Capacitive Deionization for Selective Extraction of Lithium from Aqueous Solutions

The paper deals with extraction of lithium by means of two capacitive deionization systems: one composed of lithium selective electrode and second with electrode wrapped with Li-selective membrane. In the case of the first system, hybrid electrodes where obtained by mixing λ-MnO2sorbent with activated carbon .The best Li-capacity was determined for electrode with 20 wt.-% of manganese oxide. For larger amounts of λ-MnO2 the electrode capacity decreased significantly. The second system was composed of carbon electrodes wrapped with ion-exchange membranes. The lithium selective membranes were synthesized by plasma induced interpolymerization of (meth)acrylic monomersinpores of Celgard 2400 support. Two functional monomers, poly(di(ethylene glycol)methyl ether methacrylate) and poly(glycidylmethacylate modified with hydroxymethyl-12-crown-4) were copolymerized with acrylic acid. It was found that the extraction of lithium chloride was the best for membrane caring copolymers of acrylic acid and glycidyl methacrylate modified with crown ether, andit was better than for membranes with sole poly(acrylic acid)

Advanced data fusion: Random forest proximities and pseudo-sample principle towards increased prediction accuracy and variable interpretation

Data fusion has gained much attention in the field of life sciences, and this is because analysis of biological samples may require the use of data coming from multiple complementary sources to express the samples fully. Data fusion lies in the idea that different data platforms detect different biological entities. Therefore, if these different biological compounds are then combined, they can provide comprehensive profiling and understanding of the research question in hand. Data fusion can be performed in three different traditional ways: low-level, mid-level, and high-level data fusion. However, the increasing complexity and amount of generated data require the development of more sophisticated fusion approaches. In that regard, the current study presents an advanced data fusion approach (i.e. proximities stacking) based on random forest proximities coupled with the pseudo-sample principle. Four different data platforms of 130 samples each (faecal microbiome, blood, blood headspace, and exhaled breath samples of patients who have Crohn's disease) were used to demonstrate the classification performance of this new approach. More specifically, 104 samples were used to train and validate the models, whereas the remaining 26 samples were used to validate the models externally. Mid-level, high-level, as well as individual platform classification predictions, were made and compared against the proximities stacking approach. The performance of each approach was assessed by calculating the sensitivity and specificity of each model for the external test set, and visualized by performing principal component analysis on the proximity matrices of the training samples to then, subsequently, project the test samples onto that space. The implementation of pseudo-samples allowed for the identification of the most important variables per platform, finding relations among variables of the different data platforms, and the ex-amination of how variables behave in the samples. The proximities stacking approach outperforms both mid-level and high-level fusion approaches, as well as all individual platform predictions. Concurrently, it tackles significant bottlenecks of the traditional ways of fusion and of another advanced fusion way discussed in the paper, and finally, it contradicts the general belief that the more data, the merrier the result, and therefore, considerations have to be taken into account before any data fusion analysis is conducted. (c) 2021 Published by Elsevier B.V

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

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

Src Kinases Are Required for a Balanced Production of IL-12/IL-23 in Human Dendritic Cells Activated by Toll-Like Receptor Agonists

BACKGROUND: Pathogen recognition by dendritic cells (DC) is crucial for the initiation of both innate and adaptive immune responses. Activation of Toll-like Receptors (TLRs) by microbial molecular patterns leads to the maturation of DC, which present the antigen and activate T cells in secondary lymphoid tissues. Cytokine production by DC is critical for shaping the adaptive immune response by regulating T helper cell differentiation. It was previously shown by our group that Src kinases play a key role in cytokines production during TLR4 activation in human DC. PRINCIPAL FINDINGS: In this work we investigated the role of Src kinases during different TLRs triggering in human monocyte-derived DC (MoDC). We found that Src family kinases are important for a balanced production of inflammatory cytokines by human MoDC upon stimulation of TLR3 and 8 with their respective agonists. Disruption of this equilibrium through pharmacological inhibition of Src kinases alters the DC maturation pattern. In particular, while expression of IL-12 and other inflammatory cytokines depend on Src kinases, the induction of IL-23 and co-stimulatory molecules do not. Accordingly, DC treated with Src inhibitors are not compromised in their ability to induce CD4 T cell proliferation and to promote the Th17 subset survival but are less efficient in inducing Th1 differentiation. CONCLUSIONS: We suggest that the pharmacological modulation of DC maturation has the potential to shape the quality of the adaptive immune response and could be exploited for the treatment of inflammation-related diseases

Themis2/ICB1 Is a Signaling Scaffold That Selectively Regulates Macrophage Toll-Like Receptor Signaling and Cytokine Production

BACKGROUND: Thymocyte expressed molecule involved in selection 1 (Themis1, SwissProt accession number Q8BGW0) is the recently characterised founder member of a novel family of proteins. A second member of this family, Themis2 (Q91YX0), also known as ICB1 (Induced on contact with basement membrane 1), remains unreported at the protein level despite microarray and EST databases reporting Themis2 mRNA expression in B cells and macrophages. METHODOLOGY/PRINCIPAL FINDINGS: Here we characterise Themis2 protein for the first time and show that it acts as a macrophage signalling scaffold, exerting a receptor-, mediator- and signalling pathway-specific effect on TLR responses in RAW 264.7 macrophages. Themis2 over-expression enhanced the LPS-induced production of TNF but not IL-6 or Cox-2, nor TNF production induced by ligands for TLR2 (PAM3) or TLR3 (poly IratioC). Moreover, LPS-induced activation of the MAP kinases ERK and p38 was enhanced in cells over-expressing Themis2 whereas the activation of JNK, IRF3 or NF-kappaB p65, was unaffected. Depletion of Themis2 protein by RNA inteference inhibited LPS-induced TNF production in primary human macrophages demonstrating a requirement for Themis2 in this event. Themis2 was inducibly tyrosine phosphorylated upon LPS challenge and interacted with Lyn kinase (P25911), the Rho guanine nucleotide exchange factor, Vav (P27870), and the adaptor protein Grb2 (Q60631). Mutation of either tyrosine 660 or a proline-rich sequence (PPPRPPK) simultaneously interrupted this complex and reduced by approximately 50% the capacity of Themis2 to promote LPS-induced TNF production. Finally, Themis2 protein expression was induced during macrophage development from murine bone marrow precursors and was regulated by inflammatory stimuli both in vitro and in vivo. CONCLUSIONS/SIGNIFICANCE: We hypothesise that Themis2 may constitute a novel, physiological control point in macrophage inflammatory responses

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