Adaptive neural control of nonlinear systems with hysteresis

Ph.DDOCTOR OF PHILOSOPH

Deterministic learning enhanced neutral network control of unmanned helicopter

In this article, a neural network-based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design

EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration

In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. Previous calibration methods often required specific targets or manual adjustments, making them both labor-intensive and costly. Online calibration methods based on features have been proposed, but these methods encounter challenges such as imprecise feature extraction, unreliable cross-modality associations, and high scene-specific requirements. To address this, we introduce an edge-based approach for automatic online calibration of LiDAR and cameras in real-world scenarios. The edge features, which are prevalent in various environments, are aligned in both images and point clouds to determine the extrinsic parameters. Specifically, stable and robust image edge features are extracted using a SAM-based method and the edge features extracted from the point cloud are weighted through a multi-frame weighting strategy for feature filtering. Finally, accurate extrinsic parameters are optimized based on edge correspondence constraints. We conducted evaluations on both the KITTI dataset and our dataset. The results show a state-of-the-art rotation accuracy of 0.086{\deg} and a translation accuracy of 0.977 cm, outperforming existing edge-based calibration methods in both precision and robustness

Isolation and Characterization of Two New Deoxynivalenol-Degrading Strains, Bacillus sp. HN117 and Bacillus sp. N22

peer reviewedDeoxynivalenol (DON), produced by Fusarium species, is one of the most common trichothecenes detected in cereals pre- and post-harvest, which poses a great threat to the health of livestock and human beings due to its strong toxicity. In this study, we isolated and characterized two DON-degrading bacterial strains, Bacillus sp. HN117 and Bacillus sp. N22. Both strains could degrade DON efficiently in a wide range of temperatures (from 25 °C to 42 °C) and concentrations (from 10 mg/L to 500 mg/L). After optimization of the degradation conditions, 29.0% DON was eliminated by HN117 in 72 h when it was incubated with 1000 mg/L DON; meanwhile, the DON degradation rate of N22 was boosted notably from 7.41% to 21.21% within 120 h at 500 mg/L DON. Degradation products analysis indicated HN117 was able to transform DON into a new isomer M-DOM, the possible structure of which was deduced based on LC-MS and NMR analysis, and N22 could convert DON into potential low-toxic derivatives norDON E and 9-hydroxymethyl DON lactone. These two strains have the potential to be developed as new biodegrading agents to control DON contamination in food and feed industries

Pharmaco-transcriptomic correlation analysis reveals novel responsive signatures to HDAC inhibitors and identifies Dasatinib as a synergistic interactor in small-cell lung cancer.

BACKGROUND Histone acetylation/deacetylase process is one of the most studied epigenetic modifications. Histone deacetylase inhibitors (HDACis) have shown clinical benefits in haematological malignancies but failed in solid tumours due to the lack of biomarker-driven stratification. METHODS We perform integrative pharmaco-transcriptomic analysis by correlating drug response profiles of five pan-HDACis with transcriptomes of solid cancer cell lines (n=659) to systematically identify generalizable gene signatures associated with HDACis sensitivity and resistance. The established signatures are then applied to identify cancer subtypes that are potentially sensitive or resistant to HDACis, and drugs that enhance the efficacy of HDACis. Finally, the reproductivity of the established HDACis signatures is evaluated by multiple independent drug response datasets and experimental assays. FINDINGS We successfully delineate generalizable gene signatures predicting sensitivity (containing 46 genes) and resistance (containing 53 genes) to all five HDACis, with their reproductivity confirmed by multiple external sources and independent internal assays. Using the gene signatures, we identify low-grade glioma harbouring isocitrate dehydrogenase 1/2 (IDH1/2) mutation and non-YAP1-driven subsets of small-cell lung cancer (SCLC) that particularly benefit from HDACis monotherapy. Further, based on the resistance gene signature, we identify clinically-approved Dasatinib as a synthetic lethal drug with HDACi, synergizing in inducing apoptosis and reactive oxygen species on a panel of SCLC. Finally, Dasatinib significantly enhances the therapeutic efficacy of Vorinostat in SCLC xenografts. INTERPRETATION Our work establishes robust gene signatures predicting HDACis sensitivity/resistance in solid cancer and uncovers combined Dasatinib/HDACi as a synthetic lethal combination therapy for SCLC

Multi-scale integrative analyses identify THBS2+ cancer-associated fibroblasts as a key orchestrator promoting aggressiveness in early-stage lung adenocarcinoma.

