Network Theory Analysis of Antibody-Antigen Reactivity Data: The Immune Trees at Birth and Adulthood

Motivation: New antigen microarray technology enables parallel recording of antibody reactivities with hundreds of antigens. Such data affords system level analysis of the immune system’s organization using methods and approaches from network theory. Here we measured the reactivity of 290 antigens (for both the IgG and IgM isotypes) of 10 healthy mothers and their term newborns. We constructed antigen correlation networks (or immune networks) whose nodes are the antigens and the edges are the antigen-antigen reactivity correlations, and we also computed their corresponding minimum spanning trees (MST) – maximal information reduced sub-graphs. We quantify the network organization (topology) in terms of the network theory divergence rate measure and rank the antigen importance in the full antigen correlation networks by the eigen-value centrality measure. This analysis makes possible the characterization and comparison of the IgG and IgM immune networks at birth (newborns) and adulthood (mothers) in terms of topology and node importance. Results: Comparison of the immune network topology at birth and adulthood revealed partial conservation of the IgG immune network topology, and significant reorganization of the IgM immune networks. Inspection of the antigen importance revealed some dominant (in terms of high centrality) antigens in the IgG and IgM networks at birth, which retain their importance at adulthood

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Neural network predictions of oxygen interactions on a dynamic Pd surface

<p>Artificial neural networks (NNs) are increasingly common in quantum chemistry applications. These models can be trained to higher-level <i>ab-initio</i> calculations and are capable of achieving arbitrary levels of accuracy. The most common applications thus far have been specialised for either bulk or surface structures of up to two chemical components. However, very few of these studies utilise NNs trained to high-dimensional potential energy surfaces, and there are even fewer studies which examine adsorbate–adsorbate and adsorbate–surface interactions with those NNs. The goal of this work is to determine the feasibility of and develop methodologies for producing a high-dimensional NN capable of reproducing coverage-dependent oxygen interactions with a dynamic Pd fcc(1 1 1) surface. We utilise the atomistic machine-learning potential software package to generate a Behler–Parrinello local symmetry function NN trained on a large database of density functional theory (DFT) calculations. These training methods are flexible, and thus easily expanded upon as demonstrated in previous work. This allows the database of high quality PdO DFT calculations to be used as a basis for future work, such as the inclusion of a third chemical species, for example a binary Pd alloy, or another adsorbate atom such as hydrogen.</p

Modeling Segregation on AuPd(111) Surfaces with Density Functional Theory and Monte Carlo Simulations

The simulation of segregation in multicomponent alloy surfaces is challenging with atomistic approaches because of the need to model a very large number of possible configurations with a high degree of accuracy. Density functional theory (DFT) is too expensive to use directly, and atomistic potentials are often a compromise between accuracy and computational speed. In this work we develop a neural network (NN) atomistic potential capable of predicting accurate energies for any configuration of a AuPd(111) slab. The fully trained neural network spanning all configurations and lattice constants of a AuPd binary alloy is trained from only 3914 DFT calculations. Using this NN, segregation profiles are created spanning bulk compositions between 10 and 90% Au, and at temperatures ranging from 700 to 1000 K using Monte Carlo simulations. These profiles are then fit to the Langmuir–McLean formulation of the Gibbs-isotherm with a model for the enthalpy of segregation. The simulation results are in excellent agreement with available experimental LEIS data for the composition of the top layer. Site distributions were computed and compared to random distributions, indicating the presence of some short-range ordering favoring the formation of Au–Pd surface bonds

Catalysis-hub.org: An Open Electronic Structure Database for Surface Reactions

We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org. The featured Surface Reactions database contains more than 100,000 chemical adsorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the atomic surface geometries used in the calculations together with the calculation parameters and output, which are essential for data reproducibility. Data can be accessed from the web-interface as well as from a Python API providing direct access from a local workstation. This enables researchers to efficiently use the data as a basis for further calculations and to generate surrogate models for accelerating the discovery of catalytic materials for sustainable energy applications

Estimating Bulk-Composition-Dependent H₂ Adsorption Energies on Cu_<i>x</i>Pd_1–<i>x</i> Alloy (111) Surfaces

The bulk-composition-dependent dissociative adsorption energy of hydrogen on CuPd alloys has been measured experimentally and modeled using density functional theory. The hydrogen adsorption energy cannot be simply defined by a single reactive site or as a composition weighted average of the pure metal components. We developed a modeling approach that uses a basis of active sites weighted by a model site probability distribution to estimate a bulk-composition-dependent adsorption energy. The approach includes segregation under reaction conditions. With this method, we can explain the composition-dependent adsorption energy of hydrogen on Cu-rich alloy surfaces. In Pd-rich alloys, a Pd-hydride phase may form, which results in deviations from trends on the metallic alloy surface