On "Sexual contacts and epidemic thresholds," models and inference for Sexual partnership distributions

Recent work has focused attention on statistical inference for the population distribution of the number of sexual partners based on survey data. The characteristics of these distributions are of interest as components of mathematical models for the transmission dynamics of sexually-transmitted diseases (STDs). Such information can be used both to calibrate theoretical models, to make predictions for real populations, and as a tool for guiding public health policy. Our previous work on this subject has developed likelihood-based statistical methods for inference that allow for low-dimensional, semi-parametric models. Inference has been based on several proposed stochastic process models for the formation of sexual partnership networks. We have also developed model selection criteria to choose between competing models, and assessed the fit of different models to three populations: Uganda, Sweden, and the USA. Throughout this work, we have emphasized the correct assessment of the uncertainty of the estimates based on the data analyzed. We have also widened the question of interest to the limitations of inferences from such data, and the utility of degree-based epidemiological models more generally. In this paper we address further statistical issues that are important in this area, and a number of confusions that have arisen in interpreting our work. In particular, we consider the use of cumulative lifetime partner distributions, heaping and other issues raised by Liljeros et al. in a recent working paper.Comment: 22 pages, 5 figures in linked working pape

Cancer immunology and canine malignant melanoma: a comparative review

Oral canine malignant melanoma (CMM) is a spontaneously occurring aggressive tumour with relatively few medical treatment options, which provides a suitable model for the disease in humans. Historically, multiple immunotherapeutic strategies aimed at provoking both innate and adaptive anti-tumour immune responses have been published with varying levels of activity against CMM. Recently, a plasmid DNA vaccine expressing human tyrosinase has been licensed for the adjunct treatment of oral CMM. This article reviews the immunological similarities between CMM and the human counterpart; mechanisms by which tumours evade the immune system; reasons why melanoma is an attractive target for immunotherapy; the premise of whole cell, dendritic cell (DC), viral and DNA vaccination strategies alongside preliminary clinical results in dogs. Current “gold standard” treatments for advanced human malignant melanoma are evolving quickly with remarkable results being achieved following the introduction of immune checkpoint blockade and adoptively transferred cell therapies. The rapidly expanding field of cancer immunology and immunotherapeutics means that rational targeting of this disease in both species should enhance treatment outcomes in veterinary and human clinics

An Extended Star Formation History for the Galactic Center from Hubble Space Telescope/NICMOS Observations

We present Hubble Space Telescope (HST) Near-Infrared Camera and Multiobject Spectrometer (NICMOS) observations as evidence that continuous star formation has created much of the central stellar cusp of the Galaxy. The data are the deepest ever obtained for a Galactic Center (GC) population, being $>$50% complete for \mnk$<19.3$, or initial stellar masses $\gtrsim$2 \Msun. We use Geneva and Padova stellar evolution models to produce synthetic luminosity functions for burst and continuous star formation scenarios, finding that the observations are fit best by continuous star formation at a rate that is consistent with the recent star formation activity that produced the three massive young clusters in the central 50 \pc. Further, it is not possible to fit the observations with ancient burst models, such as would be appropriate for an old population like that in Baade's Window or NGC6528

Ordinal Probit Functional Regression Models with Application to Computer-Use Behavior in Rhesus Monkeys

Research in functional regression has made great strides in expanding to non-Gaussian functional outcomes, however the exploration of ordinal functional outcomes remains limited. Motivated by a study of computer-use behavior in rhesus macaques (\emph{Macaca mulatta}), we introduce the Ordinal Probit Functional Regression Model or OPFRM to perform ordinal function-on-scalar regression. The OPFRM is flexibly formulated to allow for the choice of different basis functions including penalized B-splines, wavelets, and O'Sullivan splines. We demonstrate the operating characteristics of the model in simulation using a variety of underlying covariance patterns showing the model performs reasonably well in estimation under multiple basis functions. We also present and compare two approaches for conducting posterior inference showing that joint credible intervals tend to out perform point-wise credible. Finally, in application, we determine demographic factors associated with the monkeys' computer use over the course of a year and provide a brief analysis of the findings

A statnet Tutorial

The statnet suite of R packages contains a wide range of functionality for the statistical analysis of social networks, including the implementation of exponential-family random graph (ERG) models. In this paper we illustrate some of the functionality of statnet through a tutorial analysis of a friendship network of 1,461 adolescents.

ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks

We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and inter-related, tasks involving exponential-family random graph models (ERGMs): estimation, simulation, and goodness of fit. More precisely, ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets from a fitted ERGM using Markov chain Monte Carlo; and assessing how well a fitted ERGM does at capturing characteristics of a particular network data set.

statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data

statnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM). The components of the package provide a comprehensive framework for ERGM-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed and robustness.

Nuclear imaging in the diagnosis of primary aldosteronism.

PURPOSE OF REVIEW: Primary aldosteronism is increasingly recognized as a common secondary cause of hypertension. Successful demonstration of a unilateral cause (e.g. a classical 'Conn's adenoma') offers the potential for curative adrenalectomy. Adrenal vein sampling (AVS), in conjunction with cross-sectional imaging, remains the 'gold standard' for distinguishing unilateral and bilateral disease, but is technically demanding and frequently unsuccessful or inconclusive. As such, alternative strategies for lateralization, including nuclear medicine techniques, are being developed and brought into clinical practice. RECENT FINDINGS: Metomidate, a potent ligand of CYP11B1 and CYP11B2, can be C11H3-labelled as a PET tracer and has been shown to offer a rapid noninvasive alternative to AVS for localizing unilateral aldosterone-producing adenomas. SUMMARY: Increasing experience with 11C-metomidate PET-CT supports its use as an adjunct to AVS when this has failed, is ambiguous, or cannot be undertaken.A.S.P. and M.G. are supported by the National Institute for Health Research Cambridge Biomedical Research Centre. M.J.B. is a National Institute of Health Research Senior Investigator.This is the final published version. It first appeared at http://journals.lww.com/co-endocrinology/Fulltext/2015/06000/Nuclear_imaging_in_the_diagnosis_of_primary.3.aspx

HST/NICMOS Observations of Massive Stellar Clusters Near the Galactic Center

We report Hubble Space Telescope (HST) Near-infrared Camera and Multi-object Spectrometer (NICMOS) observations of the Arches and Quintuplet clusters, two extraordinary young clusters near the Galactic Center. For the first time, we have identified main sequence stars in the Galactic Center with initial masses well below 10 Msun. We present the first determination of the initial mass function (IMF) for any population in the Galactic Center, finding an IMF slope which is significantly more positive (Gamma approx -0.65) than the average for young clusters elsewhere in the Galaxy (Gamma approx -1.4). The apparent turnoffs in the color-magnitude diagrams suggest cluster ages which are consistent with the ages implied by the mixture of spectral types in the clusters; we find tau(age) approx 2+/-1 Myr for the Arches cluster, and tau(age) approx 4+/-1 Myr for the Quintuplet. We estimate total cluster masses by adding the masses of observed stars down to the 50% completeness limit, and then extrapolating down to a lower mass cutoff of 1 Msun. Using this method, we find > 10^4 Msun for the total mass of the Arches cluster. Such a determination for the Quintuplet cluster is complicated by the double-valued mass-magnitude relationship for clusters with ages > 3 Myr. We find a lower limit of 6300 Msun for the total cluster mass, and suggest a best estimate of twice this value which accounts for the outlying members of the cluster. Both clusters have masses which place them as the two most massive clusters in the Galaxy.Comment: accepted by ApJ higher resolution versions of figures 1 and 2 can be found at: ftp://quintup.astro.ucla.edu/nicmos1

Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects

Exponential-family random graph models (ERGMs) represent the processes that govern the formation of links in networks through the terms selected by the user. The terms specify network statistics that are sufficient to represent the probability distribution over the space of networks of that size. Many classes of statistics can be used. In this article we describe the classes of statistics that are currently available in the ergm package. We also describe means for controlling the Markov chain Monte Carlo (MCMC) algorithm that the package uses for estimation. These controls affect either the proposal distribution on the sample space used by the underlying Metropolis-Hastings algorithm or the constraints on the sample space itself. Finally, we describe various other arguments to core functions of the ergm package