The statistical analysis of tidal rhythms: Tests of the relative effectiveness of five methods using model simulations and actual data

Endogenous rhythms in intertidal organisms are often very complex and imprecise. Thus, confusing results are sometimes obtained when applying various interpretive analytical techniques. In an attempt to resolve this problem, ten different models representing typical organismic tidal‐rhythm displays were created and examined with five different inferential statistical techniques. The exercise was designed to test the relative effectiveness of these techniques in detecting the presence of known cycles in the models, and estimating their period lengths. The same comparison was then repeated on sets of animal‐derived data.

All of the five methods had their merits, but, depending on the model being examined, the results from the various methods were not identical. Three of the techniques produce harmonics, making data that contain multiple periods especially difficult to decipher. Often both tidal and circadian periods are displayed by shore dwellers; all five methods were able to find these two periods. But when the difference in circa period length was close, only one technique (array analysis) could make a distinction. This technique was also the only one able to handle data in which the period was not constant. Interestingly, this simplest of methods is probably the best all‐round method of discovery. Many more subtle, but important, differences were also noted, and it is recommended that more than one method always be used to ensure accuracy.     

Multiscale methods for macromolecular simulations

In this article we review the key modeling tools available for simulating biomolecular systems. We consider recent developments and representative applications of mixed quantum mechanics/molecular mechanics (QM/MM), elastic network models (ENMs), coarse-grained molecular dynamics, and grid-based tools for calculating interactions between essentially rigid protein assemblies. We consider how the different length scales can be coupled, both in a sequential fashion (e.g. a coarse-grained or grid model using parameterization from MD simulations), and via concurrent approaches, where the calculations are performed together and together control the progression of the simulation. We suggest how the concurrent coupling approach familiar in the context of QM/MM calculations can be generalized, and describe how this has been done in the CHARMM macromolecular simulation package.     

The relative sensitivity of different alignment methods and character codings in sensitivity analysis

Sensitivity analysis provides a way to measure robustness of clades in sequence‐based phylogenetic analyses to variation in alignment parameters rather than measuring their branch support. We compared three different approaches to multiple sequence alignment in the context of sensitivity analysis: progressive pairwise alignment, as implemented in MUSCLE; simultaneous multiple alignment of sequence fragments, as implemented in DCA; and direct optimization followed by generation of the implied alignment(s), as implemented in POY. We set out to determine the relative sensitivity of these three alignment methods using rDNA sequences and randomly generated sequences. A total of 36 parameter sets were used to create the alignments, varying the transition, transversion, and gap costs. Tree searches were performed using four different character‐coding and weighting approaches: the cost function used for alignment or equally weighted parsimony with gap positions treated as missing data, separate characters, or as fifth states. POY was found to be as sensitive, or more sensitive, to variation in alignment parameters than DCA and MUSCLE for the three empirical datasets, and POY was found to be more sensitive than MUSCLE, which in turn was found to be as sensitive, or more sensitive, than DCA when applied to the randomly generated sequences when sensitivity was measured using the averaged jackknife values. When significant differences in relative sensitivity were found between the different ways of weighting character‐state changes, equally weighted parsimony, for all three ways of treating gapped positions, was less sensitive than applying the same cost function used in alignment for phylogenetic analysis. When branch support is incorporated into the sensitivity criterion, our results favour the use of simultaneous alignment and progressive pairwise alignment using the similarity criterion over direct optimization followed by using the implied alignment(s) to calculate branch support.     

Recent advances in implicit solvent-based methods for biomolecular simulations

Implicit solvent-based methods play an increasingly important role in molecular modeling of biomolecular structure and dynamics. Recent methodological developments have mainly focused on the extension of the generalized Born (GB) formalism for variable dielectric environments and accurate treatment of nonpolar solvation. Extensive efforts in parameterization of GB models and implicit solvent force fields have enabled ab initio simulation of protein folding to native or near-native structures. Another exciting area that has benefited from the advances in implicit solvent models is the development of constant pH molecular dynamics methods, which have recently been applied to the calculations of protein pK(a) values and the studies of pH-dependent peptide and protein folding.     

