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Find video protocols related to scientific articles indexed in Pubmed.
RandomForest4Life: a Random Forest for predicting ALS disease progression.
Amyotroph Lateral Scler Frontotemporal Degener
PUBLISHED: 08-21-2014
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We describe a method for predicting disease progression in amyotrophic lateral sclerosis (ALS) patients. The method was developed as a submission to the DREAM Phil Bowen ALS Prediction Prize4Life Challenge of summer 2012. Based on repeated patient examinations over a three- month period, we used a random forest algorithm to predict future disease progression. The procedure was set up and internally evaluated using data from 1197 ALS patients. External validation by an expert jury was based on undisclosed information of an additional 625 patients; all patient data were obtained from the PRO-ACT database. In terms of prediction accuracy, the approach described here ranked third best. Our interpretation of the prediction model confirmed previous reports suggesting that past disease progression is a strong predictor of future disease progression measured on the ALS functional rating scale (ALSFRS). We also found that larger variability in initial ALSFRS scores is linked to faster future disease progression. The results reported here furthermore suggested that approaches taking the multidimensionality of the ALSFRS into account promise some potential for improved ALS disease prediction.
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Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression.
Nat. Biotechnol.
PUBLISHED: 07-17-2014
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Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development.
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Estimation of a Predictor's Importance by Random Forests When There Is Missing Data: RISK Prediction in Liver Surgery using Laboratory Data.
Int J Biostat
PUBLISHED: 06-11-2014
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Abstract In the last few decades, new developments in liver surgery have led to an expanded applicability and an improved safety. However, liver surgery is still associated with postoperative morbidity and mortality, especially in extended resections. We analyzed a large liver surgery database to investigate whether laboratory parameters like haemoglobin, leucocytes, bilirubin, haematocrit and lactate might be relevant preoperative predictors. It is not uncommon to observe missing values in such data. This also holds for many other data sources and research fields. For analysis, one can make use of imputation methods or approaches that are able to deal with missing values in the predictor variables. A representative of the latter are Random Forests which also provide variable importance measures to assess a variable's relevance for prediction. Applied to the liver surgery data, we observed divergent results for the laboratory parameters, depending on the method used to cope with missing values. We therefore performed an extensive simulation study to investigate the properties of each approach. Findings and recommendations: Complete case analysis should not be used as it distorts the relevance of completely observed variables in an undesirable way. The estimation of a variable's importance by a self-contained measure that can deal with missing values appropriately reflects the decreased relevance of variables with missing values. It can therefore be used to obtain insight into Random Forests which are commonly fit without preprocessing of missing values in the data. By contrast, multiple imputation allows for the assessment of a variable's relevance one would potentially observe in complete-data situations, if imputation performs well. For the laboratory data, lactate and bilirubin seem to be associated with the risk of liver failure and postoperative complications. These relations should be investigated by future studies in more detail. However, it is important to carefully consider the method used for analysis when there are missing values in the predictor variables.
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Multiple curve comparisons with an application to the formation of the dorsal funiculus of mutant mice.
Int J Biostat
PUBLISHED: 06-06-2014
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Abstract Much biological experimental data are represented as curves, including measurements of growth, hormone, or enzyme levels, and physical structures. Here we consider the multiple testing problem of comparing two or more nonlinear curves. We model smooth curves of unknown form nonparametrically using penalized splines. We use random effects to model subject-specific deviations from the group-level curve. We present an approach that allows examination of overall differences between the curves of multiple groups and detection of sections in which the curves differ. Adjusted p-values for each single comparison can be obtained by exploiting the connection between semiparametric mixed models and linear mixed models and employing an approach for multiple testing in general parametric models. In simulations, we show that the probability of false-positive findings of differences between any two curves in at least one position can be controlled by a pre-specified error level. We apply our method to compare curves describing the form of the mouse dorsal funiculus - a morphological curved structure in the spinal cord - in mice wild-type for the gene encoding EphA4 or heterozygous with one of two mutations in the gene.
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Predicting birth weight with conditionally linear transformation models.
Stat Methods Med Res
PUBLISHED: 05-10-2014
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Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs.
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Identifying Homogeneous Subgroups in Neurological Disorders: Unbiased Recursive Partitioning in Cervical Complete Spinal Cord Injury.
Neurorehabil Neural Repair
PUBLISHED: 01-28-2014
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Background. The reliable stratification of homogeneous subgroups and the prediction of future clinical outcomes within heterogeneous neurological disorders is a particularly challenging task. Nonetheless, it is essential for the implementation of targeted care and effective therapeutic interventions. Objective. This study was designed to assess the value of a recently developed regression tool from the family of unbiased recursive partitioning methods in comparison to established statistical approaches (eg, linear and logistic regression) for predicting clinical endpoints and for prospective patients' stratification for clinical trials. Methods. A retrospective, longitudinal analysis of prospectively collected neurological data from the European Multicenter study about Spinal Cord Injury (EMSCI) network was undertaken on C4-C6 cervical sensorimotor complete subjects. Predictors were based on a broad set of early (<2 weeks) clinical assessments. Endpoints were based on later clinical examinations of upper extremity motor scores and recovery of motor levels, at 6 and 12 months, respectively. Prediction accuracy for each statistical analysis was quantified by resampling techniques. Results. For all settings, overlapping confidence intervals indicated similar prediction accuracy of unbiased recursive partitioning to established statistical approaches. In addition, unbiased recursive partitioning provided a direct way of identification of more homogeneous subgroups. The partitioning is carried out in a data-driven manner, independently from a priori decisions or predefined thresholds. Conclusion. Unbiased recursive partitioning techniques may improve prediction of future clinical endpoints and the planning of future SCI clinical trials by providing easily implementable, data-driven rationales for early patient stratification based on simple decision rules and clinical read-outs.
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New insights into the consequences of post-windthrow salvage logging revealed by functional structure of saproxylic beetles assemblages.
PLoS ONE
PUBLISHED: 01-01-2014
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Windstorms, bark beetle outbreaks and fires are important natural disturbances in coniferous forests worldwide. Wind-thrown trees promote biodiversity and restoration within production forests, but also cause large economic losses due to bark beetle infestation and accelerated fungal decomposition. Such damaged trees are often removed by salvage logging, which leads to decreased biodiversity and thus increasingly evokes discussions between economists and ecologists about appropriate strategies. To reveal the reasons behind species loss after salvage logging, we used a functional approach based on four habitat-related ecological traits and focused on saproxylic beetles. We predicted that salvage logging would decrease functional diversity (measured as effect sizes of mean pairwise distances using null models) as well as mean values of beetle body size, wood diameter niche and canopy cover niche, but would increase decay stage niche. As expected, salvage logging caused a decrease in species richness, but led to an increase in functional diversity by altering the species composition from habitat-filtered assemblages toward random assemblages. Even though salvage logging removes tree trunks, the most negative effects were found for small and heliophilous species and for species specialized on wood of small diameter. Our results suggested that salvage logging disrupts the natural assembly process on windthrown trees and that negative ecological impacts are caused more by microclimate alteration of the dead-wood objects than by loss of resource amount. These insights underline the power of functional approaches to detect ecosystem responses to anthropogenic disturbance and form a basis for management decisions in conservation. To mitigate negative effects on saproxylic beetle diversity after windthrows, we recommend preserving single windthrown trees or at least their tops with exposed branches during salvage logging. Such an extension of the green-tree retention approach to windthrown trees will preserve natural succession and associated communities of disturbed spruce forests.
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Boosting structured additive quantile regression for longitudinal childhood obesity data.
Int J Biostat
PUBLISHED: 07-30-2013
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Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.
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Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.
PLoS ONE
PUBLISHED: 01-01-2013
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Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited.
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Insects overshoot the expected upslope shift caused by climate warming.
