Metastasis of a Perianal Gland Adenocarcinoma in a Dog

A seven-year-old male Airdale was found upon clinical examination to have neoplasms in the lungs as well as on the back of the lumbar region. The grose and microscopic post mortem examination revealed the neoplasms to originate from the perianal glands and to be metastatic in the lungs, kidneys, liver, lymph nodes, and intestine

The effect of conflicting public health guidance on smokers' and vapers’ e-cigarette harm perceptions

BACKGROUND: E-cigarettes are increasingly being viewed, incorrectly, as more harmful than cigarettes. This may discourage smokers from switching to e-cigarettes. One potential explanation for these increasingly harmful attitudes is conflicting information presented in the media and online, and from public health bodies. AIMS AND METHODS: In this prospectively registered online study, we aimed to examine the impact of conflicting public health information on smokers’ and vapers’ e-cigarette harm perceptions. Daily UK smokers who do not vape (n = 334) and daily UK vapers (n = 368) were randomized to receive either: (1) a consistent harm reduction statement from two different public health bodies (Harm Reduction), (2) a consistent negative statement about e-cigarette harms from two different public health bodies (Negative), (3) a harm reduction statement from one public health body and a negative statement from another (Conflict), and (4) a statement of the risks of smoking followed by a harm reduction statement from one public health body and a negative statement from another (Smoking Risk + Conflict). Participants then answered questions regarding their perceptions of e-cigarette harm. RESULTS: The Negative condition had the highest e-cigarette harm perceptions, significantly higher than the Smoking Risk + Conflict condition (MD = 5.4, SE = 1.8, p < .016, d = 0.3 [CI 0.73 to 10.04]), which did not differ from the Conflict condition (MD = 1.5, SE = 1.8, p = .836, d = 0.1 [CI −3.14 to 6.17]). The Conflict condition differed from the Harm Reduction condition, where harm perceptions were lowest (MD = 5.4, SE = 1.8, p = .016, d = 0.3 [CI 0.74 to 10.07]). CONCLUSIONS: These findings are the first to demonstrate that, compared to harm reduction information, conflicting information increases e-cigarette harm perceptions amongst vapers, and smokers who do not vape. IMPLICATIONS: This research provides the first empirical evidence that conflicting information increases smokers’ and vapers’ e-cigarette harm perceptions, compared to harm reduction information. This may have a meaningful impact on public health as e-cigarette harm perceptions can influence subsequent smoking and vaping behavior. Conflicting information may dissuade smokers, who have the most to gain from accurate e-cigarette harm perceptions, from switching to e-cigarettes. These findings indicate that public health communications that are consensus-based can lower harm perceptions of e-cigarettes, and have the potential to reduce morbidity and mortality attributable to tobacco smoking

Dynamical frustration in ANNNI model and annealing

Zero temperature quench in the Axial Next Nearest Neighbour Ising (ANNNI) model fails to bring it to its ground state for a certain range of values of the frustration parameter $\kappa$, the ratio of the next nearest neighbour antiferromagnetic interaction strength to the nearest neighbour one. We apply several annealing methods, both classical and quantum, and observe that the behaviour of the residual energy and the order parameter depends on the value of $\kappa$ strongly. Classical or thermal annealing is found to be adequate for small values of $\kappa$. However, neither classical nor quantum annealing is effective at values of $\kappa$ close to the fully frustrated point $\kappa=0.5$, where the residual energy shows a very slow algebraic decay with the number of MCS.Comment: 6 pages,10 figures, to be published in Proceedings of " The International Workshop on Quantum annealing and other Optimization Methods

Cleaning Genotype Data from Diversity Outbred Mice.

Data cleaning is an important first step in most statistical analyses, including efforts to map the genetic loci that contribute to variation in quantitative traits. Here we illustrate approaches to quality control and cleaning of array-based genotyping data for multiparent populations (experimental crosses derived from more than two founder strains), using MegaMUGA array data from a set of 291 Diversity Outbred (DO) mice. Our approach employs data visualizations that can reveal problems at the level of individual mice or with individual SNP markers. We find that the proportion of missing genotypes for each mouse is an effective indicator of sample quality. We use microarray probe intensities for SNPs on the X and Y chromosomes to confirm the sex of each mouse, and we use the proportion of matching SNP genotypes between pairs of mice to detect sample duplicates. We use a hidden Markov model (HMM) reconstruction of the founder haplotype mosaic across each mouse genome to estimate the number of crossovers and to identify potential genotyping errors. To evaluate marker quality, we find that missing data and genotyping error rates are the most effective diagnostics. We also examine the SNP genotype frequencies with markers grouped according to their minor allele frequency in the founder strains. For markers with high apparent error rates, a scatterplot of the allele-specific probe intensities can reveal the underlying cause of incorrect genotype calls. The decision to include or exclude low-quality samples can have a significant impact on the mapping results for a given study. We find that the impact of low-quality markers on a given study is often minimal, but reporting problematic markers can improve the utility of the genotyping array across many studies

Ising model in small-world networks

The Ising model in small-world networks generated from two- and three-dimensional regular lattices has been studied. Monte Carlo simulations were carried out to characterize the ferromagnetic transition appearing in these systems. In the thermodynamic limit, the phase transition has a mean-field character for any finite value of the rewiring probability p, which measures the disorder strength of a given network. For small values of p, both the transition temperature and critical energy change with p as a power law. In the limit p -> 0, the heat capacity at the transition temperature diverges logarithmically in two-dimensional (2D) networks and as a power law in 3D.Comment: 6 pages, 7 figure

Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice.

We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects

Clinical and medication profiles stratified by household income in patients referred for diabetes care

BACKGROUND: Low income individuals with diabetes are at particularly high risk for poor health outcomes. While specialized diabetes care may help reduce this risk, it is not currently known whether there are significant clinical differences across income groups at the time of referral. The objective of this study is to determine if the clinical profiles and medication use of patients referred for diabetes care differ across income quintiles. METHODS: This cross-sectional study was conducted using a Canadian, urban, Diabetes Education Centre (DEC) database. Clinical information on the 4687 patients referred to the DEC from May 2000 – January 2002 was examined. These data were merged with 2001 Canadian census data on income. Potential differences in continuous clinical parameters across income quintiles were examined using regression models. Differences in medication use were examined using Chi square analyses. RESULTS: Multivariate regression analysis indicated that income was negatively associated with BMI (p < 0.0005) and age (p = 0.023) at time of referral. The highest income quintiles were found to have lower serum triglycerides (p = 0.011) and higher HDL-c (p = 0.008) at time of referral. No significant differences were found in HBA1C, LDL-c or duration of diabetes. The Chi square analysis of medication use revealed that despite no significant differences in HBA1C, the lowest income quintiles used more metformin (p = 0.001) and sulfonylureas (p < 0.0005) than the wealthy. Use of other therapies were similar across income groups, including lipid lowering medications. High income patients were more likely to be treated with diet alone (p < 0.0005). CONCLUSION: Our findings demonstrate that low income patients present to diabetes clinic older, heavier and with a more atherogenic lipid profile than do high income patients. Overall medication use was higher among the lower income group suggesting that differences in clinical profiles are not the result of under-treatment, thus invoking lifestyle factors as potential contributors to these findings

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence.

Recent developments allowed generating multiple high-quality \u27omics\u27 data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values

The random K-satisfiability problem: from an analytic solution to an efficient algorithm

We study the problem of satisfiability of randomly chosen clauses, each with K Boolean variables. Using the cavity method at zero temperature, we find the phase diagram for the K=3 case. We show the existence of an intermediate phase in the satisfiable region, where the proliferation of metastable states is at the origin of the slowdown of search algorithms. The fundamental order parameter introduced in the cavity method, which consists of surveys of local magnetic fields in the various possible states of the system, can be computed for one given sample. These surveys can be used to invent new types of algorithms for solving hard combinatorial optimizations problems. One such algorithm is shown here for the 3-sat problem, with very good performances.Comment: 38 pages, 13 figures; corrected typo