Monitoring Method Research Articles

Electrochemical determination of nitazoxanide in environmental and pharmaceutical samples using a boron-doped diamond electrode

Consumption of Nitazoxanide (NTZ), a compound belonging to the class of nitrothiazoles, has increased due to its efficiency in treating viral gastroenteritis caused by rotavirus and norovirus “coronavirus disease”. Thus, developing a method capable of monitoring NTZ in wastewater and pharmaceutical formulations is relevant to human health control. However, most methods for NTZ determination are expensive and time-consuming. Hence, we present a fast, efficient, and low-waste method for NTZ monitoring in drugs and water samples using a boron-doped diamond electrode. A 3D-printed electrochemical cell with a capacity of 2 mL was used for all studies. Besides the simplicity of low waste generation, the method provided good analytical performance with a linear range of 2.0–20.0 µmol/L of NTZ, and a limit of detection (LOD) of 0.57 µmol/L. Recovery percentages between 99 and 107 % were obtained to analyze spiked samples. In addition, we demonstrate that the LOD value can be significantly improved by 50 times after applying a pre-concentration step using solid-phase extraction. Therefore, we believe the described approach is a simple and efficient electrochemical method for monitoring NTZ levels in pharmaceutical and environmental samples without laborious sample preparation procedures. This is the first report to describe the use of BDD electrodes for NTZ monitoring and environmental sample analysis using an eco-friendly approach.

Multiscale Spatio-Temporal Feature Fusion Based Non-Intrusive Appliance Load Monitoring for Multiple Industrial Industries

The appliance types and power consumption patterns vary greatly across different industries. This can lead to unstable identification results of traditional appliance load monitoring methods in different industries. A non-intrusive appliance load monitoring (NIALM) method for multiple industries based on multiscale spatio-temporal feature fusion has been proposed. Firstly, the ConvNeXt Block with efficient channel attention has strong feature extraction capability. Spatial features of appliance state changes and micro-variations generated during operation can be extracted from mixed industrial load information by it. Meanwhile, the bidirectional gated recurrent neural network is used to learn the bidirectional dependencies of the load data, obtaining temporal features. Then, the multi-scale feature extraction module is used to extract temporal and spatial features from different depths of the network layers. And the extracted multi-scale temporal and spatial features are fully integrated. Finally, the proposed model is optimized using the Stochastic Weight Averaging method. During the training process, a certain number of model weights are randomly averaged, which can improve the model's generalization ability and identification accuracy. The experiment was conducted on six different industries. The evaluation indexes such as accuracy, F1 score, and Wasserstein distance are also used to verify the effectiveness and superiority of the method.

Life Extension of Wind Turbine Drivetrains by means of SCADA Data: Case Study of Generator Bearings in an Onshore Wind Farm

Accurate predictions for remaining useful life (RUL) of wind turbine drivetrains is crucial in reducing downtime, optimising maintenance strategies, extending operational life and improving costs. The purpose of this study is to present a method which utilises SCADA data for RUL estimation. It aims to answer whether, by only having the basic SCADA data, normally collected from all wind turbines, valuable RUL prediction results can be obtained. The work intends to develop a reliable, accurate, user-friendly and informative tool to enable observation of any growing trends in the proposed metrics, such as temperature difference, cumulative sum of temperature difference and the moving average of the cumulative sum. These metrics are defined based on the differences between the actual temperatures of the components, that might have undergone some damage and the model predicted temperatures of those components if they would have remained healthy, throughout years of operation. A machine learning model is used, along with selected SCADA input parameters, to predict the healthy state temperature of components. The proposed method is implemented on SCADA data collected from an actual wind farm over seven years. The results of this study show that while the SCADA data analysis can contribute to fault detection, the estimation of RUL purely based on SCADA appears to be uncertain. Such tools however, can be used as a monitoring method, during operation, to record abnormality trend of the components over the years and can be used as important inputs for the life extension evaluation of wind turbine drivetrains.

Photoelectric sensor-based belt-type high-speed seed guiding device performance monitoring method and system

The belt-type high-speed seed guiding device encounters difficulties in seed guidance due to its distinctive seed transporting belt structure. The monitoring point is situated between the seed belt support plate and the seeds, which pass through it, making it difficult to distinguish the characteristics of the seed pulses. This presents challenges in monitoring the device’s performance. This study proposes a monitoring method for the belt-type high-speed seed guiding device using photoelectric sensors. The monitoring module employs a photoelectric sensor as a signal source and designs hardware and software for the monitoring module. The research introduces the deviation pulse extraction algorithm (DPEA), which utilises the seed guidance performance monitoring mechanism. This achieves precise monitoring of seed transport on the transporting seed belt, thereby eliminating the increased multiple index caused by “pulse sinking”. Simulation experiments demonstrate that the optimal deviation pulse extraction algorithm (ODPEA) is highly effective in identifying and eliminating “pulse sinking”, resolving the issue of increased multiple index. The DPEA is unable to achieve the same effect. The results of comparative tests of seeding monitoring accuracy indicate that both algorithms exhibit average monitoring accuracy above 96% across varying operating speeds. The DPEA, however, demonstrates heightened sensitivity to changes in operating speed. It can be observed that the operating speed has a significant impact on the monitoring accuracy of both algorithms. The monitoring system performance evaluation comparative test reveals that the DPEA-based monitoring system registers the smallest deviations in qualified feed index, miss index, and multiple index at 0.25 percentage points, 0.10 percentage points, and 0.35 percentage points, respectively. In contrast, the ODPEA-based system shows minimum deviations in qualified feed index and miss index at 0.12 percentage points each, and no deviation in multiple index. Both algorithms demonstrate minimal influence of operating speed on missing and multiple seeding rates. The impact of changes in operating speed on these metrics is significantly lower. The efficacy of the ODPEA in eliminating ’pulse sinking’ enhances the algorithm’s ability to assess multiple seeding rates and overall accuracy. The ODPEA-based monitoring system exhibits superior performance, providing theoretical support for the development of monitoring systems in high-speed belt-type seed guiding devices.

Real-Time Structural Health Monitoring of Bridges Using Convolutional Neural Network-Based Loss Factor Analysis for Enhanced Energy Dissipation Detection

This study introduces a novel method for real-time structural health monitoring (SHM) of bridges using a convolutional neural network (CNN) model that leverages loss factor analysis. As bridge structures deteriorate over time, often accelerated by operational loads, maintaining structural integrity and safety becomes critical. The proposed approach utilizes the concept of a loss factor, which represents the process of energy dissipation across different vibration states, as a key indicator of structural health. This factor is computed from vibration energy spectra, which include amplitude and frequency components, processed through the CNN model for high sensitivity to structural changes. The results demonstrate that the energy dissipation of the bridge during operation can be categorized into signals from three distinct sources: structural responses, defects-related indicators, and noise interference. By monitoring variations in the loss factor over time, the model effectively identifies early signs of structural deterioration, which is critical for timely maintenance interventions. The study also highlights the adaptability to different load conditions and environmental factors, ensuring robust performance in various operational scenarios. The findings underscore the potential of the CNN model to transform SHM practices by enhancing early defect detection, supporting preventive maintenance, and ultimately extending the lifespan of bridge infrastructure.

Construction error control method of large-span spatial structures based on digital twin

In the process of construction of large-span spatial structures, there are many control elements, which need to carry on the dual control of internal force and shape. The traditional control method cannot measure the construction deviation completely and accurately, and there is a lag in information feedback. In this paper, spatio-temporal information is integrated into digital twin and a construction error control method is proposed for large-span spatial structures. By analyzing the fusion mechanism of spatio-temporal information and digital twin, a closed-loop control system for structural errors before, during and after construction is formed. Driven by the control architecture, the method of structure simulation analysis and real-time monitoring during construction is proposed, and the functional relationship between cable force and cable length is established. Based on point cloud data, construction error optimization detection method is proposed for structure formation state. The nonlinear relation between elevation change rate and cable force deviation is formed and the control measures of formation state error are obtained. The experimental model of cable truss structure is taken as an example to apply and verify the theoretical method. The construction error control twin platform is formed in the experiment. Driven by the platform, the twinning analysis and real-time monitoring of the construction process are carried out to control the error of the structure formation. The results show that this method integrates spatio-temporal information into digital twin to realize data integration processing. The optimized error detection method can save 30% time cost and effectively control the construction error within 3%. The integration of spatio-temporal information and digital twin provides theoretical and algorithm support for the intelligent management and control of large-span spatial structure construction.

