Interpretation of individual differences in computational neuroscience using a latent input approach.

Computational neuroscience offers a valuable opportunity to understand the neural mechanisms underlying behavior. However, interpreting individual differences in these mechanisms, such as developmental differences, is less straightforward. We illustrate this challenge through studies that examine individual differences in reinforcement learning.

Interaction of light with gas-liquid interfaces: influence on photon absorption in continuous-flow photoreactors.

Light interacts with gas bubbles in various ways, potentially leading to photon losses in gas-liquid photochemical applications. Given that light is a valuable 'reagent', understanding these losses is crucial for optimizing reactor efficiency. In this study, we address the challenge of quantifying these interactions by implementing a method that separately determines the photon flux and utilizes actinometric experiments to determine the effective optical path length, a key descriptor of photon absorption.

Development and expert inspections of the user interface for a primary care decision support system.

Background: General practitioners play a unique key role in diagnosing patients with unclear diseases. Decision support systems in primary care can assist with diagnosis provided that they are efficient and user-friendly.

Objectives: The objective of this study is to develop a high-fidelity prototype of the user interface of a clinical decision support system for primary care, particularly for diagnosis support in unclear diseases, using expert inspections at an early stage of development to ensure a high level of usability.

How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.

Background: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases.

Secondary use of patient data within decentralized studies using the example of rare diseases in Germany: A data scientist's exploration of process and lessons learned.

Objective: Unlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance.