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Background: Treatment of nasal tumors in dogs is associated with high morbidity and reliable prognostic factors are lacking. Dynamic contrast-enhanced computed tomography (DCECT) can be used to assess tumor perfusion., Objectives: To assess perfusion parameters of nasal tumors (correlating with tumor type) before and during radiotherapy (RT) and find potential correlation with survival., Animals: Twenty-four client-owned dogs with nasal tumors, including 16 epithelial tumors and 8 sarcomas., Methods: Prospective cross-sectional study. All dogs had baseline DCECT to assess fractional vascular volume (BV), blood flow (BF), and transit time (TT). Thirteen dogs had repeat DCECT after 12 Gy of megavoltage RT. Survival times were calculated., Results: Median BV was 17.83 mL/100 g (range, 3.63-66.02), median BF was 122.63 mL/100 g/minute (range, 23.65-279.99), and median TT was 8.91 seconds (range, 4.57-14.23). Sarcomas had a significantly lower BF than adenocarcinomas (P = .002), carcinomas (P = .01), and other carcinomas (P = .001), and significantly lower BV than adenocarcinomas (P = .03) and other carcinomas (P = .004). Significant associations were found between epithelial tumors and sarcoma for change in tumor volume (P = .01), width (P = .004), and length (P = .02) in that epithelial tumors decreased in volume whereas sarcomas increased in volume. Perfusion parameters were not correlated with survival., Conclusions and Clinical Importance: Nasal sarcomas have lower BV and BF than nasal carcinomas, and sarcomas have a lower size reduction than carcinomas early on during RT. Baseline results and changes in perfusion parameters may not be correlated with survival., (© 2023 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.)

Introduction: Treatment of brain tumors in dogs can be associated with significant morbidity and reliable prognostic factors are lacking. Dynamic contrast-enhanced computed tomography (DCECT) can be used to assess tumor perfusion. The objectives of this study were to assess perfusion parameters and change in size of suspected brain tumors before and during radiotherapy (RT) depending on their location and find a potential correlation with survival., Methods: Seventeen client-owned dogs with suspected brain tumors were prospectively recruited. All dogs had a baseline DCECT to assess mass size, blood volume (BV), blood flow (BF), and transit time (TT). Twelve dogs had a repeat DCECT after 12 Gy of megavoltage RT. Survival times were calculated., Results: Intra-axial masses had lower BF ( p  = 0.005) and BV ( p  < 0.001) than extra-axial masses but not than pituitary masses. Pituitary masses had lower BF ( p  = 0.001) and BV ( p  = 0.004) than extra-axial masses. The volume of the mass was positively associated with TT ( p  = 0.001) but not with BF and BV. Intra-axial masses showed a more marked decrease in size than extra-axial and pituitary masses during RT ( p  = 0.022 for length, p  = 0.05 for height). Extra-axial masses showed a greater decrease in BF ( p  = 0.011) and BV ( p  = 0.012) during RT than pituitary masses and intra-axial masses. Heavier dogs had a shorter survival time ( p  = 0.011). Perfusion parameters were not correlated with survival., Conclusion: DCECT perfusion parameters and change in size of brain masses during RT might be different based on the location of the mass., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mortier, Maddox, Blackwood, La Fontaine and Busoni.)

Objective: Treatment of orofacial tumors in dogs is associated with high morbidity and reliable prognostic factors are lacking. Dynamic contrast-enhanced computed tomography (DCECT) can be used to assess tumor perfusion. The objectives of this study were to describe the perfusion parameters of different types of orofacial tumors and to describe the changes in perfusion parameters during radiotherapy (RT) in a subset of them., Animals: 11 dogs with orofacial tumors prospectively recruited., Clinical Presentation and Procedures: All dogs had baseline DCECT to assess blood volume (BV), blood flow (BF), and transit time (TT). Five dogs had repeat DCECT during megavoltage RT., Results: 5 squamous cell carcinomas, 3 sarcomas, 1 melanoma, 1 histiocytic sarcoma, and 1 acanthomatous ameloblastoma were included. Blood volume and BF were higher in squamous cell carcinomas than in sarcomas, although no statistical analysis was performed. At repeat DCECT, 4 dogs showed a reduction in the size of their tumor during RT. Among these dogs, 3 showed an increase in BV and BF and 1 a decrease in these parameters between the baseline and the follow-up DCECT. The only dog whose tumor increased in size between the first and the second DCECT showed a decrease in BV and BF., Clinical Relevance: Perfusion parameters derived from DCECT were described in a series of dogs with various types of orofacial tumors. The results suggest that epithelial tumors could have higher BV and BF than mesenchymal tumors, although larger sample sizes are needed to support these preliminary findings.

Purpose: To test if the relative change in FDG-PET SUV max over the course of treatment was associated with disease progression and overall survival. Additionally, the prognostic values of other first-order PET-metric changes were investigated., Methods: The study included 38 patients with stage II-III NSCLC, who underwent concurrent chemoradiotherapy. Patients received two pre-treatment FDG-PET scans and four during-treatment scans at weekly intervals. SUV max was normalized to the start of treatment and analyzed using linear regression. Linear regression coefficients of other first order PET-metrics were grouped according to dissimilarity. Associations to patient outcome were analyzed using Cox hazard ratio., Results: Twenty-eight patients satisfied the criteria for analysis. All PET-metrics demonstrated a strong linear correlation with time during treatment [median R-range: -0.87: -0.97]. No strong associations (p > 0.10) were found for the relative slope of SUV max to patient outcomes. Other first-order metrics did correlate with outcome but the single imaging time-point maximizing the association of PET response with outcome varied per PET metric and outcome parameter., Conclusion: All investigated FDG PET metrics linearly decreased during treatment. Relative change in SUV max was not associated to patient outcome while several other first order PET-metrics were related to patient outcome. A single optimal imaging time-point could not be identified., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The availability of large-scale naturalistic driving data provides enormous opportunities for studying relationships between instantaneous driving decisions prior to involvement in safety critical events (SCEs). This study investigates the role of driving instability prior to involvement in SCEs. While past research has studied crash types and their contributing factors, the role of pre-crash behavior in such events has not been explored as extensively. The research demonstrates how measures and analysis of driving volatility can be leading indicators of crashes and contribute to enhancing safety. Highly detailed microscopic data from naturalistic driving are used to provide the analytic framework to rigorously analyze the behavioral dimensions and driving instability that can lead to different types of SCEs such as roadway departures, rear end collisions, and sideswipes. Modeling results reveal a positive association between volatility and involvement in SCEs. Specifically, increases in both lateral and longitudinal volatilities represented by Bollinger bands and vehicular jerk lead to higher likelihoods of involvement in SCEs. Further, driver behavior related factors such as aggressive driving and lane changing also increases the likelihood of involvement in SCEs. Driver distraction, as represented by the duration of secondary tasks, also increases the risk of SCEs. Likewise, traffic flow parameters play a critical role in safety risk. The risk of involvement in SCEs decreases under free flow traffic conditions and increases under unstable traffic flow. Further, the model shows prediction accuracy of 88.1 % and 85.7 % for training and validation data. These results have implications for proactive safety and providing in-vehicle warnings and alerts to prevent the occurrence of such SCEs., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Connected and automated vehicles (CAVs) offer a huge potential to improve the operations and safety of transportation systems. However, the use of smart devices and communications in CAVs introduce new risks. CAVs would leverage vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, thus providing additional system access points compared to traditional systems. Automation makes these systems more vulnerable and increases the consequences of cyberattacks. This study utilizes an infrastructure-based communication platform consisting of cooperative adaptive cruise control and lane control advisories developed by the authors to perform cyber risk assessment of CAVs. The study emulates three types of cyberattacks (message falsification, dedicated denial of service, and spoofing attacks) in a representative traffic environment consisting of multiple CAV platoons and lane change events to analyze the safety and stability impacts of the cyberattacks. Simulation experiments using VISSIM reveals that traffic stream and CAV string is unstable under all three types of cyberattacks. The worst case is represented by the message falsification attack. Increases in volatility are observed over a no attack case, with variations increasing by an average of 43%-51% along with an increase of over 3000 crash conflicts. Similarly, lane change crash conflicts are observed to be more severe compared to rear end crash conflicts, showing a higher probability of severe injuries. Further, the case of slight cyberattack on a single CAV also creates significant disruption in the traffic stream. Analysis of variance (ANOVA) reveals the statistical significance of the results. These results pave the way for future design of secure systems from a monitoring perspective., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Purpose: We investigated in a single-center prospective trial (NCT03376386) the use of serial fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) to determine the boost dose and to guide boost segmentation in head and neck cancer., Methods and Materials: Patients were eligible when treated with curative radiation therapy with or without systemic treatment for T2-4 squamous cell carcinoma of the hypopharynx, larynx, or oropharynx (20 patients in total). FDG-PET/CT scans were made at baseline and for redelineation and replanning at the end of weeks 2 and 4 of radiation therapy. The metabolically active part of the primary tumor received a 4 Gy boost on top of the 70 Gy baseline dose per partial metabolic response. The study would be considered feasible when ≥80% of adaptations were successful and no Common Terminology Criteria for Adverse Events grade ≥4 acute toxicity occurred., Results: One patient received 70 Gy after complete metabolic response in week 2, and 12 patients received 78 Gy because of partial metabolic response at weeks 2 and 4. Seven patients received 74 Gy, either because of complete metabolic response at week 4 (n = 3) or a missed FDG-PET/CT (n = 4). The patients missed their FDG-PET/CT scans because they did not fast (n = 2) or at patients' request (n = 2). In addition to the 4 missed FDG-PET/CT scans, 2 adaptive plans could not be finished successfully owing to logistical problems. In total, 85% of adaptations were completed correctly. No patient experienced grade ≥4 toxicity, and 40% had grade 3 dysphagia (tube feeding) during treatment. This decreased at 12 weeks posttreatment to 20%., Conclusions: This prospective trial demonstrates the feasibility of serial FDG-PET/CT scans for dose escalation and patient selection., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. This paper used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities. The estimation results revealed lower severities with ATM deployment. Marginal effects for ATM deployments that featured hard shoulder running (HSR) revealed lower likelihoods for severe and moderate injury crashes of 15.9 % and for minor injury crashes of 10.1 %. The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 % and 8.33 % with ATM without HSR. The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models, revealing better performance with validation data. These results have implications for improving freeway crash risk at critical locations., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The use of traffic simulation to analyze complex transportation issues has become common practice in transportation engineering. The further application of microsimulation to the analysis of traffic safety in a systematic, rigorous, and controlled fashion is becoming increasingly viable as simulation models improve and tools for quantifying surrogate safety measures become readily accessible. Using a calibrated traffic microsimulation model and surrogate safety assessment model analysis, this paper examined how the risk for left-turn crashes varied as traffic conditions changed at a signalized intersection. Safety impacts for 750 unique combinations of intersection geometry, traffic, and signal timing parameters were simulated and the number of left-turn conflicts per hour noted. Results of the simulation analyses were used to develop statistical models that expressed the risk of occurrence of a left-turn crash during a given hour as a function of the left-turn phasing mode and prevailing conditions during that hour. The study was motivated by the recent widespread application of the flashing yellow arrow (FYA) which provides the opportunity to vary left-turn phasing mode by time of day-potentially leading to more efficient traffic operations at signalized intersections. In this regard, the study addresses a basic need for tools that predict how the risk for left-turn crashes might vary at a more disaggregated level than that provided by existing crash prediction models, which typically predict yearly totals of left-turn crashes, often based on annual average daily traffic volumes. Potential application of the model to the implementation of a time-variable safety-based left-turn phasing selection scheme using FYA was successfully demonstrated., (Published by Elsevier Ltd.)

In locally advanced lung cancer, established baseline clinical variables show limited prognostic accuracy and 18F-fluorodeoxyglucose positron emission tomography (FDG PET) radiomics features may increase accuracy for optimal treatment selection. Their robustness and added value relative to current clinical factors are unknown. Hence, we identify robust and independent PET radiomics features that may have complementary value in predicting survival endpoints. A 4D PET dataset (n = 70) was used for assessing the repeatability (Bland-Altman analysis) and independence of PET radiomics features (Spearman rank: |ρ|<0.5). Two 3D PET datasets combined (n = 252) were used for training and validation of an elastic net regularized generalized logistic regression model (GLM) based on a selection of clinical and robust independent PET radiomics features (GLMall). The fitted model performance was externally validated (n = 40). The performance of GLMall (measured with area under the receiver operating characteristic curve, AUC) was highest in predicting 2-year overall survival (0.66±0.07). No significant improvement was observed for GLMall compared to a model containing only PET radiomics features or only clinical variables for any clinical endpoint. External validation of GLMall led to AUC values no higher than 0.55 for any clinical endpoint. In this study, robust independent FDG PET radiomics features did not have complementary value in predicting survival endpoints in lung cancer patients. Improving risk stratification and clinical decision making based on clinical variables and PET radiomics features has still been proven difficult in locally advanced lung cancer patients., Competing Interests: The authors have declared that no competing interests exist.

The World Health Organization monitoring and evaluation framework for the International Health Regulations (IHR, 2005) describes the targets for the Joint External Evaluation (JEE) indicators. For workforce development, the JEE defines the optimal target for attaining and complying with the IHR (2005) as 1 trained field epidemiologist (or equivalent) per 200,000 population. We explain the derivation and use of the current field epidemiology workforce development target and identify the limitations and lessons learned in applying it to various countries' public health systems. This article also proposes a way forward for improvements and implementation of this workforce development target.

Traditional traffic safety analyses use highly aggregated data, typically annual average daily traffic (AADT) and annual crash counts. This approach neglects the time-varying nature of critical factors such as traffic speed, volume, and density, and their effects on traffic safety. This paper evaluated the relationship between crashes and quality of flow at different levels of temporal aggregation using continuous count station data and probe data from 4 lane rural freeway and 6 lane urban freeway segments in Virginia. The performance of crash prediction models using traffic and geometric information at 15-minute, hourly, and annual aggregation intervals were contrasted. This study also assessed whether inclusion of speed data improved model performance and examined the effects of using speeds from physical sensors versus speed estimates from private-sector probe speed data. The results showed that using average hourly volume along with average speed and selected geometric variables improved predictions compared to annual models that did not use speed information. When comparing an AADT-based model to an average hourly volume model for total crashes, the mean absolute prediction error improved by 11% for rural models and 20% for urban models. This result was based on volume and speed data from continuous count stations. When private sector probe speed data was used, the rural model performance improved by 10% and urban models by 20%. This trend was consistent for all crash types irrespective of level of injury or number of vehicles involved. Even though models using private sector data performed slightly worse than the ones based on continuous count data, they were still far better than AADT based models. These results indicate that probe based data can be used in developing crash models without harming prediction capability., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Purpose: Oropharynx cancer (OPC) is heterogeneous; human papillomavirus (HPV)-positive and HPV tumors represent 2 disease entities with a different prognosis. Earlier studies investigating the prognostic value of pretreatment F-FDG PET in OPC are small or included patients with unknown HPV status. This study assessed the prognostic value of PET variables, in a large cohort with balanced HPV status., Methods: Retrospectively, primary tumor SUVmax, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were extracted from baseline FDG PET/CT of patients with OPC treated with (chemo)radiation. The Pearson correlation between the PET variables was calculated. With linear regression, the correlation between the PET variables and HPV status, age, smoking status, T stage, N stage, and American Joint Committee on Cancer stage was calculated. Univariable and multivariable Cox models analyzed local control, overall survival, and disease-free survival (DFS)., Results: Of 201 patients, 109 were HPV. Metabolic tumor volume and TLG correlated (r = 0.96), as did SUVpeak and SUVmax (r = 0.97). The PET variables correlated strongest with HPV status and T stage. These two accounted for 40% of the variance of MTV and 33% of TLG. Human papillomavirus-negative tumors had a significantly higher SUVmax, SUVpeak, MTV, and TLG. In univariable analysis, all PET variables were significantly associated with local control, overall survival, and DFS. In multivariable analysis, TLG was significantly associated to DFS in patients with HPV OPC (hazard ratio, 1.005; 95% confidence interval, 1.001-1.010; P = 0.03). However, we did not observe this in HPV patients., Conclusions: Increased baseline TLG is associated with worse DFS in HPV OPC and might be used as biomarker for risk stratification in these patients. Interestingly, we could not identify this association in HPV patients.

