A5088672765- Malaria Research and Control
- Bioinformatics and Genomic Networks
- Mosquito-borne diseases and control
- Complex Network Analysis Techniques
- Genetics, Bioinformatics, and Biomedical Research
- Aquaculture disease management and microbiota
- Advanced Graph Neural Networks
- Neural Networks and Applications
- Blind Source Separation Techniques
- Electronic Health Records Systems
- SARS-CoV-2 detection and testing
- Data Visualization and Analytics
- Interconnection Networks and Systems
- Extenics and Innovation Methods
- Evolution and Genetic Dynamics
- Graph Theory and Algorithms
- Hemoglobinopathies and Related Disorders
- Target Tracking and Data Fusion in Sensor Networks
- Research Data Management Practices
- Metabolomics and Mass Spectrometry Studies
- Sparse and Compressive Sensing Techniques
- Time Series Analysis and Forecasting
- Power Systems and Renewable Energy
- Rough Sets and Fuzzy Logic
- Higher Education and Teaching Methods
Tsinghua University
2023-2025
Center for Devices and Radiological Health
2024
United States Food and Drug Administration
2024
Tsinghua–Berkeley Shenzhen Institute
2023
University of Georgia
2015-2020
The University of Adelaide
2014
The recent scale-up in malaria control measures Latin America has resulted a significant decrease the number of reported cases several countries including Ecuador, where it presented low incidence years (558 2015) with occasional outbreaks both Plasmodium falciparum and vivax coastal Amazonian regions. This success led Ecuador to transition its policy from elimination. study evaluated general knowledge, attitude practices (KAP) about malaria, as well prevalence four communities an endemic...
Data that house topological information is manifested as relationships between multiple variables via a graph formulation. Various methods have been developed for analyzing time series on the nodes of graphs but research works signals with volatility are limited. In this paper, we propose framework multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models from spectral perspective Laplacian matrix. We introduce three graphical GARCH models: one symmetric Graph...
The goals of the Triple Aim health care and P4 medicine outline objectives that require a significant informatics component. However, do not provide specifications about how all new individual patient data will be combined in meaningful ways with from other sources, like epidemiological data, to promote individuals society. We seem have more than ever before but few resources means use it efficiently. need general, extensible solution integrates homogenizes disparate origin, incompatible...
Graph signal processing refers to dealing with irregularly structured data. Compared traditional processing, it can preserve the complex interactions within irregular In this work, we devise a robust algorithm recover band-limited graph signals in presence of impulsive noise. First, observed data vector is recast, such that noise component divided into two vectors, representing dense-noise and sparse outliers, respectively. We then exploit ℓ0-norm characterize as regularization term....
Projects in the life sciences continue to increase complexity as they scale answer deeper and more diverse questions. They employ technologies that generate increasingly large ‘omic’ datasets research teams regularly include experts ranging from animal care technicians, veterinarians, human health clinicians, geneticists, immunologists, biochemists computer scientists, mathematical modelers, data often located at different institutions. Providing cyberinfrastructure support framework (IT,...
<title>Abstract</title> Molecular assays are critical tools for the diagnosis of infectious diseases. These have been extremely valuable during COVID pandemic, used to guide both patient management and infection control strategies. Sustained transmission unhindered proliferation virus pandemic resulted in many variants with unique mutations. Some these mutations could lead signature erosion, where tests developed using genetic sequence an earlier version pathogen may produce false negative...
The assumption of using a static graph to represent multivariate time-varying signals oversimplifies the complexity modeling their interactions over time. We propose Dynamic Multi-hop model that captures dynamic among node signals, while also accounting for edge by extracting latent edges through topological diffusion and pruning. resulting graphs are sparse, capturing key representing signal both near distant neighbors Estimation algorithm is further proposed, accurately interaction...
This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message passing method that simultaneously conducts online prediction, missing data imputation, and noise removal on time-varying graph signals. Unlike conventional Processing methods apply the same filter to entire graph, spatiotemporal updates of GSAMP employ distinct approach utilizes localized computations at each node. update is based an adaptive solution obtained from optimization problem designed minimize...
This work introduces the LLM Online Spatial-temporal Reconstruction (LLM-OSR) framework, which integrates Graph Signal Processing (GSP) and Large Language Models (LLMs) for online spatial-temporal signal reconstruction. The LLM-OSR utilizes a GSP-based handler to enhance graph signals employs LLMs predict missing values based on spatiotemporal patterns. performance of is evaluated traffic meteorological datasets under varying Gaussian noise levels. Experimental results demonstrate that...
Background: The past few years have seen a tremendous increase in the size and complexity of datasets. Scientific clinical studies must to incorporate datasets that cross multiple spatial temporal scales describe particular phenomenon. storage accessibility these heterogeneous way is useful researchers yet extensible new data types major challenge. Methods: In order overcome obstacles, we propose use primitives as common currency between analytical methods. four identified are time series,...
In this paper, we propose a fast and robust tracking method based on reversed sparse representation.Be different from other representation visual methods, the target template is sparsely represented by candidate particles which are gotten particle filter.In order to improve robustness of method, use set.Meanwhile, two level competition mechanism also introduced.In first level, each all compete with similarity calculation, coefficients.Then, winners construct set.In second candidates in set...
Quantification of system-wide perturbations from time series -omic data (i.e. a large number variables with multiple measures in time) provides the basis for many downstream hypothesis generating tools. Here we propose method, Massively Parallel Analysis Time Series (MPATS) that can be applied to quantify transcriptome-wide perturbations. The proposed method characterizes each individual through its $\ell_1$ distance every other series. Application MPATS compare biological conditions...
Characterization of host responses associated with severe malaria through an integrative approach is necessary to understand the dynamics a \textit{Plasmodium cynomolgi} infection. In this study, we conducted temporal immune profiling, cytokine profiling and transcriptomic analysis five \textit{Macaca mulatta} infected \textit{P. cynomolgi}. This experiment resulted in two infections, mild infections. Our reveals that differential transcriptional up-regulation genes linked response...
We derived an ordinary differential equation model to capture the disease dynamics during blood-stage malaria. The was directly from earlier age-structured partial model. original simplified due experimental constraints. Here we calibrated with data using a multiple objective genetic algorithm. Through calibration process, quantified removal of healthy red blood cells and preferential infection reticulocytes \textit{Plamodium cynomolgi} \textit{Macaca mulatta}. our also revealed existence...