In this large retrospective study, the administration of GBCAs was not associated with higher rates of AKI, which remained consistent across varying levels of baseline renal function. Furthermore, no significant increase in GBCA-associated acute ADRs was observed in patients with impaired renal function. These findings suggest that GBCA administration is generally well-tolerated across a wide spectrum of renal function, without increasing the risk of AKI or GBCA-associated acute ADRs.

Hydride transfer is an essential elementary reaction across the chemical value chain, but there are limited methods available for quantifying thermodynamic hydricity (Δ<i>G</i>_H-), particularly among main group reagents. Herein, we exploit facile H₂ activation and reversible hydride transfer from a metal surface to a molecular reagent, the net hydrogen reduction reaction (HRR), to develop a potentiometric method for quantifying Δ<i>G</i>_H- of main group reagents recalcitrant to conventional methods. HRR potentiometry is first validated with a benzimidazole-based hydride donor and then applied to uncover the impact of the reaction environment on hydricity. For a benzimidazole-based hydride donor, HRR equilibrium potentials are roughly invariant across solvents, indicating that the solvent dependence of its hydricity largely reflects the differential solvation of H^- across media. For formate, HRR potentials and corresponding hydricities depend strongly on water content. For borohydrides, HRR potentiometry reveals that effective hydricity values are strongly influenced by Lewis acid-base adduct formation with the hydride acceptor but are minimally influenced by the countercation. Together with these studies, the advantages, limitations, and practical considerations of the HRR potentiometry method are discussed, highlighting the power of this methodology as a complementary tool to measure molecular hydricity.

We study community detection in multiple networks with jointly correlated node attributes and edges. This setting arises naturally in applications such as social platforms, where a shared set of users may exhibit both correlated friendship patterns and correlated attributes across different platforms. Extending the classical Stochastic Block Model (SBM) and its contextual counterpart (Contextual SBM or CSBM), we introduce the correlated CSBM, which incorporates structural and attribute correlations across graphs. To build intuition, we first analyze correlated Gaussian Mixture Models, wherein only correlated node attributes are available without edges, and identify the conditions under which an estimator minimizing the distance between attributes achieves exact matching of nodes across the two databases. For the correlated CSBMs, we develop a two-step procedure that first applies <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i>-core matching to most nodes using edge information, then refines the matching for the remaining unmatched nodes by leveraging their attributes with a distance-based estimator. We identify the conditions under which the algorithm recovers the exact node correspondence, enabling us to merge the correlated edges and average the correlated attributes for enhanced community detection. Crucially, by aligning and combining graphs, we identify regimes in which community detection is impossible in a single graph but becomes feasible when side information from correlated graphs is incorporated. Our results illustrate how the interplay between graph matching and community recovery can boost performance, broadening the scope of multi-graph, attribute-based community detection.

We study the statistical decision process of detecting the low-rank signal from various signal-plus-noise type data matrices, known as the spiked random matrix models. We first show that the principal component analysis can be improved by entrywise pre-transforming the data matrix if the noise is non-Gaussian, generalizing the known results for the spiked random matrix models with rank-1 signals. As an intermediate step, we find out sharp phase transition thresholds for the extreme eigenvalues of spiked random matrices, which generalize the Baik-Ben Arous-Péché (BBP) transition. We also prove the central limit theorem for the linear spectral statistics for the spiked random matrices and propose a hypothesis test based on it, which does not depend on the distribution of the signal or the noise. When the noise is non-Gaussian noise, the test can be improved with an entrywise transformation to the data matrix with additive noise. We also introduce an algorithm that estimates the rank of the signal when it is not known a priori.

In this large retrospective study, the administration of GBCAs was not associated with higher rates of AKI, which remained consistent across varying levels of baseline renal function. Furthermore, no significant increase in GBCA-associated acute ADRs was observed in patients with impaired renal function. These findings suggest that GBCA administration is generally well-tolerated across a wide spectrum of renal function, without increasing the risk of AKI or GBCA-associated acute ADRs.

Hydride transfer is an essential elementary reaction across the chemical value chain, but there are limited methods available for quantifying thermodynamic hydricity (Δ<i>G</i>_H-), particularly among main group reagents. Herein, we exploit facile H₂ activation and reversible hydride transfer from a metal surface to a molecular reagent, the net hydrogen reduction reaction (HRR), to develop a potentiometric method for quantifying Δ<i>G</i>_H- of main group reagents recalcitrant to conventional methods. HRR potentiometry is first validated with a benzimidazole-based hydride donor and then applied to uncover the impact of the reaction environment on hydricity. For a benzimidazole-based hydride donor, HRR equilibrium potentials are roughly invariant across solvents, indicating that the solvent dependence of its hydricity largely reflects the differential solvation of H^- across media. For formate, HRR potentials and corresponding hydricities depend strongly on water content. For borohydrides, HRR potentiometry reveals that effective hydricity values are strongly influenced by Lewis acid-base adduct formation with the hydride acceptor but are minimally influenced by the countercation. Together with these studies, the advantages, limitations, and practical considerations of the HRR potentiometry method are discussed, highlighting the power of this methodology as a complementary tool to measure molecular hydricity.

