Identification Of Modules Research Articles

Concealed cardiomyopathy as an emerging cause of sudden cardiac arrest and sudden cardiac death.

The inherited cardiomyopathies exhibit a broad spectrum of disease, with some patients remaining asymptomatic throughout life, while, for others, the first symptom of disease is sudden cardiac death at a young age. The risk of malignant ventricular arrhythmia in these conditions has traditionally been linked to the degree of structural myocardial abnormalities and functional impairment. However, recent advances in genetic testing and knowledge of the genetic basis of the diseases have led to the identification of concealed cardiomyopathy, in which sudden cardiac arrest or sudden cardiac death occurs in the absence of observable clinical features of cardiomyopathy, with a diagnosis being made only after the identification of a causative genetic variant. Increased awareness of concealed cardiomyopathy, a better understanding of mechanisms of arrhythmia and identification of risk modulators will be vital to improve care for families with concealed cardiomyopathy.

Comparative small molecule screening of primary human acute leukemias, engineered human leukemia and leukemia cell lines.

Targeted therapeutics for high-risk cancers remain an unmet medical need. Here we report the results of a large-scale screen of over 11,000 molecules for their ability to inhibit the survival and growth in vitro of human leukemic cells from multiple sources including patient samples, de novo generated human leukemia models, and established human leukemic cell lines. The responses of cells from de novo models were most similar to those of patient samples, both of which showed striking differences from the cell-line responses. Analysis of differences in subtype-specific therapeutic vulnerabilities made possible by the scale of this screen enabled the identification of new specific modulators of apoptosis, while also highlighting the complex polypharmacology of anti-leukemic small molecules such as shikonin. These findings introduce a new platform for uncovering new therapeutic options for high-risk human leukemia, in addition to reinforcing the importance of the test sample choice for effective drug discovery.

Galectin-3 zebrafish model: identification and pharmacological modulation of arrhythmogenic cardiomyopathy-related phenotypes

Abstract Background Arrhythmogenic Cardiomyopathy (ACM) is rare hereditary cardiac condition characterized by the substitution of ventricular myocardium with fibro-fatty tissue leading to high risk of life-threatening arrhythmias and sudden cardiac death, particularly among young individuals and athletes. Based on genotype-phenotype correlation, the spectrum of the ACM phenotype was shown to be broader than previously believed. Approximately 40% of ACM patients carry one or more putative pathogenic variants in genes encoding desmosomal proteins. Recently LGALS3 gene involvement in ACM early pathogenesis has been advocated. Aim To achieve a comprehensive understanding of LGALS3 role in the pathogenesis of AC. Methods This stable mutant line was generated by CRISPR/Cas9 strategy and then deeply characterized. Phenotyping included heart activity characterization using a recently published software specific for zebrafish, the pyHeart4Fish. Gene expression analysis was carried out by using RNAseq, Real Time PCR and signaling pathway reporter zebrafish lines. Immunofluorescence analysis were performed using and antibody against L-plastin, a leukocyte marker for the detection of inflammatory cells and Acridine Orange/Ethidium Bromide staining to check the possible presence of apoptotic and necrotic events. Results Morphological investigation revealed notable structural abnormalities such as pericardial effusion and/or hemopericardium. The heart activity analysis detected a diminished ventricle contractility and ejection fraction, with bradycardia and occurrence of arrhythmia events. Rna analysis highlighted the dysregulation of Wnt/β-catenin signaling pathway. Moreover, the L-plastin inflammatory marker revealed a significant infiltration of inflammatory cells in the heart as well as an elevated number of apoptotic and necrotic cells (figure 1). The pharmacological treatment, partially rescued a subset of AC-related cardiac phenotypes observed, including heart contractility, bradycardia and arrhythmia. Conclusion Our findings strongly validate our lgals3a zebrafish mutant as an effective model to elucidate its role in ACM related phenotype. This model accurately replicates the cardiac abnormalities and dysregulation in cell signaling that are characteristic of ACM. Further, observed cell death and inflammation and the partial recovery of cardiac phenotypes through pharmacological intervention, underscores the potential of targeting the Wnt/β-catenin signaling pathway as future perspective for ACM.Figure 1

Myeloid-Derived Suppressor Cells in Bladder Cancer: An Emerging Target

Bladder cancer remains a prevalent and challenging malignancy. Myeloid-derived suppressor cells (MDSCs) have emerged as key contributors to the immunosuppressive tumor microenvironment, facilitating tumor progression, immune evasion, and resistance to therapies. This review explores the role of MDSC in bladder cancer, highlighting their involvement in immune regulation; tumor progression; and resistance to therapies such as bacillus Calmette–Guérin (BCG) therapy, chemotherapy, and immune checkpoint inhibitors (ICIs). We also discuss their potential as biomarkers and therapeutic targets, with current evidence suggesting that targeting MDSCs, either alone or in combination with existing treatments such as BCG and ICIs, may enhance anti-tumor immunity and improve clinical outcomes. However,, challenges remain, particularly regarding the identification and therapeutic modulation of MDSC subpopulations. Further research is warranted to fully elucidate their role in bladder cancer and to optimize MDSC-targeted therapies.

