Cosmology at the Millennium

One hundred years ago we did not know how stars generate energy, the age of the Universe was thought to be only millions of years, and our Milky Way galaxy was the only galaxy known. Today, we know that we live in an evolving and expanding Universe comprising billions of galaxies, all held together by dark matter. With the hot big-bang model, we can trace the evolution of the Universe from the hot soup of quarks and leptons that existed a fraction of a second after the beginning to the formation of galaxies a few billion years later, and finally to the Universe we see today 13 billion years after the big bang, with its clusters of galaxies, superclusters, voids, and great walls. The attractive force of gravity acting on tiny primeval inhomogeneities in the distribution of matter gave rise to all the structure seen today. A paradigm based upon deep connections between cosmology and elementary particle physics -- inflation + cold dark matter -- holds the promise of extending our understanding to an even more fundamental level and much earlier times, as well as shedding light on the unification of the forces and particles of nature. As we enter the 21st century, a flood of observations is testing this paradigm.Comment: 44 pages LaTeX with 14 eps figures. To be published in the Centennial Volume of Reviews of Modern Physic

Encapsulation of cadmium selenide nanocrystals in biocompatible nanotubes: DFT calculations, X-ray diffraction investigations and confocal fluorescence imaging

The encapsulation of CdSe nanocrystals within singlewalled carbon nanotubes (SWNTs) cavities of varying dimensions at elevated temperatures under strictly air-tight conditions is described for the first time. The structures of CdSe nanocrystals under confinement inside SWNTs was established in a comprehensive study, combining both experimental and DFT level theoretical investigations. The calculated binding energies show that all considered polymorphs (3, 3), (4, 4) and (4,2) may be obtained experimentally. The most thermodynamically stable structure (3:3) is directly compared to the experimentally observed CdSe structures inside carbon nanotubes. The gas-phase density functional theorycalculated energy differences between “free” 3:3 and 4:2 structures (e.g. whereby 3:3 models a novel tubular structure in which both Cd and Se form three coordination as observed experimentally for HgTe inside SWNT and 4:2 is a motif derived from the hexagonal CuI bulk structure in which both Cd and Se form 4 or 2 coordinations) are surprisingly small, only 0.06 eV per formula unit.. X-ray powder diffraction, Raman spectroscopy, High-resolution transmission electron microscopy (HRTEM) and Energy Dispersive X-ray (EDX) analyses led to the full characterization of the SWNTs filled with the CdSe nanocrystals, shedding light on the composition, structure and the electronic interactions of the new nanohybrid materials on an atomic level. A new emerging hybrid nanomaterial, simultaneously filled and beta-D-glucan coated was obtained using pristine nanotubes and bulk CdSe powder as starting materials. This displayed fluorescence in water dispersions and unexpected biocompatibility was found to be mediated by the beta-D-glucan (a biopolymer extracted from barley) with respect to that of the individual inorganic materials components. For the first time, such supramolecular nanostructures are investigated by life-sciences techniques applied to functional nanomaterials characterization opening the doors for future nano-biotechnological applications

A fluorescent Arg–Gly–Asp (RGD) peptide–naphthalenediimide (NDI) conjugate for imaging integrin <em>α_vβ₃in vitro</em>

We have developed a fluorescent peptide conjugate (TrpNDIRGDfK) based on the coupling of cyclo(RGDfK) to a new tryptophan-tagged amino acid naphthalenediimide (TrpNDI).</p

Maternal and perinatal outcomes of pregnancies delivered at 23 weeks' gestation.

OBJECTIVE: To evaluate the maternal and perinatal outcomes of pregnancies delivered at 23+0 to 23+6 weeks' gestation. METHODS: This prospective cohort study included women in the Canadian Perinatal Network who were admitted to one of 16 Canadian tertiary perinatal units between August 1, 2005, and March 31, 2011, and who delivered at 23+0 to 23+6 weeks' gestation. Women were included in the network if they were admitted with spontaneous preterm labour with contractions, a short cervix without contractions, prolapsing membranes with membranes at or beyond the external os or a dilated cervix, preterm premature rupture of membranes, intrauterine growth restriction, gestational hypertension, or antepartum hemorrhage. Maternal outcomes included Caesarean section, placental abruption, and serious complication. Perinatal outcomes were mortality and serious morbidity. RESULTS: A total of 248 women and 287 infants were included in the study. The rate of Caesarean section was 10.5% (26/248) and 40.3% of women (100/248) had a serious complication, the most common being chorioamnionitis (38.6%), followed by blood transfusion (4.5%). Of infants with known outcomes, perinatal mortality was 89.9% (223/248) (stillbirth 23.3% [67/287] and neonatal death 62.9% [156/248]). Of live born neonates with known outcomes (n = 181), 38.1% (69/181) were admitted to NICU. Of those admitted to NICU, neonatal death occurred in 63.8% (44/69). Among survivors at discharge, the rate of severe brain injury was 44.0% (11/25), of retinopathy of prematurity 58.3% (14/24), and of any serious neonatal morbidity 100% (25/25). Two subgroup analyses were performed: in one, antepartum stillbirths were excluded, and in the other only centres that indicated they offered fetal monitoring at 23 weeks' gestation were included and antepartum stillbirths were excluded. In each of these, perinatal outcomes similar to the overall group were found. CONCLUSION: Pregnant women delivering at 23 weeks' gestation are at risk of morbidity. Their infants have high rates of serious morbidity and mortality. Further research is needed to identify strategies and forms of management that not only increase perinatal survival but also reduce morbidities in these extremely low gestational age infants and reduce maternal morbidity

RAIphy: Phylogenetic classification of metagenomics samples using iterative refinement of relative abundance index profiles

Background: Computational analysis of metagenomes requires the taxonomical assignment of the genome contigs assembled from DNA reads of environmental samples. Because of the diverse nature of microbiomes, the length of the assemblies obtained can vary between a few hundred bp to a few hundred Kbp. Current taxonomic classification algorithms provide accurate classification for long contigs or for short fragments from organisms that have close relatives with annotated genomes. These are significant limitations for metagenome analysis because of the complexity of microbiomes and the paucity of existing annotated genomes. Results: We propose a robust taxonomic classification method, RAIphy, that uses a novel sequence similarity metric with iterative refinement of taxonomic models and functions effectively without these limitations. We have tested RAIphy with synthetic metagenomics data ranging between 100 bp to 50 Kbp. Within a sequence read range of 100 bp-1000 bp, the sensitivity of RAIphy ranges between 38%-81% outperforming the currently popular composition-based methods for reads in this range. Comparison with computationally more intensive sequence similarity methods shows that RAIphy performs competitively while being significantly faster. The sensitivityspecificity characteristics for relatively longer contigs were compared with the PhyloPythia and TACOA algorithms. RAIphy performs better than these algorithms at varying clade-levels. For an acid mine drainage (AMD) metagenome, RAIphy was able to taxonomically bin the sequence read set more accurately than the currently available methods, Phymm and MEGAN, and more accurately in two out of three tests than the much more computationally intensive method, PhymmBL. Conclusions: With the introduction of the relative abundance index metric and an iterative classification method, we propose a taxonomic classification algorithm that performs competitively for a large range of DNA contig lengths assembled from metagenome data. Because of its speed, simplicity, and accuracy RAIphy can be successfully used in the binning process for a broad range of metagenomic data obtained from environmental samples

Confidence from uncertainty - A multi-target drug screening method from robust control theory

<p>Abstract</p> <p>Background</p> <p>Robustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty.</p> <p>Results</p> <p>We present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty.</p> <p>Conclusions</p> <p>The paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.</p

Fundamental issues in systems biology.

types: Journal Article; Research Support, Non-U.S. Gov'tIn the context of scientists' reflections on genomics, we examine some fundamental issues in the emerging postgenomic discipline of systems biology. Systems biology is best understood as consisting of two streams. One, which we shall call 'pragmatic systems biology', emphasises large-scale molecular interactions; the other, which we shall refer to as 'systems-theoretic biology', emphasises system principles. Both are committed to mathematical modelling, and both lack a clear account of what biological systems are. We discuss the underlying issues in identifying systems and how causality operates at different levels of organisation. We suggest that resolving such basic problems is a key task for successful systems biology, and that philosophers could contribute to its realisation. We conclude with an argument for more sociologically informed collaboration between scientists and philosophers.Funding received from the Economic and Social Research Council (ESRC), UK, and Overseas Conference Funding from the British Academy

LEDGF gene silencing impairs the tumorigenicity of prostate cancer DU145 cells by abating the expression of Hsp27 and activation of the Akt/ERK signaling pathway

Lens epithelium-derived growth factor (LEDGF) maintains survival pathways by augmenting the transcription of stress-response genes such as small heat-shock protein 27. Recently, aberrant expression of LEDGF was found in prostate cancer (PC). Herein, we showed that LEDGF overexpression upregulated Hsp27 in PC cells, DU145, PC-3 and LNCaP and promoted antiapoptotic pathways in PCs. We found that these cells had higher abundance of Hsp27, which was correlated with the levels of LEDGF expression. Transactivation assay in DU145 cells revealed that transactivation of Hsp27 was related to the magnitude of LEDGF expression. Silencing of LEDGF in DU145 cells abrogated Hsp27 expression and inhibited stimulated cell proliferation, invasiveness and migration. These cells were arrested in S and G2 phase, and failed to accumulate cyclin B1, and showed increased apoptosis. Furthermore, LEDGF-depleted DU145 cells displayed elevated Bax and cleaved caspase 9 expression and reduced levels of Bcl2, Bcl-XL. The activated survival pathway(s), ERK1/2 and Akt, were selectively decreased in these cells, which characteristically have lower tumorigenicity. Conversely, the depleted cells, when re-overexpressed with LEDGF or Hsp27, regained tumorigenic properties. Collectively, results reveal the involvement of LEDGF-mediated elevated expression of Hsp27-dependent survival pathway(s) in PC. Our findings suggest new lines of investigation aimed at developing therapies by targeting LEDGF or its aberrant expression-associated stimulated antiapoptotic pathway(s)

Methods of model reduction for large-scale biological systems: a survey of current methods and trends

Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed

Mathematical modeling of intracellular signaling pathways

Dynamic modeling and simulation of signal transduction pathways is an important topic in systems biology and is obtaining growing attention from researchers with experimental or theoretical background. Here we review attempts to analyze and model specific signaling systems. We review the structure of recurrent building blocks of signaling pathways and their integration into more comprehensive models, which enables the understanding of complex cellular processes. The variety of mechanisms found and modeling techniques used are illustrated with models of different signaling pathways. Focusing on the close interplay between experimental investigation of pathways and the mathematical representations of cellular dynamics, we discuss challenges and perspectives that emerge in studies of signaling systems