Change Detection Research Articles

Deconvolution of Human Urine across the Transcriptome and Metabolome.

Early detection of the cell type changes underlying several genitourinary tract diseases largely remains an unmet clinical need, where existing assays, if available, lack the cellular resolution afforded by an invasive biopsy. While messenger RNA in urine could reflect the dynamic signal that facilitates early detection, current measurements primarily detect single genes and thus do not reflect the entire transcriptome and the underlying contributions of cell type-specific RNA. We isolated and sequenced the cell-free RNA (cfRNA) and sediment RNA from human urine samples (n = 6 healthy controls and n = 12 kidney stone patients) and measured the urine metabolome. We analyzed the resulting urine transcriptomes by deconvolving the noninvasively measurable cell type contributions and comparing to plasma cfRNA and the measured urine metabolome. Urine transcriptome cell type deconvolution primarily yielded relative fractional contributions from genitourinary tract cell types in addition to cell types from high-turnover solid tissues beyond the genitourinary tract. Comparison to plasma cfRNA yielded enrichment of metabolic pathways and a distinct cell type spectrum. Integration of urine transcriptomic and metabolomic measurements yielded enrichment for metabolic pathways involved in amino acid metabolism and overlapped with metabolic subsystems associated with proximal tubule function. Noninvasive whole transcriptome measurements of human urine cfRNA and sediment RNA reflects signal from hard-to-biopsy tissues exhibiting low representation in blood plasma cfRNA liquid biopsy at cell type resolution and are enriched in signal from metabolic pathways measurable in the urine metabolome.

Object-based change detection (OBCD): a case study for measuring retail led regeneration

ABSTRACT The Canadian retail landscape experienced a defining shift in the 1990s with the emergence of “big box” retailers and the proliferation of power centers nationwide. Within this context, the Ontario Stockyards neighborhood in Toronto, Canada, has undergone a remarkable transformation over the past three decades. It has evolved from a predominantly meat-packing industrial hub into a versatile urban environment featuring a mix of retail and residential spaces. This research seeks to elucidate the dynamics of commercial/industrial and residential area changes within this evolving landscape. The study spans a 23-year period, during which the distribution of land use categories undergoes notable fluctuations. This study sheds light on the intricate relationship between retail-driven urban regeneration, commercial/industrial transformations, and residential development. This paper uses the Stockyards neighborhood as a case study, to demonstrate the practical application of Object-Based Change Detection (OBCD) in real-world contexts. Through this case study, the potential and effectiveness of OBCD as a valuable tool for analyzing complex urban development is presented. The findings underscore the significance of integrated urban development strategies that leverage the transformative potential of retail-led initiatives.

Evaluating the damage of collapsed bridges using remote sensing technologies: Case study: Baltimore’s Francis Scott Key Bridge

Bridges are vital for linking communities and facilitating economic activity. However, in the face of disaster, like ship collisions pose a significant threat to bridge infrastructure, causing structural damage and potential safety hazards. Rapid and precise assessment of the damage is essential for effective emergency response and recovery operations. Remote sensing with near-real-time satellite imagery provides the disaster scenario. This paper presents a change detection using pre- and post-disaster satellite data for Baltimore’s Francis Scott Key Bridge to identify structural damage due to the collision of the ship with support pillars on 26 March 2024. Both optical and microwave satellite data were used from open data sources and analyzed based on geospatial techniques such as change detection and surface profiling. It is estimated that an 1100-meter span of bridge was affected due to this collision, which helped to estimate the damage and mobilize the rescue operations. It may need further validation from ground truth information. Hence, the current study emphasizes the potential of remote sensing satellite data to provide near-real-time impact on disaster analysis.

