Parasitic Cape honeybee workers, Apis mellifera capensis, evade policing

Relocation of the Cape honeybee, Apis mellifera capensis, by bee-keepers from southern to northern South Africa in 1990 has caused widespread death of managed African honeybee, A. m. scutellata, colonies. Apis mellifera capensis worker bees are able to lay diploid, female eggs without mating by means of automictic thelytoky (meiosis followed by fusion of two meiotic products to restore egg diploidy), whereas workers of other honeybee subspecies are able to lay only haploid, male eggs. The A. m. capensis workers, which are parasitizing and killing A. m. scutellata colonies in northern South Africa, are the asexual offspring of a single, original worker in which the small amount of genetic variation observed is due to crossing over during meiosis (P. Kryger, personal communication). Here we elucidate two principal mechanisms underlying this parasitism. Parasitic A. m. capensis workers activate their ovaries in host colonies that have a queen present (queenright colonies), and they lay eggs that evade being killed by other workers (worker policing)—the normal fate of worker-laid eggs in colonies with a queen. This unique parasitism by workers is an instance in which a society is unable to control the selfish actions of its members

Anarchy in the UK: Detailed genetic analysis of worker reproduction in a naturally occurring British anarchistic honeybee, Apis mellifera, colony using DNA microsatellites

Anarchistic behaviour is a very rare phenotype of honeybee colonies. In an anarchistic colony, many workers’ sons are reared in the presence of the queen. Anarchy has previously been described in only two Australian colonies. Here we report on a first detailed genetic analysis of a British anarchistic colony. Male pupae were present in great abundance above the queen excluder, which was clearly indicative of extensive worker reproduction and is the hallmark of anarchy. Seventeen microsatellite loci were used to analyse these male pupae, allowing us to address whether all the males were indeed workers’ sons, and how many worker patrilines and individual workers produced them. In the sample, 95 of 96 of the males were definitely workers’ sons. Given that ≈ 1% of workers’ sons were genetically indistinguishable from queen’s sons, this suggests that workers do not move any queen-laid eggs between the part of the colony where the queen is present to the area above the queen excluder which the queen cannot enter. The colony had 16 patrilines, with an effective number of patrilines of 9.85. The 75 males that could be assigned with certainty to a patriline came from 7 patrilines, with an effective number of 4.21. They were the offspring of at least 19 workers. This is in contrast to the two previously studied Australian naturally occurring anarchist colonies, in which most of the workers’ sons were offspring of one patriline. The high number of patrilines producing males leads to a low mean relatedness between laying workers and males of the colony. We discuss the importance of studying such colonies in the understanding of worker policing and its evolution

Honey bee foraging distance depends on month and forage type

To investigate the distances at which honey bee foragers collect nectar and pollen, we analysed 5,484 decoded waggle dances made to natural forage sites to determine monthly foraging distance for each forage type. Firstly, we found significantly fewer overall dances made for pollen (16.8 %) than for non-pollen, presumably nectar (83.2 %; P < 2.2 × 10−23). When we analysed distance against month and forage type, there was a significant interaction between the two factors, which demonstrates that in some months, one forage type is collected at farther distances, but this would reverse in other months. Overall, these data suggest that distance, as a proxy for forage availability, is not significantly and consistently driven by need for one type of forage over the other

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Sequence and expression pattern of the germ line marker vasa in honey bees and stingless bees

Queens and workers of social insects differ in the rates of egg laying. Using genomic information we determined the sequence of vasa, a highly conserved gene specific to the germ line of metazoans, for the honey bee and four stingless bees. The vasa sequence of social bees differed from that of other insects in two motifs. By RT-PCR we confirmed the germ line specificity of Amvasa expression in honey bees. In situ hybridization on ovarioles showed that Amvasa is expressed throughout the germarium, except for the transition zone beneath the terminal filament. A diffuse vasa signal was also seen in terminal filaments suggesting the presence of germ line cells. Oocytes showed elevated levels of Amvasa transcripts in the lower germarium and after follicles became segregated. In previtellogenic follicles, Amvasa transcription was detected in the trophocytes, which appear to supply its mRNA to the growing oocyte. A similar picture was obtained for ovarioles of the stingless bee Melipona quadrifasciata, except that Amvasa expression was higher in the oocytes of previtellogenic follicles. The social bees differ in this respect from Drosophila, the model system for insect oogenesis, suggesting that changes in the sequence and expression pattern of vasa may have occurred during social evolution

Evidence for Genetic Overlap Between Schizophrenia and Age at First Birth in Women

IMPORTANCE: A recently published study of national data by McGrath et al in 2014 showed increased risk of schizophrenia (SCZ) in offspring associated with both early and delayed parental age, consistent with a U-shaped relationship. However, it remains unclear if the risk to the child is due to psychosocial factors associated with parental age or if those at higher risk for SCZ tend to have children at an earlier or later age. OBJECTIVE: To determine if there is a genetic association between SCZ and age at first birth (AFB) using genetically informative but independently ascertained data sets. DESIGN, SETTING, AND PARTICIPANTS: This investigation used multiple independent genome-wide association study data sets. The SCZ sample comprised 18 957 SCZ cases and 22 673 controls in a genome-wide association study from the second phase of the Psychiatric Genomics Consortium, and the AFB sample comprised 12 247 genotyped women measured for AFB from the following 4 community cohorts: Estonia (Estonian Genome Center Biobank, University of Tartu), the Netherlands (LifeLines Cohort Study), Sweden (Swedish Twin Registry), and the United Kingdom (TwinsUK). Schizophrenia genetic risk for each woman in the AFB community sample was estimated using genetic effects inferred from the SCZ genome-wide association study. MAIN OUTCOMES AND MEASURES: We tested if SCZ genetic risk was a significant predictor of response variables based on published polynomial functions that described the relationship between maternal age and SCZ risk in offspring in Denmark. We substituted AFB for maternal age in these functions, one of which was corrected for the age of the father, and found that the fit was superior for the model without adjustment for the father's age. RESULTS: We observed a U-shaped relationship between SCZ risk and AFB in the community cohorts, consistent with the previously reported relationship between SCZ risk in offspring and maternal age when not adjusted for the age of the father. We confirmed that SCZ risk profile scores significantly predicted the response variables (coefficient of determination R2 = 1.1E-03, P = 4.1E-04), reflecting the published relationship between maternal age and SCZ risk in offspring by McGrath et al in 2014. CONCLUSIONS AND RELEVANCE: This study provides evidence for a significant overlap between genetic factors associated with risk of SCZ and genetic factors associated with AFB. It has been reported that SCZ risk associated with increased maternal age is explained by the age of the father and that de novo mutations that occur more frequently in the germline of older men are the underlying causal mechanism. This explanation may need to be revised if, as suggested herein and if replicated in future studies, there is also increased genetic risk of SCZ in older mothers

Spatial effects, sampling errors, and task specialization in the honey bee

Task allocation patterns should depend on the spatial distribution of work within the nest, variation in task demand, and the movement patterns of workers, however, relatively little research has focused on these topics. This study uses a spatially explicit agent based model to determine whether such factors alone can generate biases in task performance at the individual level in the honey bees, Apis mellifera. Specialization (bias in task performance) is shown to result from strong sampling error due to localized task demand, relatively slow moving workers relative to nest size, and strong spatial variation in task demand. To date, specialization has been primarily interpreted with the response threshold concept, which is focused on intrinsic (typically genotypic) differences between workers. Response threshold variation and sampling error due to spatial effects are not mutually exclusive, however, and this study suggests that both contribute to patterns of task bias at the individual level. While spatial effects are strong enough to explain some documented cases of specialization; they are relatively short term and not explanatory for long term cases of specialization. In general, this study suggests that the spatial layout of tasks and fluctuations in their demand must be explicitly controlled for in studies focused on identifying genotypic specialists

