982 resultados para Score reading introduction


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Emerging evidence suggests that cancers arise in stem/progenitor cells. Yet, the requirements for transformation of these primitive cells remains poorly understood. In this study, we have exploited the `mammosphere' system that selects for primitive mammary stem/progenitor cells to explore their potential and requirements for transformation. Introduction of Simian Virus 40 Early Region and hTERT into mammosphere-derived cells led to the generation of NBLE, an immortalized mammary epithelial cell line. The NBLEs largely comprised of bi-potent progenitors with long-term self-renewal and multi-lineage differentiation potential. Clonal and karyotype analyses revealed the existence of heterogeneous population within NBLEs with varied proliferation, differentiation and sphere-forming potential. Significantly, injection of NBLEs into immunocompromised mice resulted in the generation of invasive ductal adenocarcinomas. Further, these cells harbored a sub-population of CD44(+)/CD24(-) fraction that alone had sphere- and tumor-initiating potential and resembled the breast cancer stem cell gene signature. Interestingly, prolonged in vitro culturing led to their further enrichment. The NBLE cells also showed increased expression of stemness and epithelial to mesenchymal transition markers, deregulated self-renewal pathways, activated DNA-damage response and cancer-associated chromosomal aberrations-all of which are likely to have contributed to their tumorigenic transformation. Thus, unlike previous in vitro transformation studies that used adherent, more differentiated human mammary epithelial cells our study demonstrates that the mammosphere-derived, less-differentiated cells undergo tumorigenic conversion with only two genetic elements, without requiring oncogenic Ras. Moreover, the striking phenotypic and molecular resemblance of the NBLE-generated tumors with naturally arising breast adenocarcinomas supports the notion of a primitive breast cell as the origin for this subtype of breast cancer. Finally, the NBLEs represent a heterogeneous population of cells with striking plasticity, capable of differentiation, self-renewal and tumorigenicity, thus offering a unique model system to study the molecular mechanisms involved with these processes. Oncogene (2012) 31, 1896-1909; doi:10.1038/onc.2011.378; published online 29 August 2011

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We propose a new paradigm for displaying comments: showing comments alongside parts of the article they correspond to. We evaluate the effectiveness of various approaches for this task and show that a combination of bag of words and topic models performs the best.

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The ribosomal P-site hosts the peptidyl-tRNAs during translation elongation. Which P-site elements support these tRNA species to maintain codon-anticodon interactions has remained unclear. We investigated the effects of P-site features of methylations of G966, C967, and the conserved C-terminal tail sequence of Ser, Lys, and Arg (SKR) of the S9 ribosomal protein in maintenance of the translational reading frame of an mRNA. We generated Escherichia coli strains deleted for the SKR sequence in S9 ribosomal protein, RsmB (which methylates C967), and RsmD (which methylates G966) and used them to translate LacZ from its +1 and -1 out-of-frame constructs. We show that the S9 SKR tail prevents both the +1 and -1 frameshifts and plays a general role in holding the P-site tRNA/peptidyl-tRNA in place. In contrast, the G966 and C967 methylations did not make a direct contribution to the maintenance of the translational frame of an mRNA. However, deletion of rsmB in the S9 Delta 3 background caused significantly increased -1 frameshifting at 37 degrees C. Interestingly, the effects of the deficiency of C967 methylation were annulled when the E. coli strain was grown at 30 degrees C, supporting its context-dependent role.

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General circulation models (GCMs) use transient climate simulations to predict climate conditions in the future. Coarse-grid resolutions and process uncertainties necessitate the use of downscaling models to simulate precipitation. However, in the downscaling models, with multiple GCMs now available, selecting an atmospheric variable from a particular model which is representative of the ensemble mean becomes an important consideration. The variable convergence score (VCS) provides a simple yet meaningful approach to address this issue, providing a mechanism to evaluate variables against each other with respect to the stability they exhibit in future climate simulations. In this study, VCS methodology is applied to 10 atmospheric variables of particular interest in downscaling precipitation over India and also on a regional basis. The nested bias-correction methodology is used to remove the systematic biases in the GCMs simulations, and a single VCS curve is developed for the entire country. The generated VCS curve is expected to assist in quantifying the variable performance across different GCMs, thus reducing the uncertainty in climate impact-assessment studies. The results indicate higher consistency across GCMs for pressure and temperature, and lower consistency for precipitation and related variables. Regional assessments, while broadly consistent with the overall results, indicate low convergence in atmospheric attributes for the Northeastern parts of India.

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A new 1D NMR experiment cited as `Quick G-SERF', which re-introduces selective proton-proton scalar interactions in a pure shift spectrum during real time data acquisition, is reported. The method provides information on multiple proton-proton couplings from a single experiment, analogous to the 2D G-SERF technique, while significantly shortening the experimental time by 1-2 orders of magnitude due to reduced dimension and enhanced sensitivity.

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Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders.

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Computing the maximum of sensor readings arises in several environmental, health, and industrial monitoring applications of wireless sensor networks (WSNs). We characterize the several novel design trade-offs that arise when green energy harvesting (EH) WSNs, which promise perpetual lifetimes, are deployed for this purpose. The nodes harvest renewable energy from the environment for communicating their readings to a fusion node, which then periodically estimates the maximum. For a randomized transmission schedule in which a pre-specified number of randomly selected nodes transmit in a sensor data collection round, we analyze the mean absolute error (MAE), which is defined as the mean of the absolute difference between the maximum and that estimated by the fusion node in each round. We optimize the transmit power and the number of scheduled nodes to minimize the MAE, both when the nodes have channel state information (CSI) and when they do not. Our results highlight how the optimal system operation depends on the EH rate, availability and cost of acquiring CSI, quantization, and size of the scheduled subset. Our analysis applies to a general class of sensor reading and EH random processes.

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Reactive oxygen species (ROS)-mediated diseased states are of major concern in modern day life. Under oxidative stress conditions, the cellular antioxidants deplete, leading to several biological disorders. Small molecule mimics of different antioxidant enzymes are found to be useful in supplementing the biological systems to detoxify ROS. In this study, we have synthesized a series of amine or amide-based diselenides containing an additional amino group as glutathione peroxidase (GPx) mimetics. These diselenides act as a catalytic triad model of the native GPx featuring two basic amino groups near the selenium centre. A comparison of the catalytic activities reveals that the additional amino group increases the activity significantly in the presence of aromatic thiols. Deprotonation of thiol by an additional amine either stabilizes the selenolate intermediate or facilitates the nucleophilic attack of thiol in other intermediates. The Se-77 NMR experiments and DFT calculations show that the amino group does not have any significant effect on the catalytic intermediates. Although the amino moiety increases the nucleophilicity of the thiol, it does not prevent the thiol exchange reactions that take place in the selenenyl sulfide intermediates.

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We give a review on (a) elements of (2 + 1)-dimensional gravity, (b) some aspects of its relation to Chern-Simons theory, (c) its generalization to couple higher spins, and (d) cosmic singularity resolution as an application in the context of flat space higher spin theory. A knowledge of the Einstein-Hilbert action, classical non-Abelian gauge theory and some (negotiable amount of) maturity are the only pre-requisites.

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