Mechanoluminescence (ML) is generated during exposures of certain materials to mechanical stimuli. Many solid materials produce ML during their fracturing, however, the irreversibility of fracto-induced ML limits the practical applications of these materials. In 1999, Chao-Nan Xu discovered an intense and reproducible ML from trap-controlled materials, including ZnS:Mn 2+ and SrAl 2 O 4 :Eu 2+ , and introduced the principles and applications of hybrid inorganic/organic mechanoluminescent (ML) composites, and related sensors to visualize stress/strain in target structures. This discovery has triggered intense research interest in trap-controlled ML materials and composites over the past 2 decades. Notable achievements of this research include the development of trap-controlled materials that exhibit bright ML emission from the ultraviolet to the near infra-red, and multiscale mechano-optical sensitivities. This research has also increased our understanding of the mechanisms of ML phenomena, enabling the rational design of trap-controlled ML materials. Practical applications of ML are also being driven by the discovery that ML composites can serve as “mechano-optical sensitive skin” for structural health diagnosis, stress sensors for biomechanics, and mechanically-activated light sources. This review focuses on the design, synthesis, characterization, optimization and application of trap-controlled ML materials, and concludes with discussions on future directions of ML research and specific challenges to improve ML materials for real-world applications.

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. Coexpression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response modules defining previously unidentified neural-immune mechanisms. We integrated genetic and genomic data to perform a transcriptome-wide association study, prioritizing disease loci likely mediated by cis effects on brain expression. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.