Wandering behaviour prevents inter and intra oceanic speciation in a coastal pelagic fish

Small pelagic fishes have the ability to disperse over long distances and may present complex evolutionary histories. Here, Old World Anchovies (OWA) were used as a model system to understand genetic patterns and connectivity of fish between the Atlantic and Pacific basins. We surveyed 16 locations worldwide using mtDNA and 8 microsatellite loci for genetic parameters, and mtDNA (cyt b; 16S) and nuclear (RAG1; RAG2) regions for dating major lineage-splitting events within Engraulidae family. The OWA genetic divergences (0-0.4%) are compatible with intra-specific divergence, showing evidence of both ancient and contemporary admixture between the Pacific and Atlantic populations, enhanced by high asymmetrical migration from the Pacific to the Atlantic. The estimated divergence between Atlantic and Pacific anchovies (0.67 [0.53-0.80] Ma) matches a severe drop of sea temperature during the Gunz glacial stage of the Pleistocene. Our results support an alternative evolutionary scenario for the OWA, suggesting a coastal migration along south Asia, Middle East and eastern Africa continental platforms, followed by the colonization of the Atlantic via the Cape of the Good Hope.Portuguese Foundation for Science & Technology (FCT) [SFRH/BD/36600/2007]; FCT [UID/MAR/04292/2013, SFRH/BPD/65830/2009]; FCT strategic plan [UID/Multi/04326/2013]info:eu-repo/semantics/publishedVersio

Transient Cognitive Impairment and White Matter Hyperintensities in Severely Depressed Older Patients Treated With Electroconvulsive Therapy

BACKGROUND: Although electroconvulsive therapy (ECT) is a safe and effective treatment for patients with severe late life depression (LLD), transient cognitive impairment can be a reason to discontinue the treatment. The aim of the current study was to evaluate the association between structural brain characteristics and general cognitive function during and after ECT. METHODS: A total of 80 patients with LLD from the prospective naturalistic follow-up Mood Disorders in Elderly treated with Electroconvulsive Therapy study were examined. Magnetic resonance imaging scans were acquired before ECT. Overall brain morphology (white and grey matter) was evaluated using visual rating scales. Cognitive functioning before, during, and after ECT was measured using the Mini Mental State Examination (MMSE). A linear mixed-model analysis was performed to analyze the association between structural brain alterations and cognitive functioning over time. RESULTS: Patients with moderate to severe white matter hyperintensities (WMH) showed significantly lower MMSE scores than patients without severe WMH (F(1,75.54) = 5.42, p = 0.02) before, during, and post-ECT, however their trajectory of cognitive functioning was similar as no time × WMH interaction effect was observed (F(4,65.85) = 1.9, p = 0.25). Transient cognitive impairment was not associated with medial temporal or global cortical atrophy (MTA, GCA). CONCLUSION: All patients showed a significant drop in cognitive functioning during ECT, which however recovered above baseline levels post-ECT and remained stable until at least 6 months post-ECT, independently of severity of WMH, GCA, or MTA. Therefore, clinicians should not be reluctant to start or continue ECT in patients with severe structural brain alterations

Localized Basis for Effective Lattice Hamiltonians: Lattice Wannier Functions

A systematic method is presented for constructing effective Hamiltonians for general phonon-related structural transitions. The key feature is the application of group theoretical methods to identify the subspace in which the effective Hamiltonian acts and construct for it localized basis vectors, which are the analogue of electronic Wannier functions. The results of the symmetry analysis for the perovskite, rocksalt, fluorite and A15 structures and the forms of effective Hamiltonians for the ferroelectric transition in $PbTiO_3$ and $BaTiO_3$, the oxygen-octahedron rotation transition in $SrTiO_3$, the Jahn-Teller instability in $La_{1-x}(Ca,Sr,Ba)_xMnO_3$ and the antiferroelectric transition in $PbZrO_3$ are discussed. For the oxygen- octahedron rotation transition in $SrTiO_3$, this method provides an alternative to the rotational variable approach which is well behaved throughout the Brillouin zone. The parameters appearing in the Wannier basis vectors and in the effective Hamiltonian, given by the corresponding invariant energy expansion, can be obtained for individual materials using first- principles density-functional-theory total energy and linear response techniques, or any technique that can reliably calculate force constants and distortion energies. A practical approach to the determination of these parameters is presented and the application to ferroelectric $PbTiO_3$ discussed.Comment: extensive revisions in presentation, 32 pages, Revtex, 7 Postscript figure

What is progressive business?

