Data Processing Strategies Research Articles

Automation and Data Integrity in Regulatory Submissions: Innovations for Decentralized Clinical Trials

This article explores the evolving landscape of regulatory submissions in clinical trials, with a particular focus on the challenges and opportunities presented by decentralized clinical trials (DCTs). It examines the role of automation in streamlining regulatory document preparation and validation processes, highlighting the benefits of improved accuracy, enhanced productivity, and accelerated timelines. The article delves into the unique data integrity challenges posed by DCTs, including the integration of diverse data sources such as wearables, mobile applications, and remote monitoring tools. It discusses innovative solutions for ensuring regulatory compliance, such as AI-driven monitoring systems, blockchain technology for audit trails, and automated query resolution workflows. The importance of traceability in regulatory submissions is emphasized, along with its role in enhancing reproducibility and accountability in clinical research. The article also addresses the impact of these changes on statistical programming, outlining the challenges faced by programmers and strategies for regulatory-compliant data processing. Finally, it looks toward future directions in automation and data integrity, exploring the potential of AI/ML integration for adaptive workflows and the development of seamless connections between clinical programming tools and regulatory platforms. Throughout, the article underscores the industry's ongoing efforts to balance technological advancement with regulatory compliance and data integrity in the pursuit of more efficient and effective clinical trials.

Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm

In response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segmentation framework based on Elite Strategy-based Improved Red-billed Blue Magpie Optimization (ES-RBMO) Algorithm. The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. Four key phenotypic parameters of the plant were extracted. The phenotypic parameters of plant height, stem thickness, leaf area and leaf inclination were analyzed by comparing the values extracted by manual measurements with the values extracted by the 3D point cloud technique. The results showed that the coefficients of determination (R2) for these parameters were 0.932, 0.741, 0.938 and 0.935, respectively, indicating high correlation. The root mean square error (RMSE) was 0.511, 0.135, 0.989 and 3.628, reflecting the level of error between the measured and extracted values. The absolute percentage errors (APE) were 1.970, 4.299, 4.365 and 5.531, which further quantified the measurement accuracy. In this study, an efficient and adaptive intelligent optimization framework was constructed, which is capable of optimizing data processing strategies to achieve efficient and accurate processing of tomato point cloud data. This study provides a new technical tool for plant phenotyping and helps to improve the intelligent management in agricultural production.

Signal Processing Workflow for Suspect Screening in LC × LC-HRMS: Efficient Extraction of Pure Mass Spectra for Identification of Suspects in Complex Samples Using a Mass Filtering Algorithm.

The data processing workflows for comprehensive two-dimensional liquid chromatography (LC × LC) hyphenated to high-resolution mass spectrometry (HRMS) operated in data-independent acquisition (DIA) are limited compared to their one-dimensional counterparts. A two-step workflow is proposed to extract pure mass spectra from LC × LC-HRMS. First, a mass filtering (MF) algorithm groups ions belonging to the same compound based on their elution profile similarity in the first (1D) and second dimension (2D). Second, the filtered data are deconvoluted using multivariate curve resolution (MCR) to address potential coelution. The presented workflow is termed MF + MCR and was tested on pulsed elution-LC × LC-HRMS data from a wastewater effluent extract. The proposed workflow was benchmarked to the following three data processing strategies for mass spectra extraction: peak apex (PAM), using the MF approach alone, or using MCR without prior MF. The MF + MCR workflow identified 25 suspect compounds, compared to 23, 16, and 10 identified by MF, MCR, and PAM, respectively. The nine suspects that could not be identified using MCR compared to the MF + MCR all had low total signal contributions, i.e., low intensities compared to the TIC. This showed that adequate preprocessing prior to MCR is essential for trace level analysis. Additionally, it was shown that the MF + MCR workflow extracted statistically significantly purer mass spectra compared to PAM (p-value: 0.003) and MCR (p-value: 0.04) from a spiked blank sample. The results highlight that by utilizing the elution profiles in both chromatographic dimensions, clean mass spectra of analytes at trace levels measured in DIA can be extracted, allowing for more reliable compound identification compared to the workflows that were used for benchmarking.

Integrating Particle Motion Tracking into Thermal Gel Electrophoresis for Label-Free Sugar Sensing.

Bioanalytical sensors are adept at quantifying target analytes from complex sample matrices with high sensitivity, but their multiplexing capacity is limited. Conversely, analytical separations afford great multiplexing capacity but typically require analyte labeling to increase sensitivity. Here, we report the development of a separation-based sensor to sensitively quantify unlabeled polysaccharides using particle motion tracking within a microfluidic electrophoresis platform. Carboxymethyl dextran (20 kDa) was spiked into Pluronic thermal gel along with fluorescent nanoparticles (200 nm diameter) and loaded into single-channel microfluidic devices. Upon voltage application, the soluble sugar enriched into a concentrated band that induced motion of the insoluble particles as it passed. Bead displacement was tracked over time to produce electropherograms where peak areas were proportional to analyte concentrations. Key studies herein established the range of acceptable operating conditions (e.g., gel concentration, temperature) to characterize how the temperature-dependent rigidity of thermal gel influenced the analysis. Data processing strategies were then evaluated to identify conditions (e.g., exposure intervals, particle averaging, motion directionality) to maximize sensitivity. The quantitative response of the method was evaluated over a broad concentration range (0.5-5000 nM) where detection limits were found to be 520 pM for the 20 kDa sugar, providing a 106-fold superior mass LOD than a gold standard UV-vis absorbance method. Studies into the detection mechanism found that sensitivity was dependent on the molecular weight of the sugar as larger sugars produced greater responses. Collectively, these studies established best practices for integrating particle sensing into thermal gel separations for label-free polysaccharide quantitation.

Revolutionizing Tissue Clearing and 3-Dimensional Imaging: Transparent Embedding Solvent System for Uniform High-Resolution Imaging.

Combining transparent embedding with sectioning is likely to be the future direction for tissue clearing and 3-dimensional (3D) imaging. A newly published transparent embedding system, TESOS (Transparent Embedding Solvent System), ensures consistent submicron resolution imaging throughout the entire sample, and can be compatible with different microscopy systems. This method shows great potential in connectome mapping, and might be an optimal option for future 3D multiplex immunofluorescence and RNA insitu hybridization imaging. Additional efforts would be needed to innovate labeling, imaging, and data processing strategies to fully utilize the potential of transparent embedding systems in high-resolution imaging of large-scale samples.

Single-Cell Hi-C Analysis Workflow with Pairtools.

Single-cell Hi-C (scHi-C) is a collection of protocols for studying genomic interactions within individual cells. Although data analysis for scHi-C resembles data analysis for bulk Hi-C, the unique challenges of scHi-C, such as high noise and protocol-specific biases, require specialized data processing strategies. In this tutorial chapter, we focus on using pairtools, a suite of tools optimized for scHi-C data, demonstrating its application on a Drosophila snHi-C dataset. While centered on pairtools for snHi-C data, the principles outlined are applicable across scHi-C variants with minor adjustments. This educational chapter aims to guide researchers in using open-source tools for scHi-C analysis, emphasizing critical steps of contact pair extraction, detection of ligation junctions, filtration, and deduplication.

Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamics

BackgroundSenescence is a complex developmental process that is regulated by a multitude of environmental, genetic, and physiological factors. Optimizing the timing and dynamics of this process has the potential to significantly impact crop adaptation to future climates and for maintaining grain yield and quality, particularly under terminal stress. Accurately capturing the dynamics of senescence and isolating the genetic variance component requires frequent assessment as well as intense field testing. Here, we evaluated and compared the potential of temporally dense drone-based RGB- and multispectral image sequences for this purpose. Regular measurements were made throughout the grain filling phase for more than 600 winter wheat genotypes across three experiments in a high-yielding environment of temperate Europe. At the plot level, multispectral and RGB indices were extracted, and time series were modelled using different parametric and semi-parametric models. The capability of these approaches to track senescence was evaluated based on estimated model parameters, with corresponding parameters derived from repeated visual scorings as a reference. This approach represents the need for remote-sensing based proxies that capture the entire process, from the onset to the conclusion of senescence, as well as the rate of the progression.ResultsOur results indicated the efficacy of both RGB and multispectral reflectance indices in monitoring senescence dynamics and accurately identifying key temporal parameters characterizing this phase, comparable to more sophisticated proximal sensing techniques that offer limited throughput. Correlation coefficients of up to 0.8 were observed between multispectral (NDVIred668-index) and visual scoring, respectively 0.9 between RGB (ExGR-index) and visual scoring. Sub-sampling of measurement events demonstrated that the timing and frequency of measurements were highly influential, arguably even more than the choice of sensor.ConclusionsRemote-sensing based proxies derived from both RGB and multispectral sensors can capture the senescence process accurately. The sub-sampling emphasized the importance of timely and frequent assessments, but also highlighted the need for robust methods that enable such frequent assessments to be made under variable environmental conditions. The proposed measurement and data processing strategies can improve the measurement and understanding of senescence dynamics, facilitating adaptive crop breeding strategies in the context of climate change.

Integrating a hybrid data processing strategy into an optimized light gradient boosting machine for photovoltaic power forecasting

Integrating a hybrid data processing strategy into an optimized light gradient boosting machine for photovoltaic power forecasting

Vehicle Target Detection Using the Improved YOLOv5s Algorithm

This paper explores the application of the YOLOv5s algorithm integrated with the DeepSORT tracking detection algorithm in vehicle target detection, leveraging its advantages in data processing, loss function, network structure, and training strategy. Regarding detection frame regression, adopting Focal-EIOU can improve vehicle detection accuracy by precisely measuring overlap and better handle complex scenarios, enhancing the overall performance. The CoordConv convolution layer with more spatial position information is employed to enhance the original network structure’s convolution layer and improve vehicle positioning accuracy. The principle and effectiveness of the Shuffle Attention mechanism are analyzed and added to the YOLOv5s network structure to enhance training, improve detection accuracy and running speed. And the DeepSORT tracking detection algorithm is designed to achieve high-speed operation and high-accuracy matching in target tracking, enabling efficient and reliable tracking of objects. Simultaneously, the network structure is optimized to enhance algorithmic speed and performance. To meet the requirements of vehicle detection in practical transportation systems, real-world vehicle images are collected as a dataset for model training to achieve accurate vehicle detection. The results show that the accuracy rate P of the improved YOLOv5s algorithm is increased by 0.484%, and mAP_0.5:0.95 reaches 92.221%, with an increase of 1.747%.

Massive Point Cloud Processing for Efficient Construction Quality Inspection and Control.

The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method.

The Zenith Total Delay Combination of International GNSS Service Repro3 and the Analysis of Its Precision

Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction.

On-orbit Moving Target Detection Method Based on Dual Images from Taijing-IV 01 Satellite

Abstract. With the evolution of satellite video technology, the domain has garnered increasing attention. Concurrently, advancements in deep learning have yielded numerous outcomes in target detection. This paper introduces a novel method for detecting moving targets, offering a broader detection range compared to traditional satellite video techniques, facilitating orbital target recognition from dual panchromatic image strips. Our experimental setup on the Taijing-IV 01 satellite, launched on February 27, 2022, successfully acquired two image strips separated by one second. These strips contain speed and directional information of moving objects, extractable through the frame differencing technique. We propose combining frame differencing with lightweight deep learning for target detection, extracting regions of interest (ROIs) to focus on areas with potential moving targets. This approach reduces the workload of wholeimage target detection, decreasing data processing volume by 89%. By optimizing the YOLOv8 network and using techniques like feature map fusion of low-level and high-resolution features, we enhance sensitivity to small targets. Consequently, the model size is reduced by 79%, the mean Average Precision (mAP) increases by approximately 1.8% and 4.5%, and detection speed rises by 26%. This method introduces a new paradigm in remote sensing data services, facilitating rapid acquisition and real-time transmission of positions and image information of moving targets to the ground. This significantly reduces bandwidth requirements for transmitting remote sensing information, presenting a novel strategy for data acquisition and processing in large-scale Earth observation systems and geoscientific applications.

The Application of Big Data Technology in the Analysis of Commercial Circulation Data in Emerging Industries

Inside the generation of rapid technological development, rising industries are increasingly counting on big facts to pressure growth and innovation. This examine explores the transformative effect of huge facts generation on the analysis of commercial circulate statistics in those burgeoning sectors. By way of harnessing numerous and voluminous datasets, organisations inside rising industries can uncover crucial insights, optimise delivery chains, are expecting marketplace developments, and enhance patron reviews. The paper begins by using outlining the unique challenges and possibilities that emerging industries face within the virtual landscape, emphasising the want for strong facts-driven techniques. We delve into the methodologies of large facts analytics, together with data acquisition, garage, processing, and visualization strategies tailor-made for the nuanced requirements of these industries. A crucial examination of case research wherein huge facts has been efficaciously carried out offers realistic insights into its effectiveness and barriers. The examine similarly investigates the role of advanced analytics, system studying, and AI in refining data evaluation techniques, providing a complete view of the way those technologies synergistically make contributions to strategic selection-making. Moral issues, especially regarding records of privateness and safety, are also addressed, acknowledging the responsibilities that accompany the utilization of huge information. The paper concludes by projecting future traits in big statistics programs within rising industries, which includes the capacity for predictive analytics and the integration of IoT gadgets. This research now not only underscores the significance of big statistics in revolutionising business flow however also serves as a guiding framework for industry leaders and stakeholders trying to navigate the complexities of the digital age in rising markets.

The SDUST2022GRA global marine gravity anomalies recovered from radar and laser altimeter data: contribution of ICESat-2 laser altimetry

Abstract. The global marine gravity anomaly model is predominantly recovered from along-track radar altimeter data. Despite significant advances in gravity anomaly recovery, the improvement of the gravity anomaly model remains constrained by the absence of cross-track geoid gradients and the reduction in radar altimeter data, especially in coastal and high-latitude regions. ICESat-2 laser altimetry, with a three-pair laser beam configuration, a small footprint, and a near-polar orbit, facilitates the determination of cross-track geoid gradients and provides valid observations in certain regions. We present an ICESat-2 altimeter data processing strategy that includes the determination of cross-track geoid gradients and the combination of along-track and cross-track geoid gradients. Utilizing these methods, we developed a new global marine gravity model, SDUST2022GRA, from radar and laser altimeter data. Different weight determination methods were applied to each type of altimeter datum. The precision and spatial resolution of SDUST2022GRA were assessed against published altimeter-derived global gravity anomaly models (DTU17, V32.1, NSOAS22) and shipborne gravity measurements. SDUST2022GRA achieved a global precision of 4.43 mGal, representing an improvement of approximately 0.22 mGal over existing altimeter-derived models. In local coastal and high-latitude regions, SDUST2022GRA showed an enhancement of 0.16–0.24 mGal compared to the other models. The spatial resolution of SDUST2022GRA is approximately 20 km in certain regions, which is slightly superior to the other models. The percentage contribution of ICESat-2 to the improvement of the gravity anomaly model is 4.3 % in low- to mid-latitude regions by comparing SDUST2022GRA with ICESat-2 to SDUST2021GRA without ICESat-2, and this is increasing in coastal regions. These assessments suggest that SDUST2022GRA is a reliable global marine gravity anomaly model. The SDUST2022GRA data are freely available at https://doi.org/10.5281/zenodo.8337387 (Li et al., 2023).

The Real-Time Detection of Vertical Displacements by Low-Cost GNSS Receivers Using Precise Point Positioning.

Vertical displacements are traditionally measured with precise levelling, which is inherently time consuming. Rapid or even real-time height determination can be achieved by the Global Navigation Satellite System (GNSS). Nevertheless, the accuracy of real-time GNSS positioning is limited, and the deployment of a network of continuously operating GNSS receivers is not cost effective unless low-cost GNSS receivers are considered. In this study, we examined the use of geodetic-grade and low-cost GNSS receivers for static and real-time GNSS levelling, respectively. The results of static GNSS levelling were processed in four different software programs or services. The largest differences for ellipsoidal/normal heights reached 0.054 m/0.055 m, 0.046 m/0.047 m, and 0.058 m/0.058 m for points WRO1, BM_ROOF, and BM_CP, respectively. In addition, the values depended on the software used and the location of the point. However, the multistage experiment was designed to analyze various strategies for GNSS data processing and to define a method for detecting vertical displacement in a time series of receiver coordinates. The developed method combined time differentiation of coordinates estimated for a single GNSS receiver using the Precise Point Positioning (PPP) technique and Butterworth filtering. It demonstrated the capability of real-time detection of six out of eight displacements in the range between 20 and 55 mm at the three-sigma level. The study showed the potential of low-cost GNSS receivers for real-time displacement detection, thereby suggesting their applicability to structural health monitoring, positioning, or early warning systems.

Exploring chemical markers and identifying phenolic markers using a metabolomics strategy and chemometrics to study the different origins of defatted Coix seed

Coix seed, a prevalent medicinal and food-homologous plant, is extensively consumed in Asia. It has various pharmacological properties, such as anti-inflammatory and anticancer effects. Coix seed oil, as its main component, is widely produced. However, during the industrial production process of Coix seed oil, substantial byproducts are produced, namely, defatted Coix seeds, which are also worth researching. Currently, it remains unclear whether there will be differences in defatted Coix seeds obtained from different geographical locations, with previous studies reporting that phenolic compounds in defatted Coix seeds have a significant utilization value. In this study, firstly, the TPC and TFC of samples collected in three temperature zones were detected. Subsequently, UPLC-Q-TOF/MS was used to analyze the samples, and a metabolomics data processing strategy and chemometric analysis method were established. We have confirmed the presence of flavonoids and phenolic compounds in 30 batches of Coix seed from different temperature zones in China, and concluded that the overall quality of Coix seed from different batches is relatively stable. With the established strategy, 12 characteristic chemical markers were identified, and 5 valuable phenolic chemical markers were selected for distinguishing the origin of Coix seed and evaluating the quality of defatted Coix seed. Among them, proanthocyanidin A2 has the highest content in defatted Coix seed in subtropical regions, while the content of caffeic acid, naringin, rutin, and chlorogenic acid decreases from north to south. The strategy proposed in this study may provide some basis for the quality control and rational use of defatted Coix seeds.

ProteinFlow: An advanced framework for feature engineering in protein data analysis.

In the burgeoning field of proteins, the effective analysis of intricate protein data remains a formidable challenge, necessitating advanced computational tools for data processing, feature extraction, and interpretation. This study introduces ProteinFlow, an innovative framework designed to revolutionize feature engineering in protein data analysis. ProteinFlow stands out by offering enhanced efficiency in data collection and preprocessing, along with advanced capabilities in feature extraction, directly addressing the complexities inherent in multidimensional protein data sets. Through a comparative analysis, ProteinFlow demonstrated a significant improvement over traditional methods, notably reducing data preprocessing time and expanding the scope of biologically significant features identified. The framework's parallel data processing strategy and advanced algorithms ensure not only rapid data handling but also the extraction of comprehensive, meaningful insights from protein sequences, structures, and interactions. Furthermore, ProteinFlow exhibits remarkable scalability, adeptly managing large-scale data sets without compromising performance, a crucial attribute in the era of big data.

