Signal Recognition Research Articles

Multiple Self-Powered Sensor-Integrated Mobile Manipulator for Intelligent Environment Detection.

A multiple self-powered sensor-integrated mobile manipulator (MSIMM) system was proposed to address challenges in existing exploration devices, such as the need for a constant energy supply, limited variety of sensed information, and difficult human-computer interfaces. The MSIMM system integrates triboelectric nanogenerator (TENG)-based self-powered sensors, a bionic manipulator, and wireless gesture control, enhancing sensor data usability through machine learning. Specifically, the system includes a tracked vehicle platform carrying the manipulator and electronics, including a storage battery and a microcontroller unit (MCU). An integrated sensor glove and terminal application (APP) enable intuitive manipulator control, improving human-computer interaction. The system responds to and analyzes various environmental stimuli, including the droplet and fall height, temperature, pressure, material type, angles, angular velocity direction, and acceleration amplitude and direction. The manipulator, fabricated using 3D printing technology, integrates multiple sensors that generate electrical signals through the triboelectric effect of mechanical motion. These signals are classified using convolutional neural networks for accurate environmental monitoring. Our database shows signal recognition and classification accuracy exceeding 94%, with specific accuracies of 100% for pressure sensors, 99.55% for angle sensors, and 98.66, 95.91, 96.27, and 94.13% for material, droplet, temperature, and acceleration sensors, respectively.

Research on a Method for Classifying Bolt Corrosion Based on an Acoustic Emission Sensor System.

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions.

Deciphering the genetic impact of signal peptide missense CTLA-4 polymorphism with rheumatoid arthritis in the Indian population: A case-control and in silico studies

Cytoplasmic T Lymphocyte Antigen-4 (CTLA-4) gene encodes for a glycoprotein, expressed on activated T-cells to transfer an inhibitory signal to control T-cell activation and proliferation. Techniques coupled with Real-time Polymerase Chain Reaction (PCR) and High-Resolution Melting Analysis (HRMA) were used to screen a missense signal peptide polymorphism (CTLA-4 + 49 A/G rs231775) in the Indian population to detect its association with Rheumatoid Arthritis (RA). Further, the resulting outcome was confirmed by Sanger’s sequencing technique, and genotype frequencies were calculated. In eukaryotic cells, the M domain of the Signal Recognition Particle (SRP-54) recognizes the N-terminal region of the Signal Peptide (SP) sequence. SP directs the polypeptide chain into the Sec-61 translocon of the Endoplasmic Reticulum (ER) for further protein modification. As the Single Nucleotide Polymorphism (SNP) rs231775 lies in the signal peptide region of CTLA-4, an in-silico study was also performed to predict the mRNA stability and SP-SRP protein interaction. From the study, it was observed that the genotype frequency of rs231775 SNP G/G homozygous dominant was significantly higher in RA patients than G/A heterozygous dominant and A/A homozygous recessive conditions (Odd Ratio (OR) = 2.0862; 95 % Confidence Interval (C.I) = 1.2584 to 3.4584; Relative Risk (RR) = 1.8507; p = 0.0044). Moreover, the rs231775 SNP G allele frequency was higher in RA than the control group G = 0.407 (40.7 %) vs 0.32 (32 %). In silico approaches of Protein-Protein docking and Molecular Dynamics (MD) simulation reveal CTLA-4 rs231775 SNP (G allele) has destabilized the SP-SRP protein complex, which may affect the translocation of CTLA-4 nascent polypeptide chains into the ER via activating Regulation of Aberrant Protein Production (RAPP) pathway.

Feature analysis and recognition of fiber breakage AE signals after propagation

Acoustic emission (AE) is a non-destructive testing technique, and establishing correlations between AE signals and material damage modes is one of its primary challenges. However, it is difficult to identify damage modes in ceramic matrix composites (CMCs) due to AE signal attenuation occurring after propagation and complex damage modes. In this study, AE signals generated by the breakage of C and SiC fibers were monitored at different distances and angles on the C/SiC plate. The attenuation of energy and the frequency spectra were analyzed. The Mel-frequency cepstral coefficient (MFCC) method was used to analyze the waveform data of AE signals and extract MFCC features. To identify the damage caused by C and SiC fiber breakage, AE parameter features and MFCC features were selected as inputs, and a fully connected neural network was constructed to train a supervised pattern recognition model. The results show that the MFCC feature has higher recognition accuracy than the traditional feature when AE is used for damage identification.

Interactive Virtual Ankle Movement Controlled by Wrist sEMG Improves Motor Imagery: An Exploratory Study.

Virtual reality (VR) techniques can significantly enhance motor imagery training by creating a strong illusion of action for central sensory stimulation. In this article, we establish a precedent by using surface electromyography (sEMG) of contralateral wrist movement to trigger virtual ankle movement through an improved data-driven approach with a continuous sEMG signal for fast and accurate intention recognition. Our developed VR interactive system can provide feedback training for stroke patients in the early stages, even if there is no active ankle movement. Our objectives are to evaluate: 1) the effects of VR immersion mode on body illusion, kinesthetic illusion, and motor imagery performance in stroke patients; 2) the effects of motivation and attention when utilizing wrist sEMG as a trigger signal for virtual ankle motion; 3) the acute effects on motor function in stroke patients. Through a series of well-designed experiments, we have found that, compared to the 2D condition, VR significantly increases the degree of kinesthetic illusion and body ownership of the patients, and improves their motor imagery performance and motor memory. When compared to conditions without feedback, using contralateral wrist sEMG signals as trigger signals for virtual ankle movement enhances patients' sustained attention and motivation during repetitive tasks. Furthermore, the combination of VR and feedback has an acute impact on motor function. Our exploratory study suggests that the sEMG-based immersive virtual interactive feedback provides an effective option for active rehabilitation training for severe hemiplegia patients in the early stages, with great potential for clinical application.

An electrochemical sensor based on 2D Zn-MOFs and 2D C-Ti3C2Tx composite materials for rapid and direct detection of various foodborne pathogens

Rapid screening for foodborne pathogens is crucial for food safety. A rapid and one-step electrochemical sensor has been developed for the detection of Escherichia coli (E. coli), Staphylococcus aureus (S. aureus) and Salmonella typhimurium (S. typhimurium). Through the construction of aptamer/two-dimensional carboxylated Ti3C2Tx (2D C-Ti3C2Tx)/two-dimensional Zn-MOF (2D Zn-MOF) composites, the recognition elements, signal tags, and signal amplifiers are integrated on the electrode surface. Pathogens are selectively captured using the aptamer, which increases the impedance of the electrode surface，leads to a decrease in the 2D Zn-MOF current. Bacteria can be rapidly quantified using a one-step detection method and the replacement of aptamers. The detection limits for E. coli, S. aureus, and S. typhimurium are 6, 5, and 5 CFU·mL−1, respectively. The sensor demonstrated reliable detection capabilities in real-sample testing. Therefore, the one-step sensor based on the 2D Zn-MOF and 2D C-Ti3C2Tx has significant application value in the detection of foodborne pathogens.

Study on crack propagation characteristics of rocks with different lateral pressure based on joint monitoring of DIC and AE

During the process of rock failure, the characteristics of crack propagation affect the fracture characteristics and macroscopic mechanical behavior of rocks, indirectly affecting the safety and stability of rock engineering. In order to study the evolution characteristics of cracks during rock failure under different lateral pressure, based on an improved digital image correlation (DIC) and acoustic emission (AE) signal recognition method, a visual biaxial servo loading device was developed to conduct biaxial compression tests on mudstone with prefabricated cracks of the same inclination angle. The research results indicate that the stages of crack propagation include microcracks propagation, crack tip formation, stable macroscopic cracks propagation, and unstable macroscopic cracks propagation. As the lateral pressure increased, the initiation frequency of cracks decreased, the quantity of propagation decreased, and the propagation path shortened, indirectly increasing the bearing strength of rocks. The initiation stress, peak stress, and elastic modulus of pre-cracked rocks with lateral pressure ≤ 2 MPa were lower than those of pre-cracked rocks with lateral pressure > 3 MPa, with the minimum reduction amplitude of 14.1%, 21.2%, and 12.6%, respectively. As the lateral pressure decreased, the dispersion of the AE main frequency distribution increased and accelerated its downward expansion. The surface temperature curves of rocks were prone to fluctuations and rapid upward evolution characteristics corresponding to crack tip formation and crack propagation, respectively. The research results provide theoretical and engineering references for the mining of weak coal seams.

Expression of signal recognition particle 14 in hepatocellular carcinoma and its relationship with disease progression and patient survival.

To investigate the expression of signal recognition particle 14 (SRP14) in hepatocellular carcinoma (HCC) and its clinical significance. The data of SRP14 expression in HCC were obtained from bioinformatics study, and from investigation with quantitative reverse transcription polymerase chain reaction (qRT-PCR), immunohistochemical staining and Western blotting in clinical samples. The Kaplan-Meier analysis was used to determine the associations between SRP14 mRNA expression and the overall survival, progression-free survival, and disease-specific survival of HCC patients. The effect of SRP14 on the proliferation and migration of HCC cells were determined by EdU staining, MTS, Transwell and wound-healing assays. The potential mechanism for SRP14 regulating HCC was explored through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis as well as qRT-PCR. According to the data from GSE14520, TNMplot database and clinical samples, compared with paired tumor-adjacent tissues, non-paired tumor-adjacent tissues and normal tissues, the mRNA expression of SPR14 in HCC tissues was upregulated (all P<0.05). In clinical samples, compared with paired tumor-adjacent tissues, the protein expression of SPR14 in HCC tissues was increased (P<0.05). The increased mRNA expression of SRP14 was associated with good overall survival, progression-free survival, and disease-specific survival in HCC patients. SRP14 inhibited the proliferation and migration of HCC cells in vitro. According to the KEGG and GO enrichment analysis, in non-specific HCC, the genes co-expressed with SRP14 may predominantly regulate protein synthesis, processing, and transport, while in nonalcoholic fatty liver disease related HCC, the genes co-expressed with SRP14 could control multiple signaling pathways such as MAPK, cAMP, PI3K-Akt, and Wnt. Mechanistically, SRP14 up-regulated the mRNA expression of tumor suppressor gene GPRC5A inHCC cells (P<0.05). SRP14 may regulate HCC progression and influence patient prognosis.

Leveraging electrocardiography signals for deep learning-driven cardiovascular disease classification model

Electrocardiography (ECG) is the most non-invasive diagnostic tool for cardiovascular diseases (CVDs). Automatic analysis of ECG signals assists in accurately and rapidly detecting life-threatening arrhythmias like atrioventricular blockage, atrial fibrillation, ventricular tachycardia, etc. The ECG recognition models need to utilize algorithms to detect various kinds of waveforms in the ECG and identify complicated relationships over time. However, the high variability of wave morphology among patients and noise are challenging issues. Physicians frequently utilize automated ECG abnormality recognition models to classify long-term ECG signals. Recently, deep learning (DL) models can be used to achieve enhanced ECG recognition accuracy in the healthcare decision making system. In this aspect, this study introduces an automated DL enabled ECG signal recognition (ADL-ECGSR) technique for CVD detection and classification. The ADL-ECGSR technique employs three most important subprocesses: pre-processed, feature extraction, parameter tuning, and classification. Besides, the ADL-ECGSR technique involves the design of a bidirectional long short-term memory (BiLSTM) based feature extractor, and the Adamax optimizer is utilized to optimize the trained method of the BiLSTM model. Finally, the dragonfly algorithm (DFA) with a stacked sparse autoencoder (SSAE) module is applied to recognize and classify EEG signals. An extensive range of simulations occur on benchmark PTB-XL datasets to validate the enhanced ECG recognition efficiency. The comparative analysis of the ADL-ECGSR methodology showed a remarkable performance of 91.24 % on the existing methods.

In utero morphological and functional properties of bovine trophoblastic vesicles.

In many mammals, including ruminants, pregnancy requires pregnancy recognition signaling molecules secreted by the conceptus; however, the mechanism underlying pregnancy establishment in cattle remains unknown. Trophoblastic vesicles (TVs) are artificially produced from the extraembryonic tissues of the elongating conceptus and may be useful tools for understanding conception. This study investigated the morphological and functional properties of TVs in comparison to those of intact conceptuses. TVs were prepared from the extraembryonic tissues of conceptuses collected 14 days after artificial insemination (AI), cryopreserved immediately after dissection, and cultured after thawing for subsequent transplantation into the uterus. The transferred TVs were collected 7 days after transplantation and compared with extraembryonic tissue samples collected from conceptuses at 21 days post-AI. The recovered TVs were 40 times longer than those of their pre-transplant counterparts. Microscopic evaluation revealed that their membrane structures consisted of trophoblast and hypoblast layers. The expression patterns of the cell differentiation markers, CDX2, SOX2, and GATA6, and interferon tau (IFNT) protein expression levels in the TVs were similar to those in control extraembryonic tissue samples. These findings suggest that TVs are capable of morphological elongation and maintain IFNT production in a similar way as original trophoblasts.

Bacterial homologs of innate eukaryotic antiviral defenses with anti-phage activity highlight shared evolutionary roots of viral defenses

Prokaryotes have evolved a multitude of defense systems to protect against phage predation. Some of these resemble eukaryotic genes involved in antiviral responses. Here, we set out to systematically project the current knowledge of eukaryotic-like antiviral defense systems onto prokaryotic genomes, using Pseudomonas aeruginosa as a model organism. Searching for phage defense systems related to innate antiviral genes from vertebrates and plants, we uncovered over 450 candidates. We validated six of these phage defense systems, including factors preventing viral attachment, R-loop-acting enzymes, the inflammasome, ubiquitin pathway, and pathogen recognition signaling. Collectively, these defense systems support the concept of deep evolutionary links and shared antiviral mechanisms between prokaryotes and eukaryotes.

Mimicking chirality floralform heterostructure: A multivariate metal-based nanoparticles with highly charge transformation interface and multivariate recognition

Enantioselective identification of chiral molecules holds immense significance in the fields of biological processes, chemical reactions, and drug development due to the optical rotation properties of enantiomers. Moreover, the chiral nanocomposite plays an important role in capable of selectively interacting with specific chiral molecules. By utilizing the chiral surfaces of metal polyphenol framework, an enantioselective identification chiral nanocomposite is fabricated on polyphenol colloidal spheres (PCS). The multivariate metal-based nanocomposite use L-tartaric acid (TA) and copper as a chiral recognition selector and signal corrector, aminothiophenol (PATP) anchoring Ag NPs as artificial traction skeleton. Applying L-/ D-cysteine as patterns enantiomers, the chiral recognition selector Cu- L-TA framework perform stereoselective recognition of target molecules through strong affinity and hydrogen interaction. The discriminative SERS signal and CT-related totally symmetric (a1) value of Cu- PCS@Ag NPs and PATP molecules is regarded as discriminating part and internal standard to correct the signal drifting. The put forward method has a good linearity in the concentration range of 1 nM ∼ 10 mM and detection limits as low as 0.37 pM and 0.30 pM for L/D-Cys. This work provides specific method for the chiral molecule recognition and determination in complex matrix sample.