Real-Time Ubiquitous Radar Target Classification with 1D ResNet-SE-Based Multi-Channel Network

Ubiquitous radar has significant advantages over traditional radar in detecting and identifying low, slow, and small (LSS) targets in a strong clutter environment. It effectively addresses challenges faced in low-altitude target monitoring within the low-altitude economy (LAE). The working mode of ubiquitous radar, which tracks first and identifies later, provides high-resolution Doppler data to the target identification module. Utilizing high-resolution Doppler data allows for the effective identification of LSS targets. To meet the needs of real-time classification, this paper first designs a real-time classification process based on sliding window Doppler data. This process requires the classifier to classify targets based on multiple rows of high-resolution Doppler spectra within the sliding window. Secondly, a multi-channel parallel perception network based on a 1D ResNet-SE network is designed. This network captures features within the rows of sliding window data and integrates inter-row features. Experiments show that the designed real-time classification process and multi-channel parallel perception network meet real-time classification requirements. Compared to the 1D CNN-MLP multi-channel network, the proposed 1D ResNet-MLP multi-channel network improves the classification accuracy from 98.71% to 99.34%. Integrating the 1D Squeeze-and-Excitation (SE) module to form the 1D ResNet-SE-MLP network further enhances accuracy to 99.58%, with drone target accuracy, the primary focus of the LAE, increasing from 97.19% to 99.44%.

Advances in deep learning-based intelligent receivers

Abstract. This paper presents a summary of the progress of deep learning in the research of intelligent receivers and predicts its future development direction. First, the background of the era of intelligent receivers is introduced. With the rapid development of communication technology, intelligent receivers show insufficient flexibility, low accuracy, and spectral efficiency in detecting current radio signals. This is a challenge that is difficult to overcome in order to meet the needs of modern signal recovery. With the advent of artificial intelligence technologies such as deep learning, academia and industry began to explore the potential of applying these techniques to the field of communication receivers, leading to the emergence of intelligent receivers. This paper then introduces the fundamentals of wireless communication systems and deep learning neural networks, laying the groundwork for a deeper understanding of the subsequent content. Chapter 3 provides a comprehensive overview of the application of intelligent receivers in signal reception, including channel estimation, signal detection, modulation identification, demodulation and decoding. Finally, the paper considers the practical applications of intelligent receivers and speculates on their future development.

Pharmacology of PIEZO1 channels.

PIEZO1 is a eukaryotic membrane protein that assembles as trimers to form calcium-permeable, non-selective cation channels with exquisite capabilities for mechanical force sensing and transduction of force into effect in diverse cell types that include blood cells, endothelial cells, epithelial cells, fibroblasts and stem cells and diverse systems that include bone, lymphatics and muscle. The channel has wide-ranging roles and is considered as a target for novel therapeutics in ailments spanning cancers and cardiovascular, dental, gastrointestinal, hepatobiliary, infectious, musculoskeletal, nervous system, ocular, pregnancy, renal, respiratory and urological disorders. The identification of PIEZO1 modulators is in its infancy but useful experimental tools emerged for activating, and to a lesser extent inhibiting, the channels. Elementary structure-activity relationships are known for the Yoda series of small molecule agonists, which show the potential for diverse physicochemical and pharmacological properties. Intriguing effects of Yoda1 include the stimulated removal of excess cerebrospinal fluid. Despite PIEZO1's broad expression, opportunities are suggested for selective positive or negative modulation without intolerable adverse effects. Here we provide a focused, non-systematic, narrative review of progress with this pharmacology and discuss potential future directions for research in the area.

DNA logic gate for cancer cells identification, detection, and modulation of cell–cell interactions

The precise detection of cancer cells and modulation of cell–cell interactions would be highly desired for noninvasive and nontoxic cancer treatments but it remains challenging due to the existence of biomarkers on both normal and tumor cells. In this work, we engineered a logic-gated DNA molecular machine incorporating a membrane protein aptamer and an extracellular acid responsive split i-motif for the precise identification and detection of cancer cells, as well as the mediation of cell–cell interactions. In this system, only in the presence of both the membrane protein and protons, the logic-gated DNA molecular machine is activated to perform the “AND” logic operation to induce catalytic hairpin assembly (CHA) on the electrode, leading to the formation of G-quadruplex/hemin and the generation of an amplified electrochemical signal. On the other hand, the formed “AND” logic could hybridize with the DNA fragment on T-cell, effectively shorting the distance between T-cell and cancer cells. Benefiting from the DNA logic gate and CHA, this strategy could not only precisely identify cancer cells, but also show a sensitive analysis of cancer cells with a detection limit of 15 cells/mL. The logic-gated DNA molecular machine achieved the identification of cancer cells and modulation of intercellular interactions, providing a new idea for precise disease diagnosis and therapy.

Modulation Pattern Identification Algorithm for Model Transfer Based on Machine Learning

To overcome the shortcomings of traditional modulation identification methods in small sample signal processing, this paper proposes a new model transfer modulation identification algorithm that integrates machine learning. The algorithm first uses a combination of convolutional neural network (CNN) and long short-term memory network (LSTM) to fully tap and utilize its feature extraction capabilities in the spatial and temporal dimensions. It significantly improves the performance of the model in these two dimensions through a parallel structure, which can ensure high efficiency and accuracy in signal recognition tasks. Then, through model migration technology, the common features in the pre-trained model are retained and fine-tuned to adapt to new signal recognition needs, effectively solving the problem of small sample signal recognition. Experimental results show that the algorithm proposed in this paper significantly improves signal recognition accuracy, with an average accuracy rate of 96%. In the 16APSK and 16QAM signal recognition tasks, the accuracy is as high as 100% and 99%, respectively, which demonstrates excellent performance.

Identification of function modules in the co-expression protein–protein interaction network of Bombyx mori in response to Beauveria bassiana infection

Beauveria bassiana (B. bassiana) is a common fungal disease in sericulture. Previous research has primarily focused on investigating genes involved in innate immunity. However, the response of Bombyx mori (B. mori) to B. bassiana requires the coordination of other biological processes in addition to the immune system. We measured protein expression profile of B. mori after inoculating B. bassiana using iTRAQ technology in previous. Here we constructed a co-expression protein–protein interaction network of B. mori in response to B. bassiana infection. Subnetworks and modules were analyzed, and the functions of these modules were annotated. The results revealed the identification of numerous proteins associated with cellular immunity, including those involved in phagosomes, lysosomes, mTOR signaling, sugar metabolism, and the ubiquitin–proteasome pathway. Meanwhile, we observed that the pathways involved in protein synthesis were activated, including pyruvate and purine metabolism, RNA transport, ribosome, protein processing in endoplasmic reticulum, and protein export pathways, during B. bassiana infection. Based on this analysis, we selected six candidate genes (shock protein, ribosome, translocon, actin muscle-type A2, peptidoglycan recognition protein, and collagenase) that were found to be related to the response to B. bassiana. Further verification experiments demonstrated significant changes in their expression levels after inoculation with B. bassiana. These research findings provide new insights into the molecular mechanism of insect immune response to fungal infection.

Evaluating the use of grey-box system identification for digital twins in manufacturing automation

ABSTRACT A key element of any digital twin is the digital replica, or model, of its physical counterpart. When applied to industrial automation systems, it is important to consider the trade-off between model fidelity and computational complexity when developing this model. In this paper, we investigate the use of grey-box system identification as a means for producing digital twin models, as the approach reduces computational complexity while maintaining model fidelity. Using a three-link robotic manipulator, we evaluate the ability the digital twin’s system identification module to produce a high-fidelity model while under the influence of input disturbances. We then expand these results by evaluating the ability of the determined model to accurately emulate the robotic manipulator for tasks given to digital twins through the system simulation and monitoring modules. The results of this testing demonstrate that the grey-box system identification module is prone to error and sensitive to input disturbances; however, it produces models that are still able to accurately predict the dynamic response of the robotic manipulator. Furthermore, the tests demonstrate the determined models can be used by the digital twin for simulation and monitoring applications with certain limitations.

Multiomics approach discloses lipids and metabolites profiles associated to Parkinson's disease stages and applied therapies

Profiling circulating lipids and metabolites in Parkinson's disease (PD) patients could be useful not only to highlight new pathways affected in PD condition but also to identify sensitive and effective biomarkers for early disease detection and potentially effective therapeutic interventions. In this study we adopted an untargeted omics approach in three groups of patients (No L-Dopa, L-Dopa and DBS) to disclose whether long-term levodopa treatment with or without deep brain stimulation (DBS) could reflect a characteristic lipidomic and metabolomic signature at circulating level. Our findings disclosed a wide up regulation of the majority of differentially regulated lipid species that increase with disease progression and severity. We found a relevant modulation of triacylglycerols and acyl-carnitines, together with an altered profile in adiponectin and leptin, that can differentiate the DBS treated group from the others PD patients. We found a highly significant increase of exosyl ceramides (Hex2Cer) and sphingoid bases (SPB) in PD patients mainly in DBS group (p < 0.0001), which also resulted in a highly accurate diagnostic performance. At metabolomic level, we found a wide dysregulation of pathways involved in the biosynthesis and metabolism of several amino acids. The most interesting finding was the identification of a specific modulation of L-glutamic acid in the three groups of patients. L-glutamate levels increased slightly in No L-Dopa and highly in L-Dopa patients while decreased in DBS, suggesting that DBS therapy might have a beneficial effect on the glutamatergic cascade. All together, these data provide novel insights into the molecular and metabolic alterations underlying PD therapy and might be relevant for PD prediction, diagnosis and treatment.

Identification of Key Immune and Cell Cycle Modules and Prognostic Genes for Glioma Patients through Transcriptome Analysis.

Gliomas, the most prevalent type of primary brain tumor, stand out as one of the most aggressive and lethal types of human cancer. To uncover potential prognostic markers, we employed the weighted correlation network analysis (WGCNA) on the Chinese Glioma Genome Atlas (CGGA) 693 dataset to reveal four modules significantly associated with glioma clinical traits, primarily involved in immune function, cell cycle regulation, and ribosome biogenesis. Using the least absolute shrinkage and selection operator (LASSO) regression algorithm, we identified 11 key genes and developed a prognostic risk score model, which exhibits precise prognostic prediction in the CGGA 325 dataset. More importantly, we also validated the model in 12 glioma patients with overall survival (OS) ranging from 4 to 132 months using mRNA sequencing and immunohistochemical analysis. The analysis of immune infiltration revealed that patients with high-risk scores exhibit a heightened immune infiltration, particularly immune suppression cells, along with increased expression of immune checkpoints. Furthermore, we explored potentially effective drugs targeting 11 key genes for gliomas using the library of integrated network-based cellular signatures (LINCS) L1000 database, identifying that in vitro, both torin-1 and clofarabine exhibit promising anti-glioma activity and inhibitory effect on the cell cycle, a significant pathway enriched in the identified glioma modules. In conclusion, our study provides valuable insights into molecular mechanisms and identifying potential therapeutic targets for gliomas.

Saffron (Crocus sativus) and its constituents in ovalbumin-induced asthma model: a preclinical systematic review and meta-analysis.

Animal and human studies have demonstrated that the saffron and the active components of saffron, including crocin, crocetin, and safranal, possess anti-inflammatory, antioxidant, and immunomodulatory properties. In this meta-analysis, the preclinical evidence and potential mechanism of saffron were explored in an animal model of ovalbumin-induced asthma. Studies related to saffron and its constituents in an animal model of ovalbumin-induced asthma from the beginning to March 2024 were searched from Scopus, PubMed, and Web of Science databases. The methodological quality of the studies was evaluated using the 15-item CAMARADES checklist. Data analysis was performed using STATA software version 17. Thirteen studies with 536 animals (268 animals in the intervention group and 268 animals in the ovalbumin-induced group) were analyzed. The meta-analysis findings demonstrated that saffron and its constituents played a significant role in reducing total WBC, eosinophil, lymphocyte, and monocyte counts. Moreover, saffron showed a significant decrease in the levels of IL-4, IL-5, IL-13, IgE, histamine, endothelin, nitric oxide, and nitrite. Moreover, saffron was found to elevate EC50 thresholds and lower maximum response rates in experimental animals. The analysis revealed a significant identification of modulation in endoplasmic reticulum (ER) stress markers and miRNAs pathways. Saffron and its components may impact ovalbumin-induced asthma model in animals through anti-inflammatory, antioxidant, and immunomodulatory pathways, as well as improving pulmonary function and modulating ER stress markers and miRNAs pathways. As a result, saffron should be considered for further clinical trials in individuals suffering from asthma.

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

Fluorescence-Based HTS Assays for Ion Channel Modulation in Drug Discovery Pipelines.

