Automatic early detection of induced colic in horses using accelerometer devices

BACKGROUND

To seek appropriate veterinary attention for horses with colic, owners must recognise early signs. Direct observation of horse behaviour has several drawbacks: it is time-consuming, hard to see subtle and common behavioural signs, and is based on intuition and subjective decisions. Due to recent advances in wearables and artificial intelligence, it may be possible to develop diagnostic software that can automatically detect colic signs.

OBJECTIVES

To develop a software algorithm to aid in the detection of colic signs and levels of pain.

STUDY DESIGN

In vivo experiments.

METHODS

Transient colic was induced in eight experimental mares with luteolytic doses of prostaglandin. Veterinarians observed the horses before and throughout the interventions and assigned pain scores which were used to separate colic episodes into none (pain score ≤5), level 1 (pain score 6-10) or level 2 (pain score ≥11). Accelerometric data and videos were collected throughout the experiments and using accelerometric data, the horse's behaviour was classified into normal and 10 pain-related behaviours and an activity index was calculated. Models were designed that utilised behaviour and activity index characteristics both detecting the presence of colic and assessing its severity. To determine the accuracy of the model, the ground truth, that is the veterinarians' observation of colic signs and assessment of pain level, was compared with the automatic detection system.

RESULTS

The cross-validation analysis demonstrated an accuracy of 91.2% for detecting colic and an accuracy of 93.8% in differentiating between level 1 colic and level 2 colic. The model was able to accurately classify 10 pain-related behaviours and distinguish them from normal behaviour with a high accuracy.

MAIN LIMITATIONS

We included a limited number of horses with severe pain related behaviours in the dataset. This constraint affects the accuracy of categorising colic severity rather than limiting the algorithms' capacity to identify early colic signs.

CONCLUSIONS

Our system for early detection of colic in horses is unique and innovative, and it can distinguish between colic of varying severity.

Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body-tag-anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error.

Optimal Frequency and Wireless Power Budget for Miniature Receivers in Obese People

This study investigates wireless power transfer for deep in-body receivers, determining the optimal frequency, power budget, and design for the transmitter and receiver. In particular, the focus is on small, in-body receivers at large depths up to 20 cm for obese patients. This enables long-term monitoring of the gastrointestinal tract for all body types. Numerical simulations are used to investigate power transfer and losses as a function of frequency and to find the optimal design at the selected frequency for an obese body model. From all ISM-frequencies in the investigated range (1 kHz-10 GHz), the value of 13.56 MHz yields the best performance. This optimum corresponds to the transition from dominant copper losses in conductors to dominant losses in conductive tissue. At this frequency, a transmitting and receiving coil are designed consisting of 12 and 23 windings, respectively. With a power transfer efficiency of 2.70×10-5, 18 µW can be received for an input power of 0.68 W while still satisfying exposure guidelines. The power transfer is validated by measurements. For the first time, efficiency values and the power budget are reported for WPT through 20 cm of tissue to mm sized receivers. Compared to WPT at higher frequencies, as commonly used for small receivers, the proposed system is more suitable for WPT to large depths in-body and comes with the advantage that no focusing is required, which can accommodate multiple receivers and uncertainty about receiver location more easily. The received power allows long-term sensing in the gastrointestinal tract by, e.g., temperature, pressure, and pH sensors, motility sensing, or even gastric stimulation.

Quantitative analysis of the optogenetic excitability of CA1 neurons

Introduction

Optogenetics has emerged as a promising technique for modulating neuronal activity and holds potential for the treatment of neurological disorders such as temporal lobe epilepsy (TLE). However, clinical translation still faces many challenges. This in-silico study aims to enhance the understanding of optogenetic excitability in CA1 cells and to identify strategies for improving stimulation protocols.

Methods

Employing state-of-the-art computational models coupled with Monte Carlo simulated light propagation, the optogenetic excitability of four CA1 cells, two pyramidal and two interneurons, expressing ChR2(H134R) is investigated.

Results and discussion

The results demonstrate that confining the opsin to specific neuronal membrane compartments significantly improves excitability. An improvement is also achieved by focusing the light beam on the most excitable cell region. Moreover, the perpendicular orientation of the optical fiber relative to the somato-dendritic axis yields superior results. Inter-cell variability is observed, highlighting the importance of considering neuron degeneracy when designing optogenetic tools. Opsin confinement to the basal dendrites of the pyramidal cells renders the neuron the most excitable. A global sensitivity analysis identified opsin location and expression level as having the greatest impact on simulation outcomes. The error reduction of simulation outcome due to coupling of neuron modeling with light propagation is shown. The results promote spatial confinement and increased opsin expression levels as important improvement strategies. On the other hand, uncertainties in these parameters limit precise determination of the irradiance thresholds. This study provides valuable insights on optogenetic excitability of CA1 cells useful for the development of improved optogenetic stimulation protocols for, for instance, TLE treatment.

Comparison of Low-Cost 5G Electromagnetic Field Sensors

This paper compares different low-cost sensors that can measure (5G) RF-EMF exposure. The sensors are either commercially available (off-the-shelf Software Defined Radio (SDR) Adalm Pluto) or constructed by a research institution (i.e., imec-WAVES, Ghent University and Smart Sensor Systems research group (S³R), The Hague University of Applied Sciences). Both in-lab (GTEM cell) and in-situ measurements have been performed for this comparison. The in-lab measurements tested the linearity and sensitivity, which can then be used to calibrate the sensors. The in-situ testing confirmed that the low-cost hardware sensors and SDR can be used to assess the RF-EMF radiation. The variability between the sensors was 1.78 dB on average, with a maximum deviation of 5.26 dB. Values between 0.09 V/m and 2.44 V/m were obtained at a distance of about 50 m from the base station. These devices can be used to provide the general public and governments with temporal and spatial 5G electromagnetic field values.

RF-EMF Exposure near 5G NR Small Cells

Of particular interest within fifth generation (5G) cellular networks are the typical levels of radiofrequency (RF) electromagnetic fields (EMFs) emitted by 'small cells', low-power base stations, which are installed such that both workers and members of the general public can come in close proximity with them. In this study, RF-EMF measurements were performed near two 5G New Radio (NR) base stations, one with an Advanced Antenna System (AAS) capable of beamforming and the other a traditional microcell. At various positions near the base stations, with distances ranging between 0.5 m and 100 m, both the worst-case and time-averaged field levels under maximized downlink traffic load were assessed. Moreover, from these measurements, estimates were made of the typical exposures for various cases involving users and non-users. Comparison to the maximum permissible exposure limits issued by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) resulted in maximum exposure ratios of 0.15 (occupational, at 0.5 m) and 0.68 (general public, at 1.3 m). The exposure of non-users was potentially much lower, depending on the activity of other users serviced by the base station and its beamforming capabilities: 5 to 30 times lower in the case of an AAS base station compared to barely lower to 30 times lower for a traditional antenna.

MmWave Physical Layer Network Modeling and Planning for Fixed Wireless Access Applications

The large bandwidths that are available at millimeter-wave frequencies enable fixed wireless access (FWA) applications, in which fixed point-to-point wireless links are used to provide internet connectivity. In FWA networks, a wireless mesh is created and data are routed from the customer premises equipment (CPE) towards the point of presence (POP), which is the interface with the wired internet infrastructure. The performance of the wireless links depends on the radio propagation characteristics, as well as the wireless technology that is used. The radio propagation characteristics depend on the environment and on the considered frequency. In this work, we analyzed the network characteristics of FWA networks using radio propagation models for different wireless technologies using millimeter-wave (mmWave) frequencies of 28 GHz, 60 GHz, and 140 GHz. Different scenarios and environments were considered, and the influence of rain, vegetation, and the number of subscribers was investigated. A network planning algorithm is presented that defines a route for each CPE towards the POP based on a predefined location of customer devices and considering the available capacity of the wireless links. Rain does not have a considerable effect on the system capacity. Even though the higher frequencies exhibit a larger path loss, resulting in a lower power of the received signal, the larger bandwidths enable a higher channel capacity.

Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium

Fixed wireless access (FWA) provides a solution to compete with fiber deployment while offering reduced costs by using the mmWave bands, including the unlicensed 60 GHz one. This paper evaluates the deployment of FWA networks in the 60 GHz band in realistic urban and rural environment in Belgium. We developed a network planning tool that includes novel backhaul based on the IEEE 802.11ay standard with multi-objective capabilities to maximise the user coverage, providing at least 1 Gbps of bit rate while minimising the required network infrastructure. We evaluate diverse serving node locations, called edge nodes (EN), and the impact of environmental factors such as rain and vegetation on the network design. Extensive simulation results show that defining a proper EN's location is essential to achieve viable user coverage higher than 95%, particularly in urban scenarios where street canyons affect propagation. Rural scenarios require nearly 75 ENs per km2 while urban scenarios require four times (300 ENs per km2) this infrastructure. Finally, vegetation can reduce the coverage by 3% or increment infrastructure up to 7%, while heavy rain can reduce coverage by 5% or increment infrastructure by 15%, depending on the node deployment strategy implemented.

Combined Antenna-Channel Modeling for the Harsh Horse Hoof Environment

This paper describes the exploration of the combined antenna-channel model for a horse hoof. An antenna of 25 mm × 40 mm is designed in the ISM 868 MHz band. During the characterization and design of the antenna, the dynamic and harsh environment of the horse hoof is taken into account throughout every step of the procedure because it is impossible to de-embed the antenna from its environment. The antenna and channel model are verified extensively by measurements in phantom and ex vivo. The antenna is verified to be robust against changes in the morphology of the horse's hoof up to 50%. The dynamic environment was captured by considering different soil types and air, and the design was verified to be resilient against changes herein. The antenna performs well within the targeted band, with a fractional bandwidth of 8% and a gain of -2 dBi. Furthermore, a path loss model was constructed for a typical barn environment, and the antenna reaches a range of 250 m in the studied environment based on the LoRa technology. This research is important for monitoring horse health.

Influence of Temporal Interference Stimulation Parameters on Point Neuron Excitability

Temporal interference (TI) stimulation is a technique in which two high frequency sinusoidal electric fields, oscillating at a slightly different frequency are sent into the brain. The goal is to achieve stimulation at the place where both fields interfere. This study uses a simplified version of the Hodgkin - Huxley model to analyse the different parameters of the TI-waveform and how the neuron reacts to this waveform. In this manner, the underlying mechanism of the reaction of the neuron to a TI -signal is investigated. Clinical relevance- This study shows the importance of the parameter choice of the temporal interference waveform and provides insights into the underlying mechanism of the neuronal response to a beating sine for the application of temporal interference stimulation.

Geodesic Path Model for Indoor Propagation Loss Prediction of Narrowband Channels

Indoor path loss models characterize the attenuation of signals between a transmitting and receiving antenna for a certain frequency and type of environment. Their use ranges from network coverage planning to joint communication and sensing applications such as localization and crowd counting. The need for this proposed geodesic path model comes forth from attempts at path loss-based localization on ships, for which the traditional models do not yield satisfactory path loss predictions. In this work, we present a novel pathfinding-based path loss model, requiring only a simple binary floor map and transmitter locations as input. The approximated propagation path is determined using geodesics, which are constrained shortest distances within path-connected spaces. However, finding geodesic paths from one distinct path-connected space to another is done through a systematic process of choosing space connector points and concatenating parts of the geodesic path. We developed an accompanying tool and present its algorithm which automatically extracts model parameters such as the number of wall crossings on the direct path as well as on the geodesic path, path distance, and direction changes on the corners along the propagation path. Moreover, we validate our model against path loss measurements conducted in two distinct indoor environments using DASH-7 sensor networks operating at 868 MHz. The results are then compared to traditional floor-map-based models. Mean absolute errors as low as 4.79 dB and a standard deviation of the model error of 3.63 dB is achieved in a ship environment, almost half the values of the next best traditional model. Improvements in an office environment are more modest with a mean absolute error of 6.16 dB and a standard deviation of 4.55 dB.

IN-SITU 5G NR BASE STATION EXPOSURE OF THE GENERAL PUBLIC: COMPARISON OF ASSESSMENT METHODS

New measurement methods and equipment for correct 5G New Radio (NR) electromagnetic field (EMF) in-situ exposure assessment of instantaneous time-averaged exposure (Eavg) and maximum extrapolated field exposure (Emax) are proposed. The different options are investigated with in-situ measurements around 5G NR base stations (FR1) in different countries. The maximum electric field values satisfy the ICNIRP 2020 limit (maximum 7.7%). The difference between Emax and Eavg is <3 dB for the different measurement equipment at multiple sites in case there is only self-generated traffic. However, in a more realistic scenario, Eavg cannot be used to assess the exposure correctly due to influence of other users as the spatial distribution of user equipment (UE) influences Eavg, while Emax is not affected. However, when multiple UEs are collocated, there is no influence of the number of UEs. A broadband measurement can give a first impression of the RF-EMF exposure up to 700 m, but is not enough to assess the 5G-NR exposure.

Estimated all-day and evening whole-brain radiofrequency electromagnetic fields doses, and sleep in preadolescents

OBJECTIVE

To investigate the association of estimated all-day and evening whole-brain radiofrequency electromagnetic field (RF-EMF) doses with sleep disturbances and objective sleep measures in preadolescents.

METHODS

We included preadolescents aged 9-12 years from two population-based birth cohorts, the Dutch Generation R Study (n = 974) and the Spanish INfancia y Medio Ambiente Project (n = 868). All-day and evening overall whole-brain RF-EMF doses (mJ/kg/day) were estimated for several RF-EMF sources including mobile and Digital Enhanced Cordless Telecommunications (DECT) phone calls (named phone calls), other mobile phone uses, tablet use, laptop use (named screen activities), and far-field sources. We also estimated all-day and evening whole-brain RF-EMF doses in these three groups separately (i.e. phone calls, screen activities, and far-field). The Sleep Disturbance Scale for Children was completed by mothers to assess sleep disturbances. Wrist accelerometers together with sleep diaries were used to measure sleep characteristics objectively for 7 consecutive days.

RESULTS

All-day whole-brain RF-EMF doses were not associated with self-reported sleep disturbances and objective sleep measures. Regarding evening doses, preadolescents with high evening whole-brain RF-EMF dose from phone calls had a shorter total sleep time compared to preadolescents with zero evening whole-brain RF-EMF dose from phone calls [-11.9 min (95%CI -21.2; -2.5)].

CONCLUSIONS

Our findings suggest the evening as a potentially relevant window of RF-EMF exposure for sleep. However, we cannot exclude that observed associations are due to the activities or reasons motivating the phone calls rather than the RF-EMF exposure itself or due to chance finding.

Lessons Learned from a Distributed RF-EMF Sensor Network

In an increasingly wireless world, spatiotemporal monitoring of the exposure to environmental radiofrequency (RF) electromagnetic fields (EMF) is crucial to appease public uncertainty and anxiety about RF-EMF. However, although the advent of smart city infrastructures allows for dense networks of distributed sensors, the costs of accurate RF sensors remain high, and dedicated RF monitoring networks remain rare. This paper describes a comprehensive study comprising the design of a low-cost RF-EMF sensor node capable of monitoring four frequency bands used by wireless telecommunications with an unparalleled temporal resolution, its application in a small-scale distributed sensor network consisting of both fixed (on building façades) and mobile sensor nodes (on postal vans), and the subsequent analysis of over a year of data between January 2019 and May 2020, during which slightly less than 10 million samples were collected. From the fixed nodes’ results, the potential errors were determined that are induced when sampling at lower speeds (e.g., one sample per 15 min) and measuring for shorter periods of time (e.g., a few weeks), as well as an adequate resolution (30 min) for diurnal and weekly temporal profiles which sufficiently preserves short-term variations. Furthermore, based on the correlation between the sensors, an adequate density of 100 sensor nodes per km² was deduced for future networks. Finally, the mobile sensor nodes were used to identify potential RF-EMF exposure hotspots in a previously unattainable area of more than 60 km². In summary, through the analysis of a small number of RF-EMF sensor nodes (both fixed and mobile) in an urban area, this study offers invaluable insights applicable to future designs and deployments of distributed RF-EMF sensor networks.

Vehicle Localization Using Doppler Shift and Time of Arrival Measurements in a Tunnel Environment

Most applications and services of Cooperative Intelligent Transport Systems (C-ITS) rely on accurate and continuous vehicle location information. The traditional localization method based on the Global Navigation Satellite System (GNSS) is the most commonly used. However, it does not provide reliable, continuous, and accurate positioning in all scenarios, such as tunnels. Therefore, in this work, we present an algorithm that exploits the existing Vehicle-to-Infrastructure (V2I) communication channel that operates within the LTE-V frequency band to acquire in-tunnel vehicle location information. We propose a novel solution for vehicle localization based on Doppler shift and Time of Arrival measurements. Measurements performed in the Beveren tunnel in Antwerp, Belgium, are used to obtain results. A comparison between estimated positions using Extended Kalman Filter (EKF) on Doppler shift measurements and individual Kalman Filter (KF) on Doppler shift and Time of Arrival measurements is carried out to analyze the filtering methods performance. Findings show that the EKF performs better than KF, reducing the average estimation error by 10 m, while the algorithm accuracy depends on the relevant RF channel propagation conditions and other in-tunnel-related environment knowledge included in the estimation. The proposed solution can be used for monitoring the position and speed of vehicles driving in tunnel environments.

Improved alpha-beta power reduction via combined electrical and ultrasonic stimulation in a parkinsonian cortex-basal ganglia-thalamus computational model

Objective. To investigate computationally the interaction of combined electrical and ultrasonic modulation of isolated neurons and of the parkinsonian cortex-basal ganglia-thalamus loop.Approach. Continuous-wave or pulsed electrical and ultrasonic neuromodulation is applied to isolated Otsuka plateau-potential generating subthalamic nucleus (STN) and Pospischil regular, fast and low-threshold spiking cortical cells in a temporally alternating or simultaneous manner. Similar combinations of electrical/ultrasonic waveforms are applied to a parkinsonian biophysical cortex-basal ganglia-thalamus neuronal network. Ultrasound-neuron interaction is modelled respectively for isolated neurons and the neuronal network with the NICE and SONIC implementations of the bilayer sonophore underlying mechanism. Reduction inα-βspectral energy is used as a proxy to express improvement in Parkinson's disease by insonication and electrostimulation.Main results. Simultaneous electro-acoustic stimulation achieves a given level of neuronal activity at lower intensities compared to the separate stimulation modalities. Conversely, temporally alternating stimulation with50 Hzelectrical and ultrasound pulses is capable of eliciting100 HzSTN firing rates. Furthermore, combination of ultrasound with hyperpolarizing currents can alter cortical cell relative spiking regimes. In the parkinsonian neuronal network, continuous-wave and pulsed ultrasound reduce pathological oscillations by different mechanisms. High-frequency pulsed separated electrical and ultrasonic deep brain stimulation (DBS) reduce pathologicalα-βpower by entraining STN-neurons. In contrast, continuous-wave ultrasound reduces pathological oscillations by silencing the STN. Compared to the separated stimulation modalities, temporally simultaneous or alternating electro-acoustic stimulation can achieve higher reductions inα-βpower for the same safety contraints on electrical/ultrasonic intensity.Significance. Focused ultrasound has the potential of becoming a non-invasive alternative of conventional DBS for the treatment of Parkinson's disease. Here, we elaborate on proposed benefits of combined electro-acoustic stimulation in terms of improved dynamic range, efficiency, spatial resolution, and neuronal selectivity.

Radio-frequency electromagnetic field exposure and contribution of sources in the general population: an organ-specific integrative exposure assessment

In order to achieve an integrated radio-frequency electromagnetic fields (RF-EMF) dose assessment, detailed information about source-specific exposure duration and output power is needed. We developed an Integrated Exposure Model (IEM) to combine energy absorbed due to use of and exposure to RF-EMF sources and applied it to a sample of the general population to derive population RF-EMF estimates. The IEM used specific absorption rate transfer algorithms to provide RF-EMF daily dose estimates (mJ/kg/day) using source-specific attributes (e.g. output power, distance), personal characteristics and usage patterns. Information was obtained from an international survey performed in four European countries with 1755 participants. We obtained median whole-body and whole-brain doses of 183.7 and 204.4 mJ/kg/day. Main contributors to whole-brain dose were mobile phone near the head for calling (2G networks) and far-field sources, whereas the latter together with multiple other RF-EMF sources were main contributors for whole-body dose. For other anatomical sites, 2G phone calls, mobile data and far-field exposure were important contributors. The IEM provides insight into main contributors to total RF-EMF dose and, applied to an international survey, provides an estimate of population RF-dose. The IEM can be used in future epidemiological studies, risk assessments and exposure reduction strategies.

Exposure to radiofrequency electromagnetic fields: Comparison of exposimeters with a novel body-worn distributed meter

BACKGROUND

Exposure to radiofrequency electromagnetic fields (RF-EMF) is often measured with personal exposimeters, but the accuracy of measurements can be hampered as carrying the devices on-body may result in body shielding. Further, the compact design may compromise the frequency selectivity of the sensor. The aim of this study was to compare measurements obtained using a multi-band body-worn distributed-exposimeter (BWDM) with two commercially available personal exposimeters (ExpoM-RF and EmeSpy 200) under real-life conditions.

METHODS

The BWDM measured power density in 10 frequency bands (800, 900, 1800, 2100, 2600 MHz, DECT 1900 MHz, WiFi 2.4 GHz; with separate uplink/downlink bands for 900, 1800 and 2100 MHz); using 20 separate antennas integrated in a vest and placed on diametrically opposite locations on the body, to minimize body-shielding. RF-EMF exposure data were collected from several microenvironments (e.g. shopping areas, train stations, outdoor rural/ urban residential environments, etc.) by walking around pre-defined areas/routes in Belgium, Spain, France, the Netherlands and Switzerland. Measurements were taken every 1-4 s with the BWDM in parallel with an ExpoM-RF and an EmeSpy 200 exposimeter. We calculated medians and interquartile ranges (IQRs) and compared difference, ratios and correlations of geometric mean RF-EMF exposure levels per microenvironment as measured with the exposimeters and the BWDM.

RESULTS

Across 267 microenvironments, medians and IQR of total BWDM measured RF-EMF exposure was 0.13 (0.05-0.33) mW/m². Difference: IQR of exposimeters minus BWDM exposure levels was -0.011 (-0.049 to 0.0095) mW/m² for the ExpoM-RF and -0.056 (-0.14 to -0.017) for the EmeSpy 200; ratios (exposimeter/BWDM) of total exposure had an IQR of 0.79 (0.55-1.1) for the ExpoM-RF and 0.29 (0.22-0.38) for the EmeSpy 200. Spearman correlations were 0.93 for the ExpoM-RF vs the BWDM and 0.96 for the EmeSpy 200 vs the BWDM.

DISCUSSION AND CONCLUSIONS

Results indicate that exposimeters worn on-body provide somewhat lower total RF-EMF exposure as compared to measurements conducted with the BWDM, in line with effects from body shielding. Ranking of exposure levels of microenvironments showed high correspondence between the different device types. Our results are informative for the interpretation of existing epidemiological research results.

Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data

Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.

Use of Machine Learning for the Estimation of Down- and Up-Link Field Exposure in Multi-Source Indoor WiFi Scenarios

A novel Machine Learning (ML) method based on Neural Networks (NN) is proposed to assess radio‐frequency (RF) exposure generated by WiFi sources in indoor scenarios. The aim was to build an NN capable of addressing the complexity and variability of real‐life exposure setups, including the effects of not only down‐link transmission access points (APs) but also up‐link transmission by different sources (e.g. laptop, printers, tablets, and smartphones). The NN was fed with easy to be found data, such as the position and type of WiFi sources (APs, clients, and other users) and the position and material characteristics (e.g. penetration loss) of walls. The NN model was assessed using an additional new layout, distinct from that one used to build and optimize the NN coefficients. The NN model achieved a remarkable field prediction accuracy across exposure conditions in both layouts, with a median prediction error of −0.4 to 0.6 dB and a root mean square error of 2.5−5.1 dB, compared with the target electric field estimated by a deterministic indoor network planner. The proposed approach performs well for the different layouts and is thus generally used to assess RF exposure in indoor scenarios. © 2021 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.

Double Two-State Opsin Model With Autonomous Parameter Inference

Optogenetics has a lot of potential to become an effective neuromodulative therapy for clinical applications. Selecting the correct opsin is crucial to have an optimal optogenetic tool. With computational modeling, the neuronal response to the current dynamics of an opsin can be extensively and systematically tested. Unlike electrical stimulation where the effect is directly defined by the applied field, the stimulation in optogenetics is indirect, depending on the selected opsin's non-linear kinetics. With the continuous expansion of opsin possibilities, computational studies are difficult due to the need for an accurate model of the selected opsin first. To this end, we propose a double two-state opsin model as alternative to the conventional three and four state Markov models used for opsin modeling. Furthermore, we provide a fitting procedure, which allows for autonomous model fitting starting from a vast parameter space. With this procedure, we successfully fitted two distinctive opsins (ChR2(H134R) and MerMAID). Both models are able to represent the experimental data with great accuracy and were obtained within an acceptable time frame. This is due to the absence of differential equations in the fitting procedure, with an enormous reduction in computational cost as result. The performance of the proposed model with a fit to ChR2(H134R) was tested, by comparing the neural response in a regular spiking neuron to the response obtained with the non-instantaneous, four state Markov model (4SB), derived by Williams et al. (2013). Finally, a computational speed gain was observed with the proposed model in a regular spiking and sparse Pyramidal-Interneuron-Network-Gamma (sPING) network simulation with respect to the 4SB-model, due to the former having two differential equations less. Consequently, the proposed model allows for computationally efficient optogenetic neurostimulation and with the proposed fitting procedure will be valuable for further research in the field of optogenetics.

Membrane Charge Oscillations During Ultrasonic Neuromodulation by Intramembrane Cavitation

OBJECTIVE

To investigate the importance of membrane charge oscillations and redistribution in multi-compartmental ultrasonic neuromodulation (UNMOD) intramembrane cavitation models.

METHODS

The Neuronal Intramembrane Cavitation Excitation (NICE) model and multiScale Optimized model of Neuronal Intramembrane Cavitation (SONIC) of UNMOD are compared for a nanoscale multi-compartmental and point neuron approximation of the bilayer sonophore and surrounding proteins. The temporal dynamics of charge oscillations and their effect on the resulting voltage oscillations are investigated by fourier series analysis.

RESULTS

Comparison of excitation thresholds and neuronal response between nanoscale multi-compartmental and point models, implemented in the SONIC and NICE framework, demonstrates that the explicit modeling of fast spatial charge redistribution is critical for an accurate multi-compartmental UNMOD-model. Furthermore, the importance of modeling partial protein coverage is quantified by the excitability thresholds. Subsequently, we establish by fourier analysis that these charge oscillations are slowly changing in time.

CONCLUSION

Fast charge redistribution significantly alters neuronal excitability in a multi-compartmental nanoscale UNMOD-model. Also the mutual exclusivity between protein and sonophore coverage should be taken into account, when simulating the dependency of neuronal excitability on coverage fractions. Charge oscillations are periodic and their fourier components change on a slow timescale. Furthermore, the resulting voltage oscillations decrease in energy with overtone number, implying that an extension of the existing multiscale model (SONIC) to multi-compartmental neurons is possible by taking into account a limited number of fourier components.

SIGNIFICANCE

First steps are taken towards a morphologically realistic and computationally efficient UNMOD-model, improving our understanding of the underlying ultrasonic neuromodulation mechanisms.

Protocol for personal RF-EMF exposure measurement studies in 5th generation telecommunication networks

BACKGROUND

The general population is exposed to Radio-Frequency Electromagnetic Fields (RF-EMFs) used by telecommunication networks. Previous studies developed methods to assess this exposure. These methods will be inadequate to accurately assess exposure in 5G technologies or other wireless technologies using adaptive antennas. This is due to the fact that 5G NR (new radio) base stations will focus actively on connected users, resulting in a high spatio-temporal variations in the RF-EMFs. This increases the measurement uncertainty in personal measurements of RF-EMF exposure. Furthermore, a user's exposure from base stations will be dependent on the amount of data usage, adding a new component to the auto-induced exposure, which is often omitted in current studies.

GOALS

The objective of this paper is to develop a general study protocol for future personal RF-EMF exposure research adapted to 5G technologies. This protocol will include the assessment of auto-induced exposure of both a user's own devices and the networks' base stations.

METHOD

This study draws from lessons learned from previous RF-EMF exposure research and current knowledge on 5G technologies, including studies simulating 5G NR base stations and measurements around 5G NR test sites.

RESULTS

To account for auto-induced exposure, an activity-based approach is introduced. In survey studies, an RF-EMF sensor is fixed on the participants' mobile device(s). Based on the measured power density, GPS data and movement and proximity sensors, different activities can be clustered and the exposure during each activity is evaluated. In microenvironmental measurements, a trained researcher performs measurements in predefined microenvironments with a mobile device equipped with the RF-EMF sensor. The mobile device is programmed to repeat a sequence of data transmission scenarios (different amounts of uplink and downlink data transmissions). Based on simulations, the amount of exposure induced in the body when the user device is at a certain location relative to the body, can be evaluated.

CONCLUSION

Our protocol addresses the main challenges to personal exposure measurement introduced by 5G NR. A systematic method to evaluate a user's auto-induced exposure is introduced.

Radiofrequency electromagnetic fields from mobile communication: Description of modeled dose in brain regions and the body in European children and adolescents

BACKGROUND

Little is known about radiofrequency electromagnetic fields (RF) from mobile technology and resulting dose in young people. We describe modeled integrated RF dose in European children and adolescents combining own mobile device use and surrounding sources.

METHODS

Using an integrated RF model, we estimated the daily RF dose in the brain (whole-brain, cerebellum, frontal lobe, midbrain, occipital lobe, parietal lobe, temporal lobes) and the whole-body in 8358 children (ages 8-12) and adolescents (ages 14-18) from the Netherlands, Spain, and Switzerland during 2012-2016. The integrated model estimated RF dose from near-field sources (digital enhanced communication technology (DECT) phone, mobile phone, tablet, and laptop) and far-field sources (mobile phone base stations via 3D-radiowave modeling or RF measurements).

RESULTS

Adolescents were more frequent mobile phone users and experienced higher modeled RF doses in the whole-brain (median 330.4 mJ/kg/day) compared to children (median 81.8 mJ/kg/day). Children spent more time using tablets or laptops compared to adolescents, resulting in higher RF doses in the whole-body (median whole-body dose of 81.8 mJ/kg/day) compared to adolescents (41.9 mJ/kg/day). Among brain regions, temporal lobes received the highest RF dose (medians of 274.9 and 1786.5 mJ/kg/day in children and adolescents, respectively) followed by the frontal lobe. In most children and adolescents, calling on 2G networks was the main contributor to RF dose in the whole-brain (medians of 31.1 and 273.7 mJ/kg/day, respectively).

CONCLUSION

This first large study of RF dose to the brain and body of children and adolescents shows that mobile phone calls on 2G networks are the main determinants of brain dose, especially in temporal and frontal lobes, whereas whole-body doses were mostly determined by tablet and laptop use. The modeling of RF doses provides valuable input to epidemiological research and to potential risk management regarding RF exposure in young people.

A Millimeter-Scale Crystal-Less MICS Transceiver for Insertable Smart Pills

This paper presents a millimeter-scale crystal-less wireless transceiver for volume-constrained insertable pills. Operating in the 402-405 MHz medical implant communication service (MICS) band, the phase-tracking receiver-based over-the-air carrier recovery has a ±160 ppm coverage. A fully integrated adaptive antenna impedance matching solution is proposed to calibrate the antenna impedance variation inside the body. A tunable matching network (TMN) with single inductor performs impedance matching for both transmitter (TX) and receiver (RX) and TX/RX mode switching. To dynamically calibrate the antenna impedance variation over different locations and diet conditions, a loop-back power detector using self-mixing is adopted, which expands the power contour up to 4.8 VSWR. The transceiver is implemented in a 40-nm CMOS technology, occupying 2 mm² die area. The transceiver chip and a miniature antenna are integrated in a 3.5 × 15 mm² area prototype wireless module. It has a receiver sensitivity of -90 dBm at 200 kbps data rate and delivers up to - 25 dBm EIRP in the wireless measurement with a liquid phantom.

Association between estimated whole-brain radiofrequency electromagnetic fields dose and cognitive function in preadolescents and adolescents

OBJECTIVE

To investigate the association between estimated whole-brain radiofrequency electromagnetic fields (RF-EMF) dose, using an improved integrated RF-EMF exposure model, and cognitive function in preadolescents and adolescents.

METHODS

Cross-sectional analysis in preadolescents aged 9-11 years and adolescents aged 17-18 years from the Dutch Amsterdam Born Children and their Development Study (n = 1664 preadolescents) and the Spanish INfancia y Medio Ambiente Project (n = 1288 preadolescents and n = 261 adolescents), two population-based birth cohort studies. Overall whole-brain RF-EMF doses (mJ/kg/day) were estimated for several RF-EMF sources together including mobile and Digital Enhanced Cordless Telecommunications phone calls (named phone calls), other mobile phone uses than calling, tablet use, laptop use (named screen activities), and far-field sources. We also estimated whole-brain RF-EMF doses in these three groups separately (i.e. phone calls, screen activities, and far-field) that lead to different patterns of RF-EMF exposure. We assessed non-verbal intelligence in the Dutch and Spanish preadolescents, information processing speed, attentional function, and cognitive flexibility in the Spanish preadolescents, and working memory and semantic fluency in the Spanish preadolescents and adolescents using validated neurocognitive tests.

RESULTS

Estimated overall whole-brain RF-EMF dose was 90.1 mJ/kg/day (interquartile range (IQR) 42.7; 164.0) in the Dutch and Spanish preadolescents and 105.1 mJ/kg/day (IQR 51.0; 295.7) in the Spanish adolescents. Higher overall estimated whole-brain RF-EMF doses from all RF-EMF sources together and from phone calls were associated with lower non-verbal intelligence score in the Dutch and Spanish preadolescents (-0.10 points, 95% CI -0.19; -0.02 per 100 mJ/kg/day increase in each exposure). However, none of the whole-brain RF-EMF doses was related to any other cognitive function outcome in the Spanish preadolescents or adolescents.

CONCLUSIONS

Our results suggest that higher brain exposure to RF-EMF is related to lower non-verbal intelligence but not to other cognitive function outcomes. Given the cross-sectional nature of the study, the small effect sizes, and the unknown biological mechanisms, we cannot discard that our resultsare due to chance finding or reverse causality. Longitudinal studies on RF-EMF brain exposure and cognitive function are needed.

EVALUATION OF SPECIFIC ABSORPTION RATE IN THE FAR-FIELD, NEAR-TO-FAR FIELD AND NEAR-FIELD REGIONS FOR INTEGRATIVE RADIOFREQUENCY EXPOSURE ASSESSMENT

The specific absorption rate (SAR) induced by wireless radiofrequency (RF) systems depends on different parameters. Previously, SAR was mainly assessed under conditions of a single frequency and technology and for a limited number of localized RF sources. The current and emerging mobile systems involve a wider range of usage scenarios and are frequently used simultaneously, leading to combined exposures for which almost no exposure evaluation exists. The aim and novelty of this study is to close this gap of knowledge by developing new methods to rapidly evaluate the SAR induced by RF systems in such scenarios at frequencies from 50 MHz to 5.5 GHz. To this aim, analytical methods for SAR estimation in several usage scenarios were derived through a large-scale numerical study. These include subject-specific characteristics, properties of the RF systems and provide an estimation of the SAR in the whole body, tissues and organs, and different brain regions.

FIELD ENHANCEMENT AND SIZE OF RADIO-FREQUENCY HOTSPOTS INDUCED BY MAXIMUM RATIO FIELD COMBINING IN FIFTH GENERATION NETWORK

The goal of this paper is to experimentally assess the field enhancement and hotspot size of radio frequency electromagnetic fields created by the Maximum Ratio Combining (MRC) precoding scheme using lab measurements at 3.5, 5.5 and 11 GHz. MRC is an adaptive precoding scheme used by Massive Multiple Input Multiple Output systems, one of the enabling techniques of the fifth generation of telecommunications (5G). A virtual antenna array was used to compare MRC with two passive precoding schemes: the Random Phase Model (RPM) and the Centerline Beam Model (CBM). The field enhancement going from CBM to MRC was largest in obstructed line of sight (OLOS), ranging from 1.9 to 7.4 dB. The field enhancement going from RPM to MRC was about 9.5 dB across frequency bands in both line of sight (LOS) and OLOS. The hotspot size, quantified by the full width at half maximum (FWHM), ranged from 0.5 wavelengths to one wavelength.

SECONIC: Towards multi-compartmental models for ultrasonic brain stimulation by intramembrane cavitation

OBJECTIVE

To design a computationally efficient model for ultrasonic neuromodulation (UNMOD) of morphologically realistic multi-compartmental neurons based on intramembrane cavitation.

APPROACH

A Spatially Extended Neuronal Intramembrane Cavitation model that accurately predicts observed fast Charge Oscillations (SECONIC) is designed. A regular spiking cortical Hodgkin-Huxley type nanoscale neuron model of the bilayer sonophore and surrounding proteins is used. The accuracy and computational efficiency of SECONIC is compared with the Neuronal Intramembrane Cavitation Excitation (NICE) and multiScale Optimized model of Neuronal Intramembrane Cavitation (SONIC).

MAIN RESULTS

Membrane charge redistribution between different compartments should be taken into account via fourier series analysis in an accurate multi-compartmental UNMOD-model. Approximating charge and voltage traces with the harmonic term and first two overtones results in reasonable goodness-of-fit, except for high ultrasonic pressure (adjusted R-squared ≥0.61). Taking into account the first eight overtones results in a very good fourier series fit (adjusted R-squared ≥0.96) up to 600 kPa. Next, the dependency of effective voltage and rate parameters on charge oscillations is investigated. The two-tone SECONIC-model is one to two orders of magnitude faster than the NICE-model and demonstrates accurate results for ultrasonic pressure up to 100 kPa.

SIGNIFICANCE

Up to now, the underlying mechanism of UNMOD is not well understood. Here, the extension of the bilayer sonophore model to spatially extended neurons via the design of a multi-compartmental UNMOD-model, will result in more detailed predictions that can be used to validate or falsify this tentative mechanism. Furthermore, a multi-compartmental model for UNMOD is required for neural engineering studies that couple finite difference time domain simulations with neuronal models. Here, we propose the SECONIC-model, extending the SONIC-model by taking into account charge redistribution between compartments.

A Comprehensive Study on Light Signals of Opportunity for Subdecimetre Unmodulated Visible Light Positioning

Currently, visible light positioning (VLP) enabling an illumination infrastructure requires a costly retrofit. Intensity modulation systems not only necessitate changes to the internal LED driving module, but decrease the LEDs’ radiant flux as well. This hinders the infrastructure’s ability to meet the maintained illuminance standards. Ideally, the LEDs could be left unmodulated, i.e., unmodulated VLP (uVLP). uVLP systems, inherently low-cost, exploit the characteristics of the light signals of opportunity (LSOOP) to infer a position. In this paper, it is shown that proper signal processing allows using the LED’s characteristic frequency (CF) as a discriminative feature in photodiode (PD)-based received signal strength (RSS) uVLP. This manuscript investigates and compares the aptitude of (future) RSS-based uVLP and VLP systems in terms of their feasibility, cost and accuracy. It demonstrates that CF-based uVLP exhibits an acceptable loss of accuracy compared to (regular) VLP. For point source-like LEDs, uVLP only worsens the trilateration-based median p50 and 90th percentile root-mean-square error p90 from 5.3cm to 7.9cm (+50%) and from 9.6cm to 15.6cm (+62%), in the 4m × 4m room under consideration. A large experimental validation shows that employing a robust model-based fingerprinting localisation procedure, instead of trilateration, further boosts uVLP’s p50 and p90 accuracy to 5.0cm and 10.6cm. When collating with VLP’s p50=3.5cm and p90=6.8cm, uVLP exhibits a comparable positioning performance at a significantly lower cost and at a higher maintained illuminance, all of which underline uVLP’s high adoption potential. With this work, a significant step is taken towards the development of an accurate and low-cost tracking system.

Estimated whole-brain and lobe-specific radiofrequency electromagnetic fields doses and brain volumes in preadolescents

OBJECTIVE

To assess the association between estimated whole-brain and lobe-specific radiofrequency electromagnetic fields (RF-EMF) doses, using an improved integrated RF-EMF exposure model, and brain volumes in preadolescents at 9-12 years old.

METHODS

Cross-sectional analysis in preadolescents aged 9-12 years from the Generation R Study, a population-based birth cohort set up in Rotterdam, The Netherlands (n = 2592). An integrated exposure model was used to estimate whole-brain and lobe-specific RF-EMF doses (mJ/kg/day) from different RF-EMF sources including mobile and Digital Enhanced Cordless Telecommunications (DECT) phone calls, other mobile phone uses than calling, tablet use, laptop use, and far-field sources. Whole-brain and lobe-specific RF-EMF doses were estimated for all RF-EMF sources together (i.e. overall) and for three groups of RF-EMF sources that lead to a different pattern of RF-EMF exposure. Information on brain volumes was extracted from magnetic resonance imaging scans.

RESULTS

Estimated overall whole-brain RF-EMF dose was 84.3 mJ/kg/day. The highest overall lobe-specific dose was estimated in the temporal lobe (307.1 mJ/kg/day). Whole-brain and lobe-specific RF-EMF doses from all RF-EMF sources together, from mobile and DECT phone calls, and from far-field sources were not associated with global, cortical, or subcortical brain volumes. However, a higher whole-brain RF-EMF dose from mobile phone use for internet browsing, e-mailing, and text messaging, tablet use, and laptop use while wirelessly connected to the internet was associated with a smaller caudate volume.

CONCLUSIONS

Our results suggest that estimated whole-brain and lobe-specific RF-EMF doses were not related to brain volumes in preadolescents at 9-12 years old. Screen activities with mobile communication devices while wirelessly connected to the internet lead to low RF-EMF dose to the brain and our observed association may thus rather reflect effects of social or individual factors related to these specific uses of mobile communication devices. However, we cannot discard residual confounding, chance finding, or reverse causality. Further studies on mobile communication devices and their potential negative associations with brain development are warranted, regardless whether associations are due to RF-EMF exposure or to other factors related to their use.

Design of an Integrated Platform for Mapping Residential Exposure to Rf-Emf Sources

Nowadays, information and communication technologies (mobile phones, connected objects) strongly occupy our daily life. The increasing use of these technologies and the complexity of network infrastructures raise issues about radiofrequency electromagnetic fields (Rf-Emf) exposure. Most previous studies have assessed individual exposure to Rf-Emf, and the next level is to assess populational exposure. In our study, we designed a statistical tool for Rf-Emf populational exposure assessment and mapping. This tool integrates geographic databases and surrogate models to characterize spatiotemporal exposure from outdoor sources, indoor sources, and mobile phones. A case study was conducted on a 100 × 100 m grid covering the 14th district of Paris to illustrate the functionalities of the tool. Whole-body specific absorption rate (SAR) values are 2.7 times higher than those for the whole brain. The mapping of whole-body and whole-brain SAR values shows a dichotomy between built-up and non-built-up areas, with the former displaying higher values. Maximum SAR values do not exceed 3.5 and 3.9 mW/kg for the whole body and the whole brain, respectively, thus they are significantly below International Commission on Non-Ionizing Radiation Protection (ICNIRP) recommendations. Indoor sources are the main contributor to populational exposure, followed by outdoor sources and mobile phones, which generally represents less than 1% of total exposure.

AB0827 IMPACT OF BASELINE BODY MASS INDEX ON THE EFFICACY AND SAFETY OF TOFACITINIB IN PATIENTS WITH PSORIATIC ARTHRITIS

Background:

Obesity is highly prevalent in PsA (~45%)1and is associated with a reduced response to TNF inhibitors.2Tofacitinib is an oral Janus kinase inhibitor for the treatment of PsA.

Objectives:

This post hoc analysis assessed tofacitinib efficacy and safety in patients (pts) with PsA by baseline (BL) body mass index (BMI) category.

Methods:

Data were pooled from two placebo (PBO)-controlled, double-blind, Phase 3 studies in pts with active PsA and an inadequate response to ≥1 conventional synthetic DMARD (OPAL Broaden [12 months;NCT01877668]) or to ≥1 TNF inhibitor (OPAL Beyond [6 months;NCT01882439]).3,4This analysis included pts randomised to tofacitinib 5 mg twice daily (BID), tofacitinib 10 mg BID or PBO, stratified by BL BMI: <25 kg/m2, ≥25–<30 kg/m2, ≥30–<35 kg/m2, or ≥35 kg/m2. Efficacy and safety were reported to Month (M)3. M3 efficacy outcomes included ACR20/50/70 and HAQ-DI responses, dactylitis and enthesitis resolution rates and changes from BL in HAQ-DI, Short Form-36 Version 2 (SF-36v2) Physical (PCS) and Mental Component Summary (MCS) scores, and Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scores. Safety outcomes included adverse events (AEs), such as cardiovascular (CV) events and changes in lipid levels and liver function tests (LFTs).

Results:

This analysis included 710 pts; 43.8% were obese (BMI ≥30). At BL, 161 (22.7%) pts had a BMI <25, 238 (33.5%) had a BMI ≥25–<30, 186 (26.2%) had a BMI ≥30–<35 and 125 (17.6%) had a BMI ≥35. Most pts were white (92.5–96.8%), middle-aged (mean: 44.5–51.2 yrs) and female (49.5–65.6%). Greater proportions of obese pts were from Russia/Eastern Europe (35.0%) and USA/Canada (31.8%), vs the rest of world. At BL, higher BMI correlated with an increased prevalence of metabolic syndrome (4.3% in BMI <25 to 76.0% in BMI ≥35) and CRP levels >2.87 mg/L (49.1% in BMI <25 to 84.0% in BMI ≥35). Higher proportions of pts (42.5–47.9%) in BL BMI categories <35 reported no prior biologic DMARD use, vs pts with a BL BMI ≥35 (33.6%). At M3, efficacy improvements were greater in tofacitinib-treated pts vs PBO-treated pts (Figure 1). In pts with a BL BMI ≥35, a trend towards fewer pts responding was observed (Figure 1) and mean changes from baseline in SF-36v2 PCS and MCS and FACIT-F generally appeared lower (Figure 2) vs pts in lower BL BMI categories. Up to M3, the proportions of pts with AEs, and percentage change from BL in lipid levels and LFTs, were generally similar across all BL BMI categories. Three CV events were reported: non-fatal cerebrovascular accident, transient ischemic attack (both tofacitinib 5 mg BID, BMI ≥30–<35) and coronary artery revascularisation (PBO; BMI ≥35). Limitations include the 3-month observation time, particularly for safety findings, thus longer observation times are warranted.

Conclusion:

Regardless of BL BMI, tofacitinib demonstrated greater efficacy than PBO at M3 in pts with PsA. Similar to other advanced therapies,2reduced efficacy was generally observed in tofacitinib and PBO pts with a BL BMI ≥35. Tofacitinib safety appeared consistent across all BL BMI categories.

References:

[1]Labitigan et al. Arthritis Care Res (Hoboken) 2014;66:600-07.

[2]Singh et al. PLoS One 2018;13:e0195123.

[3]Mease et al. NEJM 2017;377:1537-50.

[4]Gladman et al. NEJM 2017;377:1525-36.

Acknowledgments:

Medical writing support was provided by Mark Bennett of CMC Connect, McCann Health Medical Communications, and funded by Pfizer Inc.

Disclosure of Interests:

Christopher T. Ritchlin Grant/research support from: UCB Pharma, AbbVie, Amgen, Consultant of: UCB Pharma, Amgen, AbbVie, Lilly, Pfizer, Novartis, Gilead, Janssen, Alexis Ogdie Shareholder of: Amgen, Novartis, Pfizer Inc, Grant/research support from: Novartis, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Corrona, Eli Lilly, Novartis, Pfizer Inc, Jon T Giles Grant/research support from: Pfizer Inc, Juan Jesus Gomez-Reino Grant/research support from: AbbVie, Novartis, Pfizer Inc, Roche, UCB, Consultant of: Pfizer Inc, Speakers bureau: AbbVie, Bristol-Myers Squibb, Janssen, MSD, Pfizer Inc, Roche, UCB, Philip Helliwell: None declared, Lori Stockert Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Pamela Young Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Wael Joseph Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Rajiv Mundayat Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Daniela Graham Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, John Woolcott Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ana Belen Romero Shareholder of: Pfizer Inc, Employee of: Pfizer Inc

Spatial and temporal assessment of radiofrequency electromagnetic fields emitted by smart meters and smart meter banks in urban environments

This paper describes radiofrequency (RF) electromagnetic field (EMF) measurements in the vicinity of single and banks of advanced metering infrastructure (AMI) smart meters. The measurements were performed in a meter testing and distribution facility as well as in-situ at five urban locations. The measurements consisted of gauging the RF environment at the place of assessment, evaluating the worst-case electric-field levels at various positions around the assessed AMI meter configuration (spatial assessment), which ranged from a single meter to a bank of 81 m, and calculating the duty cycle of the system, i.e. the fraction of time that the AMI meters were actually transmitting (12-h temporal assessment). Both in-situ and in the meter facility, the maximum field levels at 0.3 m from the meter configurations were 10-13 V/m for a single meter and 18-38 V/m for meter banks with 20-81 m. Furthermore, 6-min average duty cycles of 0.01% (1 m) up to 13% (81-m bank) were observed. Next, two general statistical models (one for a single meter and one for a meter bank) were constructed to predict the electric-field strength as a function of distance to any configuration of the assessed AMI meters. For all scenarios, the measured exposure levels (at a minimum distance of 0.3 m) were well below the maximum permissible exposure limits issued by the International Commission on Non-Ionizing Radiation Protection (ICNIRP), the U.S. Federal Communications Commission (FCC), and the Institute of Electrical and Electronics Engineers (IEEE). Indeed, the worst-case time-average exposure level at a distance of 0.3 m from an AMI installation was 5.39% of the FCC/IEEE and 9.43% of the ICNIRP reference levels.

Stimulation Pattern Efficiency in Percutaneous Auricular Vagus Nerve Stimulation: Experimental Versus Numerical Data

OBJECTIVE

Percutaneous electrical stimulation of the auricular vagus nerve (pVNS) is an electroceutical technology. The selection of stimulation patterns is empirical, which may lead to under-stimulation or over-stimulation. The objective is to assess the efficiency of different stimulation patterns with respect to individual perception and to compare it with numerical data based on in-silico ear models.

METHODS

Monophasic (MS), biphasic (BS) and triphasic stimulation (TS) patterns were tested in volunteers. Different clinically-relevant perception levels were assessed. In-silico models of the human ear were created with embedded fibers and vessels to assess different excitation levels.

RESULTS

TS indicates experimental superiority over BS which is superior to MS while reaching different perception levels. TS requires about 57% and 35% of BS and MS magnitude, respectively, to reach the comfortable perception. Experimental thresholds decrease from non-bursted to bursted stimulation. Numerical results indicate a slight superiority of BS and TS over MS while reaching different excitation levels, whereas the burst length has no influence. TS yields the highest number of asynchronous action impulses per stimulation symbol for the used tripolar electrode set-up.

CONCLUSION

The comparison of experimental and numerical data favors the novel TS pattern. The analysis separates excitatory pVNS effects in the auricular periphery, as accounted by in-silico data, from the combination of peripheral and central pVNS effects in the brain, as accounted by experimental data.

SIGNIFICANCE

The proposed approach moves from an empirical selection of stimulation patterns towards efficient and optimized pVNS settings.

P12.02 Glioblastoma Multiforme immunological blockades and the implications of glioma cancer stem cells

BACKGROUND

Glioblastoma Multiforme (GBM) is classified as a WHO grade IV astrocytoma that continues to circumvent classical and novel chemo-, radio- and immuno-therapies. The recent FDA approvals for the use of targeted immunotherapies against inhibitory checkpoint ligands (for melanoma; ipilimumab and nivolumab) have brought the use of monoclonal antibody therapies to the forefront of GBM research. However, poor immunological responses, exemplified by down-regulation of anti-tumour T-cell activity, and up-regulation of immunosuppressive cells and secreted factors within the tumour micro-environment, have limited the effectiveness of immunotherapy in GBM to date. Therefore, understanding how GBM modulates an extensive repertoire of immune checkpoint ligands and the functional consequence on immune evasion is necessary to develop more targeted immuno-therapeutics.

MATERIAL AND METHODS

Patient derived glioblastoma cell lines were cultured using established serum-based or glioma cancer stem cell (gCSC) conditions. The phenotypes of resultant GBM cells and gCSC’s were characterised using flow cytometry and immunocytochemistry, to assess expression of neural lineage and stem cell-associated markers. Thereafter, cells were screened for the expression of an extensive range of inhibitory checkpoint ligands by flow cytometry. Finally, the secretion of immune modulating factors by GBM cells and gCSC’s were evaluated by using XL cytokine proteome arrays. Cytokines that appeared to be differentially expressed were subsequently measured using Cytometric Bead Arrays.

RESULTS

Adherent gCSC’s and gCSC derived glioma-spheres express nestin, CD44, A2B5 and vimentin, consistent with a stem cell phenotype. Furthermore, the gCSC’s exhibited reduced expression of the neural lineage markers NeuN and OSP. Flow cytometry analyses revealed that glioblastoma cells expressed all 11 checkpoint ligands investigated. Interestingly, gCSC’s showed higher levels of PD-L1, B7-H3, CD155 and HVEM expression than GBM cells.

CONCLUSION

Glioblastoma Multiforme is highly immuno-suppressive, which is reinforced by this study. Glioblastoma cells express all the inhibitory checkpoint ligands investigated and glioma cancer stem cell cultures up-regulate expression levels further. This implies that GBM cells are heavily equipped to inhibit infiltrating T-cells, exemplifying the need to find suitable therapeutics that target multiple immuno-suppressive mechanisms simultaneously.

Current Directions in the Auricular Vagus Nerve Stimulation I - A Physiological Perspective

Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging technology in the field of bioelectronic medicine with applications in therapy. Modulation of the afferent vagus nerve affects a large number of physiological processes and bodily states associated with information transfer between the brain and body. These include disease mitigating effects and sustainable therapeutic applications ranging from chronic pain diseases, neurodegenerative and metabolic ailments to inflammatory and cardiovascular diseases. Given the current evidence from experimental research in animal and clinical studies we discuss basic aVNS mechanisms and their potential clinical effects. Collectively, we provide a focused review on the physiological role of the vagus nerve and formulate a biology-driven rationale for aVNS. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the framework of EU COST Action "European network for innovative uses of EMFs in biomedical applications (BM1309)." Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on physiological aspects - a discussion of engineering aspects is provided by our accompanying article (Kaniusas et al., 2019). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.

Mobile phones: A trade-off between speech intelligibility and exposure to noise levels and to radio-frequency electromagnetic fields

When making phone calls, cellphone and smartphone users are exposed to radio-frequency (RF) electromagnetic fields (EMFs) and sound pressure simultaneously. Speech intelligibility during mobile phone calls is related to the sound pressure level of speech relative to potential background sounds and also to the RF-EMF exposure, since the signal quality is correlated with the RF-EMF strength. Additionally, speech intelligibility, sound pressure level, and exposure to RF-EMFs are dependent on how the call is made (on speaker, held at the ear, or with headsets). The relationship between speech intelligibility, sound exposure, and exposure to RF-EMFs is determined in this study. To this aim, the transmitted RF-EMF power was recorded during phone calls made by 53 subjects in three different, controlled exposure scenarios: calling with the phone at the ear, calling in speaker-mode, and calling with a headset. This emitted power is directly proportional to the exposure to RF EMFs and is translated into specific absorption rate using numerical simulations. Simultaneously, sound pressure levels have been recorded and speech intelligibility has been assessed during each phone call. The results show that exposure to RF-EMFs, quantified as the specific absorption in the head, will be reduced when speaker-mode or a headset is used, in comparison to calling next to the ear. Additionally, personal exposure to sound pressure is also found to be highest in the condition where the phone is held next to the ear. On the other hand, speech perception is found to be the best when calling with a phone next to the ear in comparison to the other studied conditions, when background noise is present.

