634 research outputs found
Peabody Laptop Ensemble
No full textProgram for concert featuring works by Joo Won Park. Peabody Laptop Ensemble: Soo Hyun Bahn; Ben Jin; Matthew Bielski; Joe Gipple; Shawn Guo; Zichen Huang; Jingchen Jiang; Yichen Liu; Andrew Mo; Ben Stewart; Zexin Wang; Xintong Yuan; Yunxiang Zhang
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DNA Methylation and Human Diseases: Applied and Methodological Studies
No full textDNA methylation plays important roles in regulating gene expression and chromosome integrity via addition of methyl groups to cytosine residues. A growing number of human diseases have been found to be associated with aberrant DNA methylation. DNA methylation also provides potential cancer biomarkers and therapeutic targets.
Lung cancer remains the leading cause of cancer-related mortality worldwide, with an estimated 224,390 new cases and 158,080 deaths in the U.S. alone in 2016. In Chapter 1, the association between overall survival and DNA methylation of a tumor-suppressor gene named LRRC3B was investigated in 1,230 early-stage non-small cell lung cancer patients. It provides evidence of plausibility for building biomarkers on DNA methylation of LRRC3B for overall survival of early-stage non-small cell lung cancer, thus filling a gap between previous in vitro studies and clinical applications.
Acute respiratory distress syndrome (ARDS) is a severe lung disease with a mortality rate of over 40% among moderate-to-severe patients. In Chapter 2, we conducted an epigenome-wide association study (EWAS) between DNA methylation and 28-day survival time in 185 moderate-to-severe ARDS patients from intensive care units (ICUs). We identified four CpG sites that were significantly associated with ARDS survival in two independent cohorts. By integrating all four statistically significant methylation sites, we built a methylation risk score for each patient. Patients with a higher methylation risk score had a significantly higher hazard of death within 28 days.
As the next generation sequencing (NGS) technology becomes more and more affordable, sequencing-based methylation measurements would become popular in epigenetic studies. But it also presents challenge in data analysis, including co-methylation network analysis. In Chapter 3, we showed that while standard sequencing-based methylation measurement provides an unbiased estimate for the methylation level, it leads to a biased estimate for correlation between methylation sites. In Chapter 4, we also showed that sequencing-based methylation measure also leads to biased estimates for linear regression coefficients. We proposed a new method to obtain unbiased estimate for methylation correlation and linear coefficients based on bisulfite sequencing data. We demonstrated its performance using various simulation settings as well as real data generated using bisulfite sequencing technique.DNA methylation; lung cancer; ARDS; bisulfite sequencin
Development of a Risk Prediction Model for Infection After Kidney Transplantation Transmitted from Bacterial Contaminated Preservation Solution
No full textMingxing Guo,1,* Chen Pan,1,* Ying Zhao,1 Wanyi Xu,1 Ye Xu,1 Dandan Li,1 Yichen Zhu,2 Xiangli Cui1 1Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiangli Cui, Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of China, Email [email protected] Yichen Zhu, Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of China, Email [email protected]: The risk of transplant recipient infection is unknown when the preservation solution culture is positive.Methods: We developed a prediction model to evaluate the infection in kidney transplant recipients within microbial contaminated preservation solution. Univariate logistic regression was utilized to identify risk factors for infection. Both stepwise selection with Akaike information criterion (AIC) was used to identify variables for multivariate logistic regression. Selected variables were incorporated in the nomograms to predict the probability of infection for kidney transplant recipients with microbial contaminated preservation solution.Results: Age, preoperative creatinine, ESKAPE, PCT, hemofiltration, and sirolimus had a strongest association with infection risk, and a nomogram was established with an AUC value of 0.72 (95% confidence interval, 0.64– 0.80) and Brier index 0.20 (95% confidence interval, 0.18– 0.23). Finally, we found that when the infection probability was between 20% and 80%, the model oriented antibiotic strategy should have higher net benefits than the default strategy using decision curve analysis.Conclusion: Our study developed and validated a risk prediction model for evaluating the infection of microbial contaminated preservation solutions in kidney transplant recipients and demonstrated good net benefits when the total infection probability was between 20% and 80%.Keywords: risk prediction model, kidney transplant, nomogram, risk factor
GPU-accelerated Linear Solvers for High-order Finite Element Methods in Poisson Problems
No full textSolving large-scale linear systems arising from high-order finite element discretizations for Poisson equations often represents the most expensive component of the high-order finite element solver. This dissertation develops and analyzes numerical methods and algorithms aimed at improving the convergence and efficiency of the preconditioned conjugate gradient (PCG) algorithm for high-order finite element methods on Graphics Processing Units (GPUs).
The conjugate gradient algorithm iteratively refines an initial approximate solution until a specified stopping criterion is met, with its convergence rate enhanced through the application of a preconditioner.
Novel smoothers are constructed within a multigrid preconditioner to improve the convergence rate on highly deformed meshes.
Additionally, new stopping criteria are introduced to balance various error sources, thereby reducing the number of iterations.
An adaptive mixed precision conjugate gradient algorithm is proposed to exploit the superior computational performance of GPUs at lower precisions while maintaining convergence and accuracy.
