Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Design, construction and evaluation of systems to predict risk in obstetrics

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy. We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Feature selection using expected attainable discrimination

We propose expected attainable discrimination (EAD) as a measure to select discrete valued features for reliable discrimination between two classes of data. EAD is an average of the area under the ROC curves obtained when a simple histogram probability density model is trained and tested on many random partitions of a data set. EAD can be incorporated into various stepwise search methods to determine promising subsets of features, particularly when misclassification costs are difficult or impossible to specify. Experimental application to the problem of risk prediction in pregnancy is described.

The clinical utility of the Cornell Scale for Depression in Dementia as a routine assessment in nursing homes

Objective To examine the clinical utility of the Cornell Scale for Depression in Dementia (CSDD) in nursing homes. Setting 14 nursing homes in Sydney and Brisbane, Australia. Participants 92 residents with a mean age of 85 years. Measurements Consenting residents were assessed by care staff for depression using the CSDD as part of their routine assessment. Specialist clinicians conducted assessment of depression using the Semi-structured Clinical Diagnostic Interview for DSM-IV-TR Axis I Disorders for residents without dementia or the Provisional Diagnostic Criteria for Depression in Alzheimer Disease for residents with dementia to establish expert clinical diagnoses of depression. The diagnostic performance of the staff completed CSDD was analyzed against expert diagnosis using receiver operating characteristic (ROC) curves. Results The CSDD showed low diagnostic accuracy, with areas under the ROC curve being 0.69, 0.68 and 0.70 for the total sample, residents with dementia and residents without dementia, respectively. At the standard CSDD cutoff score, the sensitivity and specificity were 71% and 59% for the total sample, 69% and 57% for residents with dementia, and 75% and 61% for residents without dementia. The Youden index (for optimizing cut-points) suggested different depression cutoff scores for residents with and without dementia. Conclusion When administered by nursing home staff the clinical utility of the CSDD is highly questionable in identifying depression. The complexity of the scale, the time required for collecting relevant information, and staff skills and knowledge of assessing depression in older people must be considered when using the CSDD in nursing homes.

Competitive interactions of anti-carcinogens with serum albumin: A spectroscopic study of bendamustine and dexamethasone with the aid of chemometrics

Interactions between the anti-carcinogens, bendamustine (BDM) and dexamethasone (DXM), with bovine serum albumin (BSA) were investigated with the use of fluorescence and UV–vis spectroscopies under pseudo-physiological conditions (Tris–HCl buffer, pH 7.4). The static mechanism was responsible for the fluorescence quenching during the interactions; the binding formation constant of the BSA–BDM complex and the binding number were 5.14 × 105 L mol−1 and 1.0, respectively. Spectroscopic studies for the formation of BDM–BSA complex were interpreted with the use of multivariate curve resolution – alternating least squares (MCR–ALS), which supported the complex formation. The BSA samples treated with site markers (warfarin – site I and ibuprofen – site II) were reacted separately with BDM and DXM; while both anti-carcinogens bound to site I, the binding constants suggested that DXM formed a more stable complex. Relative concentration profiles and the fluorescence spectra associated with BDM, DXM and BSA, were recovered simultaneously from the full fluorescence excitation–emission data with the use of the parallel factor analysis (PARAFAC) method. The results confirmed that on addition of DXM to the BDM–BSA complex, the BDM was replaced and the DXM–BSA complex formed; free BDM was released. This finding may have consequences for the transport of these drugs during any anti-cancer treatment.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Determination of effective moisture diffusivity of banana using thermogravimetric analysis

