cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12800855
title: Matlab Code File 3: Helper Function from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

description: Estrogen receptor (ER) positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either estrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and estrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of estrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.
DOI: 10.6084/m9.figshare.12800858.v1
title: Matlab Code File 1: Main File from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

description: Estrogen receptor (ER) positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either estrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and estrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of estrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.
DOI: 10.6084/m9.figshare.12800855.v1
title: Matlab Code File 3: Helper Function from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

description: Estrogen receptor (ER) positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either estrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and estrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of estrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.
DOI: 10.6084/m9.figshare.12800870.v1
title: Matlab Code File 2: Differential Equations from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

description: Estrogen receptor (ER) positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either estrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and estrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of estrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.
DOI: 10.6084/m9.figshare.12800858
title: Matlab Code File 1: Main File from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

description: Estrogen receptor (ER) positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either estrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and estrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of estrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.
DOI: 10.6084/m9.figshare.12800867
title: SBML Version of the Model from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

description: Estrogen receptor (ER) positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either estrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and estrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of estrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.
DOI: 10.6084/m9.figshare.13323647.v1
title: Additional file 3 of HDAC2 inhibits EMT-mediated cancer metastasis by downregulating the long noncoding RNA H19 in colorectal cancer

description: Additional file 3.
DOI: 10.6084/m9.figshare.19092330.v1
title: Additional file 1 of A risk scoring system to predict the individual incidence of early-onset colorectal cancer

description: Additional file 1. The individual risk scores of 10,000 simulated subjects.
DOI: 10.6084/m9.figshare.12800873.v1
title: Parameter Cohort: Listing of the 400 Parameter Sets in the Cohort from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

description: Estrogen receptor (ER) positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either estrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and estrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of estrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.
DOI: 10.6084/m9.figshare.13323632.v1
title: Additional file 1 of Moose genomes reveal past glacial demography and the origin of modern lineages

description: Additional file 1.
DOI: 10.6084/m9.figshare.19090823
title: Additional file 2 of Identification of novel signaling components in N,N’-Dinitrosopiperazine-mediated metastasis of nasopharyngeal Carcinoma by quantitative phosphoproteomics

description: Additional file 2: Table S2: Different phosphopeptides mediated by DNP. (XLS 44 KB)
DOI: 10.6084/m9.figshare.19090823.v1
title: Additional file 2 of Identification of novel signaling components in N,N’-Dinitrosopiperazine-mediated metastasis of nasopharyngeal Carcinoma by quantitative phosphoproteomics

description: Additional file 2: Table S2: Different phosphopeptides mediated by DNP. (XLS 44 KB)
DOI: 10.6084/m9.figshare.19092330
title: Additional file 1 of A risk scoring system to predict the individual incidence of early-onset colorectal cancer

description: Additional file 1. The individual risk scores of 10,000 simulated subjects.
DOI: 10.6084/m9.figshare.12800861.v1
title: Raw Data from the Experiments: Western Blot Intensities and Coulter Counter Counts from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

description: Estrogen receptor (ER) positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either estrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and estrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of estrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.
DOI: 10.6084/m9.figshare.13849633.v1
title: PRKAA/AMPKα phosphorylation switches the role of RASAL2 from a suppressor to an activator of autophagy

description: RASAL2 (RAS protein activator like 2), a RASGTPase activating protein, can catalyze the hydrolysis of RAS-GTP into RAS-GDP to inactivate the RAS pathway in various types of cancer cells. However, the cellular function of RASAL2 remains elusive. Here we showed that RASAL2 can attenuate PRKAA/AMPKα phosphorylation by recruiting phosphatase PPM1B/pp2cβ, thus inhibiting the initiation of basal autophagy under normal conditions. In addition, we found that glucose starvation could induce dissociation of PPM1B from RASAL2 and then RASAL2 at S351 be phosphorylated by PRKAA, followed by the binding of phosphorylated-RASAL2 with to PIK3C3/VPS34-ATG14-BECN1/Beclin1 complex to increase PIK3C3 activity and autophagy. Furthermore, RASAL2 S351 phosphorylation facilitated breast tumor growth and correlated to poor clinical outcomes in breast cancer patients. Our study demonstrated that the phosphorylation status of RASAL2 S351 can function as a molecular switch to either suppress or promote AMPK-mediated autophagy. Inhibition of RASAL2 S351 phosphorylation might be a potential therapeutic strategy to overcome the resistance of AMPK-activation agents. <b>Abbreviations:</b> AICAR: aminoimidazole carboxamide ribonucleotide; AMPK: adenosine 5‘-monophosphate (AMP)-activated protein kinase; ATG14: autophagy related 14; C.C: compound C; CQ: chloroquine; DKO: double-knockout; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MTOR: mechanistic target of rapamycin kinase; PIK3C3/VPS34: phosphatidylinositol 3-kinase catalytic subunit type 3; PIK3R4/VPS15: phosphoinositide-3-kinase regulatory subunit 4; PPM1B/pp2cβ: protein phosphatase, Mg2+/Mn2+ dependent 1B; PRKAA/AMPKα: protein kinase AMP-activated catalytic subunit alpha; PtdIns: phosphatidylinositol; PtdIns3P: phosphatidylinositol-3-phosphate; RASAL2: RAS protein activator like 2; RasGAPs: RasGTPase activating proteins; SQSTM1/p62: sequestosome 1; TNBC: triple-negative breast cancer.
DOI: 10.6084/m9.figshare.12800873
title: Parameter Cohort: Listing of the 400 Parameter Sets in the Cohort from Mathematical modelling of breast cancer cells in response to endocrine therapy and Cdk4/6 inhibition

description: Estrogen receptor (ER) positive breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of any targeted therapy often results in resistance to the therapy. Our ultimate goal is to use mathematical modelling to optimize alternating therapies that not only decrease proliferation but also stave off resistance. Toward this end, we measured levels of key proteins and proliferation over a 7-day time course in ER+ MCF-7 breast cancer cells. Treatments included endocrine therapy, either estrogen deprivation, which mimics the effects of an aromatase inhibitor, or fulvestrant, an ER degrader. These data were used to calibrate a mathematical model based on key interactions between ER signalling and the cell cycle. We show that the calibrated model is capable of predicting the combination treatment of fulvestrant and estrogen deprivation. Further, we show that we can add a new drug, palbociclib, to the model by measuring only two key proteins, cMyc and hyperphosphorylated RB1, and adjusting only parameters associated with the drug. The model is then able to predict the combination treatment of estrogen deprivation and palbociclib. We illustrate the model's potential to explore protocols that limit proliferation and hold off resistance by not depending on any one therapy.
DOI: 10.6084/m9.figshare.11755143
title: Legacy temperature zone map

description: This is the temperature zone map of Zheng et al. 2008, used as legacy map of this study.
DOI: 10.6084/m9.figshare.19090820.v1
title: Additional file 1 of Identification of novel signaling components in N,N’-Dinitrosopiperazine-mediated metastasis of nasopharyngeal Carcinoma by quantitative phosphoproteomics

description: Additional file 1: Table S1: DNP-regulated phosphopeptides identified using a SILAC quantitative proteomics approach. (XLS 212 KB)
DOI: 10.6084/m9.figshare.19092568.v1
title: Additional file 16 of An integrative multi-omics analysis based on liquid–liquid phase separation delineates distinct subtypes of lower-grade glioma and identifies a prognostic signature

description: Additional file 16: Table S6. The LLPS-related DEGs between LGG samples and normal samples.
DOI: 10.6084/m9.figshare.23290115
title: Hydrogen Combustion supplemetary data from an active learning study

description: This data is generated in an active learning study of hydrogen combustion, with energies and forces generated using the ωB97X-V DFT functional with the cc-pVTZ basis set. It is build on top of the original HCombustion dataset: https://figshare.com/articles/dataset/Hydrogen_Combustion_using_IRC_AIMD_and_normal_modes/19601689<br>There are two parts to this data. al_added_npz which are data added during the active learning cycle, and irc_dialation which are the irc dialation data described in the paper Beyond potential energy surface benchmarking: a complete application of machine learning to chemical reactivity (link to be added).<br>All data are stored in npz format. They can be loaded through numpy by using:np.load("name.npz", allow_pickle=True)This will load a dictionary with the following keys:'R': positions, array of (n_snapshots, n_atoms, 3)'Z': atomic numbers, array of (n_snapshots, n_atoms)'E': energy, array of (n_snapshots, 1)'F': forces, array of (n_snapshots, n_atoms, 3)coordinates are in the unit of Angstrom, energy in kcal/mol, forces in kcal/mol/A.
DOI: 10.6084/m9.figshare.16998951
title: XRD raw data of (Al63Cu25Fe12)100-xCex (x = 1 at%) sample from Influence of cerium content on the microstructure and thermal expansion properties of suction cast Al-Cu-Fe alloys

description: 4CExrd
DOI: 10.6084/m9.figshare.16998954
title: XRD raw data of (Al63Cu25Fe12)100-xCex (x = 0.1 at%) sample from Influence of cerium content on the microstructure and thermal expansion properties of suction cast Al-Cu-Fe alloys

description: 2CExrd
DOI: 10.6084/m9.figshare.16998957
title: XRD raw data of (Al63Cu25Fe12)100-xCex (x = 2 at%) sample from Influence of cerium content on the microstructure and thermal expansion properties of suction cast Al-Cu-Fe alloys

description: 5CExrd
DOI: 10.6084/m9.figshare.16998960
title: XRD raw data of (Al63Cu25Fe12)100-xCex (x = 0 at%) sample from Influence of cerium content on the microstructure and thermal expansion properties of suction cast Al-Cu-Fe alloys

description: 1CExrd
DOI: 10.6084/m9.figshare.16998963
title: XRD raw data of (Al63Cu25Fe12)100-xCex (x = 0.5 at%) sample from Influence of cerium content on the microstructure and thermal expansion properties of suction cast Al-Cu-Fe alloys

description: 3CExrd
DOI: 10.6084/m9.figshare.7037501
title: A pivotal role of FOS-mediated BECN1/Beclin 1 upregulation in dopamine D2 and D3 receptor agonist-induced autophagy activation

description: Autophagy dysfunction is implicated in the pathogenesis of Parkinson disease (PD). BECN1/Beclin 1 acts as a critical regulator of autophagy and other cellular processes; yet, little is known about the function and regulation of BECN1 in PD. In this study, we report that dopamine D2 and D3 receptor (DRD2 and DRD3) activation by pramipexole and quinpirole could enhance <i>BECN1</i> transcription and promote autophagy activation in several cell lines, including PC12, MES23.5 and differentiated SH-SY5Y cells, and also in tyrosine hydroxylase positive primary midbrain neurons. Moreover, we identified a novel FOS (FBJ murine osteosarcoma viral oncogene homolog) binding sequence (5′-TGCCTCA-3′) in the rat and human <i>Becn1</i>/<i>BECN1</i> promoter and uncovered an essential role of FOS binding in the enhancement of <i>Becn1</i> transcription in PC12 cells in response to the dopamine agonist(s). In addition, we demonstrated a critical role of intracellular Ca<sup>2+</sup> elevation, followed by the enhanced phosphorylation of CAMK4 (calcium/calmodulin-dependent protein kinase IV) and CREB (cAMP responsive element binding protein) in the increases of FOS expression and autophagy activity. More importantly, pramipexole treatment ameliorated the SNCA/α-synuclein accumulation in rotenone-treated PC12 cells that overexpress wild-type or A53T mutant SNCA by promoting autophagy flux. This effect was also demonstrated in the substantia nigra and the striatum of <i>SNCA</i><sup>A53T</sup> transgenic mice. The inhibition of SNCA accumulation by pramipexole was attenuated by cotreatment with the DRD2 and DRD3 antagonists and <i>Becn1</i> siRNAs. Thus, our findings suggest that DRD2 and DRD3 agonist(s) may induce autophagy activation via a BECN1-dependent pathway and have the potential to reduce SNCA accumulation in PD.
DOI: 10.6084/m9.figshare.16998975
title: MicroRNA-29a May Influence Myopia Development by Regulating Collagen I

description: The aim of this study was to characterize the regulatory role of microRNA-29a (miR-29a) in myopia, providing support for potential biomarkers and new therapeutic targets of myopia in humans. The miR-29a expression level was detected in the aqueous humor and peripheral blood plasma of 21 high myopic patients and eight cataract control patients using quantitative polymerase chain reaction. iTRAQ analysis of proteomes was conducted to show the regulatory effect of miR-29a on human scleral fibroblasts (SFs) cultured <i>in vitro</i>. We also assessed proliferation, migration, and collagen I synthesis in SF cells, mediated by miR-29a. MiR-29a expression was significantly higher in the aqueous humor of highly myopic patients than in the cataract control patients (fold change: 4.861, p = 0.001). miR-29a inhibited the synthesis of type I collagen in human SF cells and enhanced cell migration, but had no significant effect on cell proliferation. MiR-29a was highly expressed in aqueous humor of myopia patients and inhibited the synthesis of type I collagen in human SF cells <i>in vivo</i>, thereby it may play an important role in myopia development.