Human Neutrophil Elastase Degrades SPLUNC1 and Impairs Airway Epithelial Defense against Bacteria

Background:Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are a significant cause of mortality of COPD patients, and pose a huge burden on healthcare. One of the major causes of AECOPD is airway bacterial (e.g. nontypeable Haemophilus influenzae [NTHi]) infection. However, the mechanisms underlying bacterial infections during AECOPD remain poorly understood. As neutrophilic inflammation including increased release of human neutrophil elastase (HNE) is a salient feature of AECOPD, we hypothesized that HNE impairs airway epithelial defense against NTHi by degrading airway epithelial host defense proteins such as short palate, lung, and nasal epithelium clone 1 (SPLUNC1).Methodology/Main Results:Recombinant human SPLUNC1 protein was incubated with HNE to confirm SPLUNC1 degradation by HNE. To determine if HNE-mediated impairment of host defense against NTHi was SPLUNC1-dependent, SPLUNC1 protein was added to HNE-treated primary normal human airway epithelial cells. The in vivo function of SPLUNC1 in NTHi defense was investigated by infecting SPLUNC1 knockout and wild-type mice intranasally with NTHi. We found that: (1) HNE directly increased NTHi load in human airway epithelial cells; (2) HNE degraded human SPLUNC1 protein; (3) Recombinant SPLUNC1 protein reduced NTHi levels in HNE-treated human airway epithelial cells; (4) NTHi levels in lungs of SPLUNC1 knockout mice were increased compared to wild-type mice; and (5) SPLUNC1 was reduced in lungs of COPD patients.Conclusions:Our findings suggest that SPLUNC1 degradation by neutrophil elastase may increase airway susceptibility to bacterial infections. SPLUNC1 therapy likely attenuates bacterial infections during AECOPD. © 2013 Jiang et al

IFN-gamma is associated with risk of Schistosoma japonicum infection in China.

Before the start of the schistosomiasis transmission season, 129 villagers resident on a Schistosoma japonicum-endemic island in Poyang Lake, Jiangxi Province, 64 of whom were stool-positive for S. japonicum eggs by the Kato method and 65 negative, were treated with praziquantel. Forty-five days later the 93 subjects who presented for follow-up were all stool-negative. Blood samples were collected from all 93 individuals. S. japonicum soluble worm antigen (SWAP) and soluble egg antigen (SEA) stimulated IL-4, IL-5 and IFN-gamma production in whole-blood cultures were measured by ELISA. All the subjects were interviewed nine times during the subsequent transmission season to estimate the intensity of their contact with potentially infective snail habitats, and the subjects were all re-screened for S. japonicum by the Kato method at the end of the transmission season. Fourteen subjects were found to be infected at that time. There was some indication that the risk of infection might be associated with gender (with females being at higher risk) and with the intensity of water contact, and there was evidence that levels of SEA-induced IFN-gamma production were associated with reduced risk of infection

Performance modelling and analysis of software defined networking

Software Defined Networking (SDN) is an emerging architecture for the next-generation Internet, providing unprecedented network programmability to handle the explosive growth of Big Data driven by the popularisation of smart mobile devices and the pervasiveness of content-rich multimedia applications. In order to quantitatively investigate the performance characteristics of SDN networks, several research efforts from both simulation experiments and analytical modelling have been reported in the current literature. Among those studies, analytical modelling has demonstrated its superiority in terms of cost-effectiveness in the evaluation of large-scale networks. However, for analytical tractability and simplification, existing analytical models are derived based on the unrealistic assumptions that the network traffic follows the Poisson process which is suitable to model non-bursty text data and the data plane of SDN is modelled by one simplified Single Server Single Queue (SSSQ) system. Recent measurement studies have shown that, due to the features of heavy volume and high velocity, the multimedia big data generated by real-world multimedia applications reveals the bursty and correlated nature in the network transmission. With the aim of the capturing such features of realistic traffic patterns and obtaining a comprehensive and deeper understanding of the performance behaviour of SDN networks, this paper presents a new analytical model to investigate the performance of SDN in the presence of the bursty and correlated arrivals modelled by Markov Modulated Poisson Process (MMPP). The Quality-of-Service performance metrics in terms of the average latency and average network throughput of the SDN networks are derived based on the developed analytical model. To consider realistic multi-queue system of forwarding elements, a Priority-Queue (PQ) system is adopted to model SDN data plane. To address the challenging problem of obtaining the key performance metrics, e.g., queue length distribution of PQ system with a given service capacity, a versatile methodology extending the Empty Buffer Approximation (EBA) method is proposed to facilitate the decomposition of such a PQ system to two SSSQ systems. The validity of the proposed model is demonstrated through extensive simulation experiments. To illustrate its application, the developed model is then utilised to study the strategy of the network configuration and resource allocation in SDN networksThis work is supported by the EU FP7 “QUICK” Project (Grant NO. PIRSES-GA-2013-612652) and the National Natural Science Foundation of China (Grant NO. 61303241)

Automated adaptive analysis of tagged magnetic resonance images of the mouse heart

The full potential of tagged MRI of the mouse heart for non-invasive evaluation of cardiac mechanics in transgenic animals has not been realized due to excessive user involvement with available image processing algorithms. Therefore, we developed an automated, rapid, high-resolution analysis technique, called High Density Mapping (HDM), that uses spectral correlation to efficiently quantify regional wall deformation, does not entail tracking of individual tags, and involves minimal user interaction. HDM analysis distinguishes regional mechanics in healthy and infarcted mice within 2 minutes. This new method may help promote the practical use of tagged MRI in mice and other species.published_or_final_versio

A pancake-shaped nano-aggregate for focusing surface plasmons

We proposed a pancake-shaped nano-aggregate that highly focuses surface plasmons. The structure is a superposition of bowtie-shaped dimers, where surface plasmons are excited, resonated with the structure, and coupled. Surface integral equation method (Poggio-Miller-Chang-Harrington-Wu-Tsai method) is used to predict the performance of the proposed structure. It is a method which can accurately calculate the near-fields of nanoparticles. Based on the numerical prediction, the proposed structure shows an electric field (E-field) enhancement of more than 400 times, which is equivalent to a Raman enhancement factor of more than 2.5 e 10 times. It is promising for single molecule detections using surface-enhanced Raman scattering. The physics of the proposed structure are revealed. It is useful to design nanostructures for high E-field enhancement. © 2012 American Institute of Physics.published_or_final_versio

Secondary Sex Ratio among Women Exposed to Diethylstilbestrol in Utero

BACKGROUND. Diethylstilbestrol (DES), a synthetic estrogen widely prescribed to pregnant women during the mid-1900s, is a potent endocrine disruptor. Previous studies have suggested an association between endocrine-disrupting compounds and secondary sex ratio. METHODS. Data were provided by women participating in the National Cancer Institute (NCI) DES Combined Cohort Study. We used generalized estimating equations to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the relation of in utero DES exposure to sex ratio (proportion of male births). Models were adjusted for maternal age, child's birth year, parity, and cohort, and accounted for clustering among women with multiple pregnancies. RESULTS. The OR for having a male birth comparing DES-exposed to unexposed women was 1.05 (95% CI, 0.95-1.17). For exposed women with complete data on cumulative DES dose and timing (33%), those first exposed to DES earlier in gestation and to higher doses had the highest odds of having a male birth. The ORs were 0.91 (95% C, 0.65-1.27) for first exposure at ≥ 13 weeks gestation to < 5 g DES; 0.95 (95% CI, 0.71-1.27) for first exposure at ≥ 13 weeks to ≥ 5 g; 1.16 (95% CI, 0.96-1.41) for first exposure at < 13 weeks to < 5 g; and 1.24 (95% CI, 1.04-1.48) for first exposure at < 13 weeks to ≥ 5 g compared with no exposure. Results did not vary appreciably by maternal age, parity, cohort, or infertility history. CONCLUSIONS. Overall, no association was observed between in utero DES exposure and secondary sex ratio, but a significant increase in the proportion of male births was found among women first exposed to DES earlier in gestation and to a higher cumulative dose.National Cancer Institute (N01-CP-21168, N01-CP-51017, N01-CP-01289

Integrating microalgae production with anaerobic digestion: a biorefinery approach

This is the peer reviewed version of the following article: [Uggetti, E. , Sialve, B. , Trably, E. and Steyer, J. (2014), Integrating microalgae production with anaerobic digestion: a biorefinery approach. Biofuels, Bioprod. Bioref, 8: 516-529. doi:10.1002/bbb.1469], which has been published in final form at https://doi.org/10.1002/bbb.1469. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-ArchivingIn the energy and chemical sectors, alternative production chains should be considered in order to simultaneously reduce the dependence on oil and mitigate climate change. Biomass is probably the only viable alternative to fossil resources for production of liquid transportation fuels and chemicals since, besides fossils, it is one of the only available sources of carbon-rich material on Earth. Over recent years, interest in microalgae biomass has grown in both fundamental and applied research fields. The biorefinery concept includes different technologies able to convert biomass into added-value chemicals, products (food and feed) and biofuels (biodiesel, bioethanol, biohydrogen). As in oil refinery, a biorefinery aims at producing multiple products, maximizing the value derived from differences in biomass components, including microalgae. This paper provides an overview of the various microalgae-derived products, focusing on anaerobic digestion for conversion of microalgal biomass into methane. Special attention is paid to the range of possible inputs for anaerobic digestion (microalgal biomass and microalgal residue after lipid extraction) and the outputs resulting from the process (e.g. biogas and digestate). The strong interest in microalgae anaerobic digestion lies in its ability to mineralize microalgae containing organic nitrogen and phosphorus, resulting in a flux of ammonium and phosphate that can then be used as substrate for growing microalgae or that can be further processed to produce fertilizers. At present, anaerobic digestion outputs can provide nutrients, CO2 and water to cultivate microalgae, which in turn, are used as substrate for methane and fertilizer generation.Peer ReviewedPostprint (author's final draft

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

Improving corporate governance in state-owned corporations in China: which way forward?

This article discusses corporate governance in China. It outlines the basic agency problem in Chinese listed companies and questions the effectiveness of the current mechanisms employed to improve their standards of governance. Importantly, it considers alternative means through which corporate practice in China can be brought into line with international expectations and stresses the urgency with which this task must be tackled. It concludes that regulators in China must construct a corporate governance model which is compatible with its domestic setting and not rush to adopt governance initiatives modelled on those in cultures which are fundamentally different in the hope of also reproducing their success

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe