3,481 research outputs found

    Modification of conductive polymer for polymeric anodes of flexible organic light-emitting diodes

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    Author name used in this publication: Guang-Feng WangAuthor name used in this publication: Xiao-Ming TaoAuthor name used in this publication: John H. Xin2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Synthesis and characterization of folate-poly(ethylene glycol) chitosan graft-polyethylenimine as a non-viral carrier for tumor-targeted gene delivery

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    The use of chitosan and chitosan derivatives for gene delivery is limited due to the low transfection efficiency and difficulty in transfecting into a variety of cell types, including some cancer cells overexpressing folate receptor (FRs). In order to solve this problem, folate (FA) and poly(ethylene glycol) (PEG) was conjugated to chitosan-graft-polyethylenimine (CHI-g-PEI) to enhance water-solubility and the transfection efficiency. In the present study, a cell specific targeting molecule FA was linked on PEG and then grafted the FA-PEG onto CHI-g-PEI. The FA-PEG-grafted CHI-g-PEI (FA-PEG-CHI-g-PEI) effectively condensed the plasmid DNA (pDNA) into nanoparticles with positive surface charge under the suitable nitrogen/phosphorus (N/P) ratio. In vitro, transfection efficiency of the FA-PEG-CHI-g-PEI /pDNA complex in 293T cells and LoVo cells (FRs over-expressing cell lines) increased with increasing N/P ratio under N/P = 15 and was more than 50%, but no significant difference in human lung carcinoma cells (A549) cells (FRs deficient cell lines). Importantly, in vivo luciferase expression showed that the efficiency of FA-PEG-CHI-g-PEI -mediated transfection (50 ΞΌg luciferase plasmid (pLuc), N/P ratio = 15) was comparable to that of adenovirus-mediated luciferase transduction (1 Γ— 109 pfu) in melanomabearing mice. It was concluded that FA-PEG-CHI-g-PEI, which has improved transfection efficiency and FRs specificity in vitro and in vivo, may be useful in gene therapy.Key words: Folate poly(ethylene glycol)-chitosan-grafted-polyethylenimine (FA-PEG-CHI-g-PEI), gene transfection, non-virus vector, in vitro, in viv

    Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery

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    Semantic tool segmentation in surgical videos is important for surgical scene understanding and computer-assisted interventions as well as for the development of robotic automation. The problem is challenging because different illumination conditions, bleeding, smoke and occlusions can reduce algorithm robustness. At present labelled data for training deep learning models is still lacking for semantic surgical instrument segmentation and in this paper we show that it may be possible to use robot kinematic data coupled with laparoscopic images to alleviate the labelling problem. We propose a new deep learning based model for parallel processing of both laparoscopic and simulation images for robust segmentation of surgical tools. Due to the lack of laparoscopic frames annotated with both segmentation ground truth and kinematic information a new custom dataset was generated using the da Vinci Research Kit (dVRK) and is made available

    A False Start in the Race Against Doping in Sport: Concerns With Cycling’s Biological Passport

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    Professional cycling has suffered from a number of doping scandals. The sport’s governing bodies have responded by implementing an aggressive new antidoping program known as the biological passport. Cycling’s biological passport marks a departure from traditional antidoping efforts, which have focused on directly detecting prohibited substances in a cyclist’s system. Instead, the biological passport tracks biological variables in a cyclist’s blood and urine over time, monitoring for fluctuations that are thought to indirectly reveal the effects of doping. Although this method of indirect detection is promising, it also raises serious legal and scientific concerns. Since its introduction, the cycling community has debated the reliability of indirect biological-passport evidence and the clarity, consistency, and transparency of its use in proving doping violations. Such uncertainty undermines the legitimacy of finding cyclists guilty of doping based on this indirect evidence alone. Antidoping authorities should address these important concerns before continuing to pursue doping sanctions against cyclists solely on the basis of their biological passports

    Colored Resonant Signals at the LHC: Largest Rate and Simplest Topology

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    We study the colored resonance production at the LHC in a most general approach. We classify the possible colored resonances based on group theory decomposition, and construct their effective interactions with light partons. The production cross section from annihilation of valence quarks or gluons may be on the order of 400 - 1000 pb at LHC energies for a mass of 1 TeV with nominal couplings, leading to the largest production rates for new physics at the TeV scale, and simplest event topology with dijet final states. We apply the new dijet data from the LHC experiments to put bounds on various possible colored resonant states. The current bounds range from 0.9 to 2.7 TeV. The formulation is readily applicable for future searches including other decay modes.Comment: 29 pages, 9 figures. References updated and additional K-factors include

    Clinical-pathological study on Ξ²-APP, IL-1Ξ², GFAP, NFL, SpectrinΒ II, 8OHdG, TUNEL, miR-21, miR-16, miR-92 expressions to verify DAI-diagnosis, grade and prognosis

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    Traumatic brain injury (TBI) is one of the most important death and disability cause, involving substantial costs, also in economic terms, when considering the young age of the involved subject. Aim of this paper is to report a series of patients treated at our institutions, to verify neurological results at six months or survival; in fatal cases we searched for Ξ²APP, GFAP, IL-1Ξ², NFL, Spectrin II, TUNEL and miR-21, miR-16, and miR-92 expressions in brain samples, to verify DAI diagnosis and grade as strong predictor of survival and inflammatory response. Concentrations of 8OHdG as measurement of oxidative stress was performed. Immunoreaction of Ξ²-APP, IL-1Ξ², GFAP, NFL, Spectrin II and 8OHdG were significantly increased in the TBI group with respect to control group subjects. Cell apoptosis, measured by TUNEL assay, were significantly higher in the study group than control cases. Results indicated that miR-21, miR-92 and miR-16 have a high predictive power in discriminating trauma brain cases from controls and could represent promising biomarkers as strong predictor of survival, and for the diagnosis of postmortem traumatic brain injury

    Using metrics and sustainability considerations to evaluate the use of bio-based and non-renewable BrΓΈnsted acidic ionic liquids to catalyse Fischer esterification reactions

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    Background Ionic liquids have found uses in many applications, one of which is the joint solvation and catalysis of chemical transformations. Suitable BrΓΈnsted acidic ionic liquids can be formed by combining lactams with sulphonic acids. This work weighs up the relative benefits and disadvantages of applying these BrΓΈnsted acidic ionic liquid catalysts in esterifications through a series of comparisons using green chemistry metrics. Results A new bio-based ionic liquid was synthesised from N-methyl pyrrolidinone and p-cymenesulphonic acid, and tested as a catalyst in three Fischer esterifications under different conditions. An evaluation of the performance of this BrΓΈnsted acidic ionic liquid was made through the comparison to other ionic liquid catalysts as well as conventional homogeneous BrΓΈnsted acids. Conclusion Extending the argument to feedstock security as well as mass utilisation, ultimately in most instances traditional mineral acids appear to be the most sensible option for BrΓΈnsted acid esterification catalysts. Ester yields obtained from BrΓΈnsted acidic ionic liquid catalysed procedures were modest. This calls into question the diversity of research exploring esterification catalysis and the role of ionic liquids in esterifications

    Determining the neurotransmitter concentration profile at active synapses

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    Establishing the temporal and concentration profiles of neurotransmitters during synaptic release is an essential step towards understanding the basic properties of inter-neuronal communication in the central nervous system. A variety of ingenious attempts has been made to gain insights into this process, but the general inaccessibility of central synapses, intrinsic limitations of the techniques used, and natural variety of different synaptic environments have hindered a comprehensive description of this fundamental phenomenon. Here, we describe a number of experimental and theoretical findings that has been instrumental for advancing our knowledge of various features of neurotransmitter release, as well as newly developed tools that could overcome some limits of traditional pharmacological approaches and bring new impetus to the description of the complex mechanisms of synaptic transmission

    Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models

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    <p>Abstract</p> <p>Background</p> <p>In recent years, artificial neural network is advocated in modeling complex multivariable relationships due to its ability of fault tolerance; while decision tree of data mining technique was recommended because of its richness of classification arithmetic rules and appeal of visibility. The aim of our research was to compare the performance of ANN and decision tree models in predicting hospital charges on gastric cancer patients.</p> <p>Methods</p> <p>Data about hospital charges on 1008 gastric cancer patients and related demographic information were collected from the First Affiliated Hospital of Anhui Medical University from 2005 to 2007 and preprocessed firstly to select pertinent input variables. Then artificial neural network (ANN) and decision tree models, using same hospital charge output variable and same input variables, were applied to compare the predictive abilities in terms of mean absolute errors and linear correlation coefficients for the training and test datasets. The transfer function in ANN model was sigmoid with 1 hidden layer and three hidden nodes.</p> <p>Results</p> <p>After preprocess of the data, 12 variables were selected and used as input variables in two types of models. For both the training dataset and the test dataset, mean absolute errors of ANN model were lower than those of decision tree model (1819.197 vs. 2782.423, 1162.279 vs. 3424.608) and linear correlation coefficients of the former model were higher than those of the latter (0.955 vs. 0.866, 0.987 vs. 0.806). The predictive ability and adaptive capacity of ANN model were better than those of decision tree model.</p> <p>Conclusion</p> <p>ANN model performed better in predicting hospital charges of gastric cancer patients of China than did decision tree model.</p
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