We are the Change that we Seek: Information Interactions During a Change of Viewpoint

There has been considerable hype about filter bubbles and echo chambers influencing the views of information consumers. The fear is that these technologies are undermining democracy by swaying opinion and creating an uninformed, polarised populace. The literature in this space is mostly techno-centric, addressing the impact of technology. In contrast, our work is the first research in the information interaction field to examine changing viewpoints from a human-centric perspective. It provides a new understanding of view change and how we might support informed, autonomous view change behaviour. We interviewed 18 participants about a self-identified change of view, and the information touchpoints they engaged with along the way. In this paper we present the information types and sources that informed changes of viewpoint, and the ways in which our participants interacted with that information. We describe our findings in the context of the techno-centric literature and suggest principles for designing digital information environments that support user autonomy and reflection in viewpoint formation

On Birthing Dancing Stars: The Need for Bounded Chaos in Information Interaction

While computers causing chaos is acommon social trope, nearly the entirety of the history of computing is dedicated to generating order. Typical interactive information retrieval tasks ask computers to support the traversal and exploration of large, complex information spaces. The implicit assumption is that they are to support users in simplifying the complexity (i.e. in creating order from chaos). But for some types of task, particularly those that involve the creative application or synthesis of knowledge or the creation of new knowledge, this assumption may be incorrect. It is increasingly evident that perfect order—and the systems we create with it—support highly-structured information tasks well, but provide poor support for less-structured tasks.We need digital information environments that help create a little more chaos from order to spark creative thinking and knowledge creation. This paper argues for the need for information systems that offerwhat we term ‘bounded chaos’, and offers research directions that may support the creation of such interface

Can tDCS enhance treatment of aphasia after stroke?

Background: Recent advances in the application of transcranial direct current stimulation (tDCS) in healthy populations have led to the exploration of the technique as an adjuvant method to traditional speech therapies in patients with post-stroke aphasia. Aims: The purpose of the review is: (i) to review the features of tDCS that make it an attractive tool for research and potential future use in clinical contexts; (ii) to describe recent studies exploring the facilitation of language performance using tDCS in post-stroke aphasia; (iii) to explore methodological considerations of tDCS that may be key to understanding tDCS in treatment of aphasia post stroke; and (iv) to highlight several caveats and outstanding questions that need to be addressed in future work. Main Contribution: This review aims to highlight our current understanding of the methodological and theoretical issues surrounding the use of tDCS as an adjuvant tool in the treatment of language difficulties after stroke. Conclusions: Preliminary evidence shows that tDCS may be a useful tool to complement treatment of aphasia, particularly for speech production in chronic stroke patients. To build on this exciting work, further systematic research is needed to understand the mechanisms of tDCS-induced effects, its application to current models of aphasia recovery, and the complex interactions between different stimulation parameters and language rehabilitation techniques. The potential of tDCS is to optimise language rehabilitation techniques and promote long-term recovery of language. A stimulating future for aphasia rehabilitation

CT AND MRI OF THYROGLOSSAL DUCT CYST

The thyroglossal duct is a narrow tubular structure connecting the primordium of the thyroid gland to the tongue in the midline. If the duct does not involute at 8-10th weeks of gestation, secretion for infection or inflammation causes a thyroglossal duct cyst (TDC). TDC accounts for 70% of all congenital neck anomalies. The aim of this paper is to describe MR and CT findings of TDC including 3d MR sequences and a rare branched thyroglossal duct cyst

Noise estimation from averaged diffusion weighted images: Can unbiased quantitative decay parameters assist cancer evaluation?

Purpose: Multiexponential decay parameters are estimated from diffusion-weighted-imaging that generally have inherently low signal-to-noise ratio and non-normal noise distributions, especially at high b-values. Conventional nonlinear regression algorithms assume normally distributed noise, introducing bias into the calculated decay parameters and potentially affecting their ability to classify tumors. This study aims to accurately estimate noise of averaged diffusion-weighted-imaging, to correct the noise induced bias, and to assess the effect upon cancer classification. Methods: A new adaptation of the median-absolute-deviation technique in the wavelet-domain, using a closed form approximation of convolved probability-distribution-functions, is proposed to estimate noise. Nonlinear regression algorithms that account for the underlying noise (maximum probability) fit the biexponential/stretched exponential decay models to the diffusion-weighted signal. A logistic-regression model was built from the decay parameters to discriminate benign from metastatic neck lymph nodes in 40 patients. Results: The adapted median-absolute-deviation method accurately predicted the noise of simulated (R=0.96) and neck diffusion-weighted-imaging (averaged once or four times). Maximum probability recovers the true apparent-diffusion-coefficient of the simulated data better than nonlinear regression (up to 40%), whereas no apparent differences were found for the other decay parameters. Conclusions: Perfusion-related parameters were best at cancer classification. Noise-corrected decay parameters did not significantly improve classification for the clinical data set though simulations show benefit for lower signal-to-noise ratio acquisitions. © 2013 Wiley Periodicals, Inc