Integrated Bionic Human Retina Process and In‐Sensor RC System Based on 2D Retinomorphic Memristor Array

AbstractThe rapid development of machine vision has put forward higher requirements for image perception and visual learning systems. However, limited by the von Neumann bottleneck, traditional artificial vision systems rely on complex optical and circuit designs, and the separate distribution of sensors, memory, and computing modules results in high energy consumption and high latency. Memristor‐based reservoir computing (RC) system with integrated sensing and memory functions provides a solution to effectively improve the computational efficiency of artificial vision networks. Here, a complete artificial visual recognition system based on in‐sensor RC is developed by combining the excellent optoelectronic synaptic properties of 2D WS2 memristor array. Through effectively regulating the conductance of memristors by light or electrical stimulation, image information is converted into high‐dimensional information in the reservoir layer, and the classification can be completed by simple training, significantly reducing the training complexity and cost consumption. With this system, an effective recognition of hand‐written numbers with an accuracy of 88.3% is demonstrated, and a 100% recognition of traffic signals of different colors is achieved. The proposed in‐sensor RC system advances the development and application of artificial vision recognition, paving the way for efficient machine learning and neuromorphic vision systems.

Sig-RFPNet: multi-branch network and multi-classifier method for offline signature recognition

Sig-RFPNet: multi-branch network and multi-classifier method for offline signature recognition

KDM6A regulates immune response genes in multiple myeloma

AbstractThe histone H3 at lysine 27 (H3K27) demethylase lysine demethylase 6A (KDM6A) is a tumor suppressor in multiple cancers, including multiple myeloma (MM). We created isogenic MM cells disrupted for KDM6A and tagged the endogenous protein to facilitate genome-wide studies. KDM6A binds genes associated with immune recognition and cytokine signaling. Most importantly, KDM6A binds and activates NLRC5 and CIITA, which encode regulators of major histocompatibility complex genes. Patient data indicate that NLRC5 and CIITA are downregulated in MM with low KDM6A expression. Chromatin analysis shows that KDM6A binds poised and active enhancers and KDM6A loss led to decreased H3K27ac at enhancers, increased H3K27me3 levels in body of genes bound by KDM6A, and decreased gene expression. Reestablishing histone acetylation with an HDAC3 inhibitor leads to upregulation of major histocompatibility complex expression, offering a strategy to restore immunogenicity of KDM6A-deficient tumors. Loss of Kdm6a in Kirsten rat sarcoma virus (K-RAS)-transformed murine fibroblasts led to increased growth in vivo associated with decreased T-cell infiltration.

An Intrinsic Host Defense against HSV-1 Relies on the Activation of Xenophagy with the Active Clearance of Autophagic Receptors.

Autophagy engulfs cellular components in double-membrane-bound autophagosomes for clearance and recycling after fusion with lysosomes. Thus, autophagy is a key process for maintaining proteostasis and a powerful cell-intrinsic host defense mechanism, protecting cells against pathogens by targeting them through a specific form of selective autophagy known as xenophagy. In this context, ubiquitination acts as a signal of recognition of the cargoes for autophagic receptors, which direct them towards autophagosomes for subsequent breakdown. Nevertheless, autophagy can carry out a dual role since numerous viruses including members of the Orthoherpesviridae family can either inhibit or exploit autophagy for its own benefit and to replicate within host cells. There is growing evidence that Herpes simplex virus type 1 (HSV-1), a highly prevalent human pathogen that infects epidermal keratinocytes and sensitive neurons, is capable of negatively modulating autophagy. Since the effects of HSV-1 infection on autophagic receptors have been poorly explored, this study aims to understand the consequences of HSV-1 productive infection on the levels of the major autophagic receptors involved in xenophagy, key proteins in the recruitment of intracellular pathogens into autophagosomes. We found that productive HSV-1 infection in human neuroglioma cells and keratinocytes causes a reduction in the total levels of Ub conjugates and decreases protein levels of autophagic receptors, including SQSTM1/p62, OPTN1, NBR1, and NDP52, a phenotype that is also accompanied by reduced levels of LC3-I and LC3-II, which interact directly with autophagic receptors. Mechanistically, we show these phenotypes are the result of xenophagy activation in the early stages of productive HSV-1 infection to limit virus replication, thereby reducing progeny HSV-1 yield. Additionally, we found that the removal of the tegument HSV-1 protein US11, a recognized viral factor that counteracts autophagy in host cells, enhances the clearance of autophagic receptors, with a significant reduction in the progeny HSV-1 yield. Moreover, the removal of US11 increases the ubiquitination of SQSTM1/p62, indicating that US11 slows down the autophagy turnover of autophagy receptors. Overall, our findings suggest that xenophagy is a potent host defense against HSV-1 replication and reveals the role of the autophagic receptors in the delivery of HSV-1 to clearance via xenophagy.

