Comprehensive Experimental Validation Research Articles

Robust Algorithms for the Analysis of Fast-Field-Cycling Nuclear Magnetic Resonance Dispersion Curves

Fast-Field-Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry is a powerful, non-destructive magnetic resonance technique that enables, among other things, the investigation of slow molecular dynamics at low magnetic field intensities. FFC-NMR relaxometry measurements provide insight into molecular motion across various timescales within a single experiment. This study focuses on a model-free approach, representing the NMRD profile R1 as a linear combination of Lorentzian functions, thereby addressing the challenges of fitting data within an ill-conditioned linear least-squares framework. Tackling this problem, we present a comprehensive review and experimental validation of three regularization approaches to implement the model-free approach to analyzing NMRD profiles. These include (1) MF-UPen, utilizing locally adapted L2 regularization; (2) MF-L1, based on L1 penalties; and (3) a hybrid approach combining locally adapted L2 and global L1 penalties. Each method’s regularization parameters are determined automatically according to the Balancing and Uniform Penalty principles. Our contributions include the implementation and experimental validation of the MF-UPen and MF-MUPen algorithms, and the development of a “dispersion analysis” technique to assess the existence range of the estimated parameters. The objective of this work is to delineate the variance in fit quality and correlation time distribution yielded by each algorithm, thus broadening the set of software tools for the analysis of sample structures in FFC-NMR studies. The findings underline the efficacy and applicability of these algorithms in the analysis of NMRD profiles from samples representing different potential scenarios.

JGULF: Joint global and unilateral local feature network for micro-expression recognition

Micro-expression is a subtle facial movement that is fleeting and manifest in localized areas, making it difficult for the human eye to detect and recognize it. Although algorithms that extract facial features from specific regions or the entire face have shown potential, the classification of micro-expressions using features from symmetrical left and right regions can be challenging in the presence of unilateral movements. This can ultimately affect the performance of micro-expression recognition. To address this issue, we propose a network called Joint Global and Unilateral Local Features (JGULF) for micro-expression recognition. Initially, we employ a Convolutional Neural Network (CNN) and an adjusted Vision Transformer (ViT) model to extract global features from micro-expressions. The local feature extraction module is designed based on global features. The facial features are divided into multiple local regions with varying scales. After that, local feature learning and selection are performed to filter out unilateral local features related to micro-expression movements efficiently. Finally, global and local features are combined to classify micro-expressions. Through comprehensive experimental validation, our algorithm achieves state-of-the-art classification performance on the SMIC, CASMEII, and SAMM micro-expression datasets, demonstrating the effectiveness of combining global features and selecting local features.

Adaptive Control Algorithm for Quadruped Robots in Unknown High-slope Terrain

The capability to navigate high-slope terrains is crucial for quadruped robots engaged in field operations. We propose a novel terrain estimation and adaptation strategy tailored for facilitating quadruped robot locomotion on complex slope terrains. Our approach involves predicting terrain slopes by analyzing foot positions and IMU data, subsequently adjusting the robot’s body orientation and height in real-time to accommodate varying slope conditions. To effectively control the motion of quadruped robots on such challenging terrains, we introduce a multimodal motion control algorithm that integrates Model Predictive Control (MPC) with Quadratic Programming (QP) torque control. This combined approach ensures stable and efficient locomotion even on steep slopes. Furthermore, we present a state estimation method based on Kalman filtering, enabling accurate self-assessment by the robot without heavy reliance on visual sensors. In simulation validation, our quadruped robot achieved notable performance metrics. It achieved a forward speed of 0.7 m/s on slopes with angles up to 43∘ and demonstrated stable rotational capability at a speed of 2 rad/s on a 32∘ slope. When subjected to external force interference, the robot exhibited resilience, withstanding constant external forces of up to 60Nm and external torques of up to 35Nm on flat ground. Moreover, on a 30∘ slope, the robot maintained stable locomotion in the face of impulses reaching 64Nm ⋅ s along the x and y directions of its body coordinate system and 7Nm ⋅ s along the z-axis. This comprehensive strategy and experimental validation highlight the efficacy and robustness of our adaptive control algorithm, paving the way for enhanced performance of quadruped robots navigating high-slope terrains with unpredictable characteristics.

Frequency tuning and automatic frequency tracking of shunted piezoelectric transducers

Piezoelectric ultrasonic transducers, vital in medical devices and aerospace, often face challenges like resonant frequency shifts and impedance variations affecting their operational efficiency. This paper introduces a shunted piezoelectric transducer which could tune itself by digitally programmable inductance. A transformer and inductance-capacitance matching network ensures enhanced compatibility and impedance management. Proposing a fuzzy PI-based phase control method achieves resonant frequency tracking, synchronizing operational frequency with the transducer. In contrast to traditional methods, our approach enables faster and more precise fine-tuning, detecting and rectifying real-world deviations for optimal performance. A comprehensive experimental validation, based on fundamental knowledge analysis, confirms the feasibility and superiority of our proposed method, and the commonly encountered issues of resonance frequency deviation and impedance variation in high-power piezoelectric transducer applications can be effectively mitigated.

Comprehensive Analysis and Experimental Validation of FOXD2 as a Novel Potential Prognostic Biomarker Associated with Immune Infiltration in Head and Neck Squamous Cell Carcinoma

Comprehensive Analysis and Experimental Validation of FOXD2 as a Novel Potential Prognostic Biomarker Associated with Immune Infiltration in Head and Neck Squamous Cell Carcinoma

Experimental validation and comprehensive analysis of m6A methylation regulators in intervertebral disc degeneration subpopulation classification

Intervertebral disc degeneration (IVDD) is one of the most prevalent causes of chronic low back pain. The role of m6A methylation modification in disc degeneration (IVDD) remains unclear. We investigated immune-related m6A methylation regulators as IVDD biomarkers through comprehensive analysis and experimental validation of m6A methylation regulators in disc degeneration. The training dataset was downloaded from the GEO database and analysed for differentially expressed m6A methylation regulators and immunological features, the differentially regulators were subsequently validated by a rat IVDD model and RT-qPCR. Further screening of key m6A methylation regulators based on machine learning and LASSO regression analysis. Thereafter, a predictive model based on key m6A methylation regulators was constructed for training sets, which was validated by validation set. IVDD patients were then clustered based on the expression of key m6A regulators, and the expression of key m6A regulators and immune infiltrates between clusters was investigated to determine immune markers in IVDD. Finally, we investigated the potential role of the immune marker in IVDD through enrichment analysis, protein-to-protein network analysis, and molecular prediction. By analysising of the training set, we revealed significant differences in gene expression of five methylation regulators including RBM15, YTHDC1, YTHDF3, HNRNPA2B1 and ALKBH5, while finding characteristic immune infiltration of differentially expressed genes, the result was validated by PCR. We then screen the differential m6A regulators in the training set and identified RBM15 and YTHDC1 as key m6A regulators. We then used RBM15 and YTHDC1 to construct a predictive model for IVDD and successfully validated it in the training set. Next, we clustered IVDD patients based on the expression of RBM15 and YTHDC1 and explored the immune infiltration characteristics between clusters as well as the expression of RBM15 and YTHDC1 in the clusters. YTHDC1 was finally identified as an immune biomarker for IVDD. We finally found that YTHDC1 may influence the immune microenvironment of IVDD through ABL1 and TXK. In summary, our results suggest that YTHDC1 is a potential biomarker for the development of IVDD and may provide new insights for the precise prevention and treatment of IVDD.

A security-enhanced scheme for MQTT protocol based on domestic cryptographic algorithm

Recently, the rapid advancement of Industrial Internet of Things (IIoT) technology has led to the utilization of various communication protocols. Among these, the Message Queue Telemetry Transport (MQTT) protocol has become prevalent. Operating on a publish/subscribe model, MQTT’s popularity in IIoT because of simplicity, lightweight nature, and ease of implementation. However, traditional MQTT protocol standards have neglected critical security concerns during the communication process, such as ineffective identity authentication, weak data confidentiality, and a lack of access control functions. To address these challenges, this paper introduces a security-enhanced scheme for the MQTT protocol based on domestic cryptographic algorithms. The proposed method enforces mutual identity authentication between the MQTT Client and the MQTT Broker using digital certificates. It also implements access control through an Access Control List (ACL) and ensures end-to-end data security via the symmetric encryption algorithm SM4. Through comprehensive security analysis and experimental validation, this paper demonstrates that the enhanced scheme effectively rectifies the MQTT protocol’s security deficiencies, achieving these improvements with small overhead.

A comprehensive analysis and experimental validation of TK1 in uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is becoming a main malignant cancer that threaten to women's health. Thymidine kinase 1 (TK1) is considering to be associated with tumorigenesis and development. Nevertheless, the function of TK1 in UCEC is still unclear. Herein, we analyzed the TK1 expression level in pan-cancer and found that TK1 was upregulated in a variety of cancers including UCEC. Patients of UCEC with high expression of TK1 were related to poor outcome. TK1 was also related to clinical stage, histologic grade and lymph node metastasis. Abnormal expression of TK1 in UCEC was related to promoter methylation while gene mutation was not frequent. TK1 and its associated genes appeared to be prominent in cell cycle and DNA replication, according to GO and KEGG analysis. Analysis of immune infiltration revealed a negative correlation between TK1 and CD8 + T cells, macrophages, and dendritic cells. In vitro experiments, TK1 knockdown resulted in the inhibition of proliferation, migration, invasion and EMT in UCEC cell lines.

Design and Optimization of a Robot Dosing Device for Aliquoting of Biological Samples Based on Genetic Algorithms

Aliquoting of biological samples refers to the process of dividing a larger biological sample into smaller, representative portions known as aliquots. This procedure is commonly employed in laboratories, especially in fields like molecular biology, genetics, and clinical research. Currently, manual dosing devices are commonplace in laboratories, but they demand a significant amount of time for their manual operation. The automated dosing devices available are integrated into narrowly focused aliquoting systems and lack versatility as manipulator equipment. Addressing this limitation, a novel technical solution is proposed in this paper for a modular dosing device compatible with robotic manipulators. The paper introduces and details a mathematical model, optimizes its parameters, and constructs a detailed 3D model using the NX environment to demonstrate the engineering feasibility of our concept. It further outlines the development of a three-dimensional dynamic simulation model for the dosing device, comparing analytical calculations with simulation results. The construction of a dosing device prototype is discussed, followed by a comprehensive experimental validation.

ZNF695, A Potential Prognostic Biomarker, Correlates with Immune Infiltrates in Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma: Bioinformatic Analysis and Experimental Verification.

The role of Zinc Finger Protein 695 (ZNF695) is unclear in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). The objective of this study was to conduct a comprehensive analysis and experimental validation of ZNF695 in CESC. The study investigated the expression of ZNF695 in both pan-cancer and CESC, utilizing data from The Cancer Genome Atlas (TCGA) database to assess its diagnostic value. The present study investigated the association between ZNF695 expression levels and clinical characteristics, as well as prognosis, in patients with CESC. The study explored potential regulatory networks involving ZNF695, including its association with immune infiltration, immune score, stemness index based on mRNA expression (mRNAsi), and drug sensitivity in CESC. We explored the expression of ZNF695 in CESC single cells. ZNF695 expression was validated using GSE29570. ZNF695 was found to be aberrantly expressed in pan-cancer and CESC. There was a significant correlation observed between an elevated level of ZNF695 expression in patients with CESC and histological grade (p = 0.017). Furthermore, a strong association was found between high ZNF695 expression in CESC patients and poorer overall survival (OS) (HR: 1.87; 95% CI: 1.17-3.00; p = 0.009), Progression-free Survival (PFS) (HR: 1.86; 95% CI: 1.16-2.98; p = 0.010), and Disease-specific Survival (DSS) (HR: 1.98; 95% CI: 1.15-3.42; p = 0.014). The expression of ZNF695 in CESC patients (p = 0.006) was identified as an independent prognostic determinant. ZNF695 was associated with steroid hormone biosynthesis, oxidative phosphorylation, and so on. ZNF695 expression correlated with immune infiltration, immune score, and mRNAsi in CESC. ZNF695 expression significantly and negatively correlated with AICA ribonucleotide, BIX02189, QL-XI-92, STF-62247, and SNX-2112 in CESC. ZNF695 gene was upregulated in CESC tissues and cell lines. ZNF695 was significantly upregulated in the CESC cell lines. ZNF695 may be a potential prognostic biomarker and immunotherapeutic target for CESC patients.

In vitro and in vivo validation of the antiviral effect of hCypA against SARS-CoV-2 via binding to the RBD of spike protein

The novel coronavirus disease 2019 has stimulated the rapid development of new biological therapeutics to inhibit SARS-CoV-2 infection; however, this remains a challenging task. In a previous study using structural analysis, we revealed that human cyclophilin A inhibits the entry of SARS-CoV-2 into host cells by interfering with the interaction of the receptor-binding domain of the spike protein with angiotensin-converting enzyme 2 on the host cell surface, highlighting its potential for antiviral therapy. For a comprehensive experimental validation, in this study, we verified the antiviral effects of human cyclophilin A against SARS-CoV-2, including its variants, using in vitro assays and experiments on an in vivo mouse model. Human cyclophilin A demonstrated a highly effective antiviral effect, with an 85% survival rate upon SARS-CoV-2 infection. It also reduced viral titers, inflammation in the lungs and brain, and cytokine release in the serum, suggesting a controlled immune response and potentially faster recovery. Overall, our study provides insights into the potential of human cyclophilin A as a therapeutic agent against SARS-CoV-2, which should guide future clinical trials that might provide an additional therapeutic option for patients.

AFANet: Adaptive feature aggregation for polyp segmentation

In terms of speed and accuracy, the deep learning-based polyp segmentation method is superior. It is essential for the early detection and treatment of colorectal cancer and has the potential to greatly reduce the disease's overall prevalence. Due to the various forms and sizes of polyps, as well as the blurring of the boundaries between the polyp region and the surrounding mucus, most existing algorithms are unable to provide highly accurate colorectal polyp segmentation. Therefore, to overcome these obstacles, we propose an adaptive feature aggregation network (AFANet). It contains two main modules: the Multi-modal Balancing Attention Module (MMBA) and the Global Context Module (GCM). The MMBA extracts improved local characteristics for inference by integrating local contextual information while paying attention to them in three regions: foreground, background, and border. The GCM takes global information from the top of the encoder and sends it to the decoder layer in order to further investigate global contextual feature information in the pathologic picture. Dice of 92.11 % and 94.76 % and MIoU of 91.07 % and 94.54 %, respectively, are achieved by comprehensive experimental validation of our proposed technique on two benchmark datasets, Kvasir-SEG and CVCClinicDB. The experimental results demonstrate that the strategy outperforms other cutting-edge approaches.

Comprehensive Analysis and Experimental Validation of the Parkinson's Disease Lysosomal Gene ACP2 and Pan-cancer.

