Our data show that spliceosome assembly can be inhibited by targeting UHM-ULM interactions by small molecules, thus extending the toolkit of splicing modulators for structural and biochemical studies of the spliceosome and splicing regulation.The Berezinskii-Kosterlitz-Thouless (BKT) mechanism, building upon proliferation of topological defects in 2D systems, is the first example of phase transition beyond the Landau-Ginzburg paradigm of symmetry breaking. Such a topological phase transition has long been sought yet undiscovered directly in magnetic materials. https://www.selleckchem.com/products/ldk378.html Here, we pin down two transitions that bound a BKT phase in an ideal 2D frustrated magnet TmMgGaO4, via nuclear magnetic resonance under in-plane magnetic fields, which do not disturb the low-energy electronic states and allow BKT fluctuations to be detected sensitively. Moreover, by applying out-of-plane fields, we find a critical scaling behavior of the magnetic susceptibility expected for the BKT transition. The experimental findings can be explained by quantum Monte Carlo simulations applied on an accurate triangular-lattice Ising model of the compound which hosts a BKT phase. These results provide a concrete example for the BKT phase and offer an ideal platform for future investigations on the BKT physics in magnetic materials.Recently, stretchable electronics combined with wireless technology have been crucial for realizing efficient human-machine interaction. Here, we demonstrate highly stretchable transparent wireless electronics composed of Ag nanofibers coils and functional electronic components for power transfer and information communication. Inspired by natural systems, various patterned Ag nanofibers electrodes with a net structure are fabricated via using lithography and wet etching. The device design is optimized by analyzing the quality factor and radio frequency properties of the coil, considering the effects of strain. Particularly, the wireless transmission efficiency of a five-turn coil drops by approximately only 50% at 10 MHz with the strain of 100%. Moreover, various complex functional wireless electronics are developed using near-field communication and frequency modulation technology for applications in content recognition and long-distance transmission (>1 m), respectively. In summary, the proposed device has considerable potential for applications in artificial electronic skins, human healthcare monitoring and soft robotics.Skeletal reorganization is a type of intriguing processes because of their interesting mechanism, high atom-economy and synthetic versatility. Herein, we describe an unusual, divergent skeletal reorganization of N-sulfonyl ynamides. Upon treatment with lithium diisopropylamine (LDA), N-sulfonyl ynamides undergo a skeletal reorganization to deliver thiete sulfones, while the additional use of 1,3-dimethyl-tetrahydropyrimidin-2(1H)-one (DMPU) shifts the process to furnish propargyl sulfonamides. This skeletal reorganization divergence features broad substrate scope and scalability. Mechanistically, experimental and computational studies reveal that these processes may initiate from a lithiation/4-exo-dig cyclization cascade, and the following ligand-dependent 1,3-sulfonyl migration or β-elimination would control the chemodivergence. This protocol additionally provides a facile access to a variety of privileged molecules from easily accessible ynamides.Lymphangioleiomyomatosis (LAM) is a rare fatal cystic lung disease due to bi-allelic inactivating mutations in tuberous sclerosis complex (TSC1/TSC2) genes coding for suppressors of the mechanistic target of rapamycin complex 1 (mTORC1). The origin of LAM cells is still unknown. Here, we profile a LAM lung compared to an age- and sex-matched healthy control lung as a hypothesis-generating approach to identify cell subtypes that are specific to LAM. Our single-cell RNA sequencing (scRNA-seq) analysis reveals novel mesenchymal and transitional alveolar epithelial states unique to LAM lung. This analysis identifies a mesenchymal cell hub coordinating the LAM disease phenotype. Mesenchymal-restricted deletion of Tsc2 in the mouse lung produces a mTORC1-driven pulmonary phenotype, with a progressive disruption of alveolar structure, a decline in pulmonary function, increase of rapamycin-sensitive expression of WNT ligands, and profound female-specific changes in mesenchymal and epithelial lung cell gene expression. Genetic inactivation of WNT signaling reverses age-dependent changes of mTORC1-driven lung phenotype, but WNT activation alone in lung mesenchyme is not sufficient for the development of mouse LAM-like phenotype. The alterations in gene expression are driven by distinctive crosstalk between mesenchymal and epithelial subsets of cells observed in mesenchymal Tsc2-deficient lungs. This study identifies sex- and age-specific gene changes in the mTORC1-activated lung mesenchyme and establishes the importance of the WNT signaling pathway in the mTORC1-driven lung phenotype.Cellular signaling systems play a vital role in maintaining homeostasis when a cell is exposed to different perturbations. Components of the systems are organized as hierarchical networks, and perturbing different components often leads to transcriptomic profiles that exhibit compositional statistical patterns. Mining such patterns to investigate how cellular signals are encoded is an important problem in systems biology, where artificial intelligence techniques can be of great assistance. Here, we investigated the capability of deep generative models (DGMs) to modeling signaling systems and learn representations of cellular states underlying transcriptomic responses to diverse perturbations. Specifically, we show that the variational autoencoder and the supervised vector-quantized variational autoencoder can accurately regenerate gene expression data in response to perturbagen treatments. The models can learn representations that reveal the relationships between different classes of perturbagens and enable mappings between drugs and their target genes. In summary, DGMs can adequately learn and depict how cellular signals are encoded. The resulting representations have broad applications, demonstrating the power of artificial intelligence in systems biology and precision medicine.