Showing papers by "Liang Ding published in 2023"

NetBID2 provides comprehensive hidden driver analysis

Abstract: Many signaling and other genes known as "hidden" drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .

Random Smoothing Regularization in Kernel Gradient Descent Learning

Abstract: Random smoothing data augmentation is a unique form of regularization that can prevent overfitting by introducing noise to the input data, encouraging the model to learn more generalized features. Despite its success in various applications, there has been a lack of systematic study on the regularization ability of random smoothing. In this paper, we aim to bridge this gap by presenting a framework for random smoothing regularization that can adaptively and effectively learn a wide range of ground truth functions belonging to the classical Sobolev spaces. Specifically, we investigate two underlying function spaces: the Sobolev space of low intrinsic dimension, which includes the Sobolev space in $D$-dimensional Euclidean space or low-dimensional sub-manifolds as special cases, and the mixed smooth Sobolev space with a tensor structure. By using random smoothing regularization as novel convolution-based smoothing kernels, we can attain optimal convergence rates in these cases using a kernel gradient descent algorithm, either with early stopping or weight decay. It is noteworthy that our estimator can adapt to the structural assumptions of the underlying data and avoid the curse of dimensionality. This is achieved through various choices of injected noise distributions such as Gaussian, Laplace, or general polynomial noises, allowing for broad adaptation to the aforementioned structural assumptions of the underlying data. The convergence rate depends only on the effective dimension, which may be significantly smaller than the actual data dimension. We conduct numerical experiments on simulated data to validate our theoretical results.

mcGP: mesh-clustered Gaussian process emulator for partial differential equation systems

Abstract: Partial differential equations (PDEs) have become an essential tool for modeling complex physical systems. Such equations are typically solved numerically via mesh-based methods, such as the finite element method, the outputs of which consist of the solutions on a set of mesh nodes over the spatial domain. However, these simulations are often prohibitively costly to survey the input space. In this paper, we propose an efficient emulator that simultaneously predicts the outputs on a set of mesh nodes, with theoretical justification of its uncertainty quantification. The novelty of the proposed method lies in the incorporation of the mesh node coordinates into the statistical model. In particular, the proposed method segments the mesh nodes into multiple clusters via a Dirichlet process prior and fits a Gaussian process model in each. Most importantly, by revealing the underlying clustering structures, the proposed method can extract valuable flow physics present in the systems that can be used to guide further investigations. Real examples are demonstrated to show that our proposed method has smaller prediction errors than its main competitors, with competitive computation time, and provides valuable insights about the underlying physics of the systems. An R package for the proposed methodology is provided in an open repository.

[Resveratrol improves cognitive function in severely burned rats by inhibiting hippocampal NF-κB/JNK pathway].

Abstract: Objective To investigate the protective effect of resveratrol (RSV) on improving cognitive function in severely burned rats and its possible mechanism. Methods 18 male SD rats aged 18-20 months were randomly divided into 3 groups: control group, model group and RSV group, with 6 rats in each group. After successful modeling, the rats in RSV group were gavaged once daily with RSV (20 mg/kg). Meanwhile, the rats in control group and model group were gavaged once daily with an equal volume of sodium chloride solution. After 4 weeks, the cognitive function of all rats was estimated by Step-down Test. The concentration of tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6) protein in serum of rats were detected by ELISA. The expression of IL-6, TNF-α mRNA and protein were estimated by real-time PCR and Western blotting. The apoptosis of hippocampal neurons was tested by terminal deoxynuclectidyl transferase-mediated dUTP-biotin nick end labeling assay (TUNEL). The expression of nuclear transcription factor-κB (NF-κB)/c-Jun N-terminal kinase (JNK) pathway-related proteins in hippocampus were assessed by Western blotting. Results Compared with the rats in model group, rats in RSV group exhibited improved cognitive function. Consistently, the rats in RSV group had a reduced concentration of TNF-α and IL-6 in serum, decreased mRNA and protein expressions of TNF-α and IL-6 in hippocampus, and decreased apoptosis rate and relative expression of p-NF-κB p65/NF-κB p65 and p-JNK/JNK in hippocampal neurons. Conclusion RSV alleviates inflammatory response and hippocampal neuronal apoptosis by inhibiting NF-κB/JNK pathway, thereby improving cognitive function in severely burned rats.