Rationale: Subsets of patients with early-stage lung adenocarcinoma (LUAD) have a poor post-surgical course after curative surgery. However, biomarkers stratifying this high-risk subset and molecular underpinnings underlying the aggressive phenotype remain unclear. Methods: We integrated bulk and single-cell transcriptomics, proteomics, secretome and spatial profiling of clinical early-stage LUAD samples to identify molecular underpinnings that promote the aggressive phenotype. Results: We identified and validated THBS2, at multi-omic levels, as a tumor size-independent biomarker that robustly predicted post-surgical survival in multiple independent clinical cohorts of early-stage LUAD. Furthermore, scRNA-seq data revealed that THBS2 is exclusively derived from a specific cancer-associated fibroblast (CAF) subset that is distinct from CAFs defined by classical markers. Interestingly, our data demonstrated that THBS2 was preferentially secreted via exosomes in early-stage LUAD tumors with high aggressiveness, and its levels in the peripheral plasma associated with short recurrence-free survival. Further characterization showed that THBS2-high early-stage LUAD was characterized by suppressed antitumor immunity. Specifically, beyond tumor cells, THBS2+ CAFs mainly interact with B and CD8+ T lymphocytes as well as macrophages within tumor microenvironment of early-stage LUAD, and THBS2-high LUAD was associated with decreased immune cell infiltrates but increased immune exhaustion marker. Clinically, high THBS2 expression predicted poor response to immunotherapies and short post-treatment survival of patients. Finally, THBS2 recombinant protein suppressed ex vivo T cells proliferation and promoted in vivo LUAD tumor growth and distant micro-metastasis. Conclusions: Our multi-level analyses uncovered tumor-specific THBS2+ CAFs as a key orchestrator promoting aggressiveness in early-stage LUAD

Bounded droop controller for parallel operation of inverters

In this paper, the stability of parallel-operated inverters in the sense of boundedness is investigated. At first, the non-linear model of parallelled inverters with a generic linear or non-linear load is obtained by using the generalised dissipative Hamiltonian structure and then the robust droop controller, recently proposed in the literature for parallel operation of inverters, is implemented in a way to produce a bounded control output. The proposed controller is called the bounded droop controller (BDC). It introduces a zero-gain property and can guarantee the boundedness of the closed-loop system solution. Therefore, for the first time, the closed-loop stability in the sense of boundedness is guaranteed for parallelled inverters feeding generic non-linear/linear loads. The controller structure is further improved to increase its robustness with respect to initial conditions, numerical errors or external disturbances while maintaining the stability property. Moreover, the controller is tuned to avoid any possible limit cycles in the voltage dynamics. Real-time simulation results for two single-phase inverters operated in parallel loaded with a non-linear load are presented to verify the effectiveness of the proposed BDC

Model validation and stochastic stability of a hydro-turbine governing system under hydraulic excitations

This paper addresses the stability of a hydro-turbine governing system under hydraulic excitations. During the operation of a hydro-turbine, water hammer with different intensities occurs frequently, resulting in the stochastic change of the cross-sectional area (A) of the penstock. In this study, we first introduce a stochastic variable u to the cross-sectional area (A) of the penstock related to the intensity of water hammer. Using the Chebyshev polynomial approximation, the stochastic hydro-turbine governing model is simplified to its equivalent deterministic model, by which the dynamic characteristics of the stochastic hydro-turbine governing system can be obtained from numerical experiments. From comparisons based on an operational hydropower station, we verify that the stochastic model is suitable for describing the dynamic behaviors of the hydro-turbine governing system in full-scale applications. We also analyze the change laws of the dynamic variables under increasing stochastic intensity. Moreover, the differential coefficient with different values is used to study the stability of the system, and stability of the hydro-turbine flow with the increasing load disturbance is also presented. Finally, all of the above numerical results supply some basis for modeling efficiently the operation of large hydropower stations

The antibody response to SARS-CoV-2 Beta underscores the antigenic distance to other variants

Alpha-B.1.1.7, Beta-B.1.351, Gamma-P.1 and Delta-B.1.617.2 variants of SARS-CoV-2 express multiple mutations in the spike protein (S). These may alter the antigenic structure of S, causing escape from natural or vaccine-induced immunity. Beta is particularly difficult to neutralize using serum induced by early pandemic SARS-CoV-2 strains and is most antigenically separated from Delta. To understand this, we generated 674 mAbs from Beta infected individuals and performed a detailed structure-function analysis of the 27 most potent mAbs: one binding the spike N-terminal domain (NTD), the rest the receptor binding domain (RBD). Two of these RBD-binding mAbs recognise a neutralizing epitope conserved between SARS-CoV-1 and -2, whilst 18 target mutated residues in Beta: K417N, E484K, and N501Y. There is a major response to N501Y including a public IgVH4-39 sequence, with E484K and K417N also targeted. Recognition of these key residues underscores why serum from Beta cases poorly neutralizes early pandemic and Delta viruses