Mean first-passage times of non-Markovian random walkers in confinement

The first-passage time (FPT), defined as the time a random walker takes to reach a target point in a confining domain, is a key quantity in the theory of stochastic processes. Its importance comes from its crucial role to quantify the efficiency of processes as varied as diffusion-limited reactions, target search processes or spreading of diseases. Most methods to determine the FPT properties in confined domains have been limited to Markovian (memoryless) processes. However, as soon as the random walker interacts with its environment, memory effects can not be neglected. Examples of non Markovian dynamics include single-file diffusion in narrow channels or the motion of a tracer particle either attached to a polymeric chain or diffusing in simple or complex fluids such as nematics \cite{turiv2013effect}, dense soft colloids or viscoelastic solution. Here, we introduce an analytical approach to calculate, in the limit of a large confining volume, the mean FPT of a Gaussian non-Markovian random walker to a target point. The non-Markovian features of the dynamics are encompassed by determining the statistical properties of the trajectory of the random walker in the future of the first-passage event, which are shown to govern the FPT kinetics.This analysis is applicable to a broad range of stochastic processes, possibly correlated at long-times. Our theoretical predictions are confirmed by numerical simulations for several examples of non-Markovian processes including the emblematic case of the Fractional Brownian Motion in one or higher dimensions. These results show, on the basis of Gaussian processes, the importance of memory effects in first-passage statistics of non-Markovian random walkers in confinement.Comment: Submitted version. Supplementary Information can be found on the Nature website : http://www.nature.com/nature/journal/v534/n7607/full/nature18272.htm

Approach for the classification of polliniferous vegetation using multispectral imaging and neural networks

Beekeeping has suffered a serious deterioration in the regions of the world. The possibility of nectar-polliniferous resources has decreased and, therefore, the feeding of bees, with the consequent decrease in production. There is, therefore, a need to improve marketing and diversification systems and to update production techniques and the management of the production process by beekeepers to obtain the quality of honey required by the market [1]. This work proposes the use of spectral information to identify the different pollen-producing plants using remote vision, image processing, and artificial neural networks

Photon Management in Two-Dimensional Disordered Media

Elaborating reliable and versatile strategies for efficient light coupling between free space and thin films is of crucial importance for new technologies in energy efficiency. Nanostructured materials have opened unprecedented opportunities for light management, notably in thin-film solar cells. Efficient coherent light trapping has been accomplished through the careful design of plasmonic nanoparticles and gratings, resonant dielectric particles and photonic crystals. Alternative approaches have used randomly-textured surfaces as strong light diffusers to benefit from their broadband and wide-angle properties. Here, we propose a new strategy for photon management in thin films that combines both advantages of an efficient trapping due to coherent optical effects and broadband/wide-angle properties due to disorder. Our approach consists in the excitation of electromagnetic modes formed by multiple light scattering and wave interference in two-dimensional random media. We show, by numerical calculations, that the spectral and angular responses of thin films containing disordered photonic patterns are intimately related to the in-plane light transport process and can be tuned through structural correlations. Our findings, which are applicable to all waves, are particularly suited for improving the absorption efficiency of thin-film solar cells and can provide a novel approach for high-extraction efficiency light-emitting diodes

Matched Ascertainment of Informative Families for Complex Genetic Modelling

Family data are used extensively in quantitative genetic studies to disentangle the genetic and environmental contributions to various diseases. Many family studies based their analysis on population-based registers containing a large number of individuals composed of small family units. For binary trait analyses, exact marginal likelihood is a common approach, but, due to the computational demand of the enormous data sets, it allows only a limited number of effects in the model. This makes it particularly difficult to perform joint estimation of variance components for a binary trait and the potential confounders. We have developed a data-reduction method of ascertaining informative families from population-based family registers. We propose a scheme where the ascertained families match the full cohort with respect to some relevant statistics, such as the risk to relatives of an affected individual. The ascertainment-adjusted analysis, which we implement using a pseudo-likelihood approach, is shown to be efficient relative to the analysis of the whole cohort and robust to mis-specification of the random effect distribution

Optimization of ex vivo hematopoietic stem cell expansion in intermittent dynamic cultures

For the ex vivo expansion of CD34+ cells, culture conditions were optimized using cytokine cocktails and media change methods. In addition, static, orbital-shake, and stirred cultures were compared. After cultivation, total cell expansion, immunophenotypes, clonogenic ability, and metabolite concentration in media were analyzed. Optimized media change methods enhanced the number of total nucleated cells (TNCs) by 600-fold (from 104 to 6 × 106 cells) in static cultures. Furthermore, intermittent orbital-shake cultures gave the highest fold increase of TNCs and CD34+/CD38− cells. These results imply that proliferation of CD34+ cells in intermittent shake cultures was more efficient than that in static cultures under optimized culture conditions

Polypyrrole-Fe2O3 nanohybrid materials for electrochemical storage

We report on the synthesis and electrochemical characterization of nanohybrid polypyrrole (PPy) (PPy/Fe2O3) materials for electrochemical storage applications. We have shown that the incorporation of nanoparticles inside the PPy notably increases the charge storage capability in comparison to the “pure” conducting polymer. Incorporation of large anions, i.e., paratoluenesulfonate, allows a further improvement in the capacity. These charge storage modifications have been attributed to the morphology of the composite in which the particle sizes and the specific surface area are modified with the incorporation of nanoparticles. High capacity and stability have been obtained in PC/NEt4BF4 (at 20 mV/s), i.e., 47 mAh/g, with only a 3% charge loss after one thousand cyles. The kinetics of charge–discharge is also improved by the hybrid nanocomposite morphology modifications, which increase the rate of insertion–expulsion of counter anions in the bulk of the film. A room temperature ionic liquid such as imidazolium trifluoromethanesulfonimide seems to be a promising electrolyte because it further increases the capacity up to 53 mAh/g with a high stability during charge–discharge processes

