Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates

Gas hydrates represent one of the main flow assurance challenges in the oil and gas industry as they can lead to plugging of pipelines and process equipment. In this paper we present a literature study performed to evaluate the current state of the use of machine learning methods within the field of gas hydrates with specific focus on the oil chemistry. A common analysis technique for crude oils is Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) which could be a good approach to achieving a better understanding of the chemical composition of hydrates, and the use of machine learning in the field of FT-ICR MS was therefore also examined. Several machine learning methods were identified as promising, their use in the literature was reviewed and a text analysis study was performed to identify the main topics within the publications. The literature search revealed that the publications on the combination of FT-ICR MS, machine learning and gas hydrates is limited to one. Most of the work on gas hydrates is related to thermodynamics, while FT-ICR MS is mostly used for chemical analysis of oils. However, with the combination of FT-ICR MS and machine learning to evaluate samples related to gas hydrates, it could be possible to improve the understanding of the composition of hydrates and thereby identify hydrate active compounds responsible for the differences between oils forming plugging hydrates and oils forming transportable hydrates.Current overview and way forward for the use of machine learning in the field of petroleum gas hydratespublishedVersio

Design of Selective Inhibitors of Tyrosine Kinase 2

Abstract: Selective inhibitors of tyrosine kinase 2 (Tyk2) were searched for using database screening, de novo ligand design and computational docking in Tyk2 and seven other protein kinases. None of the structures in the National Cancer Institute database seem to inhibit Tyk2 selectively, but five of the designed structures seem promising

Using machine learning-based variable selection to identify hydrate related components from FT-ICR MS spectra

The blockages of pipelines caused by agglomeration of gas hydrates is a major flow assurance issue in the oil and gas industry. Some crude oils form gas hydrates that remain as transportable particles in a slurry. It is commonly believed that naturally occurring components in those crude oils alter the surface properties of gas hydrate particles when formed. The exact structure of the crude oil components responsible for this surface modification remains unknown. In this study, a successive accumulation and spiking of hydrate-active crude oil fractions was performed to increase the concentration of hydrate related compounds. Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) was then utilised to analyse extracted oil samples for each spiking generation. Machine learning-based variable selection was used on the FT-ICR MS spectra to identify the components related to hydrate formation. Among six different methods, Partial Least Squares Discriminant Analysis (PLS-DA) was selected as the best performing model and the 23 most important variables were determined. The FT-ICR MS mass spectra for each spiking level was compared to samples extracted before the successive accumulation, to identify changes in the composition. Principal Component Analysis (PCA) exhibited differences between the oils and spiking levels, indicating an accumulation of hydrate active components. Molecular formulas, double bond equivalents (DBE) and hydrogen-carbon (H/C) ratios were determined for each of the selected variables and evaluated. Some variables were identified as possibly asphaltenes and naphthenic acids which could be related to the positive wetting index (WI) for the oils.publishedVersio

Grayscale representation of infrared microscopy images by Extended Multiplicative Signal Correction for registration with histological images

Fourier-transform infrared (FTIR) microspectroscopy is rounding the corner to become a label-free routine method for cancer diagnosis. In order to build infrared-spectral based classifiers, infrared images need to be registered with Hematoxylin and Eosin (H&E) stained histological images. While FTIR images have a deep spectral domain with thousands of channels carrying chemical and scatter information, the H&E images have only three color channels for each pixel and carry mainly morphological information. Therefore, image representations of infrared images are needed that match the morphological information in H&E images. In this paper, we propose a novel approach for representation of FTIR images based on extended multiplicative signal correction highlighting morphological features that showed to correlate well with morphological information in H&E images. Based on the obtained representations, we developed a strategy for global-to-local image registration for FTIR images and H&E stained histological images of parallel tissue sections.publishedVersio

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

<p>Abstract</p> <p>Background</p> <p>Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy <it>C</it>-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function.</p> <p>Results</p> <p>Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops.</p> <p>Conclusions</p> <p>HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.</p

Early Induction of Cross-Reactive CD8+ T-Cell Responses in Tonsils After Live-Attenuated Influenza Vaccination in Children

Background Live-attenuated influenza vaccine (LAIV) was licensed for prophylaxis of children 2–17 years old in Europe in 2012 and is administered as a nasal spray. Live-attenuated influenza vaccine induces both mucosal and systemic antibodies and systemic T-cell responses. Tonsils are the lymph nodes serving the upper respiratory tract, acting as both induction and effector site for mucosal immunity. Methods Here, we have studied the early tonsillar T-cell responses induced in children after LAIV. Thirty-nine children were immunized with trivalent LAIV (containing A/H1N1, A/H3N2, and B viruses) at days 3, 7, and 14 before tonsillectomy. Nonvaccinated controls were included for comparison. Tonsils and peripheral blood (pre- and postvaccination) were collected to study T-cell responses. Results Tonsillar and systemic T-cell responses differed between influenza strains, and both were found against H3N2 and B viruses, whereas only systemic responses were observed against A/H1N1. A significant increase in cross-reactive tonsillar CD8+ T cells recognizing conserved epitopes from a broad range of seasonal and pandemic viruses occurred at day 14. Tonsillar T cells showed significant cytokine responses (Th1, Th2, and granulocyte-macrophage colony-stimulating factor). Conclusions Our findings support the use of LAIV in children to elicit broadly cross-reactive T cells, which are not induced by traditional inactivated influenza vaccines and may provide protection to novel virus strains.publishedVersio

Attack rates amongst household members of outpatients with confirmed COVID-19 in Bergen, Norway: A case-ascertained study

Background Households studies reflect the natural spread of SARS-CoV-2 in immunologically naive populations with limited preventive measures to control transmission. We hypothesise that seropositivity provides more accurate household attack rates than RT-PCR. Here, we investigated the importance of age in household transmission dynamics. Methods We enroled 112 households (291 participants) in a case-ascertained study in Bergen, Norway from 28th February to 4th April 2020, collecting demographic and clinical data from index patients and household members. SARS-CoV-2-specific antibodies were measured in sera collected 6–8 weeks after index patient nasopharyngeal testing to define household attack rates. Findings The overall attack rate was 45% (95% CI 38–53) assessed by serology, and 47% when also including seronegative RT-PCR positives. Serology identified a higher number of infected household members than RT-PCR. Attack rates were equally high in children (48%) and young adults (42%). The attack rate was 16% in asymptomatic household members and 42% in RT-PCR negative contacts. Older adults had higher antibody titres than younger adults. The risk of household transmission was higher when the index patient had fever (aOR 3.31 [95% CI 1.52–7.24]; p = 0.003) or dyspnoea (aOR 2.25 [95% CI 1.80–4.62]; p = 0.027) during acute illness. Interpretation Serological assays provide more sensitive and robust estimates of household attack rates than RT-PCR. Children are equally susceptible to infection as young adults. Negative RT-PCR or lack of symptoms are not sufficient to rule out infection in household members.publishedVersio

