Deep Brain Stimulation (DBS) is an effective treatment for several neurological and psychiatric disorders. In order to gain insights into the therapeutic mechanisms of DBS and to advance future therapies a better understanding of the effects of DBS on large-scale brain networks is required.In this paper, we describe an experimental protocol and analysis pipeline for simultaneously performing DBS and intracranial local field potential (LFP) recordings at a target brain region during concurrent magnetoencephalography (MEG) measurement. Firstly we describe a phantom setup that allowed us to precisely characterise the MEG artefacts that occurred during DBS at clinical settings.Using the phantom recordings we demonstrate that with MEG beamforming it is possible to recover oscillatory activity synchronised to a reference channel, despite the presence of high amplitude artefacts evoked by DBS. Finally, we highlight the applicability of these methods by illustrating in a single patient with Parkinson's disease (PD), that changes in cortical-subthalamic nucleus coupling can be induced by DBS.To our knowledge this paper provides the first technical description of a recording and analysis pipeline for combining simultaneous cortical recordings using MEG, with intracranial LFP recordings of a target brain nucleus during DBS. One condition in which DBS therapy has proven to be particularly effective is Parkinson's Disease (PD) . In PD, Each DBS procedure presents a unique opportunity to gain valuable translational insights about electrophysiological brain activity in pathological disease states . DBS elePrevious work using simultaneous MEG and resting intracranial LFP recordings has shown the existence of two spatially and spectrally distinct networks between the STN and cortical regions in PD . An alphAlthough it has been possible to simultaneously stimulate and record from the STN, and also to record from the STN during concurrent MEG, no methods have as yet been developed to combine these two approaches and allow simultaneous MEG and intracranial LFP recordings during DBS. The utility for such recordings is clear, since they would enable the characterisation of both local effects of DBS and of the effects of DBS on connectivity between target nuclei and distal brain regions. Such information could benefit current understanding of the therapeutic mechanisms of DBS in addition to informing future developments in DBS technologies.In this paper we describe the experimental setup for simultaneous MEG and intracranial LFP recordings during DBS. We also detail the analysis procedure making it possible to recover coherence between the LFP and the MEG in the presence of stimulation, despite artefacts due to ferromagnetic extension wires, and those due to DBS currents that will not be obviated by the use of non-ferromagnetic extension wires. The proposed methods are validated using a dipole phantom and applied to data from a single PD patient with electrodes in the STN.22.1All the MEG recordings described in the present paper were performed using a CTF 275-channel MEG system . An important advantage of this system is the high dynamic range of its sensors and their robustness to strong external interferences.In our previous studies we used the EEG system integrated in CTF-MEG to record the intracranial data . Our ratAny noise generator capable of producing wide-band noise with an appropriate amplitude can be used. Our generator was built utilising an 8.2 V Zener diode biased close to its avalanche region. This was achieved using a variable potentiometer, while an oscilloscope was used to ensure the maximum noise was produced. The noise was amplified to 500 mV peak-to-peak range. To control the exact output amplitude a variable resistor on the output was included. Note that connecting the noise generator with cables to both the MEG and the EEG amplifier would create a breach in the optical isolation and defeat its purpose. Fortunately, the BrainAmp system provides a solution by combining two optically isolated amplifiers into one system with synchronous sampling—accordingly one of these amplifiers was used to record the noise signal, whilst the other was used to record physiological activity.2.2In this section we detail our approach for simultaneous DBS and LFP recording during MEG. We used a purpose built stimulation-record amplifier that was a variant of the design used in previous studies not involving MEG .This design is based on the idea that when stimulating one of the two middle contacts (contact 1 or 2) monopolarly while recording from the two adjacent contacts of the DBS electrode  one can use the common mode rejection property of the front stage differential amplifier to reduce the DBS artefact and line noise. Both these signals are seen similarly by the recording contacts. The stimulation-record amplifier is used in combination with a clinically approved external DBS stimulator  connected to the intracranial electrode via the amplifier circuit.(1)Front stage amplifier with a gain of ×10 (any higher could saturate the amplifier due to DC voltages on the contacts).(2)DC blocking component, to remove any offsets that could cause saturation of the subsequent stages.(3)Low pass filter at 40 Hz to further reduce the line noise and DBS artefacts (when 130 Hz DBS is delivered). This filter is based on a Sallen–Key low pass design with −12 db per stage. Two stages were used giving −24 db of attenuation for each doubling of frequency after 40 Hz, which is in most cases sufficient to recover the frequencies of interest.(4)In addition to the LFP from the stimulated location the amplifier also generates a copy of the stimulation train which can later be used e.g., for segmenting the data. This is achieved by connecting the stimulator signal to an opto-isolator in parallel with the patient. The stimulator pulse of about 60 μs is too fast for sampling by the EEG amplifier requiring the use of a 555 timer circuit to stretch the pulse to approximately 2 ms. The pulse is then reduced in amplitude to a few mV.The amplifier consists of the following components:The amplifier produces two bipolar output signals compatible with the BrainAmp ExG box: LFP and stimulation train copy. Consequently during blocks of recordings with DBS on, we were able to record both a single bipolar LFP from the side being stimulated, and 3 bipolar LFPs from the contralateral side. Further details regarding this amplifier are available from the authors on request.2.3To demonstrate that MEG activity coherent with a reference channel can be accurately recovered in the presence of DBS artefacts we performed an experiment with a MEG phantom. We used a CTF current dipole phantom comprising a spherical plastic container, holding saline, in which a dipolar source can be immersed  with four platinum-iridium cylindrical surfaces (1.27 mm diameter and 1.5 mm length) and a centre-to-centre separation of 2 mm. Contact 0 was the closest to the electrode tip and contact 3—the farthest. To emulate monopolar stimulation we immersed an additional anodal reference electrode in the phantom. Accordingly, in our recordings monopolar DBS was administered between contact 1 of the DBS electrode and the anode. Bipolar DBS in contrast is administered between adjacent contacts of the DBS electrode. In our phantom recordings, bipolar stimulation was administered between contacts 0 (cathode) and 1 (anode) of the DBS electrode. Note that in our recordings with the phantom, we tested monopolar and bipolar DBS separately in order to characterise the different kinds of sensor artefacts produced by each of these stimulation regimes. All analyses of coherence, however, are presented only during monopolar DBS.A copy of the sinusoidal input to the current dipole was recorded bipolarly with the BrainAmp amplifier system as would be done with the intracranial LFP. Note that although in the monopolar arrangement we could also record the potential difference between contacts 0 and 2 of the DBS electrode, using it as a surrogate LFP would not mimic the patient recording well. This is because the dipole signal would only be picked up by volume conduction in water from a distant source, whereas in the real patient the LFP signal is picked up from neurons surrounding the electrode. Therefore, we did not record from the stimulating electrode in the phantom experiment.Three Head Position Indicator (HPI) coils were attached to the phantom in order to facilitate co-registration between the MEG co-ordinate system and the phantom. The coils were driven with sinusoidal signals at the frequencies of 1425 1475 and 1525 Hz. The phantom was raised into the MEG helmet and whilst the dipolar source was active, three minute recordings were performed for each of the following experimental conditions: (1) No DBS, (2) Monopolar DBS at 130 Hz (3) Monopolar DBS at 20 Hz (4) Bipolar DBS at 130 Hz (5) Bipolar DBS at 20 Hz.2 x phase. These values are similar to those measured for real DBS in patients  via the stimulation-record amplifier with a pulse width of 60 μs and amplitude of 3 V. Our measurements showed that the current flowing through the phantom for these parameters was 6 mA which corresponds to charge of 0.36 μC/phase and charge density of 6 μC/cmpatients . The risAlthough stimulation at 130 Hz produces the greatest clinical benefit, we also explored the effect of stimulating at 20 Hz as a means of probing beta band resonance within the cortico-subthalamic nucleus network.In order to make our phantom simulation as realistic as possible, we added two additional and related artefacts which are seen in recordings with patients. Firstly we simulated low amplitude movements to replicate arterial pulsations and slight head movements resulting from each heartbeat. These movements were created by placing an inflatable balloon under the phantom, which was periodically inflated with air at a rate of 60 times per minute. Air inflation was performed using custom made electronics, connected to a pressurised air supply which was controlled using a MATLAB script. We ensured that the resulting movements of the phantom were similar in terms of their magnitude to head movements that are observed in patient recordings by ensuring that the channel spectra were comparable (see Results section). To simulate the second type of artefact, we placed two ferromagnetic extension wires which were identical to those used in patient recordings on the spherical surface of the phantom. We have previously shown that movements of the ferromagnetic wires are related to arterial pulsations and that the interaction of these two phenomena is major source of artefacts in recordings without stimulation .2.4We present data from a 43 year old male patient with a nine year history of PD, who underwent implantation of bilateral DBS electrodes in the STN. The recordings were approved by the local ethics committee, and the patient gave written informed