Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

Human anatomy, morphology, and associated diseases can be studied using medical imaging data. However, access to medical imaging data is restricted by governance and privacy concerns, data ownership, and the cost of acquisition, thus limiting our ability to understand the human body. A possible solution to this issue is the creation of a model able to learn and then generate synthetic images of the human body conditioned on specific characteristics of relevance (e.g., age, sex, and disease status). Deep generative models, in the form of neural networks, have been recently used to create synthetic 2D images of natural scenes. Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations. This work proposes a generative model that can be scaled to produce anatomically correct, high-resolution, and realistic images of the human brain, with the necessary quality to allow further downstream analyses. The ability to generate a potentially unlimited amount of data not only enables large-scale studies of human anatomy and pathology without jeopardizing patient privacy, but also significantly advances research in the field of anomaly detection, modality synthesis, learning under limited data, and fair and ethical AI. Code and trained models are available at: https://github.com/AmigoLab/SynthAnatomy.Comment: 13 pages, 3 figures, 2 tables, accepted at SASHIMI MICCAI 202

Cumulative risk effects for the development of behaviour difficulties in children with special educational needs and disabilities

Research has identified multiple risk factors for the development of behaviour difficulties. What have been less explored are the cumulative effects of exposure to multiple risks on behavioural outcomes, with no study specifically investigating these effects within a population of young people with special educational needs and disabilities (SEND). Furthermore, it is unclear whether a threshold or linear risk model better fits the data for this population. The sample included 2660 children and 1628 adolescents with SEND. Risk factors associated with increases in behaviour difficulties over an 18-month period were summed to create a cumulative risk score, with this explanatory variable being added into a multi-level model. A quadratic term was then added to test the threshold model. There was evidence of a cumulative risk effect, suggesting that exposure to higher numbers of risk factors, regardless of their exact nature, resulted in increased behaviour difficulties. The relationship between risk and behaviour difficulties was non-linear, with exposure to increasing risk having a disproportionate and detrimental impact on behaviour difficulties in child and adolescent models. Interventions aimed at reducing behaviour difficulties need to consider the impact of multiple risk variables. Tailoring interventions towards those exposed to large numbers of risks would be advantageous

Portable Multi- and Many-Core Performance for Finite Difference Codes; Application to the Free-Surface Component of NEMO

Abstract. We present an approach which we call PSyKAl that is designed to achieve portable performance for parallel, finite-difference Ocean models. In PSyKAl the code related to the underlying science is formally separated from code related to parallelisation and single-core optimisations. This separation of concerns allows scientists to code their science independently of the underlying hardware architecture and for optimisation specialists to be able to tailor the code for a particular machine independently of the science code. We have taken the free-surface part of the NEMO ocean model and created a new, shallow-water model named NEMOLite2D. In doing this we have a code which is of a manageable size and yet which incorporates elements of full ocean models (input/output, boundary conditions, etc.). We have then manually constructed a PSyKAl version of this code and investigated the transformations that must be applied to the middle/PSy layer in order to achieve good performance, both serial and parallel. We have produced versions of the PSy layer parallelised with both OpenMP and OpenACC; in both cases we were able to leave the natural-science parts of the code unchanged while achieving good performance on both multi-core CPUs and GPUs. In quantifying whether or not the obtained performance is `good' we also consider the limitations of the basic roofline model and improve on it by generating kernel-specific CPU ceilings. </jats:p

High prevalence of sessile serrated adenomas with BRAF mutations: A prospective study of patients undergoing colonoscopy

Background & Aims: Sporadic colorectal cancers with a high degree of microsatellite instability are a clinically distinct subgroup with a high incidence of BRAF mutation and are widely considered to develop from serrated polyps. Previous studies of serrated polyps have been highly selected and largely retrospective. This prospective study examined the prevalence of sessile serrated adenomas and determined the incidence of BRAF and K-ras mutations in different types of polyps. Methods: An unselected consecutive series of 190 patients underwent magnifying chromoendoscopy. Polyp location, size, and histologic classification were recorded. All polyps were screened for BRAF V600E and K-ras codon 12 and 13 mutations. Results: Polyps were detected in 72% of patients. Most (60%) were adenomas (tubular adenomas, tubulovillous adenomas), followed by hyperplastic polyps (29%), sessile serrated adenomas (SSAs; 9%), traditional serrated adenomas (0.7%), and mixed polyps (1.7%). Adenomas were more prevalent in the proximal colon (73%), as were SSAs (75%), which tended to be large (64%> 5 mm). The presence of at least one SSA was associated with increased polyp burden (5.0 vs 2.5;