Why Was There a Harmful Algal Bloom in 2015: The Relative Growth of Toxic and Non-toxic Diatoms as a Function of Temperature

A coastwide bloom of the toxigenic diatom Pseudo-nitzschia in 2015 resulted in the largest recorded outbreak and unprecedented levels of the neurotoxin, domoic acid (DA), along the North American west coast. The scientific community has suggested that warmer ocean temperatures were the main cause of this harmful algal bloom (HAB), but little scientific evidence to support the relationship between temperature, and the growth and toxicity of Pseudo-nitzschia has been provided for local isolates of these diatoms. To gain insight into bloom dynamics, a laboratory study was conducted to examine the growth of toxic and non-toxic phytoplankton species at a range of temperatures. Non- (or low) toxic diatoms Pseudo-nitzschia fraudulenta, Skeletonema costatum, and Chaetoceros decipiens were isolated from the 2015 bloom, and cultured at eight temperature conditions (5.6, 6.8, 8.7, 10.8, 13.3, 15.2, 17.2, 19.0°C). A total of 48 cultures (6 tubes per condition), with duplicates at each temperature, were grown in a temperature gradient incubator and monitored for 31 days over three complete growth cycles (runs) of exponential and stationary growth. Specific growth rates, determined from daily measures of in vivo fluorescence, indicate that by Run 3, there was no growth at 5.6°C for Chaetoceros decipiens, and a large decline in the growth rate for Skeletonema costatum at 17.2 and 19.0°C. Pseudo-nitzschia fraudulenta demonstrated the greatest growth rates of all species from 10.8 to 19.0°C, and exhibited the broadest range of elevated growth rates. These temperature results indicate that Skeletonema costatum does not thrive in ocean temperatures above 15°C, and is outcompeted by other algae, including both species of Pseudo-nitzschia. Results of this study will greatly aid oceanographers in determining the dominant species in a coastal region as a function of ambient ocean temperature conditions

Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

Structure determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure but is constrained by the need for large, well-ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction, and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular weight threshold required by single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure determination methods. Here we present a data processing scheme that combines routines from X-ray crystallography and new algorithms we developed to solve structures from tomograms of nanocrystals. This pipeline handles image processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. We also assess the tolerance of this workflow to the effects of radiation damage. Our simulations indicate a trade-off between a wider tilt-range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors but not merging errors can be overcome with additional datasets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure

Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular-weight threshold required for single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure-determination methods. Here, a data-processing scheme is presented that combines routines from X-ray crystallography and new algorithms that have been developed to solve structures from tomograms of nanocrystals. This pipeline handles image-processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. The tolerance of this workflow to the effects of radiation damage is also assessed. The simulations indicate a trade-off between a wider tilt range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors, but not merging errors, can be overcome with additional data sets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure

Plant Trait Diversity Buffers Variability in Denitrification Potential over Changes in Season and Soil Conditions

BACKGROUND: Denitrification is an important ecosystem service that removes nitrogen (N) from N-polluted watersheds, buffering soil, stream, and river water quality from excess N by returning N to the atmosphere before it reaches lakes or oceans and leads to eutrophication. The denitrification enzyme activity (DEA) assay is widely used for measuring denitrification potential. Because DEA is a function of enzyme levels in soils, most ecologists studying denitrification have assumed that DEA is less sensitive to ambient levels of nitrate (NO(3)(-)) and soil carbon and thus, less variable over time than field measurements. In addition, plant diversity has been shown to have strong effects on microbial communities and belowground processes and could potentially alter the functional capacity of denitrifiers. Here, we examined three questions: (1) Does DEA vary through the growing season? (2) If so, can we predict DEA variability with environmental variables? (3) Does plant functional diversity affect DEA variability? METHODOLOGY/PRINCIPAL FINDINGS: The study site is a restored wetland in North Carolina, US with native wetland herbs planted in monocultures or mixes of four or eight species. We found that denitrification potentials for soils collected in July 2006 were significantly greater than for soils collected in May and late August 2006 (p<0.0001). Similarly, microbial biomass standardized DEA rates were significantly greater in July than May and August (p<0.0001). Of the soil variables measured--soil moisture, organic matter, total inorganic nitrogen, and microbial biomass--none consistently explained the pattern observed in DEA through time. There was no significant relationship between DEA and plant species richness or functional diversity. However, the seasonal variance in microbial biomass standardized DEA rates was significantly inversely related to plant species functional diversity (p<0.01). CONCLUSIONS/SIGNIFICANCE: These findings suggest that higher plant functional diversity may support a more constant level of DEA through time, buffering the ecosystem from changes in season and soil conditions

Stabilized UAV footage from Challenges and solutions for studying collective animal behaviour in the wild

ESM 1. Stabilized UAV video footage of wildebeest herd crossing a river, demonstrating the feasibility of using remote sensing tools to study collective animal behaviors across large distances in a natural setting. Video was reproduced with permission from Colin J. Torney and Elaine Ferguson

Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation.

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular-weight threshold required for single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure-determination methods. Here, a data-processing scheme is presented that combines routines from X-ray crystallography and new algorithms that have been developed to solve structures from tomograms of nanocrystals. This pipeline handles image-processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. The tolerance of this workflow to the effects of radiation damage is also assessed. The simulations indicate a trade-off between a wider tilt range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors, but not merging errors, can be overcome with additional data sets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure

Analogical reasoning and the content of creation stories : Quantitative comparisons of preindustrial societies

A long-standing question in sociology concerns preindustrial societies and the relationship between their subsistence technology and ideas about god. This article proposes a shift from questions regarding gods who now and then create to questions about creations that sometimes involve a god. For preindustrial societies, it addresses the relation between their subsistence technology and the content of their creation stories. This article’s answer combines Hume’s general hypothesis that people reason by analogy with Topitsch’s specification that invokes vital, technical, and social analogies. This conjunction yields concrete hypotheses about the substance of creation stories in societies with varying levels of subsistence technology according to Lenski’s typology. To test these hypotheses, the authors used Murdock’s Standard Cross-Cultural Sample and the Human Relations Area Files. Field reports were coded for 116 preindustrial societies. The findings show that people use different thought models to explain the unknown, depending on the society’s level of subsistence technology.