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The mating of budding yeast depends on chemotropism, a fundamental cellular process. Haploid yeast cells of opposite mating type signal their positions to one another through mating pheromones. We have proposed a deterministic gradient sensing model that explains how these cells orient toward their mating partners. Using the cell-cycle determined default polarity site (DS), cells assemble a gradient tracking machine (GTM) composed of signaling, polarity, and trafficking proteins. After assembly, the GTM redistributes up the gradient, aligns with the pheromone source, and triggers polarized growth toward the partner. Since positive feedback mechanisms drive polarized growth at the DS, it is unclear how the GTM is released for tracking. What prevents the GTM from triggering polarized growth at the DS? Here, we describe two mechanisms that are essential for tracking: inactivation of the Ras GTPase Bud1 and positioning of actin-independent vesicle delivery upgradient.

Abstract The abrupt occurrence of twinning when Mg is deformed leads to a highly anisotropic response, making it too unreliable for structural use and too unpredictable for observation. Here, we describe an in-situ transmission electron microscopy experiment on Mg crystals with strategically designed geometries for visualization of a long-proposed but unverified twinning mechanism. Combining with atomistic simulations and topological analysis, we conclude that twin nucleation occurs through a pure-shuffle mechanism that requires prismatic-basal transformations. Also, we verified a crystal geometry dependent twin growth mechanism, that is the early-stage growth associated with instability of plasticity flow, which can be dominated either by slower movement of prismatic-basal boundary steps, or by faster glide-shuffle along the twinning plane. The fundamental understanding of twinning provides a pathway to understand deformation from a scientific standpoint and the microstructure design principles to engineer metals with enhanced behavior from a technological standpoint.

In photosynthesis, plants use energy from sunlight to turn carbon from CO 2 in the air into a solid form of carbon that can build the plant’s body. Photosynthesis consists of two portions: the reactions that absorb sunlight energy and another set of reactions called the Calvin-Benson-Bassham (CBB) cycle. When the plant “wakes up” in the morning, after a night of darkness, these two processes do not wake up at the same pace, which can damage the plant cells. However, plant cells prevent this problem by regulating these two processes carefully. To understand how photosynthetic organisms switch from night to day, a type of photosynthetic bacteria called cyanobacteria were used to explore how another pathway, called the oxidative pentose phosphate (OPP) pathway, helps with this dark-to-light transition. Our research found that the OPP pathway can help photosynthesis quickly reactivate when light is available and can prevent cell damage from too much light.

The massive use of vehicles as a primary means of transportation as well the increasing adoption of vehicles’ on-board sensors represents a unique opportunity for sensing and data collection. However, vehicles tend to cluster in specific regions such as highways and a few popular roads, making their utilization for data collection in isolated regions with low-density traffic difficult. We address this problem by proposing an incentive mechanism that encourages vehicles to deviate from their pre-planned trajectories to visit these isolated places. At the core of our proposal is the idea of compensation based on participants’ location diversity, which allows for rewarding vehicles in low-density traffic areas more than those located in high-density ones. We model this problem as a non-cooperative game in which participants are the vehicles and their new trajectories are their strategies. The output of this game is a new set of stable trajectories that maximize spatial coverage. Simulations show our approach outperforms the approach that doesn't take into account participants’ location diversity in terms of spatial coverage and road utilization.