A Bayesian method to estimate the optimal threshold of a longitudinal biomarker

The objective of this study was to develop methods to estimate the optimal threshold of a longitudinal biomarker and its credible interval when the diagnostic test is based on a criterion that reflects a dynamic progression of that biomarker. Two methods are proposed: one parametric and one non‐parametric. In both the cases, the Bayesian inference was used to derive the posterior distribution of the optimal threshold from which an estimate and a credible interval could be obtained. A numerical study shows that the bias of the parametric method is low and the coverage probability of the credible interval close to the nominal value, with a small coverage asymmetry in some cases. This is also true for the non‐parametric method in case of large sample sizes. Both the methods were applied to estimate the optimal prostate‐specific antigen nadir value to diagnose prostate cancer recurrence after a high‐intensity focused ultrasound treatment. The parametric method can also be applied to non‐longitudinal biomarkers.     

Towards robust and repeatable sampling methods in eDNA‐based studies

DNA‐based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the methods used to collect samples upon which these later steps are based. This represents a critical knowledge gap, as methodologically sound field sampling is the foundation for subsequent analyses. We reviewed field sampling methods used for metabarcoding studies of both terrestrial and freshwater ecosystem biodiversity over a nearly three‐year period (n = 75). We found that 95% (n = 71) of these studies used subjective sampling methods and inappropriate field methods and/or failed to provide critical methodological information. It would be possible for researchers to replicate only 5% of the metabarcoding studies in our sample, a poorer level of reproducibility than for ecological studies in general. Our findings suggest greater attention to field sampling methods, and reporting is necessary in eDNA‐based studies of biodiversity to ensure robust outcomes and future reproducibility. Methods must be fully and accurately reported, and protocols developed that minimize subjectivity. Standardization of sampling protocols would be one way to help to improve reproducibility and have additional benefits in allowing compilation and comparison of data from across studies.     

StructureSelector: A web‐based software to select and visualize the optimal number of clusters using multiple methods

Inferences of population genetic structure are of great importance to the fields of ecology and evolutionary biology. The program structure has been widely used to infer population genetic structure. However, previous studies demonstrated that uneven sampling often leads to wrong inferences on hierarchical structure. The most widely used ΔK method tends to identify the uppermost hierarchy of population structure. Recently, four alternative statistics (medmedk , medmeak , maxmedk and maxmeak ) were proposed, which appear to be more accurate than the previously used methods for both even and uneven sampling data. However, the lack of easy‐to‐use software limits the use of these appealing new estimators. Here, we developed a web‐based user‐friendly software structureselector to calculate the four appealing alternative statistics together with the commonly used Ln Pr(X|K) and ΔK statistics. structureselector accepts the result files of structure , admixture or faststructure as input files. It reports the “best” K for each estimator, and the results are available as HTML or tab separated tables. The program can also generate graphical representations for specific K, which can be easily downloaded from the server. The software is freely available at http://lmme.qdio.ac.cn/StructureSelector/ .     

A Bayesian Approach to Joint Mixed‐Effects Models with a Skew‐Normal Distribution and Measurement Errors in Covariates

Summary In recent years, nonlinear mixed‐effects (NLME) models have been proposed for modeling complex longitudinal data. Covariates are usually introduced in the models to partially explain intersubject variations. However, one often assumes that both model random error and random effects are normally distributed, which may not always give reliable results if the data exhibit skewness. Moreover, some covariates such as CD4 cell count may be often measured with substantial errors. In this article, we address these issues simultaneously by jointly modeling the response and covariate processes using a Bayesian approach to NLME models with covariate measurement errors and a skew‐normal distribution. A real data example is offered to illustrate the methodologies by comparing various potential models with different distribution specifications. It is showed that the models with skew‐normality assumption may provide more reasonable results if the data exhibit skewness and the results may be important for HIV/AIDS studies in providing quantitative guidance to better understand the virologic responses to antiretroviral treatment.     

OptimClass: Using species‐to‐cluster fidelity to determine the optimal partition in classification of ecological communities

Question: Community ecologists are often confronted with multiple possible partitions of a single set of records of species composition and/or abundances from several sites. Different methods of numerical classification produce different results, and the question is which of them, and how many clusters, should be selected for interpretation. We demonstrate a new method for identifying the optimal partition from a series of partitions of the same set of sites, based on number of species with high fidelity to clusters in a partition (faithful species). Methods: The new method, OptimClass, has two variants. OptimClass 1 searches the partition with the maximum number of faithful species across all clusters, while OptimClass 2 searches the partition with the maximum number of clusters that contain at least a preselected minimum number of faithful species. Faithful species are determined based on the P value of the Fisher's exact test, as a measure of fidelity. OptimClass was tested on three vegetation datasets that varied in species richness and internal heterogeneity, using several classification algorithms, resemblance measures and cover transformations. Results: Results from both variants of OptimClass depended on the preselected threshold P value for faithful species: higher P gave higher probability that a partition with more clusters was selected as optimal. Good partitions, in terms of OptimClass criteria, involved flexible beta clustering, and also ordinal clustering. Good partitions were also obtained with TWINSPAN when the required number of clusters was small, or UPGMA when the required number of clusters was large. Poor partitions usually resulted from classifications that used resemblance measures and cover transformations emphasizing differences in species cover; this is not unexpected because OptimClass uses a presence/absence‐based fidelity measure. Conclusions: If the aim of a classification is to obtain clusters rich in faithful species, which can be subsequently used as diagnostic species for identification of community types, OptimClass is a suitable method for simultaneous choice of the optimal classification algorithm and optimal number of clusters. It can be computed in the JUICE program.     

