Global patterns in anuran–prey networks: structure mediated by latitude

Life on Earth is supported by an infinite number of interactions among organisms. Species interactions in these networks are influenced by latitude, evolutionary history and species traits. We performed a global‐scale literature analysis to build up a database of interactions between anuran communities and their preys, from a wide range of geographical areas, using a network approach. For this purpose, we compiled a total of 55 weighted anuran–prey interaction networks, 39 located in the tropics and 16 in temperate areas. We tested the influence of latitude, as well as anuran taxonomic, functional and phylogenetic richness on network metrics. We found that anuran–prey networks are not nested, exhibit low complementary specialization and modularity and high connectance when compared to other types of networks. The main effects on network metrics were related to latitude, followed by anuran taxonomic, functional and phylogenetic richness, a pattern similar to the emerging in mutualistic networks. Our study is the first integrated analysis of the structural patterns in anuran–prey antagonistic interaction networks in different parts of the world. We suggest that different processes, mediated mainly by latitude, are modeling the architecture of anuran–prey networks across the globe.     

Use of artificial neural networks for analysis of the factors affecting particle size in mebudipine nanoemulsion

In this study, a nanoemulsion containing mebudipine [composed of ethyl oleate (oil phase), Tween 80 (T80), Span 80 (S80) (surfactants), polyethylene glycol 400, ethanol (cosurfactants), and deionized water] was prepared with the aim of improving its bioavailability for an effective antihypertensive therapy. Particle size of the formulation was measured by dynamic light scattering. Then, artificial neural networks were used in identifying factors that influence the particle size of the nanoemulsion. Three variables, namely, amount of surfactant system (T80?+?S80), amount of polyethylene glycol, and amount of ethanol as cosurfactants, were considered as input values and the particle size was used as output. The developed model showed that all the three inputs had some degrees of effect on particles size: increasing the value of each input decreased the size. Furthermore, amount of surfactant was found to be the dominant factor in controlling the final particle size of nanoemulsion.

Communicated by Ramaswamy H. Sarma     

Testing the link between species interactions and species co‐occurrence in a trophic network

Species interactions are dynamic processes that vary across environmental and ecological contexts, and operate across scale boundaries, making them difficult to quantify. Nevertheless, ecologists are increasingly interested in inferring species interactions from observational data using statistical analyses of their spatial co‐occurrence patterns. Trophic interactions present a particular challenge, as predators and prey may frequently or rarely co‐occur, depending on the spatial or temporal scale of observation. In this study, we investigate the accuracy of inferred interactions among species that both compete and trophically interact. We utilized a long‐term dataset of pond‐breeding amphibian co‐occurrences from Mt Rainier National Park (Washington, USA) and compiled a new dataset of their empirical interactions from the literature. We compared the accuracy of four statistical methods in inferring these known species interactions from spatial associations. We then used the best performing statistical method, the Markov network, to further investigate the sensitivity of interaction inference to spatial scale‐dependence and the presence of predators. We show that co‐occurrence methods are generally inaccurate when estimating trophic interactions. Further the strength and sign of inferred interactions were dependent upon the spatial scale of observation and predator presence influenced the detectability of competitive interactions among prey species. However, co‐occurrence analysis revealed new patterns of spatial association among pairs of species with known interactions. Overall, our study highlights a limiting frontier in co‐occurrence theory and the disconnect between widely implemented methodologies and their ability to accurately infer interactions in trophically‐structured communities.     

Matrix metalloproteinases as target genes for gene regulatory networks driving molecular and cellular pathways related to a multistep pathogenesis of cerebrovascular disease

The present study investigated a joint contribution of matrix metalloproteinases (MMPs) genes to ischemic stroke (IS) development and analyzed interactions between MMP genes and genome-wide associated loci for IS. A total of 1288 unrelated Russians (600 IS patients and 688 healthy individuals) from Central Russia were recruited for the study. Genotyping of seven single nucleotide polymorphisms (SNPs) of MMP genes (rs1799750, rs243865, rs3025058, rs11225395, rs17576, rs486055, and rs2276109) and eight genome-wide associated loci for IS were done using Taq-Man–based assays and MALDI-TOF mass spectrometry iPLEX platform, respectively. Allele − 799T at rs11225395 of the MMP8 gene was significantly associated with a decreased risk of IS after adjustment for sex and age (OR = 0.82; 95%CI, 0.70-0.96; P = 0.016). The model-based multifactor dimensionality reduction method has revealed 21 two-order, 124 three-order, and 474 four-order gene-gene (G×G) interactions models meaningfully (P_perm < 0.05) associated with the IS risk. The bioinformatic analysis enabled establishing the studied MMP gene polymorphisms possess a clear regulatory potential and may be targeted by gene regulatory networks driving molecular and cellular pathways related to the pathogenesis of IS. In conclusion, the present study was the first to identify an association between polymorphism rs11225395 of the MMP8 gene and IS risk. The study findings also indicate that MMPs deserve special attention as a potential class of genes influencing the multistep mechanisms of cerebrovascular disease including atherosclerosis in cerebral arteries, acute cerebral artery occlusion as well as the ischemic injury of the brain and its recovery.     

