The method has been validated according to the guideline of International Conference on Harmonization and parameters like linearity, range, accuracy, precision, sensitivity, and robustness have been studied. Although the method was found to be efficient for the determination of TA in all solvents on the basis of statistical data 1-octanol, followed by ethanol and methanol, was found to be comparatively better than the other studied solvents. No change in the stock solution stability of TA has been observed in each solvent for 24 hours stored either at room (25 ± 1°C) or at refrigerated temperature (2–8°C). A shift in the absorption maxima has been observed for TA in various solvents indicating drug-solvent interactions. The studied method is simple, rapid, economical, accurate, and precise for the assay of TA in different organic solvents.The present study has been carried out to validate a UV spectrometric method for the assay of tolfenamic acid (TA) in organic solvents. TA is insoluble in water; therefore, a total of thirteen commonly used organic solvents have been selected in which the drug is soluble. Fresh stock solutions of TA in each solvent in a concentration of 1 × 10 Tolfenamic acid (TA) is a nonsteroidal anti-inflammatory drug (NSAID) that belongs to the family of fenamates and is used in both humans and animals for the management of pain and inflammation . RecentlTA occurs as a white or slightly yellow crystalline powder and is practically insoluble in water , 30. TheThe present study has been designed to validate the UV spectrometric assay procedure for the analysis of pure TA in different organic solvents according to the guidelines of International Conference on Harmonization (ICH) . TA is aTolfenamic acid was purchased from Sigma-Aldrich Company Ltd., Dorset, UK. All the solvents used in this study were of analytical grade having the highest degree of purity. The details of the solvents used in this study are reported in μL of the solution was applied to 250 μm silica gel GF254 plates. It was developed with the mobile phase up to 2/3 distance of the plate. The plate was dried and viewed under 254 nm UV lamp .TLC was performed to check the purity of TA used in this study according to the method reported in British Pharmacopoeia . The subAll absorbance measurements and spectral determinations were carried out on a Shimadzu UV-visible spectrophotometer (model UV-1601) using quartz cell of 10 mm path length. The cells were employed always in the same orientation using appropriate control solutions in the reference beam. The baseline correction was made by the built-in baseline memory at the initializing period while auto-zero adjustment was made by one-touch operation. The wavelength scale was also calibrated automatically by the instrument. The instrument was calibrated for the absorbance scale according to the method described in British Pharmacopoeia , by usinThe absorbencies of the corresponding series of solutions in each solvent were measured against a reference of the same solvent in the region of 250–400 nm. Quartz cells were closed with a cap to prevent evaporation of the organic solvent during absorbance measurements.−4 M (2.62 mg%) in the individual solvent  of the mean recoveries.y-intercept and slope of the calibration curve using the following formulae:σ is the standard deviation of the intercept and S is the slope of the calibration curve.LOD and LOQ of the developed method were calculated from the standard deviation of the −5 M). The accuracy and precision of the method were determined.The robustness of the method was determined by studying small changes in the assay wavelength (±2 nm). This parameter was studied thrice in the similar range used for the determination of TA . The samples were stored in tightly sealed glass containers protected from light. A 5 mL aliquot of the sample was taken each time and the absorbencies were measured at 0-, 1-, 2-, 3-, and 24-hour time interval.The stability of stock solutions of TA was studied at room (25 ± 1°C) and refrigerated temperature (2–8°C). The stock solutions of TA were prepared in pure solvents at a concentration of 1 × 10In order to study spectrometric characteristics of a compound, it is necessary to confirm the purity of the material to avoid any effect on the position and intensity of the absorption maxima as well as on the validation of the assay method. In the case of TA a thin layer chromatography (TLC) examination was conducted to detect any spots other than that of TA on TLC plates. The TLC test for TA has been carried out according to the method described in British Pharmacopoeia . TA appeλmax and ϵmax) can be expected. Such a change has been described as solvatochromism  in various organic solvents are reported in λ1max range from 286 to 294 nm and λ2max from 332 to 354 nm (R2) of 0.99905–0.99988 showing very small scatter of the points around the calibration curves amino]benzoic acid and gives two peaks in the region of 280–360 nm . The shoIt is defined as the interval between the upper and lower concentrations of the analyte that have been demonstrated to be determined with acceptable precision, accuracy, and linearity. The absorbance values in the range of 0.2–0.8 are known to offer the highest precision . TherefoThe accuracy of an analytical method is defined as the degree to which the determined value of an analyte in a sample corresponds to the true value. The results for the percent recovery of TA in different organic solvents are reported in Precision of an analytical method is the closeness of agreement between a series of measurements obtained from multiple samples of the studied drug under prescribed conditions. The results for the precision of the method for the assay of TA in various solvents are