The effect of washing on the electrical performance of knitted textile strain sensors for quantifying joint motion

Successful market penetration of textile-based strain sensors requires long-term reliability which in turn relies on the washability of the sensor. First, this paper presents an evaluation of the effect of 5 washing cycles on the electrical performance of a knitted conductive transducer, over 1500 cycles of repetitive elongation. The promising behaviour of the textile sensor in this study showed that it might be possible to make a smart garment, capable of quantifying elbow flexion-extension motion, by integrating it into an elbow sleeve. Second, a prototype sleeve, incorporating a knitted sensor (the so-called smart sleeve), was tested in a simulated training/clinical setting by performing 50 flexion-extension cycles after 1, 5, 15, 25, 50 and 75 washes. In both studies, the electrical resistance of the sensor increased with the number of washes in a predictable manner and exhibited a repeatable, reliable and prompt response to elongation. In particular, the electrical pattern representing flexion-extension motion measured using the sleeve was clear and distinguishable up to the 75th wash. Moreover, resistance measurements within the same trial were repeatable at maximum flexion (≤2% variation) and at maximum extension (≤3% variation) and predictable with increasing washes (R2 = 0.992 at maximum flexion and R2 = 0.989 at maximum extension). The good washability of the smart sleeve, evidenced by its ability to detect, distinguish and measure parameters of flexion-extension motion up to 75 washes, makes it a suitable and sustainable choice for applications, such as strength conditioning or rehabilitation, where repetition count and speed are useful.

EP.TU.657Scoping the role and content of a simulator-based endosim curriculum

Background Endoscopy training requires simultaneous acquisition of practical skill and knowledge. Virtual reality Simulators, such as EndoSim (SurgicalScience), offer the opportunity to deconstruct a skill into fundamental components (1), to allow repetitive practice, and achievement of task-specific objectives. Trainees with the least endoscopy experience benefit most from early simulated training (2,3), supporting the introduction of a simulation curriculum into SPRINT: Structured PRogramme for INduction and Training, an existing initiative to improve endoscopy training delivery in Wales. (4) The aim of this study was to design a pilot simulator curriculum for the EndoSim (Surgical Science, Gothemberg) simulator. Methods A focus group completed all EndoSim modules. Each EndoSim exercise was cross-examined against the relevant DOPS tool “Direct Observation of Procedural Skill” used by the Joint Advisory Group for Endoscopy Training and Certification. Exercises were chosen that represented each DOPS domain to teach basic skills in endoscopy scope handling. Results 12 exercises were chosen. These exercises addressed the insertion and withdrawal, and visualisation components of the JAG DOPS tool. Pre-procedural skills, management of findings, post-procedural skills and endoscopic non-technical Skills (ENTS) are beyond the scope of this simulator and require additional taught sessions to provide the context for current simulation training. Discussion This is the first step in developing and refining appropriate exercises to inform the proposed curriculum. The next step will be validating the chosen exercises against expert benchmark performance.

Customized de novo mutation detection for any variant calling pipeline: SynthDNM

Motivation As sequencing technologies and analysis pipelines evolve, de novo mutation (DNM) calling tools must be adapted. Therefore, a flexible approach is needed that can accurately identify DNMs from genome or exome sequences from a variety of datasets and variant calling pipelines. Results Here, we describe SynthDNM, a random-forest based classifier that can be readily adapted to new sequencing or variant-calling pipelines by applying a flexible approach to constructing simulated training examples from real data. The optimized SynthDNM classifiers predict de novo SNPs and indels with robust accuracy across multiple methods of variant calling. Availabilityand implementation SynthDNM is freely available on Github (https://github.com/james-guevara/synthdnm). Supplementary information Supplementary data are available at Bioinformatics online.

Customized de novo mutation detection for any variant calling pipeline: SynthDNM

MotivationAs sequencing technologies and analysis pipelines evolve, DNM calling tools must be adapted. Therefore, a flexible approach is needed that can accurately identify de novo mutations from genome or exome sequences from a variety of datasets and variant calling pipelines.ResultsHere, we describe SynthDNM, a random-forest based classifier that can be readily adapted to new sequencing or variant-calling pipelines by applying a flexible approach to constructing simulated training examples from real data. The optimized SynthDNM classifiers predict de novo SNPs and indels with robust accuracy across multiple methods of variant calling.AvailabilitySynthDNM is freely available on Github (https://github.com/james-guevara/synthdnm)[email protected] informationSupplementary data are available at Bioinformatics online.

TAPPING INTO INTERPRETING STUDENTS’ MOTIVATION TO ENGAGE IN TARGETED TRANSLATION PRACTICE

Translation and interpreting are different in many aspects. For the former, the source and target text remain available and communication between participants happens asynchronously; the latter demands immediate interaction and speech signals are fast fading. The two activities and their respective contexts, including working conditions, are also dissimilar in the professional world. A quick glance may leave an impression that entirely different training is in order. However, translation and interpreting as a profession also share tremendous similarities — the European Master’s Translation competence framework adequately applies to interpreting. This action research study aimed to motivate beginning interpreting students to overcome challenges in interpreting practice via translation activities. A two-stage translation workshop was designed, and the results show that students became more engaged in the workshop when the authenticity of the tasks and the relevance between translation practice and interpreting performance were elucidated. Keywords: motivation, situated translation, simulated training, project-based learning, entrepreneurship, action research