Ion channels represent a druggable family of transmembrane pore-forming proteins with important (patho)physiological functions. While electrophysiological measurement (manual patch clamp) remains the only direct method for detection of ion currents, it is a labor-intensive technique. Although automated patch clamp instruments have become available to date, their high costs limit their use to large pharma companies or commercial screening facilities. Therefore, fluorescence-based assays are particularly important for initial screening of compound libraries. Despite their numerous disadvantages, they are highly amenable to high-throughput screening and in many cases, no sophisticated instrumentation or materials are required. These features predispose them for implementation in early phases of drug discovery pipelines (hit identification), even in an academic environment. This review summarizes the advantages and pitfalls of individual methodological approaches for identification of ion channel modulators employing fluorescent probes (i.e., membrane potential and ion flux assays) with emphasis on practical aspects of their adaptation to high-throughput format.

Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)

This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). The proposed method incorporates a pair of modifications to the standard slime mould algorithm (SMA). Primary modification encloses computation of random average position between the slimes' current individual position and best individual position towards resolution of local optima issue. Subsequent modification then involves substitution of an exponential function to the existing tangent hyperbolic function within formula p of the standard SMA in enabling improved probability variants via the selection of updated equations. Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification.

Intra-pulse modulation discrimination using a self-supervised attention-driven CNN-BiLSTM-VAE combination

Identification of intra-pulse modulation (IPM) of radar signals is a crucial part of contemporary electronic support systems and electronic intelligence reconnaissance. Artificial intelligence (AI)-based methods can be very effective in recognising the IPM of radar signals. In this direction, an automatic method is proposed for recognising a few IPMs of radar signals based on continuous wavelet transform (CWT) and a hybrid model of self-attention (SA)-aided convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Firstly, time–frequency attributes of different radar signals are obtained using CWT, and thereafter CNN-SA-BiLSTM is utilised for feature extraction from the 2D scalograms formed by the time–frequency components. The CNN extracts features from the scalograms, SA enhances the discriminative power of the feature map, and BiLSTM detects radar signals based on these features. Additionally, the study addresses real-world data imbalance issues by incorporating a generative AI model, namely the Variational Autoencoder (VAE). The VAE-based approach effectively mitigates challenges arising from data imbalance situations. This method is tested at varying noise levels to give a proper representation of the actual electronic warfare environment. The simulation results demonstrate that the best overall recognition accuracy of the proposed method is 98.4%, even at low signal-to-noise ratios (SNR).

Identification of susceptibility modules and characteristic genes to osteoarthritis by WGCNA.

The susceptibility modules and characteristic genes of patients with osteoarthritis (OA) were determined by weighted gene co-expression network analysis (WGCNA), and the role of immune cells in OA related microenvironment was analyzed. GSE98918 and GSE117999 data sets are from GEO database. R language was used to conduct difference analysis for the new data set after merging. The formation of gene co-expression network, screening of susceptibility modules and screening of core genes are all through WGCNA. GO and KEGG enrichment analyses were used for Hub genes. The characteristic genes of the disease were obtained by Lasso regression screening. SSGSEA was used to estimate immune cell abundance in sample and a series of correlation analyses were performed. WGCNA was used to form 6 gene co-expression modules. The yellow-green module is identified as the susceptible module of OA. 202 genes were identified as core genes. Finally, RHOT2, FNBP4 and NARF were identified as the characteristic genes of OA. The results showed that the characteristic genes of OA were positively correlated with plasmacytoid dendritic cells, NKT cells and immature dendritic cells, but negatively correlated with active B cells. MDSC were the most abundant immune cells in cartilage. This study identified the Hippo signaling pathway, mTOR signaling pathway, and three characteristic genes (RHOT2, FNBP4, NARF) as being associated with osteoarthritis (OA). These three genes are downregulated in the cartilage of OA patients and may serve as biomarkers for early diagnosis and targeted therapy. Proper regulation of immune cells may aid in the treatment of OA. Future research should focus on developing tools to detect these genes and exploring their therapeutic applications.

FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization

Framing theory is a widely accepted theoretical framework in the field of news communication studies, frequently employed to analyze the content of news reports. This paper innovatively introduces framing theory into the text summarization task and proposes a news text summarization method based on framing theory to address the global context of rapidly increasing speed and scale of information dissemination. Traditional text summarization methods often overlook the implicit deep-level semantic content and situational frames in news texts, and the method proposed in this paper aims to fill this gap. Our deep learning-based news frame identification module can automatically identify frame elements in the text and predict the dominant frame of the text. The frame-aware summarization generation model (FrameSum) can incorporate the identified frame feature into the text representation and attention mechanism, ensuring that the generated summary focuses on the core content of the news report while maintaining high information coverage, readability, and objectivity. Through empirical studies on the standard CNN/Daily Mail dataset, we found that this method performs significantly better in improving summary quality and maintaining the accuracy of news facts.