Overcoming network overload and redundancy in inter-organizational networks:the roles of potential and latent ties

This paper builds on Granovetter's distinction between strong and weak ties [Granovetter, M. S. 1973. The strength of weak ties. Amer. J. Sociol. 78(6) 1360–1380] in order to respond to recent calls for a more dynamic and processual understanding of networks. The concepts of potential and latent tie are deductively identified, and their implications for understanding how and why networks emerge, evolve, and change are explored. A longitudinal empirical study conducted with companies operating in the European motorsport industry reveals that firms take strategic actions to search for potential ties and reactivate latent ties in order to solve problems of network redundancy and overload. Examples are given, and their characteristics are examined to provide theoretical elaboration of the relationship between the types of tie and network evolution. These conceptual and empirical insights move understanding of the managerial challenge of building effective networks beyond static structural contingency models of optimal network forms to highlight the processes and capabilities of dynamic relationship building and network development. In so doing, this paper highlights the interrelationship between search and redundancy and the scope for strategic action alongside path dependence and structural influences on network processes.

Reducing suffering in animal models and procedures involving seizures, convulsions and epilepsy

This report is based on discussions and submissions from an expert working group consisting of veterinarians, animal care staff and scientists with expert knowledge relevant to the field and aims to facilitate the implementation of the Three Rs (replacement, reduction and refinement) in the use of animal models or procedures involving seizures, convulsions and epilepsy. Each of these conditions will be considered, the specific welfare issues discussed, and practical measures to reduce animal use and suffering suggested. The emphasis is on refinement since this has the greatest potential for immediate implementation, and some general issues for refinement are summarised to help achieve this, with more detail provided on a range of specific refinements.

Therapeutic potential of statins and the induction of heme oxygenase-1 in preeclampsia

Heme oxygenase (Hmox) is an endogenous system that offers protection against placental cytotoxic damage associated with preeclampsia. The Hmox1/carbon monoxide (CO) pathway inhibits soluble Flt-1 (sFlt-1) and soluble Endoglin (sEng). More importantly, statins induce Hmox1 and suppress the release of sFlt-1 and sEng; thus, statins and Hmox1 activators are potential novel therapeutic agents for treating preeclampsia. The contribution of the Hmox system to the pathogenesis of preeclampsia has been further indicated by the incidence of preeclampsia being reduced by a third in smokers, who had reduced levels of circulating sFlt-1. Interestingly, preeclamptic women exhale less CO compared with women with healthy pregnancies. Hmox1 is reduced prior to the increase in sFlt-1 as Hmox1 mRNA expression in the trophoblast is decreased in the first trimester in women who go on to develop preeclampsia. Induction of Hmox1 or exposure to CO or bilirubin has been shown to inhibit the release of sFlt-1 and sEng in animal models of preeclampsia. The functional benefit of statins and Hmox1 induction in women with preeclampsia is valid not only because they inhibit sFlt-1 release, but also because statins and Hmox1 are associated with anti-apoptotic, anti-inflammatory, and anti-oxidant properties. The StAmP trial is the first randomized control trial (RCT) evaluating the use of pravastatin to ameliorate severe preeclampsia. This proof-of-concept study will pave the way for future global RCT, the success of which will greatly contribute to achieving the United Nations Millennium Development Goals (MDG4 and MDG5) and offering an affordable and easily accessible therapy for preeclampsia. © 2014 The Authors.

Tissue Transglutaminase (TG2) is a potential therapeutic target in the treatment of chemoresistant breast cancer

Tissue transglutaminase (TG2) has been suggested to be a key player in the progression and metastasis of chemoresistant breast cancer. One of the foremost survival signalling pathways implicated in causing drug resistance in breast cancer is the constitutive activation of NFκB (Nuclear Factor -kappa B) induced by TG2. This study provides a mechanism by which TG2 constitutively activates NFκB which in turn confers chemoresistance to breast cancer cells against doxorubicin. Breast cancer cell lines with varying expression levels of TG2 as well as TG2 null breast cancer cells transfected with TG2 were used as the major cell models for this study. This study made use of cell permeable and impermeable TG2 inhibitors, specific TG2 and Rel A/ p65 targeting siRNA, TG2 functional blocking antibodies, IKK inhibitors and a specific targeting peptide against Rel A/p65 to investigate the pathway of activation involved in the constitutive activation of NFκB by TG2 leading to drug resistance. Crucial to the activation of Rel A/p65 and drug resistance in the breast cancer cells is the interaction between the complex of IκBα and Rel A/p65 with TG2 which results in the dimerization of Rel A/p65 and polymerization of IκBα. The association of TG2 with the IκBα-NFκB complex was determined to be independent of IKKα/β function. The polymerized IκBα is degraded in the cytoplasm by the μ-calpain pathway which allows the cross linked Rel A/ p65 dimers to translocate into the nucleus. Using R283 and ZDON (cell permeable TG2 activity inhibitors) and specific TG2 targeting siRNA, the Rel A/ p65 dimer formation could be inhibited. Co-immunoprecipitation studies confirmed that the phosphorylation of the Rel A/p65 dimers at the Ser536 residue by IKKε took place in the cell nucleus. Importantly, this study also investigated the transcriptional regulation of the TGM2 gene by the pSer536 Rel A/ p65 dimer and the importance of this TG2-NFκB feedback loop in conferring drug resistance to breast cancer cells. This data provides evidence that TG2 could be a key therapeutic target in the treatment of chemoresistant breast cancer.

E-procurement in the aviation industry:value creation potential of B2B e-marketplaces

In academic literature, only limited research has been undertaken in exploring the value creation of B2B (Business-to-Business) e-Marketplace models in the aviation industry. The aim of this publication is a theoretical analysis to explore whether or not B2B e-Marketplaces can make a contribution to the achievement of competitive advantage in procurement in the aviation industry. The research focuses on the potential of B2B e-Marketplaces in terms of improving an airline’s competitiveness in its procurement value chain and discusses empirical results from a survey among international e-Marketplace / portal operators

Developing accurate models of the human airways

Objectives Particle delivery to the airways is an attractive prospect for many potential therapeutics, including vaccines. Developing strategies for inhalation of particles provides a targeted, controlled and non-invasive delivery route but, as with all novel therapeutics, in vitro and in vivo testing are needed prior to clinical use. Whilst advanced vaccine testing demands the use of animal models to address safety issues, the production of robust in vitro cellular models would take account of the ethical framework known as the 3Rs (Replacement, Reduction and Refinement of animal use), by permitting initial screening of potential candidates prior to animal use. There is thus a need for relevant, realistic in vitro models of the human airways. Key findings Our laboratory has designed and characterised a multi-cellular model of human airways that takes account of the conditions in the airways and recapitulates many salient features, including the epithelial barrier and mucus secretion. Summary Our human pulmonary models recreate many of the obstacles to successful pulmonary delivery of particles and therefore represent a valid test platform for screening compounds and delivery systems.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Impairment of calcium ATPases by high glucose and potential pharmacological protection

The review deals with impairment of Ca2+-ATPases by high glucose or its derivatives in vitro, as well as in human diabetes and experimental animal models. Acute increases in glucose level strongly correlate with oxidative stress. Dysfunction of Ca2+-ATPases in diabetic and in some cases even in nondiabetic conditions may result in nitration of and in irreversible modification of cysteine-674. Nonenyzmatic protein glycation might lead to alteration of Ca2+-ATPase structure and function contributing to Ca2+ imbalance and thus may be involved in development of chronic complications of diabetes. The susceptibility to glycation is probably due to the relatively high percentage of lysine and arginine residues at the ATP binding and phosphorylation domains. Reversible glycation may develop into irreversible modifications (advanced glycation end products, AGEs). Sites of SERCA AGEs are depicted in this review. Finally, several mechanisms of prevention of Ca2+-pump glycation, and their advantages and disadvantages are discussed. © 2013 Informa UK, Ltd.

An automated tool for semantic accessing to formal software models

Many software engineers have found that it is difficult to understand, incorporate and use different formal models consistently in the process of software developments, especially for large and complex software systems. This is mainly due to the complex mathematical nature of the formal methods and the lack of tool support. It is highly desirable to have software models and their related software artefacts systematically connected and used collaboratively, rather than in isolation. The success of the Semantic Web, as the next generation of Web technology, can have profound impact on the environment for formal software development. It allows both the software engineers and machines to understand the content of formal models and supports more effective software design in terms of understanding, sharing and reusing in a distributed manner. To realise the full potential of the Semantic Web in formal software development, effectively creating proper semantic metadata for formal software models and their related software artefacts is crucial. This paper proposed a framework that allows users to interconnect the knowledge about formal software models and other related documents using the semantic technology. We first propose a methodology with tool support is proposed to automatically derive ontological metadata from formal software models and semantically describe them. We then develop a Semantic Web environment for representing and sharing formal Z/OZ models. A method with prototype tool is presented to enhance semantic query to software models and other artefacts. © 2014.

Models@run.time:foundations, applications, and roadmaps

Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@run.time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.

On Multi-Dimensional Random Walk Models Approximating Symmetric Space-Fractional Diffusion Processes

Mathematics Subject Classification: 26A33, 47B06, 47G30, 60G50, 60G52, 60G60.

Detection and quantification of protein oxidation in sarcopenic models:a mass spectrometry study

Oxidised biomolecules in aged tissue could potentially be used as biomarkers for age-related diseases; however, it is still unclear whether they causatively contribute to ageing or are consequences of the ageing process. To assess the potential of using protein oxidation as markers of ageing, mass spectrometry (MS) was employed for the identification and quantification of oxidative modifications in obese (ob/ob) mice. Lean muscle mass and strength is reduced in obesity, representing a sarcopenic model in which the levels of oxidation can be evaluated for different muscular systems including calcium homeostasis, metabolism and contractility. Several oxidised residues were identified by tandem MS (MS/MS) in both muscle homogenate and isolated sarcoplasmic reticulum (SR), an organelle that regulates intracellular calcium levels in muscle. These modifications include oxidation of methionine, cysteine, tyrosine, and tryptophan in several proteins such as sarcoplasmic reticulum calcium ATPase (SERCA), glycogen phosphorylase, and myosin. Once modifications had been identified, multiple reaction monitoring MS (MRM) was used to quantify the percentage modification of oxidised residues within the samples. Preliminary data suggests proteins in ob/ob mice are more oxidised than the controls. For example SERCA, which constitutes 60-70% of the SR, had approximately a 2-fold increase in cysteine trioxidation of Cys561 in the obese model when compared to the control. Other obese muscle proteins have also shown a similar increase in oxidation for various residues. Further analysis with complex protein mixtures will determine the potential diagnostic use of MRM experiments for analysing protein oxidation in small biological samples such as muscle needle biopsies.

Distribution of Diatoms and Development of Diatom-Based Models for Inferring Salinity and Nutrient Concentrations in Florida Bay and Adjacent Coastal Wetlands of South Florida (USA)

The composition and distribution of diatom algae inhabiting estuaries and coasts of the subtropical Americas are poorly documented, especially relative to the central role diatoms play in coastal food webs and to their potential utility as sentinels of environmental change in these threatened ecosystems. Here, we document the distribution of diatoms among the diverse habitat types and long environmental gradients represented by the shallow topographic relief of the South Florida, USA, coastline. A total of 592 species were encountered from 38 freshwater, mangrove, and marine locations in the Everglades wetland and Florida Bay during two seasonal collections, with the highest diversity occurring at sites of high salinity and low water column organic carbon concentration (WTOC). Freshwater, mangrove, and estuarine assemblages were compositionally distinct, but seasonal differences were only detected in mangrove and estuarine sites where solute concentration differed greatly between wet and dry seasons. Epiphytic, planktonic, and sediment assemblages were compositionally similar, implying a high degree of mixing along the shallow, tidal, and storm-prone coast. The relationships between diatom taxa and salinity, water total phosphorus (WTP), water total nitrogen (WTN), and WTOC concentrations were determined and incorporated into weighted averaging partial least squares regression models. Salinity was the most influential variable, resulting in a highly predictive model (r apparent 2  = 0.97, r jackknife 2  = 0.95) that can be used in the future to infer changes in coastal freshwater delivery or sea-level rise in South Florida and compositionally similar environments. Models predicting WTN (r apparent 2  = 0.75, r jackknife 2  = 0.46), WTP (r apparent 2  = 0.75, r jackknife 2  = 0.49), and WTOC (r apparent 2  = 0.79, r jackknife 2  = 0.57) were also strong, suggesting that diatoms can provide reliable inferences of changes in solute delivery to the coastal ecosystem.

Statistical Models for Predicting College Success

Colleges base their admission decisions on a number of factors to determine which applicants have the potential to succeed. This study utilized data for students that graduated from Florida International University between 2006 and 2012. Two models were developed (one using SAT as the principal explanatory variable and the other using ACT as the principal explanatory variable) to predict college success, measured using the student’s college grade point average at graduation. Some of the other factors that were used to make these predictions were high school performance, socioeconomic status, major, gender, and ethnicity. The model using ACT had a higher R^2 but the model using SAT had a lower mean square error. African Americans had a significantly lower college grade point average than graduates of other ethnicities. Females had a significantly higher college grade point average than males.

Integrated Carbon Budget Models for the Everglades Terrestrial-Coastal-Oceanic Gradient: Current Status and Needs for Inter-Site Comparisons

Recent studies suggest that coastal ecosystems can bury significantly more C than tropical forests, indicating that continued coastal development and exposure to sea level rise and storms will have global biogeochemical consequences. The Florida Coastal Everglades Long Term Ecological Research (FCE LTER) site provides an excellent subtropical system for examining carbon (C) balance because of its exposure to historical changes in freshwater distribution and sea level rise and its history of significant long-term carbon-cycling studies. FCE LTER scientists used net ecosystem C balance and net ecosystem exchange data to estimate C budgets for riverine mangrove, freshwater marsh, and seagrass meadows, providing insights into the magnitude of C accumulation and lateral aquatic C transport. Rates of net C production in the riverine mangrove forest exceeded those reported for many tropical systems, including terrestrial forests, but there are considerable uncertainties around those estimates due to the high potential for gain and loss of C through aquatic fluxes. C production was approximately balanced between gain and loss in Everglades marshes; however, the contribution of periphyton increases uncertainty in these estimates. Moreover, while the approaches used for these initial estimates were informative, a resolved approach for addressing areas of uncertainty is critically needed for coastal wetland ecosystems. Once resolved, these C balance estimates, in conjunction with an understanding of drivers and key ecosystem feedbacks, can inform cross-system studies of ecosystem response to long-term changes in climate, hydrologic management, and other land use along coastlines.