Impact of Summer Heat on Urban Population Mortality in Europe during the 1990s: An Evaluation of Years of Life Lost Adjusted for Harvesting

Background

Efforts to prevent and respond to heat-related illness would benefit by quantifying the impact of summer heat on acute population mortality. We estimated years of life lost due to heat in 14 European cities during the 1990s accounting for harvesting.

Methods

We combined the number of deaths attributable to heat estimated by the PHEWE project with life expectancy derived from population life tables. The degree of harvesting was quantified by comparing the cumulative effect of heat up to lagged day 30 with the immediate effect of heat, by geographical region and age. Next, an evaluation of years of life lost adjusted for harvesting was obtained.

Results

Without accounting for harvesting, we estimated more than 23,000 years of life lost per year, 55% of which was among individuals younger than 75. When 30 day mortality displacement was taken into account, the overall impact reduced on average by 75%. Harvesting was more pronounced in North-continental cities than in Mediterranean cities and was stronger among young people than among elderly.

Conclusions

High ambient temperatures during summer were responsible for many deaths in European cities during the 1990s, but a large percentage of these deaths likely involved frail persons whose demise was only briefly hastened by heat exposure. Differences in harvesting across regions and classes of age could reflect different proportions of frail individuals in the population or could be indicative of heterogeneous dynamics underlying the entry and exit of individuals from the high-risk pool which is subject to mortality displacement.     

Tailoring of Localized Surface Plasmon Resonances of Core-Shell Au@Ag Nanorods by Changing the Thickness of Ag Shell

We report the synthesis and the characterization of core-shell Au@Ag nanorods through reduction by the wet chemical method. UV-visible absorption spectra of core-shell Au@Ag nanorods demonstrate the longitudinal mode of localized surface plasmon resonances (LSPRs) can be tailored from 724 to 786 nm by controlling the thickness of the silver shell, as is assessed by transmission electron microscope (TEM). Furthermore, the tunable and well-controlled LSPRs of core-shell Au@Ag nanorods are also investigated by numerical simulation using the finite difference time domain (FDTD) method, which strongly supports the experimental observations. The growth mechanism for core-shell Au@Ag nanorods is proposed, according to experimental observations and numerical calculations.     

Broad Tunable Nanoantenna Based on Graphene Log-Periodic Toothed Structure

A log-periodic toothed nanoantenna based on graphene is proposed, and its multi-resonance properties with respect to the variations of the chemical potential are investigated. The field enhancement and radar cross-section of the antenna for different chemical potentials are calculated, and the effect of the chemical potential on the resonance frequency is analyzed. In addition, the dependence of the resonance frequency on the substrate is also discussed. It is shown that large modulation of resonance intensity in log-periodic toothed nanoantenna can be achieved via turning the chemical potential of graphene. The tunability of the resonant frequencies of the antenna can be used to broad tuning of spectral features. The property of tunable multi-resonant field enhancement has great prospect in the field of graphene-based broadband nanoantenna, which can be applied in non-linear spectroscopy, optical sensor, and near-field optical microscopy.     

Numerical Simulation of Broadband Scattering by Coated and Noncoated Metal Nanostructures Using Discrete Dipole Approximation Method

We suggest numerical method to study the optical response of metal nanostructures. The analysis of optical properties such as scattering and absorption by coated and noncoated nanogeometry has been done using discrete dipole approximation (DDA) method. The core-shell nanogeometry supports surface plasmon resonances, which are highly tunable from 400 to 1100 nm. The tunability of surface plasmon resonance (SPR) highly depends on the structural anisotropy and chosen core-shell material. Further, we have observed that aspect ratio is one of the key parameter to decide the nature and position of the plasmonic peaks and magnitude of optical cross section. We have also shown that coated nanospheroid is a more appropriate geometry as compared to coated nanosphere and noncoated nanospheroid in terms of wide tunability of surface plasmon resonance. The wide tunability in SPR is observed for the effective radii 90 nm core-shell (Au@SiO₂) nanospheroid with aspect ratio 0.1.     

Intensive determination of storage condition effects on human plasma metabolomics

Introduction

Human plasma metabolomics offer powerful tools for understanding disease mechanisms and identifying clinical biomarkers for diagnosis, efficacy prediction and patient stratification. Although storage conditions can affect the reliability of data from metabolites, strict control of these conditions remains challenging, particularly when clinical samples are included from multiple centers. Therefore, it is necessary to consider stability profiles of each analyte.

Objectives

The purpose of this study was to extract unstable metabolites from vast metabolome data and identify factors that cause instability.

Method

Plasma samples were obtained from five healthy volunteers, were stored under ten different conditions of time and temperature and were quantified using leading-edge metabolomics. Instability was evaluated by comparing quantitation values under each storage condition with those obtained after ?80 °C storage.

Result

Stability profiling of the 992 metabolites showed time- and temperature-dependent increases in numbers of significantly changed metabolites. This large volume of data enabled comparisons of unstable metabolites with their related molecules and allowed identification of causative factors, including compound-specific enzymatic activity in plasma and chemical reactivity. Furthermore, these analyses indicated extreme instability of 1-docosahexaenoylglycerol, 1-arachidonoylglycerophosphate, cystine, cysteine and N⁶-methyladenosine.

Conclusion

A large volume of data regarding storage stability was obtained. These data are a contribution to the discovery of biomarker candidates without misselection based on unreliable values and to the establishment of suitable handling procedures for targeted biomarker quantification.

     

Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.     

Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways

Background

Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.

Results

We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.

Conclusion

The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.

     

Theoretical study of electron transfer process between fullerenes and neurotransmitters; acetylcholine,dopamine, serotonin and epinephrine in nanostructures [neurotransmitters].C<Subscript><Emphasis Type="Italic">n</Emphasis></Subscript> complexes

Neurotransmitters are the compounds which allow the transmission of signals from one neuron to the next across synapses. They are the brain chemicals that communicate information throughout brain and body. Fullerenes are a family of carbonallotropes, molecules composed entirely of carbon, that take the forms of spheres, ellipsoids, and cylinders. Various empty carbon fullerenes (C_n) with different carbon atoms have been obtained and investigated. Topological indices have been successfully used to construct effective and useful mathematical methods to establish clear relationships between structural data and the physical properties of these materials. In this study, the number of carbon atoms in the fullerenes was used as an index to establish a relationship between the structures of neurotransmitters (NTs) acetylcholine (AC) 1, dopamine (DP) 2, serotonin (SE) 3, and epinephrine (EP) 4 as the well-known redox systems and fullerenes C_n (n = 60, 70, 76, 82, and 86) which create [NT].C_n; A-1 to A-5 up to D-1 to D-5. The relationship between the number of carbon atoms and the free energy of electron transfer (ΔG_et(n); n = 1–4) is assessed using the Rehm-Weller equation for A-1 to A-5 up to D-1 to D-5 supramolecular [NT].C_n complexes. The calculations are presented for the four reduction potentials (^Red.E₁ to ^Red.E₄) of fullerenes C_n. The results were used to calculate the four free energy values of electron transfer (ΔG_et(1) to ΔG_et(4)) of the supramolecular complexes A-1 to A-8 up to D-1 to D-8 for fullerenes C₆₀ to C₁₂₀. The first to fourth free activation energy values of electron transfer and the maximum wavelength of the electron transfers, ΔG^#_et(n) and λ_et (n = 1–4), respectively, were also calculated in this study for A-1 to A-8 up to D-1 to D-8 in accordance with the Marcus theory.