Showing papers by "Michael S. Lan published in 2012"
University of British Columbia1, Fraser Health2, Children's Hospital Oakland Research Institute3, Fred Hutchinson Cancer Research Center4, University of Western Ontario5, University of California, Riverside6, French Alternative Energies and Atomic Energy Commission7, French Institute of Health and Medical Research8, University of Duisburg-Essen9, Imperial College London10, Newcastle University11, Spanish National Research Council12, University of Warwick13, Paris Diderot University14, University of Southern Denmark15, BC Cancer Agency16, Eppley Institute for Research in Cancer and Allied Diseases17, Scripps Research Institute18, John Radcliffe Hospital19, University of Illinois at Chicago20, Stanford University21, University of Bern22, Johns Hopkins University School of Medicine23, National Research Council24, University of Southern California25, Utrecht University26, Université de Montréal27, Centre national de la recherche scientifique28, Indiana University – Purdue University Indianapolis29, University Medical Center Utrecht30, Autonomous University of Madrid31, University of Zurich32, Harvard University33, University of Adelaide34, LSU Health Sciences Center New Orleans35, Queen's University36, University of Erlangen-Nuremberg37, University of Birmingham38, Oklahoma Medical Research Foundation39, Thomas Jefferson University40, University of Oklahoma Health Sciences Center41, McMaster University42, Johns Hopkins University43, University of Amsterdam44, University of California, Davis45, University of California, San Diego46, University at Buffalo47, Sunnybrook Health Sciences Centre48, University of California, Los Angeles49, Morehouse School of Medicine50, University of Pennsylvania51, Seattle Children's Research Institute52, University of Lausanne53, Virginia Tech54, National Institutes of Health55, Karolinska Institutet56
TL;DR: The Transcription Factor Encyclopedia (TFe), a new web-based compendium of mini review articles on transcription factors (TFs) that is founded on the principles of open access and collaboration, is presented.
Abstract: Here we present the Transcription Factor Encyclopedia (TFe), a new web-based compendium of mini review articles on transcription factors (TFs) that is founded on the principles of open access and collaboration. Our consortium of over 100 researchers has collectively contributed over 130 mini review articles on pertinent human, mouse and rat TFs. Notable features of the TFe website include a high-quality PDF generator and web API for programmatic data retrieval. TFe aims to rapidly educate scientists about the TFs they encounter through the delivery of succinct summaries written and vetted by experts in the field. TFe is available at http://www.cisreg.ca/tfe.
137 citations
TL;DR: Both the AR42J cell line and the primary cultured mouse acinar cells support INSM1 induced acini trans‐differentiation model.
Abstract: In this study, the functional role of INSM1 is examined with an AR42J acinar cell model for trans-differentiation into insulin-positive cells. Islet transcription factors (ITFs: INSM1, Pdx-1, and NeuroD1) are over-expressed in AR42J cells using adenoviral vectors. Addition of Ad-INSM1 alone or the combination of three ITFs to the AR42J cells triggers cellular trans-differentiation. Ectopic expression of INSM1 directly induces insulin, Pax6, and Nkx6.1 expression, whereas Pdx-1 and NeuroD1 were slightly suppressed by INSM1. Addition of Pdx-1 and NeuroD1 with INSM1 further enhances endocrine trans-differentiation by increasing both the numbers and intensity of the insulin-positive cells with simultaneous activation of ITFs, Ngn3 and MafA. INSM1 expression alone partially inhibits dexamethasone-induced exocrine amylase expression. The combination of the three ITFs completely inhibits amylase expression and concomitantly induces greater acinar cell trans-differentiation into endocrine cells. Also, addition of the three ITFs promotes EGF and TGFβ receptors expression. Stimulation by the three ITFs along with the EGF/TGFβ growth factors strongly promotes insulin gene expression. The combination of the three ITFs and EGF/TGFβ growth factors with the primary cultured pancreatic acini also facilitates exocrine to endocrine cell differentiation. Taken together, both the AR42J cell line and the primary cultured mouse acinar cells support INSM1 induced acini trans-differentiation model.
24 citations
DOI•
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01 Jan 2012
TL;DR: To analyze estimation behavior, two main characteristics were investigated: the temporal changes of the estimation results, called temporal sensitivity, was evaluated and the standard deviation over time, which is the outcome measure for the spatial sensitivity analysis.
Abstract: Sensitivity Study Study design The estimation results of the Full and Equilibrium methods are both based on measured force/torque sensor data and therefore the arm weight force (f̂ua, f̂la) and for the Full method additionally the center of mass location vector (r̂ua, r̂la) need to be estimated with the subject inside the ARMin. Since the estimation quality differs for different subject anthropometries and different estimation poses, an analysis of the estimation behavior to finally achieve higher and more robust estimation quality is needed. To analyze estimation behavior, two main characteristics were investigated: First, the temporal changes of the estimation results, called temporal sensitivity, was evaluated with the estimation results’ standard deviation over time. Second, the influence of the estimation pose on the estimation result, called spatial sensitivity, was tested. The standard deviation of the estimation results over all spatial estimation poses for each subject is the outcome measure for the spatial sensitivity analysis. For clinical evaluation of the temporal and spatial sensitivities of the Full and Equilibrium methods, the subject’s arm was driven by the position-controlled ARMin in p = 27 different poses. In each of these poses the weight of the arm was estimated over six seconds during each iteration with the 1800Hz real time system. The p = 27 poses were defined to cover the functional arm ROM [1] adapted to the passive ROM of stroke patients (see Tab. 1).
18 citations
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01 Jan 2012
TL;DR: Exp 1: control vs 30” K+ (P<0.0001); control vs. 2’ K+ ($1,000,000; $1,500,000) (P.<0.0005; £1,200,000).
Abstract: Exp 1: control vs. 30” K+ (P<0.0001); control vs. 2’ K+ (P<0.0001). Exp 2: control vs. 3’ K+ (P<0.0001); 3’ K vs. 3’ K + 5’ recovery (P<0.0001). Exp 3: control vs. 3’ K+ (P<0.0001); 3’ K vs. 3’ K + 10’ recovery (P<0.0001). Exp 4: control vs. 1’ K+ (P<0.0001); control vs. 1’ K + 1’ recovery (P<0.005); 1’ K vs. 1’ K + 1’ recovery (P<0.01); 1’ K vs. 1’ K + 5’ recovery (P<0.0001); 1’ K + 1’ recovery vs. 1’ K + 5’ recovery (P<0.05).
10 citations
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01 Jan 2012
TL;DR: Data extraction form in this form includes all the information the authors intend to extract from the included studies.
Abstract: Figure S1 The relevant question from the clinical reporting form Shows the question patients were asked to grade severity of LS from 1 to 4 Figure S2 Maximum spinal cord dose in patients with unilateral and bilateral neck radiation Box and whisker plot showing no difference in Dmax for patients with unilateral and bilateral neck radiation Figure S3 Dose parameters in patients with no LS symptoms (unshaded, n = 75), and with LS (shaded, n = 42) A – Dose to spinal cord B – Volume of spinal cord receiving 10, 20, 30, and 40 Gy C – Percentage of spinal cord receiving 10, 20, 30, and 40 Gy Figure S4 Dose parameter multi-collinearity plots A – V20% vs V30% B – V20% vs V40% Scatter plots showing significant multicollinearity between V20% and V30%, but less collinearity between V20% and V40% Table S1 Collinearity statistics for models containing V20%, V30%, and V40% Tables showing variance inflation factor and tolerance statistics for logistic regression models containing A V20%, V30%, and V40% (high collinearity); and B V20% and V40% (low collinearity) Table S2 Ordinal logistic regression with highest reported grade of LS as the dependent variable Logistic regression output showing younger age and absence of diabetes are significantly associated with higher grade LS Table S3 Binary logistic regression with LS vs Non-LS as the dependent variable, and absolute dose volumes (in cc) Logistic regression output showing that using absolute volume or percentage volume makes little difference to the predictive power of the model or the odds ratio for variables in the refined model Table S4 Ordinal logistic regression with highest reported grade of LS as the dependent variable, and absolute dose volumes (in cc) Logistic regression output showing that using absolute volume or percentage volume makes little difference to the predictive power of the model or the odds ratio for variables in the refined model (PDF 234 kb)
8 citations
TL;DR: The alterations increase the therapeutic potential of adenoviral INSM1 promoter-driven suicide gene therapy for the treatment of a variety of neuroendocrine tumors.