Case contamination in electronic health records based case‐control studies

Clinically relevant information from electronic health records (EHRs) permits derivation of a rich collection of phenotypes. Unlike traditionally designed studies where scientific hypotheses are specified a priori before data collection, the true phenotype status of any given individual in EHR‐based studies is not directly available. Structured and unstructured data elements need to be queried through preconstructed rules to identify case and control groups. A sufficient number of controls can usually be identified with high accuracy by making the selection criteria stringent. But more relaxed criteria are often necessary for more thorough identification of cases to ensure achievable statistical power. The resulting pool of candidate cases consists of genuine cases contaminated with noncase patients who do not satisfy the control definition. The presence of patients who are neither true cases nor controls among the identified cases is a unique challenge in EHR‐based case‐control studies. Ignoring case contamination would lead to biased estimation of odds ratio association parameters. We propose an estimating equation approach to bias correction, study its large sample property, and evaluate its performance through extensive simulation studies and an application to a pilot study of aortic stenosis in the Penn medicine EHR. Our method holds the promise of facilitating more efficient EHR studies by accommodating enlarged albeit contaminated case pools.     

Application of iterative robust model‐based optimal experimental design for the calibration of biocatalytic models

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model‐based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω‐transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278–1293, 2017     

Advances in spatial,individual‐based modelling of forest dynamics

Many individual‐based models of forest dynamics lack spatial complexity. Although, in certain cases, spatially simple models may not be substantially inferior to spatially complex models, advances in vegetation science indicate potential weaknesses, particularly the lack of consideration of propagule availability in horizontal space, and varying patch (or canopy gap) dimensions. Models with vertical and horizontal spatial complexity can address these issues, but, thus far, evidence that they outperform patch (or gap) models is limited. Comparison of projections from models that differ only in their spatial complexity is needed to address the effects of propagule availability in space, spatial pattern of canopy tree mortality, and spatial resolution.     

Identifyability measures to select the parameters to be estimated in a solid‐state fermentation distributed parameter model

Process modeling can lead to of advantages such as helping in process control, reducing process costs and product quality improvement. This work proposes a solid‐state fermentation distributed parameter model composed by seven differential equations with seventeen parameters to represent the process. Also, parameters estimation with a parameters identifyability analysis (PIA) is performed to build an accurate model with optimum parameters. Statistical tests were made to verify the model accuracy with the estimated parameters considering different assumptions. The results have shown that the model assuming substrate inhibition better represents the process. It was also shown that eight from the seventeen original model parameters were nonidentifiable and better results were obtained with the removal of these parameters from the estimation procedure. Therefore, PIA can be useful to estimation procedure, since it may reduce the number of parameters that can be evaluated. Further, PIA improved the model results, showing to be an important procedure to be taken. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:905–917, 2016     

Normal‐mode‐based modeling of allosteric couplings that underlie cyclic conformational transition in F1 ATPase

F₁ ATPase, a rotary motor comprised of a central stalk ( γ subunit) enclosed by three α and β subunits alternately arranged in a hexamer, features highly cooperative binding and hydrolysis of ATP. Despite steady progress in biophysical, biochemical, and computational studies of this fascinating motor, the structural basis for cooperative ATPase involving its three catalytic sites remains not fully understood. To illuminate this key mechanistic puzzle, we have employed a coarse‐grained elastic network model to probe the allosteric couplings underlying the cyclic conformational transition in F₁ ATPase at a residue level of detail. We will elucidate how ATP binding and product (ADP and phosphate) release at two catalytic sites are coupled with the rotation of γ subunit via various domain motions in α ₃ β ₃ hexamer (including intrasubunit hinge‐bending motions in β subunits and intersubunit rigid‐body rotations between adjacent α and β subunits). To this end, we have used a normal‐mode‐based correlation analysis to quantify the allosteric couplings of these domain motions to local motions at catalytic sites and the rotation of γ subunit. We have then identified key amino acid residues involved in the above couplings, some of which have been validated against past studies of mutated and γ ‐truncated F₁ ATPase. Our finding strongly supports a binding change mechanism where ATP binding to the empty catalytic site triggers a series of intra‐ and intersubunit domain motions leading to ATP hydrolysis and product release at the other two closed catalytic sites. Proteins 2009. © 2009 Wiley‐Liss, Inc.