35 resultados para ROC
em Deakin Research Online - Australia
Resumo:
1. To develop a conservation management plan for a species, knowledge of its distribution and spatial arrangement of preferred habitat is essential. This is a difficult task, especially when the species of concern is in low abundance. In south-western Victoria, Australia, populations of the rare rufous bristlebird Dasyornis broadbenti are threatened by fragmentation of suitable habitat. In order to improve the conservation status of this species, critical habitat requirements must be identified and a system of corridors must be established to link known populations. A predictive spatial model of rufous bristlebird habitat was developed in order to identify critical areas requiring preservation, such as corridors for dispersal.
2. Habitat models generated using generalized linear modelling techniques can assist in delineating the specific habitat requirements of a species. Coupled with geographic information system (GIS) technology, these models can be extrapolated to produce maps displaying the spatial configuration of suitable habitat.
3. Models were generated using logistic regression, with bristlebird presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multispectral digital imagery, as the predictors. A multimodel inference approach based on Akaike’s information criterion was used and the resulting model was applied in a GIS to extrapolate predicted likelihood of occurrence across the entire area of concern. The predictive performance of the selected model was evaluated using the receiver operating characteristic (ROC) technique. A hierarchical partitioning protocol was used to identify the predictor variables most likely to influence variation in the dependent variable. Probability of species presence was used as an index of habitat suitability.
4. Negative associations between rufous bristlebird presence and increasing elevation, 'distance to cree', 'distance to coast' and sun index were evident, suggesting a preference for areas relatively low in altitude, in close proximity to the coastal fringe and drainage lines, and receiving less direct sunlight. A positive association with increasing habitat complexity also suggested that this species prefers areas containing high vertical density of vegetation.
5. The predictive performance of the selected model was shown to be high (area under the curve 0·97), indicating a good fit of the model to the data. Hierarchical partitioning analysis showed that all the variables considered had significant independent contributions towards explaining the variation in the dependent variable. The proportion of the total study area that was predicted as suitable habitat for the rufous bristlebird (using probability of occurrence at a ≥0·5 level ) was 16%.
6. Synthesis and applications. The spatial model clearly delineated areas predicted as highly suitable rufous bristlebird habitat, with evidence of potential corridors linking coastal and inland populations via gullies. Conservation of this species will depend on management actions that protect the critical habitats identified in the model. A multi-scale approach to the modelling process is recommended whereby a spatially explicit model is first generated using landscape variables extracted from a GIS, and a second model at site level is developed using fine-scale habitat variables measured on the ground. Where there are constraints on the time and cost involved in measuring finer scale variables, the first step alone can be used for conservation planning.
Resumo:
Tumor necrosis factor (TNF) plays a role in the pathogenesis of chronic hepatitis B (CHB) and chronic hepatitis C (CHC). The difference in the cytokine responses between hepatitis B virus (HBV) and hepatitis C virus (HCV) infections may have implications in the pathogenesis of these diseases. We performed a comparative study to examine the possible differences in the TNF-TNF receptor (TNFR) response between CHB and CHC. We studied the cytokine levels of 38 patients with CHB, 40 patients with CHC and 9 patients with dual hepatitis B and C, and compared them with the baseline levels of 12 healthy controls. The plasma levels of TNF-, interferon-, interleukin (IL)-2, IL-4, IL-10 and soluble TNFR-1 and 2 (sTNFR-1 and 2) were quantified by enzyme-linked immunosorbent assays. The expression of TNFR-1 and 2 in liver tissues was examined in 30 cases of CHB and 15 cases of CHC by semiquantitative reverse transcription polymerase chain reaction. The results showed that sTNFR-1 levels correlated with liver inflammation in all patients, whereas this correlation was not found with sTNFR-2 or other cytokines. Liver inflammation indicators were higher in HCV RNA+ than in HCV RNA– CHC. Most significantly, sTNFR-1 levels correlated with liver inflammation in CHB, but not in CHC. However, the expression of TNFR-1 and 2 in liver was similar between CHB and CHC. These findings suggest that the TNFR signal transduction pathway is modulated differently in HBV and HCV infection.
Resumo:
PURPOSE: To investigate prospectively the relative accuracy of computed tomographic (CT) angiography, calcium scoring (CS), and both methods combined in demonstrating coronary artery stenoses by using conventional angiography as the reference standard. MATERIALS AND METHODS: The study was approved by the institutional review board Human Research Ethics Committee, and all patients completed written informed consent. Fifty patients (40 men, 10 women) aged 62 years ± 11 (± standard deviation) who were suspected of having coronary artery disease underwent both conventional coronary angiography and multisection coronary CT angiography with CS. Sensitivity and specificity of CS, CT angiography, and both methods combined in demonstrating luminal stenosis greater than or equal to 50% were determined for each arterial segment, coronary vessel, and patient. Receiver operating characteristic (ROC) curves were generated for CS prediction of significant stenosis, and the Mann-Whitney U test was used for comparison of CS between groups. RESULTS: When used with segment-specific electrocardiographic phase reconstructions, CT angiography demonstrated stenosed segments with 79% sensitivity and 95% specificity. Mean calcium score was greater in segments, vessels, and patients with stenoses than in segments, vessels, and patients without stenoses (P < .001 for all); nine (16%) of 56 stenosed segments, however, had a calcium score of 0. The patient calcium score correlated strongly with the number of stenosed arteries (Spearman {rho} = 0.75, P < .001). CS was more accurate in demonstrating stenosis in patients than in segments (areas under ROC curve were 0.88 and 0.74, respectively). CT angiography, however, was more accurate than CS in demonstrating stenosis in patients, vessels, and segments. The sensitivity and specificity of CS varied according to the threshold used, but when the calcium score cutoff (ie, >150) matched the specificity of CT angiography (95%), the sensitivity of CS in demonstrating stenosed segments was 29% (compared with 79% for CT angiography). Combining CT angiography with CS (at threshold of 400) improved the sensitivity of CT angiography (from 93% to 100%) in demonstrating significant coronary disease in patients, without a loss of specificity (85%); this finding, however, was not statistically significant. CONCLUSION: CT angiography is more accurate than CS in demonstrating coronary stenoses. A patient calcium score of greater than or equal to 400, however, can be used to potentially identify patients with significant coronary stenoses not detected at CT angiography.
Resumo:
The Theory of Homeostasis posits that Subjective Well-being (SWB) is regulated by a dynamic biological mechanism, assisting to maintain a positive view of life. Further, the theory suggests that clinical depression is the loss of SWB due to the defeat of this homeostatic defence system. To test this hypothesis it was predicted that people who were diagnosed as clinically depressed with the Semi-structured Clinical Interview (SCID-1/NP) based on the DSM-IV-TR Axis 1 would have a Personal Well-being Index-Adult (PWI-A) score below the normative range (70–80% of scale maximum). Following ethical approval a sample of 146 men was obtained and each was assessed on the SCID-1/NP and on the PWI-A. Subjects diagnosed as having one of several pathologies such as post traumatic stress disorder, panic disorder, social phobia and specific phobia were found to score significantly lower on the PWI-A compared to participants who received no diagnosis. However, as the data did not discriminate between currently depressed and persons with other non-depressive psychopathologies, a Receiver Operating Characteristics (ROC) curve analysis was used to explore this data further. Results indicated that the PWI-A was significantly better than guessing in discriminating clinically depressed cases, but only just so. Therefore, while this research found support for the proposition that the loss of SWB indicated clinical depression, the PWI-A is not sufficiently specific for diagnosis, nor can it be concluded that all instances of depression is the failure of SWB.
Resumo:
The aim of this article is to review the development and assessment of cardiovascular risk prediction models and to discuss the predictive value of a risk factor as well as to introduce new assessment methods to evaluate a risk prediction model. Many cardiovascular risk prediction models have been developed during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an existing model. The area under the receiver operating characteristic (ROC) curve has traditionally been used to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has its limitations and cannot be the sole approach to evaluate the usefulness of a new marker. New assessment methods are being developed to appropriately assess a risk prediction model and they will be gradually used in clinical and epidemiological studies.
Resumo:
Background: The utility of fatty acids (FAs) as biomarkers of total fat intake is unknown.
Objective: We compared FA changes in red cells (RCs), plasma phospholipids (PLs), and cholesterol esters (CEs) in response to a low-fat diet (LFD) and a moderate-fat diet (MFD) and assessed whether individual or combination of FAs predict LFD.
Design: Postmenopausal women (n = 66) were randomly assigned to receive an LFD (17% of energy from fat) or an MFD (34% of energy from fat) for 6 wk. All foods were provided. FAs in diets and blood were determined by gas-liquid chromatography. FA changes between baseline and end of study were compared across diets by using t tests. FA predictors of an LFD were selected by logistic regression.
