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Abstract Quantum spin liquids can arise from Kitaev magnetic interactions, and exhibit fractionalized excitations with the potential for a topological form of quantum computation. This review surveys recent experimental and theoretical progress on the pursuit of phenomena related to Kitaev magnetism in layered and exfoliatable materials, which offer numerous opportunities to apply powerful techniques from the field of atomically thin materials. We primarily focus on the antiferromagnetic Mott insulator $\alpha$ -RuCl ${\mathrm{}}_3$ , which exhibits Kitaev couplings and is readily exfoliated to single- or few-layer sheets, and thus serves as a test bed for developing probes of Kitaev phenomena in atomically thin materials and devices. We introduce the Kitaev model and how it is realized in $\alpha$ -RuCl ${\mathrm{}}_3$ and other material candidates; and cover $\alpha$ -RuCl ${\mathrm{}}_3$ synthesis and fabrication into van der Waals heterostructure devices. A key discovery is a work-function-mediated charge transfer that heavily dopes both the $\alpha$ -RuCl ${\mathrm{}}_3$ and proximate materials, and can enhance Kitaev interactions by up to 50%. We further discuss a wide range of recent results in electronic transport and optical and tunneling spectroscopies of $\alpha$ -RuCl ${\mathrm{}}_3$ devices. The experimental techniques and theoretical insights developed for $\alpha$ -RuCl ${\mathrm{}}_3$ establish a framework for discovering and engineering superior two-dimensional Kitaev materials that may ultimately realize elusive quantum spin liquid phases. 

Dynamic treatment regimes (DTRs) are critical to precision medicine, optimizing long-term outcomes through personalized, real-time decision making in evolving clinical contexts, but require careful supervision for unsafe treatment risks. Existing efforts rely primarily on clinician prescribed gold standards despite the absence of a known optimal strategy, and predominantly using structured EHR data without extracting valuable insights from clinical notes, limiting their reliability for treatment recommendations. In this work, we introduce SAFER, a calibrated risk-aware tabular-language recommendation framework for DTR that integrates both structured EHR and clinical notes, enabling them to learn from each other, and addresses inherent label uncertainty by assuming ambiguous optimal treatment solution for deceased patients. Moreover, SAFER employs conformal prediction to provide statistical guarantees, ensuring safe treatment recommendations while filtering out uncertain predictions. Experiments on two publicly available sepsis datasets demonstrate that SAFER outperforms state-of-the-art baselines across multiple recommendation metrics and counterfactual mortality rate, while offering robust formal assurances. These findings underscore SAFER’s potential as a trustworthy and theoretically grounded solution for high-stakes DTR applications. 

Accurately inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing (scRNA‐seq) data is critical for understanding cellular dynamics in both normal development and disease. However, existing computational methods often suffer from low precision and high false‐positive rates due to the intrinsic noise and complex regulatory architecture in scRNA‐seq data. We introduce scTIGER2.0, a deep‐learning‐based framework that integrates expression correlation, pseudotime ordering, temporal causal discovery, and bootstrap‐based significance testing to infer high‐confidence, directional gene–gene interactions. Benchmarking against five popular GRN inference methods using large‐scale datasets, scTIGER2.0 consistently achieved superior specificity, especially in linear developmental trajectories. In real applications, scTIGER2.0 identified an APOE‐centered GRN from Alzheimer's disease scRNA‐seq data and uncovered interconnected GRNs for FOS, FOXP1, JUN, KLF6, NCOA4, and RUNX1 from acute myeloid leukemia data, where 87.5% of the predicted targets show promoter‐binding peaks in the corresponding ChIP‐seq data. These results demonstrate that scTIGER2.0 is a robust, accurate and fully integrated platform for uncovering biologically meaningful GRNs from noisy scRNA‐seq data. 

Abstract Optical phonon engineering through nonlinear effects has been utilized in ultrafast control of material properties. However, nonlinear optical phonons typically exhibit rapid decay due to strong mode-mode couplings, limiting their effectiveness in temperature or frequency sensitive applications. Here we report the observation of long-lived nonlinear optical phonons through the spontaneous formation of phonon frequency combs in the van der Waals material CrXTe₃(X=Ge, Si) using high-resolution Raman scattering. Unlike conventional optical phonons, the highestA_gmode in CrGeTe₃splits into equidistant, sharp peaks forming a frequency comb that persists for hundreds of oscillations and survives up to 200K. These modes correspond to localized oscillations of Ge₂Te₆clusters, isolated from Cr hexagons, behaving as independent quantum oscillators. Introducing a cubic nonlinear term to the harmonic oscillator model, we simulate the phonon time evolution and successfully replicate the observed comb structure. Similar frequency comb behavior is observed in CrSiTe₃, demonstrating the generalizability of this phenomenon. Our findings demonstrate that Raman scattering effectively probes high-frequency nonlinear phonon modes, offering insight into the generation of long-lived, tunable phonon frequency combs with potential applications in ultrafast material control and phonon-based technologies.