Lee Silverman Voice Treatment versus standard speech and language therapy versus control in Parkinson’s disease: a pilot randomised controlled trial (PD COMM pilot)

Background: Speech-related problems are common in Parkinson’s disease (PD), but there is little evidence for the effectiveness of standard speech and language therapy (SLT) or Lee Silverman Voice Treatment (LSVT LOUD®). Methods: The PD COMM pilot was a three-arm, assessor-blinded, randomised controlled trial (RCT) of LSVT LOUD®, SLT and no intervention (1:1:1 ratio) to assess the feasibility and to inform the design of a full-scale RCT. Non-demented patients with idiopathic PD and speech problems and no SLT for speech problems in the past 2 years were eligible. LSVT LOUD® is a standardised regime (16 sessions over 4 weeks). SLT comprised individualised content per local practice (typically weekly sessions for 6–8 weeks). Outcomes included recruitment and retention, treatment adherence, and data completeness. Outcome data collected at baseline, 3, 6, and 12 months included patient-reported voice and quality of life measures, resource use, and assessor-rated speech recordings. Results: Eighty-nine patients were randomised with 90% in the therapy groups and 100% in the control group completing the trial. The response rate for Voice Handicap Index (VHI) in each arm was ≥ 90% at all time-points. VHI was highly correlated with the other speech-related outcome measures. There was a trend to improvement in VHI with LSVT LOUD® (difference at 3 months compared with control: − 12.5 points; 95% CI − 26.2, 1.2) and SLT (difference at 3 months compared with control: − 9.8 points; 95% CI − 23.2, 3.7) which needs to be confirmed in an adequately powered trial. Conclusion: Randomisation to a three-arm trial of speech therapy including a no intervention control is feasible and acceptable. Compliance with both interventions was good. VHI and other patient-reported outcomes were relevant measures and provided data to inform the sample size for a substantive trial

Case Studies in Teaching

This chapter will:; ; ; Present a final set of three teaching case studies to illustrate the points that we have made throughout this volume;; ; ; Discuss extensive tasks designed for business students to enable them to practise and develop the skills of negotiating, relationship-building, decision-making, and problem-solving, as these are used by business professionals;; ; ; Conclude with recommendations for further readings based on research into spoken and written business discourse

M3G: Maximum Margin Microarray Gridding

<p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p

Negotiating the Meaning of Team Expertise: A Firefighter Team’s Epistemic Denial

In this case study, we report how a team of firefighters critiqued one of its member’s decisions to facilitate learning and process improvement. The study is supported by 500+ hr of ethnographic observations, documents, and 11 retrospective interviews, which captured how the team’s talk about the member’s decision shaped their interpretations of their own and others’ expertise—interpretations that ironically undermined learning. Constant comparative analysis revealed that these firefighters positioned themselves as experts by crediting either personal experience or technical knowledge and then discrediting the alternative way of knowing. We labeled this process epistemic denial. The process of epistemic denial was rooted in identity concern; specifically, veteran team members relied on personal experience and newer members relied on technical information gained from training to assert their expertise, and to devalue others’ expertise. The article concludes with recommendations for avoiding problems associated with epistemic denial in high-reliability teams.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline