Signal Window Research Articles

Evaluating the Impact of Windowing Techniques on Fourier Transform-Preprocessed Signals for Deep Learning-Based ECG Classification

(1) Background: Arrhythmias, or irregular heart rhythms, are a prevalent cardiovascular condition and are diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends greatly on the quality of signal preprocessing. This study evaluated the impact of different windowing techniques applied to Fourier transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A one-dimensional convolutional neural network was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal. (3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications.

Worse arterial pressure reservoir capacity is associated with exacerbated hemodynamic response to capnogram changes and more labile intraoperative cardiopulmonary-metabolic coupling

Abstract Background The apparent promise of capnography as a cardiac monitor based on the coupled cardiopulmonary response to metabolic changes remains hampered by inconsistent results in studies investigating previous correlations between hemodynamics (e.g.cardiac output) and end-tidal CO2 (EtCO2).(1,2,3) Excess pressure (Pexcess), a dynamic sub-component of pressure waveform representing pressure beyond buffering capacity (reservoir pressure) of elastic large arteries, strongly correlates with aortic flow in humans, suggesting the Pexcess as a promising tool for non-invasive hemodynamic monitoring.(4,5) Purpose We aimed to clarify potential cofounders and pathophysiological mechanisms modifying the EtCO2-arterial hemodynamic coupling, by analysing the intra-operative temporal relations of paired reservoir-excess and capnogram parameters Methods In 190 patients with normal pre-operative pulmonary function tests who underwent non-cardiac surgery, simultaneous capnogram and intra-arterial pressure signals were analysed off-line. Signal windows (>2 minutes) of maximum and minimum mean arterial pressure were autoidentified, the capnogram and pressure signals were averaged into a single representative cycle, to extract reservoir-excess pressure and capnogram parameters (EtCO2, expiratory slope, alpha slope, and plateau slope) accordingly.(6,7) Arterial reservoir capacity (ARC) was defined as Preservoir/Pexcess, which was used to stratify patients into quartiles. Relationships between maximum changes in capnogram and pressure parameters were analysed (Figure 1). Results The ΔEtCO2 and ΔPa mean were correlated significantly in all groups. In patients with the highest ARC (Q4), Pexcess remained unaltered in response to ΔEtCO2 (r:0.095 p:0.545), unlike other subgroups with significant positive correlations. In Q4 (the highest ARC group), the increment in Preservoir was the main cause of ΔPa in relation to ΔEtCO2. In contrast, the ΔEtCO2 - ∆Pexcess relationship was most pronounced in the group with the poorest ARC (r:0.634 p:0.005), underlying the significance of the pressure reservoir behaviour as a determinant of metabolic oscillations in arterial hemodynamics, even though the changes in ΔEtCO2 were equivalent.(Figure 2) Conclusion(s) Worse arterial pressure reserve capacity is associated with an exacerbated hemodynamic response to capnogram changes and a more labile cardiopulmonary-metabolic coupling, characterised by a pronounced increase in excess pressure in response to increased EtCO2 in patients with low ARC. Substantial Pa alterations accompanying EtCO2 changes had opposite drivers in low and high ARC groups (predominant augmentation of Preservoir vs Pexcess). Utilisation of the capnogram for cardiac hemodynamic monitoring is substantially affected by ARC, hence it should be considered while developing capnogram-based monitoring indices.Figure 1.Methodology and example casesFigure 2.Main results

