What You Get Out of Memory Depends on the Question You Ask

Following study, participants received 2 tests. The 1st was a recognition test; the 2nd was designed to tap recollection. The objective was to examine performance on Test I conditional on Test 2 performance. In Experiment 1, contrary to process dissociation assumptions, exclusion errors better predicted subsequent recollection than did inclusion errors. In Experiments 2 and 3, with alternate questions posed on Test 2, words having high estimates of recollection with one question had high estimates of familiarity with the other question. Results supported the following: (a) the 2-test procedure has considerable potential for elucidating the relationship between recollection and familiarity; (b) there is substantial evidence for dependency between such processes when estimates are obtained using the process dissociation and remember-know procedures; and (c) order of information access appears to depend on the question posed to the memory system.

Precision-recall operating characteristic (P-ROC) curves in imprecise environments

Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. Specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. Therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (ANOVA). The increased sensitivity and reliability of this approach is demonstrated in a remote sensing application.

Validation and refinement of survival models for liver retransplantation

Orthotopic liver retransplantation (re-OLT) is highly controversial. The objectives of this study were to determine the validity of a recently developed United Network for Organ Sharing (UNOS) multivariate model using an independent cohort of patients undergoing re-OLT outside the United States, to determine whether incorporation of other variables that were incomplete in the UNOS registry would provide additional prognostic information, to develop new models combining data sets from both cohorts, and to evaluate the validity of the model for end-stage liver disease (MELD) in patients undergoing re-OLT. Two hundred eighty-one adult patients undergoing re-OLT (between 1986 and 1999) at 6 foreign transplant centers comprised the validation cohort. We found good agreement between actual survival and predicted survival in the validation cohort; 1-year patient survival rates in the low-, intermediate-, and high-risk groups (as assigned by the original UNOS model) were 72%, 68%, and 36%, respectively (P < .0001). In the patients for whom the international normalized ratio (INR) of prothrombin time was available, MELD correlated with outcome following re-OLT; the median MELD scores for patients surviving at least 90 days compared with those dying within 90 days were 20.75 versus 25.9, respectively (P = .004). Utilizing both patient cohorts (n = 979), a new model, based on recipient age, total serum bilirubin, creatinine, and interval to re-OLT, was constructed (whole model χ(2) = 105, P < .0001). Using the c-statistic with 30-day, 90-day, 1-year, and 3-year mortality as the end points, the area under the receiver operating characteristic (ROC) curves for 4 different models were compared. In conclusion, prospective validation and use of these models as adjuncts to clinical decision making in the management of patients being considered for re-OLT are warranted.

The use of a change in gentamicin clearance as an early predictor of gentamicin-induced nephrotoxicity

A retrospective review was undertaken in 744 patients who were dose-individualized with gentamicin once daily to evaluate a change in gentamicin clearance as a potential predictor of nephrotoxicity. The definition of nephrotoxicity was chosen to be a change in creatinine clearance greater than 20%. Similarly, a change in gentamicin clearance of greater than 20% was also considered a possible index of nephrotoxicity. Four criteria were developed to assess the usefulness of gentamicin clearance as a predictor of nephrotoxicity. Following the application of the inclusion/exclusion criteria, 132 patients were available for the analysis. The sensitivity, specificity, positive predictive value, and negative predictive value were assessed for each of the criteria. Receiver operating characteristic (ROC) curves were produced to determine if an optimum value in the change of gentamicin clearance could be found to maximize sensitivity and specificity. The overall incidence of nephrotoxicity based on a decrease in creatinine clearance by 20% or more was 3.8%. Women were overrepresented in the nephrotoxic group [71.4% versus 40.1% (P = 0.0025)]. Patients with nephrotoxicity had statistically longer treatment periods, increased cumulative dose, and more dosing predictions (P < 0.05 in each case). The sensitivity of the criteria ranged from 43 to 46%, and specificity ranged from 93 to 99%. The positive and negative predictive values ranged from 63 to 94% and 86 to 89%, respectively. In those patients in whom nephrotoxicity was predicted from a change in gentamicin clearance, this change occurred on average 3 days before the change in creatinine clearance (P < 0.05). A change in gentamicin clearance to predict nephrotoxicity may be a useful addition to current monitoring methods, although it is not the complete answer.

