An exploration of the factors influencing the appointment of women to publicly quoted company boards

This thesis is comprised of three papers: a systematic review of the factors influencing the appointment of women to public company boards, an empirical study of the factors that influence these appointments and a process review. Efforts made in recent decades to increase women’s representation at the top of business have shown slow and inconsistent progress. The first paper examines existing literature about female board appointments. Findings show that countries have introduced regulation to encourage organisations to appoint women to their boards with differing degrees of success. Enforced quotas have been the most successful way of increasing progress, while authors propose that selection processes need improvement. The paper also offers some albeit mixed evidence that increasing the number of women on boards can improve organisational performance. The empirical paper builds on work by the Female FTSE Board Report (Vinnicombe, Doldor & Sealy, 2018) which highlighted the increase in women appointed to FTSE boards as non-executive directors and the lack of progress in increasing the number of women as executive directors. It makes a unique contribution by providing a detailed analysis of the views and experiences of appointing female directors of 12 top executives from some of the UK’s most prominent companies. Analysis identified four main themes: diversity, barriers, facilitators and conditions for change. Findings showed that while the idea of women on boards as non-executive directors is well embedded and efforts have been made to select women to these posts, there are many remaining barriers to increasing the numbers of women in executive director roles. External focus now needs to be applied to galvanise efforts in public companies to develop their female talent pipeline and increase the number of executive directors on their boards. The process review provides reflections on the doctoral research process

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry

Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model

Rapid Temperature-Dependent Rheological Measurements of Non-Newtonian Solutions Using a Machine-Learning Aided Microfluidic Rheometer

Biofluids such as synovial fluid, blood plasma, and saliva contain several proteins which impart non-Newtonian properties to the biofluids. The concentration of such protein macromolecules in biofluids is regarded as an important biomarker for the diagnosis of several health conditions, including cardiovascular disorders, joint quality, and Alzheimer’s. Existing technologies for the measurements of macromolecules in biofluids are limited; they require a long turnaround time, or require complex protocols, thus calling for alternative, more suitable, methodologies aimed at such measurements. According to the well-established relations for polymer solutions, the concentration of macromolecules in solutions can also be derived via measurement of rheological properties such as shear-viscosity and the longest relaxation time. We here introduce a microfluidic rheometer for rapid simultaneous measurement of shear viscosity and longest relaxation time of non-Newtonian solutions at different temperatures. At variance with previous technologies, our microfluidic rheometer provides a very short turnaround time of around 2 min or less thanks to the implementation of a machine-learning algorithm. We validated our platform on several aqueous solutions of poly(ethylene oxide). We also performed measurements on hyaluronic acid solutions in the clinical range for joint grade assessment. We observed monotonic behavior with the concentration for both rheological properties, thus speculating on their use as potential rheo-markers, i.e., rheological biomarkers, across several disease states

The Implicit Relational Assessment Procedure (IRAP) and the malleability of ageist attitudes

The current study examined the malleability of implicit attitudes using the Implicit Relational Assessment Procedure (IRAP). In Experiment 1, “similar” and “opposite” were presented as response options with the sample terms “old people” and “young people” and various positive and negative target stimuli. Results showed significantly faster response latencies for consistent (e.g., Similar- Positive-Young People) compared to inconsistent tasks (e.g., Similar-Positive-Old People). Explicit measures did not correlate with this IRAP effect. Experiment 2 determined whether prior exposure to pictures of admired and disliked old and young individuals had an impact on IRAP performance. Results revealed that pro-old exemplars reduced the pro-young IRAP effect, but reversed the anti-old effect, and this held for 24 h; explicit measures were largely unaffected. The findings suggest that the IRAP provides an informative measure of attitudechange following pro- versus anti-exemplar training

Exploring Screen Presentations in the Implicit Relational Assessment Procedure (IRAP)

The current study attempted to systematically manipulate stimulus presentations in the Implicit Relational Assessment Procedure (IRAP) to determine the potential impact of this variable on implicit responding. The study comprised of four conditions that systematically manipulated the positions of the sample stimuli and the response options. Specifically, the Random-Random Condition randomized both sample stimuli and response options; Random-Fixed randomized sample stimuli, but response options remained in fixed locations; Fixed-Random Condition fixed sample stimuli but randomized response options; and Fixed-Fixed Condition fixed both sample stimuli and response options. The results demonstrated strong and predicted IRAP effects in all four conditions. Although the Random-Fixed presentation generated the strongest D-IRAP score, the randomization of the sample stimuli and response options were both critical to producing strong and significant D-IRAP scores because the Random-Random and Fixed-Random Conditions were only marginally smaller. The implications of the findings for existing and future research with the IRAP are discussed

Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D

Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study’s objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows

Establishing mand emergence: The effects of three training procedures and modified antecedent conditions

This study examined the effects of a modified antecedent during probes for emergent mands following listener versus tact training for children with autism. Eight students, aged 7 to 11, were trained to respond to 3 sets of relational responses (front/back, left/right, on/under), each assigned a nonsense label. Three training types were evaluated: listener training, tact training, and listener– tact training combined. Following the experimental training, probes for emergent mands were conducted under modified antecedent conditions. Results showed that modified antecedent conditions were critical in demonstrating mand emergence for some participants

Reliability of Semiautomated Kinetic Perimetry (SKP) and Goldmann Kinetic Perimetry in Children and Adults With Retinal Dystrophies.

PurposeTo investigate the precision of visual fields (VFs) from semiautomated kinetic perimetry (SKP) on Octopus 900 perimeters, for children and adults with inherited retinal degenerations (IRDs). Goldmann manual kinetic perimetry has long been used in the diagnosis and follow-up of these patients, but SKP is becoming increasingly common. Octopus VFs (OVFs) and Goldmann VFs (GVFs) were both mapped on two occasions.MethodsNineteen females and 10 males with IRDs were tested on OVFs and GVFs, with two targets per test (V4e and one smaller target). Tests were performed in the same (randomized) order at two visits about 1 week apart. The VFs were digitized to derive isopter solid angles. Comparisons, within and between visits, were performed with paired t-tests and Bland-Altman plots.ResultsMedian age was 20 years (range, 7-70; 10 participants aged ≤17 years old). There were no significant differences in solid angles between OVFs and GVFs (P ≥ 0.06) or between the two visits' solid angles on either perimeter (P ≥ 0.30). Between-visit test-retest variability for GVFs and OVFs was similar (P ≥ 0.73), with median values of approximately 9% to 13%. Overall variability was lower for children than adults (medians of 7.5% and 12.8%, respectively).ConclusionsOctopus SKP and Goldmann perimetry produced VFs of similar size and variability.Translational relevanceOur study indicates that SKP provides a viable alternative to traditional Goldmann perimetry in clinical trials or care involving both children and adults with IRDs

An experimental test of a cognitive defusion exercise.

This study investigated the impact of defusion on a nonclinical sample ( n = 60) in the context of negative (e.g., “I am a bad person”) and positive (e.g., “I am whole”) self-statements. Participants were assigned to one of three experimental conditions (Pro-Defusion, Anti-Defusion, and Neutral) that manipulated instruc - tions about the impact of a defusion strategy. Defusion was also manipulated through the visual presentation of the self-statements, with each presented in three formats (Normal, Defused, Abnormal). Participants rated each self-state - ment for comfort, believability, and willingness. Although the instructions did not affect ratings, negative statements presented in the defused format de - creased discomfort and increased willingness and believability relative to the nondefused statements. The findings suggest using defusion strategies in coping with negative psychological conten