Variational Supertrees for Bayesian Phylogenetics.

Bayesian phylogenetic inference is powerful but computationally intensive. Researchers may find themselves with two phylogenetic posteriors on overlapping data sets and may wish to approximate a combined result without having to re-run potentially expensive Markov chains on the combined data set. This raises the question: given overlapping subsets of a set of taxa (e.g. species or virus samples), and given posterior distributions on phylogenetic tree topologies for each of these taxon sets, how can we optimize a probability distribution on phylogenetic tree topologies for the entire taxon set? In this paper we develop a variational approach to this problem and demonstrate its effectiveness. Specifically, we develop an algorithm to find a suitable support of the variational tree topology distribution on the entire taxon set, as well as a gradient-descent algorithm to minimize the divergence from the restrictions of the variational distribution to each of the given per-subset probability distributions, in an effort to approximate the posterior distribution on the entire taxon set.

Pathways by which case managers' match support influences youth mentoring outcomes: Testing the systemic model of youth mentoring.

Keller's systemic model of youth mentoring posits there are multiple pathways through which all stakeholders in the youth mentoring process, including the program staff who support the match (or case managers), influence youth outcomes. This study examines case managers' direct and indirect contributions to match outcomes and tests how transitive interactions facilitate a theorized sequence of mentoring interactions to effect greater closeness and length, specifically in nontargeted mentoring programs. A structural equations model of case manager contributions to match outcomes was tested using data from 758 mentor-mentee matches, supported by 73 case managers across seven mentoring agencies. Results reveal direct effects of mentor-reported match support quality on match length and indirect influences on match length through increasing youth-centeredness, goal-focused orientation, and closeness. The findings confirm the presence of multiple pathways of influence, including indirect effects on outcomes via transitive interactions in match support that scaffold youth-centeredness and goal-focused interactions in the match. Findings also suggest supervisors' evaluations of case managers may provide little information about how match support influences the nature of mentor-mentee interactions.

Estimating effective population size changes from preferentially sampled genetic sequences.

Coalescent theory combined with statistical modeling allows us to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. When sequences are sampled serially through time and the distribution of the sampling times depends on the effective population size, explicit statistical modeling of sampling times improves population size estimation. Previous work assumed that the genealogy relating sampled sequences is known and modeled sampling times as an inhomogeneous Poisson process with log-intensity equal to a linear function of the log-transformed effective population size. We improve this approach in two ways. First, we extend the method to allow for joint Bayesian estimation of the genealogy, effective population size trajectory, and other model parameters. Next, we improve the sampling time model by incorporating additional sources of information in the form of time-varying covariates. We validate our new modeling framework using a simulation study and apply our new methodology to analyses of population dynamics of seasonal influenza and to the recent Ebola virus outbreak in West Africa.

Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference.

Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting a coalescent model for the sampled individuals' genealogy and then integrating over all possible genealogies via Monte Carlo or, less efficiently, by conditioning on one genealogy estimated from the sequence data. However, when analyzing sequences sampled serially through time, current methods implicitly assume either that sampling times are fixed deterministically by the data collection protocol or that their distribution does not depend on the size of the population. Through simulation, we first show that, when sampling times do probabilistically depend on effective population size, estimation methods may be systematically biased. To correct for this deficiency, we propose a new model that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size. We demonstrate that in the presence of preferential sampling our new model not only reduces bias, but also improves estimation precision. Finally, we compare the performance of the currently used phylodynamic methods with our proposed model through clinically-relevant, seasonal human influenza examples.

An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics.

MOTIVATION: The field of phylodynamics focuses on the problem of reconstructing population size dynamics over time using current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g. influenza virus. Phylodynamic inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. Although this approach is quite powerful, large datasets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand inferential methods with relatively low computational cost. RESULTS: To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function, we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm. AVAILABILITY AND IMPLEMENTATION: The R code for all simulation studies and real data analysis conducted in this article are publicly available at http://www.ics.uci.edu/∼slan/lanzi/CODES.html and in the R package phylodyn available at https://github.com/mdkarcher/phylodyn. CONTACT: S.Lan@warwick.ac.uk or babaks@uci.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

How Trustworthy Is Your Tree? Bayesian Phylogenetic Effective Sample Size Through the Lens of Monte Carlo Error.

