cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1405684.v3
title: BRCA2 is needed for both repair and cell cycle arrest in mammalian cells exposed to S23906, an anticancer monofunctional DNA binder

description: Repair of DNA-targeted anticancer agents is an active area of investigation of both fundamental and clinical interest. However, most studies have focused on a small number of compounds limiting our understanding of both DNA repair and the DNA damage response. S23906 is an acronycine derivative that shows strong activity toward solid tumors in experimental models. S23906 forms bulky monofunctional DNA adducts in the minor groove which leads to destabilization of the double-stranded helix. We now report that S23906 induces formation of DNA double strand breaks that are processed through homologous recombination (HR) but not Non-Homologous End-Joining (NHEJ) repair. Interestingly, S23906 exposure was accompanied by a higher sensitivity of BRCA2-deficient cells compared to other HR deficient cell lines and by an S-phase accumulation in wild-type (wt), but not in BRCA2-deficient cells. Recently, we have shown that S23906-induced S phase arrest was mediated by the checkpoint kinase Chk1. However, its activated phosphorylated form is equally induced by S23906 in wt and BRCA2-deficient cells, likely indicating a role for BRCA2 downstream of Chk1. Accordingly, override of the S phase arrest by either 7-hydroxystaurosporine (UCN-01) or AZD7762 potentiates the cytotoxic activity of S23906 in wt, but not in BRCA2-deficient cells. Together, our findings suggest that the pronounced sensitivity of BRCA2-deficient cells to S23906 is due to both a defective S-phase arrest and the absence of HR repair. Tumors with deficiencies for proteins involved in HR, and BRCA2 in particular, may thus show increased sensitivity to S23906, thereby providing a rationale for patient selection in clinical trials.
DOI: 10.6084/m9.figshare.1405686.v3
title: Tumor suppressor genes are larger than apoptosis-effector genes and have more regions of active chromatin: Connection to a stochastic paradigm for sequential gene expression programs

description: Apoptosis- and proliferation-effector genes are substantially regulated by the same transactivators, with E2F-1 and Oct-1 being notable examples. The larger proliferation-effector genes have more binding sites for the transactivators that regulate both sets of genes, and proliferation-effector genes have more regions of active chromatin, i.e, DNase I hypersensitive and histone 3, lysine-4 trimethylation sites. Thus, the size differences between the 2 classes of genes suggest a transcriptional regulation paradigm whereby the accumulation of transcription factors that regulate both sets of genes, merely as an aspect of stochastic behavior, accumulate first on the larger proliferation-effector gene “traps,” and then accumulate on the apoptosis effector genes, thereby effecting sequential activation of the 2 different gene sets. As IRF-1 and p53 levels increase, tumor suppressor proteins are first activated, followed by the activation of apoptosis-effector genes, for example during S-phase pausing for DNA repair. Tumor suppressor genes are larger than apoptosis-effector genes and have more IRF-1 and p53 binding sites, thereby likewise suggesting a paradigm for transcription sequencing based on stochastic interactions of transcription factors with different gene classes. In this report, using the ENCODE database, we determined that tumor suppressor genes have a greater number of open chromatin regions and histone 3 lysine-4 trimethylation sites, consistent with the idea that a larger gene size can facilitate earlier transcriptional activation via the inclusion of more transactivator binding sites.
DOI: 10.6084/m9.figshare.1405701.v15
title: Cuke Particle Positions - 1998-2007 - Grid sites

description: LTRANS V2 raw output: Longitude, Latitude, depth, boundary status, and release site. Directories are structured as G1998\152\1\ meaning treatmentyear\releaseday\bin\. Files are named para 1002.csv which contains all the positions (of that treatment, year, site, and release day) at the end of the first day.
DOI: 10.6084/m9.figshare.1405684.v2
title: BRCA2 is needed for both repair and cell cycle arrest in mammalian cells exposed to S23906, an anticancer monofunctional DNA binder

