Effects of Initial Flow on Close-In Planet Atmospheric Circulation

We use a general circulation model to study the three-dimensional (3-D) flow and temperature distributions of atmospheres on tidally synchronized extrasolar planets. In this work, we focus on the sensitivity of the evolution to the initial flow state, which has not received much attention in 3-D modeling studies. We find that different initial states lead to markedly different distributions-even under the application of strong forcing (large day-night temperature difference with a short "thermal drag time") that may be representative of close-in planets. This is in contrast with the results or assumptions of many published studies. In general, coherent jets and vortices (and their associated temperature distributions) characterize the flow, and they evolve differently in time, depending on the initial condition. If the coherent structures reach a quasi- stationary state, their spatial locations still vary. The result underlines the fact that circulation models are currently unsuitable for making quantitative predictions (e.g., location and size of a "hot spot") without better constrained, and well posed, initial conditions.Comment: Accepted for publication in the Astrophysical Journal; 23 pages, 9 figures

Towards Spacetime Entanglement Entropy for Interacting Theories

Entanglement entropy of quantum fields in gravitational settings is a topic of growing importance. This entropy of entanglement is conventionally computed relative to Cauchy hypersurfaces where it is possible via a partial tracing to associate a reduced density matrix to the spacelike region of interest. In recent years Sorkin has proposed an alternative, manifestly covariant, formulation of entropy in terms of the spacetime two-point correlation function. This formulation, developed for a Gaussian scalar field theory, is explicitly spacetime in nature and evades some of the possible non-covariance issues faced by the conventional formulation. In this paper we take the first steps towards extending Sorkin's entropy to non-Gaussian theories where Wick's theorem no longer holds and one would expect higher correlators to contribute. We consider quartic perturbations away from the Gaussian case and find that to first order in perturbation theory, the entropy formula derived by Sorkin continues to hold but with the two-point correlators replaced by their perturbation-corrected counterparts. We then show that our results continue to hold for arbitrary perturbations (of both bosonic and fermionic theories). This is a non-trivial and, to our knowledge, novel result. Furthermore we also derive closed-form formulas of the entanglement entropy for arbitrary perturbations at first and second order. Our work also suggests avenues for further extensions to generic interacting theories.Comment: v2: 30 pages, 2 figures. Results extended to arbitrary perturbations and a discussion of the second order contributions added. v3: minor change

A Hybrid Least Squares and Principal Component Analysis Algorithm for Raman Spectroscopy

Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm’s superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica nanoparticles

Election proximity and representation focus in party-constrained environments

Do elected representatives have a time-constant representation focus or do they adapt their focus depending on election proximity? In this article, we examine these overlooked theoretical and empirical puzzles by looking at how reelection-seeking actors adapt their legislative behavior according to the electoral cycle. In parliamentary democracies, representatives need to serve two competing principals: their party and their district. Our analysis hinges on how representatives make a strategic use of parliamentary written questions in a highly party-constrained institutional context to heighten their reselection and reelection prospects. Using an original data set of over 32,000 parliamentary questions tabled by Portuguese representatives from 2005 to 2015, we examine how time interacts with two key explanatory elements: electoral vulnerability and party size. Results show that representation focus is not static over time and, in addition, that electoral vulnerability and party size shape strategic use of parliamentary questions

Biogenic and Synthetic Polyamines Bind Cationic Dendrimers

Biogenic polyamines are essential for cell growth and differentiation, while polyamine analogues exert antitumor activity in multiple experimental model systems, including breast and lung cancer. Dendrimers are widely used for drug delivery in vitro and in vivo. We report the bindings of biogenic polyamines, spermine (spm), and spermidine (spmd), and their synthetic analogues, 3,7,11,15-tetrazaheptadecane.4HCl (BE-333) and 3,7,11,15,19-pentazahenicosane.5HCl (BE-3333) to dendrimers of different compositions, mPEG-PAMAM (G3), mPEG-PAMAM (G4) and PAMAM (G4). FTIR and UV-visible spectroscopic methods as well as molecular modeling were used to analyze polyamine binding mode, the binding constant and the effects of polyamine complexation on dendrimer stability and conformation. Structural analysis showed that polyamines bound dendrimers through both hydrophobic and hydrophilic contacts with overall binding constants of Kspm-mPEG-G3 = 7.6×104 M−1, Kspm-mPEG-PAMAM-G4 = 4.6×104 M−1, Kspm-PAMAM-G4 = 6.6×104 M−1, Kspmd-mPEG-G3 = 1.0×105 M−1, Kspmd-mPEG-PAMAM-G4 = 5.5×104 M−1, Kspmd-PAMAM-G4 = 9.2×104 M−1, KBE-333-mPEG-G3 = 4.2×104 M−1, KBe-333-mPEG-PAMAM-G4 = 3.2×104 M−1, KBE-333-PAMAM-G4 = 3.6×104 M−1, KBE-3333-mPEG-G3 = 2.2×104 M−1, KBe-3333-mPEG-PAMAM-G4 = 2.4×104 M−1, KBE-3333-PAMAM-G4 = 2.3×104 M−1. Biogenic polyamines showed stronger affinity toward dendrimers than those of synthetic polyamines, while weaker interaction was observed as polyamine cationic charges increased. The free binding energies calculated from docking studies were: −3.2 (spermine), −3.5 (spermidine) and −3.03 (BE-3333) kcal/mol, with the following order of binding affinity: spermidine-PAMAM-G-4>spermine-PAMMAM-G4>BE-3333-PAMAM-G4 consistent with spectroscopic data. Our results suggest that dendrimers can act as carrier vehicles for delivering antitumor polyamine analogues to target tissues

