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Protein translation is tightly and precisely controlled by multiple mechanisms including upstream open reading frames (uORFs), but the origins of uORFs and their role in maize are largely unexplored. In this study, an active transposition event was identified during the propagation of maize inbred line B73. The transposon, which was named BTA for ‘B73 active transposable element hAT’, creates a novel dosage-dependent hypomorphic allele of the hexose transporter gene ZmSWEET4c through insertion within the coding sequence in the first exon, and results in reduced kernel size. The BTA insertion does not affect transcript abundance but reduces protein abundance of ZmSWEET4c, probably through the introduction of a uORF. Furthermore, the introduction of BTA sequence in the exon of other genes can regulate translation efficiency without affecting their mRNA levels. A transposon capture assay revealed 79 novel insertions for BTA and BTA-like elements. These insertion sites have typical euchromatin features, including low levels of DNA methylation and high levels of H3K27ac. A putative autonomous element that mobilizes BTA and BTA-like elements was identified. Together, our results suggest a transposon-based origin of uORFs and document a new role for transposable elements to influence protein abundance and phenotypic diversity by affecting the translation rate.

 

Edge-assisted Augmented Reality (AR) which offloads computeintensive Deep Neural Network (DNN)-based AR tasks to edge servers faces an important design challenge: how to pick the DNN model out of many choices proposed for each AR task for offloading. For each AR task, e.g., depth estimation, many DNN-based models have been proposed over time that vary in accuracy and complexity. In general, more accurate models are also more complex; they are larger and have longer inference time. Thus choosing a larger model in offloading can provide higher accuracy for the offloaded frames but also incur longer turnaround time, during which the AR app has to reuse the estimation result from the last offloaded frame, which can lead to lower average accuracy. In this paper, we experimentally study this design tradeoff using depth estimation as a case study. We design optimal offloading schedule and further consider the impact of numerous factors such as on-device fast tracking, frame downsizing and available network bandwidth. Our results show that for edge-assisted monocular depth estimation, with proper frame downsizing and fast tracking, compared to small models, the improved accuracy of large models can offset its longer turnaround time to provide higher average estimation accuracy across frames under both LTE and 5G mmWave. 

Interfaces, the boundary that separates two or more chemical compositions and/or phases of matter, alters basic chemical and physical properties including the thermodynamics of selectivity, transition states, and pathways of chemical reactions, nucleation events and phase growth, and kinetic barriers and mechanisms for mass transport and heat transport. While progress has been made in advancing more interface‐sensitive experimental approaches, their interpretation requires new theoretical methods and models that in turn can further elaborate on the microscopic physics that make interfacial chemistry so unique compared to the bulk phase. In this review, we describe some of the most recent theoretical efforts in modeling interfaces, and what has been learned about the transport and chemical transformations that occur at the air–liquid and solid–liquid interfaces.

This article is categorized under:

Structure and Mechanism > Reaction Mechanisms and Catalysis

Structure and Mechanism > Computational Materials Science

Software > Quantum Chemistry

Software > Simulation Methods

 

Sn‐based perovskites are promising Pb‐free photovoltaic materials with an ideal 1.3 eV bandgap. However, to date, Sn‐based thin film perovskite solar cells have yielded relatively low power conversion efficiencies (PCEs). This is traced to their poor photophysical properties (i.e., short diffusion lengths (<30 nm) and two orders of magnitude higher defect densities) than Pb‐based systems. Herein, it is revealed that melt‐synthesized cesium tin iodide (CsSnI₃) ingots containing high‐quality large single crystal (SC) grains transcend these fundamental limitations. Through detailed optical spectroscopy, their inherently superior properties are uncovered, with bulk carrier lifetimes reaching 6.6 ns, doping concentrations of around 4.5 × 10¹⁷cm⁻³, and minority‐carrier diffusion lengths approaching 1 µm, as compared to their polycrystalline counterparts having ≈54 ps, ≈9.2 × 10¹⁸cm⁻³, and ≈16 nm, respectively. CsSnI₃SCs also exhibit very low surface recombination velocity of ≈2 × 10³cm s⁻¹, similar to Pb‐based perovskites. Importantly, these key parameters are comparable to high‐performance p‐type photovoltaic materials (e.g., InP crystals). The findings predict a PCE of ≈23% for optimized CsSnI₃SCs solar cells, highlighting their great potential.