Using textual analysis with Concept Inventories to identify root causes of misconceptions

Engineers must have deep and accurate conceptual understanding of their field and Concept inventories (CIs) are one method of assessing conceptual understanding and providing formative feedback. Current CI tests use Multiple Choice Questions (MCQ) to identify misconceptions and have undergone reliability and validity testing to assess conceptual understanding. However, they do not readily provide the diagnostic information about students’ reasoning and therefore do not effectively point to specific actions that can be taken to improve student learning. We piloted the textual component of our diagnostic CI on electrical engineering students using items from the signals and systems CI. We then analysed the textual responses using automated lexical analysis software to test the effectiveness of these types of software and interviewed the students regarding their experience using the textual component. Results from the automated text analysis revealed that students held both incorrect and correct ideas for certain conceptual areas and provided indications of student misconceptions. User feedback also revealed that the inclusion of the textual component is helpful to students in assessing and reflecting on their own understanding.

Subsurface imaging of vegetation, climate, and root-zone moisture interactions

Changes in global climate and land use affect important prolesses from evapotranspiration and groundwater recharge to carbon storage and biochemical cycling. Near surface soil moisture is pivotal to understand the consequences of these changes. However, the dynamic interactions between vegetation and soil moisture remain largely unresolved because it is difficult to monitor and quantify subsurface hydrologic fluxes at relevant scales. Here we use electrical resistivity to monitor the influence of climate and vegetation on root-zone moisture, bridging the gap between remotely-sensed and in-situ point measurements. Our research quantifies large seasonal differences in root-zone moisture dynamics for a forest-grassland ecotone. We found large differences in effective rooting depth and moisture distributions for the two vegetation types. Our results highlight the likely impacts of land transformations on groun ter recharge, streamflow, and land-atmosphere exchanges.

The root locus method: application to linear stability analysis and design of cooperative car-following models

A global framework for linear stability analyses of traffic models, based on the dispersion relation root locus method, is presented and is applied taking the example of a broad class of car-following (CF) models. This approach is able to analyse all aspects of the dynamics: long waves and short wave behaviours, phase velocities and stability features. The methodology is applied to investigate the potential benefits of connected vehicles, i.e. V2V communication enabling a vehicle to send and receive information to and from surrounding vehicles. We choose to focus on the design of the coefficients of cooperation which weights the information from downstream vehicles. The coefficients tuning is performed and different ways of implementing an efficient cooperative strategy are discussed. Hence, this paper brings design methods in order to obtain robust stability of traffic models, with application on cooperative CF models

Combining HPLC-DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex moutan

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations

Does the addition of Clonidine to lumbar nerve root blocks improve outcome? A randomised, prospective, single-blinded controlled pilot study

Purpose To evaluate if adding clonidine to a standard nerve root block containing local anaesthetic and steroid improved the outcome of patients with severe lumbar nerve root pain secondary to MRI proven lumbar disc prolapse. Methods We undertook a single blind, prospective, randomised controlled trial evaluating 100 consecutive patients with nerve root pain secondary to lumbar disc prolapse undergoing trans-foraminal epidural steroid injection either with or without the addition of clonidine. 50 patients were allocated to each arm of the study. The primary outcome measure was the avoidance of a second procedure- repeat injection or micro-discectomy surgery. Secondary outcome measures were also studied: pain scores for leg and back pain using a visual analogue scale (VAS), the Roland Morris Disability Questionnaire (RMDQ) and the Measure Your Own Medical Outcome Profile (MYMOP). Follow up was carried out at 6 weeks, 6 months and 1 year. Results No serious complications occurred. Of the 50 patients who received the addition of clonidine, 56% were classified as successful injections, with no further intervention required, as opposed to 40% who received the standard injection. This difference did not reach statistical significance (p=0.109, chi-squared test). All secondary measures showed no statistically significant differences between the groups except curiously, the standard group who had been classified as successful had better leg pain relief than the clonidine group (p=0.026) at 1 year. Conclusions This pilot study has shown a 16% treatment effect with adding clonidine to lumbar nerve root blocks and that it is a safe injectate for this purpose.

Lung cancer stem cells: The root of resistance

In the absence of specific treatable mutations, platinum-based chemotherapy remains the gold standard of treatment for lung cancer patients. However, 5-year survival rates remain poor due to the development of resistance and eventual relapse. Resistance to conventional cytotoxic therapies presents a significant clinical challenge in the treatment of this disease. The cancer stem cell (CSC) hypothesis suggests that tumors are arranged in a hierarchical structure, with the presence of a small subset of stem-like cells that are responsible for tumor initiation and growth. This CSC population has a number of key properties such as the ability to asymmetrically divide, differentiate and self-renew, in addition to having increased intrinsic resistance to therapy. While cytotoxic chemotherapy kills the bulk of tumor cells, CSCs are spared and have the ability to recapitulate the heterogenic tumor mass. The identification of lung CSCs and their role in tumor biology and treatment resistance may lead to innovative targeted therapies that may ultimately improve clinical outcomes in lung cancer patients. This review will focus on lung CSC markers, their role in resistance and their relevance as targets for future therapies.