reported in −6 M (0.05 mg%) has been found in 1-octanol while the highest of 6.47 × 10−6 M (0.17 mg%) has been found in 1-butanol. This indicates that the UV spectrometric technique is highly sensitive for the detection of TA in various organic solvents.It is the lowest concentration of an analyte in a sample that can be detected but not necessarily quantified. It is considered as limit test that indicates that the analyte is above or below a certain value which is usually expressed as percentage of the analyte in the sample. The LOD of TA in each solvent is reported in −6 M (0.16 mg%) has been found in 1-octanol while the highest of 1.96 × 10−5 M (0.51 mg%) has been found in 1-butanol. The values of LOQ of TA in each solvent are reported in The LOQ determines the lowest concentration of an analyte in a sample that can be quantified with acceptable precision and accuracy under the documented operational conditions of the drug being assayed. The minimum quantification limit of 5.98 × 10The robustness of an analytical method is a measure of its capacity to obtain acceptable results when perturbed by small but deliberate variations. It is basically an indicator of method suitability and reliability during normal use. Absorbance of a solution is dependent upon wavelength, solvent, pH, and temperature. Therefore, these parameters should remain constant throughout the course of the analysis; otherwise significant errors may arise in the quantitative analysis of the samples . In the The solution stability is a measure of the extent to which the studied drug is stable in a solvent being used for the assay over a particular period of time under specified conditions. It is an essential requirement that the analyte should not undergo any chemical change and should remain stable in the particular solvent .The study of TA was carried out at room temperature (25 ± 1°C) and refrigerated temperature (2–8°C). The consistency in absorbance indicated the stability of TA solutions. In all solvents no significant change has been observed in the absorbance of TA after 24 hours of storage either at room temperature or in a refrigerator. However, in spite of the stability of TA in the organic solvents for at least 24 hours, fresh solutions were used for the validation study.The present study has employed thirteen commonly used solvents for the validation of a UV spectrometric method for the determination of TA. The results indicated that the method is accurate, precise, robust, economical, and rapid for the assay of TA with a stock solution stability of 24 hours in each solvent. TA exhibits two peaks in the UV region of 280–360 nm. The major short wavelength peak is in the region of 285–295 nm that showed good results for the assay of TA. Those solvents that have a cutoff point in this region or interfere with the major peak can also be used for the determination of TA with respect to the minor long wavelength peak in the region of 335–355 nm.The results of this study highlight the effect of different solvents on the spectral characteristics of organic molecules of pharmaceutical importance. Some shifts in the absorption maxima of TA have been noted probably due to drug-solvent interaction while absorptivity constants of TA in each solvent have also been determined. These shifts can affect the wavelengths used for the assay of a compound and, therefore, it is necessary to use a particular solvent for assay purpose. It is also necessary to confirm the purity of the solvent used for assay and its interference in the spectral region of the compound to be studied. A detailed investigation of the effect of solvent parameters on the spectral characteristics of a compound is required to develop an understanding of the changes observed. Such study would help the pharmaceutical formulators and analysts to determine TA in pharmaceutical systems incorporating organic solvents.


The intra-pulse measurements of the beam steering in an AlGaAs/GaAs quantum cascade laser are presented in this paper. The experimental results are explained by a two-mode theoretical model. The near field of the laser radiation is calculated according to the effective index method and transposed to the far field numerically according to Huygens principle. The maximal observed value of deflection of the beam has been found to be  Quantum cascade lasers emitting in the mid-infrared band are valuable sources for molecular spectroscopy, telecommunication and medicine. Methods of their fabrication are currently relatively mature. They work at high temperatures, under continuous wave regime and can deliver high output power. However, to make them fully useful , it is required to assure that they emit stable and well-shaped output beams. This in practice might be difficult because of the so called beam steering which is a phenomenon manifested by changing direction of the beam emission. This phenomenon occurs in various types of semiconductor lasers including quantum cascade lasers. In the latter the beam steering was already observed upon change of the driving current (Bewley et al. Research on the intra-pulse beam steering was based so far on comparing two parts of the driving current pulses that were several tens of nanoseconds long (Yu et al. The asymmetry of the spatial power distribution in the far field must arise from interference of two or more transverse mode of the same frequency. Should the modes be of different frequencies, they would not interfere and the far field distribution would be symmetrical even if the near field was asymmetrical. That has been confirmed experimentally by Yu et al.  who publThe origin of the beam steering phenomenon has been thus ascribed to a lateral hole burning in the population inversion. Such an idea in respect of