Volatile organic compounds in headspace characterize isolated bacterial strains independent of growth medium or antibiotic sensitivity.

INTRODUCTION: Early and reliable determination of bacterial strain specificity and antibiotic resistance is critical to improve sepsis treatment. Previous research demonstrated the potential of headspace analysis of volatile organic compounds (VOCs) to differentiate between various microorganisms associated with pulmonary infections in vitro. This study evaluates whether VOC analysis can also discriminate antibiotic sensitive from resistant bacterial strains when cultured on varying growth media. METHODS: Both antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumonia were cultured on 4 different growth media, i.e. Brain Heart Infusion, Marine Broth, Müller-Hinton and Trypticase Soy Agar. After overnight incubation at 37°C, the headspace air of the cultures was collected on stainless steel desorption tubes and analyzed by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). Statistical analysis was performed using regularized multivariate analysis of variance and cross validation. RESULTS: The three bacterial species could be correctly recognized based on the differential presence of 14 VOCs (p<0.001). This discrimination was not influenced by the different growth media. Interestingly, a clear discrimination could be made between the antibiotic-resistant and -sensitive variant of Pseudomonas aeruginosa (p<0.001) based on their species-specific VOC signature. CONCLUSION: This study demonstrates that isolated microorganisms, including antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, could be identified based on their excreted VOCs independent of the applied growth media. These findings suggest that the discriminating volatiles are associated with the microorganisms themselves rather than with their growth medium. This study exemplifies the potential of VOC analysis as diagnostic tool in medical microbiology. However, validation of our results in appropriate in vivo models is critical to improve translation of breath analysis to clinical applications.

Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer.

Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images. Specifically, we compare U-Net, DenseNet and EfficientNet. Models were developed on a multi-center cohort with 582 WSIs and subsequently evaluated on two independent test sets including 85 and 50 WSIs, respectively, to show the robustness of the proposed method to differing staining protocols and scanner types. We also investigated the application of normalization as a pre-processing step by two techniques, the whole-slide image color standardizer (WSICS) algorithm, and a cycle-GAN based method. For the two independent datasets we obtained an AUC of 0.92 and 0.83 respectively. After rescanning the AUC improves to 0.91/0.88 and after style normalization to 0.98/0.97. In the future our algorithm could be used to automatically pre-screen prostate biopsies to alleviate the workload of pathologists.

Constructing bi-plots for random forest: Tutorial.

Current technological developments have allowed for a significant increase and availability of data. Consequently, this has opened enormous opportunities for the machine learning and data science field, translating into the development of new algorithms in a wide range of applications in medical, biomedical, daily-life, and national security areas. Ensemble techniques are among the pillars of the machine learning field, and they can be defined as approaches in which multiple, complex, independent/uncorrelated, predictive models are subsequently combined by either averaging or voting to yield a higher model performance. Random forest (RF), a popular ensemble method, has been successfully applied in various domains due to its ability to build predictive models with high certainty and little necessity of model optimization. RF provides both a predictive model and an estimation of the variable importance. However, the estimation of the variable importance is based on thousands of trees, and therefore, it does not specify which variable is important for which sample group. The present study demonstrates an approach based on the pseudo-sample principle that allows for construction of bi-plots (i.e. spin plots) associated with RF models. The pseudo-sample principle for RF. is explained and demonstrated by using two simulated datasets, and three different types of real data, which include political sciences, food chemistry and the human microbiome data. The pseudo-sample bi-plots, associated with RF and its unsupervised version, allow for a versatile visualization of multivariate models, and the variable importance and the relation among them.

Identification of Mycobacterium tuberculosis using volatile biomarkers in culture and exhaled breath.

In this pilot study, volatile molecules produced by cultures of Mycobacterium tuberculosis were evaluated to determine whether they could be used to discriminate between uninfected and M. tuberculosis-infected macaques. Thirty seven of the culture biomarkers were detectable in macaque breath and were shown to discriminate between uninfected and infected animals with an area under the curve (AUC) of 87%. An AUC of 98% was achieved when using the top 38 discriminatory molecules detectable in breath. We report two newly discovered volatile biomarkers, not previously associated with M. tuberculosis, that were selected in both our in vitro and in vivo discriminatory biomarker suites: 4-(1,1-dimethylpropyl)phenol and 4-ethyl-2,2,6,6-tetramethylheptane. Additionally, we report the detection of heptanal, a previously identified M. tuberculosis breath biomarker in humans, as an in vitro culture biomarker that was detected in every macaque breath sample analyzed, though not part of the in vivo discriminatory suite. This pilot study suggests that molecules from the headspace of M. tuberculosis culture show potential to translate as breath biomarkers for macaques infected with the same strain.

Volatile metabolites in breath strongly correlate with gut microbiome in CD patients.

Microbiota composition and its metabolic capacity are very important for host health. Evidence suggests that gut microbiome is involved in the metabolites production by host-microbiome interaction. These metabolites can be absorbed in blood and excreted in exhaled air. Although, profiles of gut microbiota and exhaled metabolites were associated with gastrointestinal diseases, a direct link between them has not yet been investigated. The aim of the study was to investigate the relation between volatiles in breath and gut microbiome in active and quiescent Crohn's disease (CD) via a multivariate statistical approach. Canonical correlation analysis (CCA) was used to assess the relation between exhaled metabolites and faecal bacterial species. From 68 CD patients, 184 repeated faecal and breath samples were collected (92 active and 92 quiescent disease). The microbiota composition was assessed by the pyrosequencing of the 16 S rRNA V1-V3 gene region and breath metabolites by gas chromatography mass spectrometry. In active disease, CCA analysis identified 18 metabolites significantly correlated with 19 faecal bacterial taxa (R = 0.91 p-value 3.5*10-4). In quiescent disease 17 volatile metabolites were correlated with 17 bacterial taxa (R = 0.96 p-value 2.8*10-4). Nine metabolites and three bacteria taxa overlapped in active and inactive CD. This is the first study that shows a significant relation between gut microbiome and exhaled metabolites, and was found to differ between active and quiescent CD, indicating various underlying mechanisms. Unravelling this link is essential to increase our understanding on the functional effects of the microbiome and may provide new leads for microbiome-targeted intervention.

A new method to evaluate macaque health using exhaled breath: A case study of M. tuberculosis in a BSL-3 setting.

Breath is hypothesized to contain clinically relevant information, useful for the diagnosis and monitoring of disease, as well as understanding underlying pathogenesis. Nonhuman primates, such as the cynomolgus macaque, serve as an important model for the study of human disease, including over 70 different human infections. In this feasibility study, exhaled breath was successfully collected in less than 5 min under Biosafety Level 3 conditions from five anesthetized, intubated cynomolgus and rhesus macaques, before and after lung infection with M. tuberculosis The breath was subsequently analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. A total of 384 macaque breath features were detected, with hydrocarbons being the most abundant. We provide putative identification for 19 breath molecules and report on overlap between the identified macaque breath compounds and those identified in previous human studies.NEW & NOTEWORTHY To the best of our knowledge, this is the first time the volatile molecule content of macaque breath has been comprehensively sampled and analyzed. We do so here in a Biosafety Level 3 setting in the context of M. tuberculosis lung infection. The breath of nonhuman primates represents a novel fluid that could provide insight into disease pathogenesis.

Data Fusion in Metabolomics and Proteomics for Biomarker Discovery.

Proteomics and metabolomics provide key insights into status and dynamics of biological systems. These molecular studies reveal the complex mechanisms involved in disease or aging processes. Invaluable information can be obtained using various analytical techniques such as nuclear magnetic resonance, liquid chromatography, or gas chromatography coupled to mass spectrometry. Each method has inherent advantages and drawbacks, but they are complementary in terms of biological information.The fusion of different measurements is a complex topic. We describe here a framework allowing combining multiple data sets, provided by different analytical platforms. For each platform, the relevant information is extracted in the first step. The obtained latent variables are then fused and further analyzed. The influence of the original variables is then calculated back and interpreted.

Increased mitochondrial ATP production capacity in brain of healthy mice and a mouse model of isolated complex I deficiency after isoflurane anesthesia.

We reported before that the minimal alveolar concentration (MAC) of isoflurane is decreased in complex I-deficient mice lacking the NDUFS4 subunit of the respiratory chain (RC) (1.55 and 0.81% at postnatal (PN) 22-25 days and 1.68 and 0.65% at PN 31-34 days for wildtype (WT) and CI-deficient KO, respectively). A more severe respiratory depression was caused by 1.0 MAC isoflurane in KO mice (respiratory rate values of 86 and 45 at PN 22-25 days and 69 and 29 at PN 31-34 days for anesthetized WT and KO, respectively). Here, we address the idea that isoflurane anesthesia causes a much larger decrease in brain mitochondrial ATP production in KO mice thus explaining their increased sensitivity to this anesthetic. Brains from WT and KO mice of the above study were removed immediately after MAC determination at PN 31-34 days and a mitochondria-enriched fraction was prepared. Aliquots were used for measurement of maximal ATP production in the presence of pyruvate, malate, ADP and creatine and, after freeze-thawing, the maximal activity of the individual RC complexes in the presence of complex-specific substrates. CI activity was dramatically decreased in KO, whereas ATP production was decreased by only 26% (p < 0.05). The activities of CII, CIII, and CIV were the same for WT and KO. Isoflurane anesthesia decreased the activity of CI by 30% (p < 0.001) in WT. In sharp contrast, it increased the activity of CII by 37% (p < 0.001) and 50% (p < 0.001) and that of CIII by 37% (p < 0.001) and 40% (p < 0.001) in WT and KO, respectively, whereas it tended to increase that of CIV in both WT and KO. Isoflurane anesthesia increased ATP production by 52 and 69% in WT (p < 0.05) and KO (p < 0.01), respectively. Together these findings indicate that isoflurane anesthesia interferes positively rather than negatively with the ability of CI-deficient mice brain mitochondria to convert their main substrate pyruvate into ATP.

Toward high-content screening of mitochondrial morphology and membrane potential in living cells.

Mitochondria are double membrane organelles involved in various key cellular processes. Governed by dedicated protein machinery, mitochondria move and continuously fuse and divide. These "mitochondrial dynamics" are bi-directionally linked to mitochondrial and cell functional state in space and time. Due to the action of the electron transport chain (ETC), the mitochondrial inner membrane displays a inside-negative membrane potential (Δψ). The latter is considered a functional readout of mitochondrial "health" and required to sustain normal mitochondrial ATP production and mitochondrial fusion. During the last decade, live-cell microscopy strategies were developed for simultaneous quantification of Δψ and mitochondrial morphology. This revealed that ETC dysfunction, changes in Δψ and aberrations in mitochondrial structure often occur in parallel, suggesting they are linked potential targets for therapeutic intervention. Here we discuss how combining high-content and high-throughput strategies can be used for analysis of genetic and/or drug-induced effects at the level of individual organelles, cells and cell populations. This article is part of a Directed Issue entitled: Energy Metabolism Disorders and Therapies.

Quantifying small molecule phenotypic effects using mitochondrial morpho-functional fingerprinting and machine learning.