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data

Abstract: The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as “hidden drivers”, are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

Ultrabroadband Absorber Based on Layered Inkjet-Printing Resistive Film

Abstract: This letter proposes an ultrabroadband absorber (UBA) based on the layered inkjet-printing resistive film (IPRF) composed of three lossy layers. The equivalent circuit model of the UBA is put forward to analyze the impedance matching with air under broadband conditions. The prototype was fabricated using IPRF as the lossy layers and the polymethacrylimide (PMI) foam as the dielectric layers. The simulated and measured results show that the ultrabroadband 0 dB absorption performance covers 2–23 GHz with the fractional bandwidth of 168% and maintains stable absorbing performance under the incidence angle of 45°. The total thickness of the proposed design is only 0.1λL at the lowest operating frequency. Experimental validation of the fabricated prototype has also confirmed the potential using of IPRF-based broadband absorbers in several applications.

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data

Abstract: The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as “hidden drivers”, are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

W-Band Low-Sidelobe Series-Fed Slot Array Antenna Based on Groove Gap Waveguide

Abstract: In this letter, a low-sidelobe, high-efficiency, series-fed 2 × 24 slot array antenna at 93 GHz is proposed. The antenna can be divided into three separated layers and there is no need of electric contact between layers according to gap waveguide (GW) theory. To control the magnitude of the coupling energy and achieve −30 dB sidelobe quasi-Taylor weighing in radiation slots, transverse coupling slots in the middle layer are placed in a certain distance relative to the bottom gap waveguide center. Measure results agree well with simulation result, which demonstrates that the reflection coefficient of the antenna is lower than −10 dB from 91.6 to 94.4 GHz, with over 23 dB gain in the operating band. The 3 dB beamwidth of the E-plane pattern is lower than 4°, while the sidelobe level is lower than −20 dB. The whole antenna's efficiency is higher than 85%.

Representing Additive Gaussian Processes by Sparse Matrices

Abstract: Among generalized additive models, additive Mat\'ern Gaussian Processes (GPs) are one of the most popular for scalable high-dimensional problems. Thanks to their additive structure and stochastic differential equation representation, back-fitting-based algorithms can reduce the time complexity of computing the posterior mean from $O(n^3)$ to $O(n\log n)$ time where $n$ is the data size. However, generalizing these algorithms to efficiently compute the posterior variance and maximum log-likelihood remains an open problem. In this study, we demonstrate that for Additive Mat\'ern GPs, not only the posterior mean, but also the posterior variance, log-likelihood, and gradient of these three functions can be represented by formulas involving only sparse matrices and sparse vectors. We show how to use these sparse formulas to generalize back-fitting-based algorithms to efficiently compute the posterior mean, posterior variance, log-likelihood, and gradient of these three functions for additive GPs, all in $O(n \log n)$ time. We apply our algorithms to Bayesian optimization and propose efficient algorithms for posterior updates, hyperparameters learning, and computations of the acquisition function and its gradient in Bayesian optimization. Given the posterior, our algorithms significantly reduce the time complexity of computing the acquisition function and its gradient from $O(n^2)$ to $O(\log n)$ for general learning rate, and even to $O(1)$ for small learning rate.

Harmonising compliance in china and malaysia cross border commercial relationship: an alternative dispute resolution perspective

Abstract: The cross-border commercial relationship between China and Malaysia has continued to grow over the years, with China being Malaysia’s largest trading and business partner and a major source of foreign direct investment. However, the relationship has not been without issues and challenges within the realm of cross-border business, trade and commercial formation of contracts and agreements and its implementation between both contracting parties. Holistic dispute resolution mechanisms are therefore crucial towards harmonising compliance in cross-border commercial relations between China and Malaysia. Alternative dispute resolution (ADR) mechanisms such as negotiation, mediation, arbitration, online dispute resolution, and expert determination can be used to overcome contractual issues and legal comprehension challenges such as the language barrier, cultural differences, in view of differing legal systems within historical and contemporary legal norms and developments. These mechanisms can provide parties with a cost-effective, efficient, and flexible means of resolving conflicts while preserving long-term cross-border business inter-relationships between two unique jurisdictions comprising China and Malaysia. Alternative dispute resolution mechanisms can strengthen business, trade, and commercial engagements at many levels-both public and private venturesand thus considerably enhance cross-border compliance, and foster a more stable ease of doing business environment. Alternative dispute resolution mechanisms can facilitate harmonious compliance in cross-border commercial relations between China and Malaysia. The use of these mechanisms could overcome commercial and business disputes and uphold bilateral business integrity and build long -term commercial interests in the long run. By adopting ADR mechanisms, China and Malaysia can strengthen their trade and business ties and foster a more stable and holistic business environment at both ASEAN and global environment.