Global trends in oxidative stress in the Retina: A bibliometric analysis of 2013-2023.

Background: Oxidative stress plays a significant role in the pathogenesis of many retinal diseases. However, only a few systematic bibliometric studies have been conducted. This study aims to visualize research hotspots and developmental trends in oxidative stress in the retina from 2013 to 2023 by analyzing bibliometric data. Methods: We retrieved papers on oxidative stress in the retina published between 2013 and 2023 from the Web of Science Core Collection. The data were visually analyzed using CiteSpace and VOSviewer software. Results: The total number of 2100 publications were included in the analysis. An overall increasing trend in the number of publications is observed between 2013 and 2023. Chinese publications were the most contributive, but United States publications were the most influential. Shanghai Jiao Tong University was the most active and prolific institution. Antioxidants was the most productive journal, while Oxidative Medicine and Cellular Longevity were the journals with the most-cited articles. Kaarniranta K, from Finland, was the most productive and influential author. Examination of co-cited references revealed that researchers in the field are primarily focused on investigating the molecular mechanisms, preventive strategies, and utilization of antioxidants to address retinal oxidative damage. Diabetic retinopathy, endothelial growth factor, retinitis pigmentosa, retinal degeneration, antioxidant response, retinal ganglion cells, and genes are the research hotspots in this field. Metabolism, sodium iodate, and system are at the forefront of research in this field. Conclusion: Attention toward retinal oxidative stress has increased over the past decade. Current research focuses on the mechanisms of retinal diseases related to oxidative stress and the experimental study of antioxidants in retinal diseases, which may continue to be a trend in the future.

Atractylenolide I Alleviates Indomethacin-Induced Gastric Ulcers in Rats by Inhibiting NLRP3 Inflammasome Activation.

Atractylodes macrocephala Koidz, a traditional Chinese medicine, contains atractylenolide I (ATR-I), which has potential anticancer, anti-inflammatory, and immune-modulating properties. This study evaluated the therapeutic potential of ATR-I for indomethacin (IND)-induced gastric mucosal lesions and its underlying mechanisms. Noticeable improvements were observed in the histological morphology and ultrastructures of the rat gastric mucosa after ATR-I treatment. There was improved blood flow, a significant decrease in the expression of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), IL-1ß, and IL-18, and a marked increase in prostaglandin E2 (PGE2) expression in ATR-I-treated rats. Furthermore, there was a significant decrease in the mRNA and protein expression levels of NOD-like receptor thermal protein domain associated protein 3 (NLRP3), apoptosis-associated speck-like protein (ASC), cysteinyl aspartate specific proteinase-1 (caspase-1), and nuclear factor-κB (NF-κB) in rats treated with ATR-I. The results show that ATR-I inhibits the NLRP3 inflammasome signaling pathway and effectively alleviates local inflammation, thereby improving the therapeutic outcomes against IND-induced gastric ulcers in rats.

Astragaloside IV ameliorates indomethacin-induced intestinal inflammation in rats through inhibiting the activation of NLRP3 inflammasome.

The administration of nonsteroidal anti-inflammatory drugs (NSAIDs) may cause significant intestinal alteration and inflammation and lead to the occurrence of inflammatory diseases resembling duodenal ulcers. Astragaloside IV (AS-IV) is a glycoside of cycloartane-type triterpene isolated from the dried root of Astragalus membranaceus (Fisch.) Bge. (family Fabaceae), and has been used for ameliorating the NSAID-induced inflammation in the small intestine. The present study aimed to investigate the effects of AS-IV on indomethacin (IND)-induced inflammation in the small intestine of rats and its underlying mechanisms. Hematoxylin-eosin (H&E) staining, transmission and scanning electron microscopy were carried out to observe the surface morphology and ultrastructure of the small intestinal mucosa. Immunofluorescence and ELISA tests were employed to detect the expressions of NLRP3, ASC, caspase-1, and NF-κB proteins, as well as inflammatory factors IL-1ß and IL-18, to uncover potential molecular mechanisms responsible for mitigating small intestinal inflammation. The results demonstrated that AS-IV significantly decreased the ulcer index, improved the surface morphology and microstructure of the small intestinal mucosa, and increased mucosal blood flow. Molecular docking revealed a strong and stable binding capacity of AS-IV to NLRP3, ASC, caspase-1, and NF-κB proteins. Further experimental validation exhibited that AS-IV markedly decreased levels of IL-1ß and IL-18, and inhibited the protein expression of NLRP3, ASC, caspase-1, and NF-κB. Our data demonstrate that AS-IV ameliorates IND-induced intestinal inflammation in rats by inhibiting the activation of NLRP3 inflammasome and reducing the release of IL-1ß and IL-18, thereby representing a promising therapy for IND-induced intestinal inflammation.

A Heart Image Segmentation Method Based on Position Attention Mechanism and Inverted Pyramid.

In the realm of modern medicine, medical imaging stands as an irreplaceable pillar for accurate diagnostics. The significance of precise segmentation in medical images cannot be overstated, especially considering the variability introduced by different practitioners. With the escalating volume of medical imaging data, the demand for automated and efficient segmentation methods has become imperative. This study introduces an innovative approach to heart image segmentation, embedding a multi-scale feature and attention mechanism within an inverted pyramid framework. Recognizing the intricacies of extracting contextual information from low-resolution medical images, our method adopts an inverted pyramid architecture. Through training with multi-scale images and integrating prediction outcomes, we enhance the network's contextual understanding. Acknowledging the consistent patterns in the relative positions of organs, we introduce an attention module enriched with positional encoding information. This module empowers the network to capture essential positional cues, thereby elevating segmentation accuracy. Our research resides at the intersection of medical imaging and sensor technology, emphasizing the foundational role of sensors in medical image analysis. The integration of sensor-generated data showcases the symbiotic relationship between sensor technology and advanced machine learning techniques. Evaluation on two heart datasets substantiates the superior performance of our approach. Metrics such as the Dice coefficient, Jaccard coefficient, recall, and F-measure demonstrate the method's efficacy compared to state-of-the-art techniques. In conclusion, our proposed heart image segmentation method addresses the challenges posed by diverse medical images, offering a promising solution for efficiently processing 2D/3D sensor data in contemporary medical imaging.

Helicobacter pylori infection: a dynamic process from diagnosis to treatment.

Helicobacter pylori, a gram-negative microaerophilic pathogen, causes several upper gastrointestinal diseases, such as chronic gastritis, peptic ulcer disease, and gastric cancer. For the diseases listed above, H. pylori has different pathogenic mechanisms, including colonization and virulence factor expression. It is essential to make accurate diagnoses and provide patients with effective treatment to achieve positive clinical outcomes. Detection of H. pylori can be accomplished invasively and noninvasively, with both having advantages and limitations. To enhance therapeutic outcomes, novel therapeutic regimens, as well as adjunctive therapies with probiotics and traditional Chinese medicine, have been attempted along with traditional empiric treatments, such as triple and bismuth quadruple therapies. An H. pylori infection, however, is difficult to eradicate during treatment owing to bacterial resistance, and there is no commonly available preventive vaccine. The purpose of this review is to provide an overview of our understanding of H. pylori infections and to highlight current treatment and diagnostic options.

Learning-based sliding mode synchronization for fractional-order Hindmarsh-Rose neuronal models with deterministic learning.

Introduction: In recent years, extensive research has been conducted on the synchronous behavior of neural networks. It is found that the synchronization ability of neurons is related to the performance of signal reception and transmission between neurons, which in turn affects the function of the organism. However, most of the existing synchronization methods are faced with two difficulties, one is the structural parameter dependency, which limits the promotion and application of synchronous methods in practical problems. The other is the limited adaptability, that is, even when faced with the same control tasks, for most of the existing control methods, the control parameters still need to be retrained. To this end, the present study investigates the synchronization problem of the fractional-order HindmarshRose (FOHR) neuronal models in unknown dynamic environment. Methods: Inspired by the human experience of knowledge acquiring, memorizing, and application, a learning-based sliding mode control algorithm is proposed by using the deterministic learning (DL) mechanism. Firstly, the unknown dynamics of the FOHR system under unknown dynamic environment is locally accurately identified and stored in the form of constant weight neural networks through deterministic learning without dependency of the system parameters. Then, based on the identified and stored system dynamics, the model-based and relearning-based sliding mode controller are designed for similar as well as new synchronization tasks, respectively. Results: The synchronization process can be started quickly by recalling the empirical dynamics of neurons. Therefore, fast synchronization effect is achieved by reducing the online computing time. In addition, because of the convergence of the identification and synchronization process, the control experience can be constantly replenished and stored for reutilization, so as to improve the synchronization speed and accuracy continuously. Discussion: The thought of this article will also bring inspiration to the related research in other fields.

A Salient Object Detection Method Based on Boundary Enhancement.

Visual saliency refers to the human's ability to quickly focus on important parts of their visual field, which is a crucial aspect of image processing, particularly in fields like medical imaging and robotics. Understanding and simulating this mechanism is crucial for solving complex visual problems. In this paper, we propose a salient object detection method based on boundary enhancement, which is applicable to both 2D and 3D sensors data. To address the problem of large-scale variation of salient objects, our method introduces a multi-level feature aggregation module that enhances the expressive ability of fixed-resolution features by utilizing adjacent features to complement each other. Additionally, we propose a multi-scale information extraction module to capture local contextual information at different scales for back-propagated level-by-level features, which allows for better measurement of the composition of the feature map after back-fusion. To tackle the low confidence issue of boundary pixels, we also introduce a boundary extraction module to extract the boundary information of salient regions. This information is then fused with salient target information to further refine the saliency prediction results. During the training process, our method uses a mixed loss function to constrain the model training from two levels: pixels and images. The experimental results demonstrate that our salient target detection method based on boundary enhancement shows good detection effects on targets of different scales, multi-targets, linear targets, and targets in complex scenes. We compare our method with the best method in four conventional datasets and achieve an average improvement of 6.2% on the mean absolute error (MAE) indicators. Overall, our approach shows promise for improving the accuracy and efficiency of salient object detection in a variety of settings, including those involving 2D/3D semantic analysis and reconstruction/inpainting of image/video/point cloud data.

An Interactive Image Segmentation Method Based on Multi-Level Semantic Fusion.

Understanding and analyzing 2D/3D sensor data is crucial for a wide range of machine learning-based applications, including object detection, scene segmentation, and salient object detection. In this context, interactive object segmentation is a vital task in image editing and medical diagnosis, involving the accurate separation of the target object from its background based on user annotation information. However, existing interactive object segmentation methods struggle to effectively leverage such information to guide object-segmentation models. To address these challenges, this paper proposes an interactive image-segmentation technique for static images based on multi-level semantic fusion. Our method utilizes user-guidance information both inside and outside the target object to segment it from the static image, making it applicable to both 2D and 3D sensor data. The proposed method introduces a cross-stage feature aggregation module, enabling the effective propagation of multi-scale features from previous stages to the current stage. This mechanism prevents the loss of semantic information caused by multiple upsampling and downsampling of the network, allowing the current stage to make better use of semantic information from the previous stage. Additionally, we incorporate a feature channel attention mechanism to address the issue of rough network segmentation edges. This mechanism captures richer feature details from the feature channel level, leading to finer segmentation edges. In the experimental evaluation conducted on the PASCAL Visual Object Classes (VOC) 2012 dataset, our proposed interactive image segmentation method based on multi-level semantic fusion demonstrates an intersection over union (IOU) accuracy approximately 2.1% higher than the currently popular interactive image segmentation method in static images. The comparative analysis highlights the improved performance and effectiveness of our method. Furthermore, our method exhibits potential applications in various fields, including medical imaging and robotics. Its compatibility with other machine learning methods for visual semantic analysis allows for integration into existing workflows. These aspects emphasize the significance of our contributions in advancing interactive image-segmentation techniques and their practical utility in real-world applications.

Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals.

Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

Research on antibiotic resistance in Helicobacter pylori: a bibliometric analysis of the past decade.

Resistance of Helicobacter pylori (H. pylori) to antibiotics has reached alarming levels worldwide, and the efficacy of the H. pylori eradication treatment has decreased dramatically because of antibiotic resistance. To gain a more comprehensive understanding of the development status, research hotspots, and future trends related to H. pylori antibiotic resistance, we conducted a thorough retrospective analysis via the bibliometrics method. We searched the Science Citation Index Expanded of the Web of Science Core Collection for all pertinent articles on H. pylori antibiotic resistance from 2013 to 2022. R-bibliometrix, CiteSpace, and VOSviewer tools were utilized to depict statistical evaluations in order to provide an unbiased presentation and forecasts in the field. We incorporated a total of 3,509 articles related to H. pylori antibiotic resistance. Publications were inconsistent prior to 2017, but steadily increased after 2017. China generated the most papers and the United States of America received the most citations and the highest H-index. Baylor College of Medicine was the most influential institution in this field, with the highest number of publications and citations, as well as the highest H-index. Helicobacter was the most productive journal, followed by the World Journal of Gastroenterology and Frontiers in Microbiology. The World Journal of Gastroenterology had the highest citation. Graham, David Y was the most productive and cited author. Clarithromycin resistance, prevalence, gastric cancer, quadruple therapy, sequential therapy, 23S rRNA, whole genome sequencing, bismuth, and probiotics appeared with a high frequency in the keywords. The top keywords with the highest citation bursts were vonoprazan, RdxA, biofilm formation, and fatty acid chain. Our research illustrated a multi-dimensional facet and a holistic knowledge structure for H. pylori antibiotic resistance research over the past decade, which can serve as a guide for the H. pylori research community to conduct in-depth investigations in the future.

