Binocular interaction:Contrast matching and contrast discrimination are predicted by the same model

How do signals from the 2 eyes combine and interact? Our recent work has challenged earlier schemes in which monocular contrast signals are subject to square-law transduction followed by summation across eyes and binocular gain control. Much more successful was a new 'two-stage' model in which the initial transducer was almost linear and contrast gain control occurred both pre- and post-binocular summation. Here we extend that work by: (i) exploring the two-dimensional stimulus space (defined by left- and right-eye contrasts) more thoroughly, and (ii) performing contrast discrimination and contrast matching tasks for the same stimuli. Twenty-five base-stimuli made from 1 c/deg patches of horizontal grating, were defined by the factorial combination of 5 contrasts for the left eye (0.3-32%) with five contrasts for the right eye (0.3-32%). Other than in contrast, the gratings in the two eyes were identical. In a 2IFC discrimination task, the base-stimuli were masks (pedestals), where the contrast increment was presented to one eye only. In a matching task, the base-stimuli were standards to which observers matched the contrast of either a monocular or binocular test grating. In the model, discrimination depends on the local gradient of the observer's internal contrast-response function, while matching equates the magnitude (rather than gradient) of response to the test and standard. With all model parameters fixed by previous work, the two-stage model successfully predicted both the discrimination and the matching data and was much more successful than linear or quadratic binocular summation models. These results show that performance measures and perception (contrast discrimination and contrast matching) can be understood in the same theoretical framework for binocular contrast vision. © 2007 VSP.

Developing a scalable model of recombinant protein yield from Pichia pastoris:the influence of culture conditions, biomass and induction regime

Background The optimisation and scale-up of process conditions leading to high yields of recombinant proteins is an enduring bottleneck in the post-genomic sciences. Typical experiments rely on varying selected parameters through repeated rounds of trial-and-error optimisation. To rationalise this, several groups have recently adopted the 'design of experiments' (DoE) approach frequently used in industry. Studies have focused on parameters such as medium composition, nutrient feed rates and induction of expression in shake flasks or bioreactors, as well as oxygen transfer rates in micro-well plates. In this study we wanted to generate a predictive model that described small-scale screens and to test its scalability to bioreactors. Results Here we demonstrate how the use of a DoE approach in a multi-well mini-bioreactor permitted the rapid establishment of high yielding production phase conditions that could be transferred to a 7 L bioreactor. Using green fluorescent protein secreted from Pichia pastoris, we derived a predictive model of protein yield as a function of the three most commonly-varied process parameters: temperature, pH and the percentage of dissolved oxygen in the culture medium. Importantly, when yield was normalised to culture volume and density, the model was scalable from mL to L working volumes. By increasing pre-induction biomass accumulation, model-predicted yields were further improved. Yield improvement was most significant, however, on varying the fed-batch induction regime to minimise methanol accumulation so that the productivity of the culture increased throughout the whole induction period. These findings suggest the importance of matching the rate of protein production with the host metabolism. Conclusion We demonstrate how a rational, stepwise approach to recombinant protein production screens can reduce process development time.

Institutional antecedents and performance consequences of influence strategies in export channels to transitional economies

This study examines how the institutional environment of transitional economies impacts institutional arrangements in the form of influence strategies employed by Western exporters in managing relationships with local firms. Reflecting environmental components, a Western firm’s understanding of Eastern Europe’s regulatory volatility, foreignness, and partner’s control locus is posited to impact economic performance by affecting key coercive and non-coercive influence strategies. A model specifying the effects of the institutional environment on economic outcomes is developed and tested on data from US exporters to Eastern Europe. A structural equation analysis indicates institutional components have a differential impact on the influence strategies employed by these Western firms and on export performance. In particular, use of coercive legalistic pleas is increased by regulatory volatility but reduced by perceived foreignness while use of non-coercive recommendations is increased by the partner’s external locus of control but not by perceived foreignness. Importantly, the institutional environment’s impact on economic performance is shown to be direct as well as indirect through the influence strategies Western firms employ in Eastern Europe. The study concludes with a discussion of implications for managers and researchers.

Attainment of competitive advantage by the exporter-importer dyad:the role of export offering and import objectives

Although experience shows that the exporter and importer jointly contribute towards the attainment of competitive advantage, past studies have separately examined export-related characteristics or import barriers. This article identifies a subset of critical factors that illustrate how the exporter–importer (E-I) dyad creates and maintains competitive advantage. Based on a sample of Greek importers, a path analytic model was developed that empirically demonstrates that product technology sophistication (PTS), product and service quality and importer strategic objectives are important for the attainment of competitive advantage while price competitiveness and trust upon the exporter are not.

Blur adaptation explained by a norm-based model of contrast adaptation

Adapting to blurred images makes in-focus images look too sharp, and vice-versa (Webster et al, 2002 Nature Neuroscience 5 839 - 840). We asked how such blur adaptation is related to contrast adaptation. Georgeson (1985 Spatial Vision 1 103 - 112) found that grating contrast adaptation followed a subtractive rule: perceived (matched) contrast of a grating was fairly well predicted by subtracting some fraction k(~0.3) of the adapting contrast from the test contrast. Here we apply that rule to the responses of a set of spatial filters at different scales and orientations. Blur is encoded by the pattern of filter response magnitudes over scale. We tested two versions - the 'norm model' and 'fatigue model' - against blur-matching data obtained after adaptation to sharpened, in-focus or blurred images. In the fatigue model, filter responses are simply reduced by exposure to the adapter. In the norm model, (a) the visual system is pre-adapted to a focused world and (b) discrepancy between observed and expected responses to the experimental adapter leads to additional reduction (or enhancement) of filter responses during experimental adaptation. The two models are closely related, but only the norm model gave a satisfactory account of results across the four experiments analysed, with one free parameter k. This model implies that the visual system is pre-adapted to focused images, that adapting to in-focus or blank images produces no change in adaptation, and that adapting to sharpened or blurred images changes the state of adaptation, leading to changes in perceived blur or sharpness.

Molecular phase space transport in water:non-stationary random walk model

Molecular transport in phase space is crucial for chemical reactions because it defines how pre-reactive molecular configurations are found during the time evolution of the system. Using Molecular Dynamics (MD) simulated atomistic trajectories we test the assumption of the normal diffusion in the phase space for bulk water at ambient conditions by checking the equivalence of the transport to the random walk model. Contrary to common expectations we have found that some statistical features of the transport in the phase space differ from those of the normal diffusion models. This implies a non-random character of the path search process by the reacting complexes in water solutions. Our further numerical experiments show that a significant long period of non-stationarity in the transition probabilities of the segments of molecular trajectories can account for the observed non-uniform filling of the phase space. Surprisingly, the characteristic periods in the model non-stationarity constitute hundreds of nanoseconds, that is much longer time scales compared to typical lifetime of known liquid water molecular structures (several picoseconds).

Response normalization and blur adaptation:data and multi-scale model

Adapting to blurred or sharpened images alters perceived blur of a focused image (M. A. Webster, M. A. Georgeson, & S. M. Webster, 2002). We asked whether blur adaptation results in (a) renormalization of perceived focus or (b) a repulsion aftereffect. Images were checkerboards or 2-D Gaussian noise, whose amplitude spectra had (log-log) slopes from -2 (strongly blurred) to 0 (strongly sharpened). Observers adjusted the spectral slope of a comparison image to match different test slopes after adaptation to blurred or sharpened images. Results did not show repulsion effects but were consistent with some renormalization. Test blur levels at and near a blurred or sharpened adaptation level were matched by more focused slopes (closer to 1/f) but with little or no change in appearance after adaptation to focused (1/f) images. A model of contrast adaptation and blur coding by multiple-scale spatial filters predicts these blur aftereffects and those of Webster et al. (2002). A key proposal is that observers are pre-adapted to natural spectra, and blurred or sharpened spectra induce changes in the state of adaptation. The model illustrates how norms might be encoded and recalibrated in the visual system even when they are represented only implicitly by the distribution of responses across multiple channels.

Pricing-to-market for UK export sector

This thesis investigates the pricing-to-market (PTM) behaviour of the UK export sector. Unlike previous studies, this study econometrically tests for seasonal unit roots in the export prices prior to estimating PTM behaviour. Prior studies have seasonally adjusted the data automatically. This study’s results show that monthly export prices contain very little seasonal unit roots implying that there is a loss of information in the data generating process of the series when estimating PTM using seasonally-adjusted data. Prior studies have also ignored the econometric properties of the data despite the existence of ARCH effects in such data. The standard approach has been to estimate PTM models using Ordinary Least Square (OLS). For this reason, both EGARCH and GJR-EGARCH (hereafter GJR) estimation methods are used to estimate both a standard and an Error Correction model (ECM) of PTM. The results indicate that PTM behaviour varies across UK sectors. The variables used in the PTM models are co-integrated and an ECM is a valid representation of pricing behaviour. The study also finds that the price adjustment is slower when the analysis is performed on real prices, i.e., data that are adjusted for inflation. There is strong evidence of auto-regressive condition heteroscedasticity (ARCH) effects – meaning that the PTM parameter estimates of prior studies have been ineffectively estimated. Surprisingly, there is very little evidence of asymmetry. This suggests that exporters appear to PTM at a relatively constant rate. This finding might also explain the failure of prior studies to find evidence of asymmetric exposure in foreign exchange (FX) rates. This study also provides a cross sectional analysis to explain the implications of the observed PTM of producers’ marginal cost, market share and product differentiation. The cross-sectional regressions are estimated using OLS, Generalised Method of Moment (GMM) and Logit estimations. Overall, the results suggest that market share affects PTM positively.Exporters with smaller market share are more likely to operate PTM. Alternatively, product differentiation is negatively associated with PTM. So industries with highly differentiated products are less likely to adjust their prices. However, marginal costs seem not to be significantly associated with PTM. Exporters perform PTM to limit the FX rate effect pass-through to their foreign customers, but they also avoided exploiting PTM to the full, since to do so can substantially reduce their profits.

Towards a unified understanding of event-related changes in the EEG:the Firefly model of synchronization through cross-frequency phase modulation

Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.

Pre-entry organisational identification:considering the theoretical and managerial implications of future members’ identification with an organisation

From a Social Identity Theory perspective, organisational identification arises through a cognitive process of self-categorisation. As a consequence a person need not have a formal relationship with an organisation in order to identify with it. In this conceptual paper, the authors draw on this proposal to argue that future members are capable of identifying with an organisation prior to entry, and that this initial pre-entry identification could contribute to a person’s subsequent post-entry organisational identification. The paper further suggests that because no distinction need be drawn between organisational identification in current and future members, we might expect to find the same antecedents of identification in both instances. The group engagement model (Tyler and Blader 2003) is called on to propose that when a future member experiences pride in, and respect from, an organisation before they join, this should positively influence their pre-entry organisational identification. The authors explore the managerial implications of these propositions, and argue that an organisation’s actions and practices that have been shown to influence a post-entry organisational identification should have an equivalent impact on future members’ organisational identification when observed during the pre-entry period. Two examples of such practices, organisational support and organisational communication, are used to illustrate this suggestion and a number of ways are discussed through which these practices may be experienced by a person before they join an organisation.

Pricing-to-market using EGARCH-error correction model

In this paper, the authors use an exponential generalized autoregressive conditional heteroscedastic (EGARCH) error-correction model (ECM), that is, EGARCH-ECM, to estimate the pass-through effects of foreign exchange (FX) rates and producers’ prices for 20 U.K. export sectors. The long-run adjustment of export prices to FX rates and producers’ prices is within the range of -1.02% (for the Textiles sector) and -17.22% (for the Meat sector). The contemporaneous pricing-to-market (PTM) coefficient is within the range of -72.84% (for the Fuels sector) and -8.05% (for the Textiles sector). Short-run FX rate pass-through is not complete even after several months. Rolling EGARCH-ECMs show that the short and long-run effects of FX rate and producers’ prices fluctuate substantially as are asymmetry and volatility estimates before equilibrium is achieved.

Deficiency in catechol-O-methyltransferase and 2-methoxyoestradiol is associated with pre-eclampsia

Despite intense investigation, mechanisms that facilitate the emergence of the pre-eclampsia phenotype in women are still unknown. Placental hypoxia, hypertension, proteinuria and oedema are the principal clinical features of this disease. It is speculated that hypoxia-driven disruption of the angiogenic balance involving vascular endothelial growth factor (VEGF)/placenta-derived growth factor (PLGF) and soluble Fms-like tyrosine kinase-1 (sFLT-1, the soluble form of VEGF receptor 1) might contribute to some of the maternal symptoms of pre-eclampsia. However, pre-eclampsia does not develop in all women with high sFLT-1 or low PLGF levels, and it also occurs in some women with low sFLT-1 and high PLGF levels. Moreover, recent experiments strongly suggest that several soluble factors affecting the vasculature are probably elevated because of placental hypoxia in the pre-eclamptic women, indicating that upstream molecular defect(s) may contribute to pre-eclampsia. Here we show that pregnant mice deficient in catechol-O-methyltransferase (COMT) show a pre-eclampsia-like phenotype resulting from an absence of 2-methoxyoestradiol (2-ME), a natural metabolite of oestradiol that is elevated during the third trimester of normal human pregnancy. 2-ME ameliorates all pre-eclampsia-like features without toxicity in the Comt(-/-) pregnant mice and suppresses placental hypoxia, hypoxia-inducible factor-1alpha expression and sFLT-1 elevation. The levels of COMT and 2-ME are significantly lower in women with severe pre-eclampsia. Our studies identify a genetic mouse model for pre-eclampsia and suggest that 2-ME may have utility as a plasma and urine diagnostic marker for this disease, and may also serve as a therapeutic supplement to prevent or treat this disorder.

Unravelling the theories of pre-eclampsia:Are the protective pathways the new paradigm?

Pre-eclampsia is a vascular disorder of pregnancy where anti-angiogenic factors, systemic inflammation and oxidative stress predominate, but none can claim to cause pre-eclampsia. This review provides an alternative to the 'two-stage model' of pre-eclampsia in which abnormal spiral arteries modification leads to placental hypoxia, oxidative stress and aberrant maternal systemic inflammation. Very high maternal soluble fms-like tyrosine kinase-1 (sFlt-1 also known as sVEGFR) and very low placenta growth factor (PlGF) are unique to pre-eclampsia; however, abnormal spiral arteries and excessive inflammation are also prevalent in other placental disorders. Metaphorically speaking, pregnancy can be viewed as a car with an accelerator and brakes, where inflammation, oxidative stress and an imbalance in the angiogenic milieu act as the 'accelerator'. The 'braking system' includes the protective pathways of haem oxygenase 1 (also referred as Hmox1 or HO-1) and cystathionine-γ-lyase (also known as CSE or Cth), which generate carbon monoxide (CO) and hydrogen sulphide (H2S) respectively. The failure in these pathways (brakes) results in the pregnancy going out of control and the system crashing. Put simply, pre-eclampsia is an accelerator-brake defect disorder. CO and H2S hold great promise because of their unique ability to suppress the anti-angiogenic factors sFlt-1 and soluble endoglin as well as to promote PlGF and endothelial NOS activity. The key to finding a cure lies in the identification of cheap, safe and effective drugs that induce the braking system to keep the pregnancy vehicle on track past the finishing line.

Probabilistic multiple model neural network based leak detection system:experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.