Genomic imprinting and kinship in the social Hymenoptera: what are the predictions?

The kinship theory of genomic imprinting predicts that conflicts of interest between parents can promote the evolution of opposed expression patterns of maternally and paternally derived alleles in the offspring. The social Hymenoptera (ants, some bees, and some wasps) are particularly suitable to test this theory, because a variety of social conflicts are predicted due to relatedness asymmetries between female and male nestmates that are a corollary of haplo-diploid sex determination. Here I argue that the kin-selection predictions for genomic imprinting in social Hymenoptera might in many cases be more complex than previously suggested, because the optimal strategy will have to take fitness effects in different castes and sexes into account.     

Influences of geometrical and mechanical properties of bone tissues in mandible behaviour – experimental and numerical predictions

The properties and geometry of bone in the mandible play a key role in mandible behaviour during a person’s lifetime, and attention needs to be paid to the influence of bone properties. We analysed the effect of bone geometry, size and bone properties in mandible behaviour, experimenting on cadaveric mandibles and FE models. The study was developed using the geometry of a cadaveric mandible without teeth. Three models of cadaveric condyles were experimentally tested with instrumented with four rosettes, and a condyle reaction of 300 N. Four finite element models were considered to validate the experiments and analyse mandible behaviour. One numeric model was simulated with 10 muscles in a quasi-static condition. The experimental results present different condyle stiffness’s, of 448, 215 and 254 N/mm. The values presented in the rosettes are influenced by bone geometry and bone thickness; maximum value was ?600 με in rosette #4, and the maximum strain difference between mandibles was 111%. The numerical results show that bone density decreases and strain distribution increases in the thinner mandible regions. Nevertheless, the global behaviour of the structure remains similar, but presents different strain magnitudes. The study shows the need to take into account bone characteristics and their evolutions in order to improve implant design and fixation throughout the patient life. The change in bone stiffness promotes a change in maximum strain distribution with same global behaviour.     

Do Lanchester's laws of combat describe competition in ants?

Lanchester's laws are mathematical models, originally designedto model military combat, that describe battle outcomes basedon the sizes of armies and the potencies of individual fightingunits. The "square law" describes a scenario in which small-sizedcompetitors may use a numerical advantage to overcome large-sizedcompetitors in simultaneous combat. The "linear law" describeshow a competitor with large-sized fighting units may use one-on-onecombat to favor victory when outnumbered. Lanchester's lawshave been suggested as an important regulator of interspecificcompetition in social insects, but without experimental support. In this study, experimental platforms were designed to invokethe conditions of both Lanchester's laws in a community ofants in lowland tropical wet forest in Costa Rica. I measuredbehavioral dominance at the food platforms in two separatemanners: an ordinal ranking of the number of workers present,and the monopoly of the food platforms. At the platform invokingsimultaneous combat, satisfying the square law, small-sizedants were more behaviorally dominant by numerical superiority.At the platform invoking one-on-one combat, satisfying thelinear law, larger ants were more behaviorally dominant by monopoly. These results suggest that Lanchester's laws explain,in part, the outcome of interspecific competition in ants.     

Observing populations and testing predictions about genetic drift in a computer simulation improves college students’ conceptual understanding

Background

Evolution is a difficult subject for students, with well-documented confusion about natural selection, tree thinking, and genetic drift among other topics. Here we investigate the effect of a simulation-based module about the conservation of black-footed ferrets, a module designed with pedagogical approaches that have been demonstrated to be effective, for teaching genetic drift. We compared performance on the Genetic Drift Inventory (GeDI) of students who completed the module and students who were in classes that used other methods for teaching genetic drift.

Results

Students in 19 courses using the simulation-based module improved their understanding of genetic drift significantly after completing the Ferrets module, as measured by the GeDI. Students in five control courses actually performed significantly worse on the GeDI after instruction. The lower scores in the control courses were driven by a decrease in these students’ understanding of key concepts.