This introductory chapter signposts the rationale, framework and case study contents of the book. Firstly, we offer an overview of the need for new more progressive business models than the mainstream which exists at present, identifying the current challenges facing business in Europe and beyond in its international ramifications. To remedy these challenges, we present our alternative vision of progressive business functioning, whose basic criteria comprise ecological sustainability, respect for future generations, and pro-socialness. Then, synopses of our case examples follow

Consequences of local gauge symmetry in empirical tight-binding theory

A method for incorporating electromagnetic fields into empirical tight-binding theory is derived from the principle of local gauge symmetry. Gauge invariance is shown to be incompatible with empirical tight-binding theory unless a representation exists in which the coordinate operator is diagonal. The present approach takes this basis as fundamental and uses group theory to construct symmetrized linear combinations of discrete coordinate eigenkets. This produces orthogonal atomic-like "orbitals" that may be used as a tight-binding basis. The coordinate matrix in the latter basis includes intra-atomic matrix elements between different orbitals on the same atom. Lattice gauge theory is then used to define discrete electromagnetic fields and their interaction with electrons. Local gauge symmetry is shown to impose strong restrictions limiting the range of the Hamiltonian in the coordinate basis. The theory is applied to the semiconductors Ge and Si, for which it is shown that a basis of 15 orbitals per atom provides a satisfactory description of the valence bands and the lowest conduction bands. Calculations of the dielectric function demonstrate that this model yields an accurate joint density of states, but underestimates the oscillator strength by about 20% in comparison to a nonlocal empirical pseudopotential calculation.Comment: 23 pages, 7 figures, RevTeX4; submitted to Phys. Rev.

Structure Discovery in Mixed Order Hyper Networks

Background  Mixed Order Hyper Networks (MOHNs) are a type of neural network in which the interactions between inputs are modelled explicitly by weights that can connect any number of neurons. Such networks have a human readability that networks with hidden units lack. They can be used for regression, classification or as content addressable memories and have been shown to be useful as fitness function models in constraint satisfaction tasks. They are fast to train and, when their structure is fixed, do not suffer from local minima in the cost function during training. However, their main drawback is that the correct structure (which neurons to connect with weights) must be discovered from data and an exhaustive search is not possible for networks of over around 30 inputs.  Results  This paper presents an algorithm designed to discover a set of weights that satisfy the joint constraints of low training error and a parsimonious model. The combined structure discovery and weight learning process was found to be faster, more accurate and have less variance than training an MLP.  Conclusions  There are a number of advantages to using higher order weights rather than hidden units in a neural network but discovering the correct structure for those weights can be challenging. With the method proposed in this paper, the use of high order networks becomes tractable

Postglacial expansion of the arctic keystone copepod calanus glacialis

Calanus glacialis, a major contributor to zooplankton biomass in the Arctic shelf seas, is a key link between primary production and higher trophic levels that may be sensitive to climate warming. The aim of this study was to explore genetic variation in contemporary populations of this species to infer possible changes during the Quaternary period, and to assess its population structure in both space and time. Calanus glacialis was sampled in the fjords of Spitsbergen (Hornsund and Kongsfjorden) in 2003, 2004, 2006, 2009 and 2012. The sequence of a mitochondrial marker, belonging to the ND5 gene, selected for the study was 1249 base pairs long and distinguished 75 unique haplotypes among 140 individuals that formed three main clades. There was no detectable pattern in the distribution of haplotypes by geographic distance or over time. Interestingly, a Bayesian skyline plot suggested that a 1000-fold increase in population size occurred approximately 10,000 years before present, suggesting a species expansion after the Last Glacial Maximum.GAME from the National Science Centre, the Polish Ministry of Science and Higher Education Iuventus Plus [IP2014 050573]; FCT-PT [CCMAR/Multi/04326/2013]; [2011/03/B/NZ8/02876

The moisture response of soil heterotrophic respiration: Interaction with soil properties

Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4% in global soil carbon stock predictions by 2100. The necessity of improving the representation of this relationship in models has been highlighted in recent studies. Here we present a data-driven analysis of soil moisture-respiration relations based on 90 soils. With the use of linear models we show how the relationship between soil heterotrophic respiration and different measures of soil moisture is consistently affected by soil properties. The empirical models derived include main effects and moisture interaction effects of soil texture, organic carbon content and bulk density. When compared to other functions currently used in different soil biogeochemical models, we observe that our results can correct biases and reconcile differences within and between such functions. Ultimately, accurate predictions of the response of soil carbon to future climate scenarios will require the integration of soil-dependent moisture-respiration functions coupled with realistic representations of soil water dynamic

Structure of a lectin from Canavalia gladiata seeds: new structural insights for old molecules

<p>Abstract</p> <p>Background</p> <p>Lectins are mainly described as simple carbohydrate-binding proteins. Previous studies have tried to identify other binding sites, which possible recognize plant hormones, secondary metabolites, and isolated amino acid residues. We report the crystal structure of a lectin isolated from <it>Canavalia gladiata </it>seeds (CGL), describing a new binding pocket, which may be related to pathogen resistance activity in ConA-like lectins; a site where a non-protein amino-acid, α-aminobutyric acid (Abu), is bound.</p> <p>Results</p> <p>The overall structure of native CGL and complexed with α-methyl-mannoside and Abu have been refined at 2.3 Å and 2.31 Å resolution, respectively. Analysis of the electron density maps of the CGL structure shows clearly the presence of Abu, which was confirmed by mass spectrometry.</p> <p>Conclusion</p> <p>The presence of Abu in a plant lectin structure strongly indicates the ability of lectins on carrying secondary metabolites. Comparison of the amino acids composing the site with other legume lectins revealed that this site is conserved, providing an evidence of the biological relevance of this site. This new action of lectins strengthens their role in defense mechanisms in plants.</p

Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

<p>Abstract</p> <p>Background</p> <p>Tuberculosis is a contagious disease caused by <it>Mycobacterium tuberculosis </it>(Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.</p> <p>Results</p> <p>We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.</p> <p>Conclusions</p> <p>This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.</p