BMSeNet: Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network for Real-Time Semantic Segmentation.

Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network's receptive field. Concurrently, feature information extraction is restricted to a single scale, which reduces the network's ability to generalize and maintain robustness. Furthermore, loss of image spatial details negatively impacts segmentation accuracy. To address these limitations, this paper proposes a Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network (BMSeNet). First, to address the limitation of singular semantic feature scales, a Multiscale Context Pyramid Pooling Module (MSCPPM) is introduced. By leveraging various pooling operations, this module efficiently enlarges the receptive field and better aggregates multiscale contextual information. Moreover, a Spatial Detail Enhancement Module (SDEM) is designed, to effectively compensate for lost spatial detail information and significantly enhance the perception of spatial details. Finally, a Bilateral Attention Fusion Module (BAFM) is proposed. This module leverages pixel positional correlations to guide the network in assigning appropriate weights to the features extracted from the two branches, effectively merging the feature information of both branches. Extensive experiments were conducted on the Cityscapes and CamVid datasets. Experimental results show that the proposed BMSeNet achieves a good balance between inference speed and segmentation accuracy, outperforming some state-of-the-art real-time semantic segmentation methods.

Semantic Segmentation Network Based on Adaptive Attention and Deep Fusion Utilizing a Multi-Scale Dilated Convolutional Pyramid.

Deep learning has recently made significant progress in semantic segmentation. However, the current methods face critical challenges. The segmentation process often lacks sufficient contextual information and attention mechanisms, low-level features lack semantic richness, and high-level features suffer from poor resolution. These limitations reduce the model's ability to accurately understand and process scene details, particularly in complex scenarios, leading to segmentation outputs that may have inaccuracies in boundary delineation, misclassification of regions, and poor handling of small or overlapping objects. To address these challenges, this paper proposes a Semantic Segmentation Network Based on Adaptive Attention and Deep Fusion with the Multi-Scale Dilated Convolutional Pyramid (SDAMNet). Specifically, the Dilated Convolutional Atrous Spatial Pyramid Pooling (DCASPP) module is developed to enhance contextual information in semantic segmentation. Additionally, a Semantic Channel Space Details Module (SCSDM) is devised to improve the extraction of significant features through multi-scale feature fusion and adaptive feature selection, enhancing the model's perceptual capability for key regions and optimizing semantic understanding and segmentation performance. Furthermore, a Semantic Features Fusion Module (SFFM) is constructed to address the semantic deficiency in low-level features and the low resolution in high-level features. The effectiveness of SDAMNet is demonstrated on two datasets, revealing significant improvements in Mean Intersection over Union (MIOU) by 2.89% and 2.13%, respectively, compared to the Deeplabv3+ network.

YOLOv7-TS: A Traffic Sign Detection Model Based on Sub-Pixel Convolution and Feature Fusion.

In recent years, significant progress has been witnessed in the field of deep learning-based object detection. As a subtask in the field of object detection, traffic sign detection has great potential for development. However, the existing object detection methods for traffic sign detection in real-world scenes are plagued by issues such as the omission of small objects and low detection accuracies. To address these issues, a traffic sign detection model named YOLOv7-Traffic Sign (YOLOv7-TS) is proposed based on sub-pixel convolution and feature fusion. Firstly, the up-sampling capability of the sub-pixel convolution integrating channel dimension is harnessed and a Feature Map Extraction Module (FMEM) is devised to mitigate the channel information loss. Furthermore, a Multi-feature Interactive Fusion Network (MIFNet) is constructed to facilitate enhanced information interaction among all feature layers, improving the feature fusion effectiveness and strengthening the perception ability of small objects. Moreover, a Deep Feature Enhancement Module (DFEM) is established to accelerate the pooling process while enriching the highest-layer feature. YOLOv7-TS is evaluated on two traffic sign datasets, namely CCTSDB2021 and TT100K. Compared with YOLOv7, YOLOv7-TS, with a smaller number of parameters, achieves a significant enhancement of 3.63% and 2.68% in the mean Average Precision (mAP) for each respective dataset, proving the effectiveness of the proposed model.

Comparative study of the dynamics of colloidal glass and gel.

