Simultaneous Inference Using Multiple Marginal Models.

This tutorial describes single-step low-dimensional simultaneous inference with a focus on the availability of adjusted p values and compatible confidence intervals for more than just the usual mean value comparisons. The basic idea is, first, to use the influence of correlation on the quantile of the multivariate t-distribution: the higher the less conservative. In addition, second, the estimability of the correlation matrix using the multiple marginal models approach (mmm) using multiple models in the class of linear up to generalized linear mixed models. The underlying maxT-test using mmm is discussed by means of several real data scenarios using selected R packages. Surprisingly, different features are highlighted, among them: (i) analyzing different-scaled, correlated, multiple endpoints, (ii) analyzing multiple correlated binary endpoints, (iii) modeling dose as qualitative factor and/or quantitative covariate, (iv) joint consideration of several tuning parameters within the poly-k trend test, (v) joint testing of dose and time, (vi) considering several effect sizes, (vii) joint testing of subgroups and overall population in multiarm randomized clinical trials with correlated primary endpoints, (viii) multiple linear mixed effect models, (ix) generalized estimating equations, and (x) nonlinear regression models.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step p $p$ -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.

Three Arabidopsis UMP kinases have different roles in pyrimidine nucleotide biosynthesis and (deoxy)CMP salvage.

Pyrimidine nucleotide monophosphate biosynthesis ends in the cytosol with uridine monophosphate (UMP). UMP phosphorylation to uridine diphosphate (UDP) by UMP KINASEs (UMKs) is required for the generation of all pyrimidine (deoxy)nucleoside triphosphates as building blocks for nucleic acids and central metabolites like UDP-glucose. The Arabidopsis (Arabidopsis thaliana) genome encodes five UMKs and three belong to the AMP KINASE (AMK)-like UMKs, which were characterized to elucidate their contribution to pyrimidine metabolism. Mitochondrial UMK2 and cytosolic UMK3 are evolutionarily conserved, whereas cytosolic UMK1 is specific to the Brassicaceae. In vitro, all UMKs can phosphorylate UMP, cytidine monophosphate (CMP) and deoxycytidine monophosphate (dCMP), but with different efficiencies. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated nuclease 9 (Cas9)-induced null mutants were generated for UMK1 and UMK2, but not for UMK3, since frameshift alleles were lethal for germline cells. However, a mutant with diminished UMK3 activity showing reduced growth was obtained. Metabolome analyses of germinating seeds and adult plants of single- and higher-order mutants revealed that UMK3 plays an indispensable role in the biosynthesis of all pyrimidine (deoxy)nucleotides and UDP-sugars, while UMK2 is important for dCMP recycling that contributes to mitochondrial DNA stability. UMK1 is primarily involved in CMP recycling. We discuss the specific roles of these UMKs referring also to the regulation of pyrimidine nucleoside triphosphate synthesis.

A Multifunctional Nanostructured Hydrogel as a Platform for Deciphering Niche Interactions of Hematopoietic Stem and Progenitor Cells.

For over half a century, hematopoietic stem cells (HSCs) have been used for transplantation therapy to treat severe hematologic diseases. Successful outcomes depend on collecting sufficient donor HSCs as well as ensuring efficient engraftment. These processes are influenced by dynamic interactions of HSCs with the bone marrow niche, which can be revealed by artificial niche models. Here, a multifunctional nanostructured hydrogel is presented as a 2D platform to investigate how the interdependencies of cytokine binding and nanopatterned adhesive ligands influence the behavior of human hematopoietic stem and progenitor cells (HSPCs). The results indicate that the degree of HSPC polarization and motility, observed when cultured on gels presenting the chemokine SDF-1α and a nanoscale-defined density of a cellular (IDSP) or extracellular matrix (LDV) α4ß1 integrin binding motif, are differently influenced on hydrogels functionalized with the different ligand types. Further, SDF-1α promotes cell polarization but not motility. Strikingly, the degree of differentiation correlates negatively with the nanoparticle spacing, which determines ligand density, but only for the cellular-derived IDSP motif. This mechanism potentially offers a means of predictably regulating early HSC fate decisions. Consequently, the innovative multifunctional hydrogel holds promise for deciphering dynamic HSPC-niche interactions and refining transplantation therapy protocols.

