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The peel stripe margin pattern is one of the most important quality traits of watermelon. In this study, two contrasted watermelon lines [slb line (P1) with a clear peel stripe margin pattern and GWAS-38 line (P2) with a blurred peel stripe margin pattern] were crossed, and biparental F2 mapping populations were developed. Genetic segregation analysis revealed that a single recessive gene is modulating the main-effect genetic locus (Clcsm) of the clear stripe margin pattern of peel. Bulked segregant analysis-based sequencing (BSA-Seq) and fine genetic mapping exposed the delimited Clcsm locus to a 19.686-kb interval on chromosome 6, and the Cla97C06G126680 gene encoding the MYB transcription factor family was identified. The gene mutation analysis showed that two non-synonymous single-nucleotide polymorphism (nsSNP) sites [Chr6:28438793 (A-T) and Chr6:28438845 (A-C)] contribute to the clear peel stripe margin pattern, and quantitative real-time polymerase chain reaction (qRT-PCR) also showed a higher expression trend in the slb line than in the GWAS-38 line. Further, comparative transcriptomic analysis identified major differentially expressed genes (DEGs) in three developmental periods [4, 12, and 20 days after pollination (DAP)] of both parental lines. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses indicated highly enriched DEGs involved in metabolic processes and catalytic activity. A total of 44 transcription factor families and candidate genes belonging to the ARR-B transcription factor family are believed to regulate the clear stripe margin trait of watermelon peel. The gene structure, sequence polymorphism, and expression trends depicted significant differences in the peel stripe margin pattern of both parental lines. The ClMYB36 gene showed a higher expression trend for regulating the clear peel stripe margin of the slb line, and the ClAPRR5 gene depicted a higher expression for modulating the blurred peel stripe margin in the GWAS-38 line. Overall, our fine genetic mapping and transcriptomic analysis revealed candidate genes differentiating the clear and blurred peel stripe patterns of watermelon fruit.

Abstract Myocardial infarction (MI) poses a significant clinical challenge, necessitating expeditious and precise detection to mitigate potentially fatal outcomes. Current MI diagnosis predominantly relies on electrocardiography (ECG); however, it is fraught with limitations, including inter-observer variability and a reliance on expert interpretation. This study introduces an automated MI detection framework that capitalizes on hybrid signal processing methodologies and deterministic learning theory. The initial step involves the extraction of the Shannon energy envelope (SEE) and its derivative from a single-lead ECG. Integration of the SEE into the ECG’s phase portrait provides a means to capture the underlying nonlinear system dynamics. Subsequently, the application of fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) yields discriminative features originating from the most energetically dominant intrinsic mode components (IMFs) within the SEE. Profound dissimilarities are discernible between ECG signals recorded from healthy subjects and those afflicted with MI. In the subsequent phase, deterministic learning theory, implemented through neural networks, is employed to facilitate the classification of ECG signals into two distinct groups. The method’s efficacy is meticulously evaluated using the PTB diagnostic ECG database, resulting in a noteworthy average classification accuracy of 99.21 $$\%$$ % within a tenfold cross-validation framework. In summation, the findings affirm that the proposed features not only complement conventional ECG attributes but also align closely with the underlying dynamics of the ECG system, ultimately fortifying the automatic detection of MI. The imperative requirement for early and accurate MI diagnosis is addressed through our approach, offering a robust and dependable means to fulfill this pivotal clinical need.

The marine ecosystem is one of the most extensive and abundant ecosystems on Earth. Marine plankton is an important component, and its abundance, number of species, and dominant species are regarded as important monitoring indicators. Aiming at the problems of low accuracy and high complexity in identifying plankton based on convolutional neural networks, this study proposes a lightweight identification algorithm for plankton images based on the improved MobileNetV2. Firstly, the network layer structure is extracted by redesigning features to balance the depth and width of the network to reduce the model parameters; secondly, the lightweight coordinate attention (CA) mechanism is introduced to strengthen the attention and extraction ability of key areas; in addition, the structure of the network classifier is optimized to improve the utilization efficiency of the model parameters. The results show that the model achieves a 95.46% accuracy and 94.48% recall in 12 kinds of images. Compared with the initial MobileNetV2, the parameters and calculation amount are reduced by 72.47% and 52.09%, respectively, and the reasoning time for a single image is 6.15 ms. The model realizes the accurate identification of plankton in situ under the premise of ensuring it is lightweight. Combining time information and depth data, it is of great significance for marine ecological environment monitoring and prediction to obtain the abundance of various plankton.

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.

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.

IntroductionIn 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.MethodsInspired 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.ResultsThe 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.DiscussionThe thought of this article will also bring inspiration to the related research in other fields.