MACHINE-LEARNING TECHNIQUES IN MULTIPLE SCLEROSIS PREDICTION USING EEG

The diagnosis and quantification of Multiple Sclerosis (MS) have typically depended on skilled doctors recognizing visual patterns, such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), which resulted in a costly, time-consuming and non-reproducible process. The application of Machine Learning (ML) in MS diagnosis has been getting a lot of attention in the last few years due to the volume of scientific data, the heterogeneity of disease courses, and the variety of diagnostic methods. The EEG has the capability to detect important changes in the brain’s inherent electrical activity, which are influenced by changes in the neural network connections associated with inflammatory demyelinating and neural damage characteristic of the MS. Utilizing multimodal machine learning over the clinically available data may be a contemporary strategy with amazing potential to facilitate early diagnosis. Considering recent EEG investigations as well as the accessibility to their datasets would be the initial steps in utilizing the ML in MS diagnosis. This paper provides a systematic review of the latest techniques for MS diagnostic based on PRISMA guidelines with the prospect of ML application in their investigations. The goal is to find if EEG could be considered a robust and accurate technique for MS diagnosis. In accordance with PRISMA guidelines, we consider 111 papers. In our review, 404 people are considered including 209 with MS and 195 healthy controls. As a result, we generated an updated investigation looking at the ML approaches as well as utilizing EEG as an accurate, but less often used method to help with early diagnosis of MS. We summarize, analyze, discuss, and synthesize the recently published works, current trends, and open research issues. The review also points out knowledge gaps about the necessity of validating results and addressing constraints such as limited sample number. Our investigation proves that the precision of the supervised strategies such as [Formula: see text]NN and SVM is typically higher than that of the unsupervised strategies. On the other hand, by utilizing various techniques such as splitting EEG signal sub-bands, signal windowing, and identifying effective features from the data analysis approaches, we have been able to achieve significant classification accuracy higher than 99%. According to the high degree of accuracy of the results, this approach is becoming the focal point of the research works. The high diagnostic accuracy of the proposed machine learning techniques on the EEG signal analysis shows its potential capacity to become a more widespread procedure as the MS diagnostic techniques. To develop the reliability of these methods, gathering, and analyzing more EEG signals from MS patients and creating more suitable EEG protocols are essential in future studies. In the same way, for the dynamic and online analyses of the EEG signals, in-depth studies are needed to determine more effective EEG protocols and machine-learning techniques.

ZGSO:Cr3+,Ni2+ Persistent Phosphors with Dual Emission in NIR-I and SWIR Ranges for Bio-Imaging Applications.

Persistent luminescence (PersL) is widely used for near infrared (NIR-I, 650-950nm) imaging as they allow getting images without background. Bio-imaging in the second shortwave-infrared region SWIR-II (NIR-II, 1000-1400nm) is less widespread but is growing as it offers the advantages of low photon scattering, increased in vivo penetration depth, and improved imaging clarity. In this work, the preparation and the complete optical properties of a new material is reported, Zn1.33Ga1.33Ni0.005Cr0.005Sn0.33O3.995 (ZGSO:Cr3+, Ni2+) able of emitting in both deep-red/NIR-I and SWIR (NIR-II) and shows its potential in bioimaging. ZGSO:Cr3+, Ni2+ can be excited using different sources such as X-rays, UV, and visible light to emit persistent signals in dual biological windows (dual-BW). By integrating an energy transfer process from Cr3+ to Ni2+ within this newly synthesized material, the influence of co-dopants on signal intensity and emission wavelengths is sought to explore. PersL at ≈700nm (NIR-I) and ≈1300nm (NIR-II) have been tested in preliminary bioimaging experiments using different protocols, allowing signal detection with good spatial resolution and depth sensitivity. The dual-BW PersL imaging strategy expands the toolbox for highly accurate analysis and has, for the first time, allowed access to accurately high-resolution sensing, and tracing.

Defect classification and quantification method based on AC magnetic flux leakage time domain signal characteristics

Pipelines play a crucial role in industries such as petroleum and nuclear power, where non-destructive testing is essential. Magnetic flux leakage testing methods are widely used for pipeline inspection due to their ability to detect both internal and external defects. However, accurately classifying and evaluating the size of such defects poses challenges due to the complex coupling relationship between ferromagnetic materials and defect magnetic fields. This paper proposes a method for defect classification and quantification based on the time domain characteristics of AC magnetic flux leakage signals. Firstly, the paper explores the shielding effects caused by transient high magnetic permeability within the material on signals from internal defects. It analyzes the differences in signals between internal and external defects. Then, based on the nonlinear attributes of defect signals, the paper proposes a defect classification method based on derivatives analysis of the windowed time-domain signal. Moreover, the study finds that rising time and zero-crossing time can be used to assess the depth of internal and external defects separately, which can decouple the width and depth. Finally, experimental validation confirms the effectiveness of defects classification and quantification. This paper provides a feasible method for evaluating the defect of ferromagnetic materials.

A robust high-throughput screening system to assess bacterial tyrosine ammonia lyase activity in the context of tyrosine inherited metabolic disorders.

