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In June 2013, the legal blood alcohol concentration (BAC) limit for driving was lowered from 0.05 to 0.03 mg/mL in Taiwan. Thus, this study aimed to assess the epidemiological changes in terms of drinking among drivers in southern Taiwan before and after the law was imposed. Only patients who had undergone the BAC test at the emergency room were included in the study. The patients during the study period before (n = 2735) and after (n = 2413) the implementation of the law were selected for comparison. Drunk patients were defined as those who had a BAC ≥0.005 and were considered as driving under the influence (DUI) of alcohol. Meanwhile, driving while intoxicated (DWI) was defined as a BAC ≥0.05, which was the level adopted in the new law. Since the BAC limit lowered to 0.03, the number of DUI patients significantly decreased from 340 (12.4%) to 171 (7.1%), and that of DWI patients significantly reduced from 273 (10.0%) to 146 (6.1%) based on the alcohol test. In addition, after the implementation of the law, the number of associated injuries did not significantly decrease from that before the law was implemented in patients involved in alcohol-related crashes. After lowering the legal BAC limit from 0.05 to 0.03, responsiveness to the change in law was observed among the studied population. However, such responsiveness may not be observed in some citizens who may need special interventions to help reduce their behavior of drinking and driving.

Significance Diffusion-weighted MRI (DWI) tractography is widely used to map structural connections of the human brain in vivo and has been adopted by large-scale initiatives such as the human connectome project. Our results indicate that, even with high-quality data, DWI tractography alone is unlikely to provide an anatomically accurate map of the brain connectome. It is crucial to complement tractography results with a combination of histological or neurophysiological methods to map structural connectivity accurately. Our findings, however, do not diminish the importance of diffusion MRI as a noninvasive tool that offers important quantitative measures related to brain tissue microstructure and white matter architecture. Tractography based on diffusion-weighted MRI (DWI) is widely used for mapping the structural connections of the human brain. Its accuracy is known to be limited by technical factors affecting in vivo data acquisition, such as noise, artifacts, and data undersampling resulting from scan time constraints. It generally is assumed that improvements in data quality and implementation of sophisticated tractography methods will lead to increasingly accurate maps of human anatomical connections. However, assessing the anatomical accuracy of DWI tractography is difficult because of the lack of independent knowledge of the true anatomical connections in humans. Here we investigate the future prospects of DWI-based connectional imaging by applying advanced tractography methods to an ex vivo DWI dataset of the macaque brain. The results of different tractography methods were compared with maps of known axonal projections from previous tracer studies in the macaque. Despite the exceptional quality of the DWI data, none of the methods demonstrated high anatomical accuracy. The methods that showed the highest sensitivity showed the lowest specificity, and vice versa. Additionally, anatomical accuracy was highly dependent upon parameters of the tractography algorithm, with different optimal values for mapping different pathways. These results suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone.

Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.

To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2). T2w, DWI (12 b values, 0-2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS. In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82-0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments. Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.

[This corrects the article DOI: 10.1371/journal.pone.0228113.].

Background Available data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI. Methods In this retrospective study, 95 patients who underwent multiparametric MRI with DCE and DWI from September 2007 to July 2013 and who were diagnosed with a suspicious NME (BI-RADS 4/5) were included. Twenty-nine patients were excluded for lesion non-visibility on DWI ( n  = 24: 12 benign and 12 malignant) and poor DWI quality ( n  = 5: 1 benign and 4 malignant). Two readers independently assessed DWI and DCE-MRI findings in two separate randomized readings using different ADC metrics and ROI approaches. NME lesions were classified as either benign (> 1.3 × 10 −3  mm 2 /s) or malignant (≤ 1.3 × 10 −3  mm 2 /s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured. Results There were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = − 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy. Conclusions Diagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.

I test the effect of harsher punishments and sanctions on driving under the influence (DUI). In this setting, punishments are determined by strict rules on blood alcohol content (BAC) and previous offenses. Regression discontinuity derived estimates suggest that having a BAC above the DUI threshols reduces recidivism by up to 2 percentage points (17 percent). Likewise having a BAC over the aggravated DUI threshold reduces recidivism by an additional percentage point (9 percent). The results suggest that the additional sanctions experienced by drunk drivers at BAC thresholds are effective in reducing repeat drunk driving.