Working Memory Capacity for Mid-Air Gestures in Human–Computer Interaction

Mid-air gesture interactions have emerged as one of the predominant modalities used in human-computer interaction (HCI). However, extant research on gestures predominantly concentrates on the design of gestures for singular functionalities, which requires a comprehensive investigation into gesture interaction design from an integrative HCI system perspective. Working memory capacity plays a pivotal role in constraining the number of gestures accommodated within an HCI system. This study adopts the change detection paradigm to conduct an empirical investigation into the working memory capacities associated with mid-air gestures, aiming to elucidate the working memory capacity for such gestures. The experimental design included two independent variables: five distinct categories of mid-air gestures (physical, symbolic, metaphorical, abstract, and mixed) and memory set sizes ranging from two to nine items. The findings revealed that working memory capacities for different types of gestures fluctuate between three and five items, with symbolic gestures presenting the most negligible cognitive load for recall and abstract gestures proving to be the most challenging. The outcomes of this research have significant implications for both the design of individual gestures and the overarching design of gesture interaction systems, serving as a valuable reference for future endeavors in HCI design optimization.

Longitudinal study on peak expiratory flow monitoring and its impact on quality of life in childhood asthma

Objective To evaluate the impact of peak expiratory flow (PEF) monitoring using a smart peak flow (SPF) device on the quality of life (QoL) and satisfaction among children with asthma. Methods This 3-month prospective cohort study enrolled 71 children aged 7 to 17 years with physician-diagnosed asthma. Participants used the SPF device twice daily, with measurements recorded automatically. Quality of life was assessed using the Pediatric Asthma Quality of Life Questionnaire (PAQLQ), and asthma control was assessed using the Asthma Control Test (ACT) or Childhood Asthma Control Test (C-ACT). Adherence to PEF measurements and satisfaction with the device were evaluated. Results Seventy-one children (mean age 11.4 years) completed the study. Adherence to twice-daily PEF measurements decreased significantly over three months (from 50.0% at 1 month to 39.9% at 3 months, p < 0.001). Children with good adherence (38.0%) showed significant improvements in PAQLQ scores, while those with poor adherence (62.0%) did not. COVID-19 infection resulted in a significant decrease in %PEF rate and increased peak flow variability. Despite device-related issues, overall satisfaction was high (85.19% for good adherence users vs. 88.64% for poor adherence users, p = 0.671). Conclusion Regular PEF monitoring improves QoL in children with asthma by enabling early detection of symptom changes and better management. However, maintaining adherence to regular PEF monitoring is challenging. Further research with control groups is needed to validate these findings.

Passive control and damage assessment with a pendulum tuned mass damper using mean square equations and second-order eigen-perturbation techniques

This paper proposes a novel framework for damage detection and retuning of a Pendulum Tuned Mass Damper (PTMD) addressing effects of stochasticity via mean-square equations and establishing rapid change detection. The framework was made possible via a Stochastic Differential Equation (SDE) formulation of the equations of motion considering a two degree of freedom base and structure system equipped with a PTMD. The system was assumed to be excited at the base with Gaussian white noise and the solutions were computed using a stochastic integration scheme. A Second Order Eigen-Perturbation (SOPS) estimate is used to carry out detection of sudden stiffness reduction in the base floor of the structure. Once damage is detected, mean square equations on monitored displacements were used to retune the PTMD by adapting the length of the pendulum. It was found that damage could be detected rapidly and successive retunings were able to minimize the mean square displacement of the damaged structure and stabilise it over time.

The Effects of Ageing on the Organisation of Visual Short-term Memory

Past research has reported a deterioration in the corpus callosum of older adults that causes less interhemispheric communication between the hemispheres. However, this study did not measure the corpus callosum; instead, it assumes that older people have smaller corpus callosum based on past research. Past research has also reported a considerable impairment in the elders' capacity to handle global statistics instead of local figures but a global preceding effect in younger adults. To study the ageing effects on the organisation of VSTM, 45 younger adults aged between 18-28 and 44 older adults aged between 62-80 (some exclusions were made later) participated in a change detection task. A memory display was presented to the participants. After a short delay, a test display was presented with one of the four conditions: single probe, whole probe, Half probe (across hemifield) or half probe (within hemifield). Participants had to choose if the colours were similar or distinct between the memory and the test display. Only the colours and probe conditions could change between memory and the test display, but the locations stayed the same. The study builds on the present knowledge of VSTM-related age decline in cognition. It was reported that overall, the older participants performed less accurately on the change detection task than the younger adults. The effects of the answer and the interaction between the probe condition and the answer were also reported. However, there was no significant interaction between the probe condition and the group. It was a complex online study which was a significant limitation of the study. Future research can investigate memory consolidation in older adults using a simpler design in a lab-based study. The present study is a follow-up study for Anonymous (2020).

Unsupervised Change Detection Methods Applied to Landslide Mapping: Case Study in São Sebastião, Brazil

ABSTRACTLandslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context.

MATNet: Multilevel attention-based transformers for change detection in remote sensing images

Remote sensing image change detection is crucial for natural disaster monitoring and land use change. As the resolution increases, the scenes covered by remote sensing images become more complex, and traditional methods have difficulties in extracting detailed information. With the development of deep learning, the field of change detection has new opportunities. However, existing algorithms mainly focus on the difference analysis between bi-temporal images, while ignoring the semantic information between images, resulting in the global and local information not being able to interact effectively. In this paper, we introduce a new transformer-based multilevel attention network (MATNet), which is capable of extracting multilevel features of global and local information, enabling information interaction and fusion, and thus modeling the global context more effectively. Specifically, we extract multilevel semantic features through the Transformer encoder, and utilize the Feature Enhancement Module (FEM) to perform feature summing and differencing on the multilevel features in order to better extract the local detail information, and thus better detect the changes in small regions. In addition, we employ a multilevel attention decoder (MAD) to obtain information in spatial and spectral dimensions, which can effectively fuse global and local information. In experiments, our method performs excellently on CDD, DSIFN-CD, LEVIR-CD, and SYSU-CD datasets, with F1 scores and OA reaching 95.67%∕87.75%∕90.94%∕86.82% and 98.95%∕95.93%∕99.11%∕90.53% respectively.

Relation between Urine Cytological Findings and Renal Function in Patients with Kidney Stones in Taif, Saudi Arabia

Background and Objectives: Urine serves as a vital diagnostic fluid, and urine cytology analysis plays a crucial role in identifying urinary system illnesses such as bladder cancer and kidney stones. The Paris System for Reporting Urinary Cytology establishes a uniform method for diagnosing urinary tract cancer. This study aimed to provide valuable insights that can inform diagnostic strategies related to kidney stones and ultimately improve patient outcomes via the early detection of the cellular changes associated with kidney stones and their relation to kidney function tests. Materials and Methods: A comparative study was conducted and comprised two groups: group 1, consisting of 50 patients diagnosed with kidney stones, and group 2, comprising 50 patients diagnosed with other kidney diseases. Renal function tests and urinalysis (via the PAP staining of urine cellular deposits to detect nuclear changes) were performed, and the results were analyzed. Results: There was a statistically significant increase in urinary red blood cells, white blood cells, and nuclear reactive atypical changes in urinary sediments of kidney stone patients compared to the patients without stones, while there was a decrease in the estimated glomerular filtration rate (eGFR). eGFR showed a 96.7% specificity in detecting cases with nuclear reactive atypia. Conclusions: eGFR emerges as a reliable diagnostic marker for the comprehensive assessment of kidney stones, particularly when associated with nuclear atypia. The significant correlation between the indicators of chronic kidney disease, such as decreased eGFR, and the presence of kidney stones emphasizes the urgent need for efficient diagnostic practices.

A chromatic feature detector in the retina signals visual context changes.

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

ECG classification based on guided attention mechanism

Background and ObjectiveIntegrating domain knowledge into deep learning models can improve their effectiveness and increase explainability. This study aims to enhance the classification performance of electrocardiograms (ECGs) by customizing specific guided mechanisms based on the characteristics of different cardiac abnormalities. MethodsTwo novel guided attention mechanisms, Guided Spatial Attention (GSA) and CAM-based spatial guided attention mechanism (CGAM), were introduced. Different attention guidance labels were created based on clinical knowledge for four ECG abnormality classification tasks: ST change detection, premature contraction identification, Wolf-Parkinson-White syndrome (WPW) classification, and atrial fibrillation (AF) detection. The models were trained and evaluated separately for each classification task. Model explainability was quantified using Shapley values. ResultsGSA improved the F1 score of the model by 5.74%, 5%, 8.96%, and 3.91% for ST change detection, premature contraction identification, WPW classification, and AF detection, respectively. Similarly, CGAM exhibited improvements of 3.89%, 5.40%, 8.21%, and 1.80% for the respective tasks. The combined use of GSA and CGAM resulted in even higher improvements of 6.26%, 5.58%, 8.85%, and 4.03%, respectively. Moreover, when all four tasks were conducted simultaneously, a notable overall performance boost was achieved, demonstrating the broad adaptability of the proposed model. The quantified Shapley values demonstrated the effectiveness of the guided attention mechanisms in enhancing the model's explainability. ConclusionsThe guided attention mechanisms, utilizing domain knowledge, effectively directed the model's attention, leading to improved classification performance and explainability. These findings have significant implications in facilitating accurate automated ECG classification.

Application of remote sensing and GIS techniques for monitoring water volume variations in inaccessible reservoirs

ABSTRACT Change detection processes using remote sensing and Geographic Information System (GIS) techniques were applied to monitor and estimate the water volume in the Mosul Dam reservoir during a period in which it was inaccessible due to ISIS occupation of the Province of Mosul. A total of 17 scenes of LANDSAT 8 images have been captured and the calculated areas have been used to estimate the water level (WL) and water volume (V) in the reservoir using the volume–area–elevation curve that was developed in 2011. The results indicated that WL ranged between a minimum of 308.5 m and a maximum of 321 m, while V ranged between 3.95 and 6.8 km3 for the minimum and maximum WL, respectively. For the blackout period with missing data, the monthly reservoir inflows (RIs) from the drainage watershed and the mean inflows were calculated and found to be compatible with the mean inflows of Mosul reservoir for the years 1986–2011.

Nested transformer decoder using dense skip-connections for change detection in high-resolution remote sensing image

ABSTRACT Change detection is a critical task in remote sensing image analysis, with applications in land management and urban development. Convolutional neural networks (CNNs) have shown success in this area, but existing CNN-based methods often struggle with irrelevant changes caused by interfering factors, leading to unsatisfactory results. To address this, some studies have replaced CNN layers with transformers to enhance the receptive field and utilize global information better. However, this can make models overly reliant on global information, potentially overlooking shallow, fine-grained features, especially at the edges of changed targets. To overcome these limitations, we propose a Nested Transformer Decoder (NTD) that combines the strengths of transformers and CNNs by introducing dense skip connections. While having a large receptive field, it can also better retain shallow and fine-grained features by adding skip-connection. These innovations enable our model to more accurately identify changes and preserve the edges of the changed areas, including small changes that are difficult to detect using existing methods. Additionally, we introduce a Residual Fusion Module (RFM) to reduce false detections caused by irrelevant factors like light and shadow. The RFM employs residual information and spatiotemporal attention to suppress differences in multi-scale features, leading to better detection outcomes. The experimental results demonstrate that our proposed model outperforms the current state-of-the-art change detection methods in various metrics, and represents a novel and effective solution to the challenge of change detection in remote sensing images. Experimental results show that our model surpasses state-of-the-art change detection methods across various metrics. Tested on three public change detection datasets, our method improves the Intersection over Union (IoU) for the change class by 2.83% to 16.39% and enhances the F1-score by 1.60% to 9.63%. These results demonstrate that the proposed approach provides an effective solution to the challenges of change detection in remote sensing images.

