Avalanche phenomena leverage steeply nonlinear dynamics to generate disproportionately high responses from small perturbations and are found in a multitude of events and materials 1 , enabling technologies including optical phase-conjugate imaging, 2 infrared quantum counting, 3 and efficient upconverted lasing 4-6 . However, the photon avalanching (PA) mechanism underlying these optical innovations has been observed only in bulk materials and aggregates 6,7 , and typically at cryogenic temperatures 5-8 , limiting its utility and impact in many applications. Here, we report the realization of PA at room temperature in single nanostructures -small, Tm 3+ -doped upconverting nanocrystals -and demonstrate their use in superresolution imaging at wavelengths that fall within near-infrared (NIR) spectral windows of maximal biological transparency. Avalanching nanoparticles (ANPs) can be pumped by either continuous-wave or pulsed lasers and exhibit all of the defining features of PA. These hallmarks include clear excitation power thresholds, exceptionally long rise time at threshold, and a dominant excited-state absorption that is >13,000 times larger than ground-state absorption. Beyond the avalanching threshold, ANP emission scales nonlinearly with the 26 th power of pump intensity, resulting from induced positive optical feedback in each nanocrystal. This enables the experimental realization of photon-avalanche single-beam superresolution imaging (PASSI) 7 , achieving sub-70 nm spatial resolution using only simple scanning confocal microscopy and before any computational analysis. Pairing their steep nonlinearity with existing superresolution techniques and computational methods 9-11 , ANPs allow for imaging with higher resolution and at ca. 100-fold lower excitation intensities than is possible with other probes. The low PA threshold and exceptional photostability of ANPs also suggest their utility in a diverse array of applications 7 including subwavelength bioimaging 7,12,13 , IR detection, temperature [14][15][16] and pressure 17 transduction, neuromorphic computing 18 , and quantum optics 19,20 . Main

Dilated cardiomyopathy (DCM) is characterized by reduced cardiac output, as well as thinning and enlargement of left ventricular chambers. These characteristics eventually lead to heart failure. Current standards of care do not target the underlying molecular mechanisms associated with genetic forms of heart failure, driving a need to develop novel therapeutics for DCM. To identify candidate therapeutics, we developed an in vitro DCM model using induced pluripotent stem cell–derived cardiomyocytes (iPSC-CMs) deficient in B-cell lymphoma 2 (BCL2)-associated athanogene 3 (BAG3). With these BAG3-deficient iPSC-CMs, we identified cardioprotective drugs using a phenotypic screen and deep learning. From a library of 5500 bioactive compounds and siRNA validation, we found that inhibiting histone deacetylase 6 (HDAC6) was cardioprotective at the sarcomere level. We translated this finding to a BAG3 cardiomyocyte–knockout (BAG3 cKO ) mouse model of DCM, showing that inhibiting HDAC6 with two isoform-selective inhibitors (tubastatin A and a novel inhibitor TYA-018) protected heart function. In BAG3 cKO and BAG3 E455K mice, HDAC6 inhibitors improved left ventricular ejection fraction and reduced left ventricular diameter at diastole and systole. In BAG3 cKO mice, TYA-018 protected against sarcomere damage and reduced Nppb expression. Based on integrated transcriptomics and proteomics and mitochondrial function analysis, TYA-018 also enhanced energetics in these mice by increasing expression of targets associated with fatty acid metabolism, protein metabolism, and oxidative phosphorylation. Our results demonstrate the power of combining iPSC-CMs with phenotypic screening and deep learning to accelerate drug discovery, and they support developing novel therapies that address underlying mechanisms associated with heart disease.

PurposeThe study aims to examine the impact of six events related to the escalating Indo-China border conflicts in 2020 on the Indian stock market, including the role of firm-specific variables.Design/methodology/approachThis study employs an event-study method on a sample of 481 firms from August 23, 2019 to March 3, 2022. A cross-sectional regression is employed to examine the association between event-led abnormal returns and firm characteristics.FindingsThe results show that, although the individual events reflect heterogeneous effects on stock market returns, the average impact of the event categories is negative. The study also found that net working capital, current ratio, financial leverage and operating cash flows are significant financial performance indicators and drive cumulative abnormal returns. Further, size anomaly is absent, indicating that more prominent firms are resilient to new information.Research limitations/implicationsThe ongoing conflict between Russia and Ukraine is an example of how these disagreements can devolve into a disaster for the parties to the war. Although wars have an impact on markets at the global level, the impacts of border disputes are local. Border disputes are ongoing, and the study's findings can be used to empower investors to make risk-averting decisions that make their portfolios resilient to such events.Originality/valueThis study provides firm-level insight into the impacts of border conflicts on stock markets. The authors compare the magnitude of such impacts on two types of events, namely injuries and casualties due to country-specific border tensions and a government ban on Chinese apps. Key implications for policymakers, stakeholders and academics are presented.

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