The pivotal role of lysosomal function in preserving neuronal homeostasis is recognized, with its dysfunction being implicated in neurodegenerative processes, notably in Parkinson's disease (PD). Yet, the molecular underpinnings of lysosome-related genes (LRGs) in the context of PD remain partially elucidated. We collected RNA-seq data from the brain substantia nigra of 30 PD patients and 20 normal subjects from the GEO database. We obtained molecular classification clusters from the screened lysosomal expression patterns. The lysosome-related diagnostic model of Parkinson's disease was constructed by XGBoost and Random Forest. And we validated the expression patterns of signature LRGs in the diagnostic model by constructing a PD rat model. Finally, the linkage between PD and cancer through signature genes was explored. The expression patterns of the 33 LRGs screened can be divided into two groups of PD samples, enabling exploration of the variance in biological processes and immune elements. Cluster A had a higher disease severity. Subsequently, critical genes were sieved through the application of machine learning methodologies culminating in the identification of two intersecting feature genes (ACP2 and LRP2). A PD risk prediction model was constructed grounded on these signature genes. The model's validity was assessed through nomogram evaluation, which demonstrated robust confidence validity. Then we analyzed the correlation analysis, immune in-filtration, biological function, and rat expression validation of the two genes with common pathogenic genes in Parkinson's disease, indicating that these two genes play an important role in the pathogenesis of PD. We then selected ACP2, which had a significant immune infiltration correlation, as the entry gene for the pan-cancer analysis. The pan-cancer analysis revealed that ACP2 has profound associations with prognostic indicators, immune infiltration, and tumor-related regulatory processes across various neoplasms, suggesting its potential as a therapeutic target in a range of human diseases, including PD and cancers. Our study comprehensively analyzed the molecular grouping of LRGs expression patterns in Parkinson's disease, and the disease progression was more severe in cluster A. And the PD diagnosis model related to LRGs is constructed. Finally, ACP2 is a potential target for the relationship between Parkinson's disease and tumor.

Integrated analysis reveals crosstalk between pyroptosis and immune regulation in renal fibrosis.

The pathogenesis of renal fibrosis (RF) involves intricate interactions between profibrotic processes and immune responses. This study aimed to explore the potential involvement of the pyroptosis signaling pathway in immune microenvironment regulation within the context of RF. Through comprehensive bioinformatics analysis and experimental validation, we investigated the influence of pyroptosis on the immune landscape in RF. We obtained RNA-seq datasets from Gene Expression Omnibus (GEO) databases and identified Pyroptosis-Associated Regulators (PARs) through literature reviews. Systematic evaluation of alterations in 27 PARs was performed in RF and normal kidney samples, followed by relevant functional analyses. Unsupervised cluster analysis revealed distinct pyroptosis modification patterns. Using single-sample gene set enrichment analysis (ssGSEA), we examined the correlation between pyroptosis and immune infiltration. Hub regulators were identified via weighted gene coexpression network analysis (WGCNA) and further validated in a single-cell RNA-seq dataset. We also established a unilateral ureteral obstruction-induced RF mouse model to verify the expression of key regulators at the mRNA and protein levels. Our comprehensive analysis revealed altered expression of 19 PARs in RF samples compared to normal samples. Five hub regulators, namely PYCARD, CASP1, AIM2, NOD2, and CASP9, exhibited potential as biomarkers for RF. Based on these regulators, a classifier capable of distinguishing normal samples from RF samples was developed. Furthermore, we identified correlations between immune features and PARs expression, with PYCARD positively associated with regulatory T cells abundance in fibrotic tissues. Unsupervised clustering of RF samples yielded two distinct subtypes (Subtype A and Subtype B), with Subtype B characterized by active immune responses against RF. Subsequent WGCNA analysis identified PYCARD, CASP1, and NOD2 as hub PARs in the pyroptosis modification patterns. Single-cell level validation confirmed PYCARD expression in myofibroblasts, implicating its significance in the stress response of myofibroblasts to injury. In vivo experimental validation further demonstrated elevated PYCARD expression in RF, accompanied by infiltration of Foxp3+ regulatory T cells. Our findings suggest that pyroptosis plays a pivotal role in orchestrating the immune microenvironment of RF. This study provides valuable insights into the pathogenesis of RF and highlights potential targets for future therapeutic interventions.