Bayesian inference is a popular and widely-used approach to infer phylogenies (evolutionary trees). However, despite decades of widespread application, it remains difficult to judge how well a given Bayesian Markov chain Monte Carlo (MCMC) run explores the space of phylogenetic trees. In this paper, we investigate the Monte Carlo error of phylogenies, focusing on high-dimensional summaries of the posterior distribution, including variability in estimated edge/branch (known in phylogenetics as "split") probabilities and tree probabilities, and variability in the estimated summary tree. Specifically, we ask if there is any measure of effective sample size (ESS) applicable to phylogenetic trees which is capable of capturing the Monte Carlo error of these three summary measures. We find that there are some ESS measures capable of capturing the error inherent in using MCMC samples to approximate the posterior distributions on phylogenies. We term these tree ESS measures, and identify a set of three which are useful in practice for assessing the Monte Carlo error. Lastly, we present visualization tools that can improve comparisons between multiple independent MCMC runs by accounting for the Monte Carlo error present in each chain. Our results indicate that common post-MCMC workflows are insufficient to capture the inherent Monte Carlo error of the tree, and highlight the need for both within-chain mixing and between-chain convergence assessments.

Shine a light: Under-ice light and its ecological implications in a changing Arctic Ocean.

The Arctic marine ecosystem is shaped by the seasonality of the solar cycle, spanning from 24-h light at the sea surface in summer to 24-h darkness in winter. The amount of light available for under-ice ecosystems is the result of different physical and biological processes that affect its path through atmosphere, snow, sea ice and water. In this article, we review the present state of knowledge of the abiotic (clouds, sea ice, snow, suspended matter) and biotic (sea ice algae and phytoplankton) controls on the underwater light field. We focus on how the available light affects the seasonal cycle of primary production (sympagic and pelagic) and discuss the sensitivity of ecosystems to changes in the light field based on model simulations. Lastly, we discuss predicted future changes in under-ice light as a consequence of climate change and their potential ecological implications, with the aim of providing a guide for future research.

A multicultural assessment of adolescent connectedness: testing measurement invariance across gender and ethnicity.

Counselors, psychologists, and evaluators of intervention programs for youth increasingly view the promotion of connectedness as an important intervention outcome. When evaluating these programs, researchers frequently test whether the treatment effects differ across gender and ethnic or racial groups. Doing so necessitates the availability of culturally and gender-invariant measures. We used the Hemingway: Measure of Adolescent Connectedness to estimate the factor structure invariance and equality of means across gender and 3 racial/ethnic groups with a large sample of middle school adolescents. From a practical perspective, the 10-scale model suggested factor structure invariance across gender and racial or ethnic (i.e., African American, Caucasian, and Latina/o) groups of adolescents. However, tests for partial invariance revealed some group difference on the factor loadings and intercepts between gender and ethnic/racial groups. When testing for mean equivalence, girls reported higher connectedness to friends, siblings, school, peers, teachers, and reading but lower connectedness to their neighborhoods. Caucasians reported higher connectedness to their neighborhoods and friends but lower connectedness to siblings than African Americans and Latinos. African Americans reported the highest connectedness to self (present and future) but lowest connectedness to teachers. Latinos reported the lowest connectedness to reading, self-in-the-present, and self-in-the-future. Overall, this study reveals racial/ethnic and gender mean differences on several connectedness subscales and suggests the Hemingway subscales are, from a practical perspective, invariant across gender and ethnicity and therefore appropriate for most assessment and evaluation purposes.

Youth mentoring with a balanced focus, shared purpose, and collaborative interactions.

This opening article defines the ways in which three mentoring interaction elements--focus, purpose, and authorship--distinguish between effective and ineffective mentoring relationship styles. The framework described can help mentors better understand the difference between prescriptive and instrumental styles and differentiate laissez-faire from developmental mentoring. It also reveals unique ways for program staff to develop training materials and for researchers to better study mentoring activities. The authors suggest that being able to articulate the importance of focus, purpose, and authorship is critical for shaping program practices, designing relevant research, and guiding program evaluations.

"I dunno, what do you wanna do?": testing a framework to guide mentor training and activity selection.

Whether relational or goal-directed interactions are most useful in youth mentoring has been frequently debated, but until recently, little work had been done to understand how such interactions manifest to create viable relationship styles. The authors' findings in the first study they explore in this article support Karcher and Nakkula's assertion that relational and goal-directed interactions are distinct. The authors found that both made significant contributions to relationship quality, but for children and preadolescents, relational interactions appeared to be more strongly associated with the quality of the mentoring relationship than did goal-oriented interactions. Karcher and Nakkula further suggest that it is when the focus of interactions (relational and goal-directed) emerges through collaborative dialogue that the strongest relationship styles emerge. In their second study, the authors found that cross-age peer mentoring (school-based) matches that selected their activities collaboratively had higher-quality mentoring relationships (mentor and youth reported) than those matches whose activities were typically selected unilaterally by either the program or the mentee alone.

