Thermodynamic properties of the exactly solvable transverse Ising model on decorated planar lattices

The generalized mapping transformation technique is used to obtain the exact solution for the transverse Ising model on decorated planar lattices. Within this scheme, the basic thermodynamic quantities are calculated for different planar lattices with arbitrary spins of decorating atoms. The particular attention has been paid to the investigation of the transverse-field effects on magnetic properties of the system under investigation. The most interesting numerical results for the phase diagrams, compensation temperatures and several thermodynamic quantities are discussed in detail for the ferrimagnetic version of the model.Comment: 9 pages, 13 figures, submitted to Journal of Magnetism and Magnetic Material

Magnetic properties of exactly solvable doubly decorated Ising-Heisenberg planar models

Applying the decoration-iteration procedure, we introduce a class of exactly solvable doubly decorated planar models consisting both of the Ising- and Heisenberg-type atoms. Exact solutions for the ground state, phase diagrams and basic physical quantities are derived and discussed. The detailed analysis of the relevant quantities suggests the existence of an interesting quantum antiferromagnetic phase in the system.Comment: 9 pages, 9 figures, submitted to Physical Review

Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery

Retrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (∆Rrs) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations in cyanobacteria biomass in specific regions, their performance has not been assessed on hyperspectral images from satellite sensors. Our work leverages a machine-learning model, Mixture Density Networks (MDNs), trained on a large (N = 939) dataset of collocated in situ chlorophyll-a concentrations (Chla), PCs, and remote sensing reflectance (Rrs) measurements to estimate PC from all relevant spectral bands. The performance of the developed model is demonstrated via PC maps produced from select images of the Hyperspectral Imager for the Coastal Ocean (HICO) and Italian Space Agency’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) using a matchup dataset. As input to the MDN, we incorporate a combination of widely used band ratios (BRs) and line heights (LHs) taken from existing multispectral algorithms, that have been proven for both Chla and PC esti�mation, as well as novel BRs and LHs to increase the overall cyanobacteria biomass estimation accuracy and reduce the sensitivity to ∆Rrs. When trained on a random half of the dataset, the MDN achieves uncertainties of 44.3%, which is less than half of the uncertainties of all viable optimized multispectral PC algorithms. The MDN is notably better than multispectral algorithms at preventing overestimation on low (10 mg m− 3). According to our extensive assessments, the developed model is anticipated to enable practical PC products from PRISMA and HICO, therefore the model is promising for planned hyperspectral missions, such as the Plankton Aerosol and Cloud Ecosystem (PACE). This advancement will enhance the complementary roles of hyperspectral radiometry from satellite and low-altitude platforms for quantifying and monitoring cyanobacteria harmful algal blooms at both large and local spatial scales

Accounting for Redundancy when Integrating Gene Interaction Databases

During the last years gene interaction networks are increasingly being used for the assessment and interpretation of biological measurements. Knowledge of the interaction partners of an unknown protein allows scientists to understand the complex relationships between genetic products, helps to reveal unknown biological functions and pathways, and get a more detailed picture of an organism's complexity. Being able to measure all protein interactions under all relevant conditions is virtually impossible. Hence, computational methods integrating different datasets for predicting gene interactions are needed. However, when integrating different sources one has to account for the fact that some parts of the information may be redundant, which may lead to an overestimation of the true likelihood of an interaction. Our method integrates information derived from three different databases (Bioverse, HiMAP and STRING) for predicting human gene interactions. A Bayesian approach was implemented in order to integrate the different data sources on a common quantitative scale. An important assumption of the Bayesian integration is independence of the input data (features). Our study shows that the conditional dependency cannot be ignored when combining gene interaction databases that rely on partially overlapping input data. In addition, we show how the correlation structure between the databases can be detected and we propose a linear model to correct for this bias. Benchmarking the results against two independent reference data sets shows that the integrated model outperforms the individual datasets. Our method provides an intuitive strategy for weighting the different features while accounting for their conditional dependencies

Characterization of the Fecal Microbiome from Non-Human Wild Primates Reveals Species Specific Microbial Communities

BACKGROUND: Host-associated microbes comprise an integral part of animal digestive systems and these interactions have a long evolutionary history. It has been hypothesized that the gastrointestinal microbiome of humans and other non-human primates may have played significant roles in host evolution by facilitating a range of dietary adaptations. We have undertaken a comparative sequencing survey of the gastrointestinal microbiomes of several non-human primate species, with the goal of better understanding how these microbiomes relate to the evolution of non-human primate diversity. Here we present a comparative analysis of gastrointestinal microbial communities from three different species of Old World wild monkeys. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed fecal samples from three different wild non-human primate species (black-and-white colobus [Colubus guereza], red colobus [Piliocolobus tephrosceles], and red-tailed guenon [Cercopithecus ascanius]). Three samples from each species were subjected to small subunit rRNA tag pyrosequencing. Firmicutes comprised the vast majority of the phyla in each sample. Other phyla represented were Bacterioidetes, Proteobacteria, Spirochaetes, Actinobacteria, Verrucomicrobia, Lentisphaerae, Tenericutes, Planctomycetes, Fibrobacateres, and TM7. Bray-Curtis similarity analysis of these microbiomes indicated that microbial community composition within the same primate species are more similar to each other than to those of different primate species. Comparison of fecal microbiota from non-human primates with microbiota of human stool samples obtained in previous studies revealed that the gut microbiota of these primates are distinct and reflect host phylogeny. CONCLUSION/SIGNIFICANCE: Our analysis provides evidence that the fecal microbiomes of wild primates co-vary with their hosts, and that this is manifested in higher intraspecies similarity among wild primate species, perhaps reflecting species specificity of the microbiome in addition to dietary influences. These results contribute to the limited body of primate microbiome studies and provide a framework for comparative microbiome analysis between human and non-human primates as well as a comparative evolutionary understanding of the human microbiome

Thermomechanical couplings in shape memory alloy materials

In this work we address several theoretical and computational issues which are related to the thermomechanical modeling of shape memory alloy materials. More specifically, in this paper we revisit a non-isothermal version of the theory of large deformation generalized plasticity which is suitable for describing the multiple and complex mechanisms occurring in these materials during phase transformations. We also discuss the computational implementation of a generalized plasticity based constitutive model and we demonstrate the ability of the theory in simulating the basic patterns of the experimentally observed behavior by a set of representative numerical examples

Overexpression of DNA Polymerase Zeta Reduces the Mitochondrial Mutability Caused by Pathological Mutations in DNA Polymerase Gamma in Yeast

In yeast, DNA polymerase zeta (Rev3 and Rev7) and Rev1, involved in the error-prone translesion synthesis during replication of nuclear DNA, localize also in mitochondria. We show that overexpression of Rev3 reduced the mtDNA extended mutability caused by a subclass of pathological mutations in Mip1, the yeast mitochondrial DNA polymerase orthologous to human Pol gamma. This beneficial effect was synergistic with the effect achieved by increasing the dNTPs pools. Since overexpression of Rev3 is detrimental for nuclear DNA mutability, we constructed a mutant Rev3 isoform unable to migrate into the nucleus: its overexpression reduced mtDNA mutability without increasing the nuclear one

Dopaminergic Activation of Estrogen Receptors Induces Fos Expression within Restricted Regions of the Neonatal Female Rat Brain

Steroid receptor activation in the developing brain influences a variety of cellular processes that endure into adulthood, altering both behavior and physiology. Recent data suggests that dopamine can regulate expression of progestin receptors within restricted regions of the developing rat brain by activating estrogen receptors in a ligand-independent manner. It is unclear whether changes in neuronal activity induced by dopaminergic activation of estrogen receptors are also region specific. To investigate this question, we examined where the dopamine D1-like receptor agonist, SKF 38393, altered Fos expression via estrogen receptor activation. We report that dopamine D1-like receptor agonist treatment increased Fos protein expression within many regions of the developing female rat brain. More importantly, prior treatment with an estrogen receptor antagonist partially reduced D1-like receptor agonist-induced Fos expression only within the bed nucleus of the stria terminalis and the central amygdala. These data suggest that dopaminergic activation of estrogen receptors alters neuronal activity within restricted regions of the developing rat brain. This implies that ligand-independent activation of estrogen receptors by dopamine might organize a unique set of behaviors during brain development in contrast to the more wide spread ligand activation of estrogen receptors by estrogen

A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.

As modeling becomes a more widespread practice in the life sciences and biomedical sciences, researchers need reliable tools to calibrate models against ever more complex and detailed data. Here we present an approximate Bayesian computation (ABC) framework and software environment, ABC-SysBio, which is a Python package that runs on Linux and Mac OS X systems and that enables parameter estimation and model selection in the Bayesian formalism by using sequential Monte Carlo (SMC) approaches. We outline the underlying rationale, discuss the computational and practical issues and provide detailed guidance as to how the important tasks of parameter inference and model selection can be performed in practice. Unlike other available packages, ABC-SysBio is highly suited for investigating, in particular, the challenging problem of fitting stochastic models to data. In order to demonstrate the use of ABC-SysBio, in this protocol we postulate the existence of an imaginary reaction network composed of seven interrelated biological reactions (involving a specific mRNA, the protein it encodes and a post-translationally modified version of the protein), a network that is defined by two files containing 'observed' data that we provide as supplementary information. In the first part of the PROCEDURE, ABC-SysBio is used to infer the parameters of this system, whereas in the second part we use ABC-SysBio's relevant functionality to discriminate between two different reaction network models, one of them being the 'true' one. Although computationally expensive, the additional insights gained in the Bayesian formalism more than make up for this cost, especially in complex problems