The importance of fission–fusion social group dynamics in birds

Almost all animal social groups show some form of fission–fusion dynamics, whereby group membership is not spatio‐temporally stable. These dynamics have major implications at both population and individual levels, exerting an important influence on patterns of social behaviour, information transfer and epidemiology. However, fission–fusion dynamics in birds have received relatively little attention. We review the existing evidence for fission–fusion sociality in birds alongside a more general explanation of the social and ecological processes that may drive fission–fusion dynamics. Through a combination of recent methodological developments and novel technologies with well‐established areas of ornithological research, avian systems offer great potential to further our understanding of fission–fusion social systems and the consequences they have at an individual and population level. In particular, investigating the interaction between social structure and environmental covariates can promote a deeper understanding of the evolution of social behaviour and the adaptive value of group living, as well as having important consequences for applied research.     

Modelling parasite aggregation: disentangling statistical and ecological approaches

The overdispersion in macroparasite infection intensity among host populations is commonly simulated using a constant negative binomial aggregation parameter. We describe an alternative to utilising the negative binomial approach and demonstrate important disparities in intervention efficacy projections that can come about from opting for pattern-fitting models that are not process-explicit. We present model output in the context of the epidemiology and control of soil-transmitted helminths due to the significant public health burden imposed by these parasites, but our methods are applicable to other infections with demonstrable aggregation in parasite numbers among hosts.     

Reduced and reversed temperature dependence of blood oxygenation in an ectothermic scombrid fish: implications for the evolution of regional heterothermy?

Tunas (family Scombridae) are exceptional among most teleost fishes in that they possess vascular heat exchangers which allow heat retention in specific regions of the body (termed ‘regional heterothermy’). Seemingly exclusive to heterothermic fishes is a markedly reduced temperature dependence of blood–oxygen (blood–O₂) binding, or even a reversed temperature dependence where increasing temperature increases blood–O₂ affinity. These unusual binding properties have been documented in whole blood and in haemoglobin (Hb) solutions, and they are hypothesised to prevent oxygen loss from arteries to veins within the vascular heat exchangers and/or to prevent excessive oxygen unloading to the warm tissues and ensure an adequate supply of oxygen to tissues positioned efferent to the heat exchangers. The temperature sensitivity of blood–O₂ binding has not been characterised in an ectothermic scombrid (mackerels and bonitos), but the existence of the unusual binding properties in these fishes would have clear implications for their proposed association with regional heterothermy. Accordingly, the present study examined oxygenation of whole blood of the chub mackerel (Scomber japonicus) at 10, 20 and 30°C and at 0.5, 1 and 2% CO₂. Oxygen affinity was generally highest at 20°C for all levels of CO₂. Temperature-independent binding was observed at low (0.5%) CO₂, where the PO₂ at 50% blood–O₂ saturation (P ₅₀) was not statistically different at 10 and 30°C (2.58 vs. 2.78 kPa, respectively) with an apparent heat of oxygenation (∆H°) close to zero (−6 kJ mol⁻¹). The most significant temperature-mediated difference occurred at high (2%) CO₂, where the P ₅₀ at 10°C was twofold higher than that at 20°C with a corresponding ∆H° of +43 kJ mol⁻¹. These results provide clear evidence of independent and reversed open-system temperature effects on blood oxygenation in S. japonicus, and it is therefore speculated that these unusual blood–O₂ binding characteristics may have preceded the evolution of vascular heat exchangers and regional heterothermy in fishes.     

Young’s modulus of elasticity of Schlemm’s canal endothelial cells

Schlemm’s canal (SC) endothelial cells are likely important in the physiology and pathophysiology of the aqueous drainage system of the eye, particularly in glaucoma. The mechanical stiffness of these cells determines, in part, the extent to which they can support a pressure gradient and thus can be used to place limits on the flow resistance that this layer can generate in the eye. However, little is known about the biomechanical properties of SC endothelial cells. Our goal in this study was to estimate the effective Young’s modulus of elasticity of normal SC cells. To do so, we combined magnetic pulling cytometry of isolated cultured human SC cells with finite element modeling of the mechanical response of the cell to traction forces applied by adherent beads. Preliminary work showed that the immersion angles of beads attached to the SC cells had a major influence on bead response; therefore, we also measured bead immersion angle by confocal microscopy, using an empirical technique to correct for axial distortion of the confocal images. Our results showed that the upper bound for the effective Young’s modulus of elasticity of the cultured SC cells examined in this study, in central, non-nuclear regions, ranged between 1,007 and 3,053 Pa, which is similar to, although somewhat larger than values that have been measured for other endothelial cell types. We compared these values to estimates of the modulus of primate SC cells in vivo, based on images of these cells under pressure loading, and found good agreement at low intraocular pressure (8–15 mm Hg). However, increasing intraocular pressure (22–30 mm Hg) appeared to cause a significant increase in the modulus of these cells. These moduli can be used to estimate the extent to which SC cells deform in response to the pressure drop across the inner wall endothelium and thereby estimate the extent to which they can generate outflow resistance.     

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.     

Large-scale properties of clustered networks: implications for disease dynamics

We consider previously proposed procedures for generating clustered networks and investigate how these procedures lead to differences in network properties other than clustering. We interpret our findings in terms of the effect of the network structure on the disease outbreak threshold and disease dynamics. To generate null-model networks for comparison, we implement an assortativity-conserving rewiring algorithm that alters the level of clustering while causing minimal impact on other properties. We show that many theoretical network models used to generate networks with a particular property often lead to significant changes in network properties other than that of interest. For high levels of clustering, different procedures lead to networks that differ in degree heterogeneity and assortativity, and in broader scale measures such as ?(0) and the distribution of shortest path lengths. Hence, care must be taken when investigating the implications of network properties for disease transmission or other dynamic process that the network supports.