Predicting the Impact of Climate Change on Threatened Species in UK Waters

Global climate change is affecting the distribution of marine species and is thought to represent a threat to biodiversity. Previous studies project expansion of species range for some species and local extinction elsewhere under climate change. Such range shifts raise concern for species whose long-term persistence is already threatened by other human disturbances such as fishing. However, few studies have attempted to assess the effects of future climate change on threatened vertebrate marine species using a multi-model approach. There has also been a recent surge of interest in climate change impacts on protected areas. This study applies three species distribution models and two sets of climate model projections to explore the potential impacts of climate change on marine species by 2050. A set of species in the North Sea, including seven threatened and ten major commercial species were used as a case study. Changes in habitat suitability in selected candidate protected areas around the UK under future climatic scenarios were assessed for these species. Moreover, change in the degree of overlap between commercial and threatened species ranges was calculated as a proxy of the potential threat posed by overfishing through bycatch. The ensemble projections suggest northward shifts in species at an average rate of 27 km per decade, resulting in small average changes in range overlap between threatened and commercially exploited species. Furthermore, the adverse consequences of climate change on the habitat suitability of protected areas were projected to be small. Although the models show large variation in the predicted consequences of climate change, the multi-model approach helps identify the potential risk of increased exposure to human stressors of critically endangered species such as common skate (Dipturus batis) and angelshark (Squatina squatina)

Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique.

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1-4 min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s-1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena

Public perceptions of tidal energy: Can you predict social acceptability across coastal communities in England?

Early consideration of potential societal issues faced by the nascent tidal industry is important to facilitate public engagement and potentially avoid levels of conflict that have arisen within other renewable energy sectors; general expressions of public support (as reported in national-scale attitude surveys) do not always translate into approval for local developments. It is a very appealing idea that the likely response of different types of communities to marine energy developments can be mapped and used to support planning. This study examined the attitudes of 963 people in South West England to hypothetical local tidal energy projects, analysing the results both by geographic location and according to the coastal community typology developed for England by the Marine Management Organisation. With the exception of age, demographic variables had little influence on the level of opposition to tidal energy, which instead was affected more by factors such as attitudes towards tidal energy in general (in particular its likely environmental impact), activities undertaken at the coast, and place attachment. These significant factors are typically not captured by the national census data used to determine community types. Any predictions about the acceptability of energy projects made as a result of community mapping based on demographic variables will not be a substitute for thorough public engagement and consultation, which should centre on the implications of tidal developments for the environment

Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.

BACKGROUND: There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. METHODS: DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. RESULTS: Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. CONCLUSIONS: D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning

The impact of chromosomal translocation locus and fusion oncogene coding sequence in synovial sarcomagenesis.

Synovial sarcomas are aggressive soft-tissue malignancies that express chromosomal translocation-generated fusion genes, SS18-SSX1 or SS18-SSX2 in most cases. Here, we report a mouse sarcoma model expressing SS18-SSX1, complementing our prior model expressing SS18-SSX2. Exome sequencing identified no recurrent secondary mutations in tumors of either genotype. Most of the few mutations identified in single tumors were present in genes that were minimally or not expressed in any of the tumors. Chromosome 6, either entirely or around the fusion gene expression locus, demonstrated a copy number gain in a majority of tumors of both genotypes. Thus, by fusion oncogene coding sequence alone, SS18-SSX1 and SS18-SSX2 can each drive comparable synovial sarcomagenesis, independent from other genetic drivers. SS18-SSX1 and SS18-SSX2 tumor transcriptomes demonstrated very few consistent differences overall. In direct tumorigenesis comparisons, SS18-SSX2 was slightly more sarcomagenic than SS18-SSX1, but equivalent in its generation of biphasic histologic features. Meta-analysis of human synovial sarcoma patient series identified two tumor-gentoype-phenotype correlations that were not modeled by the mice, namely a scarcity of male hosts and biphasic histologic features among SS18-SSX2 tumors. Re-analysis of human SS18-SSX1 and SS18-SSX2 tumor transcriptomes demonstrated very few consistent differences, but highlighted increased native SSX2 expression in SS18-SSX1 tumors. This suggests that the translocated locus may drive genotype-phenotype differences more than the coding sequence of the fusion gene created. Two possible roles for native SSX2 in synovial sarcomagenesis are explored. Thus, even specific partial failures of mouse genetic modeling can be instructive to human tumor biology

Knowledge sharing in infection prevention in routine and outbreak situations: a survey of the Society for Healthcare Epidemiology of America Research Network

Abstract In this cross-sectional Society for Healthcare Epidemiology of America Research Network survey on knowledge sharing in infection prevention we identified a rudimentary understanding of how to communicate and share knowledge within healthcare institutions. Our data support the need of further research in this important field

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management

Target product profiles for protecting against outdoor malaria transmission.

BACKGROUND\ud \ud Long-lasting insecticidal nets (LLINs) and indoor residual sprays (IRS) have decimated malaria transmission by killing indoor-feeding mosquitoes. However, complete elimination of malaria transmission with these proven methods is confounded by vectors that evade pesticide contact by feeding outdoors.\ud \ud METHODS\ud \ud For any assumed level of indoor coverage and personal protective efficacy with insecticidal products, process-explicit malaria transmission models suggest that insecticides that repel mosquitoes will achieve less impact upon transmission than those that kill them outright. Here such models are extended to explore how outdoor use of products containing either contact toxins or spatial repellents might augment or attenuate impact of high indoor coverage of LLINs relying primarily upon contact toxicity.\ud \ud RESULTS\ud \ud LLIN impact could be dramatically enhanced by high coverage with spatial repellents conferring near-complete personal protection, but only if combined indoor use of both measures can be avoided where vectors persist that prefer feeding indoors upon humans. While very high levels of coverage and efficacy will be required for spatial repellents to substantially augment the impact of LLINs or IRS, these ambitious targets may well be at least as practically achievable as the lower requirements for equivalent impact using contact insecticides.\ud \ud CONCLUSIONS\ud \ud Vapour-phase repellents may be more acceptable, practical and effective than contact insecticides for preventing outdoor malaria transmission because they need not be applied to skin or clothing and may protect multiple occupants of spaces outside of treatable structures such as nets or houses

Gender representation in science publication: evidence from <i>Brain Communications</i>

The persistent underrepresentation of women in Science, Technology, Engineering, Mathematics and Medicine (STEMM) points to the need to continue promoting the awareness and understanding of this phenomenon. Being one of the main outputs of scientific work, academic publications provide the opportunity to quantify the gender gap in science as well as to identify possible sources of bias and areas of improvement. Brain Communications is a ‘young’ journal founded in 2019, committed to transparent publication of rigorous work in neuroscience, neurology and psychiatry. For all manuscripts (n = 796) received by the journal between 2019 and 2021, we analysed the gender of all authors (n = 7721) and reviewers (n = 4492). Overall, women were 35.3% of all authors and 31.3% of invited reviewers. A considerably higher proportion of women was found in first authorship (42.4%) than in last authorship positions (24.9%). The representation of women authors and reviewers decreased further in the months following COVID-19 restrictions, suggesting a possible exacerbating role of the pandemic on existing disparities in science publication. The proportion of manuscripts accepted for publication was not significantly different according to the gender of the first, middle or last authors, meaning we found no evidence of gender bias within the review or editorial decision-making processes at Brain Communications