Inverse Problem in Materials Science

Abstract

The importance of the inverse problem in materials science, that is, the determination of unknown parameters in the physico-chemical models from the experimental values is emphasized. The main attention is given to the problem of how to take into account experimental systematic errors during the inverse problem.     

On the Sampling Method for Grand Canonical Monte Carlo Simulations

Abstract

A bulk Lennard-Jones fluid was simulated using the grand canonical Monte Carlo method. Three different sampling methods were used in the transition matrix, namely the Metropolis, Barker and a third novel method. While it can be shown that the Metropolis method will give the most accurate ensemble averages in the limit of an infinitely long run, the new method termed “Modified Barker Sampling” (MBS), is shown to be superior for the runs of practical length for the particular system studied.     

Enhanced sampling of molecular dynamics simulation of peptides and proteins by double coupling to thermal bath

Low sampling efficiency in conformational space is the well-known problem for conventional molecular dynamics. It greatly increases the difficulty for molecules to find the transition path to native state, and costs amount of CPU time. To accelerate the sampling, in this paper, we re-couple the critical degrees of freedom in the molecule to environment temperature, like dihedrals in generalized coordinates or nonhydrogen atoms in Cartesian coordinate. After applying to ALA dipeptide model, we find that this modified molecular dynamics greatly enhances the sampling behavior in the conformational space and provides more information about the state-to-state transition, while conventional molecular dynamics fails to do so. Moreover, from the results of 16 independent 100?ns simulations by the new method, it shows that trpzip2 has one-half chances to reach the naive state in all the trajectories, which is greatly higher than conventional molecular dynamics. Such an improvement would provide a potential way for searching the conformational space or predicting the most stable states of peptides and proteins.     

Hierarchical sampling for metastable conformers determines biomolecular recognition: the case of malectin and diglucosylated N-glycan interactions

Structural information over the entire course of binding interactions based on the analyses of energy landscapes is described, which provides a framework to understand the events involved during biomolecular recognition. Conformational dynamics of malectin’s exquisite selectivity for diglucosylated N-glycan (Dig-N-glycan), a highly flexible oligosaccharide comprising of numerous dihedral torsion angles, are described as an example. For this purpose, a novel approach based on hierarchical sampling for acquiring metastable molecular conformations constituting low-energy minima for understanding the structural features involved in a biologic recognition is proposed. For this purpose, four variants of principal component analysis were employed recursively in both Cartesian space and dihedral angles space that are characterized by free energy landscapes to select the most stable conformational substates. Subsequently, k-means clustering algorithm was implemented for geometric separation of the major native state to acquire a final ensemble of metastable conformers. A comparison of malectin complexes was then performed to characterize their conformational properties. Analyses of stereochemical metrics and other concerted binding events revealed surface complementarity, cooperative and bidentate hydrogen bonds, water-mediated hydrogen bonds, carbohydrate–aromatic interactions including CH–π and stacking interactions involved in this recognition. Additionally, a striking structural transition from loop to β-strands in malectin CRD upon specific binding to Dig-N-glycan is observed. The interplay of the above-mentioned binding events in malectin and Dig-N-glycan supports an extended conformational selection model as the underlying binding mechanism.     

Mate sampling and choosiness in the sand goby

To date, mate choice studies have mostly focused on establishing which mates are chosen or how the choices are performed. Here, we combined these two approaches by empirically testing how latency to mate is affected by various search costs, variation in mate quality and female quality in the sand goby (Pomatoschistus minutus). Our results show that females adjust their mating behaviour according to the costs and benefits of the choice situation. Specifically, they mated sooner when access to males was delayed and when the presence of other females presented a mate sampling cost. We also found a positive link between size variation among potential mating partners and spawning delay in some (but not all) experimental conditions. By contrast, we did not find the number of available males or the females'' own body size (‘quality’) to affect mating latency. Finally, female mating behaviour varied significantly between years. These findings are notable for demonstrating that (i) mate sampling time is particularly sensitive to costs and, to a lesser degree, to variation among mate candidates, (ii) females'' mating behaviour is sensitive to qualitative rather than to quantitative variation in their environment, and (iii) a snapshot view may describe mate sampling behaviour unreliably.     

Application of Adaptive Cluster Sampling with a Data‐Driven Stopping Rule to Plant Disease Incidence

Plant pathologists need to manage plant diseases at low incidence levels. This needs to be performed efficiently in terms of precision, cost and time because most plant infections spread rapidly to other plants. Adaptive cluster sampling with a data‐driven stopping rule (ACS*) was proposed to control the final sample size and improve efficiency of the ordinary adaptive cluster sampling (ACS) when prior knowledge of population structure is not known. This study seeks to apply the ACS* design to plant diseases at various levels of clustering and incidences levels. Results from simulation study show that the ACS* is as efficient as the ordinary ACS design at low levels of disease incidence with highly clustered diseased plants and is an efficient design compared with simple random sampling (SRS) and ordinary ACS for some highly to less clustered diseased plants with moderate to higher levels of disease incidence.     

