Extending metacognition : an account of how procedural and analytic metacognitive processes interact with extended cognition : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Psychology at Massey University, Manawatū, New Zealand

This thesis examines the relationship between extended cognition and metacognition by way of three interlocking proposals. First of all, both extended cognition and metacognition should be conceptualised as sub-personal-level explanations that are implemented in the brain and environment and in cultural practices that inform individual skill. Secondly, the procedural metacognition norm of fluency, analytic metacognition, and cognitive skill mutually reinforce and enrich each other when dealing with cognitive obstacles. Finally, my third claim, builds on and refines claims one and two when I examine the involvement of metacognition in relation to expertise; specifically, I focus on the skilled interplay of automaticity and metacognitive control when confronted with cognitive obstacles. To this end, I build on hybrid accounts of skilled cognitive performance to provide a framework that isolates cases of metacognitive extension. This thesis concludes that metacognition, rather than being viewed as wholly internal, can be partially externalised across the environment when the individual exhibits high levels of automaticity and control when using an artefact

Eyetracking metrics reveal impaired spatial anticipation in behavioural variant frontotemporal dementia.

Eyetracking technology has had limited application in the dementia field to date, with most studies attempting to discriminate syndrome subgroups on the basis of basic oculomotor functions rather than higher-order cognitive abilities. Eyetracking-based tasks may also offer opportunities to reduce or ameliorate problems associated with standard paper-and-pencil cognitive tests such as the complexity and linguistic demands of verbal test instructions, and the problems of tiredness and attention associated with lengthy tasks that generate few data points at a slow rate. In the present paper we adapted the Brixton spatial anticipation test to a computerized instruction-less version where oculomotor metrics, rather than overt verbal responses, were taken into account as indicators of high level cognitive functions. Twelve bvFTD (in whom spatial anticipation deficits were expected), six SD patients (in whom deficits were predicted to be less frequent) and 38 healthy controls were presented with a 10 × 7 matrix of white circles. During each trial (N = 24) a black dot moved across seven positions on the screen, following 12 different patterns. Participants' eye movements were recorded. Frequentist statistical analysis of standard eye movement metrics were complemented by a Bayesian machine learning (ML) approach in which raw eyetracking time series datasets were examined to explore the ability to discriminate diagnostic group performance not only on the overall performance but also on individual trials. The original pen and paper Brixton test identified a spatial anticipation deficit in 7/12 (58%) of bvFTD and in 2/6 (33%) of SD patients. The eyetracking frequentist approach reported the deficit in 11/12 (92%) of bvFTD and in none (0%) of the SD patients. The machine learning approach had the main advantage of identifying significant differences from controls in 24/24 individual trials for bvFTD patients and in only 12/24 for SD patients. Results indicate that the fine grained rich datasets obtained from eyetracking metrics can inform us about high level cognitive functions in dementia, such as spatial anticipation. The ML approach can help identify conditions where subtle deficits are present and, potentially, contribute to test optimisation and the reduction of testing times. The absence of instructions also favoured a better distinction between different clinical groups of patients and can help provide valuable disease-specific markers

Fluconazole-Associated Birth Defects: A Comprehensive Review

Background: The August 2013 publication of a large historical cohort study in the New England Journal of Medicine has reignited interest in the potential teratogenic effects of fluconazole when used in pregnant females. Fluconazole is an effective and commonly-utilized antifungal medication. Thus, maternal and fetal exposure to fluconazole is expected in the general population, and pharmacists are expected to counsel patients regarding any risks to their prescribed treatment. Methods: A literature review of all published literature indexed to PubMed (January 1966 to October 2013) and International Pharmaceutical Abstracts (January 1975 to October 2013) including fluconazole and teratogenic effects and published in the English language was conducted. Results: Fourteen publications were included for analysis including case reports (n=7), cross-sectional research (n=2), and historical cohort studies (n=5). Conclusion: There appears to be little to no fetal risk resulting from a single dose or short duration antifungal therapy with fluconazole. However, prolonged high-dose fluconazole therapy has increased potential to confer teratogenic effects. In those cases, the risks of such therapy should be weighed against potential benefits

Eyetracking metrics reveal impaired spatial anticipation in behavioural variant frontotemporal dementia.

