LPS remodeling triggers formation of outer membrane vesicles in Salmonella.

Outer membrane vesicles (OMV) are proposed to mediate multiple functions during pathogenesis and symbiosis. However, the mechanisms responsible for OMV formation remain poorly understood. It has been shown in eukaryotic membranes that lipids with an inverted-cone shape favor the formation of positive membrane curvatures. Based on these studies, we formulated the hypothesis that lipid A deacylation might impose shape modifications that result in the curvature of the outer membrane (OM) and subsequent OMV formation. We tested the effect of lipid A remodeling on OMV biogenesis employing Salmonella enterica serovar Typhimurium as a model organism. Expression of the lipid A deacylase PagL resulted in increased vesiculation, without inducing an envelope stress response. Mass spectrometry analysis revealed profound differences in the patterns of lipid A in OM and OMV, with accumulation of deacylated lipid A forms exclusively in OMV. OMV biogenesis by intracellular bacteria upon macrophage infection was drastically reduced in a pagL mutant strain. We propose a novel mechanism for OMV biogenesis requiring lipid A deacylation in the context of a multifactorial process that involves the orchestrated remodeling of the outer membrane

The O-antigen flippase Wzk can substitute for MurJ in peptidoglycan synthesis in Helicobacter pylori and Escherichia coli

The peptidoglycan (PG) cell wall is an essential component of the cell envelope of most bacteria. Biogenesis of PG involves a lipid-linked disaccharide-pentapeptide intermediate called lipid II, which must be translocated across the cytoplasmic membrane after it is synthesized in the inner leaflet of this bilayer. Accordingly, it has been demonstrated that MurJ, the proposed lipid II flippase in Escherichia coli, is required for PG biogenesis, and thereby viability. In contrast, MurJ is not essential in Bacillus subtilis because this bacterium produces AmJ, an unrelated protein that is functionally redundant with MurJ. In this study, we investigated why MurJ is not essential in the prominent gastric pathogen, Helicobacter pylori. We found that in this bacterium, Wzk, the ABC (ATP-binding cassette) transporter that flips the lipid-linked O- or Lewis- antigen precursors across the inner membrane, is redundant with MurJ for cell viability. Heterologous expression of wzk in E. coli also suppresses the lethality caused by the loss of murJ. Furthermore, we show that this cross-species complementation is abolished when Wzk is inactivated by mutations that target a domain predicted to be required for ATPase activity. Our results suggest that Wzk can flip lipid II, implying that Wzk is the flippase with the most relaxed specificity for lipid-linked saccharides ever identified

Endocytosis of commensal antigens by intestinal epithelial cells regulates mucosal T cell homeostasis

Commensal bacteria influence host physiology, without invading host tissues. We show that proteins from segmented filamentous bacteria (SFB) are transferred into intestinal epithelial cells (IECs) through adhesion-directed endocytosis that is distinct from the clathrin-dependent endocytosis of invasive pathogens. This process transfers microbial cell wall–associated proteins, including an antigen that stimulates mucosal T helper 17 (T_H17) cell differentiation, into the cytosol of IECs in a cell division control protein 42 homolog (CDC42)–dependent manner. Removal of CDC42 activity in vivo led to disruption of endocytosis induced by SFB and decreased epithelial antigen acquisition, with consequent loss of mucosal T_H17 cells. Our findings demonstrate direct communication between a resident gut microbe and the host and show that under physiological conditions, IECs acquire antigens from commensal bacteria for generation of T cell responses to the resident microbiota

The multifaceted virulence of adherent-invasive Escherichia coli

ABSTRACTThe surge in inflammatory bowel diseases, like Crohn’s disease (CD), is alarming. While the role of the gut microbiome in CD development is unresolved, the frequent isolation of adherent-invasive Escherichia coli (AIEC) strains from patient biopsies, together with their propensity to trigger gut inflammation, underpin the potential role of these bacteria as disease modifiers. In this review, we explore the spectrum of AIEC pathogenesis, including their metabolic versatility in the gut. We describe how AIEC strains hijack the host defense mechanisms to evade immune attrition and promote inflammation. Furthermore, we highlight the key traits that differentiate AIEC from commensal E. coli. Deciphering the main components of AIEC virulence is cardinal to the discovery of the next generation of antimicrobials that can selectively eradicate CD-associated bacteria

A Simulated Annealing for Optimizing Assignment of E-Scooters to Freelance Chargers

First- and last-mile trips are becoming increasingly expensive and detrimental to the environment, especially within dense cities. Thus, new micro-mobility transportation modes such as e-scooter sharing systems have been introduced to fill the gaps in the transportation network. Furthermore, some recent studies examined e-scooters as a green option from the standpoint of environmental sustainability. Currently, e-scooter charging is conducted by competitive freelancers who do not consider the negative environmental impact resulting from not optimizing the fuel efficiency of their charging trips. Several disputes have been recorded among freelance chargers, especially when simultaneously arriving at an e-scooters location. The paper aims to find the optimal tours for all chargers to pick up e-scooters in the form of routes, such that each route contains one charger, and each e-scooter is visited only once by the set of routes, which are typically called an E-Scooter-Chargers Allocation (ESCA) solution. This study develops a mathematical model for the assignment of e-scooters to freelance chargers and adapts a simulated annealing metaheuristic to determine a near-optimal solution. We evaluated the proposed approach using real-world instances and a benchmark-simulated dataset. Moreover, we compare the proposed model benchmark dataset to the baseline (i.e., state-of-practice). The results show a reduction of approximately 61–79% in the total distance traveled, leading to shorter charging trips.</p

Driving behavior classification at signalized intersections using vehicle kinematics: Application of unsupervised machine learning

Driving behavior is considered as a unique driving habit of each driver and has a significant impact on road safety. This study proposed a novel data-driven Machine Learning framework that can classify driving behavior at signalized intersections considering two different signal conditions. To the best of our knowledge, this is the first study that investigates driving behavior at signalized intersections with two different conditions that are mostly used in practice, i.e., the control setting with the signal order of green-yellow-red and a flashing green setting with the signal order of green-flashing green-yellow-red. A driving simulator dataset collected from participants at Qatar University’s Qatar Transportation and Traffic Safety Center, driving through multiple signalized intersections, was used. The proposed framework extracts volatility measures from vehicle kinematic parameters including longitudinal speed and acceleration. K-means clustering algorithm with elbow method was used as an unsupervised machine learning to cluster driving behavior into three classes (i.e., conservative, normal, and aggressive) and investigate the impact of signal conditions. The framework confirmed that in general driving behavior at a signalized intersection reflects drivers’ habits and personality rather than the signal condition, still, it manifests the intersection nature that usually requires drivers to be more vigilant and cautious. Nonetheless, the results suggested that flashing green condition could make drivers more conservative, which could be due to the limited capabilities of human to estimate the remaining distance and the prolonged duration of the additional flashing green interval. The proposed framework and findings of the study were promising that can be used for clustering drivers into different styles for different conditions and might be beneficial for policymakers, researchers, and engineers.The NPRP award [NPRP 9- 360-2-150] from the Qatar National Research Fund (a member of Qatar Foundation)

A Simulated Annealing for Optimizing Assignment of E-Scooters to Freelance Chargers

First- and last-mile trips are becoming increasingly expensive and detrimental to the environment, especially within dense cities. Thus, new micro-mobility transportation modes such as e-scooter sharing systems have been introduced to fill the gaps in the transportation network. Furthermore, some recent studies examined e-scooters as a green option from the standpoint of environmental sustainability. Currently, e-scooter charging is conducted by competitive freelancers who do not consider the negative environmental impact resulting from not optimizing the fuel efficiency of their charging trips. Several disputes have been recorded among freelance chargers, especially when simultaneously arriving at an e-scooters location. The paper aims to find the optimal tours for all chargers to pick up e-scooters in the form of routes, such that each route contains one charger, and each e-scooter is visited only once by the set of routes, which are typically called an E-Scooter-Chargers Allocation (ESCA) solution. This study develops a mathematical model for the assignment of e-scooters to freelance chargers and adapts a simulated annealing metaheuristic to determine a near-optimal solution. We evaluated the proposed approach using real-world instances and a benchmark-simulated dataset. Moreover, we compare the proposed model benchmark dataset to the baseline (i.e., state-of-practice). The results show a reduction of approximately 61&ndash;79% in the total distance traveled, leading to shorter charging trips

Fuel consumption at signalized intersections: Investigating the impact of different signal indication settings

The fuel consumption of vehicles depends on various factors including vehicle design, driving style, traffic management, and road design. Many manufacturers have been developing efficient and smart vehicles, which contributes to minimizing vehicle fuel consumption. However, traffic management and control could restrict the efficiency of having a sustainable mobility system. Intersections are considered as critical locations, in terms of fuel consumption, due to the significant impact of traffic control at these locations on the vehicle maneuver either by stopping or acceleration to clear these bottleneck points. Analyzing the effect of different intersection signal settings is, therefore, important to optimize vehicle fuel consumption. In this study, we used simulator data of sixty-six drivers going through signalized intersections equipped with two different signal indication settings, namely, control and flashing green conditions. We calculated total fuel consumption using the VT-CPFM and COPERT models and then applied GLME with two different model distributions: normal and log-normal to study the correlation between the two treatments and fuel consumption. Results showed that by displaying the remaining green time, flashing green treatment (i.e., signals with traffic light sequence: green, flashing green, yellow and red-green) produced a lower fuel consumption in comparison to control condition (green, yellow and red sequence), yielding to a similar performance of eco-driving. It was found that as drivers become aware ahead of time when the traffic light will be turning red due to the flashing green signal indication, eventually they either speed up a little to cross the intersection in time, or they early start decelerating, which creates a more optimal deceleration pattern. Results also showed that the VT-CPFM model resulted in more realistic results than COPERT due to its ability to capture the transient changes in speed and acceleration.</p

Application of Unsupervised Machine Learning Classification for the Analysis of Driver Behavior in Work Zones in the State of Qatar

Work zone areas are commonly known as crash-prone areas. Thus, they usually receive high priority by road operators as drivers and workers have higher chances of being involved in road crashes. The paper aims to investigate driving behavior in work zones using unsupervised machine learning and vehicle kinematic data. A dataset of 67 participants was gathered through an experiment using a driving simulator located at the Qatar Transportation and Traffic Safety Center (QTTSC). The study considered two different work zone scenarios where the leftmost lane was closed for maintenance. In the first scenario, drivers drove on the leftmost lane (Drive 1), while in the second, they drove on the second leftmost lane (Drive 2). The results show that the number of aggressive and conservative drivers was surprisingly more than normal drivers, as most participants either cautiously drove through or failed to drive without being aggressive. The results also show that drivers acted more aggressively in the leftmost lane rather than in the second leftmost lane. We also found that female drivers and drivers with relatively little driving experience were more likely to be aggressive as they drove through a work zone. The framework was found to be promising and can help policymakers take optimal safety countermeasures in work zones during construction.</p