Draft Genome Assembly and Population Genetics of an Agricultural Pollinator, the Solitary Alkali Bee (Halictidae: Nomia melanderi).

Alkali bees (Nomia melanderi) are solitary relatives of the halictine bees, which have become an important model for the evolution of social behavior, but for which few solitary comparisons exist. These ground-nesting bees defend their developing offspring against pathogens and predators, and thus exhibit some of the key traits that preceded insect sociality. Alkali bees are also efficient native pollinators of alfalfa seed, which is a crop of major economic value in the United States. We sequenced, assembled, and annotated a high-quality draft genome of 299.6 Mbp for this species. Repetitive content makes up more than one-third of this genome, and previously uncharacterized transposable elements are the most abundant type of repetitive DNA. We predicted 10,847 protein coding genes, and identify 479 of these undergoing positive directional selection with the use of population genetic analysis based on low-coverage whole genome sequencing of 19 individuals. We found evidence of recent population bottlenecks, but no significant evidence of population structure. We also identify 45 genes enriched for protein translation and folding, transcriptional regulation, and triglyceride metabolism evolving slower in alkali bees compared to other halictid bees. These resources will be useful for future studies of bee comparative genomics and pollinator health research

A SNP test to identify Africanized honey bees via proportion of 'African' ancestry

The honey bee, Apis mellifera, is the world’s most important pollinator and is ubiquitous in most agricultural ecosystems. Four major evolutionary lineages and at least 24 subspecies are recognised. Commercial populations are mainly derived from subspecies originating in Europe (75-95%). The Africanized honey bee is a New World hybrid of A. m. scutellata from Africa and European subspecies, with the African component making up 50-90% of the genome. Africanized honey bees are considered undesirable for beekeeping in most countries, due to their extreme defensiveness and poor honey production. The international trade in honey bees is restricted, due in part to bans on importation of queens (and semen) from countries where Africanized honey bees are extant. Some desirable strains from the United States of America that have been bred for traits such as resistance to the mite Varroa destructor are unfortunately excluded from export to countries like Australia due to the presence of Africanized honey bees in the USA. This study shows that a panel of 95 single nucleotide polymorphisms, chosen to differentiate between the African, Eastern European and Western European lineages, can detect Africanized honey bees with a high degree of confidence via ancestry assignment. Our panel therefore offers a valuable tool to mitigate the risks of spreading Africanized honey bees across the globe and may enable the resumption of queen and bee semen imports from the Americas

Understanding the semantic structure of human fMRI brain recordings with formal concept analysis

We investigate whether semantic information related to object categories can be obtained from human fMRI BOLD responses with Formal Concept Analysis (FCA). While the BOLD response provides only an indirect measure of neural activity on a relatively coarse spatio-temporal scale, it has the advantage that it can be recorded from humans, who can be questioned about their perceptions during the experiment, thereby obviating the need of interpreting animal behavioral responses. Furthermore, the BOLD signal can be recorded from the whole brain simultaneously. In our experiment, a single human subject was scanned while viewing 72 gray-scale pictures of animate and inanimate objects in a target detection task. These pictures comprise the formal objects for FCA. We computed formal attributes by learning a hierarchical Bayesian classifier, which maps BOLD responses onto binary features, and these features onto object labels. The connectivity matrix between the binary features and the object labels can then serve as the formal context. In line with previous reports, FCA revealed a clear dissociation between animate and inanimate objects with the inanimate category also including plants. Furthermore, we found that the inanimate category was subdivided between plants and non-plants when we increased the number of attributes extracted from the BOLD response. FCA also allows for the display of organizational differences between high-level and low-level visual processing areas. We show that subjective familiarity and similarity ratings are strongly correlated with the attribute structure computed from the BOLD signal