Experimental evaluation of hotspot identification methods

Identification of hot spots, also known as the sites with promise, black spots, accident-prone locations, or priority investigation locations, is an important and routine activity for improving the overall safety of roadway networks. Extensive literature focuses on methods for hot spot identification (HSID). A subset of this considerable literature is dedicated to conducting performance assessments of various HSID methods. A central issue in comparing HSID methods is the development and selection of quantitative and qualitative performance measures or criteria. The authors contend that currently employed HSID assessment criteria–namely false positives and false negatives–are necessary but not sufficient, and additional criteria are needed to exploit the ordinal nature of site ranking data. With the intent to equip road safety professionals and researchers with more useful tools to compare the performances of various HSID methods and to improve the level of HSID assessments, this paper proposes four quantitative HSID evaluation tests that are, to the authors’ knowledge, new and unique. These tests evaluate different aspects of HSID method performance, including reliability of results, ranking consistency, and false identification consistency and reliability. It is intended that road safety professionals apply these different evaluation tests in addition to existing tests to compare the performances of various HSID methods, and then select the most appropriate HSID method to screen road networks to identify sites that require further analysis. This work demonstrates four new criteria using 3 years of Arizona road section accident data and four commonly applied HSID methods [accident frequency ranking, accident rate ranking, accident reduction potential, and empirical Bayes (EB)]. The EB HSID method reveals itself as the superior method in most of the evaluation tests. In contrast, identifying hot spots using accident rate rankings performs the least well among the tests. The accident frequency and accident reduction potential methods perform similarly, with slight differences explained. The authors believe that the four new evaluation tests offer insight into HSID performance heretofore unavailable to analysts and researchers.

Identification of crash hot spots is the critical component of the highway safety management process. Errors in hot spot identification (HSID) may result in the inefficient use of resources for safety improvements. One HSID method that is based on the empirical Bayesian (EB) method has been widely used as an effective approach for identifying crash-prone sites. For the EB method, the negative binomial (NB) model is usually needed to obtain the EB estimates. Recently, some studies have shown that the Sichel (SI) model can be easily used in the EB modeling framework and potentially yield better EB estimates. The objective of this study was to compare the performance of the two crash prediction (SI and NB) models in identifying hot spots with the EB method. To accomplish the objective of this study, empirical crash data collected at highway segments in Texas were used to generate simulated crash counts. Three commonly used HSID methods (simple ranking, confidence interval, and EB) were applied with the use of simulated data. False positives, false negatives, and false identifications were calculated and compared across the methods. The simulation results in this study suggested that the SI-based EB method could consistently provide a better HSID result than the NB-based EB method. Moreover, EB methods yielded the lowest error percentage of the three HSID methods. This study confirmed that the EB technique was an effective method for identifying hazardous sites. On the basis of the findings in this study, it is recommended that transportation safety researchers consider the SI model as an alternative crash prediction model when the EB approach is used.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Motivation: Certain studies use short-TE MRS to estimate metabolites such as gamma-aminobutyric acid. The findings of such studies hinge on the correlation of this technique to the gold-standard edited MRS technique. Goal(s): We investigate how the false negative rate and false positive rate of studies vary as a function of correlation between short-TE and MEGA-edited metabolite estimates. Approach: Simulated data was used to numerically estimate the false positive and false negative rates, using parameters obtained from the literature. Results: We show that these studies can have largely inflated false positive and false negative rates as compared to their nominally designed values. Impact: The false negative and false positive rate can be markedly inflated when using short-TE MRS to estimate metabolites such as gamma-aminobutyric acid. This calls for increased caution in interpreting results, and the accommodation of these effects when designing statistical analyses.