description: Repair of DNA-targeted anticancer agents is an active area of investigation of both fundamental and clinical interest. However, most studies have focused on a small number of compounds limiting our understanding of both DNA repair and the DNA damage response. S23906 is an acronycine derivative that shows strong activity toward solid tumors in experimental models. S23906 forms bulky monofunctional DNA adducts in the minor groove which leads to destabilization of the double-stranded helix. We now report that S23906 induces formation of DNA double strand breaks that are processed through homologous recombination (HR) but not Non-Homologous End-Joining (NHEJ) repair. Interestingly, S23906 exposure was accompanied by a higher sensitivity of BRCA2-deficient cells compared to other HR deficient cell lines and by an S-phase accumulation in wild-type (wt), but not in BRCA2-deficient cells. Recently, we have shown that S23906-induced S phase arrest was mediated by the checkpoint kinase Chk1. However, its activated phosphorylated form is equally induced by S23906 in wt and BRCA2-deficient cells, likely indicating a role for BRCA2 downstream of Chk1. Accordingly, override of the S phase arrest by either 7-hydroxystaurosporine (UCN-01) or AZD7762 potentiates the cytotoxic activity of S23906 in wt, but not in BRCA2-deficient cells. Together, our findings suggest that the pronounced sensitivity of BRCA2-deficient cells to S23906 is due to both a defective S-phase arrest and the absence of HR repair. Tumors with deficiencies for proteins involved in HR, and BRCA2 in particular, may thus show increased sensitivity to S23906, thereby providing a rationale for patient selection in clinical trials.
DOI: 10.6084/m9.figshare.1405683
title: Dispersion relations for MAPI deposited on a Si wafer

description: Dispersion relations for MAPI deposited on a Si wafer. Measured using variable angle spectroscopic ellipsometry and fit using the following oscillators: - Tauc-Lorentz<br>Eg 1.55<br>E 0 1.63<br>A0 23.9<br>
DOI: 10.6084/m9.figshare.1405686.v4
title: Tumor suppressor genes are larger than apoptosis-effector genes and have more regions of active chromatin: Connection to a stochastic paradigm for sequential gene expression programs

description: Apoptosis- and proliferation-effector genes are substantially regulated by the same transactivators, with E2F-1 and Oct-1 being notable examples. The larger proliferation-effector genes have more binding sites for the transactivators that regulate both sets of genes, and proliferation-effector genes have more regions of active chromatin, i.e, DNase I hypersensitive and histone 3, lysine-4 trimethylation sites. Thus, the size differences between the 2 classes of genes suggest a transcriptional regulation paradigm whereby the accumulation of transcription factors that regulate both sets of genes, merely as an aspect of stochastic behavior, accumulate first on the larger proliferation-effector gene “traps,” and then accumulate on the apoptosis effector genes, thereby effecting sequential activation of the 2 different gene sets. As IRF-1 and p53 levels increase, tumor suppressor proteins are first activated, followed by the activation of apoptosis-effector genes, for example during S-phase pausing for DNA repair. Tumor suppressor genes are larger than apoptosis-effector genes and have more IRF-1 and p53 binding sites, thereby likewise suggesting a paradigm for transcription sequencing based on stochastic interactions of transcription factors with different gene classes. In this report, using the ENCODE database, we determined that tumor suppressor genes have a greater number of open chromatin regions and histone 3 lysine-4 trimethylation sites, consistent with the idea that a larger gene size can facilitate earlier transcriptional activation via the inclusion of more transactivator binding sites.
DOI: 10.6084/m9.figshare.1405683.v1
title: Dispersion relations for MAPI deposited on a Si wafer

description: Dispersion relations for MAPI deposited on a Si wafer. Measured using variable angle spectroscopic ellipsometry and fit using the following oscillators: - Tauc-Lorentz<br>Eg 1.55<br>E 0 1.63<br>A0 23.9<br>
DOI: 10.6084/m9.figshare.1405686
title: Tumor suppressor genes are larger than apoptosis-effector genes and have more regions of active chromatin: Connection to a stochastic paradigm for sequential gene expression programs

description: Apoptosis- and proliferation-effector genes are substantially regulated by the same transactivators, with E2F-1 and Oct-1 being notable examples. The larger proliferation-effector genes have more binding sites for the transactivators that regulate both sets of genes, and proliferation-effector genes have more regions of active chromatin, i.e, DNase I hypersensitive and histone 3, lysine-4 trimethylation sites. Thus, the size differences between the 2 classes of genes suggest a transcriptional regulation paradigm whereby the accumulation of transcription factors that regulate both sets of genes, merely as an aspect of stochastic behavior, accumulate first on the larger proliferation-effector gene “traps,” and then accumulate on the apoptosis effector genes, thereby effecting sequential activation of the 2 different gene sets. As IRF-1 and p53 levels increase, tumor suppressor proteins are first activated, followed by the activation of apoptosis-effector genes, for example during S-phase pausing for DNA repair. Tumor suppressor genes are larger than apoptosis-effector genes and have more IRF-1 and p53 binding sites, thereby likewise suggesting a paradigm for transcription sequencing based on stochastic interactions of transcription factors with different gene classes. In this report, using the ENCODE database, we determined that tumor suppressor genes have a greater number of open chromatin regions and histone 3 lysine-4 trimethylation sites, consistent with the idea that a larger gene size can facilitate earlier transcriptional activation via the inclusion of more transactivator binding sites.
DOI: 10.6084/m9.figshare.1405686.v6
title: Tumor suppressor genes are larger than apoptosis-effector genes and have more regions of active chromatin: Connection to a stochastic paradigm for sequential gene expression programs

description: Apoptosis- and proliferation-effector genes are substantially regulated by the same transactivators, with E2F-1 and Oct-1 being notable examples. The larger proliferation-effector genes have more binding sites for the transactivators that regulate both sets of genes, and proliferation-effector genes have more regions of active chromatin, i.e, DNase I hypersensitive and histone 3, lysine-4 trimethylation sites. Thus, the size differences between the 2 classes of genes suggest a transcriptional regulation paradigm whereby the accumulation of transcription factors that regulate both sets of genes, merely as an aspect of stochastic behavior, accumulate first on the larger proliferation-effector gene “traps,” and then accumulate on the apoptosis effector genes, thereby effecting sequential activation of the 2 different gene sets. As IRF-1 and p53 levels increase, tumor suppressor proteins are first activated, followed by the activation of apoptosis-effector genes, for example during S-phase pausing for DNA repair. Tumor suppressor genes are larger than apoptosis-effector genes and have more IRF-1 and p53 binding sites, thereby likewise suggesting a paradigm for transcription sequencing based on stochastic interactions of transcription factors with different gene classes. In this report, using the ENCODE database, we determined that tumor suppressor genes have a greater number of open chromatin regions and histone 3 lysine-4 trimethylation sites, consistent with the idea that a larger gene size can facilitate earlier transcriptional activation via the inclusion of more transactivator binding sites.
DOI: 10.6084/m9.figshare.1405680
title: Data S5 - Gene–Tissue associations coverage and quality analysis

description: This file contains: - Gene–tissue associations from the integration of the transcriptomics and proteomics datasets - GNF atlas gene–tissue associations used in the analysis - Overlap between the integrated set and UniProtKB - Overlap between the GNF atlas studied set and UniProtKB
DOI: 10.6084/m9.figshare.1405686.v5
title: Tumor suppressor genes are larger than apoptosis-effector genes and have more regions of active chromatin: Connection to a stochastic paradigm for sequential gene expression programs

description: Apoptosis- and proliferation-effector genes are substantially regulated by the same transactivators, with E2F-1 and Oct-1 being notable examples. The larger proliferation-effector genes have more binding sites for the transactivators that regulate both sets of genes, and proliferation-effector genes have more regions of active chromatin, i.e, DNase I hypersensitive and histone 3, lysine-4 trimethylation sites. Thus, the size differences between the 2 classes of genes suggest a transcriptional regulation paradigm whereby the accumulation of transcription factors that regulate both sets of genes, merely as an aspect of stochastic behavior, accumulate first on the larger proliferation-effector gene “traps,” and then accumulate on the apoptosis effector genes, thereby effecting sequential activation of the 2 different gene sets. As IRF-1 and p53 levels increase, tumor suppressor proteins are first activated, followed by the activation of apoptosis-effector genes, for example during S-phase pausing for DNA repair. Tumor suppressor genes are larger than apoptosis-effector genes and have more IRF-1 and p53 binding sites, thereby likewise suggesting a paradigm for transcription sequencing based on stochastic interactions of transcription factors with different gene classes. In this report, using the ENCODE database, we determined that tumor suppressor genes have a greater number of open chromatin regions and histone 3 lysine-4 trimethylation sites, consistent with the idea that a larger gene size can facilitate earlier transcriptional activation via the inclusion of more transactivator binding sites.
DOI: 10.6084/m9.figshare.1405678
title: Data S2 - Common associations transcriptomic methods

description: This file contains the following information: - The list of genes studied at the different cutoffs - The list of common associations to all datasets at the different cutoffs - The list of common association for at least 4 datasets at the different cutoffs
DOI: 10.6084/m9.figshare.1405679
title: Data S4 - Common and unique gene–tissue associations to all the sets

description: This file contains: - Overlap between all the sets (transcriptomic set, UniProtKB, Text-mining and proteomics set) - Overlap between the transcriptomic and the proteomic set - The list of gene–tissue associations unique to each set
DOI: 10.6084/m9.figshare.1405678.v1
title: Data S2 - Common associations transcriptomic methods

description: This file contains the following information: - The list of genes studied at the different cutoffs - The list of common associations to all datasets at the different cutoffs - The list of common association for at least 4 datasets at the different cutoffs
DOI: 10.6084/m9.figshare.1405680.v2
title: Data S5 - Gene–Tissue associations coverage and quality analysis

description: This file contains: - Gene–tissue associations from the integration of the transcriptomics and proteomics datasets - GNF atlas gene–tissue associations used in the analysis - Overlap between the integrated set and UniProtKB - Overlap between the GNF atlas studied set and UniProtKB
DOI: 10.6084/m9.figshare.1405679.v2
title: Data S4 - Common and unique gene–tissue associations to all the sets

description: This file contains: - Overlap between all the sets (transcriptomic set, UniProtKB, Text-mining and proteomics set) - Overlap between the transcriptomic and the proteomic set - The list of gene–tissue associations unique to each set
DOI: 10.6084/m9.figshare.1405680.v1
title: Data S5 - Gene–Tissue associations coverage and quality analysis

description: This file contains: - Gene–tissue associations from the integration of the transcriptomics and proteomics datasets - GNF atlas gene–tissue associations used in the analysis - Overlap between the integrated set and UniProtKB - Overlap between the GNF atlas studied set and UniProtKB
DOI: 10.6084/m9.figshare.1405677
title: Data S1 – Mapping of tissue names to Brenda Tissue Ontology terms

description: This file contains the mapping from the tissue names from the original sources to the standardized BTO terms.
DOI: 10.6084/m9.figshare.1405677.v1
title: Data S1 – Mapping of tissue names to Brenda Tissue Ontology terms

description: This file contains the mapping from the tissue names from the original sources to the standardized BTO terms.
DOI: 10.6084/m9.figshare.1405679.v1
title: Data S4 - Common and unique gene–tissue associations to all the sets

description: This file contains: - Overlap between all the sets (transcriptomic set, UniProtKB, Text-mining and proteomics set) - Overlap between the transcriptomic and the proteomic set - The list of gene–tissue associations unique to each set
DOI: 10.6084/m9.figshare.1405666
title: Data S3 - mRNA reference set associations

description: This file contains the gene–tissue associations that form the mRNA reference set used in the fold-enrichment analysis
DOI: 10.6084/m9.figshare.1405638
title: GEUVADIS expressed regions coverage matrix

description: coverageMatrix file produced by the derfinder Bioconductor package for the expressed region (ERs) analysis of the Rail-RNA paper. Scripts that reproduce this file are available via https://github.com/nellore/rail/tree/master/eval.
DOI: 10.6084/m9.figshare.1405638.v1
title: GEUVADIS expressed regions coverage matrix

description: coverageMatrix file produced by the derfinder Bioconductor package for the expressed region (ERs) analysis of the Rail-RNA paper. Scripts that reproduce this file are available via https://github.com/nellore/rail/tree/master/eval.
DOI: 10.6084/m9.figshare.1405636
title: Szkolenie: Analiza i ocena opłacalności i ryzyka projektów