Inborn errors of tyrosine metabolism result in patient's inability to degrade tyrosine. Current treatment consists of a phenylalanine and tyrosine restricted diet and nitisinone, causing a block in the tyrosine degradation pathway. However, tyrosine levels will increase, leading to acquired hypertyrosinemia, implying the need for an add-on treatment. Tyrosine ammonia lyases (TAL) can provide such an add-on treatment as they catalyze the deamination of tyrosine into p-coumaric acid and ammonia. In this study, we developed a robust high-throughput screening (HTS) assay to assess the capacity of bacterial TAL enzymes to decrease excessive tyrosine. The assay is based on the spectrophotometric quantification of p-coumaric acid after conversion of tyrosine by bacterial TAL. As a benchmark, TAL from Flavobacterium johnsoniae (FjTAL) was used to optimize the assay. Optimal growth conditions for high-level protein expression were determined by incubating transformed Escherichia coli BL21 (DE3) cells at different temperatures during various incubation times. Subsequently, assay temperature and pH were optimized followed by testing different ratios of tyrosine assay mixes to bacterial lysate. Finally, assay robustness and functionality were evaluated. Optimal FjTAL expression was obtained after incubation for 24h at 22°C. Ideal assay conditions consist of a 80/20 ratio of 1mM tyrosine assay mix to FjTAL lysate performed at pH 9.2 and 37°C. The robustness test showed Z' values > 0.4 and signal window values > 2 without edge or drift effects. As proof-of-principle, we successfully determined the catalytic activity of two other bacterial TAL enzymes RsTAL (5.718.10-3 ± 0.21.10-3) and SeSAM8 (4.658.10-3 ± 0.37.10-3). A robust, simple and reliable HTS assay was thus developed to evaluate the tyrosine degradation capacity of bacterial TAL enzymes.

Exploring P2X receptor activity: A journey from cellular impact to electrophysiological profiling

The development of in vitro pharmacological assays relies on creating genetically modified cell lines that overexpress the target protein of interest. However, the choice of the host cell line can significantly impact the experimental outcomes. This study explores the functional characterization of P2X7 and P2X4 receptor modulators through cellular assays and advanced electrophysiological techniques. The influence of different host cell lines (HEK-293, HEK-293FT, and 1321N1) on the activity of reference agonists and antagonists targeting human and murine P2X4 and P2X7 receptors was systematically investigated, highlighting the significant impact of the host cell on experimental results. The 1321N1 cell line was identified as the preferred host cell line when investigating the human P2X4 receptor due to more consistent agonist activities, antagonist potencies, and a more stable assay signal window. Furthermore, a patch-clamp protocol that allows for the repetitive recording of ATP-mediated inward currents from isolated human CD4+ T-cells was established, revealing that both P2X7 and P2X4 receptors are crucial for immune cell regulation, positioning them as promising therapeutic targets for managing inflammatory disorders.

A Plug-In Graph Neural Network to Boost Temporal Sensitivity in fMRI Analysis.

Learning-based methods offer performance leaps over traditional methods in classification analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning models that analyze functional connectivity (FC) features among brain regions have been particularly promising. However, many existing models receive as input temporally static FC features that summarize inter-regional interactions across an entire scan, reducing the temporal sensitivity of classifiers by limiting their ability to leverage information on dynamic FC features of brain activity. To improve the performance of baseline classification models without compromising efficiency, here we propose a novel plug-in based on a graph neural network, GraphCorr, to provide enhanced input features to baseline models. The proposed plug-in computes a set of latent FC features with enhanced temporal information while maintaining comparable dimensionality to static features. Taking brain regions as nodes and blood-oxygen-level-dependent (BOLD) signals as node inputs, GraphCorr leverages a node embedder module based on a transformer encoder to capture dynamic latent representations of BOLD signals. GraphCorr also leverages a lag filter module to account for delayed interactions across nodes by learning correlational features of windowed BOLD signals across time delays. These two feature groups are then fused via a message passing algorithm executed on the formulated graph. Comprehensive demonstrations on three public datasets indicate improved classification performance for several state-of-the-art graph and convolutional baseline models when they are augmented with GraphCorr.

Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor.

Spike extraction by blind source separation (BSS) algorithms can successfully extract physiologically meaningful information from the sEMG signal, as they are able to identify motor unit (MU) discharges involved in muscle contractions. However, BSS approaches are currently restricted to isometric contractions, limiting their applicability in real-world scenarios. We present a strategy to track MUs across different dynamic hand gestures using adaptive independent component analysis (ICA): first, a pool of MUs is identified during isometric contractions, and the decomposition parameters are stored; during dynamic gestures, the decomposition parameters are updated online in an unsupervised fashion, yielding the refined MUs; then, a Pan-Tompkins-inspired algorithm detects the spikes in each MUs; finally, the identified spikes are fed to a classifier to recognize the gesture. We validate our approach on a 4-subject, 7-gesture + rest dataset collected with our custom 16-channel dry sEMG armband, achieving an average balanced accuracy of 85.58±14.91% and macro-F1 score of 85.86±14.48%. We deploy our solution onto GAP9, a parallel ultra-low-power microcontroller specialized for computation-intensive linear algebra applications at the edge, obtaining an energy consumption of 4.72 mJ @ 240 MHz and a latency of 121.3 ms for each 200 ms-long window of sEMG signal.

Intrinsic neural timescales exhibit different lengths in distinct meditation techniques

Meditation encompasses a range of practices employing diverse induction techniques, each characterized by a distinct attentional focus. In Mantra meditation, for instance, practitioners direct their attention narrowly to a given sentence that is recursively repeated, while other forms of meditation such as Shoonya meditation are induced by a wider attentional focus. Here we aimed to identify the neural underpinnings and correlates associated with this spectrum of distinct attentional foci. To accomplish this, we used EEG data to estimate the brain's intrinsic neural timescales (INTs), that is, its temporal windows of activity, by calculating the Autocorrelation Window (ACW) of the EEG signal. The autocorrelation function measures the similarity of a timeseries with a time-lagged version of itself by correlating the signal with itself on different time lags, consequently providing an estimation of INTs length. Therefore, through using the ACW metric, our objective was to explore whether there is a correspondence between the length of the brain's temporal windows of activity and the width of the attentional scope during various meditation techniques. This was performed on three groups of highly proficient practitioners belonging to different meditation traditions, as well as a meditation-naïve control group. Our results indicated that practices with a wider attentional focus, like Shoonya meditation, exhibit longer ACW durations compared to practices requiring a narrower attentional focus, such as Mantra meditation or body-scanning Vipassana meditation. Together, we demonstrated that distinct meditation techniques with varying widths of attentional foci exhibit unique durations in their brain's INTs. This may suggest that the width of the attentional scope during meditation relates and corresponds to the width of the brain's temporal windows in its neural activity. Significance StatementOur research uncovered the neural mechanisms that underpin the attentional foci in various meditation techniques. We revealed that distinct meditation induction techniques, featured by their range of attentional widths, are characterized by varying lengths of intrinsic neural timescales (INTs) within the brain, as measured by the Autocorrelation Window function. This finding may bridge the gap between the width of attentional windows (subjective) and the width of the temporal windows in the brain's neural activity (objective) during different meditation techniques, offering a new understanding of how cognitive and neural processes are related to each other. This work holds significant implications, especially in the context of the increasing use of meditation in mental health and well-being interventions. By elucidating the distinct neural foundations of different meditation techniques, our research aims to pave the way for developing more tailored and effective meditation-based treatments.

Method for extracting temperature distribution characteristics of municipal asphalt pavement construction in high-temperature weather under compaction quality constraint

ABSTRACT In order to improve the temperature detection ability of municipal asphalt pavement in high-temperature weather, a method to extract the temperature distribution characteristics of municipal asphalt pavement in high-temperature weather under the constraint of compaction quality was proposed. Using a digital image system, laser profiler, and ground penetrating radar system, collect construction temperature data of urban asphalt pavement under the constraints of compaction quality under high-temperature weather conditions, and use time series analysis to reconstruct the data. Combined with the nondestructive detection method of ground penetrating radar, the echo analysis model of temperature information is established, and the characteristics of temperature distribution are reconstructed using the spectral analysis method. The time–frequency transformation method is used to transform the time series of construction temperature under the constraint of compaction quality. The discrete signal window method was used to analyse the temperature characteristics of construction. According to the characteristic state monitoring, the temperature distribution characteristics are extracted. The test results show that the method is reliable in extracting the temperature distribution characteristics of municipal asphalt pavement construction under high-temperature weather and improves the real-time monitoring capability of asphalt pavement temperature.

Experimental Study on the Detection of the Existence and Location of Mimicked and Unexpected Interface Debonding Defects in an Existing Rectangular CFST Column with PZT Materials.

Interface bonding conditions between concrete and steel materials play key roles in ensuring the composite effect and load-carrying capacity of concrete-steel composite structures such as concrete-filled steel tube (CFST) members in practice. A method using both surface wave and electromechanical impedance (EMI) measurement for detecting the existence and the location of inaccessible interface debonding defects between the concrete core and steel tube in CFST members using piezoelectric lead zirconate titanate (PZT) patches as actuators and sensors is proposed. A rectangular CFST specimen with two artificially mimicked interface debonding defects was experimentally verified using PZT patches as the actuator and sensor. By comparing the surface wave measurement of PZT sensors at different surface wave travelling paths under both a continuous sinusoidal signal and a 10-period sinusoidal windowed signal, three potential interface debonding defects are quickly identified. Furthermore, the accurate locations of the three detected potential interface debonding defects are determined with the help of EMI measurements from a number of additional PZT sensors around the three potential interface debonding defects. Finally, the accuracy of the proposed interface debonding detection method is verified with a destructive observation by removing the local steel tube at the three detected interface debonding locations. The observation results show that the three detected interface debonding defects are two mimicked interface debonding defects, and an unexpected debonding defect occurred spontaneously due to concrete shrinkage in the past one and a half years before conducting the test. Results in this study indicate that the proposed method can be an efficient and accurate approach for the detection of unknown interface debonding defects in existing CFST members.

Application of 2E-2U method for free-field underwater calibrations of hydrophones and projectors in a reverberant laboratory test tank

In this paper, application of a Two-Equations Two-Unknowns (2E-2U) method is described for calibration of hydrophones and projectors below 1 kHz in a laboratory test tank. At low frequencies, amplitude and phase measurements for the calibration of the hydrophones and projectors in the test tank are difficult to perform since echo-free time of the laboratory test tank is not large enough due to transducer initial transients and tank wall boundary reflections. To overcome these difficulties, the 2E-2U method is applied to received (windowed) signals obtained during calibration measurements. Thus, calibration measurements become possible at a frequency down to 250 Hz. These measurements in the test tank are performed for a hydrophone and a developed flextensional projector. First, the received sensitivities for the hydrophone are calculated and validated by comparisons with pressure calibration in a closed chamber. Good agreements are obtained between the two measurement platforms, with a maximum difference of 0.5 dB and uncertainty of 1.3 dB. Then, the transmitting voltage response (TVR) of the flextensional projector is calculated and compared with the calibration data obtained from the method defined in the relevant standards. Good agreements are obtained between the two TVR data with a maximum difference of 1.1 dB and uncertainty of 1.7 dB.

State-of-charge estimation of lithium-ion battery: Joint long short-term memory network and adaptive extended Kalman filter online estimation algorithm

With the widespread use of lithium-ion batteries, estimating the State of Charge (SOC) has been one of the most critical tasks in the battery management system (BMS). In this paper, a joint long short-term memory recurrent neural network (LSTM-RNN) and adaptive extended Kalman filter (AEKF) algorithm is proposed to achieve accurate SOC estimation. The proposed algorithm generates an initial SOC estimation value through a trained LSTM-RNN with a sliding time window signal sequence as input. The initial estimation value is then used as a feedback input to the AEKF. The results show that only a 30-s time series as the input of the LSTM-RNN can achieve satisfactory estimation results. Compared with traditional adaptive extended Kalman filter and unscented Kalman filter (UKF), the proposed algorithm has a better performance of estimation accuracy under various discharging conditions, especially when applied at low temperatures. Finally, the SOC estimation results under four operating conditions and various temperatures show that the maximum error is less than 2.5 %, in particular, the root mean square error (RMSE) and the mean absolute error (MAE) are below 1.01 % and 0.64 %. The proposed algorithm is an effective SOC estimation method with good robustness and high estimation accuracy.

Osteosarcoma-targeted Cu and Ce based oxide nanoplatform for NIR II fluorescence/magnetic resonance dual-mode imaging and ros cascade amplification along with immunotherapy

BackgroundAs the lethal bone tumor, osteosarcoma often frequently occurs in children and adolescents with locally destructive and high metastasis. Distinctive kinds of nanoplatform with high therapeutical effect and precise diagnosis for osteosarcoma are urgently required. Multimodal optical imaging and programmed treatment, including synergistic photothermal-chemodynamic therapy (PTT-CDT) elicits immunogenetic cell death (ICD) is a promising strategy that possesses high bio-imaging sensitivity for accurate osteosarcoma delineating as well as appreciable therapeutic efficacy with ignorable side-effects.Methods and resultsIn this study, mesoporous Cu and Ce based oxide nanoplatform with Arg-Gly-Asp (RGD) anchoring is designed and successfully constructed. After loading with indocyanine green, this nanoplatform can be utilized for precisely targeting and efficaciously ablating against osteosarcoma via PTT boosted CDT and the closely following ICD stimulation both in vitro and in vivo. Besides, it provides off-peak fluorescence bio-imaging in the second window of near-infrared region (NIR II, 1000-1700 nm) and Magnetic resonance signal, serves as the dual-mode contrast agents for osteosarcoma tissue discrimination.ConclusionTumor targeted Cu&Ce based mesoporous nanoplatform permits efficient osteosarcoma suppression and dual-mode bio-imaging that opens new possibility for effectively diagnosing and inhibiting the clinical malignant osteosarcoma.

A New and Lightweight R-Peak Detector Using the TEDA Evolving Algorithm

The literature on ECG delineation algorithms has seen significant growth in recent decades. However, several challenges still need to be addressed. This work aims to propose a lightweight R-peak-detection algorithm that does not require pre-setting and performs classification on a sample-by-sample basis. The novelty of the proposed approach lies in the utilization of the typicality eccentricity detection anomaly (TEDA) algorithm for R-peak detection. The proposed method for R-peak detection consists of three phases. Firstly, the ECG signal is preprocessed by calculating the signal’s slope and applying filtering techniques. Next, the preprocessed signal is inputted into the TEDA algorithm for R-peak estimation. Finally, in the third and last step, the R-peak identification is carried out. To evaluate the effectiveness of the proposed technique, experiments were conducted on the MIT-BIH arrhythmia database (MIT-AD) for R-peak detection and validation. The results of the study demonstrated that the proposed evolutive algorithm achieved a sensitivity (Se in %), positive predictivity (+P in %), and accuracy (ACC in %) of 95.45%, 99.61%, and 95.09%, respectively, with a tolerance (TOL) of 100 milliseconds. One key advantage of the proposed technique is its low computational complexity, as it is based on a statistical framework calculated recursively. It employs the concepts of typicity and eccentricity to determine whether a given sample is normal or abnormal within the dataset. Unlike most traditional methods, it does not require signal buffering or windowing. Furthermore, the proposed technique employs simple decision rules rather than heuristic approaches, further contributing to its computational efficiency.

Fault location in high-voltage direct-current grids based on panoramic voltage features and vision transformers

This paper presents a novel fault location method designed for high-voltage direct-current (HVDC) grids equipped with quick-action protections and circuit breakers. The method relies on a very short data window of single-ended voltage signals, which allows the acquisition of necessary measurements before the ultra-fast fault-isolation stage. Through fault analysis of the simulated four-terminal HVDC grid, it is observed that the fault distance induces a unique transient response in the voltage signal. However, due to the complex mathematical representation of loop components and the multiple fault factors involved, a data-driven strategy is adopted for fault distance estimation. Based on this strategy, four signal processing techniques, including Gramian angular field, recurrence plot, Markov transition field, and continuous wavelet transform, are used to extract panoramic voltage features. Additionally, four advanced lightweight variants of vision transformers (ViT), including MobileViT, EfficientFormer, Efficient-Model, and EdgeViT, are considered for constructing the regression model. The optimal combination of panoramic voltage features and regression models for configuring the locator is determined by statistical evaluation. The results of semi-physical experiments based on real-time digital simulators demonstrate that the proposed method, first, can accurately estimate the fault distance, second, possesses robustness against high transition resistance, low sampling frequency, and strong noise interference, and third, is practically feasible.

Abstract 3114: Development of HTS enzymatic assays for RNA helicases: DDX3, DDX5, DDX17, RIG-1 and MDA5

Abstract RNA helicases are a broad class of enzymes that bind and modify RNA in an ATP-dependent manner and play diverse roles in RNA metabolism, regulation of gene expression, and innate immunity. Several RNA helicases, especially in the dead box helicase (DDX) family, have been shown to be dysregulated in cancer, and there have been limited efforts to develop small molecule inhibitors. To accelerate development of selective helicase inhibitors while avoiding off target effects, especially with helicases that promote tumor immunity, we are assembling a panel of high throughput biochemical assays using the Transcreener ADP2 assay for homogenous detection of RNA-dependent ATPase activity with a far-red fluorescence polarization (FP) readout. Here we describe development of assays for DDX3, DDX17 and DDX5, which have all been implicated in cancer, and the Rig-1-like receptor (RLR) helicases, RIG-1 and MDA5, which trigger expression of type I interferons important for tumor immunity. We produced the recombinant enzymes using BaV-infected insect cells, as full-length polypeptides, or in some cases truncated to remove disordered N-terminal domains. We determined RNA substrate and cofactor requirements and kinetic parameters with the purified enzymes and optimized the assays for initial velocity detection of RNA-dependent ATPase activity with a Z’ value greater than 0.7, ensuring a sufficient signal window for inhibitor screening and dose response assays. We validated each of the helicase ATPase assays for HTS by screening a collection of bioactives (Tocris 2.0) and confirming hits in dose response mode with the enzymatic assay and with thermal shift assays to confirm target binding. These assays will enable screening and hit-to-lead/SAR for DDX helicases that drive tumorigenesis as well as selectivity profiling to avoid off target effects with RLR helicases that promote tumor immunity. Citation Format: Mahbbat Ali, Ha Pham, Anibal Ramos Martinez, Robert Lowery. Development of HTS enzymatic assays for RNA helicases: DDX3, DDX5, DDX17, RIG-1 and MDA5 [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 3114.

Abstract 7178: Neuronal cells derived from iPSCs cell to evaluate neurotoxicity after 48 or 72 hours in high-through put screening format

Abstract Neurotoxicity is a major concern in central nervous system drug discovery and a frequent cause of attrition in clinical trials or approved drug withdrawal. Neuropathy is a side effect defined by the injury of peripheral nerves causing a loss of sensation/motion. Drug development pipelines typically involve testing in cell lines followed by animal investigations with translation to humans. The induced pluripotent stem cells (iPSCs) have widened new model systems to study adverse toxicities. With evolution in regulatory guidance the use of human- iPSC-derived tissue provides high-throughput access and a more relevant testing environment. Neuropathy is one of the most chemotherapy adverse effects. In this study we have screened 8 compounds. Bortezomib (proteasome inhibitor), gemcitabine (approved ovarian cancer treatment), ivermectin (parasitic diseases treatment), flavopiridol (approved CDKs inhibitors), SNS-032 (approved CDKs inhibitor), mefloquine (anti-malaria drug), digitonin (nonionic detergent) and tamoxifen (hormone receptor-positive breast cancer treatment). These molecule toxicities were tested on neuronal-derived iPSCs Dopaneurone, GABAneurone, Glutaneurone, Motorneurone, Microglya and Astrocytes in high throughput screening. For the project, cells were plated in 384 well/plates. DMSO tolerance was performed (up to 10%). Drugs were added, then, after 48h or 72h, total protease and protease activities were measured using CytoTox-GloTM. Compounds were tested at 7 concentrations (from 10−4 to 10−9M).In this study we have validated cell culture and the assay conditions. The signal window range was good after 48h and 72h of treatment. No toxicity was found until 10% DMSO. A specific neurotoxicity signature was measurable for each drug tested. Digitonin, our positive control, induced toxicity in all IPSCs after 48h and 72h of incubation. On the contrary, Tamoxifen, our negative control, did not induce any neurotoxicity. The other molecules generated a signature of severe to mild neurotoxicity. These results are in agreement with their known adverse side-effects. In conclusion, this panel is a good tool to anticipate possible neurotoxicity within the 3Rs respect. It can be used in early drug de-risking or neuroprotection screening, with the aim of preventing/reducing/curing neuropathy in at-risk populations. Citation Format: Fabienne Sabatier, Justine Chaigne, Maryse Martin, Rodolphe Barbeau, Luisa Houssin, Stephane Bonnin, Alastair King, Vanessa Jahnke. Neuronal cells derived from iPSCs cell to evaluate neurotoxicity after 48 or 72 hours in high-through put screening format [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 7178.

A fast gradient convolution kernel compensation method for surface electromyogram decomposition

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 ms average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.