whole brain, including gray/white matter and deep brain structures, to determine the global model coefficients. Synthetic R1 values were then calculated using these coefficients and compared with the measured R1 maps. RESULTS: The model's validity was demonstrated by correspondence between the synthetic and measured R1 values and by high stability of the model coefficients across a large cohort. CONCLUSION: A single set of global coefficients can be used to relate R1 , MT, and R2 * across the whole brain. Our population study demonstrates the robustness and stability of the model. Magn Reson Med, 2014. © 2014 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. Magn Reson Med 73:1309-1314, 2015. © 2014 Wiley Periodicals, Inc.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

Derivation of an observer model adapted to irregular signals based on convolution channels.

Anthropomorphic model observers are mathe- matical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the "Filtered Channel observer" (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal.

Female adolescents' views on a youth-friendly clinic.

OBJECTIVE: To assess satisfaction among female patients of a youth friendly clinic and to determine with which factors this was associated. METHODS: A cross-sectional survey was conducted in an adolescent clinic in Lausanne, Switzerland, between March and May 2008. All female patients who had made at least one previous visit were eligible. Three hundred and eleven patients aged 12-22 years were included. We performed bivariate analysis to compare satisfied and non-satisfied patients and constructed a log-linear model. RESULTS: Ninety-four percent of patients were satisfied. Satisfied female adolescents were significantly more likely to feel that their complaints were heard, that the caregiver understood their problems, to have no change of physician, to have received the correct treatment/help and to follow the caregiver's advice. The log-linear model highlighted four factors directly linked with patient satisfaction: outcome of care, continuity of care, adherence to treatment and the feeling of being understood. CONCLUSIONS: The main point for female adolescent patient satisfaction lies in a long term, trustworthy relationship with their caregiver. Confidentiality and accessibility were secondary for our patients.

The ghost of past species occurrence: improving species distribution models for presence-only data

1. Model-based approaches have been used increasingly in conservation biology over recent years. Species presence data used for predictive species distribution modelling are abundant in natural history collections, whereas reliable absence data are sparse, most notably for vagrant species such as butterflies and snakes. As predictive methods such as generalized linear models (GLM) require absence data, various strategies have been proposed to select pseudo-absence data. However, only a few studies exist that compare different approaches to generating these pseudo-absence data. 2. Natural history collection data are usually available for long periods of time (decades or even centuries), thus allowing historical considerations. However, this historical dimension has rarely been assessed in studies of species distribution, although there is great potential for understanding current patterns, i.e. the past is the key to the present. 3. We used GLM to model the distributions of three 'target' butterfly species, Melitaea didyma, Coenonympha tullia and Maculinea teleius, in Switzerland. We developed and compared four strategies for defining pools of pseudo-absence data and applied them to natural history collection data from the last 10, 30 and 100 years. Pools included: (i) sites without target species records; (ii) sites where butterfly species other than the target species were present; (iii) sites without butterfly species but with habitat characteristics similar to those required by the target species; and (iv) a combination of the second and third strategies. Models were evaluated and compared by the total deviance explained, the maximized Kappa and the area under the curve (AUC). 4. Among the four strategies, model performance was best for strategy 3. Contrary to expectations, strategy 2 resulted in even lower model performance compared with models with pseudo-absence data simulated totally at random (strategy 1). 5. Independent of the strategy model, performance was enhanced when sites with historical species presence data were not considered as pseudo-absence data. Therefore, the combination of strategy 3 with species records from the last 100 years achieved the highest model performance. 6. Synthesis and applications. The protection of suitable habitat for species survival or reintroduction in rapidly changing landscapes is a high priority among conservationists. Model-based approaches offer planning authorities the possibility of delimiting priority areas for species detection or habitat protection. The performance of these models can be enhanced by fitting them with pseudo-absence data relying on large archives of natural history collection species presence data rather than using randomly sampled pseudo-absence data.

History matters: relating paths and rates of landscape change to butterfly species decline

Western European landscapes have drastically changed since the 1950s, with agricultural intensifications and the spread of urban settlements considered the most important drivers of this land-use/land-cover change. Losses of habitat for fauna and flora have been a direct consequence of this development. In the present study, we relate butterfly occurrence to land-use/land-cover changes over five decades between 1951 and 2000. The study area covers the entire Swiss territory. The 10 explanatory variables originate from agricultural statistics and censuses. Both state as well as rate was used as explanatory variables. Species distribution data were obtained from natural history collections. We selected eight butterfly species: four species occur on wetlands and four occur on dry grasslands. We used cluster analysis to track land-use/land-cover changes and to group communes based on similar trajectories of change. Generalized linear models were applied to identify factors that were significantly correlated with the persistence or disappearance of butterfly species. Results showed that decreasing agricultural areas and densities of farms with more than 10 ha of cultivated land are significantly related with wetland species decline, and increasing densities of livestock seem to have favored disappearance of dry grassland species. Moreover, we show that species declines are not only dependent on land-use/land-cover states but also on the rates of change; that is, the higher the transformation rate from small to large farms, the higher the loss of dry grassland species. We suggest that more attention should be paid to the rates of landscape change as feasible drivers of species change and derive some management suggestions.

Copeptin Levels Remain Unchanged during the Menstrual Cycle.

BACKGROUND: Copeptin, a surrogate marker for arginin vasopressin production, is evaluated as an osmo-dependent stress and inflammatory biomarker in different diseases. We investigated copeptin during the menstrual cycle and its relationship to sex hormones, markers of subclinical inflammation and estimates of body fluid. METHODS: In 15 healthy women with regular menstrual cycles, blood was drawn on fifteen defined days of their menstrual cycle and was assayed for copeptin, progesterone, estradiol, luteinizing hormone, high-sensitive C-reactive protein, tumor necrosis factor-alpha and procalcitonin. Symptoms of fluid retention were assessed on each visit, and bio impedance analysis was measured thrice to estimate body fluid changes. Mixed linear model analysis was performed to assess the changes of copeptin across the menstrual cycle and the relationship of sex hormones, markers of subclinical inflammation and estimates of body fluid with copeptin. RESULTS: Copeptin levels did not significantly change during the menstrual cycle (p = 0.16). Throughout the menstrual cycle, changes in estradiol (p = 0.002) and in the physical premenstrual symptom score (p = 0.01) were positively related to copeptin, but changes in other sex hormones, in markers of subclinical inflammation or in bio impedance analysis-estimated body fluid were not (all p = ns). CONCLUSION: Although changes in estradiol and the physical premenstrual symptom score appear to be related to copeptin changes, copeptin does not significantly change during the menstrual cycle.

Comparability of clinical wear measurements by optical 3D laser scanning in two different centers.

OBJECTIVE: The purpose of this study was to compare the use of different variables to measure the clinical wear of two denture tooth materials in two analysis centers. METHODS: Twelve edentulous patients were provided with full dentures. Two different denture tooth materials (experimental material and control) were placed randomly in accordance with the split-mouth design. For wear measurements, impressions were made after an adjustment phase of 1-2 weeks and after 6, 12, 18, and 24 months. The occlusal wear of the posterior denture teeth of 11 subjects was assessed in two study centers by use of plaster replicas and 3D laser-scanning methods. In both centers sequential scans of the occlusal surfaces were digitized and superimposed. Wear was described by use of four different variables. Statistical analysis was performed after log-transformation of the wear data by use of the Pearson and Lin correlation and by use of a mixed linear model. RESULTS: Mean occlusal vertical wear of the denture teeth after 24 months was between 120μm and 212μm, depending on wear variable and material. For three of the four variables, wear of the experimental material was statistically significantly less than that of the control. Comparison of the two study centers, however, revealed correlation of the wear variables was only moderate whereas strong correlation was observed among the different wear variables evaluated by each center. SIGNIFICANCE: Moderate correlation was observed for clinical wear measurements by optical 3D laser scanning in two different study centers. For the two denture tooth materials, wear measurements limited to the attrition zones led to the same qualitative assessment.

Substitution of (R,S)-methadone by (R)-methadone: Impact on QTc interval.

Methadone is administered as a chiral mixture of (R,S)-methadone. The opioid effect is mainly mediated by (R)-methadone, whereas (S)-methadone blocks the human ether-à-go-go-related gene (hERG) voltage-gated potassium channel more potently, which can cause drug-induced long QT syndrome, leading to potentially lethal ventricular tachyarrhythmias. To investigate whether substitution of (R,S)-methadone by (R)-methadone could reduce the corrected QT (QTc) interval, (R,S)-methadone was replaced by (R)-methadone (half-dose) in 39 opioid-dependent patients receiving maintenance treatment for 14 days. (R)-methadone was then replaced by the initial dose of (R,S)-methadone for 14 days (n = 29). Trough (R)-methadone and (S)-methadone plasma levels and electrocardiogram measurements were taken. The Fridericia-corrected QT (QTcF) interval decreased when (R,S)-methadone was replaced by a half-dose of (R)-methadone; the median (interquartile range [IQR]) values were 423 (398-440) milliseconds (ms) and 412 (395-431) ms (P = .06) at days 0 and 14, respectively. Using a univariate mixed-effect linear model, the QTcF value decreased by a mean of -3.9 ms (95% confidence interval [CI], -7.7 to -0.2) per week (P = .04). The QTcF value increased when (R)-methadone was replaced by the initial dose of (R,S)-methadone for 14 days; median (IQR) values were 424 (398-436) ms and 424 (412-443) ms (P = .01) at days 14 and 28, respectively. The univariate model showed that the QTcF value increased by a mean of 4.7 ms (95% CI, 1.3-8.1) per week (P = .006). Substitution of (R,S)-methadone by (R)-methadone reduces the QTc interval value. A safer cardiac profile of (R)-methadone is in agreement with previous in vitro and pharmacogenetic studies. If the present results are confirmed by larger studies, (R)-methadone should be prescribed instead of (R,S)-methadone to reduce the risk of cardiac toxic effects and sudden death.

Reproducibility and within-day variability of body fat masurements using segmental bipolar bioelectrical impedance in women

Objective: to assess the between and within-device reproducibility, as well as within-day variability of body fat measurements. Methods: body fat percentage (%BF) was measured twice on seventeen female students aged between 18 and 20 with a body mass index of 21.9 22.6 kg/m2 (mean SD) using seven bipolar bioelectrical impedance devices (BF-306) according to the manufacturer's recommendations. Each student was also measured each hour between 7:00 and 22:00. Statistical analysis was conducted using a general linear model for repeated measurements. Results: the correlation between first and second measurements was very high (Pearson r between 0.985 and 1.000, p<0.001), as well as the correlation between devices (Pearson r between 0.986 and 0.999, all p<0.001). Repeated measurements analysis showed no differences were between devices (F test=0.83, p=0.59) or readings (first vs. second: F test=0.12, p=0.74). Conversely, significant differences were found between assessment periods throughout the day, measurements made in the morning being lower than those made in the afternoon. Assuming an overall daily average of 100 (based on all measurements), the values were 95.8 3.2 (mean SD) at 8:00 versus 101.3 3.0 at 20:00, corresponding to a mean change of 2.2 1.1 in %BF (F test for repeated values=6.58, p<0.001). Conclusions: the between and within-device reproducibility for measuring body fat is high, enabling the use of multiple devices in a single study. Conversely, small but significant changes in body fat measurements occur during the day, urging body fat measurements to be performed at fixed times.

Interregional compensatory mechanisms of motor functioning in progressing preclinical neurodegeneration.

