Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data.

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increase available data (e.g., in low-density regions-i.

On the need to integrate interannual natural variability into coastal multihazard assessments.

The co-occurrence of multiple hazards can either exacerbate or mitigate risks. The interrelationships between multiple hazards greatly depend on the spatiotemporal scale and can be difficult to detect from large to local scales. In this paper, we identified coastal regions worldwide where the leading tropical (El Niño-Southern Oscillation, ENSO) and polar (Arctic Oscillation, AO; Southern Annular Mode, SAM) modes of climate variability simultaneously modify the seasonal conditions of multiple hazards, including the near-surface wind speed and swell and wind-sea wave powers.

Bridging pharmacology and neural networks: A deep dive into neural ordinary differential equations.

The advent of machine learning has led to innovative approaches in dealing with clinical data. Among these, Neural Ordinary Differential Equations (Neural ODEs), hybrid models merging mechanistic with deep learning models have shown promise in accurately modeling continuous dynamical systems. Although initial applications of Neural ODEs in the field of model-informed drug development and clinical pharmacology are becoming evident, applying these models to actual clinical trial datasets-characterized by sparse and irregularly timed measurements-poses several challenges.

How Rigidity and Conjugation of Bidentate Ligands Affect the Geometry and Photophysics of Iron -Heterocyclic Complexes: A Comparative Study.

Two iron complexes featuring the bidentate, nonconjugated N-heterocyclic carbene (NHC) 1,1'-methylenebis(3-methylimidazol-2-ylidene) (mbmi) ligand, where the two NHC moieties are separated by a methylene bridge, have been synthesized to exploit the combined influence of geometric and electronic effects on the ground- and excited-state properties of homoleptic Fe-hexa-NHC [Fe(mbmi)](PF) and heteroleptic Fe-tetra-NHC [Fe(mbmi)(bpy)](PF) (bpy = 2,2'-bipyridine) complexes. They are compared to the reported Fe-hexa-NHC [Fe(btz)](PF) and Fe-tetra-NHC [Fe(btz)(bpy)](PF) complexes containing the conjugated, bidentate mesoionic NHC ligand 3,3'-dimethyl-1,1'-bis(-tolyl)-4,4'-bis(1,2,3-triazol-5-ylidene) (btz). The observed geometries of [Fe(mbmi)](PF) and [Fe(mbmi)(bpy)](PF) are evaluated through L-Fe-L bond angles and ligand planarity and compared to those of [Fe(btz)](PF) and [Fe(btz)(bpy)](PF).

Photoredox matching of earth-abundant photosensitizers with hydrogen evolving catalysts by first-principles predictions.

Photoredox properties of several earth-abundant light-harvesting transition metal complexes in combination with cobalt-based proton reduction catalysts have been investigated computationally to assess the fundamental viability of different photocatalytic systems of current experimental interest. Density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations using several GGA (BP86, BLYP), hybrid-GGA (B3LYP, B3LYP*), hybrid meta-GGA (M06, TPSSh), and range-separated hybrid (ωB97X, CAM-B3LYP) functionals were used to calculate relevant ground and excited state reduction potentials for photosensitizers, catalysts, and sacrificial electron donors. Linear energy correction factors for the DFT/TD-DFT results that provide the best agreement with available experimental reference results were determined in order to provide more accurate predictions.