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.

MRI-based deep learning for differentiating between bipolar and major depressive disorders.

Mood disorders, particularly bipolar disorder (BD) and major depressive disorder (MDD), manifest changes in brain structure that can be detected using structural magnetic resonance imaging (MRI). Although structural MRI is a promising diagnostic tool, prevailing diagnostic criteria for BD and MDD are predominantly subjective, sometimes leading to misdiagnosis. This challenge is compounded by a limited understanding of the underlying causes of these disorders. In response, we present SE-ResNet, a Residual Network (ResNet)-based framework designed to discriminate between BD, MDD, and healthy controls (HC) using structural MRI data. Our approach extends the traditional Squeeze-and-Excitation (SE) layer by incorporating a dedicated branch for spatial attention map generation, equipped with soft-pooling, a 7 × 7 convolution, and a sigmoid function, intended to detect complex spatial patterns. The fusion of channel and spatial attention maps through element-wise addition aims to enhance the model's ability to discriminate features. Unlike conventional methods that use max-pooling for downsampling, our methodology employs soft-pooling, which aims to preserve a richer representation of input features and reduce data loss. When evaluated on a proprietary dataset comprising 303 subjects, the SE-ResNet achieved an accuracy of 85.8 %, a recall of 85.7 %, a precision of 85.9 %, and an F1 score of 85.8 %. These performance metrics suggest that the SE-ResNet framework has potential as a tool for detecting psychiatric disorders using structural MRI data.

Analysis of electrode locations on limb condition effect for myoelectric pattern recognition.

BACKGROUND: Gesture recognition using surface electromyography (sEMG) has garnered significant attention due to its potential for intuitive and natural control in wearable human-machine interfaces. However, ensuring robustness remains essential and is currently the primary challenge for practical applications. METHODS: This study investigates the impact of limb conditions and analyzes the influence of electrode placement. Both static and dynamic limb conditions were examined using electrodes positioned on the wrist, elbow, and the midpoint between them. Initially, we compared classification performance across various training conditions at these three electrode locations. Subsequently, a feature space analysis was conducted to quantify the effects of limb conditions. Finally, strategies for group training and feature selection were explored to mitigate these effects. RESULTS: The results indicate that with the state-of-the-art method, classification performance at the wrist was comparable to that at the middle position, both of which outperformed the elbow, consistent with the findings from the feature space analysis. In inter-condition classification, training under dynamic limb conditions yielded better results than training under static conditions, especially at the positions covered by dynamic training. Additionally, fast and slow movement speeds produced similar performance outcomes. To mitigate the effects of limb conditions, adding more training conditions reduced classification errors; however, this reduction plateaued after four conditions, resulting in classification errors of 22.72%, 22.65%, and 26.58% for the wrist, middle, and elbow, respectively. Feature selection further improved classification performance, reducing errors to 19.98%, 19.75%, and 27.14% at the respective electrode locations, using three optimal features derived from single-condition training. CONCLUSIONS: The study demonstrated that the impact of limb conditions was mitigated when electrodes were placed near the wrist. Dynamic limb condition training, combined with feature optimization, proved to be an effective strategy for reducing this effect. This work contributes to enhancing the robustness of myoelectric-controlled interfaces, thereby advancing the development of wearable intelligent devices.

Molecular mechanisms of emerging inflammasome complexes and their activation and signaling in inflammation and pyroptosis.

Inflammasomes are multi-protein complexes that assemble within the cytoplasm of mammalian cells in response to pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs), driving the secretion of the pro-inflammatory cytokines IL-1ß and IL-18, and pyroptosis. The best-characterized inflammasome complexes are the NLRP3, NAIP-NLRC4, NLRP1, AIM2, and Pyrin canonical caspase-1-containing inflammasomes, and the caspase-11 non-canonical inflammasome. Newer inflammasome sensor proteins have been identified, including NLRP6, NLRP7, NLRP9, NLRP10, NLRP11, NLRP12, CARD8, and MxA. These inflammasome sensors can sense PAMPs from bacteria, viruses and protozoa, or DAMPs in the form of mitochondrial damage, ROS, stress and heme. The mechanisms of action, physiological relevance, consequences in human diseases, and avenues for therapeutic intervention for these novel inflammasomes are beginning to be realized. Here, we discuss these emerging inflammasome complexes and their putative activation mechanisms, molecular and signaling pathways, and physiological roles in health and disease.

The role of pattern recognition receptors in the innate immune system of Chinese mitten crab (Eriocheir sinensis).

Eriocheir sinensis (Chinese mitten crab) is one of the main economic species in China, which has evolved an extremely sophisticated innate immune system to fend off disease invasions. However, bacterial and viral infections have caused significant financial losses for the E. sinensis aquaculture in recent years. Making well-informed judgments for the control microbial infections would require a thorough understanding and clarification of the intricate innate immune system of E. sinensis. Innate immunity is essential for the host's defense against invasive pathogens. Pattern recognition receptors (PRRs) initially recognize pathogen-associated molecular patterns (PAMPs) and trigger an innate immune response, causing the generation of inflammatory cytokine and promoting the clearance and control of pathogens. In E. sinensis, Toll/Toll-like receptors, lipopolysaccharide and ß-1,3-glucan binding proteins, C-type lectins, galactoside-binding lectins, L-type lectins, scavenger receptors, and down syndrome cell adhesion molecules have been identified to be PRRs that are involved in the recognition of bacteria, fungi, and viruses. In this review, we give a comprehensive overview of the literature regarding PRRs' roles in the immunological defenses of E. sinensis, with the aim of providing clues to the mechanisms of innate immunity.

Recognition of phylogenetically diverse pathogens through enzymatically amplified recruitment of RNF213.

Innate immunity senses microbial ligands known as pathogen-associated molecular patterns (PAMPs). Except for nucleic acids, PAMPs are exceedingly taxa-specific, thus enabling pattern recognition receptors to detect cognate pathogens while ignoring others. How the E3 ubiquitin ligase RNF213 can respond to phylogenetically distant pathogens, including Gram-negative Salmonella, Gram-positive Listeria, and eukaryotic Toxoplasma, remains unknown. Here we report that the evolutionary history of RNF213 is indicative of repeated adaptation to diverse pathogen target structures, especially in and around its newly identified CBM20 carbohydrate-binding domain, which we have resolved by cryo-EM. We find that RNF213 forms coats on phylogenetically distant pathogens. ATP hydrolysis by RNF213's dynein-like domain is essential for coat formation on all three pathogens studied as is RZ finger-mediated E3 ligase activity for bacteria. Coat formation is not diffusion-limited but instead relies on rate-limiting initiation events and subsequent cooperative incorporation of further RNF213 molecules. We conclude that RNF213 responds to evolutionarily distant pathogens through enzymatically amplified cooperative recruitment.

Enhanced Accumulation of Health-Promoting Cajaninstilbene Acid in Pigeon Pea Hairy Root Cultures Cocultured with an Endophytic Fungus during Early Stages of Colonization.

Endophytic fungi can effectively regulate the biosynthesis of health-beneficial metabolites in plants. However, few studies have revealed how the accumulation of host metabolites varies during interactions with endophytic fungi. Here, pigeon pea hairy root cultures (PPHRCs) were cocultured with an endophytic fungus Penicillium rubens to explore the impact on the biosynthesis and accumulation of cajaninstilbene acid (CSA). The results showed that CSA accumulation in PPHRCs increased significantly (15.29-fold) during the early stages of P. rubens colonization (fungal attachment and invasion phases). Once P. rubens successfully colonized the intercellular gap of hairy roots to form a symbiotic relationship, the CSA levels in PPHRCs decreased drastically. Moreover, P. rubens could be recognized by plant pattern recognition receptors that regulate immunity/symbiosis, triggering the expression of genes related to pathogenesis, CSA biosynthesis, and ABC transporter. Overall, P. rubens could enhance the accumulation of health-promoting CSA in PPHRCs during the early stages of colonization.

Intra-tumoral delivery of 5'ppp-dsRNA induces robust anti-tumor response via RIG-I activation and Bcl-2 gene downregulation in murine model of prostate cancer.

Onco-immunotherapy via blocking checkpoint-inhibitors has revolutionized the treatment-landscape of several malignancies, though not in the metastatic castration-resistant prostate cancer (PCa) owing to immunosuppressive and poorly immunogenic "cold" tumor microenvironment (TME). Turning up the heat of such cold TME via triggering innate immunity is now of increasing interest to restore immune-surveillance. Retinoic acid-inducible gene- I (RIG-I)-like receptors (RLRs) are cytosolic innate-sensors that can detect exogenous RNAs and induce type-I interferons and other pro-inflammatory signaling. RIG-I activation is suggested to be a valuable addition to the treatment approaches for several cancers. However, the knowledge about RIG-I signaling in PCa remains elusive. The present study evaluated the expression of two important RLRs, RIG-I and melanoma differentiation-associated protein 5 (MDA5) along with their downstream partners, mitochondrial antiviral-signaling protein (MAVS) and ERA G-protein-like 1 (ERAL1) during PCa progression in the transgenic adenocarcinoma of mouse prostate (TRAMP) model. The early stage of PCa revealed a significant increment in the expression of RLRs, but not MAVS. However, the advanced stage showed downregulated RLR signaling. Further, the therapeutic implication of 5'ppp-dsRNA, a synthetic RIG-I agonist and Bcl2 gene silencer has been investigated in vitro and in vivo. Intra-tumoral delivery of 5'ppp-dsRNA regressed tumor growth via triggering tumor cells apoptosis, immunomodulation, and inducing phagocytic "eat me" signals. These findings highlight that, for the first time, RIG-I activation and Bcl-2 silencing with 5'ppp-dsRNA can serve as a potent tumor-suppressor strategy in PCa and has a significant clinical implication in transforming "cold" TME into immunogenic "hot" TME of PCa.

The Science of Cholesteatoma.

Cholesteatoma is a potential end-stage outcome of chronic ear infections that can result in the destruction of temporal bone structures with potential resultant hearing loss, vertigo, and intracranial infectious complications. There is currently no treatment apart from surgery for this condition, and despite years of study, the histopathogenesis of this disease remains poorly understood. This review is intended to summarize our accumulated knowledge of the mechanisms of cholesteatoma development and the underlying molecular biology. Attention will be directed particularly to recent developments, covering many potential pharmacologic targets that could be used to treat this disease in the future.

Auditory processing in patients with temporal lobe epilepsy: A systematic review and meta-analysis.

BACKGROUND: Hearing efficiency is known to influence and interact with communication and mental health. Hearing impairment may be hidden when co-occurring with neurological disorders. PURPOSE: We performed a systematic review and meta-analysis in order to address the following questions: 1) which specific tools of auditory processing show clear deficits, separating Temporal Lobe Epilepsy (TLE) patients from normal controls,2) How well is TLE evaluated in terms of hearing and auditory processing? METHODS: The study inclusion criteria were: 1) patients diagnosed with temporal lobe epilepsy, 2) presence of a normal control group, 3) auditory processing assessment using auditory stimuli with behavioral tests and/or P300 or Mitch Match Negativity (MMN) latency and/or amplitude, 4) publications written in English, 5) publication date after 2000. 132 articles were retrieved and based on PRISMA & PICO criteria 23 articles were analyzed. RESULTS: Temporal resolution and processing as measured by the behavioral tests of Gaps-In-Noise (GIN) and Duration Pattern Test (DPT) document deficiencies in TLE patients and separate them from normal controls. Electrophysiology as measured by MMN & P300 shows statistically significant differences in TLE patients compared to controls with patients showing deficient auditory processing. A clear difference between studies with psychoacoustic assessment as opposed to electrophysiology ones may be due to lacking or incomplete evaluation of peripheral hearing by gold standard tools (76.9% in electrophysiology studies). CONCLUSION: Auditory processing is deficient in patients with TLE. There is a clear need to evaluate hearing efficiency before proceeding to auditory processing evaluation with behavioral or electrophysiological tests.

Infrared spectroscopy as a new approach for early fabry disease screening: a pilot study.

BACKGROUND: Fabry disease (FD) is a rare X-linked lysosomal storage disorder marked by alpha-galactosidase-A (α-Gal A) deficiency, caused by pathogenic mutations in the GLA gene, resulting in the accumulation of glycosphingolipids within lysosomes. The current screening test relies on measuring α-Gal A activity. However, this approach is limited to males. Infrared (IR) spectroscopy is a technique that can generate fingerprint spectra of a biofluid's molecular composition and has been successfully applied to screen numerous diseases. Herein, we investigate the discriminating vibration profile of plasma chemical bonds in patients with FD using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. RESULTS: The Fabry disease group (n = 47) and the healthy control group (n = 52) recruited were age-matched (39.2 ± 16.9 and 36.7 ± 10.9 years, respectively), and females were predominant in both groups (59.6% and 65.4%, respectively). All patients had the classic phenotype (100%), and no late-onset phenotype was detected. A generated partial least squares discriminant analysis (PLS-DA) classification model, independent of gender, allowed differentiation of samples from FD vs. control groups, reaching 100% sensitivity, specificity and accuracy. CONCLUSION: ATR-FTIR spectroscopy harnessed to pattern recognition algorithms can distinguish between FD patients and healthy control participants, offering the potential of a fast and inexpensive screening test.

Effects of dietary Lactobacillus postbiotics and bacitracin on the modulation of mucosa-associated microbiota and pattern recognition receptors affecting immunocompetence of jejunal mucosa in pigs challenged with enterotoxigenic F18+ Escherichia coli.

BACKGROUND: Enterotoxigenic Escherichia coli (E. coli) is a threat to humans and animals that causes intestinal disorders. Antimicrobial resistance has urged alternatives, including Lactobacillus postbiotics, to mitigate the effects of enterotoxigenic E. coli. METHODS: Forty-eight newly weaned pigs were allotted to NC: no challenge/no supplement; PC: F18+ E. coli challenge/no supplement; ATB: F18+ E. coli challenge/bacitracin; and LPB: F18+ E. coli challenge/postbiotics and fed diets for 28 d. On d 7, pigs were orally inoculated with F18+ E. coli. At d 28, the mucosa-associated microbiota, immune and oxidative stress status, intestinal morphology, the gene expression of pattern recognition receptors (PRR), and intestinal barrier function were measured. Data were analyzed using the MIXED procedure in SAS 9.4. RESULTS: PC increased (P < 0.05) Helicobacter mastomyrinus whereas reduced (P < 0.05) Prevotella copri and P. stercorea compared to NC. The LPB increased (P < 0.05) P. stercorea and Dialister succinatiphilus compared with PC. The ATB increased (P < 0.05) Propionibacterium acnes, Corynebacterium glutamicum, and Sphingomonas pseudosanguinis compared to PC. The PC tended to reduce (P = 0.054) PGLYRP4 and increased (P < 0.05) TLR4, CD14, MDA, and crypt cell proliferation compared with NC. The ATB reduced (P < 0.05) NOD1 compared with PC. The LPB increased (P < 0.05) PGLYRP4, and interferon-γ and reduced (P < 0.05) NOD1 compared with PC. The ATB and LPB reduced (P < 0.05) TNF-α and MDA compared with PC. CONCLUSIONS: The F18+ E. coli challenge compromised intestinal health. Bacitracin increased beneficial bacteria showing a trend towards increasing the intestinal barrier function, possibly by reducing the expression of PRR genes. Lactobacillus postbiotics enhanced the immunocompetence of nursery pigs by increasing the expression of interferon-γ and PGLYRP4, and by reducing TLR4, NOD1, and CD14.

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.

The adjuvant BcfA activates antigen presenting cells through TLR4 and supports TFH and TH1 while attenuating TH2 gene programming.

Introduction: Adjuvants added to subunit vaccines augment antigen-specific immune responses. One mechanism of adjuvant action is activation of pattern recognition receptors (PRRs) on innate immune cells. Bordetella colonization factor A (BcfA); an outer membrane protein with adjuvant function, activates TH1/TH17-polarized immune responses to protein antigens from Bordetella pertussis and SARS CoV-2. Unlike other adjuvants, BcfA does not elicit a TH2 response. Methods: To understand the mechanism of BcfA-driven TH1/TH17 vs. TH2 activation, we screened PRRs to identify pathways activated by BcfA. We then tested the role of this receptor in the BcfA-mediated activation of bone marrow-derived dendritic cells (BMDCs) using mice with germline deletion of TLR4 to quantify upregulation of costimulatory molecule expression and cytokine production in vitro and in vivo. Activity was also tested on human PBMCs. Results: PRR screening showed that BcfA activates antigen presenting cells through murine TLR4. BcfA-treated WT BMDCs upregulated expression of the costimulatory molecules CD40, CD80, and CD86 and produced IL-6, IL-12/23 p40, and TNF-α while TLR4 KO BMDCs were not activated. Furthermore, human PBMCs stimulated with BcfA produced IL-6. BcfA-stimulated murine BMDCs also exhibited increased uptake of the antigen DQ-OVA, supporting a role for BcfA in improving antigen presentation to T cells. BcfA further activated APCs in murine lungs. Using an in vitro TH cell polarization system, we found that BcfA-stimulated BMDC supernatant supported TFH and TH1 while suppressing TH2 gene programming. Conclusions: Overall, these data provide mechanistic understanding of how this novel adjuvant activates immune responses.

Structural and Phylogenetic In Silico Characterization of Vitis vinifera PRR Protein as Potential Target for Plasmopara viticola Infection.

Fungi infection, especially derived from Plasmopara viticola, causes severe grapevine economic losses worldwide. Despite the availability of chemical treatments, looking for eco-friendly ways to control Vitis vinifera infection is gaining much more attention. When a plant is infected, multiple disease-control molecular mechanisms are activated. PRRs (Pattern Recognition Receptors) and particularly RLKs (receptor-like kinases) take part in the first barrier of the immune system, and, as a consequence, the kinase signaling cascade is activated, resulting in an immune response. In this context, discovering new lectin-RLK (LecRLK) membrane-bounded proteins has emerged as a promising strategy. The genome-wide localization of potential LecRLKs involved in disease defense was reported in two grapevine varieties of great economic impact: Chardonnay and Pinot Noir. A total of 23 potential amino acid sequences were identified, exhibiting high-sequence homology and evolution related to tandem events. Based on the domain architecture, a carbohydrate specificity ligand assay was conducted with docking, revealing two sequences as candidates for specific Vitis vinifera-Plasmopara viticola host-pathogen interaction. This study confers a starting point for designing new effective antifungal treatments directed at LecRLK targets in Vitis vinifera.

The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR.

This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual selection of cluster centers. And the clustering process of the improved DPC is automatic without manual intervention. The cut-off distance is avoided by forming a voxel structure and using the number of points in the voxel as the local density of the voxel. The automatic selection of cluster centers is realized by selecting the voxels whose gamma values are greater than the gamma value of the inflection point of the fitted γ curve as cluster centers. Finally, a new merging strategy is introduced to overcome the over-segmentation problem and obtain the final clustering result. To verify the effectiveness of the improved DPC, experiments on point cloud segmentation of LiDAR under different scenes were conducted. The basic DPC, K-means, and DBSCAN were introduced for comparison. The experimental results showed that the improved DPC is effective and its application to point cloud segmentation of LiDAR is successful. Compared with the basic DPC, K-means, the improved DPC has better clustering accuracy. And, compared with DBSCAN, the improved DPC has comparable or slightly better clustering accuracy without nontrivial parameters.

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.

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.

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.