Search for: All records

Enhancing accurate molecular property predic- tion relies on effective and proficient representa- tion learning. It is crucial to incorporate diverse molecular relationships characterized by multi- similarity (self-similarity and relative similarities) (Wang et al., 2019) between molecules. However, current molecular representation learning meth- ods fall short in exploring multi-similarity and of- ten underestimate the complexity of relationships between molecules. Additionally, previous multi- similarity approaches require the specification of positive and negative pairs to attribute distinct pre- defined weights to different relative similarities, which can introduce potential bias. In this work, we introduce Graph Multi-Similarity Learning for Molecular Property Prediction (GraphMSL) framework, along with a novel approach to for- mulate a generalized multi-similarity metric with- out the need to define positive and negative pairs. In each of the chemical modality spaces (e.g., molecular depiction image, fingerprint, NMR, and SMILES) under consideration, we first de- fine a self-similarity metric (i.e., similarity be- tween an anchor molecule and another molecule), and then transform it into a generalized multi- similarity metric for the anchor through a pair weighting function. GraphMSL validates the effi- cacy of the multi-similarity metric across Molecu- leNet datasets. Furthermore, these metrics of all modalities are integrated into a multimodal multi-similarity metric, which showcases the po- tential to improve the performance. Moreover, the focus of the model can be redirected or cus- tomized by altering the fusion function. Last but not least, GraphMSL proves effective in drug dis- covery evaluations through post-hoc analyses of the learnt representations. 

Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors. While AI-enhanced NMR prediction models hold promise, challenges still persist in tasks such as molecular retrieval, iso- mer recognition, and peak assignment. In response, this paper introduces a novel solution, Knowledge-Guided Multi-Level Multimodal Alignment with Instance-Wise Discrimination (K-M3 AID), which establishes correspondences between two heterogeneous modalities: molecular graphs and NMR spectra. K- M3AID employs a dual-coordinated contrastive learning architecture with three key modules: a graph-level alignment module, a node-level alignment module, and a communication channel. Notably, K-M3AID introduces knowledge- guided instance-wise discrimination into contrastive learning within the node-level alignment module. In addition, K-M3 AID demonstrates that skills acquired during node-level alignment have a positive impact on graph-level alignment, acknowledging meta-learning as an inherent property. Empirical validation underscores the effectiveness of K-M3AID in multiple zero- shot tasks. 

Abstract Seismic anisotropy provides essential information for characterizing the orientation of deformation and flow in the crust and mantle. The isotropic structure of the Antarctic crust and upper mantle has been determined by previous studies, but the azimuthal anisotropy structure has only been constrained by mantle core phase (SKS) splitting observations. This study determines the azimuthal anisotropic structure of the crust and mantle beneath the central and West Antarctica based on 8—55 s Rayleigh wave phase velocities from ambient noise cross‐correlation. An anisotropic Rayleigh wave phase velocity map was created using a ray—based tomography method. These data are inverted using a Bayesian Monte Carlo method to obtain an azimuthal anisotropy model with uncertainties. The azimuthal anisotropy structure in most of the study region can be fit by a two‐layer structure, with one layer at depths of 0–15 km in the shallow crust and the other layer in the uppermost mantle. The azimuthal anisotropic layer in the shallow crust of West Antarctica, where it coincides with strong positive radial anisotropy quantified by the previous study, shows a fast direction that is subparallel to the inferred extension direction of the West Antarctic Rift System. Fast directions of upper mantle azimuthal anisotropy generally align with teleseismic shear wave splitting fast directions, suggesting a thin lithosphere or similar lithosphere‐asthenosphere deformation. However, inconsistencies in this exist in Marie Byrd Land, indicating differing ancient deformation patterns in the shallow mantle lithosphere sampled by the surface waves and deformation in the deeper mantle and asthenosphere sampled more strongly by splitting measurements. 

Active nematics are a class of far-from-equilibrium materials characterized by local orientational order of force-generating, anisotropic constitutes. Traditional methods for predicting the dynamics of active nematics rely on hydrodynamic models, which accurately describe idealized flows and many of the steady-state properties, but do not capture certain detailed dynamics of experimental active nematics. We have developed a deep learning approach that uses a Convolutional Long-Short-Term-Memory (ConvLSTM) algorithm to automatically learn and forecast the dynamics of active nematics. We demonstrate our purely data-driven approach on experiments of 2D unconfined active nematics of extensile microtubule bundles, as well as on data from numerical simulations of active nematics. 

Summary Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero‐inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study‐level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta‐analysis. The current study proposes a novel statistical approach, the Zero‐inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero‐inflated Poisson regression model, thus it is attractive for meta‐analysis when individual participant‐level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero‐inflation bias in most situations.