Fire and Gold Build Seattle

The final decade of the 19th century established Seattle as the preeminent city in the Pacific Northwest. Prodigious changes resulting from the Fire of 1889 paved the way for Seattle to take full advantage of the Klondike Gold Rush eight years later. This work details the impact that each of these events had on Seattle and concludes that the compound effects of two events of happenstance created the foundation for the Seattle we know today

The Geography of Fast Food Outlets: A Review

The availability of food high in fat, salt and sugar through Fast Food (FF) or takeaway outlets, is implicated in the causal pathway for the obesity epidemic. This review aims to summarise this body of research and highlight areas for future work. Thirty three studies were found that had assessed the geography of these outlets. Fourteen studies showed a positive association between availability of FF outlets and increasing deprivation. Another 13 studies also included overweight or obesity data and showed conflicting results between obesity/overweight and FF outlet availability. There is some evidence that FF availability is associated with lower fruit and vegetable intake. There is potential for land use policies to have an influence on the location of new FF outlets. Further research should incorporate good quality data on FF consumption, weight and physical activity

An Analysis of a Ring Attractor Model for Cue Integration

Animals and robots must constantly combine multiple streams of noisy information from their senses to guide their actions. Recently, it has been proposed that animals may combine cues optimally using a ring attractor neural network architecture inspired by the head direction system of rats augmented with a dynamic re-weighting mechanism. In this work we report that an older and simpler ring attractor network architecture, requiring no re-weighting property combines cues according to their certainty for moderate cue conflicts but converges on the most certain cue for larger conflicts. These results are consistent with observations in animal experiments that show sub-optimal cue integration and switching from cue integration to cue selection strategies. This work therefore demonstrates an alternative architecture for those seeking neural correlates of sensory integration in animals. In addition, performance is shown robust to noise and miniaturization and thus provides an efficient solution for artificial systems

Molecular Adaptations for Sensing and Securing Prey and Insight into Amniote Genome Diversity from the Garter Snake Genome

Colubridae represents the most phenotypically diverse and speciose family of snakes, yet no well-assembled and annotated genome exists for this lineage. Here, we report and analyze the genome of the garter snake, Thamnophis sirtalis, a colubrid snake that is an important model species for research in evolutionary biology, physiology, genomics, behavior, and the evolution of toxin resistance. Using the garter snake genome, we show how snakes have evolved numerous adaptations for sensing and securing prey, and identify features of snake genome structure that provide insight into the evolution of amniote genomes. Analyses of the garter snake and other squamate reptile genomes highlight shifts in repeat element abundance and expansion within snakes, uncover evidence of genes under positive selection, and provide revised neutral substitution rate estimates for squamates. Our identification of Z and W sex chromosome-specific scaffolds provides evidence for multiple origins of sex chromosome systems in snakes and demonstrates the value of this genome for studying sex chromosome evolution. Analysis of gene duplication and loss in visual and olfactory gene families supports a dim-light ancestral condition in snakes and indicates that olfactory receptor repertoires underwent an expansion early in snake evolution. Additionally, we provide some of the first links between secreted venom proteins, the genes that encode them, and their evolutionary origins in a rear-fanged colubrid snake, together with new genomic insight into the coevolutionary arms race between garter snakes and highly toxic newt prey that led to toxin resistance in garter snakes

Mouse models of rhinovirus-induced disease and exacerbation of allergic airway inflammation

Rhinoviruses cause serious morbidity and mortality as the major etiological agents of asthma exacerbations and the common cold. A major obstacle to understanding disease pathogenesis and to the development of effective therapies has been the lack of a small-animal model for rhinovirus infection. Of the 100 known rhinovirus serotypes, 90% (the major group) use human intercellular adhesion molecule-1 (ICAM-1) as their cellular receptor and do not bind mouse ICAM-1; the remaining 10% (the minor group) use a member of the low-density lipoprotein receptor family and can bind the mouse counterpart. Here we describe three novel mouse models of rhinovirus infection: minor-group rhinovirus infection of BALB/c mice, major-group rhinovirus infection of transgenic BALB/c mice expressing a mouse-human ICAM-1 chimera and rhinovirus-induced exacerbation of allergic airway inflammation. These models have features similar to those observed in rhinovirus infection in humans, including augmentation of allergic airway inflammation, and will be useful in the development of future therapies for colds and asthma exacerbations

Lifestyle modification and metformin as long-term treatment options for obese adolescents: study protocol

<p>Abstract</p> <p>Background</p> <p>Childhood obesity is a serious health concern affecting over 155 million children in developed countries worldwide. Childhood obesity is associated with significantly increased risk for development of type 2 diabetes, cardiovascular disease and psychosocial functioning problems (i.e., depression and decreased quality of life). The two major strategies for management of obesity and associated metabolic abnormalities are lifestyle modification and pharmacologic therapy. This paper will provide the background rationale and methods of the REACH childhood obesity treatment program.</p> <p>Methods/design</p> <p>The REACH study is a 2-year multidisciplinary, family-based, childhood obesity treatment program. Seventy-two obese adolescents (aged 10-16 years) and their parents are being recruited to participate in this randomized placebo controlled trial. Participants are randomized to receive either metformin or placebo, and are then randomized to a moderate or a vigorous intensity supervised exercise program for the first 12-weeks. After the 12-week exercise program, participants engage in weekly exercise sessions with an exercise facilitator at a local community center. Participants engage in treatment sessions with a dietitian and social worker monthly for the first year, and then every three months for the second year. The primary outcome measure is change in body mass index and the secondary outcome measures are changes in body composition, risk factors for type 2 diabetes and cardiovascular disease, changes in diet, physical activity, and psychosocial well-being (e.g., quality of life). It is hypothesized that participants who take metformin and engage in vigorous intensity exercise will show the greatest improvements in body mass index. In addition, it is hypothesized that participants who adhere to the REACH program will show improvements in body composition, physical activity, diet, psychosocial functioning and risk factor profiles for type 2 diabetes and cardiovascular disease. These improvements are expected to be maintained over the 2-year program.</p> <p>Discussion</p> <p>The findings from this study will advance the knowledge regarding the long-term efficacy and sustainability of interventions for childhood obesity.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov number NCT00934570</p

Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning

Reinforcement learning (RL) provides an influential characterization of the brain's mechanisms for learning to make advantageous choices. An important problem, though, is how complex tasks can be represented in a way that enables efficient learning. We consider this problem through the lens of spatial navigation, examining how two of the brain's location representations—hippocampal place cells and entorhinal grid cells—are adapted to serve as basis functions for approximating value over space for RL. Although much previous work has focused on these systems' roles in combining upstream sensory cues to track location, revisiting these representations with a focus on how they support this downstream decision function offers complementary insights into their characteristics. Rather than localization, the key problem in learning is generalization between past and present situations, which may not match perfectly. Accordingly, although neural populations collectively offer a precise representation of position, our simulations of navigational tasks verify the suggestion that RL gains efficiency from the more diffuse tuning of individual neurons, which allows learning about rewards to generalize over longer distances given fewer training experiences. However, work on generalization in RL suggests the underlying representation should respect the environment's layout. In particular, although it is often assumed that neurons track location in Euclidean coordinates (that a place cell's activity declines “as the crow flies” away from its peak), the relevant metric for value is geodesic: the distance along a path, around any obstacles. We formalize this intuition and present simulations showing how Euclidean, but not geodesic, representations can interfere with RL by generalizing inappropriately across barriers. Our proposal that place and grid responses should be modulated by geodesic distances suggests novel predictions about how obstacles should affect spatial firing fields, which provides a new viewpoint on data concerning both spatial codes

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead