An investigation of the genus Mesacanthus (Chordata: Acanthodii) from the Orcadian Basin and Midland Valley areas of Northern and Central Scotland using traditional morphometrics.

Mesacanthus is a common and speciose genus of acanthodian fish from Lower Old Red Sandstone and Middle Old Red Sandstone assemblages (representing the Lower Devonian and Middle Devonian respectively) and is well represented in many palaeoichthyology collections in the UK. Based upon descriptions given during the 19th century, specimens of the genus Mesacanthus from the Orcadian Basin and Midland Valley areas of Northern and Central Scotland have historically been referred to a number of different species; of these, the most frequently discussed in the literature are M. mitchelli, M. peachi and M. pusillus. In order to test the validity of these three species, traditional morphometric analyses were carried out on over 100 specimens of Mesacanthus, from both the Lower Devonian and the Middle Devonian, that cover the full range of known localities for these taxa in Northern and Central Scotland. Based upon morphological and morphometric comparisons, this investigation has found that at least two species of Mesacanthus are valid (M. mitchelli and M. pusillus) as specimens from the Lower Devonian and Middle Devonian have been shown to differ significantly in a number of important ways. However, no evidence has been found for the validity of the second and distinct Middle Devonian species, M. peachi.This work was funded by a NERC/CASE doctoral studentship (NE/L501578/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PeerJ via http://dx.doi.org/10.7717/peerj.133

Development and characterisation of a three-dimensional in vitro hepatic fish model to investigate xenobiotic metabolism

Full version unavailable due to 3rd party copyright restrictions.Regulatory and academic studies use large numbers of fish annually. The use of fish primary cells offers an in vitro alternative for the assessment of chemical toxicity and the evaluation of environmental samples in ecotoxicology. Their uses however are not without limitations which includes short culture periods (i.e. longevity and loss of organ-specific functionality over time. Three-dimensional (3-D spheroid) technology is now established for in vitro mammalian toxicity studies and offers significant advantages for environmental applications in a model fish species. This thesis reports development of a reproducible six-well plate, gyratory-mediated method for rainbow trout (Oncorhynchus mykiss) hepatocyte spheroid culture and compares morphological, functional and biochemical status with two-dimensional (2-D) monolayer hepatocytes. The work further assesses the bio-transformation potential of developed 3-D spheroids to a range of environmentally relevant pharmaceuticals. The study suggests that mature spheroids retain some organotypic responses over-time in culture including morphological (viz., smooth outer surface, tight cell–cell contacts); functional (viz., histo-architecture; cell adhesion molecule expression) and biochemical properties (viz., protein, glucose, albumin- and enzyme levels) that is superior to conventional 2-D monolayer cells. These 3-D spheroids also demonstrate a capacity for the metabolism of environmentally-relevant pharmaceuticals that could be utilised to better understand their bio-accumulation potential in fish. This is an important step forward for developing alternative in vitro tools in future fish ecotoxicological studies as well as for fundamental understanding of the interaction of chemicals with biomolecules which could potentially lead to detrimental responses at different levels of biological organisation.BBSRC, AstraZenec

A re-evaluation of the enigmatic dinosauriform Caseosaurus crosbyensis from the Late Triassic of Texas, USA and its implications for early dinosaur evolution

The holotype specimen of the Late Triassic dinosauriform Caseosaurus crosbyensis is redescribed and evaluated phylogenetically for the first time, providing new anatomical information and data on the earliest dinosaurs and their evolution within the dinosauromorph lineage. Historically, Caseosaurus crosbyensis has been considered to represent an early saurischian dinosaur, and often a herrerasaur. More recent work on Triassic dinosaurs has cast doubt over its supposed dinosaurian affinities and uncertainty about particular features in the holotype and only known specimen has led to the species being regarded as a dinosauriform of indeterminate position. Here, we present a new diagnosis for Caseosaurus crosbyensis and refer additional material to the taxon—a partial right ilium from Snyder Quarry. Our comparisons and phylogenetic analyses suggest that Caseosaurus crosbyensis belongs in a clade with herrerasaurs and that this clade is the sister taxon of Dinosauria, rather than positioned within it. This result, along with other recent analyses of early dinosaurs, pulls apart what remains of the “traditional” group of dinosaurs collectively termed saurischians into a polyphyletic assemblage and implies that Dinosauria should be regarded as composed exclusively of Ornithoscelida (Ornithischia + Theropoda) and Sauropodomorpha. In addition, our analysis recovers the enigmatic European taxon Saltopus elginensis among herrerasaurs for the first time. This result suggests a greater body-size range for herrerasaurs than previously thought and provides further evidence for their presence in Europe during the Late Triassic. If this hypothesis is correct then this clade of herrerasaurs also represents the first clade of non-dinosaurian dinosauromorphs known to contain large-bodied carnivorous species. The results of our analyses also highlight the distinction between the clades Herrerasauridae and Herrerasauria, as they are currently defined, and necessitate a provisional revival of the latter until future works can better resolve the relationships among these important early taxa

Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.

BackgroundAutism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.MethodsWe introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.LimitationsWhile this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.ResultsOur models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity.ConclusionThis study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism

Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.

BackgroundAutism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.MethodsWe introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.LimitationsWhile this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.ResultsOur models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity.ConclusionThis study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism

Detail-oriented cognitive style and social communicative deficits, within and beyond the autism spectrum: independent traits that grow into developmental interdependence

At the heart of debates over underlying causes of autism is the "Kanner hypothesis" that autistic deficits in social reciprocity, and a cognitive/perceptual 'style' favouring detail-oriented cognition, co-vary in autistic individuals. A separate line of work indicates these two domains are normally distributed throughout the population, with autism representing an extremity. This realisation brings the Kanner debate into the realm of normative co-variation, providing more ways to test the hypothesis, and insights into typical development; for instance, in the context of normative functioning, the Kanner hypothesis implies social costs to spatial/numerical prowess

A novel hypothesis of dinosaur relationships and early dinosaur evolution

For nearly 130 years dinosaurs have been divided into two distinct clades Ornithischia and Saurischia. Here, we present a radical new hypothesis for the phylogenetic relationships of the major dinosaurian groups, one that challenges the current consensus concerning early dinosaur evolution and highlights problematic aspects of current cladistic definitions. Our study recovers, for the first time, a sister-group relationship between Ornithischia and Theropoda (Ornithoscelida), with Sauropodomorpha + Herrerasauridae forming its monophyletic outgroup. This new tree topology requires redefinition and rediagnosis of Dinosauria and the subsidiary dinosaurian clades. In addition, it forces re-evaluations of early dinosaur cladogenesis and character evolution, suggests the independent acquisition of hypercarnivory in herrerasaurids and theropods, and offers an explanation for many of the anatomical features previously regarded as striking convergences between theropods and early ornithischians