Azospirillum brasilense is a nitrogen-fixing bacterium living in association with plant roots. The genome of the strain Sp245, isolated in Brazil from wheat roots, consists of one chromosome and six plasmids. In this work, the A. brasilense Sp245 plasmids were analyzed in order to shed some light on the evolutionary pathways they followed over time. To this purpose, a similarity network approach was applied in order to identify the evolutionary relationships among all the A. brasilense plasmids encoded proteins; in this context a computational pipeline specifically devoted to the analysis and the visualization of the network-like evolutionary relationships among different plasmids molecules was developed. This information was supplemented with a detailed (in silico) functional characterization of both the connected  and the unconnected  components of the network. Furthermore, the most likely source organism for each of the genes encoded by A. brasilense plasmids was checked, allowing the identification of possible trends of gene loss/gain in this microorganism. Data obtained provided a detailed description of the evolutionary landscape of the plasmids of A. brasilense Sp245, suggesting some of the molecular mechanisms responsible for the present-day structure of these molecules. Azospirillum brasilense is a Gram-negative, aerobic, free-living nitrogen-fixing bacterium found in the soil associated with roots [ A. brasilense strains have been isolated over time. One of the most studied ones, reported as Sp245, was isolated in Brazil in 1986 from surface-sterilized wheat roots where it interacts endosymbiotically with the plant and performs nitrogen fixation [ Azospirillum might represent an evolutionary bridge between water- and soil-living bacteria [ Azospirillum plays in the interaction between plants and bacteria but also to gain a deeper knowledge of the mechanisms that brought bacteria from water to soil environments. Plasmids are particularly intriguing from this viewpoint because of their high variability and the ability to easily exchange genetic material among them and/or with the genome either of the same or of other strains. For these reasons they represent excellent candidates for a research that aims at improving the current knowledge in the fields of Azospirillum genomics and phylogenetics. Available studies showed that plasmids evolve primarily through the horizontal gene transfer (HGT) occurring between different strains belonging to the same or to different species/genera, and it is possible to map such exchanges by constructing networks linking the genes of the studied organism with the most similar ones found in the DNA molecules harbored by other strains [th roots , 2. Sevefixation . The who Synechococcus sp. PCC 7002 [ Burkholderia vietnamiensis G4 [The availability of massive database of plasmid sequences, together with the development of bioinformatics tools enabling the visualization of sequence homology relationships through similarity networks , can be PCC 7002  and Burkensis G4 . With thensis G4 , 12, 13  A. brasilense Sp245 in order to clarify (i) which and how many paralogous genes do plasmids feature, (ii) whether it might be possible to identify any correlation between the number of paralogs and the plasmid size, (iii) how paralogous genes are arranged (tandem or scattered) and organized  onto their plasmid backbone, (iv) which the functions performed by paralogs are , (v) if different plasmids have different or similar evolutionary trajectories both at intrareplicon and interreplicon level, and (vi) the extent of HGT with other bacteria.In this work a computational network-oriented visualisation and analysis pipeline was developed and used to analyse the evolutionary relationships existing within and among the replicons of A. brasilense Sp245 plasmidome .The graphical representation of the plasmids was obtained with Circos , whereasE-value.The pipeline that brings from the FASTA sequences file to the formatted table was developed specifically for this work. The programming languages used were Ruby and C++. In detail, the first script performs a BLAST search of the protein sequences against themselves, in order to find possible connections. From the BLAST result, it removes the self-hits and those that do not meet (simultaneously) all the above-mentioned criteria that are user-defined to achieve either lower or higher stringency in the analyses. Finally, the script creates two files, one with the connected sequences (the sequences scoring significant BLAST hits) and the other one with those without relevant hits. These files can be directly used to feed BLAST2GO. The text results of the BLAST online search performed inside BLAST2GO are passed to the second script, which extracts the first hit not belonging to the same strain of the query organism and builds up the table as a tab-separated text file which can be opened by any spreadsheet application or by a simple text editor. The table summarizes the features of the proteins analyzed: GI, function (Gene Ontology annotation), whether it has orthologs or not, organism with the most similar sequence (if any), and, in case, the identity percent and the e-value of 1e − 20. This annotation was automatically obtained with a custom made script, that we called BLAST2COG.Functional annotation of each of sequences in the assembled dataset was obtained according to the best BLAST hit in the COG database, with a threshold The Preferential Organismal Sharing (POS) has been recently defined as the strongest evolutionary relatedness (based on sequence identity) of each gene of a given replicon with taxonomically and/or ecologically correlated organisms .https://www.blast2go.com/). A custom made script filters the results of BLAST2GO, finding the first BLAST match belonging to a strain other than the one the analyzed gene belongs to.To find the POS of a gene, an online BLAST search has been performed, using the translated protein sequence of the gene as query, with the software BLAST2GO  relatedness to the former. This supposition is often supported by the fact that the organisms found as POS have known relationships with the analyzed strain. This relationship can be taxonomical, ecological, or both, with a prominence of the organisms sharing the same niche over the ones taxonomically near but living in a different environment. A. brasilense Sp245 plasmid genes at three different levels: (i) intraplasmid level, that is, the molecular evolution events occurring within the same plasmid; (ii) interplasmids level, that is, the evolutionary relatedness among different plasmids; (iii) interplasmid/chromosome level, that is, the cross talk among plasmids and chromosome.Replicon networks construction and analysis may allow a multilevel observation and analysis of DNA molecules structure and evolution. Such approach was applied here to study the number, the physical organization, and the function of paralogous genes ofIn this analysis, the existence of a gene coancestry is expressed as a link connecting two or more nodes, in case all the sequence identity minimal parameters . The networks obtained for the six plasmids at the identity threshold of ≥40% are shown in  A. brasilense Sp245 plasmid encoded proteins are reported in Results concerning the function performed by the A. brasilense Sp245 plasmid are summarized in  A. brasilense) and the proportion of connected sequences on the total. In Supplementary file 2 other data are reported for a more complete picture: the predicted function of the proteins, the Preferential Organismal Sharing (POS), and the identity percent and E-value of the match.The most important features of eachThe whole body of these data is visualized in From  A. brasilense.The deviation of the GC content from the molecule's mean (shown by the inner histogram) is another feature that is interesting to focus on. It is expected for a plasmid , to be q in silico characterization of connected and not connected genes (at intra- or interplasmid level) according to the COG database was made, and the results are reported in AnThe network of connected components  of plasmids is represented in  A. brasilense Sp245 plasmids , is reported in A magnification of the largest network component, including 104 proteins encoded from five A. lipoferum, which has been shown [ A. brasilense, while, with the decrease of plasmids size, more and more strains are featured, as can be seen visually by the mosaic-like structure of the inner circle.For the proteins belonging to the plasmids, the identification of the POS was carried out. Data obtained are reported in en shown  being th A. brasilense Sp245 plasmids and chromosome were constructed; two of them (at an identity threshold of ≥50% and 100%) are shown in  A. brasilense Sp245 chromosome, even though their number is variable. Overall, as expected, the higher the plasmid size is, the higher the number of links and genes shared with the chromosomes is. A further analysis , especially A. lipoferum 4B . With the decrease in size of the molecule, the POS becomes more and more varied. Observing the organisms whose proteins are homologous to those of A. brasilense (Supplementary file 2), it can be noted that there are no clusters of genes related to the same strain, exception made for A. lipoferum. This could lead to the supposition that no particular clusters of genes are involved in horizontal transfer with distantly related strain. To support this hypothesis, it can be useful to look at the GC content since compositional features can be used to trace back possible HGT events [The main results of theT events : indeed,Concerning the intraplasmid network presented in The graphs classifying the connected plasmid sequences with the chromosome according to their identity percentage  show tha Azospirillum genus [ A. brasilense plasmids encoded proteins we focused our attention on the connected components of the overall network built in the first stages of this analysis.As for the COG characterization of the proteins , dividedum genus . In ordeIn  Azospirillum associates to. As with the other networks components, also here we can see the tendency to have nodes scattered through all the plasmids , without particular differences among them, thus confirming the general observation of an intense exchange of genes among plasmids.From Finally, analyzing the connections of the plasmids versus the chromosome (Supplementary file 3) the idea that different replicon types  inside the same cytoplasm may follow different evolutionary trajectories showing a different level of trade-off and reshuffling with the chromosome genome is reinforced. Azospirillum Sp245, which plays an important role in many ecosystems. Duplication events shaped the architecture of A. brasilense replicons as highlighted by the presence of paralogous genes in other parts of the plasmidome, usually on a different plasmid. From this we supposed an intense activity of gene exchange among plasmids residing in the same cell. We looked for the POS  and found out that the taxonomical proximity is more important than the sharing of the same ecological niche, for most of the POS were related to A. brasilense close species Azospirillum lipoferum.The analyses performed in this work shed more light on the plasmidome ofInterestingly, both the intraplasmid and plasmid versus chromosome networks highlight that paralogous gene families may show a different distribution in identity thresholds categories in the different plasmids. This finding strongly suggests that different replicons inside the same cytoplasm may follow unequal molecular evolution processes: in this specific study case, plasmids AZOBR4 and AZOBR5  are enriched in high identity (recently formed) paralogous gene families.A functional annotation of the connected proteins revealed that a large set of them belongs to the ABC superfamily, primarily involved in the transport of amino acids. This may suggest the presence of an evolutionary trend in the  sharing of such genes in this particular organism.Finally, in this work a simple yet effective bioinformatics pipeline for the network-oriented investigation of informative molecule was presented, allowing also scientists lacking a strong bioinformatic background to perform the analyses presented herein.Supplementary File 1: Pipeline followed to find the connected proteins coded by the Azospirillum brasilense plasmidome and to characterize their function.Supplementary File 2: Predicted function, Preferential Organismal Sharing (POS), and identity percent of the first BLAST match for each connected protein of the Azospirillum brasilense plasmidome.Supplementary File 3: Plasmids – Chromosome connections at different identity thresholds, from 40% to 100%.Supplementary File 4: List of all proteins coded by the Azospirillum brasilense plasmidome, with their functional characterization according to the COG (Cluster of Orthologous Groups) database.