consent prior to participation. The permanent DBS macroelectrode implanted in the STN was model 3389 as in the phantom experiment (see above). Further details of the operative procedure can be found in . The loc2.5Recordings were performed after overnight withdrawal from dopaminergic medication. The patient was requested to keep his eyes open and to stay still during recordings. A neurologist was present inside the magnetically shielded scanner room at all times during the recordings, to monitor the wellbeing of the patient and to administer DBS.The experiment started with a 4 min resting block during which data were collected directly via BrainAmp without the stim-record amplifier. The stim-record amplifier and external stimulator were then added to the setup and two additional recording runs were performed. Two stimulation conditions were tested independently for the right and left STNs: 0 Hz (no DBS), and 130 Hz monopolar DBS. The right and left STNs were stimulated separately in different recording runs. Each recording run was 7 min long and included two conditions, each lasting 3 min, separated by a 1 min interval for washout of the effect of the first condition. Stimulation conditions were assigned to runs in random order. We also tested DBS at 5 Hz and 20 Hz in the same experiment but the data are not presented here.Monopolar DBS, between contact 1 and an external reference applied to the patient's clavicle was administered by the neurologist. The stimulation pulse width and amplitude were 60 μs and 3 V respectively. At the onset of DBS, the stimulation voltage was increased slowly, in increments of 0.5 V, whilst checking for clinical improvement and for the presence of any stimulation related side effects. Prior to experimentation, clinical assessment of the patient confirmed an improvement in motor (Part III) Unified Parkinson's Disease Rating Scale (UPDRS) scores with monopolar DBS at 3 V and 130 Hz.2.6MEG data were sampled at 2400 Hz and stored to disk for subsequent offline analyses. Head location was continuously monitored throughout the experiment. For runs with two active DBS conditions a recording of ∼1 min without DBS was added at the beginning to aid extraction of head location.LFP recordings from both STNs were simultaneously collected using the BrainAmp system. Patient data were collected via the 8-channel bipolar headbox and BrainAmp ExG amplifier, as shown in After the initial resting recording the right side channels and L0-R0 channel were disconnected and bipolar channels #1 and #2 of the headbox were used for the stimulus train copy and the LFP recording from the stimulation-record amplifier. Channels #3 and #4 were left unused and channels #5–7 recorded from the unstimulated side. Initially this was the left side, but after the first two stimulation runs the connectors were swapped so that left became the stimulated side and right the unstimulated side.LFP recordings were high pass filtered at 1 Hz in the hardware so as to avoid amplifier saturation due to large DC offsets. The data were sampled at 2500 Hz which was the closest available sampling rate to the one used in MEG. LFPs were recorded onto a laptop that was optically isolated from the BrainAmp hardware. Random noise from the noise generator was recorded simultaneously on the BrainAmp system via the 32-channel monopolar BrainAmp MR Plus amplifier and the MEG via one of the Analogue to Digital Converter (ADC) channels see .2.7http://www.fil.ion.ucl.ac.uk/spm/software/, http://www.fil.ion.ucl.ac.uk/spm/ext/#DAiSS) and Fieldtrip  and continuous head location data for the whole run, part of which might also be invalid. The idea of the analysis described below is to generate the best estimate of the head location in each run even if a large portion of the head location measurements are invalid. The analysis is based on the assumption that for valid head location measurements the pairwise distances between HPI coils will be the same  whereas for invalid measurements they will be highly variable. Our approach had been optimised to work on a large group of recordings without concurrent DBS and was robust enough to work unmodified also with recordings including DBS despite the fact that during stimulation delivery head tracking was clearly disrupted.We performed two passes on combined head tracking data from all runs. The aim of the first pass was to determine the distances between HPI coils. For this purpose we collected from each run two head location estimates. The first was the initial head location measurement. The second was extracted from continuous location data by computing pairwise distances between fiducials in cm, rounding to the nearest integer and finding the most common value across time (using ‘mode’ function in MATLAB). Time points for which one of the distances differed from its mode value by more than 1 cm were marked as invalid and interpolated using linear interpolation and extrapolation over the whole run. Corrected head location was then computed as the median over all points, original and interpolated.We then examined pairwise distances between fiducials for all the collected measurements. In a typical stimulation experiment there would be 5 runs, yielding 10 measurements. Again, ‘mode’ function was used to find the most common set of distances and measurements with distances differing from the mode by more than 1 cm were marked as invalid. Averaged distances for the valid measurements were used in the second pass over the data. This pass again involved correction and interpolation of the original continuous head tracking traces with the difference being that distances for each time point were compared not to the mode of that same run but to the valid distances computed across all runs in the first pass. The rationale for that was that there might be some cases where tracking was valid only briefly so that the true values were not even most common in a run. Still if those brief moments could be identified one would get a better estimate of head location in that run than by taking it from a different run.Finally, the original and corrected head locations were examined for the second time across all runs and their validity was determined by comparing distances to their most common values as described above. The average of all the valid locations was then computed. Each run was assigned a head location in the following order of preference. If the location computed from continuous head tracking data was valid, it was used because it best reflected the true head location during the measurement. If not, the initial measurement was used. If both run-specific measurements were not valid, the average over the valid measurements from other runs was used.In practice, for the subject shown in the present paper, and all the subjects we studied for the group study not shown here, a run-specific head location could always be successfully recovered. Head and sensor locations were always visually inspected and compared across runs to make sure that there are no gross outliers and misregistrations. Note that our aim in this analysis was to recover a single most representative head location for each run rather than completely reconstruct the head movements during the run and compensate for them. There was no severe head movement problem in our study with the patient off dopaminergic medication. Thus, the inability to recover valid head location during active DBS is not very problematic in our case because we recorded at least 30 s of data with no stimulation in each run and recovered the head location from it.2.9As the first processing step the raw data files from the CTF-MEG and BrainVision systems were converted to SPM MATLAB-based format. The LFP data were downsampled to 2400 Hz, matching the sampling rate of the MEG data. The white noise recordings from the MEG and BrainVision systems were linearly de-trended prior to cross-correlating the two time series. If both recordings were valid and the MEG and LFP files were matched correctly a clear peak could be detected in the cross correlation exceeding by far the background cross-correlation values. Our criterion for detecting this peak was that the ratio of the peak value to the median of the absolute values of cross-correlation across all delays was above 25. Based on the location of the peak with respect to the zero lag we determined the offset of the two recordings. This value could be used for the initial rough alignment. By computing local cross-correlations for different parts of the recording we found that this rough alignment would result in a slow drift between the two noise traces which would typically accumulate to 0.7 samples per minute of recording. The reason for this drift could be the fact that the true sampling rates of the two recordings were slightly different. We thus refined the alignment further using the following procedure. Non overlapping 1-s segments were defined in the units of samples for the MEG data starting with the first sample. Corresponding segments were then defined for LFP data by adding the offset value determined from global cross-correlation to the MEG segment definition. MEG segments for which there was no LFP counterpart (due to asynchronous start and end of the two recordings) were discarded. Cross-correlations were then computed segment-wise between the MEG and LFP noise copies. For each segment, if a clear peak  was found in the lag range of ±20 samples (±8.3 ms) the corresponding offset was added to its LFP segment definition, so that repeating the procedure with corrected LFP segment definition would only yield peaks at zero lag.The resulting segment definitions for MEG and LFP were then used to epoch the full MEG and LFP data. The epoched datasets were fused and then converted again into a continuous recording (which was straightforward because the segments were consecutive in time). In this new dataset MEG and LFP data were fused and aligned. We chose to perform the fusion as early as possible in the processing pipeline to be able to process our older datasets recorded with the CTF EEG system and the new fused datasets in the same way.2.10Visual inspection of the individual channel data obtained during monopolar DBS revealed high amplitude jumps in some of the channels that lasted only a few samples see . The jum5 fT. Channels with more than 1000 jumps in any experimental condition were removed from all experimental conditions, in order to keep the conditions comparable. This threshold for channel rejection was determined empirically as it allowed us to remove channels that were worst affected by jumps, leaving those that could be easily repaired, using an interpolation-based approach as follows. A fixed segment of data (120 ms) either side of the jump was examined and DBS stimulation pulse peaks within it were identified. The data between DBS pulse peaks greater than two pulses away from the jump on either side were