Adaptive finite element method assisted by stochastic simulation of chemical systems

Stochastic models of chemical systems are often analysed by solving the corresponding\ud Fokker-Planck equation which is a drift-diffusion partial differential equation for the probability\ud distribution function. Efficient numerical solution of the Fokker-Planck equation requires adaptive mesh refinements. In this paper, we present a mesh refinement approach which makes use of a stochastic simulation of the underlying chemical system. By observing the stochastic trajectory for a relatively short amount of time, the areas of the state space with non-negligible probability density are identified. By refining the finite element mesh in these areas, and coarsening elsewhere, a suitable mesh is constructed and used for the computation of the probability density

Implementation of an Optimization and Simulation-Based Approach for Detecting and Resolving Conflicts at Airport

International audienceIn this paper is presented a methodology that uses simulation together with optimization techniques for a conflict detection and resolution at airports. This approach provides more robust solutions to operative problems, since, optimization allows to come up with optimal or suboptimal solutions, on the other hand, simulation allows to take into account other aspects as stochasticity and interactions inside the system. Both the airport airspace (terminal manoeuvring area), and airside (runway taxiways and terminals), were modelled. In this framework, different restrictions such as speed, separation minima between aircraft, and capacity of airside components were taken into account. The airspace was modeled as a network of links and nodes representing the different routes, while the airside was modeled in a low detail, where runway, taxiways and terminals were modeled as servers with a specific capacity. The objective of this work is to detect and resolve conflicts both in the airspace and in the airside and have a balanced traffic load on the ground

The interplay of fungal and bacterial microbiomes on rainforest frogs following a disease outbreak

Emerging infectious diseases are a serious threat to wildlife populations, and there is growing evidence that host microbiomes play important roles in infection dynamics, possibly even mitigating diseases. Nevertheless, most research on this topic has focused only on bacterial microbiomes, while fungal microbiomes have been largely neglected. To help fill this gap in our knowledge, we examined both the bacterial and fungal microbiomes of four sympatric Australian frog species, which had different population-level responses to the emergence of chytridiomycosis, a widespread disease caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd). We sequenced 16,884 fungal amplicon sequence variants (ASVs) and 41,774 bacterial ASVs. Bacterial communities had higher richness and were less variable within frog species than were fungal communities. Nevertheless, both communities were correlated for both ASV richness and beta diversity (i.e., frogs with similar bacterial richness and community composition tended to also have similar fungal richness and community composition). This suggests that either one microbial community was having a large impact on the other or that they were both being driven by similar environmental factors. For both microbial taxa, we found little evidence of associations between Bd (prevalence or intensity) and either individuals' ASVs or beta diversity. However, there was mixed evidence of associations between richness (both bacterial and fungal) and Bd, with high richness potentially providing a protective effect. Surprisingly, the relative abundance of bacteria that have previously been shown to inhibit Bd was also positively associated with Bd infection intensity, suggesting that a high relative abundance of those bacteria provides poor protection against infection

The interplay of fungal and bacterial microbiomes on rainforest frogs following a disease outbreak

Emerging infectious diseases are a serious threat to wildlife populations, and there is growing evidence that host microbiomes play important roles in infection dynamics, possibly even mitigating diseases. Nevertheless, most research on this topic has focused only on bacterial microbiomes, while fungal microbiomes have been largely neglected. To help fill this gap in our knowledge, we examined both the bacterial and fungal microbiomes of four sympatric Australian frog species, which had different population-level responses to the emergence of chytridiomycosis, a widespread disease caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd). We sequenced 16,884 fungal amplicon sequence variants (ASVs) and 41,774 bacterial ASVs. Bacterial communities had higher richness and were less variable within frog species than were fungal communities. Nevertheless, both communities were correlated for both ASV richness and beta diversity (i.e., frogs with similar bacterial richness and community composition tended to also have similar fungal richness and community composition). This suggests that either one microbial community was having a large impact on the other or that they were both being driven by similar environmental factors. For both microbial taxa, we found little evidence of associations between Bd (prevalence or intensity) and either individuals' ASVs or beta diversity. However, there was mixed evidence of associations between richness (both bacterial and fungal) and Bd, with high richness potentially providing a protective effect. Surprisingly, the relative abundance of bacteria that have previously been shown to inhibit Bd was also positively associated with Bd infection intensity, suggesting that a high relative abundance of those bacteria provides poor protection against infection

microDecon: a highly accurate read‐subtraction tool for the post‐sequencing removal of contamination in metabarcoding studies