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.

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.

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.

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.

In this paper, a new concept of “similar fault” is introduced to the field of fault isolation (FI) of discrete-time nonlinear uncertain systems, which defines a new and important class of faults that have small mutual differences in fault magnitude and fault-induced system trajectories. Effective isolation of such similar faults is rather challenging as their small mutual differences could be easily concealed by other system uncertainties (e.g., modeling uncertainty/disturbances). To this end, a novel similar fault isolation (sFI) scheme is proposed based on an adaptive threshold mechanism. Specifically, an adaptive dynamics learning approach based on the deterministic learning theory is first introduced to locally accurately learn/identify the uncertain system dynamics under each faulty mode using radial basis function neural networks (RBF NNs). Based on this, a bank of sFI estimators are then developed using a novel mechanism of absolute measurement of fault dynamics differences. The resulting residual signals can be used to effectively capture the small mutual differences of similar faults and distinguish them from other system uncertainties. Finally, an adaptive threshold is designed for real-time sFI decision making. One important feature of the proposed sFI scheme is that: it is capable of not only isolating similar faults that belong to a pre-defined fault set (used in the training/learning process), but also identifying new faults that do not match any pre-defined faults. Rigorous analysis on isolatability conditions and isolation time is conducted to characterize the performance of the proposed sFI scheme. Simulation results on a practical application example of a single-link flexible joint robot arm are used to show the effectiveness and advantages of the proposed scheme over existing approaches.

This paper presents a novel frequency-domain approach to reveal the exact range of the imaginary spectra and the stability of linear time-invariant systems with two delays. First, an exact relation, i.e., the Rekasius substitution, is used to replace the exponential term caused by the delays in order to transform the transcendental characteristic equation to a quasi-polynomial. Second, this quasi-polynomial is uniquely tackled by our proposed Dixon resultant and discriminant theory, leading to the elimination of delay-related elements and the revelation of the exact range of the frequency spectra of the original system of interest. Then, by sweeping the frequency over this obtained range, the stability switching curves are declared exhaustively. Last, we deploy the cluster treatment of characteristic roots (CTCR) paradigm to reveal the exact and complete stability map. The proposed methodologies are tested and verified by a numerical method called Quasi-Polynomial mapping-based Root finder (QPmR) over an example case.

This paper deals with the problem of leaderless consensus control for a class of non-introspective linear multi-agent systems (MASs) subject to input saturation and external disturbances. A novel dynamic relative output-feedback saturated consensus control protocol is proposed, which utilizes not only relative system outputs but also relative controller states from neighboring agents for distributed feedback control. With this new controller structure, the associated control synthesis conditions that guarantee optimal disturbance attenuation performance are fully characterized as linear matrix inequalities (LMIs) using a complete form of Lyapunov function matrix, which can be solved efficiently via convex optimization. The proposed approach unifies the designs for both continuous-time and discrete-time MASs. Two application examples are used to demonstrate effectiveness and usefulness of the proposed results.

Abstract Background The anterior cruciate ligament (ACL) plays an important role in stabilizing translation and rotation of the tibia relative to the femur. ACL injury alters knee kinematics and usually links to the alternation of gait patterns. The aim of this study is to develop a new method to distinguish between gait patterns of patients with anterior cruciate ligament deficient (ACL-D) knees and healthy controls with ACL-intact (ACL-I) knees based on nonlinear features and neural networks. Therefore ACL injury will be automatically and objectively detected. Methods First knee rotation and translation parameters are extracted and phase space reconstruction (PSR) is employed. The properties associated with the gait system dynamics are preserved in the reconstructed phase space. For the purpose of classification of ACL-D and ACL-I knee gait patterns, three-dimensional (3D) PSR together with Euclidean distance computation has been used. These measured parameters show significant difference in gait dynamics between the two groups and have been utilized to form a feature set. Neural networks are then constructed to identify gait dynamics and are utilized as the classifier to distinguish between ACL-D and ACL-I knee gait patterns based on the difference of gait dynamics between the two groups. Results Experiments are carried out on a database containing 18 patients with ACL injury and 28 healthy controls to assess the effectiveness of the proposed method. By using the twofold and leave-one-subject-out cross-validation styles, the correct classification rates for ACL-D and ACL-I knees are reported to be 91.3$$\%$$ % and 95.65$$\%$$ % , respectively. Conclusion Compared with other state-of-the-art methods, the results demonstrate that gait alterations in the presence of ACL deficiency can be detected with superior performance. The proposed method is a potential candidate for the automatic and non-invasive classification between patients with ACL deficiency and healthy subjects.