Arctic Climate Change, Economy and Society (ACCESS): Integrated perspectives.

This introduction to the special issue presents an overview of the wide range of results produced during the European Union project Arctic Climate Change, Economy and Society (ACCESS). This project assessed the main impacts of climate change on Arctic Ocean's geophysical variables and how these impending changes could be expected to impact directly and indirectly on socio-economic activities like transportation, marine sea food production and resource exploitation. Related governance issues were examined. These results were used to develop several management tools that can live on beyond ACCESS. In this article, we synthesize most of the project results in the form of tentative responses to questions raised during the project. By doing so, we put the findings of the project in a broader perspective and introduce the contributions made in the different articles published in this special issue.

phylodyn: an R package for phylodynamic simulation and inference.

We introduce phylodyn, an r package for phylodynamic analysis based on gene genealogies. The package's main functionality is Bayesian nonparametric estimation of effective population size fluctuations over time. Our implementation includes several Markov chain Monte Carlo-based methods and an integrated nested Laplace approximation-based approach for phylodynamic inference that have been developed in recent years. Genealogical data describe the timed ancestral relationships of individuals sampled from a population of interest. Here, individuals are assumed to be sampled at the same point in time (isochronous sampling) or at different points in time (heterochronous sampling); in addition, sampling events can be modelled with preferential sampling, which means that the intensity of sampling events is allowed to depend on the effective population size trajectory. We assume the coalescent and the sequentially Markov coalescent processes as generative models of genealogies. We include several coalescent simulation functions that are useful for testing our phylodynamics methods via simulation studies. We compare the performance and outputs of various methods implemented in phylodyn and outline their strengths and weaknesses. r package phylodyn is available at https://github.com/mdkarcher/phylodyn.

Future sea ice conditions and weather forecasts in the Arctic: Implications for Arctic shipping.

The ability to forecast sea ice (both extent and thickness) and weather conditions are the major factors when it comes to safe marine transportation in the Arctic Ocean. This paper presents findings focusing on sea ice and weather prediction in the Arctic Ocean for navigation purposes, in particular along the Northeast Passage. Based on comparison with the observed sea ice concentrations for validation, the best performing Earth system models from the Intergovernmental Panel on Climate Change (IPCC) program (CMIP5-Coupled Model Intercomparison Project phase 5) were selected to provide ranges of potential future sea ice conditions. Our results showed that, despite a general tendency toward less sea ice cover in summer, internal variability will still be large and shipping along the Northeast Passage might still be hampered by sea ice blocking narrow passages. This will make sea ice forecasts on shorter time and space scales and Arctic weather prediction even more important.

Arctic pathways of Pacific Water: Arctic Ocean Model Intercomparison experiments.

Pacific Water (PW) enters the Arctic Ocean through Bering Strait and brings in heat, fresh water, and nutrients from the northern Bering Sea. The circulation of PW in the central Arctic Ocean is only partially understood due to the lack of observations. In this paper, pathways of PW are investigated using simulations with six state-of-the art regional and global Ocean General Circulation Models (OGCMs). In the simulations, PW is tracked by a passive tracer, released in Bering Strait. Simulated PW spreads from the Bering Strait region in three major branches. One of them starts in the Barrow Canyon, bringing PW along the continental slope of Alaska into the Canadian Straits and then into Baffin Bay. The second begins in the vicinity of the Herald Canyon and transports PW along the continental slope of the East Siberian Sea into the Transpolar Drift, and then through Fram Strait and the Greenland Sea. The third branch begins near the Herald Shoal and the central Chukchi shelf and brings PW into the Beaufort Gyre. In the models, the wind, acting via Ekman pumping, drives the seasonal and interannual variability of PW in the Canadian Basin of the Arctic Ocean. The wind affects the simulated PW pathways by changing the vertical shear of the relative vorticity of the ocean flow in the Canada Basin.

Optimisation of sampling for the temporal monitoring of technetium-99 in the Arctic marine environment.

Monitoring of the marine environment for radioactivity, for both radiological protection and oceanographic purposes, remains an expensive and labour intensive activity due to the large sample volumes needed and the complex and lengthy analytical procedures required to measure low levels of contamination. Because of this, some consideration must be given to the design of sampling plans to ensure effective and efficient sampling that can be defended on the basis of scientific rationale. This article tests the hypothesis that geostatistical techniques may prove of use in the optimisation and design of sampling regimes for the monitoring of temporal fluctuations in the levels of technetium at a location in the Norwegian Arctic marine environment. The level of temporal correlation exhibited by two relevant time series was investigated and the information used to observe the effect of sampling frequency on the production of monthly estimates of activity of technetium in both seawater and seaweed. The results indicate that reduced sampling frequency allows production of estimates that acceptably replicate the actual data and that use of geostatistical procedures may offer advantages in the planning of monitoring systems for marine radioactivity. The use of an oceanographic model was also investigated as a means of assessing the temporal correlation prior to actual sampling, an approach that may offer significant advantages by reducing the need to have lengthy time series prior to designing sampling regimes.

