Machine learning methods for compound annotation in non-targeted mass spectrometry-A brief overview of fingerprinting, in silico fragmentation and de novo methods.

Non-targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance-based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML).

Quality Tolerance Limits: A General Guidance for Parameter Selection and Threshold Setting.

The past years have sharpened the industry's understanding of a Quality by Design (QbD) approach toward clinical trials. Using QbD encourages designing quality into a trial during the planning phase. The identification of Critical to Quality (CtQs) factors and specifically Critical Data and Processes (CD&Ps) is key to such a risk-based monitoring approach.

A Quantum-Chemical Bonding Database for Solid-State Materials.

An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis.

Neural ordinary differential equations with irregular and noisy data.

Measurement noise is an integral part of collecting data of a physical process. Thus, noise removal is necessary to draw conclusions from these data, and it often becomes essential to construct dynamical models using these data. We discuss a methodology to learn differential equation(s) using noisy and irregularly sampled measurements.

Twice weekly polymerase chain reaction (PCR) surveillance swabs are not as effective as daily antigen testing for containment of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) outbreaks: A modeling study based on real world data from a child and adolescent psychiatry clinic.

Objective: In the coronavirus disease 2019 (COVID-19) pandemic, child and adolescent psychiatry wards face the risk of severe acute respiratory coronavirus 2 (SARS-CoV-2) introduction and spread within the facility. In this setting, mask and vaccine mandates are hard to enforce, especially for younger children. Surveillance testing may detect infection early and enable mitigation measures to prevent viral spread.