Physics-informed multi-step real-time conflict-based vehicle safety prediction.

Real-time vehicle safety prediction is critical in roadway safety management as drivers or vehicles can be altered beforehand to take corresponding evasive actions and avoid possible collisions. This study proposes a physics-informed multi-step real-time conflict-based vehicle safety prediction model to enhance roadway safety. Physics insights (i.e., traffic shockwave properties) are combined with data-driven features extracted from deep-learning techniques to improve prediction accuracy. A time series of future vehicle safety indicators are predicted such that vehicles/drivers have enough time to take precautions. The safety indicator at each time stamp is a continuous value that the sign reflects the presence of conflict risks, and the absolute value indicates the conflict risk level to advise different magnitudes of evasive actions. A customized loss function is developed for the proposed prediction model to give more attention to risky events, which are the focus of safety management. The prediction superiority of the proposed model is proven through numerical experiments by comparing it with three benchmarks constructed based on the literature. Further, sensitivity analysis on key model parameters is carried out to advise parameter selections in developing real-world conflict-based vehicle safety prediction applications.

Cycle-level traffic conflict prediction at signalized intersections with LiDAR data and Bayesian deep learning.

Real-time safety prediction models are vital in proactive road safety management strategies. This study develops models to predict traffic conflicts at signalized intersections at the signal cycle level, using advanced Bayesian deep learning techniques and efficient LiDAR points. The modeling framework contains three phases, which are data preprocessing, base deep learning model development, and Bayesian deep learning model development. The core of the framework is the long short-term memory (LSTM) employed to predict the conflict frequency of a cycle by using traffic features of the previous five cycles (e.g., dynamic traffic parameters, traffic conflict frequency). Four Bayesian deep learning models were developed, including Bayesian-Standard LSTM, Bayesian-Hybrid-LSTM, Bayesian-Stacked-LSTM Encoder-Decoder, and Bayesian-Multi-head Stacked-LSTM Encoder-Decoder. The developed models were applied to traffic conflicts extracted from LiDAR points that were collected from a signalized intersection in Harbin, China with a total duration of seven days. Traffic conflicts, measured by the modified time-to-collision conflict indicator, were identified using the peak over threshold approach. The models were thoroughly evaluated from the aspects of reliability, transferability, sensitivity, and robustness. The results show that the developed four models can predict traffic conflict frequency per cycle per lane simultaneously with its uncertainty. Moreover, the two Bayesian encoder-decoder models perform better than Bayesian-Standard LSTM and Bayesian-Hybrid-LSTM in the four tests. Bayesian-Multi-head Stacked-LSTM Encoder-Decoder is suggested as the optimal model for its high reliability under uncertainty, good transferability in three scenarios, low sensitivity to different parameters, and sound robustness against small noise. The proposed framework could benefit studies on the state-of-the-art data-driven approach for real-time safety prediction.

Uncertainty prediction of mining safety production situation.

In order to explore the occurrence and development law of mining safety production accidents, analyze its future change trends, and aim at the ambiguity, non-stationarity, and randomness of mining safety production accidents, an uncertainty prediction model for mining safety production situation is proposed. Firstly, the time series effect evaluation function is introduced to determine the optimal time granularity, which is used as the window width of fuzzy information granulation (FIG), and the time series of mining safety production situation is mapped to Low, R, and Up three granular parameter sequences, according to the triangular fuzzy number; then, the mean value of the intrinsic mode function (IMF) is maintained in the normal dynamic filtering range. After the ensemble empirical mode decomposition (EEMD), the three non-stationary granulation parameter sequences of Low, R, and Up are decomposed into the intrinsic mode function components representing the detail information and the trend components representing the overall change, and then the sub-sequences are reconstructed according to the sample entropy to highlight the correlation among the sub-sequences; finally, the cloud model language rules of mining safety production situation prediction are created. Through time series discretization, cloud transformation, concept jump, time series set division, association rule mining, and uncertain reasoning, the reconstructed component sequence is modeled and predicted by uncertainty information extraction. The accuracy of the uncertainty prediction model was verified by 21 sets of test samples. The average relative errors of Low, R, and Up sequences were 9.472 %, 16.671 %, and 3.625 %, respectively. The research shows that the uncertainty prediction model of mining safety production situation overcomes the fuzziness, non-stationarity, and uncertainty of safety production accidents, and provides theoretical reference and practical guidance for mining safety management and decision-making.

Multi-Criteria Decision Analysis Model for Assessing the Risk from Multi-Ingredient Dietary Supplements (MIDS).

Military personnel use dietary supplements (DS) for performance enhancement, bodybuilding, weight loss, and to maintain health. Adverse events, including cardiovascular (CV) effects, have been reported in military personnel taking supplements. Previous research determined that ingestion of multi-ingredient dietary supplements (MIDS), can lead to signals of safety concerns. Therefore, to assess the safety of MIDS, the Department of Defense via a contractor explored the development of a model-based risk assessment tool. We present a strategy and preliminary novel multi-criteria decision analysis (MCDA)-based tool for assessing the risk of adverse CV effects from MIDS. The tool integrates toxicology and other relevant data available on MIDS; likelihood of exposure, and biologic plausibility that could contribute to specific aspects of risk.Inputs for the model are values of four measures assigned based on the available evidence supplemented with the opinion of experts in toxicology, modeling, risk assessment etc. Measures were weighted based on the experts' assessment of measures' relative importance. Finally, all data for the four measures were integrated to provide a risk potential of 0 (low risk) to 100 (high risk) that defines the relative risk of a MIDS to cause adverse reactions.We conclude that the best available evidence must be supplemented with the opinion of experts in medicine, toxicology and pharmacology. Model-based approaches are useful to inform risk assessment in the absence of data. This MCDA model provides a foundation for refinement and validation of accuracy of the model predictions as new evidence becomes available.

Safety biomarkers for development of vaccines and biologics: Report from the safety biomarkers symposium held on November 28-29, 2017, Marcy l'Etoile, France.

Vaccines prevent infectious diseases, but vaccination is not without risk and adverse events are reported although they are more commonly reported for biologicals than for vaccines. Vaccines and biologicals must undergo vigorous assessment before and after licensure to minimise safety concerns. Potential safety concerns should be identified as early as possible during the development for vaccines and biologicals to minimize investment risk. State-of-the art tools and methods to identify safety concerns and biomarkers that are predictive of clinical outcomes are indispensable. For vaccines and adjuvant formulations, systems biology approaches, supported by single-cell microfluidics applied to translational studies between preclinical and clinical studies, could improve reactogenicity and safety predictions. Next-generation animal models for clinical assessment of injection-site reactions with greater relevance for target human population and criteria to define the level of acceptability of local reactogenicity at vaccine injection sites in pre-clinical animal species should be assessed. Advanced in silico machine-learning-based analytics, species-specific cell or tissue expression, receptor occupancy and kinetics and cell-based assays for functional activity are needed to improve pre-clinical safety assessment of biologicals. The in vitro MIMIC® system could be used to compliment preclinical and clinical studies for assessing immune-toxicity, immunogenicity, immuno-inflammatory and mode of action of biologicals and vaccines. Sanofi Pasteur brought together leading experts in this field to review the state-of-the-art at a unique 'Safety Biomarkers Symposium' on 28-29 November 2017. Here we summarise the proceedings of this symposium. This unique scientific meeting confirmed the importance for institutions and industrial organizations to collaborate to develop tools and methods needed for predicting reactogenicity and immune-inflammatory reactions to vaccines and biologicals, and to develop more accuracy, reliability safety biomarkers, to inform decisions on the attrition or advancement of vaccines and biologicals.

In Silico Profiling of Clinical Phenotypes for Human Targets Using Adverse Event Data.

We present a novel approach for the molecular transformation and analysis of patient clinical phenotypes. Building on the fact that drugs perturb the function of targets/genes, we integrated data from 8.2 million clinical reports detailing drug-induced side effects with the molecular world of drug-target information. Using this dataset, we extracted 1.8 million associations of clinical phenotypes to 770 human drug-targets. This collection is perhaps the largest phenotypic profiling reference of human targets to-date, and unique in that it enables rapid development of testable molecular hypotheses directly from human-specific information. We also present validation results demonstrating analytical utilities of the approach, including drug safety prediction, and the design of novel combination therapies. Challenging the long-standing notion that molecular perturbation studies cannot be performed in humans, our data allows researchers to capitalize on the vast tomes of clinical information available throughout the healthcare system.

Sensitivity analysis in the context of regional safety modeling: identifying and assessing the modifiable areal unit problem.

A wide array of spatial units has been explored in current regional safety analysis. Since traffic crashes exhibit extreme spatiotemporal heterogeneity which has rarely been a consideration in partitioning these zoning systems, research based on these areal units may be subjected to the modifiable areal unit problem (MAUP). This study attempted to conduct a sensitivity analysis to quantitatively investigate the MAUP effect in the context of regional safety modeling. The emerging regionalization method-RECDAP (regionalization with dynamically constrained agglomerative clustering and partitioning) was employed to aggregate 738 traffic analysis zones in the county of Hillsborough to 14 zoning schemes at an incremental step-size of 50 zones based on spatial homogeneity of crash risk. At each level of aggregation, a Bayesian Poisson lognormal model and a Bayesian spatial model were calibrated to explain observed variations in total/severe crash counts given a number of zone-level factors. Results revealed that as the number of zones increases, the spatial autocorrelation of crash data increases. The Bayesian spatial model outperforms the Bayesian Poisson-lognormal model in accurately accounting for spatial autocorrelation effects, unbiased parameter estimates, and model performance, especially in cases with higher disaggregated levels. Zoning schemes with higher number of zones tend to have increasing number of significant variables, more stable coefficient estimation, smaller standard error, whereas worse model performance. The variables of population density and median household income show consistently significant effects on crash risk and are robust to variation in data aggregation. The MAUP effects may be significantly reduced if we just maintain at about 50% of the original number of zones (350 or larger). The present study highlights MAUP that is generally ignored by transportation safety analysts, and provides insights into the nature of parameter sensitivity to data aggregation in the context of regional safety modeling.

Modelling the effect of lactic acid bacteria from starter- and aroma culture on growth of Listeria monocytogenes in cottage cheese.

Four mathematical models were developed and validated for simultaneous growth of mesophilic lactic acid bacteria from added cultures and Listeria monocytogenes, during chilled storage of cottage cheese with fresh- or cultured cream dressing. The mathematical models include the effect of temperature, pH, NaCl, lactic- and sorbic acid and the interaction between these environmental factors. Growth models were developed by combining new and existing cardinal parameter values. Subsequently, the reference growth rate parameters (µref at 25°C) were fitted to a total of 52 growth rates from cottage cheese to improve model performance. The inhibiting effect of mesophilic lactic acid bacteria from added cultures on growth of L. monocytogenes was efficiently modelled using the Jameson approach. The new models appropriately predicted the maximum population density of L. monocytogenes in cottage cheese. The developed models were successfully validated by using 25 growth rates for L. monocytogenes, 17 growth rates for lactic acid bacteria and a total of 26 growth curves for simultaneous growth of L. monocytogenes and lactic acid bacteria in cottage cheese. These data were used in combination with bias- and accuracy factors and with the concept of acceptable simulation zone. Evaluation of predicted growth rates of L. monocytogenes in cottage cheese with fresh- or cultured cream dressing resulted in bias-factors (Bf) of 1.07-1.10 with corresponding accuracy factor (Af) values of 1.11 to 1.22. Lactic acid bacteria from added starter culture were on average predicted to grow 16% faster than observed (Bf of 1.16 and Af of 1.32) and growth of the diacetyl producing aroma culture was on average predicted 9% slower than observed (Bf of 0.91 and Af of 1.17). The acceptable simulation zone method showed the new models to successfully predict maximum population density of L. monocytogenes when growing together with lactic acid bacteria in cottage cheese. 11 of 13 simulations of L. monocytogenes growth were within the acceptable simulation zone, which demonstrated good performance of the empirical inter-bacterial interaction model. The new set of models can be used to predict simultaneous growth of mesophilic lactic acid bacteria and L. monocytogenes in cottage cheese during chilled storage at constant and dynamic temperatures. The applied methodology is likely to be applicable for safety prediction of other types of fermented and unripened dairy products where inhibition by lactic acid bacteria is important for growth of pathogenic microorganisms.