Swarming Behavior in Plant Roots

Interactions between individuals that are guided by simple rules can generate swarming behavior. Swarming behavior has been observed in many groups of organisms, including humans, and recent research has revealed that plants also demonstrate social behavior based on mutual interaction with other individuals. However, this behavior has not previously been analyzed in the context of swarming. Here, we show that roots can be influenced by their neighbors to induce a tendency to align the directions of their growth. In the apparently noisy patterns formed by growing roots, episodic alignments are observed as the roots grow close to each other. These events are incompatible with the statistics of purely random growth. We present experimental results and a theoretical model that describes the growth of maize roots in terms of swarming

Methanethiol-dependent dimethylsulfide production in soil environments

Dimethylsulfide (DMS) is an environmentally important trace gas with roles in sulfur cycling, signalling to higher organisms and in atmospheric chemistry. DMS is believed to be predominantly produced in marine environments via microbial degradation of the osmolyte dimethylsulfoniopropionate (DMSP). However, significant amounts of DMS are also generated from terrestrial environments, for example, peat bogs can emit ~6 μmol DMS m−2 per day, likely via the methylation of methanethiol (MeSH). A methyltransferase enzyme termed ‘MddA’, which catalyses the methylation of MeSH, generating DMS, in a wide range of bacteria and some cyanobacteria, may mediate this process, as the mddA gene is abundant in terrestrial metagenomes. This is the first study investigating the functionality of MeSH-dependent DMS production (Mdd) in a wide range of aerobic environments. All soils and marine sediment samples tested produced DMS when incubated with MeSH. Cultivation-dependent and cultivation-independent methods were used to assess microbial community changes in response to MeSH addition in a grassland soil where 35.9% of the bacteria were predicted to contain mddA. Bacteria of the genus Methylotenera were enriched in the presence of MeSH. Furthermore, many novel Mdd+ bacterial strains were isolated. Despite the abundance of mddA in the grassland soil, the Mdd pathway may not be a significant source of DMS in this environment as MeSH addition was required to detect DMS at only very low conversion rates

SIP metagenomics identifies uncultivated Methylophilaceae as dimethylsulphide degrading bacteria in soil and lake sediment.

Dimethylsulphide (DMS) has an important role in the global sulphur cycle and atmospheric chemistry. Microorganisms using DMS as sole carbon, sulphur or energy source, contribute to the cycling of DMS in a wide variety of ecosystems. The diversity of microbial populations degrading DMS in terrestrial environments is poorly understood. Based on cultivation studies, a wide range of bacteria isolated from terrestrial ecosystems were shown to be able to degrade DMS, yet it remains unknown whether any of these have important roles in situ. In this study, we identified bacteria using DMS as a carbon and energy source in terrestrial environments, an agricultural soil and a lake sediment, by DNA stable isotope probing (SIP). Microbial communities involved in DMS degradation were analysed by denaturing gradient gel electrophoresis, high-throughput sequencing of SIP gradient fractions and metagenomic sequencing of phi29-amplified community DNA. Labelling patterns of time course SIP experiments identified members of the Methylophilaceae family, not previously implicated in DMS degradation, as dominant DMS-degrading populations in soil and lake sediment. Thiobacillus spp. were also detected in (13)C-DNA from SIP incubations. Metagenomic sequencing also suggested involvement of Methylophilaceae in DMS degradation and further indicated shifts in the functional profile of the DMS-assimilating communities in line with methylotrophy and oxidation of inorganic sulphur compounds. Overall, these data suggest that unlike in the marine environment where gammaproteobacterial populations were identified by SIP as DMS degraders, betaproteobacterial Methylophilaceae may have a key role in DMS cycling in terrestrial environments.HS was supported by a UK Natural Environment Research Council Advanced Fellowship NE/E013333/1), ÖE by a postgraduate scholarship from the University of Warwick and an Early Career Fellowship from the Institute of Advanced Study, University of Warwick, UK, respectively. Lawrence Davies is acknowledged for help with QIIME