carrier distribution was analysed in detail by Yang et al. . KeepingThe investigated device was an l et al. . The lasThe diagram of the experimental set-up is shown in Fig. The measurement of the far field along the slow axis is performed by rotating the stage with the laser chamber, while along the fast axis by moving the goniometric cradle with the detector.A schematic diagram of the theoretical model geometry is presented in Fig. For calculation of the near field we use the effective index method and consider only the slow axis direction. In this direction the active region is confined by trenches filled in by metal. Therefore, the field distribution In our model we consider two first modes with maximal values of Intensities of the near and far field are linked to the field amplitudes by the formulaAll the near and far field calculations are made numerically using a program written in Scilab. Figures The threshold current for the studied laser in the temperature of 260 K is 6 A. The characteristic temperature We have measured the far field of the laser for currents from 7 to 15 A with the step of 1 A. No beam steering has been observed for the current lower than 10 A. Also no such effect has been observed in the temperature higher than 260 K. At 260 K and for currents in the range from 10 to 14 A the beam is steered on one side, while for the current of 15 A, the beam steering alternately on both sides has been observed. The change of the beam direction for several currents in the range from 10 to 15 A is presented in Fig. The maximum range of the beam steering changes from For the highest current of 15 A, the dominant mode is initially also the 0th order. Then, the amplitude ratio of the modes varies, and after 80 ns the 0th mode temporarily almost disappears. Unlike for the lower currents, the phase difference changes abruptly,within the time lower than 1 ns, and the most frequently takes the value of y et al.  and exply et al. .Figures We have observed the beam steering along the slow axis of the quantum cascade laser beam for the currents We used a two-mode model to describe the beam steering phenomenon. Addition of an another mode to the model would slightly improve the results of calculations but would also increase dramatically the calculation time. In our opinion, the two-mode model is sufficient to describe the beam steering in the studied laser, but in other structures it may require addition of one or more higher-order modes. However, we have shown that even such a very simple model gives results well matched to the experiment.Undoubtedly the theoretical model could be greatly improved by taking into an account not only the interfering modes, but also additional modes with different frequencies. Neglecting the noninterfering modes can make the calculated phase differences and the second mode contribution underestimated. To take these modes into account, additional measurements of the spatially dependent spectra for the studied laser have to be performed, which is what we are planning for the near future.


The correct segmentation of myofibres in histological muscle biopsy images is a critical step in the automatic analysis process. Errors occurring as a result of incorrect segmentations have a compounding effect on latter morphometric analysis and as such it is vital that the fibres are correctly segmented. This paper presents a new automatic approach to myofibre segmentation in H&E stained adult skeletal muscle images that is based on Coherence-Enhancing Diffusion filtering.The procedure can be broadly divided into four steps: 1) pre-processing of the images to extract only the eosinophilic structures, 2) performing of Coherence-Enhancing Diffusion filtering to enhance the myofibre boundaries whilst smoothing the interior regions, 3) morphological filtering to connect unconnected boundary regions and remove noise, and 4) marker controlled watershed transform to split touching fibres.The method has been tested on a set of adult cases with a total of 2,832 fibres. Evaluation was done in terms of segmentation accuracy and other clinical metrics.The results show that the proposed approach achieves a segmentation accuracy of 89% which is a significant improvement over existing methods. The examination of stained biopsies from skeletal muscle is a vital component in the diagnostic pathway for the vast majority of neuromuscular disorders and remains essential as a preliminary investigation despite the availability of electron microscopic, genetic, and molecular tests for specific conditions. Detection of variation in fibre size is seen as a first step in the diagnosis of the majority of neuromuscular disorders including myopathic, dystrophic, neurogenic, and inflammatory conditions; examples of which include mitochondrial cytopathies, muscular dystrophies, motor neuron disease, and dermatomyositis , 2. MuscOne of the key steps for such automatic methods is the accurate segmentation of the myofibres from the surrounding connective structures Figure . InaccurThe simplest automatic approach is to perform thresholding to segment the image into foreground and background regions, one corresponding to myofibres and the other representing background and connective tissue. The central step of such an approach is the selection of a threshold value to separate the two regions such that they are well represented. Although methods exist to estimate the threshold automatically , or manuThese more advanced approaches can be broadly split into two categories: those that use edge detection to identify the perimysium or endomysium (the myofibre boundaries), and those that use deformable models to identify the myofibres. This dichotomy is by no means exclusive as many approaches will use methods from both of these categories.a priori information for further segmentation; it is worth noting however that the approach to such a segmentation is not mentioned in the original paper. Sertel et al. [Edge detection methods seek to segment the perimysium and endomysium so that the fibres can be segmented from these boundaries. Therefore, edge detection methods are often used as a pre-processing step to fibre segmentation with the justification being that, if the boundaries are correctly identified, the fibres themselves can be segmented as the interior of the boundary regions. Tzekis et al.  presentel et al.  present l et al.  is appliThe second category of segmentation method seeks to apply deformable models, such as active contours , to idenOne of the main limitations of the above methods is that they can often fail in cases where noise is introduced as a result of the staining or sectioning process, and also in cases with weak boundaries. As well as this, there is no previous work that directly compares the different approaches to myofibre segmentation in adult cases. Motivated by these drawbacks, this paper presents a method for myofibre segmentation based on Coherence-Enhancing Diffusion (CED) filtering  that is Therefore, the main contributions of this work are twofold. First, a new method for myofibre segmentation is presented using a multi-step process based upon Coherence-Enhancing Diffusion filtering . This neμm) using a frozen cryostat . The sections were then placed on glass slides and stained with haematoxylin and eosin (H&E) using standard protocols [μm) viewed at 10× zoom and stored as uncompressed TIFF files. A total of 2832 fibres were identified across the region of interest images.The muscle tissue, obtained following open or Bergstrom needle biopsy, was frozen onto cork blocks and sectioned with the fibres orientated in the transverse plane (5 - 7 rotocols . Entire The ground truth data was generated by a single user (HS) tracing the boundaries of the fibres across all images. In order to validate the manual segmentations as ground truth, a repeatability study was performed whereby the same image was segmented 10 times by the same person. This process resulted in multiple ground truth regions which were then co-registered using a group-wise registration algorithm  to obtaiThis work uses Coherence-Enhancing Diffusion (CED) filtering to segment the boundary regions whose interiors contain the myofibres. CED filtering  seeks toThe proposed method can be coarsely divided into four main steps: 1) pre-processing, 2) Coherence-Enhacing Diffusion filtering, 3) morphological filtering, and 4) watershed transform. A flow-chart of this process is shown in Figure The first step is to apply a stain decomposition method  to the mThe purpose of performing Coherence-Enhancing Diffusion filtering  is to enThe central step of CED is to determine the directions along which the image should be smoothed, this is achieved by utilising the eigendecomposition of the second moment matrix, or structure tensor. As explained in , the heau is the image under evolution at diffusion time t. The diffusion tensor, D, is a positive semidefinite matrix which is adapted to the local image structure. The local image structure can be measured using the structure tensor [where e tensor , 26 whicuσ is defined as u convolved with a Gaussian with standard deviation σ such that uσ=Gσ∗u. As such, uσ is a regularised version of u obtained via convolution with a Gaussian Gσ. The standard deviation, σ, can be thought to represent the noise scale since it forces the edge detector to ignore details smaller than O (σ) [ρ, defines the integration scale which describes the characteristic size of the texture [Jρ describe the local orientations, or coherence orientations, and the eigenvalues describe the average contrast along the eigendirections. The eigenvalues of where an O (σ) . The sta texture . The eigD. The diffusion tensor of coherence-enhancing diffusion uses the same eigenvectors as Jρ and the eigenvalues are assembled such thatThese eigenvalues are used to assemble the eigenvectors of the diffusion tensor c1 ∈  serves as a regularisation parameter that ensures that the diffusion tensor remains uniformly positive definite, and c2 > 0 serves as a threshold parameter.where τ, as the difference between two calculated time levels. For full details on the implementation of CED filtering the reader is pointed towards Section 5 of [The solution to Eq. ion 5 of  and Section 5 of .μm) are removed. This ensures that any erroneous regions corresponding to staining or preparation artefacts are ignored from the final segmentation. The final morphological filtering step is to perform morphological closing [μm) is used. The complement of this binary image then gives the segmentation of the myofibres. The final step is to apply a marker controlled watershed transformation [μm), the Euclidean distance transform of this binary image is then used to compute the watershed transform.The output of the Coherence-Enhancing Diffusion step described above is a grayscale image with enhanced myofibre boundaries and smoothed interiors. Although the fibre boundaries in general will be well connected, due to inherent noise within the images further processing needs to be performed to connect any disconnected boundaries and remove any remaining noise. The image produced from CED can be thought of as a probability map describing the likelihood of a pixel belonging to a boundary region. As such, to convert the given image into a binary segmentation a thresholding method is used to identify only the pixels corresponding to boundary regions. This threshold is automatically estimated using the well known Otsu method . Followi closing  so as toormation  to splitUse of human tissue is covered by a research tissue bank ethical approval which covers the diagnostic archive from which the material was drawn. The BioBank reference number is ACP000090. Use of material also conforms with Codes of Practice of the Human Tissue Authority.To assess the effectiveness of the proposed method it is tested against various existing myofibre segmentation algorithms using different measures of accuracy. Multiple evaluation criteria need to be employed as a single measure of accuracy is not appropriate and could lead to an unfair or biased view of an algorithm’s performance. As such, multiple criteria are used to build a bigger picture of the effectiveness of a segmentation algorithm. As well as this, the effect of each of the proposed method’s parameters are investigated and discussed.For each method, the segmentation accuracy is measured using both image segmentation evaluation criteria and clinical morphometric measures.The overall segmentation accuracy of a given method is measured as the number of correctly segmented myofibres (in relation to the given ground truth) divided by the total number of true positive, false positive and false negative regions. To ensure that a segmented myofibre is not counted across multiple regions, a one-to-one mapping is enforced between the ground truth and the automatic segmentations. Each ground truth region is mapped to a single segmented region that is encompassed within the ground truth boundary. Any remaining regions are then counted as false positives.fragmentation and congealment.To evaluate whether or not the different algorithms over or under segment the myofibre regions two new measures are proposed: F = p/n, where p is the total number of fragmented fibres in the output segmentation and n is the total number of fibres in the ground truth. The fragmented fibres are found by counting the number of connected components within a masked region of the output segmentation with the mask being set as each fibre in the ground truth segmentation.Fragmentation is used to measure to what extent an algorithm fragments a myofibre in the output segmentation. Given the ground truth segmentations and the output segmentation, fragmentation is defined as C=q/n, where q is the total number of congealed fibres in the output segmentation and n is the total number of fibres in the ground truth. A congealed fibre is found by counting the number of connected components within a masked region in the ground truth segmentation with the masks corresponding to the regions in the output segmentation.Congealment is measured in a similar fashion to fragmentation, however, the purpose of congealment is to measure the number of segmentations that span across multiple fibre boundaries. As such, congealment is defined as For both fragmentation and congealment a value of 0 indicates a low fragmentation or congealment factor. As the value increases, the quality of the segmentation decreases since the algorithm is seen to fragment or congeal the segmentation.S = {pi,i = 1,…,n}, represents the set of all pixel based misclassifications on a per image basis such that, pi, represents the percentage of misclassified pixels for the i-th image with respect to the ground truth segmentation. A pixel is considered a misclassified pixel if it appears as a 1 in the ground truth but a 0 in the segmentation, or similarly, if it appears as a 0 in the ground truth and a 1 in the segmentation. Each algorithm will have a unique distribution of misclassification percentages and the distribution of misclassifications can be characterised by its cumulative distribution function (CDF). The CDFs are maximum likelihood estimators and are defined asTo estimate how well each algorithm performs with respect to the ground truth a measure is used based on the cumulative distribution function of misclassification errors . For a gfK is the kernel density estimate of the histogram estimate based on S. The optimal algorithm has a CDF which corresponds to the unit step function [where function . TherefoThe two morphometric measures used to evaluate the segmentation algorithms are mean fibre diameter and the variability coefficient . The meawhere abnormal . Since tτ = 1,σ = 1 × 10-4 px (0.89 μm),ρ = 4 px (3.56 μm),c1 = 1 × 10-10,c2 = 1 × 10-3. The relative influence of each parameter was then assessed by varying the value of each parameter and assessing the change in the accuracy of the segmentation. A generalised linear regression model [ρ, has a medium influence on the final segmentation quality. This is not surprising as ρ controls the characteristic size of the texture regions [ρ will lead to either a lack of coherence within myofibres such that the “noisy” aspects within the fibres are not smoothed, or the coherence-enhancement occurs over too large a scale so the fibre boundary details are destroyed. As can be seen in Table ρ will need tuning since the other parameters have a low influence on the final segmentation quality.The initial parameters for the CED algorithm were set according to the values laid out and discussed in  and subson model  was then0-4 px 0.9 μm,ρ =  regions . As suchFigure μ = 100. The propagation weight was set to 0.1 and the advection term was set to 500. For Chan-Vese active contours, the algorithm was run for 500 iterations and the weight of length term was set to μ =0.1. In both cases, an initial circular mask was used. The segmentations obtained from all methods are post-processed to remove any segmented fibres on the boundary of the image. As well as this, any segmented region smaller than 750 pixels in area (674 μm) is removed and a watershed segmentation is performed to split any touching fibres. This ensures that the segmentations are in keeping with the ground truth and also enables more accurate morphometric measurements to be taken. It also ensures that the comparisons are focused on the core algorithms as opposed to any post-processing method used. It is worth noting that the majority of works published on myofibre segmentation include these post-processing steps ..μ = 100.p-value of p = 0.01. Many images within the dataset will contain fibres with weak boundaries and prominent myofibrils which is equivalent to “noisy” regions. When the myofibre regions are well defined with a strong endomysium or perimysium then all methods are able to segment the myofibres adequately. However, the data contains many examples where the myofibres are heterogeneous in appearance, due to problems with the staining  of the different methods are shown in Figure % of the standard deviation out from the mark of 250. All other methods have a variability coefficient of greater than 300 which indicates abnormal cases, even though the data is normal. The reason for this large variability coefficient can be attributed to high fragmentation and congealment factors which lead to outliers within the segmentations as shown in Table When considering the clinical metrics, all methods achieve a mean diameter that is within the standard deviation range of the ground truth segmentations . As well as this, the mean variability coefficient for this case across the other methods is 553.82 (±119.31) compared to 420.23 for the proposed method.Figure The segmentation in Figure %. As well as this, through the use of morphometric features such as mean fibre diameter and the variability coefficient, the proposed CED method is able to closely match ground truth segmentations and produce results that are clinically consistent.A new method for myofibre segmentation in adult skeletal muscle cases has been presented based on Coherence-Enhacing Diffusion (CED) filtering. This proposed method allows for the myofibre boundaries to be enhanced whilst maintaing the coherence of the pixels within a myofibre region. The evaluation shows that when compared against existing methods for myofibre segmentation such as hybrid level sets, Chan-Vese active contours, and line enhancement, CED is able to achieve superior segmentation accuracies of approximately 89The implementations were done in MATLAB and run on an Apple iMac with a 3.4 GHz Intel Core i7 processor with 16 GB or RAM. The average run-times for each method is shown in Table Other future work will be focused on applying the proposed CED algorithm to abnormal cases so as to build a complete framework for myofibre segmentation and abnormality detection. Moreover, morphometric measurements will be used to automatically identify clinically interesting aspects such as atrophy and hypertrophy factors as well as fibre type proportions and prominence. However, to achieve this the proposed method will need to be tested and evaluated on ATPase stained sections. As well as this, future research will investigate employing CED filtering to whole slide images so as to obtain an estimate of fibre size variation across the full area of the biopsy. To achieve this, an automatic region detection algorithm will be employed to select the best possible regions for analysis. This will in turn reduce the number of segmentation errors since analysis will only be performed on “clean” regions.In conclusion, an accurate technique has been presented for segmenting adult myofibres in H&E stained cases. The technique was evaluated on a number of representative regions and was compared against existing approaches. The results show that the proposed approach is able to show significantly improved results over current methods.


One of the recommended treatments for disorders associated with the lower extremity venous insufficiency is the application of external mechanical compression. Compression stockings and elastic bandages are widely used for the purpose of compression therapy and are usually designed to exert a specified value or range of compression on the leg. However, the leg deforms under external compression, which can lead to undesirable variations in the amount of compression applied by the compression bandages. In this paper, the use of an active compression bandage (ACB), whose compression can be regulated through an electrical signal, is investigated. The ACB is based on the use of dielectric elastomer actuators. This paper specifically investigates, via both analytical and non-linear numerical simulations, the potential pressure the ACB can apply when the compliancy of the human leg is taken into account. The work underpins the need to account for the compressibility of the leg when designing compression garments for lower extremity venous insufficiency.A mathematical model is used to simulate the volumetric change of a calf when compressed. Suitable parameters for this calf model are selected from the literature where the calf, from ankle to knee, is divided into six different regions. An analytical electromechanical model of the ACB, which considers its compliancy as a function of its pre-stretch and electricity applied, is used to predict the ACB’s behavior. Based on these calf and ACB analytical models, a simulation is performed to investigate the interaction between the ACB and the human calf with and without an electrical stimulus applied to the ACB. This simulation is validated by non-linear analysis performed using a software based on the finite element method (FEM). In all simulations, the ACB’s elastomer is stretched to a value in the range between 140 and 220 % of its initial length.Using data from the literature, the human calf model, which is examined in this work, has different compliancy in its different regions. For example, when a 28.5 mmHg (3.8 kPa) of external compression is applied to the entire calf, the ankle shows a 3.7 % of volume change whereas the knee region undergoes a 2.7 % of volume change. The paper presents the actual pressure in the different regions of the calf for different values of the ACB’s stretch ratio when it is either electrically activated or not activated, and when compliancy of the leg is either considered or not considered. For example, results of the performed simulation show that about 10 % variation in compression in the ankle region is expected when the ACB initially applies 6 kPa and the compressibility of the calf is first considered and then not considered. Such a variation reduces to 5 % when the initial pressure applied by the ACB reduced by half.Comparison with non-linear FEM simulations show that the analytical models used in this work can closely estimate interaction between an active compression bandage and a human calf. In addition, compliancy of the leg should not be neglected when either designing a compression band or predicting the compressive force it can exert. The methodology proposed in this work can be extended to other types of elastic compression bandages and garments for biomedical applications.The online version of this article (doi:10.1186/s12938-015-0088-3) contains supplementary material, which is available to authorized users. Venous dysfunction in human lower extremities causes disorders that impact the life quality of considerable amount of individuals. Disorders such as orthostatic hypotension, edema and deep vein thrombosis are closely tied to the venous system failure to pump the blood back to the heart. Typically certain body mechanisms including vein valve and muscle contraction help the blood return. Sometimes these mechanisms fail to operate properly and lead to decrease in blood flow and an increase in blood volume accumulated in the lower extremities. In particular, any malfunction in capillary filtration causes abnormal balance between blood in capillaries and fluids in interstitial tissue. While standing, venous pressure in the lower leg increases which in turn results in an increase of oncotic pressure. This results in greater pressure gradient in the capillaries and thus more filtration. An increase in filtration causes accumulation of more fluid in interstitial tissue which results in edema . Also, dcompression stocking (CS) is one of the recommended devices that apply mechanical compressions to the lower leg area. CS reduces venous hypertension and helps the leg muscle pump, which improves filtration, prevents skin breakdown, and eventually results in reduction of edema and venous pooling [intermittent pneumatic compression (IPC). IPC increases blood flow and improves blood return by simulating normal leg muscle activity [Dielectric elastomer actuators (DEAs) are introduced as active compression bandages (ACBs) to address the shortcomings of the previous devices [In order to prevent and cure these disorders, different methods have been suggested . The com pooling , 6. Anotactivity . The effactivity , 9 and I devices . In part devices , 12. Mor devices .DEAs are a category of electro-active polymers (EAPs) which have drawn much attention due to their simplicity, stability, fast response, high stretch ratio, high applicable force, and low cost . NumerouThe quantity of external compression is an important factor in treating the previously mentioned disorders on the lower extremities. For instance, it was shown that an external compression of 35–40 mmHg was required at the ankle to prevent edema in patients with severe venous disease . Also 2 suggesteIn this paper, an analytical study and a numerical simulation based on the finite element method (FEM) are proposed to simulate the interaction between the ACB and the calf. The results of this study assist predicting how calf compressibility affects performance of an ACB and the use of this technology in future clinical studies.C):The human calf compresses upon application of pressure. The geometry and size of the calf change considerably with the application of an external compression. The relationship between changes in volume to changes in external pressure is often determined by its compliance  in “Pa” on the calf and the true radial strain (εr). The stress–strain curves for all calf regions are depicted in Fig. 1In this work, the compliance data acquired by Thirsk et al.  was digimechanical pressure which is the pressure resulting from the mechanical stress in the ACB before actuating the ACB, and the actuation pressure, which is the pressure variation after actuating the ACB. The ACB is assumed to be made out of a flat DEA with length L [λ can be used:r is the deformed radius of the ACB when it conforms the calf. Since the geometry of calf is conical, the ACB undergoes different stretch ratios along the height of the calf. In the analytical modeling of this work, it was assumed that the conical geometry of each calf region was consisted of small cylindrical geometries with finite height. The simulation procedure, which is explained in this section, is done for each of these small cylindrical geometries and the final values of radius and pressure was averaged over the entire calf region. As the stretch ratio of the ACB changes the amount of compression that is exerted on the calf is also changed. The amount of this compression is obtained using the following equation [In this section, an analytical model of the cylindrical DEA , 16 is clength L . This flλa and λb are stretch ratios of inner and outer radii of the cylindrical DEA respectively, and μ1, μ2, α1, α2 are the ogden parameters for the DEA material. As this compression from the ACB is applied, the calf radius changes. Assuming Rc as the radius of the undeformed calf   