In primary fibroblasts from Leigh Syndrome (LS) patients, isolated mitochondrial complex I deficiency is associated with increased reactive oxygen species levels and mitochondrial morpho-functional changes. Empirical evidence suggests these aberrations constitute linked therapeutic targets for small chemical molecules. However, the latter generally induce multiple subtle effects, meaning that in vitro potency analysis or single-parameter high-throughput cell screening are of limited use to identify these molecules. We combine automated image quantification and artificial intelligence to discriminate between primary fibroblasts of a healthy individual and a LS patient based upon their mitochondrial morpho-functional phenotype. We then evaluate the effects of newly developed Trolox variants in LS patient cells. This revealed that Trolox ornithylamide hydrochloride best counterbalanced mitochondrial morpho-functional aberrations, effectively scavenged ROS and increased the maximal activity of mitochondrial complexes I, IV and citrate synthase. Our results suggest that Trolox-derived antioxidants are promising candidates in therapy development for human mitochondrial disorders.

Photo-induction and automated quantification of reversible mitochondrial permeability transition pore opening in primary mouse myotubes.

Opening of the mitochondrial permeability transition pore (mPTP) is involved in various cellular processes including apoptosis induction. Two distinct states of mPTP opening have been identified allowing the transfer of molecules with a molecular weight <1500 Da or <300 Da. The latter state is considered to be reversible and suggested to play a role in normal cell physiology. Here we present a strategy combining live-cell imaging and computer-assisted image processing allowing spatial visualization and quantitative analysis of reversible mPTP openings ("ΔΨ flickering") in primary mouse myotubes. The latter were stained with the photosensitive cation TMRM, which partitions between the cytosol and mitochondrial matrix as a function of mitochondrial membrane potential (ΔΨ). Controlled illumination of TMRM-stained primary mouse myotubes induced ΔΨ flickering in particular parts of the cell ("flickering domains"). A novel quantitative automated analysis was developed and validated to detect and quantify the frequency, size, and location of individual ΔΨ flickering events in myotubes.

Towards the disease biomarker in an individual patient using statistical health monitoring.

In metabolomics, identification of complex diseases is often based on application of (multivariate) statistical techniques to the data. Commonly, each disease requires its own specific diagnostic model, separating healthy and diseased individuals, which is not very practical in a diagnostic setting. Additionally, for orphan diseases such models cannot be constructed due to a lack of available data. An alternative approach adapted from industrial process control is proposed in this study: statistical health monitoring (SHM). In SHM the metabolic profile of an individual is compared to that of healthy people in a multivariate manner. Abnormal metabolite concentrations, or abnormal patterns of concentrations, are indicated by the method. Subsequently, this biomarker can be used for diagnosis. A tremendous advantage here is that only data of healthy people is required to construct the model. The method is applicable in current-population based -clinical practice as well as in personalized health applications. In this study, SHM was successfully applied for diagnosis of several orphan diseases as well as detection of metabotypic abnormalities related to diet and drug intake.

Breath acetone to monitor life style interventions in field conditions: an exploratory study.

OBJECTIVE: To assess whether breath acetone concentration can be used to monitor the effects of a prolonged physical activity on whole body lipolysis and hepatic ketogenesis in field conditions. METHODS: Twenty-three non-diabetic, 11 type 1 diabetic, and 17 type 2 diabetic subjects provided breath and blood samples for this study. Samples were collected during the International Four Days Marches, in the Netherlands. For each participant, breath acetone concentration was measured using proton transfer reaction ion trap mass spectrometry, before and after a 30-50 km walk on four consecutive days. Blood non-esterified free fatty acid (NEFA), beta-hydroxybutyrate (BOHB), and glucose concentrations were measured after walking. RESULTS: Breath acetone concentration was significantly higher after than before walking, and was positively correlated with blood NEFA and BOHB concentrations. The effect of walking on breath acetone concentration was repeatedly observed on all four consecutive days. Breath acetone concentrations were higher in type 1 diabetic subjects and lower in type 2 diabetic subjects than in control subjects. CONCLUSIONS: Breath acetone can be used to monitor hepatic ketogenesis during walking under field conditions. It may, therefore, provide real-time information on fat burning, which may be of use for monitoring the lifestyle interventions.