PLoS ONE
PUBLISHED: 01-01-2013
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Along elevational gradients, climate warming may lead to an upslope shift of the lower and upper range margin of organisms. A recent meta-analysis concluded that these shifts are species specific and considerably differ among taxonomic lineages. We used the opportunity to compare upper range margins of five lineages (plants, beetles, flies, hymenoptera, and birds) between 1902-1904 and 2006-2007 within one region (Bavarian Forest, Central Europe). Based on the increase in the regional mean annual temperature during this period and the regional lapse rate, the upslope shift is expected to be between 51 and 201 m. Averaged across species within lineages, the range margin of all animal lineages shifted upslope, but that of plants did not. For animals, the observed shifts were probably due to shifts in temperature and not to changes in habitat conditions. The range margin of plants is therefore apparently not constrained by temperature, a result contrasting recent findings. The mean shift of birds (165 m) was within the predicted range and consistent with a recent global meta-analysis. However, the upslope shift of the three insect lineages (>260 m) exceeded the expected shift even after considering several sources of uncertainty, which indicated a non-linear response to temperature. Our analysis demonstrated broad differences among lineages in their response to climate change even within one region. Furthermore, on the considered scale, the response of ectothermic animals was not consistent with expectations based on shifts in the mean annual temperature. Irrespective of the reasons for the overshooting of the response of the insects, these shifts lead to reorganizations in the composition of assemblages with consequences for ecosystem processes.
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Monotonicity-constrained species distribution models.
Ecology
PUBLISHED: 11-12-2011
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Flexible modeling frameworks for species distribution models based on generalized additive models that allow for smooth, nonlinear effects and interactions are of increasing importance in ecology. Commonly, the flexibility of such smooth function estimates is controlled by means of penalized estimation procedures. However, the actual shape remains unspecified. In many applications, this is not desirable as researchers have a priori assumptions on the shape of the estimated effects, with monotonicity being the most important. Here we demonstrate how monotonicity constraints can be incorporated in a recently proposed flexible framework for species distribution models. Our proposal allows monotonicity constraints to be imposed on smooth effects and on ordinal, categorical variables using an additional asymmetric L2 penalty. Model estimation and variable selection for Red Kite (Milvus milvus) breeding was conducted using the flexible boosting framework implemented in R package mboost.
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Dunnett-type inference in the frailty Cox model with covariates.
Stat Med
PUBLISHED: 07-19-2011
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A frequent objective in medical research is the investigation of differences in patient survival between several experimental treatments and one standard treatment. In order to assess these differences statistically, we have to apply adjustments for multiple comparisons to prevent an increased number of false-positive findings. The most prominent procedure of this type is the Bonferroni correction, which maintains the error level but leads to conservative results. On the basis of a general statistical framework for simultaneous inference, we propose a new statistical procedure for many-to-one comparisons of treatments with adjustment for covariates for clustered survival data modeled by a frailty Cox model. In contrast to the Bonferroni method, dependencies between estimated effects are taken into account. The resulting simultaneous confidence intervals for the hazard ratios of the experimental treatments compared with a control can be interpreted in terms of both statistical significance and clinical importance. The quality of the new procedure is judged by the coverage probability for the simultaneous confidence intervals. Simulation results show an acceptable performance in balanced and various unbalanced designs. The practical merits are demonstrated by a reanalysis of a chronic myelogeneous leukemia clinical trial. The procedure presented here works well for multiple comparisons with a control with adjustment for covariates for survival data from multicenter clinical trials.
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BARD1 expression predicts outcome in colon cancer.
Clin. Cancer Res.
PUBLISHED: 06-21-2011
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BARD1 is a BRCA1-binding partner with tumor suppressive properties. Aberrant splice variants of BARD1 have been detected in various cancers, and it has been postulated that the presence of some splice variants is cancer specific. This is the first study assessing BARD1 expression patterns and correlation with clinical outcome in colon cancer.
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Case studies in reproducibility.
Brief. Bioinformatics
PUBLISHED: 01-28-2011
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Reproducible research is a concept of providing access to data and software along with published scientific findings. By means of some case studies from different disciplines, we will illustrate reasons why readers should be given the possibility to look at the data and software independently from the authors of the original publication. We report results of a survey comprising 100 papers recently published in Bioinformatics. The main finding is that authors of this journal share a culture of making data available. However, the number of papers where source code for simulation studies or analyzes is available is still rather limited.
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Testing the additional predictive value of high-dimensional molecular data.
BMC Bioinformatics
PUBLISHED: 02-08-2010
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While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature.
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A robust procedure for comparing multiple means under heteroscedasticity in unbalanced designs.
PLoS ONE
PUBLISHED: 01-26-2010
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Investigating differences between means of more than two groups or experimental conditions is a routine research question addressed in biology. In order to assess differences statistically, multiple comparison procedures are applied. The most prominent procedures of this type, the Dunnett and Tukey-Kramer test, control the probability of reporting at least one false positive result when the data are normally distributed and when the sample sizes and variances do not differ between groups. All three assumptions are non-realistic in biological research and any violation leads to an increased number of reported false positive results. Based on a general statistical framework for simultaneous inference and robust covariance estimators we propose a new statistical multiple comparison procedure for assessing multiple means. In contrast to the Dunnett or Tukey-Kramer tests, no assumptions regarding the distribution, sample sizes or variance homogeneity are necessary. The performance of the new procedure is assessed by means of its familywise error rate and power under different distributions. The practical merits are demonstrated by a reanalysis of fatty acid phenotypes of the bacterium Bacillus simplex from the "Evolution Canyons" I and II in Israel. The simulation results show that even under severely varying variances, the procedure controls the number of false positive findings very well. Thus, the here presented procedure works well under biologically realistic scenarios of unbalanced group sizes, non-normality and heteroscedasticity.
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Variable selection and model choice in geoadditive regression models.
Biometrics
PUBLISHED: 09-03-2009
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Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.
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Order-restricted scores test for the evaluation of population-based case-control studies when the genetic model is unknown.
Biom J
PUBLISHED: 08-04-2009
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The Cochran-Armitage (CA) linear trend test for proportions is often used for genotype-based analysis of candidate gene association. Depending on the underlying genetic mode of inheritance, the use of model-specific scores maximises the power. Commonly, the underlying genetic model, i.e. additive, dominant or recessive mode of inheritance, is a priori unknown. Association studies are commonly analysed using permutation tests, where both inference and identification of the underlying mode of inheritance are important. Especially interesting are tests for case-control studies, defined by a maximum over a series of standardised CA tests, because such a procedure has power under all three genetic models. We reformulate the test problem and propose a conditional maximum test of scores-specific linear-by-linear association tests. For maximum-type, sum and quadratic test statistics the asymptotic expectation and covariance can be derived in a closed form and the limiting distribution is known. Both the limiting distribution and approximations of the exact conditional distribution can easily be computed using standard software packages. In addition to these technical advances, we extend the area of application to stratified designs, studies involving more than two groups and the simultaneous analysis of multiple loci by means of multiplicity-adjusted p-values for the underlying multiple CA trend tests. The new test is applied to reanalyse a study investigating genetic components of different subtypes of psoriasis. A new and flexible inference tool for association studies is available both theoretically as well as practically since already available software packages can be easily used to implement the suggested test procedures.
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Trend tests for the evaluation of exposure-response relationships in epidemiological exposure studies.
Epidemiol Perspect Innov
PUBLISHED: 03-06-2009
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One possibility for the statistical evaluation of trends in epidemiological exposure studies is the use of a trend test for data organized in a 2 x k contingency table. Commonly, the exposure data are naturally grouped or continuous exposure data are appropriately categorized. The trend test should be sensitive to any shape of the exposure-response relationship. Commonly, a global trend test only determines whether there is a trend or not. Once a trend is seen it is important to identify the likely shape of the exposure-response relationship. This paper introduces a best contrast approach and an alternative approach based on order-restricted information criteria for the model selection of a particular exposure-response relationship. For the simple change point alternative H1 : pi1 = ...= piq
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A PAUC-based estimation technique for disease classification and biomarker selection.
Stat Appl Genet Mol Biol
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The partial area under the receiver operating characteristic curve (PAUC) is a well-established performance measure to evaluate biomarker combinations for disease classification. Because the PAUC is defined as the area under the ROC curve within a restricted interval of false positive rates, it enables practitioners to quantify sensitivity rates within pre-specified specificity ranges. This issue is of considerable importance for the development of medical screening tests. Although many authors have highlighted the importance of PAUC, there exist only few methods that use the PAUC as an objective function for finding optimal combinations of biomarkers. In this paper, we introduce a boosting method for deriving marker combinations that is explicitly based on the PAUC criterion. The proposed method can be applied in high-dimensional settings where the number of biomarkers exceeds the number of observations. Additionally, the proposed method incorporates a recently proposed variable selection technique (stability selection) that results in sparse prediction rules incorporating only those biomarkers that make relevant contributions to predicting the outcome of interest. Using both simulated data and real data, we demonstrate that our method performs well with respect to both variable selection and prediction accuracy. Specifically, if the focus is on a limited range of specificity values, the new method results in better predictions than other established techniques for disease classification.
