Developing an intervention to improve reading comprehension for children and young people with autism spectrum disorders

Aim: A substantial proportion of children and young people with autism demonstrate accurate word reading but struggle to understand the content of what they are reading. There is an urgent need for further research in this area to enable educational professionals to implement evidence-based reading interventions. / Method/Rationale: This study analyses the effectiveness of an intervention designed to improve the reading comprehension of young people with autism and reading comprehension diffculties (mean age 13 years, 6 months). The intervention was delivered twice a week over a period of six weeks. / Findings: The results indicate that the intervention group (N=15) demonstrated a signifcantly greater increase in their reading comprehension than a ‘treatment as usual’ control group (N=14), showing an average of three years’ improvement in their reading comprehension. Semi-structured interviews with participants indicated that many demonstrated a shift in their approach to reading, with a greater focus on comprehension and an awareness of transferring the skills they had learnt to other areas of the curriculum. Participants also identifed that the intervention supported their speaking and listening skills. / Limitations: The small size of the sample in this study limits the generalisation of the fndings. The robustness of the fndings would be increased by including long-term outcome measures. / Conclusions: These fndings present important implications for professionals working with young people and suggest that school-based reading interventions may be effective at developing the reading comprehension of individuals with autism

Antenatal origins of reduced lung function-but not asthma?

Peer reviewedPostprin

POCUS: mining genomic sequence annotation to predict disease genes

Here we present POCUS (prioritization of candidate genes using statistics), a novel computational approach to prioritize candidate disease genes that is based on over-representation of functional annotation between loci for the same disease. We show that POCUS can provide high (up to 81-fold) enrichment of real disease genes in the candidate-gene shortlists it produces compared with the original large sets of positional candidates. In contrast to existing methods, POCUS can also suggest counterintuitive candidates

Mimicking exercise in three-dimensional bioengineered skeletal muscle to investigate cellular and molecular mechanisms of physiological adaptation

Bioengineering of skeletal muscle in vitro in order to produce highly aligned myofibres in relevant three dimensional (3D) matrices have allowed scientists to model the in vivo skeletal muscle niche. This review discusses essential experimental considerations for developing bioengineered muscle in order to investigate exercise mimicking stimuli. We identify current knowledge for the use of electrical stimulation and co-culture with motor neurons to enhance skeletal muscle maturation and contractile function in bioengineered systems in vitro. Importantly, we provide a current opinion on the use of acute and chronic exercise mimicking stimuli (electrical stimulation and mechanical overload) and the subsequent mechanisms underlying physiological adaptation in 3D bioengineered muscle. We also identify that future studies using the latest bioreactor technology, providing simultaneous electrical and mechanical loading and flow perfusion in vitro, may provide the basis for advancing knowledge in the future. We also envisage, that more studies using genetic, pharmacological, and hormonal modifications applied in human 3D bioengineered skeletal muscle may allow for an enhanced discovery of the in-depth mechanisms underlying the response to exercise in relevant human testing systems. Finally, 3D bioengineered skeletal muscle may provide an opportunity to be used as a pre-clinical in vitro test-bed to investigate the mechanisms underlying catabolic disease, while modelling disease itself via the use of cells derived from human patients without exposing animals or humans (in phase I trials) to the side effects of potential therapies

Reproducible kk-means clustering in galaxy feature data from the GAMA survey

A fundamental bimodality of galaxies in the local Universe is apparent in many of the features used to describe them. Multiple sub-populations exist within this framework, each representing galaxies following distinct evolutionary pathways. Accurately identifying and characterising these sub-populations requires that a large number of galaxy features be analysed simultaneously. Future galaxy surveys such as LSST and Euclid will yield data volumes for which traditional approaches to galaxy classification will become unfeasible. To address this, we apply a robust $k$-means unsupervised clustering method to feature data derived from a sample of 7338 local-Universe galaxies selected from the Galaxy And Mass Assembly (GAMA) survey. This allows us to partition our sample into $k$ clusters without the need for training on pre-labelled data, facilitating a full census of our high dimensionality feature space and guarding against stochastic effects. We find that the local galaxy population natively splits into $2$, $3$, $5$ and a maximum of $6$ sub-populations, with each corresponding to a distinct ongoing evolutionary mechanism. Notably, the impact of the local environment appears strongly linked with the evolution of low-mass ($M_{*} < 10^{10}$ M$_{\odot}$) galaxies, with more massive systems appearing to evolve more passively from the blue cloud onto the red sequence. With a typical run time of $\sim3$ minutes per value of $k$ for our galaxy sample, we show how $k$-means unsupervised clustering is an ideal tool for future analysis of large extragalactic datasets, being scalable, adaptable, and providing crucial insight into the fundamental properties of the local galaxy population

Literature-based discovery of diabetes- and ROS-related targets

Abstract Background Reactive oxygen species (ROS) are known mediators of cellular damage in multiple diseases including diabetic complications. Despite its importance, no comprehensive database is currently available for the genes associated with ROS. Methods We present ROS- and diabetes-related targets (genes/proteins) collected from the biomedical literature through a text mining technology. A web-based literature mining tool, SciMiner, was applied to 1,154 biomedical papers indexed with diabetes and ROS by PubMed to identify relevant targets. Over-represented targets in the ROS-diabetes literature were obtained through comparisons against randomly selected literature. The expression levels of nine genes, selected from the top ranked ROS-diabetes set, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biological relevance of literature-derived targets in the pathogenesis of diabetic neuropathy. Results SciMiner identified 1,026 ROS- and diabetes-related targets from the 1,154 biomedical papers (http://jdrf.neurology.med.umich.edu/ROSDiabetes/). Fifty-three targets were significantly over-represented in the ROS-diabetes literature compared to randomly selected literature. These over-represented targets included well-known members of the oxidative stress response including catalase, the NADPH oxidase family, and the superoxide dismutase family of proteins. Eight of the nine selected genes exhibited significant differential expression between diabetic and non-diabetic mice. For six genes, the direction of expression change in diabetes paralleled enhanced oxidative stress in the DRG. Conclusions Literature mining compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the pathogenesis of diabetic neuropathy.http://deepblue.lib.umich.edu/bitstream/2027.42/78315/1/1755-8794-3-49.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/2/1755-8794-3-49-S7.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/3/1755-8794-3-49-S10.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/4/1755-8794-3-49-S8.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/5/1755-8794-3-49-S3.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/6/1755-8794-3-49-S1.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/7/1755-8794-3-49-S4.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/8/1755-8794-3-49-S2.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/9/1755-8794-3-49-S12.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/10/1755-8794-3-49-S11.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/11/1755-8794-3-49-S9.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/12/1755-8794-3-49-S5.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/13/1755-8794-3-49-S6.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/14/1755-8794-3-49.pdfPeer Reviewe