NMR and pattern recognition methods in metabolomics: from data acquisition to biomarker discovery: a review.

Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).

Confirmation of brand identity of a Trappist beer by mid-infrared spectroscopy coupled with multivariate data analysis.

Authentication of foods is of importance both to consumers and producers for e.g. confidence in label descriptions and brand protection, respectively. The authentication of beers has received limited attention and in most cases only small data sets were analysed. In this study, Fourier-transform infrared attenuated total reflectance (FT-IR ATR) spectroscopy was applied to a set of 267 beers (53 different brands) to confirm claimed identity for samples of a single beer brand based on their spectral profiles. Skewness-adjusted robust principal component analysis (ROBPCA) was deployed to detect outliers in the data. Subsequently, extended canonical variates analysis (ECVA) was used to reduce the dimensionality of the data while simultaneously achieving maximum class separation. Finally, the reduced data were used as inputs to various linear and non-linear classifiers. Work focused on the specific identification of Rochefort 8° (a Trappist beer) and both direct and indirect (using an hierarchical approach) identification strategies were studied. For the classification problems Rochefort vs. non-Rochefort, Rochefort 8° vs. non-Rochefort 8° and Rochefort 8° vs. Rochefort 6° and 10°, correct prediction abilities of 93.8%, 93.3% and 97.3%, respectively were achieved.

Interpretation and visualization of non-linear data fusion in kernel space: study on metabolomic characterization of progression of multiple sclerosis.

BACKGROUND: In the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required. METHODOLOGY: The main aim is to demonstrate a novel approach for data fusion for classification; the approach is applied to metabolomics datasets coming from patients suffering from MScl at a different stage of the disease. The approach involves data fusion in kernel space and consists of four main steps. The first one is to extract the significant information per data source using Support Vector Machine Recursive Feature Elimination. This method allows one to select a set of relevant variables. In the next step the optimized kernel matrices are merged by linear combination. In step 3 the merged datasets are analyzed with a classification technique, namely Kernel Partial Least Square Discriminant Analysis. In the final step, the variables in kernel space are visualized and their significance established. CONCLUSIONS: We find that fusion in kernel space allows for efficient and reliable discrimination of classes (MScl and early stage). This data fusion approach achieves better class prediction accuracy than analysis of individual datasets and the commonly used mid-level fusion. The prediction accuracy on an independent test set (8 samples) reaches 100%. Additionally, the classification model obtained on fused kernels is simpler in terms of complexity, i.e. just one latent variable was sufficient. Finally, visualization of variables importance in kernel space was achieved.

Simultaneous analysis of plasma and CSF by NMR and hierarchical models fusion.

Because cerebrospinal fluid (CSF) is the biofluid which interacts most closely with the central nervous system, it holds promise as a reporter of neurological disease, for example multiple sclerosis (MScl). To characterize the metabolomics profile of neuroinflammatory aspects of this disease we studied an animal model of MScl-experimental autoimmune/allergic encephalomyelitis (EAE). Because CSF also exchanges metabolites with blood via the blood-brain barrier, malfunctions occurring in the CNS may be reflected in the biochemical composition of blood plasma. The combination of blood plasma and CSF provides more complete information about the disease. Both biofluids can be studied by use of NMR spectroscopy. It is then necessary to perform combined analysis of the two different datasets. Mid-level data fusion was therefore applied to blood plasma and CSF datasets. First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination. The selected variables from each dataset were concatenated for joint analysis by partial least squares discriminant analysis (PLS-DA). The combined metabolomics information from plasma and CSF enables more efficient and reliable discrimination of the onset of EAE. Second, we introduced hierarchical models fusion, in which previously developed PLS-DA models are hierarchically combined. We show that this approach enables neuroinflamed rats (even on the day of onset) to be distinguished from either healthy or peripherally inflamed rats. Moreover, progression of EAE can be investigated because the model separates the onset and peak of the disease.