We investigate and compare the difference in the dynamics of two arrested states: colloidal glass and colloidal gel. Real-space experiments reveal two distinct nonergodicity origins for their slow dynamics, namely, cage effects for the glass and attractive bondings for the gel. Such distinct origins lead to a faster decay of the correlation function and a smaller nonergodicity parameter of the glass than those of the gel. We also find that the gel exhibits stronger dynamical heterogeneity compared with the glass due to the greater correlated motions in the gel. Moreover, a logarithmic decay in the correlation function is observed as the two nonergodicity origins merge, consistent with the mode coupling theory.

Complex and dynamic transcriptional changes allow the helminth Fasciola gigantica to adjust to its intermediate snail and definitive mammalian hosts.

BACKGROUND: The tropical liver fluke, Fasciola gigantica causes fasciolosis, an important disease of humans and livestock. We characterized dynamic transcriptional changes associated with the development of the parasite in its two hosts, the snail intermediate host and the mammalian definitive host. RESULTS: Differential gene transcription analysis revealed 7445 unigenes transcribed by all F. gigantica lifecycle stages, while the majority (n = 50,977) exhibited stage-specific expression. Miracidia that hatch from eggs are highly transcriptionally active, expressing a myriad of genes involved in pheromone activity and metallopeptidase activity, consistent with snail host finding and invasion. Clonal expansion of rediae within the snail correlates with increased expression of genes associated with transcription, translation and repair. All intra-snail stages (miracidia, rediae and cercariae) require abundant cathepsin L peptidases for migration and feeding and, as indicated by their annotation, express genes putatively involved in the manipulation of snail innate immune responses. Cercariae emerge from the snail, settle on vegetation and become encysted metacercariae that are infectious to mammals; these remain metabolically active, transcribing genes involved in regulation of metabolism, synthesis of nucleotides, pH and endopeptidase activity to assure their longevity and survival on pasture. Dramatic growth and development following infection of the mammalian host are associated with high gene transcription of cell motility pathways, and transport and catabolism pathways. The intra-mammalian stages temporally regulate key families of genes including the cathepsin L and B proteases and their trans-activating peptidases, the legumains, during intense feeding and migration through the intestine, liver and bile ducts. While 70% of the F. gigantica transcripts share homology with genes expressed by the temperate liver fluke Fasciola hepatica, gene expression profiles of the most abundantly expressed transcripts within the comparable lifecycle stages implies significant species-specific gene regulation. CONCLUSIONS: Transcriptional profiling of the F. gigantica lifecycle identified key metabolic, growth and developmental processes the parasite undergoes as it encounters vastly different environments within two very different hosts. Comparative analysis with F. hepatica provides insight into the similarities and differences of these parasites that diverged > 20 million years ago, crucial for the future development of novel control strategies against both species.

Proteomic analysis of Fasciola gigantica excretory and secretory products (FgESPs) interacting with buffalo serum of different infection periods by shotgun LC-MS/MS.

Fasciolosis, caused by Fasciola hepatica and Fasciola gigantica, is an important zoonotic disease in the world. It affects livestock, especially for sheep and cattle, causing major economic loss due to morbidity and mortality. Although the excretory and secretory products (ESPs) of F. hepatica have been relatively well studied, little is known about the interaction between the ESP and host, and the mechanism of the key proteins involved in interaction. In this study, buffaloes were infected by Fasciola gigantica, and infection serum was collected at three different periods (42dpi, 70dpi, and 98dpi). The interaction proteins were pulled down with three different period serum by Co-IP assay, respectively, and then identified by LC-MS/MS analysis. A number of proteins were identified; some of them related to the biological function of the parasite, while most of them the functions were unknown. For the annotated proteins, 13, 5, and 7 proteins were pulled down by the infected serum in 42dpi, 70dpi, and 98dpi, respectively, and 18 proteins could be detected in all three periods. Among them, 13 belong to the cathepsin family, 4 proteins related to glutathione S-transferase, and 3 proteins are calcium-binding protein; other proteins related to catalytic activity and cellular process. This study could provide new insights into the central role played by ESPs in the protection of F. gigantica from the host immune response. At the same time, our research provided material for further studies about the interaction between F. gigantica and host.

Vehicle Detection in Urban Traffic Surveillance Images Based on Convolutional Neural Networks with Feature Concatenation.

Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance. The difficulty of this task lies in the fact that it requires accurate localization of relatively small vehicles in complex scenes and expects real-time detection. In this paper, we present a vehicle detection framework that improves the performance of the conventional Single Shot MultiBox Detector (SSD), which effectively detects different types of vehicles in real-time. Our approach, which proposes the use of different feature extractors for localization and classification tasks in a single network, and to enhance these two feature extractors through deconvolution (D) and pooling (P) between layers in the feature pyramid, is denoted as DP-SSD. In addition, we extend the scope of the default box by adjusting its scale so that smaller default boxes can be exploited to guide DP-SSD training. Experimental results on the UA-DETRAC and KITTI datasets demonstrate that DP-SSD can achieve efficient vehicle detection for real-world traffic surveillance data in real-time. For the UA-DETRAC test set trained with UA-DETRAC trainval set, DP-SSD with the input size of 300 × 300 achieves 75.43% mAP (mean average precision) at the speed of 50.47 FPS (frames per second), and the framework with a 512 × 512 sized input reaches 77.94% mAP at 25.12 FPS using an NVIDIA GeForce GTX 1080Ti GPU. The DP-SSD shows comparable accuracy, which is better than those of the compared state-of-the-art models, except for YOLOv3.

Immune response and protective effect against chronic Toxoplasma gondii infection induced by vaccination with a DNA vaccine encoding profilin.

BACKGROUND: Toxoplasma gondii is an obligate intracellular parasite that can infect almost all warm-blooded animals. T. gondii profilin (TgPF) plays a crucial role in parasite motility and host cell invasion, and has shown promise against toxoplasmosis. DNA vaccine was considered to elicit effective humoral and cell-mediated immunity against T. gondii infection. The objective of the present study was to evaluate the immunogenicity of TgPF in mice using a DNA vaccination strategy. METHODS: A DNA vaccine (pVAX-PF) encoding TgPF gene was constructed and then was intramuscularly injected into mice with and without a plasmid encoding IL-15 (pVAX-IL-15). The immune responses in immunized Kunming mice including lymphocyte proliferation, levels of cytokines, antibody titers and T lymphocyte subclasses were analyzed. The protective efficacy against chronic T. gondii infection was observed at 4 weeks post-infection with the cyst-forming PRU strain of T. gondii (Genotype II). RESULTS: EitherpVAX-PF with or without pVAX-IL-15 could elicit higher level of IgG and IgG2a antibodies and produce strong cellular immune responses in the immunized mice. The brain cyst numbers in mice immunized with pVAX-PF + pVAX-IL-15 (1843 ± 215.7) and pVAX-PF (1897 ± 337.8) were reduced 40.82% and 39.08%, respectively, compared to that in mice received nothing (3114 ± 168.8), and the differences were statistically significant (P < 0.0001). However, the T. gondii cyst numbers in mice immunized with pVAX-PF + pVAX-IL-15 were not statistically significantly different compared to that in mice immunized with pVAX-PF alone [t(10) = 0.33, P > 0.05]. CONCLUSIONS: The present study indicated that TgPF could be a promising vaccine candidate against chronic toxoplasmosis, which can be further used to develop multi-epitope vaccine formulations in food-producing animals against T. gondii infection.

Characterization of the complete mitochondrial genome of Marshallagia marshalli and phylogenetic implications for the superfamily Trichostrongyloidea.

Marshallagia marshalli (Nematoda: Trichostrongylidae) infection can lead to serious parasitic gastroenteritis in sheep, goat, and wild ruminant, causing significant socioeconomic losses worldwide. Up to now, the study concerning the molecular biology of M. marshalli is limited. Herein, we sequenced the complete mitochondrial (mt) genome of M. marshalli and examined its phylogenetic relationship with selected members of the superfamily Trichostrongyloidea using Bayesian inference (BI) based on concatenated mt amino acid sequence datasets. The complete mt genome sequence of M. marshalli is 13,891 bp, including 12 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. All protein-coding genes are transcribed in the same direction. Phylogenetic analyses based on concatenated amino acid sequences of the 12 protein-coding genes supported the monophylies of the families Haemonchidae, Molineidae, and Dictyocaulidae with strong statistical support, but rejected the monophyly of the family Trichostrongylidae. The determination of the complete mt genome sequence of M. marshalli provides novel genetic markers for studying the systematics, population genetics, and molecular epidemiology of M. marshalli and its congeners.

Structural maintenance of chromosomes (SMC) proteins are required for DNA elimination in Paramecium.

Chromosome (SMC) proteins are a large family of ATPases that play important roles in the organization and dynamics of chromatin. They are central regulators of chromosome dynamics and the core component of condensin. DNA elimination during zygotic somatic genome development is a characteristic feature of ciliated protozoa such as Paramecium This process occurs after meiosis, mitosis, karyogamy, and another mitosis, which result in the formation of a new germline and somatic nuclei. The series of nuclear divisions implies an important role of SMC proteins in Paramecium sexual development. The relationship between DNA elimination and SMC has not yet been described. Here, we applied RNA interference, genome sequencing, mRNA sequencing, immunofluorescence, and mass spectrometry to investigate the roles of SMC components in DNA elimination. Our results show that SMC4-2 is required for genome rearrangement, whereas SMC4-1 is not. Functional diversification of SMC4 in Paramecium led to a formation of two paralogues where SMC4-2 acquired a novel, development-specific function and differs from SMC4-1. Moreover, our study suggests a competitive relationship between these two proteins.

Dynamic learning from adaptive neural control for full-state constrained strict-feedback nonlinear systems.

This study focuses on the learning and control issues of strict-feedback systems with full-state constraints. To achieve learning capability under constraints, transformation mapping is utilized to convert the original system with full-state constraints into a quasi-pure-feedback unconstrained system. Utilizing the system transformation technique, only a single neural network (NN) is required to identify the unknown dynamics within the transformed system. Combining the dynamic surface control design, a novel adaptive neural control scheme is developed to ensure that all closed-loop signals are uniformly bounded, and every system state remains within the predefined constraint range. In addition, the precise convergence of NN weights is further transformed into an exponential stability problem for a category of linear time-varying systems under persistent excitation conditions. Subsequently, the converged NN weights are efficiently stored and utilized to create a learning controller to achieve better control performance while abiding by the full-state constraints. The viability of this control strategy is demonstrated via simulations.

Deterministic learning-based neural identification and knowledge fusion.

Recent deterministic learning methods have achieved locally-accurate identification of unknown system dynamics. However, the locally-accurate identification means that the neural networks can only capture the local dynamics knowledge along the system trajectory. In order to capture a broader knowledge region, this article investigates the knowledge fusion problem of deterministic learning, that is, the integration of different knowledge regions along different individual trajectories. Specifically, two kinds of knowledge fusion schemes are systematically introduced: an online fusion scheme and an offline fusion scheme. The online scheme can be viewed as an extension of distributed cooperative learning control to cooperative neural identification for sampled-data systems. By designing an auxiliary information transmission strategy to enable the neural network to receive information learned from other tasks while learning its own task, it is proven that the weights of all localized RBF networks exponentially converge to their common true/ideal values. The offline scheme can be regarded as a knowledge distillation strategy, in which the fused network is obtained by offline training through the knowledge learned from all individual system trajectories via deterministic learning. A novel weight fusion algorithm with low computational complexity is proposed based on the least squares solution under subspace constraints. Simulation studies show that the proposed fusion schemes can successfully integrate the knowledge regions of different individual trajectories while maintaining the learning performance, thereby greatly expanding the knowledge region learned from deterministic learning.

An interpretable ensemble trees method with joint analysis of static and dynamic features for myocardial infarction detection.

Objective.In recent years, artificial intelligence-based electrocardiogram (ECG) methods have been massively applied to myocardial infarction (MI). However, the joint analysis of static and dynamic features to achieve accurate and interpretable MI detection has not been comprehensively addressed.Approach.This paper proposes a simplified ensemble tree method with a joint analysis of static and dynamic features to solve this issue for MI detection. Initially, the dynamic features are extracted by modeling the intrinsic dynamics of ECG via dynamic learning in addition to extracting classical static features. Secondly, a two-stage feature selection strategy is designed to identify a few significant features, which substitute the original variables that are employed in constructing the ensemble tree. This approach enhances the discriminative ability by selecting significant static and dynamic features. Subsequently, this paper presents an interpretable classification method named StackTree by introducing a stacked ensemble scheme to modify the ensemble tree simplification algorithm. The representative rules of the raw ensemble trees are selected as the intermediate training data that is used to retrain a decision tree with performance close to that of the source ensemble model. Using this scheme, the significant precision and interpretability of MI detection are thus comprehensively addressed.Main results.The effectiveness of our method in detecting MI is evaluated using the Physikalisch-Technische Bundesanstalt (PTB) and clinical database. The findings suggest that our algorithm outperforms the traditional methods based on a single type of feature. Additionally, it is comparable to the conventional random forest, achieving 97.1% accuracy under the inter-patient framework on the PTB database. Furthermore, feature subsets trained on PTB are validated using the clinical database, resulting in an accuracy of 84.5%. The chosen important features demonstrate that both static and dynamic information have crucial roles in MI detection. Crucially, the proposed method provides clear internal workings in an easy-to-understand visual manner.

An interpretable shapelets-based method for myocardial infarction detection using dynamic learning and deep learning.

Objective.Myocardial infarction (MI) is a prevalent cardiovascular disease that contributes to global mortality rates. Timely diagnosis and treatment of MI are crucial in reducing its fatality rate. Currently, electrocardiography (ECG) serves as the primary tool for clinical diagnosis. However, detecting MI accurately through ECG remains challenging due to the complex and subtle pathological ECG changes it causes. To enhance the accuracy of ECG in detecting MI, a more thorough exploration of ECG signals is necessary to extract significant features.Approach.In this paper, we propose an interpretable shapelet-based approach for MI detection using dynamic learning and deep learning. Firstly, the intrinsic dynamics of ECG signals are learned through dynamic learning. Then, a deep neural network is utilized to extract and select shapelets from ECG dynamics, which can capture locally specific ECG changes, and serve as discriminative features for identifying MI patients. Finally, the ensemble model for MI detection is built by integrating shapelets of multi-dimensional ECG dynamic signals.Main results.The performance of the proposed method is evaluated on the public PTB dataset with accuracy, sensitivity, and specificity of 94.11%, 94.97%, and 90.98%.Significance.The shapelets obtained in this study exhibit significant morphological differences between MI and healthy subjects.

Deterministic learning-based neural control for output-constrained strict-feedback nonlinear systems.

This paper studies learning from adaptive neural control of output-constrained strict-feedback uncertain nonlinear systems. To overcome the constraint restriction and achieve learning from the closed-loop control process, there are several significant steps. Firstly, a state transformation is introduced to convert the original constrained system output into an unconstrained one. Then an equivalent n-order affine nonlinear system is constructed based on the transformed unconstrained output state in norm form by the system transformation method. By combining dynamic surface control (DSC) technique, an adaptive neural control scheme is proposed for the transformed system. Then all closed-loop signals are uniformly ultimately bounded and the system output tracks the expected trajectory well with satisfying the constraint requirement. Secondly, the partial persistent excitation condition of the radial basis function neural network (RBF NN) could be verified to achieve. Therefore, the uncertain dynamics can be precisely approximated by RBF NN. Subsequently, the learning ability of RBF NN is achieved, and the knowledge acquired from the neural control process is stored in the form of constant neural networks (NNs). By reutilizing the knowledge, a novel learning controller is established to improve the control performance when facing the similar or same control task. The proposed learning control (LC) scheme can avoid repeating the online adaptation of neural weight estimates, which saves computing resources and improves transient performance. Meanwhile, the LC method significantly raises the tracking accuracy and the speed of error convergence while satisfying of the constraint condition simultaneously. Simulation studies demonstrate the efficiency of this proposed control scheme.

New Results on Rapid Dynamical Pattern Recognition via Deterministic Learning From Sampling Sequences.

Rapid dynamical pattern recognition based on the deterministic learning method (DLM-based RDPR) aims to rapidly recognize the most similar dynamical pattern pair from perspectives of differences in inherent system dynamics. The basic mechanism is to use available recognition errors to reflect the differences in the dynamics of dynamical pattern pairs and then to make a decision based on a minimal recognition error (MRE) principle. This article focuses on providing a rigorous theoretical analysis of the MRE principle in DLM-based RDPR under the sampled-data framework. Specifically, we seek a unified methodology from the similarity definition to the measure implementation and then to derive general sufficient conditions and necessary conditions for the MRE principle. The main idea is to: 1) from the average signal energy aspect, define a time-dependent dynamics-based similarity in dynamical pattern pairs and reestablish the measure of recognition errors generated from the DLM-based RDPR; 2) introduce the energy-based Lyapunov method to establish the interrelation between the dynamical distance and the recognition error; and 3) derive sufficient conditions and necessary conditions from two directions of the interrelation. The proposed conditions distinguish themselves from virtually all of the existing DLM-based RDPR works with only sufficient conditions in the sense that it is shown in a rigorous analysis that under what conditions, the pattern pair recognized based on the MRE principle is indeed the most similar one. Therefore, the proposed work makes the DLM-based RDPR possess good interpretability and provides strong theoretical guidance in engineering applications.