Contamination is a ubiquitous problem in microbiome research and can skew results, especially when small amounts of target DNA are available. Nevertheless, no clear solution has emerged for removing microbial contamination. To address this problem, we developed the R package microDecon (https://github.com/donaldtmcknight/microDecon), which uses the proportions of contaminant operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) in blank samples to systematically identify and remove contaminant reads from metabarcoding data sets. We rigorously tested microDecon using a series of computer simulations and a sequencing experiment. We also compared it to the common practice of simply removing all contaminant OTUs/ASVs and other methods for removing contamination. Both the computer simulations and our sequencing data confirmed the utility of microDecon. In our largest simulation (100,000 samples), using microDecon improved the results in 98.1% of samples. Additionally, in the sequencing data and in simulations involving groups, it enabled accurate clustering of groups as well as the detection of previously obscured patterns. It also produced more accurate results than the existing methods for identifying and removing contamination. These results demonstrate that microDecon effectively removes contamination across a broad range of situations. It should, therefore, be widely applicable to microbiome studies, as well as to metabarcoding studies in general

Genomic selection in aquaculture: application, limitations and opportunities with special reference to marine shrimp and pearl oysters

Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements, and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopenaeus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer the solutions to the genotyping by sequencing approach and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps that impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short- and long-term solutions to sustainable and profitable aquaculture industries

Infection dynamics, dispersal, and adaptation: understanding the lack of recovery in a remnant frog population following a disease outbreak

Emerging infectious diseases can cause dramatic declines in wildlife populations. Sometimes, these declines are followed by recovery, but many populations do not recover. Studying differential recovery patterns may yield important information for managing disease-afflicted populations and facilitating population recoveries. In the late 1980s, a chytridiomycosis outbreak caused multiple frog species in Australia's Wet Tropics to decline. Populations of some species (e.g., Litoria nannotis) subsequently recovered, while others (e.g., Litoria dayi) did not. We examined the population genetics and current infection status of L. dayi, to test several hypotheses regarding the failure of its populations to recover: (1) a lack of individual dispersal abilities has prevented recolonization of previously occupied locations, (2) a loss of genetic variation has resulted in limited adaptive potential, and (3) L. dayi is currently adapting to chytridiomycosis. We found moderate-to-high levels of gene flow and diversity (Fst range: <0.01-0.15; minor allele frequency (MAF): 0.192-0.245), which were similar to previously published levels for recovered L. nannotis populations. This suggests that dispersal ability and genetic diversity do not limit the ability of L. dayi to recolonize upland sites. Further, infection intensity and prevalence increased with elevation, suggesting that chytridiomycosis is still limiting the elevational range of L. dayi. Outlier tests comparing infected and uninfected individuals consistently identified 18 markers as putatively under selection, and several of those markers matched genes that were previously implicated in infection. This suggests that L. dayi has genetic variation for genes that affect infection dynamics and may be undergoing adaptation

Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators

Prior knowledge about the imaging physics provides a mechanistic forward operator that plays an important role in image reconstruction, although myriad sources of possible errors in the operator could negatively impact the reconstruction solutions. In this work, we propose to embed the traditional mechanistic forward operator inside a neural function, and focus on modeling and correcting its unknown errors in an interpretable manner. This is achieved by a conditional generative model that transforms a given mechanistic operator with unknown errors, arising from a latent space of self-organizing clusters of potential sources of error generation. Once learned, the generative model can be used in place of a fixed forward operator in any traditional optimization-based reconstruction process where, together with the inverse solution, the error in prior mechanistic forward operator can be minimized and the potential source of error uncovered. We apply the presented method to the reconstruction of heart electrical potential from body surface potential. In controlled simulation experiments and in-vivo real data experiments, we demonstrate that the presented method allowed reduction of errors in the physics-based forward operator and thereby delivered inverse reconstruction of heart-surface potential with increased accuracy.Comment: 11 pages, Conference: Medical Image Computing and Computer Assisted Interventio

Standing out from the crowd:Dedicated institutional investors and strategy uniqueness

This paper examines the effect of dedicated institutional investors on firms' strategy uniqueness. We build on the uniqueness paradox where unique strategies are important drivers of economic rent, yet create an information problem whereby CEOs face discounts from the capital market, thus discouraging them from selecting unique strategies. We propose dedicated institutional investors as a partial remedy to the uniqueness paradox. Dedicated institutional investors invest in gaining private information about their investments, devote effort to understanding firms' strategies, and reduce capital market pressure. Thus, dedicated institutional investors can encourage CEOs to pursue more unique strategies. Our empirical results show the positive influence of dedicated institutional investors on strategic uniqueness, which is even stronger when firms operate in industries that are hard to value

Nonmonotonic inelastic tunneling spectra due to surface spin excitations in ferromagnetic junctions

The paper addresses inelastic spin-flip tunneling accompanied by surface spin excitations (magnons) in ferromagnetic junctions. The inelastic tunneling current is proportional to the magnon density of states which is energy-independent for the surface waves and, for this reason, cannot account for the bias-voltage dependence of the observed inelastic tunneling spectra. This paper shows that the bias-voltage dependence of the tunneling spectra can arise from the tunneling matrix elements of the electron-magnon interaction. These matrix elements are derived from the Coulomb exchange interaction using the itinerant-electron model of magnon-assisted tunneling. The results for the inelastic tunneling spectra, based on the nonequilibrium Green's function calculations, are presented for both parallel and antiparallel magnetizations in the ferromagnetic leads.Comment: 9 pages, 4 figures, version as publishe