Measuring the value of air quality: application of the spatial hedonic model

This study applies a hedonic model to assess the economic benefits of air quality improvement following the 1990 Clean Air Act Amendment at the county level in the lower 48 United States. An instrumental variable approach that combines geographically weighted regression and spatial autoregression methods (GWR-SEM) is adopted to simultaneously account for spatial heterogeneity and spatial autocorrelation. SEM mitigates spatial dependency while GWR addresses spatial heterogeneity by allowing response coefficients to vary across observations. Positive amenity values of improved air quality are found in four major clusters: (1) in East Kentucky and most of Georgia around the Southern Appalachian area; (2) in a few counties in Illinois; (3) on the border of Oklahoma and Kansas, on the border of Kansas and Nebraska, and in east Texas; and (4) in a few counties in Montana. Clusters of significant positive amenity values may exist because of a combination of intense air pollution and consumer awareness of diminishing air quality

Susceptibility to Predation Affects Trait-Mediated Indirect Interactions by Reversing Interspecific Competition

Numerous studies indicate that the behavioral responses of prey to the presence of predators can have an important role in structuring assemblages through trait-mediated indirect interactions. Few studies, however, have addressed how relative susceptibility to predation influences such interactions. Here we examine the effect of chemical cues from the common shore crab Carcinus maenas on the foraging behavior of two common intertidal gastropod molluscs. Of the two model consumers studied, Littorina littorea is morphologically more vulnerable to crab predation than Gibbula umbilicalis, and it exhibited greater competitive ability in the absence of predation threat. However, Littorina demonstrated a greater anti-predator response when experimentally exposed to predation cues, resulting in a lower level of foraging. This reversed the competitive interaction, allowing Gibbula substantially increased access to shared resources. Our results demonstrate that the susceptibility of consumers to predation can influence species interactions, and suggest that inter-specific differences in trait-mediated indirect interactions are another mechanism through which non-consumptive predator effects may influence trophic interactions

Using the RDP Classifier to Predict Taxonomic Novelty and Reduce the Search Space for Finding Novel Organisms

BACKGROUND: Currently, the naïve Bayesian classifier provided by the Ribosomal Database Project (RDP) is one of the most widely used tools to classify 16S rRNA sequences, mainly collected from environmental samples. We show that RDP has 97+% assignment accuracy and is fast for 250 bp and longer reads when the read originates from a taxon known to the database. Because most environmental samples will contain organisms from taxa whose 16S rRNA genes have not been previously sequenced, we aim to benchmark how well the RDP classifier and other competing methods can discriminate these novel taxa from known taxa. PRINCIPAL FINDINGS: Because each fragment is assigned a score (containing likelihood or confidence information such as the boostrap score in the RDP classifier), we "train" a threshold to discriminate between novel and known organisms and observe its performance on a test set. The threshold that we determine tends to be conservative (low sensitivity but high specificity) for naïve Bayesian methods. Nonetheless, our method performs better with the RDP classifier than the other methods tested, measured by the f-measure and the area-under-the-curve on the receiver operating characteristic of the test set. By constraining the database to well-represented genera, sensitivity improves 3-15%. Finally, we show that the detector is a good predictor to determine novel abundant taxa (especially for finer levels of taxonomy where novelty is more likely to be present). CONCLUSIONS: We conclude that selecting a read-length appropriate RDP bootstrap score can significantly reduce the search space for identifying novel genera and higher levels in taxonomy. In addition, having a well-represented database significantly improves performance while having genera that are "highly" similar does not make a significant improvement. On a real dataset from an Amazon Terra Preta soil sample, we show that the detector can predict (or correlates to) whether novel sequences will be assigned to new taxa when the RDP database "doubles" in the future

Bacterial Genomes: Habitat Specificity and Uncharted Organisms

The capability and speed in generating genomic data have increased profoundly since the release of the draft human genome in 2000. Additionally, sequencing costs have continued to plummet as the next generation of highly efficient sequencing technologies (next-generation sequencing) became available and commercial facilities promote market competition. However, new challenges have emerged as researchers attempt to efficiently process the massive amounts of sequence data being generated. First, the described genome sequences are unequally distributed among the branches of bacterial life and, second, bacterial pan-genomes are often not considered when setting aims for sequencing projects. Here, we propose that scientists should be concerned with attaining an improved equal representation of most of the bacterial tree of life organisms, at the genomic level. Moreover, they should take into account the natural variation that is often observed within bacterial species and the role of the often changing surrounding environment and natural selection pressures, which is central to bacterial speciation and genome evolution. Not only will such efforts contribute to our overall understanding of the microbial diversity extant in ecosystems as well as the structuring of the extant genomes, but they will also facilitate the development of better methods for (meta)genome annotation

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Reduced Stability and Increased Dynamics in the Human Proliferating Cell Nuclear Antigen (PCNA) Relative to the Yeast Homolog

Proliferating Cell Nuclear Antigen (PCNA) is an essential factor for DNA replication and repair. PCNA forms a toroidal, ring shaped structure of 90 kDa by the symmetric association of three identical monomers. The ring encircles the DNA and acts as a platform where polymerases and other proteins dock to carry out different DNA metabolic processes. The amino acid sequence of human PCNA is 35% identical to the yeast homolog, and the two proteins have the same 3D crystal structure. In this report, we give evidence that the budding yeast (sc) and human (h) PCNAs have highly similar structures in solution but differ substantially in their stability and dynamics. hPCNA is less resistant to chemical and thermal denaturation and displays lower cooperativity of unfolding as compared to scPCNA. Solvent exchange rates measurements show that the slowest exchanging backbone amides are at the β-sheet, in the structure core, and not at the helices, which line the central channel. However, all the backbone amides of hPCNA exchange fast, becoming undetectable within hours, while the signals from the core amides of scPCNA persist for longer times. The high dynamics of the α-helices, which face the DNA in the PCNA-loaded form, is likely to have functional implications for the sliding of the PCNA ring on the DNA since a large hole with a flexible wall facilitates the establishment of protein-DNA interactions that are transient and easily broken. The increased dynamics of hPCNA relative to scPCNA may allow it to acquire multiple induced conformations upon binding to its substrates enlarging its binding diversity

Genome-wide DNA methylation levels and altered cortisol stress reactivity following childhood trauma in humans

DNA methylation likely plays a role in the regulation of human stress reactivity. Here we show that in a genome-wide analysis of blood DNA methylation in 85 healthy individuals, a locus in the Kit ligand gene (KITLG; cg27512205) showed the strongest association with cortisol stress reactivity (P=5.8 � 10?6). Replication was obtained in two independent samples using either blood (N=45, P=0.001) or buccal cells (N=255, P=0.004). KITLG methylation strongly mediates the relationship between childhood trauma and cortisol stress reactivity in the discovery sample (32% mediation). Its genomic location, a CpG island shore within an H3K27ac enhancer mark, and the correlation between methylation in the blood and prefrontal cortex provide further evidence that KITLG methylation is functionally relevant for the programming of stress reactivity in the human brain. Our results extend preclinical evidence for epigenetic regulation of stress reactivity to humans and provide leads to enhance our understanding of the neurobiological pathways underlying stress vulnerability

Structural Constraints Identified with Covariation Analysis in Ribosomal RNA

Covariation analysis is used to identify those positions with similar patterns of sequence variation in an alignment of RNA sequences. These constraints on the evolution of two positions are usually associated with a base pair in a helix. While mutual information (MI) has been used to accurately predict an RNA secondary structure and a few of its tertiary interactions, early studies revealed that phylogenetic event counting methods are more sensitive and provide extra confidence in the prediction of base pairs. We developed a novel and powerful phylogenetic events counting method (PEC) for quantifying positional covariation with the Gutell lab’s new RNA Comparative Analysis Database (rCAD). The PEC and MI-based methods each identify unique base pairs, and jointly identify many other base pairs. In total, both methods in combination with an N-best and helix-extension strategy identify the maximal number of base pairs. While covariation methods have effectively and accurately predicted RNAs secondary structure, only a few tertiary structure base pairs have been identified. Analysis presented herein and at the Gutell lab’s Comparative RNA Web (CRW) Site reveal that the majority of these latter base pairs do not covary with one another. However, covariation analysis does reveal a weaker although significant covariation between sets of nucleotides that are in proximity in the three-dimensional RNA structure. This reveals that covariation analysis identifies other types of structural constraints beyond the two nucleotides that form a base pair

APOBEC3G-Induced Hypermutation of Human Immunodeficiency Virus Type-1 Is Typically a Discrete “All or Nothing” Phenomenon

The rapid evolution of Human Immunodeficiency Virus (HIV-1) allows studies of ongoing host–pathogen interactions. One key selective host factor is APOBEC3G (hA3G) that can cause extensive and inactivating Guanosine-to-Adenosine (G-to-A) mutation on HIV plus-strand DNA (termed hypermutation). HIV can inhibit this innate anti-viral defense through binding of the viral protein Vif to hA3G, but binding efficiency varies and hypermutation frequencies fluctuate in patients. A pivotal question is whether hA3G-induced G-to-A mutation is always lethal to the virus or if it may occur at sub-lethal frequencies that could increase viral diversification. We show in vitro that limiting-levels of hA3G-activity (i.e. when only a single hA3G-unit is likely to act on HIV) produce hypermutation frequencies similar to those in patients and demonstrate in silico that potentially non-lethal G-to-A mutation rates are ∼10-fold lower than the lowest observed hypermutation levels in vitro and in vivo. Our results suggest that even a single incorporated hA3G-unit is likely to cause extensive and inactivating levels of HIV hypermutation and that hypermutation therefore is typically a discrete “all or nothing” phenomenon. Thus, therapeutic measures that inhibit the interaction between Vif and hA3G will likely not increase virus diversification but expand the fraction of hypermutated proviruses within the infected host