Comprehensive experimental and numerical validation of Lattice Boltzmann fluid flow and particle simulations in a child respiratory tract

The numerical simulation of inhaled aerosols in medical research starts to play a crucial role in understanding local deposition within the respiratory tract, a feat often unattainable experimentally. Research on children is particularly challenging due to the limited availability of in vivo data and the inherent morphological intricacies. CFD solvers based on Finite Volume Methods (FVM) have been widely employed to solve the flow field in such studies. Recently, Lattice Boltzmann Methods (LBM), a mesoscopic approach, have gained prominence, especially for their scalability on High-Performance Computers.This study endeavours to compare the effectiveness of LBM and FVM in simulating particulate flows within a child’s respiratory tract, supporting research related to particle deposition and medication delivery using LBM. Considering a 5-year-old child’s airway model at a steady inspiratory flow, the results are compared with in vitro experiments. Notably, both LBM and FVM exhibit favourable agreement with experimental data for the mean velocity field and the turbulence intensity.For particle deposition, both numerical methods yield comparable results, aligning well with in vitro experiments across a particle size range of 0.1–20 µm. Discrepancies are identified in the upper airways and trachea, indicating a lower deposition fraction than in the experiment. Nonetheless, both LBM and FVM offer invaluable insights into particle behaviour for different sizes, which are not easily achievable experimentally.In terms of practical implications, the findings of this study hold significance for respiratory medicine and drug delivery systems — potential health impacts, targeted drug delivery strategies or optimisation of respiratory therapies.

Omicron variant (B.1.1.529) challenge the integrity of blood brain barrier: Evidence from protein structural analysis

Studies on nonhuman primates, wild-type and transgenic mice have shown the presence of SARS-CoV-2 RNA components in the brains. Despite the Blood-Brain Barrier (BBB) provides protection there are less evidences on how the SARS-CoV-2 crosses the BBB. Given that there is an increase of Omicron reinfection rates, transmissibility rate and involvement to cause neurological dysfunctions, we hypothesized to investigate how the Omicron variant (B.1.1.529) binds structurally to key BBB-maintaining proteins and thus can possibly challenge the integrity and transportation to the brain. By using molecular dynamics simulation approaches we examined the interaction of Omicron variant (B.1.1.529) with different structural and transporter proteins located at the BBB. Our results show that in Zona Ocludin 1-RBD complex, we observe a distinct pattern. Omicron demonstrates a docking score of −88.9 ± 6.8 kcal/mol and six interactions, while the wild type (WT) presents a higher score of −94.0 ± 2.3 kcal/mol, forming eight interactions. Comparing affinities, the WT-RBD displays a stronger preference for Claudin-5, boasting a docking score of −110.2 ± 3.0 and nine interactions, versus Omicron-RBD's slightly reduced engagement, with a docking score of −105.6 ± 0.2 and seven interactions. Interestingly, the Omicron variant exhibits heightened stability in interactions with Glucose Transporter and ABC transporters, registering docking scores of −110.6 ± 1.9 and −112.0 ± 3.6 kcal/mol, respectively. This surpasses the WT's respective scores of −95.2 ± 2.2 and −104.0 ± 6.2 kcal/mol, reflecting a unique interaction profile. Rigorous molecular dynamics simulations validate our findings. Our study emphasizes the Omicron variant's increased affinity towards transporter proteins, illuminating potential implications for BBB integrity and brain transportation. While these insights offer a valuable framework, comprehensive experimental validation is indispensable for a comprehensive understanding.

Vibration control of interconnected composite beams: Dynamical analysis and experimental validations

This paper presents a comprehensive theoretical investigation and experimental validation of vibration and nonlinear vibration control in interconnected composite beams with fixed ends operating within a thermal environment. The study employs the Rayleigh-Ritz method, utilizing a series of orthogonal polynomials as interpolation basis functions to derive the vibration mode functions of the composite structure under thermal conditions. Furthermore, the motion control equation for the system is derived based on Lagrange's equation. These equations of motion are then simplified using the Galerkin method, leading to ordinary differential equations. The response of the system is subsequently computed using the harmonic balance method and validated through the Runge-Kutta method. Finally, experimental results confirm that the NiTiNOL-steel wire rope effectively controls the vibration of interconnected composite beams in a thermal environment and is consistent with the theoretical results.

