State practitioner insights into local public health challenges and opportunities in obesity prevention: a qualitative study.

IntroductionThe extent of obesity prevention activities conducted by local health departments (LHDs) varies widely. The purpose of this qualitative study was to characterize how state obesity prevention program directors perceived the role of LHDs in obesity prevention and factors that impact LHDs' success in obesity prevention.MethodsFrom June 2011 through August 2011, we conducted 28 semistructured interviews with directors of federally funded obesity prevention programs at 22 state and regional health departments. Interviews were transcribed verbatim, coded, and analyzed to identify recurring themes and key quotations.ResultsMain themes focused on the roles of LHDs in local policy and environmental change and on the barriers and facilitators to LHD success. The role LHDs play in obesity prevention varied across states but generally reflected governance structure (decentralized vs centralized). Barriers to local prevention efforts included competing priorities, lack of local capacity, siloed public health structures, and a lack of local engagement in policy and environmental change. Structures and processes that facilitated prevention were having state support (eg, resources, technical assistance), dedicated staff, strong communication networks, and a robust community health assessment and planning process.ConclusionsThese findings provide insight into successful strategies state and local practitioners are using to implement innovative (and evidence-informed) community-based interventions. The change in the nature of obesity prevention requires a rethinking of the state-local relationship, especially in centralized states

Efficient Detection of Repeating Sites to Accelerate Phylogenetic Likelihood Calculations

The phylogenetic likelihood function (PLF) is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection, and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory savings attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 12-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the PLF currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation

Coupling SIMD and SIMT Architectures to Boost Performance of a Phylogeny-aware Alignment Kernel

Background: Aligning short DNA reads to a reference sequence alignment is a prerequisite for detecting their biological origin and analyzing them in a phylogenetic context. With the PaPaRa tool we introduced a dedicated dynamic programming algorithm for simultaneously aligning short reads to reference alignments and corresponding evolutionary reference trees. The algorithm aligns short reads to phylogenetic profiles that correspond to the branches of such a reference tree. The algorithm needs to perform an immense number of pairwise alignments. Therefore, we explore vector intrinsics and GPUs to accelerate the PaPaRa alignment kernel. Results: We optimized and parallelized PaPaRa on CPUs and GPUs. Via SSE 4.1 SIMD (Single Instruction, Multiple Data) intrinsics for x86 SIMD architectures and multi-threading, we obtained a 9-fold acceleration on a single core as well as linear speedups with respect to the number of cores. The peak CPU performance amounts to 18.1 GCUPS (Giga Cell Updates per Second) using all four physical cores on an Intel i7 2600 CPU running at 3.4 GHz. The average CPU performance (averaged over all test runs) is 12.33 GCUPS. We also used OpenCL to execute PaPaRa on a GPU SIMT (Single Instruction, Multiple Threads) architecture. A NVIDIA GeForce 560 GPU delivered peak and average performance of 22.1 and 18.4 GCUPS respectively. Finally, we combined the SIMD and SIMT implementations into a hybrid CPU-GPU system that achieved an accumulated peak performance of 33.8 GCUPS. Conclusions: This accelerated version of PaPaRa (available at www.exelixis-lab.org/software.html) provides a significant performance improvement that allows for analyzing larger datasets in less time. We observe that state-of-the-art SIMD and SIMT architectures deliver comparable performance for this dynamic programming kernel when the “competing programmer approach” is deployed. Finally, we show that overall performance can be substantially increased by designing a hybrid CPU-GPU system with appropriate load distribution mechanisms

Entropic and gradient flow formulations for nonlinear diffusion

Nonlinear diffusion $\partial_t \rho = \Delta(\Phi(\rho))$ is considered for a class of nonlinearities $\Phi$. It is shown that for suitable choices of $\Phi$, an associated Lyapunov functional can be interpreted as thermodynamics entropy. This information is used to derive an associated metric, here called thermodynamic metric. The analysis is confined to nonlinear diffusion obtainable as hydrodynamic limit of a zero range process. The thermodynamic setting is linked to a large deviation principle for the underlying zero range process and the corresponding equation of fluctuating hydrodynamics. For the latter connections, the thermodynamic metric plays a central role

Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data

We present genesis, a library for working with phylogenetic data, and gappa, an accompanying command-line tool for conducting typical analyses on such data. The tools target phylogenetic trees and phylogenetic placements, sequences, taxonomies and other relevant data types, offer high-level simplicity as well as lowlevel customizability, and are computationally efficient, well-tested and field-proven

Correlating Antiagglomerant Performance with Gas Hydrate Cohesion

Although inhibiting hydrate formation in hydrocarbon–water systems is paramount in preventing pipe blockage in hydrocarbon transport systems, the molecular mechanisms responsible for antiagglomerant (AA) performance are not completely understood. To better understand why macroscopic performance is affected by apparently small changes in the AA molecular structure, we perform molecular dynamics simulations. We quantify the cohesion energy between two gas hydrate nanoparticles dispersed in liquid hydrocarbons in the presence of different AAs, and we achieve excellent agreement against experimental data obtained at high pressure using the micromechanical force apparatus. This suggests that the proposed simulation approach could provide a screening method for predicting, in silico, the performance of new molecules designed to manage hydrates in flow assurance. Our results suggest that entropy and free energy of solvation of AAs, combined in some cases with the molecular orientation at hydrate–oil interfaces, are descriptors that could be used to predict performance, should the results presented here be reproduced for other systems as well. These insights could help speed up the design of new AAs and guide future experiments