Infrared Spectroscopic Studies of Cells and Tissues: Triple Helix Proteins as a Potential Biomarker for Tumors

In this work, the infrared (IR) spectra of living neural cells in suspension, native brain tissue, and native brain tumor tissue were investigated. Methods were developed to overcome the strong IR signal of liquid water so that the signal from the cellular biochemicals could be seen. Measurements could be performed during surgeries, within minutes after resection. Comparison between normal tissue, different cell lineages in suspension, and tumors allowed preliminary assignments of IR bands to be made. The most dramatic difference between tissues and cells was found to be in weaker IR absorbances usually assigned to the triple helix of collagens. Triple helix domains are common in larger structural proteins, and are typically found in the extracellular matrix (ECM) of tissues. An algorithm to correct offsets and calculate the band heights and positions of these bands was developed, so the variance between identical measurements could be assessed. The initial results indicate the triple helix signal is surprisingly consistent between different individuals, and is altered in tumor tissues. Taken together, these preliminary investigations indicate this triple helix signal may be a reliable biomarker for a tumor-like microenvironment. Thus, this signal has potential to aid in the intra-operational delineation of brain tumor borders. © 2013 Stelling et al

A fractal nature for polymerized laminin

Polylaminin (polyLM) is a non-covalent acid-induced nano- and micro-structured polymer of the protein laminin displaying distinguished biological properties. Polylaminin stimulates neuritogenesis beyond the levels achieved by ordinary laminin and has been shown to promote axonal regeneration in animal models of spinal cord injury. Here we used confocal fluorescence microscopy (CFM), scanning electron microscopy (SEM) and atomic force microscopy (AFM) to characterize its three-dimensional structure. Renderization of confocal optical slices of immunostained polyLM revealed the aspect of a loose flocculated meshwork, which was homogeneously stained by the antibody. On the other hand, an ordinary matrix obtained upon adsorption of laminin in neutral pH (LM) was constituted of bulky protein aggregates whose interior was not accessible to the same anti-laminin antibody. SEM and AFM analyses revealed that the seed unit of polyLM was a flat polygon formed in solution whereas the seed structure of LM was highly heterogeneous, intercalating rod-like, spherical and thin spread lamellar deposits. As polyLM was visualized at progressively increasing magnifications, we observed that the morphology of the polymer was alike independently of the magnification used for the observation. A search for the Hausdorff dimension in images of the two matrices showed that polyLM, but not LM, presented fractal dimensions of 1.55, 1.62 and 1.70 after 1, 8 and 12 hours of adsorption, respectively. Data in the present work suggest that the intrinsic fractal nature of polymerized laminin can be the structural basis for the fractal-like organization of basement membranes in the neurogenic niches of the central nervous system.This work was supported by a grant from the Brazilian National Research Council (CNPq; 476772/2008-7) to TCS. MSS acknowledges support from the European Research Council through ERC - 306990. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Hochman Méndez, C.; Cantini ., M.; Moratal Pérez, D.; Salmerón Sánchez, M.; Coelho-Sampaio, T. (2014). A fractal nature for polymerized laminin. PLoS ONE. 9(10):109388-1-109388-11. https://doi.org/10.1371/journal.pone.0109388S109388-1109388-11910Durbeej, M. (2009). Laminins. Cell and Tissue Research, 339(1), 259-268. doi:10.1007/s00441-009-0838-2Miner, J. H., & Yurchenco, P. D. (2004). LAMININ FUNCTIONS IN TISSUE MORPHOGENESIS. Annual Review of Cell and Developmental Biology, 20(1), 255-284. doi:10.1146/annurev.cellbio.20.010403.094555Yurchenco, P. D. (2010). Basement Membranes: Cell Scaffoldings and Signaling Platforms. Cold Spring Harbor Perspectives in Biology, 3(2), a004911-a004911. doi:10.1101/cshperspect.a004911Hohenester, E., & Yurchenco, P. D. (2013). Laminins in basement membrane assembly. Cell Adhesion & Migration, 7(1), 56-63. doi:10.4161/cam.21831Freire, E., & Coelho-Sampaio, T. (2000). Self-assembly of Laminin Induced by Acidic pH. Journal of Biological Chemistry, 275(2), 817-822. doi:10.1074/jbc.275.2.817Freire, E., Sant’Ana Barroso, M. M., Klier, R. N., & Coelho-Sampaio, T. (2011). Biocompatibility and Structural Stability of a Laminin Biopolymer. Macromolecular Bioscience, 12(1), 67-74. doi:10.1002/mabi.201100125Freire, E. (2002). Structure of laminin substrate modulates cellular signaling for neuritogenesis. Journal of Cell Science, 115(24), 4867-4876. doi:10.1242/jcs.00173Hochman-Mendez, C., Lacerda de Menezes, J. R., Sholl-Franco, A., & Coelho-Sampaio, T. (2013). Polylaminin recognition by retinal cells. Journal of Neuroscience Research, 92(1), 24-34. doi:10.1002/jnr.23298Menezes, K., Ricardo Lacerda de Menezes, J., Assis Nascimento, M., de Siqueira Santos, R., & Coelho-Sampaio, T. (2010). Polylaminin, a polymeric form of laminin, promotes regeneration after spinal cord injury. The FASEB Journal, 24(11), 4513-4522. doi:10.1096/fj.10-157628Barroso, M. M. S., Freire, E., Limaverde, G. S. C. S., Rocha, G. M., Batista, E. J. O., Weissmüller, G., … Coelho-Sampaio, T. (2008). Artificial Laminin Polymers Assembled in Acidic pH Mimic Basement Membrane Organization. Journal of Biological Chemistry, 283(17), 11714-11720. doi:10.1074/jbc.m709301200Freire, E. (2004). Sialic acid residues on astrocytes regulate neuritogenesis by controlling the assembly of laminin matrices. Journal of Cell Science, 117(18), 4067-4076. doi:10.1242/jcs.01276Hausdorff, F. (1918). Dimension und �u�eres Ma�. Mathematische Annalen, 79(1-2), 157-179. doi:10.1007/bf01457179Soille, P., & Rivest, J.-F. (1996). On the Validity of Fractal Dimension Measurements in Image Analysis. Journal of Visual Communication and Image Representation, 7(3), 217-229. doi:10.1006/jvci.1996.0020Theiler, J. (1990). Estimating fractal dimension. Journal of the Optical Society of America A, 7(6), 1055. doi:10.1364/josaa.7.001055Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66. doi:10.1109/tsmc.1979.4310076Iranfar, H., Rajabi, O., Salari, R., & Chamani, J. (2012). Probing the Interaction of Human Serum Albumin with Ciprofloxacin in the Presence of Silver Nanoparticles of Three Sizes: Multispectroscopic and ζ Potential Investigation. The Journal of Physical Chemistry B, 116(6), 1951-1964. doi:10.1021/jp210685qPalmero, C. Y., Miranda-Alves, L., Sant’Ana Barroso, M. M., Souza, E. C. L., Machado, D. E., Palumbo-Junior, A., … Nasciutti, L. E. (2013). The follicular thyroid cell line PCCL3 responds differently to laminin and to polylaminin, a polymer of laminin assembled in acidic pH. Molecular and Cellular Endocrinology, 376(1-2), 12-22. doi:10.1016/j.mce.2013.05.020Behrens, D. T., Villone, D., Koch, M., Brunner, G., Sorokin, L., Robenek, H., … Hansen, U. (2012). The Epidermal Basement Membrane Is a Composite of Separate Laminin- or Collagen IV-containing Networks Connected by Aggregated Perlecan, but Not by Nidogens. Journal of Biological Chemistry, 287(22), 18700-18709. doi:10.1074/jbc.m111.336073Colognato, H., Winkelmann, D. A., & Yurchenco, P. D. (1999). Laminin Polymerization Induces a Receptor–Cytoskeleton Network. The Journal of Cell Biology, 145(3), 619-631. doi:10.1083/jcb.145.3.619Liesi, P., & Silver, J. (1988). Is astrocyte laminin involved in axon guidance in the mammalian CNS? Developmental