The dispersion of 99Tc in the Nordic Seas and the Arctic Ocean: a comparison of model results and observations.

The radionuclide (99)Tc had been discharged from the nuclear reprocessing facility in Sellafield (UK) into the Irish Sea in increased amounts in the 1990s. We compare the simulated dispersion of (99)Tc in surface water as calculated by a hydrodynamic model and an assessment box model with field-observations from 1996 to 1999 to study concentrations, pathways and travel times. The model results are consistent with the observations and show the typical pathway of dissolved radionuclides from the Irish Sea via the North Sea along the Norwegian Coast. Subsequently the contaminated water separates into three branches of which the two Arctic branches bear the potential for future monitoring of the signal in the next decades. The results of the hydrodynamic model indicate a large variability of surface concentrations in the West Spitsbergen Current which has implications for future monitoring strategies. According to the observed and simulated distributions we suggest an improved box model structure to better capture the pattern for concentrations at the surface.

On the potential for climate change impacts on marine anthropogenic radioactivity in the Arctic regions.

Current predictions as to the impacts of climate change in general and Arctic climate change in particular are such that a wide range of processes relevant to Arctic contaminants are potentially vulnerable. Of these, radioactive contaminants and the processes that govern their transport and fate may be particularly susceptible to the effects of a changing Arctic climate. This paper explores the potential changes in the physical system of the Arctic climate system as they are deducible from present day knowledge and model projections. As a contribution to a better preparedness regarding Arctic marine contamination with radioactivity we present and discuss how a changing marine physical environment may play a role in altering the current understanding pertaining to behavior of contaminant radionuclides in the marine environment of the Arctic region.

The study of mentoring in the learning environment (SMILE): a randomized evaluation of the effectiveness of school-based mentoring.

The effect of providing youth school-based mentoring (SBM), in addition to other school-based support services, was examined with a sample of 516 predominately Latino students across 19 schools. Participants in a multi-component, school-based intervention program run by a youth development agency were randomly assigned to one of two conditions: (1) supportive services alone or (2) supportive services plus SBM. Compared to community-based mentoring, the duration of the SBM was brief (averaging eight meetings), partly because the agency experienced barriers to retaining mentors. Intent-to-treat (ITT) main effects of SBM were tested using hierarchical linear modeling (HLM) and revealed small, positive main effects of mentoring on self-reported connectedness to peers, self-esteem (global and present-oriented), and social support from friends, but not on several other measures, including grades and social skills. Three-way cross-level interactions of sex and school level (elementary, middle, and high school) revealed that elementary school boys and high school girls benefited the most from mentoring. Among elementary school boys, those in the mentoring condition reported higher social skills (empathy and cooperation), hopefulness, and connectedness both to school and to culturally different peers. Among high school girls, those mentored reported greater connectedness to culturally different peers, self-esteem, and support from friends. Findings suggest no or iatrogenic effects of mentoring for older boys and younger girls. Therefore, practitioners coordinating multi-component programs that include SBM would be wise to provide mentors to the youth most likely to benefit from SBM and bolster program practices that help to support and retain mentors.

How connectedness contributes to experimental smoking among rural youth: developmental and ecological analyses.

The influences of peer, sibling, and parents' smoking on adolescents' initiation of tobacco use have been explained as a function of peer pressure, genetics, and social learning processes, but rarely in combination or with assessments of the quality of these relationships. This study examined the additional contributions of connectedness to friends, siblings, parents, and teachers beyond the effects of friend, sibling, and parental smoking using logistic regression analyses with a cross-sectional middle and high school sample of 303 rural adolescents. Friends' and siblings' smoking, and connectedness to friends, were the strongest predictors of experimental smoking. Parental smoking and connectedness to parents and to teachers were significant predictors of experimental smoking when considered independently, but not after accounting for friend and sibling factors. Connectedness to parents and teachers decreased the odds of experimental smoking, while connectedness to friends increased the odds. EDITORS' STRATEGIC IMPLICATIONS: This strategy of assessing connectedness to-and smoking behaviors of-friends, siblings, teachers, and parents shows promise in predicting teens' cigarette smoking choices. The finding that the negative effects of unconventional connectedness and smoking by friends and siblings outweigh positive effects of connectedness to adults awaits replication with different samples and measures.