Combined XPS and contact angle studies of ethylene vinyl acetate and polyvinyl acetate blends

Cataloged from PDF version of article.In this study, we prepared thin films by blending ethylene vinyl acetate copolymers (EVA) containing 12-33 (wt.%) vinyl acetate (VA) with polyvinyl acetate (PVAc) and high density polyethylene homopolymers. Large area micropatterns having controlled protrusion sizes were obtained by phase-separation especially for the PVAc/EVA-33 blends using dip coating. These surfaces were characterized by XPS and contact angle measurements. A reasonably linear relation was found between the VA content on the surface (wt.%) obtained from XPS analysis and the VA content in bulk especially for PVAc/EVA-33 blend surfaces. PE segments were more enriched on the surface than that of the bulk for pure EVA copolymer surfaces similar to previous reports and VA enrichment was found on the EVA/HDPE blend surfaces due to high molecular weight of HDPE. Water theta(e) decreased with the increase in the VA content on the blend surface due to the polarity of VA. A good agreement was obtained between gamma(-)(s) and atomic oxygen surface concentration with the increase of VA content. The applicability of Cassie-Baxter equation was tested and found that it gave consistent results with the experimental water contact angles for the case where VA content was lower than 55 wt.% in the bulk composition. (C) 2011 Elsevier B. V. All rights reserved

The effect of distributed exchange parameters on magnetocaloric refrigeration capacity in amorphous and nanocomposite materials

The temperature dependent magnetization of nanocomposite alloys has been fit with a modified Handrich-Kobe equation with an asymmetric exchange fluctuation parameter combined with the Arrott-Noakes equation. The two equations of state are combined to calculate the entropy change in the magnetocaloric effect associated with the ferromagnetic to paramagnetic phase transformation. The complete fit for the M(T) of (Fe70Ni30)88Zr7B4Cu nanocomposite powder is accomplished by combining the two theories. We investigate the broadening of the second-order transition arising from asymmetric exchange parameters and resulting from the fluctuations of interatomic spacing found in an amorphous matrix and the asymmetric dependence of exchange energy on interatomic spacing. The magnetic entropy curve revealed extra broadening with a refrigeration capacity (RC) value of 135 J/kg at 5 T, which is comparable to (Fe76Cr8-xMoxCu1B15) ribbons, which have a RC value of 180 J/kg for the same applied field. Broadening of the magnetic entropy can lead to larger RC values and a wider working temperature range, making nanocomposite alloys promising for magnetocaloric applications

miR-132/212 knockout mice reveal roles for these miRNAs in regulating cortical synaptic transmission and plasticity

miR-132 and miR-212 are two closely related miRNAs encoded in the same intron of a small non-coding gene, which have been suggested to play roles in both immune and neuronal function. We describe here the generation and initial characterisation of a miR-132/212 double knockout mouse. These mice were viable and fertile with no overt adverse phenotype. Analysis of innate immune responses, including TLR-induced cytokine production and IFNβ induction in response to viral infection of primary fibroblasts did not reveal any phenotype in the knockouts. In contrast, the loss of miR-132 and miR-212, while not overtly affecting neuronal morphology, did affect synaptic function. In both hippocampal and neocortical slices miR-132/212 knockout reduced basal synaptic transmission, without affecting paired-pulse facilitation. Hippocampal long-term potentiation (LTP) induced by tetanic stimulation was not affected by miR-132/212 deletion, whilst theta burst LTP was enhanced. In contrast, neocortical theta burst-induced LTP was inhibited by loss of miR-132/212. Together these results indicate that miR-132 and/or miR-212 play a significant role in synaptic function, possibly by regulating the number of postsynaptic AMPA receptors under basal conditions and during activity-dependent synaptic plasticity

Single Cell Analysis of Blood Mononuclear Cells Stimulated Through Either LPS or Anti-CD3 and Anti-CD28.

Immune cell activation assays have been widely used for immune monitoring and for understanding disease mechanisms. However, these assays are typically limited in scope. A holistic study of circulating immune cell responses to different activators is lacking. Here we developed a cost-effective high-throughput multiplexed single-cell RNA-seq combined with epitope tagging (CITE-seq) to determine how classic activators of T cells (anti-CD3 coupled with anti-CD28) or monocytes (LPS) alter the cell composition and transcriptional profiles of peripheral blood mononuclear cells (PBMCs) from healthy human donors. Anti-CD3/CD28 treatment activated all classes of lymphocytes either directly (T cells) or indirectly (B and NK cells) but reduced monocyte numbers. Activated T and NK cells expressed senescence and effector molecules, whereas activated B cells transcriptionally resembled autoimmune disease- or age-associated B cells (e.g., CD11c, T-bet). In contrast, LPS specifically targeted monocytes and induced two main states: early activation characterized by the expression of chemoattractants and a later pro-inflammatory state characterized by expression of effector molecules. These data provide a foundation for future immune activation studies with single cell technologies (https://czi-pbmc-cite-seq.jax.org/)

