Stability of Hydrogen Hydrates from Second-Order Møller–Plesset Perturbation Theory

The formation of gas hydrates and clathrates critically depends on the interaction between the host water network and the guest gas species. Density functional calculations can struggle to quantitatively capture these dispersion-type interactions. Here, we report wave function-based calculations on hydrogen hydrates that combine periodic Hartree–Fock with a localized treatment of electronic correlation. We show that local second-order Møller–Plesset perturbation theory (LMP2) reproduces the stability of the different filled-ice-like hydrates in excellent agreement with experimental data. In contrast to various dispersion-corrected density functional theory implementations, LMP2 correctly identifies the pressures needed to stabilize the C₀, C₁, and C₂ hydrates and does not find a spurious region of stability for an ice-I_h-based dihydrate. Our results suggest that LMP2 or similar approaches can provide quantitative insights into the mechanisms of formation and eventual decomposition of molecular host–guest compounds

A computational approach to managing coupled human–environmental systems: the POSEIDON model of ocean fisheries

Sustainable management of complex human–environment systems, and the essential services they provide, remains a major challenge, felt from local to global scales. These systems are typically highly dynamic and hard to predict, particularly in the context of rapid environmental change, where novel sets of conditions drive coupled socio-economic-environmental responses. Faced with these challenges, our tools for policy development, while informed by the past experience, must not be unduly constrained; they must allow equally for both the fine-tuning of successful existing approaches and the generation of novel ones in unbiased ways. We study ocean fisheries as an example class of complex human–environmental systems, and present a new model (POSEIDON) and computational approach to policy design. The model includes an adaptive agent-based representation of a fishing fleet, coupled to a simplified ocean ecology model. The agents (fishing boats) do not have programmed responses based on empirical data, but respond adaptively, as a group, to their environment (including policy constraints). This conceptual model captures qualitatively a wide range of empirically observed fleet behaviour, in response to a broad set of policies. Within this framework, we define policy objectives (of arbitrary complexity) and use Bayesian optimization over multiple model runs to find policy parameters that best meet the goals. The trade-offs inherent in this approach are explored explicitly. Taking this further, optimization is used to generate novel hybrid policies. We illustrate this approach using simulated examples, in which policy prescriptions generated by our computational methods are counterintuitive and thus unlikely to be identified by conventional frameworks

Identification and characterization of two novel primate-specific histone H3 variants, H3.X and H3.Y

The expression of a new histone variant H3.Y increases during cellular stress to regulate cell cycle progression and gene expression

Acute abdomen—Rare cause in an 80-year-old female patient under immunosuppressive treatment

Eine 80-jährige Frau stellte sich zur Abklärung abdomineller Schmerzen vor. Vorausgegangen war die Diagnosestellung einer Autoimmunhepatitis mit Einleitung einer immunsuppressiven Therapie und Auftritt zweier Pneumonien mit opportunistischen Erregern. Die Bildgebung erbrachte einen „omental cake“ mit Verdacht auf Peritonealkarzinose. Bei Auftritt eines akuten Abdomens erfolgte eine explorative Laparotomie, hierbei zeigten sich intraabdominelle Abszesse. Anhand von Blutkulturen und des intraoperativ gewonnenen Materials wurde eine disseminierte Nocardiose diagnostiziert. Die Patientin verstarb aufgrund einer fulminant verlaufenen Sepsis

Solitary waves in the Nonlinear Dirac Equation

In the present work, we consider the existence, stability, and dynamics of solitary waves in the nonlinear Dirac equation. We start by introducing the Soler model of self-interacting spinors, and discuss its localized waveforms in one, two, and three spatial dimensions and the equations they satisfy. We present the associated explicit solutions in one dimension and numerically obtain their analogues in higher dimensions. The stability is subsequently discussed from a theoretical perspective and then complemented with numerical computations. Finally, the dynamics of the solutions is explored and compared to its non-relativistic analogue, which is the nonlinear Schr{\"o}dinger equation. A few special topics are also explored, including the discrete variant of the nonlinear Dirac equation and its solitary wave properties, as well as the PT-symmetric variant of the model

PIPS: Pathogenicity Island Prediction Software

The adaptability of pathogenic bacteria to hosts is influenced by the genomic plasticity of the bacteria, which can be increased by such mechanisms as horizontal gene transfer. Pathogenicity islands play a major role in this type of gene transfer because they are large, horizontally acquired regions that harbor clusters of virulence genes that mediate the adhesion, colonization, invasion, immune system evasion, and toxigenic properties of the acceptor organism. Currently, pathogenicity islands are mainly identified in silico based on various characteristic features: (1) deviations in codon usage, G+C content or dinucleotide frequency and (2) insertion sequences and/or tRNA genetic flanking regions together with transposase coding genes. Several computational techniques for identifying pathogenicity islands exist. However, most of these techniques are only directed at the detection of horizontally transferred genes and/or the absence of certain genomic regions of the pathogenic bacterium in closely related non-pathogenic species. Here, we present a novel software suite designed for the prediction of pathogenicity islands (pathogenicity island prediction software, or PIPS). In contrast to other existing tools, our approach is capable of utilizing multiple features for pathogenicity island detection in an integrative manner. We show that PIPS provides better accuracy than other available software packages. As an example, we used PIPS to study the veterinary pathogen Corynebacterium pseudotuberculosis, in which we identified seven putative pathogenicity islands

Automatic Labeling of Self-Organizing Maps: Making a Treasure--Map Reveal its Secrets

. Self-organizing maps are an unsupervised neural network model which lends itself to the cluster analysis of high-dimensional input data. However, interpreting a trained map proves to be difficult because the features responsible for a specific cluster assignment are not evident from the resulting map representation. In this paper we present our LabelSOM approach for automatically labeling a trained self-organizing map with the features of the input data that are the most relevant ones for the assignment of a set of input data to a particular cluster. The resulting labeled map allows the user to better understand the structure and the information available in the map and the reason for a specific map organization, especially when only little prior information on the data set and its characteristics is available. 1 Introduction The self-organizing map (SOM) [2, 3] is a prominent unsupervised neural network model for cluster analysis. Data from a high-dimensional input space is mapped..

Creating an Order in Distributed Digital Libraries by Integrating Independent Self-Organizing Maps

Digital document libraries are an almost perfect application arena for unsupervised neural networks. This because many of the operations computers have to perform on text documents are classification tasks based on "noisy" input patterns. The "noise" arises because of the known inaccuracy of mapping natural language to an indexing vocabulary representing the contents of the documents. A growing number of papers is dedicated to the usage of self-organizing maps to organize the contents of such digital libraries. These papers assume the central availability of the data; an assumption that is questionable given the massive amount of available information. In this paper we describe an approach for organizing distributed digital libraries based on a system of independent self-organizing maps each of which representing just a portion of the complete digital library. Furthermore, we argue in favor of integrating these independent maps in a hierarchical fashion, again by means of self-organizi..

Uncovering the Hierarchical Structure of Text Archives by Using an Unsupervised Neural Network with Adaptive Architecture

. Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires the development of stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In this paper we present the Growing Hierarchical Self-Organizing Map (GH-SOM), a neural network model based on the self-organizing map. The main feature of this extended model is its capability of growing both in terms of map size as well as in a three-dimensional tree-structure in order to represent the hierarchical structure present in a data collection. This capability, combined with the stability of the self-organizing map for high-dimensional feature space representation, makes it an ideal tool for data analysis and exploration. We demonstrate the potential of this method with an application from the information retrieval domain, which is prototypical of the high-dimensional feature spaces frequently encountered in toda..