Browsing by Author "Cameron, David"

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    Classification of endocrine resistant breast cancers from transcriptomic datasets using multi-gene signatures
    (Cranfield University, 2012-09) Larionov, Alexey; Cameron, David; Morgan, Sarah
    Breast cancer is the most frequent cancer in women in developed countries. Endocrine treatment is indicated to the majority of breast cancer patients. However, in some cases it does not work despite the current clinical indications. Eventually the resistance may develop in many of those who initially respond. Re-analysis of available breast cancer transcriptomic datasets using new multi-gene signatures associated with endocrine resistance may help to understand and overcome endocrine resistance. The goal of this project was to develop a bioinformatics pipeline to (i) select endocrine resistant cases from the available breast cancer datasets and (ii) classify the selected cases by multiple multi-gene signatures. The pipeline has been successfully designed and applied for classification of endocrineresistant samples from 9 breast cancer datasets using 7 transcriptional signatures. The obtained results have been presented in a dedicated web site. The pipeline consists of:  Procedures for a manually curated selection of relevant datasets and signatures;  Procedures for semi-automatic data pre-processing, allowing cross-platform analysis;  A new, fully automated, classification algorithm (Iterative Consensus PAM). The main features of the developed classification algorithm include:  It is based on un-supervised partitioning;  It allows for “non-classifiable” samples;  The procedure does not require a training set;  The procedure can be used in a cross-platform context (Affymetrix & Illumina). The developed pipeline and web site may constitute a prototype for a future web-hub collecting (i) data on endocrine-resistant breast cancer specimens, (ii) collecting multigene signatures relevant to endocrine resistance and (iii) providing tools to apply the signatures to the data. The web-repository could provide a tool to integrate the data and signatures and to produce new clinical and biological knowledge about endocrine resistance in breast cancer.
  • No Thumbnail Available
    ItemOpen Access
    Data: Dynamic Graphical Instructions Result in Improved Attitudes and Decreased Task Completion Time in Human–Robot Co-Working: An Experimental Manufacturing Study
    (Cranfield University, 2023-08-08 17:52) Eimontaite, Iveta; Cameron, David
    This study explores how dynamic graphical signage could aid a manufacturing task. Forty employees from one UK manufacturing company participated in a field experiment to complete a precision pick-and-place task working in conjunction with a collaborative robotic arm. Twenty-one participants completed the task with the support of dynamic graphical signage that provided information about the robot and the activity, while the rest completed the same task with no signage. Participants accuracy, response time, negative attitudes to robot, and outcome expectancy were assessed in this study. Data from: Eimontaite, I.; Cameron, D.; Rolph, J.; Mokaram, S.; Aitken, J.M.; Gwilt, I. Dynamic GraphicalInstructions Result in ImprovedAttitudes and Decreased TaskCompletion Time in Human–Robot Co-Working: An ExperimentalManufacturing Study. Sustainability 2022, 14
  • Thumbnail Image
    ItemOpen Access
    Dynamic graphical instructions result in improved attitudes and decreased task completion time in human–robot co-working: an experimental manufacturing study
    (MDPI, 2022-03-11) Eimontaite, Iveta; Cameron, David; Rolph, Joe; Mokaram, Saeid; Aitken, Jonathan M.; Gwilt, Ian; Law, James
    Collaborative robots offer opportunities to increase the sustainability of work and workforces by increasing productivity, quality, and efficiency, whilst removing workers from hazardous, repetitive, and strenuous tasks. They also offer opportunities for increasing accessibility to work, supporting those who may otherwise be disadvantaged through age, ability, gender, or other characteristics. However, to maximise the benefits, employers must overcome negative attitudes toward, and a lack of confidence in, the technology, and must take steps to reduce errors arising from misuse. This study explores how dynamic graphical signage could be employed to address these issues in a manufacturing task. Forty employees from one UK manufacturing company participated in a field experiment to complete a precision pick-and-place task working in conjunction with a collaborative robotic arm. Twenty-one participants completed the task with the support of dynamic graphical signage that provided information about the robot and the activity, while the rest completed the same task with no signage. The presence of the signage improved the completion time of the task as well as reducing negative attitudes towards the robots. Furthermore, participants provided with no signage had worse outcome expectancies as a function of their response time. Our results indicate that the provision of instructional information conveyed through appropriate graphical signage can improve task efficiency and user wellbeing, contributing to greater workforce sustainability. The findings will be of interest for companies introducing collaborative robots as well as those wanting to improve their workforce wellbeing and technology acceptance.
  • Thumbnail Image
    ItemOpen Access
    The social triad model: considering the deployer in a novel approach to trust in human–robot interaction
    (Springer, 2023-09-13) Cameron, David; Collins, Emily C.; de Saille, Stevienna; Eimontaite, Iveta; Greenwood, Alice; Law, James
    There is an increasing interest in considering, measuring, and implementing trust in human-robot interaction (HRI). New avenues in this field include identifying social means for robots to influence trust, and identifying social aspects of trust such as a perceptions of robots’ integrity, sincerity or even benevolence. However, questions remain regarding robots’ authenticity in obtaining trust through social means and their capacity to increase such experiences through social interaction with users. We propose that the dyadic model of HRI misses a key complexity: a robot’s trustworthiness may be contingent on the user’s relationship with, and opinion of, the individual or organisation deploying the robot (termed here, Deployer). We present a case study in three parts on researching HRI and a LEGO® Serious® Play focus group on care robotics to indicate how Users’ trust towards the Deployer can affect trust towards robots and robotic research. Our Social Triad model (User, Robot, Deployer) offers novel avenues for exploring trust in a social context.