Understanding brain reserve in preclinical stages of neurodegenerative disorders allows determination of which brain regions contribute to normal functioning despite accelerated neuronal loss. Besides the recruitment of additional regions, a reorganisation and shift of relevance between normally engaged regions are a suggested key mechanism. Thus, network analysis methods seem critical for investigation of changes in directed causal interactions between such candidate brain regions. To identify core compensatory regions, fifteen preclinical patients carrying the genetic mutation leading to Huntington's disease and twelve controls underwent fMRI scanning. They accomplished an auditory paced finger sequence tapping task, which challenged cognitive as well as executive aspects of motor functioning by varying speed and complexity of movements. To investigate causal interactions among brain regions a single Dynamic Causal Model (DCM) was constructed and fitted to the data from each subject. The DCM parameters were analysed using statistical methods to assess group differences in connectivity, and the relationship between connectivity patterns and predicted years to clinical onset was assessed in gene carriers. In preclinical patients, we found indications for neural reserve mechanisms predominantly driven by bilateral dorsal premotor cortex, which increasingly activated superior parietal cortices the closer individuals were to estimated clinical onset. This compensatory mechanism was restricted to complex movements characterised by high cognitive demand. Additionally, we identified task-induced connectivity changes in both groups of subjects towards pre- and caudal supplementary motor areas, which were linked to either faster or more complex task conditions. Interestingly, coupling of dorsal premotor cortex and supplementary motor area was more negative in controls compared to gene mutation carriers. Furthermore, changes in the connectivity pattern of gene carriers allowed prediction of the years to estimated disease onset in individuals. Our study characterises the connectivity pattern of core cortical regions maintaining motor function in relation to varying task demand. We identified connections of bilateral dorsal premotor cortex as critical for compensation as well as task-dependent recruitment of pre- and caudal supplementary motor area. The latter finding nicely mirrors a previously published general linear model-based analysis of the same data. Such knowledge about disease specific inter-regional effective connectivity may help identify foci for interventions based on transcranial magnetic stimulation designed to stimulate functioning and also to predict their impact on other regions in motor-associated networks.

Proximal tibia volumetric bone mineral density is correlated to the magnitude of local acceleration in male long distance runners.

Introduction: The beneficial effect of physical exercise on bone mineral density (BMD) is at least partly explained by the forces exerted directly on the bones. Male runners present generally higher BMD than sedentary individuals. We postulated that the proximal tibia BMD is related to the running distance as well as to the magnitude of the shocks (while running) in male runners. Methods: A prospective study (three yearly measurements) included 81 healthy male subjects: 16 sedentary lean subjects and three groups of runners (5-30 km/week, n=19; 30-50 km/week, n=29; 50-100 km/week, n=17). Several measurements were performed at the proximal tibia level: volumetric BMD (vBMD), cortical index (CI) i.e. an index of cortical bone thickness and peak accelerations (an index of shocks during heel strike) while running (measured by a 3-D accelerometer). A general linear model assessed the prediction of vBMD or CI by a) simple effects (running distance, peak accelerations, time) and b) interactions (for instance if vBMD prediction by peak acceleration depends on running distance). Results: CI and vBMD a) increase with running distance to reach a plateau over 30 km/wk, b) are positively associated with peak accelerations over 30 km/week. Discussion: Running may be associated with high peak accelerations in order to have beneficial effects on BMD. More important strains are needed to be associated with the same increase in BMD during running sessions of short duration than those of long duration. Conclusion: CI and vBMD are associated with the magnitude of the shocks during heel strike in runners. Key words: Bone mineral density, strains, physical exercise, running distance.

Opportunity for catch-up HPV vaccination in young women after first delivery.

Background Early age at first delivery has been identified as a risk factor for high-risk HPV-type infection and cervical cancer development. Methods A cross-sectional study was carried out in a large public maternity hospital in Sao Paulo, Brazil. During June 2006 to February 2007, 301 women aged 15-24 years who gave birth to their first child were recruited between 43 and 60 days after delivery. Detection of HPV DNA in cervical specimens was performed using a standardised PCR protocol with PGMY09/11 primers. The association of selected factors with HPV infection was assessed by using a Generalised Linear Model. Results HPV DNA was detected in 58.5% (95% CI 52.7% to 64.0%) of the enrolled young women. The most common types of HPV found were: HPV16, HPV51, HPV52, HPV58 and HPV71. The overall prevalence of HPV types targeted by the HPV prophylactic vaccines was: HPV 16-12.0%, HPV 18-2.3% and HPV 6 and 11 4.3%. In the multivariate analysis, only age (inversely, p for trend=0.02) and smoking habits were independently associated with HPV infection. Conclusions The findings show that these young primiparous women had high cervical HPV prevalence, suggesting that this is a high-risk group for cervical cancer development. Nevertheless, 17.3% were positive for any of the four HPV types included in HPV vaccines (HPV6, 11, 16 or 18), with 13.3% positive for HPV 16 or 18 and only 1.0% having both vaccine related-oncogenic HPV types. Thus, young primiparous women could benefit from catch-up HPV vaccination programmes.

iLOGP: A Simple, Robust, and Efficient Description of n-Octanol/Water Partition Coefficient for Drug Design Using the GB/SA Approach.

The n-octanol/water partition coefficient (log Po/w) is a key physicochemical parameter for drug discovery, design, and development. Here, we present a physics-based approach that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the experimental log Po/w on a cleansed data set of more than 17,500 molecules. After internal validation by five-fold cross-validation and data randomization, the predictive power of the most interesting multiple linear model, based on two GB/SA parameters solely, was tested on two different external sets of molecules. On the Martel druglike test set, the predictive power of the best model (N = 706, r = 0.64, MAE = 1.18, and RMSE = 1.40) is similar to six well-established empirical methods. On the 17-drug test set, our model outperformed all compared empirical methodologies (N = 17, r = 0.94, MAE = 0.38, and RMSE = 0.52). The physical basis of our original GB/SA approach together with its predictive capacity, computational efficiency (1 to 2 s per molecule), and tridimensional molecular graphics capability lay the foundations for a promising predictor, the implicit log P method (iLOGP), to complement the portfolio of drug design tools developed and provided by the SIB Swiss Institute of Bioinformatics.

Do habitat suitability models reliably predict the recovery areas of threatened species?

1. Identifying those areas suitable for recolonization by threatened species is essential to support efficient conservation policies. Habitat suitability models (HSM) predict species' potential distributions, but the quality of their predictions should be carefully assessed when the species-environment equilibrium assumption is violated.2. We studied the Eurasian otter Lutra lutra, whose numbers are recovering in southern Italy. To produce widely applicable results, we chose standard HSM procedures and looked for the models' capacities in predicting the suitability of a recolonization area. We used two fieldwork datasets: presence-only data, used in the Ecological Niche Factor Analyses (ENFA), and presence-absence data, used in a Generalized Linear Model (GLM). In addition to cross-validation, we independently evaluated the models with data from a recolonization event, providing presences on a previously unoccupied river.3. Three of the models successfully predicted the suitability of the recolonization area, but the GLM built with data before the recolonization disagreed with these predictions, missing the recolonized river's suitability and badly describing the otter's niche. Our results highlighted three points of relevance to modelling practices: (1) absences may prevent the models from correctly identifying areas suitable for a species spread; (2) the selection of variables may lead to randomness in the predictions; and (3) the Area Under Curve (AUC), a commonly used validation index, was not well suited to the evaluation of model quality, whereas the Boyce Index (CBI), based on presence data only, better highlighted the models' fit to the recolonization observations.4. For species with unstable spatial distributions, presence-only models may work better than presence-absence methods in making reliable predictions of suitable areas for expansion. An iterative modelling process, using new occurrences from each step of the species spread, may also help in progressively reducing errors.5. Synthesis and applications. Conservation plans depend on reliable models of the species' suitable habitats. In non-equilibrium situations, such as the case for threatened or invasive species, models could be affected negatively by the inclusion of absence data when predicting the areas of potential expansion. Presence-only methods will here provide a better basis for productive conservation management practices.