Spring constant calibration of the atomic force microscope (AFM) cantilever is of fundamental importance for quantifying the force between the AFM cantilever tip and the sample. The calibration within the framework of thin plate theory undoubtedly has a higher accuracy and broader scope than that within the well-established beam theory. However, thin plate theory-based accurate analytic determination of the constant has been perceived as an extremely difficult issue. In this paper, we implement the thin plate theory-based analytic modeling for the static behavior of rectangular AFM cantilevers, which reveals that the three-dimensional effect and Poisson effect play important roles in accurate determination of the spring constants. A quantitative scaling law is found that the normalized spring constant depends only on the Poisson’s ratio, normalized dimension and normalized load coordinate. Both the literature and our refined finite element model validate the present results. The developed model is expected to serve as the benchmark for accurate calibration of rectangular AFM cantilevers. Spring constant calibration of rectangular atomic force microscope (AFM) cantilevers is of fundamental importance in the measurement of pico/nano-Newton scale forces by an AFM with applications to many emerging technologies such as atomic manipulation7820According to the dimensional method, the beam theory-based equation of spring constant for a rectangular cantilever is19E is the Young’s modulus of the material, t are the width, length and thickness of the cantilever, respectively. It is well known that equation  << b. For relatively wide cantilevers, the plate theory should be used, instead of the beam theory, to obtain more accurate results. However, the accurate analytic solution to the governing equation of a rectangular cantilever plate was extremely difficult to obtain due to the complexity of the mathematical model. Accordingly, the approximate/numerical solutions had to be developed to calibrate the AFM spring constants on a case-by-case basis. Obviously, the analytic solutions are necessary to capture the essence of the problem by quantitatively realizing the relation among the key parameters/quantities, which cannot be realized by an approximate/numerical solution.where According to the classical Kirchhoff thin plate theorywhere We examine the mechanical behavior of the rectangular AFM cantilever by constructing the Hamiltonian variational principle from the original Hellinger-Reissner variational principle for the thin plate bending problem. The corresponding Hamiltonian system-based governing matrix equation is derived . The normalized load-point deflections P at the central line .


A 47-year-old woman presented for surgical resection of basal cell carcinoma of the right lower eyelid with immediate reconstruction. Resection and reconstruction were successfully performed; however, intraoperatively, the patient rapidly developed profound chemosis.What is chemosis and what are some predisposing factors?What is the pathogenesis of chemosis?What is the preferred management of chemosis?What surgical techniques may be used to minimize the risk for developing chemosis?Chemosis is defined as edema of the conjunctiva. It is most commonly seen immediately following surgical procedures on the eye, especially the lower eyelid, but may arise any time during the postoperative period.2,Chemosis is commonly caused by impaired lymphatic drainage, increased inflammation, and/or venous congestion. Allergic reaction, infection, or trauma may be the precipitating event that induces an inflammatory conjunctival response. The course of chemosis is thought to be caused by a positive feedback loop: conjunctival swelling leads to desiccation and increased inflammation. As the swelling increases, there is worsening malposition of the eyelid and the cornea, resulting in a disruption in tear flow dynamics over the surface of the eye.2,2The treatment of chemosis is based on blocking or disrupting the positive feedback loop. Severe intraoperative chemosis is treated with a conjunctival and Tenon's capsule incision to release fluid from the subconjunctival space.2In procedures such as cantholyses or canthotomies, care should be taken to avoid the lymphatic vessels lateral to the eye and surrounding the cheek. Both veins and lymphatic vessels course under the superficial musculoaponeurotic system (SMAS) in the cheek region. Oftentimes, dissection in this area is carried out in the sub-SMAS layer, leading to destruction of the lymphatic vessels and an increased risk of chemosis. Alternatively, a dissection under the periosteum avoids significant trauma to the sub-SMAS layer and markedly reduces the risk of chemosis.6In the case described earlier, the patient developed chemosis intraoperatively, which is rarely seen.


Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content. The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can provide a non-invasive comprehensive view of brain activity with high temporal (EEG) and spatial (fMRI) resolution. The EEG technique gives a measure of the synchronized electrical activity of large populations of neurons. Despite its high temporal resolution, which is in the order of tens of milliseconds, the EEG suffers from the spatial inverse problem, related to the difficulty in inferring the spatial location of neuronal sources in the brain from the potentials recorded at scalp level The fMRI technique based on blood oxygen level-dependent (BOLD) contrast gives information about the hemodynamic processes associated with the neuronal activity. The BOLD measure is extended to the whole brain and has a spatial resolution in the order of mm, but suffers from an ill-posed temporal problem, as it is hard to extract the timings of events that caused the measured hemodynamic modifications As the strengths and weaknesses of the two approaches are complementary, the simultaneous recording of EEG and fMRI holds great promise for cognitive neuroscience. Among the possible applications, it is worth mentioning the pre-surgical evaluation for epileptic diseases An area of increasing interest is the analysis of EEG and BOLD signals during resting wakefulness. The spontaneous electrophysiological activity exerts a large influence on sensory, cognitive and motor-driven processes Despite growing knowledge of BOLD RSNs, their underlying electrophysiological signature is still a matter of discussion. One of the main topics to clarify is how the coherent slow fMRI hemodynamic fluctuations are coupled to the fast neuronal activity recorded with EEG. However, a meaningful exploitation of EEG-fMRI information relies on good data quality, especially in the case of resting-state applications. Indeed, while in the study of event-related brain response the interesting information is usually restricted to a group of channels and to specific intervals and is known a priori, in resting-state analysis the global state of the brain is of interest.Despite the potential advantages of EEG and fMRI integration, its main concern regards the removal of artefacts from the EEG signal recorded in the magnetic resonance (MR) environment. The main artefact affecting the EEG signal is the gradient artefact (GA), caused by the switching of magnetic field gradients required for MR image acquisition. Its amplitude can be up to 100 times larger than the original EEG signal, but since it occurs at fixed time intervals, it is easily removable by subtracting an average GA template from the EEG signal at the channel level The combination of these factors increases the spatial and temporal complexity of the PA. Up to now, several methods have been proposed for its removal. A first group of techniques operates at the channel level by subtracting from each EEG channel a template of the artefact. There are two common ways to estimate such a template. The averaged artefact subtraction method (AAS) However, for the ICA decomposition to be meaningful, the sources should be stationary in space, and often this is not the case with EEG signals. Indeed, not only the spatial topography of PA contribution changes during the cardiac cycle, but the neuronal signals themselves can also be strongly non-stationary over time. Although the ability of ICA to remove PA has been confirmed in more than one work Recently it was proposed to apply OBS before ICA in order to help in meeting the ICA assumptions and to check if the ICA performance could improve if a reduced amount of artefact was present. The OBS-ICA combines the strengths of both approaches and was confirmed capable of producing satisfactorily improved corrections Although Vanderperren et al., Starting from the assumption that OBS-ICA has the potential to improve the quality of EEG signal retrieval Twelve healthy right-handed volunteers with no history of neurological disorders took part to the study . All of them signed a written informed consent to the protocol, in accordance with local ethical committee guidelines.All EEG data were recorded simultaneously with fMRI recordings in a Siemens 3T Trio MR scanner (Germany). EEG data were acquired using an MR-compatible EEG system . The EEG cap  included 63 scalp electrodes distributed according to the 10–20 system and one additional ECG electrode placed on the participants’ back. EEG signals were acquired relative to an FCz reference, with the ground in correspondence of Iz (10-5 electrode system). The EEG data were sampled at 5000 Hz, with a band-pass filtering of 0.016–250 Hz. The impedance at each electrode was kept lower than 10 kΩ.The study protocol was approved by the local human subjects review board at RWTH Aachen University and was carried out in accordance with the Declaration of Helsinki. Two phases of rest lasting 6 minutes (i.e. 180 fMRI scans) were separated by 3 runs of a visual oddball task lasting 10 minutes and 8 seconds (i.e. 304 fMRI scans) per run. During resting wakefulness the subjects were asked to keep their eyes closed. The analysis was performed only on the EEG resting-state recordings, the data from the visual oddball task are presented elsewhere A schematic illustration of the entire processing stream is provided in Each component was back-projected to the EEG signal space, and the variance of the resulting signal across the PA interval (0–700 ms after R) was calculated and compared to the initial EEG variance during the same interval, following the same procedure described in We evaluated the cross-correlation between each IC and two PA templates. Since the cardiac-related artefact was found to change polarity from one side of the head to the other Blocks formed by four PA intervals were averaged and the PACF was calculated, similarly to that performed in The thresholds of the automatic selection criteria were chosen on an empirical basis, expressly equal across subjects.For each IC, a continuous wavelets transform (CWT) was performed in each PA interval, and the CWTs across intervals were averaged. We used the Morlet complex family of wavelets  and investigated frequencies going from 1 Hz to 20 Hz with twenty steps in between. Basing on the time-varying frequency content of the PA templates, the ICs having a peak time locked to the R peak between the delta and alpha band were selected and removed. To check the quality of PA removal, EEG epochs from −200 ms to 1 s w.r.t. R peaks, before and after ICA correction, were extracted and compared. We then assessed the performance of the different ICA corrections by means of three different criteria. When the effects of PA correction on the alpha content were examined, only the occipital channels were considered, otherwise all the EEG channels were used to assess the quality of PA correction. In each validation, the quality measures of the eight ICA-based methods were compared through a non parametric Kruskal Wallis (KW) test, for each dataset separately; if significant differences emerged at the group level, the KW statistics were used in a multiple comparison test to extract the pairwise differences. This was followed by two further comparisons, between 1) the four selection methods  and 2) the two ICA intervals (across datasets and selection methods).Assuming that the maximum signal amplitude corresponds to PA occurrence, the ratio between the maximum signal variation after and before ICA correction is proportional to the amount of artefact removed by ICA. This ratio, averaged over all the EEG channels, was therefore used to estimate the effectiveness of the PA correction.Subjects having an evident alpha peak in the mean PSD before ICA were selected  with the aim of checking if the alpha rhythm could be retrieved after ICA correction. For each epoch, we computed the power spectral density (PSD) of the occipital channels with an autoregressive (AR) spectrum. The PA shows a main contribution in the low frequency range (between around 4 and 8 Hz) and an additional one in the alpha range (from 8 to 13 Hz). Since during rest the signal of interest in the occipital channels is mainly in the alpha band, assuming that the neuronal alpha rhythm contributes to the most of the alpha power, a good PA correction should remove as much of the low frequency contribution as possible while maintaining most of the alpha power. For this purpose, we estimated the mean PSD across epochs and occipital channels  and looked at the ratio between delta (delta ratio), theta (theta ratio) and alpha  power after and before ICA. Additionally, we defined a quality coefficient (QC) as the ratio between the alpha ratio and the mean value between delta and theta ratios: such a measure is proportional to the amount of 1) low frequency power cancelled and 2) alpha power preserved.In addition to the statistical analysis of the QC values (QC_test), a comparison including all subjects and channels was performed (group_test). In this case, the previous assumptions on the alpha contribution were no longer reliable, furthermore no information of interest was expected in the higher frequency bands. Therefore, the quality of correction was only evaluated in terms of the proportion of delta and theta power that was removed.We added this validation criterion to provide further details about the ICA correction effects on the signal spectral content. Indeed, the change of frequency power can give ambiguous information, especially if the alpha band is considered. Since the percentages with which PA and neuronal signals contribute to the total alpha power are not known a priori, it is difficult to state whether the physiological alpha rhythm is preserved or not just by looking at the alpha power change. Nevertheless, the alpha temporal properties can help in distinguishing PA alpha from neuronal alpha, since the latter is not temporarily locked to PA occurrence. As a consequence, the modifications in time-varying alpha content induced by ICA can provide further information on the correction quality.post) from the CWT of the uncorrected signal (CWTpre). After visual inspection of such difference, dubbed CWToff, we used its time derivative (averaged over both time and frequency) as metric for the correction quality. Under the hypothesis that low and high values of derivative can be associated to physiological and PA-related frequency components respectively, the selection methods corresponding to higher mean derivatives of CWToff were evaluated better than others.More generally, in the entire frequency spectrum a good correction can be assessed by looking at the continuous frequency component  compared to the PA-locked ones , without having any a priori knowledge on 1) their contribution to the total power and 2) the frequency band of interest. The reader can have a better idea of the difference between physiological and artefactual frequency contributions by looking at Across datasets and ICA intervals, it emerged that the variance-based selection criterion removed less components compared to the others, with 9.2±2.6 (mean ± standard deviation) out of 63 ICs removed, against 19.6±7.3 of the correlation method, 21.4±4.9 of the wavelets method and 20±5.3 of the partial autocorrelation function method. The results of each validation method relative to both the datasets are shown below.The PTP value comparison  led to the best results in terms of PTP ratio, because in both datasets the mean percentage of residual PTP associated with this method was minor compared to the others. The KW test between all different methods showed significant differences only in Dataset2 (p<0.02), where the multiple pairwise comparison showed that when the ICA matrix was calculated from the whole dataset, the variance selection method (ICA_whole_pvaf) performed significantly better than the correlation method (ICA_whole_corr) (p<0.05). By comparing the four selection methods across ICA intervals and datasets, significant differences emerged (p<0.01). In particular, the pvaf method performed better than the corr method (p<0.01) and pacf method (p<0.05). Despite the lower PTP ratio, no significant differences were detected with respect to the wave method. The performance of the corr method was poor in comparison to the others. The statistical test between the two ICA intervals showed no significant differences; indeed, the performance of ICA_whole with respect to ICA_R was variable and dependent on the selection method and the dataset under examination.The comparison between the eight ICA-based methods based on their frequency content led to partially conflicting results. Indeed, while the group_test results were in line with the PTP value results, the QC_test provided discordant information with respect to them.The group_test  confirmed the capability of the pvaf method to remove the low frequency artefactual contribution. The values of the ratio between the delta and theta power after and before the eight ICA corrections are listed in the upper panel of The results of the QC_test  are listed (mean ± standard deviation) in Nevertheless, we could identify differences in the eight ICA-based methods’ performance. In contrast with the PTP validation, the selection method based on variance contribution was associated with the lowest QC, regardless of the interval used for ICA calculation: the pvaf method reduced the LF power more than the others, but it also cancelled the majority of the alpha power. When looking at both datasets, the other selection methods led to good and comparable results in terms of QC. In some cases, the corr and pacf methods preserved a higher percentage of alpha power than the wave method, but in such cases they were less effective in removing the LF contribution. By comparing the two ICA intervals (across datasets and selection methods) and the four selection methods  separately, no significant differences emerged. However, the wave selection method had the highest mean QC value (QC = 1.01±0.4), immediately followed by corr (QC = 0.95±0.42) and then by pacf (QC = 0.89±0.39) and pvaf (QC = 0.75±0.28) ones respectively. The wave method removed more alpha power compared to the pacf and corr ones, but the method also removed more components in the delta and theta frequency ranges (data not shown). off of each ICA-based method, representing the time-varying frequency components removed at the group level, allowed us to easily discriminate between poor and good corrections. The qualitative and quantitative comparisons based on CWToff_occ  and CWToff_all  led to results that were in line with the findings of the previous QC_test: indeed, they confirmed the higher reliability of the wave method with respect to the pvaf one in preserving the information of interest.The visual inspection of the CWToff are described hereinafter. The MD values are listed in off_all, the KW statistics showed significant differences between the eight ICA corrections (p<0.01 for both datasets). Looking at the pairwise comparisons, in Dataset1, ICA_R_wave method performed significantly better than ICA_R_corr method (p<0.03), whereas no significant pairwise differences emerged in Dataset2. No significant differences emerged from the comparison between the two ICA intervals (across datasets and selection methods), whereas the comparison between the four selection methods showed significant differences (p<0.01), with the corr and pacf methods significantly worse than the wave and pvaf methods (p<0.01). In particular, the wave method was first-ranked, followed by pvaf, pacf and corr methods.The results of the quantative comparison based on the mean derivative (MD) of the CWToff_occ, where significant differences were found between the eight ICA corrections in Dataset1 (p<0.03), but not within the single pairs of methods. Again, significant differences emerged between the four selection methods but not between the two ICA intervals (p<0.01). The rank was the same as in CWToff_all. The pairwise comparison showed that the corr method was significantly worse than pvaf and wave methods (p<0.01), while the pacf method was just worse than the wave method (p<0.02).Similar results emerged from the analysis of CWToff and CWTpost , from which emerged the capability of the wave method to remove the time-varying alpha, locked to the PA, while leaving intact the continuous alpha. The visual inspection proved the poor performance of the corr method, which left the time-varying contribution related to the PA untouched, and confirmed the tendency of the pvaf method to remove information of interest. The pacf method performed better than corr but worse than wave and pvaf methods.These quantitative findings were confirmed by the visual inspection of CWTpost_occ and the CWToff_occ of the eight ICA corrections, relative to Dataset1, are shown as example in off_occ (right panels), displaying that 1) the wave method removed only the PA-related alpha and 2) the pvaf method removed the PA more than the others but together with a portion of continuous alpha power.The CWTThe main objective of the present study was to identify the optimal ICA parameters for the removal of PA from EEG data recorded in an MR environment, after OBS correction. Since our interest was the analysis of spontaneous brain activity with EEG and fMRI, we discussed the effects of different ICA parameter settings on resting-state EEG data recorded at 3T. We compared two intervals for the calculation of the ICA mixing matrix, 1) the entire signal and 2) the PA intervals, together with four methods for selecting the PA-related ICs, based on their 1) contribution to the artefact variance, 2) correlation with PA templates, 3) wavelets transform and 4) partial autocorrelation function. The quality of the EEG cleaning was assessed by looking at the changes occurring after ICA correction in the EEG signal around the R peaks (from −200 ms to 1 s after it). Three different criteria were considered, based on the EEG 1) peak to peak amplitude, 2) batch spectral content and 3) time-varying spectral content. The comparison was performed on two groups of datasets relative to the same 12 subjects: the general agreement between the outcomes of the two comparisons highlighted the reliability of each ICA correction, whose performances were usually reproducible across datasets. The selection of PA-related ICs based on their wavelets transform emerged as the best compromise between the amount of removed PA and the preservation of the neuronal alpha content.To the best of our knowledge, this is the first study comparing different ICA corrections in terms of their capability to retrieve resting-state data information from data measured in an MR environment. Indeed, the widespread comparison between OBS, ICA and OBS-ICA methods described in In our work, among the several methods proposed to remove the PA, we focused on OBS-ICA combination, found to be capable of improving the correction performed separately by each of the two techniques The quality of PA correction was evaluated from different perspectives. The PTP comparison looked at the change in the maximal variation of the EEG signal due to ICA correction, assuming the PTP value to correspond with PA occurrence. However, this criterion provided information regarding the amount of PA removal only, whereas the validations based on the frequency content change were also potentially sensitive to the deterioration of the signal of interest; in this application, the latter was identified as the alpha rhythm clearly visible in the occipital channels of four subjects , and more in general as the frequency components unlocked to PA occurrence .The BFC criterion inspected the modifications induced by ICA correction to the EEG batch frequency spectrum, giving a quantitative measure of the power change in each band of interest . This approach assumes that the PA spectrum is characterized by peaks at heart-rate frequency and its harmonics A first quality check was performed on the occipital channels of the four subjects with a visible alpha peak, where the PA contribution to the alpha band was estimated to be less than the neuronal one. We therefore considered the quality of each ICA correction as proportional to the percent of 1) removed low frequency power (delta and theta bands) and 2) preserved alpha power. For this purpose, we defined a quality coefficient as the ratio between the former and the latter. With respect to the PTP comparison, this validation accounted for both the artefact removal and the recovery of the underlying information; on the other hand, we could not define a range of QC values determining a good correction, since the proportion of alpha power related to PA was unknown. In this application, we considered the ICA corrections associated with higher QC as better than the others, but in doing this we discarded the presence of a PA contribution to the alpha power.To make the statistical analysis stronger, we additionally evaluated the change in batch frequency power in all subjects and channels. However, the assumptions on the predominance of alpha physiological content in the total alpha were not valid anymore, therefore we only investigated the amount of low frequency power that was removed.In summary, the BFC validation provided more detailed information with respect to the PTP comparison, but was limited by the impossibility to distinguish between PA-related and physiological contributions in the same frequency band. The investigation of the time-frequency information proved to be more suitable for this purpose.The TFC criterion inspected the modifications induced by ICA-based methods to the EEG time-varying frequency content using the CWTs. Since the PA-related frequency content was locked in time to PA occurrence, while the physiological one was independent, we evaluated the quality of cleaning in terms of the capability to preserve the long lasting contribution and cancel the one locked in time to the PA occurrence. In particular, we analyzed the time-frequency components that were removed by each ICA correction, both visually and quantitatively. This criterion helped in discriminating the neuronal and PA-related frequency contribution and provided further valuable information for the quality assessment.It is worth mentioning that the two frequency-validation criteria (BFC and TFC) are specific to our work with respect to the previous methodological comparisons and proved to be extremely useful in the assessment of the effects of each ICA correction on the signal of interest. The results of the comparative analysis strengthen the need to consider different factors when assessing the quality of PA correction. While the validation criteria agreed on the similar performance of the two intervals for ICA calculation, their findings on the four selection methods were more discordant. Indeed, the PTP criterion led to conclusions that were partially in disagreement with the other validation criteria. The former showed that the selection of components based on variance led to the greatest PA removal and marked it as the best selection method. On the contrary, the QC and TFC validations revealed that this criterion removed a higher percent of alpha power than the other selection methods, including part of the neuronal alpha signal. The visual inspection and quantitative TFC comparison revealed the inability of the variance selection method to discriminate between neuronal and PA-related components and the higher reliability of the wavelets selection method to this end.Summarizing the results of the three validation criteria across the two datasets, the selection of the PA-related ICs based on their wavelets transform emerged as the best compromise between the reduction in the PA amplitude and the preservation of the underlying resting-state information. Indeed, in most cases looking at the time varying frequency content allowed us to distinguish easily between neuronal and artefactual components, given the differences in their temporal properties. The wavelets-based selection criterion represents a novelty of our comparison with respect to previous ones. Among the other selection methods, the one based on the ICs variance showed a more robust performance compared to the correlation and autocorrelation ones. The pvaf method may represent a good choice in cases where an automatic method is required, such as when a big dataset has to be analyzed in a short time, or in datasets recorded in conditions different from rest. In future applications, it could be worth combining the results of pvaf and wave methods, in order to benefit from their strengths and overcome their complementary weaknesses. In principle, their integration allows to remove from low to high frequency PA contributions and at the same time eliminates the risk of removing physiological components.Regarding the two signal lengths for ICA calculation, i.e. the whole dataset or only the PA intervals, both of them led to acceptable results and none outperformed the other, as determined by the quantitative comparisons. The variability in the performance of the selection methods across the subjects of the group was hypothesized to be related to the overall quality of the EEG acquisition that in turn influences the goodness of the detection of the R peaks and the performance of OBS and ICA decomposition.The setting of proper parameters represents a crucial step that influences the performance of each selection method. The parameters chosen in our study led to the removal of almost one third of the components in the corr, wave and pacf methods, a higher percentage with respect to the pvaf method. Nevertheless, the latter influenced the original signal more than the others, indicating that the information about which components are removed is more relevant than the number itself.In the variance contribution method, the 2.5% threshold was chosen based on empirical observations: when using higher thresholds, such as 5%, only a few components were removed (the first sorted by energy), which typically did not fully resemble the artefact. Instead, the 2.5% threshold setting also allowed to remove cardiac related artefacts with lower energy. It is worth mentioning that the interval used for computing the explained variance has an effect on the results and in turn influences the choice of an appropriate threshold. In our study, the fact that the highest variance contributions were found in the first components indicates that our PA interval (from 0 to 700 ms after the R peaks) did not always match the PA occurrence. On the other hand, the high temporal and spatial variability of the PA within and between subjects makes the choice of an interval appropriate for all subjects very challenging.In the selection based on correlation, two factors are determinant, i.e. the quality of the ECG signal or template and the correlation threshold. In our study, we decided to use an artefact template instead of the ECG signal, since the latter seemed to be different from the artefact occurrences in the EEG signal; nevertheless, from our results we could deduce that it is possible that our template did not resemble the cardiac artefact, at least in the majority of subjects. The reason for this could be that the template was estimated from the EEG signal before OBS correction. Regarding the thresholds, the choices between absolute or relative thresholds and of the threshold value are not trivial.The selection method based on wavelets has the major drawback of being manual, relying on the user’s ability to recognize the PA frequency contributions. To optimize the quality of PA removal, the user has to train himself to inspect the IC signals together with their time-varying spectral content. In the current application, the performances of the wave method were not optimal, because the low frequency contribution of the PA was not removed as efficiently as when using the pvaf method. After proper training, the user may be able to identify all the artefactual components. Either way, the combination of visual inspection and quantitative indices for the selection of components would be very beneficial.In the selection method based on the ICs PACF, the most delicate step was related to the setting of the threshold, at least in our application. Since many ICs had a peak in correspondence of the R–R distance, we decided to remove only the ones with a peak amplitude above a certain threshold, i.e. one third of the maximum amplitude. A comparison between different thresholds would also be useful in this case.It is worth mentioning that the overall performance of the selection methods strongly depends on the quality of the ICA decomposition. In this study we used the Infomax ICA algorithm, which was proved to be effective when used for PA correction in A full exploitation of the potentials of EEG-fMRI integration is possible only if an optimal cleaning of the EEG signal from the MR related artefacts is performed. The cardiac-related artefact has variable characteristics over space and time that make it difficult to remove. This study focused on the PA correction based on the combination of OBS and ICA and compared eight different ICA corrections, i.e. two intervals for the ICA calculation and four methods for selecting the PA-related components. Different criteria for the assessment of the quality of PA removal were used, some sensitive to the artefact removal, others also to the preservation of the information of interest. The two intervals of ICA calculation led to similar results, whereas the selection of the artefactual components based on their wavelets transform emerged as preferable to the other selection methods, since it resulted in the ability to highlight the PA-related components, making them easily distinguishable from the neuronal ones. The results were usually in agreement across the two datasets, thus confirming the reproducibility of the performance of each ICA correction algorithm. Even though the quality of the PA removal largely depends on the performance of the ICA decomposition, the present work provides valuable information on the optimization of the selection of PA-related ICs and on the assessment of the effects that each PA correction has on the EEG signal.


A cognitive radio sensor network (CRSN) is a wireless sensor network whose sensor nodes are equipped with cognitive radio capability. Clustering is one of the most challenging issues in CRSNs, as all sensor nodes, including the cluster head, have to use the same frequency band in order to form a cluster. However, due to the nature of heterogeneous channels in cognitive radio, it is difficult for sensor nodes to find a cluster head. This paper proposes a novel energy-efficient and compact clustering scheme named clustering with temporary support nodes (CENTRE). CENTRE efficiently achieves a compact cluster formation by adopting two-phase cluster formation with fixed duration. By introducing a novel concept of temporary support nodes to improve the cluster formation, the proposed scheme enables sensor nodes in a network to find a cluster head efficiently. The performance study shows that not only is the clustering process efficient and compact but it also results in remarkable energy savings that prolong the overall network lifetime. In addition, the proposed scheme decreases both the clustering overhead and the average distance between cluster heads and their members. The senRecently, research activities in the field of cognitive radio have been increasing rapidly. Usually, a cognitive radio network is assumed to be implemented as a secondary network that exploits the intermittent vacant channels of a primary network . The ideClustering in a CRSN is similar to clustering in an ordinary WSN. Each cluster consists of one cluster head and a number of cluster members. The cluster members sense the attributes of the target environment and send the sensed data to the cluster head. The cluster head aggregates the data sent from its members and forwards the aggregated data to the data collection entity called the sink or base station. Indeed, clustering is considered a proper topology handling method for a CRSN, primarily because only the cluster heads need to perform cognitive radio management tasks instead of all the sensor nodes, which reduces the total energy consumption. However, clustering in a CRSN has an additional requirement: that is, to form a cluster, the sensor nodes not only have to be in the transmission range of one another but also have to operate in the same communication channel, as illustrated in In some works in the field of CRSNs, the clustering methods are assumed or fixed –9. The cTemporary support node: The cluster head assigns one sensor node called the temporary support (TS) node to serve as a support node for a short time. The TS node broadcasts an invitation packet on each channel. Because the cluster head should stay on its operating channel to accept sensor nodes' registration, it needs the help of the temporary support node to send out the invitation. The invitation packet contains the information about the existence and operating channel of the cluster head of the TS node. As each sensor node might tune to a different channel, the sensor node might be unable to discover a cluster head even though the cluster head is located within the sensor node's transmission range. Therefore, the role of the TS node is to alert the sensor nodes that have not joined any cluster about the presence of the TS node's cluster head.Two sub-phases of cluster formation: The cluster formation process consists of two sub-phases: cluster head discovery and cluster member invitation. In CRSNs, it is difficult for sensor nodes to find a cluster head. The two sub-phases enable the sensor nodes to find a cluster head effectively.Partial spectrum sensing: The sensor nodes do not carry out the spectrum sensing process on all the channels but only on some part of the channels to conserve energy and time. This is not a usual approach because the sensor nodes are usually required to perform full spectrum sensing or even cooperative spectrum sensing ,15. In oCommunication frequency selection: Intra-cluster transmissions are assigned a high frequency, whereas the inter-cluster transmissions are assigned a low frequency. High frequency channels support higher data transmission rates but shorter transmission ranges and, thus, they are suitable for data transmission between a cluster head and the cluster members. Low frequency channels, on the other hand, support longer transmission ranges and consume less energy and, thus, they are suitable for one-hop data collections from cluster heads to the sink.Fundamentally, clustering in CRSNs should consider the energy consumption of the sensor nodes because this directly affects the network lifetime. In the same approach, clustering overhead has to be minimized in order to achieve efficient power consumption, while supporting both event-driven and regular data collection. Motivated by providing a suitable clustering method for CRSNs, as described above, we propose a novel energy-efficient and compact clustering scheme called clustering with temporary support nodes (CENTRE). CENTRE aims to improve the network performances with the deployment of temporary support nodes. Here, we present the main features of CENTRE:The deployment of sensor nodes is random, dense, and redundant. This assumption implies that a number of sensor nodes might be excluded (put to sleep) during the data collection activity, without affecting the WSN's sensing coverage functionality.Each sensor node is equipped with one cognitive radio transmitter.When a sensor node (of any class) simultaneously receives more than one packet, it receives one packet successfully while discarding the others.The medium access control protocol is based on time division multiple access (TDMA).The CRSN application requires periodic data collection and there is at least one reserved, low-frequency common control channel between the sink and the cluster heads. However, there is no common control channel among the high-frequency channels.The CRSN is deployed in a remote location where no primary user is present. Even though there is no primary user, the network could be considered a CRSN because the sensor nodes are equipped with cognitive radio capabilities such as dynamic spectrum access and transmission parameters reconfigurability. We are aware that wireless networks employing cognitive radios should consider the interference to the primary users. Indeed, we plan to include primary users in the future work (Section 4), but in this paper, our main objective is to introduce our novel approach for clustering that involves a particular cluster member to be a temporary support node and to evaluate its performance. Even though we do not include primary users, our underlying network still can be categorized as a CRSN because it is a wireless sensor network and the sensor nodes are equipped with cognitive radio capability, according to the definition of cognitive radio given in .In CENTRE, the following assumptions are made:The principal contribution of this paper is the development of a clustering method suitable for CRSNs with low energy consumption. Besides energy conservation, the proposed work is shown to have low clustering overhead and a short distance between cluster heads and their members. Another contribution is that a novel approach of clustering is developed by introducing the concept of a temporary support node. With the help of this temporary support node, the proposed work is able to perform well under CRSN environment. The rest of this paper is organized as follows: Section 2 explains the proposed CENTRE algorithm and its features. In Section 3, the performance of CENTRE is evaluated. Finally, Section 4 presents the conclusions drawn.2.CENTRE, the clustering method designed for CRSNs, is performed in rounds. Each round consists of two phases: the cluster formation phase and the data transmission phase. The cluster formation phase consists of two sub-phases: cluster head discovery  and cluster member invitation . The durations of each phase and sub-phase are fixed and predetermined.In sub-phase 1, the sensor nodes search for the cluster head. In a CRSN, however, the sensor nodes might not be able to locate a cluster head even though the cluster head may be inside their transmission range because the sensor nodes and the cluster head use different channels. This condition is anticipated in sub-phase 2 when each cluster head actively search for sensor nodes that can become its members, with the help of a TS node. After the completion of the cluster formation phase, the clustering process is completed with one cluster head in each cluster. The data transmission phase that follows involves multiple pairs of intra-cluster and inter-cluster data transmission. During the intra-cluster data collection, the cluster members send the sensed data  to their cluster heads using one of the high-frequency channels agreed upon with the cluster head. During the inter-cluster data transmission, the cluster heads send the data to the sink by one-hop transmission using one of the low-frequency channels assigned by the sink.Sensor node that does not belong to any cluster, simply sensor node (SN)Cluster head (CH)Sensor node that connects to a cluster head, namely cluster member (CM)Temporary support (TS) nodeThe sensor nodes are divided into four classes:During the initial deployment, all sensor nodes are in the class SN. By adopting CENTRE, some sensor nodes would become CHs and CMs by the end of cluster formation sub-phase 1 while the rest stay as SNs. During cluster formation sub-phase 2, a number of CMs might be selected to act as TSs (maximum one TS per cluster). When the tasks of the TSs are completed, they return to function as CMs. However, at the end of sub-phase 2, some sensor nodes might still be SNs; in other words, they do not belong to any cluster. These SNs will be unable to participate in the following data transmissions. However, by assuming a dense and redundant deployment of the sensor nodes in the network, the sensing coverage is expected to be tolerable.Partial spectrum sensing and cluster head discovery: The sensor nodes carry out the spectrum sensing process on some of the channels to find cluster heads.Cluster head declaration: The sensor nodes declare themselves as cluster heads after they fail to discover any cluster head on their current operating channels by following a predetermined probability.Registration to a cluster head: The sensor nodes that find a cluster head proceed to join the cluster.TS node assignment and cluster member invitation: The cluster heads might assign the closest cluster member as a support node temporarily. The TS node broadcasts invitation packets on each channel.Data transmission: Data transmission includes both intra-cluster and inter-cluster data transmissions.The five major activities in CENTRE are given below.These activities are performed regularly during the CENTRE rounds, as illustrated in 2.1.The cluster formation phase aims to build an optimal cluster topology in a distributive manner. As mentioned earlier, this phase consists of two sub-phases: cluster head discovery (sub-phase 1) and cluster member invitation (sub-phase 2). In sub-phase 1, the sensor nodes search for cluster heads, whereas, in sub-phase 2, the cluster heads search for new cluster members, with the help of TS nodes. Three activities take place in sub-phase 1. They are: (1) partial spectrum sensing and cluster head discovery, (2) registration of sensor nodes to a cluster head, and (3) cluster head declaration. The first step in cluster formation is partial spectrum sensing, in which the sensor nodes perform spectrum sensing on a part of the entire spectrum. The purpose of partial spectrum sensing is to save energy and time, because sensing on the entire spectrum requires considerably higher processing tasks and time, but the sensor nodes have only limited resources. The sensor nodes start partial spectrum sensing after a random delay. The random delay is applied to increase the probability of discovering a cluster head. If all the sensor nodes start spectrum sensing at the same initiation time, then no cluster head would be found because all the sensor nodes are performing spectrum sensing. If a sensor node waits, then by the time it starts spectrum sensing, there is a possibility of it finding a cluster head as some sensor nodes might have declared themselves as cluster heads earlier. However, the random delay is kept short to minimize the cluster formation duration.Each sensor node keeps a list of cluster heads that it has found during partial spectrum sensing and, the sensor node registers to the first-listed cluster head. No additional computation is performed to select among cluster heads from the list to reduce energy consumption. The packet exchanges that take place between a sensor node and the cluster heads during the registration period is shown in The sensor node sends a join packet to the cluster head first on the list and sets its timer. The join packet may fail to reach the cluster head or collide with other packets at the cluster head. Hence, if the timer expires but the sensor node has not received a response packet, it sends a join packet to the same cluster head one more time. When the second join packet also fails to elicit a response from the first cluster head, the sensor node sends a join packet to the next cluster head on the list. The cluster head that receives the join packet sends back a response packet to the sensor node. Here, it is assumed that when a cluster head receives many join packets simultaneously, one join packet is received successfully and the other join packets are dropped. Moreover, the cluster head does not send any notification to the sending nodes about the dropped join packets.Once the sensor node receives a response packet from the cluster head, it calculates the distance and required transmission power based on the received power level. The sensor node reconfigures its transmission power level to the minimum required power level (reconfiguration is enabled by cognitive radio) and sends an acknowledgement packet to the cluster head at the new transmission power level. The acknowledgement packet also contains the distance information. The purpose of the acknowledgement packet is to ensure that the cluster head can successfully receive and decode a packet transmitted at the new transmission power level. The distance information is used during sub-phase 2. When the cluster head receives the acknowledgement packet, it replies with a confirmation packet, sets the sensor node as its cluster member, and records its distance information. Similarly, the sensor node sets itself as the cluster member of the respective cluster head after it receives the confirmation packet. Then, the cluster member goes to the sleep state until the end of sub-phase 1.In case the registration fails and there is no cluster head on the list, the sensor node checks the remaining time (the duration of sub-phase 1 is fixed and predetermined). If the remaining time is sufficient to support another round of partial spectrum sensing and registration trial, then the sensor node waits for a random delay period and repeats partial spectrum sensing. If the remaining time is insufficient, then the sensor node goes to the sleep state until the end of sub-phase 1.If no cluster head is found at the end of partial spectrum sensing, then the sensor node declares itself as a cluster head with a certain predetermined probability. When the sensor node becomes a cluster head, it beacons about its presence periodically on its operating channel and stays ready to process registration requests. When the sensor node fails to become a cluster head, it again checks the remaining time. If the time is sufficient, it waits and repeats partial spectrum sensing; otherwise, it goes to the sleep state until the end of sub-phase 1.Sub-phase 2 starts with the assignment of TS nodes. Each cluster head computes the number of cluster members that belong to the cluster. If the cluster head does not have any member, then it becomes a sensor node. If the number of members in the cluster is less than the predefined threshold, then the cluster head assigns the closest cluster member as a TS node; otherwise, the cluster head and its members go to the sleep state until the end of sub-phase 2. As the cluster head has recorded the distance information between itself and each of its cluster members during the registration procedure, it easily decides the closest cluster member. The cluster head then sends a TS node assignment packet to that cluster member and stays ready to process registration requests from sensor nodes until the end of sub-phase 2.The consideration to assign the closest cluster member as the temporary support node is as follows: as the closest cluster member/temporary support node broadcasts an invitation, the sensor nodes within its transmission range could receive it. However, these sensor nodes need to transmit their registration packet to the cluster head, not to the temporary support node. Because the temporary support node is the closest node to the cluster head, if a sensor node can receive a packet form a temporary support node, then it is highly probable that it can send a packet successfully to the cluster head.Each cluster member wakes up from the sleep state and waits for the TS node assignment packet from the cluster head. If the cluster member receives a TS node packet, then it becomes a TS node. Otherwise, the cluster member recalls its distance to the cluster head. The cluster members that are relatively closer to the cluster head go to the sleep state until the end of sub-phase 2, whereas the cluster members that are farther from the cluster head stay in the active state.i.e., becomes a cluster member) and goes to sleep until the end of sub-phase 2. The receivers of the invitation packets are active cluster members and sensor nodes. The cluster head of the TS node is called the suggested cluster head. The receivers of the invitation packets record up to two invitation packets. The exchange of packets during sub-phase 2 is shown in The TS node sets its transmission power level to the default setting (maximum) and broadcasts an invitation packet on each available channel. The invitation packet contains the address and operating channel of the TS node's cluster head. After the TS node finishes the broadcasts, it returns to its original class  efficiently to prolong the network lifetime. Energy consumption per round is also analyzed to evaluate the energy consumption trend, in which low and stable energy consumption is desired. Because we propose a clustering method in this paper, we need to measure its effectiveness by measuring the normalized clustering overhead and average distance between cluster heads and the cluster members. To achieve superior performance, the clustering method should have not only low clustering overhead to reduce the energy consumption during cluster formation but also compact clustering (short distance between the cluster heads and the cluster members) to reduce the energy consumption in intra-cluster data transmission.The performance parameters are measured at the end of each round. However, as DSAC requires iteration to reach the optimal number of clusters, its cluster formation duration is varied per round. Therefore, the performance parameters of both CENTRE and DSAC are evaluated based on the CENTRE's round duration. The network topology in which sensor nodes are randomly deployed is shown in To further observe the energy consumption and network lifetime, the snapshots of network composition when half of the sensor nodes are alive are shown in To further evaluate the clustering methods, we measure the normalized clustering overhead and the average distance between the cluster heads and their cluster members. The clustering overhead is normalized in terms of time, that is, we define the normalized clustering overhead as the clustering time divided by the data transmission time per round. A small ratio of overhead is desired because it means that the time spent in creating the cluster topology is negligible compared to the actual data transmission time. The simulation results have shown the superiority of CENTRE against DSAC in terms of network lifetime, energy consumption, clustering overhead, and distance between the cluster heads and their members. These performance improvements are due to the properties of CENTRE, which are: (1) it does not require multiple beacon/data broadcasts during the cluster formation, (2) it has fixed cluster formation duration which does not depend on the number of sensor nodes, (3) it enables transmission power adjustment, (4) it has organized sleep mode, and (5) it adopts temporary support nodes which results in compact clustering. These properties make CENTRE an effective and efficient clustering method for CRSNs. In practice, we expect that CENTRE would perform the best under the condition of dense and random deployment of sensor nodes and the sensing applications that require regular data collection. However, CENTRE would need a common control channel.4.In this paper, a novel energy-efficient and compact clustering scheme called CENTRE with temporary support nodes is proposed for CRSNs. The proposed cluster formation process has two sub-phases: cluster head discovery and cluster member invitation. Even though it is difficult for sensor nodes to find a cluster head in CRSNs, the two sub-phases enable the sensor nodes to find a cluster head efficiently. CENTRE also decreases the average distance between cluster heads and their members, resulting in compact clustering. In addition, adopting a fixed duration for cluster formation results in remarkable energy saving. The performance evaluation shows that CENTRE achieves 34% longer network lifetime with less clustering overhead. The average distance between of cluster heads and their cluster members is also decreased. The main reasons for the performance improvement of the CENTRE scheme include the following: the fixed cluster formation duration, the adjustment of the transmission power of the cluster members based on the distance to the cluster head, the refinement of the cluster formation process by the use of TS nodes, and the use of the sleep mode when the sensors are not active. In our future works, we will consider primary users involvement, backup channels selection, and network coverage analysis.


