552 research outputs found
Supplemental Material - A Novel Nomogram for Identifying Candidates for Adjuvant Chemotherapy in Patients With Stage IB Non-small Cell Lung Cancer
No full textSupplemental Material for A Novel Nomogram for Identifying Candidates for Adjuvant Chemotherapy in Patients With Stage IB Non-small Cell Lung Cancer by Xue Song, Yangyang Xie, Haoran Deng, Fei Yu, Shiqiang Wang, and Yafang Lou in Cancer Control</p
The CSPG4-specific monoclonal antibody enhances and prolongs the effects of the BRAF inhibitor in melanoma cells
No full textSupplementary_material – Supplemental material for The Stroke Stigma Scale: a reliable and valid stigma measure in patients with stroke
No full textSupplemental material, Supplementary_material for The Stroke Stigma Scale: a reliable and valid stigma measure in patients with stroke by Minfang Zhu, Hongzhen Zhou, Weibin Zhang, Yingying Deng, Xiaoyan Wang, Xuejie Bai, Muling Li, Ruidan Hu, Jiakun Hou and Yangyang Liu in Clinical Rehabilitation</p
Fundamental parameters of 49 new star clusters in GAIA DR2
No full textThis is a database of the fundamental parameters and color-magnitude diagrams of 49 star clusters.
It corresponds to the study of Zhongmu Li, Yangyang Deng and Jing Chen in 2021, which was submitted to ApJS. When one use these data, please cite to that work.
The fundamental parameters are fitted from the color-magnitude diagrams in V and I bands, via the ASPS model and Poerful CMD code.
The V and I magnitudes are transformed from the Gaia DR2 magnitudes using some fitting correlations.
The database contains three parts, i.e., a parameter file and three directories. They are explained as follows.
The file "parameters.dat" gives the basic information, best-fit parameters from ASPS model and isochrones.
There are 15 columns in this file. The colums are for Name, FoF_ID, l, l_err, b, b_err, m-M, E(V-I), Z, t, f_b, f_r, Z_old, t_old, t_old_err respectively.
They correspond to the contents of a manuscript that was submitted to ApJS.
The directory "obseved_cmds_data" contains the data of observed VI band color-magnitude diagrams of 49 star clusters.
The first and second columns are for V magnitude and (V-I) color, respectively.
The directory "bestfit_cmds_data" contains the data of best-fit color-magnitude diagrams of 49 star clusters.
The first and second columns are for (V-I) color and V magnitude respectively.
The directory "cmds_figs" contains the comparison figures of the observed and best-fit color-magnitude diagrams.
If you have any problems with using these data, send an email to [email protected]
Interleukin-1 mediated cell-type specific signaling in hippocampal neurons and astrocytes
No full textInterleukin-1β (IL-1β) is a pro-inflammatory cytokine that is implicated in immune and inflammatory responses. In the central nervous system (CNS), IL-1β is synthesized and released during injury, infection, and many neurodegenerative diseases, but also under physiological conditions. Several IL-1-mediated signaling pathways and effects have been identified in hippocampal neurons and astrocytes, but their mechanisms have not been fully defined. IL-1 signaling requires the type one IL-1 receptor (IL-1RI) as well as IL-1 receptor accessory protein (IL-1RAcP) as a receptor partner. A novel isoform of the IL-1 receptor accessory protein, AcPb, has also been found in the CNS, but its role remains unclear. This thesis examined AcPb function in regulating IL-1β signaling. The results showed that IL-1β activated p38 MAPK but not NFκB in neurons. In astrocytes, IL-1β induced both p38 and NFκB pathways in regulating inflammatory responses. AcPb was not involved in mediating either p38 or NFκB in either cell type. In contrast, a physiological level of IL-1β treatment (0.01ng/ml) activated p-Src in neurons via AcPb in vitro. In addition, overexpression of AcPb in astrocytes was sufficient to induce p-Src mediated by IL-1β. Taken together, these results suggest that the restricted expression of AcPb in CNS neurons may mediate neuronal specific IL-1 pathways and outcomes, and that physiological and pathophysiological levels of IL-1β mediate particular neuronal functions via separate pathways.Ph. D.Includes abstractIncludes bibliographical referencesby Yangyang Huan
How Does Artificial Intelligence Shape Innovation?
No full text© 2025 Yangyang DengMy dissertation aims to explore how artificial intelligence (AI) shapes innovation processes and outcomes through three interconnected studies. In the first study, I explore the link between AI and innovation through a systematic literature review, examining AI’s roles and effects in innovation activities. I identify AI’s dual roles as both an innovation component and a research tool, with both roles exerting both positive and negative effects on innovation. The literature review uncovers a critical gap in understanding AI’s impact on innovation as a research tool, specifically the mechanisms through which AI influences innovation and its impact on innovation outcomes. To address this gap, I conduct a second study to explore how AI is applied in the drug discovery process, investigating the mechanisms through which AI affects innovation. I employ a qualitative research design, analyzing AI-driven biotech firms in drug discovery and conducting interviews with industry experts. I find that the use of AI enhances innovation performance primarily by addressing the inherent uncertainty and complexity of innovation processes. Moreover, AI exhibits significant bottlenecks in innovation and requires various complementary resources to overcome these bottlenecks and achieve desired outcomes. In the third study, I employ a quantitative research design to examine how AI adoption influences innovation outcomes, particularly breakthrough innovations. Using patent data, I find that, on its own, using AI does not significantly increase the likelihood of breakthroughs. However, inventors equipped with rich domain knowledge can amplify AI’s advantages, thereby enhancing the likelihood of achieving breakthrough innovations. Overall, my three studies advance our understanding of how AI shapes innovation by providing valuable insights into how AI’s capabilities and limitations influence innovation and identifying key complementarities in AI applications
Molecular simulations of rheological, mechanical and transport properties of solid-fluid systems:
No full textIn this dissertation, two distinct but relevant systems are chosen as representatives of interesting solid-fluid systems. Molecular dynamics (MD) and Monte Carlo techniques are applied to investigate the rheological, mechanical and transport properties of these systems.
Firstly, polyethylene melt embedded with silica nanoparticles is examined to be of our interest. Since it is computationally impractical to model a complex system with a molecular description, a multiscale modeling approach, which combines both atomistic and mesoscale simulations, is employed to efficiently represent and study the polymer nanoparticle systems. Based on a coarse-grained force field for polyethylene, a novel method is developed for determining the solid-fluid interaction at the spherical interface. Our coarse grained model is designed to mimic 4 nm silica nanoparticles in polyethylene melt at 423K. A series of MD simulations are performed to investigate the factors that control the homogeneity of nanofillers inside polymer matrix, also in the presence of nonionic surfactants (short chain alcohols). The effects of nanoparticle filling fraction, polymer chain length, and relative sizes between nanoparticles and polymer chains on the particle dispersion are explored. In addition, a fundamental relationship is pursued between the microstructure and macroscopic properties (transport and rheological) of polymer nanoparticle composites.
In this work another method for determining the solid-fluid interaction parameter is presented: the experimental adsorption isotherms are used to validate the potential parameters. The rapid expansion of silica nanoparticle agglomerates in supercritical carbon dioxide (RESS process) is chosen to be the system of interest. The simulations show that the effective attraction between two identical nanoparticles is most prominent for densely hydroxylated particle surfaces that interact strongly with CO2 via hydrogen bonds, while it is significantly weaker for dehydroxylated particles. We also explore the shearing forces necessary to break an agglomerate in supercritical fluid. The agglomerate experiences deformation followed by elongation, and finally break-up. The calculated diffusion coefficient of CO2 is expected to be smaller than the experimental value, because the nanoparticle agglomerate hinders fluid movement. In the direction of shearing forces, the diffusion of CO2 shows a steep increase after the breakup, confirming the rupture of the agglomerate.Ph.D.Includes bibliographical references (p. 136-142)by Yangyang She
Shedding light on the black box: integrating prediction models and explainability using explainable machine learning
No full textIn contemporary organizational research, when dealing with large heterogeneous datasets and complex relationships, statistical modeling focused on developing substantive explanations typically results in low predictive accuracy. In contrast, machine learning (ML) exhibits remarkable strength for prediction, but suffers from an unexplainable analytical process and output—thus ML is often known as a “black box” approach. The recent development of explainable machine learning (XML) integrates high predictive accuracy with explainability, which combines the advantages inherent in both statistical modeling and ML paradigms. This paper compares XML with statistical modeling and the traditional ML approaches, focusing on an advanced application of XML known as evolving fuzzy system (EFS), which enhances model transparency by clarifying the unique contribution of each modeled predictor. In an illustrative study, we demonstrate two EFS-based XML models and conduct comparative analyses among XML, ML, and statistical models with a commonly-used database in organizational research. Our study offers a thorough description of analysis procedures for implementing XML in organizational research, along with best-practice recommendations for each step as well as Python code to aid future research using XML. Finally, we discuss the benefits of XML for organizational research and its potential development.</p
Stable isotope data of rivers over the Qinghai-Tibet Plateau
No full text<p>Stable isotope data of rivers over the Qinghai-Tibet Plateau.</p>
Stable isotope data of rivers over the Qinghai-Tibet Plateau
No full text<p>Stable isotope data of rivers over the Qinghai-Tibet Plateau.</p>
- …