A comparison of Bayesian and non-linear regression methods for robust estimation of pharmacokinetics in DCE-MRI and how it affects cancer diagnosis

The aim of this work is to compare Bayesian Inference for nonlinear models with commonly used traditional non-linear regression (NR) algorithms for estimating tracer kinetics in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The algorithms are compared in terms of accuracy, and reproducibility under different initialization settings. Further it is investigated how a more robust estimation of tracer kinetics affects cancer diagnosis. The derived tracer kinetics from the Bayesian algorithm were validated against traditional NR algorithms (i.e. Levenberg-Marquardt, simplex) in terms of accuracy on a digital DCE phantom and in terms of goodness-of-fit (Kolmogorov-Smirnov test) on ROI-based concentration time courses from two different patient cohorts. The first cohort consisted of 76 men, 20 of whom had significant peripheral zone prostate cancer (any cancer-core-length (CCL) with Gleason>3+3 or any-grade with CCL>=4mm) following transperineal template prostate mapping biopsy. The second cohort consisted of 9 healthy volunteers and 24 patients with head and neck squamous cell carcinoma. The diagnostic ability of the derived tracer kinetics was assessed with receiver operating characteristic area under curve (ROC AUC) analysis. The Bayesian algorithm accurately recovered the ground-truth tracer kinetics for the digital DCE phantom consistently improving the Structural Similarity Index (SSIM) across the 50 different initializations compared to NR. For optimized initialization, Bayesian did not improve significantly the fitting accuracy on both patient cohorts, and it only significantly improved the ve ROC AUC on the HN population from ROC AUC=0.56 for the simplex to ROC AUC=0.76. For both cohorts, the values and the diagnostic ability of tracer kinetic parameters estimated with the Bayesian algorithm weren't affected by their initialization. To conclude, the Bayesian algorithm led to a more accurate and reproducible quantification of tracer kinetic parameters in DCE-MRI, improving their ROC-AUC and decreasing their dependence on initialization settings

Incidence and Risk Factors of Recurrent Clostridioides difficile Infection in Patients With Cirrhosis

INTRODUCTION: Clostridioides difficile infection (CDI) is common in patients with cirrhosis and is associated with poor outcomes. CDI risk factors in this population have been well characterized; however, risk factors of recurrent CDI (R-CDI) after treatment have not been explored. We sought to estimate the incidence of R-CDI and its associated risk factors in patients with cirrhosis. METHODS: We performed a cohort study of patients with cirrhosis hospitalized with CDI between 2012 and 2016. We collected patient characteristics, including detailed information on the CDI, features of the underlying liver disease, and outcomes including R-CDI, hospital readmission, and mortality. R-CDI was defined as CDI occurring 2–8 weeks after the initial episode. Cox proportional hazards model was used to identify variables independently associated with the outcomes. RESULTS: A total of 257 hospitalized patients with cirrhosis and CDI were included. CDI was community associated in 22.6%. The incidence of R-CDI was 11.9%. R-CDI was not significantly associated with medications at hospital admission or discharge. Independent risk factors of R-CDI included increased Charlson Comorbidity Index (hazard ratio [HR] 1.30; 95% confidence interval [CI]: 1.09–1.55) and use of lactulose (HR 2.58; 95% CI: 1.09–6.09). The 30-day readmission rate was 37%, and readmission was associated with increased Charlson Comorbidity Index (HR 1.12; 95% CI: 1.03–1.23) and Model for End-Stage Liver Disease score (HR 1.04; 95% CI: 1.01–1.07). The 90-day mortality was 22.8%. DISCUSSION: In patients with cirrhosis, R-CDI is associated with comorbidity burden and lactulose use. Attention to these factors might aid clinicians in efforts to prevent R-CDI and improve outcomes in this population