Reconciling a significant hierarchical assembly of massive early-type galaxies at z<~1 with mass downsizing

Hierarchical models predict that massive early-type galaxies (mETGs) are the latest systems to be in place into the cosmic scenario (at z<~0.5), conflicting with the observational phenomenon of galaxy mass downsizing, which poses that the most massive galaxies have been in place earlier that their lower-mass counterparts (since z~0.7). We have developed a semi-analytical model to test the feasibility of the major-merger origin hypothesis for mETGs, just accounting for the effects on galaxy evolution of the major mergers strictly reported by observations. The most striking model prediction is that very few present-day mETGs have been really in place since z~1, because ~90% of the mETGs existing at z~1 are going to be involved in a major merger between z~1 and the present. Accounting for this, the model derives an assembly redshift for mETGs in good agreement with hierarchical expectations, reproducing observational mass downsizing trends at the same time.Comment: 2 pages, 1 figure, Proceedings of Symposium 2 of JENAM 2010, "Environment and the Formation of Galaxies: 30 years later", ed. I. Ferreras and A. Pasquali, Astrophysics & Space Science Proceedings, Springe

Distribution of melanopsin positive neurons in pigmented and albino mice: evidence for melanopsin interneurons in the mouse retina.

Here we have studied the population of intrinsically photosensitive retinal ganglion cells (ipRGCs) in adult pigmented and albino mice. Our data show that although pigmented (C57Bl/6) and albino (Swiss) mice have a similar total number of ipRGCs, their distribution is slightly different: while in pigmented mice ipRGCs are more abundant in the temporal retina, in albinos the ipRGCs are more abundant in superior retina. In both strains, ipRGCs are located in the retinal periphery, in the areas of lower Brn3a(+)RGC density. Both strains also contain displaced ipRGCs (d-ipRGCs) in the inner nuclear layer (INL) that account for 14% of total ipRGCs in pigmented mice and 5% in albinos. Tracing from both superior colliculli shows that 98% (pigmented) and 97% (albino) of the total ipRGCs, become retrogradely labeled, while double immunodetection of melanopsin and Brn3a confirms that few ipRGCs express this transcription factor in mice. Rather surprisingly, application of a retrograde tracer to the optic nerve (ON) labels all ipRGCs, except for a sub-population of the d-ipRGCs (14% in pigmented and 28% in albino, respectively) and melanopsin positive cells residing in the ciliary marginal zone (CMZ) of the retina. In the CMZ, between 20% (pigmented) and 24% (albino) of the melanopsin positive cells are unlabeled by the tracer and we suggest that this may be because they fail to send an axon into the ON. As such, this study provides the first evidence for a population of melanopsin interneurons in the mammalian retina

Deep learning generalizes because the parameter-function map is biased towards simple functions

This is the final version. Available from ICLR via the link in this recordDeep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning theory would instead predict that they would severely overfit. While many proposals for some kind of implicit regularization have been made to rationalise this success, there is no consensus for the fundamental reason why DNNs do not strongly overfit. In this paper, we provide a new explanation. By applying a very general probability-complexity bound recently derived from algorithmic information theory (AIT), we argue that the parameter-function map of many DNNs should be exponentially biased towards simple functions. We then provide clear evidence for this strong bias in a model DNN for Boolean functions, as well as in much larger fully conected and convolutional networks trained on CIFAR10 and MNIST. As the target functions in many real problems are expected to be highly structured, this intrinsic simplicity bias helps explain why deep networks generalize well on real world problems. This picture also facilitates a novel PAC-Bayes approach where the prior is taken over the DNN input-output function space, rather than the more conventional prior over parameter space. If we assume that the training algorithm samples parameters close to uniformly within the zero-error region then the PAC-Bayes theorem can be used to guarantee good expected generalization for target functions producing high-likelihood training sets. By exploiting recently discovered connections between DNNs and Gaussian processes to estimate the marginal likelihood, we produce relatively tight generalization PAC-Bayes error bounds which correlate well with the true error on realistic datasets such as MNIST and CIFAR10and for architectures including convolutional and fully connected networks

Operators on the Fréchet sequence space ces(p+), 1≤p<∞1 \leq p < \infty

[EN] The Fréchet sequence spaces ces(p+) are very different to the Fréchet sequence spaces ¿p+,1¿pp}\ell ^q$$ ℓ p + = ∩ q > p ℓ q . Math. Nachr. 147, 7–12 (1990)Pérez Carreras, P., Bonet, J.: Barrelled Locally Convex Spaces. North Holland, Amsterdam (1987)Pitt, H.R.: A note on bilinear forms. J. Lond. Math. Soc. 11, 171–174 (1936)Ricker, W.J.: A spectral mapping theorem for scalar-type spectral operators in locally convex spaces. Integral Equ. Oper. Theory 8, 276–288 (1985)Robertson, A.P., Robertson, W.: Topological Vector Spaces. Cambridge University Press, Cambridge (1973)Waelbroeck, L.: Topological vector spaces and algebras. Lecture Notes in Mathematics, vol. 230. Springer, Berlin (1971

Multidrug resistance proteins preferentially regulate natriuretic peptide-driven cGMP signalling in the heart & vasculature.

BACKGROUND & PURPOSE: Cyclic-3',5'-guanosine monophosphate (cGMP) underpins the bioactivity of nitric oxide (NO) and natriuretic peptides and is key to cardiovascular homeostasis. Cyclic GMP-driven responses are terminated primarily by phosphodiesterases but cellular efflux via multidrug resistance proteins (MRPs) might contribute. Herein, the effect of pharmacological blockade of MRPs on cGMP signalling in the heart and vasculature was investigated in vitro and in vivo. EXPERIMENTAL APPROACHES: Proliferation of human coronary artery smooth muscle cells (hCASMC), vasorelaxation of murine aorta and reductions in mean arterial blood pressure (MABP) in response to NO-donors or natriuretic peptides was determined in the absence and presence of the MRP inhibitor MK571. The ability of MRP inhibition to reverse morphological and contractile deficits in a murine model of pressure overload-induced HF was also explored. KEY RESULTS: MK571 attenuated hCASMC growth and enhanced the anti-proliferative effects of NO and ANP. MRP blockade caused concentration-dependent relaxations of murine aorta and augmented responses to ANP (and to a lesser extent NO). MK571 did not decrease MABP, but enhanced the hypotensive actions of ANP and improved structural and functional indices of disease severity in experimental HF. These beneficial actions of MRP inhibition were associated with a greater intra:extra -cellular cGMP ratio in vitro and in vivo. CONCLUSIONS & IMPLICATIONS: MRP blockade promotes the cardiovascular functions of natriuretic peptides in vitro and in vivo, with more modest effects on NO. MRP inhibition may have therapeutic utility in cardiovascular diseases triggered by dysfunctional cGMP signalling, particularly those associated with altered natriuretic peptide bioactivity

Recent trends in molecular diagnostics of yeast infections : from PCR to NGS

The incidence of opportunistic yeast infections in humans has been increasing over recent years. These infections are difficult to treat and diagnose, in part due to the large number and broad diversity of species that can underlie the infection. In addition, resistance to one or several antifungal drugs in infecting strains is increasingly being reported, severely limiting therapeutic options and showcasing the need for rapid detection of the infecting agent and its drug susceptibility profile. Current methods for species and resistance identification lack satisfactory sensitivity and specificity, and often require prior culturing of the infecting agent, which delays diagnosis. Recently developed high-throughput technologies such as next generation sequencing or proteomics are opening completely new avenues for more sensitive, accurate and fast diagnosis of yeast pathogens. These approaches are the focus of intensive research, but translation into the clinics requires overcoming important challenges. In this review, we provide an overview of existing and recently emerged approaches that can be used in the identification of yeast pathogens and their drug resistance profiles. Throughout the text we highlight the advantages and disadvantages of each methodology and discuss the most promising developments in their path from bench to bedside

