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. (c) 2020 Elsevier B.V. All rights reserved.</p

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

Screening of chorioamnionitis using volatile organic compound detection in exhaled breath: a pre-clinical proof of concept study

Chorioamnionitis is a major risk factor for preterm birth and an independent risk factor for postnatal morbidity for which currently successful therapies are lacking. Emerging evidence indicates that the timing and duration of intra-amniotic infections are crucial determinants for the stage of developmental injury at birth. Insight into the dynamical changes of organ injury after the onset of chorioamnionitis revealed novel therapeutic windows of opportunity. Importantly, successful development and implementation of therapies in clinical care is currently impeded by a lack of diagnostic tools for early (prenatal) detection and surveillance of intra-amniotic infections. In the current study we questioned whether an intra-amniotic infection could be accurately diagnosed by a specific volatile organic compound (VOC) profile in exhaled breath of pregnant sheep. For this purpose pregnant Texel ewes were inoculated intra-amniotically with Ureaplasma parvum and serial collections of exhaled breath were performed for 6 days. Ureaplasma parvum infection induced a distinct VOC-signature in expired breath of pregnant sheep that was significantly different between day 0 and 1 vs. day 5 and 6. Based on a profile of only 15 discriminatory volatiles, animals could correctly be classified as either infected (day 5 and 6) or not (day 0 and 1) with a sensitivity of 83% and a specificity of 71% and an area under the curve of 0.93. Chemical identification of these distinct VOCs revealed the presence of a lipid peroxidation marker nonanal and various hydrocarbons including n-undecane and n-dodecane. These data indicate that intra-amniotic infections can be detected by VOC analyses of exhaled breath and might provide insight into temporal dynamics of intra-amniotic infection and its underlying pathways. In particular, several of these volatiles are associated with enhanced oxidative stress and undecane and dodecane have been reported as predictive biomarker of spontaneous preterm birth in humans. Applying VOC analysis for the early detection of intra-amniotic infections will lead to appropriate surveillance of these high-risk pregnancies, thereby facilitating appropriate clinical course of action including early treatment of preventative measures for pre-maturity-associated morbidities

The SARS-CoV-2 viral load in COVID-19 patients is lower on face mask filters than on nasopharyngeal swabs.

Face masks and personal respirators are used to curb the transmission of SARS-CoV-2 in respiratory droplets; filters embedded in some personal protective equipment could be used as a non-invasive sample source for applications, including at-home testing, but information is needed about whether filters are suited to capture viral particles for SARS-CoV-2 detection. In this study, we generated inactivated virus-laden aerosols of 0.3-2 microns in diameter (0.9 µm mean diameter by mass) and dispersed the aerosolized viral particles onto electrostatic face mask filters. The limit of detection for inactivated coronaviruses SARS-CoV-2 and HCoV-NL63 extracted from filters was between 10 to 100 copies/filter for both viruses. Testing for SARS-CoV-2, using face mask filters and nasopharyngeal swabs collected from hospitalized COVID-19-patients, showed that filter samples offered reduced sensitivity (8.5% compared to nasopharyngeal swabs). The low concordance of SARS-CoV-2 detection between filters and nasopharyngeal swabs indicated that number of viral particles collected on the face mask filter was below the limit of detection for all patients but those with the highest viral loads. This indicated face masks are unsuitable to replace diagnostic nasopharyngeal swabs in COVID-19 diagnosis. The ability to detect nucleic acids on face mask filters may, however, find other uses worth future investigation

Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis

Contains fulltext : 93904.pdf (publisher's version ) (Open Access

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

Contains fulltext : 91843.pdf (publisher's version ) (Open Access)12 p

Chemometrics and NMR spectroscopy for metabolomics analysis of neurological disorders

Contains fulltext : 94175.pdf (publisher's version ) (Open Access)Radboud Universiteit Nijmegen, 28 augustus 2012Promotores : Buydens, L.M.C., Wijmenga, S.S.335 p

