Tree Sequences as a General-Purpose Tool for Population Genetic Inference.

As population genetic data increase in size, new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks applied to population genetic alignments. To better utilize these new data structures, we propose and implement a graph convolutional network to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format.

Mechanical and suture-holding properties of a UV-cured atelocollagen membrane with varied crosslinked architecture.

The mechanical competence and suturing ability of collagen-based membranes are paramount in guided bone regeneration (GBR) therapy, to ensure damage-free implantation, fixation and space maintenance. However, contact with the biological medium can induce swelling of collagen molecules, yielding risks of membrane sinking into the bone defect, early loss of barrier function, and irreversibly compromised clinical outcomes. To address these challenges, this study investigates the effect of the crosslinked network architecture on both mechanical and suture-holding properties of a new atelocollagen (AC) membrane.

Tree sequences as a general-purpose tool for population genetic inference.

As population genetics data increases in size new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient, but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks (CNNs) applied to population genetic alignments. To better utilize these new data structures we propose and implement a graph convolutional network (GCN) to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format.

Collaborative Recognition of Wellbeing Needs: A Novel Approach to Universal Psychosocial Screening on the Neonatal Unit.

Universal screening for the psychological needs of families in neonatal care is internationally recommended, but is not routinely practiced in the United Kingdom (UK). The present quality improvement project explores the clinical and operational feasibility of a novel approach to universal screening on a neonatal intensive care unit in the UK. The approach to screening taken adopts collaborative, strengths-based and dialogical methods for recognising the psychological needs of families whose baby is in hospital.

PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature.

Summary: Large-scale comparative studies rely on the application of both phylogenetic trees and phenotypic data, both of which come from a variety of sources, but due to the changing nature of phylogenetic classification over time, many taxon names in comparative datasets do not match the nomenclature in phylogenetic trees. Manual curation of taxonomic synonyms in large comparative datasets can be daunting. To address this issue, we introduce PhyloMatcher, a tool which allows for programmatic querying of the National Center for Biotechnology Information Taxonomy and Global Biodiversity Information Facility databases to find associated synonyms with given target species names.