Materials preparation and longevity in hyperthermal atomic oxygen

Flight hardware fabrication, the design and fabrication of an atom beam source, construction of a surface science laboratory, and progress in research on processes and mechanisms of interaction of hyperthermal atoms at solid surfaces are discussed

A Revealed and Stated Preference Latent Class Model to Examine Homogenous Subgroup Consumer Behavior Responses to Information and Food Safety Technology Treatments

The combination and joint estimation of revealed and stated preference (RP/SP) data approach to examining consumer preferences to relevant policy-based measures has exclusively considered aggregate data and behavior of the average individual. However, in policy-based analyses, where the research is often driven by understanding how different individuals react to different or similar scenarios, a preferred approach would be to analyze preferences of homogenous population subgroups. We accomplish this by developing a latent class RP/SP analysis that examines whether homogenous subgroups (or classes) of the population, based on individual health and behavioral characteristics, respond differently to health-risk information and new food safety technology. The ongoing efforts by the U.S. Food and Drug Administration to reduce illness and death associated with consuming raw Gulf of Mexico oysters provide an ideal platform for the analysis as the health risks only relate to a very specific subgroup of consumer. Results from the probabilistic latent class model indicate that the vulnerable at-risk consumers respond differently to the information treatments than other subgroups, illustrating why educational information brochures have had little impact on reducing annual deaths from consuming raw oysters. Also, findings across all subgroups provide strong empirical evidence that the new FDA policy requiring processing technology to be used in oyster production will have detrimental effects on the oyster industry. Key Words: Food safety technology, health-risk information, latent class analysis, revealed preference, stated preference

Childhood injury after a parental cancer diagnosis

A parental cancer diagnosis is psychologically straining for the whole family. We investigated whether a parental cancer diagnosis is associated with a higher-than-expected risk of injury among children by using a Swedish nationwide register-based cohort study. Compared to children without parental cancer, children with parental cancer had a higher rate of hospital contact for injury during the first year after parental cancer diagnosis (hazard ratio [HR] = 1.27, 95% confidence interval [CI] = 1.22-1.33), especially when the parent had a comorbid psychiatric disorder after cancer diagnosis (HR = 1.41, 95% CI = 1.08-1.85). The rate increment declined during the second and third year after parental cancer diagnosis (HR = 1.10, 95% CI = 1.07-1.14) and became null afterwards (HR = 1.01, 95% CI = 0.99-1.03). Children with parental cancer also had a higher rate of repeated injuries than the other children (HR = 1.13, 95% CI = 1.12-1.15). Given the high rate of injury among children in the general population, our findings may have important public health implications.NonePublishe

The Generation Challenge Programme comparative plant stress-responsive gene catalogue

The Generation Challenge Programme (GCP; www.generationcp.org) has developed an online resource documenting stress-responsive genes comparatively across plant species. This public resource is a compendium of protein families, phylogenetic trees, multiple sequence alignments (MSA) and associated experimental evidence. The central objective of this resource is to elucidate orthologous and paralogous relationships between plant genes that may be involved in response to environmental stress, mainly abiotic stresses such as water deficit (‘drought’). The web-based graphical user interface (GUI) of the resource includes query and visualization tools that allow diverse searches and browsing of the underlying project database. The web interface can be accessed at http://dayhoff.generationcp.org

Climatic and Resource Determinants of Forest Elephant Movements

As a keystone megafaunal species, African forest elephants (Loxodonta cyclotis) influence the structure and composition of tropical forests. Determining the links between food resources, environmental conditions and elephant movement behavior is crucial to understanding their habitat requirements and their effects on the ecosystem, particularly in the face of poaching and global change. We investigate whether fruit abundance or climate most strongly influence forest elephant movement behavior at the landscape scale in Gabon. Trained teams of “elephant trackers” performed daily fruit availability and dietary composition surveys over a year within two relatively pristine and intact protected areas. With data from 100 in-depth field follows of 28 satellite-collared elephants and remotely sensed environmental layers, we use linear mixed-effects models to assess the effects of sites, seasons, focal elephant identification, elephant diet, and fruit availability on elephant movement behavior at monthly and 3-day time scales. At the month-level, rainfall, and to a lesser extent fruit availability, most strongly predicted the proportion of time elephants spent in long, directionally persistent movements. Thus, even elephants in moist tropical rainforests show seasonal behavioral phenotypes linked to rainfall. At the follow-level (2–4 day intervals), relative support for both rainfall and fruit availability decreased markedly, suggesting that at finer spatial scales forest elephants make foraging decisions largely based on other factors not directly assessed here. Focal elephant identity explained the majority of the variance in the data, and there was strong support for interindividual variation in behavioral responses to rainfall. Taken together, this highlights the importance of approaches which follow individuals through space and time. The links between climate, resource availability and movement behavior provide important insights into the behavioral ecology of forest elephants that can contribute to understanding their role as seed dispersers, improving management of populations, and informing development of solutions to human-elephant conflict

Wide dynamic range magnetic field cycler: Harnessing quantum control at low and high fields

We describe the construction of a fast field cycling device capable of sweeping a 4-order-of-magnitude range of magnetic fields, from ~1mT to 7T, in under 700ms. Central to this system is a high-speed sample shuttling mechanism between a superconducting magnet and a magnetic shield, with the capability to access arbitrary fields in between with high resolution. Our instrument serves as a versatile platform to harness the inherent dichotomy of spin dynamics on offer at low and high fields - in particular, the low anisotropy, fast spin manipulation, and rapid entanglement growth at low field as well as the long spin lifetimes, spin specific control, and efficient inductive measurement possible at high fields. Exploiting these complementary capabilities in a single device open up applications in a host of problems in quantum control, sensing, and information storage, besides in nuclear hypepolarization, relaxometry and imaging. In particular, in this paper, we focus on the ability of the device to enable low-field hyperpolarization of 13C nuclei in diamond via optically pumped electronic spins associated with Nitrogen Vacancy (NV) defect centers

Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct ‘metacommunities’, and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the ‘evidence framework’ (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the ‘Anna Karenina principle (AKP)’ applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community

Efficient large-scale protein sequence comparison and gene matching to identify orthologs and co-orthologs

Broadly, computational approaches for ortholog assignment is a three steps process: (i) identify all putative homologs between the genomes, (ii) identify gene anchors and (iii) link anchors to identify best gene matches given their order and context. In this article, we engineer two methods to improve two important aspects of this pipeline [specifically steps (ii) and (iii)]. First, computing sequence similarity data [step (i)] is a computationally intensive task for large sequence sets, creating a bottleneck in the ortholog assignment pipeline. We have designed a fast and highly scalable sort-join method (afree) based on k-mer counts to rapidly compare all pairs of sequences in a large protein sequence set to identify putative homologs. Second, availability of complex genomes containing large gene families with prevalence of complex evolutionary events, such as duplications, has made the task of assigning orthologs and co-orthologs difficult. Here, we have developed an iterative graph matching strategy where at each iteration the best gene assignments are identified resulting in a set of orthologs and co-orthologs. We find that the afree algorithm is faster than existing methods and maintains high accuracy in identifying similar genes. The iterative graph matching strategy also showed high accuracy in identifying complex gene relationships. Standalone afree available from http://vbc.med.monash.edu.au/∼kmahmood/afree. EGM2, complete ortholog assignment pipeline (including afree and the iterative graph matching method) available from http://vbc.med.monash.edu.au/∼kmahmood/EGM2

ModBase, a database of annotated comparative protein structure models, and associated resources

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence–structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains 10 355 444 reliable models for domains in 2 421 920 unique protein sequences. ModBase allows users to update comparative models on demand, and request modeling of additional sequences through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are available through the ModBase interface as well as the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the SALIGN server for multiple sequence and structure alignment (http://salilab.org/salign), the ModEval server for predicting the accuracy of protein structure models (http://salilab.org/modeval), the PCSS server for predicting which peptides bind to a given protein (http://salilab.org/pcss) and the FoXS server for calculating and fitting Small Angle X-ray Scattering profiles (http://salilab.org/foxs)

Collaborative annotation of genes and proteins between UniProtKB/Swiss-Prot and dictyBase

UniProtKB/Swiss-Prot, a curated protein database, and dictyBase, the Model Organism Database for Dictyostelium discoideum, have established a collaboration to improve data sharing. One of the major steps in this effort was the ‘Dicty annotation marathon’, a week-long exercise with 30 annotators aimed at achieving a major increase in the number of D. discoideum proteins represented in UniProtKB/Swiss-Prot. The marathon led to the annotation of over 1000 D. discoideum proteins in UniProtKB/Swiss-Prot. Concomitantly, there were a large number of updates in dictyBase concerning gene symbols, protein names and gene models. This exercise demonstrates how UniProtKB/Swiss-Prot can work in very close cooperation with model organism databases and how the annotation of proteins can be accelerated through those collaborations