Shortcomings of protein removal prior to high performance liquid chromatographic analysis - A case study using method development for BAY 11-7082

During the analytical method development for BAY 11-7082 ((E)-3-[4-methylphenylsulfonyl]-2-propenenitrile), using HPLC-MS-MS and HPLC-UV, we observed that the protein removal process (both ultrafiltration and precipitation method using organic solvents) prior to HPLC brought about a significant reduction in the concentration of this compound. The use of a structurally similar internal standard, BAY 11-7085 ((E)-3-[4-t-butylphenylsulfonyl]-2-propenenitrile), was not effective in compensating for the loss of analyte as the extent of reduction was different to that of the analyte. We present here a systematic investigation of this problem and a new validated method for the determination of BAY 11-7082. (c) 2006 Elsevier B.V. All rights reserved.

Non-zero mean Gaussian process prior wind field models

This report outlines the derivation and application of a non-zero mean, polynomial-exponential covariance function based Gaussian process which forms the prior wind field model used in 'autonomous' disambiguation. It is principally used since the non-zero mean permits the computation of realistic local wind vector prior probabilities which are required when applying the scaled-likelihood trick, as the marginals of the full wind field prior. As the full prior is multi-variate normal, these marginals are very simple to compute.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

The role of prior exposure on the capture of attention by items in working memory

The Biased Competition Model (BCM) suggests both top-down and bottom-up biases operate on selective attention (e.g., Desimone & Duncan, 1995). It has been suggested that top-down control signals may arise from working memory. In support, Downing (2000) found faster responses to probes presented in the location of stimuli held vs. not held in working memory. Soto, Heinke, Humphreys, and Blanco (2005) showed the involuntary nature of this effect and that shared features between stimuli were sufficient to attract attention. Here we show that stimuli held in working memory had an influence on the deployment of attentional resources even when: (1) It was detrimental to the task, (2) there was equal prior exposure, and (3) there was no bottom-up priming. These results provide further support for involuntary top-down guidance of attention from working memory and the basic tenets of the BCM, but further discredit the notion that bottom-up priming is necessary for the effect to occur.

'I like this interview; I get cakes and cats!':the effect of prior relationships on interview talk

Research interviews are a form of interaction jointly constructed by the interviewer and interviewee, what Silverman (2001: 104) calls 'interview-as-local-accomplishment'. From this perspective, interviews are an interpretative practice in which what is said is inextricably tied to where it is said, how it is said and, importantly, to whom it is said (Holstein and Gubrium, 2004). The relationship between interviewer and interviewee, then, is fundamental in research interviews. But what happens when the relationship between interviewer and interviewee is not only that of researcher-informant but also involves other roles such as colleague and friend? In this article we will show how prior relationships are invoked and made relevant by both parties during educational research interviews and how these prior relationships therefore contribute to the 'generation' (Baker, 2004: 163) of interview data. © 2010 The Author(s).

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

Placement year academic benefit revisited:effects of demographics, prior achievement and degree programme

Investigations of whether students taking undergraduate work placements show greater academic improvement than those who do not have shown inconsistent results. In most studies, sample sizes have been relatively small and few studies have taken into account pre-existing student differences.Here data from over 6000 students at one university over six cohorts and a range of programmes are analysed. Consistent academic benefit from placement experience, regardless of ethnicity, gender, socioeconomic background and subject is shown. However the impact of demographic factors on both achievement and on the probability of taking a placement suggests that future research should take these factors into account. The role of placements in promoting employability is contextualised as a secondary benefit to the primary goal of educating the mind in the Newman (1852) tradition. Possible causes of, and further research into, the improved academic performance identified are discussed.

Prior action execution has no effect on corticospinal facilitation during action observation

Transcranial magnetic stimulation (TMS) has been used widely in research investigating corticospinal (CS) excitability during action observation. Generally, this work has shown that observation of an action performed by others, in the absence of overt movement, modulates the excitability of the CS pathway in humans. Despite the extent of the literature exploring action observation effects, however, there has been little research to date that has compared observation with the combination of observation and execution directly. Here, we report a single-pulse TMS study that investigated whether CS excitability during action observation was modulated by actions performed by the observers prior to viewing a ball pinching action. The results showed that CS excitability during action observation increased when compared to observation of a static hand, but that there was no additional motor facilitation when participants performed the same action prior to observing it. Our findings highlight the importance of action observation and its consequences on the CS system, whilst also illustrating the limited effect of prior action execution on the CS pathway for a simple action task.