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Improving Nanoparticle Size Estimation from Scanning Transmission Electron Micrographs with a Multislice Surrogate Model.

Nano Lett

January 2025

Electron Microscopy Center, Empa - Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.

The computational cost of simulating scanning transmission electron microscopy (STEM) images limits the curation of large enough data sets to train accurate and robust machine learning networks for deep feature extraction from atomically resolved STEM images. For nanoparticle size estimation in particular, a diverse data set is essential due to the large variations in size, shape, crystallinity, orientation, and dynamical diffraction effects in experimental data. To address this, we train a 3D convolutional neural network to predict STEM images from voxelized atomic models, achieving a 100x speed-up compared to traditional multislice simulations while maintaining high image quality.

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Gas phase bond dissociation energies (BDE) O-H/N-H in hydroquinone (HQ), 4-aminophenol (AP), 1,4-phenylenediamine (PDA), 4-hydroxydiphenylamine (HDPA), N,N'-diphenyl-1,4-phenylenediamine (DPPDA) as well as in their phenoxyl/aminyl radicals have been determined using a combined technique of quantum chemical calculation. The technique included a series of DFT (PBE1PBE, TPSSTPSS, M06-2X), ab initio (DLPNO-CCSD(T)) methods with valence 3ξ-basis sets, composite methods of Gaussian family (G4) and Weizmann theory with ab initio Brueckner Doubles (W1BD), as well as reference reactions of different levels of structural similarity. W1BD method was used in combination with isodesmic reactions for BDE estimation (kJ∙mol) of compounds with the only aromatic fragment: BDE = 352.

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The National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) milk protein yield (MPY) prediction equation includes independent and additive effects of digestible energy intake and absorbed EAA. Our objective was to evaluate the NASEM MPY prediction and EAA use efficiency in Holstein cows in pens from commercial farms. Data collected from 12 Brazilian herds were used.

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Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data.

J Am Med Inform Assoc

January 2025

Information Systems and Business Administration, Johannes Gutenberg University, Mainz 55128, Germany.

Objectives: Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalous.

Materials And Methods: The dataset analyzed consists of 18 587 truncated real-world cancer registry records containing 8 categorical variables describing patients diagnosed with bladder and lung tumors.

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Show the generalizability of an ingredient-based method to automatically create an up-to-date, error-free, complete list of medication codes (e.g., opioid medications with at least one opioid ingredient) from an ingredient list (e.

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