The biophysical framework of collective cell migration has been extensively investigated in recent years; however, it remains elusive how chemical inputs from neighboring cells are integrated to coordinate the collective movement. Here, we provide evidence that propagation waves of extracellular signal-related kinase (ERK) mitogen-activated protein kinase activation determine the direction of the collective cell migration. A wound-healing assay of Mardin-Darby canine kidney (MDCK) epithelial cells revealed two distinct types of ERK activation wave, a "tidal wave" from the wound, and a self-organized "spontaneous wave" in regions distant from the wound. In both cases, MDCK cells collectively migrated against the direction of the ERK activation wave. The inhibition of ERK activation propagation suppressed collective cell migration. An ERK activation wave spatiotemporally controlled actomyosin contraction and cell density. Furthermore, an optogenetic ERK activation wave reproduced the collective cell migration. These data provide new mechanistic insight into how cells sense the direction of collective cell migration.

The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical benefits still remain unproven in real-world applications, particularly in polymer science. We demonstrate the successful discovery of new polymers with high thermal conductivity, inspired by machine-learning-assisted polymer chemistry. This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data, expertise from laboratory synthesis and advanced technologies for thermophysical property measurements. Using a molecular design algorithm trained to recognize quantitative structure-property relationships with respect to thermal conductivity and other targeted polymeric properties, we identified thousands of promising hypothetical polymers. From these candidates, three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications. The synthesized polymers reached thermal conductivities of 0.18-0.41 W/mK, which are comparable to those of state-of-the-art polymers in non-composite thermoplastics .

We aim to elucidate the cosurfactant effects, which are expected when junctions of two block copolymers share a common microdomain interface, on morphology and phase behavior of mixtures. Especially this paper addresses the effects involved for binary mixtures composed of polystyrene-block-polyisoprene having about equal molecular weights but complementary compositions, one forming polystyrene (PS) cylinders in polyisoprene (PI) matrix and the other forming PI cylinders in PS matrix. Transmission electron microscopy and small-angle X-ray scattering were used to characterize the phase behavior and domain spacing of the binary mixtures. First, we found an expanding composition range for hexagonally packed cylindrical morphology and a narrowed composition range for lamellae relative to the corresponding composition ranges for neat SI block copolymer under a strong segregation condition. The result indicates that the cosurfactant effects help a block copolymer to take its spontaneous curvature. Second, it was found that the effects enlarged the domain size and interdomain distance of the binary mixtures. Those results were compared with the theory by Birshtein and co-workers, which is proposed to describe the microdomain morphology for strongly segregated binary block copolymers. We found good agreements between experimental and theoretical results in terms of (i) domain size, (ii) interdomain distance, and (iii) the blending compositions where morphological transitions occur.