Magnetic fields around evolved stars: further observations of H2_2O maser polarization

We aim to detect the magnetic field and infer its properties around four AGB stars using H$_2$O maser observations. The sample we observed consists of the following sources: the semi-regular variable RT Vir and the Mira variables AP Lyn, IK Tau, and IRC+60370. We observed the 6$_{1,6}-5_{2,3}$ H$_2$O maser rotational transition, in full-polarization mode, to determine its linear and circular polarization. Based on the Zeeman effect, one can infer the properties of the magnetic field from the maser polarization analysis. We detected a total of 238 maser features, in three of the four observed sources. No masers were found toward AP Lyn. The observed masers are all located between 2.4 and 53.0 AU from the stars. Linear and circular polarization was found in 18 and 11 maser features, respectively. We more than doubled the number of AGB stars in which magnetic field has been detected from H$_2$O maser polarization, as our results confirm the presence of fields around IK Tau, RT Vir and IRC+60370. The strength of the field along the line of sight is found to be between 47 and 331 mG in the H$_2$O maser region. Extrapolating this result to the surface of the stars, assuming a toroidal field ($\propto$ r$^{-1}$), we find magnetic fields of 0.3-6.9 G on the stellar surfaces. If, instead of a toroidal field, we assume a poloidal field ($\propto$ r$^{-2}$), then the extrapolated magnetic field strength on the stellar surfaces are in the range between 2.2 and $\sim$115 G. Finally, if a dipole field ($\propto$ r$^{-3}$) is assumed, the field strength on the surface of the star is found to be between 15.8 and $\sim$1945 G. The magnetic energy of our sources is higher than the thermal and kinetic energy in the H$_2$O maser region of this class of objects. This leads us to conclude that, indeed, magnetic fields probably play an important role in shaping the outflows of evolved stars. (abridged)Comment: 15 pages, 5 figures, 7 tables. Accepted for publication in A&

Towards integrated control of varroa: effect of variation in hygienic behaviour among honey bee colonies on mite population increase and deformed wing virus incidence

Hygienic behaviour in the honey bee, Apis mellifera, is the uncapping and removal of dead, diseased or infected brood from sealed cells by worker bees. We determined the effect of hygienic behaviour on varroa population growth and incidence of deformed wing virus (DWV), which can be transmitted by varroa. We treated 42 broodless honey bee colonies with oxalic acid in early January 2013 to reduce varroa populations to low levels, which we quantified by extracting mites from a sample of worker bees. We quantified varroa levels, again when the colonies were broodless, 48 weeks later. During the summer the hygienic behaviour in each colony was quantified four times using the Freeze Killed Brood (FKB) removal assay, and ranged from 27.5 % to 100 %. Varroa population increased greatly over the season, and there was a significant negative correlation between varroa increase and FKB removal. This was entirely due to fully hygienic colonies with >95 % FKB having only 43 % of the varroa build up of the less hygienic colonies.None of the 14 colonies with >80 % FKB removal had overt symptoms of DWV, whilst 36 % of the less hygienic colonies did. Higher levels of FKB removal also correlated significantly with lower numbers of DWV RNA copies in worker bees, but not in varroa mites. On average, fully hygienic colonies had c. 10,000 times less viral RNA than less hygienic colonies

Utility of B-type natriuretic peptide in predicting medium-term mortality in patients undergoing major non-cardiac surgery

We assessed the ability of pre-operative B-type natriuretic peptide (BNP) levels to predict medium-term mortality in patients undergoing major noncardiac surgery. During a median 654 days follow-up 33 patients from a total cohort of 204 patients (16%) died. The optimal cut-off in this cohort, determined using a receiver operating characteristic curve, was >35pg.mL-1. This was associated with a 3.47-fold increase in the hazard of death (p=0.001) and had a sensitivity of 70% and a specificity of 68% for this outcome. These findings extend recent work demonstrating that BNP levels obtained before major noncardiac surgery can be used to predict peri-operative morbidity, and indicate that they also forecast medium-term mortality.This work was supported by a grant from TENOVUS Scotland. The Health Services Research Unit is core-funded by the Chief Scientists Office of the Scottish Executive Health Department.Peer reviewedAuthor versio

