MOESM2 of Metabolic network model guided engineering ethylmalonyl-CoA pathway to improve ascomycin production in Streptomyces hygroscopicus var. ascomyceticus

Additional file 2. Metabolic network model of S. hygroscopicus var. ascomyceticus FS35 and potential targets identified using this model

Influence of violation under different traffic volume.

<p>Influence of violation under different traffic volume.</p

MOESM3 of Metabolic network model guided engineering ethylmalonyl-CoA pathway to improve ascomycin production in Streptomyces hygroscopicus var. ascomyceticus

Additional file 3: Table S1. Primers used in this section. Figure S1. Heat map illustrating the conservation of metabolic enzymes in different metabolic subsystems among 31 Streptomyces strains. Figure S2. Homology analysis of proteins related to the primary metabolism of S. coelicolor A3(2) and S16-shyl. Figure S3. Production profiles of the stain S. hygroscopicus var. ascomyceticus FS35 in batch fermentation. Table S2. Genetic targets chosen for experimental implementation. Table S3. The encoded enzymes involved in the ethylmalonyl-CoA pathways of the 31 Streptomyces strains

A Bayesian Tobit quantile regression approach for naturalistic longitudinal driving capability assessment

© 2020 Elsevier Ltd Given the severe traffic safety issue, tremendous efforts have been devoted to identify the crash contributing factors for developing and implementing safety improvement countermeasures. According to the study findings, driving behaviors have attributed to the majority crash occurrence, among which inadequate driving capability is a key factor. Therefore, a number of studies have been conducted for developing techniques associated with the driving capability assessment and its various improvement. However, the conventional assessment approaches, such as driving license exams and vehicle insurance quotes, have only focused on basic driving skill evaluations or aggregated driving style classifications, which failed to quantify driving capability from the safety perspective with respect to the complex driving scenarios. In this study, a novel longitudinal driving capacity assessment and ranking approach was developed with naturalistic driving data. Two Responsibility-Sensitive Safety (RSS) based driving capability indicators from the perspectives of risk exposure and severity were first proposed. Then, Bayesian Tobit quantile regression (BTQR) models were introduced to explore the relationships between driving capability indicators with trip level characteristics from the aspects of travel features, operational conditions, and roadway characteristics. The modeling results concluded that nighttime driving and higher average speed would lead to higher longitudinal collision risk and its severity. Besides, the BTQR models have provided varying factors significances among different quantile levels, for instance, driving duration is only significant at high quantiles for the driving capability indicators, implying that duration only affects drivers with large longitudinal risk exposures and strong close following tendencies. Furthermore, the case studies provided how to deploy the developed model to obtain the relative longitudinal driving capability rankings. Finally, the model applications from the aspects of commercial fleet safety management and comparing the autonomous vehicles’ longitudinal driving behaviors with human drivers have been discussed

Space-time diagram under different traffic condition with no violations.

<p>Space-time diagram under different traffic condition with no violations.</p

Vehicle collision distance-time-velocity diagram.

<p>Vehicle collision distance-time-velocity diagram.</p

Largest cluster center value (<i>V</i>_<i>C</i>) with different cluster number for different volume conditions.

<p>Largest cluster center value (<i>V</i>_<i>C</i>) with different cluster number for different volume conditions.</p

Largest cluster center with different cluster number.

<p>Largest cluster center with different cluster number.</p

Aimsun interface of the simulated section.

<p>Aimsun interface of the simulated section.</p

Methodology framework.

<p>Methodology framework.</p