Ridehailing services such as Uber have been promoted as viable interventions for curbing alcohol-involved driving fatalities. However, evidence of ridehailing's impact has been mixed, with some studies finding no association but others finding either an increase or a decrease in fatalities. We contribute to this literature by examining more recent years of data, which capture a period during which Uber ridership has grown substantially and alcohol-involved fatalities have increased. Furthermore, we test whether the relationship between Uber availability and traffic fatalities depends on local characteristics. We employ multivariate regression models to test the association between Uber availability and total, alcohol-involved, and weekend and holiday-specific traffic fatalities in the 100 most populated metropolitan areas in the United States between 2009 and 2017. We find that Uber availability is not associated with changes in total, alcohol-involved, and weekend and holiday-specific traffic fatalities in aggregate, yet it is associated with increased traffic fatalities in urban, densely populated counties.

Background There is concern about the detrimental effects of shift-workers’ increasing working hours particularly when driving sleep deprived. The approach to measuring the magnitude of driving impairment caused by sleep deprivation was by comparing it to alcohol. The study compared driving performance after 24-h of wakefulness to performance with a BrAC of just over 22 μg/100mls of breath which is equal to 50 mg of alcohol per 100mls of blood (Scottish drink-drive limit). The effectiveness of coffee as a countermeasure for driver fatigue and the association between subjective impairment and actual performance was also investigated. Methods A study of 30 participants (11 male and 19 female; mean age 21) was conducted. Subjects were tested under three conditions: fully rested, sleep deprived, and alcohol intoxicated – BrAC mean [SD] 25.95 μg [2.78]. Under each condition, subjects were tested before and after coffee ingestion. This involved driving simulation (Lane Change Task and Reaction Test) and subjective Likert scales (Karolinska Sleepiness Scale and driver impairment scale). Outcome measures included lane tracking adaptive mean deviation, reaction time, and subjective sleepiness and impairment ratings. Results Compared to alcohol, sleep deprived mean reaction times were slower (2.86 s vs. 2.34 s) and lateral control of the vehicle was reduced (lane tracking adaptive mean deviation: 0.5 vs. 0.3). Coffee did not produce an improvement when sleep deprived, and instead, performance deteriorated. Females were less impaired following sleep deprivation than males. Following prolonged wakefulness, the correlation between subjective impairment and actual performance was significant. Conclusions It was concluded that sleep deprivation has a greater impact on driving performance than a BrAC of 22 μg/100mls of breath, as measured by driving simulation. Coffee is not an effective countermeasure for sleep deprived driving and drivers’ ability to judge this impairment is suggested to be limited.

We elected to analyze the correlation between the pre-treatment apparent diffusion coefficient (ADC) and the clinical, histological, and immunohistochemical status of rectal cancers. Forty-nine rectal cancer patients who received surgical resection without neoadjuvant therapy were selected that underwent primary MRI and diffusion-weighted imaging (DWI). Tumor ADC values were determined and analyzed to identify any correlations between these values and pre-treatment CEA or CA19-9 levels, and/or the histological and immunohistochemical properties of the tumor. Inter-observer agreement of confidence levels from two separate observers was suitable for ADC measurement (k  =  0.775). The pre-treatment ADC values of different T stage tumors were not equal (p  =  0.003). The overall trend was that higher T stage values correlated with lower ADC values. ADC values were also significantly lower for the following conditions: tumors with the presence of extranodal tumor deposits (p  =  0.006) and tumors with CA19-9 levels ≥ 35 g/ml (p  =  0.006). There was a negative correlation between Ki-67 LI and the ADC value (r  =  -0.318, p  =  0.026) and between the AgNOR count and the ADC value (r  =  -0.310, p  =  0.030). Significant correlations were found between the pre-treatment ADC values and T stage, extranodal tumor deposits, CA19-9 levels, Ki-67 LI, and AgNOR counts in our study. Lower ADC values were associated with more aggressive tumor behavior. Therefore, the ADC value may represent a useful biomarker for assessing the biological features and possible relationship to the status of identified rectal cancers.