description: Szkolenie: Analiza i ocena opłacalności i ryzyka projektów inwestycyjnych Szkolenie o tej tematyce lub podobnej może również być zorganizowane:<br>- w dowolnym innym terminie<br>- w miejscu najbliżej Państwa siedziby<br>W celu uzgodnienia szczegółów proszę o kontakt:<br>Grzegorz Michalski<br>tel: 503452860<br>tel. 791214963<br>Grzegorz.Michalski@gmail.com<br>http://eunicacon.wordpress.com/<br>http://michalskig.com/ Prowadzący<br>doc. Grzegorz Michalski
DOI: 10.6084/m9.figshare.1405637
title: Szkolenie: Analiza i ocena opłacalności i ryzyka projektów inwestycyjnych

description: Szkolenie: Analiza i ocena opłacalności i ryzyka projektów inwestycyjnych Szkolenie o tej tematyce lub podobnej może również być zorganizowane:<br>- w dowolnym innym terminie<br>- w miejscu najbliżej Państwa siedziby<br>W celu uzgodnienia szczegółów proszę o kontakt:<br>Grzegorz Michalski<br>tel: 503452860<br>tel. 791214963<br>Grzegorz.Michalski@gmail.com<br>http://eunicacon.wordpress.com/<br>http://michalskig.com/ Prowadzący<br>doc. Grzegorz Michalski
DOI: 10.6084/m9.figshare.1405666.v2
title: Data S3 - mRNA reference set associations

description: This file contains the gene–tissue associations that form the mRNA reference set used in the fold-enrichment analysis
DOI: 10.6084/m9.figshare.1405635
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405637.v1
title: Szkolenie: Analiza i ocena opłacalności i ryzyka projektów inwestycyjnych

description: Szkolenie: Analiza i ocena opłacalności i ryzyka projektów inwestycyjnych Szkolenie o tej tematyce lub podobnej może również być zorganizowane:<br>- w dowolnym innym terminie<br>- w miejscu najbliżej Państwa siedziby<br>W celu uzgodnienia szczegółów proszę o kontakt:<br>Grzegorz Michalski<br>tel: 503452860<br>tel. 791214963<br>Grzegorz.Michalski@gmail.com<br>http://eunicacon.wordpress.com/<br>http://michalskig.com/ Prowadzący<br>doc. Grzegorz Michalski
DOI: 10.6084/m9.figshare.1405634
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405635.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405666.v1
title: Data S3 - mRNA reference set associations

description: This file contains the gene–tissue associations that form the mRNA reference set used in the fold-enrichment analysis
DOI: 10.6084/m9.figshare.1405631
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405632
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405633
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405634.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405631.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405632.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405633.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405630
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405629
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405627
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405629.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405630.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405628
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405625
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405626
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405627.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405628.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405626.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405623
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405624
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405622
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405624.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405625.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405621
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405623.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405620
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405622.v1
title: szkolenie

description: szkolenie
DOI: 10.6084/m9.figshare.1405617
title: Rydberg states of the HeH<sup>+</sup> ion calculated by <i>ab initio</i> methods: potential energies and effective principal quantum numbers

description: More than 80 excited electronic states of the hydrohelium ion HeH<sup>+</sup> of <sup>1, 3</sup>Σ<sup>+</sup>, <sup>1, 3</sup>Π, <sup>1, 3</sup>Δ, <sup>1, 3</sup>Φ and <sup>1, 3</sup>Γ symmetry have been calculated <i>ab initio</i> up to <i>n</i> = 6 for internuclear distances ranging from 0.5 to 100 bohr. The computations involve a configuration interaction (CI) treatment based on a home-made suite of programs that uses special basis sets designed for the representation of molecular Rydberg states. The results are compared with previous computations where these are available (up to <i>n</i> = 4), and it is found that except for the very lowest excited states, the present energies are consistently lower than those obtained previously, with an average lowering corresponding to several hundred cm<sup>−1</sup>. It is shown that with the exception of its ground state, HeH<sup>+</sup> is an effective one-electron system having an overall electronic structure similar to H<sup>+</sup><sub>2</sub>. The interaction of the excited electron with the He<sup>+</sup> 1<i>s</i> core electron causes small singlet–triplet splittings to appear and ℓ-mixing interactions to occur, that are not present in H<sup>+</sup><sub>2</sub>.
DOI: 10.6084/m9.figshare.1405621.v1
title: szkolenie

description: szkolenie