Comparative proteomics of uropathogenic Escherichia coli during growth in human urine identify UCA-like (UCL) fimbriae as an adherence factor involved in biofilm formation and binding to uroepithelial cells

Uropathogenic Escherichia coli (UPEC) are the primary cause of urinary tract infection (UTI) in humans. For the successful colonisation of the human urinary tract, UPEC employ a diverse collection of secreted or surface-exposed virulence factors including toxins, iron acquisition systems and adhesins. In this study, a comparative proteomic approach was utilised to define the UPEC pan and core surface proteome following growth in pooled human urine. Identified proteins were investigated for subcellular origin, prevalence and homology to characterised virulence factors. Fourteen core surface proteins were identified, as well as eleven iron uptake receptor proteins and four distinct fimbrial types, including type 1, P, F1C/S and a previously uncharacterised fimbrial type, designated UCA-like (UCL) fimbriae in this study. These pathogenicity island (PAI)-associated fimbriae are related to UCA fimbriae of Proteus mirabilis, associated with UPEC and exclusively found in members of the E. coli B2 and D phylogroup. We further demonstrated that UCL fimbriae promote significant biofilm formation on abiotic surfaces and mediate specific attachment to exfoliated human uroepithelial cells. Combined, this study has defined the surface proteomic profiles and core surface proteome of UPEC during growth in human urine and identified a new type of fimbriae that may contribute to UTI.

The Learning Component of Dynamic Allocation Indices

For a multiarmed bandit problem with exponential discounting the optimal allocation rule is defined by a dynamic allocation index defined for each arm on its space. The index for an arm is equal to the expected immediate reward from the arm, with an upward adjustment reflecting any uncertainty about the prospects of obtaining rewards from the arm, and the possibilities of resolving those uncertainties by selecting that arm. Thus the learning component of the index is defined to be the difference between the index and the expected immediate reward. For two arms with the same expected immediate reward the learning component should be larger for the arm for which the reward rate is more uncertain. This is shown to be true for arms based on independent samples from a fixed distribution with an unknown parameter in the cases of Bernoulli and normal distributions, and similar results are obtained in other cases.

A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm

During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.

Ultrafast and reversible multiblock formation by the SET-Nitroxide radical coupling reaction

The single electron transfer-nitroxide radical coupling (SET-NRC) reaction has been used to produce multiblock polymers with high molecular weights in under 3 min at 50◦C by coupling a difunctional telechelic polystyrene (Br-PSTY-Br)with a dinitroxide. The well known combination of dimethyl sulfoxide as solvent and Me6TREN as ligand facilitated the in situ disproportionation of CuIBr to the highly active nascent Cu0 species. This SET reaction allowed polymeric radicals to be rapidly formed from their corresponding halide end-groups. Trapping of these carbon-centred radicals at close to diffusion controlled rates by dinitroxides resulted in high-molecular-weight multiblock polymers. Our results showed that the disproportionation of CuI was critical in obtaining these ultrafast reactions, and confirmed that activation was primarily through Cu0. We took advantage of the reversibility of the NRC reaction at elevated temperatures to decouple the multiblock back to the original PSTY building block through capping the chain-ends with mono-functional nitroxides. These alkoxyamine end-groups were further exchanged with an alkyne mono-functional nitroxide (TEMPO–≡) and ‘clicked’ by a CuI-catalyzed azide/alkyne cycloaddition (CuAAC) reaction with N3–PSTY–N3 to reform the multiblocks. This final ‘click’ reaction, even after the consecutive decoupling and nitroxide-exchange reactions, still produced high molecular-weight multiblocks efficiently. These SET-NRC reactions would have ideal applications in re-usable plastics and possibly as self-healing materials.

Depositional model for the early Miocene turbidite sequence, the Whakataki Formation, NZ

This research examined the influence of tectonic activity on submarine sedimentation processes, through a deposit-based analysis of turbidites in outcrop. A comprehensive field study of the Miocene Whakataki Formation yielded significant data that was analysed using methods of process-sedimentology, stratigraphy, and ichnology. Signatures of the tectonically active depositional environment were identifiable at very high resolution, from grain composition and texture to trace-fossil assemblages, as well as on a broader-scale in stratigraphic stacking patterns and structural deformation. From these results and environmental interpretations, an original facies characterisation and conceptual depositional model have been established.

Atmospheric visibility and PM10 as indicators of new particle formation in an urban environment

It is well-known that new particle formation (NPF) in the atmosphere is inhibited by pre-existing particles in the air that act as condensation sinks to decrease the concentration and, thus, the supersaturation of precursor gases. In this study, we investigate the effects of two parameters - atmospheric visibility, expressed as the particle back-scatter coefficient (BSP), and PM10 particulate mass concentration, on the occurrences of NPF events in an urban environment where the majority of precursor gases originate from motor vehicle and industrial sources. This is the first attempt to derive direct relationships between each of these two parameters and the occurrence of NPF. NPF events were identified from data obtained with a neutral cluster and air ion spectrometer over 245 days within a calendar year. Bayesian logistic regression was used to determine the probability of observing NPF as functions of BSP and PM10. We show that the BSP at 08 h on a given day is a reliable indicator of an NPF event later that day. The posterior median probability of observing an NPF event was greater than 0.5 (95%) when the BSP at 08 h was less than 6.8 Mm-1.

Direct electrochemical formation of nanostructured amorphous Co(OH)2 on gold electrodes with enhanced activity for the oxygen evolution reaction.

The oxides of cobalt have recently been shown to be highly effective electrocatalysts for the oxygen evolution reaction (OER) under alkaline conditions. In general species such as Co3O4 and CoOOH have been investigated that often require an elevated temperature step during their synthesis to create crystalline materials. In this work we investigate the rapid and direct electrochemical formation of amorphous nanostructured Co(OH)2 on gold electrodes under room temperture conditions which is a highly active precursor for the OER. During the OER some conversion to crystalline Co3O4 occurs at the surface, but the bulk of the material remains amorphous. It is found that the underlying gold electrode is crucial to the materials enhanced performance and provides higher current density than can be achieved using carbon, palladium or copper support electrodes. This catalyst exhibits excellent activity with a current density of 10 mA cm-2 at an overpotential of 360 mV with a high turnover frequency of 2.1 s-1 in 1 M NaOH. A Tafel slope of 56 mV dec-1 at low overpotentials and a slope of 122 mV dec-1 at high overpotentials is consistent with the dual barrier model for the electrocatalytic evolution of oxygen. Significantly, the catalyst maintains excellent activity for up to 24 hr of continuous operation and this approach offers a facile way to create a highly effective and stable material.

Modeling dynamic demand response using Monte Carlo simulation and interval mathematics for boundary estimation

With the rapid development of various technologies and applications in smart grid implementation, demand response has attracted growing research interests because of its potentials in enhancing power grid reliability with reduced system operation costs. This paper presents a new demand response model with elastic economic dispatch in a locational marginal pricing market. It models system economic dispatch as a feedback control process, and introduces a flexible and adjustable load cost as a controlled signal to adjust demand response. Compared with the conventional “one time use” static load dispatch model, this dynamic feedback demand response model may adjust the load to a desired level in a finite number of time steps and a proof of convergence is provided. In addition, Monte Carlo simulation and boundary calculation using interval mathematics are applied for describing uncertainty of end-user's response to an independent system operator's expected dispatch. A numerical analysis based on the modified Pennsylvania-Jersey-Maryland power pool five-bus system is introduced for simulation and the results verify the effectiveness of the proposed model. System operators may use the proposed model to obtain insights in demand response processes for their decision-making regarding system load levels and operation conditions.

Correction factors for dynamic state estimation of aggregated generators

This paper presents an approach for dynamic state estimation of aggregated generators by introducing a new correction factor for equivalent inter-area power flows. The spread of generators from the center of inertia of each area is summarized by the correction term α on the equivalent power flow between the areas and is applied to the identification and estimation process. A nonlinear time varying Kalman filter is applied to estimate the equivalent angles and velocities of coherent areas by reducing the effect of local modes on the estimated states. The approach is simulated on two test systems and the results show the effect of the correction factor and the performance of the state estimation by estimating the inter-area dynamics of the system.

PMU measurement based dynamic load modeling using SVC devices in online environment

Online dynamic load modeling has become possible with the availability of Static Voltage Compensator (SVC) and Phasor Measurement Unit (PMU) devices. The power of the load response to the small random bounded voltage fluctuations caused from SVC can be measured by PMU for modelling purposes. The aim of this paper is to illustrate the capability of identifying an aggregated load model from high voltage substation level in the online environment. The induction motor is used as the main test subject since it contributes the majority of the dynamic loads. A test system representing simple electromechanical generator model serving dynamic loads through the transmission network is used to verify the proposed method. Also, dynamic load with multiple induction motors are modeled to achieve a better realistic load representation.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

Efficient surface modification of biomaterial to prevent biofilm formation and the attachment of microorganisms

Biomaterials play a fundamental role in disease management and the improvement of health care. In recent years, there has been a significant growth in the diversity, function, and number of biomaterials used worldwide. Yet, attachment of pathogenic microorganisms onto biomaterial surfaces remains a significant challenge that substantially undermines their clinical applicability, limiting the advancement of these systems. The emergence and escalating pervasiveness of antibiotic-resistant bacterial strains makes the management of biomaterial-associated nosocomial infections increasingly difficult. The conventional post-operative treatment of implant-caused infections using systemic antibiotics is often marginally effective, further accelerating the extent of antimicrobial resistance. Methods by which the initial stages of bacterial attachment and biofilm formation can be restricted or prevented are therefore sought. The surface modification of biomaterials has the potential to alleviate pathogenic biofouling, therefore preventing the need for conventional antibiotics to be applied.

Influence of interleukin-1 beta on platelet-poor plasma clot formation: A potential impact on early bone healing

Objectives Hematoma quality (especially the fibrin matrix) plays an important role in the bone healing process. Here, we investigated the effect of interleukin-1 beta (IL-1β) on fibrin clot formation from platelet-poor plasma (PPP). Methods Five-milliliter of rat whole-blood samples were collected from the hepatic portal vein. All blood samples were firstly standardized via a thrombelastograph (TEG), blood cell count, and the measurement of fibrinogen concentration. PPP was prepared by collecting the top two-fifths of the plasma after centrifugation under 400 × g for 10min at 20°C. The effects of IL-1β cytokines on artificial fibrin clot formation from PPP solutions were determined by scanning electronic microscopy (SEM), confocal microscopy (CM), turbidity, and clot lysis assays. Results The lag time for protofibril formation was markedly shortened in the IL-1β treatment groups (243.8 ± 76.85 in the 50 pg/mL of IL-1β and 97.5 ± 19.36 in the 500 pg/mL of IL-1β) compared to the control group without IL-1β (543.8 ± 205.8). Maximal turbidity was observed in the control group. IL-1β (500 pg/mL) treatment significantly decreased fiber diameters resulting in smaller pore sizes and increased density of the fibrin clot structure formed from PPP (P < 0.05). The clot lysis assay revealed that 500 pg/mL IL-1β induced a lower susceptibility to dissolution due to the formation of thinner and denser fibers. Conclusion IL-1β can significantly influence PPP fibrin clot structure, which may affect the early bone healing process.

Institutional constraints on altruism in the formation of a venture idea

This thesis is about the social orientation of new venture ideas and how the degree of social orientation is influenced by the entrepreneur's level of altruism, by industry norms, and by nonprofit work experience. Potential entrepreneurs were asked to generate new venture ideas based on 3D-printing and their ideas were rated for degree of social orientation. It was found that while greater altruism leads to more socially-oriented venture ideas, the influence of altruism on the venture idea is constrained by the profit-maximization norm that is prevalent in most industries.