Three-factor models versus time series models:quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data

Signal integration determines cell fate on the cellular level, affects cognitive processes and affective responses on the behavioural level, and is likely to be involved in psychoneurobiological processes underlying mood disorders. Interactions between stimuli may subjected to time effects. Time-dependencies of interactions between stimuli typically lead to complex cell responses and complex responses on the behavioural level. We show that both three-factor models and time series models can be used to uncover such time-dependencies. However, we argue that for short longitudinal data the three factor modelling approach is more suitable. In order to illustrate both approaches, we re-analysed previously published short longitudinal data sets. We found that in human embryonic kidney 293 cells cells the interaction effect in the regulation of extracellular signal-regulated kinase (ERK) 1 signalling activation by insulin and epidermal growth factor is subjected to a time effect and dramatically decays at peak values of ERK activation. In contrast, we found that the interaction effect induced by hypoxia and tumour necrosis factor-alpha for the transcriptional activity of the human cyclo-oxygenase-2 promoter in HEK293 cells is time invariant at least in the first 12-h time window after stimulation. Furthermore, we applied the three-factor model to previously reported animal studies. In these studies, memory storage was found to be subjected to an interaction effect of the beta-adrenoceptor agonist clenbuterol and certain antagonists acting on the alpha-1-adrenoceptor / glucocorticoid-receptor system. Our model-based analysis suggests that only if the antagonist drug is administer in a critical time window, then the interaction effect is relevant.

A review of multi-factor capacity expansion models for manufacturing plants:searching for a holistic decision aid

Investment in capacity expansion remains one of the most critical decisions for a manufacturing organisation with global production facilities. Multiple factors need to be considered making the decision process very complex. The purpose of this paper is to establish the state-of-the-art in multi-factor models for capacity expansion of manufacturing plants within a corporation. The research programme consisting of an extensive literature review and a structured assessment of the strengths and weaknesses of the current research is presented. The study found that there is a wealth of mathematical multi-factor models for evaluating capacity expansion decisions however no single contribution captures all the different facets of the problem.

Wealth effects of mergers and acquisitions for US firms: using alternative pricing models

This empirical study employs a different methodology to examine the change in wealth associated with mergers and acquisitions (M&As) for US firms. Specifically, we employ the standard CAPM, the Fama-French three-factor model and the Carhart four-factor models within the OLS and GJR-GARCH estimation methods to test the behaviour of the cumulative abnormal returns (CARs). Whilst the standard CAPM captures the variability of stock returns with the overall market, the Fama-French factors capture the risk factors that are important to investors. Additionally, augmenting the Fama-French three-factor model with the Carhart momentum factor to generate the four-factor captures additional pricing elements that may affect stock returns. Traditionally, estimates of abnormal returns (ARs) in M&As situations rely on the standard OLS estimation method. However, the standard OLS will provide inefficient estimates of the ARs if the data contain ARCH and asymmetric effects. To minimise this problem of estimation efficiency we re-estimated the ARs using GJR-GARCH estimation method. We find that there is variation in the results both as regards the choice models and estimation methods. Besides these variations in the estimated models and the choice of estimation methods, we also tested whether the ARs are affected by the degree of liquidity of the stocks and the size of the firm. We document significant positive post-announcement cumulative ARs (CARs) for target firm shareholders under both the OLS and GJR-GARCH methods across all three methodologies. However, post-event CARs for acquiring firm shareholders were insignificant for both sets of estimation methods under the three methodologies. The GJR-GARCH method seems to generate larger CARs than those of the OLS method. Using both market capitalization and trading volume as a measure of liquidity and the size of the firm, we observed strong return continuations in the medium firms relative to small and large firms for target shareholders. We consistently observed market efficiency in small and large firm. This implies that target firms for small and large firms overreact to new information resulting in a more efficient market. For acquirer firms, our measure of liquidity captures strong return continuations for small firms under the OLS estimates for both CAPM and Fama-French three-factor models, whilst under the GJR-GARCH estimates only for Carhart model. Post-announcement bootstrapping simulated CARs confirmed our earlier results.