Magnetic fluid hyperthermia simulations in evaluation of SAR calculation methods

PurposeThe purpose of this study is to employ magnetic fluid hyperthermia simulations in the precise computation of Specific Absorption Rate functions -SAR(T)-, and in the evaluation of the predictive capacity of different SAR calculation methods.MethodsMagnetic fluid hyperthermia experiments were carried out using magnetite-based nanofluids. The respective SAR values were estimated through four different calculation methods including the initial slope method, the Box-Lucas method, the corrected slope method and the incremental analysis method (INCAM). A novel numerical model combining the heat transfer equations and the Navier-Stokes equations was developed to reproduce the experimental heating process. To address variations in heating efficiency with temperature, the expression of the power dissipation as a Gaussian function of temperature was introduced and the Levenberg-Marquardt optimization algorithm was employed to compute the function parameters and determine the function’s effective branch within each measurement’s temperature range. The power dissipation function was then reduced to the respective SAR function.ResultsThe INCAM exhibited the lowest relative errors ranging between 0.62 and 15.03% with respect to the simulations. SAR(T) functions exhibited significant variations, up to 45%, within the MFH-relevant temperature range.ConclusionsThe examined calculation methods are not suitable to accurately quantify the heating efficiency of a magnetic fluid. Numerical models can be exploited to effectively compute SAR(T) and contribute to the development of robust hyperthermia treatment planning applications.     

Comparison of methods for calculating relative survival in population-based studies

Background: It is vital that unbiased estimates of relative survival are estimated and reported by cancer registries. A single figure of relative survival is often required to make reporting simpler. This can be obtained by pooling all ages or, more commonly, by using age-standardisation. The various methods for providing a single figure estimate of relative survival can give very different estimates. Methods: The problem is illustrated through an example using Finnish thyroid cancer data. The differences are further explored through a simulation study that investigates the effect of age on the estimates of relative survival. Results: The example highlights that in practice the all-age estimates from the various methods can be substantially different (up to 6 percentage units at 15 years of follow-up). The simulation study confirms the finding that differing estimates for the all-age estimates of relative survival are obtained. Performing age-standardisation makes the methods more comparable and results in better estimation of the true net survival. Conclusions: The all-age estimates of relative survival rarely give an appropriate estimate of net survival. We feel that modelling or stratifying by age when calculating relative survival is vitally important as the lack of homogeneity in the cohort of patients leads to potentially biased estimates. We feel that the methods using modelling provide a greater flexibility than life-table based approaches. The flexible parametric approach does not require an arbitrary splitting of the time-scale, which makes it more computationally efficient. It also has the advantage of easily being extended to incorporate time-dependent effects.     

Computer assisted simulations and molecular graphics methods in molecular design

A survey is presented of computer-assisted statistical mechanical methods. The general theoretical background is described and special methods are discussed in detail. Practical procedures allowing for the calculation of binding energies are examined. A recent perturbation-relaxation procedure is summarized.     

Recruiting machine learning methods for molecular simulations of proteins

Abstract

Molecular dynamics (MD) simulations are critical to understanding the movements of proteins in time. Yet, MD simulations are limited due to the availability of high-resolution protein structures, accuracy of the underlying force-field, computational expense, and difficulty in analysing big data-sets. Machine learning algorithms are now routinely used to circumvent many of these limitations and computational biophysicists are continuously making progress in developing novel applications. Here, we discuss some of these methods, varying from traditional dimensionality reduction approaches to more recent abstractions such as transfer learning and reinforcement learning, and how they have been used to deal with the challenges in MD. We conclude with the prospective issues in the application of machine learning methods in MD, to increase accuracy and efficiency of protein dynamics studies in general.     

Cardiac performance correlates of relative heart ventricle mass in amphibians

This study used an in situ heart preparation to analyze the power output and stroke work of spontaneously beating hearts of four anurans (Rhinella marina, Lithobates catesbeianus, Xenopus laevis, Pyxicephalus edulis) and three urodeles (Necturus maculosus, Ambystoma tigrinum, Amphiuma tridactylum) that span a representative range of relative ventricle mass (RVM) found in amphibians. Previous research has documented that RVM correlates with dehydration tolerance and maximal aerobic capacity in amphibians. The power output (mW g^?1 ventricle mass) and stroke work (mJ g^?1 ventricle muscle mass) were independent of RVM and were indistinguishable from previously published results for fish and reptiles. RVM was significantly correlated with maximum power output (P _max, mW kg^?1 body mass), stroke volume, cardiac output, afterload pressure (P _O) at P _max, and preload pressure (P _I) at P _max. P _I at P _max and P _O at P _max also correlated very closely with each other. The increases in both P _I and P _O at maximal power outputs in large hearts suggest that concomitant increases in blood volume and/or increased modulation of vascular compliance either anatomically or via sympathetic tone on the venous vasculature would be necessary to achieve P _max in vivo. Hypotheses for variation in RVM and its concomitant increased P _max in amphibians are developed.     

Design and performance frameworks for constructing problem-solving simulations

Rapid advancements in hardware, software, and connectivity are helping to shorten the times needed to develop computer simulations for science education. These advancements, however, have not been accompanied by corresponding theories of how best to design and use these technologies for teaching, learning, and testing. Such design frameworks ideally would be guided less by the strengths/limitations of the presentation media and more by cognitive analyses detailing the goals of the tasks, the needs and abilities of students, and the resulting decision outcomes needed by different audiences. This article describes a problem-solving environment and associated theoretical framework for investigating how students select and use strategies as they solve complex science problems. A framework is first described for designing on-line problem spaces that highlights issues of content, scale, cognitive complexity, and constraints. While this framework was originally designed for medical education, it has proven robust and has been successfully applied to learning environments from elementary school through medical school. Next, a similar framework is detailed for collecting student performance and progress data that can provide evidence of students' strategic thinking and that could potentially be used to accelerate student progress. Finally, experimental validation data are presented that link strategy selection and use with other metrics of scientific reasoning and student achievement.     

Recent developments in QM/MM methods towards open-boundary multi-scale simulations

Combined quantum mechanics/molecular mechanics (QM/MM) methods have been widely used in multi-scale modelling and simulations of physical, chemical and biological processes in complex environments. In this review, we provide an overview of the recently developed QM/MM algorithms, with emphasis on our works, towards the ultimate goal of establishing an open boundary between the QM and MM subsystems. The open boundary is characterised by on-the-fly exchanges of partial charges and atoms between the QM and MM subsystems, allowing us to focus on the small QM subsystem of primary interest in dynamics simulations. An open-boundary scheme has the promise to the utilisations of small QM subsystems, high-levels of QM theory and long simulation times, which can potentially lead to new insights.     

High performance computing in biology: multimillion atom simulations of nanoscale systems

Computational methods have been used in biology for sequence analysis (bioinformatics), all-atom simulation (molecular dynamics and quantum calculations), and more recently for modeling biological networks (systems biology). Of these three techniques, all-atom simulation is currently the most computationally demanding, in terms of compute load, communication speed, and memory load. Breakthroughs in electrostatic force calculation and dynamic load balancing have enabled molecular dynamics simulations of large biomolecular complexes. Here, we report simulation results for the ribosome, using approximately 2.64 million atoms, the largest all-atom biomolecular simulation published to date. Several other nano-scale systems with different numbers of atoms were studied to measure the performance of the NAMD molecular dynamics simulation program on the Los Alamos National Laboratory Q Machine. We demonstrate that multimillion atom systems represent a 'sweet spot' for the NAMD code on large supercomputers. NAMD displays an unprecedented 85% parallel scaling efficiency for the ribosome system on 1024 CPUs. We also review recent targeted molecular dynamics simulations of the ribosome that prove useful for studying conformational changes of this large biomolecular complex in atomic detail.     

雄激素和雌激素受体药物筛选方法的研究进展

雄激素和雌激素受体通过与相应激素特异性结合促进细胞分化和组织生长,发挥重要的生理功能,其功能失调可诱发多种疾病。雄激素和雌激素受体的选择性调节剂是治疗相关疾病的重要药物。基于基因组学、分子生物学、细胞生物学和生物信息学等最新研究成果而发展形成的实验技术或方法被用于新型雄激素和雌激素受体调节剂的筛选,显著加快了新药开发的进程。     

Assessing the performance of DNA barcoding using posterior predictive simulations

Accurate estimates of biodiversity are required for research in a broad array of biological subdisciplines including ecology, evolution, systematics, conservation and biodiversity science. The use of statistical models and genetic data, particularly DNA barcoding, has been suggested as an important tool for remedying the large gaps in our current understanding of biodiversity. However, the reliability of biodiversity estimates obtained using these approaches depends on how well the statistical models that are used describe the evolutionary process underlying the genetic data. In this study, we utilize data from the Barcode of Life Database and posterior predictive simulations to assess the performance of DNA barcoding under commonly used substitution models. We demonstrate that the success of DNA barcoding varies widely across DNA substitution models and that model choice has a substantial impact on the number of operational taxonomic units identified (changing results by ~4–31%). Additionally, we demonstrate that the widely followed practice of a priori assuming the Kimura 2‐parameter model for DNA barcoding is statistically unjustified and should be avoided. Using both data‐based and inference‐based test statistics, we detect variation in model performance across taxonomic groups, clustering algorithms, genetic divergence thresholds and substitution models. Taken together, these results illustrate the importance of considering both model selection and model adequacy in studies quantifying biodiversity.