Analogies between geminivirus and oncovirus: Cell cycle regulation

Geminiviruses are a large family of plant viruses whose genome is composed of one or two circular and single strand of DNA. They replicate in the cell nucleus being Rep protein, the only viral protein necessary for their replication process. Geminiviruses as same as animal DNA oncoviruses, like SV40, adenovirus and papillomavirus, use the host replication machinery to replicate their DNA. Consequently, they alter host cell cycle regulation to create a suitable environment for their replication. One of the events involved in this alteration would be the inactivation of the retinoblastoma protein (pRb) that negatively regulates the G1/S transition in cells. The discovery of one homologue of the pRb in plants and the finding that Rep protein of some geminiviruses interacts with human retinoblastoma protein, as well as animal virus oncoproteins, is very interesting. This finding laid the groundwork for subsequent detection of analogies between geminiviruses and animal DNA tumor viruses, especially in their interaction with pRb. Moreover, the finding allowed the determination of how this interaction affects the regulation of the cell cycle in plants and animals. Accumulated knowledge generates new interesting questions and possible implications, and so, in this document, we dare to watch in that direction.Key words: Geminivirus, oncovirus, retinoblastoma protein, cell cycle regulation, endoreduplication

Virtual screening for inhibitors of the human TSLP:TSLPR interaction

The pro-inflammatory cytokine thymic stromal lymphopoietin (TSLP) plays a pivotal role in the pathophysiology of various allergy disorders that are mediated by type 2 helper T cell (Th2) responses, such as asthma and atopic dermatitis. TSLP forms a ternary complex with the TSLP receptor (TSLPR) and the interleukin-7-receptor subunit alpha (IL-7Ra), thereby activating a signaling cascade that culminates in the release of pro-inflammatory mediators. In this study, we conducted an in silico characterization of the TSLP: TSLPR complex to investigate the drugability of this complex. Two commercially available fragment libraries were screened computationally for possible inhibitors and a selection of fragments was subsequently tested in vitro. The screening setup consisted of two orthogonal assays measuring TSLP binding to TSLPR: a BLI-based assay and a biochemical assay based on a TSLP: alkaline phosphatase fusion protein. Four fragments pertaining to diverse chemical classes were identified to reduce TSLP: TSLPR complex formation to less than 75% in millimolar concentrations. We have used unbiased molecular dynamics simulations to develop a Markov state model that characterized the binding pathway of the most interesting compound. This work provides a proof-ofprinciple for use of fragments in the inhibition of TSLP: TSLPR complexation

Electrically-driven phase transition in magnetite nanostructures

Magnetite (Fe$_{3}$O$_{4}$), an archetypal transition metal oxide, has been used for thousands of years, from lodestones in primitive compasses[1] to a candidate material for magnetoelectronic devices.[2] In 1939 Verwey[3] found that bulk magnetite undergoes a transition at T$_{V}$ $\approx$ 120 K from a high temperature "bad metal" conducting phase to a low-temperature insulating phase. He suggested[4] that high temperature conduction is via the fluctuating and correlated valences of the octahedral iron atoms, and that the transition is the onset of charge ordering upon cooling. The Verwey transition mechanism and the question of charge ordering remain highly controversial.[5-11] Here we show that magnetite nanocrystals and single-crystal thin films exhibit an electrically driven phase transition below the Verwey temperature. The signature of this transition is the onset of sharp conductance switching in high electric fields, hysteretic in voltage. We demonstrate that this transition is not due to local heating, but instead is due to the breakdown of the correlated insulating state when driven out of equilibrium by electrical bias. We anticipate that further studies of this newly observed transition and its low-temperature conducting phase will shed light on how charge ordering and vibrational degrees of freedom determine the ground state of this important compound.Comment: 17 pages, 4 figure

The Phyre2 web portal for protein modeling, prediction and analysis

Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission