Our results show that this approach enables the accurate detection and quantification of Aβ oligomers in vitro, and in Caenorhabditis elegans and mouse hippocampal tissues.This paper explores the knowledge of linguistic structure learned by large artificial neural networks, trained via self-supervision, whereby the model simply tries to predict a masked word in a given context. Human language communication is via sequences of words, but language understanding requires constructing rich hierarchical structures that are never observed explicitly. The mechanisms for this have been a prime mystery of human language acquisition, while engineering work has mainly proceeded by supervised learning on treebanks of sentences hand labeled for this latent structure. However, we demonstrate that modern deep contextual language models learn major aspects of this structure, without any explicit supervision. We develop methods for identifying linguistic hierarchical structure emergent in artificial neural networks and demonstrate that components in these models focus on syntactic grammatical relationships and anaphoric coreference. Indeed, we show that a linear transformation of learned embeddings in these models captures parse tree distances to a surprising degree, allowing approximate reconstruction of the sentence tree structures normally assumed by linguists. These results help explain why these models have brought such large improvements across many language-understanding tasks.While colocalization within a bacterial operon enables coexpression of the constituent genes, the mechanistic logic of clustering of nonhomologous monocistronic genes in eukaryotes is not immediately obvious. Biosynthetic gene clusters that encode pathways for specialized metabolites are an exception to the classical eukaryote rule of random gene location and provide paradigmatic exemplars with which to understand eukaryotic cluster dynamics and regulation. Here, using 3C, Hi-C, and Capture Hi-C (CHi-C) organ-specific chromosome conformation capture techniques along with high-resolution microscopy, we investigate how chromosome topology relates to transcriptional activity of clustered biosynthetic pathway genes in Arabidopsis thaliana Our analyses reveal that biosynthetic gene clusters are embedded in local hot spots of 3D contacts that segregate cluster regions from the surrounding chromosome environment. The spatial conformation of these cluster-associated domains differs between transcriptionally active and silenced clusters. We further show that silenced clusters associate with heterochromatic chromosomal domains toward the periphery of the nucleus, while transcriptionally active clusters relocate away from the nuclear periphery. Examination of chromosome structure at unrelated clusters in maize, rice, and tomato indicates that integration of clustered pathway genes into distinct topological domains is a common feature in plant genomes. Our results shed light on the potential mechanisms that constrain coexpression within clusters of nonhomologous eukaryotic genes and suggest that gene clustering in the one-dimensional chromosome is accompanied by compartmentalization of the 3D chromosome.The recent advent of immune checkpoint inhibitor (CPI) antibodies has revolutionized many aspects of cancer therapy, but the efficacy of these breakthrough therapeutics remains limited, as many patients fail to respond for reasons that still largely evade understanding. An array of studies in human patients and animal models has demonstrated that local signaling can generate strongly immunosuppressive microenvironments within tumors, and emerging evidence suggests that delivery of immunostimulatory molecules into tumors can have therapeutic effects. Nanoparticle formulations of these cargoes offer a promising way to maximize their delivery and to enhance the efficacy of checkpoint inhibitors. We developed a modular nanoparticle system capable of encapsulating an array of immunostimulatory oligonucleotides that, in some cases, greatly increase their potency to activate inflammatory signaling within immune cells in vitro. We hypothesized that these immunostimulatory nanoparticles could suppress tumor growth by activating similar signaling in vivo, and thereby also improve responsiveness to immune checkpoint inhibitor antibody therapies. We found that our engineered nanoparticles carrying a CpG DNA ligand of TLR9 can suppress tumor growth in several animal models of various cancers, resulting in an abscopal effect on distant tumors, and improving responsiveness to anti-CTLA4 treatment with combinatorial effects after intratumoral administration. Moreover, by incorporating tumor-homing peptides, immunostimulatory nucleotide-bearing nanoparticles facilitate antitumor efficacy after systemic intravenous (i.v.) administration.Nanostructured plasmonic materials can lead to the extremely compact pixels and color filters needed for next-generation displays by interacting with light at fundamentally small length scales. read more However, previous demonstrations suffer from severe angle sensitivity, lack of saturated color, and absence of black/gray states and/or are impractical to integrate with actively addressed electronics. Here, we report a vivid self-assembled nanostructured system which overcomes these challenges via the multidimensional hybridization of plasmonic resonances. By exploiting the thin-film growth mechanisms of aluminum during ultrahigh vacuum physical vapor deposition, dense arrays of particles are created in near-field proximity to a mirror. The sub-10-nm gaps between adjacent particles and mirror lead to strong multidimensional coupling of localized plasmonic modes, resulting in a singular resonance with negligible angular dispersion and ∼98% absorption of incident light at a desired wavelength. The process is compatible with arbitrarily structured substrates and can produce wafer-scale, diffusive, angle-independent, and flexible plasmonic materials. We then demonstrate the unique capabilities of the strongly coupled plasmonic system via integration with an actively addressed reflective liquid crystal display with control over black states. The hybrid display is readily programmed to display images and video.