Mid-infrared emissivity of partially dehydrated asteroid (162173) Ryugu shows strong signs of aqueous alteration.

The near-Earth asteroid (162173) Ryugu, the target of Hayabusa2 space mission, was observed via both orbiter and the lander instruments. The infrared radiometer on the MASCOT lander (MARA) is the only instrument providing spectrally resolved mid-infrared (MIR) data, which is crucial for establishing a link between the asteroid material and meteorites found on Earth. Earlier studies revealed that the single boulder investigated by the lander belongs to the most common type found on Ryugu.

A continued learning approach for model-informed precision dosing: Updating models in clinical practice.

Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown.

Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology.

Model-informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. Current strategies comprise model-informed dosing tables or are based on maximum a posteriori estimates. These approaches, however, lack a quantification of uncertainty and/or consider only part of the available patient-specific information.

Reconstructing regime-dependent causal relationships from observational time series.

Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past few decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this difficult task. In this paper, we focus on an important challenge that is at the core of time series causal discovery: regime-dependent causal relations.

Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy.

An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity.

Photic entrainment of circadian activity patterns in the tropical labrid fish Halichoeres chrysus.

Yellow wrasses (Halichoeres chrysus) show clear daily activity patterns. The fish hide in the substrate at (subjective) night, during the distinct rest phase. Initial entrainment in a 12h:12h light-dark (12:12 LD) cycle (mean period 24.