Environmentally compatible solid rocket propellants

Hercules' clean propellant development research is exploring three major types of clean propellant: (1) chloride-free formulations (no chlorine containing ingredients), being developed on the Clean Propellant Development and Demonstration (CPDD) contract sponsored by Phillips Laboratory, Edwards Air Force Base, CA; (2) low HCl scavenged formulations (HCl-scavenger added to propellant oxidized with ammonium perchlorate (AP)); and (3) low HCl formulations oxidized with a combination of AN and AP (with or without an HCl scavenger) to provide a significant reduction (relative to current solid rocket boosters) in exhaust HCl. These propellants provide performance approaching that of current systems, with less than 2 percent HCl in the exhaust, a significant reduction (greater than or equal to 70 percent) in exhaust HCl levels. Excellent processing, safety, and mechanical properties were achieved using only readily available, low cost ingredients. Two formulations, a sodium nitrate (NaNO3) scavenged HTPB and a chloride-free hydroxy terminated polyether (HTPE) propellant, were characterized for ballistic, mechanical, and rheological properties. In addition, the hazards properties were demonstrated to provide two families of class 1.3, 'zero-card' propellants. Further characterization is planned which includes demonstration of ballistic tailorability in subscale (one to 70 pound) motors over the range of burn rates required for retrofit into current Hercules space booster designs (Titan 4 SRMU and Delta 2 GEM)

Fisheries Bycatch Risk to Marine Megafauna Is Intensified in Lagrangian Coherent Structures

Incidental catch of nontarget species (bycatch) is a major barrier to ecological and economic sustainability in marine capture fisheries. Key to mitigating bycatch is an understanding of the habitat requirements of target and nontarget species and the influence of heterogeneity and variability in the dynamic marine environment. While patterns of overlap among marine capture fisheries and habitats of a taxonomically diverse range of marine vertebrates have been reported, a mechanistic understanding of the real-time physical drivers of bycatch events is lacking. Moving from describing patterns toward understanding processes, we apply a Lagrangian analysis to a high-resolution ocean model output to elucidate the fundamental mechanisms that drive fisheries interactions. We find that the likelihood of marine megafauna bycatch is intensified in attracting Lagrangian coherent structures associated with submesoscale and mesoscale filaments, fronts, and eddies. These results highlight how the real-time tracking of dynamic structures in the oceans can support fisheries sustainability and advance ecosystem-based managemen

Fit to Predict? Ecoinformatics for Predicting the Catchability of a Pelagic Fish in Near Real-Time

The ocean is a dynamic environment inhabited by a diverse array of highly migratory species, many of which are under direct exploitation in targeted fisheries. The timescales of variability in the marine realm coupled with the extreme mobility of ocean-wandering species such as tuna and billfish complicates fisheries management. Developing ecoinformatics solutions that allow for near real-time prediction of the distributions of highly mobile marine species is an important step towards the maturation of dynamic ocean management and ecological forecasting. Using 25 years (1990-2014) of NOAA fisheries\u27 observer data from the California drift gillnet fishery, we model relative probability of occurrence (presence-absence) and catchability (total catch) of broadbill swordfish Xiphias gladius in the California Current System (CCS). Using freely-available environmental datasets and open source software, we explore the physical drivers of regional swordfish distribution. Comparing models built upon remotely-sensed datasets with those built upon a data-assimilative configuration of the Regional Ocean Modelling System (ROMS), we explore trade-offs in model construction and address how physical data can affect predictive performance and operational capacity. Swordfish catchability was found to be highest in deeper waters (\u3e1500m) with surface temperatures in the 14-20 degrees C range, isothermal layer depth (ILD) of 20-40m, positive sea surface height anomalies and during the new moon

Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modeling species distributions are often remotely sensed, and as such have limited capacity for interpreting the vertical structure of the water column, or are sampled in situ, offering minimal spatial and temporal coverage. Advances in ocean models have improved our capacity to explore subsurface ocean features, yet there has been limited integration of such features in SDMs. Using output from a data-assimilative configuration of the Regional Ocean Modeling System, we examine the effect of including dynamic subsurface variables in SDMs to describe the habitats of four pelagic predators in the California Current System (swordfish Xiphias gladius, blue sharks Prionace glauca, common thresher sharks Alopias vulpinus, and shortfin mako sharks lsurus oxyrinchus). Species data were obtained from the California Drift Gillnet observer program (1997-2017). We used boosted regression trees to explore the incremental improvement enabled by dynamic subsurface variables that quantify the structure and stability of the water column: isothermal layer depth and bulk buoyancy frequency. The inclusion of these dynamic subsurface variables significantly improved model explanatory power for most species. Model predictive performance also significantly improved, but only for species that had strong affiliations with dynamic variables (swordfish and shortfin mako sharks) rather than static variables (blue sharks and common thresher sharks). Geospatial predictions for all species showed the integration of isothermal layer depth and bulk buoyancy frequency contributed value at the mesoscale level (\u3c 100 km) and varied spatially throughout the study domain. These results highlight the utility of including dynamic subsurface variables in SDM development and support the continuing ecological use of biophysical output from ocean circulation models

