Signature Extraction Research Articles

Whole Genome Sequencing Analysis of Model Organisms Elucidates the Association Between Environmental Factors and Human Cancer Development

Determining a novel etiology and mechanism of human cancer requires extraction of characteristic mutational signatures derived from chemical substances. This study explored the mutational signatures of N-nitroso bile acid conjugates using Salmonella strains. Exposing S. typhimurium TA1535 to N-nitroso-glycine/taurine bile acid conjugates induced a predominance of C:G to T:A transitions. Two mutational signatures, B1 and B2, were extracted. Signature B1 is associated with N-nitroso-glycine bile acid conjugates, while Signature B2 is linked to N-nitroso-taurine bile acid conjugates. Signature B1 revealed a strong transcribed strand bias with GCC and GCT contexts, and the mutation pattern of N-nitroso-glycine bile acid conjugates in YG7108, which lacks O6-methylguanine DNA methyltransferases, matched that of the wild-type strain TA1535, suggesting that O6-methyl-deoxyguanosine contributes to mutations in the relevant regions. COSMIC database-based similarity analysis revealed that Signature B1 closely resembled SBS42, which is associated with occupational cholangiocarcinoma caused by overexposure to 1,2-dichlolopropane (1,2-DCP) and/or dichloromethane (DCM). Moreover, the inflammatory response pathway was induced by 1,2-DCP exposure in a human cholangiocyte cell line, and iNOS expression was positive in occupational cholangiocarcinomas. These results suggest that 1,2-DCP triggers an inflammatory response in biliary epithelial cells by upregulating iNOS and N-nitroso-glycine bile acid conjugate production, resulting in cholangiocarcinoma via DNA adduct formation.

Diagnosis of Schizophrenia Using EEG Sensor Data: A Novel Approach with Automated Log Energy-Based Empirical Wavelet Reconstruction and Cepstral Features.

Schizophrenia (SZ) is a severe mental disorder characterised by disruptions in cognition, behaviour, and perception, significantly impacting an individual's life. Traditional SZ diagnosis methods are labour-intensive and prone to errors. This study presents an innovative automated approach for detecting SZ acquired through electroencephalogram (EEG) sensor signals, aiming to improve diagnostic efficiency and accuracy. We utilised Fast Independent Component Analysis to remove artefacts from raw EEG sensor data. A novel Automated Log Energy-based Empirical Wavelet Reconstruction (ALEEWR) technique was introduced to reconstruct decomposed modes based on their variability, ensuring effective extraction of meaningful EEG signatures. Cepstral-based features-cepstral activity, cepstral mobility, and cepstral complexity-were used to capture the power, rate of change, and irregularity of the cepstrum of preprocessed EEG signals. ANOVA-based feature selection was applied to refine these features before classification using the K-Nearest Neighbour (KNN) algorithm. Our approach achieved an exceptional accuracy of 99.4%, significantly surpassing previous methods. The proposed ALEEWR and cepstral analysis demonstrated high precision, sensitivity, and specificity in the automated diagnosis of schizophrenia. This study introduces a highly accurate and efficient method for SZ detection using EEG technology. The proposed techniques offer significant improvements in diagnostic accuracy, with potential implications for enhancing SZ diagnosis and patient care through automated systems.

Fault signature extraction of rolling bearings under variable speed via time–frequency overlap group sparse representation

The high-fidelity extraction of the nonperiodic fault transients of rolling bearings under variable speed is of significant importance, while still a challenge for early-stage fault diagnosis of major equipment such as aero engines and wind turbines. A method termed continuous symmetric Laplace wavelet transform (CSLWT) enhanced time–frequency overlap group sparse (OGS) representation is proposed in this paper. A symmetric Laplace mother wavelet function is proposed which could satisfy the admissibility condition of wavelet decomposition and reconstruction, upon which the CSLWT is fabricated and adopted as the sparse representation dictionary to match the nonperiodic fault transients. Since the decomposition coefficients of nonperiodic fault impulses via the CSLWT exhibit group sparsity on the time–frequency plane with unknown intervals, the time–frequency OGS model is adopted and then solved for extraction of the nonperiodic fault transients. Moreover, a data driven, multi-objective optimization strategy is presented for determining the hyper-parameters of the sparse model. The correlated kurtosis of angular envelope signal (CK-AE) and harmonic-to-noise energy ratio of the order spectrum (HNR-OS) are introduced as the optimization objectives, and the Pareto front is solved based on the NSGA-II algorithm, upon which the acceptable solutions set is chosen from the Pareto front, and the proper hyper-parameters are further determined via proposed normalized solutions distance density sorting of the acceptable solutions set. The performance of the proposed method is validated via processing both simulated and experimental vibration signals, as well as compared with some tradition fault diagnosis methods.

Convolutional Autoencoders for Signal Reconstruction and their Application to Damage Signature Extraction

The utilization of Machine Learning (ML) techniques for Structural Health Monitoring (SHM) has increased in the last years. Using ML techniques for signal reconstruction has been explored in the literature of this field. In this work, a database of Ultrasonic Guided Waves (UGW), propagated through a thermoplastic composite plate, and generated and acquired with piezoelectric transducers (PZT), has been preprocessed, organized, and analyzed to reconstruct signals by using Convolutional Autoencoders (cAE) neural networks. This special type of autoencoders (AE) is based on the utilization of convolutional stages, which are formed by deep-learning layers that are especially useful for their utilization on discretized time-series signals, due to their ability to convolve different sections of the signal, thus extracting different features which are automatically learned by the network. The cAE, which have a bottleneck section in their middle part (between the encoder and the decoder sections), have the ability to reduce the dimensionality of the input data, thus allowing the extraction of a minimum number of relevant features of the signals. Summarizing, this paper presents a neural network designed as a cAE which is able to reconstruct data from a reduced number of features, reaching a correlation value between the real and the artificial data higher than 98%.

Temporally-preserving latent variable models: Offline and online training for reconstruction and interpretation of fault data for gearbox condition monitoring

Latent variable models can effectively determine the condition of essential rotating machinery without needing labelled data. These models analyse vibration data via an unsupervised learning strategy. Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task. In a temporal-preserving context, two approaches exist to develop a condition-monitoring methodology: offline and online. For latent variable models, the available training modes are no different. While many traditional methods use offline training, online training can dynamically adjust the latent manifold, possibly leading to better fault signature extraction from the vibration data. This study explores online training using temporal-preserving latent variable models. Within online training, there are two main methods: one focuses on reconstructing data and the other on interpreting the data components. Both are considered to evaluate how they diagnose faults over time. Using two experimental datasets, the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions. Importantly, the complementarity of offline and online models is emphasised, reassuring their versatility in fault diagnostics. Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable model-based fault diagnostics. Conflict of Interest Statement The authors declare no conflicts of interest.

Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification.

Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis, and treatment of cancer patients. We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics. MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE.

Enhancing Fault Identification, Classification and Location Accuracy in Transmission Lines

This research paper presents a proposed system for fault identification, classification and location in transmission lines using a Support Vector Machine (SVM)-based technique in conjunction with a Positive Sequence Analyzer. The objective is to develop an accurate and reliable method for identifying, classifying and locating different fault types in transmission lines. The proposed system leverages the capabilities of SVMs in handling high-dimensional feature spaces and the fault signature extraction capabilities of the Positive Sequence Analyzer. Experimental evaluations are conducted to assess the performance and effectiveness of the proposed system, comparing it with existing fault identification and classification methods. The results demonstrate the superior performance and robustness of the SVM-based technique utilizing the Positive Sequence Analyzer, providing a valuable contribution to fault management and system reliability in transmission line networks.

Autonomous Tumor Signature Extraction Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study.

Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI. Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing "green" in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) and p-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer. The highest R (R > 0.45), AUC (>0.90), and lowest p-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the "greenest" parts from the selected blob. The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness.

