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Polymeric materials have been used to realize optical systems that, through periodic variations of their structural or optical properties, interact with light-generating holographic signals. Complex holographic systems can also be dynamically controlled through exposure to external stimuli, yet they usually contain only a single type of holographic mode. Here, we report a conjugated organogel that reversibly displays three modes of holograms in a single architecture. Using dithering mask lithography, we realized two-dimensional patterns with varying cross-linking densities on a conjugated polydiacetylene. In protic solvents, the organogel contracts anisotropically to develop optical and structural heterogeneities along the third dimension, displaying holograms in the form of three-dimensional full parallax signals, both in fluorescence and bright-field microscopy imaging. In aprotic solvents, these heterogeneities diminish as organogels expand, recovering the two-dimensional periodicity to display a third hologram mode based on iridescent structural colours. Our study presents a next-generation hologram manufacturing method for multilevel encryption technologies. Periodic patterns with varying cross-linking densities are realized in conjugated polydiacetylene films, creating multiple holographic images—all dynamically responsive to exposure to various solvents—simultaneously in the same polymeric structures.

Portable and personalized artificial intelligence (AI)‐driven sensors mimicking human olfactory and gustatory systems have immense potential for the large‐scale deployment and autonomous monitoring systems of Internet of Things (IoT) devices. In this study, an artificial Q‐grader comprising surface‐engineered zinc oxide (ZnO) thin films is developed as the artificial nose, tongue, and AI‐based statistical data analysis as the artificial brain for identifying both aroma and flavor chemicals in coffee beans. A poly(vinylidene fluoride‐co‐hexafluoropropylene)/ZnO thin film transistor (TFT)‐based liquid sensor is the artificial tongue, and an Au, Ag, or Pd nanoparticles/ZnO nanohybrid gas sensor is the artificial nose. In order to classify the flavor of coffee beans (acetic acid (sourness), ethyl butyrate and 2‐furanmethanol (sweetness), caffeine (bitterness)) and the origin of coffee beans (Papua New Guinea, Brazil, Ethiopia, and Colombia‐decaffeine), rational combination of TFT transfer and dynamic response curves capture the liquids and gases‐dependent electrical transport behavior and principal component analysis (PCA)‐assisted machine learning (ML) is implemented. A PCA‐assisted ML model distinguished the four target flavors with >92% prediction accuracy. ML‐based regression model predicts the flavor chemical concentrations with >99% accuracy. Also, the classification model successfully distinguished four different types of coffee‐bean with 100% accuracy. In this study, AI‐driven sensors using surface‐engineered ZnO thin films are developed and designed to mimic human olfactory and taste systems. Employing machine learning models, the system achieves over 92% accuracy in identifying coffee bean flavors and 99% in predicting chemical concentrations. This sensor system constitutes a promising candidate for IoT applications in environmental monitoring and early disease diagnosis.

The physical structure of an organic solid is strongly affected by the surface of the underlying substrate. Controlling this interface is an important issue to improve device performance in the organic electronics community. Here we report an approach that utilizes an organic heterointerface to improve the crystallinity and control the morphology of an organic thin film. Pentacene is used as an active layer above, and m -bis(triphenylsilyl)benzene is used as the bottom layer. Sequential evaporations of these materials result in extraordinary morphology with far fewer grain boundaries and myriad nanometre-sized pores. These peculiar structures are formed by difference in molecular interactions between the organic layers and the substrate surface. The pentacene film exhibits high mobility up to 6.3 cm 2  V −1  s −1 , and the pore-rich structure improves the sensitivity of organic-transistor-based chemical sensors. Our approach opens a new way for the fabrication of nanostructured semiconducting layers towards high-performance organic electronics. High-performance organic electronics require minimal grain boundaries in an organic semiconductor active layer. Here, Kang et al. report the growth of pentacene thin films in a macroporous structure with improved crystallinity, which is guided by a chemically heterogeneous, rubber-like substrate.

Flotillin-1 (Flot-1) has been shown to regulate cancer progression, but the regulatory role of post-translational modifications of Flot-1 on cancers remains elusive. Herein, we show that up-regulated E2 conjugating enzyme UBC9 sumoylates Flot-1 at Lys-51 and Lys-195 with small ubiquitin-like modifier (SUMO)-2/3 modification in metastatic prostate cancer. Mitogen induced the sumoylation and nuclear translocation of Flot-1. The nuclear-targeted Flot-1 physically interacted with Snail, and inhibited Snail degradation through the proteasome in a sumoylation-dependent manner, thereby promoting epithelial-to-mesenchymal transition (EMT). Sumoylation of Flot-1 by up-regulated UBC9 in human metastatic prostate cancer tissues and prostate cancer cells with high metastatic potential positively correlated with the stabilization of Snail and the induction of Snail-mediated EMT genes in the metastatic prostate cancer. Our study reveals a new mechanism of sumoylated Flot-1-mediating Snail stabilization, and identifies a novel sumoylated Flot-1-Snail signaling axis in EMT of metastatic prostate cancer.

We have shown that insulin-like growth factor-1 (IGF-1) induces palmitoylation turnover of Flotillin-1 (Flot-1) in the plasma membrane (PM) for cell proliferation, after IGF-1 receptor (IGF-1R) signaling activation. However, the enzymes responsible for the turnover have not been identified. Herein, we show that acyl protein thioesterases-1 (APT-1) catalyzes Flot-1 depalmitoylation, and zinc finger DHHC domain-containing protein palmitoyltransferase-19 (ZDHHC-19) repalmitoylation of the depalmitoylated Flot-1 for the turnover in cervical cancer cells. The turnover prevented desensitization of IGF-1R via endocytosis and lysosomal degradation, thereby exerting excessive IGF-1R activation in cervical cancer cells. FLOT1, LYPLA1 and ZDHHC19 were highly expressed, and epithelial-to-mesenchymal transition (EMT)-inducing TIAM1 and GREM1 coordinately upregulated in malignant cervical cancer tissues. And blocking the turnover suppressed the EMT, migration, and invasion of cervical cancer cells. Our study identifies the specific enzymes regulating Flot-1 palmitoylation turnover, and reveals a novel regulatory mechanism of IGF-1-mediated cervical cancer progression.

Given a large tensor, how can we decompose it to sparse core tensor and factor matrices such that it is easier to interpret the results? How can we do this without reducing the accuracy? Existing approaches either output dense results or give low accuracy. In this paper, we propose VeST, a tensor factorization method for partially observable data to output a very sparse core tensor and factor matrices. VeST performs initial decomposition, determines unimportant entries in the decomposition results, removes the unimportant entries, and carefully updates the remaining entries. To determine unimportant entries, we define and use entry-wise 'responsibility' for the decomposed results. The entries are updated iteratively in a coordinate descent manner in parallel for scalable computation. Extensive experiments show that our method VeST is at least 2.2 times more sparse and at least 2.8 times more accurate compared to competitors. Moreover, VeST is scalable in terms of input order, dimension, and the number of observable entries. Thanks to VeST, we successfully interpret the result of real-world tensor data based on the sparsity pattern of the resulting factor matrices.