Application of Statistical Methods for Central Statistical Monitoring and Implementations on the German Multiple Sclerosis Registry.

Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD).

Pyrimidine catabolism is required to prevent the accumulation of 5-methyluridine in RNA.

5-Methylated cytosine is a frequent modification in eukaryotic RNA and DNA influencing mRNA stability and gene expression. Here we show that free 5-methylcytidine (5mC) and 5-methyl-2'-deoxycytidine are generated from nucleic acid turnover in Arabidopsis thaliana, and elucidate how these cytidines are degraded, which is unclear in eukaryotes. First CYTIDINE DEAMINASE produces 5-methyluridine (5mU) and thymidine which are subsequently hydrolyzed by NUCLEOSIDE HYDROLASE 1 (NSH1) to thymine and ribose or deoxyribose. Interestingly, far more thymine is generated from RNA than from DNA turnover, and most 5mU is directly released from RNA without a 5mC intermediate, since 5-methylated uridine (m5U) is an abundant RNA modification (m5U/U ∼1%) in Arabidopsis. We show that m5U is introduced mainly by tRNA-SPECIFIC METHYLTRANSFERASE 2A and 2B. Genetic disruption of 5mU degradation in the NSH1 mutant causes m5U to occur in mRNA and results in reduced seedling growth, which is aggravated by external 5mU supplementation, also leading to more m5U in all RNA species. Given the similarities between pyrimidine catabolism in plants, mammals and other eukaryotes, we hypothesize that the removal of 5mU is an important function of pyrimidine degradation in many organisms, which in plants serves to protect RNA from stochastic m5U modification.

A comparison of strategies for generating artificial replicates in RNA-seq experiments.

Due to the overall high costs, technical replicates are usually omitted in RNA-seq experiments, but several methods exist to generate them artificially. Bootstrapping reads from FASTQ-files has recently been used in the context of other NGS analyses and can be used to generate artificial technical replicates. Bootstrapping samples from the columns of the expression matrix has already been used for DNA microarray data and generates a new artificial replicate of the whole experiment. Mixing data of individual samples has been used for data augmentation in machine learning. The aim of this comparison is to evaluate which of these strategies are best suited to study the reproducibility of differential expression and gene-set enrichment analysis in an RNA-seq experiment. To study the approaches under controlled conditions, we performed a new RNA-seq experiment on gene expression changes upon virus infection compared to untreated control samples. In order to compare the approaches for artificial replicates, each of the samples was sequenced twice, i.e. as true technical replicates, and differential expression analysis and GO term enrichment analysis was conducted separately for the two resulting data sets. Although we observed a high correlation between the results from the two replicates, there are still many genes and GO terms that would be selected from one replicate but not from the other. Cluster analyses showed that artificial replicates generated by bootstrapping reads produce it p values and fold changes that are close to those obtained from the true data sets. Results generated from artificial replicates with the approaches of column bootstrap or mixing observations were less similar to the results from the true replicates. Furthermore, the overlap of results among replicates generated by column bootstrap or mixing observations was much stronger than among the true replicates. Artificial technical replicates generated by bootstrapping sequencing reads from FASTQ-files are better suited to study the reproducibility of results from differential expression and GO term enrichment analysis in RNA-seq experiments than column bootstrap or mixing observations. However, FASTQ-bootstrapping is computationally more expensive than the other two approaches. The FASTQ-bootstrapping may be applicable to other applications of high-throughput sequencing.

Simultaneous confidence intervals for contrasts of quantiles.

