Characterizing hypervelocity impact (HVI)-induced pitting damage using active guided ultrasonic waves : from linear to nonlinear

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe

Interrogating Institutionalized Establishments: Urban-Rural Inequalities in China’s Higher Education

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The influence of synaptic activity on neuronal health

According to the theory of neuronal health, neurons exist in a spectrum of states ranging from highly resilient to vulnerable. An unhealthy neuron may be rendered dysfunctional or non-viable by an insult that would ordinarily be non-toxic to a healthy neuron. Over the years it has become clear that a neuron’s health is subject to dynamic regulation by electrical or synaptic activity. This review highlights recently identified activity dependent signalling events which boost neuronal health through the transcriptional control of pro- and anti-apoptotic genes, the enhancement of antioxidant defences, and the regulation of mitochondrial and neurotrophic factor availability. Furthermore, activity dependent signals have recently been shown to influence a variety of events specific to individual neurodegenerative diseases, which will also be highlighted

Immune Protection Induced on Day 10 Following Administration of the 2009 A/H1N1 Pandemic Influenza Vaccine

BACKGROUND: The 2009 swine-origin influenza virus (S-OIV) H1N1 pandemic has caused more than 18,000 deaths worldwide. Vaccines against the 2009 A/H1N1 influenza virus are useful for preventing infection and controlling the pandemic. The kinetics of the immune response following vaccination with the 2009 A/H1N1 influenza vaccine need further investigation. METHODOLOGY/PRINCIPAL FINDINGS: 58 volunteers were vaccinated with a 2009 A/H1N1 pandemic influenza monovalent split-virus vaccine (15 µg, single-dose). The sera were collected before Day 0 (pre-vaccination) and on Days 3, 5, 10, 14, 21, 30, 45 and 60 post vaccination. Specific antibody responses induced by the vaccination were analyzed using hemagglutination inhibition (HI) assay and enzyme-linked immunosorbent assay (ELISA). After administration of the 2009 A/H1N1 influenza vaccine, specific and protective antibody response with a major subtype of IgG was sufficiently developed as early as Day 10 (seroprotection rate: 93%). This specific antibody response could maintain for at least 60 days without significant reduction. Antibody response induced by the 2009 A/H1N1 influenza vaccine could not render protection against seasonal H1N1 influenza (seroconversion rate: 3% on Day 21). However, volunteers with higher pre-existing seasonal influenza antibody levels (pre-vaccination HI titer ≥1∶40, Group 1) more easily developed a strong antibody protection effect against the 2009 A/H1N1 influenza vaccine as compared with those showing lower pre-existing seasonal influenza antibody levels (pre-vaccination HI titer <1∶40, Group 2). The titer of the specific antibody against the 2009 A/H1N1 influenza was much higher in Group 1 (geometric mean titer: 146 on Day 21) than that in Group 2 (geometric mean titer: 70 on Day 21). CONCLUSIONS/SIGNIFICANCE: Recipients could gain sufficient protection as early as 10 days after vaccine administration. The protection could last at least 60 days. Individuals with a stronger pre-existing seasonal influenza antibody response may have a relatively higher potential for developing a stronger humoral immune response after vaccination with the 2009 A/H1N1 pandemic influenza vaccine

Supramolecular thermoplastics and thermoplastic elastomer materials with self-healing ability based on oligomeric charged triblock copolymers

Supramolecular polymeric materials constitute a unique class of materials held together by non-covalent interactions. These dynamic supramolecular interactions can provide unique properties such as a strong decrease in viscosity upon relatively mild heating, as well as self-healing ability. In this study we demonstrate the unique mechanical properties of phase-separated electrostatic supramolecular materials based on mixing of low molar mass, oligomeric, ABA-triblock copolyacrylates with oppositely charged outer blocks. In case of well-chosen mixtures and block lengths, the charged blocks are phase separated from the uncharged matrix in a hexagonally packed nanomorphology as observed by transmission electron microscopy. Thermal and mechanical analysis of the material shows that the charged sections have a T-g closely beyond room temperature, whereas the material shows an elastic response at temperatures far above this T-g ascribed to the electrostatic supramolecular interactions. A broad set of materials having systematic variations in triblock copolymer structures was used to provide insights in the mechanical properties and and self-healing ability in correlation with the nanomorphology of the materials

Inhibition of StearoylCoA Desaturase Activity Blocks Cell Cycle Progression and Induces Programmed Cell Death in Lung Cancer Cells

Lung cancer is the most frequent form of cancer. The survival rate for patients with metastatic lung cancer is ∼5%, hence alternative therapeutic strategies to treat this disease are critically needed. Recent studies suggest that lipid biosynthetic pathways, particularly fatty acid synthesis and desaturation, are promising molecular targets for cancer therapy. We have previously reported that inhibition of stearoylCoA desaturase-1 (SCD1), the enzyme that produces monounsaturated fatty acids (MUFA), impairs lung cancer cell proliferation, survival and invasiveness, and dramatically reduces tumor formation in mice. In this report, we show that inhibition of SCD activity in human lung cancer cells with the small molecule SCD inhibitor CVT-11127 reduced lipid synthesis and impaired proliferation by blocking the progression of cell cycle through the G1/S boundary and by triggering programmed cell death. These alterations resulting from SCD blockade were fully reversed by either oleic (18:1n-9), palmitoleic acid (16:1n-7) or cis-vaccenic acid (18:1n-7) demonstrating that cis-MUFA are key molecules for cancer cell proliferation. Additionally, co-treatment of cells with CVT-11127 and CP-640186, a specific acetylCoA carboxylase (ACC) inhibitor, did not potentiate the growth inhibitory effect of these compounds, suggesting that inhibition of ACC or SCD1 affects a similar target critical for cell proliferation, likely MUFA, the common fatty acid product in the pathway. This hypothesis was further reinforced by the observation that exogenous oleic acid reverses the anti-growth effect of SCD and ACC inhibitors. Finally, exogenous oleic acid restored the globally decreased levels of cell lipids in cells undergoing a blockade of SCD activity, indicating that active lipid synthesis is required for the fatty acid-mediated restoration of proliferation in SCD1-inhibited cells. Altogether, these observations suggest that SCD1 controls cell cycle progression and apoptosis and, consequently, the overall rate of proliferation in cancer cells through MUFA-mediated activation of lipid synthesis

Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies