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Membraneless Compartmentalization of Cell-Free Transcription-Translation by Polymer-Assisted Liquid-Liquid Phase Separation
(2024-11-20) Izri, Ziane; Noireaux, Vincent; noireaux@umn.edu; Noireaux, Vincent; Noireaux Lab
Living cells use liquid-liquid phase separation (LLPS) to compartmentalize metabolic functions into mesoscopic-sized droplets. Deciphering the mechanisms at play in LLPS is therefore critical to understanding the structuration and functions of cells at the subcellular level. Although observed and achieved to a significant degree of control in vivo, the reconstitution of LLPS integrating advanced biological functions, such as gene expression, has been so far limited in vitro. LLPS of cell-free transcription-translation (TXTL) reactions requires multi-step experimental approaches that lack biomimetism and have relatively poor efficacy, thus limiting their usage in cell-free engineered systems such as synthetic cells. Here we report the polymer-assisted LLPS of TXTL reactions as the single-pot one-step compartmentalization of a model complex metabolic system obtained without using solvents or surfactants. LLPS occurs by adding the biocompatible polymers poly(ethylene glycol), poly(vinyl alcohol), and dextran to a TXTL reaction, that remains highly active. These polymers serve as partitioning agents that localize TXTL in mesoscopic-sized droplets rich in dextran. Cytoplasmic and membrane-interacting proteins are synthesized preferentially inside these droplets, and localize either uniformly or preferentially at the interface, depending on their nature. The LLPS-TXTL system presented in this work is a step toward the design of synthetic membraneless active organelles.
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Transfer Behavior and Off-Peak Commutes
(Center for Transportation Studies, University of Minnesota, 2024-10) Baek, Kwangho; Khani, Alireza
To improve transit service for off-peak travelers, an essential yet often underrepresented group, and promote social equity, this study examines off-peak transit commutes and transfers, with a focus on the transitway system in the Twin Cities. The research contrasts off-peak and peak travel behaviors using an onboard survey (OBS) from 2016 and automatic fare collection (AFC) data from 2018 to 2023. The initial analysis involved clustering trips from OBS into 16 regional zones and creating origin-destination matrices to explore spatial travel patterns. Key findings include longer peak-time trips (8.51 miles) compared to off-peak trips (5.74 miles) and a higher concentration of non-work trips during off-peak times. The study also reveals that off-peak trips are more dispersed geographically. In the second phase, path choice sets were generated for each respondent from OBS, and logistic regression models were used to analyze preferences for transitway versus bus-only routes. The results indicated a strong preference for transitways, with 60% of passengers opting for them over buses when travel times were equal. Finally, AFC data was integrated with OBS using machine learning techniques to examine long-term trends, including the impact of the COVID-19 pandemic. Post-pandemic data show an increase in off-peak commutes and transit trips with transfers despite an overall decline in transfers. This study provides insights into evolving transit usage behaviors and highlights the importance of the transitway system in facilitating efficient travel.
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Shared Transportation Goals Workshops
(Center for Transportation Studies, University of Minnesota, 2024-08) Center for Transportation Studies
This report summarizes four Shared Transportation Goals Workshops held by the Center for Transportation Studies in April through June 2024. These included the Equity Workshop, the Climate Change and Natural Systems Workshop, the Our Region is Dynamic and Resilient Workshop and the Our Communities are Healthy and Safe Workshop. Participants were representatives of the University of Minnesota and local, regional, and state transportation professionals. Metropolitan Council's Transportation Policy Plan 2050 (TPP) was used as the scaffolding for the discussion.
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Toward implementation of max-pressure control on Minnesota roads: Phase 2
(Minnesota Department of Transportation, 2024-10) Stern, Raphael; Levin, Michael W.; Kiani, Amirhossein
Max-pressure (MP) traffic signal control is a new and innovative control algorithm that uses upstream and downstream vehicle counts to determine signal timing that maximizes throughput. While this method has been extensively tested in simulation, it has not yet been tested on actual traffic signals in the US. To close this gap, this report presents the results of the development of a hardware-in-the-loop traffic signal testbed where microsimulation is used to simulate realistic traffic conditions, and the MP algorithm is used to control the signal display using a traffic controller (Q-Free MaxTime controller). The hardware-in-the-loop results demonstrate that MP can be safely deployed on North American traffic signal control hardware.
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Development of a smart phone app to warn the driver of unintentional lane departure using GPS technology
(Minnesota Department of Transportation, 2024-10) Hayee, M. I.; Tasnim, N. Z.
Unintentional lane departure is a significant safety risk. Currently, available commercial lane departure warning systems use vision-based or GPS technology with lane-level resolution. These techniques have their own performance limitations in poor weather conditions. We have previously developed a lane departure detection (LDD) algorithm using standard GPS technology. Our algorithm acquires the trajectory of a moving vehicle in real-time from a standard GPS receiver and compares it with a road reference heading (RRH) to detect any potential lane departure. The necessary RRH is obtained from one or more past trajectories using our RRH generation algorithm. This approach has a significant limitation due to its dependency on past trajectories. To overcome this limitation, we have integrated Google routes in addition to past trajectories to extract the RRH of any given road. This advancement has been incorporated into a newly developed smartphone app, which now combines our previously developed LDD algorithm with the enhanced RRH generation algorithm. The app effectively detects lane departures and provides real-time audible warnings to drivers. Additionally, we have designed the app's database structure and completed the programming of the necessary algorithms. To evaluate the performance of the newly developed smartphone app, we perform many field tests on a freeway. Our field test results show that our smartphone app can accurately detect all lane departures on long straight sections of the freeway irrespective of whether the RRH is generated from a Google route or past trajectory.