Real-Time Measurement of a Weak Interaction of a Transcription Factor Motif with a Protein Hub at Single-Molecule Precision.

Transcription factors often interact with other protein cofactors, regulating gene expression. Direct detection of these brief events using existing technologies remains challenging due to their transient nature. In addition, intrinsically disordered domains, intranuclear location, and lack of cofactor-dependent active sites of transcription factors further complicate the quantitative analysis of these critical processes. Here, we create a genetically encoded label-free sensor to identify the interaction between a motif of the MYC transcription factor, a primary cancer driver, and WDR5, a chromatin-associated protein hub. Using an engineered nanopore equipped with this motif, WDR5 is probed through reversible captures and releases in a one-by-one and time-resolved fashion. Our single-molecule kinetic measurements indicate a weak-affinity interaction arising from a relatively slow complex association and a fast dissociation of WDR5 from the tethered motif. Further, we validate this subtle interaction by determinations in an ensemble using single nanodisc-wrapped nanopores immobilized on a biolayer interferometry sensor. This study also provides the proof-of-concept for a sensor that reveals unique recognition signatures of different protein binding sites. Our foundational work may be further developed to produce sensing elements for analytical proteomics and cancer nanomedicine.

Research on a soft saturation nonlinear SSVEP signal feature extraction algorithm

Brain–computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.

Acquisition and Analysis of Facial Electromyographic Signals for Emotion Recognition.

The objective of the article is to recognize users' emotions by classifying facial electromyographic (EMG) signals. A biomedical signal amplifier, equipped with eight active electrodes positioned in accordance with the Facial Action Coding System, was used to record the EMG signals. These signals were registered during a procedure where users acted out various emotions: joy, sadness, surprise, disgust, anger, fear, and neutral. Recordings were made for 16 users. The mean power of the EMG signals formed the feature set. We utilized these features to train and evaluate various classifiers. In the subject-dependent model, the average classification accuracies were 96.3% for KNN, 94.9% for SVM with a linear kernel, 94.6% for SVM with a cubic kernel, and 93.8% for LDA. In the subject-independent model, the classification results varied depending on the tested user, ranging from 91.4% to 48.6% for the KNN classifier, with an average accuracy of 67.5%. The SVM with a cubic kernel performed slightly worse, achieving an average accuracy of 59.1%, followed by the SVM with a linear kernel at 53.9%, and the LDA classifier at 41.2%. Additionally, the study identified the most effective electrodes for distinguishing between pairs of emotions.

A stage-dependent seed defense response to explain efficient seed transmission of Xanthomonas citri pv. fuscans to common bean.

Although seed represents an important means of plant pathogen dispersion, the seed-pathogen dialogue remains largely unexplored. A multiomic approach was performed at different seed developmental stages of common bean (Phaseolus vulgaris L.) during asymptomatic colonization by Xanthomonas citri pv. fuscans (Xcf), At the early seed developmental stages, we observed high transcriptional changes both in seeds with bacterial recognition and defense signal transduction genes, and in bacteria with up-regulation of the bacterial type 3 secretion system. This high transcriptional activity of defense genes in Xcf-colonized seeds during maturation refutes the widely diffused assumption considering seeds as passive carriers of microbes. At later seed maturation stages, few transcriptome changes indicated a less intense molecular dialogue between the host and the pathogen, but marked by changes in DNA methylation of plant defense genes, in response to Xcf colonization. We showed examples of pathogen-specific DNA methylations in colonized seeds acting as plant defense silencing to repress plant immune response during the germination process. Finally, we propose a novel plant-pathogen interaction model, specific to the seed tissues, highlighting the existence of distinct phases during seed-pathogen interaction with seeds being actively interacting with colonizing pathogens, then both belligerents switching to more passive mode at later stages.