Fast radio bursts (FRBs) are enigmatic high-energy events with unknown origins, which are observationally divided into two categories, i.e., repeaters and nonrepeaters. However, there are potentially a number of nonrepeaters that may be misclassified, as repeating bursts are missed due to the limited sensitivity and observation periods, thus misleading the investigation of their physical properties. In this work, we propose a repeater identification method based on the t-distributed Stochastic Neighbor Embedding algorithm and apply the classification to the first Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) catalog. We find that the spectral morphology parameters, specifically spectral running (r), represent the key features for identifying repeaters from the nonrepeaters. Also, the results suggest that repeaters are more biased toward narrowband emission, whereas nonrepeaters are inclined toward broadband emission. We provide a list of 163 repeater candidates, five of which are confirmed with an updated repeater catalog from CHIME/FRB. Our findings improve our understanding of the various properties underlying repeaters and nonrepeaters, as well as guidelines for future FRB detection and categorization.

Fast radio bursts (FRBs) have been found in great numbers, but the physical mechanism of these sources is still a mystery. The redshift evolutions of the FRB energy distribution function and the volumetric rate shed light on the origin of FRBs. However, such estimations rely on the dispersion measurement (DM)–redshift (z) relationship. A few FRBs that have been detected recently show large excess DMs beyond the expectation from the cosmological and Milky Way contributions, which indicates large spread of DMs from their host galaxies. In this work, we adopt two lognormal-distributed ${\mathrm{DM}}_{\mathrm{host}}$ models and estimate the energy function using the non-repeating FRBs selected from the Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB Catalog 1. By comparing the lognormal-distributed ${\mathrm{DM}}_{\mathrm{host}}$ models to a constant ${\mathrm{DM}}_{\mathrm{host}}$ model, the FRB energy function results are consistent within the measurement uncertainty. We also estimate the volumetric rate of the non-repeating FRBs in three different redshift bins. The volumetric rate shows that the trend is consistent with the stellar-mass density redshift evolution. Since the lognormal-distributed ${\mathrm{DM}}_{\mathrm{host}}$ model increases the measurement errors, the inference of FRBs tracking the stellar-mass density is nonetheless undermined.

Doxorubicin (Dox) is a chemotherapeutic drug used to treat a wide range of cancers, but its clinical application is limited due to its cardiotoxicity. Protein kinase C-ζ (PKC-ζ) is a serine/threonine kinase belonging to atypical protein kinase C (PKC) subfamily, and is activated by its phosphorylation. We and others have reported that PKC-ζ induced cardiac hypertrophy by activating the inflammatory signaling pathway. This study focused on whether PKC-ζ played an important role in Dox-induced cardiotoxicity. We found that PKC-ζ phosphorylation was increased by Dox treatment in vivo and in vitro. PKC-ζ overexpression exacerbated Dox-induced cardiotoxicity. Conversely, knockdown of PKC-ζ by siRNA relieved Dox-induced cardiotoxicity. Similar results were observed when PKC-ζ enzyme activity was inhibited by its pseudosubstrate inhibitor, Myristoylated. PKC-ζ interacted with β-catenin and inhibited Wnt/β-catenin signaling pathway. Activation of Wnt/β-catenin signaling by LiCl protected against Dox-induced cardiotoxicity. The Wnt/β-catenin inhibitor XAV-939 aggravated Dox-caused decline of β-catenin and cardiomyocyte apoptosis and mitochondrial damage. Moreover, activation of Wnt/β-catenin suppressed aggravation of Dox-induced cardiotoxicity due to PKC-ζ overexpression. Taken together, our study revealed that inhibition of PKC-ζ activity was a potential cardioprotective approach to preventing Dox-induced cardiac injury.

The repeating behavior of fast radio bursts (FRBs) is regarded as a key clue to understanding their physical origin, yet reliably distinguishing repeaters from apparent nonrepeaters with current observations remains challenging. Here we propose a physically interpretable and practically quantifiable classification framework based on spectral morphology. Using dimensionality reduction, clustering, and feature-importance analysis, we identify the spectral running r and spectral index γ as the most critical parameters for distinguishing repeaters from apparent nonrepeaters in the CHIME/FRB sample. In the γ– r space, repeaters preferentially occupy regions with steeper, narrower-band spectra, whereas nonrepeaters cluster in flatter, broader-band regions, resulting in a clear density separation. We further construct a probability map in the γ – r space based on Gaussian mixture model posterior analysis, revealing a clear gradient of repetition likelihood from ∼67% in the high-repetition region to ∼7% in the low-repetition region. This model also identifies several apparent nonrepeaters with high inferred repetition probability, highlighting them as priority targets for future monitoring. This framework provides a simple and generalizable tool for assessing repeatability in the CHIME/FRB sample and highlights the diagnostic power of spectral morphology in unveiling FRB origins.

Interacting dark energy (IDE) scenario assumes that there exists a direct interaction between dark energy and cold dark matter, but this interaction is hard to be tightly constrained by the current data. Fast radio bursts (FRBs) will be seen in large numbers by future radio telescopes, and thus they have potential to become a promising low-redshift cosmological probe. In this work, we investigate the capability of future FRBs of constraining the dimensionless coupling parameter $\beta$ in four phenomenological IDE models. If we fix the FRB properties, about $10^5$ FRB data can give constraints on $\beta$ tighter than the current cosmic microwave background data in the IDE models with the interaction proportional to the energy density of dark energy. In all the IDE models, about $10^6$ FRB data can achieve the absolute errors of $\beta$ to less than $0.10$, providing a way to precisely measure $\beta$ by only one cosmological probe. Jointly constraining the FRB properties and cosmological parameters would increase the constraint errors of $\beta$ by a factor of about 0.5-2.