Risk assessment and antibody responses to SARS-CoV-2 in healthcare workers

Background: Preventing infection in healthcare workers (HCWs) is crucial for protecting healthcare systems during the COVID-19 pandemic. Here, we investigated the seroepidemiology of SARS-CoV-2 in HCWs in Norway with low-transmission settings. Methods: From March 2020, we recruited HCWs at four medical centres. We determined infection by SARS-CoV-2 RT-PCR and serological testing and evaluated the association between infection and exposure variables, comparing our findings with global data in a meta-analysis. Anti-spike IgG antibodies were measured after infection and/or vaccination in a longitudinal cohort until June 2021. Results: We identified a prevalence of 10.5% (95% confidence interval, CI: 8.8–12.3) in 2020 and an incidence rate of 15.0 cases per 100 person-years (95% CI: 12.5–17.8) among 1,214 HCWs with 848 person-years of follow-up time. Following infection, HCWs (n = 63) mounted durable anti-spike IgG antibodies with a half-life of 4.3 months since their seropositivity. HCWs infected with SARS-CoV-2 in 2020 (n = 46) had higher anti-spike IgG titres than naive HCWs (n = 186) throughout the 5 months after vaccination with BNT162b2 and/or ChAdOx1-S COVID-19 vaccines in 2021. In a meta-analysis including 20 studies, the odds ratio (OR) for SARS-CoV-2 seropositivity was significantly higher with household contact (OR 12.6; 95% CI: 4.5–35.1) and occupational exposure (OR 2.2; 95% CI: 1.4–3.2). Conclusion: We found high and modest risks of SARS-CoV-2 infection with household and occupational exposure, respectively, in HCWs, suggesting the need to strengthen infection prevention strategies within households and medical centres. Infection generated long-lasting antibodies in most HCWs; therefore, we support delaying COVID-19 vaccination in primed HCWs, prioritising the non-infected high-risk HCWs amid vaccine shortage.publishedVersio

The Performances of Three Commercially Available Assays for the Detection of SARS‐CoV‐2 Antibodies at Different Time Points Following SARS‐CoV‐2 Infection

The aim of this study was to evaluate the performances of three commercially available antibody assays for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies at different time points following SARS-CoV-2 infection. Sera from 536 cases, including 207 SARS-CoV-2 PCR positive, were tested for SARS-CoV-2 antibodies with the Wantai receptor binding domain (RBD) total antibody assay, Liaison S1/S2 IgG assay and Alinity i nucleocapsid IgG assay and compared to a two-step reference ELISA (SARS-CoV-2 RBD IgG and SARS-CoV-2 spike IgG). Diagnostic sensitivity, specificity, predictive values and Cohen’s kappa were calculated for the commercial assays. The assay’s sensitivities varied greatly, from 68.7% to 95.3%, but the specificities remained high (96.9%–99.1%). The three tests showed good performances in sera sampled 31 to 60 days after PCR positivity compared to the reference ELISA. The total antibody test performed better than the IgG tests the first 30 days and the nucleocapsid IgG test showed reduced sensitivity two months or more after PCR positivity. Hence, the test performances at different time points should be taken into consideration in clinical practice and epidemiological studies. Spike or RBD IgG tests are preferable in sera sampled more than two months following SARS-CoV-2 infection.publishedVersio

Live attenuated influenza vaccine in children induces b-cell responses in tonsils

Background. Tonsils play a key role in eliciting immune responses against respiratory pathogens. Little is known about how tonsils contribute to the local immune response after intranasal vaccination. Here, we uniquely report the mucosal humoral responses in tonsils and saliva after intranasal live attenuated influenza vaccine (LAIV) vaccination in children. Methods. Blood, saliva, and tonsils samples were collected from 39 children before and after LAIV vaccination and from 16 agematched, nonvaccinated controls. Serum antibody responses were determined by a hemagglutination inhibition (HI) assay. The salivary immunoglobulin A (IgA) level was measured by an enzyme-linked immunosorbent assay. Antibody-secreting cell (ASC) and memory B-cell (MBC) responses were enumerated in tonsils and blood. Results. Significant increases were observed in levels of serum antibodies and salivary IgA to influenza A(H3N2) and influenza B virus strains as early as 14 days after vaccination but not to influenza A(H1N1). Influenza virus-specific salivary IgA levels correlated with serum HI responses, making this a new possible indicator of vaccine immunogenicity in children. LAIV augmented influenza virus-specific B-cell responses in tonsils and blood. Tonsillar MBC responses correlated with systemic MBC and serological responses. Naive children showed significant increases in MBC counts after LAIV vaccination. Conclusions. This is the first study to demonstrate that LAIV elicits humoral B-cell responses in tonsils of young children. Furthermore, salivary IgA analysis represents an easy method for measuring immunogenicity after vaccination