Free mycophenolic acid should be monitored in renal transplant recipients with hypoalbuminemia

The current approach for therapeutic drug monitoring in renal transplant recipients receiving mycophenolate mofetil (MMF) is measurement of total mycophenolic acid (MPA) concentration. Because MPA is highly bound, during hypoalbuminemia the total concentration no longer reflects the free (pharmacologically active) concentration. The authors investigated what degree of hypoalbuminemia causes a significant change in protein binding and thus percentage free MPA. Forty-two renal transplant recipients were recruited for the study. Free and total concentrations of MPA (predose, and 1, 3, and 6 hours post-MMF dose samples) and plasma albumin concentrations were determined on day 5 posttransplantation. Six-hour area under the concentration-time curve (AUC(0-6)) values were calculated for free and total MPA, and percentage free MPA was determined for each patient. The authors found a significant relationship between low albumin concentrations and increased percentage free MPA (Spearman correlation = -0.54, P < 0.0001). Receiver operating characteristic (ROC) curve analysis was performed on the albumin versus percentage free MPA data. The cutoff value of albumin determined from the ROC analysis that differentiated normal from elevated percentage free MPA (defined as greater than or equal to3%) in this patient population was 31 g/L. At this cutoff value albumin was found to be a good predictor of altered free MPA percentage, with a sensitivity and specificity of 0.75 and 0.80, respectively, and an area under the ROC curve of 0.79. To rationalize MMF dosing regimens in hypoalbuminemic patients (plasma albumin less than or equal to 31 g/L), clinicians should consider monitoring the free MPA concentration.

Determining the fetal scalp lactate level that indicates the need for intervention in labour

Background: Fetal scalp lactate testing has been shown to be as useful as pH with added benefits. One remaining question is What level of lactate should trigger intervention in the first stage of labour?' Aims: This study aimed to establish the lactate level in the first stage of labour that indicates the need for intervention to ensure satisfactory outcomes for both babies and mothers. Methods: A prospective study at Mater Mothers' Hospital, Brisbane, Australia, a tertiary referral centre. One hundred and forty women in labour, with non-reassuring fetal heart rate traces, were tested using fetal blood scalp sampling of 5 mu L of capillary blood tested on an Accusport (Boeringer, Mannheim, East Sussex, UK) lactate meter. Decision to intervene in labour was based on clinical assessment plus a predetermined cut off. Main outcome measures were APGAR scores, cord arterial pH, meconium stained liquor and Intensive Care Nursery admission. Results: Two-graph receiver operating characteristic (TG-ROC) analysis showed optimal specificity, and sensitivity for predicting adverse neonatal outcomes was a scalp lactate level above 4.2 mmol/L. Conclusions: Fetal blood sampling remains the standard for further investigating-non-reassuring cardiotocograph (CTG) traces. Even so, it is a poor predictor of fetal outcomes. Scalp lactate has been shown to be at least as good a predictor as scalp pH, with the advantages of being easier, cheaper and with a lower rate of technical failure. Our study, found that a cut off fetal scalp lactate level of 4.2 mmol/L, in combination with an assessment of the entire clinical picture, is a useful tool in identifying those women who need intervention.

Myocardial blood volume and perfusion reserve responses to combined dipyridamole and exercise stress: A quantitative approach to contrast stress echocardiography

Background: Qualitative interpretation of myocardial contrast echocardiography (MCE) improves the accuracy of wall-motion analysis for assessment of coronary artery disease (CAD). We examined the feasibility and accuracy of quantitative MCE for diagnosis of CAD. Methods: Dipyridamole/exercise stress MCE (destruction-replenishment protocol with real-time imaging) was performed in 90 patients undergoing quantitative coronary angiography, 48 of whom had significant (> 50%) stenoses. MCE was repeated with exercise alone in 18 patients. Myocardial blood flow (A*beta) was obtained from blood volume (A) and time to refill (beta). Results: Quantification of flow reserve was feasible in 88%. The mean A*beta reserve in the anterior wall was significantly impaired for patients with left anterior descending coronary artery disease (n = 28) compared with those with no disease (1.6 +/- 1.2 vs; 4.0 +/- 2.5, P <=.001). This reflected impaired beta reserve, with no difference in the A reserve. Applying a receiver operating characteristic curve derived cutoff of 2.0 for A*beta reserve, quantitative MCE was 76% sensitive and 71% specific for the diagnosis of significant left anterior descending coronary artery stenosis. Posterior circulation results were similar, with 78% sensitivity and 59% specificity for detection of posterior CAD. Overall, quantitative MCE was similarly sensitive to qualitative approach for diagnosis of CAD (88% vs 93%), but with lower specificity (52% vs 65%, P =.07). In 18 patients restudied with pure exercise stress, the mean myocardial blood flow reserve was less than after combined stress (2.1 +/- 1.6 vs 3.7 +/- 1.9, P =.01). Conclusion: Quantitative MCE is feasible for the diagnosis of CAD with dipyridamole/exercise stress. Dipyridamole prolongs postexercise hyperemia, augmenting the degree of hyperemia at the time of imaging.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.

A Test of Performance of Breast MRI Interpretation in a Multicentre Screening Study

Objectives: The aim of this study was to assess the consistency and performance of radiologists interpreting breast magnetic resonance imaging (MRI) examinations. Materials and Methods: Two test sets of eight cases comprising cancers, benign disease, technical problems and parenchymal enhancement were prepared from two manufacturers' equipment (X and Y) and reported by 15 radiologists using the recording form and scoring system of the UK MRI breast screening study [(MAgnetic Resonance Imaging in Breast Screening (MARIBS)]. Variations in assessments of morphology, kinetic scores and diagnosis were measured by assessing intraobserver and interobserver variability and agreement. The sensitivity and specificity of reporting performances was determined using receiver operating characteristic (ROC) curve analysis. Results: Intraobserver variation was seen in 13 (27.7%) of 47 of the radiologists' conclusions (four technical and seven pathological differences). Substantial interobserver variation was observed in the scores recorded for morphology, pattern of enhancement, quantification of enhancement and washout pattern. The overall sensitivity of breast MRI was high [88.6%, 95% confidence interval (CI) 77.4-94.7%], combined with a specificity of 69.2% (95% CI 60.5-76.7%). The sensitivities were similar for the two test sets (P=.3), but the specificity was significantly higher for the Manufacturer X dataset (P

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.

Low mannose-binding lectin function is associated with sepsis in adult patients

Mannose-binding lectin (MBL) is an innate immune system pattern recognition molecule that kills a wide range of pathogens via the lectin complement pathway. MBL deficiency is associated with severe infection but the best measure of this deficiency is undecided. We investigated the influence of MBL functional deficiency on the development of sepsis in 195 adult patients, 166 of whom had bloodstream infection and 35 had pneumonia. Results were compared with 236 blood donor controls. MBL function (C4b deposition) and levels were measured by enzyme-linked immunosorbent assay. Using receiver-operator characteristics of MBL function in healthy controls, we identified a level of < 0.2 U mu L-1 as a highly discriminative marker of low MBL2 genotypes. Median MBL function was lower in sepsis patients (0.18 U mu L-1) than in controls (0.48 U mu L-1, P < 0.001). MBL functional deficiency was more common in sepsis patients than controls (P < 0.001). MBL functional deficient patients had significantly higher sequential organ failure assessment (SOFA) scores and higher MBL function and levels were found in patients with SOFA scores predictive of good outcome. Deficiency of MBL function appears to be associated with bloodstream infection and the development of septic shock. High MBL levels may be protective against severe sepsis. © 2006 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved.

Predictive validity of three ActiGraph energy expenditure equations for children

Purpose: This Study evaluated the predictive validity of three previously published ActiGraph energy expenditure (EE) prediction equations developed for children and adolescents. Methods: A total of 45 healthy children and adolescents (mean age: 13.7 +/- 2.6 yr) completed four 5-min activity trials (normal walking. brisk walking, easy running, and fast running) in ail indoor exercise facility. During each trial, participants were all ActiGraph accelerometer oil the right hip. EE was monitored breath by breath using the Cosmed K4b(2) portable indirect calorimetry system. Differences and associations between measured and predicted EE were assessed using dependent t-tests and Pearson correlations, respectively. Classification accuracy was assessed using percent agreement, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve, Results: None of the equations accurately predicted mean energy expenditure during each of the four activity trials. Each equation, however, accurately predicted mean EE in at least one activity trial. The Puyau equation accurately predicted EE during slow walking. The Trost equation accurately predicted EE during slow running. The Freedson equation accurately predicted EE during fast running. None of the three equations accurately predicted EE during brisk walking. The equations exhibited fair to excellent classification accuracy with respect to activity intensity. with the Trost equation exhibiting the highest classification accuracy and the Puyau equation exhibiting the lowest. Conclusions: These data suggest that the three accelerometer prediction equations do not accurately predict EE on a minute-by-minute basis in children and adolescents during overground walking and running. The equations maybe, however, for estimating participation in moderate and vigorous activity.

Prediction of HLA-DQ3.2 beta ligands: evidence of multiple registers in class II binding peptides

Motivation: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. Methods: We describe a structure-based prediction model for modeling peptide-DQ3.2 beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2 beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2 beta binding and non-binding peptides obtained from biochemical and functional studies. Results: Our model predicts DQ3.2 beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2 beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.