914 resultados para true abnormal dta elimination


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Memorial discourse

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http://www.archive.org/details/metlakahtltruena00davirich

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Treatment of Zn(Si(SiMe3)3)2 with ZnX2 (X = Cl, Br, I) in tetrahydrofuran (THF) at 23 °C afforded [Zn(Si(SiMe3)3)X(THF)]2 in 83–99% yield. X-ray crystal structures revealed dimeric structures with Zn2X2 cores. Thermogravimetric analyses of [Zn(Si(SiMe3)3)X(THF)]2 demonstrated a loss of coordinated THF between 50 and 155 °C and then single-step weight losses between 200 and 275 °C. The nonvolatile residue was zinc metal in all cases. Bulk thermolyses of [Zn(Si(SiMe3)3)X(THF)]2 between 210 and 250 °C afforded zinc metal in 97–99% yield, Si(SiMe3)3X in 91–94% yield, and THF in 81–98% yield. Density functional theory calculations confirmed that zinc formation becomes energetically favorable upon THF loss. Similar reactions are likely to be general for M(SiR3)n/MXn pairs and may lead to new metal-film-growth processes for chemical vapor deposition and atomic layer deposition.

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Diarthrodial joints are well suited to intra-articular injection, and the local delivery of therapeutics in this fashion brings several potential advantages to the treatment of a wide range of arthropathies. Possible benefits over systemic delivery include increased bioavailability, reduced systemic exposure, fewer adverse events, and lower total drug costs. Nevertheless, intra-articular therapy is challenging because of the rapid egress of injected materials from the joint space; this elimination is true of both small molecules, which exit via synovial capillaries, and of macromolecules, which are cleared by the lymphatic system. In general, soluble materials have an intra-articular dwell time measured only in hours. Corticosteroids and hyaluronate preparations constitute the mainstay of FDA-approved intra-articular therapeutics. Recombinant proteins, autologous blood products and analgesics have also found clinical use via intra-articular delivery. Several alternative approaches, such as local delivery of cell and gene therapy, as well as the use of microparticles, liposomes, and modified drugs, are in various stages of preclinical development.

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Increased understanding of the precise molecular mechanisms involved in cell survival and cell death signaling pathways offers the promise of harnessing these molecules to eliminate cancer cells without damaging normal cells. Tyrosine kinase oncoproteins promote the genesis of leukemias through both increased cell proliferation and inhibition of apoptotic cell death. Although tyrosine kinase inhibitors, such as the BCR-ABL inhibitor imatinib, have demonstrated remarkable efficacy in the clinic, drug-resistant leukemias emerge in some patients because of either the acquisition of point mutations or amplification of the tyrosine kinase, resulting in a poor long-term prognosis. Here, we exploit the molecular mechanisms of caspase activation and tyrosine kinase/adaptor protein signaling to forge a unique approach for selectively killing leukemic cells through the forcible induction of apoptosis. We have engineered caspase variants that can directly be activated in response to BCR-ABL. Because we harness, rather than inhibit, the activity of leukemogenic kinases to kill transformed cells, this approach selectively eliminates leukemic cells regardless of drug-resistant mutations.

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Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high-dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high-dimensional data with survival outcomes. In this paper, we develop a novel method based on the random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis.Additionally, we have shown the advantages of our approach over single-gene-based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to better utilize the information available from microarray data with survival outcomes.

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© 2015 The Authors. Synapse elimination occurs in development, plasticity, and disease. Although the importance of synapse elimination has been documented in many studies, the molecular mechanisms underlying this process are unclear. Here, using the development of C. elegans RME neurons as a model, we have uncovered a function for the apoptosis pathway in synapse elimination. We find that the conserved apoptotic cell death (CED) pathway and axonal mitochondria are required for the elimination of transiently formed clusters of presynaptic components in RME neurons. This function of the CED pathway involves the activation of the actin-filament-severing protein, GSNL-1. Furthermore, we show that caspase CED-3 cleaves GSNL-1 at a conserved C-terminal region and that the cleaved active form of GSNL-1 promotes its actin-severing ability. Our data suggest that activation of the CED pathway contributes to selective elimination of synapses through disassembly of the actin filament network. Meng et al. find that activation of the cell death pathway in C. elegans neurons contributes to selective elimination of synapses through disassembly of the actin filament network.

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A flexible elimination algorithm is presented and is applied to the solution of dense systems of linear equations. Properties of the algorithm are exploited in relation to panel element methods for potential flow and subsonic compressible flow. Further properties in relation to distributed computing are also discussed.

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