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Unreliable numbers: error and harm induced by bad design can be reduced by better design.

ABSTRACT: Number entry is a ubiquitous activity and is often performed in safety- and mission-critical procedures, such as healthcare, science, finance, aviation and in many other areas. We show that Monte Carlo methods can quickly and easily compare the reliability of different number entry systems. A surprising finding is that many common, widely used systems are defective, and induce unnecessary human error. We show that Monte Carlo methods enable designers to explore the implications of normal and unexpected operator behaviour, and to design systems to be more resilient to use error. We demonstrate novel designs with improved resilience, implying that the common problems identified and the errors they induce are avoidable.

SUBMITTER: Thimbleby H

PROVIDER: S-EPMC4614478 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Unreliable numbers: error and harm induced by bad design can be reduced by better design.

Thimbleby Harold H Oladimeji Patrick P Cairns Paul P

Journal of the Royal Society, Interface 20150901 110

Number entry is a ubiquitous activity and is often performed in safety- and mission-critical procedures, such as healthcare, science, finance, aviation and in many other areas. We show that Monte Carlo methods can quickly and easily compare the reliability of different number entry systems. A surprising finding is that many common, widely used systems are defective, and induce unnecessary human error. We show that Monte Carlo methods enable designers to explore the implications of normal and une ...[more]

PMID: 26354830

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Project description:BackgroundEndometriosis is an estrogen dependent, inflammatory disorder occurring in 5-10% of reproductive-aged women. Women with endometriosis have a lower body mass index (BMI) and decreased body fat compared to those without the disease, yet few studies have focused on the metabolic abnormalities in adipose tissue in women with endometriosis. Previously, we identified microRNAs that are differentially expressed in endometriosis and altered in the serum of women with the disease. Here we explore the effect of endometriosis on fat tissue and identified a role for endometriosis-related microRNAs in fat metabolism and a reduction in adipocyte stem cell number.MethodsPrimary adipocyte cells cultured from 20 patients with and without endometriosis were transfected with mimics and inhibitors of microRNAs 342-3p or Let 7b-5p to model the status of these microRNAs in endometriosis. RNA was extracted for gene expression analysis by qRT-PCR. PCNA expression was used as a marker of adipocyte proliferation. Endometriosis was induced experimentally in 9-week old female C57BL/6 mice and after 10 months fat tissue was harvested from both the subcutaneous (inguinal) and visceral (mesenteric) tissue. Adipose-derived mesenchymal stem cells in fat tissue were characterized in both endometriosis and non-endometriosis mice by FACS analysis.ResultsGene expression analysis showed that endometriosis altered the expression of Cebpa, Cebpb, Ppar-γ, leptin, adiponectin, IL-6, and HSL, which are involved in driving brown adipocyte differentiation, appetite, insulin sensitivity and fat metabolism. Each gene was regulated by an alteration in microRNA expression known to occur in endometriosis. Analysis of the stem cell content of adipose tissue in a mouse model of endometriosis demonstrated a reduced number of adipocyte stem cells.ConclusionsWe demonstrate that microRNAs Let-7b and miR-342-3p affected metabolic gene expression significantly in adipocytes of women with endometriosis. Similarly, there is a reduction in the adipose stem cell population in a mouse model of endometriosis. Taken together these data suggest that endometriosis alters BMI in part through an effect on adipocytes and fat metabolism.

Dataset Information

Unreliable numbers: error and harm induced by bad design can be reduced by better design.

Publications

Unreliable numbers: error and harm induced by bad design can be reduced by better design.

Similar Datasets

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