Modelling fungal colonies and communities:challenges and opportunities

This contribution, based on a Special Interest Group session held during IMC9, focuses on physiological based models of filamentous fungal colony growth and interactions. Fungi are known to be an important component of ecosystems, in terms of colony dynamics and interactions within and between trophic levels. We outline some of the essential components necessary to develop a fungal ecology: a mechanistic model of fungal colony growth and interactions, where observed behaviour can be linked to underlying function; a model of how fungi can cooperate at larger scales; and novel techniques for both exploring quantitatively the scales at which fungi operate; and addressing the computational challenges arising from this highly detailed quantification. We also propose a novel application area for fungi which may provide alternate routes for supporting scientific study of colony behaviour. This synthesis offers new potential to explore fungal community dynamics and the impact on ecosystem functioning

Reverse engineering of biochar

This study underpins quantitative relationships that account for the combined effects that starting biomass and peak pyrolysis temperature have on physico-chemical properties of biochar. Meta-data was assembled from published data of diverse biochar samples (n = 102) to (i) obtain networks of intercorrelated properties and (ii) derive models that predict biochar properties. Assembled correlation networks provide a qualitative overview of the combinations of biochar properties likely to occur in a sample. Generalized Linear Models are constructed to account for situations of varying complexity, including: dependence of biochar properties on single or multiple predictor variables, where dependence on multiple variables can have additive and/or interactive effects; non-linear relation between the response and predictors; and non-Gaussian data distributions. The web-tool Biochar Engineering implements the derived models to maximize their utility and distribution. Provided examples illustrate the practical use of the networks, models and web-tool to engineer biochars with prescribed properties desirable for hypothetical scenarios

PEPVAC: a web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands

Prediction of peptide binding to major histocompatibility complex (MHC) molecules is a basis for anticipating T-cell epitopes, as well as epitope discovery-driven vaccine development. In the human, MHC molecules are known as human leukocyte antigens (HLAs) and are extremely polymorphic. HLA polymorphism is the basis of differential peptide binding, until now limiting the practical use of current epitope-prediction tools for vaccine development. Here, we describe a web server, PEPVAC (Promiscuous EPitope-based VACcine), optimized for the formulation of multi-epitope vaccines with broad population coverage. This optimization is accomplished through the prediction of peptides that bind to several HLA molecules with similar peptide-binding specificity (supertypes). Specifically, we offer the possibility of identifying promiscuous peptide binders to five distinct HLA class I supertypes (A2, A3, B7, A24 and B15). We estimated the phenotypic population frequency of these supertypes to be 95%, regardless of ethnicity. Targeting these supertypes for promiscuous peptide-binding predictions results in a limited number of potential epitopes without compromising the population coverage required for practical vaccine design considerations. PEPVAC can also identify conserved MHC ligands, as well as those with a C-terminus resulting from proteasomal cleavage. The combination of these features with the prediction of promiscuous HLA class I ligands further limits the number of potential epitopes. The PEPVAC server is hosted by the Dana-Farber Cancer Institute at the site

Recognition and classification of histones using support vector machine

Histones are DNA-binding proteins found in the chromatin of all eukaryotic cells. They are highly conserved and can be grouped into five major classes: H1/H5, H2A, H2B, H3, and H4. Two copies of H2A, H2B, H3, and H4 bind to about 160 base pairs of DNA forming the core of the nucleosome (the repeating structure of chromatin) and H1/H5 bind to its DNA linker sequence. Overall, histones have a high arginine/lysine content that is optimal for interaction with DNA. This sequence bias can make the classification of histones difficult using standard sequence similarity approaches. Therefore, in this paper, we applied support vector machine (SVM) to recognize and classify histones on the basis of their amino acid and dipeptide composition. On evaluation through a five-fold cross-validation, the SVM-based method was able to distinguish histones from nonhistones (nuclear proteins) with an accuracy around 98%. Similarly, we obtained an overall >95% accuracy in discriminating the five classes of histones through the application of 1-versus-rest (1-v-r) SVM. Finally, we have applied this SVM-based method to the detection of histones from whole proteomes and found a comparable sensitivity to that accomplished by hidden Markov motifs (HMM) profiles