A multi-agent adaptive deep learning framework for online intrusion detection

The network security analyzers use intrusion detection systems (IDSes) to distinguish malicious traffic from benign ones. The deep learning-based (DL-based) IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and costly signature extraction process. However, this new generation of IDSes still needs to overcome a number of challenges to be employed in practical environments. One of the main issues of an applicable IDS is facing traffic concept drift, which manifests itself as new (i.e. , zero-day) attacks, in addition to the changing behavior of benign users/applications. Furthermore, a practical DL-based IDS needs to be conformed to a distributed (i.e. , multi-sensor) architecture in order to yield more accurate detections, create a collective attack knowledge based on the observations of different sensors, and also handle big data challenges for supporting high throughput networks. This paper proposes a novel multi-agent network intrusion detection framework to address the above shortcomings, considering a more practical scenario (i.e., online adaptable IDSes). This framework employs continual deep anomaly detectors for adapting each agent to the changing attack/benign patterns in its local traffic. In addition, a federated learning approach is proposed for sharing and exchanging local knowledge between different agents. Furthermore, the proposed framework implements sequential packet labeling for each flow, which provides an attack probability score for the flow by gradually observing each flow packet and updating its estimation. We evaluate the proposed framework by employing different deep models (including CNN-based and LSTM-based) over the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. Through extensive evaluations and experiments, we show that the proposed distributed framework is well adapted to the traffic concept drift. More precisely, our results indicate that the CNN-based models are well suited for continually adapting to the traffic concept drift (i.e. , achieving an average detection rate of above 95% while needing just 128 new flows for the updating phase), and the LSTM-based models are a good candidate for sequential packet labeling in practical online IDSes (i.e. , detecting intrusions by just observing their first 15 packets).

Abstract P30: MUTATIONAL SIGNATURE ASSIGNMENT HETEROGENEITY IS WIDESPREAD AND CAN BE ADDRESSED BY ENSEMBLE APPROACHES

Abstract Single base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard Index = 0.38 – 0.83) and quantitatively (Kendall tau-b = 0.18 – 0.76). This phenomenon was exacerbated for “flat” signatures such as the homologous-recombination repair deficiency (HRD) signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2-deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9 – 24.7% on average using COSMIC and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution. Citation Format: Wu Jialun Andy, Akila Perera, Linganesan Kularatnarajah, Anna Korsakova, Jason James Pitt. MUTATIONAL SIGNATURE ASSIGNMENT HETEROGENEITY IS WIDESPREAD AND CAN BE ADDRESSED BY ENSEMBLE APPROACHES [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P30.

Abstract 4355: Characterizing HRD in ovarian cancer: Insights from copy number signatures

Abstract Copy number changes are defining features in ovarian cancers. Two distinct methods by Steele et al. and Drews et al., published back-to-back in 2022, detail features and categories of copy number signatures. However, these methods have not been benchmarked for ovarian cancer. Mutational signatures are pivotal in understanding cancer biology and serve as predictive and prognostic biomarkers. This study aims to examine the landscape of copy number signatures to characterize the diversity of Homologous Recombination (HR) impairment within ovarian cancer. While assessing Homologous Recombination Deficiency (HRD) is crucial for targeted treatments, current HRD tests lack clarity. Using signature extraction tools SigProfilerExtractor and CINSignature, we analyzed whole-genome sequencing data (mean depth 100x) from 416 ovarian cases from Genomics England (GeL), employing both copy number signature methods. Among the signatures extracted, HRD signature CN17 from Steele et al. methodology was identified in 37% of the samples and primarily found in the high-grade serous ovarian cancer (HGSOC) subtype (77%). Additionally, higher exposure levels (median of 0.4 in n=154 compared to median of 0.29 in n=416) of Drews et al. methodology’s most complex HRD signature, CX3, was detected in samples exhibiting CN17 activity. This ongoing exploration of the copy number signatures landscape works towards identifying the spectrum of HR impairment, aiming to benchmark these two methods for a standardized diagnostic tool. This study contributes to our understanding of the mutational landscape of ovarian cancer and its potential implications for prevention and personalized treatment strategies. Citation Format: Aliah Hawari, Avraam Tapinos, Claire J. Kramer, Andreas J. Gruber, Richard Houlston, James Brenton, David Wedge. Characterizing HRD in ovarian cancer: Insights from copy number signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4355.