Comparison of BER estimation methods in numerical simulation of 40 Gbit/s RZ-DPSK transmission

This study analyzes the validity of different Q-factor models in the BER estimation in RZ-DPSK transmission at 40 Gb/s channel rate. The impact of the duty cycle of the carrier pulses on the accuracy of the BER estimates through the different models has also been studied.

Comparison of BER estimation methods in numerical simulation of 40 Gbit/s RZ-DPSK transmission

This study analyzes the validity of different Q-factor models in the BER estimation in RZ-DPSK transmission at 40 Gb/s channel rate. The impact of the duty cycle of the carrier pulses on the accuracy of the BER estimates through the different models has also been studied.

Recent advances in explaining hedge fund returns:implicit factors and exposures

We survey articles covering how hedge fund returns are explained, using largely non-linear multifactor models that examine the non-linear pay-offs and exposures of hedge funds. We provide an integrated view of the implicit factor and statistical factor models that are largely able to explain the hedge fund return-generating process. We present their evolution through time by discussing pioneering studies that made a significant contribution to knowledge, and also recent innovative studies that examine hedge fund exposures using advanced econometric methods. This is the first review that analyzes very recent studies that explain a large part of hedge fund variation. We conclude by presenting some gaps for future research.

Hypoxia-inducible factor (HIF) network:insights from mathematical models

Oxygen is a crucial molecule for cellular function. When oxygen demand exceeds supply, the oxygen sensing pathway centred on the hypoxia inducible factor (HIF) is switched on and promotes adaptation to hypoxia by up-regulating genes involved in angiogenesis, erythropoiesis and glycolysis. The regulation of HIF is tightly modulated through intricate regulatory mechanisms. Notably, its protein stability is controlled by the oxygen sensing prolyl hydroxylase domain (PHD) enzymes and its transcriptional activity is controlled by the asparaginyl hydroxylase FIH (factor inhibiting HIF-1).To probe the complexity of hypoxia-induced HIF signalling, efforts in mathematical modelling of the pathway have been underway for around a decade. In this paper, we review the existing mathematical models developed to describe and explain specific behaviours of the HIF pathway and how they have contributed new insights into our understanding of the network. Topics for modelling included the switch-like response to decreased oxygen gradient, the role of micro environmental factors, the regulation by FIH and the temporal dynamics of the HIF response. We will also discuss the technical aspects, extent and limitations of these models. Recently, HIF pathway has been implicated in other disease contexts such as hypoxic inflammation and cancer through crosstalking with pathways like NF?B and mTOR. We will examine how future mathematical modelling and simulation of interlinked networks can aid in understanding HIF behaviour in complex pathophysiological situations. Ultimately this would allow the identification of new pharmacological targets in different disease settings.

Generating music with graphical models

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Graphical modelling for brain connectivity via partial coherence

Spectral and coherence methodologies are ubiquitous for the analysis of multiple time series. Partial coherence analysis may be used to try to determine graphical models for brain functional connectivity. The outcome of such an analysis may be considerably influenced by factors such as the degree of spectral smoothing, line and interference removal, matrix inversion stabilization and the suppression of effects caused by side-lobe leakage, the combination of results from different epochs and people, and multiple hypothesis testing. This paper examines each of these steps in turn and provides a possible path which produces relatively ‘clean’ connectivity plots. In particular we show how spectral matrix diagonal up-weighting can simultaneously stabilize spectral matrix inversion and reduce effects caused by side-lobe leakage, and use the stepdown multiple hypothesis test procedure to help formulate an interaction strength.

Equilibrium properties of disordered spin models with two-scale interactions

Methods for understanding classical disordered spin systems with interactions conforming to some idealized graphical structure are well developed. The equilibrium properties of the Sherrington-Kirkpatrick model, which has a densely connected structure, have become well understood. Many features generalize to sparse Erdös- Rényi graph structures above the percolation threshold and to Bethe lattices when appropriate boundary conditions apply. In this paper, we consider spin states subject to a combination of sparse strong interactions with weak dense interactions, which we term a composite model. The equilibrium properties are examined through the replica method, with exact analysis of the high-temperature paramagnetic, spin-glass, and ferromagnetic phases by perturbative schemes. We present results of replica symmetric variational approximations, where perturbative approaches fail at lower temperature. Results demonstrate re-entrant behaviors from spin glass to ferromagnetic phases as temperature is lowered, including transitions from replica symmetry broken to replica symmetric phases. The nature of high-temperature transitions is found to be sensitive to the connectivity profile in the sparse subgraph, with regular connectivity a discontinuous transition from the paramagnetic to ferromagnetic phases is apparent.

Effects of oxidative stress and neuroprotection in apoptosis in neuronal cell models

The PC12 and SH-SY5Y cell models have been proposed as potentially realistic models to investigate neuronal cell toxicity. The effects of oxidative stress (OS) caused by both H2O2 and Aβ on both cell models were assessed by several methods. Cell toxicity was quantitated by measuring cell viability using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) viability assay, an indicator of the integrity of the electron transfer chain (ETC), and cell morphology by fluorescence and video microscopy, both of which showed OS to cause decreased viability and changes in morphology. Levels of intracellular peroxide production, and changes in glutathione and carbonyl levels were also assessed, which showed OS to cause increases in intracellular peroxide production, glutathione and carbonyl levels. Differentiated SH-SY5y cells were also employed and observed to exhibit the greatest sensitivity to toxicity. The neurotrophic factor, nerve growth factor (NGF) was shown to cause protection against OS. Cells pre-treated with NGF showed higher viability after OS, generally less apoptotic morphology, recorded less apoptotic nucleiods, generally lower levels of intracellular peroxides and changes in gene expression. The neutrophic factor, brain derived growth factor (BDNF) and ascorbic acid (AA) were also investigated. BDNF showed no specific neuroprotection, however the preliminary data does warrant further investigation. AA showed a 'janus face' showing either anti-oxidant action and neuroprotection or pro-oxidant action depending on the situation. Results showed that the toxic effects of compounds such as Aβ and H2O2 are cell type dependent, and that OS alters glutathione metabolism in neuronal cells. Following toxic insult, glutathione levels are depleted to low levels. It is herein suggested that this lowering triggers an adaptive response causing alterations in glutathione metabolism as assessed by evaluation of glutathione mRNA biosynthetic enzyme expression and the subsequent increase in glutathione peroxidase (GPX) levels.

Non-linear hierarchical visualisation

This thesis applies a hierarchical latent trait model system to a large quantity of data. The motivation for it was lack of viable approaches to analyse High Throughput Screening datasets which maybe include thousands of data points with high dimensions. High Throughput Screening (HTS) is an important tool in the pharmaceutical industry for discovering leads which can be optimised and further developed into candidate drugs. Since the development of new robotic technologies, the ability to test the activities of compounds has considerably increased in recent years. Traditional methods, looking at tables and graphical plots for analysing relationships between measured activities and the structure of compounds, have not been feasible when facing a large HTS dataset. Instead, data visualisation provides a method for analysing such large datasets, especially with high dimensions. So far, a few visualisation techniques for drug design have been developed, but most of them just cope with several properties of compounds at one time. We believe that a latent variable model (LTM) with a non-linear mapping from the latent space to the data space is a preferred choice for visualising a complex high-dimensional data set. As a type of latent variable model, the latent trait model can deal with either continuous data or discrete data, which makes it particularly useful in this domain. In addition, with the aid of differential geometry, we can imagine the distribution of data from magnification factor and curvature plots. Rather than obtaining the useful information just from a single plot, a hierarchical LTM arranges a set of LTMs and their corresponding plots in a tree structure. We model the whole data set with a LTM at the top level, which is broken down into clusters at deeper levels of t.he hierarchy. In this manner, the refined visualisation plots can be displayed in deeper levels and sub-clusters may be found. Hierarchy of LTMs is trained using expectation-maximisation (EM) algorithm to maximise its likelihood with respect to the data sample. Training proceeds interactively in a recursive fashion (top-down). The user subjectively identifies interesting regions on the visualisation plot that they would like to model in a greater detail. At each stage of hierarchical LTM construction, the EM algorithm alternates between the E- and M-step. Another problem that can occur when visualising a large data set is that there may be significant overlaps of data clusters. It is very difficult for the user to judge where centres of regions of interest should be put. We address this problem by employing the minimum message length technique, which can help the user to decide the optimal structure of the model. In this thesis we also demonstrate the applicability of the hierarchy of latent trait models in the field of document data mining.