Drying has been extensively used as a food preservation procedure. The longer life attained by drying is however accompanied by huge energy consumption and deterioration of quality. Moisture diffusivity is an important factor that is considered essential to understand for design, analysis, and optimization of drying processes for food and other materials. Without an accurate value of moisture diffusivity, drying kinetics, energy consumption, quality attributes such as shrinkage, texture, and microstructure cannot be predicted properly. However, moisture diffusivities differ due to variation of composition and microstructure of foodstuff and drying variables. For a particular food, it changes with many factors including moisture content, water holding capacity, process variables and physiochemical attributes of food. Published information on moisture diffusivities of banana is inadequate and sometimes inconsistent due to lack of precise repeatable analysis techniques. In this work, the effective moisture diffusivity of banana was determined by Thermogravimetric Analysis (TGA), which ensures precise measurements and reproduction of experiments. A TGA Q500 V20.13 Build 39 was deployed to obtain the drying curve of the food material. It was found that effective moisture diffusivity ranged from 6.63 x10-10 to 1.03 x10-9 and 1.34 x10-10 to 6.60 x10-10 for isothermal at 70 0C and non-isothermal process respectively.These values are consistent with the value of moisture diffusivity found in the literature.

Exploration of the fundamental equilibrium behaviour of calcium exchange with weak acid cation resins

This study evaluated the complexity of calcium ion exchange with sodium exchanged weak acid cation resin (DOW MAC-3). Exchange equilibria recorded for a range of different solution normalities revealed profiles which were represented by conventional “L” or “H” type isotherms at low values of equilibrium concentration (Ce) of calcium ions, plus a superimposed region of increasing calcium uptake was observed at high Ce values. The loading of calcium ions was determined to be ca. 53.5 to 58.7 g/kg of resin when modelling only the sorption curve created at low Ce values,which exhibited a well-defined plateau. The calculated calcium ion loading capacity for DOWMAC-3 resin appeared to correlate with the manufacturer's recommendation. The phenomenon of super equivalent ion exchange (SEIX) was observed when the “driving force” for the exchange process was increased in excess of 2.25 mmol calcium ions per gram of resin in the starting solution. This latter event was explained in terms of displacement of sodium ions from sodium hydroxide solution which remained in the resin bead following the initial conversion of the as supplied “H+” exchanged resin sites to the “Na+” version required for softening studies. Evidence for hydrolysis of a small fraction of the sites on the sodium exchanged resin surface was noted. The importance of carefully choosing experimental parameters was discussed especially in relation to application of the Langmuir–Vageler expression. This latter model which compared the ratio of the initial calcium ion concentration in solution to resin mass, versus final equilibrium loading of the calcium ions on the resin; was discovered to be an excellent means of identifying the progress of the calcium–sodium ion exchange process. Moreover, the Langmuir–Vageler model facilitated standardization of various calcium–sodium ion exchange experiments which allowed systematic experimental design.

A method to evaluate the response of the coronary circulation of perfused rat heart to adenosine

Exogenous adenosine causes a monophasic dilation of the coronary vessels in paced, perfused rat heart preparations. Because levels of endogenous adenosine in paced hearts may mask the presence of high potency adenosine receptors, we have developed a method to measure coronary vascular responses in a potassium-arrested heart. Hearts from adult male, Wistar rats were perfused at a constant flow rate of 10 mL/min in the nonrecirculating, Langendorff mode, using Krebs-Henseleit buffer. After 30 min, coronary perfusion pressure was 44 +/- 1 mmHg (mean +/- SEM). Hearts were then perfused with a modified Krebs-Henseleit buffer containing 35 mM potassium. Coronary perfusion pressure increased by 84 +/- 3 mmHg. Adenosine-induced reductions in coronary perfusion pressure were expressed as a percentage of the maximal increase in pressure produced by modified Krebs-Henseleit buffer from the equilibration level. A concentration-response curve for adenosine (n = 6) was biphasic and best described by the presence of two adenosine receptors, with negative log EC50 values of 8.8 +/- 0.3 and 4.3 +/- 0.1, representing 29 +/- 3 and 71 +/- 3%, respectively, of the observed response. Interstitial adenosine sampled by microdialysis during potassium arrest was 25% of the concentration found in paced hearts. Endogenous adenosine in nonarrested hearts may obscure the biphasic response of the coronary vessels to adenosine.