averaged to produce a signal for interpolating the region contaminated with the jump. The contaminated region of data was defined as the segment of data between two DBS pulse peaks of the jump on either side. Finally, the mean of the post-jump data segment was adjusted to correspond to that of the pre-jump data, ensuring smooth fixing of the jump  were identified. Margins of 20 s were then removed from the beginning and the end of the segment to only include a stable stimulation period sufficiently distant from any parameter changes. This period was epoched into 4 s segments.2.12We characterised the time and spectral domain components of DBS artefacts. Spectral analysis of individual channel data was performed by pre-multiplying the data with a hanning taper prior to spectral decomposition using the Fast Fourier Transform. Further details on the artefacts may be found in Section t-test using standard SPM analyses (p < 0.01 (family wise error corrected) to enable the identification of significant regions in frequency and sensor space.We have previously defined methodology for determining the statistical significance of resting coherence between a bipolar contact pair of the STN DBS electrode and all MEG sensors . This apanalyses . The SPM2.13Sources coherent with the reference channels were localised using DICS beamforming  as descrFor the phantom lead fields were computed using a single sphere model coregistered to MEG sensors based on HPI coil location measurements. For the patient we used a single shell head model  pairwise comparisons  we constructed the null distribution from the maximal absolute coherence difference between the three conditions at each permutation, following the procedure described by . Followi33.1y axis for comparison of the signal amplitudes. The middle plots (Panel B) show only 0.08 s of data and the y axes are varied so that the signal can be visualised in all 3 conditions. The plots on the far right are the spectra of the data presented in Panel A. The effects of movements of the ferromagnetic wires which occur at a rate of approximately 1 Hz are clearly visualised in Panel A. Furthermore, the effects of ferromagnetic wire movement appear similar across the three different stimulation conditions.Raw data recorded in the absence of DBS in the patient showed artefacts related to the movement of ferromagnetic wires due to arterial pulsations caused by the heartbeat. This type of artefact has been described in detail in  and was Data recorded during stimulation in both the patient and the phantom showed two additional kinds of artefacts: (1) DBS pulses followed by ringing (2) jump artefacts. The first type of artefact was observed during both bipolar and monopolar DBS, whereas the second type of artefact were observed only during monopolar DBS. In the following paragraphs we will describe these artefacts in further detail.Firstly, each DBS pulse produced a large artefact followed by a ringing response in all channels whose amplitude ranged from hundreds to several thousands of femtoTesla (fT). This ringing arises from the response of the antialiasing hardware filter in the CTF machinery to a stimulation pulse. In our recordings, with a sampling frequency of 2400 Hz, the antialiasing filter was an 8th order elliptic low-pass filter with a cut off of 600 Hz. This filter resulted in a ringing decay time constant of 4.8 ms. As a result, most of the period between stimulation pulses (7.7 ms) was affected by ringing during both monopolar and bipolar DBS at 130 Hz . Comparing monopolar . This procedure enabled us to recover 30 channels in the phantom experiment and 35 in the patient experiment. The second type of artefact we noted was jumps affecting a subset of the channels during monopolar DBS at either 20 Hz or 130 Hz. Jumps comprised abrupt changes in the signal level with a fixed amplitude of ∼3.5e5 fT , followed by a ringing artefact as described above. This value corresponds to Although our experiments revealed that bipolar DBS resulted in fewer artefacts than monopolar DBS . The reason for this is that a major source of this artefact is the percutaneous externalisation wire that moves with arterial pulses on the scalp . This pe3P  , tempora3P  can remo3P . The appearance of jumps is also affected by the intrinsic noise of the SQUID. If the external interference and the intrinsic noise are small, no unlocks will occur, while in an intermediate range the jumps will occur occasionally as in B.The problem could potentially be mitigated by changing the DBS pulse shape to exclude very high frequencies . However, the safety and clinical efficacy of such modified pulses has not been demonstrated. Thus we preferred to keep DBS settings close to those used in routine clinical practice and tackle the jump problem by data post-processing. But if there is further interest in combining MEG with monopolar DBS in the future, optimizing both MEG and DBS hardware settings is definitely a promising direction to reduce artefact contamination.We have dealt with jumps in three different ways: (1) excluding severely affected channels, (2) correcting by interpolation and (3) using robust methods for computing coherence. The vast majority of jumps were contained within the excluded channels.The second type of artefact we observed were DBS stimulation pulses followed by ringing, the latter being the result of antialiasing filters. The antialiasing filters had a beneficial effect of prevented aliasing artefacts, which have been observed in other DBS MEG studies , but hadOne approach for shortening the ringing artefacts would be to use a much higher sampling rate and hence a higher antialiasing filter cut off frequency, since aliasing would only be observed for frequencies greater than the Nyquist frequency . Using a higher cut off frequency for the antialiasing filter has the beneficial effect of shortening the time constant of the ringing artefacts, but comes at a computational cost of having to deal with large volumes of data acquired at high sampling rates.Despite the presence of the observed artefacts we were able to recover simulated coherence in source space using beamforming in the phantom experiment. More specifically, source space coherence values were similar when DBS was off and artefacts were absent to when DBS was on. It is worth mentioning that although we attempted to make the phantom experiment as similar as possible to a patient recording, there are still some differences. For instance, we used a pure sinusoid as the ‘cortical’ signal, and did not incorporate other competing sources. These factors resulted in unrealistically high coherence with the reference channels that we had to artificially reduce by adding random noise to the latter.Analysis of single patient data also revealed comparable coherence spectra when DBS was on and off. We will leave a detailed discussion of the effects of DBS on cortico-STN coupling to be the subject of a future report, but it should be noted that both the STN LFP and the cortico-STN coherence may be confounded by a temporary stun effect due to recent surgery. This may lead to baseline changes but should spare the difference between spectra recorded acutely on and off DBS, unless the stun effect were to totally suppress baseline beta activity.4.5The present study paves the way to using MEG as a means of studying DBS effects on oscillatory connectivity between subcortical stimulation targets and the rest of the brain. We have demonstrated that physiological components of interest can be recovered from MEG data in the presence of DBS artefacts as well as artefacts from ferromagnetic percutaneous wires. Accordingly, the effects of DBS on LFP-MEG coherence can be studied.


We present a computationally efficient reconstruction method for the limited-data chemical species tomography problem that incorporates projection of the unknown gas concentration function onto a low-dimensional subspace, and regularization using prior information obtained from a simple flow model. In this context, the contribution of this work is on the analysis of the projection-induced data errors and the calculation of bounds for the overall image error incorporating the impact of projection and regularization errors as well as measurement noise. As an extension to this methodology, we present a variant algorithm that preserves the positivity of the concentration image. Chemical species tomography (CST) addresses this challenge by imaging quantitatively the concentration of chemical particles in gaseous media and exhaust plumes × m, which incorporates the gas plume. The measurement plane is normal to the plume propagation axis at a distance of 2 m from the engine's nozzle as shown in At the FLITES experiment , 126 ligΩ=−0.5,0.75×.The choice of the regularization parameter λ has a significant impact on the reconstructed Tikhonov estimator ormation . The genormation  on theirormation . As in oB. This reduction will lead to a minimum near the optimal λ*, before the residual norm increases again in response to the regularization beginning to affect some of the larger singular values that are clustered closely together. Thereafter, we expect the r0. The graphs of c3, c5 and c6 are shown in r*=Φ†c for each value of λ in the same interval, regarding as optimal parameter choice λ* the argument that minimizes this error discrepancy. In reality of course Our criterion for choosing λ from this curve is that in the neighbourhood of the optimal λ the value of 7.This work proposes a computationally efficient solution of the inverse problem in limited-data chemical species attenuation tomography with flow model-based prior information. We have showed that projection of the unknown function in a smooth low-dimensional basis reduces drastically the dimensionality of the problem and probably yields rank-deficient linear systems that are suitable to Tikhonov regularization. This image reconstruction approach is computationally efficient as it avoids inverting high-dimensional matrices; however, it incurs errors due to the subspace projection and the need for regularization in obtaining a stable solution of the projected inverse problem. To quantify these errors and their impact on the reconstructed image, we provide an upper bound on the overall image error which incorporates the subspace approximation and regularization errors and can be used in the interpretation of the reconstructed images. In this bound the approximation error appears both as an offset as well as a component of the data error, however, the amplification factor of these errors in the image reconstruction is maintained at small levels. This framework is complemented by an variant algorithm for solving the positively constrained imaging problem and a heuristic method for selecting the required regularization parameter. The numerical simulations affirm the performance of the algorithms proposed and the validate the error bounds.


The selection and configuration of building materials for optimal energy efficiency in a building require some assumptions and models for the thermal behavior of the utilized materials. Although the models for many materials can be considered acceptable for simulation and calculation purposes, the work for modeling the real time behavior of phase change materials is still under development. The data given in this article shows the thermal behavior of a flat roof element with a phase change material (PCM) layer. The temperature and energy given to and taken from the building element are reported. In addition the solid–liquid behavior of the PCM is tracked through images. The resulting thermal behavior of the phase change material is discussed and simulated in  Specifications tableValue of the data•The data contain thermal measurements from a laboratory experiment of a flat roof element with a phase change material layer.•The data provide a reference and may serve to benchmark thermal models for the used phase change material by other groups.•In addition, the phase change material was observed in minute intervals and these images can be used to further the understanding of the the real time thermal behavior of a paraffin based phase change material and the effect of natural convection during phase change.1Three types of data are presented and given in 1.1The During the experiment, the roof element was put into a box with 15 cm thermal insulation on all sides. The surface temperatures were stabilized by copper serpentines, through which conditioned water runs. Therefore they give energy to and take energy from the upper and lower surfaces of the roof that represent the outdoor surface and indoor surface respectively. The thermal changes within the roof element are measured and the energy given to and taken from a surface, and the energy stored inside the roof are calculated to ensure the validity of the experiment. The experimental method is discussed in more detail in 1.2All of the data acquisition points are shown in The thermocouples and PT-100 were calibrated at EGEKALMEM laboratory. The 95% sensitivity of the PT-100 measurement during calibration is calculated by Eq. Td shows the resolution of the datalogger and is taken as 0.001 Refmm is the reference value of the multimeter and is 0.0024 St) is 0.01 at the maximum. The thermal stability of the constant temperature baths (Bs) is 0.01 RefPt) is taken as 0.01 at 0 °C and 0.02 at 100 °C, therefore the sensitivity of PT-100 measurements is ±0.035 with 95% confidence.f(x) is as given in Eq. The calibration curves of the thermocouples are calculated with 95% confidence with matlab software. The calculations can be found in For example, TC210 measures the temperature in the middle of the PCM layer. The calibration curve for TC210 is given in The a, b, c, and d coefficients derived for calibration curves are given in Supplementary Table 21.3In addition to measurement of the temperature changes inside the PCM, the behavior of the PCM was also observed by taking photographs of the PCM layer during the experiment. The left side of


Case definition has long been an issue for comparability of results obtained for musculoskeletal pain prevalence, however the test-retest reliability of questions used to determine joint pain prevalence has not been examined. The objective of this study was to determine question reliability and the impact of question wording, ordering and the time between questions on responses.A Computer Assisted Telephone Interviewing (CATI) survey was used to re-administer questions collected as part of a population-based longitudinal cohort study. On two different occasions questions were asked of the same sample of 203 community dwelling respondents  aged 18 years and over at two time points 14 to 27 days apart (average 15 days). Reliability of the questions was assessed using Cohen’s kappa (κ) and intraclass correlation coefficient (ICC) and whether question wording and period effects existed was assessed using a crossover design.The self-reported prevalence of doctor diagnosed arthritis demonstrated excellent reliability (κ = 0.84 and κ = 0.79 for questionnaires 1 and 2 respectively). The reliability of questions relating to musculoskeletal pain and/or stiffness ranged from moderate to excellent for both types of questions, that is, those related to ever having joint pain on most days for at least a month (κ = 0.52 to κ = 0.95) and having pain and/or stiffness on most days for the last month (κ = 0.52 to κ = 0.90). However there was an effect of question wording on the results obtained for hand, foot and back pain and/or stiffness indicating that the area of pain may influence prevalence estimates.Joint pain and stiffness questions are reliable and can be used to determine prevalence. However, question wording and pain area may impact on estimates with issues such as pain perception and effect on activities playing a possible role in the recall of musculoskeletal pain. Recent work, undertaken as part of the Global Burden of Disease Study 2010, has highlighted the impact of musculoskeletal conditions worldwide . HoweverOther issues which are likely to impact on prevalence estimates obtained from questionnaires include response category wording and mode of administration. Questionnaire wording has been shown to influence the reported prevalence of conditions such as wheezing in asthma , and in It is essential that data used in the planning and monitoring of health services and disease prevalence in populations, are as accurate as possible , particuOne way of assessing reliability is by using a test-retest methodology where responses to questions are assessed in the same group of people, after a specified time period, to see if they provide similar results. This is especially important for questions that are used in regular or ongoing surveys. The reliability of questions in telephone health survey questionnaires, such as the Behavioral Risk Factor Surveillance System (BRFSS) in the United States, has been addressed in the literature , 21–25. In population studies, test-retest methods have been used to assess the reliability of different questionnaire tools associated with musculoskeletal pain. Examples include: Balogh et al.  who examPicavet and Hazes  also exaThe aims of this study were to examine (i) the test-retest reliability of self-reported doctor diagnosed arthritis and non-specific musculoskeletal pain in six areas of the body and (ii) the effect of questionnaire wording, order and time between questionnaires.The North West Adelaide Health Study (NWAHS) is a representative longitudinal study of 4056 randomly selected adults aged 18 years and over at the time of recruitment from the north-west region of Adelaide, South Australia. The sample region represents approximately half of the metropolitan area  and almost one-third of the population in South Australia (population of approximately 1.7 million), which has the second highest elderly population of all the Australian states and territories . The aimA Computer Assisted Telephone Interview (CATI), a self-completed questionnaire and a clinic assessment has been used at each stage , 40 in oi.Determine the test-retest reliability of each of the questions at two time pointsii.Determine whether the questions asked in the different stages of the NWAHS could be combined in order to provide longitudinal prevalence estimates and comparison of changes in prevalence over time.As a result of the different time frames used for prevalence across the stages (most days for at least a month vs. over the past month…on most days), it was decided to conduct a study in order to:In order to achieve this, a separate random sample of community dwelling adults aged 18 years and over was contacted by telephone by a health survey research company. Potential participants were broadly informed of the nature of the research  and that they would be contacted on two different occasions (at the current time and in approximately two weeks’ time) in order to test questions so as to provide the best information about joint pain. If participants gave their consent, the short survey was commenced. Participants were randomly assigned to one of four groups: (i) those who were asked the musculoskeletal questions from the NWAHS Stage 2 (questionnaire 1) twice, (ii) those who were asked the questions from Stage 3 (questionnaire 2) twice, (iii) those who were asked questionnaire 1 and then questionnaire 2 at the second interview and (iv) those who were asked questionnaire 2 and then questionnaire 1. A diagram detailing the order of the questionnaires is presented in Fig. Overall, 52 participants provided responses at time 1 and 2 for questionnaire 1, 51 participants provided responses at the two time points for questionnaire 2 and 50 participants each provided responses at time 1 and 2 for questionnaire 1 then 2 and for questionnaire 2 then 1. Thus responses were obtained from 203 respondents in total.n = 203). In order to obtain the prevalence of musculoskeletal pain as determined by the questions used at Stage 2 of the NWAHS, the response obtained from all participants that answered questionnaire 1 first were combined with the response from participants who answered questionnaire 1 after questionnaire 2 (n =152). To determine the prevalence of musculoskeletal pain using the questions from Stage 3 of the NWAHS, the response obtained from all participants who answered questionnaire 2 first were combined with the response from participants who answered questionnaire 2 after questionnaire 1 (n =151). The prevalence obtained from questionnaire 1 and 2 individually (n =102 and n =101 respectively) was also determined.Statistical analyses were conducted using G*Power , to deten =52) or questionnaire 2 (n =51) at the first and second telephone call and also to assess the agreement between time 1 and time 2 for the arthritis prevalence questions, which was asked of all respondents (n =203). Reliability values of between 0.81 and 1.00 were considered to be “excellent” agreement, between 0.61 and 0.80 “good” agreement, between 0.41 and 0.60 “moderate”, between 0.21 and 0.40 “fair” and less than 0.20 were considered “poor” agreement [Cohen’s kappa (κ) , 43 was greement . For eacSecondly the pkcross command in STATA  was usedEthical approval for the study was obtained from the Human Research Ethics Committee of the University of Adelaide (H-2012-098). All participants provided informed consent.Overall a complete response  was obtained from 203 respondents. There was an average of 15 (SD 1.7) days between each questionnaire (range 14–27 days). The mean age of participants was 60 years (range 19–91) and 60.6 % were female. There was complete agreement between the two time points in responses to sex and age (κ = 1.00 and ICC = 1.00 respectively). While the final sample size was limited by time and costs, the retrospective power calculation indicated that with a sample size of 100, the achieved power for a McNemar test to detect an odds ratio of 2 ranged between 20.9 and 62.4 %.The results of the reliability testing for the arthritis question are presented in Table The results of the crossover study are shown in Table It must be noted that the prevalence results provided in Table Issues of case definition and variation across studies that examine the prevalence of musculoskeletal disorders have long been an issue for researchers and policy makers alike . The lacIn line with previous reliability studies conducted in South Australia , age andThe overall prevalence of arthritis variable has been shown to have good reliability in previous work . It is aThe questions relating to “ever” having back pain or stiffness on most days for at least a month demonstrated good and excellent agreement beyond chance respectively. However questions relating to back pain on most days over the past month had only moderate reliability. While respondents were likely to remember if they had “ever” had back pain or stiffness, the presence of pain in the last month in particular could however have been impacted by the time between each of the surveys and this may be reflected in the significant period effect also obtained from the crossover analysis. Back stiffness also demonstrated a significant period effect and there was a significant effect as a result of the change in question wording for both back pain and stiffness. While the timeframe between questionnaires was only approximately two weeks, the period effect obtained for the back pain and stiffness questions appears to indicate a recall bias may exist.Ever having hip pain or stiffness demonstrated good agreement as did having hip pain or stiffness in the last month although there was a difference between the right and left sides in the level of agreement. Ever having knee pain, aching or stiffness and knee pain, aching or stiffness in the last month had good reliability, ever having shoulder pain or stiffness demonstrated moderate reliability and shoulder pain in the last month demonstrated excellent reliability and stiffness good reliability. It may be that some for some joint areas it is easier to recall current pain and the relative impact on activities of daily life may also influence the recollection of joint pain. However, the crossover analysis indicated no impact of question wording on these results.The foot pain questions were “On most days, do you have pain, aching or stiffness in either of your feet?” and “Over the past month, have you had pain, aching or stiffness in either of your feet on most days?” The reliability for the first question was good, however the reliability for the second question ranged between good and moderate, possibly because it requires participants to place their symptoms within a time frame of one month. The first question also does not provide a specific time frame over which the foot pain, aching or stiffness is present and this was reflected in a significant effect of questionnaire wording on the estimates as a result of the crossover analysis.Finally having hand pain, aching in the last month demonstrated good reliability, while stiffness demonstrated fair to moderate reliability. The first question combined pain, aching or stiffness and also, like the foot question, did not include the word “ever”. The questions relating to prevalence in the past month separated pain and aching and stiffness and these variables had to be combined for analysis purposes. Consequently there was a significant effect of question wording on responses for hand pain and stiffness and also an effect of the time between questions.The prevalence estimates of musculoskeletal conditions obtained in this community sample were generally high. The introduction to the survey did indicate that the questions would be about pain in the joints and thus those with joint pain may have been more likely to continue the survey. While the prevalence of back pain was within the range of one month prevalences described by Hoy et al.  of 24.0 A possible limitation of the study was the lack of facial and/or body cues, as a result of using the telephone, which may indicate misunderstanding or incorrect interpretation of a question. In order to address this; highly skilled telephone interviewers who undertake many