Results: Many FAs in RCs, PLs, and CEs responded differently to the 2 diets. Changes from baseline with an LFD for palmitic acid (16:0) (3–11% increase), behenic (22:0) and lignoceric (24:0) acids (3–20% decrease, in RCs and PLs only), cis-monounsaturated FA (MUFA) (25–35% increase), linoleic acid (18:2n–6) (11–13% decrease), trans octadecanoic acids (trans 18:1) (7–20% decrease), and n–6 highly unsaturated FA (HUFA) (2–8% increase) were significantly different from changes with an MFD. Individually, 18:2n–6 and trans 18:1 were strong predictors of an LFD [receiver operating characteristic (ROC) curves: 0.92–0.80). A logistic regression model with trans 18:1, 18:2n–6, and vaccenic acid (18:1n–7) predicted an LFD with high specificity and sensitivity (ROC curves: 0.99).
Conclusions: Saturated FA, cisMUFA, n–6 HUFA, and exogenous FAs greatly differed in their response to the LFD and MFD. Parallel responses were observed in RCs, PLs, and CEs. A model with a combination of FAs almost perfectly differentiated the consumption of 34% fat from that of 17% fat.
Resumo:
Many risk prediction models have been developed for cardiovascular diseases in different countries during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an established risk prediction model. Researchers often use the area under the receiver operating characteristic curve (ROC) to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has serious limitations and cannot be the sole approach to evaluate the usefulness of a new marker in clinical and epidemiological studies. To overcome the shortcomings of this traditional method, new assessment methods have been proposed. The aim of this article is to overview various risk prediction models for cardiovascular diseases, to describe the receiver operating characteristic curve method and discuss some new assessment methods proposed recently. Some of the methods were illustrated with figures from a cardiovascular disease study in Australia.
Resumo:
The prevalence of depression in the Australian workforce is unknown. Epidemiological surveys (e.g., the National Health Survey and National Survey of Mental Health and Wellbeing) do not routinely include a depression scale and within the mental health field, few studies focus on depression and employment groups specifically. Although the inclusion of a direct measure of depression in national surveys is preferable, the prevalence of depression may be inferred from short screening scales of general mental health. In this paper, scores on the General Health Questionnaire (GHQ-12) and the Kessler psychological distress scale (K10) for a sample of employed persons were mapped onto the CES-D (Iowa) measure of depression. The results of this study indicate that the recommended GHQ-12 cut-off point is appropriate for estimating work-related depression prevalence. However, the cut-off point on the K10 (the short-scale currently used in Australian national surveys) may need to be substantially reduced if scores on the K10 are to be used to identify workers at risk of depression. The routine inclusion of a direct depression measure in national surveys is recommended, particularly considering the number of employed persons in Australia and large proportion of the sample classified as depressed in this study.
Resumo:
Measurement of Health-Related Quality of Life (HRQoL) of the elderly requires instruments with demonstrated sensitivity, reliability, and validity, particularly with the increasing proportion of older people entering the health care system. This article reports the psychometric properties of the 12-item Assessment of Quality of Life (AQoL) instrument in chronically ill community-dwelling elderly people with an 18-month follow-up. Comparator instruments included the SF-36 and the OARS. Construct validity of the AQoL was strong when examined via factor analysis and convergent and divergent validity against other scales. Receiver Operator Characteristic (ROC) curve analyses and relative efficiency estimates indicated the AQoL is sensitive, responsive, and had the strongest predicative validity for nursing home entry. It was also sensitive to economic prediction over the follow-up. Given these robust psychometric properties and the brevity of the scale, AQoL appears to be a suitable instrument for epidemiologic studies where HRQoL and utility data are required from elderly populations.
Resumo:
Aims : Comparing waist circumference (WC) role in diabetes risk prediction and diagnosis of metabolic syndrome (MS) in different populations.
Methods : Population-based samples from Australia (n = 9026) and Iran (n = 8259) were studied in 2000 and followed for 4 years. Follow-up attendance was 58% and mean age was 51 vs. 47. Pearson correlations calculated between WC and other MS components. ROC for the role of WC in the prediction of incident diabetes was used.
Results : Prevalences of MS (48% vs. 28%), an increased WC (58.5% vs. 54.5%), low HDL-C (35% vs. 11.2%), high triglyceride (52.2% vs. 29.6%) were significantly higher in Iran. Fasting glucose ≥5.6 mmol/L was higher in Australia (26% vs. 23%). Hypertension was no different (38%). Pearson correlations between WC and other MS components were stronger in Australians: FPG (0.32 vs. 0.2), HDL (0.47 vs. 0.16), TG (0.38 vs. 0.30) and SBP (0.38 vs. 0.36). Among women, area under ROC curve for WC as a predictor for diabetes was significantly higher for Australians (0.76 vs. 0.68, p < 0.001) with no difference among men (0.69 vs. 0.71, p = 0.4).