Conclusions

The Ferrets module appears to be an effective way to teach genetic drift. In the control courses, students’ progress in understanding genetic drift may pass through a stage where their understanding of key concepts is worse than it was prior to instruction. However, students who learned genetic drift in courses that used the Ferrets module showed a more rapid increase in their understanding of key concepts related to genetic drift. This result suggests that the paths that students can take to move from novice to expert understanding may be more varied than was previously predicted.

     

Evaluation of 2-indolcarbohydrazones as potent α-glucosidase inhibitors,in silico studies and DFT based stereochemical predictions

2-Indolcarbohydrazones 1–28 were synthesized and evaluated for their α-glucosidase inhibitory potential. A varying degree of inhibitory potential with IC₅₀ values in the range of 2.3 ± 0.11–226.4 ± 6.8 μM was observed while comparing these outcomes with the standard acarbose (IC₅₀ = 906.0 ± 6.3 μM). The stereochemistry of ten (10) randomly selected compounds (1, 3, 6, 8, 12, 18, 19, 23, 25 and 28) was predicted by Density Functional Theory (DFT). The stability of E isomer was deduced by comparing the calculated and experimental vibration modes of ν_CO, ν_NC and ν_CH (CH in NCH-R). It was observed that except compound 18, all other compounds were deduced to have E configuration while molecular modeling studies revealed the key interactions between enzyme and synthesized compounds.     

Phenylalanine hydroxylase deficiency in the Slovak population: Genotype–phenotype correlations and genotype-based predictions of BH4-responsiveness

We investigated the mutation spectrum of the phenylalanine hydroxylase gene (PAH) in a cohort of patients from 135 Slovak PKU families. Mutational screening of the known coding region, including conventional intron splice sites, was performed using high-resolution melting analysis, with subsequent sequencing analysis of the samples showing deviated melting profiles compared to control samples. The PAH gene was also screened for deletions and duplications using MLPA analysis. Forty-eight different disease causing mutations were identified in our patient group, including 30 missense, 8 splicing, 7 nonsense, 2 large deletions and 1 small deletion with frameshift; giving a detection rate of 97.6%. The most prevalent mutation was the p.R408W, occurring in 47% of all alleles, which concurs with results from neighboring and other Slavic countries. Other frequent mutations were: p.R158Q (5.3%), IVS12 + 1G>A (5.3%), p.R252W (5.1%), p.R261Q (3.9%) and p.A403V (3.6%). We also identified three novel missense mutations: p.F233I, p.R270I, p.F331S and one novel variant: c.− 30A>T in the proximal part of the PAH gene promoter. A spectrum of 84 different genotypes was observed and a genotype based predictions of BH4-responsiveness were assessed. Among all genotypes, 36 were predicted to be BH4-responsive represented by 51 PKU families. In addition, genotype–phenotype correlations were performed.     

Computational exploration of metal–organic frameworks: examples of advances in crystal structure predictions and electronic structure tuning

The purpose of this article is to consider some recent developments in the area of the computational chemistry of metal–organic frameworks (MOFs), and more specifically on their crystal structure prediction and electronic structures. We intend here to illustrate how computational approaches might be powerful tool for the discovery of new families of hybrid frameworks, helping to understand their often complex energy landscapes. Also, MOFs have attracted a lot of attention due to their potential use for photocatalysis and optoelectronic, making it necessary to develop strategies to control their electronic structures. We will show how recent computational studies in this area have allowed a better understanding of their electronic properties and their potential tunability, highlighting when they have given successful guidelines for the discovery of novel MOFs with targeted properties.     

Do generalized scaling laws exist for species abundance distribution in mountains?

Knowing the global pattern of species diversity is a central goal of the science of ecology, and scaling laws can be useful for analysis of cross-scale biodiversity patterns. An elevational gradient in a warm temperate zone of the Donglingshan mountains (China) is used to test the scaling laws of species abundance distribution using multifractal analysis. We show that the power law scaling relationship holds for not just the classical SAR (species–area relationship for richness), but also for Shannon and Simpson diversity. In fact, we find power-laws in the generalized species abundance distribution at all stratal levels of the forest (trees, shrubs and herbs). The fact that these laws exist across a heterogeneous landscape representing a strong bioclimatic gradient suggests that biodiversity scaling laws may be more robust than previously thought.     

pKa predictions with a coupled finite difference Poisson-Boltzmann and Debye-Hückel method

Modeling charge interactions is important for understanding many aspects of biological structure and function, and continuum methods such as Finite Difference Poisson-Boltzmann (FDPB) are commonly employed. Calculations of pH-dependence have identified separate populations; surface groups that can be modeled with a simple Debye-Hückel (DH) model, and buried groups, with stronger resultant interactions that are dependent on detailed conformation. This observation led to the development of a combined FDPB and DH method for pK(a) prediction (termed FD/DH). This study reports application of this method to ionizable groups, including engineered buried charges, in staphylococcal nuclease. The data had been made available to interested research groups before publication of mutant structures and/or pK(a) values. Overall, FD/DH calculations perform as intended with low ΔpK(a) values for surface groups (RMSD between predicted and experimental pK(a) values of 0.74), and much larger ΔpK(a) values for the engineered internal groups, with RMSD = 1.64, where mutant structures were known and RMSD = 1.80, where they were modeled. The weaker resultant interactions of the surface groups are determined mostly by charge-charge interactions. For the buried groups, R(2) for correlation between predicted and measured ΔpK(a) values is 0.74, despite the high RMSDs. Charge-charge interactions are much less important, with the R(2) value for buried group ΔpK(a) values increasing to 0.80 when the term describing charge desolvation alone is used. Engineered charge burial delivers a relatively uniform, nonspecific effect, in terms of pK(a) . How the protein environment adapts in atomic detail to deliver this resultant effect is still an open question.     

Numbats and aardwolves—how low is low? A re-affirmation of the need for statistical rigour in evaluating regression predictions

Many comparative physiological studies aim to determine if a particular species differs from a prediction based on a linear allometric regression for other species. However, the judgment as to whether the species in question conforms to this allometric relationship is often not based on any formal statistical analysis. An appropriate statistical method is to compare the new species’ value with the 95% confidence limits for predicting an additional datum from the relationship for the other species. We examine the basal metabolic rate (BMR) of the termitivorous numbat (Myrmecobius fasciatus) and aardwolf (Proteles cristatus) to demonstrate the use of the 95% prediction limits to determine statistically if they have a lower-than-expected BMR compared to related species. The numbat’s BMR was 83.6% of expected from mass, but fell inside the 95% prediction limits for a further datum; a BMR < 72.5% of predicted was required to fall below the one-tail 95% prediction limits. The aardwolf had a BMR that was only 74.2% of predicted from the allometric equation, but it also fell well within the 95% prediction limits; a BMR of only 41.8% of predicted was necessary to fall below the one-tail 95% prediction limits. We conclude that a formal statistical approach is essential, although it is difficult to demonstrate that a single species statistically differs from a regression relationship for other species.     

How accurate and statistically robust are catalytic site predictions based on closeness centrality?

Background  

We examine the accuracy of enzyme catalytic residue predictions from a network representation of protein structure. In this model, amino acid α-carbons specify vertices within a graph and edges connect vertices that are proximal in structure. Closeness centrality, which has shown promise in previous investigations, is used to identify important positions within the network. Closeness centrality, a global measure of network centrality, is calculated as the reciprocal of the average distance between vertex i and all other vertices.     

Taking phylogenetics beyond pattern analysis: can models of genome dynamics guide predictions about homoplasy in morphological and behavioral data sets?

Despite the considerable amount of interest in phylogeny reconstruction, patterns of homoplasy in morphological and behavioral data have received only limited attention to date, whereas the patterns of homoplasy in molecular data are relatively well understood. First, because the number of alternative molecular character states is strictly limited (particularly for nucleotide sequence data), higher rates of substitution generate higher levels of homoplasy. Second, depending on the relative proportions of constrained and unconstrained sites, each molecular data set has a time frame of applicability outside of which resolution becomes ambiguous. There is good evidence to suggest that numbers of alternative character states for morphological and even behavioral data may be similarly limited and that higher rates of evolution are often linked to higher rates of homoplasy. Like molecular data sets, morphological and behavioral data sets contain rapidly evolving characters as well as more conservative elements. Morphologies and behaviors related to sexual recognition and reproduction show low levels of intraspecific variation, but high levels of lability between species, making them crucial for species identification but often poor as markers of relationship at greater time depths. The organization theory of speciation derived by Carson is a model based on genome dynamics, and it predicts exactly this window of applicability for characters related to sexual reproduction. Nonsexual characters related to environmental adaptation should be applicable at greater phylogenetic depths. A better understanding of patterns of homoplasy enables a more sophisticated approach to the assessment of the relative reliabilities of alternative tree topologies.     

Thermodynamic resolution: How do errors in modeled protein structures affect binding affinity predictions?

The present study addresses the effect of structural distortion, caused by protein modeling errors, on calculated binding affinities toward small molecules. The binding affinities to a total of 300 distorted structures based on five different protein–ligand complexes were evaluated to establish a broadly applicable relationship between errors in protein structure and errors in calculated binding affinities. Relatively accurate protein models (less than 2 Å RMSD within the binding site) demonstrate a 14.78 (±7.5)% deviation in binding affinity from that calculated by using the corresponding crystal structure. For structures of 2–3 Å, 3–4 Å, and >4 Å RMSD within the binding site, the error in calculated binding affinity increases to 20.8 (±5.98), 22.79 (±11.3), and 29.43 (±11.47)%, respectively. The results described here may be used in combination with other tools to evaluate the utility of modeled protein structures for drug development or other ligand‐binding studies. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

How can soil monitoring networks be used to improve predictions of organic carbon pool dynamics and CO2 fluxes in agricultural soils?

As regional and continental carbon balances of terrestrial ecosystems become available, it becomes clear that the soils are the largest source of uncertainty. Repeated inventories of soil organic carbon (SOC) organized in soil monitoring networks (SMN) are being implemented in a number of countries. This paper reviews the concepts and design of SMNs in ten countries, and discusses the contribution of such networks to reducing the uncertainty of soil carbon balances. Some SMNs are designed to estimate country-specific land use or management effects on SOC stocks, while others collect soil carbon and ancillary data to provide a nationally consistent assessment of soil carbon condition across the major land-use/soil type combinations. The former use a single sampling campaign of paired sites, while for the latter both systematic (usually grid based) and stratified repeated sampling campaigns (5?C10 years interval) are used with densities of one site per 10?C1,040 km². For paired sites, multiple samples at each site are taken in order to allow statistical analysis, while for the single sites, composite samples are taken. In both cases, fixed depth increments together with samples for bulk density and stone content are recommended. Samples should be archived to allow for re-measurement purposes using updated techniques. Information on land management, and where possible, land use history should be systematically recorded for each site. A case study of the agricultural frontier in Brazil is presented in which land use effect factors are calculated in order to quantify the CO₂ fluxes from national land use/management conversion matrices. Process-based SOC models can be run for the individual points of the SMN, provided detailed land management records are available. These studies are still rare, as most SMNs have been implemented recently or are in progress. Examples from the USA and Belgium show that uncertainties in SOC change range from 1.6?C6.5 Mg C ha^?1 for the prediction of SOC stock changes on individual sites to 11.72 Mg C ha^?1 or 34% of the median SOC change for soil/land use/climate units. For national SOC monitoring, stratified sampling sites appears to be the most straightforward attribution of SOC values to units with similar soil/land use/climate conditions (i.e. a spatially implicit upscaling approach).