Activity Monitoring Research Articles

Clinicopathological characteristics and typing of multilocular cystic renal neoplasm of low malignant potential

BACKGROUND Up until now, no research has been reported on the association between the clinical growth rate of multilocular cystic renal neoplasm of low malignant potential (MCRNLMP) and computed tomography (CT) imaging characteristics. Our study sought to examine the correlation between them, with the objective of distinguishing unique features of MCRNLMP from renal cysts and exploring effective management strategies. AIM To investigate optimal management strategies of MCRNLMP. METHODS We retrospectively collected and analyzed data from 1520 patients, comprising 1444 with renal cysts and 76 with MCRNLMP, who underwent renal cyst decompression, radical nephrectomy, or nephron-sparing surgery for renal cystic disease between January 2013 and December 2021 at our institution. Detection of MCRNLMP utilized the Bosniak classification for imaging and the 2016 World Health Organization criteria for clinical pathology. RESULTS Our meticulous exploration has revealed compelling findings on the occurrence of MCRNLMP. Precisely, it comprises 1.48% of all cases involving simple renal cysts, 5.26% of those with complex renal cysts, and a noteworthy 12.11% of renal tumors coexisting with renal cysts, indicating a statistically significant difference (P = 0.001). Moreover, MCRNLMP constituted a significant 22.37% of the patient population whose cysts demonstrated a rapid growth rate of ≥ 2.0 cm/year, whereas it only represented 0.66% among those with a growth rate below 2.0 cm/year. Of the 76 MCRNLMP cases studied, none of the nine patients who underwent subsequent nephron-sparing surgery or radical nephrectomy following renal cyst decompression experienced recurrence or metastasis. In the remaining 67 patients, who were actively monitored over a 3-year postoperative period, only one showed suspicious recurrence on CT scans. CONCLUSION MCRNLMP can be tentatively identified and categorized into three types based on CT scanning and growth rate indicators. In treating MCRNLMP, partial nephrectomy is preferred, while radical nephrectomy should be minimized. After surgery, active monitoring is advisable to prevent unnecessary nephrectomy.

Optimization of quenched fluorescent peptide substrates of SARS-CoV-2 3CLpro main protease (Mpro) from proteomic identification of P6-P6' active site specificity.

SARS-CoV-2 3C-like main protease (3CLpro) is essential for protein excision from the viral polyprotein. 3CLpro inhibitor drug development to block SARS-CoV-2 replication focuses on the catalytic non-prime (P) side for specificity and potency, but the importance of the prime (P') side in substrate specificity and for drug development remains underappreciated. We determined the P6-P6' specificity for 3CLpro from >800 cleavage sites that we identified using Proteomic Identification of Cleavage site Specificity (PICS). Cleavage occurred after the canonical P1-Gln and non-canonical P1-His and P1-Met residues. Moreover, P3 showed a preference for Arg/Lys and P3' for His. Essential H-bonds between the N-terminal Ser1 of protomer-B in 3CLpro dimers form with P1-His, but not with P1-Met. Nonetheless, cleavage occurs at P1-Met456 in native MAP4K5. Elevated reactive oxygen species in SARS-CoV-2 infection oxidize methionines. Molecular simulations revealed P1-MetOX forms an H-bond with Ser1 and notably, strong positive cooperativity between P1-Met with P3'-His was revealed, which enhanced peptide-cleavage rates. The highly plastic S3' subsite accommodates P3'-His that displays stabilizing backbone H-bonds with Thr25 lying central in a "'threonine trio" (Thr24-Thr25-Thr26) in the P'-binding domain I. Molecular docking simulations unveiled structure-activity relationships impacting 3CLpro-substrate interactions, and the role of these structural determinants was confirmed by MALDI-TOF-MS cleavage assays of P1'- and P3'-positional scanning peptide libraries carrying a 2nd optimal cut-site as an internal positive control. These data informed the design of two new and highly soluble 3CLproquenched-fluorescent peptide substrates for improved FRET monitoring of 3CLpro activity with 15× improved sensitivity over current assays.IMPORTANCEFrom global proteomics identification of >800 cleavage sites, we characterized the P6-P6' active site specificity of SARS-CoV-2 3CLpro using proteome-derived peptide library screens, molecular modeling simulations, and focussed positional peptide libraries. In P1', we show that alanine and serine are cleaved 3× faster than glycine and the hydrophobic small amino acids Leu, Ile, or Val prevent cleavage of otherwise optimal non-prime sequences. In characterizing non-canonical non-prime P1 specificity, we explored the unusual P1-Met specificity, discovering enhanced cleavage when in the oxidized state (P1-MetOX). We unveiled unexpected amino acid cooperativity at P1-Met with P3'-His and noncanonical P1-His with P2-Phe, and the importance of the threonine trio (Thr24-Thr25-Thr26) in the prime side binding domain I in defining prime side binding in SARS-CoV-2 3CLpro. From these analyses, we rationally designed quenched-fluorescence natural amino acid peptide substrates with >15× improved sensitivity and high peptide solubility, facilitating handling and application for screening of new antiviral drugs.

Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery

This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019–2022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R2 > 0.9, a Spectral Angle < 10° and water quality mapping errors < 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities’ monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.

A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor

Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation.

Using an mHealth approach to collect patient-generated health data for predicting adverse health outcomes among adult survivors of childhood cancer

IntroductionCancer therapies predispose childhood cancer survivors to various treatment-related late effects, which contribute to a higher symptom burden, chronic health conditions (CHCs), and premature mortality. Regular monitoring of symptoms between clinic visits is useful for timely medical consultation and interventions that can improve quality of life (QOL). The Health Share Study aims to utilize mHealth to collect patient-generated health data (PGHD; daily symptoms, momentary physical health status) and develop survivor-specific risk prediction scores for mitigating adverse health outcomes including poor QOL and emergency room admissions. These personalized risk scores will be integrated into the hospital-based electronic health record (EHR) system to facilitate clinician communications with survivors for timely management of late effects.MethodsThis prospective study will recruit 600 adult survivors of childhood cancer from the St. Jude Lifetime Cohort study. Data collection include 20 daily symptoms via a smartphone, objective physical health data (physical activity intensity, sleep performance, and biometric data including resting heart rate, heart rate variability, oxygen saturation, and physical stress) via a wearable activity monitor, patient-reported outcomes (poor QOL, unplanned healthcare utilization) via a smartphone, and clinically ascertained outcomes (physical performance deficits, onset of/worsening CHCs) assessed in the survivorship clinic. Participants will complete health surveys and physical/functional assessments in the clinic at baseline, 2) report daily symptoms, wear an activity monitor, measure blood pressure at home over 4 months, and 3) complete health surveys and physical/functional assessments in the clinic 1 and 2 years from the baseline. Socio-demographic and clinical data abstracted from the EHR will be included in the analysis. We will invite 20 cancer survivors to investigate suitable formats to display predicted risk information on a dashboard and 10 clinicians to suggest evidence-based risk management strategies for adverse health outcomes.AnalysisMachine and statistical learning will be used in prediction modeling. Both approaches can handle a large number of predictors, including longitudinal patterns of daily symptoms/other PGHD, along with cancer treatments and socio-demographics.ConclusionThe individualized risk prediction scores and added communications between providers and survivors have the potential to improve survivorship care and outcomes by identifying early clinical presentations of adverse events.

Measurement of Visitors’ Subjective and Objective Emotional Response to Congestion in Public Spaces

Research on the ties between congestion and visitors’ experiences in a destination has primarily used subjective methods. The current study proposes that the emotional, subjective visitor response to congestion be measured alongside the objective experience, in real time and real-world conditions. To do so, advanced tracking methods in time and space (GPS) were combined with the Experience Sampling Method (ESM), which includes self-reporting and physiological monitoring of electrodermal activity (SCL). An intra-subject experiment was designed, with all participants asked to traverse an identical path in Jerusalem’s main market, Mahane Yehuda, under two different crowding conditions. The partial concordance between subjective and objective arousal highlights the methodology’s significance for increasing our understanding of a visit’s emotional dimension in urban destinations, particularly congested public spaces. Alongside the study’s limitations, the practical application of the methodology for tourist destinations and urban settings that face congestion and overcrowding is discussed, aiming to improve the quality of the visitor experience and urban well-being.

