Pronounced impairment of activities of daily living in posterior cortical atrophy

Introduction : The impact of several dementia syndromes on activities of daily living (ADLs) has been well documented, but no study has yet investigated functional ability in posterior cortical atrophy (PCA). The primarily visual nature of deficits in this condition is likely to have a pronounced impact on ADLs. Objective : The aim of this study was to profile functional change in PCA and identify predictors of change. Method : Twenty-nine PCA patients and 25 patients with typical Alzheimer’s disease (AD) and their caregivers were included in this cross-sectional study. ADLs were assessed using the Disability Assessment for Dementia (DAD), administered to caregivers, assessing basic ADLs (e.g., eating, dressing) and instrumental ADLs (e.g., managing finances, meal preparation). The predictive utility of cognitive domains (Addenbrooke’s Cognitive Examination), behavioural impairment (Cambridge Behavioural Inventory-Revised) and demographic variables on ADL ability was also examined. Results : PCA patients showed significantly reduced total ADL scores compared to AD patients (medium effect size, d = –0.7; p 0.05). A model combining patient mood, disinhibition, apathy, symptom duration, and memory and attention/orientation scores explained the variance of scores in functional decline (61.2%), but the key factor predicting ADL scores was attention/orientation (p = 0.048). Conclusion : This study shows the profound impact of PCA on ADLs and factors underpinning patients’ disability. Attention/orientation deficits were found to correlate and contribute to variance in ADL scores. Future work to develop tailored interventions to manage ADL impairment in PCA should take these findings into account

Combining metabolic modelling with machine learning accurately predicts yeast growth rate

New metabolic engineering techniques hold great potential for a range of bio-industrial applications. However, their practical use is hindered by the huge number of possible modifications, especially in eukaryotic organisms. To address this challenge, we present a methodology combining genome-scale metabolic modelling and machine learning to precisely predict cellular phenotypes starting from gene expression readouts. Our methodology enables the identification of candidate genetic manipulations that maximise a desired output--potentially reducing the number of in vitro experiments otherwise required. We apply and validate this methodology to a screen of 1,143 Saccharomyces cerevisiae knockout strains. Within the proposed framework, we compare different combinations of feature selection and supervised machine/deep learning approaches to identify the most effective model

Managing complex engineering projects: What can we learn from the evolving digital footprint?

The challenges of managing large complex engineering projects, such as those involving the design of infrastructure, aerospace and industrial systems; are widely acknowledged. While there exists a mature set of project management tools and methods, many of today's projects overrun in terms of both time and cost. Existing literature attributes these overruns to factors such as: unforeseen dependencies, a lack of understanding, late changes, poor communication, limited resource availability (inc. personnel), incomplete data and aspects of culture and planning. Fundamental to overcoming these factors belies the challenge of how management information relating to them can be provided, and done so in a cost eff; ective manner. Motivated by this challenge, recent research has demonstrated how management information can be automatically generated from the evolving digital footprint of an engineering project, which encompasses a broad range of data types and sources. In contrast to existing work that reports the generation, verification and application of methods for generating management information, this paper reviews all the reported methods to appraise the scope of management information that can be automatically generated from the digital footprint. In so doing, the paper presents a reference model for the generation of managerial information from the digital footprint, an appraisal of 27 methods, and a critical reflection of the scope and generalisability of data-driven project management methods. Key findings from the appraisal include the role of email in providing insights into potential issues, the role of computer models in automatically eliciting process and product dependencies, and the role of project documentation in assessing project norms. The critical reflection also raises issues such as privacy, highlights the enabling technologies, and presents opportunities for new Business Intelligence tools that are based on real-time monitoring and analysis of digital footprints.</p

Variation in creative behaviour during the later stages of the design process

This paper presents results from an experiment studying the creative behaviour of 14 engineering designers during a later stage engineering design activity; with the aim of identifying important considerations that must be made when supporting designers in later stage design situations. Data gathered demonstrates the variation in designer behaviour that occurs even when completing identical activities; and differing creative approaches that designers may follow within the later stages of the design process. By understanding the individual behaviour of designers, it will be possible to better inform the use of methods for creative support within the later stage engineering design process