Hidden negative linear compressibility in lithium L-tartrate†

Development of artificial muscles, next-generation pressure sensors and precision optics relies on advances in materials with anomalous mechanical properties. Negative linear compressibility, NLC, is one such rare, counterintuitive phenomenon, in which a material expands along one axis under hydrostatic pressure. Both classical and recent NLC materials face a pay-off between the active pressure range and magnitude of NLC, and in the vast majority of cases the NLC effect decreases with pressure. By decoupling the mechanical behaviour of building units for the first time in a winerack framework containing two different strut types, we show that lithium L-tartrate exhibits NLC with a maximum value, Kmax = -21 TPa^-1, and an overall NLC capacity, χNLC = 5.1 %, that are comparable to the most exceptional materials to date. Furthermore, the contributions from molecular strut compression and angle opening interplay to give rise to so-called “hidden” negative linear compressibility, in which NLC is absent at ambient pressure, switched on at 2 GPa and sustained up to the limit of our experiment, 5.5 GPa. Analysis of the changes in crystal structure using variable-pressure synchrotron X-ray diffraction reveals new chemical and geometrical design rules to assist the discovery of other materials with exciting hidden anomalous mechanical properties

Domain Growth in a 1-D Driven Diffusive System

The low-temperature coarsening dynamics of a one-dimensional Ising model, with conserved magnetisation and subject to a small external driving force, is studied analytically in the limit where the volume fraction \mu of the minority phase is small, and numerically for general \mu. The mean domain size L(t) grows as t^{1/2} in all cases, and the domain-size distribution for domains of one sign is very well described by the form P_l(l) \propto (l/L^3)\exp[-\lambda(\mu)(l^2/L^2)], which is exact for small \mu (and possibly for all \mu). The persistence exponent for the minority phase has the value 3/2 for \mu \to 0.Comment: 8 pages, REVTeX, 7 Postscript figures, uses multicol.sty and epsf.sty. Submitted to Phys. Rev.

Lineage Divergence and Historical Gene Flow in the Chinese Horseshoe Bat (Rhinolophus sinicus)

PMCID: PMC3581519This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

18S rRNA is a reliable normalisation gene for real time PCR based on influenza virus infected cells

Background: One requisite of quantitative reverse transcription PCR (qRT-PCR) is to normalise the data with an internal reference gene that is invariant regardless of treatment, such as virus infection. Several studies have found variability in the expression of commonly used housekeeping genes, such as beta-actin (ACTB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), under different experimental settings. However, ACTB and GAPDH remain widely used in the studies of host gene response to virus infections, including influenza viruses. To date no detailed study has been described that compares the suitability of commonly used housekeeping genes in influenza virus infections. The present study evaluated several commonly used housekeeping genes [ACTB, GAPDH, 18S ribosomal RNA (18S rRNA), ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide (ATP5B) and ATP synthase, H+ transporting, mitochondrial Fo complex, subunit C1 (subunit 9) (ATP5G1)] to identify the most stably expressed gene in human, pig, chicken and duck cells infected with a range of influenza A virus subtypes. Results: The relative expression stability of commonly used housekeeping genes were determined in primary human bronchial epithelial cells (HBECs), pig tracheal epithelial cells (PTECs), and chicken and duck primary lung-derived cells infected with five influenza A virus subtypes. Analysis of qRT-PCR data from virus and mock infected cells using NormFinder and BestKeeper software programmes found that 18S rRNA was the most stable gene in HBECs, PTECs and avian lung cells. Conclusions: Based on the presented data from cell culture models (HBECs, PTECs, chicken and duck lung cells) infected with a range of influenza viruses, we found that 18S rRNA is the most stable reference gene for normalising qRT-PCR data. Expression levels of the other housekeeping genes evaluated in this study (including ACTB and GPADH) were highly affected by influenza virus infection and hence are not reliable as reference genes for RNA normalisation

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data

<p>Abstract</p> <p>Background</p> <p>The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.</p> <p>Results</p> <p>Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for <it>eve </it>mRNA pattern formation in the <it>Drosophila melanogaster </it>blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same <it>cis</it>-regulatory module depending on the factors' concentration, and implies different modes of activation and repression.</p> <p>Conclusions</p> <p>Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.</p

A neural circuit model of decision uncertainty and change-of-mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind

Comparative Pathogenesis of Three Human and Zoonotic SARS-CoV Strains in Cynomolgus Macaques

The severe acute respiratory syndrome (SARS) epidemic was characterized by increased pathogenicity in the elderly due to an early exacerbated innate host response. SARS-CoV is a zoonotic pathogen that entered the human population through an intermediate host like the palm civet. To prevent future introductions of zoonotic SARS-CoV strains and subsequent transmission into the human population, heterologous disease models are needed to test the efficacy of vaccines and therapeutics against both late human and zoonotic isolates. Here we show that both human and zoonotic SARS-CoV strains can infect cynomolgus macaques and resulted in radiological as well as histopathological changes similar to those seen in mild human cases. Viral replication was higher in animals infected with a late human phase isolate compared to a zoonotic isolate. While there were significant differences in the number of host genes differentially regulated during the host responses between the three SARS-CoV strains, the top pathways and functions were similar and only apparent early during infection with the majority of genes associated with interferon signaling pathways. This study characterizes critical disease models in the evaluation and licensure of therapeutic strategies against SARS-CoV for human use