Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic

Native drivers of fish life history traits are lost during the invasion process

Rapid adaptation to global change can counter vulnerability of species to population declines and extinction. Theoretically, under such circumstances both genetic variation and phenotypic plasticity can maintain population fitness, but empirical support for this is currently limited. Here, we aim to characterize the role of environmental and genetic diversity, and their prior evolutionary history (via haplogroup profiles) in shaping patterns of life history traits during biological invasion. Data were derived from both genetic and life history traits including a morphological analysis of 29 native and invasive populations of topmouth gudgeon Pseudorasbora parva coupled with climatic variables from each location. General additive models were constructed to explain distribution of somatic growth rate (SGR) data across native and invasive ranges, with model selection performed using Akaike's information criteria. Genetic and environmental drivers that structured the life history of populations in their native range were less influential in their invasive populations. For some vertebrates at least, fitness-related trait shifts do not seem to be dependent on the level of genetic diversity or haplogroup makeup of the initial introduced propagule, nor of the availability of local environmental conditions being similar to those experienced in their native range. As long as local conditions are not beyond the species physiological threshold, its local establishment and invasive potential are likely to be determined by local drivers, such as density-dependent effects linked to resource availability or to local biotic resistance

Reconciling the biogeography of an invader through recent and historic genetic patterns: the case of topmouth gudgeon Pseudorasbora parva

© 2018 The Author(s) The genetic variability and population structure of introduced species in their native range are potentially important determinants of their invasion success, yet data on native populations are often poorly represented in relevant studies. Consequently, to determine the contribution of genetic structuring in the native range of topmouth gudgeon Pseudorasbora parva to their high invasion success in Europe, we used a dataset comprising of 19 native and 11 non-native populations. A total of 666 samples were analysed at 9 polymorphic microsatellite loci and sequenced for 597 bp of mitochondrial DNA. The analysis revealed three distinct lineages in the native range, of which two haplogroups were prevalent in China (100%), with a general split around the Qinling Mountains. Dating of both haplogroups closely matched past geological events. More recently, its distribution has been influenced by fish movements in aquaculture, resulting in gene flow between previously separated populations in Northern and Southern China. Their phylogeography in Europe indicate as few as two introductions events and two dispersal routes. Microsatellite data revealed native populations had higher genetic diversity than those in the invasive range, a contrast to previous studies on P. parva. This study confirms the importance of extensive sampling in both the native and non-native range of invasive species in evaluating the influence of genetic variability on invasion success

Early-life intestinal microbiome in Trachemys scripta elegans analyzed using 16S rRNA sequencing

During the early-life period, the hatchlings of red-eared slider turtles (Trachemys scripta elegans) rely on their own post-hatching internal yolk for several days before beginning to feed. The gut microbiome is critical