Differential Methylation Profile of Ovarian Cancer in Tissues and Plasma

Road fatalities are among the major causes of mortality for Strigiformes species and may affect the population's survival. The use of mitigation strategies must be considered to overcome this problem. However, because mitigation along the total length of all roads is not financially feasible, the locations where Strigiformes roadkills are more frequent (i.e., road fatality hotspots) must be identified. In addition to hotspot identification, factors that influence the occurrence of such fatalities should be recognized to allow mitigation measures to be delineated. We used road fatality data collected from 311 km of southern Portugal roads over a 2‐year period to compare the performance of five hotspot identification methods: binary logistic regression (BLR), ecological niche factor analysis (ENFA), Kernel density estimation, nearest neighbor hierarchical clustering (NNHC), and Malo's method. BLR and ENFA modelling were also used for recognizing roadkill deterministic factors. Our results suggest that Malo's method should be preferred for hotspot identification. The main factors driving owl roadkillings are those associated with good habitat conditions for species occurrence and specific conditions that promote hunting behavior near roads. Based on these factors, several mitigation measures are recommended.

Ranaviruses have been identified as the etiologic agent in many amphibian die-offs across the globe. Polymerase chain reaction (PCR) is commonly used to detect ranavirus infection in amphibian hosts, but the test results may vary between tissue samples obtained by lethal and non-lethal procedures. Testing liver samples for infection is a common lethal sampling technique to estimate ranavirus prevalence because the pathogen often targets this organ and the liver is easy to identify and collect. However, tail clips or swabs may be more practicable for ranavirus surveillance programs compared with collecting and euthanizing animals, especially for uncommon species. Using PCR results from liver samples for comparison, we defined false-positive test results as occurrences when a non-lethal technique indicated positive but the liver sample was negative. Similarly, we defined false-negative test results as occurrences when a non-lethal technique was negative but the liver sample was positive. Using these decision rules, we estimated false-negative and false-positive rates for tail clips and swabs. Our study was conducted in a controlled facility using American bullfrog Lithobates catesbeianus tadpoles; false-positive and false-negative rates were estimated after different periods of time following exposure to ranavirus. False-negative and false-positive rates were 20 and 6%, respectively, for tail samples, and 22 and 12%, respectively, for swabs. False-negative rates were constant over time, but false-positive rates decreased with post-exposure duration. Our results suggest that non-lethal sampling techniques can be useful for ranavirus surveillance, although the prevalence of infection may be underestimated when compared to results obtained with liver samples.

The Effect of Testing Reliability on Visual Field Sensitivity in Normal Eyes: The Singapore Chinese Eye Study

Screening Efectiveness with SERUM Galactomannan in High and Low Risk Patients for Fungal Infection in the Post ALLO-HTC

Bridging the Gap: Screening for Nonalcoholic Fatty Liver Disease in the Primary Care Population

To evaluate the false-negative and false-positive error rates both in a screening and a non-screening population. A total of 4192 prostatic biopsies were reported in a 6-year period by 15 consultant histopathologists, two of whom had an interest in uropathology and were deemed to be specialists (J.O. and C.S.). All biopsies were reviewed prior to the multidisciplinary team (MDT) meeting. The overall false-negative rate was 1.7% (screening 2.1%, non-screening 1.5%). The overall false-positive rate was 0.5% (screening 0.9%, non-screening 0.4%). These error rates varied among pathologists, with the false-negative rate ranging from 0% to 9.3%, and the false-positive rate ranging from 0% to 3.8%. The false-negative rate was three times greater than the false-positive rate, showing that detection of significant pathology is far greater in the negative biopsies. More errors occurred in the screening population than in the non-screening population. The consultants making the most errors were non-specialists, but the specialists also made false-negative errors, suggesting that just using specialist reporting alone would not have eradicated errors.