We study community detection in multiple networks with jointly correlated node attributes and edges. This setting arises naturally in applications such as social platforms, where a shared set of users may exhibit both correlated friendship patterns and correlated attributes across different platforms. Extending the classical Stochastic Block Model (SBM) and its contextual counterpart (Contextual SBM or CSBM), we introduce the correlated CSBM, which incorporates structural and attribute correlations across graphs. To build intuition, we first analyze correlated Gaussian Mixture Models, wherein only correlated node attributes are available without edges, and identify the conditions under which an estimator minimizing the distance between attributes achieves exact matching of nodes across the two databases. For the correlated CSBMs, we develop a two-step procedure that first applies <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i>-core matching to most nodes using edge information, then refines the matching for the remaining unmatched nodes by leveraging their attributes with a distance-based estimator. We identify the conditions under which the algorithm recovers the exact node correspondence, enabling us to merge the correlated edges and average the correlated attributes for enhanced community detection. Crucially, by aligning and combining graphs, we identify regimes in which community detection is impossible in a single graph but becomes feasible when side information from correlated graphs is incorporated. Our results illustrate how the interplay between graph matching and community recovery can boost performance, broadening the scope of multi-graph, attribute-based community detection.

We study the statistical decision process of detecting the low-rank signal from various signal-plus-noise type data matrices, known as the spiked random matrix models. We first show that the principal component analysis can be improved by entrywise pre-transforming the data matrix if the noise is non-Gaussian, generalizing the known results for the spiked random matrix models with rank-1 signals. As an intermediate step, we find out sharp phase transition thresholds for the extreme eigenvalues of spiked random matrices, which generalize the Baik-Ben Arous-Péché (BBP) transition. We also prove the central limit theorem for the linear spectral statistics for the spiked random matrices and propose a hypothesis test based on it, which does not depend on the distribution of the signal or the noise. When the noise is non-Gaussian noise, the test can be improved with an entrywise transformation to the data matrix with additive noise. We also introduce an algorithm that estimates the rank of the signal when it is not known a priori.

MR imaging of bone abnormalities, extraarticular lesions, and associated abscesses provides useful information in the differentiation of tuberculous arthritis and pyogenic arthritis.

Hydride transfer is an essential elementary reaction across the chemical value chain, but there are limited methods available for quantifying thermodynamic hydricity (ΔGH−), particularly amongst main group reagents. Herein, we exploit facile H2 activation and reversible hydride transfer from a metal surface to a molecular reagent, the net hydrogen reduction reaction (HRR), to develop a potentiometric method for quantifying ΔGH− of main group reagents recalcitrant to conventional methods. HRR potentiometry is first validated with a benzimidazolium hydride donor and then applied to uncover the impact of the reaction environment on hydricity. Across benzimidazolium hydride donors, HRR equilibrium potentials are roughly invariant across solvents, indicating that the solvent dependence of its hydricity largely reflects the differential solvation of H− across media. For formate, HRR potentials and corresponding hydricities depend strongly on water content. For borohydrides, HRR potentiometry reveals that effective hydricity values are strongly influenced by Lewis acid-base adduct formation with the hydride acceptor but are minimally influenced by the counter cation. These studies highlight the power of HRR potentiometry to both quantify and uncover trends in hydricity across main group reagents.

Graph matching problem aims to identify node correspondence between two or more correlated graphs. Previous studies have primarily focused on models where only edge information is provided. However, in many social networks, not only the relationships between users, represented by edges, but also their personal information, represented by features, are present. In this paper, we address the challenge of identifying node correspondence in correlated graphs, where additional node features exist, as in many real-world settings. We propose a two-step procedure, where we initially match a subset of nodes only using edge information, and then match the remaining nodes using node features. We derive information-theoretic limits for exact graph matching on this model. Our approach provides a comprehensive solution to the real-world graph matching problem by providing systematic ways to utilize both edge and node information for exact matching of the graphs.

Graph matching problem aims to identify node correspondence between two or more correlated graphs. Previous studies have primarily focused on models where only edge information is provided. However, in many social networks, not only the relationships between users, represented by edges, but also their personal information, represented by features, are present. In this paper, we address the challenge of identifying node correspondence in correlated graphs, where additional node features exist, as in many real-world settings. We propose a two-step procedure, where we initially match a subset of nodes only using edge information, and then match the remaining nodes using node features. We derive information-theoretic limits for exact graph matching on this model. Our approach provides a comprehensive solution to the real-world graph matching problem by providing systematic ways to utilize both edge and node information for exact matching of the graphs.