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Background: Atrial high-rate episodes (AHREs) are frequent in patients with cardiac implantable electronic devices. A decrease in device-detected P-wave amplitude may be an indicator of periods of increased risk of AHRE., Objective: The objective of this study was to assess the association between P-wave amplitude and AHRE incidence., Methods: Remote monitoring data from 2579 patients with no history of atrial fibrillation (23% pacemakers and 77% implantable cardioverter-defibrillators, of which 40% provided cardiac resynchronization therapy) were used to calculate the mean P-wave amplitude during 1 month after implantation. The association with AHRE incidence according to 4 strata of daily burden duration (≥15 minutes, ≥6 hours, ≥24 hours, ≥7 days) was investigated by adjusting the hazard ratio with the CHA 2 DS 2 -VASc score., Results: The adjusted hazard ratio for 1-mV lower mean P-wave amplitude during the first month increased from 1.10 (95% confidence interval [CI], 1.05-1.15; P < .001) to 1.18 (CI, 1.09-1.28; P < .001) with AHRE duration strata from ≥15 minutes to ≥7 days independent of the CHA 2 DS 2 -VASc score. Of 871 patients with AHREs, those with 1-month P-wave amplitude <2.45 mV had an adjusted hazard ratio of 1.51 (CI, 1.19-1.91; P = .001) for progression of AHREs from ≥15 minutes to ≥7 days compared with those with 1-month P-wave amplitude ≥2.45 mV. Device-detected P-wave amplitudes decreased linearly during the 1 year before the first AHRE by 7.3% (CI, 5.1%-9.5%; P < .001 vs patients without AHRE)., Conclusion: Device-detected P-wave amplitudes <2.45 mV were associated with an increased risk of AHRE onset and progression to persistent forms of AHRE independent of the patient's risk profile., Competing Interests: Disclosures Mauro Biffi has held educational activity and participated in speaker’s bureau on behalf of Boston Scientific, Biotronik, and Medtronic. Matteo Ziacchi has held educational activity and participated in speaker’s bureau on behalf of Medtronic. Daniele Giacopelli and Alessio Gargaro are employees of Biotronik Italia S.p.a. The remaining authors have no major conflicts of interest to disclose., (Copyright © 2024 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)

Background: Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and effective feature extraction are challenges for achieving accurate soil water estimation using this technology., Methods: This study proposes a method for hyperspectral remote sensing soil water content estimation based on a combination of continuous wavelet transform (CWT) and competitive adaptive reweighted sampling (CARS). Hyperspectral data were collected from soil samples with different water contents prepared in the laboratory. CWT, with two wavelet basis functions (mexh and gaus2), was used to pre-process the hyperspectral reflectance to eliminate noise interference. The correlation analysis was conducted between soil water content and wavelet coefficients at ten scales. The feature variables were extracted from these wavelet coefficients using the CARS method and used as input variables to build linear and non-linear models, specifically partial least squares (PLSR) and extreme learning machine (ELM), to estimate soil water content., Results: The results showed that the correlation between wavelet coefficients and soil water content decreased as the decomposition scale increased. The corresponding bands of the extracted wavelet coefficients were mainly distributed in the near-infrared region. The non-linear model (ELM) was superior to the linear method (PLSR). ELM demonstrated satisfactory accuracy based on the feature wavelet coefficients of CWT with the mexh wavelet basis function at a decomposition scale of 1 (CWT(mexh&#95;1)), with R 2 , RMSE, and RPD values of 0.946, 1.408%, and 3.759 in the validation dataset, respectively. Overall, the CWT(mexh&#95;1)-CARS-ELM systematic modeling method was feasible and reliable for estimating the water content of sandy clay loam., Competing Interests: Lihua Luo is an employee of the Yunnan Institute of Water and Hydropower Engineering Investigation and Design, Co., LTD., (©2024 Chen et al.)

Objective. We demonstrate detection of high energy particle current (HEC) for MeV therapeutic electron beams. Detection of HEC comprises of remote sensing or acquiring information about HEC inside radiation transport medium from a distance outside of the medium. Approach. HEC is self-propelled motion of charged particles through a radiation transport medium. Remote sensing of HEC is embodied in an experimental setup, which includes homogeneous and heterogeneous phantoms irradiated with 4-15 MeV electron beams and two large area parallel-plane electrodes extraneous to the phantoms providing two-parameter detection. We also introduce a new type of scanning method (depth-scan) for probing object properties along the beamline axis. Main Results. Deterministic radiation transport simulations and measurements agree, considering differences in simulation vs experimental geometry and experimental uncertainties. Significance. This method may be suitable for range detection of charged particle beams, or for probing of radiation opaque objects in non-destructive testing., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Seagrasses provide critical ecosystem services but cumulative human pressure on coastal environments has seen a global decline in their health and extent. Key processes of anthropogenic disturbance can operate at local spatio-temporal scales that are not captured by conventional satellite imaging. Seagrass management strategies to prevent longer-term loss and ensure successful restoration require effective methods for monitoring these fine-scale changes. Current seagrass monitoring methods involve resource-intensive fieldwork or recurrent image classification. This study presents an alternative method using iteratively reweighted multivariate alteration detection (IR-MAD), an unsupervised change detection technique originally developed for satellite images. We investigate the application of IR-MAD to image data acquired using an unoccupied aerial vehicle (UAV). UAV images were captured at a 14-week interval over two seagrass beds in Brisbane Water, NSW, Australia using a 10-band Micasense RedEdge-MX Dual camera system. To guide sensor selection, a further three band subsets representing simpler sensor configurations (6, 5 and 3 bands) were also analysed using eight categories of seagrass change. The ability of the IR-MAD method, and for the four different sensor configurations, to distinguish the categories of change were compared using the Jeffreys-Matusita (JM) distance measure of spectral separability. IR-MAD based on the full 10-band sensor images produced the highest separability values indicating that human disturbances (propeller scars and other seagrass damage) were distinguishable from all other change categories. IR-MAD results for the 6-band and 5-band sensors also distinguished key seagrass change features. The IR-MAD results for the simplest 3-band sensor (an RGB camera) detected change features, but change categories were not strongly separable from each other. Analysis of IR-MAD weights indicated that additional visible bands, including a coastal blue band and a second red band, improve change detection. IR-MAD is an effective method for seagrass monitoring, and this study demonstrates the potential for multispectral sensors with additional visible bands to improve seagrass change detection., (© 2024. The Author(s).)

Introduction: Patients with Brugada syndrome (BrS) face an increased risk of ventricular arrhythmias and sudden cardiac death. Implantable cardiac monitors (ICMs) have emerged as effective tools for detecting arrhythmias in BrS. Technological advancements, including temperature sensors and improved subcutaneous electrocardiogram (subECG) signal quality, hold promise for further enhancing their utility in this population., Methods and Results: We present a case of a 40-year-old man exhibiting a BrS type 2 pattern on 12-lead ECG, who underwent ICM insertion (BIOMONITOR IIIm, BIOTRONIK) due to drug-induced BrS type 1 pattern and a history of syncope, with a negative response to programmed ventricular stimulation. The device contains an integrated temperature sensor and can transmit daily vital data, such as mean heart rate and physical activity. Several months later, remote alerts indicated a temperature increase, along with transmitted subECGs suggesting a fever-induced BrS type 1 pattern. The patient was promptly advised to commence antipyretic therapy. Over the following days, remotely monitored parameters showed decreases in mean temperature, physical activity, and mean heart rate, without further recurrence of abnormal subECGs., Conclusion: ICMs offer valuable insights beyond arrhythmia detection in BrS. Early detection of fever using embedded temperature sensors may improve patient management, while continuous subECG morphological analysis has the potential to enhance risk stratification in BrS patients., (© 2024 Wiley Periodicals LLC.)

Monitoring systems that incentivize, track and verify compliance with social and environmental standards are widespread in food systems. In particular, digital monitoring approaches using remote sensing, machine learning, big data, smartphones, platforms and blockchain are proliferating. The increasing use and availability of these technologies put us at a critical juncture to leverage these innovations for enhanced transparency, fairness and open access, rather than descending into a dystopian landscape of digital surveillance and division perpetuated by a powerful few. Here we discuss opportunities and risks, and highlight research gaps linked to the ongoing digitalization of monitoring approaches., (© 2024. Springer Nature Limited.)

A substantial number of critically ill patients in intensive care units (ICUs) rely on indwelling urinary catheters (IDCs), demanding regular monitoring of urine bags. This process increases the workload for healthcare providers and elevates the risk of exposure to contagious diseases. Moreover, IDCs are a primary cause of catheter-associated urinary tract infections (UTIs) in ICU patients whose delayed detection can have life-threatening complications. To address this, we have developed a Sticker Type Antenna for Remote Sensing (STARS) system capable of measuring urine flow rate and conductivity as early-risk markers for UTIs, alongside tracking patients' urine bag status to facilitate medical automation for healthcare providers. STARS comprises a simple, low-cost, disposable antenna module for contactless measurements of urine volume and conductivity, and a reusable wireless module for real-time data transmission. Systematic studies on STARS revealed its stable performance within physiologically relevant ranges of urine volume (0 to 2000 ml) and conductivity (5 to 40 mS/cm) in urine bags. As a proof-of-concept, STARS was tested in artificially created healthy and infected urine specimens to validate its non-contact sensing performance in detecting the onset of UTIs in catheterized patients within a hospital-like environment. STARS represents the first application of a real-time, contactless, wireless monitoring platform for simultaneous urine bag management and early risk detection of UTIs.

Despite treatment advancements, HF mortality remains high, prompting interest in reducing HF-related hospitalizations through remote monitoring. These advances are necessary considering the rapidly rising prevalence and incidence of HF worldwide, presenting a burden on hospital resources. While traditional approaches have failed in predicting impending HF-related hospitalizations, remote hemodynamic monitoring can detect changes in intracardiac filling pressure weeks prior to HF-related hospitalizations which makes timely pharmacological interventions possible. To ensure successful implementation, structural integration, optimal patient selection, and efficient data management are essential. This review aims to provide an overview of the rationale, the available devices, current evidence, and the implementation of remote hemodynamic monitoring., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Despite recent popularization and widespread use in modern electronic devices, LiDAR technology remains expensive for research purposes, in part due to the very high performance offered by commercially available LiDAR scanners. However, such high performance is not always needed, and the expensive price ends up making LiDAR scanners inaccessible for research projects with reduced budget, such as those in developing countries. Here we designed and built a simple ground-based LiDAR scanner, with performance sufficient to fulfil the requirements for a variety of ecological research projects, while being cheap and easy to build. We managed to assemble a LiDAR scanner under 400 USD (as of 2021), and it is simple enough to be built by personnel with minimal engineering background. We also demonstrated the quality of the resulting point clouds by scanning a test site and producing some common LiDAR products. Although not adequate for mapping large area due to its limited range, our LiDAR design is open, customizable, and can produce adequate results while costing ~1% of "low-cost" scanners available in the market. As such, our LiDAR scanner opens a world of new opportunities, particularly for projects in developing countries., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pereira Mendes, Lim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In the current biodiversity crisis, populations of many species have alarmingly declined, and insects are no exception to this general trend. Biodiversity monitoring has become an essential asset to detect biodiversity change but remains patchy and challenging for organisms that are small, inconspicuous or make (nocturnal) long-distance movements. Radars are powerful remote-sensing tools that can provide detailed information on intensity, timing, altitude and spatial scale of aerial movements and might therefore be particularly suited for monitoring aerial insects and their movements. Importantly, they can contribute to several essential biodiversity variables (EBVs) within a harmonized observation system. We review existing research using small-scale biological and weather surveillance radars for insect monitoring and outline how the derived measures and quantities can contribute to the EBVs 'species population', 'species traits', 'community composition' and 'ecosystem function'. Furthermore, we synthesize how ongoing and future methodological, analytical and technological advancements will greatly expand the use of radar for insect biodiversity monitoring and beyond. Owing to their long-term and regional-to-large-scale deployment, radar-based approaches can be a powerful asset in the biodiversity monitoring toolbox whose potential has yet to be fully tapped. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Insects are the most diverse group of animals on Earth, yet our knowledge of their diversity, ecology and population trends remains abysmally poor. Four major technological approaches are coming to fruition for use in insect monitoring and ecological research-molecular methods, computer vision, autonomous acoustic monitoring and radar-based remote sensing-each of which has seen major advances over the past years. Together, they have the potential to revolutionize insect ecology, and to make all-taxa, fine-grained insect monitoring feasible across the globe. So far, advances within and among technologies have largely taken place in isolation, and parallel efforts among projects have led to redundancy and a methodological sprawl; yet, given the commonalities in their goals and approaches, increased collaboration among projects and integration across technologies could provide unprecedented improvements in taxonomic and spatio-temporal resolution and coverage. This theme issue showcases recent developments and state-of-the-art applications of these technologies, and outlines the way forward regarding data processing, cost-effectiveness, meaningful trend analysis, technological integration and open data requirements. Together, these papers set the stage for the future of automated insect monitoring. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red&#95;edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Accelerometer-derived physical activity (PA) from cardiac devices are available via remote monitoring platforms yet rarely reviewed in clinical practice. We aimed to investigate the association between PA and clinical measures of frailty and physical functioning., Methods: The PATTErn study (A study of Physical Activity paTTerns and major health Events in older people with implantable cardiac devices) enrolled participants aged 60 + undergoing remote cardiac monitoring. Frailty was measured using the Fried criteria and gait speed (m/s), and physical functioning by NYHA class and SF-36 physical functioning score. Activity was reported as mean time active/day across 30-days prior to enrolment (30-day PA). Multivariable regression methods were utilised to estimate associations between PA and frailty/functioning (OR = odds ratio, β = beta coefficient, CI = confidence intervals)., Results: Data were available for 140 participants (median age 73, 70.7% male). Median 30-day PA across the analysis cohort was 134.9 min/day (IQR 60.8-195.9). PA was not significantly associated with Fried frailty status on multivariate analysis, however was associated with gait speed (β = 0.04, 95% CI 0.01-0.07, p = 0.01) and measures of physical functioning (NYHA class: OR 0.73, 95% CI 0.57-0.92, p = 0.01, SF-36 physical functioning: β = 4.60, 95% CI 1.38-7.83, p = 0.005)., Conclusions: PA from cardiac devices was associated with physical functioning and gait speed. This highlights the importance of reviewing remote monitoring PA data to identify patients who could benefit from existing interventions. Further research should investigate how to embed this into clinical pathways., (© 2024. The Author(s).)

With the accelerated development of the technological power of society, aerial images of drones gradually penetrated various industries. Due to the variable speed of drones, the captured images are shadowed, blurred, and obscured. Second, drones fly at varying altitudes, leading to changing target scales and making it difficult to detect and identify small targets. In order to solve the above problems, an improved ASG-YOLOv5 model is proposed in this paper. Firstly, this research proposes a dynamic contextual attention module, which uses feature scores to dynamically assign feature weights and output feature information through channel dimensions to improve the model's attention to small target feature information and increase the network's ability to extract contextual information; secondly, this research designs a spatial gating filtering multi-directional weighted fusion module, which uses spatial filtering and weighted bidirectional fusion in the multi-scale fusion stage to improve the characterization of weak targets, reduce the interference of redundant information, and better adapt to the detection of weak targets in images under unmanned aerial vehicle remote sensing aerial photography; meanwhile, using Normalized Wasserstein Distance and CIoU regression loss function, the similarity metric value of the regression frame is obtained by modeling the Gaussian distribution of the regression frame, which increases the smoothing of the positional difference of the small targets and solves the problem that the positional deviation of the small targets is very sensitive, so that the model's detection accuracy of the small targets is effectively improved. This paper trains and tests the model on the VisDrone2021 and AI-TOD datasets. This study used the NWPU-RESISC dataset for visual detection validation. The experimental results show that ASG-YOLOv5 has a better detection effect in unmanned aerial vehicle remote sensing aerial images, and the frames per second (FPS) reaches 86, which meets the requirement of real-time small target detection, and it can be better adapted to the detection of the weak and small targets in the aerial image dataset, and ASG-YOLOv5 outperforms many existing target detection methods, and its detection accuracy reaches 21.1% mAP value. The mAP values are improved by 2.9% and 1.4%, respectively, compared with the YOLOv5 model. The project is available at https://github.com/woaini-shw/asg-yolov5.git., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Aims: Remote dielectric sensing (ReDS) represents a contemporary non-invasive technique reliant on electromagnetic energy to quantify pulmonary congestion. Its prognostic significance within the context of heart failure (HF) patients remains elusive. This study aimed to assess the prognostic implications of residual pulmonary congestion, as gauged by the ReDS system, among patients admitted due to congestive HF., Methods and Results: We enrolled hospitalized HF patients who underwent ReDS assessments upon admission and discharge in a blinded manner, independent of attending physicians. We evaluated the prognostic impact of the ReDS ratio between admission and discharge on the primary outcome, which encompassed all-cause mortality and HF-related re-hospitalizations. A cohort of 133 patients (median age 78 [72, 84] years, 78 male [59%]) was included. Over a median observation period of 363 days post-index discharge, an escalated ReDS group (ReDS ratio > 100%), determined through statistical calculation, emerged as an independent predictor of the primary outcome, exhibiting an adjusted hazard ratio of 4.37 (95% confidence interval 1.13-16.81, P = 0.032). The cumulative incidence of the primary outcome was notably higher in the increased ReDS group compared with the decreased ReDS group (50.1% vs. 8.5%, P = 0.034)., Conclusions: Elevated ReDS ratios detected during the index hospitalization could serve as a promising prognostic indicator in HF patients admitted for treatment. The clinical ramifications of ReDS-guided HF management warrant validation in subsequent studies., (© 2024 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Background: Eating disorders (EDs) are serious, often chronic, conditions associated with pronounced morbidity, mortality, and dysfunction increasingly affecting young people worldwide. Illness progression, stages and recovery trajectories of EDs are still poorly characterised. The STORY study dynamically and longitudinally assesses young people with different EDs (restricting; bingeing/bulimic presentations) and illness durations (earlier; later stages) compared to healthy controls. Remote measurement technology (RMT) with active and passive sensing is used to advance understanding of the heterogeneity of earlier and more progressed clinical presentations and predictors of recovery or relapse., Methods: STORY follows 720 young people aged 16-25 with EDs and 120 healthy controls for 12 months. Online self-report questionnaires regularly assess ED symptoms, psychiatric comorbidities, quality of life, and socioeconomic environment. Additional ongoing monitoring using multi-parametric RMT via smartphones and wearable smart rings ('Ōura ring') unobtrusively measures individuals' daily behaviour and physiology (e.g., Bluetooth connections, sleep, autonomic arousal). A subgroup of participants completes additional in-person cognitive and neuroimaging assessments at study-baseline and after 12 months., Discussion: By leveraging these large-scale longitudinal data from participants across ED diagnoses and illness durations, the STORY study seeks to elucidate potential biopsychosocial predictors of outcome, their interplay with developmental and socioemotional changes, and barriers and facilitators of recovery. STORY holds the promise of providing actionable findings that can be translated into clinical practice by informing the development of both early intervention and personalised treatment that is tailored to illness stage and individual circumstances, ultimately disrupting the long-term burden of EDs on individuals and their families., (© 2024. The Author(s).)

Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc ., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R 2 ) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts., Competing Interests: Subhrangshu Adhikary is employed by Spiraldevs Automation Industries Pvt. Ltd. and Saikat Banerjee is employed by Wingbiotics., (© 2024 Adhikary et al.)

Purpose: We aimed to design, develop, and evaluate an internet of things-enabled patch (IoT patch) for real-time remote monitoring of adherence (or patch wear time) during patch treatment in child participants in clinical trials. This study provides healthcare providers with a tool for objective, real-time, and remote assessment of adherence and for making required adjustments to treatment plans., Methods: The IoT patch had two temperature microsensors and a wireless chip. One sensor was placed closer to the skin than the other, resulting in a temperature difference depending on whether the patch was worn. When the patch was worn, it measured temperatures every 30 seconds and transmitted temperature data to a cloud server via a mobile application every 15 seconds. The patch was evaluated via 2 experiments with 30 healthy adults and 40 children with amblyopia., Results: Excellent monitoring accuracy was observed in both adults (mean delay of recorded time data, 0.4 minutes) and children (mean, 0.5 minutes). The difference between manually recorded and objectively recorded patch wear times showed good agreement in both groups. Experiment 1 showed accurate monitoring over a wide range of temperatures (from 0 to 30°C). Experiment 2 showed no significant differences in wearability (ease-of-use and comfort scores) between the IoT and conventional patches., Conclusions: The IoT patch offers an accurate, real-time, and remote system to monitor adherence to patch treatment. The patch is comfortable and easy to use. The utilization of an IoT patch may increase adherence to patch treatment based on accurate monitoring., Translational Relevance: Results show that the IoT patch can enable real-time adherence monitoring in clinical trials, improving treatment precision, and patient compliance to enhance outcomes.

Background: There is an urgent need for objective and sensitive measures to quantify clinical disease progression and gauge the response to treatment in clinical trials for amyotrophic lateral sclerosis (ALS). Here, we evaluate the ability of an accelerometer-derived outcome to detect differential clinical disease progression and assess its longitudinal associations with overall survival in patients with ALS., Methods: Patients with ALS wore an accelerometer on the hip for 3-7 days, every 2-3 months during a multi-year observation period. An accelerometer-derived outcome, the Vertical Movement Index (VMI), was calculated, together with predicted disease progression rates, and jointly analysed with overall survival. The clinical utility of VMI was evaluated using comparisons to patient-reported functionality, while the impact of various monitoring schemes on empirical power was explored through simulations., Findings: In total, 97 patients (70.1% male) wore the accelerometer for 1995 days, for a total of 27,701 h. The VMI was highly discriminatory for predicted disease progression rates, revealing faster rates of decline in patients with a worse predicted prognosis compared to those with a better predicted prognosis (p < 0.0001). The VMI was strongly associated with the hazard for death (HR 0.20, 95% CI: 0.09-0.44, p < 0.0001), where a decrease of 0.19-0.41 unit was associated with reduced ambulatory status. Recommendations for future studies using accelerometery are provided., Interpretation: The results serve as motivation to incorporate accelerometer-derived outcomes in clinical trials, which is essential for further validation of these markers to meaningful endpoints., Funding: Stichting ALS Nederland (TRICALS-Reactive-II)., Competing Interests: Declaration of interests Nothing to report., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km 2 ), followed by deciduous forest (1,591 km 2 ) and sub-perennial forest (1,317 km 2 ). About 1,608 km 2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km 2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km 2 , with the agricultural class as the most impacted (735 km 2 ). Our analysis detected the saltmarsh class occupied 541 km 2 in the LULC map, and around 328 km 2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change., Competing Interests: The authors declare there are no competing interests., (©2024 Hernández-Guzmán and Ruiz-Luna.)

Peripheral blood oxygen saturation (SpO 2 ) is an essential indicator of respiratory functionality and received increasing attention during the COVID-19 pandemic. Clinical findings show that COVID-19 patients can have significantly low SpO 2 before any obvious symptoms. Measuring an individual's SpO 2 without having to come into contact with the person can lower the risk of cross contamination and blood circulation problems. The prevalence of smartphones has motivated researchers to investigate methods for monitoring SpO 2 using smartphone cameras. Most prior schemes involving smartphones are contact-based: They require using a fingertip to cover the phone's camera and the nearby light source to capture reemitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO 2 estimation scheme using smartphone cameras. The scheme analyzes the videos of an individual's hand for physiological sensing, which is convenient and comfortable for users and can protect their privacy and allow for keeping face masks on. We design explainable neural network architectures inspired by the optophysiological models for SpO 2 measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO 2 measurement, showing the potential of the proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO 2 estimation performance.

Lower extremity strength (LES) is essential to support activities in daily living. To extend healthy life expectancy of elderly people, early detection of LES weakness is important. In this study, we challenge to develop a method for LES assessment in daily living via an in-shoe motion sensor (IMS). To construct the estimation model, we collected data from 62 subjects. We used the outcome of the five-times-sit-to-stand test to represent the performance of LES as the target variable. Predictors were constructed from the subjects' foot motions measured by the IMS during straight path walking. We used the leave-one-subject-out least absolute shrinkage and selection operator algorithm to select features and construct respective models for the males and females. As a result, the models achieved fair and a good intra-class correlation coefficient agreement between the true and estimation values, with mean absolute errors of 2.14 and 1.21 s (variation of 23.6 and 16.0%), respectively. To validate the models, we separately collected data from 45 subjects. The models successfully predicted 100% and 90% of the male and female subjects' data, respectively, which suggests the robustness of the constructed estimation models. The results suggested that LES can be identified more effectively in daily living by wearing an IMS, and the use of an IMS has the potential for future frailty and fall risk assessment applications.

In the recent years, Active Assisted Living (AAL) technologies used for autonomous tracking and activity recognition have started to play major roles in geriatric care. From fall detection to remotely monitoring behavioral patterns, vital functions and collection of air quality data, AAL has become pervasive in the modern era of independent living for the elderly section of the population. However, even with the current rate of progress, data access and data reliability has become a major hurdle especially when such data is intended to be used in new age modelling approaches such as those using machine learning. This paper presents a comprehensive data ecosystem comprising remote monitoring AAL sensors along with extensive focus on cloud native system architecture, secured and confidential access to data with easy data sharing. Results from a validation study illustrate the feasibility of using this system for remote healthcare surveillance. The proposed system shows great promise in multiple fields from various AAL studies to development of data driven policies by local governments in promoting healthy lifestyles for the elderly alongside a common data repository that can be beneficial to other research communities worldwide.Clinical Relevance- This study creates a cloud-based smart home data ecosystem, which can achieve the remote healthcare monitoring for aging population, enabling them to live more independently and decreasing hospital admission rates.

Dens are a crucial component of the life history of most shallow water octopuses. However, den usage dynamics have only been explored in a few species over relatively short durations, and Octopus rubescens denning behavior has never been explored in situ. We built four underwater camera traps to observe the behavior of O. rubescens in and around their dens. To distinguish individuals, octopuses were captured and given a unique identifiable visible implant elastomer tag on the dorsal side of their mantle. After being tagged and photographed, each octopus was released back to its original capture site within its original den bottle. The site is unique in that octopuses reside almost exclusively in discarded bottles, therefore aiding in locating and monitoring dens. Motion-activated cameras were suspended in a metal field-of-view above bottle dens of released octopuses to observe den-associated behaviors. Cameras were regularly retrieved and replaced to allow continuous monitoring of den locations in 71 h intervals for over a month. We found that O. rubescenswas primarily active during the day and had frequent interactions with conspecifics (other members within the species). We also found that rockfish and red rock crabs tended to frequent den locations more often when octopuses were not present, while kelp greenling both visited dens more frequently and stayed longer when octopuses were present. Our results, demonstrate the utility of motion-activated camera traps for behavioral and ecological studies of nearshore mobile organisms., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.)

Recent advances in flexible and stretchable electronics have led to a surge of electronic skin (e-skin)-based health monitoring platforms. Conventional wireless e-skins rely on rigid integrated circuit chips that compromise the overall flexibility and consume considerable power. Chip-less wireless e-skins based on inductor-capacitor resonators are limited to mechanical sensors with low sensitivities. We report a chip-less wireless e-skin based on surface acoustic wave sensors made of freestanding ultrathin single-crystalline piezoelectric gallium nitride membranes. Surface acoustic wave-based e-skin offers highly sensitive, low-power, and long-term sensing of strain, ultraviolet light, and ion concentrations in sweat. We demonstrate weeklong monitoring of pulse. These results present routes to inexpensive and versatile low-power, high-sensitivity platforms for wireless health monitoring devices.

A resorbable closed-loop sensor-actuator implant can temporarily control heart rate.

Background: Whether the amount of atrial fibrillation (AF) patients experience conveys important prognostic information beyond that provided by the diagnosis of AF is uncertain. The study objective was to assess the dose-response relationship between device-detected AF burden and subsequent cardiovascular outcomes., Methods: Among patients with paroxysmal AF who underwent cardiac implantable electronic device implantation (2010-2016), Merlin.net remote-monitoring data were linked to Medicare claims to assess the magnitude and strength of the associations between device-based AF burden (defined as a daily percentage of time spent in AF or maximal AF episode duration ascertained at baseline over 30 days) and key cardiovascular outcomes., Results: Among 39 710 patients (mean age 77.1±8.7 years, 60.7% male, and a mean CHA 2 DS 2 -VASc score 4.9±1.3), all-cause mortality at 1-year increased with baseline AF burden: 8.54% with AF burden 0%, 8.9% with AF burden 0% to 5%, and 10.9% with AF burden 5% to 98% ( P <0.001) There was also a dose-response relationship between increasing AF burden and all-cause or cardiovascular hospitalization and ischemic stroke. Updating AF burden data every 30 days did not alter the AF burden-prognostic relationships determined from the use of baseline data alone. Results were also consistent when 3-year outcomes were considered and after accounting for the use of oral anticoagulants., Conclusions: In paroxysmal AF, there is a clinically relevant dose-response relationship between increasing AF burden and rates of adverse outcomes at 1- and 3-years, including increasing risks of cardiovascular hospitalization, ischemic stroke, and mortality.

In this paper, we have reviewed and presented a critical overview of "energy-efficient and reliable routing solutions" in the field of wireless body area networks (WBANs). In addition, we have theoretically analysed the importance of energy efficiency and reliability and how it affects the stability and lifetime of WBANs. WBAN is a type of wireless sensor network (WSN) that is unique, wherever energy-efficient operations are one of the prime challenges, because each sensor node operates on battery, and where an excessive amount of communication consumes more energy than perceiving. Moreover, timely and reliable data delivery is essential in all WBAN applications. Moreover, the most frequent types of energy-efficient routing protocols include crosslayer, thermal-aware, cluster-based, quality-of-service, and postural movement-based routing protocols. According to the literature review, clustering-based routing algorithms are the best choice for WBAhinwidth, and low memory WBAN, in terms of more computational overhead and complexity. Thus, the routing techniques used in WBAN should be capable of energy-efficient communication at desired reliability to ensure the improved stability period and network lifetime. Therefore, we have highlighted and critically analysed various performance issues of the existing "energy-efficient and reliable routing solutions" for WBANs. Furthermore, we identified and compiled a tabular representation of the reviewed solutions based on the most appropriate strategy and performance parameters for WBAN. Finally, concerning to reliability and energy efficiency in WBANs, we outlined a number of issues and challenges that needs further consideration while devising new solutions for clustered-based WBANs., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Farman Ullah et al.)

Neural activity research has recently gained significant attention due to its association with sensory information and behavior control. However, the current methods of brain activity sensing require expensive equipment and physical contact with the tested subject. We propose a novel photonic-based method for remote detection of human senses. Physiological processes associated with hemodynamic activity due to activation of the cerebral cortex affected by different senses have been detected by remote monitoring of nano-vibrations generated by the transient blood flow to the specific regions of the human brain. We have found that a combination of defocused, self-interference random speckle patterns with a spatiotemporal analysis, using Deep Neural Network, allows associating between the activated sense and the seemingly random speckle patterns., (© 2022. The Author(s).)

Forest fires have become a major threat around the world, causing many negative impacts on human habitats and forest ecosystems. Climatic changes and the greenhouse effect are some of the consequences of such destruction. Interestingly, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, there is a need to detect forest fires at their initial stage. This paper proposes a system and methodology that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Because of the primary power supply provided by rechargeable batteries with a secondary solar power supply, a solution is readily implementable as a standalone system for prolonged periods. Moreover, in-depth attention is given to sensor node design and node placement requirements in harsh forest environments and to minimize the damage and harmful effects caused by wild animals, weather conditions, etc. to the system. Numerous trials conducted in real tropical forest sites found that the proposed system is effective in alerting forest fires with lower latency than the existing systems., (© 2022. The Author(s).)

Remote dielectric sensing (ReDS) is a recently introduced non-invasive electromagnetic-based device used to quantify lung fluid levels. Nevertheless, its inter-rater and intra-rater reliability remain uncertain. In 10 healthy volunteers, ReDS values were measured three times successively by the officially trained expert examiner to validate intra-rater reliability. Similar measures were performed by a total of three examiners to validate inter-rater reliability. Intra-class correlation (ICC) was applied to validate each reliability. Ten healthy volunteers [median 34 (32, 40) years old, 10 men, body mass index 23.0 (21.2, 23.9) ] were included. Median ReDS value was 28% (25%, 31%). For the intra-rater reliability, ICC (1, 1) and ICC (1, 3) were 0.966 and 0.988, respectively (P < 0.001). For the inter-rater reliability, ICC (2, 1) and ICC (2, 3) were 0.683 and 0.866, respectively (P < 0.001). Given almost perfect intra-rater reliability, an examiner does not need to repeat ReDS measurement. Given substantial inter-rater reliability, ReDS measurements had better be measured by multiple examiners if possible.

An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy., Competing Interests: The authors have declared that no competing interests exist.

In Italy, a strict lockdown was imposed from 8 March 2020 to stop the spread of the coronavirus disease 2019 (COVID-19). We explored the effect of this lockdown on data transmitted by remote monitoring (RM) of implantable cardioverter and cardiac resynchronization therapy defibrillators (ICDs/CRT-Ds). RM daily transmissions from ICDs and CRT-Ds were analyzed and compared in two consecutive 1 month frames pre and post-lockdown: period I (7 February-7 March 2020) and period II (8 March-7 April 2020). The study cohort included 180 patients (81.1% male, 63.3% ICDs and 36.7% CRT-Ds) with a median age of 70 (interquartile range 62-78) years. The median value of physical activity provided by accelerometric sensors showed a significant reduction between period I and II [13.1% (8.2-18.1%) versus 9.4% (6.3-13.8%), p < 0.001]. Eighty nine % of patients decreased their activity, for 43.3% the relative reduction was ≥ 25%. The mean heart rate decreased significantly [69.2 (63.8-75.6) bpm vs 67.9 (62.7-75.3) bpm, p < 0.001], but with greater reduction (≈3 beats/minute) in patients aged < 70 years. Resting heart rate and thoracic impedance showed minor variations. No differences were observed in device pacing % and arrhythmias. In cardiac patients, the lockdown imposed to contain COVID-19 outbreak significantly reduced the amount of physical activity and the mean heart rate. These side effects of in-home confinement quarantine should be taken in consideration for frail patients., (© 2021. Springer Japan KK, part of Springer Nature.)

Background: Kidney function may promote progression of AF., Objective: We evaluated the association of kidney function to AF progression and resultant clinical outcomes in patients with cardiac implantable electronic devices (CIED)., Methods: We performed a retrospective cohort study using national clinical data from the Veterans Health Administration linked to CIED data from the Carelink® remote monitoring data warehouse (Medtronic Inc, Mounds View, MN). All devices had atrial leads and at least 75% of remote monitoring transmission coverage. Patients were included at the date of the first AF episode lasting ≥6 minutes, and followed until the occurrence of persistent AF in the first year, defined as ≥7 consecutive days with continuous AF. We used Cox regression analyses with persistent AF as a time-varying covariate to examine the association to stroke, myocardial infarction, heart failure and death., Results: Of, 10,323 eligible patients, 1,771 had a first CIED-detected AF (mean age 69 ± 10 years, 1.2% female). In the first year 355 (20%) developed persistent AF. Kidney function was not associated with persistent AF after multivariable adjustment including CHA 2 DS 2 -VASc variables and prior medications. Only higher age increased the risk (HR: 1.37 per 10 years; 95% CI:1.22-1.54). Persistent AF was associated to higher risk of heart failure (HR: 2.27; 95% CI: 1.88-2.74) and death (HR: 1.60; 95% CI: 1.30-1.96), but not stroke (HR: 1.28; 95% CI: 0.62-2.62) or myocardial infarction (HR: 1.43; 95% CI: 0.91-2.25)., Conclusion: Kidney function was not associated to AF progression, whereas higher age was. Preventing AF progression could reduce the risk of heart failure and death., (Published by Elsevier Inc.)

Heart failure (HF) is associated with considerable morbidity and mortality. The increasing prevalence of HF and inpatient HF hospitalization has a considerable burden on healthcare cost and utilization. The recognition that hemodynamic changes in pulmonary artery pressure (PAP) and left atrial pressure precede the signs and symptoms of HF has led to interest in hemodynamic guided HF therapy as an approach to allow earlier intervention during a heart failure decompensation. Remote patient monitoring (RPM) utilizing telecommunication, cardiac implantable electronic device parameters and implantable hemodynamic monitors (IHM) have largely failed to demonstrate favorable outcomes in multicenter trials. However, one positive randomized clinical trial testing the CardioMEMS device (followed by Food and Drug Administration approval) has generated renewed interest in PAP monitoring in the HF population to decrease hospitalization and improve quality of life. The COVID-19 pandemic has also stirred a resurgence in the utilization of telehealth to which RPM using IHM may be complementary. The cost effectiveness of these monitors continues to be a matter of debate. Future iterations of devices aim to be smaller, less burdensome for the patient, less dependent on patient compliance, and less cumbersome for health care providers with the integration of artificial intelligence coupled with sophisticated data management and interpretation tools. Currently, use of IHM may be considered in advanced heart failure patients with the support of structured programs., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sardar Hassan Ijaz: None. Sachin P. Shah: None. Arjun Majithia, MD: reports receiving consulting fees from Abbott., (Copyright © 2021. Published by Elsevier Inc.)