Adaptive signal control technology (ASCT) is an intelligent transportation systems (ITS) technology that optimizes signal timings in real time to improve corridor flow. While a few past studies have examined the impact of ASCT on crash frequency, little is known about its effect on injury severity outcomes. Similarly, the impact of different types of ASCTs deployed across different states is also uncertain. This paper therefore, used ordered probit models with random parameters to estimate the injury severity outcomes resulting from ASCT deployment across Pennsylvania and Virginia. Two disparate systems deployed across the two different states were analyzed to assess whether they had similar impacts on injury severity, although signal timings are optimized using different algorithms by both systems. The estimation results revealed that both ASCT systems were associated with reductions in injury severity levels. Marginal effects showed that Type A ASCT systems reduced the propensity of severe plus moderate and minor injury crashes by 11.70% and 10.36% while type B ASCT reduced the propensity of severe plus moderate and minor injury crashes by 4.39% and 6.92%. Similarly, the ASCTs deployed across the two states were also observed to reduce injury severities. The combined best fit model also revealed a similar trend towards reductions in severe plus moderate and minor injury crashes by 5.24% and 9.91%. This model performed well on validation data with a low forecast error of 0.301 and was also observed to be spatially transferable. These results encourage the consideration of ASCT deployments at intersections with high crash severities and have practical implications for aiding agencies in making future deployment decisions about ASCT., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Introduction: Adaptive signal control technology (ASCT) has long been investigated for its operational benefits, but the safety impacts of this technology are still unclear. The main purpose of this study was to determine the safety effect of ASCT at urban/suburban intersections by assessing two different systems., Method: Crash data for 41 intersections from the Pennsylvania Department of Transportation (PennDOT), along with crash frequencies computed through Safety Performance Functions (SPFs), were used to perform the Empirical Bayes (E-B) method to develop crash modification factors (CMF) for ASCT. Moreover, a crash type analysis was conducted to examine the safety impact of ASCT on a regional scale and the variation of safety among type of crashes observed., Results: The results from this study indicated the potential of ASCT to reduce crashes since the Crash Modification Factor (CMF) values for both ASCT systems (SURTRAC and InSync) showed significant reductions in crashes. Average CMF values of 0.87 and 0.64 were observed for total and fatal and injury crash categories at a 95% confidence level, and results were consistent between systems. While a reduction in the proportion of rear end crashes was observed, the change was not determined to be statistically significant. The overall distribution of crash types did not change significantly when ASCT was deployed., Conclusion and Practical Application: The results indicate that safety benefits of ASCT were generally consistent across systems, which should aid agencies in making future deployment decisions on ASCT., (Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.)

Background and Purpose: We aimed to identify tumour subregions with characteristic phenotypes based on pre-treatment multi-parametric functional imaging and correlate these subregions to treatment outcome. The subregions were created using imaging of metabolic activity (FDG-PET/CT), hypoxia (HX4-PET/CT) and tumour vasculature (DCE-CT)., Materials and Methods: 36 non-small cell lung cancer (NSCLC) patients underwent functional imaging prior to radical radiotherapy. Kinetic analysis was performed on DCE-CT scans to acquire blood flow (BF) and volume (BV) maps. HX4-PET/CT and DCE-CT scans were non-rigidly co-registered to the planning FDG-PET/CT. Two clustering steps were performed on multi-parametric images: first to segment each tumour into homogeneous subregions (i.e. supervoxels) and second to group the supervoxels of all tumours into phenotypic clusters. Patients were split based on the absolute or relative volume of supervoxels in each cluster; overall survival was compared using a log-rank test., Results: Unsupervised clustering of supervoxels yielded four independent clusters. One cluster (high hypoxia, high FDG, intermediate BF/BV) related to a high-risk tumour type: patients assigned to this cluster had significantly worse survival compared to patients not in this cluster (p = 0.035)., Conclusions: We designed a subregional analysis for multi-parametric imaging in NSCLC, and showed the potential of subregion classification as a biomarker for prognosis. This methodology allows for a comprehensive data-driven analysis of multi-parametric functional images., (Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT)., Material and Methods: For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i.e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUV mean ) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR > 1.2) were compared., Results: Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 ± 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 ± 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV > 1 cm 3 ) by the best performing model., Conclusions: We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The model correctly classifies all tumors, and could therefore, aid tumor hypoxia classification and patient stratification.

Kinetic parameter variability may be sensitive to kinetic model choice, kinetic model implementation or patient-specific effects. The purpose of this study was to assess their impact on the variability of dynamic contrast-enhanced computed tomography (DCE-CT) kinetic parameters. A total of 11 canine patients with sinonasal tumours received high signal-to-noise ratio, test-double retest DCE-CT scans. The variability for three distributed parameter (DP)-based models was assessed by analysis of variance. Mixed-effects modelling evaluated patient-specific effects. Inter-model variability (CV inter ) was comparable to or lower than intra-model variability (CV intra ) for blood flow (CV inter :[4-28%], CV intra :[28-31%]), fractional vascular volume (CV inter :[3-17%], CV intra :[16-19%]) and permeability-surface area product (CV inter :[5-12%], CV intra :[14-15%]). The kinetic models were significantly (P<0.05) impacted by patient characteristics for patient size, area underneath the curve of the artery and of the tumour. In conclusion, DP-based models demonstrated good agreement with similar differences between models and scans. However, high variability in the kinetic parameters and their sensitivity to patient size may limit certain quantitative applications., (© 2015 John Wiley & Sons Ltd.)

Traffic data is commonly collected from widely deployed sensors in urban areas. This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. This research, taking into consideration the significant increase in the amount of traffic data and the complexity of data analysis, focuses mainly on the challenge of solving data-intensive and computation-intensive problems. As a solution to the problems, this paper proposes a Cyber-ITS framework to perform data analysis on Cyber Infrastructure (CI), by nature parallel-computing hardware and software systems, in the context of ITS. The techniques of the framework include data representation, domain decomposition, resource allocation, and parallel processing. All these techniques are based on data-driven and application-oriented models and are organized as a component-and-workflow-based model in order to achieve technical interoperability and data reusability. A case study of the Cyber-ITS framework is presented later based on a traffic state estimation application that uses the fusion of massive Sydney Coordinated Adaptive Traffic System (SCATS) data and GPS data. The results prove that the Cyber-ITS-based implementation can achieve a high accuracy rate of traffic state estimation and provide a significant computational speedup for the data fusion by parallel computing.

Uncertain population behaviors in a regional emergency could potentially harm the performance of the region's transportation system and subsequent evacuation effort. The integration of behavioral survey data with travel demand modeling enables an assessment of transportation system performance and the identification of operational and public health countermeasures. This paper analyzes transportation system demand and system performance for emergency management in three disaster scenarios. A two-step methodology first estimates the number of trips evacuating the region, thereby capturing behavioral aspects in a scientifically defensible manner based on survey results, and second, assigns these trips to a regional highway network, using geographic information systems software, thereby making the methodology transferable to other locations. Performance measures are generated for each scenario including maps of volume-to-capacity ratios, geographic contours of evacuation time from the center of the region, and link-specific metrics such as weighted average speed and traffic volume. The methods are demonstrated on a 600 segment transportation network in Washington, DC (USA) and are applied to three scenarios involving attacks from radiological dispersion devices (e.g., dirty bombs). The results suggests that: (1) a single detonation would degrade transportation system performance two to three times more than that which occurs during a typical weekday afternoon peak hour, (2) volume on several critical arterials within the network would exceed capacity in the represented scenarios, and (3) resulting travel times to reach intended destinations imply that un-aided evacuation is impractical. These results assist decisions made by two categories of emergency responders: (1) transportation managers who provide traveler information and who make operational adjustments to improve the network (e.g., signal retiming) and (2) public health officials who maintain shelters, food and water stations, or first aid centers along evacuation routes. This approach may also interest decisionmakers who are in a position to influence the allocation of emergency resources, including healthcare providers, infrastructure owners, transit providers, and regional or local planning staff., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

As part of our investigations into the design of more cytotoxic analogues of the experimental anticancer drug tallimustine, 1, C-terminus modified aminoalkyl-, 2a-c, diaminoalkyl-, 3, and anilino-containing, 4, derivatives have been synthesized. Compounds 2a-c differ by 2, 3, or 4 methylene units in the C-terminus, respectively. Results from an ethidium displacement study on poly(dA-dT), poly(dG-dC), calf thymus DNA and T4 coliphage DNA showed that compounds 2-4 interact in the minor groove of the polynucleotides with a preference for poly(dA-dT) over poly(dG-dC). Compound 4 bound more weakly to the DNAs than 2a-c and 3. Using a CD dilution assay compounds 2a-c and 3 were demonstrated to bind irreversibly to calf thymus DNA. The sequence selectivity by which compounds 2-4 alkylate DNA was demonstrated using a Taq polymerase stop assay. All the compounds alkylated preferentially at the 3'-purine residue in a 5'-TTTTGPu-3' sequence (Pu = A or G). This observed sequence specificity is similar to that of tallimustine and a related compound 5. At an equimolar concentration the aminoalkyl compounds 2a-c (2b > 2a > 2c), and diaminoalkyl compound 3 were more efficient at alkylating these sequences than the anilino compound 4. Following a one hour exposure of human chronic myeloid leukemia K562 cells, compounds 2b and 3 have lower IC50 values (1.64 microM and 3.03 microM, respectively) than tallimustine (5 microM) and similar values to a related compound 5 (2.2 microM). The order of cytotoxicity for all the compounds is 2b > 5 > 3 > 2a > 1 > 2c = 4. These results indicate that the cytotoxicities of these compounds are related to their relative ability to alkylate the consensus DNA binding sequence.

The O-linked acidic oligosaccharides of the jelly coat surrounding the eggs of Ambystoma maculatum were analyzed by NMR spectroscopy. The structures of the major oligosaccharides were established as follows where Kdn represents 3-deoxy-D-glycero-D-galacto-nonulsonic acid and GalNAc-ol is N-acetylgalactosaminitol: [sequence: see text] As shown for five other amphibian species, the structures of these carbohydrate chains appear to be species specific and can afford a basis for molecular taxonomy. These new sequences also reflect the occurrence of specific fucosyltransferase activities that are characteristic of Ambystoma maculatum.

Several O-linked oligosaccharides of the jelly coat surrounding the eggs of Axolotl maculatum were analysed by 1H-NMR spectroscopy. The four major oligosaccharidealditols released by reductive beta-elimination display either the Lewisx (Lex) determinant or the sequence GalNAc(beta 1-4)[Fuc(alpha 1-3)]GlcNAc. This last structure has previously been characterized in allergenically active oligosaccharides isolated from the sea squirt H-antigen, and in the N-linked glycans of Schistosoma mansoni and human urokinase. It represents the major carbohydrate chain found in A. maculatum, the oviduct of which constitutes an excellent source of beta 1-4-acetylgalactosaminyl transferase activity. Moreover, the carbohydrate chains isolated from A. maculatum are quite different from those found in seven other amphibian species, in which the presence of species-specific material has been characterized. The role of carbohydrates appears more and more apparent during the fertilization process, and the diversity of the O-linked oligosaccharides supports such a biological role.