Concurrent control chart pattern recognition in manufacturing processes based on zero-shot learning.

In real industrial settings, collecting and labeling concurrent abnormal control chart pattern (CCP) samples are challenging, thereby hindering the effectiveness of current CCP recognition (CCPR) methods. This paper introduces zero-shot learning into quality control, proposing an intelligent model for recognizing zero-shot concurrent CCPs (C-CCPs). A multiscale ordinal pattern (OP) feature considering data sequential relationship is proposed. Drawing from expert knowledge, an attribute description space (ADS) is established to infer from single CCPs to C-CCPs. An ADS is embedded between features and labels, and the attribute classifier associates the features and attributes of CCPs. Experimental results demonstrate an accuracy of 98.73 % for 11 unseen C-CCPs and an overall accuracy of 98.89 % for all 19 CCPs, without C-CCP samples in training. Compared with other features, the multiscale OP feature has the best recognition effect on unseen C-CCPs.

Unravelling mechanisms of bacterial recognition by Acanthamoeba: insights into microbial ecology and immune responses.

Acanthamoeba, are ubiquitous eukaryotic microorganisms, that play a pivotal role in recognizing and engulfing various microbes during predation, offering insights into microbial dynamics and immune responses. An intriguing observation lies in the apparent preference of Acanthamoeba for Gram-negative over Gram-positive bacteria, suggesting potential differences in the recognition and response mechanisms to bacterial prey. Here, we comprehensively review pattern recognition receptors (PRRs) and microbe associated molecular patterns (MAMPs) that influence Acanthamoeba interactions with bacteria. We analyze the molecular mechanisms underlying these interactions, and the key finding of this review is that Acanthamoeba exhibits an affinity for bacterial cell surface appendages that are decorated with carbohydrates. Notably, this parallels warm-blooded immune cells, underscoring a conserved evolutionary strategy in microbial recognition. This review aims to serve as a foundation for exploring PRRs and MAMPs. These insights enhance our understanding of ecological and evolutionary dynamics in microbial interactions and shed light on fundamental principles governing immune responses. Leveraging Acanthamoeba as a model organism, provides a bridge between ecological interactions and immunology, offering valuable perspectives for future research.

Cross-family transfer of the Arabidopsis cell-surface immune receptor LORE to tomato confers sensing of 3-hydroxylated fatty acids and enhanced disease resistance.

Plant pathogens pose a high risk of yield losses and threaten food security. Technological and scientific advances have improved our understanding of the molecular processes underlying host-pathogen interactions, which paves the way for new strategies in crop disease management beyond the limits of conventional breeding. Cross-family transfer of immune receptor genes is one such strategy that takes advantage of common plant immune signalling pathways to improve disease resistance in crops. Sensing of microbe- or host damage-associated molecular patterns (MAMPs/DAMPs) by plasma membrane-resident pattern recognition receptors (PRR) activates pattern-triggered immunity (PTI) and restricts the spread of a broad spectrum of pathogens in the host plant. In the model plant Arabidopsis thaliana, the S-domain receptor-like kinase LIPOOLIGOSACCHARIDE-SPECIFIC REDUCED ELICITATION (AtLORE, SD1-29) functions as a PRR, which senses medium-chain-length 3-hydroxylated fatty acids (mc-3-OH-FAs), such as 3-OH-C10:0, and 3-hydroxyalkanoates (HAAs) of microbial origin to activate PTI. In this study, we show that ectopic expression of the Brassicaceae-specific PRR AtLORE in the solanaceous crop species Solanum lycopersicum leads to the gain of 3-OH-C10:0 immune sensing without altering plant development. AtLORE-transgenic tomato shows enhanced resistance against Pseudomonas syringae pv. tomato DC3000 and Alternaria solani NL03003. Applying 3-OH-C10:0 to the soil before infection induces resistance against the oomycete pathogen Phytophthora infestans Pi100 and further enhances resistance to A. solani NL03003. Our study proposes a potential application of AtLORE-transgenic crop plants and mc-3-OH-FAs as resistance-inducing biostimulants in disease management.

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.

Graph-based cell pattern recognition for merging the multi-modal optical microscopic image of neurons.

A deep understanding of neuron structure and function is crucial for elucidating brain mechanisms, diagnosing and treating diseases. Optical microscopy, pivotal in neuroscience, illuminates neuronal shapes, projections, and electrical activities. To explore the projection of specific functional neurons, scientists have been developing optical-based multimodal imaging strategies to simultaneously capture dynamic in vivo signals and static ex vivo structures from the same neuron. However, the original position of neurons is highly susceptible to displacement during ex vivo imaging, presenting a significant challenge for integrating multimodal information at the single-neuron level. This study introduces a graph-model-based approach for cell image matching, facilitating precise and automated pairing of sparsely labeled neurons across different optical microscopic images. It has been shown that utilizing neuron distribution as a matching feature can mitigate modal differences, the high-order graph model can address scale inconsistency, and the nonlinear iteration can resolve discrepancies in neuron density. This strategy was applied to the connectivity study of the mouse visual cortex, performing cell matching between the two-photon calcium image and the HD-fMOST brain-wide anatomical image sets. Experimental results demonstrate 96.67% precision, 85.29% recall rate, and 90.63% F1 Score, comparable to expert technicians. This study builds a bridge between functional and structural imaging, offering crucial technical support for neuron classification and circuitry analysis.

Identification of Nonsynonymous SNPs in Immune-Related Genes Associated with Pneumonia Severity in Pigs.

We previously showed that several polymorphisms in genes encoding pattern recognition receptors that cause amino acid substitutions alter pathogen recognition ability and disease susceptibility in pigs. In this study, we expanded our analysis to a wide range of immune-related genes and investigated polymorphism distribution and its influence on pneumonia in multiple commercial pig populations. Among the polymorphisms in 42 genes causing 634 amino acid substitutions extracted from the swine genome database, 80 in 24 genes were found to have a minor allele frequency of at least 10% in Japanese breeding stock pigs via targeted resequencing. Of these, 62 single nucleotide polymorphisms (SNPs) in 23 genes were successfully genotyped in 862 pigs belonging to four populations with data on pneumonia severity. Association analysis using a generalized linear mixed model revealed that 12 SNPs in nine genes were associated with pneumonia severity. In particular, SNPs in the cellular receptor for immunoglobulin G FCGR2B and the intracellular nucleic acid sensors IFI16 and LRRFIP1 were found to be associated with mycoplasmal pneumonia of swine or porcine pleuropneumonia in multiple populations and may therefore have wide applications in the improvement of disease resistance in pigs. Functional analyses at the cellular and animal levels are required to clarify the mechanisms underlying the effects of these SNPs on disease susceptibility.

Monocytic Phagocytes in the Immunopathogenesis of Cytokine Storm Syndromes.

Cytokine storm syndromes (CSSs) are caused by a dysregulated host immune response to an inciting systemic inflammatory trigger. This maladaptive and harmful immune response culminates in collateral damage to host tissues resulting in life-threatening multisystem organ failure. Knowledge of the various immune cells that contribute to CSS pathogenesis has improved dramatically in the past decade. Monocytes, dendritic cells, and macrophages, collective known as monocytic phagocytes, are well-positioned within the immune system hierarchy to make key contributions to the initiation, propagation, and amplification of the hyperinflammatory response in CSS. The plasticity of monocytic phagocytes also makes them prime candidates for mediating immunoregulatory and tissue-healing functions in patients who recover from cytokine storm-mediated immunopathology. Therefore, approaches to manipulate the myriad functions of monocytic phagocytes may improve the clinical outcome of CSS.

Recognition of inscribed cursive Pashtu numeral through optimized deep learning.

Pashtu is one of the most widely spoken languages in south-east Asia. Pashtu Numerics recognition poses challenges due to its cursive nature. Despite this, employing a machine learning-based optical character recognition (OCR) model can be an effective way to tackle this issue. The main aim of the study is to propose an optimized machine learning model which can efficiently identify Pashtu numerics from 0-9. The methodology includes data organizing into different directories each representing labels. After that, the data is preprocessed i.e., images are resized to 32 × 32 images, then they are normalized by dividing their pixel value by 255, and the data is reshaped for model input. The dataset was split in the ratio of 80:20. After this, optimized hyperparameters were selected for LSTM and CNN models with the help of trial-and-error technique. Models were evaluated by accuracy and loss graphs, classification report, and confusion matrix. The results indicate that the proposed LSTM model slightly outperforms the proposed CNN model with a macro-average of precision: 0.9877, recall: 0.9876, F1 score: 0.9876. Both models demonstrate remarkable performance in accurately recognizing Pashtu numerics, achieving an accuracy level of nearly 98%. Notably, the LSTM model exhibits a marginal advantage over the CNN model in this regard.

Distance and similarity measures on belief and plausibility under q-rung orthopair fuzzy sets with applications.

Belief and plausibility functions based on evidence theory (ET) have been widely used in managing uncertainty. Various generalizations of ET to fuzzy sets (FSs) have been reported in the literature, but no generalization of ET to q-rung orthopair fuzzy sets (q-ROFSs) has been made yet. Therefore, this paper proposes a novel, simple, and intuitive approach to distance and similarity measures for q-ROFSs based on belief and plausibility functions within the framework of ET. This research addresses a significant research gap by introducing a comprehensive framework for handling uncertainty in q-ROFSs using ET. Furthermore, it acknowledges the limitations inherent in the current state of research, notably the absence of generalizations of ET to q-ROFSs and the challenges in extending belief and plausibility measures to certain aggregation operators and other generalizations including Hesitant fuzzy sets, Bipolar fuzzy sets, Fuzzy soft sets etc. Our contribution lies in the proposal of a novel approach to distance and similarity measures for q-ROFSs under ET, utilizing Orthopairian belief and plausibility intervals (OBPIs). We establish new similarity measures within the generalized ET framework and demonstrate the reasonability of our method through useful numerical examples. Additionally, we construct Orthopairian belief and plausibility GRA (OBP-GRA) for managing daily life complex issues, particularly in multicriteria decision-making scenarios. Numerical simulations and results confirm the usability and practical applicability of our proposed method in the framework of ET.

A deep learning approach to identify the fetal head position using transperineal ultrasound during labor.

OBJECTIVES: To develop a deep learning (DL)-model using convolutional neural networks (CNN) to automatically identify the fetal head position at transperineal ultrasound in the second stage of labor. MATERIAL AND METHODS: Prospective, multicenter study including singleton, term, cephalic pregnancies in the second stage of labor. We assessed the fetal head position using transabdominal ultrasound and subsequently, obtained an image of the fetal head on the axial plane using transperineal ultrasound and labeled it according to the transabdominal ultrasound findings. The ultrasound images were randomly allocated into the three datasets containing a similar proportion of images of each subtype of fetal head position (occiput anterior, posterior, right and left transverse): the training dataset included 70 %, the validation dataset 15 %, and the testing dataset 15 % of the acquired images. The pre-trained ResNet18 model was employed as a foundational framework for feature extraction and classification. CNN1 was trained to differentiate between occiput anterior (OA) and non-OA positions, CNN2 classified fetal head malpositions into occiput posterior (OP) or occiput transverse (OT) position, and CNN3 classified the remaining images as right or left OT. The DL-model was constructed using three convolutional neural networks (CNN) working simultaneously for the classification of fetal head positions. The performance of the algorithm was evaluated in terms of accuracy, sensitivity, specificity, F1-score and Cohen's kappa. RESULTS: Between February 2018 and May 2023, 2154 transperineal images were included from eligible participants across 16 collaborating centers. The overall performance of the model for the classification of the fetal head position in the axial plane at transperineal ultrasound was excellent, with an of 94.5 % (95 % CI 92.0--97.0), a sensitivity of 95.6 % (95 % CI 96.8-100.0), a specificity of 91.2 % (95 % CI 87.3-95.1), a F1-score of 0.92 and a Cohen's kappa of 0.90. The best performance was achieved by the CNN1 - OA position vs fetal head malpositions - with an accuracy of 98.3 % (95 % CI 96.9-99.7), followed by CNN2 - OP vs OT positions - with an accuracy of 93.9 % (95 % CI 89.6-98.2), and finally, CNN3 - right vs left OT position - with an accuracy of 91.3 % (95 % CI 83.5-99.1). CONCLUSIONS: We have developed a DL-model capable of assessing fetal head position using transperineal ultrasound during the second stage of labor with an excellent overall accuracy. Future studies should validate our DL model using larger datasets and real-time patients before introducing it into routine clinical practice.

Therapeutic Targeting of Pattern Recognition Receptors to Modulate Inflammation in Atherosclerosis.

Atherosclerosis (AS), a potentially fatal cardiovascular disease (CVD), is a chronic inflammatory condition. The disease's onset and progression are influenced by inflammatory and immunological mechanisms. The innate immune pathways are essential in the progression of AS, as they are responsible for detecting first danger signals and causing long-term changes in immune cells. The innate immune system possesses distinct receptors known as pattern recognition receptors (PRRs) which can identify both pathogen-associated molecular patterns and danger-associated molecular signals. Activation of PRRs initiates the inflammatory response in various physiological systems, such as the cardiovascular system. This review specifically examines the contribution of the innate immune response and PRRs to the formation and advancement of AS. Studying the role of these particular receptors in AS would enhance our understanding of the development of AS and offer novel approaches for directly improving the inflammatory response associated with it.

Unravelling Influence Factors in Pattern Recognition Myoelectric Control Systems: The Impact of Limb Positions and Electrode Shifts.

Pattern recognition (PR)-based myoelectric control systems can naturally provide multifunctional and intuitive control of upper limb prostheses and restore lost limb function, but understanding their robustness remains an open scientific question. This study investigates how limb positions and electrode shifts-two factors that have been suggested to cause classification deterioration-affect classifiers' performance by quantifying changes in the class distribution using each factor as a class and computing the repeatability and modified separability indices. Ten intact-limb participants took part in the study. Linear discriminant analysis (LDA) was used as the classifier. The results confirmed previous studies that limb positions and electrode shifts deteriorate classification performance (14-21% decrease) with no difference between factors (p > 0.05). When considering limb positions and electrode shifts as classes, we could classify them with an accuracy of 96.13 ± 1.44% and 65.40 ± 8.23% for single and all motions, respectively. Testing on five amputees corroborated the above findings. We have demonstrated that each factor introduces changes in the feature space that are statistically new class instances. Thus, the feature space contains two statistically classifiable clusters when the same motion is collected in two different limb positions or electrode shifts. Our results are a step forward in understanding PR schemes' challenges for myoelectric control of prostheses and further validation needs be conducted on more amputee-related datasets.

Probing Biological Nitrogen Fixation in Legumes Using Raman Spectroscopy.

Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.

Assessment of UHF Frequency Range for Failure Classification in Power Transformers.

Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.

Comparison of Quantitative and Qualitative EDXRF Analysis for Provenance Study of Archaeological Ceramics.

The most common scientific analysis of archaeological ceramics aims to determine the raw material source and/or production technology. Scientists and archaeologists widely use XRF-based techniques as a tool in a provenance study. After conducting XRF analysis, the results are often analyzed using multivariate analysis in addition to interpretation and conclusions. Various multivariate techniques have already been applied in archaeological ceramics provenance studies to reveal different raw material sources, identify imported pieces, or determine different production recipes. This study aims to evaluate the results of multivariate analysis in the provenance study of ceramics that belong to three cultures that settled in the same area during various prehistoric periods. Portable energy-dispersive X-ray fluorescence spectrometry (pEDXRF) was used to determine the elemental composition of the ceramic material. The ceramic material was prepared in two different ways. The ceramic body material was ground into powder, homogenized, and then pressed into tablets. After that, the same fragments are polished in suitable places. Quantitative and qualitative analyses were performed on the tablets and polished pieces. The results were subjected to both unsupervised and supervised multivariate analysis. Based on the results, it was concluded that qualitative analysis of the well-prepared shards' surface using EDXRF spectrometry could be utilized in provenance studies, even when the ceramic assemblages were made of similar raw materials.

Rapid and accurate detection of cinnamon oil adulteration in perilla leaf oil using atmospheric solids analysis probe-mass spectrometry.

Perilla leaf oil (PLO) is a global premium vegetable oil with abundant nutrients and substantial economic value, rendering it susceptible to potential adulteration by unscrupulous entrepreneurs. The addition of cinnamon oil (CO) is one of the main adulteration avenues for illegal PLOs. In this study, new and real-time ambient mass spectrometric methods were developed to detect CO adulteration in PLO. First, atmospheric solids analysis probe tandem mass spectrometry combined with principal component analysis and principal component analysis-linear discriminant analysis was employed to differentiate between authentic and adulterated PLO. Then, a spectral library was established for the instantaneous matching of cinnamaldehyde in the samples. Finally, the results were verified using the SRM mode of ASAP-MS/MS. Within 3 min, the three methods successfully identified CO adulteration in PLO at concentrations as low as 5% v/v with 100% accuracy. The proposed strategy was successfully applied to the fraud detection of CO in PLO.

A rapid and accurate fluorescent sensor array based on lanthanide metal-organic framework for identification and determination of perfluorinated compounds.

Perfluorinated compounds (PFCs), as an important class of environmental pollutants, have chemical and structural similarities that make their detection a great technical challenge. This study synthesized three species of metal-organic frameworks (MOFs) using different lanthanide metal ions or organic ligands, which were integrated into a fluorescent sensor array. This innovative approach offers a straightforward, rapid, and precise detection strategy for PFCs. Different ionization properties and fluorinated hydrophobic tails of PFCs lead to different electrostatic attraction and hydrophobic effects between PFCs and sensing elements, which become the basis for differential sensing. Furthermore, the fluorescence signal is more convenient to collect, making the sensor array simple to complete the identification. Combined with pattern recognition methods, the array successfully identified seven kinds of PFCs and mixtures with a classification accuracy of 100 % and a detection limit as low as 51 nM. Finally, the utility of the sensor array in river water sample analysis was verified. The strategy provides an effective method for identifying and determining PFCs and offers new opportunities for developing sensor arrays based on lanthanide MOFs.

