ACE research vignette 026 : Do Entrepreneurs’ Personal Networks Influence Small Firm Performance?

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. This vignette, written by Dr. Rene Bakker, examines the evidence on the effects of a entrepreneurs’ personal networks on small firm performance, and the factors that moderate this relationship.

Characteristics of Bangladeshi corporate networks

Over the past 40 years, Bangladesh has been undergoing economic reforms and institutional transitions to a market economy. An important product of this strategic transition has been the emergence of interlocking directorates, where a director sits on multiple directorate boards of corporations. Given this background, this thesis seeks to examine the attributes of the corporate network of interlocking directorships in Bangladesh. To date, the study of interlocking directorates has concentrated on Western economies. This study provides the results from a systematic exploration of the corporate network of Bangladesh based on data drawn from 100 largest listed companies in 2010.

Diffusion tensor of water in partially aligned fibre networks

Monte Carlo simulations were used to investigate the relationship between the morphological characteristics and the diffusion tensor (DT) of partially aligned networks of cylindrical fibres. The orientation distributions of the fibres in each network were approximately uniform within a cone of a given semi-angle (θ0). This semi-angle was used to control the degree of alignment of the fibres. The networks studied ranged from perfectly aligned (θ0 = 0) to completely disordered (θ0 = 90°). Our results are qualitatively consistent with previous numerical models in the overall behaviour of the DT. However, we report a non-linear relationship between the fractional anisotropy (FA) of the DT and collagen volume fraction, which is different to the findings from previous work. We discuss our results in the context of diffusion tensor imaging of articular cartilage. We also demonstrate how appropriate diffusion models have the potential to enable quantitative interpretation of the experimentally measured diffusion-tensor FA in terms of collagen fibre alignment distributions.

Osteogenic differentiation of bone marrow MSCs by β-tricalcium phosphate stimulating macrophages via BMP2 signalling pathway

Immune reactions play important roles in determining the in vivo fate of bone substitute materials, either in new bone formation or inflammatory fibrous tissue encapsulation. The paradigm for the development of bone substitute materials has been shifted from inert to immunomodulatory materials, emphasizing the importance of immune cells in the material evaluation. Macrophages, the major effector cells in the immune reaction to implants, are indispensable for osteogenesis and their heterogeneity and plasticity render macrophages a primer target for immune system modulation. However, there are very few reports about the effects of macrophages on biomaterial-regulated osteogenesis. In this study, we used b-tricalcium phosphate (b-TCP) as a model biomaterial to investigate the role of macrophages on the material stimulated osteogenesis. The macrophage phenotype switched to M2 extreme in response to b-TCP extracts, which was related to the activation of calcium-sensing receptor (CaSR) pathway. Bone morphogenetic protein 2 (BMP2) was also significantly upregulated by the b-TCP stimulation, indicating that macrophage may participate in the b-TCP stimulated osteogenesis. Interestingly, when macrophageconditioned b-TCP extracts were applied to bone marrow mesenchymal stem cells (BMSCs), the osteogenic differentiation of BMSCs was significantly enhanced, indicating the important role of macrophages in biomaterial-induced osteogenesis. These findings provided valuable insights into the mechanism of material-stimulated osteogenesis, and a strategy to optimize the evaluation system for the in vitro osteogenesis capacity of bone substitute materials.

Nutrient provision increases signalling and protein synthesis in human skeletal muscle after repeated sprints

The effect of nutrient availability on the acute molecular responses following repeated sprint exercise is unknown. The aim of this study was to determine skeletal muscle cellular and protein synthetic responses following repeated sprint exercise with nutrient provision. Eight healthy young male subjects undertook two sprint cycling sessions (10 × 6 s, 0.75 N m torque kg -1, 54 s recovery) with either pre-exercise nutrient (24 g whey, 4.8 g leucine, 50 g maltodextrin) or non-caloric placebo ingestion. Muscle biopsies were taken from vastus lateralis at rest, and after 15 and 240 min post-exercise recovery to determine muscle cell signalling responses and protein synthesis by primed constant infusion of L-[ring- 13C 6] phenylalanine. Peak and mean power outputs were similar between nutrient and placebo trials. Post-exercise myofibrillar protein synthetic rate was greater with nutrient ingestion compared with placebo ( ? 48%, P<0.05) but the rate of mitochondrial protein synthesis was similar between treatments. The increased myofibrillar protein synthesis following sprints with nutrient ingestion was associated with coordinated increases in Akt-mTOR-S6KrpS6 phosphorylation 15 min post-exercise (?200-600%, P<0.05), while there was no effect on these signalling molecules when exercise was undertaken in the fasted state. For the first time we report a beneficial effect of nutrient provision on anabolic signalling and muscle myofibrillar protein synthesis following repeated sprint exercise. Ingestion of protein/carbohydrate in close proximity to high-intensity sprint exercise provides an environment that increases cell signalling and protein synthesis.

Contraction-induced changes in TNFα and Akt-mediated signalling are associated with increased myofibrillar protein in rat skeletal muscle

Resistance training results in skeletal muscle hypertrophy, but the molecular signalling mechanisms responsible for this altered phenotype are incompletely understood. We used a resistance training (RT) protocol consisting of three sessions [day 1 (d1), day 3 (d3), day 5 (d5)] separated by 48 h recovery (squat exercise, 4 sets × 10 repetitions, 3 min recovery) to determine early signalling responses to RT in rodent skeletal muscle. Six animals per group were killed 3 h after each resistance training session and 24 and 48 h after the last training session (d5). There was a robust increase in TNF? protein expression, and IKKSer180/181 and p38MAPK Thr180/Tyr182 phosphorylation on d1 (P < 0.05), which abated with subsequent RT, returning to control levels by d5 for TNF? and IKK Ser180/181. There was a trend for a decrease in MuRF-1 protein expression, 48 h following d5 of training (P = 0.08). Notably, muscle myofibrillar protein concentration was elevated compared to control 24 and 48 h following RT (P < 0.05). AktSer473 and mTORSer2448 phosphorylation were unchanged throughout RT. Phosphorylation of p70S6k Thr389 increased 3 h post-exercise on d1, d3 and d5 (P < 0.05), whilst phosphorylation of S6Ser235/236 increased on d1 and d3 (P < 0.05). Our results show a rapid attenuation of inflammatory signalling with repeated bouts of resistance exercise, concomitant with summation in translation initiation signalling in skeletal muscle. Indeed, the cumulative effect of these signalling events was associated with myofibrillar protein accretion, which likely contributes to the early adaptations in response to resistance training overload in the skeletal muscle.

Honeycomb nanogold networks with highly active sites

The formation of macroporous honeycomb gold using an electrochemically generated hydrogen bubble template is described. The synthesis procedure induces the formation of highly active surfaces with enhanced electrocatalytic and surface enhanced Raman scattering properties.

From science to management : using Bayesian networks to learn about Lyngbya

Toxic blooms of Lyngbya majuscula occur in coastal areas worldwide and have major ecological, health and economic consequences. The exact causes and combinations of factors which lead to these blooms are not clearly understood. Lyngbya experts and stakeholders are a particularly diverse group, including ecologists, scientists, state and local government representatives, community organisations, catchment industry groups and local fishermen. An integrated Bayesian Network approach was developed to better understand and model this complex environmental problem, identify knowledge gaps, prioritise future research and evaluate management options.

Tangled Webs : Using Bayesian Networks in the Fight Against Infection

Bayesian networks (BNs) provide a statistical modelling framework which is ideally suited for modelling the many factors and components of complex problems such as healthcare-acquired infections. The methicillin-resistant Staphylococcus aureus (MRSA) organism is particularly troublesome since it is resistant to standard treatments for Staph infections. Overcrowding and understa�ng are believed to increase infection transmission rates and also to inhibit the effectiveness of disease control measures. Clearly the mechanisms behind MRSA transmission and containment are very complicated and control strategies may only be e�ective when used in combination. BNs are growing in popularity in general and in medical sciences in particular. A recent Current Content search of the number of published BN journal articles showed a fi�ve fold increase in general and a six fold increase in medical and veterinary science from 2000 to 2009. This chapter introduces the reader to Bayesian network (BN) modelling and an iterative modelling approach to build and test the BN created to investigate the possible role of high bed occupancy on transmission of MRSA while simultaneously taking into account other risk factors.

Enterprise social networks : a business model perspective

Enterprise Social Networks continue to be adopted by organisations looking to increase collaboration between employees, customers and industry partners. Offering a varied range of features and functionality, this technology can be distinguished by the underlying business models that providers of this software deploy. This study identifies and describes the different business models through an analysis of leading Enterprise Social Networks: Yammer, Chatter, SharePoint, Connections, Jive, Facebook and Twitter. A key contribution of this research is the identification of consumer and corporate models as extreme approaches. These findings align well with research on the adoption of Enterprise Social Networks that has discussed bottom-up and top-down approaches. Of specific interest are hybrid models that wrap a corporate model within a consumer model and may, therefore, provide synergies on both models. From a broader perspective, this can be seen as the merging of the corporate and consumer markets for IT products and services.

Multiscale investigation of microfilament networks disruption in living cells

Actin is the most abundantly distributed protein in living cells which plays critical roles in the cell interior force generation and transmission. The fracture mechanism of microfilament networks, whose principle component is actin, would provide insights which can contribute to the understandings of self-protective characters of cytoskeleton. In this study, molecular simulations are conducted to investigate the molecular mechanisms of disruption of microfilament networks from the viewpoint of biophysics. By employing a coarse-grained (CG) model of actin filament networks, we focused on the ultimate strength and crack growth mode of microfilament networks that have dependency on the crack length. It can be found that, the fracture mechanism of microfilament network has dependency on the structural properties of microfilament networks. The structure flaws marginally change the strength of microfilament networks which would explain the self-protective characters of cytoskeleton.

Predicting voltage unbalance impacts of plug-in electric vehicles penetration in residential low-voltage distribution networks

Plug-in electric vehicles will soon be connected to residential distribution networks in high quantities and will add to already overburdened residential feeders. However, as battery technology improves, plug-in electric vehicles will also be able to support networks as small distributed generation units by transferring the energy stored in their battery into the grid. Even though the increase in the plug-in electric vehicle connection is gradual, their connection points and charging/discharging levels are random. Therefore, such single-phase bidirectional power flows can have an adverse effect on the voltage unbalance of a three-phase distribution network. In this article, a voltage unbalance sensitivity analysis based on charging/discharging levels and the connection point of plug-in electric vehicles in a residential low-voltage distribution network is presented. Due to the many uncertainties in plug-in electric vehicle ratings and connection points and the network load, a Monte Carlo-based stochastic analysis is developed to predict voltage unbalance in the network in the presence of plug-in electric vehicles. A failure index is introduced to demonstrate the probability of non-standard voltage unbalance in the network due to plug-in electric vehicles.

Sampling designs on stream networks using the pseudo-Bayesian approach

Monitoring stream networks through time provides important ecological information. The sampling design problem is to choose locations where measurements are taken so as to maximise information gathered about physicochemical and biological variables on the stream network. This paper uses a pseudo-Bayesian approach, averaging a utility function over a prior distribution, in finding a design which maximizes the average utility. We use models for correlations of observations on the stream network that are based on stream network distances and described by moving average error models. Utility functions used reflect the needs of the experimenter, such as prediction of location values or estimation of parameters. We propose an algorithmic approach to design with the mean utility of a design estimated using Monte Carlo techniques and an exchange algorithm to search for optimal sampling designs. In particular we focus on the problem of finding an optimal design from a set of fixed designs and finding an optimal subset of a given set of sampling locations. As there are many different variables to measure, such as chemical, physical and biological measurements at each location, designs are derived from models based on different types of response variables: continuous, counts and proportions. We apply the methodology to a synthetic example and the Lake Eacham stream network on the Atherton Tablelands in Queensland, Australia. We show that the optimal designs depend very much on the choice of utility function, varying from space filling to clustered designs and mixtures of these, but given the utility function, designs are relatively robust to the type of response variable.