Skewed distributions and inferences concerning quantiles are common in the health and social science researches. And most standard simultaneous inference procedures require the normality assumption. For example, few methods exist for comparing the medians of independent samples or quantiles of several distributions in general. To our knowledge, there is no easy-to-use method for constructing simultaneous confidence intervals for multiple contrasts of quantiles in a one-way layout. In this paper, we develop an asymptotic method for constructing such intervals both for differences and ratios of quantiles and extend the idea to that of right-censored time-to-event data in survival analysis. Small-sample performance of the proposed method and a bootstrap method were assessed in terms of coverage probabilities and average widths of the simultaneous confidence intervals. Good coverage probabilities were observed for most of the distributions considered in our simulations. The proposed methods have been implemented in an R package and are used to analyze two motivating datasets.

The Tukey trend test: Multiplicity adjustment using multiple marginal models.

In dose-response analysis, it is a challenge to choose appropriate linear or curvilinear shapes when considering multiple, differently scaled endpoints. It has been proposed to fit several marginal regression models that try sets of different transformations of the dose levels as explanatory variables for each endpoint. However, the multiple testing problem underlying this approach, involving correlated parameter estimates for the dose effect between and within endpoints, could only be adjusted heuristically. An asymptotic correction for multiple testing can be derived from the score functions of the marginal regression models. Based on a multivariate t-distribution, the correction provides a one-step adjustment of p-values that accounts for the correlation between estimates from different marginal models. The advantages of the proposed methodology are demonstrated through three example datasets, involving generalized linear models with differently scaled endpoints, differing covariates, and a mixed effect model and through simulation results. The methodology is implemented in an R package.

Improving Risk Assessment in Clinical Trials: Toward a Systematic Risk-Based Monitoring Approach.

Regulatory authorities have encouraged the usage of a monitoring (RBM) system in clinical trials before trial initiation for detection of potential risks and inclusion of a mitigation plan in the monitoring strategy. Several RBM tools were developed after the International Council for Harmonization gave sponsors the flexibility to initiate an approach to enhance quality management in a clinical trial. However, various studies have demonstrated the need for improvement of the available RBM tools as each does not provide a comprehensive overview of the characteristics, focus, and application. This research lays out a rationale for a risk methodology assessment (RMA) within the RBM system. The core purpose of RMA is to deliver a scientifically based evaluation and decision of any potential risk in a clinical trial. Thereby, a monitoring plan can be developed to elude prior identified risk outcome. To demonstrate RMA's theoretical approach in practice, a Shiny web application (R Foundation for Statistical Computing) was designed to describe the assessment process of risk analysis and visualization tools that eventually aid in focusing monitoring activities. RMA focuses on the identification of an individual risk and visualizes its weight on the trial. The scoring algorithm of the presented approach computes the assessment of the individual risk in a radar plot and computes the overall score of the trial. Moreover, RMA's novelty lies in its ability to decrease biased decision making during risk assessment by categorizing risk influence and detectability; a characteristic pivotal to serve RBM in assessing risks, and in contributing to a better understanding in the monitoring technique necessary for developing a functional monitoring plan. Future research should focus on validating the power of RMAs to demonstrate its efficiency. This would facilitate the process of characterizing the strengths and weaknesses of RMA in practice.

Protein interaction patterns in Arabidopsis thaliana leaf mitochondria change in dependence to light.

Mitochondrial biology is underpinned by the presence and activity of large protein assemblies participating in the organelle-located steps of respiration, TCA-cycle, glycine oxidation, and oxidative phosphorylation. While the enzymatic roles of these complexes are undisputed, little is known about the interactions of the subunits beyond their presence in these protein complexes and their functions in regulating mitochondrial metabolism. By applying one of the most important regulatory cues for plant metabolism, the presence or absence of light, we here assess changes in the composition and molecular mass of protein assemblies involved in NADH-production in the mitochondrial matrix and in oxidative phosphorylation by employing a differential complexome profiling strategy. Covering a mass up to 25 MDa, we demonstrate dynamic associations of matrix enzymes and of components involved in oxidative phosphorylation. The data presented here form the basis for future studies aiming to advance our understanding of the role of protein:protein interactions in regulating plant mitochondrial functions.

Compressed-sensing accelerated 4D flow MRI of cerebrospinal fluid dynamics.

BACKGROUND: 4D flow magnetic resonance imaging (MRI) of CSF can make an important contribution to the understanding of hydrodynamic changes in various neurological diseases but remains limited in clinical application due to long acquisition times. The aim of this study was to evaluate the accuracy of compressed SENSE accelerated MRI measurements of the spinal CSF flow. METHODS: In 20 healthy subjects 4D flow MRI of the CSF in the cervical spine was acquired using compressed sensitivity encoding [CSE, a combination of compressed sensing and parallel imaging (SENSE) provided by the manufacturer] with acceleration factors between 4 and 10. A conventional scan using SENSE was used as reference. Extracted parameters were peak velocity, absolute net flow, forward flow and backward flow. Bland-Altman analysis was performed to determine the scan-rescan reproducibility and the agreement between SENSE and compressed SENSE. Additionally, a time accumulated flow error was calculated. In one additional subject flow of the spinal canal at the level of the entire spinal cord was assessed. RESULTS: Averaged acquisition times were 10:21 min (SENSE), 9:31 min (CSE4), 6:25 min (CSE6), 4:53 min (CSE8) and 3:51 min (CSE10). Acquisition of the CSF flow surrounding the entire spinal cord took 14:40 min. Bland-Altman analysis showed good agreement for peak velocity, but slight overestimations for absolute net flow, forward flow and backward flow (< 1 ml/min) in CSE4-8. Results of the accumulated flow error were similar for CSE4 to CSE8. CONCLUSION: A quantitative analysis of acceleration factors CSE4-10 showed that CSE with an acceleration factor up to 6 is feasible. This allows a scan time reduction of 40% and enables the acquisition and analysis of the CSF flow dynamics surrounding the entire spinal cord within a clinically acceptable scan time.

Butterfly richness and abundance in flower strips and field margins: the role of local habitat quality and landscape context.

Flower strips, which are created on arable land by sowing species-rich seed mixtures, are considered to have a high potential to counteract species decline of butterflies in the agricultural landscape. However, it remains largely unexplored how various factors (design, habitat quality, landscape context) interact to determine the occurrence of butterflies in flower strips. Therefore, butterflies were surveyed in 15 flower strips differing in age (first and second growing season). Flower strips were compared with 15 field margins, which were adjacent to arable land and were dominated by grasses. The field studies were conducted during two summers (2013, 2014) in Lower Saxony (Germany). Additionally, based on a literature study, 17 environmental variables likely to be decisive for the occurrence of butterflies were identified and recorded during these field studies or analyzed in GIS. Supported by a PCA, 8 environmental variables for flower strips and 7 for field margins, were selected and included in linear mixed-effects models in order to calculate their effect on butterflies. We documented 19 butterfly species and 1,394 individuals in the flower strips and 13 species and 401 individuals in the field margins. The number of flowering plant species was the key factor for the occurrence of butterflies - both in flower strips and field margins. The diversity of the surrounding landscape (Shannon-Index H) had an additional significant influence on butterflies in flower strips, with more species and individuals being observed on areas with a lower Shannon-Index. Number of flowering plant species is the key driver of butterfly diversity and abundance, which improves the habitat quality of flower strips in agricultural landscapes. In order to promote butterflies optimally, flower strips must have a good supply of flowers even over several years. This requires careful design and management, as flower supply often decreases with increasing age of the flower strips. The study indicates that flower strips have a particularly high effect in structurally simple landscapes.

Prediction intervals for overdispersed binomial data with application to historical controls.

Bioassays are highly standardized trials for assessing the impact of a chemical compound on a model organism. In that context, it is standard to compare several treatment groups with an untreated control. If the same type of bioassay is carried out several times, the amount of information about the historical controls rises with every new study. This information can be applied to predict the outcome of one future control using a prediction interval. Since the observations are counts of success out of a given sample size, like mortality or histopathological findings, the data can be assumed to be binomial but may exhibit overdispersion caused by the variability between historical studies. We describe two approaches that account for overdispersion: asymptotic prediction intervals using the quasi-binomial assumption and prediction intervals based on the quantiles of the beta-binomial distribution. Both interval types were α-calibrated using bootstrap methods. For an assessment of the intervals coverage probabilities, a simulation study based on various numbers of historical studies and sample sizes as well as different binomial proportions and varying levels of overdispersion was run. It could be shown that α-calibration can improve the coverage probabilities of both interval types. The coverage probability of the calibrated intervals, calculated based on at least 10 historical studies, was satisfactory close to the nominal 95%. In a last step, the intervals were computed based on a real data set from the NTP homepage, using historical controls from bioassays with the mice strain B6C3F1.

Ectoparasite communities of small-bodied Malagasy primates: seasonal and socioecological influences on tick, mite and lice infestation of Microcebus murinus and M. ravelobensis in northwestern Madagascar.

BACKGROUND: Ectoparasitic infections are of particular interest for endangered wildlife, as ectoparasites are potential vectors for inter- and intraspecific pathogen transmission and may be indicators to assess the health status of endangered populations. Here, ectoparasite dynamics in sympatric populations of two Malagasy mouse lemur species, Microcebus murinus and M. ravelobensis, were investigated over an 11-month period. Furthermore, the animals' body mass was determined as an indicator of body condition, reflecting seasonal and environmental challenges. Living in sympatry, the two study species experience the same environmental conditions, but show distinct differences in socioecology: Microcebus murinus sleeps in tree holes, either solitarily (males) or sometimes in groups (females only), whereas M. ravelobensis sleeps in mixed-sex groups in more open vegetation. RESULTS: Both mouse lemur species hosted ticks (Haemaphysalis sp.), lice (Lemurpediculus sp.) and mites (Trombiculidae gen. sp. and Laelaptidae gen. sp.). Host species, as well as temporal variations (month and year), were identified as the main factors influencing infestation. Tick infestation peaked in the late dry season and was significantly more often observed in M. murinus (P = 0.011), while lice infestation was more likely in M. ravelobensis (P < 0.001) and showed a continuous increase over the course of the dry season. Genetic analyses identified Lemurpediculus sp. infesting both mouse lemur species. Ticks morphologically conform to H. lemuris, but genetic analysis showed a clear differentiation of the specimens collected in this study, suggesting a potentially new tick species. Host body mass decreased from the early to the late dry season, indicating nutritional stress during this period, which may render individuals more susceptible to parasitic infections. CONCLUSIONS: Seasonal differences and species-specific variations in sleeping site ecology in terms of sleeping site type and sociality were determined as key factors influencing ectoparasitism in M. murinus and M. ravelobensis. This needs to be taken into account when evaluating ectoparasite infestations at a given time point. The detection of the same parasite species on two closely related and sympatric host species furthermore indicates a potential pathway for disease transmission, not only within but also between lemur species.

Temperature response of permafrost soil carbon is attenuated by mineral protection.