Non-invasive regional parameter identification of degenerated human meniscus

Accurate identification of local changes in the biomechanical properties of the normal and degenerative meniscus is critical to better understand knee joint osteoarthritis onset and progression. Ex-vivo material characterization is typically performed on specimens obtained from different locations, compromising the tissue's structural integrity and thus altering its mechanical behavior. Therefore, the aim of this in-silico study was to establish a non-invasive method to determine the region-specific material properties of the degenerated human meniscus. In a previous experimental magnetic resonance imaging (MRI) study, the spatial displacement of the meniscus and its root attachments in mildly degenerated (n = 12) and severely degenerated (n = 12) cadaveric knee joints was determined under controlled subject-specific axial joint loading. To simulate the experimental response of the lateral and medial menisci, individual finite element models were created utilizing a transverse isotropic hyper-poroelastic constitutive material formulation. The superficial displacements were applied to the individual models to calculate the femoral reaction force in an inverse finite element analysis. During particle swarm optimization, the four most sensitive material parameters were varied to minimize the error between the femoral reaction force and the force applied in the MRI loading experiment. Individual global and regional parameter sets were identified. In addition to in-depth model verification, prediction errors were determined to quantify the reliability of the identified parameter sets. Both compressibility of the solid meniscus matrix (+141 %, p ≤ 0.04) and hydraulic permeability (+53 %, p ≤ 0.04) were significantly increased in the menisci of severely degenerated knees compared to mildly degenerated knees, irrespective of the meniscus region. By contrast, tensile and shear properties were unaffected by progressive knee joint degeneration. Overall, the optimization procedure resulted in reliable and robust parameter sets, as evidenced by mean prediction errors of <1 %. In conclusion, the proposed approach demonstrated high potential for application in clinical practice, where it might provide a non-invasive diagnostic tool for the early detection of osteoarthritic changes within the knee joint.

Dynamic 19F-MRI of pulmonary ventilation in lung transplant recipients with and without chronic lung allograft dysfunction

BackgroundBy the time chronic lung allograft dysfunction (CLAD), with its main phenotypes bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), is diagnosed by pulmonary function testing, irreversible damage to the lung allograft may already have occurred. Dynamic 19F-MRI of inhaled perfluoropropane may detect subtle changes in regional lung ventilation and provides a quantitative measure of regional lung function. We assessed feasibility of detecting regional ventilation dysfunction due to CLAD in lung transplant recipients. MethodsDynamic 19F-MRI was performed in ten lung transplant recipients, four without CLAD and six with CLAD (5 BOS, 1 RAS). Gas wash-in and washout dynamics were assessed and regional lung clearance index (RLCI) provided a quantitative metric of regional lung ventilation. ResultsBOS patients had substantially greater variation in regional ventilation compared with stable patients, with more regions of reduced ventilation, especially in the periphery. Tracer washout was homogeneous and rapid in stable patients but highly heterogeneous in CLAD. CLAD patients exhibited significant difference in RLCI between central and peripheral lung regions (p=0.0016) and a wider interquartile range of RLCI for wash-in compared with stable patients (no CLAD 4.1, BOS 10.5, p=0.036). FEV1 (% of baseline) negatively correlated with ventilation during wash-in, most strongly for the periphery (r=-0.844, p=0.0021). ConclusionsDynamic 19F-MRI identified quantifiable differences in regional ventilation in lung transplant recipients with and without CLAD and was well tolerated. Larger longitudinal studies using this approach will determine if early detection of changes in regional ventilation in lung transplant patients allows earlier CLAD detection.

Characterisation of an LED-based integrating sphere source for detection of changes of AERONET Europe radiometers

Abstract Within the framework of the EMPIR project 19ENV04 MAPP “Metrology for aerosol optical properties” an LED-based source was built for monitoring responsivity changes of Cimel filter radiometers from the AERONET Europe network. The developed LED device was flanged to integrating spheres to fill the field of view of the radiometers. Here we present the characterisation results of the LED-based integrating sphere source. The spectra of the radiant flux leaving a BaSO4-based integrating sphere showed positive wavelength dependent drift, which had not been reported in literature yet and did not appear by using incandescent lamps with the integrating spheres. This report shows approaches to reduce such drifts and an alternative using a PTFE-based integrating sphere, which was also tested for stability, reproducibility and homogeneity.

Longitudinal Assessment of Retinopathy of Prematurity (LONGROP) Study: Impacts of Viewing Time and Ability to Compare on Detection of Change

This study compared two imaging grading techiques to assess the utility of longitudinal image-based analysis in retinopathy of prematurity (ROP) screening: 1) time-limited without image comparison (a proxy for bedside indirect ophthalmoscopy, termed sBIO) and time-unlimited with image comparison (for telemedicine grading, termed TELE) screening. We tested two hypotheses: 1) H1: TELE was superior to sBIO for the detection of change (Tempo)-same, better, or worse-and, 2) H2: granular data of change (e.g. at the image and feature level) is integrated by graders to achieve the Tempo assessment. Prospective reliability analysis. Gold standard reference (GS) was a published curated ROP image database consisting of both Tempo and granular level changes (image and components) from 40 patients in 2 sets. Graders were divided into 2 cohorts. There were two screening techniques-1) sBIO with time limited review of 10 minutes/patient, access to prior notes and drawings and 2) TELE with unlimited review time, access to prior weeks' images, notes and schematics. Graders switched techniques and sets after 6 weeks. H1 outcome was comparison of graders' weekly Tempo scores to GS-Gestalt and for H2 was Tempo score compared to GS-View and GS-Component. H1 demonstrated no difference-accuracy of sBIO and TELE compared to GS was 51.7% and 51.9% respectively (p=0.95). Highest agreement occurred when all exams exhibited no change (91.5% sBIO vs. 93.5% TELE, p=0.46) and worst agreement was when exams always demonstrated worsening (46.5% sBIO vs. 47.1% TELE, p=0.93). Both sets of graders did worse in weeks 7-12, irrespective of technique. H2 demonstrated that Tempo assessment did not correlate with granular data changes in the GS for View level and Component level assessments-overall agreement dropped to 31.4% for Tempo vs GS-VIEW (31.2% for sBIO, 31.5% for TELE) and 4.6% for Tempo vs GS-COMPONENT (4.9% for sBIO, 4.3% for TELE). Detection of ROP Tempo was independent of screening technique by expert pediatric retina graders. Both groups did significantly better in the first half of the study, indicative of a fatigue factor. This is the first study in ROP history to demonstrate that graders integrate image and retinal features in various ways that can be in contradiction of their assessment of overall disease progression.

Phloem sap from melon plants contains extracellular vesicles that carry active proteasomes which increase in response to aphid infestation.

The morphogenesis of higher plants requires communication among distant organs throughout vascular tissues (xylem and phloem). Numerous investigations have demonstrated that phloem also act as a distribution route for signalling molecules being observed that different macromolecules translocated by the sap, including nucleic acids and proteins, change under stress situations. The participation of extracellular vesicles (EVs) in this communication has been suggested, although little is known about their role. In fact, in the last decade, the presence of EVs in plants has originated a great controversy, where major concerns arose from their origin, isolation methods, and even the appropriate nomenclature for plant nanovesicles. Phloem sap exudates from melon plants, either aphid-free or infested with Aphis gossypii, were collected by stem incision. After sap concentration (Amicon), phloem EVs (PhlEVs) were isolated by size exclusion chromatography. PhlEVs were characterised using Nanoparticle Tracking Analysis, Transmission electron microscopy and proteomic analysis. Here we confirm the presence of EVs in phloem sap in vivo and the detection of changes in the particles/protein ratio and composition of PhlEVs in response to insect feeding, revealing the presence of typical defence proteins in their cargo as well as components of the proteasome complex. PhlEVs from infested plants showed lower particles/protein ratio and almost two times more proteolytic activity than PhlEVs from aphid-free plants. In both cases, such activity was inhibited in a dose-dependent manner by the proteasome inhibitor MG132. Our results suggest that plants may use this mechanism to prepare themselves to receive infectious agents and open up the possibility of an evolutionary conserved mechanism of defence against pathogens/stresses in eukaryotic organisms.

Exploitation of long-range acoustic dependence for early change detection in dynamic machine running status

This paper suggests that the long-range dependence (LRD) in acoustic signal is an important indicator of dynamic behavior in data. Considering this information, a novel framework based on LRD in acoustic is proposed for detecting change-points in machine running status. Initially, the LRD phenomenon in acoustic is demonstrated. Subsequently, a change-point detection framework is developed, leveraging LRD to monitor machine running states. The framework includes two phases: (1) characteristic data prediction: the LRD value is predicted by the fractional autoregressive integrated moving average (FARIMA) model, which is applied to reflect changes in machine operation states. (2) date change detection: based on residual analysis, a null hypothesis testing based automatic analysis method for acoustic signals during machine successive operations is used to make decision. Experiments in real-world applications demonstrated the good potential of the proposed framework in practical engineering scenarios.