Isolated mitochondrial complex I deficiency: explorative data analysis of patient cell parameters.

Mitochondrial dysfunction has been implicated in many human diseases and off-target drug effects. Isolated deficiency of mitochondrial complex I (CI), the first complex of the oxidative phosphorylation (OXPHOS) system, can arise from mutations in nuclear DNA (nDNA)-encoded subunits. In humans, these mutations are generally associated with neurodegenerative disorders like Leigh or Leigh-like syndrome with onset in early childhood. Currently, no cure or mitigative treatment is available for these diseases. To aid the future design of rational treatment strategies, insight into the pathophysiology of CI mutations is required. To this end, we quantitatively compared various cell physiological readouts between fibroblasts from healthy individuals and patients with isolated CI deficiency. Here we review how this multivariate dataset was obtained and in which way explorative data analysis (EDA) techniques can be used for pattern analysis. Based upon 13 experimental parameters two patient groups were identified. These displayed a later (cluster I) or earlier (cluster II) age of disease onset and death. Relative to cluster I, cluster II patient cells displayed a larger reduction in CI activity, a larger increase in NADH/ROS levels, mitochondrial fragmentation and lower cellular levels of OXPHOS proteins. Our results highlight a connection between CI deficiency, ROS and mitochondrial morphology/function. This information not only contributes to our understanding of the pathophysiological mechanism of CI and mitochondrial deficiency but also suggests possible targets for cellular intervention strategies.

NMR and pattern recognition can distinguish neuroinflammation and peripheral inflammation.

Multiple Sclerosis (MScl) is a neurodegenerative disease of the CNS, associated with chronic neuroinflammation. Cerebrospinal fluid (CSF), being in closest interaction with CNS, was used to profile neuroinflammation to discover disease-specific markers. We used the commonly accepted animal model for the neuroinflammatory aspect of MScl: the experimental autoimmune/allergic encephalomyelitis (EAE). A combination of advanced (1)H NMR spectroscopy and pattern recognition methods was used to establish the metabolic profile of CSF of EAE-affected rats (representing neuroinflammation) and of two control groups (healthy and peripherally inflamed) to detect specific markers for early neuroinflammation. We found that the CSF metabolic profile for neuroinflammation is distinct from healthy and peripheral inflammation and characterized by changes in concentrations of metabolites such as creatine, arginine, and lysine. Using these disease-specific markers, we were able to detect early stage neuroinflammation, with high accuracy in a second independent set of animals. This confirms the predictive value of these markers. These findings from the EAE model may help to develop a molecular diagnosis for the early stage MScl in humans.

Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis.

BACKGROUND: Analysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these pathologies. Proteomics and metabolomics methods provide invaluable information on the biomolecular content of CSF and thereby on the possible status of the central nervous system, including neurological pathologies. The combined information provides a more complete description of CSF content. Extracting the full combined information requires a combined analysis of different datasets i.e. fusion of the data. RESULTS: A novel fusion method is presented and applied to proteomics and metabolomics data from a pre-clinical model of multiple sclerosis: an Experimental Autoimmune Encephalomyelitis (EAE) model in rats. The method follows a mid-level fusion architecture. The relevant information is extracted per platform using extended canonical variates analysis. The results are subsequently merged in order to be analyzed jointly. We find that the combined proteome and metabolome data allow for the efficient and reliable discrimination between healthy, peripherally inflamed rats, and rats at the onset of the EAE. The predicted accuracy reaches 89% on a test set. The important variables (metabolites and proteins) in this model are known to be linked to EAE and/or multiple sclerosis. CONCLUSIONS: Fusion of proteomics and metabolomics data is possible. The main issues of high-dimensionality and missing values are overcome. The outcome leads to higher accuracy in prediction and more exhaustive description of the disease profile. The biological interpretation of the involved variables validates our fusion approach.