London : Cassell, 1898.

London : Cassell, 1902.

Edinburgh : Young J. Pentland, 1887.

London : Rebman, 1897.

London : Rebman, 1900.

London : Rebman, 1902.

London : Smith, Elder, 1881.

London : H.K. Lewis, 1891.

London : printed for the booksellers, 1828.

Halifax : Milner and Sowerby, 1856.

Halifax : Milner and Sowerby, 1860.

London : printed for A. Strahan, T. Cadell, 1790.

London : And J. Balfour, and W. Creech, Edinburgh, 1791.

London : printed for A. Strahan and T. Cadell, London, 1794.

Edinburgh : printed for Thomas Nelson, 1820.

London : printed for A. Strahan, and T. Cadell jun. and W. Davies, (successors to Mr. Cadell,) in the Strand, 1797.

London : printed for A. Strahan, T. Cadell jun. and W. Davies, 1800.

London : And J. Balfour, and W. Creech, Edinburgh, 1805.

London : printed for A. Strahan, T. Cadell and W. Davies, 1803.

Manchester : printed by S. Russell, 1806.

London : J. Johnson, 1807.

London : F.C. and J. Rivington, 1813.

Dunbar : printed by and for G. Miller, 1817.

London : printed for Knight and Lacey, 1826.

London : J. and A. Churchill, 1884.

Newcastle : printed by K. Anderson, 1812.

London : J. & A. Churchill, 1877.

Detroit, Mich. : G.S. Davis, 1889.

London : J. & A. Churchill, 1887.

London : Philadelphia, 1889.

London : Taylor and Walton, 1841.

Bath : And sold by Underwood, London, 1825.

London : printed for J. Dodsley, 1782.

London : Swan Sonnenschein, 1898.

London : J. & A. Churchill, 1873.

London : J. Churchill, 1861.

Edinburgh : Young J. Pentland, 1901.

London : H. Renshaw, 1849.

One of many possible plans if the league can resume play.

The Boston Bruins had planned on opening up their 2020-21 NHL regular season overseas against the Nashville Predators in Prague as part of the NHL Global Series, but that plan to visit the Czech Republic has now been postponed.

National Hockey League regular-season games that were scheduled for early in the 2020-21 campaign in Helsinki and Prague have been called off in the wake of the coronavirus pandemic. With concern about when or if the halted 2019-20 campaign might resume due to the deadly virus outbreak, the NHL and the NHL Players Association announced the decision to scrap the matchups with an eye to a 2021 return. "The NHLPA and the NHL remain committed to maintaining and growing our international presence," a joint statement said.

The NHL announces it is postponing the Global Series games that were scheduled to be held in the Czech Republic and Finland in the 2020-21 season.

Kerfoot discussing his first Maple Leafs season spurred a larger discussion.

Former Flyer and current Red Wings scout Mark Howe said the "open-minded" NHL is determined to finish the 2019-20 season. By Joe Fordyce

Pac-12 Networks' Ashley Adamson catches up with Oregon's "Big 3" of Sabrina Ionescu, Ruthy Hebard and Satou Sabally to hear how they're adjusting to the new world without sports while still preparing for the WNBA Draft on April 17. They also share how they're staying hungry for basketball during the hiatus.

Claudia Wehrhahn, Samuel Leonard, Abel Rodriguez, Tatiana Xifara.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1449--1478.

Abstract:
Identifying disease clusters (areas with an unusually high incidence of a particular disease) is a common problem in epidemiology and public health. We describe a Bayesian nonparametric mixture model for disease clustering that constrains clusters to be made of adjacent areal units. This is achieved by modifying the exchangeable partition probability function associated with the Ewen’s sampling distribution. We call the resulting prior the Restricted Chinese Restaurant Process, as the associated full conditional distributions resemble those associated with the standard Chinese Restaurant Process. The model is illustrated using synthetic data sets and in an application to oral cancer mortality in Germany.

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson's disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pre-trained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.

Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modelling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine-scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine-scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the size of the aggregated areas decreased. When the model was well-specified, fine-scale predictions were accurate even with small numbers of observations and large aggregated areas. Under model misspecification predictive performance was significantly worse for large aggregated areas but remained high when response data was aggregated over smaller regions. Cross-validation correlation on the aggregate level was a moderately good predictor of fine-scale predictive performance. While the simulations are unlikely to capture the nuances of real-life response data, this study gives insight into the effectiveness of disaggregation regression in different contexts.

Broadcasting Health and Disease: Bodies, markets and television, 1950s–1980s An ERC BodyCapital international conference to be held at the Wellcome Trust, 19–21 February 2018 In the television age, health and the body have been broadcasted in many ways: in short… Continue reading