Monitoring the spread of a pathogenic insect on vineyards using UAS

Abstract. Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the Popillia Japonica Newman beetle, introduced to Italy in 2014, which has caused significant economic losses, mainly affecting vine cultures. Reliable identification of pests is essential for its management, but it is time-consuming and laborious. This has prompted growing interest in image-based methods, supported by computer vision (CV), which can significantly improve efficiency in insect detection. This study aims to evaluate a CV algorithm's effectiveness in identifying adult specimens of Popillia using Near-Infrared sensors on Uncrewed Aerial Systems (UAS). The project, conducted in two vineyards in northern Italy, intends to establish a replicable and standardised data acquisition protocol for future monitoring activities. Insects detected by the CV-based method are validated by manual counting performed by entomologists. In a GIS environment, prescription maps are generated in near real-time to identify where the vineyard is most affected and to guide the drone spraying treatment only on the areas in which the threshold is exceeded. The study demonstrates effective semi-automated monitoring, with a clear correlation between CV-based and manual insect measurements, as indicated by the Pearson correlation coefficients ranging from 0.89 to 0.96. Although the CV-based method may overestimate insect numbers, it provides valuable insights for targeted pest management interventions and damage assessment. The project outcomes offer a promising approach to safeguarding agriculture against invasive species, enhancing regional economic resilience while minimising the spread of insecticide, the required time, and human interaction with harmful substances.

Underwater Backscatter: What is its Practical Communication Distance?

Recent years have seen mounting interest in low-power, distributed subsea internet-of-things (IoT) networks because of their potential in environmental, industrial, and defense applications [1,2]. Energy-efficient ocean IoT networks can enable long-term sensing of ocean variables (temperature, pH, pressure, salinity, etc.) to create more accurate climate and weather prediction models and monitor the impact of climate change on the ocean [3]. Similarly, low-cost and efficient ocean IoT networks can help boost the growth of the world's blue economy by enabling active monitoring of marine infrastructures ranging from oil/gas pipelines to underwater tunnels [4]. Real-time distributed underwater sensor networks can help boost aquaculture (seafood farm) production by monitoring the farm vitals (water temperature, dissolved nutrients, pH, etc.) and detecting environmental hazards (such as harmful algae blooms) early [5].

Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper’s KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p < 0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine.

Monitoring underground hydrogen storage migration and distribution using time-lapse acoustic waveform inversion

The ongoing global energy shift from fossil fuels to renewable sources highlights the importance of underground hydrogen storage (UHS) as a sustainable mechanism to counterbalance the seasonal inconsistencies of renewable energy. Comparable to geological CO2 sequestration, UHS necessitates precise subsurface imaging and a thorough understanding of hydrogen behavior within geological formations, including its location and migration patterns. Since direct subsurface measurement is unattainable, there is a pronounced need for robust subsurface characterization and monitoring techniques. This research focuses on UHS feasibility in enduring storage scenarios, employing seismic monitoring strategies adapted from CO2 storage practices. Specifically, we integrate Continuous Active Source Seismic Monitoring (CASSM) and Time-Lapse Full Waveform Inversion (TLFWI) to scrutinize UHS. The study employs synthetic crosshole surveys that initially model the coexistence of hydrogen and water within fractured reservoirs, aiming to pinpoint hydrogen-related velocity alterations in acoustic waveforms. Subsequently, we utilize a time-lapse synthetic model of hydrogen injection to emulate CASSM observations. Utilizing TLFWI in conjunction with White's model, we successfully process the CASSM data to discern and quantify temporal variations in velocity and saturation, which effectively maps the spatial and temporal distribution of mobile hydrogen. The results affirm the capabilities of TLFWI and CASSM as proficient monitoring systems for UHS. This study aspires to offer a dependable methodology for the administration of large-scale and long-term geological hydrogen storage.

Active Chat Monitoring and Suspicious Chat Detection Over Internet

In the digital age, the proliferation of online communication platforms has facilitated unprecedented levels of connectivity and interaction. However, this interconnectedness has also introduced new challenges, particularly in ensuring the safety and security of users in online environments. One critical area of concern is the monitoring and detection of suspicious activities within chat platforms, where malicious actors may engage in harmful behaviors such as cyberbullying, harassment, or illicit activities. This research paper focuses on the development and implementation of active chat monitoring techniques for the detection of suspicious behavior over the internet. By leveraging advancements in natural language processing (NLP), machine learning, and data analytics, this study aims to explore effective methodologies for real-time monitoring and analysis of chat conversations to identify potentially harmful content. The paper will delve into various approaches for detecting suspicious patterns, including keyword analysis, sentiment analysis, and anomaly detection algorithms. Additionally, considerations for privacy, ethics, and legal implications surrounding chat monitoring will be discussed. Ultimately, this research endeavors to contribute to the enhancement of online safety measures by providing insights into the design and implementation of proactive monitoring systems capable of identifying and mitigating risks in virtual communication spaces. Keywords— Active Chat Monitoring, cybersecurity, user experience, digital era, security, user-controlled, effectiveness, evolving threat, Chat content analysis, Threat intelligence.