Recycling of end of life photovoltaic solar panels and recovery of valuable components: A comprehensive review and experimental validation

With the current rate of installation of photovoltaic (PV) modules, the total installed capacity is expected to reach 4500 GW by 2050. Given the average life of solar modules is 25 years, after their spent time the installed solar panels will eventually turn into waste. The waste from solar panel modules is expected to reach about 8600 tons by 2030 and it will further increase to 78 million tons by 2050. The waste solar panel should be discarded or recycled appropriately since the toxic substances released from them can affect human health and the environment. Therefore, there is a need to develop a recovery and recycling process for waste produced from solar modules. In this comprehensive work, we have summarized (i) the classification of photovoltaic technology, (ii) review the approaches embraced for photovoltaic (PV) waste management on a global level, (iii) the need for photovoltaic (PV) waste management and brief explanation on the recycling process (Mechanical, thermal, chemical and leaser methods) of solar cells. Also, experimental validation has been provided by the recovery of discrete components from End of Life (EoL) solar panels in an appropriate amount. The recovered material components from the modules were subjected to structural and chemical characterization to attest phase purity of the samples.

Comparative analysis of the hypothalamus transcriptome of laying ducks with different residual feeding intake

Feed costs account for approximately 60-70% of the cost of poultry farming, and feed utilization is closely related to the profitability of the poultry industry. To understand the causes of the differences in feeding in Shan Partridge ducks, we compared the hypothalamus transcriptome profiles of two groups of ducks using RNA-seq. The two groups were: 1) low residual feed intake (LRFI) group with low feed intake but high feed efficiency, and 2) high residual feed intake (HRFI) group with high feed intake but low feed efficiency. We found 78 DEGs were enriched in nine differential Kyoto Encyclopedia of Genes and Genome (KEGG) pathways, including neuroactive ligand-receptor interaction, GABAergic synapse, nitrogen metabolism, cAMP signaling pathway, calcium signaling pathway, nitrogen metabolism, tyrosine metabolism, ovarian steroidogenesis, and gluconeogenesis. To further identify core genes among the 78 DEGs, we performed protein-protein interaction and co-expression network analyses. After comprehensive analysis and experimental validation, four core genes, namely, glucagon (GCG), cholecystokinin (CCK), gamma-aminobutyric acid type A receptor subunit delta (GABRD), and gamma-aminobutyric acid type A receptor subunit beta1 (GABRB1), were identified as potential core genes responsible for the difference in residual feeding intake between the two breeds. We also investigated the level of cholecystokinin (CCK), neuropeptide Y (NPY), peptide YY(PYY), ghrelin, and glucagon-like peptide1 (GLP-1) hormones in the sera of Shan Partridge ducks at different feeding levels and found that there was a difference between the two groups with respect to GLP-1 and NPY levels. The findings will serve as a reference for future research on the feeding efficiency of Shan Partridge ducks and assist in promoting their genetic breeding.

Active disturbance rejection control for unmanned tracked vehicles in leader–follower scenarios: Discrete-time implementation and field test validation

This paper presents a systematic design of an active disturbance rejection control (ADRC) system for unmanned tracked vehicles (UTVs) in leader-follow formation. Two ADRC controllers are designed for the lateral and the longitudinal channels of the UTV based on control errors in the cross-track and the along-track directions. Through simulations, the proposed ADRC approach is first shown to outperform the conventional PI/PID controllers in scenarios involving sudden changes in the leader motion dynamics, slippage disturbances, and measurement noise. Then, a comprehensive experimental validation of the proposed leader–follower control is performed using a laboratory UTV equipped with a camera and laser sensors (to enable the calculation of error signals). In order to provide more effective interaction between the human (leader) and the UTV (follower) during the leader–follower task, a camera-based subsystem for human pose recognition is developed and deployed. Finally, the experimental results obtained outdoors demonstrate that the proposed ADRC-based leader–follower UTV control system achieves high tracking capabilities, robustness against slippage disturbances, and adaptability to changing environmental conditions.