Eyetracking technology has had limited application in the dementia field to date, with most studies attempting to discriminate syndrome subgroups on the basis of basic oculomotor functions rather than higher-order cognitive abilities. Eyetracking-based tasks may also offer opportunities to reduce or ameliorate problems associated with standard paper-and-pencil cognitive tests such as the complexity and linguistic demands of verbal test instructions, and the problems of tiredness and attention associated with lengthy tasks that generate few data points at a slow rate. In the present paper we adapted the Brixton spatial anticipation test to a computerized instruction-less version where oculomotor metrics, rather than overt verbal responses, were taken into account as indicators of high level cognitive functions. Twelve bvFTD (in whom spatial anticipation deficits were expected), six SD patients (in whom deficits were predicted to be less frequent) and 38 healthy controls were presented with a 10×7 matrix of white circles. During each trial (N=24) a black dot moved across seven positions on the screen, following 12 different patterns. Participants' eye movements were recorded. Frequentist statistical analysis of standard eye movement metrics were complemented by a Bayesian machine learning (ML) approach in which raw eyetracking time series datasets were examined to explore the ability to discriminate diagnostic group performance not only on the overall performance but also on individual trials. The original pen and paper Brixton test identified a spatial anticipation deficit in 7/12 (58%) of bvFTD and in 2/6 (33%) of SD patients. The eyetracking frequentist approach reported the deficit in 11/12 (92%) of bvFTD and in none (0%) of the SD patients. The machine learning approach had the main advantage of identifying significant differences from controls in 24/24 individual trials for bvFTD patients and in only 12/24 for SD patients. Results indicate that the fine grained rich datasets obtained from eyetacking metrics can inform us about high level cognitive functions in dementia, such as spatial anticipation. The ML approach can help identify conditions where subtle deficits are present and, potentially, contribute to test optimisation and the reduction of testing times. The absence of instructions also favoured a better distinction between different clinical groups of patients and can help provide valuable disease-specific markers

A 18F radiolabelled Zn(ii) sensing fluorescent probe

A selective fluorescent probe for Zn(ii), AQA-F, has been synthesized. AQA-F exhibits a ratiometric shift in emission of up to 80 nm upon binding Zn(ii) ([AQA-F] = 0.1 mM, [Zn(ii)Cl 2 ] = 0-300 μM). An enhancement of quantum yield from Φ = 4.2% to Φ = 35% is also observed. AQA-F has a binding constant, K d = 15.2 μM with Zn(ii). This probe has been shown to respond to endogenous Zn(ii) levels in vitro in prostate and prostate cancer cell lines. [ 18 F]AQA-F has been synthesized with a radiochemical yield of 8.6% and a radiochemical purity of 97% in 88 minutes. AQA-F shows the potential for a dual modal PET/fluorescence imaging probe for Zn(ii)

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening.

The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the common scenario in early stage drug discovery where lots of inactive data but no active data points are available (i.e., extrapolation from the low-activity to the high-activity range) has not been thoroughly examined yet. To this aim, we have designed an iterative virtual screening strategy which was evaluated on 25 diverse bioactivity data sets from ChEMBL. We benchmark the efficiency of random forest (RF), multiple linear regression, ridge regression, similarity searching, and random selection of compounds to identify a highly active molecule in the test set among a large number of low-potency compounds. We use the number of iterations required to find this active molecule to evaluate the performance of each experimental setup. We show that linear and ridge regression often outperform RF and similarity searching, reducing the number of iterations to find an active compound by a factor of 2 or more. Even simple regression methods seem better able to extrapolate to high-bioactivity ranges than RF, which only provides output values in the range covered by the training set. In addition, examination of the scaffold diversity in the data sets used shows that in some cases similarity searching and RF require two times as many iterations as random selection depending on the chemical space covered in the initial training data. Lastly, we show using bioactivity data for COX-1 and COX-2 that our framework can be extended to multitarget drug discovery, where compounds are selected by concomitantly considering their activity against multiple targets. Overall, this study provides an approach for iterative screening where only inactive data are present in early stages of drug discovery in order to discover highly potent compounds and the best experimental set up in which to do so.This project has received funding from the European Union’s Framework Programme For Research and Innovation Horizon 2020 (2014–2020) under the Marie Curie Sklodowska-Curie Grant Agreement No. 703543 (I.C.-C.). A.B. thanks the European Research Commission (Starting Grant ERC-2013-StG 336159 MIXTURE) for funding. N.C.F is funded by EPSRC (EP/M006093/1)

Construction of challenging proline–proline junctions via diselenide–selenoester ligation chemistry

Polyproline sequences are highly abundant in prokaryotic 10 and eukaryotic proteins, where they serve as key components of 11 secondary structure. To date, construction of the proline−proline motif 12 has not been possible owing to steric congestion at the ligation junction, 13 together with an n → π* electronic interaction that reduces the 14 reactivity of acylated proline residues at the C-terminus of peptides. 15 Here, we harness the enhanced reactivity of prolyl selenoesters and a 16 trans-γ-selenoproline moiety to access the elusive proline−proline 17 junction for the first time through a diselenide−selenoester ligation− 18 deselenization manifold. The efficient nature of this chemistry is 19 highlighted in the high-yielding one-pot assembly of two proline-rich 20 polypeptide targets, submaxillary gland androgen regulated protein 3B 21 and lumbricin-1. This method provides access to the most challenging of ligation junctions, thus enabling the construction of 22 previously intractable peptide and protein targets of increasing structural complexity