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

In recent years, a great deal of research has been done on the identification, monitoring, and classification of human activities. Human activity recognition (HAR) is a term commonly used to describe the automatic identification of physical activities. For activity recognition, there are primarily vision-based and sensor-based methods available. The computer vision-based method is generally effective in lab settings, but because of clutter, fluctuating light levels, and contrast, it may not perform well in real-world scenarios. Continuous monitoring and analysis of physiological signals obtained from heterogeneous sensors attached to an individual’s body is required to realise sensor-based HAR systems. Most of the previous research in human activity recognition (HAR) is biased along with feature engineering and pre-processing which requires a good amount of domain knowledge. Application-specific modelling and time-taking methods are involved in these approaches. In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and classification are involved in the form of an end-to-end classification approach. Experiments of this approach are performed by taking raw wearable sensor data with few pre-processing steps and without the involvement of a handcrafted feature extraction technique. 99.23% and 93.55% accuracy are obtained on the WISDM and UCI-HAR datasets which denoted the much improvement in the assessment of HAR over other similar approaches. The model is also tested on locally collected data from a chest mounted belt with fabric sensors and an accuracy of 87.14% has been achieved on that data.

A novel NIR fluorescent probe for enhanced β-galactosidase detection and tumor imaging in ovarian cancer models

The advancement of biological imaging techniques critically depends on the development of novel near-infrared (NIR) fluorescent probes. In this study, we introduce a designed NIR fluorescent probe, NRO-βgal, which exhibits a unique off–on response mechanism to β-galactosidase (β-gal). Emitting a fluorescence peak at a wavelength of 670 nm, NRO-βgal showcases a significant Stokes shift of 85 nm, which is indicative of its efficient energy transfer and minimized background interference. The probe achieves a remarkably low in vitro detection limit of 0.2 U/L and demonstrates a rapid response within 10 min, thereby underscoring its exceptional sensitivity, selectivity, and operational swiftness. Such superior analytical performance broadens the horizon for its application in intricate biological imaging studies. To validate the practical utility of NRO-βgal in bio-imaging, we employed ovarian cancer cell and mouse models, where the probe's efficacy in accurately delineating tumor cells was examined. The results affirm NRO-βgal's capability to provide sharp, high-contrast images of tumor regions, thereby significantly enhancing the precision of surgical tumor resection. Furthermore, the probe's potential for real-time monitoring of enzymatic activity in living tissues underscores its utility as a powerful tool for diagnostics in oncology and beyond.

Study of Physicochemical Quality and Organic Contamination in Algerian Honey.

Honey is a natural product extensively consumed in the world for its nutritional and healthy properties. However, residues of pesticides and environmental contaminants can compromise its quality. For this reason, the physicochemical parameters, and the organic contamination of monofloral and multifloral honey from three regions of Algeria (Tiaret, Laghouat, and Tindouf) were monitored to evaluate the quality of the honey and its safety for consumers. In general, the results obtained from the physicochemical analyses were in line with the EU standards. In terms of contamination, pesticides authorised and used in Algerian agriculture (metalaxyl-M and cyromazine), as well as a banned pesticide (carbaryl), were found in almost all the samples. However, only the concentration of cyromazine was higher than the relative EU maximum residue levels. PCB 180, PCB 189, anthracene, fluorene, and phenanthrene were mainly detected. All the honey shows traces of DiBP, DBP, DEHP, and DEHT, but no traces of bisphenols were found. Moreover, according to the dietary exposure assessment, a small amount of Algerian honey can be safely consumed. Overall, the data from this study should motivate the Algerian government to enhance their monitoring activities in beekeeping and to find solutions for implementing more sustainable agricultural practices harmonising with international legislation.

Deep Residual Network with a CBAM Mechanism for the Recognition of Symmetric and Asymmetric Human Activity Using Wearable Sensors

Wearable devices are paramount in health monitoring applications since they provide contextual information to identify and recognize human activities. Although sensor-based human activity recognition (HAR) has been thoroughly examined, prior studies have yet to definitively differentiate between symmetric and asymmetric motions. Determining these movement patterns might provide a more profound understanding of assessing physical activity. The main objective of this research is to investigate the use of wearable motion sensors and deep convolutional neural networks in the analysis of symmetric and asymmetric activities. This study provides a new approach for classifying symmetric and asymmetric motions using a deep residual network incorporating channel and spatial convolutional block attention modules (CBAMs). Two publicly accessible benchmark HAR datasets, which consist of inertial measurements obtained from wrist-worn sensors, are used to assess the model’s efficacy. The model we have presented is subjected to thorough examination and demonstrates exceptional accuracy on both datasets. The ablation experiment examination also demonstrates noteworthy contributions from the residual mappings and CBAMs. The significance of recognizing basic movement symmetries in increasing sensor-based activity identification utilizing wearable devices is shown by the enhanced accuracy and F1-score, especially in asymmetric activities. The technique under consideration can provide activity monitoring with enhanced accuracy and detail, offering prospective advantages in diverse domains like customized healthcare, fitness tracking, and rehabilitation progress evaluation.

Abstract PO4-13-04: Added Value from Patient Advocates in a Translational Working Group: the COMET Study

Abstract Introduction: A phase III multicenter prospective randomized clinical trial called “Comparing an Operation to Monitoring, with or without Endocrine Therapy (COMET)” (NCT02926911) assesses the risks/benefits of active monitoring (AM) versus surgery for women with low-risk ductal carcinoma in situ (DCIS). It is funded by the Patient-Centered Outcomes Research Institute and enrolled 997 women between 06/30/17 and 01/13/23. Research Patient Advocates (PAs) have been embedded from the study design stage and continue to provide guidance as part of the COMET leadership team. The COMET biobank is funded by independent foundations and is managed through monthly Translational Working Group (TWG) meetings. The TWG includes PAs, clinicians, pathologists, radiologists, and researchers from multiple institutions who discuss topics like categorization of biospecimens into discovery/validation sets; development of a pathology workflow/sample tracking process; use of small samples; and potential areas of future research/technologies that may improve DCIS diagnostics, prognostics, and care management. Methods: The TWG leveraged the existing Alliance Foundation Trials, LLC biobank infrastructure and facilitates the collection, submission, storage and analysis/use of blood, tissue, and breast images collected at specified timepoints and stored in central tissue/image repositories. PAs are active and integral in the TWG, assisting with logistical issues (contracts, transfer agreements, resource requests); communicating with sites; identifying study topics and biomarkers relevant to diverse patients; providing guidance on commercial predictive/prognostic tests; promoting effective stewardship of samples; ensuring overall focus remains on advancing clinical utility; and reporting translational study results to trial participants who agreed to donate samples/images. Results: Over 90% of requested samples/images have been submitted by ˃85 sites. Each patient’s biospecimens and pathology images are linked and de-identified for research. Use of artificial intelligence (AI) is being considered to assist in sample review. TWG members have complementary areas of expertise/experience and promote active stewardship for effective management of these scarce resources. PAs have played key roles concerning equitable data-sharing and acceleration of data and material transfer agreements. They have also been active in development of standards for discovery/training sets, validation test sets; communication procedures for patient cases that progress to future breast events; ways to catalog technologies (e.g., multi-omics, AI); and in research proposal review. Consensus has been achieved regarding major issues such as authorship, biospecimen custodianship, intellectual property, and criteria relevant to patient needs. Logistical barriers, including data sharing and technicalities of biospecimen release, have been resolved. The TWG also played an integral role in resolving recruitment challenges to COMET by creating standard pathology eligibility criteria, resulting in evidence-based protocol amendments that increased accrual. A retrospective review of biospecimens has been performed to determine adequacy for ensuing correlative molecular and spatial profiling studies. Conclusion: PAs help the COMET TWG set policy, and oversee biospecimen/image collection, and biobank use and sharing. PA input also facilitates equitable, transparent research studies and technology development that can improve personalized decisions for surgery versus AM in women with low-risk DCIS. PAs in the TWG aim to integrate the diagnostic and prognostic tools developed as part of the COMET study into future patient care. Citation Format: Deborah Collyar, Desiree Basila, Thomas Lynch, Stuart Schnitt, Jeffrey Marks, Siri Strand, Terry Hyslop, Sunil Badve, Mark Watson, H. T. Carisa Le-Petross, Lars Grimm, Robert West, Anna Weiss, Anna Rapperport, Lorraine King, Rachel Factor, Marc Ryser, Ann Partridge, E Shelley Hwang, Alastair Thompson. Added Value from Patient Advocates in a Translational Working Group: the COMET Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-13-04.

