Mutability of mononucleotide repeats, not oxidative stress, explains the discrepancy between laboratory-accumulated mutations and the natural allele-frequency spectrum in C. elegans.

Important clues about natural selection can be gleaned from discrepancies between the properties of segregating genetic variants and of mutations accumulated experimentally under minimal selection, provided the mutational process is the same in the laboratory as in nature. The base-substitution spectrum differs between C. elegans laboratory mutation accumulation (MA) experiments and the standing site-frequency spectrum, which has been argued to be in part owing to increased oxidative stress in the laboratory environment. Using genome sequence data from C. elegans MA lines carrying a mutation (mev-1) that increases the cellular titer of reactive oxygen species (ROS), leading to increased oxidative stress, we find the base-substitution spectrum is similar between mev-1, its wild-type progenitor (N2), and another set of MA lines derived from a different wild strain (PB306). Conversely, the rate of short insertions is greater in mev-1, consistent with studies in other organisms in which environmental stress increased the rate of insertion-deletion mutations. Further, the mutational properties of mononucleotide repeats in all strains are different from those of nonmononucleotide sequence, both for indels and base-substitutions, and whereas the nonmononucleotide spectra are fairly similar between MA lines and wild isolates, the mononucleotide spectra are very different, with a greater frequency of A:T → T:A transversions and an increased proportion of ±1-bp indels. The discrepancy in mutational spectra between laboratory MA experiments and natural variation is likely owing to a consistent (but unknown) effect of the laboratory environment that manifests itself via different modes of mutability and/or repair at mononucleotide loci.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Recent Advances in Understanding the Micro- and Nanoscale Phenomena of Amorphous Solid Dispersions.

Many pharmaceutical drugs in the marketplace and discovery pipeline suffer from poor aqueous solubility, thereby limiting their effectiveness for oral delivery. The use of an amorphous solid dispersion (ASD), a mixture of an active pharmaceutical ingredient and a polymer excipient, greatly enhances the aqueous dissolution performance of a drug without the need for chemical modification. Although this method is versatile and scalable, deficient understanding of the interactions between drugs and polymers inhibits ASD rational design. This current Review details recent progress in understanding the mechanisms that control ASD performance. In the solid-state, the use of high-resolution theoretical, computational, and experimental tools resolved the influence of drug/polymer phase behavior and dynamics on stability during storage. During dissolution in aqueous media, novel characterization methods revealed that ASDs can form complex nanostructures, which maintain and improve supersaturation of the drug. The studies discussed here illustrate that nanoscale phenomena, which have been directly observed and quantified, strongly affect the stability and bioavailability of ASD systems, and provide a promising direction for optimizing drug/polymer formulations.

The mutational decay of male-male and hermaphrodite-hermaphrodite competitive fitness in the androdioecious nematode C. elegans.

Androdioecious Caenorhabditis have a high frequency of self-compatible hermaphrodites and a low frequency of males. The effects of mutations on male fitness are of interest for two reasons. First, when males are rare, selection on male-specific mutations is less efficient than in hermaphrodites. Second, males may present a larger mutational target than hermaphrodites because of the different ways in which fitness accrues in the two sexes. We report the first estimates of male-specific mutational effects in an androdioecious organism. The rate of male-specific inviable or sterile mutations is ⩽5 × 10-4/generation, below the rate at which males would be lost solely due to those kinds of mutations. The rate of mutational decay of male competitive fitness is ~ 0.17%/generation; that of hermaphrodite competitive fitness is ~ 0.11%/generation. The point estimate of ~ 1.5X faster rate of mutational decay of male fitness is nearly identical to the same ratio in Drosophila. Estimates of mutational variance (VM) for male mating success and competitive fitness are not significantly different from zero, whereas VM for hermaphrodite competitive fitness is similar to that of non-competitive fitness. Two independent estimates of the average selection coefficient against mutations affecting hermaphrodite competitive fitness agree to within two-fold, 0.33-0.5%.

Enhanced Performance of Blended Polymer Excipients in Delivering a Hydrophobic Drug through the Synergistic Action of Micelles and HPMCAS.

Blends of hydroxypropyl methylcellulose acetate succinate (HPMCAS) and dodecyl (C12)-tailed poly(N-isopropylacrylamide) (PNIPAm) were systematically explored as a model system to dispense the active ingredient phenytoin by rapid dissolution, followed by the suppression of drug crystallization for an extended period. Dynamic and static light scattering revealed that C12-PNIPAm polymers, synthesized by reversible addition-fragmentation chain-transfer polymerization, self-assembled into micelles with dodecyl cores in phosphate-buffered saline (PBS, pH 6.5). A synergistic effect on drug supersaturation was documented during in vitro dissolution tests by varying the blending ratio, with HPMACS primarily aiding in rapid dissolution and PNIPAm maintaining supersaturation. Polarized light and cryogenic transmission electron microscopy experiments revealed that C12-PNIPAm micelles maintain drug supersaturation by inhibiting both crystal nucleation and growth. Cross-peaks between the phenyl group of phenytoin and the isopropyl group of C12-PNIPAm in 2D 1H nuclear Overhauser effect (NOESY) spectra confirmed the existence of drug-polymer intermolecular interactions in solution. Phenytoin and polymer diffusion coefficients, measured by diffusion-ordered NMR spectroscopy (DOSY), demonstrated that the drug-polymer association constant increased with increasing local density of the corona chains, coincident with a reduction in C12-PNIPAm molecular weight. These findings demonstrate a new strategy for exploiting the versatility of polymer blends through the use of self-assembled micelles in the design of advanced excipients.

An exploration of within-herd dynamics of a transboundary livestock disease: A foot and mouth disease case study.

Transboundary livestock diseases are a high priority for policy makers because of the serious economic burdens associated with infection. In order to make well informed preparedness and response plans, policy makers often utilize mathematical models to understand possible outcomes of different control strategies and outbreak scenarios. Many of these models focus on the transmission between herds and the overall trajectory of the outbreak. While the course of infection within herds has not been the focus of the majority of models, a thorough understanding of within-herd dynamics can provide valuable insight into a disease system by providing information on herd-level biological properties of the infection, which can be used to inform decision making in both endemic and outbreak settings and to inform larger between-herd models. In this study, we develop three stochastic simulation models to study within-herd foot and mouth disease dynamics and the implications of different empirical data-based assumptions about the timing of the onset of infectiousness and clinical signs. We also study the influence of herd size and the proportion of the herd that is initially infected on the outcome of the infection. We find that increasing herd size increases the duration of infectiousness and that the size of the herd plays a more significant role in determining this duration than the number of initially infected cattle in that herd. We also find that the assumptions made regarding the onset of infectiousness and clinical signs, which are based on contradictory empirical findings, can result in the predictions about when infection would be detectable differing by several days. Therefore, the disease progression used to characterize the course of infection in a single bovine host could have significant implications for determining when herds can be detected and subsequently controlled; the timing of which could influence the overall predicted trajectory of outbreaks.

Modeling nation-wide U.S. swine movement networks at the resolution of the individual premises.

The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between-premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within- and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data.

The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics.

Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions.

Short-term heritable variation overwhelms 200 generations of mutational variance for metabolic traits in Caenorhabditis elegans.

Metabolic disorders have a large heritable component, and have increased markedly in human populations over the past few generations. Genome-wide association studies of metabolic traits typically find a substantial unexplained fraction of total heritability, suggesting an important role of spontaneous mutation. An alternative explanation is that epigenetic effects contribute significantly to the heritable variation. Here, we report a study designed to quantify the cumulative effects of spontaneous mutation on adenosine metabolism in the nematode Caenorhabditis elegans, including both the activity and concentration of two metabolic enzymes and the standing pools of their associated metabolites. The only prior studies on the effects of mutation on metabolic enzyme activity, in Drosophila melanogaster, found that total enzyme activity presents a mutational target similar to that of morphological and life-history traits. However, those studies were not designed to account for short-term heritable effects. We find that the short-term heritable variance for most traits is of similar magnitude as the variance among MA lines. This result suggests that the potential heritable effects of epigenetic variation in metabolic disease warrant additional scrutiny.

Effects of regional differences and demography in modelling foot-and-mouth disease in cattle at the national scale.

Foot-and-mouth disease (FMD) is a fast-spreading viral infection that can produce large and costly outbreaks in livestock populations. Transmission occurs at multiple spatial scales, as can the actions used to control outbreaks. The US cattle industry is spatially expansive, with heterogeneous distributions of animals and infrastructure. We have developed a model that incorporates the effects of scale for both disease transmission and control actions, applied here in simulating FMD outbreaks in US cattle. We simulated infection initiating in each of the 3049 counties in the contiguous US, 100 times per county. When initial infection was located in specific regions, large outbreaks were more likely to occur, driven by infrastructure and other demographic attributes such as premises clustering and number of cattle on premises. Sensitivity analyses suggest these attributes had more impact on outbreak metrics than the ranges of estimated disease parameter values. Additionally, although shipping accounted for a small percentage of overall transmission, areas receiving the most animal shipments tended to have other attributes that increase the probability of large outbreaks. The importance of including spatial and demographic heterogeneity in modelling outbreak trajectories and control actions is illustrated by specific regions consistently producing larger outbreaks than others.

Critical Excipient Properties for the Dissolution Enhancement of Phenytoin.

Solubility-enhancing amorphous solid dispersions can aid in the oral delivery of hydrophobic, poorly soluble drugs. Effective solid dispersion excipients enable high supersaturation drug concentrations over biologically relevant time scales. The critical characteristics of an excipient that allow it to work well in a solid dispersion system are not well understood. We prepared poly(N-isopropylacrylamide), poly(N,N-dimethylacrylamide), and poly(N-hydroxyethylacrylamide) excipients of varying molar mass and examined their ability to improve the aqueous solubility of phenytoin, a Biopharmaceutical Class System Class II drug. Binary and ternary solid dispersions of phenytoin and these excipients, along with hydroxypropyl methylcellulose acetate succinate and hydroxypropyl methylcellulose, were prepared at 10 wt % drug loading. Dissolution behavior was studied at early time points (<1 min) and over the course of 6 h. Performance of the ternary solid dispersions was largely a function of the concentration of poly(N-isopropylacrylamide) present in micellar structures and the concentration of PNiPAm micelles in the dissolution media. We present several systems that achieved significant improvement of phenytoin solubility over a wide composition range at enhancement factors among the highest seen to date for phenytoin.

Partitioning the influence of ecology across scales on parasite evolution.

Vector-borne parasites must succeed at three scales to persist: they must proliferate within a host, establish in vectors, and transmit back to hosts. Ecology outside the host undergoes dramatic seasonal and human-induced changes, but predicting parasite evolutionary responses requires integrating their success across scales. We develop a novel, data-driven model to titrate the evolutionary impact of ecology at multiple scales on human malaria parasites. We investigate how parasites invest in transmission versus proliferation, a life-history trait that influences disease severity and spread. We find that transmission investment controls the pattern of host infectiousness over the course of infection: a trade-off emerges between early and late infectiousness, and the optimal resolution of that trade-off depends on ecology outside the host. An expanding epidemic favors rapid proliferation, and can overwhelm the evolutionary influence of host recovery rates and mosquito population dynamics. If transmission investment and recovery rate are positively correlated, then ecology outside the host imposes potent selection for aggressive parasite proliferation at the expense of transmission. Any association between transmission investment and recovery represents a key unknown, one that is likely to influence whether the evolutionary consequences of interventions are beneficial or costly for human health.

Estimating and exploring the proportions of inter- and intrastate cattle shipments in the United States.

Mathematical models are key tools for the development of surveillance, preparedness and response plans for the potential events of emerging and introduced foreign animal diseases. Creating these types of plans requires data; when data are incomplete, mathematical models can help fill in missing information, provided they are informed by the data that are available. In the United States, the most complete national-scale data available on cattle shipments are based on Interstate Certificates of Veterinary Inspection, which track the shipment of cattle between states; data on intrastate cattle shipments are lacking. Here we develop four new datasets on intrastate cattle shipments in the U.S., including an expert elicitation survey covering 19 states and territories and three state-level brand inspection data sets. The expert elicitation survey provides estimates on the proportion of shipments that travel interstate over multiple regions of the U.S. These survey data also identify differences in shipment patterns between regions, cattle commodity types, and sectors of the cattle industry. These survey data cover more states than any other source of intrastate data; however, one limitation of these data is the small number of participating experts in many of the states, only seven of the 19 responding states and territories had a group size of three or larger. The brand data sets include origin and destination information for both intra- and interstate shipments. These data, therefore, also provide detailed information on the proportion of interstate shipments in three Western states, including the temporal and geographic variation in shipments. Because the survey and brand data overlap in the Western U.S., they can be compared. We find that in the Western U.S. the expert estimates of the overall proportion of cattle shipments matched the brand data well. However, the experts estimated that there would be larger differences in beef and dairy shipments than the brand data show. This suggests the cattle industries in the West may be sending similar proportions of commodity specific cattle shipments over state lines. We additionally used the expert survey data to explore how differences in the proportion of interstate shipments can change predictions about cattle shipment patterns using the example of model-guided suggestions for targeted surveillance in Texas. Together these four data sets are the most extensive and geographically comprehensive information to date on intrastate cattle shipments. Additionally, our analyses on predicted shipment patterns suggest that assumptions about intrastate shipments could have consequences for targeted surveillance.

Network Architecture and Mutational Sensitivity of the C. elegans Metabolome.

A fundamental issue in evolutionary systems biology is understanding the relationship between the topological architecture of a biological network, such as a metabolic network, and the evolution of the network. The rate at which an element in a metabolic network accumulates genetic variation via new mutations depends on both the size of the mutational target it presents and its robustness to mutational perturbation. Quantifying the relationship between topological properties of network elements and the mutability of those elements will facilitate understanding the variation in and evolution of networks at the level of populations and higher taxa. We report an investigation into the relationship between two topological properties of 29 metabolites in the C. elegans metabolic network and the sensitivity of those metabolites to the cumulative effects of spontaneous mutation. The correlations between measures of network centrality and mutability are not statistically significant, but several trends point toward a weak positive association between network centrality and mutational sensitivity. There is a small but significant negative association between the mutational correlation of a pair of metabolites (rM ) and the shortest path length between those metabolites. Positive association between the centrality of a metabolite and its mutational heritability is consistent with centrally-positioned metabolites presenting a larger mutational target than peripheral ones, and is inconsistent with centrality conferring mutational robustness, at least in toto. The weakness of the correlation between rM and the shortest path length between pairs of metabolites suggests that network locality is an important but not overwhelming factor governing mutational pleiotropy. These findings provide necessary background against which the effects of other evolutionary forces, most importantly natural selection, can be interpreted.

The importance of temperature fluctuations in understanding mosquito population dynamics and malaria risk.

Temperature is a key environmental driver of Anopheles mosquito population dynamics; understanding its central role is important for these malaria vectors. Mosquito population responses to temperature fluctuations, though important across the life history, are poorly understood at a population level. We used stage-structured, temperature-dependent delay-differential equations to conduct a detailed exploration of the impacts of diurnal and annual temperature fluctuations on mosquito population dynamics. The model allows exploration of temperature-driven temporal changes in adult age structure, giving insights into the population's capacity to vector malaria parasites. Because of temperature-dependent shifts in age structure, the abundance of potentially infectious mosquitoes varies temporally, and does not necessarily mirror the dynamics of the total adult population. In addition to conducting the first comprehensive theoretical exploration of fluctuating temperatures on mosquito population dynamics, we analysed observed temperatures at four locations in Africa covering a range of environmental conditions. We found both temperature and precipitation are needed to explain the observed malaria season in these locations, enhancing our understanding of the drivers of malaria seasonality and how temporal disease risk may shift in response to temperature changes. This approach, tracking both mosquito abundance and age structure, may be a powerful tool for understanding current and future malaria risk.

The effect of temperature on Anopheles mosquito population dynamics and the potential for malaria transmission.

The parasites that cause malaria depend on Anopheles mosquitoes for transmission; because of this, mosquito population dynamics are a key determinant of malaria risk. Development and survival rates of both the Anopheles mosquitoes and the Plasmodium parasites that cause malaria depend on temperature, making this a potential driver of mosquito population dynamics and malaria transmission. We developed a temperature-dependent, stage-structured delayed differential equation model to better understand how climate determines risk. Including the full mosquito life cycle in the model reveals that the mosquito population abundance is more sensitive to temperature than previously thought because it is strongly influenced by the dynamics of the juvenile mosquito stages whose vital rates are also temperature-dependent. Additionally, the model predicts a peak in abundance of mosquitoes old enough to vector malaria at more accurate temperatures than previous models. Our results point to the importance of incorporating detailed vector biology into models for predicting the risk for vector borne diseases.

Evolution of structure in CH5 + and its deuterated analogues.

Diffusion Monte Carlo simulations are used to investigate the effects of deuteration on the fluxionality of CH(5)(+) or CD(5)(+), using an ab initio potential surface, developed by Jin, Braams, and Bowman [J. Phys. Chem. 2006, 110, 1569]. We find that partial deuteration quenches the fluxional behavior. The spectral consequences are also investigated. We find that, while CH(5)(+) and CD(5)(+) are nearly spherical tops, partial deuteration breaks the rotational symmetry and the mixed isotopologues are generally better characterized as symmetric tops. In addition, we investigate the effects of deuteration on the low-resolution vibrational spectrum and anticipate that signatures of this delocalization will be observable in the vibrational spectrum.

Deuteration effects on the structure and infrared spectrum of CH5(+).

The CH5+ molecular ion is well-known for its large amplitude motions that lead to complete scrambling of the hydrogen atoms, even in the vibrational ground state. Experiments have been reported that probe the consequences of these large amplitude motions. We recently reported that quantum zero-point effects partially quench the scrambling when CH5+ is partially deuterated. Here, the consequences of this quantum localization are investigated through calculations of the low-resolution spectra of CH4D+, CHD4+, and CD5+. The spectra are obtained by convoluting stick spectra, evaluated for individual stationary points on an ab initio potential surface, multiplying them by Diffusion Monte Carlo ground state density at that stationary point, and taking the sum. The CH/D stretch regions of CH4D+ and CD5+ are red-shifted relative to CH5+, while the overall shape of the envelope remains unaffected. In contrast, for CHD4+, the shape of the spectral envelope in the CH/D stretch region differs from the other three isotopologs. These signatures of the quantum localization of the deuterium on the spectra are discussed.

Quantum deconstruction of the infrared spectrum of CH5+.

We present two quantum calculations of the infrared spectrum of protonated methane (CH5+) using full-dimensional, ab initio-based potential energy and dipole moment surfaces. The calculated spectra compare well with a low-resolution experimental spectrum except below 1000 cm(-1), where the experimental spectrum shows no absorption. The present calculations find substantial absorption features below 1000 cm(-1), in qualitative agreement with earlier classical calculations of the spectrum. The major spectral bands are analyzed in terms of the molecular motions. Of particular interest is an intense feature at 200 cm(-1), which is due to an isomerization mode that connects two equivalent minima. Very recent high-resolution jet-cooled spectra in the CH stretch region (2825 to 3050 cm(-1)) are also reported, and assignments of the band origins are made, based on the present quantum calculations.