health-related telephone surveys were used. Consequently, they have the experience to detect potential question misunderstandings and provide clarification if needed. This study also used questions from the NWAHS, which are always pilot tested prior to the conduct of the main study.n =203) and while the study was broadly representative in terms of the age of participants, there was a slightly higher proportion of females interviewed compared to the NWAHS and also a higher prevalence of those reporting musculoskeletal problems. Thus there is the potential for selection bias of participants; those with musculoskeletal problems may have been more likely to participate. However, again, because the focus of the study was the reliability of questions this is likely to have little impact. The timeframe of an average of 15 days (SD 1.7) was chosen as participants were unlikely to remember their first responses but it was also unlikely that answers would change significantly. However, there are changes that could have occurred between interviews particularly with regard to the timeframe of the presence of pain on most days within the last month, which may impact of the reliability of responses. Recall bias in terms of “ever” having experienced joint pain may also have been an issue. Survey responses have also been shown to be effected by mode of collection [Other limitations of this study are the small number of respondents  “in the last month” can change over time quite legitimately, whereas changes in “ever having pain” are not legitimate if the response changes from “yes” to “no”. Consequently, it can be argued that the issue of reliability essentially only applies to questionnaire 1. The issue that remains is that of case definition or which prevalence estimate is most appropriate to use. The generally high reliability of questions relating to pain over the last month perhaps could be considered more of an indicator that these conditions tend to last longer than a month. The absence of question wording effect means that the nature of the complaint does not differ between “ever” and “last month”. Thus, either question wording should elicit the same response, so the questions can be considered equivalent subject to the caveat above. It also appears that there may not be a significant effect of question wording on the prevalence estimates for some joint areas .However for other areas of pain  question wording has a significant impact and for two of these areas (hand and back) there was also a significant effect of the time period between the responses. While questionnaire wording has been shown to have an effect on survey responses , these aJoint pain and stiffness questions are reliable and can be used to determine prevalence. However, question wording and pain location may influence prevalence estimates and impacts on the interpretation, the relevance of case definition and any analyses that may be undertaken.http://health.adelaide.edu.au/pros/data/nwahs/Data from this study form part of the NWAHS. As this is an ongoing cohort study, these data are available on application to the chief investigators. Details are available at:


Ultracold atomic systems offer a unique tool for understanding behavior of matter in the quantum degenerate regime, promising studies of a vast range of phenomena covering many disciplines from condensed matter to quantum information and particle physics. Coupling these systems to quantized light fields opens further possibilities of observing delicate effects typical of quantum optics in the context of strongly correlated systems. Measurement backaction is one of the most funda- mental manifestations of quantum mechanics and it is at the core of many famous quantum optics experiments. Here we show that quantum backaction of weak measurement can be used for tailoring long-range correlations of ultracold fermions, realizing quantum states with spatial modulations of the density and magnetization, thus overcoming usual requirement for a strong interatomic interactions. We propose detection schemes for implementing antiferromagnetic states and density waves. We demonstrate that such long-range correlations cannot be realized with local addressing, and they are a consequence of the competition between global but spatially structured backaction of weak quantum measurement and unitary dynamics of fermions. The study of quantum gases trapped in optical lattice potentials is a truly multidisciplinary field24689111213141516177202122232526L lattice sites and lattice spacing d. An optical cavity with decay rate κ selects and enhances the light scattered at a particular angle2829We consider light scattering from ultracold fermions loaded in an optical lattice with J is the tunneling amplitude, U the interaction energy between atoms with opposite spin and j. The coupling between light and matter is obtained by generalizing the model inwhere â1 is the annihilation operator for the cavity mode, a0 is a classical coherent probe (σ is an index describing the light polarization in the circular polarization basis (L or R), lσ are the coupling constants between light and atoms and Δσ is the atom-light detuning. The operator where nt probe , σ is anul(r) is the mode function of the light mode l . This property has been exploited for investigating spin-spin correlations and magnetic susceptibility of a Fermi gas in previous works where the effect of measurement backaction123132w(r) and the ladder operators of the lattice, we find that where andp is the cavity-probe detuning.The operators Cσ is the Rayleigh scattering coefficient in presence of a cavityFrom the Hamiltonian (2) and assuming that L or R), the measurement process is sensitive only to one of the two spin species x-polarized light) and y-polarized light).The polarization of the probe beam defines which linear combination of the ↑ and ↓ are addressed by the measurement scheme. For example, if the probe laser is circularly polarized  the state of the system is propagated in time using the effective Hamiltonian Ĥeff until its norm reaches r and then (iii) the quantum jump operator is applied to the atomic state which is subsequently normalized. This three-steps process is then repeated starting from (i). Note that the non-Hermitian term in Ĥeff is characterized by the energy scale γ = κ|C|2 which competes with the usual tunneling amplitude J and on-site interaction U, leading to new many-body dynamics not described by the Hubbard HamiltonianSince the photon annihilation operator Jij determines the spatial profile of the measurement operator, making our setup extremely flexible. Depending on the mode functions of the light modes and on the angles between the optical lattice, the cavity and the probe, it is possible to address different observables. Focusing on a one-dimensional atomic chain along the z axis and considering traveling waves as mode functions  and the probe beam is perpendicular to them (i.e. θ0 = π/2) so that Jii = (−1)i. The second scheme we consider probes the number of atoms at the odd sites k0 · r is equal to k1 · r and shifted such that the even sites are placed in the maxima of the light interference pattern, while the even sites are positioned at the interference zeros and do not scatter light, so that Jii = 1 for i odd while Jii = 0 for i even. Note that both the measurement scheme we consider address the system globally and do not require single-site resolution as the coefficients Jii are determined by the projections of the probe/cavity wave vectors on the direction of the optical lattice and not by their wavelengths. By changing the angles θ0 and θ1 it is possible to implement measurement operators with different spatial profiles as the periodicity of the coefficients Jij can be easily made larger  than the lattice period.In this work, we focus mainly on two different measurement schemes which partition the lattice in two spatial modes. The first one addresses the difference in occupation between odd and even lattice sites k < kF are occupied (kF being the Fermi wavevector) and the density and magnetization are uniform across the lattice. We use this state as a reference point, assuming that the atomic system is initialized in its ground state before the measurement take place. The monitoring process perturbs this state and induces AFM correlations that can drive the atomic state to a superposition of the Neel states y axis, the annihilation operator describing the photons escaping the cavity is i). The quantum jumps tend to suppress states that do not present AFM correlations since the application of ĉ on the atomic state completely vanishes its components with γ/J which determines whether the dynamics is dominated by the quantum jumps or by the usual tunneling processes. Specifically, in the strong measurement regime We first focus on non-interacting fermions with two spin components at half filling The presence of AFM correlations in the quantum state resulting from the conditional dynamics is revealed by computing the magnetic structure factorq = Q = π/d and creates an AFM state (S(Q) is directly accessible to the experiments since the probability for a photon to escape the optical cavity in a  time interval dt is proportional to Monitoring this quantity for each quantum trajectory, we find that the measurement induces a strong peak at FM state . ImportaU = 20J. The detection process is sensitive only to the spin-↑ density and therefore induces a periodic modulation of the spatial distribution of this species, favoring either odd or even lattice sites. However, since the two different spins are coupled by the repulsive interaction, spin-↓ atoms are also affected by the measurement and tend to occupy the lattice sites that are not occupied by the spin-↑ atoms, enhancing the AFM correlations of the ground state. Importantly, in this case the AFM character of the atomic state is visible in the local magnetization , the detection process creates particles and holes only on the Fermi surface, which is the typical scenario in conventional condensed matter systems. In contrast to this, the setup we propose allows us to probe states that are deep in the Fermi Sea: if where BZ indicates the first Brillouin zone. If Applying this expression on the ground states leads to Q/2 and are not necessarily confined around kF. This symmetry is reflected in the occupation of the single particle states where   is the (anti)commutator. The photocurrent escaping the cavity follows a stochastic process where the photocounts are determined by the norm of the atomic wavefunction |Ψ〉 which is given bywhen a photon is detected, the quantum jump operator is applied to the atomic state and the value of nk and αk and we solved them numerically  of the atomic system presents a periodic spatial modulation, one needs to be able to estimate the photocurrent leaking from the optical cavity. Focusing on the case illustrated in Nph which shows a “staircase” behavior. If this structure is resolved, the large-scale oscillations in the measured quantity survive and can be detected even if η < 1.The effects described in this work rely on the efficient detection  instead of performing a simple quantum trajectory calculation using a stochastic Schrödinger equation. Because of the undetected photons, the state of the system cannot be described by a pure state and it is necessary to introduce a density matrix representation. Specifically, given the jump operator ĉ and the efficiency N is a stochastic Itô increment such that where dη = 0 while it can be interpreted as stochastic Schrödinger equation (for pure states) if η = 1. Because of the stochastic nature of the detection process, the function Nph(t) which describes the number of photon detected up to the time t, is given byθ(t) is the Heaviside step function and ti is the time at which the i−th (detected) jump takes place. However, if the average time between two quantum jumps is much smaller than the typical timescale of the atomic dynamics (i.e. Nph(t) approximately follows the differential equationwhere N. If the jump operators is given by Nph(t). Focusing on a single quantum trajectory for the density matrix Ne(t) as the number of photons that escape the cavity up to the time t (and that are not necessarily detected) one has that the statistical properties of a single realization of Nph(t) follow the so-called Bernoulli process (each photon can be detected with probability η) and one haswhere the expectation value E is the mean value and Var is the variance. From these expressions, we can estimate the signal-to-noise ratio (SNR) for the conditional dynamics of a quantum system that is continuously monitored using a detector with efficiency η:where γN2). Thus, the number of photons Ne that escape the cavity during the timescale defined by the tunneling (2π/J) scales as γN2/J. Therefore, based on We can now infer what is the minimum efficiency required for determining the value of collectively by the atomic system as a whole and thanks to the fact that the creation of quantum states with AFM correlations or density modulations does not rely on the detection of a single photon, the effects that we describe in our manuscript are visible even if the detector has imperfect efficiency.making this setup robust to detection inefficiency. Because of the relatively large amount of photons scattered J, even if the detection efficiency is not perfect. Thus, this is a physical requirement for the measurement backaction to be still able to create the quantum state of interest. Instead of the oscillating photodetection rate, it is more convenient to deal with the number of detected photons, which instead of oscillations shows a growing staircase behavior with a characteristic time 2π/J  is related to the corresponding oscillations in the measurement backaction. More precisely, the rate of photodetections (and thus the measurement backaction) should show well-pronounced oscillations with the tunneling frequency shown in . Thus, aWe have shown that measurement backaction on ultracold Fermi gases can be used for realizing intriguing quantum states characterized by a periodic spatial modulation of the density and the magnetization. We have demonstrated that this spatial structure is a consequence of the global nature of the coupling between atoms and light. The competition between measurement backaction and usual dynamics determined by the tunneling enables the study of quantum magnetism without requiring extreme cooling or strong interactions between the atoms. Our method enables the possibility to engineer otherwise low entropy states . Importantly, the formation of magnetic states can be observed in real-time by measuring the photocurrent leaving the optical cavity, without the need of destructive techniques such as time-of-flight imaging. Additionally, it might be possible to engineer feedback control to stabilise a desired correlated quantum many-body state. This opens new possibility for studying the dynamics of strongly correlated materials and the effect of magnetic ordering on superconducting states534283056How to cite this article: Mazzucchi, G. et al. Quantum measurement-induced antiferromagnetic order and density modulations in ultracold Fermi gases in optical lattices. Sci. Rep.6, 31196; doi: 10.1038/srep31196 (2016).


