TMB is an open source R package that enables quick implementation of complex nonlinear random effect (latent variable) models in a manner similar to the established AD Model Builder package (ADMB, admb-project.org) (Fournier, Skaug, Ancheta, Ianelli, Magnusson, Maunder, Nielsen, and Sibert 2011). In addition, it offers easy access to parallel computations. The user defines the joint likelihood for the data and the random effects as a C++ template function, while all the other operations are done in R; e.g., reading in the data. The package evaluates and maximizes the Laplace approximation of the marginal likelihood where the random effects are automatically integrated out. This approximation, and its derivatives, are obtained using automatic differentiation (up to order three) of the joint likelihood. The computations are designed to be fast for problems with many random effects (≈ 10 6 ) and parameters (≈ 10 3 ). Computation times using ADMB and TMB are compared on a suite of examples ranging from simple models to large spatial models where the random effects are a Gaussian random field. Speedups ranging from 1.5 to about 100 are obtained with increasing gains for large problems. The package and examples are available at http://tmb-project.org.

Ecological phenomena are often measured in the form of count data. These data can be analyzed using generalized linear mixed models (GLMMs) when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the standard error distributions used in GLMMs, e.g., parasite counts may be exactly zero for hosts with effective immune defenses but vary according to a negative binomial distribution for non-resistant hosts.We present a new R package, glmmTMB, that increases the range of models that can easily be fitted to count data using maximum likelihood estimation.The interface was developed to be familiar to users of the lme4 R package, a common tool for fitting GLMMs. To maximize speed and flexibility, estimation is done using Template Model Builder (TMB), utilizing automatic differentiation to estimate model gradients and the Laplace approximation for handling random effects. We demonstrate glmmTMB and compare it to other available methods using two ecological case studies.In general, glmmTMB is more flexible than other packages available for esti- . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/132753 doi: bioRxiv preprint first posted online May. 1, 2017; mating zero-inflated models via maximum likelihood estimation and is faster than packages that use Markov chain Monte Carlo sampling for estimation; it is also more flexible for zero-inflated modelling than INLA, but speed comparisons vary with model and data structure. Our package can be used to fit GLMs and GLMMs with or without zero-inflation as well as hurdle models. By allowing ecologists to quickly estimate a wide variety of models using a single package, glmmTMB makes it easier to find appropriate models and test hypotheses to describe ecological processes.

Many commonly used fluorescently labeled lipids were found to dissociate from liposomes during incubation in blood plasma.

Effective treatments of neurodegenerative diseases require drugs to be actively transported across the blood-brain barrier (BBB). However, nanoparticle drug carriers explored for this purpose show negligible brain uptake, and the lack of basic understanding of nanoparticle-BBB interactions underlies many translational failures. Here, using two-photon microscopy in mice, we characterize the receptor-mediated transcytosis of nanoparticles at all steps of delivery to the brain in vivo. We show that transferrin receptor-targeted liposome nanoparticles are sequestered by the endothelium at capillaries and venules, but not at arterioles. The nanoparticles move unobstructed within endothelium, but transcytosis-mediated brain entry occurs mainly at post-capillary venules, and is negligible in capillaries. The vascular location of nanoparticle brain entry corresponds to the presence of perivascular space, which facilitates nanoparticle movement after transcytosis. Thus, post-capillary venules are the point-of-least resistance at the BBB, and compared to capillaries, provide a more feasible route for nanoparticle drug carriers into the brain.

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