cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1601869.v3
title: <i>MIR517C</i> inhibits autophagy and the epithelial-to-mesenchymal (-like) transition phenotype in human glioblastoma through KPNA2-dependent disruption of TP53 nuclear translocation

description: The epithelial-to-mesenchymal (-like) transition (EMT), a crucial embryonic development program, has been linked to the regulation of glioblastoma (GBM) progression and invasion. Here, we investigated the role of <i>MIR517C/miR-517c</i>, which belongs to the <i>C19MC</i> microRNA cluster identified in our preliminary studies, in the pathogenesis of GBM. We found that <i>MIR517C</i> was associated with improved prognosis in patients with GBM. Furthermore, following treatment with the autophagy inducer temozolomide (TMZ) and low glucose (LG), <i>MIR517C</i> degraded <i>KPNA2</i> (karyopherin alpha 2 [RAG cohort 1, importin alpha 1]) and subsequently disturbed the nuclear translocation of TP53 in the GBM cell line U87 in vitro. Interestingly, this microRNA could inhibit autophagy and reduce cell migration and infiltration in U87 cells harboring wild-type (WT) <i>TP53</i>, but not in U251 cells harboring mutant (MU) <i>TP53</i>. Moreover, the expression of epithelial markers (i.e., CDH13/T-cadherin and CLDN1 [claudin 1]) increased, while the expression of mesenchymal markers (i.e., CDH2/N-cadherin, SNAI1/Snail, and VIM [vimentin]) decreased, indicating that the EMT status was blocked by <i>MIR517C</i> in U87 cells. Compared with <i>MIR517C</i> overexpression, <i>MIR517C</i> knockdown promoted infiltration of U87 cells to the surrounding structures in nude mice in vivo. The above phenotypic changes were also observed in <i>TP53</i><sup><i>+/+</i></sup> and <i>TP53</i><sup><i>-/-</i></sup> HCT116 colon cancer cells. In summary, our study provided support for a link between autophagy and EMT status in WT <i>TP53</i> GBM cells and provided evidence for the signaling pathway (<i>MIR517C</i>-KPNA2-cytoplasmic TP53) involved in attenuating autophagy and eliminating the increased migration and invasion during the EMT.
DOI: 10.6084/m9.figshare.1601869.v2
title: MIR517C inhibits autophagy and the epithelial-to-mesenchymal (-like) transition phenotype in human glioblastoma through KPNA2-dependent disruption of TP53 nuclear translocation

description: The epithelial-to-mesenchymal (-like) transition (EMT), a crucial embryonic development program, has been linked to the regulation of glioblastoma (GBM) progression and invasion. Here, we investigated the role of <i>MIR517C/miR-517c</i>, which belongs to the <i>C19MC</i> microRNA cluster identified in our preliminary studies, in the pathogenesis of GBM. We found that <i>MIR517C</i> was associated with improved prognosis in patients with GBM. Furthermore, following treatment with the autophagy inducer temozolomide (TMZ) and low glucose (LG), <i>MIR517C</i> degraded <i>KPNA2</i> (karyopherin alpha 2 [RAG cohort 1, importin alpha 1]) and subsequently disturbed the nuclear translocation of TP53 in the GBM cell line U87 in vitro. Interestingly, this microRNA could inhibit autophagy and reduce cell migration and infiltration in U87 cells harboring wild-type (WT) <i>TP53</i>, but not in U251 cells harboring mutant (MU) <i>TP53</i>. Moreover, the expression of epithelial markers (i.e., CDH13/T-cadherin and CLDN1 [claudin 1]) increased, while the expression of mesenchymal markers (i.e., CDH2/N-cadherin, SNAI1/Snail, and VIM [vimentin]) decreased, indicating that the EMT status was blocked by <i>MIR517C</i> in U87 cells. Compared with <i>MIR517C</i> overexpression, <i>MIR517C</i> knockdown promoted infiltration of U87 cells to the surrounding structures in nude mice in vivo. The above phenotypic changes were also observed in <i>TP53</i><sup><i>+/+</i></sup> and <i>TP53</i><sup><i>−/−</i></sup> HCT116 colon cancer cells. In summary, our study provided support for a link between autophagy and EMT status in WT <i>TP53</i> GBM cells and provided evidence for the signaling pathway (<i>MIR517C</i>-KPNA2-cytoplasmic TP53) involved in attenuating autophagy and eliminating the increased migration and invasion during the EMT.
DOI: 10.6084/m9.figshare.1601833
title: Toolbox: Mining chemical activity status from high-throughput screening assays

description: High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote
DOI: 10.6084/m9.figshare.1601860.v4
title: Monitoring Parkinson's Disease Using Smartphones

description: A poster describing the progress of a project from the University of Manchester to monitor Parkinson's Disease longitudinally, non-intrusively and non-disruptively using smartphones.
DOI: 10.6084/m9.figshare.1601826
title: folate

description: comparison of folate vs. MTHFR in recurrent abortion
DOI: 10.6084/m9.figshare.1601833.v1
title: Toolbox: Mining chemical activity status from high-throughput screening assays

description: High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote
DOI: 10.6084/m9.figshare.1601826.v1
title: folate

description: comparison of folate vs. MTHFR in recurrent abortion
DOI: 10.6084/m9.figshare.1601819
title: Gaussian Job Archive for C18H30Br8

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1601819.v1
title: Gaussian Job Archive for C18H30Br8

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1601820
title: A review of Chinese CO<sub>2</sub> emission projections to 2030: the role of economic structure and policy

description: The projections of 89 scenarios from 12 different models for the CO<sub>2</sub> emissions of China to 2030 are reviewed, along with wider examinations of lessons from the history of energy forecasting in OECD countries, and of the Chinese macroeconomic situation. Even by 2030, emissions in the scenarios span a factor of almost 2.5, indicating significant range and uncertainty. Statistical analysis of Kaya components suggests the carbon intensity of energy supply to be the strongest determining factor. However, most scenarios assume that industry<sup>1</sup> continues to account for more than 50% of total final energy demand. This is in contrast both to historical examples, which have consistently shown economies shifting from energy-intensive industrial bases to service-based structures as income per capita rises, and to recent Chinese policy statements, which reflect a similar ambition. It is also highly salient that major failures in energy and emissions projections can frequently be accounted for in retrospect by failures to anticipate such major economic structural shifts. In conclusion, while the future trajectory of Chinese emissions remains profoundly uncertain, the potential for a significant Chinese macroeconomic transition and its implications for the scale and structure of energy demand will be a crucial factor, to which energy-climate models must pay far more attention. <b>Policy relevance</b> The dramatic growth of Chinese emissions since 2000 has become a major factor in global emission prospects and the international political agenda. Many models project rapid continued emissions growth, but an apparent halt in Chinese emissions in 2014 has amplified debate. Projections and policy need to recognise fundamental uncertainties in emission prospects, because in addition to energy/climate-specific policies, they depend on the progress in Chinese macroeconomic reforms, which are poorly represented in the models we survey. Global projections, the international process, and the design of China's own policies (most obviously, its national cap-and-trade system) need to cope with the possibility of continued growth to peaking in 2030 (the central commitment in China's Intended Nationally Determined Contribution), but must also be prepared to exploit and encourage the possibilities of low-carbon development and much earlier peaking.
DOI: 10.6084/m9.figshare.1601820.v2
title: A review of Chinese CO<sub>2</sub> emission projections to 2030: the role of economic structure and policy

description: The projections of 89 scenarios from 12 different models for the CO<sub>2</sub> emissions of China to 2030 are reviewed, along with wider examinations of lessons from the history of energy forecasting in OECD countries, and of the Chinese macroeconomic situation. Even by 2030, emissions in the scenarios span a factor of almost 2.5, indicating significant range and uncertainty. Statistical analysis of Kaya components suggests the carbon intensity of energy supply to be the strongest determining factor. However, most scenarios assume that industry<sup>1</sup> continues to account for more than 50% of total final energy demand. This is in contrast both to historical examples, which have consistently shown economies shifting from energy-intensive industrial bases to service-based structures as income per capita rises, and to recent Chinese policy statements, which reflect a similar ambition. It is also highly salient that major failures in energy and emissions projections can frequently be accounted for in retrospect by failures to anticipate such major economic structural shifts. In conclusion, while the future trajectory of Chinese emissions remains profoundly uncertain, the potential for a significant Chinese macroeconomic transition and its implications for the scale and structure of energy demand will be a crucial factor, to which energy-climate models must pay far more attention. <b>Policy relevance</b> The dramatic growth of Chinese emissions since 2000 has become a major factor in global emission prospects and the international political agenda. Many models project rapid continued emissions growth, but an apparent halt in Chinese emissions in 2014 has amplified debate. Projections and policy need to recognise fundamental uncertainties in emission prospects, because in addition to energy/climate-specific policies, they depend on the progress in Chinese macroeconomic reforms, which are poorly represented in the models we survey. Global projections, the international process, and the design of China's own policies (most obviously, its national cap-and-trade system) need to cope with the possibility of continued growth to peaking in 2030 (the central commitment in China's Intended Nationally Determined Contribution), but must also be prepared to exploit and encourage the possibilities of low-carbon development and much earlier peaking.
DOI: 10.6084/m9.figshare.1601695
title: Raw 19-channel EEG Data

description: This MATLAB workspace contains raw 19-channel EEG records (uV) for 237 cases. The epoch lengths are 16 seconds in duration, sampled at 250 Hz. All data has been re-filtered using the <em>eegfiltfft</em><em>.m</em> function within EEGLab (Delorme and Makeig, 2004). Each column pertains to one channel. Columns are: 1. Fp1 2. Fp2 3. F7 4. F3 5. Fz 6. F4 7. F8 8. T3 9. C3 10. Cz 11. C4 12. T4 13. T5 14. P3 15. Pz 16. P4 17. T6 18. O1 19. O2 All data has been collected from eyes closed baselines of various experiments. All have been collected with a Mitsar-201 amplifier running WinEEG and exported as *.txt files.
DOI: 10.6084/m9.figshare.1601820.v1
title: A review of Chinese CO<sub>2</sub> emission projections to 2030: the role of economic structure and policy

description: The projections of 89 scenarios from 12 different models for the CO<sub>2</sub> emissions of China to 2030 are reviewed, along with wider examinations of lessons from the history of energy forecasting in OECD countries, and of the Chinese macroeconomic situation.Even by 2030, emissions in the scenarios span a factor of almost 2.5, indicating significant range and uncertainty. Statistical analysis of Kaya components suggests the carbon intensity of energy supply to be the strongest determining factor. However, most scenarios assume that industry<sup>1</sup> continues to account for more than 50% of total final energy demand. This is in contrast both to historical examples, which have consistently shown economies shifting from energy-intensive industrial bases to service-based structures as income per capita rises, and to recent Chinese policy statements, which reflect a similar ambition. It is also highly salient that major failures in energy and emissions projections can frequently be accounted for in retrospect by failures to anticipate such major economic structural shifts.In conclusion, while the future trajectory of Chinese emissions remains profoundly uncertain, the potential for a significant Chinese macroeconomic transition and its implications for the scale and structure of energy demand will be a crucial factor, to which energy-climate models must pay far more attention.<b>Policy relevance</b>The dramatic growth of Chinese emissions since 2000 has become a major factor in global emission prospects and the international political agenda. Many models project rapid continued emissions growth, but an apparent halt in Chinese emissions in 2014 has amplified debate. Projections and policy need to recognise fundamental uncertainties in emission prospects, because in addition to energy/climate-specific policies, they depend on the progress in Chinese macroeconomic reforms, which are poorly represented in the models we survey. Global projections, the international process, and the design of China's own policies (most obviously, its national cap-and-trade system) need to cope with the possibility of continued growth to peaking in 2030 (the central commitment in China's Intended Nationally Determined Contribution), but must also be prepared to exploit and encourage the possibilities of low-carbon development and much earlier peaking.
DOI: 10.6084/m9.figshare.1601012
title: Rostral-Caudal Absolute Differences

