MRI and Neuropsychological Correlates of Carbon Monoxide Exposure: A Case Report

A 45-year-old woman experienced long-term, chronic exposure to carbon monoxide in the restaurant kitchen where she was employed as a cook. After returning to the restaurant after 5 days off work, she noticed that her symptoms returned immediately; she then aired out the room and called the gas company. Approximately 6 hr after a leak was detected, the patient went to the hospital, where her carboxyhemoglobin was found to be within normal limits and results of a neurologic examination were described as normal. Based on her symptoms, the patient believed she had been exposed to CO for at least 1 year before the leak was discovered. Initially, she experienced flu-like symptoms, which eventually resolved. At the time of her first neuropsychological evaluation (17 months after the exposure was identified), her persisting complaints included difficulties in reading, writing, speaking and word retrieval. The test results were consistent with secondary frontal lobe dysfunction associated with subcortical disorders such as those seen after CO exposure. Results of a subsequent neuropsychological examination (29 months postexposure) showed slight improvement in performance, but her performance was still consistent with mild frontal/subcortical dysfunction. Although the initial screening of a brain magnetic resonance image (MRI) performed 15 months after the exposure was interpreted as being within normal limits, two subsequent blind reviews of the same scans identified multiple bilateral lesions in the basal ganglia, which were consistent with chronic CO exposure. We present this case as an example of the utility of MRI and neuropsychological examinations in detecting central nervous system dysfunction secondary to CO exposure

The Unfulfilled Potential of Data-Driven Decision Making in Agile Software Development

With the general trend towards data-driven decision making (DDDM), organizations are looking for ways to use DDDM to improve their decisions. However, few studies have looked into the practitioners view of DDDM, in particular for agile organizations. In this paper we investigated the experiences of using DDDM, and how data can improve decision making. An emailed questionnaire was sent out to 124 industry practitioners in agile software developing companies, of which 84 answered. The results show that few practitioners indicated a widespread use of DDDM in their current decision making practices. The practitioners were more positive to its future use for higher-level and more general decision making, fairly positive to its use for requirements elicitation and prioritization decisions, while being less positive to its future use at the team level. The practitioners do see a lot of potential for DDDM in an agile context; however, currently unfulfilled

The unfulfilled potential of data-driven decision making in agile software development

With the general trend towards data-driven decision making (DDDM), organizations are looking for ways to use DDDM to improve their decisions. However, few studies have looked into the practitioners view of DDDM, in particular for agile organizations. In this paper we investigated the experiences of using DDDM, and how data can improve decision making. An emailed questionnaire was sent out to 124 industry practitioners in agile software developing companies, of which 84 answered. The results show that few practitioners indicated a wide-spread use of DDDM in their current decision making practices. The practitioners were more positive to its future use for higher-level and more general decision making, fairly positive to its use for requirements elicitation and prioritization decisions, while being less positive to its future use at the team level. The practitioners do see a lot of potential for DDDM in an agile context; however, currently unfulfilled

Human-computer interaction to human-computer-context interaction : towards a conceptual framework for conducting user studies for shifting interfaces

Computer interfaces have been diversifying: from mobile and wearable technologies to the human body as an interface. Moreover, new sensing possibilities have allowed input to interfaces to go beyond the traditional mouse- and keyboard. This has resulted in a shift from manifest to latent interactions, where interactions between the human and the computer are becoming less visible. Currently, there is no framework available that fully captures the complexity of the multidimensional, multimodal, often latent interactions with these constantly shifting interfaces. In this manuscript, the Hu-man-Computer-Context Interaction (HCCI) framework is proposed. This framework defines 5 relevant interaction levels to be considered during user research in all stages of the new product development process in order to optimize user experience. More specifically, the interaction context is defined in terms of user-object, user-user, user-content, user-platform and user-context interactions. The HCCI framework serves as a concrete tool to use in a new product development process by HCI researchers, design-ers, and developers and aims to be technology independent and future-proof. This framework is a preliminary suggestion to be matched against other innovation devel-opment projects and needs to be further validated

Barriers and enablers for participation in healthy lifestyle programs by adolescents who are overweight: a qualitative study of the opinions of adolescents, their parents and community stakeholders

Background: Overweight or obesity during adolescence affects almost 25% of Australian youth, yet limited research exists regarding recruitment and engagement of adolescents in weight-management or healthy lifestyle interventions, or best-practice for encouraging long-term healthy behaviour change. A sound understanding of community perceptions, including views from adolescents, parents and community stakeholders, regarding barriers and enablers to entering and engaging meaningfully in an intervention is critical to improve the design of such programs. Methods: This paper reports findings from focus groups and semi-structured interviews conducted with adolescents (n=44), parents (n=12) and community stakeholders (n=39) in Western Australia. Three major topics were discussed to inform the design of more feasible and effective interventions: recruitment, retention in the program and maintenance of healthy change. Data were analysed using content and thematic analyses.Results: Data were categorised into barriers and enablers across the three main topics. For recruitment, identified barriers included: the stigma associated with overweight, difficulty defining overweight, a lack of current health services and broader social barriers. The enablers for recruitment included: strategic marketing, a positive approach and subsidising program costs. For retention, identified barriers included: location, timing, high level of commitment needed and social barriers. Enablers for retention included: making it fun and enjoyable for adolescents, involving the family, having an on-line component, recruiting good staff and making it easy for parents to attend. For maintenance, identified barriers included: the high degree of difficulty in sustaining change and limited services to support change. Enablers for maintenance included: on-going follow up, focusing on positive change, utilisation of electronic media and transition back to community services. Conclusions: This study highlights significant barriers for adolescents and parents to overcome to engage meaningfully with weight-management or healthy lifestyle programs. A number of enablers were identified to promote ongoing involvement with an intervention. This insight into specific contextual opinions from the local community can be used to inform the delivery of healthy lifestyle programs for overweight adolescents, with a focus on maximising acceptability and feasibility

Model selection in historical research using approximate Bayesian computation

Formal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to reevaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester's laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence.Funding for this work was provided by the SimulPast Consolider Ingenio project (CSD2010-00034) of the former Ministry for Science and Innovation of the Spanish Government and the European Research Council Advanced Grant EPNet (340828).Peer ReviewedPostprint (published version