Eric Chalmers

Covering complete proteomes with X-ray structures: a current snapshot

The current and the attainable coverage by X-ray structures of proteins and their functions on the scale of the ‘protein universe’ are estimated. A detailed analysis of the coverage across nearly 2000 proteomes from all superkingdoms of life and functional annotations is performed, with particular focus on the human proteome and the family of GPCR proteins.

Development of a Pressure Control System for Brace Treatment of Scoliosis

Bracing is a common nonsurgical treatment for scoliosis, but its effectiveness has been debated. Some clinical studies have shown efficacy of brace treatment is correlated to how the brace has been worn. The more often the patients wear their braces to the prescribed tightness as well as the prescribed length of wear each day, the better the treatment outcome. A system of four wireless pressure control devices was developed to understand brace wear-time and regulate a target pressure range at the brace-body interface. Each pressure control device could function independently and be embedded in the brace at key pressure areas. Such a system could improve the quality of brace wear-making the treatment more effective and refining our understanding of the three-pressure-point brace treatment concept during daily activities. This paper reports the system development and validation. The system was tested on four healthy subjects for 2 h without pressure regulation and 2 h with regulation. The results show that the pressure regulation doubled the time spent in a desired pressure range on average (from 31% to 62%). Brace-wear time was logged correctly. The system was also validated through a seven-day continuous test, and a fully charged battery could run for 30 days without requiring recharge.

Toward maximum-predictive-value classification

Abstract Methods for tackling classification problems usually maximize prediction accuracy. However some applications require maximum predictive value instead. That is, the designer hopes to predict one of the classes with maximum precision, and is less concerned about the others. Some techniques exist for fine-tuning a model׳s predictive value, but there seems to be a shortage of methods to generate maximum-predictive-value classifiers. We propose a method using a nearest-prototype-style classifier optimized by a genetic algorithm. We test its performance using 13 publicly available data sets from the life sciences. The method generally gives more effective high-predictive-value models than standard classification methods optimized for predictive value.

Human Experts’ and a Fuzzy Model's Predictions of Outcomes of Scoliosis Treatment: A Comparative Analysis

Brace treatment is the most commonly used nonsurgical treatment for adolescents with idiopathic scoliosis. However, brace treatment is not always successful and the factors influencing its success are not completely clear. This makes treatment outcome difficult to predict. A computer model which can accurately predict treatment outcomes could potentially provide valuable treatment recommendations. This paper describes a fuzzy system that includes a prediction model and a decision support engine. The model was constructed using conditional fuzzy c-means clustering to discover patterns in retrospective patient data. The models ability to predict treatment outcome was compared to the ability of eight Scoliosis experts. The model and experts each predicted treatment outcome retrospectively for 28 braced patients, and these predictions were compared to the actual outcomes. The model outperformed all but one expert individually and performed similarly to the experts as a group. These results suggest that the fuzzy model is capable of providing meaningful treatment recommendations. This study offers the first model for this application whose performance has been shown to be at or above the human expert level.

How does pressure configuration affect Cobb angle during AIS brace casting

Methods Nine AIS patients undergoing casting for new braces participated in this pilot study (2 males, 7 females, aged 11-16, Cobb angles 16-44 degrees). An ultrasound scan was used to measure the patient’s (baseline) Cobb angle. The orthotist then used a custom standing Providence system to apply corrective pressures simulating a brace. A second ultrasound scan measured the new (corrected) Cobb angle. The orthotist could then try to achieve additional correction by adjusting the pressure magnitude/location. The process of adjusting pressures and ultrasound scanning repeated two or three times; the orthotist then chose the most satisfactory pressure configuration and performed the actual casting. The procedure produced 26 individual scans (including baseline scans) from the 9 patients. The magnitude of applied pressures was measured using inflatable air bladders fixed to the Providence pads. The air pressure was measured during the ultrasound scan. The distance between pads was measured and multiplied by the total pressure to create a torque-like measurement. Robust linear regression was used to relate pressure with Cobb angle correction, and torque with correction. Outlier points were removed if they fell more than 1.5 standard deviations from the regression line. Correlations between pressure/torque and correction were then measured. Results Two outlier points were removed both belonging to a single patient. Pressures ranged from 16-113 mmHg. The major curves’ correction ranged from 0-39%. Significant correlations existed between average pressure and Cobb angle correction (r = 0.86, p < 0.01), and average torque and Cobb angle correction (r = 0.82, p < 0.01).

Predicting the outcome of AIS brace treatment using expert judgement and a fuzzy model

Methods Data was obtained retrospectively from 28 AIS patients who had finished treatment (27 girls, 1 boy, aged 11-15 (mean 13), Cobb angles 20-44 degrees (mean 31), 21 daytime and 7 nighttime braces.) Patients were labelled ‘progressed’ if their Cobb angle had increased more than 5 degrees by the end of treatment, and ‘non-progressed’ otherwise. A fuzzy model was developed to predict treatment outcome for each patient using clinical measurements taken at the first in-brace clinic. The model considers patient age, Cobb angle, Scoliometer measurement at the apex level, and in-brace Cobb angle correction. For each patient it calculated a probability-like score for each of three possible outcomes: ‘progression’ (Cobb angle increase > 5 degrees), ‘neutral’ (Cobb angle change of 0-5 degrees), and ‘improvement’ (Cobb angle decrease). For this study, the patient was predicted to progress if the ‘progression’ score was the highest. Five AIS experts also participated: two orthopaedic surgeons, two orthotists, and one nurse practitioner. Participants were supplied with all available start-oftreatment clinical measurements for each patient, and asked to predict whether or not each patient would progress by the end of brace treatment. The multi-rater kappa was calculated to measure agreement between experts’ predictions. The correlation of each expert’s predictions and the model’s predictions with the actual treatment outcome was measured.