The article presents a new method to address matrix effects in high-dimensional spectral data, which often distort analyte quantification when blank samples or prior knowledge of the sample composition are unavailable. The authors extend the classic standard addition technique to the spectral domain by adjusting the data before applying chemometric models such as Principal Component Regression. Through simulations under various conditions (different noise levels, matrix strengths, and non-linear responses), the method significantly reduced prediction errors compared to standard approaches and proved robust. This demonstrates its potential as a practical and reliable tool for accurate chemical analysis in complex matrices.

The article examines the role of patients’ changing expectations about treatment outcomes in psychotherapy for depression. It shows that not only baseline expectations (before treatment starts) but also the within-person change in expectations over time independently predict treatment success. In a study of 75 patients undergoing 16 sessions of psychotherapy, greater increases in outcome expectancy were linked to faster and more substantial symptom improvement. The findings suggest that dynamic expectancy is a key factor in recovery and could serve both as an indicator of treatment progress and as a therapeutic target itself

This study introduces a novel mechanistic modeling and statistical framework for analyzing motion energy dynamics within psychotherapy sessions. We transform raw motion energy data into an interpretable narrative of therapist-patient interactions, thereby revealing unique insights into the nature of these dynamics. Our methodology is established through three detailed case studies, each shedding light on the complexities of dyadic interactions. A key component of our approach is an analysis spanning four years of one therapist's sessions, allowing us to distinguish between trait-like and state-like dynamics. This research represents a significant advancement in the quantitative understanding of motion dynamics in psychotherapy, with the potential to substantially influence both future research and therapeutic practice.

Ordinary differential equations (ODEs) are foundational in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different understandings of nuances in internal mechanisms or abstraction levels, presents a model selection challenge. This study introduces a testing-based approach for ODE model selection amidst statistical noise. Rooted in the model misspecification framework, we adapt foundational insights from classical statistical paradigms (Vuong and Hotelling) to the ODE context, allowing for the comparison and ranking of diverse causal explanations without the constraints of nested models. Our simulation studies validate the theoretical robustness of our proposed test, revealing its consistent size and power. Real-world data examples further underscore the algorithm's applicability in practice. To foster accessibility and encourage real-world applications, we provide a user-friendly Python implementation of our model selection algorithm, bridging theoretical advancements with hands-on tools for the scientific community.