Abstract: The INSM1 gene encodes a transcriptional repressor that is exclusively expressed in neuronal and neuroendocrine tissue during embryonic development that is re-activated in neuroendocrine tumors. Using the 1.7 kbp INSM1 promoter, an adenoviral HSV thymidine kinase gene therapy was tested for the treatment of neuroendocrine tumors. An unforeseen interference on the INSM1 promoter specificity from the adenoviral genome was observed. Attempts were made to protect the INSM1 promoter from the influence of essential adenoviral sequences and to further enhance the tissue specificity of the INSM1 promoter region. Using the chicken β-globin HS4 insulator sequence, we eliminated off-target tissue expression from the Ad-INSM1 promoter-luciferase2 constructs in vivo. In addition, inclusion of two copies of the mouse nicotinic acetylcholine receptor (n(AchR)) neuronal-restrictive silencer element (NRSE) reduced nonspecific activation of the INSM1 promoter both in vitro and in vivo. Further, inclusion of both the HS4 insulator with the n(AchR) 2 × NRSE modification showed a two log increase in luciferase activity measured from the NCI-H1155 xenograft tumors compared with the original adenovirus construct. The alterations increase the therapeutic potential of adenoviral INSM1 promoter-driven suicide gene therapy for the treatment of a variety of neuroendocrine tumors.
7 citations
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01 Jan 2012
TL;DR: Little correlation between either sequence FASTA N50, molecule map coverage or molecule map label density and final genome map N50 is found, however, there is a positive correlation between high value sequence and super scaffolding quality.
Abstract: Assembly and super scaffolding with multiple genera. We examined experiments from 16 different genera to determine if the results seen for the Tribolium castaneum genome are typical for other genomes as well. The T. castaneum genome map N50 was found to be in the high end of the probability density distribution (Additional file 6; Figure 1). The same is true for the Tcas5.0 draft sequence assembly N50 and percent of N50 improvement after super scaffolding compared to the other 17 of 19 total projects that had draft sequence genomes (Additional file 6; Figure 1). However, in no case was the T. castaneum value the highest value recorded, suggesting that a wide range of output quality is possible including values better and worse than the output for T. castaneum. We checked for evidence of correlations between a range of genomic metrics and map assembly, alignment or FASTA super scaffolding results. Because many of the genomic metrics had very broad ranges with variance that increased often for higher values the genomic metrics were log transformed to compress the upper tails and stretch the lower tails of the distributions. Overall we found little correlation between either sequence FASTA N50, molecule map coverage or molecule map label density and final genome map N50. We did, however, find correlations between finished map and sequence assembly metrics and alignment and super scaffolding quality. There is a positive correlation between high value sequence
6 citations
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01 Jan 2012
TL;DR: H om ol og y to K no w n A e a eg yp ti G en es 75 1.93 AAEL003613 1476 1.716 AAEL012608
Abstract: H om ol og y to K no w n A e. a eg yp ti G en es 75 1.1657 AAEL012117 545 1.93 AAEL003613 1476 1.9 AAEL000509 3324 1.1411 AAEL015034 3738 1.195 AAEL006222 4203 1.4383 AAEL017530 4627 1.82 1q43 AAEL801258 5546 1.1647 AAEL015242 6150 1.11 3q11 AAEL000565 6235 1.327 AAEL008459 6462 1.333 AAEL008586 7068 1.31 2p13 AAEL001388 7478 1.542 AAEL011111 7738 1.8 3p12 AAEL000429 8084 1.588 AAEL011533 8390 1.481 AAEL010501 8565 1.507 AAEL010781 8804 1.321 AAEL008382 8886 1.55 2q24 AAEL002382 9220 1.465 AAEL017054 9268 1.716 AAEL012608
6 citations
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01 Jan 2012
TL;DR: In this article, the intensity of all the proteins in four cellulosomal fractions (CB I, CB II, MCC I and MCC II) were estimated by iBAQ and LFQ methods, in order to evaluate their quantity and abundance.
Abstract: The intensities of all the proteins in four cellulosomal fractions (CB I, CB II, MCC I and MCC II) were estimated by iBAQ and LFQ methods, in order to evaluate their quantity and abundance. The iBAQ intensities were normalized by dividing each value by that of the major primary scaffoldin ScaA1 in each sample, in order to facilitate interpretation of the results and relate the cellulosomal proteins to the major primary scaffoldin within the samples. The LFQ intensities were normalized by dividing the values by the intensity of ScaA1 in CB I, in order to simplify the fold-change analysis between the different samples. A. Identified scaffoldins in each fraction. B. 166 identified dockerincontaining proteins. CB, cellobiose; MCC, microcrystalline cellulose; Doc, dockerin; GH, glycoside hydrolase; CBM, carbohydrate-binding module; CE, carbohydrate esterase; PL, polysaccharide lyases. Significant iBAQ values (≥0.10) are shown in bold. The range of LFQ values is color coded according to the scale shown at the bottom.
6 citations