Quantitative magnetophoresis of micro and nano particles

Micro- and nanoscale magnetic particles are becoming increasingly utilized in a variety of settings. Magnetophoresis is commonly used in diagnostic devices, research applications, and medicinal science. The applications of magnetophoresis in drug delivery, gene transfection, and hyperthermic treatment of tumours are in the initial phases of development. While a large body of work in magnetophoresis exists, here are few reports of the relevant magnetophoretic parameters of a system being quantitatively correlated with driven particle mobility. The relationships between the size, shape, and magnetic properties of the particles, the applied magnetic field, and the viscosity of the medium are relevant to particle magnetophoresis and the design of magnetophoretic systems. The investigation described here begins with the room temperature magnetic characterization of the three particles used: commercial beads, nanorods, and for the first time ferritin. Ferritin is a magnetic protein which has been used extensively in a research context for labelling biological particles, however such systems have not been quantifiably characterized to enable the development of loading/force causal relationships. Here, a model platform was used to correlate for the first time, the quantified ferritin loading, the empirically determined magnetic properties of the ferritin labelled particles, and the magnetophoretic forces. The quantified magnetophoresis of spheres and rods in a model viscous medium and shear thinning polymer networks was performed for the first time. This investigation also represents the first report of particle shear thinning of DNA. The decreasing viscosity experienced by the particles in DNA points toward potential implications for considering the benefits of particle induced shear thinning in the designing of magnetic particle drug delivery systems. In the final investigation, the results of the previous chapters are brought together in the fabrication and magnetophoresis of a novel, ferritin based, rod shaped, biocompatible, nanoparticles. For the first time, magnetophoresis of the nanoparticles is demonstrated and validated by spatially resolved Raman spectroscopic analysis of the magnetically concentrated material. This dual component magnetic particle has potential application in the fabrication of new functionally graded biomaterials and drug and gene delivery

Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R

Successful management of wildlife populations requires accurate estimates of abundance. Abundance estimates can be confounded by imperfect detection during wildlife surveys. N-mixture models enable quantification of detection probability and, under appropriate conditions, produce abundance estimates that are less biased. Here, we demonstrate how to use the R-INLA package for R to analyze N-mixture models, and compare performance of R-INLA to two other common approaches: JAGS (via the runjags package for R), which uses Markov chain Monte Carlo and allows Bayesian inference, and the unmarked package for R, which uses maximum likelihood and allows frequentist inference. We show that R-INLA is an attractive option for analyzing N-mixture models when (i) fast computing times are necessary (R-INLA is 10 times faster than unmarked and 500 times faster than JAGS), (ii) familiar model syntax and data format (relative to other R packages) is desired, (iii) survey-level covariates of detection are not essential, and (iv) Bayesian inference is preferred

Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes

Agriculture is being challenged to provide food, and increasingly fuel, for an expanding global population. Producing bioenergy crops on marginal lands—farmland suboptimal for food crops—could help meet energy goals while minimizing competition with food production. However, the ecological costs and benefits of growing bioenergy feedstocks—primarily annual grain crops—on marginal lands have been questioned. Here we show that perennial bioenergy crops provide an alternative to annual grains that increases biodiversity of multiple taxa and sustain a variety of ecosystem functions, promoting the creation of multifunctional agricultural landscapes. We found that switchgrass and prairie plantings harbored significantly greater plant, methanotrophic bacteria, arthropod, and bird diversity than maize. Although biomass production was greater in maize, all other ecosystem services, including methane consumption, pest suppression, pollination, and conservation of grassland birds, were higher in perennial grasslands. Moreover, we found that the linkage between biodiversity and ecosystem services is dependent not only on the choice of bioenergy crop but also on its location relative to other habitats, with local landscape context as important as crop choice in determining provision of some services. Our study suggests that bioenergy policy that supports coordinated land use can diversify agricultural landscapes and sustain multiple critical ecosystem services

Hematopoietic Cell Types: Prototype for a Revised Cell Ontology

The Cell Ontology (CL) is an OBO Foundry candidate ontology intended for the representation of cell types from all of biology. A recent workshop sponsored by NIAID on hematopoietic cell types in the CL addressed issues of both the content and structure of the CL. The section of the ontology dealing with hematopoietic cells was extensively revised, and plans were made for restructuring these cell type terms as cross-products with logical definitions based on relationships to external ontologies, such as the Protein Ontology and the Gene Ontology. The improvements to the CL in this area represent a paradigm for the future revision of the whole of the CL

Critical Media Industry Studies: A Research Approach

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73139/1/j.1753-9137.2009.01037.x.pd

Should cities hosting mass gatherings invest in public health surveillance and planning? Reflections from a decade of mass gatherings in Sydney, Australia

<p>Abstract</p> <p>Background</p> <p>Mass gatherings have been defined by the World Health Organisation as "events attended by a sufficient number of people to strain the planning and response resources of a community, state or nation". This paper explores the public health response to mass gatherings in Sydney, the factors that influenced the extent of deployment of resources and the utility of planning for mass gatherings as a preparedness exercise for other health emergencies.</p> <p>Discussion</p> <p>Not all mass gatherings of people require enhanced surveillance and additional response. The main drivers of extensive public health planning for mass gatherings reflect geographical spread, number of international visitors, event duration and political and religious considerations. In these instances, the implementation of a formal risk assessment prior to the event with ongoing daily review is important in identifying public health hazards.</p> <p>Developing and utilising event-specific surveillance to provide early-warning systems that address the specific risks identified through the risk assessment process are essential. The extent to which additional resources are required will vary and depend on the current level of surveillance infrastructure.</p> <p>Planning the public health response is the third step in preparing for mass gatherings. If the existing public health workforce has been regularly trained in emergency response procedures then far less effort and resources will be needed to prepare for each mass gathering event. The use of formal emergency management structures and co-location of surveillance and planning operational teams during events facilitates timely communication and action.</p> <p>Summary</p> <p>One-off mass gathering events can provide a catalyst for innovation and engagement and result in opportunities for ongoing public health planning, training and surveillance enhancements that outlasted each event.</p

Crop Pests and Predators Exhibit Inconsistent Responses to Surrounding Landscape Composition

The idea that noncrop habitat enhances pest control and represents a win–win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win–win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies

The Moraxella adhesin UspA1 binds to its human CEACAM1 receptor by a deformable trimeric coiled-coil

Moraxella catarrhalis is a ubiquitous human-specific bacterium commonly associated with upper and lower respiratory tract infections, including otitis media, sinusitis and chronic obstructive pulmonary disease. The bacterium uses an autotransporter protein UspA1 to target an important human cellular receptor carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1). Using X-ray crystallography, we show that the CEACAM1 receptor-binding region of UspA1 unusually consists of an extended, rod-like left-handed trimeric coiled-coil. Mutagenesis and binding studies of UspA1 and the N-domain of CEACAM1 have been used to delineate the interacting surfaces between ligand and receptor and guide assembly of the complex. However, solution scattering, molecular modelling and electron microscopy analyses all indicate that significant bending of the UspA1 coiled-coil stalk also occurs. This explains how UspA1 can engage CEACAM1 at a site far distant from its head group, permitting closer proximity of the respective cell surfaces during infection

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds