1. The detection of evolutionary shifts in trait evolution from extant taxa is motivated by the study of convergent evolution, or to correlate shifts in traits with habitat changes or with changes in other phenotypes. 2. We propose here a phylogenetic lasso method to study trait evolution from comparative data and detect past changes in the expected mean trait values. We use the Ornstein-Uhlenbeck process, which can model a changing adaptive landscape over time and over lineages. 3. Our method is very fast, running in minutes for hundreds of species, and can handle multiple traits. We also propose a phylogenetic Bayesian information criterion that accounts for the phylogenetic correlation between species, as well as for the complexity of estimating an unknown number of shifts at unknown locations in the phylogeny. This criterion does not suffer model overfitting and has high precision, so it offers a conservative alternative to other information criteria. 4. Our re-analysis of Anolis lizard data suggests a more conservative scenario of morphological adaptation and convergence than previously proposed. Software is available on GitHub.

The study of pollen morphology has historically allowed evolutionary biologists to assess phylogenetic relationships among Angiosperms, as well as to better understand the fossil record. During this process, pollen has mainly been studied by discretizing some of its main characteristics such as size, shape, and exine ornamentation. One large plant clade in which pollen has been used this way for phylogenetic inference and character mapping is the order Myrtales, composed by the small families Alzateaceae, Crypteroniaceae, and Penaeaceae (collectively the “CAP clade”), as well as the large families Combretaceae, Lythraceae, Melastomataceae, Myrtaceae, Onagraceae and Vochysiaceae. In this study, we present a novel way to study pollen evolution by using quantitative size and shape variables. We use morphometric and morphospace methods to evaluate pollen change in the order Myrtales using a time-calibrated, supermatrix phylogeny. We then test for conservatism, divergence, and morphological convergence of pollen and for correlation between the latitudinal gradient and pollen size and shape. To obtain an estimate of shape, Myrtales pollen images were extracted from the literature, and their outlines analyzed using elliptic Fourier methods. Shape and size variables were then analyzed in a phylogenetic framework under an Ornstein-Uhlenbeck process to test for shifts in size and shape during the evolutionary history of Myrtales. Few shifts in Myrtales pollen morphology were found which indicates morphological conservatism. Heterocolpate, small pollen is ancestral with largest pollen in Onagraceae. Convergent shifts in shape but not size occurred in Myrtaceae and Onagraceae and are correlated to shifts in latitude and biogeography. A quantitative approach was applied for the first time to examine pollen evolution across a large time scale. Using phylogenetic based morphometrics and an OU process, hypotheses of pollen size and shape were tested across Myrtales. Convergent pollen shifts and position in the latitudinal gradient support the selective role of harmomegathy, the mechanism by which pollen grains accommodate their volume in response to water loss.

Respondent-driven sampling (RDS) is a method of chain referral sampling popular for sampling hidden and/or marginalized populations. As such, even under the ideal sampling assumptions, the performance of RDS is restricted by the underlying social network: if the network is divided into communities that are weakly connected to each other, then RDS is likely to oversample one of these communities. In order to diminish the "referral bottlenecks" between communities, we propose anti-cluster RDS (AC-RDS), an adjustment to the standard RDS implementation. Using a standard model in the RDS literature, namely, a Markov process on the social network that is indexed by a tree, we construct and study the Markov transition matrix for AC-RDS. We show that if the underlying network is generated from the Stochastic Blockmodel with equal block sizes, then the transition matrix for AC-RDS has a larger spectral gap and consequently faster mixing properties than the standard random walk model for RDS. In addition, we show that AC-RDS reduces the covariance of the samples in the referral tree compared to the standard RDS and consequently leads to a smaller variance and design effect. We confirm the effectiveness of the new design using both the Add-Health networks and simulated networks.

We present a design and implementation of a highthroughput deep packet inspection performing both stream categorization and intrusion detection on GPU platform using CUDA. This implementation is capable of matching 64 ethernet packet streams against 25 given regular expressions at 524 Mb/s rate on a computer system with GeForce GTX 295 graphic card.