description: Details: Rostral-Caudal Data.sav<br>Data for producing Figures 1, 2 This dataset consists of spectral densities for 237 EEG records within discrete frequency bins for 19-channels. The 19 channels are: 1. Fp1<br>2. Fp2<br>3. F7<br>4. F3<br>5. Fz<br>6. F4<br>7. F8<br>8. T3<br>9. C3<br>10. Cz<br>11. C4<br>12. T4<br>13. T5<br>14. P3<br>15. Pz<br>16. P4<br>17. T6<br>18. O1<br>19. O2 The variable names are:<br>rc-absolute rostral-caudal difference<br>rl-absolute left-right difference<br>absz-absolute z-scores for rostral caudal used to select for 177 individuals<br>observed in Figures 1 and 2 (select zscores -1 -The next variables assume the general anatomy: c(x)w(y)<br>where x refers to channel number (1-19) and y refers to discrete frequency spectral density (FFT resolution 0.24 Hz) between 0 and 40 Hz. Thus: c1w56 refers to 13.44 (56*.24) Hz activity in Fp1<br>c11w89 refers to 21.36 (89*.24) Hz activity in C4…etc. -The next variables assume the general anatomy: rcw(y) where y is the absolute difference between Fp1 and O2<br>frequency spectral density (FFT resolution .24 Hz) between 0 and 40 Hz. Thus: rcw40 refers to 9.6 Hz (40*.24) absolute rostral (Fp1) caudal (O2)<br>difference. -The next variables assume the general anatomy rl(y) where y is the absolute difference between T3 and T4<br>frequency spectral density (FFT resolution .24 Hz) between 0 and 40 Hz. Thus: rl40 refers to 9.6 Hz (40*.24) absolute left (T3) right (T4)<br>difference.
DOI: 10.6084/m9.figshare.1601014
title: Real-time EEG and Magnetometer Data

description: Harmonic Synchrony Subjects.zip Dataset to recreate portions of Figures 7 and 8. These datasets are MATLAB workspaces which contain simultaneous measurements of the earth-ionosphere ultra-low frequency (ULF) activity and 19 channel quantitative electroencephalographs for 3 subjects. Subjects marked local indicate that ULF activity was monitored simultaneously in Sudbury, Canada while non-local indicates that the e-field measured in Cumiana, Italy. See links below for live data from both stations. The variables in the workspaces are: <strong>1) Raw_19ChanEEG_Mag_Refiltered</strong> Contains the raw data (19 brain leads + magnetometer) which has been filtered with <em>eegfiltfft.m</em> between 1.5 and 40 Hz. First 19 columns indicate brain channels 1. Fp1 2. Fp2 3. F7 4. F3 5. Fz 6. F4 7. F8 8. T3 9. C3 10. Cz 11. C4 12. T4 13. T5 14. P3 15. Pz 16. P4 17. T6 18. O1 19. O2 20. Induction Coil Magnetometer <strong>2) srate</strong> Sampling rate used during data collection <strong>3) Fig(x) left/right</strong> This indicates the temporal periods (in seconds) in the time series from which left or right components of Figures 7 or 8 can be reproduced exactly. <strong>4) </strong><strong>cRMS</strong> The integrated root-mean-square of the posterior channels which can be derived using the MATLAB expression &gt;&gt;[new variable]=transpose(sqrt(mean(transpose(x(:,13:19).^2))))"] where x is the "Raw_19Chan..." variable. <strong>5)Mag</strong> <strong></strong>This is the induction coil magnetometer/Marconi antenna data only <strong>6) </strong><strong>cRMS_Mag</strong> This is the integrated cRMS and induction coil data organized into two rows (1=cRMS, 2=mag). It is this signal that can be entered into the cross-coherence function within EEGLab.
DOI: 10.6084/m9.figshare.1601024
title: BenefitFunction

