Circulating Serum Exosomal miRNAs As Potential Biomarkers for Esophageal Adenocarcinoma

Author version made available in accordance with publisher policy.Abstract Background The poor prognosis and rising incidence of esophageal adenocarcinoma highlight the need for improved detection methods. The potential for circulating microRNAs (miRNAs) as biomarkers in other cancers has been shown, but circulating miRNAs have not been well characterized in esophageal adenocarcinoma. We investigated whether circulating exosomal miRNAs have potential to discriminate individuals with esophageal adenocarcinoma from healthy controls and non-dysplastic Barrett’s esophagus. Methods Seven hundred fifty-eight miRNAs were profiled in serum circulating exosomes from a cohort of 19 healthy controls, 10 individuals with Barrett’s esophagus, and 18 individuals with locally advanced esophageal adenocarcinoma. MiRNA expression was assessed using all possible permutations of miRNA ratios per individual. Four hundred eight miRNA ratios were differentially expressed in individuals with cancer compared to controls and Barrett’s esophagus (Mann-Whitney U test, P<0.05). The 179/408 ratios discriminated esophageal adenocarcinoma from healthy controls and Barrett’s esophagus (linear regression, P0.7, P<0.05). A multi-biomarker panel (RNU6-1/miR- 16-5p, miR-25-3p/miR-320a, let-7e-5p/miR-15b-5p, miR- 30a-5p/miR-324-5p, miR-17-5p/miR-194-5p) demonstrated enhanced specificity and sensitivity (area under ROC=0.99, 95 % CI 0.96–1.0) over single miRNA ratios to distinguish esophageal adenocarcinoma from controls and Barrett’s esophagus. Conclusions This study highlights the potential for serum exosomal miRNAs as biomarkers for the detection of esophageal adenocarcinoma

Blending human and artificial intelligence to support autistic children’s social communication skills

This paper examines the educational efficacy of a learning environment in which children diagnosed with Autism Spectrum Conditions (ASC) engage in social interactions with an artificially intelligent (AI) virtual agent and where a human practitioner acts in support of the interactions. A multi-site intervention study in schools across the UK was conducted with 29 children with ASC and learning difficulties, aged 4-14 years old. For reasons related to data completeness and amount of exposure to the AI environment, data for 15 children was included in the analysis. The analysis revealed a significant increase in the proportion of social responses made by ASC children to human practitioners. The number of initiations made to human practitioners and to the virtual agent by the ASC children also increased numerically over the course of the sessions. However, due to large individual differences within the ASC group, this did not reach significance. Although no evidence of transfer to the real-world post-test was shown, anecdotal evidence of classroom transfer was reported. The work presented in this paper offers an important contribution to the growing body of research in the context of AI technology design and use for autism intervention in real school contexts. Specifically, the work highlights key methodological challenges and opportunities in this area by leveraging interdisciplinary insights in a way that (i) bridges between educational interventions and intelligent technology design practices, (ii) considers the design of technology as well as the design of its use (context and procedures) on par with one another, and (iii) includes design contributions from different stakeholders, including children with and without ASC diagnosis, educational practitioners and researchers

Ablation of Barrett's oesophagus: towards improved outcomes for oesophageal cancer?

This is the accepted version of the following article: Mayne, G. C., Bright, T., Hussey, D. J. and Watson, D. I. (2012), Ablation of Barrett's oesophagus: towards improved outcomes for oesophageal cancer?. ANZ Journal of Surgery, 82: 592–598, which has been published in final form at doi:10.1111/j.1445-2197.2012.06151.xBarrett's oesophagus is the major risk factor for oesophageal adenocarcinoma. The management of Barrett’s oesophagus entails treating reflux symptoms with acid-suppressing medication or surgery (fundoplication). However neither form of anti-reflux therapy produces predictable regression, or prevents cancer development. Patients with Barrett’s oesophagus usually undergo endoscopic surveillance which aims to identify dysplastic changes or cancer at its earliest stage, when treatment outcomes should be better. Alternative endoscopic interventions are now available and are suggested for the treatment of early cancer, and prevention of progression of Barrett’s oesophagus to cancer. Such treatments could minimize the risks associated with oesophagectomy. The current status of these interventions is reviewed. Various endoscopic interventions have been described, but with long term outcomes uncertain, they remain somewhat controversial. Radiofrequency ablation (RFA) of dysplastic Barrett’s oesophagus might reduce the risk of cancer progression, although cancer development has been reported after this treatment. Endoscopic mucosal resection (EMR) allows a 1.5 to 2 cm diameter piece of oesophageal mucosa to be removed. This provides better pathology for diagnosis and staging, and if the lesion is confined to the mucosa and fully excised, EMR can be curative. The combination of EMR and RFA has been used for multifocal lesions, but long term outcomes are unknown. The new endoscopic interventions for Barrett’s oesophagus and early oesophageal cancer have potential to improve clinical outcomes, although evidence which confirms superiority over oesphagectomy is limited. Longer term outcome data and data from larger cohorts is required to confirm the appropriateness of these procedure

