Modelling knowledge production performance of research centres with a focus on triple bottom line benchmarking

We demonstrate a portable process for developing a triple bottom line model to measure the knowledge production performance of individual research centres. For the first time, this study also empirically illustrates how a fully units-invariant model of Data Envelopment Analysis (DEA) can be used to measure the relative efficiency of research centres by capturing the interaction amongst a common set of multiple inputs and outputs. This study is particularly timely given the increasing transparency required by governments and industries that fund research activities. The process highlights the links between organisational objectives, desired outcomes and outputs while the emerging performance model represents an executive managerial view. This study brings consistency to current measures that often rely on ratios and univariate analyses that are not otherwise conducive to relative performance analysis.

Ratiometric and nonratiometric Ca2+ indicators for the assessment of intracellular free Ca2+ in a breast cancer cell line using a fluorescence microplate reader

Transporters of Ca2+ are potential drug targets and Ca2+ is a useful signal in the assessment of G-protein-coupled receptor activation. Assays involving the assessment of intracellular Ca2+ using microplate readers most often use Ca2+ indicators which do not exhibit a spectra shift on Ca2+ binding (e.g. fluo-3). Indicators that do exhibit a spectral shift upon Ca2+ binding (e.g. fura-2) offer potential advantages for the calibration of intracellular Ca2+ levels. However, experimental limitations may limit the use of ratiometric dyes in microplate readers capable of screening. In this study, we compared the assessment of intracellular Ca2+ in adherent breast cancer cells using ratiometric and nonratiometric Ca2+ indicators. Our results demonstrate that both fluo-3 and fura-2 detect ATP dose-dependent increases in intracellular Ca2+ in the MCF-7 breast cancer cell line and that some of the limitations in the use of fura-2 appear to be overcome by the use of glass bottom microplates. The calibrated intracellular Ca2+ levels derived using fura-2 are consistent with those from microscopy and cuvette-based studies. Fura-2 may be useful in microplate studies, where cell lines with different properties are compared or where screening treatments lead to differences in the number of cells or dye loading. (C) 2003 Elsevier B.V. All rights reserved.

Differential motor speech outcomes in children treated for mid-line cerebellar tumour

Primary objective: To investigate the nature of the motor speech impairments and dysarthria that can arise subsequent to treatment for childhood mid-line cerebellar tumours (CMCT). Research design: The motor speech ability of six cases of children with CMCT was analysed using perceptual and physiological measures and compared with that of a group of non-neurologically impaired children matched for age and sex. Main outcome and results: Three of the children with CMCT were perceived to exhibit dysarthric speech, while the remaining three were judged to have normal speech. The speech disorder in three of the children with CMCT was marked by deviances in prosody, articulation and phonation. The underlying pathophysiology was linked to cerebellar damage and expressed as difficulty in co-ordinating the motor speech musculature as required for speech production. These deficits were not identified in the three non-dysarthric children with CMCT. Conclusion: Differential motor speech outcomes occur for children treated for CMCT and these are discussed within the realm of possible mechanisms responsible for these differences. The need for further investigation of the risk factors for development of motor speech impairment in children treated for CMCT is also highlighted.

Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Facilitating soil-and plant-water research through the use of TDR

The development of TDR for measurement of soil water content and electrical conductivity has resulted in a large shift in measurement methods for a breadth of soil and hydrological characterization efforts. TDR has also opened new possibilities for soil and plant research. Five examples show how TDR has enhanced our ability to conduct our soil- and plant-water research. (i) Oxygen is necessary for healthy root growth and plant development but quantitative evaluation of the factors controlling oxygen supply in soil depends on knowledge of the soil water content by TDR. With water content information we have modeled successfully some impact of tillage methods on oxygen supply to roots and their growth response. (ii) For field assessment of soil mechanical properties influencing crop growth, water content capability was added to two portable soil strength measuring devices; (a) A TDT (Time Domain Transmittivity)-equipped soil cone penetrometer was used to evaluate seasonal soil strengthwater content relationships. In conventional tillage systems the relationships are dynamic and achieve the more stable no-tillage relationships only relatively late in each growing season; (b) A small TDR transmission line was added to a modified sheargraph that allowed shear strength and water content to be measured simultaneously on the same sample. In addition, the conventional graphing procedure for data acquisition was converted to datalogging using strain gauges. Data acquisition rate was improved by more than a factor of three with improved data quality. (iii) How do drought tolerant plants maintain leaf water content? Non-destructive measurement of TDR water content using a flat serpentine triple wire transmission line replaces more lengthy procedures of measuring relative water content. Two challenges remain: drought-stressed leaves alter salt content, changing electrical conductivity, and drought induced changes in leaf morphology affect TDR measurements. (iv) Remote radar signals are reflected from within the first 2 cm of soil. Appropriate calibration of radar imaging for soil water content can be achieved by a parallel pair of blades separated by 8 cm, reaching 1.7 cm into soil and forming a 20 cm TDR transmission line. The correlation between apparent relative permittivity from TDR and synthetic aperture radar (SAR) backscatter coefficient was 0.57 from an airborne flyover. These five examples highlight the diversity in the application of TDR in soil and plant research.