Duke University Health System Demand Response Prospectus

The Duke University Health System Demand Response Prospectus is a client-based Masters Project that explores the profitability and environmental impacts of enrolling Duke University Health System and Duke University into Duke Energy’s PowerShare demand response program. Demand response programs are mechanisms used by utilities to decrease energy demand during high-usage periods (e.g. hot days when air conditioning use is highest) by incentivizing their customers to reduce grid consumption for a limited time. This temporary demand reduction results in cost savings to utilities because it allows them to avoid using their most inefficient and expensive power plants. In our project, we analyze the economic, environmental, and regulatory feasibility of using Duke University and Duke Medicine emergency generators in a Duke Energy demand response program called PowerShare, more specifically the Generator Curtailment Option. Duke Carbon Offset Initiative credits, a Duke University funding mechanism to reduce carbon dioxide emissions, were also considered as a potential revenue source. In order to conduct the analysis, an MS Excel and Visual Basic model was created to calculate the impacts of enrollment. The model provided to the client was designed to offer an easy user interface to quickly conduct the analyses. It was also specially designed to offer the flexibility to incorporate future changes in the energy market and user preferences. The model results indicated that, while feasible, demand response enrollment is not currently attractive from environmental and financial perspectives. The financials are poor for two mains reasons. First, expected net revenues are strictly negative because PowerShare enrollment requires Duke University to re-enroll into paying a demand side management rider (DSM) to which they are currently exempt. The DSM fee, although minimal individually, amounts to an astronomical fee for large consumers like Duke University and Duke Medicine since it is charged per unit of energy purchased. Second, PowerShare curtailment compensation is lower than current cost of diesel fuel. From an environmental perspective, PowerShare is also not a favorable option. Instead of offering a carbon emissions reduction opportunity, PowerShare participation is actually expected to increase the amount of global carbon emissions because Duke University generators emit more carbon than Duke Energy’s natural gas peak usage plants

A Qualitative Assessment of Tan Chong Motor’s Entry Into Vietnam: A Case Study

The aim of this thesis is to perform a qualitative case study on Tan Chong Motor, a Malaysian automotive conglomerate that began from being a distributor to Nissan Motor Company, as they execute Indo-China expansion plan by first penetrating into Vietnam in 2011, an emerging country that recently begun attracting Foreign Direct Investments by restructuring regulations and infrastructures. This thesis will take into study the literature on internationalization, FDI, and institutional challenges of operating in ‘South countries’ to form a framework that will then be used to assess Tan Chong Motor’s move into Vietnam. The ‘CaStER’ framework integrates four variables of Internationalisation, FDI, and cultural challenges to assess a venture of a company; Capital assessment, Structural assessment, mode of Entry analysis, and assessment of market Risk. Using ‘CaStER’, the study will firstly assess Tan Chong’s penetration to the Vietnam market. Adding value, this thesis will then assess Tan Chong Motor’s plans to duplicate the penetration method to Myanmar which in 2012 has become the next frontier-wonder after Vietnam, as is aligned with Tan Chong Motor’s Indo-China expansion plan. The study finds that with the pursuit of first mover’s advantage, it is also important to assess reservations on critical capital capacity to determine the manner and timing of penetration. This study also features a review of an interview segment series, ‘Insight Vietnam’, which discusses about Vietnam, its attractants and challenges as an emerging market attempting to attract FDI to modernize and develop the country through a panel of investors and consultants who have experience with the market

Albumin-enriched Fibrin Hydrogel Embedded in Active Ferromagnetic Networks Improves Osteoblast Differentiation and Vascular Self-organisation

Porous coatings on prosthetic implants encourage implant fixation. Enhanced fixation may be achieved using a magneto-active porous coating that can deform elastically in vivo on application of an external magnetic field, straining in-growing bone. Such coating, made of 444 ferritic stainless steel fibres, was previously characterised in terms of its mechanical and cellular responses. In this work, co-cultures of human osteoblasts and endothelial cells were seeded into a novel fibrin-based hydrogel embedded in a 444 ferritic stainless steel fibre network. Albumin was successfully incorporated into fibrin hydrogels improving the specific permeability and the diffusion of fluorescently-tagged dextrans without affecting their Young’s modulus. The beneficial effect of albumin was demonstrated by the upregulation of osteogenic and angiogenic gene expression. Furthermore, mineralisation, extracellular matrix production and formation of vessel-like structures were enhanced in albumin-enriched fibrin hydrogels compared to fibrin hydrogels. Collectively, the results indicate that the albumin-enriched fibrin hydrogel is a promising bio-matrix for bone tissue engineering and orthopaedic applications.EPSRC (EP/R511675/1) Blavatnik Family Foundation. Reuben Foundation. WD Armstrong Studentship Isaac Newton Trust Rosetrees Trust (M787)

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique to obtain structural information on complex mixtures. However, just knowing the molecular masses of the mixture’s constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present motifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an Application Programming Interface (API). MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database we can more quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks

Mycobacterium tuberculosis-infected human monocytes down-regulate microglial MMP-2 secretion in CNS tuberculosis via TNFα, NFκB, p38 and caspase 8 dependent pathways

Tuberculosis (TB) of the central nervous system (CNS) is a deadly disease characterized by extensive tissue destruction, driven by molecules such as Matrix Metalloproteinase-2 (MMP-2) which targets CNS-specific substrates. In a simplified cellular model of CNS TB, we demonstrated that conditioned medium from Mycobacterium tuberculosis-infected primary human monocytes (CoMTb), but not direct infection, unexpectedly down-regulates constitutive microglial MMP-2 gene expression and secretion by 72.8% at 24 hours, sustained up to 96 hours (P < 0.01), dependent upon TNF-α. In human CNS TB brain biopsies but not controls the p38 pathway was activated in microglia/macrophages. Inhibition of the p38 MAP kinase pathway resulted in a 228% increase in MMP-2 secretion (P < 0.01). In contrast ERK MAP kinase inhibition further decreased MMP-2 secretion by 76.6% (P < 0.05). Inhibition of the NFκB pathway resulted in 301% higher MMP-2 secretion than CoMTb alone (P < 0.01). Caspase 8 restored MMP-2 secretion to basal levels. However, this caspase-dependent regulation of MMP-2 was independent of p38 and NFκB pathways; p38 phosphorylation was increased and p50/p65 NFκB nuclear trafficking unaffected by caspase 8 inhibition. In summary, suppression of microglial MMP-2 secretion by M.tb-infected monocyte-dependent networks paradoxically involves the pro-inflammatory mediators TNF-α, p38 MAP kinase and NFκB in addition to a novel caspase 8-dependent pathway

kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity

Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or “samples”) in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct genetic lineage before conclusions are drawn using mislabelled, or misidentified samples. We present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include establishing sample identity and detecting mix-up, non-obvious genomic variation, and population structure. We show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip.This project was supported by the Australian Research Council Centre of Excellence in Plant Energy Biology (CE140100008) and by NICTA which was funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. The research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. KDM is supported by an Australian Government Research Training Program (RTP) Scholarship