Optimizing search strategies for invasive pests: Learn before you leap

Strategic searching for invasive pests presents a formidable challenge for conservation managers. Limited funding can necessitate choosing between surveying many sites cursorily, or focussing intensively on fewer sites. While existing knowledge may help to target more likely sites, e.g. with species distribution models (maps), this knowledge is not flawless and improving it also requires management investment. 2.In a rare example of trading-off action against knowledge gain, we combine search coverage and accuracy, and its future improvement, within a single optimisation framework. More specifically we examine under which circumstances managers should adopt one of two search-and-control strategies (cursory or focussed), and when they should divert funding to improving knowledge, making better predictive maps that benefit future searches. 3.We use a family of Receiver Operating Characteristic curves to reflect the quality of maps that direct search efforts. We demonstrate our framework by linking these to a logistic model of invasive spread such as that for the red imported fire ant Solenopsis invicta in south-east Queensland, Australia. 4.Cursory widespread searching is only optimal if the pest is already widespread or knowledge is poor, otherwise focussed searching exploiting the map is preferable. For longer management timeframes, eradication is more likely if funds are initially devoted to improving knowledge, even if this results in a short-term explosion of the pest population. 5.Synthesis and applications. By combining trade-offs between knowledge acquisition and utilization, managers can better focus - and justify - their spending to achieve optimal results in invasive control efforts. This framework can improve the efficiency of any ecological management that relies on predicting occurrence. © 2010 The Authors. Journal of Applied Ecology © 2010 British Ecological Society.

Towards a consistent definition of a significant delta troponin with z-scores: A way out of chaos?

Aims: We assessed the diagnostic performance of z-scores to define a significant delta cardiac troponin (cTn) in a cohort of patients with well-defined clinical outcomes. Methods: We calculated z-scores, which are dependent on the analytical precision and biological variation, to report changes in cTn. We compared the diagnostic performances of a relative delta (%Δ), actual delta (Δ), and z-scores in 762 emergency department patients with symptoms of suspected acute coronary syndrome. cTn was measured with sensitive cTnI (Beckman Coulter), highly sensitive cTnI (Abbott), and highly sensitive cTnT (Roche) assays. Results: Receiver operating characteristic analysis showed no statistically significant differences in the areas under the curve (AUC) of z-scores and Δ with both superior compared to %Δ for all three assays (p<0.001). The AUCs of z-scores measured with the Abbott hs-cTnI (0.955) and Roche hs-cTnT (0.922) assays were comparable to Beckman Coulter cTnI (0.933) (p=0.272 and 0.640, respectively). The individualized Δ cut-off values that were required to emulate a z-score of 1.96 were: Beckman Coulter cTnI 30 ng/l, Abbott hs-cTnI 20 ng/l, and Roche hs-cTnT 7 ng/l. Conclusions: z-scores allow the use of a single cut-off value at all cTn levels, for both cTnI and cTnT and for sensitive and highly sensitive assays, with comparable diagnostic performances. This strategy of reporting significant changes as z-scores may obviate the need for the empirical development of assay-specific cut-off rules to define significant troponin changes.

Evolved gas analysis of coal-derived pyrite/marcasite

The products evolved during the thermal decomposition of the coal-derived pyrite/marcasite were studied using simultaneous thermogravimetry coupled with Fourier-transform infrared spectroscopy and mass spectrometry (TG-FTIR–MS) technique. The main gases and volatile products released during the thermal decomposition of the coal-derived pyrite/marcasite are water (H2O), carbon dioxide (CO2), and sulfur dioxide (SO2). The results showed that the evolved products obtained were mainly divided into two processes: (1) the main evolved product H2O is mainly released at below 300 °C; (2) under the temperature of 450–650 °C, the main evolved products are SO2 and small amount of CO2. It is worth mentioning that SO3 was not observed as a product as no peak was observed in the m/z = 80 curve. The chemical substance SO2 is present as the main gaseous product in the thermal decomposition for the sample. The coal-derived pyrite/marcasite is different from mineral pyrite in thermal decomposition temperature. The mass spectrometric analysis results are in good agreement with the infrared spectroscopic analysis of the evolved gases. These results give the evidence on the thermal decomposition products and make all explanations have the sufficient evidence. Therefore, TG–MS–IR is a powerful tool for the investigation of gas evolution from the thermal decomposition of materials.

Can perceptual indices estimate physiological strain across a range of environments and metabolic workloads when wearing explosive ordnance disposal and chemical protective clothing?

Objective Explosive ordnance disposal (EOD) often requires technicians to wear multiple protective garments in challenging environmental conditions. The accumulative effect of increased metabolic cost coupled with decreased heat dissipation associated with these garments predisposes technicians to high levels of physiological strain. It has been proposed that a perceptual strain index (PeSI) using subjective ratings of thermal sensation and perceived exertion as surrogate measures of core body temperature and heart rate, may provide an accurate estimation of physiological strain. Therefore, this study aimed to determine if the PeSI could estimate the physiological strain index (PSI) across a range of metabolic workloads and environments while wearing heavy EOD and chemical protective clothing. Methods Eleven healthy males wore an EOD and chemical protective ensemble while walking on a treadmill at 2.5, 4 and 5.5 km·h− 1 at 1% grade in environmental conditions equivalent to wet bulb globe temperature (WBGT) 21, 30 and 37 °C. WBGT conditions were randomly presented and a maximum of three randomised treadmill walking trials were completed in a single testing day. Trials were ceased at a maximum of 60-min or until the attainment of termination criteria. A Pearson's correlation coefficient, mixed linear model, absolute agreement and receiver operating characteristic (ROC) curves were used to determine the relationship between the PeSI and PSI. Results A significant moderate relationship between the PeSI and the PSI was observed [r = 0.77; p < 0.001; mean difference = 0.8 ± 1.1 a.u. (modified 95% limits of agreement − 1.3 to 3.0)]. The ROC curves indicated that the PeSI had a good predictive power when used with two, single-threshold cut-offs to differentiate between low and high levels of physiological strain (area under curve: PSI three cut-off = 0.936 and seven cut-off = 0.841). Conclusions These findings support the use of the PeSI for monitoring physiological strain while wearing EOD and chemical protective clothing. However, future research is needed to confirm the validity of the PeSI for active EOD technicians operating in the field.

An efficient DSE using conditional multivariate complex Gaussian distribution

This paper presents an efficient noniterative method for distribution state estimation using conditional multivariate complex Gaussian distribution (CMCGD). In the proposed method, the mean and standard deviation (SD) of the state variables is obtained in one step considering load uncertainties, measurement errors, and load correlations. In this method, first the bus voltages, branch currents, and injection currents are represented by MCGD using direct load flow and a linear transformation. Then, the mean and SD of bus voltages, or other states, are calculated using CMCGD and estimation of variance method. The mean and SD of pseudo measurements, as well as spatial correlations between pseudo measurements, are modeled based on the historical data for different levels of load duration curve. The proposed method can handle load uncertainties without using time-consuming approaches such as Monte Carlo. Simulation results of two case studies, six-bus, and a realistic 747-bus distribution network show the effectiveness of the proposed method in terms of speed, accuracy, and quality against the conventional approach.

Meltdown : a tool to help in the interpretation of thermal melt curves acquired by differential scanning fluorimetry

The output of a differential scanning fluorimetry (DSF) assay is a series of melt curves, which need to be interpreted to get value from the assay. An application that translates raw thermal melt curve data into more easily assimilated knowledge is described. This program, called “Meltdown,” conducts four main activities—control checks, curve normalization, outlier rejection, and melt temperature (Tm) estimation—and performs optimally in the presence of triplicate (or higher) sample data. The final output is a report that summarizes the results of a DSF experiment. The goal of Meltdown is not to replace human analysis of the raw fluorescence data but to provide a meaningful and comprehensive interpretation of the data to make this useful experimental technique accessible to inexperienced users, as well as providing a starting point for detailed analyses by more experienced users.