Inhibition mechanism of membrane-separated silver carp hydrolysates on ice crystal growth obtained through experiments and molecular dynamics simulation.

The membrane-separated silver carp hydrolysates (>10 kD, 3-10 kD and < 3 kD) displayed abilities to mitigate oxidation and denaturation of myofibrillar protein and cryoprotective activities for frozen surimi. However, the mechanism of the membrane-separated fractions on ice crystal growth in the system is still unknown. Therefore, the cryoprotective activities (recrystallization inhibition, RI and thermal hysteresis activity, THA) of the fractions were investigated and the mechanism was explored by molecular dynamics (MD) simulation to predict the probable binding sites and model the possible interactions between the peptides and water/ice. The fractions < 3 kD displayed remarkable RI activity, with significantly higher THA (0.60 ± 0.13 °C) and lower amount of ice nuclei (4.74 ± 0.53%) than that of fractions > 10 kD and 3-10 kD. The results of MD simulation certified that the main peptides in the fractions < 3 kD interacted firmly with water molecules and inhibited growth of ice crystals with mechanism compatible with Kelvin effect. Hydrophilic and hydrophobic amino acid residues in the membrane-separated fractions offered synergistic effects on the inhibition of ice crystals.

Regulatory mechanisms governing collagen peptides and their 3D printing application for frozen surimi.

Myofibrillar proteins (MPs) are important to the gel formation that occurs in frozen surimi. Importantly, their unique gel-forming ability indicates that surimi may be a promising material for use in 3D printing. The objective of the present study was to investigate the effects of collagen peptides on the cryoprotection of MPs during freeze-thaw (FT) cycles and the subsequent printability of surimi. The results showed that the collagen peptide had both protective and destructive actions during the tested FT cycles. The addition of 1.0% collagen peptide provided significant cryoprotection to the MPs. This addition effectively maintained the structural stability of MPs while also weakening FT effects on bound water and its mobility. We also assessed the rheological and 3D-printing characteristics of surimi with 1.0% collagen peptide. The rheological results indicated that the surimi with collagen peptides had better characteristics, including shear-thinning behavior, better recovery, and improved mechanical properties. Combined with the actual printing effect, materials with good shear-thinning behavior, high apparent viscosity, and high recovery might be more suitable for 3D printing. Moreover, the high G' contributed to good structural maintenance after printing. Collectively, these results indicated that collagen peptide may serve as a new, low-sugar cryoprotectant for use in surimi. Moreover, that its use would result in a healthier system that has increased stability, precision, and formability with applications in extrusion-based 3D printing. The results of this study provide theoretical reference for the development of new surimi materials with freezing stability and good 3D printing performance. PRACTICAL APPLICATION: This study confirmed the protective action of 1.0% collagen peptides for surimi and the contribution of it to well printing precision and structure maintenance for 3D printing, providing a firm foundation for the use of collagen peptide as a low-sugar cryoprotectant and developed a new type of surimi as a food material for 3D printing.

Threshold image segmentation based on improved sparrow search algorithm.

Threshold segmentation based on swarm intelligence optimization algorithm is a research hotspot in image processing, because of its good segmentation effect and easy implementation. This paper proposes an image threshold segmentation method based on an improved sparrow search algorithm and 2-D maximum entropy method. In the proposed algorithm, the nonlinear inertia weight is introduced into the entrants' update formula to improve the local exploration ability of the algorithm, and Levy flight is introduced into the vigilant sparrows' update formula to prevent the algorithm from falling into the local optimal solution in the later stage of iteration. In addition, improved sparrow search algorithm is tested on fifteen benchmark functions. The results represent the merit of the proposed algorithm with respect to other algorithms. Finally, the proposed algorithm is applied to entropy based image segmentation. Experiment results on classical images and medical images show that the proposed method improves the segmentation effect in terms of peak signal-to-noise ratio and feature similarity.

Small Fault Detection of Discrete-Time Nonlinear Uncertain Systems.

This article investigates the problem of small fault detection (sFD) for discrete-time nonlinear systems with uncertain dynamics. The faults are considered to be "small" in the sense that the system trajectories in the faulty mode always remain close to those in the normal mode, and the magnitude of fault can be smaller than that of the system's uncertain dynamics. A novel adaptive dynamics learning-based sFD framework is proposed. Specifically, an adaptive dynamics learning approach using radial basis function neural networks (RBF NNs) is first developed to achieve locally accurate identification of the system uncertain dynamics, where the obtained knowledge can be stored and represented in terms of constant RBF NNs. Based on this, a novel residual system is designed by incorporating a newmechanism of absolute measurement of system dynamics changes induced by small faults. An adaptive threshold is then developed for real-time sFD decision making. Rigorous analysis is performed to derive the detectability condition and the analytical upper bound for sFD time. Simulation studies, including an application to a three-tank benchmark engineering system, are conducted to demonstrate the effectiveness and advantages of the proposed approach.

Distributed Cooperative Learning Control of Uncertain Multiagent Systems With Prescribed Performance and Preserved Connectivity.

For an uncertain multiagent system, distributed cooperative learning control exerting the learning capability of the control system in a cooperative way is one of the most important and challenging issues. This article aims to address this issue for an uncertain high-order nonlinear multiagent system with guaranteed transient performance and preserved initial connectivity under an undirected and static communication topology. The considered multiagent system has an identical structure and the uncertain agent dynamics are estimated by localized radial basis function (RBF) neural networks (NNs) in a cooperative way. The NN weight estimates are rigorously proven to converge to small neighborhoods of their common optimal values along the union of all agents' trajectories by a deterministic learning theory. Consequently, the associated uncertain dynamics can be locally accurately identified and can be stored and represented by constant RBF networks. Using the stored knowledge on identified system dynamics, an experience-based distributed controller is proposed to improve the control performance and reduce the computational burden. The theoretical results are demonstrated on an application to the formation control of a group of unmanned surface vehicles.

Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks.

Cardiovascular diseases (CVD) is the leading cause of human mortality and morbidity around the world, in which myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. Currently, electrocardiogram (ECG) is widely used by the clinicians to diagnose MI patients due to its inexpensiveness and non-invasive nature. Pathological alterations provoked by MI cause slow conduction by increasing axial resistance on coupling between cells. This issue may cause abnormal patterns in the dynamics of the tip of the cardiac vector in the ECG signals. However, manual interpretation of the pathological alternations induced by MI is a time-consuming, tedious and subjective task. To overcome such disadvantages, computer-aided diagnosis techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on hybrid feature extraction and artificial intelligence tools. Tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space reconstruction (PSR) are utilized to extract representative features to form cardiac vectors with synthesis of the standard 12-lead and Frank XYZ leads. They are combined with neural networks to model, identify and detect abnormal patterns in the dynamics of cardiac system caused by MI. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. Second, this vector is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Third, VMD is employed to decompose the subband of the 4-dimensional cardiac vector into different intrinsic modes, in which the first intrinsic mode contains the majority of the cardiac vector's energy and is considered to be the predominant intrinsic mode. It is selected to construct the reference variable for analysis. Fourth, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Fifth, cardiac system dynamics can be modeled and identified using neural networks, which employ the ED of 3D PSR of the reference variable as the input features. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, data sets, which include conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls from PTB diagnostic ECG database, are used for evaluation. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 97.98%. Currently, ST segment evaluation is one of the major and traditional ways for the MI detection. However, there exist weak or even undetectable ST segments in many ECG signals. Since the proposed method does not rely on the information of ST waves, it can serve as a complementary MI detection algorithm in the intensive care unit (ICU) of hospitals to assist the clinicians in confirming their diagnosis. Overall, our results verify that the proposed features may satisfactorily reflect cardiac system dynamics, and are complementary to the existing ECG features for automatic cardiac function analysis.

Joint influence of genetic origin and climate on the growth of Masson pine (Pinus massoniana Lamb.) in China.

Adaptive of trees and its correlation with the climatic are causing changes in tree species performance and distribution, which will change breeding programs and influence forest productivity. To further evaluate the joint influence of climatic factors and provenance on the ring width (RW) and ring density (RD) of Masson pine. We selected 18 provenances at Chun'an (CA) and Taizi Mountain (TZS) test site, which representing four different breeding regions, including the south, west, north and east-central regions. The results showed that the provenance effects were significantly for the RW and RD. The provenances from high temperature and low latitude regions had greater mean RW compared to species from local and cold sources. The geographical genetic variation in wood traits is generally weak. The correlation between RW of Masson pine and precipitation was stronger in the relatively arid TZS site compared with that in relatively wet CA site, as well as the effect of temperature and precipitation on RD was earlier than that in CA test site. The response relationship between establishing the width of tree rings and the environmental variables of provenance indicated that during the transition from the northern and western breeding regions to the eastern and southern breeding regions, the response of RW to climate factors changed from being temperature-based to being precipitation-based. In addition, the response of provenance to the climate of seed sources origin showed their own variation characteristics in each breeding area. Therefore, genetic improvement of big diameter wood and wood density can be gain through selection of provenance and analysis of adaptability.

Classification of gait patterns between patients with Parkinson's disease and healthy controls using phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks.

Parkinson's disease (PD) is a common neurodegenerative disorder that affects human's quality of life, especially leading to locomotor deficits such as postural instability and gait disturbances. Gait signal is one of the best features to characterize and detect movement disorders caused by a malfunction in parts of the brain and nervous system of the patients with PD. Various classification approaches using spatiotemporal gait variables have been presented earlier to classify Parkinson's gait. In this study we propose a novel method for gait pattern classification between patients with PD and healthy controls, based upon phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks. First, vertical ground reaction forces (GRFs) at specific positions of human feet are captured and then phase space is reconstructed. The properties associated with the gait system dynamics are preserved in the reconstructed phase space. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been used. These measured parameters demonstrate significant difference in gait dynamics between the two groups and have been utilized to form a reference variable set. Second, reference variables are decomposed into Intrinsic Mode Functions (IMFs) using EMD, and the third IMFs are extracted and served as gait features. Third, neural networks are then used as the classifier to distinguish between patients with PD and healthy controls based on the difference of gait dynamics preserved in the gait features between the two groups. Finally, experiments are carried out on 93 PD patients and 73 healthy subjects to assess the effectiveness of the proposed method. By using 2-fold, 10-fold and leave-one-out cross-validation styles, the correct classification rates are reported to be 91.46%, 96.99% and 98.80%, respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic and non-invasive classification between patients with PD and healthy subjects.

Adaptive Neural Control of Underactuated Surface Vessels With Prescribed Performance Guarantees.

This paper presents adaptive neural tracking control of underactuated surface vessels with modeling uncertainties and time-varying external disturbances, where the tracking errors consisting of position and orientation errors are required to keep inside their predefined feasible regions in which the controller singularity problem does not happen. To provide the preselected specifications on the transient and steady-state performances of the tracking errors, the boundary functions of the predefined regions are taken as exponentially decaying functions of time. The unknown external disturbances are estimated by disturbance observers and then are compensated in the feedforward control loop to improve the robustness against the disturbances. Based on the dynamic surface control technique, backstepping procedure, logarithmic barrier functions, and control Lyapunov synthesis, singularity-free controllers are presented to guarantee the satisfaction of predefined performance requirements. In addition to the nominal case when the accurate model of a marine vessel is known a priori, the modeling uncertainties in the form of unknown nonlinear functions are also discussed. Adaptive neural control with the compensations of modeling uncertainties and external disturbances is developed to achieve the boundedness of the signals in the closed-loop system with guaranteed transient and steady-state tracking performances. Simulation results show the performance of the vessel control systems.

Distributed model reference adaptive containment control of heterogeneous uncertain multi-agent systems.

In this paper, a distributed model reference adaptive control (MRAC) design framework is proposed for containment control of heterogeneous uncertain multi-agent systems (MAS). Both groups of leaders and followers are considered to have general linear dynamics with the leaders subject to bounded external inputs and the followers subject to uncertain system dynamics. Two distributed adaptive control protocols are developed under this framework. The first protocol assumes measurable leaders' input signals for a subset of the followers, and employs distributed observers with state-feedback adaptive controllers to achieve exact containment control performance. The second protocol incorporates robust adaptive control with nonlinear compensator techniques to handle a more challenging scenario of unmeasurable bounded leaders' inputs. Convergence of the containment control errors to an arbitrarily adjustable neighborhood of the origin is guaranteed with the second protocol. The proposed MRAC framework provides a promising alternative solution over the prevailing cooperative output regulation framework for heterogeneous linear MAS containment control. It enables us to handle more general system settings under more stringent control environments with limited accessibility of leaders' information and uncertain follower dynamics. Effectiveness and usefulness of the proposed approaches are demonstrated through extensive simulation studies, including an application to containment control of multiple nonholonomic mobile robots.