Patient safety in dentistry: development of a candidate 'never event' list for primary care

Introduction The 'never event' concept is often used in secondary care and refers to an agreed list of patient safety incidents that 'should not happen if the necessary preventative measures are in place'. Such an intervention may raise awareness of patient safety issues and inform team learning and system improvements in primary care dentistry. Objective To identify and develop a candidate never event list for primary care dentistry. Methods A literature review, eight workshops with dental practitioners and a modified Delphi with 'expert' groups were used to identify and agree candidate never events. Results Two-hundred and fifty dental practitioners suggested 507 never events, reduced to 27 distinct possibilities grouped across seven themes. Most frequently occurring themes were: 'checking medical history and prescribing' (119, 23.5%) and 'infection control and decontamination' (71, 14%). 'Experts' endorsed nine candidate never event statements with one graded as 'extreme risk' (failure to check past medical history) and four as 'high risk' (for example, extracting wrong tooth). Conclusion Consensus on a preliminary list of never events was developed. This is the first known attempt to develop this approach and an important step in determining its value to patient safety. Further work is necessary to develop the utility of this method

Nevoid basal cell carcinoma syndrome (Gorlin syndrome)

Nevoid basal cell carcinoma syndrome (NBCCS), also known as Gorlin syndrome, is a hereditary condition characterized by a wide range of developmental abnormalities and a predisposition to neoplasms

The role of hosts, plasmids and environment in determining plasmid transfer rates: a meta-analysis

Plasmids transfer at highly variable rates which spread over 10 orders of magnitude. While rates have been measured for decades and it is known that the rates are affected by biotic and abiotic factors, it is unclear how and to what extent these factors determine the rates of transfer. We performed a meta-analysis of 1224 published transfer rates from 33 papers (filtered to 612 transfer rates) to assess this variation. Over three quarters of the variation can be predicted, with plasmid repression and media type (solid versus liquid) identified as general variables explaining the most variation. Of the host and plasmid identities, identity of the recipient bacterium explained the most variation, up to 34% in some models, and more than any other explanatory variable. These results emphasize the role of the recipient in determining the rate of transfer, and show an improved range of transfer values and their correlates that can be used in future when modeling plasmid persistence

History and geography in primary schools A framework for the whole school

SIGLEAvailable from British Library Lending Division - LD:84/31038(History) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

ImaGene: a convolutional neural network to quantify natural selection from genomic data

BACKGROUND: The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework. As sites targeted by natural selection are likely to harbor important functionalities for the carrier, the identification of selection signatures in the genome has the potential to unveil the genetic mechanisms underpinning human phenotypes. Popular methods of detecting such signals rely on compressing genomic information into summary statistics, resulting in the loss of information. Furthermore, few methods are able to quantify the strength of selection. Here we explored the use of deep learning in evolutionary biology and implemented a program, called ImaGene, to apply convolutional neural networks on population genomic data for the detection and quantification of natural selection. RESULTS: ImaGene enables genomic information from multiple individuals to be represented as abstract images. Each image is created by stacking aligned genomic data and encoding distinct alleles into separate colors. To detect and quantify signatures of positive selection, ImaGene implements a convolutional neural network which is trained using simulations. We show how the method implemented in ImaGene can be affected by data manipulation and learning strategies. In particular, we show how sorting images by row and column leads to accurate predictions. We also demonstrate how the misspecification of the correct demographic model for producing training data can influence the quantification of positive selection. We finally illustrate an approach to estimate the selection coefficient, a continuous variable, using multiclass classification techniques. CONCLUSIONS: While the use of deep learning in evolutionary genomics is in its infancy, here we demonstrated its potential to detect informative patterns from large-scale genomic data. We implemented methods to process genomic data for deep learning in a user-friendly program called ImaGene. The joint inference of the evolutionary history of mutations and their functional impact will facilitate mapping studies and provide novel insights into the molecular mechanisms associated with human phenotypes

The Badger, Volume 3, Issue 6, December 12, 1966

The Badger, Volume 3, Issue 6, includes: Allen Wright agrees with BUSA criticism from Badger reporters; Mr. G. E. Dirks, a lecturer in the Department of Politics at Brock, criticizes Canadians for their “holier than thou” attitude when discussing American involvement in Vietnam; Michael A. Charles criticizes students for not paying attention to student governance, prompted by the defeat of a resolution supporting Red China’s admittance to the United Nations General Assembly and Security Council; McGill Daily EIC Sandy Gage is reinstated after overwhelming student vote