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest

Performance Evaluation of Cetacean Species Distribution Models Developed Using Generalized Additive Models and Boosted Regression Trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest

Bacterial microevolution and the Pangenome

The comparison of multiple genome sequences sampled from a bacterial population reveals considerable diversity in both the core and the accessory parts of the pangenome. This diversity can be analysed in terms of microevolutionary events that took place since the genomes shared a common ancestor, especially deletion, duplication, and recombination. We review the basic modelling ingredients used implicitly or explicitly when performing such a pangenome analysis. In particular, we describe a basic neutral phylogenetic framework of bacterial pangenome microevolution, which is not incompatible with evaluating the role of natural selection. We survey the different ways in which pangenome data is summarised in order to be included in microevolutionary models, as well as the main methodological approaches that have been proposed to reconstruct pangenome microevolutionary history

Beyond representing orthology relations by trees

Reconstructing the evolutionary past of a family of genes is an important aspect of many genomic studies. To help with this, simple relations on a set of sequences called orthology relations may be employed. In addition to being interesting from a practical point of view they are also attractive from a theoretical perspective in that e.\,g.\,a characterization is known for when such a relation is representable by a certain type of phylogenetic tree. For an orthology relation inferred from real biological data it is however generally too much to hope for that it satisfies that characterization. Rather than trying to correct the data in some way or another which has its own drawbacks, as an alternative, we propose to represent an orthology relation $\delta$ in terms of a structure more general than a phylogenetic tree called a phylogenetic network. To compute such a network in the form of a level-1 representation for $\delta$, we formalize an orthology relation in terms of the novel concept of a symbolic 3- dissimilarity which is motivated by the biological concept of a ``cluster of orthologous groups'', or COG for short. For such maps which assign symbols rather that real values to elements, we introduce the novel {\sc Network-Popping} algorithm which has several attractive properties. In addition, we characterize an orthology relation $\delta$ on some set $X$ that has a level-1 representation in terms of eight natural properties for $\delta$ as well as in terms of level-1 representations of orthology relations on certain subsets of $X$

Pattern and quality of care of cancer pain management. Results from the Cancer Pain Outcome Research Study Group

Most patients with advanced or metastatic cancer experience pain and despite several guidelines, undertreatment is well documented. A multicenter, open-label, prospective, non-randomised study was launched in Italy in 2006 to evaluate the epidemiology, patterns and quality of pain care of cancer patients. To assess the adequacy of analgesic care, we used a standardised measure, the pain management index (PMI), that compares the most potent analgesic prescribed for a patient with the reported level of the worst pain of that patient together with a selected list of clinical indicators. A total of 110 centres recruited 1801 valid cases. 61% of cases were received a WHO-level III opioid; 25.3% were classified as potentially undertreated, with wide variation (9.8–55.3%) according to the variables describing patients, centres and pattern of care. After adjustment with a multivariable logistic regression model, type of recruiting centre, receiving adjuvant therapy or not and type of patient recruited (new or already on follow-up) had a significant association with undertreatment. Non-compliance with the predefined set of clinical indicators was generally high, ranging from 41 to 76%. Despite intrinsic limitations of the PMI that may be considered as an indicator of the poor quality of cancer pain care, results suggest that the recourse to WHO third-level drugs still seems delayed in a substantial percentage of patients. This delay is probably related to several factors affecting practice in participating centres and suggests that the quality of cancer pain management in Italy deserves specific attention and interventions aimed at improving patients' outcomes

Evolutionary consequences of multidriver environmental change in an aquatic primary producer

Climate change is altering aquatic environments in a complex way, and simultaneous shifts in many properties will drive evolutionary responses in primary producers at the base of both freshwater and marine ecosystems. So far, evolutionary studies have shown how changes in environmental drivers, either alone or in pairs, affect the evolution of growth and other traits in primary producers. Here, we evolve a primary producer in 96 unique environments with different combinations of between one and eight environmental drivers to understand how evolutionary responses to environmental change depend on the identity and number of drivers. Even in multidriver environments, only a few dominant drivers explain most of the evolutionary changes in population growth rates. Most populations converge on the same growth rate by the end of the evolution experiment. However, populations adapt more when these dominant drivers occur in the presence of other drivers. This is due to an increase in the intensity of selection in environments with more drivers, which are more likely to include dominant drivers. Concurrently, many of the trait changes that occur during the initial short-term response to both single and multidriver environmental change revert after about 450 generations of evolution. In future aquatic environments, populations will encounter differing combinations of drivers and intensities of selection, which will alter the adaptive potential of primary producers. Accurately gauging the intensity of selection on key primary producers will help in predicting population size and trait evolution at the base of aquatic food webs