Multi-Source Feature-Fusion Method for the Seismic Data of Cultural Relics Based on Deep Learning.

The museum system is exposed to a high risk of seismic hazards. However, it is difficult to carry out seismic hazard prevention to protect cultural relics in collections due to the lack of real data and diverse types of seismic hazards. To address this problem, we developed a deep-learning-based multi-source feature-fusion method to assess the data on seismic damage caused by collected cultural relics. Firstly, a multi-source data-processing strategy was developed according to the needs of seismic impact analysis of the cultural relics in the collection, and a seismic event-ontology model of cultural relics was constructed. Additionally, a seismic damage data-classification acquisition method and empirical calculation model were designed. Secondly, we proposed a deep learning-based multi-source feature-fusion matching method for cultural relics. By constructing a damage state assessment model of cultural relics using superpixel map convolutional fusion and an automatic data-matching model, the quality and processing efficiency of seismic damage data of the cultural relics in the collection were improved. Finally, we formed a dataset oriented to the seismic damage risk analysis of the cultural relics in the collection. The experimental results show that the accuracy of this method reaches 93.6%, and the accuracy of cultural relics label matching is as high as 82.6% compared with many kinds of earthquake damage state assessment models. This method can provide more accurate and efficient data support, along with a scientific basis for subsequent research on the impact analysis of seismic damage to cultural relics in collections.

Fusing 1H NMR and Raman experimental data for the improvement of wine recognition models

The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of 1H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of 1H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage.

Impact of Toumeyella parvicornis outbreak in Pinus pinea L. forest of Southern Italy: First detection using a dendrochronological, isotopic and remote sensing analysis

Climate change is expected to affect forests' growth and functioning and to increase their vulnerability to stressors such as prolonged drought and pest outbreaks. Identifying vulnerable forest stands and predicting tree decline is critical for timely management interventions to preserve forests integrity and associated ecosystem services. This study combined dendrochronological and isotopic analyses with satellite remote sensing to detect early warning signs of forest decline in a Pinus pinea L. stand in southern Italy affected by one of the first outbreak of the pine parasite Toumeyella parvicornis reported in Italy. Furthermore, through a comparative study of the analysis techniques, this research aimed to identify the most effective data processing strategies for detecting tree dieback of this species. Satellite analysis revealed a gradual decline in vegetation indices (NDVI, EVI, EVI2) of the stand from 2015 onwards, which coincided with the onset of defoliation due to the pest outbreak. The decline in defoliation intensified in 2020, leading to a severe tree carbon deficit and subsequent mortality of the pine stand in 2023. In comparison, indices such as EVI and EVI2 have been shown to be more sensitive than NDVI in detecting changes in canopy cover. The inclusion of the NDMI index provided important information on the moisture dynamics of the stand. Dendrochronological analyses complemented remote sensing data: a strong decrease in growth was observed from 2020 onwards, undemanding a tipping point for the Pinus pinea stand, which led to tree mortality in 2023. The study highlighted the higher sensitivity of detrended chronologies such as BAI and TRW-I in detecting signs of forest dieback compared to raw tree-ring data. Moreover, intrinsic water use efficiency (WUEi) analysis provided insight into the eco-physiological dynamics underlying pine tree decline, revealing lower tree water retention induced by defoliation. Finally, correlations between growth and WUEi data with meteorological variables highlighted how defoliation increased the vulnerability of trees to the effects of climate, influencing their ability to recover after the pest attack. In conclusion, the combination of these analysis methods provided a detailed and comprehensive overview of tree species dieback due to new invasive pest. Our findings providing valuable insights into the eco-physiological dynamics and early detection of signs of tree decline, useful for planning effective forest management strategies to counteract the diffusion of Toumeyella parvicornis across Italy and Europe.