Previous research has shown that the temperature of underlying permafrost decreases after the ground surface is covered with sand. No significant conclusions have yet been drawn that explain why this happens, because the heat transfer mechanism effects of the sand layer on the underlying permafrost remain unclear. These mechanisms were studied in the present work. We found that the upward shortwave radiation flux of the Qinghai-Tibet Plateau ground surface with a sand layer covering was higher than that of the surface without sand; thus, the atmospheric heat reflected by the sand layer is greater than that reflected by the surface without sand. Therefore, the net radiation of the surface with the sand layer is lower than that of the surface without sand, which reduces the heat available to warm the sand layer. Because sand is both a porous medium and a weak pervious conductor with poor heat conductivity, less heat is conducted through the sand layer to the underground permafrost than in soil without the sand deposition layer. This phenomenon results in a decrease in the ground temperature of the permafrost under the sand layer, which plays a key role in protecting the permafrost. This region is the world’s largest high–altitude permafrost area in the middle and low–latitude zones and accounts for 69.3% of the total permafrost area in China. This unique environment, which is characterized by high elevation and low temperature12567812131416The Qinghai–Tibet Plateau features a permafrost region with a total area of 149 × 10Several recent studies have been conducted on variation in the underlying permafrost after the ground surface is covered with sand, including the role of a sand layer on the convective cooling effect often observed in permafrost areas1922Research data were obtained by field observation and laboratory experiments. Variations in radiation and reflectance on the surface of the sand layer, the heat flux in the sand layer, and the temperature of the permafrost underneath the sand layer were observed by means of the synchronization contrast methodThe annual average albedo of the sand layer at the Honglianghe River of the Qinghai–Tibet Plateau is 0.30, whereas the annual average albedo of the surface without the sand layer is 0.25. Based on the diurnal variation graph of the annual average albedo , the alb−2 for the annual averages and 299.81 MJ for the yearly totals. The observed net radiation values during summer and autumn are greater than those during winter and spring. The annual average value of the net radiant flux density and the yearly total value of net radiation of the sand layer are significantly lower than those of the surface without sand, with corresponding values showing decreases of 11.17 W m−2 and 352.16 MJ.The four–component radiation and net radiation observations for the two study sites are shown in −2.The diurnal variation of the annual average radiant flux density is shown in Both the sand layer and the surface without sand at the Honglianghe River of the Qinghai–Tibet Plateau are dominated by heat absorbing during summer, when the direction of soil heat flux is downwards; heat is released during winter, when the direction of soil heat flux is upwards . Heat isThe upward shortwave radiation observed after the ground surface was covered with sand is greater than that of the surface without sand because the albedo of the former is higher than that of the latter. When the radiation fluxes of other components are nearly equal, the sand layer reflects more atmospheric heat than does the surface without sand. Therefore, the net radiation of the sand layer is lower than that of the surface without the sand layer, which means that less heat is available to warm the surface of the sand layer than the surface without sand. Based on the Stefan–Boltzmann law, we have−2); εg is the emissivity of the ground surface; σ is the Stefan–Boltzmann constant (5.6696 × 10−8 W·m−2·K−4); −2).in which Thus, we have:Based on the equation λ is the thermal conductivity of the soil layer (W·m−1 °C−1). Based on the laboratory test results, the thermal conductivity of the sand and soil sample are 0.36 and 0.43 W·m−1 °C−1, respectively. Here, T is the temperature of the soil layer (°C), T1 is the temperature value of the upper surface of the soil layer, T2 is the temperature value of the lower surface of the soil layer, and Δz is the thickness of the soil layer (m).where −2, respectively. The heat fluxes of the surface without sand at depths of 0.5 and 1 m are 0.76 and 0.46 W·m−2, respectively. The heat flux in the sand layer is less than that in the surface without sand. These results are consistent with the heat flux results measured in the field.Based on equation Based on analysis of the laboratory samples, the particle sizes for the sand in the experimental field at the Honglianghe River were predominately in the range of 0.10–0.25 mm and 0.25–0.50 mm. Sand with particle sizes in the range of 0.05–0.10 mm was found infrequently, and sand with particle sizes in the range of 0.001–0.005 and 0.005–0.05 mm was not found . A signiThe thermal conductivity of the sand layer is smaller than that of the soil layer12Given the rapid developments in engineering construction projects that are undertaken in cold regions24252628293031The upward shortwave radiation flux of the ground surface of the Qinghai–Tibet Plateau covered with a sand layer increases because the surface albedo increases. When the radiation fluxes of other components are nearly equal, the atmospheric heat reflected by the sand layer increases. Therefore, the net radiation of the sand layer decreases, which reduces the heat available to warm the surface of the sand layer.The soil heat flux decreases after the ground surface of the Qinghai–Tibet Plateau is covered with sandy depositions because the sand layer is both a porous medium and a weak pervious conductor with poor heat conductivity, therefore, the heat conducted through the sand layer to the underground permafrost decreases. This phenomenon induces the ground temperature of the permafrost to decrease under the sand layer, which plays a key role in protecting the permafrost.How to cite this article: Xie, S. et al. Key evidence of the role of desertification in protecting the underlying permafrost in the Qinghai–Tibet Plateau. Sci. Rep.5, 15152; doi: 10.1038/srep15152 (2015).


