High-integrity human intervention in ecosystems: Tracking self-organization modes.

Humans play major roles in shaping and transforming the ecology of Earth. Unlike natural drivers of ecosystem change, which are erratic and unpredictable, human intervention in ecosystems generally involves planning and management, but often results in detrimental outcomes. Using model studies and aerial-image analysis, we argue that the design of a successful human intervention form calls for the identification of the self-organization modes that drive ecosystem change, and for studying their dynamics. We demonstrate this approach with two examples: grazing management in drought-prone ecosystems, and rehabilitation of degraded vegetation by water harvesting. We show that grazing can increase the resilience to droughts, rather than imposing an additional stress, if managed in a spatially non-uniform manner, and that fragmental restoration along contour bunds is more resilient than the common practice of continuous restoration in vegetation stripes. We conclude by discussing the need for additional studies of self-organization modes and their dynamics.

Association of kidney function and atrial fibrillation progression to clinical outcomes in patients with cardiac implantable electronic devices.

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 CHA2DS2-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.

Bio-optical evidence for increasing Phaeocystis dominance in the Barents Sea.

Increasing contributions of prymnesiophytes such as Phaeocystis pouchetii and Emiliania huxleyi to Barents Sea (BS) phytoplankton production have been suggested based on in situ observations of phytoplankton community composition, but the scattered and discontinuous nature of these records confounds simple inference of community change or its relationship to salient environmental variables. However, provided that meaningful assessments of phytoplankton community composition can be inferred based on their optical characteristics, ocean-colour records offer a potential means to develop a synthesis between sporadic in situ observations. Existing remote-sensing algorithms to retrieve phytoplankton functional types based on chlorophyll-a (chl-a) concentration or indices of pigment packaging may, however, fail to distinguish Phaeocystis from other blooms of phytoplankton with high pigment packaging, such as diatoms. We develop a novel algorithm to distinguish major phytoplankton functional types in the BS and apply it to the MODIS-Aqua ocean-colour record, to study changes in the composition of BS phytoplankton blooms in July, between 2002 and 2018, creating time series of the spatial distribution and intensity of coccolithophore, diatom and Phaeocystis blooms. We confirm a north-eastward expansion in coccolithophore bloom distribution, identified in previous studies, and suggest an inferred increase in chl-a concentrations, reported by previous researchers, may be partly explained by increasing frequencies of Phaeocystis blooms. This article is part of the theme issue 'The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning'.

An Advanced First Aid System Based on an Unmanned Aerial Vehicles and a Wireless Body Area Sensor Network for Elderly Persons in Outdoor Environments.

For elderly persons, a fall can cause serious injuries such as a hip fracture or head injury. Here, an advanced first aid system is proposed for monitoring elderly patients with heart conditions that puts them at risk of falling and for providing first aid supplies using an unmanned aerial vehicle. A hybridized fall detection algorithm (FDB-HRT) is proposed based on a combination of acceleration and a heart rate threshold. Five volunteers were invited to evaluate the performance of the heartbeat sensor relative to a benchmark device, and the extracted data was validated using statistical analysis. In addition, the accuracy of fall detections and the recorded locations of fall incidents were validated. The proposed FDB-HRT algorithm was 99.16% and 99.2% accurate with regard to heart rate measurement and fall detection, respectively. In addition, the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq. Mean absolute error was 1.08 × 10-5° and 2.01 × 10-5° for latitude and longitude data relative to data from the GPS Benchmark system. In addition, the results revealed that in urban areas, the UAV succeeded in all missions and arrived at the patient's locations before the ambulance, with an average time savings of 105 s. Moreover, a time saving of 31.81% was achieved when using the UAV to transport a first aid kit to the patient compared to an ambulance. As a result, we can conclude that when compared to delivering first aid via ambulance, our design greatly reduces delivery time. The proposed advanced first aid system outperformed previous systems presented in the literature in terms of accuracy of heart rate measurement, fall detection, and information messages and UAV arrival time.

Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data.

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5 µm (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10 km vs. 3 km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.

A home monitoring program including real-time wireless home spirometry in idiopathic pulmonary fibrosis: a pilot study on experiences and barriers.

In idiopathic pulmonary fibrosis (IPF), home monitoring experiences are limited, not yet real-time available nor implemented in daily care. We evaluated feasibility and potential barriers of a new home monitoring program with real-time wireless home spirometry in IPF. Ten patients with IPF were asked to test this home monitoring program, including daily home spirometry, for four weeks. Measurements of home and hospital spirometry showed good agreement. All patients considered real-time wireless spirometry useful and highly feasible. Both patients and researchers suggested relatively easy solutions for the identified potential barriers regarding real-time home monitoring in IPF.

Risk stratification of cardiovascular and heart failure hospitalizations using integrated device diagnostics in patients with a cardiac resynchronization therapy defibrillator.

Aims: Cardiac resynchronization therapy defibrillators (CRT-D) are able to monitor various parameters that may be combined by an automatic algorithm to provide a heart failure risk status (HFRS). We sought to validate the HFRS for stratifying patient risk, evaluate its association with heart failure (HF) symptoms, and investigate its utility for triage of automatic alerts. Methods and results: Data from 722 patients included in the MORE-CARE trial were analysed in a post hoc analysis. A high HFRS was associated with a significantly increased risk of admission over the next 30 days with a relative risk for cardiovascular hospitalization (CVH) of 4.5 (95% CI: 3.1-6.6, P < 0.001), of HF hospitalization of 6.3 (95% CI: 3.9-10.2, P < 0.001) and of non-HF related CVH of 3.5 (95% CI: 2.0-6.9, P < 0.001). The negative predictive value of low or medium HFRS for these admissions was ≥98%. A high HFRS was associated with an increased risk of HF symptoms. Of all the automatic remote monitoring alerts generated during the study, only 10% had a high HFRS. Conclusion: The HFRS is able to risk-stratify CRT-D patients, which is potentially useful for managing automatic remote monitoring alerts, by focusing attention on the minority of high-risk patients. Clinical Trial Registration: The trial was registered at www.clinicaltrials.gov under number NCT00885677.

A Markov approach for increasing precision in the assessment of data-intensive behavioral interventions.

Health interventions using real-time sensing technology are characterized by intensive longitudinal data, which has the potential to enable nuanced evaluations of individuals' responses to treatment. Existing analytic tools were not developed to capitalize on this opportunity as they typically focus on first-order findings such as changes in the level and/or slope of outcome variables over different intervention phases. This paper introduces an exploratory, Markov-based empirical transition method that offers a more comprehensive assessment of behavioral responses when intensive longitudinal data are available. The procedure projects a univariate time-series into discrete states and empirically determines the probability of transitioning from one state to another. State transition probabilities are summarized separately in phase-specific transition matrices. Comparing transition matrices illuminates intricate, quantifiable differences in behavior between intervention phases. Statistical significance is estimated via bootstrapping techniques. This paper introduces the methodology via three case studies from a secondhand smoke reduction trial utilizing real-time air particle sensors. Analysis enabled the identification of complex phenomena such as avoidance and escape behavior in response to punitive contingencies for tobacco use. Additionally, the largest changes in behavior dynamics were associated with the introduction of behavioral feedback. The Markov approach's ability to elucidate subtle behavioral details has not typically been feasible with standard methodologies, mainly due to historical limitations associated with infrequent repeated measures. These results suggest that the evaluation of intervention effects in data-intensive single-case designs can be enhanced, providing rich information that can ultimately be used to develop interventions uniquely tailored to specific individuals.

Correlated gamma-based hidden Markov model for the smart asthma management based on rescue inhaler usage.

Asthma is a very common chronic disease that affects a large portion of population in many nations. Driven by the fast development in sensor and mobile communication technology, a smart asthma management system has become available to continuously monitor the key health indicators of asthma patients. Such data provides opportunities for healthcare practitioners to examine patients not only in the clinic (on-site) but also outside of the clinic (off-site) in their daily life. In this paper, taking advantage from this data availability, we propose a correlated gamma-based hidden Markov model framework, which can reveal and highlight useful information from the rescue inhaler-usage profiles of individual patients for practitioners. The proposed method can provide diagnostic information about the asthma control status of individual patients and can help practitioners to make more informed therapeutic decisions accordingly. The proposed method is validated through both numerical study and case study based on real world data. Copyright © 2017 John Wiley & Sons, Ltd.

Remote sensing measurements of sea surface temperature as an indicator of Vibrio parahaemolyticus in oyster meat and human illnesses.

BACKGROUND: Vibrio parahaemolyticus (Vp) is a naturally occurring bacterium found in marine environments worldwide. It can cause gastrointestinal illness in humans, primarily through raw oyster consumption. Water temperatures, and potentially other environmental factors, play an important role in the growth and proliferation of Vp in the environment. Quantifying the relationships between environmental variables and indicators or incidence of Vp illness is valuable for public health surveillance to inform and enable suitable preventative measures. This study aimed to assess the relationship between environmental parameters and Vp in British Columbia (BC), Canada. METHODS: The study used Vp counts in oyster meat from 2002-2015 and laboratory confirmed Vp illnesses from 2011-2015 for the province of BC. The data were matched to environmental parameters from publicly available sources, including remote sensing measurements of nighttime sea surface temperature (SST) obtained from satellite readings at a spatial resolution of 1 km. Using three separate models, this paper assessed the relationship between (1) daily SST and Vp counts in oyster meat, (2) weekly mean Vp counts in oysters and weekly Vp illnesses, and (3) weekly mean SST and weekly Vp illnesses. The effects of salinity and chlorophyll a were also evaluated. Linear regression was used to quantify the relationship between SST and Vp, and piecewise regression was used to identify SST thresholds of concern. RESULTS: A total of 2327 oyster samples and 293 laboratory confirmed illnesses were included. In model 1, both SST and salinity were significant predictors of log(Vp) counts in oyster meat. In model 2, the mean log(Vp) count in oyster meat was a significant predictor of Vp illnesses. In model 3, weekly mean SST was a significant predictor of weekly Vp illnesses. The piecewise regression models identified a SST threshold of approximately 14oC for both model 1 and 3, indicating increased risk of Vp in oyster meat and Vp illnesses at higher temperatures. CONCLUSION: Monitoring of SST, particularly through readily accessible remote sensing data, could serve as a warning signal for Vp and help inform the introduction and cessation of preventative or control measures.

Using Microsensor Technology to Quantify Match Demands in Collegiate Women's Volleyball.

Vlantes, TG and Readdy, T. Using microsensor technology to quantify match demands in collegiate women's volleyball. J Strength Cond Res 31(12): 3266-3278, 2017-The purpose of this study was to quantify internal and external load demands of women's NCAA Division I collegiate volleyball competitions using microsensor technology and session rating of perceived exertion (S-RPE). Eleven collegiate volleyball players wore microsensor technology (Optimeye S5; Catapult Sports, Chicago, IL, USA) during 15 matches played throughout the 2016 season. Parameters examined include player load (PL), high impact PL, percentage of HI PL, explosive efforts (EEs), and jumps. Session rating of perceived exertion was collected 20 minutes postmatch using a modified Borg scale. The relationship between internal and external load was explored, comparing S-RPE data with the microsensor metrics (PL, HI PL, % HI PL, EEs, and jumps). The setter had the greatest mean PL and highest number of jumps of all positions in a 5-1 system, playing all 6 rotations. Playing 4 sets yielded a mean PL increase of 25.1% over 3 sets, whereas playing 5 sets showed a 31.0% increase in PL. A multivariate analysis of variance revealed significant differences (p < 0.01) across all position groups when examining % HI PL and jumps. Cohen's d analysis revealed large (≥0.8) effect sizes for these differences. Defensive specialists recorded the greatest mean S-RPE values over all 15 matches (886 ± 384.6). Establishing positional load demands allows coaches, trainers, and strength and conditioning professionals to implement training programs for position-specific demands, creating consistent peak performance, and reducing injury risk.

Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related "Big Data" Using Body Sensors information and Communication Technology.

The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered 'big data'. To our knowledge, no study has highlighted the link between 'big data' characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six 'Vs': volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.

Vehicle emission implications of drivers' smart advisory system for traffic operations in work zones.

UNLABELLED: Wireless communication systems have been broadly applied in various complicated traffic operations to improve mobility and safety on roads, which may raise a concern about the implication of the new technology on vehicle emissions. This paper explores how the wireless communication systems improve drivers' driving behaviors and its contributions to the emission reduction, in terms of Operating Mode (OpMode) IDs distribution used in emission estimation. A simulated work zone with completed traffic operation was selected as a test bed. Sixty subjects were recruited for the tests, whose demographic distribution was based on the Census data in Houston, Texas. A scene of a pedestrian's crossing in the work zone was designed for the driving test. Meanwhile, a wireless communication system called Drivers Smart Advisory System (DSAS) was proposed and introduced in the driving simulation, which provided drivers with warning messages in the work zone. Two scenarios were designed for a leading vehicle as well as for a following vehicle driving through the work zone, which included a base test without any wireless communication systems, and a driving test with the trigger of the DSAS. Subjects' driving behaviors in the simulation were recorded to evaluate safety and estimate the vehicle emission using the Environmental Protection Agency (EPA) released emission model MOVES. The correlation between drivers' driving behavior and the distribution of the OpMode ID during each scenario was investigated. Results show that the DSAS was able to induce drivers to accelerate smoothly, keep longer headway distance and stop earlier for a hazardous situation in the work zone, which driving behaviors result in statistically significant reduction in vehicle emissions for almost all studied air pollutants (p-values range from 4.10E-51 to 2.18E-03). The emission reduction was achieved by the switching the distribution of the OpMode IDs from higher emission zones to lower emission zones. IMPLICATIONS: Transportation section is a significant source of greenhouse gas emissions. Many studies demonstrate that the wireless communication system dedicated for safety and mobility issues may contribute to the induction in vehicle emissions through changing driving behaviors. An insight into the correlation between the driving behaviors and the distribution of Operating Mode (OpMode) IDs is essential to enhance the emission reduction. The result of this study shows that with a Drivers Smart Advisory System (DSAS) drivers accelerated smoothly and stopped earlier for a hazardous situation, which induce the switch of the OpMode IDs from high emission zones to lower emission zones.

Remote monitoring of implantable cardioverter-defibrillators. Problems and implications using a telemonitoring system.

The rising number of implantable devices has led to an increase in device-related workload, e.g., regular interrogation follow-up visits. Telemonitoring systems for implantable cardioverter-defibrillators (ICDs) seem to be a promising tool for reducing workload and costs, and they have the potential of optimizing patient care. However, issues such as practical functionality of ICD telemonitoring in daily routine may affect its broad implementation. The objective of this study was to evaluate potential problems during the implementation of a telemonitoring system, Medtronic CareLink™ (CL™) with respect to the installation and data transmission process. A total of 159 patients with ICDs who were equipped with the CL™ system were evaluated and followed up for 16 months regarding the success rate of the first data transmission via the telemonitoring system. In this cohort, a high rate of nontransmission of 23.9 % was observed after the 16-month follow-up. A detailed interview of these patients (no transmission) revealed that the main reasons for failed transmissions were due to the patients' loss of interest in the concept (approximately 50 %) as well as technical problems (approximately 25 %) with setting up the system. These results indicate that telemonitoring systems bear potential problems and that the evaluation of patient motivation and technical support options seems to play an important role in establishing the functionality of these systems.

Sensing coverage prediction for wireless sensor networks in shadowed and multipath environment.

Sensing coverage problem in wireless sensor networks is a measure of quality of service (QoS). Coverage refers to how well a sensing field is monitored or tracked by the sensors. Aim of the paper is to have a priori estimate for number of sensors to be deployed in a harsh environment to achieve desired coverage. We have proposed a new sensing channel model that considers combined impact of shadowing fading and multipath effects. A mathematical model for calculating coverage probability in the presence of multipath fading combined with shadowing is derived based on received signal strength (RSS). Further, the coverage probability derivations obtained using Rayleigh fading and lognormal shadowing fading are validated by node deployment using Poisson distribution. A comparative study between our proposed sensing channel model and different existing sensing models for the network coverage has also been presented. Our proposed sensing model is more suitable for realistic environment since it determines the optimum number of sensors required for desirable coverage in fading conditions.

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa.

INTRODUCTION: The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping. METHODS: A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST). RESULTS: The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points. CONCLUSIONS: Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming.

Energy Efficiency and Reliability Considerations in Wireless Body Area Networks: A Survey.

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.

Remote monitoring: a cost or an investment?

Remote management of pacemakers and implantable defibrillators is being increasingly used in Europe, due to its potential to improve follow-up efficiency and patient outcome. However, this paradigm shift needs to be economically viable for it to be sustainable in the long term. This article covers the economic aspects of remote device management, and reviews the current evidence in this field.