Predicting functionality of protein–DNA interactions by integrating diverse evidence

Chromatin immunoprecipitation (ChIP-chip) experiments enable capturing physical interactions between regulatory proteins and DNA in vivo. However, measurement of chromatin binding alone is not sufficient to detect regulatory interactions. A detected binding event may not be biologically relevant, or a known regulatory interaction might not be observed under the growth conditions tested so far. To correctly identify physical interactions between transcription factors (TFs) and genes and to determine their regulatory implications under various experimental conditions, we integrated ChIP-chip data with motif binding sites, nucleosome occupancy and mRNA expression datasets within a probabilistic framework. This framework was specifically tailored for the identification of functional and non-functional DNA binding events. Using this, we estimate that only 50% of condition-specific protein–DNA binding in budding yeast is functional. We further investigated the molecular factors determining the functionality of protein–DNA interactions under diverse growth conditions. Our analysis suggests that the functionality of binding is highly condition-specific and highly dependent on the presence of specific cofactors. Hence, the joint analysis of both, functional and non-functional DNA binding, may lend important new insights into transcriptional regulation

Sestrins induce natural killer function in senescent-like CD8(+) T cells

Aging is associated with remodeling of the immune system to enable the maintenance of life-long immunity. In the CD8⁺ T cell compartment, aging results in the expansion of highly differentiated cells that exhibit characteristics of cellular senescence. Here we found that CD27⁻CD28⁻CD8⁺ T cells lost the signaling activity of the T cell antigen receptor (TCR) and expressed a protein complex containing the agonistic natural killer (NK) receptor NKG2D and the NK adaptor molecule DAP12, which promoted cytotoxicity against cells that expressed NKG2D ligands. Immunoprecipitation and imaging cytometry indicated that the NKG2D-DAP12 complex was associated with sestrin 2. The genetic inhibition of sestrin 2 resulted in decreased expression of NKG2D and DAP12 and restored TCR signaling in senescent-like CD27⁻CD28⁻CD8⁺ T cells. Therefore, during aging, sestrins induce the reprogramming of non-proliferative senescent-like CD27⁻CD28⁻CD8⁺ T cells to acquire a broad-spectrum, innate-like killing activity

Proanthocyanidin to prevent formation of the reexpansion pulmonary edema

<p>Abstract</p> <p>Background</p> <p>We aimed to investigate the preventive effect of Proanthocyanidine (PC) in the prevention of RPE formation.</p> <p>Methods</p> <p>Subjects were divided into four groups each containing 10 rats. In the Control Group (CG): RPE wasn't performed. Then subjects were followed up for three days and they were sacrificed after the follow up period. Samplings were made from tissues for measurement of biochemical and histopathologic parameters. In the Second Group (PCG): The same protocol as CG was applied, except the administration of PC to the subjects. In the third RPE Group (RPEG): Again the same protocol as CG was applied, but as a difference, RPE was performed. In the Treatment Group (TG): The same protocol as RPEG was applied except the administration of PC to the subjects.</p> <p>Results</p> <p>In RPEG group, the most important histopathological finding was severe pulmonary edema with alveolar damage and acute inflammatory cells. These findings were less in the TG group. RPE caused increased MDA levels, and decreased GPx, SOD and CAT activity significantly in lung tissue.</p> <p>Conclusion</p> <p>PC decreased MDA levels. Oxidative stress plays an important role in pathophysiology of RPE and PC treatment was shown to be useful to prevent formation of RPE.</p

Learning pair-wise gene functional similarity by multiplex gene expression maps

Abstract Background The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute. Results Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction. Conclusions By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions

A spatio-temporal mining approach towards summarizing and analyzing protein folding trajectories

Understanding the protein folding mechanism remains a grand challenge in structural biology. In the past several years, computational theories in molecular dynamics have been employed to shed light on the folding process. Coupled with high computing power and large scale storage, researchers now can computationally simulate the protein folding process in atomistic details at femtosecond temporal resolution. Such simulation often produces a large number of folding trajectories, each consisting of a series of 3D conformations of the protein under study. As a result, effectively managing and analyzing such trajectories is becoming increasingly important. In this article, we present a spatio-temporal mining approach to analyze protein folding trajectories. It exploits the simplicity of contact maps, while also integrating 3D structural information in the analysis. It characterizes the dynamic folding process by first identifying spatio-temporal association patterns in contact maps, then studying how such patterns evolve along a folding trajectory. We demonstrate that such patterns can be leveraged to summarize folding trajectories, and to facilitate the detection and ordering of important folding events along a folding path. We also show that such patterns can be used to identify a consensus partial folding pathway across multiple folding trajectories. Furthermore, we argue that such patterns can capture both local and global structural topology in a 3D protein conformation, thereby facilitating effective structural comparison amongst conformations. We apply this approach to analyze the folding trajectories of two small synthetic proteins-BBA5 and GSGS (or Beta3S). We show that this approach is promising towards addressing the above issues, namely, folding trajectory summarization, folding events detection and ordering, and consensus partial folding pathway identification across trajectories