Mitogenomics supports an unexpected taxonomic relationship for the extinct diving duck Chendytes lawi and definitively places the extinct Labrador Duck

Chendytes lawi, an extinct flightless diving anseriform from coastal California, was traditionally classified as a sea duck, tribe Mergini, based on similarities in osteological characters. We recover and analyze mitochondrial genomes of C. lawi and five additional Mergini species, including the extinct Labrador Duck, Camptorhyncus labradorius. Despite its diving morphology, C. lawi is reconstructed as an ancient relictual lineage basal to the dabbling ducks (tribe Anatini), revealing an additional example of convergent evolution of characters related to feeding behavior among ducks. The Labrador Duck is sister to Steller’s Eider which may provide insights into the evolution and ecology of this poorly known extinct species. Our results demonstrate that inclusion of full length mitogenomes, from taxonomically distributed ancient and modern sources can improve phylogeny reconstruction of groups previously assessed with shorter single-gene mitochondrial sequences

Identification guide to the heterobranch sea slugs (Mollusca: Gastropoda) from Bocas del Toro, Panama

The Bocas del Toro Archipelago is located off the Caribbean coast of Panama. Until now, only 19 species of heterobranch sea slugs have been formally reported from this area; this number constitutes a fraction of total diversity in the Caribbean region. The Bocas del Toro Archipelago is located off the Caribbean coast of Panama. Until now, only 19 species of heterobranch sea slugs have been formally reported from this area; this number constitutes a fraction of total diversity in the Caribbean region. This increase in known diversity strongly suggests that the distribution of species within the Caribbean is still poorly known and species ranges may need to be modified as more surveys are conducted.https://doi.org/10.1186/s41200-016-0048-

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Breaking barriers: using the behavior change wheel to develop a tailored intervention to overcome workplace inhibitors to breaking up sitting time

© The Author(s). 2019. Background: The workplace is a prominent domain for excessive sitting. The consequences of increased sitting time include adverse health outcomes such as cardiovascular disease and poor mental wellbeing. There is evidence that breaking up sitting could improve health, however, any such intervention in the workplace would need to be informed by a theoretical evidence-based framework. The aim of this study was to use the Behaviour Change Wheel (BCW) to develop a tailored intervention to break up and reduce workplace sitting in desk-based workers. Methods: The BCW guide was followed for this qualitative, pre-intervention development study. Semi-structured interviews were conducted with 25 office workers (26–59years, mean age 40.9 [SD=10.8] years; 68% female) who were purposively recruited from local council offices and a university in the East of England region. The interview questions were developed using the Theoretical Domains Framework (TDF). Transcripts were deductively analysed using the COM-B (Capability, Opportunity, Motivation – Behaviour) model of behaviour. The Behaviour Change Technique Taxonomy Version 1 (BCTv1) was thereafter used to identify possible strategies that could be used to facilitate change in sitting behaviour of office workers in a future intervention. Results: Qualitative analysis using COM-B identified that participants felt that they had the physical Capability to break up their sitting time, however, some lacked the psychological Capability in relation to the knowledge of both guidelines for sitting time and the consequences of excess sitting. Social and physical Opportunity was identified as important, such as a supportive organisational culture (social) and the need for environmental resources (physical). Motivation was highlighted as a core target for intervention, both reflective Motivation, such as beliefs about capability and intention and automatic in terms of overcoming habit through reinforcement. Seven intervention functions and three policy categories from the BCW were identified as relevant. Finally, 39 behaviour change techniques (BCTs) were identified as potential active components for an intervention to break up sitting time in the workplace. Conclusions: The TDF, COM-B model and BCW can be successfully applied through a systematic process to understand the drivers of behaviour of office workers to develop a co-created intervention that can be used to break up and decrease sitting in the workplace. Intervention designers should consider the identified BCW factors and BCTs when developing interventions to reduce and break up workplace sitting

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Data from: Reduced genetic diversity and increased reproductive isolation follow population-level loss of larval dispersal in a marine gastropod

Population-level consequences of dispersal ability remain poorly understood, especially for marine animals in which dispersal is typically considered a species-level trait governed by oceanographic transport of microscopic larvae. Transitions from dispersive (planktotrophic) to non-dispersive, aplanktonic larvae are predicted to reduce connectivity, genetic diversity within populations, and the spatial scale at which reproductive isolation evolves. However, larval dimorphism within a species is rare, precluding population-level tests. We show the sea slug Costasiella ocellifera expresses both larval morphs in Florida and the Caribbean, regions with divergent mitochondrial lineages. Planktotrophy predominated at 11 sites, 10 of which formed a highly connected and genetically diverse Caribbean metapopulation. Four populations expressed mainly aplanktonic development and had markedly reduced connectivity, and lower genetic diversity at one mitochondrial and six nuclear loci. Aplanktonic dams showed partial post-zygotic isolation in most inter-population crosses, regardless of genetic or geographic distance to the sire's source, suggesting outbreeding depression affects fragmented populations. Dams from genetically isolated and neighboring populations also exhibited pre-mating isolation, consistent with reinforcement contingent on historical interaction. By increasing self-recruitment and genetic drift, the loss of dispersal may thus initiate a feedback loop resulting in the evolution of reproductive isolation over small spatial scales in the sea

Data from: Reduced genetic diversity and increased reproductive isolation follow population-level loss of larval dispersal in a marine gastropod

Population-level consequences of dispersal ability remain poorly understood, especially for marine animals in which dispersal is typically considered a species-level trait governed by oceanographic transport of microscopic larvae. Transitions from dispersive (planktotrophic) to non-dispersive, aplanktonic larvae are predicted to reduce connectivity, genetic diversity within populations, and the spatial scale at which reproductive isolation evolves. However, larval dimorphism within a species is rare, precluding population-level tests. We show the sea slug Costasiella ocellifera expresses both larval morphs in Florida and the Caribbean, regions with divergent mitochondrial lineages. Planktotrophy predominated at 11 sites, 10 of which formed a highly connected and genetically diverse Caribbean metapopulation. Four populations expressed mainly aplanktonic development and had markedly reduced connectivity, and lower genetic diversity at one mitochondrial and six nuclear loci. Aplanktonic dams showed partial post-zygotic isolation in most inter-population crosses, regardless of genetic or geographic distance to the sire's source, suggesting outbreeding depression affects fragmented populations. Dams from genetically isolated and neighboring populations also exhibited pre-mating isolation, consistent with reinforcement contingent on historical interaction. By increasing self-recruitment and genetic drift, the loss of dispersal may thus initiate a feedback loop resulting in the evolution of reproductive isolation over small spatial scales in the sea