Role of DNA polymerase theta in the genesis of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC), due to its genomic heterogeneity and lack of development of effective therapies, will become the second leading cause of cancer-related death within 10 years. Therefore, identifying novel targets that can predict response to specific treatments is a key goal to personalize pancreatic cancer therapy and improve survival. Given that the occurrence of oncogenic KRAS mutations is a characteristic event in PDAC leading to genome instability, a better understanding of the role of DNA repair mechanisms in this process is desirable. The aim of our study was to investigate the role of the error-prone DNA double strand breaks (DSBs) repair pathway, alt-EJ in the presence of KRAS G12D mutation in pancreatic cancer formation. Our findings showed that oncogenic KRAS contributes to the activation of the alt-EJ mechanism by increasing the expression of Polθ, Lig3 and Mre11, key components of alt-EJ in both mouse and human PDAC models. In addition, we demonstrated that alt-EJ has increased activity in DNA DSBs repair pathway in a mouse and human model of PDAC bearing KRAS G12D mutation. We further focused on estimating the impact of alt-EJ inactivation by polymerase theta (Polθ) deletion on pancreatic cancer development and survival in genetically engineered mouse models (GEMMs). Here, we described that although deficiency of Polθ resulted in delayed cancer progression and prolonged survival of experimental mice, it can lead to full-blown PDAC. Our study showed that disabling one component of the alt-EJ may be insufficient to fully suppress pancreatic cancer progression and a complete understanding of all alt-EJ factors and their involvement in DSB repair and oncogenesis is required.Das duktale Adenokarzinom des Pankreas (PDAC) wird aufgrund seiner genomischen Heterogenität und fehlender Entwicklung effektiver Therapien innerhalb von 10 Jahren zur zweithäufigsten krebsbedingten Todesursache werden. Daher ist die Identifizierung neuer Zielmoleküle, die das Ansprechen auf bestimmte Therapien vorhersagen können, ein wesentliches Ziel, um die Behandlung des Pankreaskarzinoms zu personalisieren und die Überlebenschancen zu verbessern. Da das Auftreten von onkogenen KRAS-Mutationen ein charakteristisches Ereignis beim PDAC ist, welches zu Genominstabilität führt, ist ein besseres Verständnis der Rolle der DNA-Reparaturmechanismen in diesem Prozess wünschenswert. Ziel unserer Studie war es, die Rolle des fehlerbehafteten DNA-Doppelstrangbrüche (DSBs) Reparaturwegs, alt-EJ, in Gegenwart einer KRAS G12D-Mutation bei der Pankreaskarzinomentstehung zu untersuchen. Unsere Ergebnisse zeigten, dass onkogenes KRAS zur Aktivierung des alt-EJ-Mechanismus beiträgt, indem es die Expression von Polθ, Lig3 und Mre11, Schlüsselkomponenten von alt-EJ, sowohl in murinen als auch humanen PDAC Modellen erhöht. Zusätzlich haben wir gezeigt, dass alt-EJ eine erhöhte Aktivität im DNA DSBs Reparaturweg in einem murinen und humanen PDAC-Modell, welches eine KRAS G12D-Mutation trägt, aufweist. Weiterhin haben wir uns darauf konzentriert, die Auswirkungen einer alt EJ Inaktivierung durch Deletion der Polymerase Theta (Polθ) auf die Entwicklung von Pankreaskarzinomen und das Überleben bei genetisch veränderten Mausmodellen (GEMMs) abzuschätzen. Hier haben wir beschrieben, dass ein Fehlen der Polθ zwar das Fortschreiten des Krebses verzögert und das Überleben von Versuchsmäusen verlängert, aber dennoch zu einem voll ausgebildeten PDAC führen kann. Unsere Studie hat gezeigt, dass die Deaktivierung einer Komponente des alt-EJ möglicherweise unzureichend ist, um das Fortschreiten des Pankreaskarzinoms vollständig zu unterdrücken, und dass ein vollständiges Verständnis aller alt EJ-Faktoren und ihrer Beteiligung an der DSB-Reparatur und Onkogenese erforderlich ist