A novel role for proliferin-2 in the ex vivo expansion of hematopoietic stem cells

AbstractA family of proliferin genes was discovered on a microarray analysis of hematopoiesis supportive stromal cell lines. Proliferin-2 (PLF2) increased the frequency of long-term culture-initiating cells (LTC-IC) from 1 in 340 to 1 in 256 of the primary hematopoietic stem cell (HSC)-enriched bone marrow cells grown on MS5.1 feeder layer. A repeat using AFT024 feeder layer also showed a similar increase in LTC-IC (from 1 in 386 cells to 1 in 260 cells). The clonogenic output of the LTC-ICs was also increased significantly. The growth of various hematopoietic and stromal cell lines treated with PLF2 was found to increase by 4–27%, as measured by cell count and DNA synthesis assay. These findings open up the possibility of using PLF2 as a new member of the growth factor cocktails for the ex vivo expansion of HSC

Text localization in web images using probabilistic candidate selection model

Master'sMASTER OF SCIENC

Study of the Hindrance Effect in Sub-barrier Fusion Reactions

We have measured the fusion cross sections of the 12C(13C, p)24Na reaction through off-line measurement of the beta-decay of 24Na using the beta-gamma coincidence method. Our new measurements in the energy range of Ec.m. = 2.6-3.0 MeV do not show an obvious S-factor maximum but a plateau. Comparison between this work and various models is presented.Comment: 3 pages, 3 figures, Talk at the "10th International Conference on Nucleus-Nucleus Collisions", Beijing, 16-21 August 200

2D and 3D video scene text classification

Text detection and recognition is a challenging problem in document analysis due 10 the presence of the unpredictable nature of video texts, such as the variations of orientation, font and size, illumination effects, and even different 20/30 text shadows. In this paper, we propose a novel horizontal and vertical symmetry feature by calculating the gradient direction and the gradient magnitude of each text candidate, which results in Potential Text Candidates (PTCs) after applying the k-means clustering algorithm on the gradient image of each input frame to verify PTC , we explore temporal information of video by proposing an iterative process that continuously verifies the PTCs of the first frame and the successive frames, until the process meets the converging criterion. This outputs Stable Potential Text Candidates (SPTCs). For each , PTC, the method obtains text representatives with the help of the edge image of the input frame. Then for each text representative, we divide it into four quadrants and check a new Mutual Nearest Neighbor Symmetry (MNNS) based on the dominant stroke width distances of the four quadrants. A voting method is finally proposed to clasify each text block as either 2D or 3D by counting the text representatives that satisfy MNNS. Experimental results on clasifying 2D and 3D text images are promising, and the result re further validated by text detection and recognition before clasification and after clasification with the exiting methods, respectively

Anomaly detection through spatio-temporal context modeling in crowded scenes

A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented in this paper. The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. During the first stage, we averagely divide the spatio-temporal volume into atomic blocks. Considering the fact that mutual interference of several human body parts potentially happen in the same block, we propose an atomic motion pattern representation using the Gaussian Mixture Model (GMM) to distinguish the motions inside each block in a refined way. Usual motion patterns can thus be defined as a certain type of steady motion activities appearing at specific scene positions. During the second stage, we further use the Markov Random Field (MRF) model to characterize the joint label distributions over all the adjacent local motion patterns inside the same crowded scene, aiming at modeling the severely occluded situations in a crowded scene accurately. By combining the determinations from the two stages, a weighted scheme is proposed to automatically detect anomaly events from crowded scenes. The experimental results on several different outdoor and indoor crowded scenes illustrate the effectiveness of the proposed algorithm

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

Computer simulated versus observed NO2 and SO2 emitted from elevated point source complex

ISC-AERMOD dispersion model was used to predict air dispersion plumes from an diesel power plant complex. Emissions of NO2 and SO2 from stacks (5 numbers) and a waste oil incinerator were studied to evaluate the pollutant dispersion patterns and the risk of nearby population. Emission source strengths from the individual point sources were also evaluated to determine the sources of significant attribution. Results demonstrated the dispersions of pollutants were influenced by the dominant easterly wind direction with the cumulative maximum ground level concentrations of 589.86 μg/m3 (1 h TWA NO2) and 479.26 μg/m3 (1 h TWA SO2). Model performance evaluation by comparing the predicted concentrations with observed values at ten locations for the individual air pollutants using rigorous statistical procedures were found to be in good agreement. Among all the emission sources within the facility complex, SESB-Power (diesel power plant) had been singled out as a significant source of emission that contributed >85% of the total pollutants emitted

The Role of Interferon Regulatory Factor-1 and Interferon Regulatory Factor-2 in IFN-γ Growth Inhibition of Human Breast Carcinoma Cell Lines

Interferon (IFN) regulatory factor-1 (IRF-1) and IRF-2 play opposing roles in the regulation of many IFN-γ-inducible genes. To investigate the signal transduction pathway in response to IFN-γ in light of differences in growth effects, we selected four human breast carcinoma cell lines based on a spectrum of growth inhibition by IFN-γ. MDA468 growth was markedly inhibited by IFN-γ, and it showed substantial induction of IRF-1 mRNA but little IRF-2 induction. SKBR3 showed little growth inhibition and little induction of IRF-1 mRNA but significant induction of IRF-2 mRNA. HS578T and MDA436 growth inhibition and IRF-1/IRF-2 induction were intermediate. All four cell lines showed intact receptor at the cell surface and Stat1 translocation to the nucleus by immunostaining. By EMSA, there were marked differences in the induced ratio of IRF-1 and IRF-2 binding activity between the cell lines that correlated with growth inhibition. Finally, antisense oligonucleotides specific for IRF-1 attenuated IFN-γ growth inhibition in MDA436 and MDA468, confirming the direct role of IRF-1 in IFN-γ growth inhibition. Induction of IRF-1 causes growth inhibition in human breast cancer cell lines, and induction of IRF-2 can oppose this. The relative induction of IRF-1 to IRF-2 is a critical control point in IFN-γ response.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63111/1/10799900360708623.pd