Biology, 130(2), 774-785. doi:10.1016/0012-1606(88)90366-1Zhou, F. C. (1990). Four patterns of laminin-immunoreactive structure in developing rat brain. Developmental Brain Research, 55(2), 191-201. doi:10.1016/0165-3806(90)90200-iGarcia-Abreu, J., Cavalcante, L. A., & Neto, V. M. (1995). Differential patterns of laminin expression in lateral and medial midbrain glia. NeuroReport, 6(5), 761-764. doi:10.1097/00001756-199503270-00014Kazanis, I., & ffrench-Constant, C. (2011). Extracellular matrix and the neural stem cell niche. Developmental Neurobiology, 71(11), 1006-1017. doi:10.1002/dneu.20970Mercier F, Schnack J, Chaumet MSG (2011) Chapter 4 Fractones: home and conductors of the neural stem cell niche. In: Seki, T., Sawamoto, K., Parent, J. M., Alvarez-Buylla, A., (Eds.) Neurogenesis in the adult brain I: neurobiology. Springer. pp 109–133.CAVALCANTIADAM, E., MICOULET, A., BLUMMEL, J., AUERNHEIMER, J., KESSLER, H., & SPATZ, J. (2006). Lateral spacing of integrin ligands influences cell spreading and focal adhesion assembly. European Journal of Cell Biology, 85(3-4), 219-224. doi:10.1016/j.ejcb.2005.09.011Frith, J. E., Mills, R. J., & Cooper-White, J. J. (2012). Lateral spacing of adhesion peptides influences human mesenchymal stem cell behaviour. Journal of Cell Science, 125(2), 317-327. doi:10.1242/jcs.087916Hernández, J. C. R., Salmerón Sánchez, M., Soria, J. M., Gómez Ribelles, J. L., & Monleón Pradas, M. (2007). Substrate Chemistry-Dependent Conformations of Single Laminin Molecules on Polymer Surfaces are Revealed by the Phase Signal of Atomic Force Microscopy. Biophysical Journal, 93(1), 202-207. doi:10.1529/biophysj.106.102491Douet, V., Kerever, A., Arikawa-Hirasawa, E., & Mercier, F. (2013). Fractone-heparan sulphates mediate FGF-2 stimulation of cell proliferation in the adult subventricular zone. Cell Proliferation, 46(2), 137-145. doi:10.1111/cpr.12023Nikolova, G., Strilic, B., & Lammert, E. (2007). The vascular niche and its basement membrane. Trends in Cell Biology, 17(1), 19-25. doi:10.1016/j.tcb.2006.11.005Yurchenco, P. D., Amenta, P. S., & Patton, B. L. (2004). Basement membrane assembly, stability and activities observed through a developmental lens. Matrix Biology, 22(7), 521-538. doi:10.1016/j.matbio.2003.10.006Nikolova, G., Jabs, N., Konstantinova, I., Domogatskaya, A., Tryggvason, K., Sorokin, L., … Lammert, E. (2006). The Vascular Basement Membrane: A Niche for Insulin Gene Expression and β Cell Proliferation. Developmental Cell, 10(3), 397-405. doi:10.1016/j.devcel.2006.01.015Qu, H., Liu, X., Ni, Y., Jiang, Y., Feng, X., Xiao, J., … Zheng, C. (2014). Laminin 411 acts as a potent inducer of umbilical cord mesenchymal stem cell differentiation into insulin-producing cells. Journal of Translational Medicine, 12(1), 135. doi:10.1186/1479-5876-12-135Kanatsu-Shinohara, M., & Shinohara, T. (2013). Spermatogonial Stem Cell Self-Renewal and Development. Annual Review of Cell and Developmental Biology, 29(1), 163-187. doi:10.1146/annurev-cellbio-101512-122353Lander, A. D., Kimble, J., Clevers, H., Fuchs, E., Montarras, D., Buckingham, M., … Oskarsson, T. (2012). What does the concept of the stem cell niche really mean today? BMC Biology, 10(1). doi:10.1186/1741-7007-10-19Loulier, K., Lathia, J. D., Marthiens, V., Relucio, J., Mughal, M. R., Tang, S.-C., … ffrench-Constant, C. (2009). β1 Integrin Maintains Integrity of the Embryonic Neocortical Stem Cell Niche. PLoS Biology, 7(8), e1000176. doi:10.1371/journal.pbio.100017

Erratum: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2\ub75th percentile and 100 as the 97\ub75th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59\ub74 (IQR 35\ub74–67\ub73), ranging from a low of 11\ub76 (95% uncertainty interval 9\ub76–14\ub70) to a high of 84\ub79 (83\ub71–86\ub77). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030