Preservation of RNA and DNA from mammal samples under field conditions

Ecological and conservation genetics require sampling of organisms in the wild. Appropriate preservation of the collected samples, usually by cryostorage, is key to the quality of the genetic data obtained. Nevertheless, cryopreservation in the field to ensure RNA and DNA stability is not always possible. We compared several nucleic acid preservation solutions appropriate for field sampling and tested them on rat (Rattus rattus) blood, ear and tail tip, liver, brain and muscle. We compared the efficacy of a nucleic acid preservation (NAP) buffer for DNA preservation against 95% ethanol and Longmire buffer, and for RNA preservation against RNAlater (Qiagen) and Longmire buffer, under simulated field conditions. For DNA, the NAP buffer was slightly better than cryopreservation or 95% ethanol, but high molecular weight DNA was preserved in all conditions. The NAP buffer preserved RNA as well as RNAlater. Liver yielded the best RNA and DNA quantity and quality; thus, liver should be the tissue preferentially collected from euthanized animals. We also show that DNA persists in nonpreserved muscle tissue for at least 1 week at ambient temperature, although degradation is noticeable in a matter of hours. When cryopreservation is not possible, the NAP buffer is an economical alternative for RNA preservation at ambient temperature for at least 2 months and DNA preservation for at least 10 months.     

A universal and reliable assay for molecular sex identification of three‐spined sticklebacks (Gasterosteus aculeatus)

In heterogametic species, biological differences between the two sexes are ubiquitous, and hence, errors in sex identification can be a significant source of noise and bias in studies where sex‐related sources of variation are of interest or need to be controlled for. We developed and validated a universal multimarker assay for reliable sex identification of three‐spined sticklebacks (Gasterosteus aculeatus). The assay makes use of genotype scores from three sex‐linked loci and utilizes Bayesian probabilistic inference to identify sex of the genotyped individuals. The results, validated with 286 phenotypically sexed individuals from six populations of sticklebacks representing all major genetic lineages (cf. Pacific, Atlantic and Japan Sea), indicate that in contrast to commonly used single‐marker‐based sex identification assays, the developed multimarker assay should be 100% accurate. As the markers in the assay can be scored from agarose gels, it provides a quick and cost‐efficient tool for universal sex identification of three‐spined sticklebacks. The general principle of combining information from multiple markers to improve the reliability of sex identification is transferable and can be utilized to develop and validate similar assays for other species.     

Sequential sampling: a novel method in farm animal welfare assessment

Lameness in dairy cows is an important welfare issue. As part of a welfare assessment, herd level lameness prevalence can be estimated from scoring a sample of animals, where higher levels of accuracy are associated with larger sample sizes. As the financial cost is related to the number of cows sampled, smaller samples are preferred. Sequential sampling schemes have been used for informing decision making in clinical trials. Sequential sampling involves taking samples in stages, where sampling can stop early depending on the estimated lameness prevalence. When welfare assessment is used for a pass/fail decision, a similar approach could be applied to reduce the overall sample size. The sampling schemes proposed here apply the principles of sequential sampling within a diagnostic testing framework. This study develops three sequential sampling schemes of increasing complexity to classify 80 fully assessed UK dairy farms, each with known lameness prevalence. Using the Welfare Quality herd-size-based sampling scheme, the first ‘basic’ scheme involves two sampling events. At the first sampling event half the Welfare Quality sample size is drawn, and then depending on the outcome, sampling either stops or is continued and the same number of animals is sampled again. In the second ‘cautious’ scheme, an adaptation is made to ensure that correctly classifying a farm as ‘bad’ is done with greater certainty. The third scheme is the only scheme to go beyond lameness as a binary measure and investigates the potential for increasing accuracy by incorporating the number of severely lame cows into the decision. The three schemes are evaluated with respect to accuracy and average sample size by running 100 000 simulations for each scheme, and a comparison is made with the fixed size Welfare Quality herd-size-based sampling scheme. All three schemes performed almost as well as the fixed size scheme but with much smaller average sample sizes. For the third scheme, an overall association between lameness prevalence and the proportion of lame cows that were severely lame on a farm was found. However, as this association was found to not be consistent across all farms, the sampling scheme did not prove to be as useful as expected. The preferred scheme was therefore the ‘cautious’ scheme for which a sampling protocol has also been developed.