Climate change in Arctic ecosystems fosters permafrost thaw and makes massive amounts of ancient soil organic carbon (OC) available to microbial breakdown. However, fractions of the organic matter (OM) may be protected from rapid decomposition by their association with minerals. Little is known about the effects of mineral-organic associations (MOA) on the microbial accessibility of OM in permafrost soils and it is not clear which factors control its temperature sensitivity. In order to investigate if and how permafrost soil OC turnover is affected by mineral controls, the heavy fraction (HF) representing mostly MOA was obtained by density fractionation from 27 permafrost soil profiles of the Siberian Arctic. In parallel laboratory incubations, the unfractionated soils (bulk) and their HF were comparatively incubated for 175 days at 5 and 15°C. The HF was equivalent to 70 ± 9% of the bulk CO2 respiration as compared to a share of 63 ± 1% of bulk OC that was stored in the HF. Significant reduction of OC mineralization was found in all treatments with increasing OC content of the HF (HF-OC), clay-size minerals and Fe or Al oxyhydroxides. Temperature sensitivity (Q10) decreased with increasing soil depth from 2.4 to 1.4 in the bulk soil and from 2.9 to 1.5 in the HF. A concurrent increase in the metal-to-HF-OC ratios with soil depth suggests a stronger bonding of OM to minerals in the subsoil. There, the younger 14 C signature in CO2 than that of the OC indicates a preferential decomposition of the more recent OM and the existence of a MOA fraction with limited access of OM to decomposers. These results indicate strong mineral controls on the decomposability of OM after permafrost thaw and on its temperature sensitivity. Thus, we here provide evidence that OM temperature sensitivity can be attenuated by MOA in permafrost soils.

Fecal parasite risk in the endangered proboscis monkey is higher in an anthropogenically managed forest environment compared to a riparian rain forest in Sabah, Borneo.

Understanding determinants shaping infection risk of endangered wildlife is a major topic in conservation medicine. The proboscis monkey, Nasalis larvatus, an endemic primate flagship species for conservation in Borneo, is endangered through habitat loss, but can still be found in riparian lowland and mangrove forests, and in some protected areas. To assess socioecological and anthropogenic influence on intestinal helminth infections in N. larvatus, 724 fecal samples of harem and bachelor groups, varying in size and the number of juveniles, were collected between June and October 2012 from two study sites in Malaysian Borneo: 634 samples were obtained from groups inhabiting the Lower Kinabatangan Wildlife Sanctuary (LKWS), 90 samples were collected from groups of the Labuk Bay Proboscis Monkey Sanctuary (LBPMS), where monkeys are fed on stationary feeding platforms. Parasite risk was quantified by intestinal helminth prevalence, host parasite species richness (PSR), and eggs per gram feces (epg). Generalized linear mixed effect models were applied to explore whether study site, group type, group size, the number of juveniles per group, and sampling month predict parasite risk. At the LBPMS, prevalence and epg of Trichuris spp., strongylids, and Strongyloides spp. but not Ascaris spp., as well as host PSR were significantly elevated. Only for Strongyloides spp., prevalence showed significant changes between months; at both sites, the beginning rainy season with increased precipitation was linked to higher prevalence, suggesting the external life cycle of Strongyloides spp. to benefit from humidity. Higher prevalence, epgs, and PSR within the LBPMS suggest that anthropogenic factors shape host infection risk more than socioecological factors, most likely via higher re-infection rates and chronic stress. Noninvasive measurement of fecal parasite stages is an important tool for assessing transmission dynamics and infection risks for endangered tropical wildlife. Findings will contribute to healthcare management in nature and in anthropogenically managed environments.

Experimental design matters for statistical analysis: how to handle blocking.

BACKGROUND: Nowadays, evaluation of the effects of pesticides often relies on experimental designs that involve multiple concentrations of the pesticide of interest or multiple pesticides at specific comparable concentrations and, possibly, secondary factors of interest. Unfortunately, the experimental design is often more or less neglected when analysing data. Two data examples were analysed using different modelling strategies. First, in a randomized complete block design, mean heights of maize treated with a herbicide and one of several adjuvants were compared. Second, translocation of an insecticide applied to maize as a seed treatment was evaluated using incomplete data from an unbalanced design with several layers of hierarchical sampling. Extensive simulations were carried out to further substantiate the effects of different modelling strategies. RESULTS: It was shown that results from suboptimal approaches (two-sample t-tests and ordinary ANOVA assuming independent observations) may be both quantitatively and qualitatively different from the results obtained using an appropriate linear mixed model. The simulations demonstrated that the different approaches may lead to differences in coverage percentages of confidence intervals and type 1 error rates, confirming that misleading conclusions can easily happen when an inappropriate statistical approach is chosen. CONCLUSION: To ensure that experimental data are summarized appropriately, avoiding misleading conclusions, the experimental design should duly be reflected in the choice of statistical approaches and models. We recommend that author guidelines should explicitly point out that authors need to indicate how the statistical analysis reflects the experimental design. © 2017 Society of Chemical Industry.

A novel multiparametric imaging approach to acute myocarditis using T2-mapping and CMR feature tracking.

BACKGROUND: The aim of this study was to evaluate the diagnostic potential of a novel cardiovascular magnetic resonance (CMR) based multiparametric imaging approach in suspected myocarditis and to compare it to traditional Lake Louise criteria (LLC). METHODS: CMR data from 67 patients with suspected acute myocarditis were retrospectively analyzed. Seventeen age- and gender-matched healthy subjects served as control. T2-mapping data were acquired using a Gradient-Spin-Echo T2-mapping sequence in short-axis orientation. T2-maps were segmented according to the 16-segments AHA-model and segmental T2 values and pixel-standard deviation (SD) were recorded. Afterwards, the parameters maxT2 (the highest segmental T2 value) and madSD (the mean absolute deviation (MAD) of the pixel-SDs) were calculated for each subject. Cine sequences in three long axes and a stack of short-axis views covering the left and right ventricle were analyzed using a dedicated feature tracking algorithm. RESULTS: A multiparametric imaging model containing madSD and LV global circumferential strain (GCSLV) resulted in the highest diagnostic performance in receiver operating curve analyses (area under the curve [AUC] 0.84) when compared to any model containing a single imaging parameter or to LLC (AUC 0.79). Adding late gadolinium enhancement (LGE) to the model resulted in a further increased diagnostic performance (AUC 0.93) and yielded the highest diagnostic sensitivity of 97% and specificity of 77%. CONCLUSIONS: A multiparametric CMR imaging model including the novel T2-mapping derived parameter madSD, the feature tracking derived strain parameter GCSLV and LGE yields superior diagnostic sensitivity in suspected acute myocarditis when compared to any imaging parameter alone and to LLC.

Microbial Community Dynamics in Soil Depth Profiles Over 120,000 Years of Ecosystem Development.

Along a long-term ecosystem development gradient, soil nutrient contents and mineralogical properties change, therefore probably altering soil microbial communities. However, knowledge about the dynamics of soil microbial communities during long-term ecosystem development including progressive and retrogressive stages is limited, especially in mineral soils. Therefore, microbial abundances (quantitative PCR) and community composition (pyrosequencing) as well as their controlling soil properties were investigated in soil depth profiles along the 120,000 years old Franz Josef chronosequence (New Zealand). Additionally, in a microcosm incubation experiment the effects of particular soil properties, i.e., soil age, soil organic matter fraction (mineral-associated vs. particulate), O2 status, and carbon and phosphorus additions, on microbial abundances (quantitative PCR) and community patterns (T-RFLP) were analyzed. The archaeal to bacterial abundance ratio not only increased with soil depth but also with soil age along the chronosequence, coinciding with mineralogical changes and increasing phosphorus limitation. Results of the incubation experiment indicated that archaeal abundances were less impacted by the tested soil parameters compared to Bacteria suggesting that Archaea may better cope with mineral-induced substrate restrictions in subsoils and older soils. Instead, archaeal communities showed a soil age-related compositional shift with the Bathyarchaeota, that were frequently detected in nutrient-poor, low-energy environments, being dominant at the oldest site. However, bacterial communities remained stable with ongoing soil development. In contrast to the abundances, the archaeal compositional shift was associated with the mineralogical gradient. Our study revealed, that archaeal and bacterial communities in whole soil profiles are differently affected by long-term soil development with archaeal communities probably being better adapted to subsoil conditions, especially in nutrient-depleted old soils.