Scalable remote homology detection and fold recognition in massive protein networks

The global connectivities in very large protein similarity networks contain traces of evolution among the proteins for detecting protein remote evolutionary relations or structural similarities. To investigate how well a protein network captures the evolutionary information, a key limitation is the intensive computation of pairwise sequence similarities needed to construct very large protein networks. In this article, we introduce label propagation on low-rank kernel approximation (LP-LOKA) for searching massively large protein networks. LP-LOKA propagates initial protein similarities in a low-rank graph by Nyström approximation without computing all pairwise similarities. With scalable parallel implementations based on distributed-memory using message-passing interface and Apache-Hadoop/Spark on cloud, LP-LOKA can search protein networks with one million proteins or more. In the experiments on Swiss-Prot/ADDA/CASP data, LP-LOKA significantly improved protein ranking over the widely used HMM-HMM or profile-sequence alignment methods utilizing large protein networks. It was observed that the larger the protein similarity network, the better the performance, especially on relatively small protein superfamilies and folds. The results suggest that computing massively large protein network is necessary to meet the growing need of annotating proteins from newly sequenced species and LP-LOKA is both scalable and accurate for searching massively large protein networks.     

The drivers of avian‐haemosporidian prevalence in tropical lowland forests of New Guinea in three dimensions

Haemosporidians are among the most common parasites of birds and often negatively impact host fitness. A multitude of biotic and abiotic factors influence these associations, but the magnitude of these factors can differ by spatial scales (i.e., local, regional and global). Consequently, to better understand global and regional drivers of avian‐haemosporidian associations, it is key to investigate these associations at smaller (local) spatial scales. Thus, here, we explore the effect of abiotic variables (e.g., temperature, forest structure, and anthropogenic disturbances) on haemosporidian prevalence and host–parasite networks on a horizontal spatial scale, comparing four fragmented forests and five localities within a continuous forest in Papua New Guinea. Additionally, we investigate if prevalence and host–parasite networks differ between the canopy and the understory (vertical stratification) in one forest patch. We found that the majority of Haemosporidian infections were caused by the genus Haemoproteus and that avian‐haemosporidian networks were more specialized in continuous forests. At the community level, only forest greenness was negatively associated with Haemoproteus infections, while the effects of abiotic variables on parasite prevalence differed between bird species. Haemoproteus prevalence levels were significantly higher in the canopy, and an opposite trend was observed for Plasmodium. This implies that birds experience distinct parasite pressures depending on the stratum they inhabit, likely driven by vector community differences. These three‐dimensional spatial analyses of avian‐haemosporidians at horizontal and vertical scales suggest that the effect of abiotic variables on haemosporidian infections are species specific, so that factors influencing community‐level infections are primarily driven by host community composition.     

Modelling spatial dynamics of fish

Our ability to model spatial distributions of fish populations is reviewed by describing the available modelling tools. Ultimate models of the individual's motivation for behavioural decisions are derived from evolutionary ecology. Mechanistic models for how fish sense and may respond to their surroundings are presented for vision, olfaction, hearing, the lateral line and other sensory organs. Models for learning and memory are presented, based both upon evolutionary optimization premises and upon neurological information processing and decision making. Functional tools for modelling behaviour and life histories can be categorized as belonging to an optimization or an adaptation approach. Among optimization tools, optimal foraging theory, life history theory, ideal free distribution, game theory and stochastic dynamic programming are presented. Among adaptation tools, genetic algorithms and the combination with artificial neural networks are described. The review advocates the combination of evolutionary and neurological approaches to modelling spatial dynamics of fish.     

Current Applications of Models of Genetic Effects with Interactions Across the Genome

Models of genetic effects integrate the action of genes, regulatory regions and interactions among alleles across the genome. Such theoretical frameworks are critical for applied studies in at least two ways. First, discovering genetic networks with specific effects underlying traits in populations requires the development of models that implement those effects as parameters—adjusting the implementation of epistasis parameters in genetic models has for instance been a requirement for properly testing for epistasis in gene-mapping studies. Second, studying the properties and implications of models of genetic effects that involve complex genetic networks has proven to be valuable, whether those networks have been revealed for particular organisms or inferred to be of interest from theoretical works and simulations. Here I review the current state of development and recent applications of models of genetic effects. I focus on general models aiming to depict complex genotype-to-phenotype maps and on applications of them to networks of interacting loci.