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971.
We present a microfluidic technique that generates asymmetric giant unilamellar vesicles (GUVs) in the size range of 2–14 μm. In our method, we (i) create water-in-oil emulsions as the precursors to build synthetic vesicles, (ii) deflect the emulsions across two oil streams containing different phospholipids at high throughput to establish an asymmetric architecture in the lipid bilayer membranes, and (iii) direct the water-in-oil emulsions across the oil–water interface of an oscillating oil jet in a co-flowing confined geometry to encapsulate the inner aqueous phase inside a lipid bilayer and complete the fabrication of GUVs. In the first step, we utilize a flow-focusing geometry with precisely controlled pneumatic pressures to form monodisperse water-in-oil emulsions. We observed different regimes in forming water-in-oil multiphase flows by changing the applied pressures and discovered a hysteretic behavior in jet breakup and droplet generation. In the second step of GUV fabrication, an oil stream containing phospholipids carries the emulsions into a separation region where we steer the emulsions across two parallel oil streams using active dielectrophoretic and pinched-flow fractionation separations. We explore the effect of applied DC voltage magnitude and carrier oil stream flow rate on the separation efficiency. We develop an image processing code that measures the degree of mixing between the two oil streams as the water-in-oil emulsions travel across them under dielectrophoretic steering to find the ideal operational conditions. Finally, we utilize an oscillating co-flowing jet to complete the formation of asymmetric giant unilamellar vesicles and transfer them to an aqueous phase. We investigate the effect of flow rates on properties of the co-flowing jet oscillating in the whipping mode (i.e., wavelength and amplitude) and define the phase diagram for the oil-in-water jet. Assays used to probe the lipid bilayer membrane of fabricated GUVs showed that membranes were unilamellar, minimal residual oil remained trapped between the two lipid leaflets, and 83% asymmetry was achieved across the lipid bilayers of GUVs. 相似文献
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Jonathan C. Hilpert Jeffrey A. Greene Matthew Bernacki 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1204-1221
Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected from n = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control, n = 48), (2) perform poorly and received intervention (treatment, n = 95) and (3) perform well (not flagged, n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments.
Practitioner notes
What is already known about this topic- Self-regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.
- SRL is a dynamic, temporal process that leads to purposeful student engagement.
- Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.
- A Markov process for measuring dynamic SRL processes using log data.
- Evidence that dynamic, interaction-dominant aspects of SRL predict student achievement.
- Evidence that SRL processes can be meaningfully impacted through educational intervention.
- Complexity approaches inform theory and measurement of dynamic SRL processes.
- Static representations of dynamic SRL processes are promising learning analytics metrics.
- Engineered features of LMS usage are valuable contributions to AI models.