Furthermore, a comprehensive optimization of the PCG algorithm, including a careful examination of the memory hierarchy, is developed.
The effectiveness of these novel approaches is demonstrated for GPU-accelerated high-order finite element discretizations in Poisson problems.Doctor of PhilosophySolving large-scale linear systems efficiently is a critical challenge in scientific computing, particularly in high-order finite element methods for simulating complex physical phenomena like heat transfer or fluid flow.
While high-order finite element methods offer superior accuracy, they often incur significant computational costs, especially for complex geometries.
This dissertation addresses this challenge by focusing on improving the performance and convergence of iterative solvers on modern hardware, Graphics Processing Units (GPUs), specifically the preconditioned conjugate gradient (PCG) method, used to solve such demanding problems.
Several key innovations are introduced to improve convergence and reduce overall computation time.
First, novel preconditioning techniques are developed to enhance solver convergence on highly deformed meshes.
Second, adaptive stopping criteria are proposed to ensure efficient solver termination without compromising accuracy.
Third, an adaptive mixed precision strategy is designed to leverage the performance advantages of GPUs, which can execute computations much faster at lower numerical precision.
Finally, the solver is carefully optimized to make better use of the GPU memory hierarchy, reducing data movement, and improving overall speed.
Together, these contributions advance the state of the art in high-performance computing for numerical simulation. The techniques presented are applicable to a wide range of problems in engineering and the physical sciences, enabling faster and more efficient solutions on modern computing architectures
Intensive Care Med
No full textT42 OH008416/OH/NIOSH CDC HHS/United StatesR01 HL060710/HL/NHLBI NIH HHS/United States1R56HL134356/NIH(NHLBI)/InternationalR01HL060710/NIH (NHLBI)/InternationalR56 HL134356/HL/NHLBI NIH HHS/United State
Raw Data for "On-demand cell-autonomous gene therapy for brain circuit disorders", Qiu et al. 2022 Science
No full textRaw Data for Qui et al. 2022 10.1126/science.abq6656
On-demand cell-autonomous gene therapy for brain circuit disorders
Yichen Qiu, Nathanael O’Neill, Benito Maffei, Clara Zourray, Amanda Almacellas Barbanoj, Jenna C. Carpenter,Steffan P. Jones, Marco Leite, Thomas J. Turner, Francisco C. Moreira, Albert Snowball, Tawfeeq Shekh-Ahmad, Vincent Magloire, Serena Barral, Manju A. Kurian, Matthew C. Walker, Stephanie Schorge, Dimitri M. Kullmann, Gabriele Lignani
Content:
EEG
MEA
Immuno
Patch Clamp Electrophysiology
All data are in a open source format. MEA files can be analysed using the MATLAB-based developed by Prof Michela Chiappalone and requests should be direct to:
Michela Chiappalone [email protected]
Ilaria Colombi [email protected]
EEG files are .zip with different transmitters. See EEG_Keys.xlxs for whcih virus was used in each animal.
MEA files are divided in 2 separate .zip: 1. Fig2 and Fig S3 and S7. 2. Fig S6.
Immuno: all the raw images are in the .zip file seprated by Figure #
Patch Clamp Electrophysiology: all the raw .abf files are in the .zip file separated by Figure #
For more info or to request materials please contact the corrsponding author Gabriele Lignani [email protected]
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Symmetry-aware recursive image similarity exploration for materials microscopy
No full textAbstractIn pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. To improve these projections, we develop and train a model to include symmetry-aware features. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We provide a customizable open-source package (https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer) of this interactive tool for researchers to use with their data.</jats:p
Collecting: a way of exploring the difficulty to leave game world
No full textDigital games have become a part of daily life for people in recent years. they have the capacity of making game users set playing games as top priority in daily life. In this sense, playing digital games can break the balance between the virtual and the reality and it is possible that some people may get lost in the virtual world. However, this thesis argues that it can be inappropriate just equating excessive play with addiction. By discussing and connecting previous literature reviews on games and addiction, as well as adding three game case studies, this thesis finally finds collecting as a different approach to explore the difficulty to leave game world. Through discussing in-game collecting and book collecting in ancient China, this thesis helps answer how people understand the heavy use on digital games and what is hidden behind choosing to stay in game world
MULTISCALE METHODS AND ANALYSIS FOR THE NONLINEAR SCHRÖDINGER EQUATION WITH WAVE OPERATOR
No full textPh.DDOCTOR OF PHILOSOPHY (FOS
Collecting: a way of exploring the difficulty to leave game world
No full textDigital games have become a part of daily life for people in recent years. they have the capacity of making game users set playing games as top priority in daily life. In this sense, playing digital games can break the balance between the virtual and the reality and it is possible that some people may get lost in the virtual world. However, this thesis argues that it can be inappropriate just equating excessive play with addiction. By discussing and connecting previous literature reviews on games and addiction, as well as adding three game case studies, this thesis finally finds collecting as a different approach to explore the difficulty to leave game world. Through discussing in-game collecting and book collecting in ancient China, this thesis helps answer how people understand the heavy use on digital games and what is hidden behind choosing to stay in game world
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