Effects of Epoxy-Polyester Hybrid and Nanoclay on Morphology, Rheological and Mechanical Properties of Styrene-Butadiene Rubber

Properties of SBR compounds filled with two kinds of filler, Epoxy-Polyester Hybrid resin(EPH) (10, 20, 30, 40 phr) and Nanoclay (Closite 15 A) (1, 3, 5, 7 phr) were studied. Microcomposite samples and nanocomposite samples were prepared by Haake internal mixer. Curing agents and additives i.e. dicumylperoxide Properties of SBR compounds filled with two kinds of filler, Epoxy-Polyester Hybrid resin(EPH) (10, 20, 30, 40 phr) and Nanoclay (Closite 15 A) (1, 3, 5, 7 phr) were studied. Microcomposite samples and nanocomposite samples were prepared by Haake internal mixer. Curing agents and additives i.e. containing 30 phr resin show higher value of modulus than sample containing 7 phr nanocaly. Rheological measurement showed that both fillers lead to an increase in viscosity and dynamic modulus of samples which is as a result of good interaction established between polymer/filler. Moreover, due to nanometer scale of nanoclay particles, reinforcing effect of Nanoclay was more noticeable and in micrometer scale of Epoxy-Polyester Hybrid particles, Epoxy resin cured by Polyester is used to improved wet ability of SBR compounds. SEM photomicrographs of cryogenically fractured samples confirmed the mentioned results. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3506

Association of low-activity MAOA allelic variants with violent crime in incarcerated offenders

This is the author's final draft. Copyright 2014 ElsevierThe main enzyme for serotonin degradation, monoamine oxidase (MAO) A, has recently emerged as a key biological factor in the predisposition to impulsive aggression. Male carriers of low-activity variants of the main functional polymorphism of the MAOA gene (MAOA-uVNTR) have been shown to exhibit a greater proclivity to engage in violent acts. Thus, we hypothesized that low-activity MAOA-uVNTR alleles may be associated with a higher risk for criminal violence among male offenders. To test this possibility, we analyzed the MAOA-uVNTR variants of violent (n = 49) and non-violent (n = 40) male Caucasian and African-American convicts in a correctional facility. All participants were also tested with the Childhood Trauma Questionnaire (CTQ), Barratt Impulsivity Scale (BIS-11) and Buss-Perry Aggression Questionnaire (BPAQ) to assess their levels of childhood trauma exposure, impulsivity and aggression, respectively. Our results revealed a robust (P < 0.0001) association between low-activity MAOA-uVNTR alleles and violent crime. This association was replicated in the group of Caucasian violent offenders (P < 0.01), but reached only a marginal trend (P = 0.08) in their African American counterparts. While violent crime charges were not associated with CTQ, BIS-11 and BPAQ scores, carriers of low-activity alleles exhibited a mild, yet significant (P < 0.05) increase in BIS-11 total and attentional-impulsiveness scores. In summary, these findings support the role of MAOA gene as a prominent genetic determinant for criminal violence. Further studies are required to confirm these results in larger samples of inmates and evaluate potential interactions between MAOA alleles and environmental vulnerability factors

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation

Elevated neutrophil and monocyte counts in peripheral blood are associated with poor survival in patients with metastatic melanoma: a prognostic model

We aimed to create a prognostic model in metastatic melanoma based on independent prognostic factors in 321 patients receiving interleukin-2 (IL-2)-based immunotherapy with a median follow-up time for patients currently alive of 52 months (range 15–189 months). The patients were treated as part of several phase II protocols and the majority received treatment with intermediate dose subcutaneous IL-2 and interferon-α. Neutrophil and monocyte counts, lactate dehydrogenase (LDH), number of metastatic sites, location of metastases and performance status were all statistically significant prognostic factors in univariate analyses. Subsequently, a multivariate Cox's regression analysis identified elevated LDH (P<0.001, hazard ratio 2.8), elevated neutrophil counts (P=0.02, hazard ratio 1.4) and a performance status of 2 (P=0.008, hazard ratio 1.6) as independent prognostic factors for poor survival. An elevated monocyte count could replace an elevated neutrophil count. Patients were assigned to one of three risk groups according to the cumulative risk defined as the sum of simplified risk scores of the three independent prognostic factors. Low-, intermediate- and high-risk patients achieved a median survival of 12.6 months (95% confidence interval (CI), 11.4–13.8), 6.0 months (95% CI, 4.8–7.2) and 3.4 months (95% CI, 1.2–5.6), respectively. The low-risk group encompassed the majority of long-term survivors, whereas the patients in the high-risk group with a very poor prognosis should probably not be offered IL-2-based immunotherapy

Therapeutic T cells induce tumor-directed chemotaxis of innate immune cells through tumor-specific secretion of chemokines and stimulation of B16BL6 melanoma to secrete chemokines

Background: The mechanisms by which tumor-specific T cells induce regression of established metastases are not fully characterized. In using the poorly immunogenic B16BL6-D5 (D5) melanoma model we reported that T cell-mediated tumor regression can occur independently of perforin, IFN-gamma or the combination of both. Characterization of regressing pulmonary metastases identified macrophages as a major component of the cells infiltrating the tumor after adoptive transfer of effector T cells. This led us to hypothesize that macrophages played a central role in tumor regression following T-cell transfer. Here, we sought to determine the factors responsible for the infiltration of macrophages at the tumor site. Methods: These studies used the poorly immunogenic D5 melanoma model. Tumor-specific effector T cells, generated from tumor vaccine-draining lymph nodes (TVDLN), were used for adoptive immunotherapy and in vitro analysis of chemokine expression. Cellular infiltrates into pulmonary metastases were determined by immunohistochemistry. Chemokine expression by the D5 melanoma following co-culture with T cells, IFN-gamma or TNF-alpha was determined by RT-PCR and ELISA. Functional activity of chemokines was confirmed using a macrophage migration assay. T cell activation of macrophages to release nitric oxide (NO) was determined using GRIES reagent. Results: We observed that tumor-specific T cells with a type 1 cytokine profile also expressed message for and secreted RANTES, MIP-1 alpha and MIP-1 beta following stimulation with specific tumor. Unexpectedly, D5 melanoma cells cultured with IFN-gamma or TNF-alpha, two type 1 cytokines expressed by therapeutic T cells, secreted Keratinocyte Chemoattractant (KC), MCP-1, IP-10 and RANTES and expressed mRNA for MIG. The chemokines released by T cells and cytokine-stimulated tumor cells were functional and induced migration of the DJ2PM macrophage cell line. Additionally, tumor-specific stimulation of wt or perforin-deficient (PKO) effector T cells induced macrophages to secrete nitric oxide (NO), providing an additional effector mechanism for T cell-mediated tumor regression. Conclusion: These data suggest two possible sources for chemokine secretion that stimulates macrophage recruitment to the site of tumor metastases. Both appear to be initiated by T cell recognition of specific antigen, but one is dependent on the tumor cells to produce the chemokines that recruit macrophages

An adaptive signaling network in melanoma inflammatory niches confers tolerance to MAPK signaling inhibition

Mitogen-activated protein kinase (MAPK) pathway antagonists induce profound clinical responses in advanced cutaneous melanoma, but complete remissions are frustrated by the development of acquired resistance. Before resistance emerges, adaptive responses establish a mutation-independent drug tolerance. Antagonizing these adaptive responses could improve drug effects, thereby thwarting the emergence of acquired resistance. In this study, we reveal that inflammatory niches consisting of tumor-associated macrophages and fibroblasts contribute to treatment tolerance through a cytokine-signaling network that involves macrophage-derived IL-1β and fibroblast-derived CXCR2 ligands. Fibroblasts require IL-1β to produce CXCR2 ligands, and loss of host IL-1R signaling in vivo reduces melanoma growth. In tumors from patients on treatment, signaling from inflammatory niches is amplified in the presence of MAPK inhibitors. Signaling from inflammatory niches counteracts combined BRAF/MEK (MAPK/extracellular signal–regulated kinase kinase) inhibitor treatment, and consequently, inhibiting IL-1R or CXCR2 signaling in vivo enhanced the efficacy of MAPK inhibitors. We conclude that melanoma inflammatory niches adapt to and confer drug tolerance toward BRAF and MEK inhibitors early during treatmen

Post-transcriptional control during chronic inflammation and cancer: a focus on AU-rich elements

A considerable number of genes that code for AU-rich mRNAs including cytokines, growth factors, transcriptional factors, and certain receptors are involved in both chronic inflammation and cancer. Overexpression of these genes is affected by aberrations or by prolonged activation of several signaling pathways. AU-rich elements (ARE) are important cis-acting short sequences in the 3′UTR that mediate recognition of an array of RNA-binding proteins and affect mRNA stability and translation. This review addresses the cellular and molecular mechanisms that are common between inflammation and cancer and that also govern ARE-mediated post-transcriptional control. The first part examines the role of the ARE-genes in inflammation and cancer and sequence characteristics of AU-rich elements. The second part addresses the common signaling pathways in inflammation and cancer that regulate the ARE-mediated pathways and how their deregulations affect ARE-gene regulation and disease outcome

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Tracing Sources of Atmospheric Methane Using Clumped Isotopes

Here we use a box model to evaluate how much additional data from Δ12CH2D2 and Δ13CH3D may add to understanding the temporal trend in atmospheric methane, and specifically, whether they may differentiate the contributions of fossil fuel and microbial sources. EDGAR (Emissions Database for Global Atmospheric Research) provides high-quality constraints on methane fluxes from major anthropogenic sources, and different versions of EDGAR reflect uncertainty in understanding of the apportionment of these fluxes over the past few decades. We used two versions of EDGAR and also considered another model of fossil fuel flux to build four different scenarios for anthropogenic source fluxes for our box model. EDGAR does not include wetland emissions and those are calculated (a free variable) to close the flux balance needed by the model. Each scenario broadly follows one of four parameterizations of anthropogenic source fluxes to obtain an estimate of the composition and evolution of Δ12CH2D2 and Δ13CH3D through time.https://www.pnas.or