Observer-based dynamical pattern recognition via deterministic learning.

In this paper, based on the sampled-data observer and the deterministic learning theory, a rapid dynamical pattern recognition approach is proposed for univariate time series composed of the output signals of the dynamical systems. Specifically, locally-accurate identification of inherent dynamics of univariate time series is first achieved by using the sampled-data observer and the radial basis function (RBF) networks. The dynamical estimators embedded with the learned knowledge are then designed by resorting to the sampled-data observer. It is proved that generated estimator residuals can reflect the difference between the system dynamics of the training and test univariate time series. Finally, a recognition decision-making scheme is proposed based on the residual norms of the dynamical estimators. Through rigorous analysis, recognition conditions are given to guarantee the accurate recognition of the dynamical pattern of the test univariate time series. The significance of this paper lies in that the difficult problems of dynamical modeling and rapid recognition for univariate time series are solved by incorporating the sampled-data observer design and the deterministic learning theory. The effectiveness of the proposed approach is confirmed by a numerical example and compressor stall warning experiments.

A dynamic learning-based ECG feature extraction method for myocardial infarction detection.

Objective.Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight.Approach.To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set.Main results.Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%.Significance.The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.

Crushing Characteristics Comparison between Aluminum/CFRP and Aluminum/CFRP/Aluminum Hybrid Tubes.

This study experimentally investigated the axial crushing characteristics of the hybrid tubes with the configuration of aluminum/carbon fiber-reinforced polymer (CFRP) (1/1) and aluminum/CFRP/aluminum (2/1). The effects of geometry size and fiber lay-up sequence on the axial crushing energy-absorption performances and failure modes of the two types of hybrid tubes were compared. The results showed that the energy absorption of the specimens with [0°/90°] lay-up sequence was better than that of the ones with [45°/-45°] lay-up sequence for both types of hybrid tubes. The proper length of the tubes should be selected to avoid too small a length-to-diameter ratio so that a stable and controllable progressive crushing failure mode can be achieved. When the crushing failure process was relatively stable, the specific energy absorption and crushing force efficiency of the 2/1 hybrid tubes were not affected by the geometric size. The energy absorption of the hybrid tubes was higher than the sum of the energy absorption of all the corresponding individual tubes, showing a positive hybrid effect.

Transcriptomic landscape of hepatic lymph nodes, peripheral blood lymphocytes and spleen of swamp buffaloes infected with the tropical liver fluke Fasciola gigantica.

The tropical liver fluke Fasciola gigantica is a parasitic helminth that has been frequently reported to infect mammals, typically involving water buffaloes. In this study, we characterized the tissue transcriptional landscape of buffaloes following infection by F. gigantica. RNAs were isolated from hepatic lymph nodes (hLNs), peripheral blood lymphocytes (pBLs), and spleen at 3-, 42- and 70-days post-infection (dpi), and all samples were subjected to RNA sequencing analyses. At 3 dpi, 2603, 460, and 162 differentially expressed transcripts (DETs) were detected in hLNs, pBLs, and spleen, respectively. At 42 dpi, 322, 937, and 196 DETs were detected in hLNs, pBLs, and spleen, respectively. At 70 dpi, 376, 334, and 165 DETs were detected in hLNs, pBLs, and spleen, respectively. Functional enrichment analysis identified upregulated immune-related pathways in the infected tissues involved in innate and adaptive immune responses, especially in hLNs at 42 and 70 dpi, and pBLs at 3 and 42 dpi. The upregulated transcripts in spleen were not enriched in any immune-related pathway. Co-expression network analysis further identified transcriptional changes associated with immune response to F. gigantica infection. Receiver operating characteristic (ROC) curve analysis showed that 107 genes in hLNs, 32 genes in pBLs, and 36 genes in spleen correlated with F. gigantica load. These findings provide new insight into molecular mechanisms and signaling pathways associated with F. gigantica infection in buffaloes.