Diabetes and breast cancer risk: a meta-analysis.

BACKGROUND: The potential of an increased risk of breast cancer in women with diabetes has been the subject of a great deal of recent research. METHODS: A meta-analysis was undertaken using a random effects model to investigate the association between diabetes and breast cancer risk. RESULTS: Thirty-nine independent risk estimates were available from observational epidemiological studies. The summary relative risk (SRR) for breast cancer in women with diabetes was 1.27 (95% confidence interval (CI), 1.16-1.39) with no evidence of publication bias. Prospective studies showed a lower risk (SRR 1.23 (95% CI, 1.12-1.35)) than retrospective studies (SRR 1.36 (95% CI, 1.13-1.63)). Type 1 diabetes, or diabetes in pre-menopausal women, were not associated with risk of breast cancer (SRR 1.00 (95% CI, 0.74-1.35) and SRR 0.86 (95% CI, 0.66-1.12), respectively). Studies adjusting for body mass index (BMI) showed lower estimates (SRR 1.16 (95% CI, 1.08-1.24)) as compared with those studies that were not adjusted for BMI (SRR 1.33 (95% CI, 1.18-1.51)). CONCLUSION: The risk of breast cancer in women with type 2 diabetes is increased by 27%, a figure that decreased to 16% after adjustment for BMI. No increased risk was seen for women at pre-menopausal ages or with type 1 diabetes.

Securing the data economy: translating privacy and enacting security in the development of DataSHIELD.

Contemporary bioscience is seeing the emergence of a new data economy: with data as its fundamental unit of exchange. While sharing data within this new 'economy' provides many potential advantages, the sharing of individual data raises important social and ethical concerns. We examine ongoing development of one technology, DataSHIELD, which appears to elide privacy concerns about sharing data by enabling shared analysis while not actually sharing any individual-level data. We combine presentation of the development of DataSHIELD with presentation of an ethnographic study of a workshop to test the technology. DataSHIELD produced an application of the norm of privacy that was practical, flexible and operationalizable in researchers' everyday activities, and one which fulfilled the requirements of ethics committees. We demonstrated that an analysis run via DataSHIELD could precisely replicate results produced by a standard analysis where all data are physically pooled and analyzed together. In developing DataSHIELD, the ethical concept of privacy was transformed into an issue of security. Development of DataSHIELD was based on social practices as well as scientific and ethical motivations. Therefore, the 'success' of DataSHIELD would, likewise, be dependent on more than just the mathematics and the security of the technology.

Competitive Hebbian learning and the hippocampal place cell system: modeling the interaction of visual and path integration cues.

The hippocampus has long been thought essential for implementing a cognitive map of the environment. However, almost 30 years since place cells were found in rodent hippocampal field CA1, it is still unclear how such an allocentric representation arises from an ego-centrically perceived world. By means of a competitive Hebbian learning rule responsible for coding visual and path integration cues, our model is able to explain the diversity of place cell responses observed in a large set of electrophysiological experiments with a single fixed set of parameters. Experiments included changes observed in place fields due to exploration of a new environment, darkness, retrosplenial cortex inactivation, and removal, rotation, and permutation of landmarks. To code for visual cues for each landmark, we defined two perceptual schemas representing landmark bearing and distance information over a linear array of cells. The information conveyed by the perceptual schemas is further processed through a network of adaptive layers which ultimately modulate the resulting activity of our simulated place cells. In path integration terms, our system is able to dynamically remap a bump of activity coding for the displacement of the animal in relation to an environmental anchor. We hypothesize that path integration information is computed in the rodent posterior parietal cortex and conveyed to the hippocampus where, together with visual information, it modulates place cell activity. The resulting network yields a more direct treatment of partial remapping of place fields than other models. In so doing, it makes new predictions regarding the nature of the interaction between visual and path integration cues during new learning and when the system is challenged with environmental changes.