Abstract 1746: KRAS mutant lung adenocarcinoma is associated with distinct mutational signature profiles

Abstract Background: Lung cancer is the 3rd most common cancer in the UK and leading cause of cancer mortality worldwide. Somatic RAS mutations are the most common oncogenes in human cancer, and KRAS mutations are the largest molecular subset of lung adenocarcinoma (LUAD). Each cancer genome accumulates a unique combination of somatic mutations, and these mutational signatures provide a genetic imprint of mutational processes that have occurred during tumorigenesis. Mutational signatures provide information on tumor aetiology/maintenance and could highlight potential therapeutic targets. This is the largest study to date to profile KRAS mutant LUAD mutational signatures using whole genome sequencing (WGS) data. Methods: Paired tumor and germline WGS data were obtained through the 100 000 Genomes Project (Genomics England). KRAS mutations were profiled using Ensembl Variant Effect Predictor (VEP) v109.0. Single base substitution (SBS), doublet base substitution (DBS), small insertion and deletions (ID) and copy number (CN) mutational signature extraction was performed using SigProfiler (COSMIC version 3.3). Statistical analysis and plotting were performed in R; a two-tailed p-value &amp;lt;0.05 was considered statistically significant. Results: 680 LUAD patients were included; 301 KRAS mutant (44%), 379 KRAS wild type (63%). Common KRAS variants were present, including G12C (38.9%), G12V (15.9%) and G12D (14.0%). 17 SBS, 9 DBS, 10 ID and 9 CN signatures were extracted and present in both KRAS mutant and wild type cohorts. KRAS mutant samples were enriched in smoking related signatures (SBS4, DBS2, ID3; odds (OR) ratio 5.54, 3.80, 4.97 respectively, p &amp;lt;0.001) and diploid CN signature (CN1, OR 1.41, p = 0.03). Conversely, KRAS mutant samples were less likely to have clocklike (ID5, OR 0.61, p = 0.012; ID8, OR 0.44, p &amp;lt;0.001), APOBEC (SBS2, OR 0.49, p &amp;lt;0.001; SBS13; 0.50, p &amp;lt;0.001), chromosomal instability (CN9, OR 0.63, p = 0.019), loss of heterozygosity (CN14, OR 0.47, p = 0.015), or homologous recombination deficiency (CN17, OR 0.58, p &amp;lt;0.001) signatures. Discussion: These results show distinct mutational signature profiles between KRAS mutant and wild type LUAD. As previously described, KRAS mutant LUAD is dominated by smoking related signatures; however, the majority of remaining signatures were more common in the KRAS wild type cohort where there was a statistically significant difference. This data suggests different mutational processes are observed in KRAS mutant compared to wild type LUAD. KRAS mutations are commonly clonal and thought to initiate tumorigenesis: timing signatures relative to KRAS mutations is necessary to further our understanding of KRAS cancer evolution and the subject of future work. Combined with clinical data integration, mutational signatures could provide prognostic information and inform predictive models. Citation Format: Laura Woodhouse, Aliah Hawari, Andreas Gruber, Colin Lindsay, David Wedge. KRAS mutant lung adenocarcinoma is associated with distinct mutational signature profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1746.

Abstract 3951: Timing the development of chemoresistance in relapsed pediatric cancer