EnsCat: clustering of categorical data via ensembling

Background: Clustering is a widely used collection of unsupervised learning techniques for identifying natural classes within a data set. It is often used in bioinformatics to infer population substructure. Genomic data are often categorical and high dimensional, e.g., long sequences of nucleotides. This makes inference challenging: The distance metric is often not well-defined on categorical data; running time for computations using high dimensional data can be considerable; and the Curse of Dimensionality often impedes the interpretation of the results. Up to the present, however, the literature and software addressing clustering for categorical data has not yet led to a standard approach. Results: We present software for an ensemble method that performs well in comparison with other methods regardless of the dimensionality of the data. In an ensemble method a variety of instantiations of a statistical object are found and then combined into a consensus value. It has been known for decades that ensembling generally outperforms the components that comprise it in many settings. Here, we apply this ensembling principle to clustering. We begin by generating many hierarchical clusterings with different clustering sizes. When the dimension of the data is high, we also randomly select subspaces also of variable size, to generate clusterings. Then, we combine these clusterings into a single membership matrix and use this to obtain a new, ensembled dissimilarity matrix using Hamming distance. Conclusions: Ensemble clustering, as implemented in R and called EnsCat, gives more clearly separated clusters than other clustering techniques for categorical data. The latest version with manual and examples is available at https://github.com/jlp2duke/EnsCat

Spatial modeling of individual-level infectious disease transmission: Tuberculosis data in Manitoba, Canada

Geographically dependent individual level models (GD-ILMs) are a class of statistical models that can be used to study the spread of infectious disease through a population in discrete-time in which covariates can be measured both at individual and area levels. The typical ILMs to illustrate spatial data are based on the distance between susceptible and infectious individuals. A key feature of GD-ILMs is that they take into account the spatial location of the individuals in addition to the distance between susceptible and infectious individuals. As a motivation of this article, we consider tuberculosis (TB) data which is an infectious disease which can be transmitted through individuals. It is also known that certain areas/demographics/communities have higher prevalent of TB (see Section 4 for more details). It is also of interest of policy makers to identify those areas with higher infectivity rate of TB for possible preventions. Therefore, we need to analyze this data properly to address those concerns. In this article, the expectation conditional maximization algorithm is proposed for estimating the parameters of GD-ILMs to be able to predict the areas with the highest average infectivity rates of TB. We also evaluate the performance of our proposed approach through some simulations. Our simulation results indicate that the proposed method provides reliable estimates of parameters which confirms accuracy of the infectivity rates

Modification of classical electron transport due to collisions between electrons and fast ions

A Fokker-Planck model for the interaction of fast ions with the thermal electrons in a quasi-neutral plasma is developed. When the fast ion population has a net flux (i.e. the distribution of the fast ions is anisotropic in velocity space) the electron distribution function is significantly perturbed from Maxwellian by collisions with the fast ions, even if the fast ion density is orders of magnitude smaller than the electron density. The Fokker-Planck model is used to derive classical electron transport equations (a generalized Ohm's law and a heat flow equation) that include the effects of the electron-fast ion collisions. It is found that these collisions result in a current term in the transport equations which can be significant even when total current is zero. The new transport equations are analyzed in the context of a number of scenarios including $\alpha$ particle heating in ICF and MIF plasmas and ion beam heating of dense plasmas

On the impact of non-IID data on the performance and fairness of differentially private federated learning

Federated Learning enables distributed data holders to train a shared machine learning model on their collective data. It provides some measure of privacy by not requiring the data be pooled and centralized but still has been shown to be vulnerable to adversarial attacks. Differential Privacy provides rigorous guarantees and sufficient protection against adversarial attacks and has been widely employed in recent years to perform privacy preserving machine learning. One common trait in many of recent methods on federated learning and federated differentially private learning is the assumption of IID data, which in real world scenarios most certainly does not hold true. In this work, we empirically investigate the effect of non-IID data on node level on federated, differentially private, deep learning. We show the non-IID data to have a negative impact on both performance and fairness of the trained model and discuss the trade off between privacy, utility and fairness. Our results highlight the limits of common federated learning algorithms in a differentially private setting to provide robust, reliable results across underrepresented groups. </p