This study is initiated to evaluate the effects, costs, and feasibility at the hospital and patient level of an evidence-based strategy to improve the use of Dutch perioperative safety guidelines. Based on current knowledge, expert opinions and expertise of the project team, a multifaceted implementation strategy has been developed.This is a stepped wedge cluster randomized trial including nine representative hospitals across The Netherlands. Hospitals are stratified into three groups according to hospital type and geographical location and randomized in terms of the period for receipt of the intervention. All adult surgical patients meeting the inclusion criteria are assessed for patient outcomes. The implementation strategy includes education, audit and feedback, organizational interventions , team-directed interventions , reminders, as well as patient-mediated interventions . To tailor the implementation activities, we developed a questionnaire to identify barriers for effective guideline adherence, based on (a) a theoretical framework for classifying barriers and facilitators, (b) an instrument for measuring determinants of innovations, and (c) 19 semi-structured interviews with perioperative key professionals. Primary outcome is guideline adherence measured at the hospital  and patient levels by a set of perioperative Patient Safety Indicators (PSIs), which was developed parallel to the perioperative guidelines. Secondary outcomes at the patient level are in-hospital complications, postoperative wound infections and mortality, length of hospital stay, and unscheduled transfer to the intensive care unit, non-elective readmission to the hospital and unplanned reoperation, all within 30 days after the initial surgery. Also, patient safety culture and team climate will be studied as potential determinants. Finally, a process evaluation is conducted to identify the compliance with the implementation strategy, as well as an economic evaluation to assess the costs. Data sources are registered clinical data and surveys. There is no form of blinding.The perioperative setting is an unexplored area with respect to implementation issues. This study is expected to yield important new evidence about the effects of a multifaceted approach on guideline adherence in the perioperative care setting.NTR3568Dutch trial registry: The online version of this article (doi:10.1186/s13012-014-0198-5) contains supplementary material, which is available to authorized users. The focus of this project is to implement perioperative safety guidelines in Dutch hospitals. Perioperative adverse events (AEs) are associated with deaths, injured and disabled patients, and high costs -5. In thImplementation of these guidelines is expected to reduce adverse patient outcomes. In addition, several studies showed that using stop moments and the timely administration of antibiotic prophylaxis improve perioperative outcome ,9,12. AdCan we improve the use of the national perioperative safety guidelines at the hospital and patient levels? In other words, does a multifaceted approach to implement the perioperative guidelines lead to a higher compliance than unsupported implementation?Other research questions are the following:Does the perioperative patient safety  improve with better adherence to the guidelines?What are the costs of this implementation strategy and what is the cost-effectiveness at the hospital level?What is the feasibility of this implementation strategy at the hospital level?The aim of this study is to investigate the (cost) effectiveness of a multifaceted approach to implement the perioperative safety guidelines. Our main research question isIn this study protocol, we describe a stepped wedge cluster randomized trial design to evaluate the effect of a multifaceted implementation strategy on guideline adherence and patient safety in Dutch hospitals (research questions 1 and 2). We also describe the economic evaluation (research question 3) and process evaluation (research question 4).Implementation of Perioperative Safety Guidelines (in Dutch: Implementatie Richtlijnen Operatieve Veiligheid (IMPROVE)) is a multicenter study in nine hospitals using an one-way  cross-over cluster trial design  where each participating cluster receives both intervention and control treatments consecutively, in separate periods.A stepped wedge design is a sequential roll-out of an intervention (here: the multifaceted implementation strategy) to participants  over a number of time periods. All groups  start with the control situation (no IMPROVE implementation activities) at the beginning of the study. At each time point, a new group of three hospitals crosses over from the control situation to the implementation situation. Good balance of hospital characteristics  over the groups  was achieved by matching. Each group will start the implementation phase of 4 months at a different time point, directly after one of the measurements T0, T1, T2). The time point a group crosses over is randomized (over the groups). Randomization was computer generated by an independent statistician when recruitment of clusters was complete  per hospital. In the interval between T0 and T1, implementation activities will take place in the first group of three hospitals. After the second measurement (T1), the second group of three hospitals crosses over to the intervention phase, while implementation activities continue in the first group and so on. At the final measurement (T3), all hospitals will be in the intervention phase , all primary and secondary outcomes will be measured per patient  is provided to hospitals that meet international standards developed and tested for external evaluation of health-care organizations . Two hosThe intervention consists of a multifaceted implementation strategy directed at the cluster. Each group of hospitals will be exposed to the intervention for 4 months. The content of the multifaceted approach is based on 1) scientific literature ; 2) The activities are tailor-made, i.e., the content depends on the analysis of the current performance and guideline adherence , local barriers in the perioperative setting , local wishes, and initiatives already realized in the hospital.We developed a questionnaire to identify barriers for effective guideline adherence experienced by the mentioned professions, as insight into the barriers and facilitators of guideline adherence is essential in deciding what kinds of implementation activities should be developed. This questionnaire is based on (a) the theoretical framework for classifying barriers and facilitators as described by Van Sluisveld et al. , (b) theSeveral outcomes are measured at the hospital  and the patient levels Table . The priThe rationale for these secondary outcomes is based on a prioritization procedure by 16 perioperative health-care professionals in the nine participating hospitals: they were invited to list ten outcomes with the highest clinical relevance to perioperative care.We define a complication as an unintended and unwanted event or state during or following medical treatment that has an unfavorable effect on the health of the patient to such an extent that adjustment of the medical treatment is necessary or irreparable harm has occurred . PostopeInformation about the processes and outcomes of perioperative care are retrospectively derived from patient medical records in the participating hospitals by two researchers (YE and GB). The first 50 patients undergoing an initial elective surgery within the measurement period and who meet the inclusion criteria are selected from the hospital databases.To measure the structure indicators of the national indicator set, the contact person in each hospital is invited to fill out a short questionnaire on the underlying items of the structure indicators on a dichotomous scale (yes/no).Health-care providers in the concerning hospitals are invited to respond to two different questionnaires and additional background questions on gender, medical position, and years of experience in the current function and in the current hospital. The Team Climate Inventory (TCI)  and the The key characteristic of cluster randomized trials (CRTs) is that the individual units (here: patients) within a cluster  are correlated, and this feature must be incorporated into power calculations and trial analysis . BesidesWe made the following assumptions: 1) nine hospitals in the Netherlands   will cooP < 0.05.After each measurement period, data are manually checked to identify out-of-range answers, inconsistent or missing data. Because of the hierarchical design , multilevel linear and logistic regression analysis will be used to compare the outcomes between treatment groups, with age, sex, the American Society of Anesthesiologists (ASA) physical status classification, and socioeconomic status (SES) as covariates. The ASA physical status classification is a five-point scale ranging from ASA 1  to ASA 5  . Time wiA process evaluation, according to Hulscher et al. , will beBetter adherence to the perioperative guidelines may reduce unnecessary perioperative harm such as complications and subsequently welfare losses. To assess the cost implications, an economic evaluation will be conducted that is based on the general principles of a cost-effectiveness analysis. The perspective of this economic evaluation will be a health-care perspective. The input of resources in the implementation strategy will be assessed by collecting volumes of consumed resources and multiplying these by the price of each resource unit  . The impNTR3568). The participation of hospitals in the study is voluntary. The research team gave oral presentations and written information about the study to each hospital. Written consent has been obtained from all participating hospitals before randomization. Because outcome data are routinely collected by the hospitals and no personal identifiers are transmitted, individual consent of patients is not required. All data will be handled strictly confidential and published anonymously. Each participant is identified in the database with a study number.This study protocol was approved by the Medical Ethical Committee of the Radboud university medical center on 9 August 2011 (registration number: 2011/318). The study conforms to Dutch law and privacy regulations and was judged not to involve human-subject research. This study is registered in the Dutch Trial Registry for clinical trials   randomized, controlled trial was not possible as all Dutch hospitals have a legal obligation to implement the perioperative safety guidelines; temporarily postponing the implementation of the guidelines would have been unethical. Finally, non-randomized designs such as pre- and post-intervention evaluations are methodologically weaker as they tend to overestimate intervention effects. Another strength of our study is the use of a multifaceted approach consisting of in total 11 different activities, covering six interventions: education, audit and feedback, organizational interventions, team-directed interventions, reminders, as well as patient-mediated interventions, of which most have been shown to be effective in other studies ,41,47-52The generalizability of the findings will be high because the study is carried out in a large, heterogeneous sample of nine hospitals across the country. A further strength of this study is the wide scope; we perform measurements at the cluster level and the level of their individual members, i.e., care providers and patients. A questionnaire is sent to the key disciplines to explore all relevant barriers and facilitators to guideline adherence and opportunities for improvement. A better understanding of the barriers and facilitators and a strategy tailored to local circumstances will enhance the implementation of the perioperative guidelines like already mentioned ,53. We aWith respect to the limitations of the study, the power of the stepped wedge cluster randomized trial design decreases if the treatment gradually reaches its expected effect after one or more time intervals after the intervention . The greOverall, knowledge of the results of this study will improve the implementation success of the perioperative guidelines. It is expected that better adherence to the perioperative safety guidelines will improve the safety of perioperative care.