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Preoperative chemoradiotherapy and postoperative chemotherapy with fluorouracil and oxaliplatin versus fluorouracil alone in locally advanced rectal cancer: initial results of the German CAO/ARO/AIO-04 randomised phase 3 trial.
Lancet Oncol.
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Preoperative chemoradiotherapy, total mesorectal excision surgery, and adjuvant chemotherapy with fluorouracil is the standard combined modality treatment for rectal cancer. With the aim of improving disease-free survival (DFS), this phase 3 study (CAO/ARO/AIO-04) integrated oxaliplatin into standard treatment.
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Large-scale model-based assessment of deer-vehicle collision risk.
PLoS ONE
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Ungulates, in particular the Central European roe deer Capreolus capreolus and the North American white-tailed deer Odocoileus virginianus, are economically and ecologically important. The two species are risk factors for deer-vehicle collisions and as browsers of palatable trees have implications for forest regeneration. However, no large-scale management systems for ungulates have been implemented, mainly because of the high efforts and costs associated with attempts to estimate population sizes of free-living ungulates living in a complex landscape. Attempts to directly estimate population sizes of deer are problematic owing to poor data quality and lack of spatial representation on larger scales. We used data on >74,000 deer-vehicle collisions observed in 2006 and 2009 in Bavaria, Germany, to model the local risk of deer-vehicle collisions and to investigate the relationship between deer-vehicle collisions and both environmental conditions and browsing intensities. An innovative modelling approach for the number of deer-vehicle collisions, which allows nonlinear environment-deer relationships and assessment of spatial heterogeneity, was the basis for estimating the local risk of collisions for specific road types on the scale of Bavarian municipalities. Based on this risk model, we propose a new "deer-vehicle collision index" for deer management. We show that the risk of deer-vehicle collisions is positively correlated to browsing intensity and to harvest numbers. Overall, our results demonstrate that the number of deer-vehicle collisions can be predicted with high precision on the scale of municipalities. In the densely populated and intensively used landscapes of Central Europe and North America, a model-based risk assessment for deer-vehicle collisions provides a cost-efficient instrument for deer management on the landscape scale. The measures derived from our model provide valuable information for planning road protection and defining hunting quota. Open-source software implementing the model can be used to transfer our modelling approach to wildlife-vehicle collisions elsewhere.
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Prediction intervals for future BMI values of individual children: a non-parametric approach by quantile boosting.
BMC Med Res Methodol
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The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age.
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Aggregative response in bats: prey abundance versus habitat.
Oecologia
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In habitats where prey is either rare or difficult to predict spatiotemporally, such as open habitats, predators must be adapted to react effectively to variations in prey abundance. Open-habitat foraging bats have a wing morphology adapted for covering long distances, possibly use information transfer to locate patches of high prey abundance, and would therefore be expected to show an aggregative response at these patches. Here, we examined the effects of prey abundance on foraging activities of open-habitat foragers in comparison to that of edge-habitat foragers and closed-habitat foragers. Bat activity was estimated by counting foraging calls recorded with bat call recorders (38,371 calls). Prey abundance was estimated concurrently at each site using light and pitfall traps. The habitat was characterized by terrestrial laser scanning. Prey abundance increased with vegetation density. As expected, recordings of open-habitat foragers clearly decreased with increasing vegetation density. The foraging activity of edge- and closed-habitat foragers was not significantly affected by the vegetation density, i.e., these guilds were able to forage from open habitats to habitats with dense vegetation. Only open-habitat foragers displayed a significant and proportional aggregative response to increasing prey abundance. Our results suggest that adaptations for effective and low-cost foraging constrains habitat use and excludes the guild of open-habitat foragers from foraging in habitats with high prey abundance, such as dense forest stands.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.