Coupled cation–electron transfer at the Pt(111)/perfluoro-sulfonic acid ionomer interface and its impact on the oxygen reduction reaction kinetics
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Amphoteric chalcogen-bonding and halogen-bonding rotaxanes for anion or cation recognition
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Recommendations for mitochondria transfer and transplantation nomenclature and characterization
Intercellular mitochondria transfer is an evolutionarily conserved process in which one cell delivers some of their mitochondria to another cell in the absence of cell division. This process has diverse functions depending on the cell types involved and physiological or disease context. Although mitochondria transfer was first shown to provide metabolic support to acceptor cells, recent studies have revealed diverse functions of mitochondria transfer, including, but not limited to, the maintenance of mitochondria quality of the donor cell and the regulation of tissue homeostasis and remodelling. Many mitochondria-transfer mechanisms have been described using a variety of names, generating confusion about mitochondria transfer biology. Furthermore, several therapeutic approaches involving mitochondria-transfer biology have emerged, including mitochondria transplantation and cellular engineering using isolated mitochondria. In this Consensus Statement, we define relevant terminology and propose a nomenclature framework to describe mitochondria transfer and transplantation as a foundation for further development by the community as this dynamic field of research continues to evolve.
Coupling of cell shape, matrix and tissue dynamics ensures embryonic patterning robustness
Tissue patterning coordinates morphogenesis, cell dynamics and fate specification. Understanding how precision in patterning is robustly achieved despite inherent developmental variability during mammalian embryogenesis remains a challenge. Here, based on cell dynamics quantification and simulation, we show how salt-and-pepper epiblast and primitive endoderm (PrE) cells pattern the inner cell mass of mouse blastocysts. Coupling cell fate and dynamics, PrE cells form apical polarity-dependent actin protrusions required for RAC1-dependent migration towards the surface of the fluid cavity, where PrE cells are trapped due to decreased tension. Concomitantly, PrE cells deposit an extracellular matrix gradient, presumably breaking the tissue-level symmetry and collectively guiding their own migration. Tissue size perturbations of mouse embryos and their comparison with monkey and human blastocysts further demonstrate that the fixed proportion of PrE/epiblast cells is optimal with respect to embryo size and tissue geometry and, despite variability, ensures patterning robustness during early mammalian development.
Revealing the molecular interplay of coverage, wettability, and capacitive response at the Pt(111)-water solution interface under bias
While electrified interfaces are crucial for electrocatalysis and corrosion, their molecular morphology remains largely unknown. Through highly realistic ab initio molecular dynamics simulations of the Pt(111)-water solution interface in reducing conditions, we reveal a deep interconnection among electrode coverage, wettability, capacitive response, and catalytic activity. We identify computationally the experimentally hypothesised states for adsorbed hydrogen on Pt, HUPD and HOPD, revealing their role in governing interfacial water reorientation and hydrogen evolution. The transition between these two H states with increasing potential, induces a shift from a hydrophobic to a hydrophilic interface and correlates with a change in the primary electrode screening mechanism. This results in a slope change in differential capacitance, marking the onset of the experimentally observed peak around the potential of zero charge. Our work produces crucial insights for advancing electrocatalytic energy conversion, developing deep understanding of electrified interfaces.
An active representation learning method for reaction yield prediction with small-scale data
Reaction optimization plays an essential role in chemical research and industrial production. To explore a large reaction system, a practical issue is how to reduce the heavy experimental load for finding the high-yield conditions. In this paper, we present an efficient machine learning tool called “RS-Coreset”, where the key idea is to take advantage of deep representation learning techniques to guide an interactive procedure for representing the full reaction space. Our proposed tool only uses small-scale data, say 2.5% to 5% of the instances, to predict the yields of the reaction space. We validate the performance on three public datasets and achieve state-of-the-art results. Moreover, we apply this tool to assist the realistic exploration of the Lewis base-boryl radicals enabled dechlorinative coupling reactions in our lab. The tool can help us to effectively predict the yields and even discover several feasible reaction combinations that were overlooked in previous articles.
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