The motion artifact is an ever-present challenge in the mobile monitoring of surface potentials. Skin-electrode impedance is investigated as an input parameter to detect the motion artifact and to reduce it using various methods. However, the impact of the used impedance measurement frequency on the relationship between measured impedance and the motion artifact and the relationship between the impedance and the motion is not well understood.In this paper, for the first time, we present the simultaneous measurement of impedance at 8 current frequencies during the application of controlled motion to the electrode at monitored electrode mounting force. Three interwoven frequency groupings are used to obtain a spectrum of 24 frequencies between 25 Hz and 1 MHz for ten volunteers. Consequently, the surface potential and one channel of ECG are measured from the electrode subject to controlled motion. The signals are then analyzed in time and frequency domain.The results show that the different frequencies of impedance measurements do not reflect the motion in the same manner. The best correlation between impedance and the applied motion was seen at impedance current frequencies above 17 kHz. For resistance this relationship existed for frequencies above 11 kHz, Reactance did not show good time domain correlation, but had good frequency domain correlation at frequencies higher than 42 kHz. Overall, we found that the impedance signal correlated well with the applied motion; however impedance had lower correlation to actual motion artifact signal.Based on our results, we can conclude that the current frequency used for the impedance measurement has a great effect on the relationship of the measurement to the applied motion and its relationship with the resulting motion artifact. Therefore, when flat textile contact biopotential electrodes are used, frequencies higher than 17 kHz are best suited for impedance measurements intended for the estimation of electrode motion or motion artifact. For resistance, the best frequencies to use are higher than 11 kHz. With the reduction in the size and power consumption of electronics, the market and the applications for biosignal monitoring systems such as mobile electrocardiogram (EKG) or mobile electromyography (EMG) are increasing. These systems are designed for use in everyday activities as well as for long-term monitoring.Wearable applications that are used for the monitoring of surface biopotentials either long-term or during everyday activities in non-medical settings have their own specific problems that do not exist to the same extent in more conventional applications in controlled environments. The long-term stability of the electrodes and the motion artifact are two of the more prevalent problems. Depending on the application, one or both of these problems can be the dominant factor that determines the reliability of the system.Many methods have been proposed to deal with the motion issue. In earlier studies on the motion issue, several methods such as adaptive filtering have been used to predict the motion artifact. These methods include optical sensors that detect the displacement of the skin –3, accelDue to its ability to also measure the change of skin-electrode contact quality over time, skin-electrode impedance measurement has garnered more interest than the other methods.Studies investigating skin-electrode impedance in relation to the motion artifact have resulted in various conclusions. Although some studies show that the change in impedance is not the cause of the motion artifact , 13, it When considering the relationship of skin-electrode impedance to motion artifact, the intuitive idea is to use impedance measurement frequency in the frequency ranges of the biosignals of interest. This issue also arose in our previous paper where we used a injected current of 100 kHz for the impedance to study the motion artifact–a biopotential with frequency components well below 100 Hz . As in oIn this study, we aim to investigate the relationship between different impedance measurement frequencies, the electrode motion pattern, and the resulting motion artifact. We apply programmable motion to the electrode and measure skin-electrode impedance at 24 different frequencies ranging from 25 Hz to 1 MHz. Following the impedance measurements, two motion artifact-containing biopotentials are measured under the same conditions and the mounting force applied to the electrodes is monitored using a novel system. Ten volunteers took part in the study. As far as we are aware, this study is the first of its kind and the results of the study can be used to guide further research into more suitable impedance frequencies that better relate to motion and/or motion artifact.In short, an electrode was mounted on the skin by a known applied force and was subjected to motion. Three consecutive impedance measurements, each done with 8 simultaneous frequencies, were carried out. Following the impedance measurements, a surface biopotential measurement was carried out under the same conditions and with the same applied motion. This was achieved by changing the connectors to the electrodes without affecting electrode location or subject positioning. An analysis of the data provided us with information on how the frequency of the injected current of the impedance measurement reflects on the relationship between skin-electrode impedance and the applied motion and between skin-electrode impedance and the motion artifact. As a reference point, these measurements also provided us with the relationship between the motion artifact and the applied motion.The electrode that was to be subjected to motion was mounted to the skin with our novel device that monitors the mounting force used to secure the electrode and applies programmed motion to the electrode. The backbone of the motion generation module was a Dremel 220 workstation . The vertically moving platform, originally designed to host a work tool, was modified so as to host a hexTronik HX12K Hi-speed servo . The 25 mm extension arm of the servo was connected to a 2 part mechanism made of 3D printed plastic parts and a Kevlar tube that acted as a housing for a FlexiForce A201  force sensor and formed a platform with which the electrode was mounted on the skin. Figure The rotational motion of the servo was translated into lateral motion of the electrode in the coordinate plane parallel to the skin, by the servo arm, as depicted in Figure As the motion was applied to the electrode, the skin-electrode interface impedance was measured as a two-electrode impedance measurement using a Zurich Instruments HF2IS Impedance Spectrometer . This device can simultaneously measure the real-time potentiostatic impedance at 8 frequencies. To achieve adequate resolution, the frequency band between 25 Hz and 1 MHz was divided into 24 logarithmically evenly distributed frequencies. Because the device can simultaneously measure 8 impedance channels at different frequencies, these 24 frequencies were distributed into three groups, as shown in Table After the impedance measurements, the surface potential change at the electrode affected by the motion and an ECG channel containing this change were measured with the BioPac Data Acquisition Unit  at a sampling rate of 200 Hz, in response to the same motion pattern. Due to safety concerns, these two devices could not be used simultaneously. The electrode setup for the surface biopotential measurements is presented in Figure This produced four data sets for each subject: Impedance measurement groups 1, 2, and 3, and the biopotential measurement. The data sets for a subject were synchronized using the first and last peaks of the opening and closing peak triplets shown in Figure Before each measurement, the force applied to the electrode to be subjected to motion was set to the equivalent of 750 +/- 100 grams reading on a Soehnle Siena kitchen scale with a precision of 1 gram . This corresponded to a force of 7.35 N exerted on the electrode, and 28 mmHg pressure exerted by the electrode to skin. The 13% tolerance level of the force seems quite large, however, in the experiments we did previously we found that the motion artifact and impedance behave similarly for electrode pressures that lie between forces needed to secure the electrode on skin and forces on the electrode that cause discomfort , and ourRMS is the root mean square of the input voltage, Ix is used to denote the measured real current (RMS) and Iy is used to denote measured imaginary current (RMS). Resistance then is the real part of the complex impedance and reactance is the imaginary part of the complex impedance. To calculate the absolute value of impedance, equation  to provide the impedance variations due to motion. The DC baseline and the low frequency baseline wander were isolated by a 0.2 Hz low-pass filter (LPF). The changes related to motion were then converted into the percentage change relative to the individual baselines by dividing the amplitude of the change of the specific signal by the baseline of the said signal. In another analysis, the absolute amplitude of the change in waveforms was separately normalized between 0 and 1 for each frequency. Normalization maps the minimum amplitude of the signal waveform at hand to 0, the maximum to 1, and the values in between to the relevant value between 0 and 1.Presentation as percentage relative to the baseline, and presentation in a normalized fashion were done in order to achieve a better comparison of the motion effect. Correlation analysis between the impedance change waveforms , the motion artifact, and the applied motion pattern was done to assess their linear relationship.In frequency domain, the power spectrum density (PSD) estimates of the waveforms were calculated. Because these PSD estimate amplitudes depend on the amplitudes of the waveforms, which vary greatly due to different skin-electrode impedance values at different frequencies, the PSD plots are presented in a normalized manner. This normalization between 0 and 1 allows for easier comparison by showing the ratio of energies of the different frequency components of the waveforms without the distraction of varying plot scales. In addition to visual analysis, these normalized PSDs were then compared between the impedance changes, the measured motion artifact, and the motion pattern in order to obtain the linear relationship between these waveforms in the manner of the contained frequency components.The same calculations were done for data obtained in the absence of motion.Sample session data from one subject, all measurements, and the calculated impedance and phase are presented in Figure For further results, we have omitted the calculated impedance and the phase, since they are directly derived from the resistance and reactance.In the previous graphs, we have presented the change of the impedance in percentages relative to the baseline of the impedance. In some cases where this percentage was higher in higher frequencies and lower in lower frequencies, the waveforms became hard to visually analyze due to overlap and being too small, relative to their specific impedance baselines. In Figure The results of previously published studies on the correlation between skin-electrode impedance and the motion artifact have varied. The studies have found that motion at the electrode location creates motion artifact and causes the skin-electrode impedance to change , 10, 11,To investigate the effect of different impedance measurement frequencies on the relationship between skin-electrode impedance and applied motion and between the skin-electrode impedance, we measured 24 frequencies of impedance in a spectrum between 25 Hz and 1 MHz and applied a programmed motion pattern under monitored electrode mounting force. Consequently, as the electrodes were subjected to the same motion pattern, we measured the motion artifact as a surface biopotential.In our results, we found that impedance measurements at different current frequencies have different waveforms in response to the same applied motion. In the context of motion studies, higher frequencies of impedance measurement possess a better relationship to motion than lower frequencies in a frequency range of 25 Hz to 1 MHz. Motion has been seen to cause the motion artifact, but the relationship is not as close and linear as it is between impedance and the motion pattern.In our measurements, the skin-electrode impedance of the textile electrode for all subjects followed a similar pattern. The resistance baseline decreased with an increasing frequency. The reactance baseline, negative for the lower frequencies, decreased in absolute magnitude, until around 257 kHz where it passed into positive, and then showed a slight increase in the positive axis. This effect is consequently seen in the phase as an overall increase from a median of -60 degrees to 21 degrees. The impedance, calculated with equation  than the other subjects and that the motion artifact of these subjects is lower than the other subjects. In addition, the ECG observed between the arm electrodes was unexpectedly high for these subjects. We are not sure if and how these are related, yet an educated guess is that these two factors, when combined, might further explain the lower correlation and also be a possible subject for further studies.When looking at the PSDs normalized for ease of comparison and the correlations of the PSD’s of the impedance to the PSD of the applied motion, the above mentioned relationship is also observed. The main frequency components of the resistance change almost completely mirror the frequency