Stable, metastable and unstable states in the mean-field RFIM at T=0

We compute the probability of finding metastable states at a given field in the mean-field random field Ising model at T=0. Remarkably, this probability is finite in the thermodynamic limit, even on the so-called ``unstable'' branch of the magnetization curve. This implies that the branch is reachable when the magnetization is controlled instead of the magnetic field, in contrast with the situation in the pure system.Comment: 10 pages, 3 figure

PVS: a web server for protein sequence variability analysis tuned to facilitate conserved epitope discovery

We have developed PVS (Protein Variability Server), a web-based tool that uses several variability metrics to compute the absolute site variability in multiple protein-sequence alignments (MSAs). The variability is then assigned to a user-selected reference sequence consisting of either the first sequence in the alignment or a consensus sequence. Subsequently, PVS performs tasks that are relevant for structure-function studies, such as plotting and visualizing the variability in a relevant 3D-structure. Neatly, PVS also implements some other tasks that are thought to facilitate the design of epitope discovery-driven vaccines against pathogens where sequence variability largely contributes to immune evasion. Thus, PVS can return the conserved fragments in the MSA—as defined by a user-provided variability threshold—and locate them in a relevant 3D-structure. Furthermore, PVS can return a variability-masked sequence, which can be directly submitted to the RANKPEP server for the prediction of conserved T-cell epitopes. PVS is freely available at: http://imed.med.ucm.es/PVS/

One-side heating test and modeling of tubular receivers equipped with turbulence promoters for solar tower applications

Tubular receivers in central tower systems suffer the high mechanical stresses caused by the temperature gradient typically established along the tube and across its circumference due to the one-side heating. In the present work, the thermal behavior of three different absorber tubes is investigated both experimentally and numerically. The tubes, manufactured in Cr alloy 718 (Inconel®), were smooth or with repeated rib-roughness (annular or helical ribs), and were tested at the solar furnace SF60 of the Plataforma Solar de Almería (PSA) in 2017 within the international access program of SFERA II project, financed by the EU. The specific focus of the tests was the assessment of the role of turbulence promoters in reducing the peak wall temperature when a strong one-side heating is present, contributing to the reduction of the thermal gradients between the irradiated and the non-irradiated (back) side of the tube. The experimental results show that the use of turbulence promoters reduce the wall temperature with respect to the case of a smooth tube, as expected, although the comparison between the samples is not trivial in view of the change in the optical properties induced by the progressive oxidation of the irradiated surface. Computational Fluid Dynamic (CFD) 3D models have been developed for the three samples and they have proven the capability to very-well reproduce the experimental results. A fair comparison between the different simulated tubes in the same controlled conditions of one-side heating has been performed numerically, assessing quantitatively the temperature reduction induced by the turbulence promoters, and the best performance of the Inconel® tube equipped with helices

Modelling avalanches in martensites

Solids subject to continuous changes of temperature or mechanical load often exhibit discontinuous avalanche-like responses. For instance, avalanche dynamics have been observed during plastic deformation, fracture, domain switching in ferroic materials or martensitic transformations. The statistical analysis of avalanches reveals a very complex scenario with a distinctive lack of characteristic scales. Much effort has been devoted in the last decades to understand the origin and ubiquity of scale-free behaviour in solids and many other systems. This chapter reviews some efforts to understand the characteristics of avalanches in martensites through mathematical modelling.Comment: Chapter in the book "Avalanches in Functional Materials and Geophysics", edited by E. K. H. Salje, A. Saxena, and A. Planes. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-45612-6_

Zero-temperature random-field Ising model on a bilayered Bethe lattice

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