Evaluation of a 3M (mistakes, mentoring, and mastery) training program for transfer of control situations in a level 2 automated driving system.

Drivers must actively supervise automation as it can only function in limited conditions. A failure to supervise the system has negative consequences in terms of missed requests to take over control and may cause crashes or jeopardize safety. The objective of this study is to determine the effect of a novel, 3M (Mistakes, Mentoring, and Mastery) training program on drivers' behavior while using level 2 driving automation systems. To achieve this, 36 participants were assigned randomly to three different training programs (3M training, User manual, and Placebo) and drove through scenarios on a fixed-based driving simulator. The results showed that drivers in the 3M training group took back control more effectively when the driving automation system reached its limits compared to drivers who received User manual or Placebo training. Drivers in the 3M training Group also had higher situation awareness and improved trust in automation. The results indicate that an interactive approach to training with regards to vehicle automation can help drivers more safely interact with automation systems.

Booster Dose of Attention Training Program for Young Novice Drivers: A Longitudinal Driving Simulator Evaluation Study.

OBJECTIVE: We examined the effectiveness of a second exposure to ACCEL, a novel driving training program, on latent hazard anticipation (HA) performance several months after their first exposure. BACKGROUND: Past research has demonstrated that PC-based driver training programs can improve latent HA performance in young novice drivers, but these improvements are below the ceiling level. METHOD: Twenty-five participants were randomly assigned to either the Placebo group, the ACCEL-1 group, or the ACCEL-2 group. Following the completion of the assigned training program, participants drove a series of eighteen scenarios incorporating latent hazards in a high-fidelity driving simulator with their eyes tracked. Participants returned two to six months following the first session and completed either the placebo program (ACCEL-1 and Placebo groups), or a second dose of training program (ACCEL-2 group), again followed by simulated evaluation drives. RESULTS: The ACCEL-2 group showed improved HA performance compared to the ACCEL-1 and Placebo groups in the second evaluation. CONCLUSION: ACCEL enhances young novice drivers' latent HA performance. The effectiveness of ACCEL is retained up to 6 months, and a second dose further improves HA performance. APPLICATION: Policy makers should consider requiring such training before the completion of graduate driver license programs. Young novice drivers that do not show successful latent HA performance could be required to complete additional training before being allowed to drive without restrictions.

Crash proximity and equivalent property damage calculation techniques: An investigation using a novel horizontal curve dataset.

Despite the numerous breakthroughs in crash analytics, there remains a lack of consensus among safety practitioners as to the optimal method for locating high crash locations. Two critical components in the traffic safety analysis process not agreed upon are 1) how the crash distance to a target location is included in the analysis and 2) how crashes are weighted based on crash-related characteristics. For example, the commonly used buffering technique to determine which crashes are associated with a specific target road segment does not associate crashes that are closer to a target road segment with any additional weight, even though it is likely to be more greatly associated with the characteristics of the target location. Additionally, the commonly used equivalent property damage only (EPDO) crash weight method has been found to weigh fatal crashes significantly more than serious injury crashes, even if the difference between the two outcomes was a single factor. This study proposes more robust crash weighting techniques for use in high-risk location identification using an application of a novel horizontal curve dataset. Specifically, a heteroscedastic censored regression approach was used to investigate the impact of different crash proximity weighting techniques and crash severity weighting methods on model outcomes. The results demonstrate that the use of a linear distance weighting factor used in conjunction with the buffering technique as well as a less precise EPDO weighting factor method results in more robust safety analysis outcomes. The improved results have the potential to improve hot spot identification and resource allocation at both the federal and regional levels by employing models that more accurately link specific crash segments with contributing crash characteristics.

Driver behavior and performances on in-vehicle display based speed compliance.

Traffic safety, and the inherent risks associated with speeding, continue to remain a national priority. Advances in both roadway and vehicle technology have created potential mechanisms to mitigate speeding behaviors. This driving simulator study evaluated the effects of alternative ways to increase driver safety by investigating the characteristics of specific driving cues and drivers' response rates to those cues. The study builds upon existing approaches to symbolically deliver Traffic Control Devices (TCDs), specifically speed alerts, at different locations within the vehicle to reduce cognitive distraction and prevent visual crowding so that drivers can properly select their speed and focus upon the roadway environment. Twenty-three participants received five visual treatments (e.g., combinations of speed alert style, presentation, and location) in a simulated environment. Participants also responded to a set of survey questions following the simulated drive. Participants were evaluated on various response factors to each visual treatment. Results showed that younger participants of the age group 18-23 responded to the visual treatment and stayed within speed limits as compared to other older and more experienced participants. Results also showed that alerts falling in the mid-peripheral visual region and alerts that flashed received an increased response rate for observing speed limits.

The role of protected intersections in improving bicycle safety and driver right-turning behavior.

Protected or separated bike lanes or cycle tracks, are increasing in popularity in North America and Europe. However, despite their documented benefits, there are concerns about potential conflicts between bicycles and vehicles when they merge back together at an intersection. The concern is that following a period of separation, drivers are less likely to anticipate and scan for the presence of bicyclists. This research examines how transitions from fully separated to mixed-traffic environments and vice versa affect driver behavior. The goal is to assess whether certain segment-intersection treatment combinations can alert drivers of the presence of bicyclists and thus, encourage them to scan for bicyclists prior to a right turn, reducing potential right-hook conflicts. Driving simulation is utilized and driver performance for right-turning vehicles is recorded under the presence of various bicycle infrastructure treatments along segments and at intersections. The experimental design includes conventional and protected bike lanes and intersections with either intersection crossing markings or protected intersections. The results show that the presence of protected bike lanes motivates fewer glances towards bicyclists traveling on those bike lanes, therefore, reducing drivers' ability to detect and in turn, perceive bicyclists. Drivers developed slightly lower speeds while driving next to protected versus conventional bike lanes. It was also found that protected intersections result in a higher rate of right glances at the intersection prior to a right turn. Drivers glancing at the intersection were also found to have lower speeds, indicating a correlation between the presence of protected intersection elements and speed selection. This research can be used to guide decisions on bicycle infrastructure implementation for safer multimodal operations.

Using SHRP2 NDS data to examine infrastructure and other factors contributing to older driver crashes during left turns at signalized intersections.

Drivers age 65 and over have higher rates of crashes and crash-related fatalities than other adult drivers and are especially over-represented in crashes during left turns at intersections. This research investigated the use of SHRP2 Naturalistic Driving Study (NDS) data to assess infrastructure and other factors contributing to left turn crashes at signalized intersections, and how to improve older driver safety during such turns. NDS data for trips involving signalized intersections and crash or near-crash events were obtained for two driver age groups: drivers age 65 and over (older drivers) and a sample of drivers age 30-49, along with NDS pre-screening and questionnaire data. Video scoring of all trips was performed to collect additional information on intersection and trip conditions. To identify the most influential factors of crash risk during left turns at signalized intersections, machine learning and regression models were used. The results found that in the obtained NDS dataset, there was a relatively small volume of crashes during left turns at signalized intersections. Further, model results found the statistically significant variables of crash risk for older drivers were associated more with health and cognitive factors rather than the infrastructure or design of the intersections. The results suggest that a study using only SHRP2 NDS data will not lead to definitive findings or recommendations for infrastructure changes to increase safety for older drivers at signalized intersections and during left turns. Moreover, the findings of this study indicates the need to consider other data sources and data collection methods to address this critical literature gap in older driver safety.

The Superior Adherence Phenotype of E. coli O104:H4 is Directly Mediated by the Aggregative Adherence Fimbriae Type I.

Whereas the O104:H4 enterohemorrhagic Escherichia coli (EHEC) outbreak strain from 2011 expresses aggregative adherence fimbriae of subtype I (AAF/I), its close relative, the O104:H4 enteroaggregative Escherichia coli (EAEC) strain 55989, encodes AAF of subtype III. Tight adherence mediated by AAF/I in combination with Shiga toxin 2 production has been suggested to result in the outbreak strain's exceptional pathogenicity. Furthermore, the O104:H4 outbreak strain adheres significantly better to cultured epithelial cells than archetypal EAEC strains expressing different AAF subtypes. To test whether AAF/I expression is associated with the different virulence phenotypes of the outbreak strain, we heterologously expressed AAF subtypes I, III, IV, and V in an AAF-negative EAEC 55989 mutant and compared AAF-mediated phenotypes, incl. autoaggregation, biofilm formation, as well as bacterial adherence to HEp-2 cells. We observed that the expression of all four AAF subtypes promoted bacterial autoaggregation, though with different kinetics. Disturbance of AAF interaction on the bacterial surface via addition of α-AAF antibodies impeded autoaggregation. Biofilm formation was enhanced upon heterologous expression of AAF variants and inversely correlated with the autoaggregation phenotype. Co-cultivation of bacteria expressing different AAF subtypes resulted in mixed bacterial aggregates. Interestingly, bacteria expressing AAF/I formed the largest bacterial clusters on HEp-2 cells, indicating a stronger host cell adherence similar to the EHEC O104:H4 outbreak strain. Our findings show that, compared to the closely related O104:H4 EAEC strain 55989, not only the acquisition of the Shiga toxin phage, but also the acquisition of the AAF/I subtype might have contributed to the increased EHEC O104:H4 pathogenicity.

Using a novel data linkage approach to investigate potential reductions in motor vehicle crash severity - An evaluation of strategic highway safety plan emphasis areas.

INTRODUCTION: With the significant number of motor-vehicle fatalities occurring on the nation's roadways in recent years, there exists a need to integrate a more complete range of data sources, available at a regional or statewide level, to effectively evaluate existing safety concerns and quantify their impacts. Crash data alone does not provide ample crash-associated citation, injury, and roadway characteristics; therefore, a more cohesive dataset is required to accurately and completely analyze the true impacts of motor-vehicle crashes. Previously developed strategies linked crash data with citation and roadway inventory data to enhance the identification and optimization of highway safety strategies. METHOD: The main objective of this research focused on developing a new deterministic linkage between crash and Emergency Medical Services (EMS) data, by utilizing the Massachusetts Crash Data System (CDS) and the Massachusetts Ambulance Trip Record Information System (MATRIS). RESULTS: After several iterations of match criterion, the validated linkage successfully matched 58.3% of MATRIS records (containing an Injury Cause of Motor Vehicle Crash) to a CDS person record (55011 linked pairs, between 2014 and 2016). The data linkage provided significant insight into injury trends in several highway safety emphasis areas such as roadway departure, speeding-related, and distraction-affected crashes. The findings from this research are twofold: (1) an established process for linking previously separate data sets, and (2) a mechanism for analysis that provides decision-makers and safety professionals with a better measure of crash outcomes.

A Wizard-of-Oz experimental approach to study the human factors of automated vehicles: Platform and methods evaluation.

OBJECTIVES: Driving simulation is an important platform for studying vehicle automation. There are different approaches to using this platform - with most using scripting or programmatic tools to simulate vehicle automation. A less frequently used approach, the Wizard-of-Oz method, has potential for increased flexibility and efficiency in designing and conducting experiments. This study designed and evaluated an experimental setup to examine the feasibility of this approach as an alternative for conducting automation studies. METHODS: Twenty-four participants experienced simulated vehicle automation in two platforms, one where the automation was controlled by algorithms, and the other where the automation was simulated by an external operator. Surveys were administered after each drive and the drivers' takeover performance after the automation disengaged was measured. RESULTS: Results indicate that while the kinematic parameters of the driving differed significantly for the two platforms, there were no significant differences in the perceptions of participants and in their takeover performance between the two platforms. CONCLUSION: These results provide evidence for the use of alternative approaches for the conduct of human factors studies on vehicle automation, potentially lowering barriers to undertaking such experiments while increasing flexibility in designing more complex studies.

Accounting for drivers' bicycling frequency and familiarity with bicycle infrastructure treatments when evaluating safety.

Municipalities in the United States often encourage bicycling for the health, economic, and environmental benefits by implementing new and innovative bicycle infrastructure treatments. Unfortunately, many treatments are unfamiliar to or misunderstood by drivers, especially when lacking explicit rules (e.g., shared lanes). To date, research has largely investigated bicycle infrastructure from a bicyclist's perspective, but with little research from the driver's perspective. The objective of this research is to utilize a driving simulator to investigate driver behavior towards different bicycle infrastructure treatments when driver behavior is not provoked by an interaction with bicyclists. More specifically, this research intends to investigate the impact of bicycling frequency and treatment familiarity, as well as the combined effect of the two, on driver behavior at each treatment type. The treatments investigated are shared lane markings called "sharrows", standard bike lanes, bike boxes, and merge lanes. The results show that bicycling frequency significantly affects the proportion of drivers making eye glances at treatments. In addition, drivers more familiar with bike boxes stopped significantly further back from bike boxes, and drivers more familiar with merge lanes performed the merge maneuver significantly earlier. Furthermore, driver speed and lane positioning at bike lanes was significantly affected by the combination of bike lane familiarity and bicycling frequency, but not individually. This research is a first step towards understanding driver behavior and expectation of bicyclists; an essential understanding for infrastructure treatments that do not provide physical barriers between bicycles and automobiles, and instead rely on driver behavior for safety.

A mixed methods investigation of bicycle exposure in crash rates.

Crash rates are an essential tool enabling researchers and practitioners to assess whether a location is truly more dangerous, or simply serves a higher volume of vehicles. Unfortunately, this simple crash rate is far more difficult to calculate for bicycles due to data challenges and the fact that they are uniquely exposed to both bicycle and automobile volumes on shared roadways. Bicycle count data, though increasingly more available, still represents a fraction of the available count data for automobiles. Further compounding on this, bicycle demand estimation methods often require more data than automobiles to account for the high variability that bicycle demand is subject to. This paper uses a combination of mixed methods to overcome these challenges and to perform an investigation of crash rates and exposure to different traffic volumes.

Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion.

According to the Federal Highway Administration, nonrecurring congestion contributes to nearly half of the overall congestion. Temporal disruptions impact the effective use of the complete roadway, due to speed reduction and rubbernecking resulting from primary incidents that in turn provoke secondary incidents. There is an additional reduction of discharge flow caused by secondary incident that significantly increases total delay. Therefore, it is important to sequentially predict the probability of secondary incidents and develop appropriate countermeasures to reduce the associated risk. Advanced computing techniques were used to easily understand and reliably predict secondary incident occurrences that have low sample mean and a small sample size. The likelihood of a secondary incident was sequentially predicted from the point of incident response to the eventual road clearance. The quality of predictions improved with the availability of additional information. The prediction performance of the principled Bayesian learning approach to neural networks (bnn) was compared to the Stochastic Gradient Boosted Decision Trees (gbdt). A pedagogical rule extraction approach, trepan, which extracts comprehensible rules from the neural networks, improved the ability to understand secondary incidents in a simplified manner. With an acceptable accuracy, gbdt is a useful tool that presents the relative importance of the predictor variables. Unexpected traffic congestion incurred by an incident is a dominant causative factor for the occurrence of secondary incidents at different stages of incident clearance. This symbolic description represents a series of decisions that may assist emergency operators by improving their decision-making capabilities. Analyzing causes and effects of traffic incidents helps traffic operators develop incident-specific strategic plans for prompt emergency response and clearance. Application of the model in connected vehicle environments will help drivers receive proactive corrective feedback before a crash. The proposed methodology can be used to alert drivers about potential highway conditions and may increase the drivers' awareness of potential events when no rerouting is possible, optimal or otherwise.

Effectiveness of visual warnings on young drivers hazard anticipation and hazard mitigation abilities.

Previous studies have demonstrated that young drivers fail both to scan for and mitigate latent hazards mostly due to their cluelessness. This study aims to investigate whether these skills could be improved by providing young drivers with alerts in advance of the upcoming threat using a driving simulator experiment. In particular, the warning was presented on the head-up displays (HUD) either 2 s, 3 s or 4 s in advance of a latent threat. The hazard anticipation, hazard mitigation and attention maintenance performance of forty-eight young drivers aged 18-25 was evaluated across eight unique scenarios either in the presence or in the absence of latent threat alerts displayed on a HUD. There were four groups overall: one control group (no alert) and three experimental groups (2 s alert, 3 s alert and 4 s alert). The analysis of the hazard anticipation data showed that all three experimental groups with HUD warnings (2 s, 3 s, 4 s) significantly increased the likelihood that drivers would glance towards latent pedestrian and vehicle hazards when compared to the control group. The hazard mitigation analysis showed that in situations involving a pedestrian threat, HUD alerts provided 3 or 4 s in advance of a potential threat led drivers to travel significantly slower than the control group or the 2 s group. No significant effect of a HUD alert on drivers' speed was found when the latent hazard was a vehicle. An analysis of eye behaviors showed that only 7 out of 597 glances at the HUD were longer than 2 s safety-threshold, indicating that the warnings do not seem to distract the driver.

The use of a driving simulator to determine how time pressures impact driver aggressiveness.

Speeding greatly attributes to traffic safety with approximately a third of fatal crashes in the United States being speeding-related. Previous research has identified being late as a primary cause of speeding. In this driving simulator study, a virtual drive was constructed to evaluate how time pressures, or hurried driving, affected driver speed choice and driver behavior. In particular, acceleration profiles, gap acceptance, willingness to pass, and dilemma zone behavior were used, in addition to speed, as measures to evaluate whether being late increased risky and aggressive driving behaviors. Thirty-six drivers were recruited with an equal male/female split and a broad distribution of ages. Financial incentives and completion time goals calibrated from a control group were used to generate a Hurried and Very Hurried experimental group. As compared to the control group, Very Hurried drivers selected higher speeds, accelerated faster after red lights, accepted smaller gaps on left turns, were more likely to pass a slow vehicle, and were more likely to run a yellow light in a dilemma zone situation. These trends were statistically significant and were also evident with the Hurried group but a larger sample would be needed to show statistical significance. The findings from this study provide evidence that hurried drivers select higher speeds and exhibit riskier driving behaviors. These conclusive results have possible implications in areas such as transportation funding and commercial motor vehicle safety.

An investigation of the speeding-related crash designation through crash narrative reviews sampled via logistic regression.

Speed is one of the most important factors in traffic safety as higher speeds are linked to increased crash risk and higher injury severities. Nearly a third of fatal crashes in the United States are designated as "speeding-related", which is defined as either "the driver behavior of exceeding the posted speed limit or driving too fast for conditions." While many studies have utilized the speeding-related designation in safety analyses, no studies have examined the underlying accuracy of this designation. Herein, we investigate the speeding-related crash designation through the development of a series of logistic regression models that were derived from the established speeding-related crash typologies and validated using a blind review, by multiple researchers, of 604 crash narratives. The developed logistic regression model accurately identified crashes which were not originally designated as speeding-related but had crash narratives that suggested speeding as a causative factor. Only 53.4% of crashes designated as speeding-related contained narratives which described speeding as a causative factor. Further investigation of these crashes revealed that the driver contributing code (DCC) of "driving too fast for conditions" was being used in three separate situations. Additionally, this DCC was also incorrectly used when "exceeding the posted speed limit" would likely have been a more appropriate designation. Finally, it was determined that the responding officer only utilized one DCC in 82% of crashes not designated as speeding-related but contained a narrative indicating speed as a contributing causal factor. The use of logistic regression models based upon speeding-related crash typologies offers a promising method by which all possible speeding-related crashes could be identified.

The primary transcriptome of the Escherichia coli O104:H4 pAA plasmid and novel insights into its virulence gene expression and regulation.

Escherichia coli O104:H4 (E. coli O104:H4), which caused a massive outbreak of acute gastroenteritis and hemolytic uremic syndrome in 2011, carries an aggregative adherence fimbriae I (AAF/I) encoding virulence plasmid, pAA. The importance of pAA in host-pathogen interaction and disease severity has been demonstrated, however, not much is known about its transcriptional organization and gene regulation. Here, we analyzed the pAA primary transcriptome using differential RNA sequencing, which allows for the high-throughput mapping of transcription start site (TSS) and non-coding RNA candidates. We identified 248 TSS candidates in the 74-kb pAA and only 21% of them could be assigned as TSS of annotated genes. We detected TSS for the majority of pAA-encoded virulence factors. Interestingly, we mapped TSS, which could allow for the transcriptional uncoupling of the AAF/I operon, and potentially regulatory antisense RNA candidates against the genes encoding dispersin and the serine protease SepA. Moreover, a computational search for transcription factor binding sites suggested for AggR-mediated activation of SepA expression, which was additionally experimentally validated. This work advances our understanding of the molecular basis of E. coli O104:H4 pathogenicity and provides a valuable resource for further characterization of pAA virulence gene regulation.

Long-term survival of the Shiga toxin-producing Escherichia coli O104:H4 outbreak strain on fenugreek seeds.

A major outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 occurred in Germany in 2011. The epidemiological investigation revealed that a contaminated batch of fenugreek seeds (Trigonella foenum-graecum) was the most probable source of the pathogen. It was suggested that the most probable point of contamination was prior to leaving the importer, meaning that the seed contamination with STEC O104:H4 should have happened more than one year before the seeds were used for sprout production. Here, we investigated the capacity of STEC O104:H4 and closely related pathogenic as well as non-pathogenic Escherichia coli strains for long-term survival on dry fenugreek seeds. We did not observe a superior survival capacity of STEC O104:H4 on dry seeds. For none of the strains tested cultivatable cells were found without enrichment on contaminated seeds after more than 24 weeks of storage. Our findings suggest that contamination previous to the distribution from the importer may be less likely than previously assumed. We show that seeds contaminated with E. coli in extremely high numbers can be completely sterilized by a short treatment with bleach. This simple and cheap procedure does not affect the germination capacity of the seeds and could significantly improve safety in sprout production.

Evaluation of the effectiveness of a multi-skill program for training younger drivers on higher cognitive skills.

Training programs exist that prove effective at teaching novice drivers to anticipate latent hazards (RAPT), mitigate hazards (ACT) and maintain attention (FOCAL). The current study (a) measures the effectiveness of a novel integrated training program (SAFE-T) that takes only a third as long to complete compared to the three individual training programs and (b) determines if integrating the training of all the three higher cognitive skills would yield results comparable to the existing programs. Three groups were evaluated: SAFE-T, RAPT and Placebo. The results show that the drivers in the SAFE-T-trained group were more likely to anticipate hazards, quicker and more effective at responding to hazards, and more likely to maintain glance durations under a critical threshold of 2 s as compared to drivers in the Placebo-trained group who received a control program that does not actively train on any of the three cognitive skills. Moreover, the results show that the drivers in the SAFE-T trained group were just as likely to anticipate hazards as the drivers in the RAPT trained group. Finally, when compared with prior studies, the drivers in the SAFE-T trained group showed similar effects of attention maintenance training.

The influence of clear zone size and roadside vegetation on driver behavior.

INTRODUCTION: Roadside vegetation provides numerous environmental and psychological benefits to drivers. Previous studies have shown that natural landscapes can effectively lower crash rates and cause less frustration and stress to the driver. However, run-off-the-road crashes resulting in a collision with a tree are twice as likely to result in a fatality, reinforcing the need to examine the placement of vegetation within the clear zone. METHOD: This study explores the relationship between the size of the clear zone and the presence of roadside vegetation on vehicle speed and lateral position. A static evaluation, distributed electronically to 100 licensed drivers, was utilized to gather speed selections for both real and virtual roads containing four combinations of clear zone sizes and roadside vegetation densities. A case study was included in the static evaluation to investigate the presence of utility poles near the edge of the road on speed selection. Validation of the static evaluation was performed by a field data collection on the same roadways shown to participants in the evaluation. RESULTS: The speeds observed in the field for roadways with medium clear zone/dense vegetation or large clear zone/spare vegetation correlated with the speeds chosen by static evaluation participants. Further field data were obtained on vehicle speeds and lateral positions for additional roads demonstrating the same clear zone size/vegetation density combinations. PRACTICAL APPLICATION: This study successfully demonstrates the relationship between clear zone design and driver behavior, which could improve clear zone design practices and thus roadway safety.

The high-affinity phosphodiesterase BcPde2 has impact on growth, differentiation and virulence of the phytopathogenic ascomycete Botrytis cinerea.

Components of the cAMP signaling pathway, such as the adenylate cyclase Bac and the protein kinase A (PKA) were shown to affect growth, morphogenesis and differentiation as well as virulence of the phytopathogenic fungus Botrytis cinerea. While loss of Bac caused drastically reduced intracellular cAMP levels, deletion of the PKA resulted in extremely increased cAMP concentrations. To regulate the intracellular level of the second messenger cAMP, a balance between its biosynthesis through adenylate cyclase activity and its hydrolysis by phosphodiesterases (PDEs) is crucial. Here, we report the functional characterization of the two PDEs in the ascomycete B. cinerea, BcPde1 and BcPde2. While deletion of bcpde2 resulted in severely affected vegetative growth, conidiation, germination and virulence, the bcpde1 deletion strain displayed a wild-type-like phenotype. However, the double bcpde1/2 deletion mutant exhibited an even stronger phenotype. Localization studies revealed that BcPde2 accumulates at the plasma membrane, but is also localized in the cytoplasm. BcPde1 was shown to be distributed in the cytoplasm as well, but also accumulates in so far unknown mobile vesicles. Overexpression of bcpde1 in the Δbcpde2 background rescued the deletion phenotype, and in addition an increased transcript level of bcpde1 in the Δbcpde2 strain was observed, indicating redundant functions of both PDEs and an interdependent gene expression.