components of the applied motion, with a median correlation of over 0.9 at impedance frequencies above 1.8 kHz. For impedance the median correlation is above 0.9 starting at 2.8 kHz and for reactance the median correlation is above 0.9 starting at 42 kHz, yet with a larger variability than resistance or impedance. The phase PSD has a correlation to the applied motion pattern that has a median 0.9 between 1.8 kHz and 404 kHz, yet at both ends of this range, the variation of the phase is higher than in the middle parts of this range, as seen in Figure The low correlations of the lower frequencies of impedance to the applied motion and the even lower correlations to the motion artifact that have large inter-subject variability might be the reason why some of the previous studies have found varying relationships between impedance and motion artifact.To observe what happens when there is no motion, we included a measurement in the absence of motion. We observed some fluctuations in the impedance waveforms and the motion artifact signal, but could not draw a relationship to the motion pattern in the time or frequency domain. These changes in the skin-electrode impedance and surface biopotential in the absence of motion and stable, applied force might be due to the subject involuntarily moving while breathing, or skin conductivity or voltage changes related to blood flow. However, the changes observed are very small and can be seen from the surface potential plot in Figure In terms of accuracy, repeatability and the effect of using a +/- 100 gram range for force levels, the differing electrical skin properties between subjects and also for the same subjects at different times cause larger differences than can be expected from the force range or system specifications. The BioPac measurement system has a resolution of 3 μV, the accuracy of the HF2IS is given as 1% for the output and max 5% at 5 MHz for the input and the force sensor has a linearity error of max +/- 3% and a repeatability error of +/-2.5%. In the context of electrode movement, especially in locations with bone and tendons close to the electrode location, small electrode location changes of even a few millimeters can have a more drastic effect on the motion artifact than caused by the inaccurancies due to the system components. Thus, we conclude that even our the system has some uncertainity due to the devices and experiment protocol, more important is electrode location selection and location standardization, and the testing of a high number of subjects. As we are measuring impedance in relation to motion and that the changes in impedance are more meaningful than absolute impedance, concluding from the similar trend for the data from our subjects, we think that the tolerance and accuracy levels of the system and protocols are adequate.To the best of our knowledge, this is the first paper in which the relationship between electrode motion and motion artifact biopotential to the impedance changes of the electrode over a wide frequency scale of continuous impedance measurements during the motion is studied. We observe that the baseline levels of skin-electrode impedances are similar to those observed by other studies: higher impedance at lower frequencies. The peak-to-peak amplitude of the impedance change initiated by the motion also follows the same pattern. The relative percentage change in the impedance is in the range below 2% for most of the frequencies and measurements.The important observation in our study is that all frequencies are not equal in electrode impedance measurement during motion. Furthermore, if the aim is to use the impedance change to detect or reduce the motion artifact, the best approach is not to use the frequencies associated with standard biosignal, ECG, and EMG that are below 500 Hz, but instead to use impedance measurement frequencies at ranges that correlate best with motion. For impedance, regarding our selected impedance measurement frequencies, this range lies between 17 kHz to 1 MHz, and for resistance it lies between 11 kHz to 1 MHz, yet for reactance a good linear time domain correlation was not present. Another advantage of using higher frequencies for impedance measurement is that the impedance measured at these current frequencies seems to be more sensitive to small movements and gives more accurate assessments of the applied motion at all motion levels.We observed that the motion applied causes motion artifact. However, the motion artifact shows lower correlation to the applied motion than the impedance. This correlation has a large variation between subjects, as seen in Figure The idea that the lower frequencies of impedance measurement could be better correlated to the motion artifact was not supported by our findings where in most cases the correlation in lower frequencies of impedance measurement to motion artifact was lower and the difference between frequencies was much higher than in the higher frequencies. Even with lower correlation coefficients, the higher frequencies posessed stability in their relationship to the motion artifact. It is important to note, however, that these findings might only be valid for textile contact electrodes with a flat contact surface.To conclude, according to our results from textile electrodes, the best way to use skin-electrode impedance to detect motion or to use it as a predictor in adaptive filtering to remove motion artifact is the use of frequencies higher than 17 kHz in the case of impedance where both the time domain signals and the frequency components respectively present a correlation higher than 0.8 and 0.9 to the applied motion. This relationship held true up to 1 Mhz, the highest frequency in our experiments. If resistance is to be used, then the frequencies higher than 11 kHz provide the best relationship to the applied motion.


We investigate the average turbulent wind field over a barchan dune by means of Computational Fluid Dynamics. We find that the fractional speed-up ratio of the wind velocity over the three-dimensional barchan shape differs from the one obtained from two-dimensional calculations of the airflow over the longitudinal cut along the dune’s symmetry axis — that is, over the equivalent transverse dune of same size. This finding suggests that the modeling of the airflow over the central slice of barchan dunes is insufficient for the purpose of the quantitative description of barchan dune dynamics as three-dimensional flow effects cannot be neglected. Sand dunes occur in many deserts and coastal areas. They form when the wind is strong enough to transport grains in saltation — which consists of particles moving in nearly ballistic trajectories and ejecting new grains upon collision with the bedQ, which is the main quantity characterizing dune dynamicsQ is a non-linear, increasing function of the wind shear velocity u*, which is proportional to the mean flow velocity gradient in turbulent boundary layer flow78Q scales approximately with ut, and with u*19One of the dune types with highest mobility is the barchan . Barchan12345et al.Measurements of the wind velocity over barchan dunes have been performed by several authors111213U increases logarithmically with the height z above the ground, that is U(z) = [u*/κ] ⋅ ln(z/z0), where u* is the wind shear velocity, κ = 0.4 is the von Kármán constant and z0 is the surface roughness7ρair = 1.225 kg/m3 standing for air density. However, the dune introduces a barrier to the wind and modifies the shear stress profile along the terrain. The wind velocity (U) measured at a given height above the surface thus changes along the terrain’s longitudinal profile in the presence of the dune. Since this velocity is an important parameter considered in field investigations, in our simulations we investigate the evolution of U with the along-wind position in the presence of the barchan dune.By means of Computational Fluid Dynamic simulations, we solve the averaged Navier-Stokes equations for the turbulent wind field over the terrain comprising the flat ground and the barchan dune. In the absence of the dune, the wind velocity Hch = 30 m and along-wind width of Lch = 200 m. et al.In our simulations, a barchan dune is placed in the center of a numerical wind-tunnel of height We perform two types of numerical simulations. In the first one we compute the wind flow over the two-dimensional profile shown in k − ε model17181920k − ε model performs poorly for problems where the surface has sharp discontinuities in its first derivative, such as in the case of a dune with a slip facek − ε model has proven to describe the main characteristics of turbulent flows in such problems with quantitative agreement with measurements — for instance the size of the separation zone of the flow over a backstep where the lee side makes an angle of 90° with the horizontal (see ref. 23k) and the turbulent dissipation (ε), which determines the scale of the turbulence. These variables are determined using the equations,The near surface wind flow is computed using the open-source CFD software package OpenFOAMandCμ is a turbulence model constant, I stands for the turbulence intensity. In our simulations, we take Cμ = 0.09 and I = 0.01. Furthermore, the turbulent length scale Hch is the height of the channel. We note that, although it would be interesting to investigate the sensitivity of the outcomes to these model parameters, the aforementioned values have been chosen here as they were adopted in previous modeling of boundary layer flow over different types of dune topography thereby leading to good agreement with measurements1921U = 8 m/s, which is larger than the minimal threshold for sand transport  is the wind velocity at downwind position x and U is the wind velocity over the flat ground (in the absence of the dune). In the main plot of δs(x) for a height of 1 m above the surface, as obtained from the calculations over the two-dimensional dune profile (δs(x) obtained for a height of 60 cm above the surface, using the same dune profile.where  profile . Furtherδs, over the longitudinal cut along the symmetry axis of the barchan. The results of this calculation for two different heights above the surface are shown in Next we compute the wind flow over the three-dimensional dune profile of δs observed in the field and in the two-dimensional calculation are observed in the simulation using the three-dimensional barchan shape. However, quantitatively the results obtained with the 2d and 3d dune shapes differ. In particular, the increase of δs(x) with x from the windward foot toward the crest is faster in the simulation using the three-dimensional dune shape than in the calculation of the airflow over the two-dimensional dune. This difference is an important result in particular since it is well-known that the shape of the central slice of a barchan can be well approximated by a transverse dune — which has a nearly fixed profile in the direction orthogonal to the wind31As we can see from et al.34Moreover, it is also well-known that the flow profile on the windward side of barchans and transverse dunes is affected by the flow structure in the lee side. In particular, one important difference between the flow within the separation bubble of barchans and transverse dunes is the occurrence of large three-dimensional flow patterns  arising in the lee of the former dunes. Using large-eddy simulations, Omidyeganeh We remark that the wind velocity profile depends on the height profile of the dune, which can be very different depending not only on wind conditions but also on dune size. In our simulations we have taken a dune profile in which the crest position  coincides with the position at which the sharp brink separating windward and lee sides occurs. Although such a profile is representative for large dunes, it was shown that as the barchan size decreases toward the minimal size  should be, thus, robust with respect to the three-dimensional barchan model used. Based on the results presented here, it would be interesting to perform systematic investigations in order to improve the quantitative assessment of the wind flow over different barchan morphologies, and to quantify the discrepancies between predictions of wind speed-up obtained from the full three-dimensional model and using the equivalent transverse dune. Furthermore, the implications of our results for dune dynamics should be also investigated by coupling the wind model with a sand transport model103238394041It should be further noted that real barchans may have a broad range of three-dimensional shapes, depending on several conditions including grain size and interdune flux — see for example refs upwind of the dune crest as shown in field measurementsenvelope comprising the dune windward side and the separation streamline of the flow in the dune lee . In this ansatz, the wind flow inside the separation bubble is then set as equal to zero, since net transport inside the bubble nearly vanishesupwind of the envelope’s crest , while the zero gradient boundary condition is taken for the pressure at the bottom wall and at the outlet. Finally, a free stream velocity of 8 m/s is applied at the inlet and at the top boundary, while at the outlet zero gradient condition is applied for the velocity. At the bottom wall, no-slip boundary condition is applied for the velocity. The value of roughness z0 is about 100 μm and the shear velocity upwind of the dune is about u* ≈ 0.4m/sThe values of turbulent parameters How to cite this article: Michelsen, B. et al. Two-dimensional airflow modeling underpredicts the wind velocity over dunes. Sci. Rep.5, 16572; doi: 10.1038/srep16572 (2015).