Abstract Survivors of pediatric cancer face lifelong battles with severe morbidities, which includes a significant risk of recurrence. Pre-existing genetic variation within primary tumors may offer certain cell populations an evolutionary advantage, increasing the likelihood that some will be resistant to treatment. But the inherent complexity of the tumor means finding these markers of resistance has yet to be fully explored across childhood cancer. To address this, we developed a pipeline that combines clonal evolution reconstruction with mutational signature extraction at the subclonal level to elucidate changes in mutational processes. Using this pipeline to analyze 1,743 pediatric tumor genomes we found a common pattern of parallel evolution in most pediatric cancers, irrespective of their tissue of origin. Further, we detected therapy-associated mutational signatures linked to specific treatments such as temozolomide, platinum, fluorouracil, and thiopurine in post-treatment tumor subpopulations. Of 235 samples with known treatment exposure, 37.9% displayed one or more treatment-associated signatures. In the context of the phylogenetic tree, we identified specific subpopulations with therapy signatures. In multi-sample cases, resistant subpopulations had ancestors present in primary tumors at the time of diagnosis, suggesting certain subpopulations possessed the ability to withstand chemotherapy-induced pressures from an early stage and then expanded following treatment. Finally, we observed differences in the timing of mutational signatures between antimetabolite and alkylating agent treatments, reflecting the mechanisms of these drugs. Antimetabolite agents showed subclonal mutational signatures occurring in later branches, likely due to dependence on cell cycling. In contrast, alkylating agents displayed signatures in both early and late subpopulations, as they are unaffected by cell cycle state. Thus, investigating mutational signatures at the subclonal level unveils new insights into the clonal dynamics of pediatric cancers and the development of chemoresistance. This research offers valuable insights for identifying pediatric cancer patients at greater risk of recurrence, enabling the development of more effective treatment plans to enhance their quality of life and overall survival rates. Citation Format: Sasha Blay, Mehdi Layeghifard, Scott Davidson, David Chen, Astra Schwertschkow, Vijay Ramaswamy, Michael Taylor, Elli Papaemmanuil, Anita Villani, David Malkin, Ludmil Alexandrov, Mark Cowley, Adam Shlien. Timing the development of chemoresistance in relapsed pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3951.

Abstract 3563: Multi-modal mutational signatures elucidate the comprehensive consequences of mutational processes

Abstract The development of mutational signatures defined individually in the contexts of single/double base substitutions, insertions and deletions, structural variants and copy number variation have led to improved understanding of cancer mutagenesis and potential for improved patient stratification. We hypothesized that combining these contexts into a multi-modal mutational signature framework may improve the accuracy of assigning mutational processes to samples, and provide a holistic view of the full repertoire of mutations conferred from such processes. In order to develop multi-modal signatures, bespoke feature engineering was required for each mutational modality to enhance signal, in addition to individual modality matrix normalization so that the data could be harmonized and analyzed as a whole. These methods, and subsequent signature extraction were developed on 2,438 whole genome sequenced cancers from 21 distinct cancer types from the PCAWG consortium for which all modalities of data were available, incorporating a 348 channel signature definition. Deployment of these methods identified multi-modal signatures that encompass multiple mutation types known to associate with specific processes, for example SBS, indel and rearrangement channels defining a signature of homologous recombination deficiency. In contrast, some signatures remain defined by a single mutational modality, highlighting the distinction between broadly mutational processes, and more focused mutation processes. Multi-modal signatures show promise in increasing accuracy for difficult to assign signatures, such as those for homologous recombination deficiency, and therefore in improving patient stratification for life-saving treatment. Citation Format: Christopher D. Steele, Ludmil Alexandrov, Azhar Khandekar. Multi-modal mutational signatures elucidate the comprehensive consequences of mutational processes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3563.

5 signature genes revealed by single-cell profiling identified unique immune subtypes affecting the prognosis of ovarian cancer.

Ovarian cancer (OC) ranks among the most prevalent gynecological malignancies, with surgery, chemotherapy, and immunotherapy constituting primary treatment modalities. However, despite advancements, immunotherapy, particularly immune checkpoint inhibitors, has yielded suboptimal outcomes. The pressing need to identify biomarkers predictive of clinical prognosis underscores our objective. We aim to discern gene signatures and establish prognostic subgroups, specifically in the context of immunotherapy and chemotherapy, guiding clinical decision-making. We used the Tumor Immunotherapy Gene Expression Resource (TIGER) and The Cancer Genome Atlas (TCGA) databases to extract signature genes of prognostic significance. Unsupervised consensus clustering was employed to classify patients based on these signature genes. The Tumor Immune Estimation Resource (TIMER) database, along with the R packages "maftools" and "ESTIMATE" facilitated immune infiltration estimation. Gene set variation analysis (GSVA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were implemented to probe immune-related cell signaling pathways among distinct subtypes. The Tumor Immune Dysfunction and Exclusion (TIDE) database was used to assess immunotherapy effects, while the R package "OncoPredict" evaluated drug sensitivity differences among subtypes. We identified five prognostically influential genes in ovarian cancer: IGFBP7, JCHAIN, CCDC80, VSIG4, and MS4A1. Utilizing these signature genes, we categorized TCGA-OV patients into five clusters, each associated with varying clinical prognoses. Notably, 2 clusters exhibited superior prognoses, accompanied by enhanced immune cell infiltration. KEGG enrichment analysis revealed their heightened enrichment in cellular immunity and immune cell interaction pathways. Given the elevated expression levels of multiple immune checkpoint molecules, these clusters may substantially benefit from immune checkpoint inhibitor therapy. Additionally, chemotherapy sensitivity analysis indicated their favorable responses to first or second-line chemotherapy regimens. We subclustered ovarian cancer patients by 5 signature genes obtained from the Single-cell RNA sequencing (scRNA-seq) dataset, which demonstrated a good typing effect. Patients in the two molecular subtypes showed better survival, higher immune cell infiltration, and higher drug sensitivity. This meticulous typing may help clinicians to quickly assess the prognosis of patients and the response to immunotherapy and chemotherapy.

Feature Extraction of Lubricating Oil Debris Signal Based on Segmentation Entropy with an Adaptive Threshold.

Ferromagnetic debris in lubricating oil, serving as an important communication carrier, can effectively reflect the wear condition of mechanical equipment and predict the remaining useful life. In practice application, the detection signals collected by using inductive sensors contain not only debris signals but also noise terms, and weak debris features are prone to be distorted, which makes it a severe challenge to debris signature identification and quantitative estimation. In this paper, a debris signature extraction method established on segmentation entropy with an adaptive threshold was proposed, based on which five identification indicators were investigated to improve detection accuracy. The results of the simulations and oil experiment show that the proposed algorithm can effectively identify wear particles and preserve debris signatures.

Spectral-Kurtosis and Image-Embedding Approach for Target Classification in Micro-Doppler Signatures

Micro-Doppler signature represents the micromotion state of a target, and it is used in target recognition and classification technology. The micro-Doppler frequency appears as a transition of the Doppler frequency due to the rotation and vibration of an object. Thus, tracking and classifying targets with high recognition accuracy is possible. However, it is difficult to distinguish the types of targets when subdividing targets with the same micromotion or classifying different targets with similar velocities. In this study, we address the problem of classification of three different targets with similar speeds and segmentation of the same type of targets. A novel signature extraction procedure is developed to automatically recognize drone, bird, and human targets by exploiting the different micro-Doppler signatures exhibited by each target. The developed algorithm is based on a novel adaptation of the spectral kurtosis technique of the radar echoes reflected by the three target types. Further, image-embedding layers are used to classify the spectral kurtosis of objects with the same micromotion. We apply a ResNet34 deep neural network to micro-Doppler images to analyze its performance in classifying objects performing micro-movements on the collected bistatic radar data. The results demonstrate that the proposed method accurately differentiates the three targets and effectively classifies multiple targets with the same micromotion.

High-fidelity fault signature extraction of rolling bearings via nonconvex regularized sparse representation enhanced by flexible analytical wavelet transform

Diagnosing the bearing fault, especially incipient fault is important for equipment health management while is still a challenge in which high-fidelity extraction of the fault signature is expected. A method termed flexible analytical wavelet transform (FAWT)-enhanced sparse representation with nonconvex regularization is proposed in this research. FAWT enjoys flexible covering along both the frequency and time axis as well as tunable oscillation property and is adopted to well match the fault impulses after parameters optimization. In the fabricated FAWT-enhanced sparse model with generalized minimax-concave regularization, an index termed harmonic-to-noise energy ratio of envelope spectrum (ES-HNER) is proposed which is found effective and robust for quantitative assessment of the richness of fault signature and could be automatically evaluated from the envelope spectrum, based on which the parameters for constructing the FAWT basis and threshold are optimized via maximizing the ES-HNER in the candidate parameters space. The sparse decomposition signals are further obtained via solving the FAWT-enhanced sparse model, upon which the bearing fault signature is expected to be exhibited on the envelope spectrum. The performance of the proposed method has been validated via analysis of both simulation and experiment signals as well as comparison with other methods.

Identifiable Solutions to Foreground Signature Extraction From Hyperspectral Images in an Intimate Mixing Scenario

Identifiable Solutions to Foreground Signature Extraction From Hyperspectral Images in an Intimate Mixing Scenario