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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. 

Abstract Background:Protein presence information is an essential component of biological pathway identification. Presence of certain enzymes in an organism points towards the metabolic pathways that occur within it, whereas the absence of these enzymes indicates either the existence of alternative pathways or a lack of these pathways altogether. The same inference applies to regulatory pathways such as gene regulation and signal transduction. Protein presence information therefore forms the basis for biological pathway studies, and patterns in presence-absence across multiple organisms allow for comparative pathway analyses. Results:Here we present ProTaxoVis, a novel bioinformatic tool that extracts protein presence information from database queries and maps it to a taxonomic tree or heatmap. ProTaxoVis generates a large-scale overview of presence patterns in taxonomic clades of interest. This overview reveals protein distribution patterns, and this can be used to deduce pathway evolution or to probe other biological questions. ProTaxoVis combines and filters sequence query results to extract information on the distribution of proteins and translates this information into two types of visual outputs: taxonomic trees and heatmaps. The trees supplement their topology with scaled pie-chart representations per node of the presence of target proteins and combinations of these proteins, such that patterns in taxonomic groups can easily be identified. The heatmap visualisation shows presence and conservation of these proteins for a user-determined set of species, allowing for a more detailed view over a larger group of proteins as compared to the trees. ProTaxoVis also allows for visual quality checks of hits based on a coverage plot and a length histogram, which can be used to determine e-value and minimum protein length cutoffs. Tabular output of resulting data from the query, combined, and heatmap building step are saved and easily accessible for further analyses. Conclusions:We evaluate our tool with the phosphoribosyltransferases, a transferase enzyme family with notable distribution patterns amongst organisms of varying complexities and across Eukaryota, Bacteria, and Archaea. ProTaxoVis is open-source and available at:https://github.com/MolecularBioinformatics/ProTaxoVis. 

Abstract The 2023/24 El Niño commenced with an exceptionally large warm water volume in the equatorial western Pacific, comparable to the extreme 1997/98 and 2015/16 events, but did not develop into a super El Niño. This study highlights the critical role of contrasting Northern Pacific Meridional Mode (NPMM) conditions in this divergence. Warm NPMM conditions during the 1997/98 and 2015/16 events created a positive zonal sea surface temperature (SST) gradient in the equatorial western-central Pacific and enhanced Madden-Julian Oscillation (MJO) propagation, driving sustained westerly wind bursts (WWBs) and downwelling Kelvin waves that intensified both events. In contrast, the cold NPMM during 2023/24 induced a negative SST gradient and suppressed MJO activity, resulting in weaker WWBs and limited eastward wave activity, preventing the event from reaching super El Niño intensity. A 2,200-year CESM1 pre-industrial simulation corroborates these observational findings, underscoring the importance of NPMM interference in improving El Niño intensity predictions. 

Abstract Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing organ shortage. While recent technological advances in organ transplantation have increased their success rate, a substantial challenge in increasing the number of DCD donations resides in the uncertainty regarding the timing of cardiac death after terminal extubation, impacting the risk of prolonged ischemic organ injury, and negatively affecting post-transplant outcomes. In this study, we trained and externally validated an ODE-RNN model, which combines recurrent neural network with neural ordinary equations and excels in processing irregularly-sampled time series data. The model is designed to predict time-to-death following terminal extubation in the intensive care unit (ICU) using the history of clinical observations. Our model was trained on a cohort of 3,238 patients from Yale New Haven Hospital, and validated on an external cohort of 1,908 patients from six hospitals across Connecticut. The model achieved accuracies of$$95.3~\pm ~1.0\%$$and$$95.4~\pm ~0.7\%$$for predicting whether death would occur in the first 30 and 60 minutes, respectively, with a calibration error of$$0.024~\pm ~0.009$$. Heart rate, respiratory rate, mean arterial blood pressure (MAP), oxygen saturation (SpO2), and Glasgow Coma Scale (GCS) scores were identified as the most important predictors. Surpassing existing clinical scores, our model sets the stage for reduced organ acquisition costs and improved post-transplant outcomes.