Position Paper: New Views of Shots - Towards Measures of Net Visibility and Reachability

In this position paper, we define two new metrics: net visibility (the fraction of the net that can be seen from the perspective of the puck) and net reachability (the fraction of the net that could be reached by the puck). Reachability is slightly different from visibility because even though there might be a small portion of the net visible in a certain area (a hole), that hole may not be large enough for the puck to pass through and reach the net. We describe a framework for computing our metrics using a combination of puck and player tracking (PPT) data and video analysis (image processing). We use data and video from an NHL game to provide a proof of concept for computing net visibility and reachability. We also describe areas where more work can be done to improve the accuracy of the results and allow the computations to be fully automated. Our position is that these metrics would be valuable in studying shooter decisions and skills, goaltender and player locations and that the technologies could be used to create virtual reality images or videos

A worldwide study of subcortical shape as a marker for clinical staging in Parkinson’s disease

Alterations in subcortical brain regions are linked to motor and non-motor symptoms in Parkinson’s disease (PD). However, associations between clinical expression and regional morphological abnormalities of the basal ganglia, thalamus, amygdala and hippocampus are not well established. We analyzed 3D T1-weighted brain MRI and clinical data from 2525 individuals with PD and 1326 controls from 22 global sources in the ENIGMA-PD consortium. We investigated disease effects using mass univariate and multivariate models on the medial thickness of 27,120 vertices of seven bilateral subcortical structures. Shape differences were observed across all Hoehn and Yahr (HY) stages, as well as correlations with motor and cognitive symptoms. Notably, we observed incrementally thinner putamen from HY1, caudate nucleus and amygdala from HY2, hippocampus, nucleus accumbens, and thalamus from HY3, and globus pallidus from HY4–5. Subregions of the thalami were thicker in HY1 and HY2. Largely congruent patterns were associated with a longer time since diagnosis and worse motor symptoms and cognitive performance. Multivariate regression revealed patterns predictive of disease stage. These cross-sectional findings provide new insights into PD subcortical degeneration by demonstrating patterns of disease stage-specific morphology, largely consistent with ongoing degeneration

Haze in Pluto's atmosphere: Results from SOFIA and ground-based observations of the 2015 June 29 Pluto occultation

On UT 29 June 2015, the occultation by Pluto of a bright star (r′ = 11.9) was observed from the Stratospheric Observatory for Infrared Astronomy (SOFIA) and several ground-based stations in New Zealand and Australia. Pre-event astrometry allowed for an in-flight update to the SOFIA team with the result that SOFIA was deep within the central flash zone (~22 km from center). Analysis of the combined data leads to the result that Pluto's middle atmosphere is essentially unchanged from 2011 and 2013 (Person et al. 2013; Bosh et al. 2015); there has been no significant expansion or contraction of the atmosphere. Additionally, our multi-wavelength observations allow us to conclude that a haze component in the atmosphere is required to reproduce the light curves obtained. This haze scenario has implications for understanding the photochemistry of Pluto's atmosphere

Internal validation of STRmix™ – A multi laboratory response to PCAST

We report a large compilation of the internal validations of the probabilistic genotyping software STRmix™. Thirty one laboratories contributed data resulting in 2825 mixtures comprising three to six donors and a wide range of multiplex, equipment, mixture proportions and templates. Previously reported trends in the LR were confirmed including less discriminatory LRs occurring both for donors and non-donors at low template (for the donor in question) and at high contributor number. We were unable to isolate an effect of allelic sharing. Any apparent effect appears to be largely confounded with increased contributor number

SciPy 1.0: fundamental algorithms for scientific computing in Python

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments