A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors.

The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the Internet to IoT applications to provide smart IoT services and products. Deploying, managing, and maintaining IoT devices for the exclusive use of an individual IoT application is inefficient and involves significant costs and effort that often outweigh the benefits. On the other hand, enabling large numbers of IoT applications to share available third-party IoT devices, which are deployed and maintained independently by a variety of IoT device providers, reduces IoT application development costs, time, and effort. To achieve a positive cost/benefit ratio, there is a need to support the sharing of third-party IoT devices globally by providing effective IoT device discovery, use, and pay between IoT applications and third-party IoT devices. A solution for global IoT device sharing must be the following: (1) scalable to support a vast number of third-party IoT devices, (2) interoperable to deal with the heterogeneity of IoT devices and their data, and (3) IoT-owned, i.e., not owned by a specific individual or organization. This paper surveys existing techniques that support discovering, using, and paying for third-party IoT devices. To ensure that this survey is comprehensive, this paper presents our methodology, which is inspired by Systematic Literature Network Analysis (SLNA), combining the Systematic Literature Review (SLR) methodology with Citation Network Analysis (CNA). Finally, this paper outlines the research gaps and directions for novel research to realize global IoT device sharing.

Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing.

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.

Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness.

Cyber-physical-social computing system integrates the interactions between cyber, physical, and social spaces by fusing information from these spaces. The result of this fusion can be used to drive many applications in areas such as intelligent transportation, smart cities, and healthcare. Situation Awareness was initially used in military services to provide knowledge of what is happening in a combat zone but has been used in many other areas such as disaster mitigation. Various applications have been developed to provide situation awareness using either IoT sensors or social media information spaces and, more recently, using both IoT sensors and social media information spaces. The information from these spaces is heterogeneous and, at their intersection, is sparse. In this paper, we propose a highly scalable, novel Cyber-physical-social Awareness (CPSA) platform that provides situation awareness by using and intersecting information from both IoT sensors and social media. By combining and fusing information from both social media and IoT sensors, the CPSA platform provides more comprehensive and accurate situation awareness than any other existing solutions that rely only on data from social media and IoT sensors. The CPSA platform achieves that by semantically describing and integrating the information extracted from sensors and social media spaces and intersects this information for enriching situation awareness. The CPSA platform uses user-provided situation models to refine and intersect cyber, physical, and social information. The CPSA platform analyses social media and IoT data using pretrained machine learning models deployed in the cloud, and provides coordination between information sources and fault tolerance. The paper describes the implementation and evaluation of the CPSA platform. The evaluation of the CPSA platform is measured in terms of capabilities such as the ability to semantically describe and integrate heterogenous information, fault tolerance, and time constraints such as processing time and throughput when performing real-world experiments. The evaluation shows that the CPSA platform can reliably process and intersect with large volumes of IoT sensor and social media data to provide enhanced situation awareness.

Improving Situation Awareness via a Situation Model-Based Intersection of IoT Sensor and Social Media Information Spaces.

Existing techniques for distilling situation awareness currently focus on information harvested from either IoT sensors or social media. While the benefits of fusing information from these two distinct information spaces for achieving enhanced situation awareness are well understood, existing techniques and related systems for fusing the IoT sensors and social media information spaces are currently embryonic. Key challenges in intersecting, combining, and fusing these information spaces to distil high-value situation awareness include devising situation models and related techniques for filtering, integrating, and fusing sparse and heterogeneous IoT sensor data and social media postings to provide a richer and more accurate situation awareness. This paper proposes novel, semantically based techniques fusing social media and IoT sensor information spaces and a comprehensive, fully implemented system that utilizes these to provide enhanced situation awareness. More specifically, this paper proposes the design of semantic-based situation models for fusing sensor and social media information spaces and presents techniques for finding similarities across these information spaces and fusing social media posting and IoT sensor data to generate an enhanced situation awareness. Furthermore, the paper presents the design and implementation of a complete system that uses the proposed models and techniques and uses that in an experimental evaluation that illustrates improvements in situation awareness from fusing the IoT sensor and social media information spaces.

IoT Device Integration and Payment via an Autonomic Blockchain-Based Service for IoT Device Sharing.

The Internet of Things (IoT) incorporates billions of IoT devices (e.g., sensors, cameras, wearables, smart phones, as well as other internet-connected machines in homes, vehicles, and industrial plants), and the number of such connected IoT devices is currently growing rapidly. This paper proposes a novel Autonomic Global IoT Device Discovery and Integration Service (which we refer to as aGIDDI) that permits IoT applications to find IoT devices that are owned and managed by other parties in IoT (which we refer to as IoT device providers), integrate them, and pay for using their data observations. aGIDDI incorporates a suite of interacting sub-services supporting IoT device description, query, integration, payment (via a pay-as-you-go payment model), and access control that utilise a special-purpose blockchain to manage all information needed for IoT applications to find, pay and use the IoT devices they need. The paper describes aGIDDI's novel protocol that allows any IoT application to discover and automatically integrate and pay for IoT devices and their data that are provided by other parties. The paper also presents aGIDDI's architecture and proof-of-concept implementation, as well as an experimental evaluation of the performance and scalability of aGIDDI in variety of IoT device integration and payment scenarios.

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms.

With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper.

Digital Twins Supporting Efficient Digital Industrial Transformation.

Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development.

Evaluation of a Josephson Arbitrary Waveform Synthesizer at Low Voltages for the Calibration of Lock-In Amplifiers.

We have evaluated a Josephson arbitrary waveform synthesizer (JAWS) in the voltage range from 1 µV to 1 mV at frequencies from 60 to 1000 Hz for the use in the calibration of lock-in amplifiers. The uncertainty contribution from the JAWS system is 45 nV for 1 mV at 1000 Hz and k = 2.0. We anticipate that the JAWS will help extend the lower voltage and frequency range of ac voltage metrology and improve the uncertainties by one order of magnitude compared to conventional techniques.

Patient dose in brain perfusion imaging using an 80-slice CT system.

BACKGROUND AND PURPOSE: Brain CT Perfusion (CTP) is an X-ray imaging technique for the assessment of brain tissue perfusion, which can be used in several different entities. The aim of this study is the evaluation of the radiation dose to patients during a comprehensive brain CT prescription protocol (CPP) consisting of an unenhanced brain CT, a brain CT angiography and a CTP scan. MATERIALS AND METHODS: Eighteen patients were studied using an 80-slice CT system, with an iterative reconstruction algorithm. The volume Computed Tomography Dose Index (CTDIvol) and dose length product (DLP) were recorded from the dose report of the system. The calculation of effective dose (ED) was accomplished using the DLP values. RESULTS: For the CTP examinations, the CTDIvol ranged from 116.0 to 134.8mGy, with the mean value 119.5mGy. The DLP ranged from 463.9 to 539.2mGy·cm, with the mean value 478mGy·cm. For the CPP, the total ED ranged from 3.31 to 5.07mSv, with the mean value 4.37mSv. CONCLUSIONS: These values are lower than the values reported in corresponding studies, including studies utilizing CT systems with more slices.

AC Voltage Measurements to 120 V With a Josephson Arbitrary Waveform Synthesizer and an Inductive Voltage Divider.

We have developed a system to extend, by a factor of up to 1000, the voltage range over which Josephson arbitrary waveform synthesizers (JAWS) can be used in ac voltage metrology. The system is based on a precision inductive voltage divider, with a lock-in amplifier as the detector. Using a JAWS with a maximum output voltage of 250 mV (root mean square), we have made accurate voltage measurements up to 120 V at 60 Hz with expanded uncertainties (k = 2) of no more than 1.5 µV/V and demonstrate that the system can operate up to 1 kHz. We anticipate that our JAWS-based system will improve uncertainties in ac voltage metrology by one order of magnitude compared to techniques based on thermal voltage converters.

Josephson Arbitrary Waveform Synthesizer as a Reference Standard for the Measurement of the Phase of Harmonics in Distorted Waveforms.

We have used the Josephson arbitrary waveform synthesizer (JAWS) to provide traceability for the phase of the harmonics, relative to their fundamental frequency, of a distorted waveform. For distorted waveforms with rms values from 0.154 to 0.2 V and harmonic magnitudes from 5% to 40% of the fundamental, our system can generate odd harmonics up to the 39th with best phase uncertainties from 0.001° to 0.010° (k = 2.0), depending on the harmonic number and harmonic magnitude. We anticipate that the ability of the JAWS to generate distorted waveforms with the lowest possible uncertainty in the magnitude, and phase spectra will make it a unique tool for low-frequency spectrum analysis.

Hemodynamic Impact of the C-Pulse Cardiac Support Device: A One-Dimensional Arterial Model Study.

The C-Pulse is a novel extra-aortic counter-pulsation device to unload the heart in patients with heart failure. Its impact on overall hemodynamics, however, is not fully understood. In this study, the function of the C-Pulse heart assist system is implemented in a one-dimensional (1-D) model of the arterial tree, and central and peripheral pressure and flow waveforms with the C-Pulse turned on and off were simulated. The results were studied using wave intensity analysis and compared with in vivo data measured non-invasively in three patients with heart failure and with invasive data measured in a large animal (pig). In all cases the activation of the C-Pulse was discernible by the presence of a diastolic augmentation in the pressure and flow waveforms. Activation of the device initiates a forward traveling compression wave, whereas a forward traveling expansion wave is associated to the device relaxation, with waves exerting an action in the coronary and the carotid vascular beds. We also found that the stiffness of the arterial tree is an important determinant of action of the device. In settings with reduced arterial compliance, the same level of aortic compression demands higher values of external pressure, leading to stronger hemodynamic effects and enhanced perfusion. We conclude that the 1-D model may be used as an efficient tool for predicting the hemodynamic impact of the C-Pulse system in the entire arterial tree, complementing in vivo observations.

Plasma Urotensin II levels in children and adolescents with chronic kidney disease: a single-centre study.

BACKGROUND: Increased plasma Urotensin II (UII) levels have been found in adults with renal diseases. Studies in children are scarce. The objective of the study is to estimate plasma UII levels in subjects with chronic kidney disease (CKD) stages 3 to 5 and renal transplant recipients (RTR). In addition, the correlation of UII with anthropometric features and biochemical parameters was assessed. METHODS: Fifty-four subjects, aged 3 to 20 years old, 23 with CKD, 13 with end-stage kidney disease (ESKD) undergoing hemodialysis (HD) and 18 RTR were enrolled. A detailed clinical evaluation was performed. Biochemical parameters of renal and liver function were measured. Plasma UII levels were measured in all patients and in 117 healthy controls, using a high sensitive enzyme immunoassay (EIA) kit. All data were analyzed using STATA™ (Version 10.1). RESULTS: Median UII and mean log-transformed UII levels were significantly higher in CKD and RTR patients compared to healthy subjects (p < 0.001). HD patients had higher but not statistically significant UII and log-UII levels than controls. UII levels increased significantly at the end of the HD session and were higher than controls and in line to those of other patients. The geometric scores of UII in HD (before dialysis), CKD and RTR patients increased respectively by 42, 136 and 164% in comparison with controls. Metabolic acidosis was associated with statistical significant change in log-UII levels (p = 0.001). Patients with metabolic acidosis had an increase in UII concentration by 76% compared to those without acidosis. CONCLUSIONS: Children and adolescents with CKD, particularly those who are not on HD and RTR, have significantly higher levels of UII than healthy subjects. UII levels increase significantly at the end of the HD session. The presence of metabolic acidosis affects significantly plasma UII levels.

Doping control container for urine stabilization: a pilot study.

Urine collection containers used in the doping control collection procedure do not provide a protective environment for urine, against degradation by microorganisms and proteolytic enzymes. An in-house chemical stabilization mixture was developed to tackle urine degradation problems encountered in human sport samples, in cases of microbial contamination or proteolytic activity. The mixture consists of antimicrobial substances and protease inhibitors for the simultaneous inactivation of a wide range of proteolytic enzymes. It has already been tested in lab-scale, as part of World Anti-Doping Agency's (WADA) funded research project, in terms of efficiency against microbial and proteolytic activity. The present work, funded also by WADA, is a follow-up study on the improvement of chemical stabilization mixture composition, application mode and limitation of interferences, using pilot urine collection containers, spray-coated in their internal surface with the chemical stabilization mixture. Urine in plastic stabilized collection containers have been gone through various incubation cycles to test for stabilization efficiency and analytical matrix interferences by three WADA accredited Laboratories (Athens, Ghent, and Rome). The spray-coated chemical stabilization mixture was tested against microorganism elimination and steroid glucuronide degradation, as well as enzymatic breakdown of proteins, such as intact hCG, recombinant erythropoietin and small peptides (GHRPs, ipamorelin), induced by proteolytic enzymes. Potential analytical interferences, observed in the presence of spray-coated chemical stabilization mixture, were recorded using routine screening procedures. The results of the current study support the application of the spray-coated plastic urine container, in the doping control collection procedure. Copyright © 2016 John Wiley & Sons, Ltd.

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt.

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

Chronic Interactions Between Carotid Baroreceptors and Chemoreceptors in Obesity Hypertension.

Carotid bodies play a critical role in protecting against hypoxemia, and their activation increases sympathetic activity, arterial pressure, and ventilation, responses opposed by acute stimulation of the baroreflex. Although chemoreceptor hypersensitivity is associated with sympathetically mediated hypertension, the mechanisms involved and their significance in the pathogenesis of hypertension remain unclear. We investigated the chronic interactions of these reflexes in dogs with sympathetically mediated, obesity-induced hypertension based on the hypothesis that hypoxemia and tonic activation of carotid chemoreceptors may be associated with obesity. After 5 weeks on a high-fat diet, the animals experienced a 35% to 40% weight gain and increases in arterial pressure from 106±3 to 123±3 mm Hg and respiratory rate from 8±1 to 12±1 breaths/min along with hypoxemia (arterial partial pressure of oxygen=81±3 mm Hg) but eucapnia. During 7 days of carotid baroreflex activation by electric stimulation of the carotid sinus, tachypnea was attenuated, and hypertension was abolished before these variables returned to prestimulation values during a recovery period. After subsequent denervation of the carotid sinus region, respiratory rate decreased transiently in association with further sustained reductions in arterial partial pressure of oxygen (to 65±2 mm Hg) and substantial hypercapnia. Moreover, the severity of hypertension was attenuated from 125±2 to 116±3 mm Hg (45%-50% reduction). These findings suggest that hypoxemia may account for sustained stimulation of peripheral chemoreceptors in obesity and that this activation leads to compensatory increases in ventilation and central sympathetic outflow that contributes to neurogenically mediated hypertension. Furthermore, the excitatory effects of chemoreceptor hyperactivity are abolished by chronic activation of the carotid baroreflex.

SensorDB: a virtual laboratory for the integration, visualization and analysis of varied biological sensor data.

BACKGROUND: To our knowledge, there is no software or database solution that supports large volumes of biological time series sensor data efficiently and enables data visualization and analysis in real time. Existing solutions for managing data typically use unstructured file systems or relational databases. These systems are not designed to provide instantaneous response to user queries. Furthermore, they do not support rapid data analysis and visualization to enable interactive experiments. In large scale experiments, this behaviour slows research discovery, discourages the widespread sharing and reuse of data that could otherwise inform critical decisions in a timely manner and encourage effective collaboration between groups. RESULTS: In this paper we present SensorDB, a web based virtual laboratory that can manage large volumes of biological time series sensor data while supporting rapid data queries and real-time user interaction. SensorDB is sensor agnostic and uses web-based, state-of-the-art cloud and storage technologies to efficiently gather, analyse and visualize data. CONCLUSIONS: Collaboration and data sharing between different agencies and groups is thereby facilitated. SensorDB is available online at http://sensordb.csiro.au.

Global- and renal-specific sympathoinhibition in aldosterone hypertension.

Recent technology for chronic electric activation of the carotid baroreflex and renal nerve ablation provide global and renal-specific suppression of sympathetic activity, respectively, but the conditions for favorable antihypertensive responses in resistant hypertension are unclear. Because inappropriately high plasma levels of aldosterone are prevalent in these patients, we investigated the effects of baroreflex activation and surgical renal denervation in dogs with hypertension induced by chronic infusion of aldosterone (12 µg/kg per day). Under control conditions, basal values for mean arterial pressure and plasma norepinephrine concentration were 100±3 mm Hg and 134±26 pg/mL, respectively. By day 7 of baroreflex activation, plasma norepinephrine was reduced by ≈40% and arterial pressure by 16±2 mm Hg. All values returned to control levels during the recovery period. Arterial pressure increased to 122±5 mm Hg concomitant with a rise in plasma aldosterone concentration from 4.3±0.4 to 70.0±6.4 ng/dL after 14 days of aldosterone infusion, with no significant effect on plasma norepinephrine. After 7 days of baroreflex activation at control stimulation parameters, the reduction in plasma norepinephrine was similar but the fall in arterial pressure (7±1 mm Hg) was diminished (≈55%) during aldosterone hypertension when compared with control conditions. Despite sustained suppression of sympathetic activity, baroreflex activation did not have central actions to inhibit either the stimulation of vasopressin secretion or drinking induced by increased plasma osmolality during chronic aldosterone infusion. Finally, renal denervation did not attenuate aldosterone hypertension. These findings suggest that aldosterone excess may portend diminished blood pressure lowering to global and especially renal-specific sympathoinhibition during device-based therapy.

Systemic vascular effects of acute electrical baroreflex stimulation.

We intended to determine if acute baroreflex activation therapy (BAT) increases venous capacitance and aortic conductance. BAT is effective in resistant hypertension, but its effect on the systemic vasculature is poorly understood. Left ventricular (LV) and aortic pressures and subdiaphragmatic aortic and caval flows (ultrasonic) were measured in six anesthetized dogs. Changes in abdominal blood volume (Vabdominal) were estimated as the integrated difference in abdominal aortic inflow and caval outflow. An electrode was implanted on the right carotid sinus. Data were measured during control and BAT. Next, sodium nitroprusside (SNP) was infused and BAT was subsequently added. Finally, angiotensin II (ANG II) was infused, and three increased BAT currents were added. We found that BAT decreased mean aortic pressure (PAo) by 22.5 ± 1.3 mmHg (P < 0.001) and increased aortic conductance by 16.2 ± 4.9% (P < 0.01) and Vabdominal at a rate of 2.2 ± 0.6 ml·kg(-1)·min(-1) (P < 0.01). SNP decreased PAo by 17.4 ± 0.7 mmHg (P < 0.001) and increased Vabdominal at a rate of 2.2 ± 0.7 ml·kg(-1)·min(-1) (P < 0.05). During the SNP infusion, BAT decreased PAo further, by 26.0 ± 2.1 mmHg (P < 0.001). ANG II increased PAo by 40.4 ± 3.5 mmHg (P = 0.001). When an increased BAT current was added, PAo decreased to baseline (P < 0.01) while aortic conductance increased from 62.3 ± 5.2% to 80.2 ± 3.3% (P < 0.05) of control. Vabdominal increased at a rate of 1.8 ± 0.9 ml·kg(-1)·min(-1) (P < 0.01), reversing the ANG II effects. In conclusion, BAT increases arterial conductance, decreases PAo, and increases venous capacitance even in the presence of powerful vasoactive drugs. Increasing venous capacitance may be an important effect of BAT in hypertension.

The baroreceptor as a therapeutic target for heart failure.

Sympathoactivation is a prominent feature of heart failure (HF). Its role in cardiac remodeling and arrhythmogenesis is well-recognized today, although incomplete understanding of autonomic mechanisms was a barrier to development of contemporary medical therapies. Despite widespread availability of drugs and devices, mortality and morbidity in HF remain unacceptably high. Recognition of an additional phenotype, HF with preserved ejection fraction (EF), poses additional challenges. New treatment options are required. Electrical modulation of the central nervous system with baroreflex activation therapy offers a new approach. Activation of this afferent pathway induces the central nervous system to rebalance autonomic modulation of the cardiovascular system. Results in animal models of HF demonstrating increased survival and beneficial cardiac remodeling recently led to a clinical feasibility study in HF with reduced EF wherein the clinical course of patients dramatically improved. Results in resistant hypertension patients further suggest potential for benefit in HF with preserved EF.