HDAC1 Inactivation Induces Mitotic Defect and Caspase-Independent Autophagic Cell Death in Liver Cancer

Histone deacetylases (HDACs) are known to play a central role in the regulation of several cellular properties interlinked with the development and progression of cancer. Recently, HDAC1 has been reported to be overexpressed in hepatocellular carcinoma (HCC), but its biological roles in hepatocarcinogenesis remain to be elucidated. In this study, we demonstrated overexpression of HDAC1 in a subset of human HCCs and liver cancer cell lines. HDAC1 inactivation resulted in regression of tumor cell growth and activation of caspase-independent autophagic cell death, via LC3B-II activation pathway in Hep3B cells. In cell cycle regulation, HDAC1 inactivation selectively induced both p21WAF1/Cip1 and p27Kip1 expressions, and simultaneously suppressed the expression of cyclin D1 and CDK2. Consequently, HDAC1 inactivation led to the hypophosphorylation of pRb in G1/S transition, and thereby inactivated E2F/DP1 transcription activity. In addition, we demonstrated that HDAC1 suppresses p21WAF1/Cip1 transcriptional activity through Sp1-binding sites in the p21WAF1/Cip1 promoter. Furthermore, sustained suppression of HDAC1 attenuated in vitro colony formation and in vivo tumor growth in a mouse xenograft model. Taken together, we suggest the aberrant regulation of HDAC1 in HCC and its epigenetic regulation of gene transcription of autophagy and cell cycle components. Overexpression of HDAC1 may play a pivotal role through the systemic regulation of mitotic effectors in the development of HCC, providing a particularly relevant potential target in cancer therapy

Perceived discrimination and health-related quality of life among Arabs and Jews in Israel: A population-based survey

<p>Abstract</p> <p>Background</p> <p>Studies have shown that perceived discrimination may be associated with impaired health. The aim of this study was to assess the levels of perceived discrimination on the basis of origin and ethnicity and measure the association with health in three population groups in Israel: non-immigrant Jews, immigrants from the former Soviet Union, and Arabs.</p> <p>Methods</p> <p>A cross sectional random telephone survey was performed in 2006 covering 1,004 Israelis aged 35-65; of these, 404 were non-immigrant Jews, 200 were immigrants from the former Soviet Union and 400 were Arabs, the final number for regression analysis was 952. Respondents were asked about their perceived experiences with discrimination in seven different areas. Quality of life, both physical and mental were measured by the Short Form 12.</p> <p>Results</p> <p>Perceived discrimination on the basis of origin was highest among immigrants. About 30% of immigrants and 20% of Arabs reported feeling discriminated against in areas such as education and employment. After adjusting for socioeconomic variables, discrimination was associated with poor physical health among non-immigrant Jews (OR = 0.42, CI = 0.19, 0.91) and immigrants (OR = 0.51, CI = 0.27, 0.94), but not among Arabs. Poor mental health was significantly associated with discrimination only among non-immigrant Jews (OR = 0.42, CI = 0.18, 0.96).</p> <p>Conclusions</p> <p>Perceived discrimination seemed high in both minority populations in Israel (Arabs and immigrants) and needs to be addressed as such. However, discrimination was associated with physical health only among Jews (non-immigrants and immigrants), and not among Arabs. These results may be due to measurement artifacts or may be a true phenomenon, further research is needed to ascertain the results.</p

First-passage times in complex scale-invariant media

How long does it take a random walker to reach a given target point? This quantity, known as a first passage time (FPT), has led to a growing number of theoretical investigations over the last decade1. The importance of FPTs originates from the crucial role played by first encounter properties in various real situations, including transport in disordered media, neuron firing dynamics, spreading of diseases or target search processes. Most methods to determine the FPT properties in confining domains have been limited to effective 1D geometries, or for space dimensions larger than one only to homogeneous media1. Here we propose a general theory which allows one to accurately evaluate the mean FPT (MFPT) in complex media. Remarkably, this analytical approach provides a universal scaling dependence of the MFPT on both the volume of the confining domain and the source-target distance. This analysis is applicable to a broad range of stochastic processes characterized by length scale invariant properties. Our theoretical predictions are confirmed by numerical simulations for several emblematic models of disordered media, fractals, anomalous diffusion and scale free networks.Comment: Submitted version. Supplementary Informations available on Nature websit

Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market

What are the dominant stocks which drive the correlations present among stocks traded in a stock market? Can a correlation analysis provide an answer to this question? In the past, correlation based networks have been proposed as a tool to uncover the underlying backbone of the market. Correlation based networks represent the stocks and their relationships, which are then investigated using different network theory methodologies. Here we introduce a new concept to tackle the above question—the partial correlation network. Partial correlation is a measure of how the correlation between two variables, e.g., stock returns, is affected by a third variable. By using it we define a proxy of stock influence, which is then used to construct partial correlation networks. The empirical part of this study is performed on a specific financial system, namely the set of 300 highly capitalized stocks traded at the New York Stock Exchange, in the time period 2001–2003. By constructing the partial correlation network, unlike the case of standard correlation based networks, we find that stocks belonging to the financial sector and, in particular, to the investment services sub-sector, are the most influential stocks affecting the correlation profile of the system. Using a moving window analysis, we find that the strong influence of the financial stocks is conserved across time for the investigated trading period. Our findings shed a new light on the underlying mechanisms and driving forces controlling the correlation profile observed in a financial market