Characterisation of spatial and temporal variability of RF-EMF exposure levels in urban environments in Flanders, Belgium

Personal exposure to Radio-Frequency Electromagnetic Fields (RF-EMFs) was studied using personal measurements in five different microenvironments in each of five cities (Brussels, Antwerp, Ghent, Bruges and Hasselt) in Flanders, Belgium. These measurements were carried out by two researchers using on-body calibrated personal exposimeters. In three out of the five studied cities (Brussels, Ghent and Bruges), temporal aspects of personal exposure to RF-EMFs were studied as well. Measurements during and outside of rush hours (7:00-9:15 and 16:30-19:00) were compared. Likewise, measurements were executed during night time and compared to the ones measured during working hours. Representativeness and repeatability of the measurement method was studied as well. The highest mean total exposure was found in Brussels (2.63 mW/m²), the most densely populated city in this study. However, we measured higher downlink exposure in Antwerp than in Brussels, which might be an effect of the stronger legislation on base stations in Brussels. The measurements and used protocol were found to be both repeatable over time (r = 0.95 for median total exposure) and representative for the studied microenvironments in terms of path selection (r = 0.88 for median total exposure). Finally, in 10 out of the 13 on-body calibrated frequency bands we found that the measurement devices underestimate the intensity of the incident RF-EMFs with median underestimations up to 68%.

Current Directions in the Auricular Vagus Nerve Stimulation II - An Engineering Perspective

Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging electroceutical technology in the field of bioelectronic medicine with applications in therapy. Artificial modulation of the afferent vagus nerve - a powerful entrance to the brain - affects a large number of physiological processes implicating interactions between the brain and body. Engineering aspects of aVNS determine its efficiency in application. The relevant safety and regulatory issues need to be appropriately addressed. In particular, in silico modeling acts as a tool for aVNS optimization. The evolution of personalized electroceuticals using novel architectures of the closed-loop aVNS paradigms with biofeedback can be expected to optimally meet therapy needs. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the scope of EU COST Action "European network for innovative uses of EMFs in biomedical applications (BM1309)." Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on engineering aspects - a discussion of physiological aspects is provided by our accompanying article (Kaniusas et al., 2019). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.

Emissions From Smart Meters and Other Residential Radiofrequency Sources

The advent of the Internet of things comes with a huge increase in wirelessly communicating devices in our environment. For example, smart energy-consumption meters are being widely deployed in residences from which they communicate their state using radiofrequency networks. Accurate characterization of the radiofrequency emissions from emerging residential wireless solutions is important to inform the public about the potential impact on their exposure to radiofrequency electromagnetic fields. A new measurement procedure to determine the exposure from residential radiofrequency devices is proposed by assessing the peak emitted fields at various distances and the proportion of time they transmit (duty cycle). Radiofrequency emissions from 55 residential devices were measured in 10 residences (Belgium and France) and compared to environmental levels, emissions from 41 mobile phones, and international standards. Overall, residential levels of radiofrequency electromagnetic field exposure are low. In addition to the continuous environmental exposure, wireless access points (due to frequent use) and especially mobile phones and other personal communication devices (due to their use close to the body) continue to represent the bulk of the radiofrequency electromagnetic field exposure in the smart home. However, some residential devices can significantly increase the exposure if their duty cycles are high enough (>10%), especially when held or used close to the body. Individual smart meters, on the other hand, will contribute only little in general, despite emissions of up to 20 V m at 50 cm, due to their low duty cycles (maximum 1%) and locations.

STATISTICAL APPROACH FOR HUMAN ELECTROMAGNETIC EXPOSURE ASSESSMENT IN FUTURE WIRELESS ATTO-CELL NETWORKS

In this article, we study human electromagnetic exposure to the radiation of an ultra dense network of nodes integrated in a floor denoted as ATTO-cell floor, or ATTO-floor. ATTO-cells are a prospective 5 G wireless networking technology, in which humans are exposed by several interfering sources. To numerically estimate this exposure we propose a statistical approach based on a set of finite difference time domain simulations. It accounts for variations of antenna phases and makes use of a large number of exposure evaluations, based on a relatively low number of required simulations. The exposure was expressed in peak-spatial 10-g SAR average (psSAR10g). The results show an average exposure level of ~4.9 mW/kg and reaching 7.6 mW/kg in 5% of cases. The maximum psSAR10g value found in the studied numerical setup equals around 21.2 mW/kg. Influence of the simulated ATTO-floor size on the resulting exposure was examined. All obtained exposure levels are far below 4 W/kg ICNIRP basic restriction for general public in limbs (and 20 W/kg basic restriction for occupational exposure), which makes ATTO-floor a potential low-exposure 5 G candidate.

Optimal Radiation of Body-Implanted Capsules

Autonomous implantable bioelectronics requires efficient radiating structures for data transfer and wireless powering. The radiation of body-implanted capsules is investigated to obtain the explicit radiation optima for E- and B-coupled sources of arbitrary dimensions and properties. The analysis uses the conservation-of-energy formulation within dispersive homogeneous and stratified canonical body models. The results reveal that the fundamental bounds exceed by far the efficiencies currently obtained by conventional designs. Finally, a practical realization of the optimal source based on a dielectric-loaded cylindrical-patch structure is presented. The radiation efficiency of the structure closely approaches the theoretical bounds and shows a fivefold improvement over existing systems.

An Unsupervised Learning Technique to Optimize Radio Maps for Indoor Localization

A major burden of signal strength-based fingerprinting for indoor positioning is the generation and maintenance of a radio map, also known as a fingerprint database. Model-based radio maps are generated much faster than measurement-based radio maps but are generally not accurate enough. This work proposes a method to automatically construct and optimize a model-based radio map. The method is based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. No measurement campaign or site survey, which are labor-intensive and time-consuming, or inertial sensor measurements, which are often not available and consume additional power, are needed for this approach. Experiments in a large office building, covering over 1100 m², resulted in median accuracies of up to 2.07 m, or a relative improvement of 28.6% with only 15 min of unlabeled training data.

Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing

Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA.

Investigation of the Stimulation Capabilities of a High-Resolution Neurorecording Probe for the Application of Closed-Loop Deep Brain Stimulation

Deep brain stimulation is an established surgical treatment for several neurological and movement disorders, such as Parkinson's disease, in which electrostimulation is applied to targeted deep nuclei in the basal ganglia through implanted electrode leads. Recent technological improvements in the field have focused on the theoretical advantage of current steering and adaptive (closed-loop) deep brain stimulation. Current steering between several active electrodes would allow for improved accuracy when targeting the desired brain structures. This has the additional benefit of avoiding undesired stimulation of neural tracts that are related to side effects, e.g., internal capsule fibres of passage in subthalamic nucleus deep brain stimulation. Closed-loop deep brain stimulation is based on the premise of continuous recording of a proxy for pathological neural activity (such as beta-band power of measured local field potentials in patients with Parkinson's disease) and accordingly adapting the used stimulus parameters. In this study, we investigate the suitability of an existing highresolution neurorecording probe for high-precision neurostimulation. If a subset of the probe's recording electrodes can be used for stimulation, then the probe would be a suitable candidate for closed-loop deep brain stimulation. A finiteelement model is used to calculate the electric potential, induced by current injection through the high-resolution probe, for different sets of active electrodes. Volumes of activated tissue are calculated and a comparison is made between the highresolution probe and a conventional stimulation lead. We investigate the capability of the probe to shift the volume of activated tissue by steering currents to different sets of active electrodes. Finally, safety limits for the injected current are used to determine the size of the volume in which neurons can be activated with the relatively small electrodes patches on the highresolution probe.