Introduction: Autoantibodies against citrullinated peptides/proteins (ACPA) are found in approximately 75% of the sera of patients with rheumatoid arthritis (RA). The RA-specific ACPA are frequently present prior to disease onset and their presence associates with a more erosive disease course. ACPA can therefore be used to aid the diagnosis and prognosis of RA. Recently, it became clear that ACPA are very heterogeneous, both in an individual patient and among different patients. The aim of this study was to investigate whether clinically meaningful ACPA profiles exist in early RA patients. Methods: Twenty citrullinated peptides and the corresponding non-citrullinated control peptides were immobilized on microarray sensor chips. Sera from 374 early arthritis patients were analyzed by surface plasmon resonance imaging (i SPR) of biomolecular interactions on the sensor chip. Results: Cluster analysis of the reactivities with the citrullinated peptides, after subtraction of the reactivities with the corresponding control peptides confirmed the heterogeneity of the ACPA response in RA and revealed 12 distinct ACPA profiles. The association of the 5 most frequent profiles with clinical features at diagnosis and during the disease course was examined, showing no statistically significant associations. Conclusions: Compared to the detection of ACPA in RA sera by CCP-based assays, ACPA profiling in early arthritis patients did not reveal associations with disease activity and progression scores.

Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging to analyze and store them within a reasonable digital infrastructure, especially in waste sorting where speed and data storage resources are limited. Additionally, as with most spectroscopic data, there is significant redundancy, making pixel and variable selection crucial for retaining chemical information. Recent high-tech developments in chemometrics enable automated and evidence-based data reduction, which can substantially enhance the speed and performance of Non-Negative Matrix Factorization (NMF), a widely used algorithm for chemical resolution of HSI data. By recovering the pure contribution maps and spectral profiles of distributed compounds, NMF can provide evidence-based sorting decisions for efficient waste management. To improve the quality and efficiency of data analysis on hyperspectral imaging (HSI) data, we apply a convex-hull method to select essential pixels and wavelengths and remove uninformative and redundant information. This process minimizes computational strain and effectively eliminates highly mixed pixels. By reducing data redundancy, data investigation and analysis become more straightforward, as demonstrated in both simulated and real HSI data for plastic sorting.

Many metabolomics studies aim to find ‘biomarkers’: sets of molecules that are consistently elevated or decreased upon experimental manipulation. Biological effects, however, often manifest themselves along a continuum of individual differences between the biological replicates in the experiment. Such differences are overlooked or even diminished by methods in standard use for metabolomics, although they may contain a wealth of information on the experiment. Properly understanding individual differences is crucial for generating knowledge in fields like personalised medicine, evolution and ecology. We propose to use simultaneous component analysis with individual differences constraints (SCA-IND), a data analysis method from psychology that focuses on these differences. This method constructs axes along the natural biochemical differences between biological replicates, comparable to principal components. The model may shed light on changes in the individual differences between experimental groups, but also on whether these differences correspond to, e.g., responders and non-responders or to distinct chemotypes. Moreover, SCA-IND reveals the individuals that respond most to a manipulation and are best suited for further experimentation. The method is illustrated by the analysis of individual differences in the metabolic response of cabbage plants to herbivory. The model reveals individual differences in the response to shoot herbivory, where two ‘response chemotypes’ may be identified. In the response to root herbivory the model shows that individual plants differ strongly in response dynamics. Thereby SCA-IND provides a hitherto unavailable view on the chemical diversity of the induced plant response, that greatly increases understanding of the system.

The aim of this study was to investigate volatile organic compounds (VOCs) in exhaled breath as possible non-invasive markers to monitor the inflammatory response in inflammatory bowel disease (IBD) patients as a result of repeated and prolonged moderate-intensity exercise. We included 18 IBD patients and 19 non-IBD individuals who each completed a 30, 40, or 50 km walking exercise over three consecutive days. Breath and blood samples were taken before the start of the exercise event and every day post-exercise to assess changes in the VOC profiles and cytokine concentrations. Proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) was used to measure exhaled breath VOCs. Multivariate analysis, particularly ANOVA-simultaneous component analysis (ASCA), was employed to extract relevant ions related to exercise and IBD. Prolonged exercise induces a similar response in breath butanoic acid and plasma cytokines for participants with or without IBD. Butanoic acid showed a significant correlation with the cytokine IL-6, indicating that butanoic acid could be a potential non-invasive marker for exercise-induced inflammation. The findings are relevant in monitoring personalized IBD management.

Volatile organic compounds (VOCs) in exhaled breath provide insights into various metabolic processes and can be used to monitor physiological response to exercise and medication. We integrated and validated in situ a sampling and analysis protocol using proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) for exhaled breath research. The approach was demonstrated on a participant cohort comprising users of the cholesterol-lowering drug statins and non-statin users during a field campaign of three days of prolonged and repeated exercise, with no restrictions on food or drink consumption. The effect of prolonged exercise was reflected in the exhaled breath of participants, and relevant VOCs were identified. Most of the VOCs, such as acetone, showed an increase in concentration after the first day of walking and subsequent decrease towards baseline levels prior to walking on the second day. A cluster of short-chain fatty acids including acetic acid, butanoic acid, and propionic acid were identified in exhaled breath as potential indicators of gut microbiota activity relating to exercise and drug use. We have provided novel information regarding the use of breathomics for non-invasive monitoring of changes in human metabolism and especially for the gut microbiome activity in relation to exercise and the use of medication, such as statins.

Accurate quantitative mineralogical data has significant implications in mining operations. However, quantitative analysis of minerals is challenging for most of the sensor outputs. Thus, it requires advances in data analytics. In this work, data fusion approaches for integrating datasets pertaining to the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions are proposed, aiming to facilitate more accurate prediction of SiO₂, Al₂O₃, and Fe₂O₃ concentrations in a polymetallic sulphide deposit. Two approaches of low-level data fusion were applied to these datasets. In the first approach, the pre-processed blocks of MWIR and LWIR data were concatenated to form a fused data block. In the second approach, a prior variable selection was performed to extract the most important features from the MWIR and LWIR datasets. The extracted informative features were subsequently concatenated to form a new fused data block. Next, prediction models that link the mineralogical concentrations with the infrared reflectance spectra were developed using partial-least squares regression (PLSR), principal component regression (PCR) and support vector regression (SVR) analytical techniques. These models were applied to the fused data blocks as well as the individual (MWIR and LWIR) data blocks. The obtained results indicate that SiO₂, Al₂O₃, and Fe₂O₃ mineral concentrations can be successfully predicted using both MWIR and LWIR spectra individually, but the prediction performance greatly improved with data fusion; where the PLSR, PCR, and SVR models provided good and acceptable results. The proposed approach could be extended for online analysis of mineral concentrations in different deposit types. Thus, it would be highly beneficial in mining operations, where indications of mineralogical concentrations can have significant financial implications.