description: Optimizing Variability to Maximize Population Benefit.<br>IDL code. Authors: Leighton T. Izu 1, Tamás Bányász and Ye Chen-Izu
DOI: 10.6084/m9.figshare.1601027
title: Sloppy Algorithm

description: Optimizing Variability to Maximize Population Benefit. <br>IDL code. Authors: Leighton T. Izu 1, Tamás Bányász and Ye Chen-Izu
DOI: 10.6084/m9.figshare.1601030
title: Companion of the Using Execution Logs to Improve the Realism of the Simulation of a Medical Imaging Application on a Distributed Computing Infrastructure article

description: Sources and supplementary data associated to the paper entitled "Using Execution Logs to Improve the Realism of the Simulation of a Medical Imaging Application on a Distributed Computing Infrastructure"
DOI: 10.6084/m9.figshare.1601011
title: Random 10 Subject Sample

description: Random 10 Sample.sav<br>For reproduction of Figure 3 Dataset contains the raw voltages (uV) for 10 randomly selected individuals from the larger dataset (N=237). Data is 16 seconds sampled at 250 Hz and filtered between 1.5 and 40 Hz. The general anatomy of each variable is: s(x)fp1 or s(x)o2 where x is the subject number (1-10). Thus s5fp1 refers to the raw time-series voltages in Fp1 sensor for subject 5<br>and s8o2 refers to the raw time-series voltages for O2 sensor for subject 8
DOI: 10.6084/m9.figshare.1601012.v1
title: Rostral-Caudal Absolute Differences

description: Details: Rostral-Caudal Data.sav<br>Data for producing Figures 1, 2 This dataset consists of spectral densities for 237 EEG records within discrete frequency bins for 19-channels. The 19 channels are: 1. Fp1<br>2. Fp2<br>3. F7<br>4. F3<br>5. Fz<br>6. F4<br>7. F8<br>8. T3<br>9. C3<br>10. Cz<br>11. C4<br>12. T4<br>13. T5<br>14. P3<br>15. Pz<br>16. P4<br>17. T6<br>18. O1<br>19. O2 The variable names are:<br>rc-absolute rostral-caudal difference<br>rl-absolute left-right difference<br>absz-absolute z-scores for rostral caudal used to select for 177 individuals<br>observed in Figures 1 and 2 (select zscores -1 -The next variables assume the general anatomy: c(x)w(y)<br>where x refers to channel number (1-19) and y refers to discrete frequency spectral density (FFT resolution 0.24 Hz) between 0 and 40 Hz. Thus: c1w56 refers to 13.44 (56*.24) Hz activity in Fp1<br>c11w89 refers to 21.36 (89*.24) Hz activity in C4…etc. -The next variables assume the general anatomy: rcw(y) where y is the absolute difference between Fp1 and O2<br>frequency spectral density (FFT resolution .24 Hz) between 0 and 40 Hz. Thus: rcw40 refers to 9.6 Hz (40*.24) absolute rostral (Fp1) caudal (O2)<br>difference. -The next variables assume the general anatomy rl(y) where y is the absolute difference between T3 and T4<br>frequency spectral density (FFT resolution .24 Hz) between 0 and 40 Hz. Thus: rl40 refers to 9.6 Hz (40*.24) absolute left (T3) right (T4)<br>difference.
DOI: 10.6084/m9.figshare.1601024.v1
title: BenefitFunction

description: Optimizing Variability to Maximize Population Benefit.<br>IDL code. Authors: Leighton T. Izu 1, Tamás Bányász and Ye Chen-Izu
DOI: 10.6084/m9.figshare.1601003
title: Cradle Shrew Stable Isotopes JAS.xlsx

description: Data from<br>Small mammal insectivore stable carbon isotope compositions as habitat proxies in a South African savanna ecosystem<br>Leichliter et al.<br>Journal of Archaeological Science: Reports