Experiences in using a contingency factor-based validation methodology for spreadsheet DSS

The use of spreadsheets has become routine in all aspects of business with usage growing across a range of functional areas and a continuing trend towards end user spreadsheet development. However, several studies have raised concerns about the accuracy of spreadsheet models in general, and of end user developed applications in particular, raising the risk element for users. High error rates have been discovered, even though the users/developers were confident that their spreadsheets were correct. The lack of an easy to use, context-sensitive validation methodology has been highlighted as a significant contributor to the problems of accuracy. This paper describes experiences in using a practical, contingency factor-based methodology for validation of spreadsheet-based DSS. Because the end user is often both the system developer and a stakeholder, the contingency factor-based validation methodology may need to be used in more than one way. The methodology can also be extended to encompass other DSS.

Fatigue cracking from stress concentration in mild steel

Pulsating; tension fatigue tests have been carried out on edge notched specimens of a mild steel. An electrical potential drop technique was used to determine the number of cycles taken to initiate cracks and the rate at which the cracks grew across the specimen. The results could be described by the range of stress intensity factor, which for crack initiation was modified to take account of the notch root radius. Analysis of elastic stress distributions at cracks and notches and models of plasticity at crack tips are used to discuss the results. Limited evidence in the literature indicates that the fracture mechanics approach may provide a general description of crack initiation and growth in notched specimens, and a simple graphical method of calculating fatigue lives is described. The results are used to illustrate the effects of specimen size and geometry on the fatigue life of notched specimens. The relevance of the work to the assessment of the significance of defects in welds is discussed.

Upregulation of urotensin II receptor in preeclampsia causes in vitro placental release of soluble vascular endothelial growth factor receptor 1 in hypoxia

Preeclampsia is a hypertensive disorder of pregnancy caused by abnormal placental function, partly because of chronic hypoxia at the utero-placental junction. The increase in levels of soluble vascular endothelial growth factor receptor 1, an antiangiogenic agent known to inhibit placental vascularization, is an important cellular factor implicated in the onset of preeclampsia. We investigated the ligand urotensin II (U-II), a potent endogenous vasoconstrictor and proangiogenic agent, for which levels have been reported to increase in patients with preeclampsia. We hypothesized that an increased sensitivity to U-II in preeclampsia might be achieved by upregulation of placental U-II receptors. We further investigated the role of U-II receptor stimulation on soluble vascular endothelial growth factor receptor 1 release in placental explants from diseased and normal patients. Immunohistochemistry, real-time PCR, and Western blotting analysis revealed that U-II receptor expression was significantly upregulated in preeclampsia placentas compared with controls (P<0.01). Cellular models of syncytiotrophoblast and vascular endothelial cells subjected to hypoxic conditions revealed an increase in U-II receptor levels in the syncytiotrophoblast model. This induction is regulated by the transcriptional activator hypoxia-inducible factor 1a. U-II treatment is associated with increased secretion of soluble vascular endothelial growth factor receptor 1 only in preeclamptic placental explants under hypoxia but not in control conditions. Interestingly, normal placental explants did not respond to U-II stimulation.