Artificial intelligence-driven tool for spectral analysis: identifying pesticide contamination in bees from reflectance profiling

Pesticide poisoning constantly threatens bees as they forage for resources in pesticide-treated crops. This poisoning requires thorough investigation to identify its causes, underscoring the importance of reliable pesticide detection methods for bee monitoring. Infrared spectroscopy provides reflectance data across hundreds of spectral bands (hyperspectral reflectance), presumably enabling the efficient classification of pesticide contamination in bee carcasses using artificial intelligence (AI) models, such as machine learning. In this study, bee contamination by commercial formulations of three insecticides—dimethoate (organophosphate), fipronil (phenylpyrazole), and imidacloprid (neonicotinoid)—as well as glyphosate, the most widely used herbicide globally, was detected using machine learning models. These models classified the hyperspectral reflectance profiles of the body surfaces of contaminated bees. The best-performing model, the linear discriminant analysis, achieved 98% accuracy in discriminating contamination across species Apis mellifera, Melipona mondury, and Partamona helleri, with prediction speeds of 0.27seconds. Our pioneering study introduced an effective method for discerning multiple classes of bees contaminated with pesticides using hyperspectral reflectance. An AI-driven spectral data analysis tool (https://github.com/bernardesrodrigoc/MACSS) was developed for the purpose of identifying and characterizing new samples through their spectral characteristics. This platform aids efforts to monitor and conserve bee populations and holds potential importance in environmental monitoring, agricultural research, and industrial quality control.

Evolution characteristics of mining-induced fractures in overburden strata under close-multi coal seams mining based on optical fiber monitoring

Large-scale mining fractures resulting from repeated mining are a major cause of surface water loss in the northern Shaanxi mining area, China. Accurately detecting the evolution of mining-induced fractures is crucial for addressing the fragile ecological environment and ensuring coalmine production safety in this area. This study focuses on the close-multi coal seams mining at the Ningtiaota coalmine, northern Shaanxi, China, investigating the failure types of overburden rock, the evolution of mining-induced fractures, and the height of fracture zones. The results indicate that the failure type of overburden strata transforms from a “trapezoid shape” to an “overlapping trapezoid shape”, with the fracture zone height extending from 64.5 m to 158.5 m due to the superposition of secondary mining. Furthermore, the evolution characteristics of mining-induced fractures shift from a “three-stage and three-step” model to a “three-stage and two-step” model. A characterization model of overburden deformation based on optical fiber sensing is proposed to effectively describe the strain distribution characteristics of overburden failure. This model reveals the spatiotemporal evolution of overburden deformation from the perspective of “horizontal three areas and vertical three zones”, enabling real-time characterization of overburden deformation. The results demonstrate a relative error of less than 5.0 % between optical fiber monitoring and other methods, excluding theoretical calculations. This study offers a technical solution for detecting mining-induced fractures in the northern Shaanxi mining area and holds significant implications for broader studies of overburden deformation and failure under repeated mining.

Hierarchical explicit–implicit combined sensing-based real-time monitoring method for the service performance of complex equipment

PurposeReal-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.Design/methodology/approachSensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.FindingsThe proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.Originality/valueThis research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

A novel online monitoring method of three-phase voltage unbalance factor with Butterworth-based improved SOGI

A novel online monitoring method of three-phase voltage unbalance factor with Butterworth-based improved SOGI

Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack

Daily behavioral analysis of group-housed pigs provides critical insights into early warning systems for pig health issues and animal welfare in smart pig farming. In this study, our main objective was to develop an automated method for monitoring and analyzing the behavior of group-reared pigs to detect health problems and improve animal welfare promptly. We have developed the method named Pig-ByteTrack. Our approach addresses target detection, Multi-Object Tracking (MOT), and behavioral time computation for each pig. The YOLOX-X detection model is employed for pig detection and behavior recognition, followed by Pig-ByteTrack for tracking behavioral information. In 1 min videos, the Pig-ByteTrack algorithm achieved Higher Order Tracking Accuracy (HOTA) of 72.9%, Multi-Object Tracking Accuracy (MOTA) of 91.7%, identification F1 Score (IDF1) of 89.0%, and ID switches (IDs) of 41. Compared with ByteTrack and TransTrack, the Pig-ByteTrack achieved significant improvements in HOTA, IDF1, MOTA, and IDs. In 10 min videos, the Pig-ByteTrack achieved the results with 59.3% of HOTA, 89.6% of MOTA, 53.0% of IDF1, and 198 of IDs, respectively. Experiments on video datasets demonstrate the method’s efficacy in behavior recognition and tracking, offering technical support for health and welfare monitoring of pig herds.

Smart Monitoring Method for Land-Based Sources of Marine Outfalls Based on an Improved YOLOv8 Model

Land-based sources of marine outfalls are a major source of marine pollution. The monitoring of land-based sources of marine outfalls is an important means for marine environmental protection and governance. Traditional on-site manual monitoring methods are inefficient, expensive, and constrained by geographic conditions. Satellite remote sensing spectral analysis methods can only identify pollutant plumes and are affected by discharge timing and cloud/fog interference. Therefore, we propose a smart monitoring method for land-based sources of marine outfalls based on an improved YOLOv8 model, using unmanned aerial vehicles (UAVs). This method can accurately identify and classify marine outfalls, offering high practical application value. Inspired by the sparse sampling method in compressed sensing, we incorporated a multi-scale dilated attention mechanism into the model and integrated dynamic snake convolutions into the C2f module. This approach enhanced the model’s detection capability for occluded and complex-feature targets while constraining the increase in computational load. Additionally, we proposed a new loss calculation method by combining Inner-IoU (Intersection over Union) and MPDIoU (IoU with Minimum Points Distance), which further improved the model’s regression speed and its ability to predict multi-scale targets. The final experimental results show that the improved model achieved an mAP50 (mean Average Precision at 50) of 87.0%, representing a 3.4% increase from the original model, effectively enabling the smart monitoring of land-based marine discharge outlets.

Health monitoring of automotive clutch system through feature fusion and application of tree-based classifiers

Clutch systems are crucial components of automotive systems, essential for transferring engine power to the wheels through gear shifts. A failed clutch can halt gear shifts and power transmission, rendering a vehicle immobile. Effective fault diagnosis is vital for ensuring reliability, preventing breakdowns, and addressing root causes promptly. Consequently, condition monitoring of the clutch is essential for maintaining system reliability and minimizing unwanted breakdowns. This paper presents an innovative condition-monitoring technique derived from vibration analysis to detect faults in the clutch system. The study involves extracting statistical, histogram, and autoregressive moving average features from acquired vibration signals. The J48 decision tree algorithm is utilized to select the most significant features, which are then classified using tree-based classifiers across three load conditions (no load, 5 kg, and 10 kg) and six clutch conditions. The experimental results demonstrate that the feature fusion strategy significantly enhances classification accuracy. The obtained results state that the classification accuracies for no load, 5 kg load, and 10 kg load were 98.33%, 100.00%, and 99.16%, respectively, while applying feature fusion strategies. This study highlights the effectiveness of feature fusion in improving fault diagnosis accuracy for clutch systems, presenting a robust method for real-time condition monitoring in automotive applications.

Rapid and Continuous Monitoring Method of Radon (222Rn) for Seawater Using a Pulsed Ionization Chamber

Rapid and Continuous Monitoring Method of Radon (²²²Rn) for Seawater Using a Pulsed Ionization Chamber

A lightweight hybrid model-based condition monitoring method for grinding wheels using acoustic emission signals

Abstract Various data-driven methods based on acoustic emission (AE) signals have been proposed to monitor and accurately identify the wear stages of the grinding wheel. However, extracting effectively generalized and discriminative features from AE signals remains a challenging task. This paper proposes a new lightweight hybrid deep learning model that combines enhanced convolution with enhanced vision transformer to effectively address the above challenges. Specifically, the key contributions of this paper are three-fold: (1) A two-stage signal preprocessing mechanism based on variational mode decomposition and continuous wavelet transform is proposed to improve the signal-to-noise ratio and feature representation of the AE signals. (2) To prevent model overfitting, a new regularization strategy based on stabilizing sparse convolutional weights and a weight penalty mechanism is designed. This approach improves the hybrid mode’s capacity to extract generalized features. (3) To concentrate on capturing multi-scale discriminative features between different wear conditions, a parameter-efficient residual convolution module based on the dropout depthwise convolution is designed, which is utilized to reconstruct the encoder of the vision transformer. In particular, to improve the training efficiency of the model, a lightweight mechanism using a stage-stride decreasing strategy is used to compress the spatial dimensions of the feature maps in the attention mechanism. The ablation experiment demonstrates the rationality of the proposed model structure. Comparative experiments show that the proposed method achieves a diagnostic accuracy of 99.6% on the test set and outperforms other state-of-the-art deep learning methods.

Design-assisted HPLC-UV method for therapeutic drug monitoring of pholcodine, ephedrine, and guaifenesin in biological fluids.

Drug-drug interactions may amplify or diminish their intended effects, or even produce entirely new effects. Multicomponent mixture HPLC analysis offers a thorough and effective method for comprehending the makeup and behavior of complicated materials, advancing research and development across a range of scientific and industrial domains. A novel experimental design-assisted HPLC methodology for the concurrent investigation of the drug-drug interaction of pholcodine, ephedrine, and guaifenesin in biological fluids has been established. Rather than the routine methodology, the application of the factorial design-HPLC method offers a powerful and efficient tool for the analysis of these compounds. Both mixed and full factorial designs were employed to assess the impact of variable factors on chromatographic results. Utilizing an isocratic elution mode on a C18 column, the chromatographic separation was carried out. 15% Methanol, 5% acetonitrile, and 80% phosphate buffer with 0.1%(v/v) triethylamine set to pH 3 make up the mobile phase flowing at rate 1.0 mL/min. The calibration curves of the drugs show excellent linearity over a concentration ranges: 0.20-13.0µg/mL for PHO, 0.50-20.0µg/mL for EPH and 0.70-20.0µg/mL for GUA with LOQ values of 0.18, 0.38 and 0.50. The fast separation and quantitation in less than 6min is an advantage. Also, the method includes a robust sample preparation protocol for the analysis of complex biological samples, ensuring high selectivity and precision. The ease, speed and cost-effectiveness of the method are ideal for supporting in vitro studies, including drug-drug interaction investigations, especially in bioanalytical labs.

Evaluating the attractiveness of different bait formulations for monitoring Liriomyza sativae (Diptera: Agromyzidae) adults

The growing need for sustainable agricultural practices drives the search for effective alternatives in pest management. Insects like Liriomyza sativae cause significant damage to crops, requiring monitoring and control methods that minimize the use of chemical pesticides. In this context, the use of natural and synthetic attractants for pest capture offers a promising solution while contributing to the preservation of ecological balance. The objective of this work was to study the attractive effect of different formulations for L. sativae adults. The formulations were divided into two groups according to their composition. For group 1, they were based on vinegar and sugar, and for group 2, formulations were based on molasses, invert sugar, hydrolyzed protein, and eugenold. The attractiveness of the formulations was evaluated based on free choice. They were placed in tube-shaped plastic containers and randomly arranged in TNT cages. Evaluations were performed every 48 hours by counting the number of adults in the containers until there was a low capture rate. Data were subjected to analysis of variance testing and means were compared using the Scott-Knott test at 5% significance level. All formulations tested were attractive to adults of L. sativae, with groups 1 and 2 reaching their maximum averages of 55.60 and 68.00 adults, respectively. Both occurred during the second evaluation. Over time, all formulations showed a reduction in capture rate. The use of effective attractants promotes traps that monitor and even control the pest population in a more sustainable way, benefiting the agroecosystem with sustainable agricultural practices. Furthermore, the research expands knowledge about the chemical ecology of insect pests, providing a basis for the chemical communication of these organisms.

A Method for Bioluminescence-Based RNA Monitoring Using Split-Luciferase Reconstitution Techniques.

A bioluminescence-based RNA monitoring method in living cells was developed using a split NanoLuc (NLuc) reconstitution technique. For specific recognition of a target RNA sequence, a mutant PUM-HD (mPUM) was used. The method was applied to β-actin mRNA in various cells, including primary cultures of rat hippocampal neurons. It allowed for continuous observation of intracellular localization distribution of the target mRNA in living cells, providing insights into various biological phenomena involving intracellular RNA localization and dynamics.

