Multiway Decomposition Followed by Reconvolution of Fluorescence Time Decay Data.

The use of time-resolved emission spectroscopy (TRES) is increasing as these instruments become more available, and the prices are decreasing, especially with the development of cheaper LED light sources. In this article, we propose a new methodology for analyzing TRES data. It combines two existing methods: PARAFAC and reconvolution. PARAFAC is a soft modeling curve resolution technique which has been extensively applied to steady-state fluorescence data, and reconvolution is the most common method for fitting TRES data. The proposed method is compared to two well-established methods of analyzing these data, namely, global reconvolution and tail fitting. In addition, we compare our approach with the SLICING method proposed in 2021 by Devos et al. which is also based on a soft model, but does not include the reconvolution step. All of these methods follow the assumption that the measured fluorescence signal is a linear combination of the underlying fluorophores. The comparison is based on a measured TRES data set with a mixture of three fluorophores and two sets of simulated data sets with up to four fluorophores. The results show that global fitting works well as long as the signal-to-noise ratio (SNR) is high (more than 15 dB), independent of the spacing between the emission peak maxima. SLICING does not give as good estimates of the time decay, mainly due to the challenge of defining the tail. Our proposed method gives robust and accurate results, outperforming the other techniques in cases with broad instrument response functions and high noise levels with SNRs down to 5 dB.

PARAFAC2×N: Coupled decomposition of multi-modal data with drift in N modes.

Analysis of GC×GC-TOFMS data for large numbers of poorly-resolved peaks, and for large numbers of samples remains an enduring problem that hinders the widespread application of the technique. For multiple samples, GC×GC-TOFMS data for specific chromatographic regions manifests as a 4th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel is for all practical purposes nonexistent. A number of solutions to handling GC×GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection. The proposed model captures over 99.9% of variance for a synthetic data set, presenting an extreme example of peak drift and co-elution across two modes of separation.

Social ties between team members affect patient satisfaction: a data-driven approach to handling complex network analyses.

Research from outside the medical field suggests that social ties between team-members influence knowledge sharing, improve coordination, and facilitate task completion. However, the relative importance of social ties among team-members for patient satisfaction remains unknown. In this study, we explored the association between social ties within emergency teams performing simulated caesarean sections (CS) and patient-actor satisfaction. Two hundred seventy-two participants were allocated to 33 teams performing two emergency CSs in a simulated setting. We collected data on social ties between team-members, measured as affective, personal and professional ties. Ties were rated on 5-point Likert scales. In addition, participants' clinical experience, demographic data and their knowledge about team members' roles were surveyed. Perceived patient satisfaction was measured on a 5-point Likert scale. Data was analysed with a linear regression model using elastic net regularization. In total, 109 predictor variables were analysed including 84 related to social ties and 25 related to clinical experience, demographics and knowledge test scores. Of the 84 variables reflecting social ties, 34 (41%) had significant association with patient satisfaction, p < 0.01. By contrast, a significant association with patient satisfaction was found for only one (4%) of the 25 variables reflecting clinical experience, demographics and knowledge of team roles. Affective ties and personal ties were found to be far more important predictors in the statistical model than professional ties and predictors relating to clinical experience. Social ties between emergency team members may be important predictors of patient satisfaction. The results from this study help to enhance our conceptual understanding of social ties and their implications for team-dynamics. Our study challenges existing views of team-performance by placing emphasis on achieving collective competence through affective and personal social ties, rather than focusing on traditional measures of expertise.