A social learning model of conflict and cooperation in human societies

Patterns of conflict and cooperation both within and between societies may be related to the degree of cultural similarity within and between the same societies. A simple model of social learning is used to predict patterns of conflict and cooperation in hypothetical societies that differ in the roles of relatives and nonrelatives in the enculturation of children. The model is illustrated by comparing its predictions to known differences in the patterns of conflict between males inpatrilocal and matrilocal societies.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

Growth-induced mass flows in fungal networks

Cord-forming fungi form extensive networks that continuously adapt to maintain an efficient transport system. As osmotically driven water uptake is often distal from the tips, and aqueous fluids are incompressible, we propose that growth induces mass flows across the mycelium, whether or not there are intrahyphal concentration gradients. We imaged the temporal evolution of networks formed by Phanerochaete velutina, and at each stage calculated the unique set of currents that account for the observed changes in cord volume, while minimizing the work required to overcome viscous drag. Predicted speeds were in reasonable agreement with experimental data, and the pressure gradients needed to produce these flows are small. Furthermore, cords that were predicted to carry fast-moving or large currents were significantly more likely to increase in size than cords with slow-moving or small currents. The incompressibility of the fluids within fungi means there is a rapid global response to local fluid movements. Hence velocity of fluid flow is a local signal that conveys quasi-global information about the role of a cord within the mycelium. We suggest that fluid incompressibility and the coupling of growth and mass flow are critical physical features that enable the development of efficient, adaptive biological transport networks.     

The Social Environment of an Aggregating, Ant-Attended Treehopper (Hemiptera: Membracidae: Vanduzea arquata)

Many membracid treehoppers are attended by honeydew-harvesting ants. Ant mutualism often favors group living, which will in turn influence social interactios and communication. I investigated aspects of life history that underlie the social behavior of an aggregating, ant-attended treehopper. The number of adults, and their patterns of distribution, changes dramatically over the course of a season. Despite the relatively low vagility and high persistence in the same clump of host plants, individuals encounter a wide range of social environments. This aggregating species differs from solitary species in the clumped distribution of females, and possibly in the intensity of acoustic competition among males, but both aggregating and solitary species exhibit large temporal changes in density. A high degree of temporal and spatial variation in the social environment is probably characteristic of many insects and may be an important source of selection on insect communication.     

Recent activities of the Microbiology in Schools Advisory Committee

Ecology and evolution

Agricultural education

Evolution and education

Education division elections

ISII in Europe

Tree project

Science teaching scholarship

Biology of terrestrial arthropods

A microscopied museum     

The role of epiphytism in architecture and evolutionary constraint within mycorrhizal networks of tropical orchids

Characterizing the architecture of bipartite networks is increasingly used as a framework to study biotic interactions within their ecological context and to assess the extent to which evolutionary constraint shape them. Orchid mycorrhizal symbioses are particularly interesting as they are viewed as more beneficial for plants than for fungi, a situation expected to result in an asymmetry of biological constraint. This study addressed the architecture and phylogenetic constraint in these associations in tropical context. We identified a bipartite network including 73 orchid species and 95 taxonomic units of mycorrhizal fungi across the natural habitats of Reunion Island. Unlike some recent evidence for nestedness in mycorrhizal symbioses, we found a highly modular architecture that largely reflected an ecological barrier between epiphytic and terrestrial subnetworks. By testing for phylogenetic signal, the overall signal was stronger for both partners in the epiphytic subnetwork. Moreover, in the subnetwork of epiphytic angraecoid orchids, the signal in orchid phylogeny was stronger than the signal in fungal phylogeny. Epiphytic associations are therefore more conservative and may co‐evolve more than terrestrial ones. We suggest that such tighter phylogenetic specialization may have been driven by stressful life conditions in the epiphytic niches. In addition to paralleling recent insights into mycorrhizal networks, this study furthermore provides support for epiphytism as a major factor affecting ecological assemblage and evolutionary constraint in tropical mycorrhizal symbioses.     

Social status and reproductive success of male Macaca fascicularis

Dominant males of nonseasonally breeding species should have more opportunity than seasonal breeders to monopolize access to estrous females and thus enhance their reproductive success. They may also use other strategies for maximizing reproductive success such as producing offspring by relatively high ranking females. To test these hypotheses, the paternity of 35 (80%) of 44 Macaca fascicularis offspring, born over a 28-month period, was established using electrophoretically and serologically defined genetic markers. The social ranks of each of the seven potential fathers and 26 potential mothers were determined by recording outcomes of dyadio agonistic interactions. No evidence could be found to support either hypothesis. Further, in view of previously reported significant, positive correlations between male social rank and sexual activity rates in this study group, sexual activity rates do not appear to predict the number of offspring a male sires in this species.