Conclusion : WC was more strongly related to other components of MS in Australia. Association between WC and MS or incident diabetes varies between ethnicities.
Resumo:
An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) Az of 0.9786 has been achieved.
Resumo:
Background
Accelerometers have been used to determine the amount of time that children spend sedentary. However, as time spent sitting may be detrimental to health, research is needed to examine whether accelerometer sedentary cut-points reflect the amount of time children spend sitting. The aim of this study was to: a) examine agreement between ActiGraph (AG) cut-points for sedentary time and objectively-assessed periods of free-living sitting and sitting plus standing time using the activPAL (aP); and b) identify cut-points to determine time spent sitting and sitting plus standing.
Methods
Forty-eight children (54% boys) aged 8–12 years wore a waist-mounted AG and thigh-mounted aP for two consecutive school days (9–3:30 pm). AG data were analyzed using 17 cut-points between 50–850 counts·min−1 in 50 counts·min−1 increments to determine sedentary time during class-time, break time and school hours. Sitting and sitting plus standing time were obtained from the aP for these periods. Limits of agreement were computed to evaluate bias between AG50 to AG850 sedentary time and sitting and sitting plus standing time. Receiver Operator Characteristic (ROC) analyses identified AG cutpoints that maximized sensitivity and specificity for sitting and sitting plus standing time.
Results
The smallest mean bias between aP sitting time and AG sedentary time was AG150 for class time (3.8 minutes), AG50 for break time (−0.8 minutes), and AG100 for school hours (−5.2 minutes). For sitting plus standing time, the smallest bias was observed for AG850. ROC analyses revealed an optimal cut-point of 96 counts·min−1 (AUC = 0.75) for sitting time, which had acceptable sensitivity (71.7%) and specificity (67.8%). No optimal cut-point was obtained for sitting plus standing (AUC = 0.51).
Conclusions
Estimates of free-living sitting time in children during school hours can be obtained using an AG cut-point of 100 counts·min−1. Higher sedentary cut-points may capture both sitting and standing time.
Resumo:
Data in many biological problems are often compounded by imbalanced class distribution. That is, the positive examples may largely outnumbered by the negative examples. Many classification algorithms such as support vector machine (SVM) are sensitive to data with imbalanced class distribution, and result in a suboptimal classification. It is desirable to compensate the imbalance effect in model training for more accurate classification. In this study, we propose a sample subset optimization technique for classifying biological data with moderate and extremely high imbalanced class distributions. By using this optimization technique with an ensemble of SVMs, we build multiple roughly balanced SVM base classifiers, each trained on an optimized sample subset. The experimental results demonstrate that the ensemble of SVMs created by our sample subset optimization technique can achieve higher area under the ROC curve (AUC) value than popular sampling approaches such as random over-/under-sampling; SMOTE sampling, and those in widely used ensemble approaches such as bagging and boosting.
Resumo:
Purpose
To test a field-based protocol using intermittent activities representative of children's physical activity behaviours, to generate behaviourally valid, population-specific accelerometer cut-points for sedentary behaviour, moderate, and vigorous physical activity.
Methods
Twenty-eight children (46% boys) aged 10–11 years wore a hip-mounted uniaxial GT1M ActiGraph and engaged in 6 activities representative of children's play. A validated direct observation protocol was used as the criterion measure of physical activity. Receiver Operating Characteristics (ROC) curve analyses were conducted with four semi-structured activities to determine the accelerometer cut-points. To examine classification differences, cut-points were cross-validated with free-play and DVD viewing activities.
Results
Cut-points of ≤372, >2160 and >4806 counts•min−1 representing sedentary, moderate and vigorous intensity thresholds, respectively, provided the optimal balance between the related needs for sensitivity (accurately detecting activity) and specificity (limiting misclassification of the activity). Cross-validation data demonstrated that these values yielded the best overall kappa scores (0.97; 0.71; 0.62), and a high classification agreement (98.6%; 89.0%; 87.2%), respectively. Specificity values of 96–97% showed that the developed cut-points accurately detected physical activity, and sensitivity values (89–99%) indicated that minutes of activity were seldom incorrectly classified as inactivity.
Conclusion
The development of an inexpensive and replicable field-based protocol to generate behaviourally valid and population-specific accelerometer cut-points may improve the classification of physical activity levels in children, which could enhance subsequent intervention and observational studies.
Resumo:
Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions.