Background: Studies relying on self-reported sleep data suggest that there is an association between short and long sleep duration and less than ideal cardiovascular health. Evidence regarding the feasibility of using digital health devices to measure sleep duration and assess its relationship to ideal cardiovascular health are lacking. The objective of the present study was to utilize digital health devices to record sleep duration and examine the relationship between sleep duration and ideal cardiovascular health., Methods: A total of 307 participants transmitted sleep duration data from digital health devices and answered the Life's Simple 7 survey instrument to assess ideal cardiovascular health. Sleep duration was defined as adequate (7 to < 9 h per night) or non-adequate (< 7 h and ≥ 9 h)., Results: We identified three sleep-cardiovascular health phenogroups: resilient (non-adequate sleep and ideal cardiovascular health), uncoupled (adequate sleep and non-ideal cardiovascular health) or concordant (sleep and cardiovascular health metrics were aligned). Participants in the resilient phenogroup (n = 83) had better cardiovascular health factor profiles (blood pressure, blood glucose and cholesterol levels) and behaviors (healthy weight, diet, exercise, smoking) than participants in the concordant (n = 171) and uncoupled (n = 53) phenogroups. This was associated with higher Life's Simple 7 Health Scores in the resilient phenogroup compared to the concordant and uncoupled phenogroups (7.8 ± 0.8 vs. 7.0 ± 1.4 vs. 5.6 ± 0.7, P < 0.01)., Conclusion: This study identified three distinct sleep-ideal cardiovascular health phenogroups and highlights the advantage of incorporating sleep assessments into studies of cardiovascular health. Future studies should focus on the relationship between sleep-cardiovascular phenogroups and clinical outcomes. Clinical Trial Registration Clinicaltrials.gov NCT02958098. Date of registration: November 11, 2016., (© 2021. The Author(s).)

Background: Early recurrences of atrial arrhythmias (ERAA) after atrial fibrillation (AF) catheter ablation do not predict procedural failure. A well-demarcated homogeneous lesion delivered by cryoballoon is less arrhythmogenic, and the recommended three-months blanking period may not refer to cryoballoon ablation (CBA)., Objective: We aimed to evaluate the predictive role of ERAA after second-generation CBA using an implantable loop recorder., Methods: This prospective observational study enrolled 100 patients (58 males, median age 58) with paroxysmal/persistent AF undergoing pulmonary vein (PV) CBA using second-generation cryoballoon with simultaneous ECG loop recorder implantation. The duration of follow-up was 12 months, with scheduled visits at 3, 6 and 12 months., Results: 99 patients from 100 completed the 12-month follow-up period. ERAA occurred in 31.3 % of patients. 83.9 % of patients with ERAA also developed late recurrences. The 12-month freedom from AF in patients with ERAA was significantly lower than in those without ERAA (p < 0.0001). Non-paroxysmal AF and longer arrhythmia history were associated with increased risk of both early (HR 3.27; 95 % CI 1.32-8.08; p = 0.010 and HR 1.0147; 95 % CI 1.008-1.086; p = 0.015, respectively) and late recurrences (HR 3.89; 95 % CI 1.67-9.04; p = 0.002 and HR 1.0142; 95 % CI 1.007-1.078; p = 0.019, respectively) of AF. ERAA were another predictor for procedural failure (HR 15.2; 95 % CI (6.42-35.99; p = 0.019)., Conclusions: ERAA occurred in the third of the patients after PV second-generation CBA and are strongly associated with procedural failure. Longer duration of AF history and persistent AF are independent predictors of AF's early and late recurrence., (© 2021. The Author(s).)

Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively., (© The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

Pine wilt disease caused by Bursaphelenchus xylophilus is a major tree disease that threatens pine forests worldwide. To diagnose this disease, we developed battery-powered remote sensing devices capable of long-range (LoRa) communication and installed them in pine trees (Pinus densiflora) in Gyeongju and Ulsan, South Korea. Upon analyzing the collected tree sensing signals, which represented stem resistance, we found that the mean absolute deviation (MAD) of the sensing signals was useful for distinguishing between uninfected and infected trees. The MAD of infected trees was greater than that of uninfected trees from August of the year, and in the two-dimensional plane, consisting of the MAD value in July and that in October, the infected and uninfected trees were separated by the first-order boundary line generated using linear discriminant analysis. It was also observed that wood moisture content and precipitation affected MAD. This is the first study to diagnose pine wilt disease using remote sensors attached to trees., Competing Interests: One of the authors, Seong Bean Park, authored Korean Patent #10-2233454, “Method and system for forecasting tree disease based on water data”. Seong Bean Park is the Director of Research Laboratory at ECONNBIZ CO., LTD. We confirm that this does not alter our adherence to PLOS ONE policies on sharing data and materials.

How do you assess the mental wellness of your work-from-home employees? This case study reports on how an occupational health nurse used work-from-home employee's own phone and Fitbit™ smartwatch to obtain heart rate data to screen for high periods of stress. Telemedicine and telemetry allowed the occupational health nurses to screen an employee when the nurse could not assess the employee face-to-face. When the occupational health nurses identified an at-risk employee, the occupational health nurses referred the employee to the Employee Assistance Program (EAP) for counseling. Leveraging heart rate data on a smartwatch is a free intervention that is scalable and has a demonstrated outcome measure with a positive return on investment.

Background: Supervised exercise training improves outcomes in patients with pulmonary arterial hypertension (PAH). The effect of an unsupervised activity intervention has not been tested., Research Question: Can a text-based mobile health intervention increase step counts in patients with PAH?, Study Design and Methods: We performed a randomized, parallel arm, single-blind clinical trial. We randomized patients to usual care or a text message-based intervention for 12 weeks. The intervention arm received three automated text messages per day with real-time step count updates and encouraging messages rooted in behavioral change theory. Individual step targets increased by 20% every 4 weeks. The primary end point was mean week 12 step counts. Secondary end points included the 6-min walk test, quality of life, right ventricular function, and body composition., Results: Among 42 randomized participants, the change in raw steps between baseline and week 12 was higher in the intervention group (1,409 steps [interquartile range, -32 to 2,220] vs -149 steps [interquartile range, -1,010 to 735]; P = .02), which persisted after adjustment for age, sex, baseline step counts, and functional class (model estimated difference, 1,250 steps; P = .03). The intervention arm took a higher average number of steps on all days between days 9 and 84 (P < .05, all days). There was no difference in week 12 six-minute walk distance. Analysis of secondary end points suggested improvements in the emPHasis-10 score (adjusted change, -4.2; P = .046), a reduction in visceral fat volume (adjusted change, -170 mL; P = .023), and nearly significant improvement in tricuspid annular plane systolic excursion (model estimated difference, 1.2 mm; P = .051)., Interpretation: This study demonstrated the feasibility of an automated text message-based intervention to increase physical activity in patients with PAH. Additional studies are warranted to examine the effect of the intervention on clinical outcomes., Clinical Trial Registration: ClinicalTrials.gov; No. NCT03069716; URL: www.clinicaltrials.gov., (Copyright © 2021 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs' feeding trails, which are unvegetated winding tracks left after feeding, have been used as an indicator of their feeding ground utilization. However, current ground-based measurements of these trails require a large amount of time and effort. Here, we developed effective methods to observe the dugongs' feeding trails using unmanned aerial vehicle (UAV) images (1) by extracting the dugong feeding trails using deep neural networks. Furthermore, we demonstrated two applications as follows; (2) extraction of the daily new feeding trails with deep neural networks and (3) estimation the direction of the feeding trails. We obtained aerial photographs from the intertidal seagrass bed at Talibong Island, Trang Province, Thailand. The F1 scores, which are a measure of binary classification model's accuracy taking false positives and false negatives into account, for the method (1) were 89.5% and 87.7% for the images with ground sampling resolutions of 1 cm/pixel and 0.5 cm/pixel, respectively, while the F1 score for the method (2) was 61.9%. The F1 score for the method (1) was high enough to perform scientific studies on the dugong. However, the method (2) should be improved, and there remains a need for manual correction. The mean area of the extracted daily new feeding trails from September 12-27, 2019, was 187.8 m2 per day (n = 9). Total 63.9% of the feeding trails was estimated to have direction within a range of 112.5° and 157.5°. These proposed new methods will reduce the time and efforts required for future feeding trail observations and contribute to future assessments of the dugongs' seagrass habitat use., Competing Interests: The authors have declared that no competing interests exist.

Farmed aquatic animals represent an increasingly important source of food for a growing human population. However, the aquaculture industry faces several challenges with regard to producing a profitable, ethical and environmentally sustainable product, which are exacerbated by the ongoing intensification of operations and increasingly extreme and unpredictable climate conditions. Fortunately, bio-sensors capable of measuring a range of environmental, behavioural and physiological variables (e.g. temperature, dissolved gases, depth, acceleration, ventilation, heart rate, blood flow, glucose and l-lactic acid) represent exciting and innovative tools for assessing the health and welfare of farmed animals in aquaculture. Here, we illustrate how these state-of-the-art technologies can provide unique insights into variables pertaining to the inner workings of the animal to elucidate animal-environment interactions throughout the production cycle, as well as to provide insights on how farmed animals perceive and respond to environmental and anthropogenic perturbations. Using examples based on current challenges (i.e. sub-optimal feeding strategies, sub-optimal animal welfare and environmental changes), we discuss how bio-sensors can contribute towards optimizing the growth, health and welfare of farmed animals under dynamically changing on-farm conditions. While bio-sensors currently represent tools that are primarily used for research, the continuing development and refinement of these technologies may eventually allow farmers to use real-time environmental and physiological data from their stock as 'early warning systems' and/or for refining day-to-day operations to ethically and sustainably optimize production. This article is part of the theme issue 'Measuring physiology in free-living animals (Part I)'.

An improved method of remote optical absorption spectroscopy and hyperspectral optical absorption imaging is described which takes advantage of the photoacoustic remote sensing detection architecture. A wide collection of photoacoustic excitation wavelengths ranging from 210 nm to 1550 nm was provided by a nanosecond tunable source allowing access to various salient endogenous chromophores such as DNA, hemeproteins, and lipids. Sensitivity of the device was demonstrated by characterizing the infrared absorption spectrum of water. Meanwhile, the efficacy of the technique was explored by recovering cell nuclei and oxygen saturation from a live chicken embryo model and by recovering adipocytes from freshly resected murine adipose tissue. This represents a continued investigation into the characteristics of the hyperspectral photoacoustic remote sensing technique which may represent an effective means of non-destructive endogenous contrast characterization and visualization.

Background: Despite more than two million pediatric operations performed in the United States annually, normal postoperative recovery remains difficult to define. Wearable sensors that assess physical activity and vital signs in real time represent a tool to assess postoperative recovery. This study examined the use of a wearable, the FitBit Inspire HR, to describe recovery in children after appendectomy and to determine the sensitivity of wearable data to distinguish disease severity., Materials and Methods: Children 3-18 y old undergoing appendectomy in a tertiary children's hospital were invited to participate. Participants wore the FitBit Inpire HR after surgery for 21 d. t-tests compared daily step counts, and piecewise linear regression models were fit to examine recovery trajectories for patients with simple and complicated appendicitis., Results: Thirty-two patients were enrolled, and 26 met eligibility criteria. Nine (35%) children had complicated appendicitis, and 14 (54%) were female; the mean age was 9.1 y (standard deviation: 2.9). Four hundred nineteen postoperative days were captured (range: 8-22 d; median: 16 d). Step counts increased after surgery; however, piecewise models showed that patients with simple appendicitis had a more rapid increase (P < 0.01) and reached a plateau (approximately 8000 steps/d) on postoperative day 9, whereas patients with complicated appendicitis did not reach a plateau and had lower step counts during the entire 21-postoperative day period (P < 0.01)., Conclusions: Recovery in children after surgery can be characterized using wearables, which can also distinguish recovery trajectories based on disease severity. Establishing such "normative" recovery patterns may lead to earlier detection of complications., (Copyright © 2021 Elsevier Inc. All rights reserved.)