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Resistant hypertension has been recognized as an additional risk in patients with hypertension, leading to an almost 1.5-fold increased risk of cardiovascular events among that population. Patients with obstructive sleep apnea (OSA) have almost five times higher risk of having resistant hypertension. Studies that evaluated the impact of treatment of OSA on blood pressure (BP) control among patients with resistant hypertension have some distinct methodological limitations, and none have included a sham positive airway pressure (PAP) control group. The aim of this study was to evaluate the effect of PAP on BP measured by 24-hour ambulatory BP (ABP) monitoring of patients with true resistant hypertension. Some of the results of our trial have previously been reported in the form of an abstract. 16 references

Housing is a significant determinant of health, particularly in developing countries such as Tonga. Currently, very little is known about the quality of the housing in Tonga, as is the case with many developing countries, nor about the interaction between children and the home environment. This study aimed to identify the nature and extent of health risk factors and behaviours in Tongan houses from a child’s perspective. An innovative methodology was used, Kids’Cam Tonga. Seventy-two Class 6 children (10 to 13-year-olds) were randomly selected from 12 randomly selected schools in Tongatapu, the main island. Each participating child wore a wearable camera on lanyards around their neck. The device automatically took wide-angled, 136° images of the child’s perspective every seven seconds. The children were instructed to wear the camera all day from Friday morning to Sunday evening, inclusive. The analysis showed that the majority of Tongan children in the study live in houses that have structural deficiencies and hazards, including water damage (42%), mould (36%), and electrical (89%) and burn risk factors (28%). The findings suggest that improvements to the housing stock may reduce the associated health burden and increase buildings’ resilience to natural hazards. A collaborative approach between communities, community leaders, government and non-governmental organisations (NGOs) is urgently needed. This research methodology may be of value to other developing countries.

Large, distributed collections of miniaturized, wireless electronic devices 1 , 2 may form the basis of future systems for environmental monitoring 3 , population surveillance 4 , disease management 5 and other applications that demand coverage over expansive spatial scales. Aerial schemes to distribute the components for such networks are required, and—inspired by wind-dispersed seeds 6 —we examined passive structures designed for controlled, unpowered flight across natural environments or city settings. Techniques in mechanically guided assembly of three-dimensional (3D) mesostructures 7 – 9 provide access to miniature, 3D fliers optimized for such purposes, in processes that align with the most sophisticated production techniques for electronic, optoelectronic, microfluidic and microelectromechanical technologies. Here we demonstrate a range of 3D macro-, meso- and microscale fliers produced in this manner, including those that incorporate active electronic and colorimetric payloads. Analytical, computational and experimental studies of the aerodynamics of high-performance structures of this type establish a set of fundamental considerations in bio-inspired design, with a focus on 3D fliers that exhibit controlled rotational kinematics and low terminal velocities. An approach that represents these complex 3D structures as discrete numbers of blades captures the essential physics in simple, analytical scaling forms, validated by computational and experimental results. Battery-free, wireless devices and colorimetric sensors for environmental measurements provide simple examples of a wide spectrum of applications of these unusual concepts. With a design inspired by wind-dispersed seeds, a series of three-dimensional passive fliers at the macro-, meso- and microscale are realized that can bear active electronic payloads.

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data. We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations. We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations. The overall model cross-validation R(2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R(2) = 0.73, regression slope = 0.91) and seasonal (R(2) = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 μg/m(3) between 2004 and 2007 and a decrease of 0.46 μg/m(3) between 2008 and 2013. Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.

The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of micro-level pollution information, including human personal exposure and acute exposure to air pollutants. A real-time system with high spatio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers focus on the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) and have achieved significant breakthroughs by utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN). However, there exist potential problems of these newly proposed systems, namely the lack of 3D data acquisition ability and the flexibility of the sensor network. In this paper, we classify the existing works into three categories as Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) based on the carriers of the sensors. Comprehensive reviews and comparisons among these three types of sensor networks were also performed. Last but not least, we discuss the limitations of the existing works and conclude the objectives that we want to achieve in future systems.

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO₂, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km² and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO₂ (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO₂ are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.

Unmanned aerial vehicles (UAVs) represent a new frontier in environmental research. Their use has the potential to revolutionise the field if they prove capable of improving data quality or the ease with which data are collected beyond traditional methods. We apply UAV technology to wildlife monitoring in tropical and polar environments and demonstrate that UAV-derived counts of colony nesting birds are an order of magnitude more precise than traditional ground counts. The increased count precision afforded by UAVs, along with their ability to survey hard-to-reach populations and places, will likely drive many wildlife monitoring projects that rely on population counts to transition from traditional methods to UAV technology. Careful consideration will be required to ensure the coherence of historic data sets with new UAV-derived data and we propose a method for determining the number of duplicated (concurrent UAV and ground counts) sampling points needed to achieve data compatibility.

The ever‐increasing concerns over indoor/outdoor air quality, industrial gas leakage, food freshness, and medical diagnosis require miniaturized gas sensors with excellent sensitivity, selectivity, stability, low power consumption, cost‐effectiveness, and long lifetime. Metal‐organic frameworks (MOFs), featuring structural diversity, large specific surface area, controllable pore size/geometry, and host‐guest interactions, hold great promises for fabricating various MOF‐based devices for diverse applications including gas sensing. Tremendous progress has been made in the past decade on the fabrication of MOF‐based sensors with elevated sensitivity and selectivity toward various analytes due to their preconcentrating and molecule‐sieving effects. Although several reviews have recently summarized different aspects of this field, a comprehensive review focusing on MOF‐based gas sensors is absent. In this review, the latest advance of MOF‐based gas sensors relying on different transduction mechanisms, for example, chemiresistive, capacitive/impedimetric, field‐effect transistor or Kelvin probe‐based, mass‐sensitive, and optical ones are comprehensively summarized. The latest progress for making large‐area MOF films essential to the mass‐production of relevant gas sensors is also included. The structural and compositional features of MOFs are intentionally correlated with the sensing performance. Challenges and opportunities for the further development and practical applications of MOF‐based gas sensors are also given. A comprehensive review on the latest progress of metal‐organic framework (MOF)‐based gas sensors relying on different transduction mechanisms is provided. The sensing performance in terms of sensitivity and selectivity is correlated with the structural and compositional features of MOFs and the transduction mechanisms. Critical future directions toward the further development of MOF‐based gas sensors are indicated.

Humans are potentially exposed to microplastics through food, drink, and air. The first two pathways have received quite some scientific attention, while little is known about the latter. We address the exposure of humans to indoor airborne microplastics using a Breathing Thermal Manikin. Three apartments were investigated, and samples analysed through FPA-µFTIR-Imaging spectroscopy followed by automatic analyses down to 11 µm particle size. All samples were contaminated with microplastics, with concentrations between 1.7 and 16.2 particles m −3 . Synthetic fragments and fibres accounted, on average, for 4% of the total identified particles, while nonsynthetic particles of protein and cellulose constituted 91% and 4%, respectively. Polyester was the predominant synthetic polymer in all samples (81%), followed by polyethylene (5%), and nylon (3%). Microplastics were typically of smaller size than nonsynthetic particles. As the identified microplastics can be inhaled, these results highlight the potential direct human exposure to microplastic contamination via indoor air.