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.

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.

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.

Modeling and classification of gait patterns between anterior cruciate ligament deficient and intact knees based on phase space reconstruction, Euclidean distance and neural networks.

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[Formula: see text] and 95.65[Formula: see text], 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.

Chromolaena odorata affects soil nitrogen transformations and competition in tropical coral islands by altering soil ammonia oxidizing microbes.

Invasive plants can change the community structure of soil ammonia-oxidizing microbes, affect the process of soil nitrogen (N) transformation, and gain a competitive advantage. However, the current researches on competition mechanism of Chromolaena odorata have not involved soil nitrogen transformation. In this study, we compared the microbially mediated soil transformations of invasive C. odorata and natives (Pisonia grandis and Scaevola taccada) of tropical coral islands. We assessed how differences in plant biomass and tissue N contents, soil nutrients, N transformation rates, microbial biomass and activity, and diversity and abundance of ammonia oxidizing microbes associated with these species impact their competitiveness. The results showed that C. odorata outcompeted both native species by allocating more proportionally biomass to aboveground parts in response to interspecific competition (12.92 % and 22.72 % more than P. grandis and S. taccada, respectively). Additionally, when C. odorata was planted with native plants, the available N and net mineralization rates in C. odorata rhizosphere soil were higher than in native plants rhizosphere soils. Higher abundance of ammonia-oxidizing bacteria in C. odorata rhizosphere soil confirmed this, being positively correlated with soil N mineralization rates and available N. Our findings help to understand the soil N acquisition and competition strategies of C. odorata, and contribute to improving evaluations and predictions of invasive plant dynamics and their ecological effects in tropical coral islands.

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.

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.

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.

Acidic "Water-in-Salt" Electrolyte Enables a High-Energy Symmetric Supercapacitor Based on Titanium Carbide MXene.

Titanium carbide MXene, Ti3C2Tx, exhibits ultrahigh capacitance in acidic electrolytes at negative potentials yet poor stability at positive potentials, resulting in low-energy densities for Ti3C2Tx-based symmetric supercapacitors. Utilizing "water-in-salt" electrolytes has successfully expanded the stable operation potential window of MXenes. However, this advancement comes at the cost of sacrificing their high capacitance in acidic electrolytes. In this work, we report an acidic "water-in-salt" (AWIS) electrolyte composed of sulfuric acid and saturated lithium halide, which effectively doubled the energy density of the Ti3C2Tx-based symmetric supercapacitor compared to those with bare acidic electrolytes. Specifically, the AWIS electrolyte successfully expanded the voltage window of the symmetric device to 1.1 V. A high specific capacitance of 112.34 F g-1 (at 10 mV s-1) was obtained due to the presence of proton redox. As a result, the symmetric device achieved a high-energy density of 19.1 Wh kg-1 and a high capacitance retention of 96.3% after 10,000 cycles. This work demonstrates the importance of designing stable and redox-active electrolytes for high-energy MXene-based symmetric supercapacitors.

Fine genetic mapping and transcriptomic analysis revealed major gene modulating the clear stripe margin pattern of watermelon peel.

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.

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.

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.

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.

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.

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.

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.

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.

Co-transfection and tandem transfection of HEK293A cells for overexpression and RNAi experiments.

pIRES2-EGFP was employed and a non-target shRNA expressing plasmid was constructed to simulate overexpression and RNAi (RNA interference) experiments. Transfection of pIRES2-EGFP into HEK293A cells by cationic lipids VigoFect demonstrated that transfection efficiency increased in a dose-dependent manner with amount of DNA plasmid used, and optimal transfection time and cell density should be identified to reach a compromise of higher transfection efficiency and lower toxicity. Co-transfection experiments indicated that the two co-transfected plasmids were equivalently delivered into the same cells, and the co-transfection efficiency was rarely affected by cell density and proportion of the two plasmids. However, plasmid-receipted cells seemed indisposed to accept plasmid again during the second transfection, and very low co-transfection efficiency was observed in tandem transfection.

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.

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.