The Molecular Signature Related to Local Inflammatory and Immune Response in Canine Cutaneous Hypersensitivity Reactions: A Preliminary Study.

Cutaneous hypersensitivity reactions (CHRs) are complex inflammatory skin disorders that affect humans and dogs. This study examined the inflammatory and immune responses leading to skin damage, inflammation, and irritation by investigating gene expression through quantitative PCR (qPCR) and protein localization through the immunohistochemistry (IHC) of specific receptors and molecules involved in CHRs. Formalin-fixed paraffin-embedded (FFPE) samples from canine CHR skin (n = 20) and healthy dog skin (n = 3) were analyzed for expression levels of eight genes, including members of the pattern recognition receptor (PRR) family, CD209 and CLEC4G, the Regakine-1-like chemokine, and acute phase proteins (APPs), LBP-like and Hp-like genes. Additionally, we examined the local involvement of IL-6, Janus Kinase 1 (JAK1), and the signal transducer activator of transcription 3 (STAT3) in the CHR cases. The study demonstrated statistically significant increases in the expression levels of CD209, Hp-like (p < 0.01), LBP-like, Regakine-1-like, and CLEC4G (p < 0.05) genes in CHRs compared to healthy controls. Conversely, IL-6, JAK1, and STAT3 showed no significant difference between the two groups (p > 0.05). Protein analysis revealed JAK1 and STAT3 expression in CHR hyperplastic epithelial cells, dermal fibroblasts, and endothelial cells of small capillaries, indicating a possible involvement in the JAK/STAT pathway in local inflammatory response regulation. Our findings suggest that the skin plays a role in the development of CHRs.

Increased viral tolerance mediates by antiviral RNA interference in bat cells.

Bats harbor highly virulent viruses that can infect other mammals, including humans, posing questions about their immune tolerance mechanisms. Bat cells employ multiple strategies to limit virus replication and virus-induced immunopathology, but the coexistence of bats and fatal viruses remains poorly understood. Here, we investigate the antiviral RNA interference pathway in bat cells and discover that they have an enhanced antiviral RNAi response, producing canonical viral small interfering RNAs upon Sindbis virus infection that are missing in human cells. Disruption of Dicer function results in increased viral load for three different RNA viruses in bat cells, indicating an interferon-independent antiviral pathway. Furthermore, our findings reveal the simultaneous engagement of Dicer and pattern-recognition receptors, such as retinoic acid-inducible gene I, with double-stranded RNA, suggesting that Dicer attenuates the interferon response initiation in bat cells. These insights advance our comprehension of the distinctive strategies bats employ to coexist with viruses.

Molecular characterization and gene expression of pattern recognition receptors in brown-marbled grouper (Epinephelus fuscoguttatus) fingerlings responding to vibriosis infection.

The pathogen recognition system involves receptors and genes that play a crucial role in activating innate immune response in brown-marbled grouper (Epinephelus fuscoguttatus) as a control agent against various infections including vibriosis. Here, we report the molecular cloning of partial open reading frames, sequences characterization, and expression profiles of Pattern Recognition Receptors (PRRs) in brown-marbled grouper. The PRRs, namely pglyrp5, tlr5, ctlD, and ctlE in brown-marbled grouper, possess conserved domains and showed shared evolutionary relationships with other fishes, humans, mammals, birds, reptilians, amphibians, and insects. In infection experiments, up to 50% mortality was found in brown-marbled grouper fingerlings infected with Vibrio alginolyticus compared to 27% mortality infected Vibrio parahaemolyticus and 100% survival of control groups. It is also demonstrated that all four PRRs had higher expression in samples infected with V. alginolyticus compared to V. parahaemolyticus. This PRRs gene expression analysis revealed that all four PRRs expressed rapidly at 4-h post-inoculation even though the Vibrio count was only detected earliest at 12-h post-inoculation in samples. The highest expression recorded was from V. alginolyticus inoculated fish spleen with up to 73-fold change for pglyrp5 gene, followed by 14 to 38-fold expression for the same treatment in spleen, head kidney, and blood samples for other PRRs, namely tlr5, ctlD, and ctlE genes. Meanwhile less than a 10% increase in expression of all four genes was detected in spleen, head kidney, and blood samples inoculated with V. parahaemolyticus. These findings indicated that pglyrp5, tlr5, ctlD, and ctlE play important roles in the early immune response to vibriosis infected, brown-marbled grouper fingerlings.