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C Lg N H Rs Z Apk Data 2....

Your operating system plays a key role in determining how well your high performance computing (HPC) infrastructure operates and performs. It connects your hardware, software, networking, and interfaces to form a unified, orchestrated environment. Red Hat Enterprise Linux provides a flexible and reliable platform for running HPC workloads at scale across datacenter, cloud, and hybrid environments.

C lg n H rs z Apk Data 2....


sys = zpk(zeros,poles,gain) creates a continuous-time zero-pole-gain model with zeros and poles specified as vectors and the scalar value of gain. The output sys is a zpk model object storing the model data. Set zeros or poles to [] for systems without zeros or poles. These two inputs need not have equal length and the model need not be proper (that is, have an excess of poles).

In many of the cohorts in public databases, decreased NQO1 transcript levels correlated with concurrent increase in expression of mesenchymal markers such as N-cadherin, fibronectin in metastasis26,27,29,30. Therefore, we assessed the role of NQO1 in prostate cancer metastasis using prostate cancer cell culture models (PC-3 NQO1 knockdown and ARCaP EMT model systems). These cellular models along with TGFβ-stimulated PC-3 model confirmed a role for NQO1 suppression in EMT progression. Our results are particularly related with metastasis since ARCaPM is a lineage-derived from ARCaPE cells that gained 100% incidence in bone and adrenal gland metastasis in mice32. PC-3 is a bone metastasis-derived cell line and TGFβ controls multiple signaling pathways associated with bone metastasis of prostate cancer cells12. Although we did not screen many markers of EMT changes, there was no consistent change in classic EMT markers in other prostate cancer cell lines with NQO1 knockdown (Supplementary Fig. 11). Additional work is needed to determine if NQO1 plays a role in the ability of cancer cells to home to different metastatic sites given our observations of heterogeneity in downstream signaling and functional output in prostate cancer cells. However, it is fascinating to note that NQO1 expression levels are significantly low in metastatic tissues derived from multiple sites of prostate cancer patients suggesting a possible gatekeeper role against metastasis (Fig. 2a).

Using a mouse model of nonalcoholic steatohepatitis (NASH), Sharma et al.43 showed that pharmacological activation of NRF2, a master regulator of NQO1, decreases TGFβ expression. However involvement of NQO1 itself or oxidative stress is not known. Our results show that decreased NQO1 in ARCaPM clone increases TGFβ signaling and expression of mesenchymal genes, suggesting the operation of a negative feedback loop. Similarly, TGFβ signaling can be inhibited by NQO1 overexpression in the same clone. The data are consistent with increased TGFβ target genes as well as TGFβ-SMAD activation caused by NQO1 knockdown in PC-3 cells. These data provide direct evidence that NQO1 inhibits TGFβ signaling. We had reported that NQO1 silencing in hormone-responsive LNCaP cells enhanced several cytokines including IL-8 that reinforces cellular pro-migratory and pro-survival signaling19. These cytokines may potentially induce the transformation of prostate cancer cells including PC-3 cells to a more migratory/mesenchymal phenotype. Cytokine array analyses revealed that NQO1 knockdown in PC-3 cells decreased macrophage migration inhibitory factor (MIF) secretion (Supplementary Fig. 12). MIF is a pleotropic cytokine and oxidative stress sensor which modulates GSH levels by altering the cellular GSH/GSSG balance. It has recently been shown that MIF knockdown leads to the activation of the TGFβ-signaling44,45. The change in MIF further supports our notion that NQO1 silencing affects the redox balance and TGFβ signaling.

In summary, we show that NQO1 regulates TGFβ signaling to inhibit EMT and migration, which are required for prostate cancer progression. The proposed model (Fig. 6f), provides an explanation for how NQO1 overcomes the vulnerability to cellular stress, to obstruct prostate cancer cell plasticity that is essential for its progression to aggressive disease. We show that inhibition of NQO1 significantly correlated with EMT-like morphological changes and increased migration. This also increased TGFβ signaling and expression of multiple genes associated with prostate cancer progression and metastasis. NQO1 knockdown increased levels of nuclear SMADs thereby activating TGFβ-SMAD-ZEB1 signaling. Importantly, our biological data provide mechanistic details for the reported correlation between decreased NQO1 transcript and poorly differentiated metastatic prostate tumors and confirms our hypothesis that attenuation of NQO1 plays a role in TGFβ signaling mediated cancer cell plasticity.

This work was supported by 5R01CA149516 (R.G.), 1R01AT7448 and CTRC 40th Anniversary Distinguished Professor of Oncology Endowment (A.P.K.). D.T. was supported in part by an American Cancer Society (Joe and Jessie Crump Foundation Medical Research Fund) Postdoctoral Fellowship (PF-15-219-01-CSM). S.B.H. was supported in part by a predoctoral fellowship from the Cancer Prevention Research Institute of Texas (RP170345). C.L.C. was supported in part by the Cancer Prevention Research Institute of Texas, RP150600 (T.H.-M.H.), 5U54CA113001-10-10 (T.H.-M.H.), DoD PC170821 (T.H.-M.H.) and the V-Foundation Translational Award. We thank the Bioanalytic Single-Cell Core (CPRIT) at UTHSCSA for partial support of data analysis. We thank Dr. David Ross (University of Colorado Medical School, Denver, CO) for NQO1 reporter and MAC220. We also thank the Optical Imaging shared resource at UTHSCSA.

All authors of this paper have read and approved the final version of the submitted manuscript. D.T., A.P.K., and R.G. developed the original hypothesis, conceived the study, and designed experiments. D.T. performed most of the in vitro experiments and analyzed data. S.-B.H., A.R.M., and X.Y. performed the animal experiments and analyzed data. R.G.B. performed immunohistochemistry. C.-N.H., C.-.L.C., and T.H.-M.H. analyzed RNA-seq data from circulating tumor cells. M.A.L. and H.M. provided clinical specimens, and H.M. also analyzed and interpreted data. R.L.R. performed pathological analysis on mouse tissues. D.T. and R.G. wrote and edited the manuscript. R.G. supervised the study.

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4. Write a Python program that calculates the area of a circle based on the radius entered by the user. Go to the editorSample Output : r = 1.1Area =3.8013271108436504Click me to see the sample solution5. Write a Python program that accepts the user's first and last name and prints them in reverse order with a space between them. Go to the editorClick me to see the sample solution6. Write a Python program that accepts a sequence of comma-separated numbers from the user and generates a list and a tuple of those numbers. Go to the editorSample data : 3, 5, 7, 23Output : List : ['3', ' 5', ' 7', ' 23'] Tuple : ('3', ' 5', ' 7', ' 23')Click me to see the sample solution7. Write a Python program that accepts a filename from the user and prints the extension of the file. Go to the editorSample filename : Output : javaClick me to see the sample solution

140. Write a Python program to convert an integer to binary that keeps leading zeros. Go to the editorSample data : x=12Expected output : 000011000000001100Click me to see the sample solution 350c69d7ab


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