The psychometric properties of the modified fear of falling avoidance behavior questionnaire in Parkinson's disease and older adults.

The Fear of Falling Avoidance Behavior Questionnaire (FFABQ) has good psychometric properties. However, we have recently modified the FFABQ (mFFABQ) to improve the clarity of the questions and Likert responses. This study aimed to examine the reliability and validity of this modified version in older adults and people with Parkinson's disease (PD). A total of 88 participants, 39 with PD (age = 72.2 ± 9.5; 29 males, 10 females) and 49 older adults (age = 72.8 ± 5.0; 13 males, 36 females), answered the mFFABQ twice, separated by 1 week, for test-retest reliability. Construct validity was evaluated through correlational analyses with fall history, Activities-Specific Balance Confidence Scale (ABC), Berg Balance Scale (BBS), Timed Up and Go, 30-Second Sit to Stand, Sensory Organization Test, Zung Anxiety Scale, Beck Depression Inventory, Consequences of Falling Questionnaire (CoFQ), and average daily activity levels using an activity monitor. The mFFABQ had good overall test-retest reliability (intraclass correlational coefficient [ICC] = 0.822; older adult ICC = 0.781, PD ICC = 0.806). The mFFABQ correlated with fall history (r = -0.430) and exhibited high correlation with the ABC (rho = -0.804) and moderate correlations with CoFQ (rho = 0.582) and BBS (rho = -0.595). The mFFABQ also correlated with time stepping (rho = -0.298) and number of steps (rho = -0.358). These results provide supportive evidence for the reliability and validity of the mFFABQ in older adults and people with PD, which supports its suitability as a clinical and research tool for the assessment of fear of falling avoidance behavior.

Wireless Sensor System Based on Organohydrogel Ionic Skin for Physiological Activity Monitoring.

Supermolecular hydrogel ionic skin (i-skin) linked with smartphones has attracted widespread attention in physiological activity detection due to its good stability in complex scenarios. However, the low ionic conductivity, inferior mechanical properties, poor contact adhesion, and insufficient freeze resistance of most used hydrogels limit their practical application in flexible electronics. Herein, a novel multifunctional poly(vinyl alcohol)-based conductive organohydrogel (PCEL5.0%) with a supermolecular structure was constructed by innovatively employing sodium carboxymethyl cellulose (CMC-Na) as reinforcement material, ethylene glycol as antifreeze, and lithium chloride as a water retaining agent. Thanks to the synergistic effect of these components, the PCEL5.0% organohydrogel shows excellent performance in terms of ionic conductivity (1.61 S m-1), mechanical properties (tensile strength of 70.38 kPa and elongation at break of 537.84%), interfacial adhesion (1.06 kPa to pig skin), frost resistance (-50.4 °C), water retention (67.1% at 22% relative humidity), and remoldability. The resultant PCEL5.0%-based i-skin delivers satisfactory sensitivity (GF = 1.38) with fast response (348 ms) and high precision under different deformations and low temperature (-25 °C). Significantly, the wireless sensor system based on the PCEL5.0% organohydrogel i-skin can transmit signals from physiological activities and sign language to a smartphone by Bluetooth technology and dynamically displays the status of these movements. The organohydrogel i-skin shows great potential in diverse fields of physiological activity detection, human-computer interaction, and rehabilitation medicine.

Abstract PO4-11-09: Feasibility of physical activity behaviour change for women living with metastatic breast cancer, a randomised controlled trial

Abstract Background Physical activity benefits women living with metastatic breast cancer by improving outcomes such as physical function, health-related quality of life and fatigue. These findings have been reported within well-resourced trials that involve exercise equipment and supervision. Home or community-based settings, however, may be preferable and more accessible. This trial evaluated the feasibility of a remotely delivered behaviour change intervention for increasing physical activity for women living with metastatic breast cancer, and evaluated the program’s efficacy. Methods A 12-week randomised controlled trial was conducted in which 20 women living with metastatic breast cancer were recruited from a metropolitan outpatient cancer clinic. Inclusion criteria were over 18 years old, English speaking, ambulatory, and not regularly active at time of recruitment. Participants were randomised 1:1 to an intervention or attention control group. Both groups received a recommendation for 150 minutes of physical activity, a wrist-worn activity monitor, a physical activity diary, and nine phone or video call sessions (six weekly, three fortnightly). The intervention group received individualised behaviour change advice on topics such as physical activity benefits, motivation, barriers and social support; the attention control group received a recurring symptom questionnaire and no behavioural advice. Feasibility outcomes included recruitment, dropout and session adherence rates, and acceptability of methods was evaluated with an interview at trial completion. Baseline and 12-week outcomes included: i) physical activity minutes measured with a 5-day Actigraph wear, ii) 6-minute walk distance, iii) 30-second sit-to-stands, iv) questionnaires for self-reported physical activity, health-related quality of life, fatigue, behavioural factors (stage of change, self-efficacy, decisional balance, processes of change, social support), and patient-specific function. Questionnaires were measured again at 18 weeks. Results Recruitment, baseline measures and within-trial sessions are completed; 12 and 18-week measures will conclude late August. Thirty-two women were approached to enrol the target sample of 20, with exclusion reasons being disinterest (n=5), health problems (n=4), loss of contact (n=2) and already physically active (n=1). Median age was 62 years (IQR: 60-69), and median time since metastatic diagnosis was 4 years (IQR: 1-5). Twelve women had bone metastases, and ten had a musculoskeletal comorbidity. Session delivery through video or phone calls were equally preferred (n=10 each). Participants completed 84% of the sessions, with reasons for missed sessions consisting of forgetting (55%), feeling unwell (24%), IT problems (7%), holiday (7%), and work commitments (7%). Median session duration for the intervention group was 23 minutes (IQR: 20-28), and control group was 17 minutes (IQR: 14-21). The preferred type of physical activity was predominantly walking, and no adverse events were reported. Four women dropped out after loss of contact. Compared to age-matched normative values, baseline outcomes varied: actigraphy-measured physical activity was low (median: 68 minutes/day, IQR: 45-88), self-reported physical activity was high (median: 289 MET-minutes/day, IQR: 52-728), 6-minute walk distance was equal (median: 529m, IQR: 416-579), 30-second sit-to-stands were low (median: 11, IQR: 8-14), health-related quality of life was high (median: 80, IQR: 70-100), and fatigue was equal (median: 30, IQR 20-40). Conclusions Women living with metastatic breast cancer were generally receptive of the trial, and recruitment, dropout and session adherence rates were positive. The women who dropped out were more physically active, physically functional and/or rated higher on the questionnaires, indicating that the trial may be more suited to those with limited physical activity experience. Citation Format: Mark Liu, Sharon Kilbreath, Elizabeth Dylke, Jasmine Yee, Jane Beith. Feasibility of physical activity behaviour change for women living with metastatic breast cancer, a randomised controlled trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-11-09.

How Sentinel-1 timeseries can improve the implementation of conservation programs in Brazil

Cumulative Sum (CuSum) change detection was applied on a Sentinel-1 backscatter time series at a spatial scale of 10 m as part of a conservation program implemented in Acre, Brazil, requiring the monitoring of deforestation activities by participants in the program. This study evaluated the results of CuSum and compared them to those obtained from conventional deforestation products, demonstrating how this method can improve the implementation of such programs. We aimed to map deforestation events with a minimum resolution of 0.1 ha to maximise event detection while minimising false positives, which could lead to unfair penalties for participants. The remarkable detection precision (ranging from 87.3 % to 96.1 %) and short delay of the CuSum algorithm make it suitable for implementing a conservation program, as illustrated in this study. Moreover, this method has the potential to accurately assess the extent of future deforestation. This study contributes to the development of effective deforestation monitoring strategies within the framework of conservation programmes to facilitate improved farming practices and climate change mitigation. This code is available at https://github.com/Pfefer/cusum.