High‐Accuracy Geological Deformation Monitoring Through Unmanned Aerial Vehicle Image Registration Without Control Points

ABSTRACTUnmanned aerial vehicles (UAVs) have gained popularity in geological deformation monitoring due to their flexibility, cost‐effectiveness, and ease of image acquisition. However, accurate co‐registration of multi‐phase UAV images, essential for effective monitoring, requires numerous ground control points (GCPs), challenging in deformation‐prone areas. This study introduces a UAV‐based monitoring method that eliminates the need for GCPs. Initially, two‐phase images are integrated for joint aerial triangulation. The first‐phase image and tie points undergo bundle adjustment refinement. Control points are identified as tie points with minimal deviation and used to refine the second‐phase image. Three‐dimensional (3D) models are generated from these refined adjustments and differential analysis between the models reveals geological deformation. Experimental results show that this method automatically identifies stable areas and effectively captures both the location and extent of geological deformation, achieving a relative change accuracy of 0.06 m, which is twice the average ground resolution. Compared to traditional aerial triangulation methods without control points, this approach offers more than a tenfold increase in accuracy. The accuracy is on par with methods that utilize multiple control points, indicating the promising potential of this approach for geological deformation monitoring.

Abstract B015: CSF liquid biopsies reveal disease status and resistance mechanisms in children undergoing targeted therapy for malignant brain tumors

Abstract BACKGROUND: Patients with relapsed or refractory brain tumors face poor outcomes due to the aggressive nature of their disease. Recent studies suggest that administration of cell cycle inhibitors may help slow tumor progression. In the Phase I trial SJDAWN (NCT03434262) conducted at St. Jude Children’s Research Hospital, CDK4/6 inhibitor therapy (ribociclib) was assessed for safety and efficacy in such patients. To understand how tumors evolve in response to this treatment, longitudinal CSF liquid biopsies from SJDAWN were retrospectively analyzed by cell-free DNA (cfDNA) sequencing. METHODS: CSF samples (n=143) were collected by lumbar puncture from 34 of 68 (50%) patients before and during treatment. cfDNA was extracted and analyzed using an innovative DNA methylation-based platform that concurrently detects tumor-associated copy number alterations, classifies tumor subtypes, and distinguishes malignant from non-malignant methylation signatures. cfDNA inputs ranged from 50 to 500 pg. CSF profiles were compared to patient-matched tumor tissue DNA methylation arrays and subsequently correlated with MRI findings at corresponding timepoints. RESULTS: Circulating tumor DNA (ctDNA) was detected in 29 of 34 (85%) patients, yielding accurate tumor classification even in the setting of diploid genomes. CNV profiles from CSF liquid biopsies strongly correlated with patient-matched tumor tissue methylation arrays and often revealed genetic events not previously captured in the tumor tissue. Resistance to CDK4/6 inhibition was characterized by focal amplification of positive cell cycle regulators, including CDK6, CCND2, MYCN, and RRM2 that were detectable in CSF ∼6 months prior to radiographic progression. Remarkably, three high-risk Group 3/4-MB patients were progression-free for >1.5 years. The trend of their longitudinal cfDNA profiles varied: one had persistent ctDNA detected throughout therapy with resistance emerging at ∼1.5 years; another had undetectable ctDNA for ∼2 years before reemergence of tumor; and the third remained MRD-negative, potentially owing to reduced ctDNA release resulting from CDK4/6 inhibition. Methylation changes in response to treatment were also observed, providing unprecedented insights into epigenetic landscapes associated with tumor evolution concurrent with targeted therapy. CONCLUSIONS: Methylation-based analysis of CSF liquid biopsies provides an ultra-sensitive and comprehensive method for serial disease monitoring. Even with sub-nanogram cfDNA inputs, tumor reemergence and resistance mechanisms were detectable months before MRI changes, enabling timely clinical intervention. Resistance to CDK4/6 inhibition is mediated by focal amplification of cell cycle regulators, suggesting alternative approaches will be required to overcome this path of resistance. CDK4/6 inhibitor therapy offers clinical benefit to a specific subset of high-risk Group 3/4-MB patients, motivating expanded evaluation of this population in future trials. Citation Format: Katie Han, Kyle S Smith, Anna Kostecka, Sandeep Dhanda, Hong Lin, Margit Mikkelsen, Giles W Robinson, Paul A Northcott. CSF liquid biopsies reveal disease status and resistance mechanisms in children undergoing targeted therapy for malignant brain tumors [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B015.