Epigenetic changes enable genomes to respond to changes in the environment, such as altered nutrition, activity, or social setting. Epigenetic modifications, thereby, provide a source of phenotypic plasticity in many species. The honey bee (Apis mellifera) uses nutritionally sensitive epigenetic control mechanisms in the development of the royal caste (queens) and the workers. The workers are functionally sterile females that can take on a range of distinct physiological and/or behavioral phenotypes in response to environmental changes. Honey bees have a wide repertoire of epigenetic mechanisms which, as in mammals, include cytosine methylation, hydroxymethylated cytosines, together with the enzymatic machinery responsible for these cytosine modifications. Current data suggests that honey bees provide an excellent system for studying the “social repertoire” of the epigenome. In this review, we elucidate what is known so far about the honey bee epigenome and its mechanisms. Our discussion includes what may distinguish honey bees from other model animals, how the epigenome can influence worker behavioral task separation, and how future studies can answer central questions about the role of the epigenome in social behavior.

The sophisticated organization of eusocial insect societies is largely based on the regulation of complex behaviors by hydrocarbon pheromones present on the cuticle. We used electrophysiology to investigate the detection of cuticular hydrocarbons (CHCs) by female-specific olfactory sensilla basiconica on the antenna of Camponotus floridanus ants through the utilization of one of the largest family of odorant receptors characterized so far in insects. These sensilla, each of which contains multiple olfactory receptor neurons, are differentially sensitive to CHCs and allow them to be classified into three broad groups that collectively detect every hydrocarbon tested, including queen and worker-enriched CHCs. This broad-spectrum sensitivity is conserved in a related species, Camponotus laevigatus, allowing these ants to detect CHCs from both nestmates and non-nestmates. Behavioral assays demonstrate that these ants are excellent at discriminating CHCs detected by the antenna, including enantiomers of a candidate queen pheromone that regulates the reproductive division of labor.

People with independent (vs. interdependent) social orientation place greater priority on personal success, autonomy, and novel experiences over maintaining ties to their communities of origin. Accordingly, an independent orientation should be linked to a motivational proclivity to move to places that offer economic opportunities, freedom, and diversity. Such places are cities that can be called “cosmopolitan.” In support of this hypothesis, Study 1 found that independently oriented young adults showed a preference to move to cosmopolitan rather than noncosmopolitan cities. Study 2 used a priming manipulation and demonstrated a causal impact of independence on residential preferences for cosmopolitan cities. Study 3 established ecological validity by showing that students who actually moved to a cosmopolitan city were more independent than those who either moved to a noncosmopolitan city or never moved. Taken together, the findings illuminate the role of cosmopolitan settlement in the contemporary cultural change toward independence and have implications for urban development and economic growth.

The shift from cookbook to authentic research-based lab courses in undergraduate biology necessitates the need for evaluation and assessment of these novel courses. Although the biology education community has made progress in this area, it is important that we interpret the effectiveness of these courses with caution and remain mindful of inherent limitations to our study designs that may impact internal and external validity. The specific context of a research study can have a dramatic impact on the conclusions. We present a case study of our own three-year investigation of the impact of a research-based introductory lab course, highlighting how volunteer students, a lack of a comparison group, and small sample sizes can be limitations of a study design that can affect the interpretation of the effectiveness of a course.

Atmospheric radiocarbon (¹⁴C) represents an important observational constraint on emissions of fossil-fuel derived carbon into the atmosphere due to the absence of ¹⁴C in fossil fuel reservoirs. The high sensitivity and precision that accelerator mass spectrometry (AMS) affords in atmospheric ¹⁴C analysis has greatly increased the potential for using such measurements to evaluate bottom-up emissions inventories of fossil fuel CO₂(CO₂ff), as well as those for other co-emitted species. Here we use observations of ¹⁴CO₂ and a series of primary hydrocarbons and combustion tracers from discrete air samples collected between June 2009 and September 2010 at the National Oceanic and Atmospheric Administration Boulder Atmospheric Observatory (BAO; Lat: 40.050° N, Lon: 105.004° W) to derive emission ratios of each species with respect to CO₂ff. The BAO tower is situated at the boundary of the Denver metropolitan area to the south and a large industrial and agricultural region to the north and east, making it an ideal location to study the contrasting mix of emissions from the activities in each region. The species considered in this analysis are carbon monoxide (CO), methane (CH₄), acetylene (C₂H₂), benzene (C₆H₆), and C₃–C₅ alkanes. We estimate emissions for a subset of these species by using the Vulcan high resolution CO2ff emission data product as a reference. We find that CO is overestimated in the 2008 National Emissions Inventory (NEI08) by a factor of ~2. A close evaluation of the inventory suggests that the ratio of CO emitted per unit fuel burned from on-road gasoline vehicles is likely over-estimated by a factor of 2.5. Using a wind-directional analysis of the data, we find enhanced concentrations of CH₄, relative to CO₂ff, in air influenced by emissions to the north and east of the BAO tower when compared to air influenced by emissions in the Denver metro region to the south. Along with enhanced CH₄, the strongest enhancements of the C₃–C₅ alkanes are also found in the north and east wind sector, suggesting that both the alkane and CH₄ enhancements are sourced from oil and gas fields located to the northeast, though it was not possible to rule out the contribution of non oil and gas CH₄ sources.

In letters to the journals Science and Nature (1, 2), 22 virologists notified the research community of their interest in expanding research to develop strains of the already deadly H7N9 Asian influenza virus that would be transmissible via aerosols among mammals, thus creating potential pandemic pathogens. PPPs are defined as pathogens that are potentially highly contagious, potentially highly deadly, and not currently present in the human population. Mammalian contagious avian flu, the 1918 pandemic flu, and SARS are examples. The letter writers cite their scientific reasons for the need for such research, much the same reasons as given by those working on similar projects for the H5N1 avian flu virus (3, 4). This new proposed research signals wider interest in making dangerous influenza viruses (5, 6) contagious in mammals via respiratory aerosols. At present, there are no international regulations or guidelines in place to decide whether such a research project should proceed.

1,1,1,2,3,3,3-Heptafluoropropane (R227ea) is a good refrigerant that reduces greenhouse effects and ozone depletion. In practical applications, we usually have to know the compressed liquid densities at different temperatures and pressures. However, the measurement requires a series of complex apparatus and operations, wasting too much manpower and resources. To solve these problems, here, Song and Mason equation, support vector machine (SVM), and artificial neural networks (ANNs) were used to develop theoretical and machine learning models, respectively, in order to predict the compressed liquid densities of R227ea with only the inputs of temperatures and pressures. Results show that compared with the Song and Mason equation, appropriate machine learning models trained with precise experimental samples have better predicted results, with lower root mean square errors (RMSEs) (e.g., the RMSE of the SVM trained with data provided by Fedele et al. [1] is 0.11, while the RMSE of the Song and Mason equation is 196.26). Compared to advanced conventional measurements, knowledge-based machine learning models are proved to be more time-saving and user-friendly.

The filamentous, non-heterocystous cyanobacterium Lyngbya aestuarii is an important contributor to marine intertidal microbial mats system worldwide. The recent isolate L. aestuarii BL J, is an unusually powerful hydrogen producer. Here we report a morphological, ultrastructural, and genomic characterization of this strain to set the basis for future systems studies and applications of this organism. The filaments contain circa 17 μm wide trichomes, composed of stacked disk-like short cells (2 μm long), encased in a prominent, laminated exopolysaccharide sheath. Cellular division occurs by transversal centripetal growth of cross-walls, where several rounds of division proceed simultaneously. Filament division occurs by cell self-immolation of one or groups of cells (necridial cells) at the breakage point. Short, sheath-less, motile filaments (hormogonia) are also formed. Morphologically and phylogenetically L. aestuarii belongs to a clade of important cyanobacteria that include members of the marine Trichodesmiun and Hydrocoleum genera, as well as terrestrial Microcoleus vaginatus strains, and alkalyphilic strains of Arthrospira. A draft genome of strain BL J was compared to those of other cyanobacteria in order to ascertain some of its ecological constraints and biotechnological potential.

The genome had an average GC content of 41.1%. Of the 6.87 Mb sequenced, 6.44 Mb was present as large contigs (>10,000 bp). It contained 6515 putative protein-encoding genes, of which, 43% encode proteins of known functional role, 26% corresponded to proteins with domain or family assignments, 19.6% encode conserved hypothetical proteins, and 11.3% encode apparently unique hypothetical proteins. The strain's genome reveals its adaptations to a life of exposure to intense solar radiation and desiccation. It likely employs the storage compounds, glycogen, and cyanophycin but no polyhydroxyalkanoates, and can produce the osmolytes, trehalose, and glycine betaine. According to its genome, BL J strain also has the potential to produce a plethora of products of biotechnological interest such as Curacin A, Barbamide, Hemolysin-type calcium-binding toxin, the suncreens scytonemin, and mycosporines, as well as heptadecane and pentadecane alkanes. With respect to hydrogen production, initial comparisons of the genetic architecture and sequence of relevant genes and loci, and a comparative model of protein structure of the NiFe bidirectional hydrogenase, did not reveal conspicuous differences that could explain its unusual hydrogen producing capacity.

Compaction waves traveling through porous cyclotetramethylene-tetranitramine (HMX) are computationally modeled using the Eulerian hydrocode CTH and validated with gas gun experimental data. The method employed use of a newly generated set of P-α parameters for granular HMX in a Mie-Gruneisen equation of state. The P-α model adds a separate parameter to differentiate between the volume changes of a solid material due to compression from the volume change due to compaction, void collapse in a granular material. Computational results are compared via five validation schema for two different initial-porosity experiments. These schema include stress measurements, velocity rise times and arrival times, elastic sound speeds though the material and final compaction densities for a series of two different percent Theoretical Maximum Density (TMD) HMX sets of experimental data. There is a good agreement between the simulations and the experimental gas gun data with the largest source of error being an 11% overestimate of the peak stress which may be due to impedance mismatch on the experimental gauge interface. Determination of these P-α parameters are important as they enable modeling of porosity and are a vital first step in modeling of precursory hotspots, caused by hydrodynamic collapse of void regions or grain interactions, prior to deflagration to detonation transition of granular explosives.