An Evaluation of Conventional Culture, invA PCR, and the Real-Time PCR iQ-Check Kit as Detection Tools for Salmonella in Naturally Contaminated Premarket and Retail Turkey

The influence of fatigue and related factors on automated perimetric testing was evaluated in both eyes of sixteen normal observers and sixteen patients with early-to-moderate visual field loss using a Digilab 750 automated perimeter and a customized test procedure. False positive rate, false negative rate, and detection sensitivity at 5,10,15, and 20 degrees eccentricity were measured in 1.5-rain intervals throughout a 21-min visual field examination. Half of the normal observers and patients with visual field loss were given a briefrest(1.5-min) midway through each visual field exam to determine whether this would reduce fatigue effects. Our findings revealed that patients displayed considerably higher average false positive and false negative rates than normal observers. However, neither the patients nor normal observers demonstrated any consistent changes in false positive rate or false negative rate as a function of testing duration. In contrast, both normal observers and patients showed an average decrease in sensitivity as a function of increasing test duration with the magnitude of the time-dependent sensitivity loss becoming greater with increasing stimulus eccentricity. Patients demonstrated a larger time-dependent sensitivity loss than normal observers, averaging ~4dB at 20 degrees eccentricity. The introduction of a brief pause midway through the test procedure appeared to reduce the time-dependent sensitivity loss for the second half of the test procedure, especially for greater eccentricities.

The authors evaluated characteristics of patients whom clinicians accurately assessed as being at high or low risk for violence and patients for whom clinicians overestimated or underestimated the risk. At admission, physicians estimated the probability that each of 226 psychiatric inpatients would physically attack someone during the first week of hospitalization. Nurses rated assaultive behavior in the hospital with the Overt Aggression Scale. Acute symptoms were rated with the Brief Psychiatric Rating Scale. For the group as a whole, assessed levels of risk were substantially related to later physical aggression (sensitivity = 67%, specificity = 69%). Multinomial logit analysis showed that patients with psychotic disorders such as schizophrenia, organic psychotic conditions, and mania were more likely to be accurately assessed by clinicians as being at high risk (true positives) than to be true negatives or false positives. A recent history of violence was associated with higher estimated risk but did not distinguish true positives from false positives. An admission mental status characterized by low levels of hostility, uncooperativeness, and suspiciousness and high levels of depression, guilt, and anxiety differentiated true negative patients from others, but symptom profiles did not differ among true positives, false positives, and false negatives. Clinical judgments emphasizing gender and race/ethnicity were associated with predictive errors: nonwhite and male patients tended to be false positives. While clinicians can accurately classify the potential for violence in the majority of patients at admission, systematic errors characterize inaccurate assessments of the risk. Awareness of these patterns may help improve assessment of the risk of violence in clinical practice.

Several different types of detectors, software, and hardware designs are employed in instruments used to monitor radioactive content of freight shipments in order to detect illicit trafficking of nuclear materials. The instruments can be container monitors, hand-held radiation detectors, mobile analysis systems, or fixed radiation portal monitors. However, within the various groupings (e.g., portal monitors), all instruments are expected to solve the same problem, that is, to detect and identify any radioactive material present according to the prescribed investigative procedure or CONOPS. The best way to compare the performance of different instruments is with a numerical score or Figure of Merit (FOM). The FOM must quantify the performance of the instrument with respect to true positives (TP), false positives (FP), and false negatives (FN). The minimization of FN for certain radionuclides (e.g., uranium and plutonium or other Special Nuclear Material (SNM)) is more important than the minimization of FN for non-threat nuclides (e.g., low level NORM). In a similar way, the minimization of FP for SNM is more important than falsely reporting the common NORM nuclides which represent no threat. System performance depends on the measurement detail (e.g., transit speed through a portal, measurement time in general, amount and distribution of NORM), therefore the test conditions should also be included in the statement of the FOM. The FOM is expected to vary significantly with the above measurement details. A FOM has been developed based on the number of true positives (TP), the number of false important positives (FIP), the number of FP, the number of true positives for SNM (TPSNM), and the number of false positives for SNM (FPSNM). This formula rates the overall performance with extra weight given to FP and FN for SNM. Examples will be shown for testing of portal monitors.