r and Pmlve Eqs.  and (7) sing Eq.  a compreOnce the bandage is wrapped and an equilibrium radius is reached for the calf and the ACB, the actuation phase is modeled and analyzed. The actuation pressure is calculated from the following equation :8\documePa and Pm and is given by:As discussed earlier the total ACB pressure on the calf is composed of r and ACB total pressure Pt. Again, this set of equation is solved similar to the previously mentioned iterative procedure. The steps of this iterative procedure are summarized in Fig. As it can be realized for Eq. , the ACBThe developed analytical model gives us the ability to predict the behavior of the ACB on the flexible calf. Moreover, it provides us with a tool to design the ACB parameters such that desired compressions on the lower leg can be achievable. Simulations by the analytical model were carried out for the six regions of the calf and the results are discussed in the following sections.ANSYS Inc V14.0) was used.Finite element modeling showed promising results for cylindrical and conical DEA geometries in the literature , 16. PouHyper-elastic Response Function model was used to model the calf material in each region. In this model, the material’s compressibility is specified by d1 parameter—which is the inverse value of the bulk modulus. Further details of this material model, which is a built-in model in Ansys, can be found in Ansys documentation [The ACB was originally modeled as a cylinder  which was chosen to vary from 1.4 to 2.2 in this study; (4) the ACB was actuated by applying an electrical potential difference of 11.3 kV between the top and bottom surfaces of the ACB. The pressure difference and volume changes were the outputs of this step. Simulation steps are shown in Fig. In reality, an ACB bandage is wrapped around the calf and then is actuated. To avoid modeling a complicated contact problem and to reduce the calculation time, an alternative procedure which gave identical results was considered. The simulation steps were: (1) the sides of the ACB were fixed (to keep the height constant at 35 mm) and the ACB was stretched radially to the size of the largest radius of the calf region; (2) the stretched ACB was then displaced to perfectly locate on the calf region; (3) the radial constraint was removed to release the ACB and make contact with the calf region. The sides of both the ACB and calf segment were kept fixed and the contact pressure and volume changes were reported as outputs. The ratio of the average calf radius  to the initial ABC’s inner radius was defined as the stretch ratio  processor and 16 GB of RAM was used. All CPU cores were fully utilized using ANSYS High Performance Computing (HPC) feature. To study the effect of mesh size on the accuracy of results, a second run was performed by selecting 4 × 20 × 25 = 2000 ACB elements, 20 × 20 × 25 = 10,000 calf elements, 500 contact elements, and 500 target elements. If the ACB sample was deformed to a stretch ratio of 2.2 the simulation finished after 42.5 h and the mechanical pressure after a full contact in step 3 was 6091.2 Pa. However, the coarser mesh size that was selected originally gave a nearly similar mechanical pressure, 6102.8 Pa. The relative error for such large pressures was less than 0.19 % with coarser mesh size but the calculation time was about 4.5 times shorter. Therefore, the coarser mesh size was chosen and simulations were performed for five different stretch ratios (range of 1.4–2.2 with 0.2 increments) and for all six calf regions. Results are discussed in the next section.Different sizes of ACBs were investigated to study the effect of the initial length of the ACB and also cover a wide range of compressions on the calf. An ACB thickness of 1 mm with a dielectric constant of 7.41 was used for the simulations. Ogden parameters similar to  were useThe initial lengths of the bandage were determined in order for the ACB to undergo a certain range of stretch ratio i.e. 1.4–2.2) considering the non-deformed shape of the calf. However, as discussed earlier, the calf deforms under compression and thus the actual stretch ratio differs from the initial desired stretch ratio. In this work, the results are presented in terms of the initial desired stretch ratio for better consistency. Table .2 considThe simulation results of the analytical and FEM modeling that were explained in “Ev), defined by Eq.  and no volume change (NVC). The maximum difference between results from the analytical and FEM methods is 2.9 % at region 6 at λ = 1.4. The FEM simulation results shown in Figs. Figure d by Eq.  versus dmentclasspt{minimaentclass1pt{minimaFigure As mentioned earlier, the amount of pressure that the ACB exerts before electrical activation is denoted by mechanical pressure. The mechanical pressure of the ACB increases as the stretch ratio of the ACB increases. Also this mechanical pressure generally increases as the radius of the calf reduces . Thus foIn Fig. As mentioned earlier, applying a graded compression that reduces from ankle to knee is one of the recommended treatments for disorders associated with lower leg blood pooling . One wayThis work investigated a recently proposed technology that potentially addresses some of the disadvantages of current compression therapy devices for the lower leg disorders. It should be noted that the investigated technology is still in its early stage of development, which justifies this study aimed at predicting the ACB behavior when a compressible human calf is considered.The ACB faces some challenges and limitations that would need to be addressed in future studies. Specifically, DEAs are generally actuated with high actuation, which may pose limitations for use in wearable devices. The use of a stack of thin actuators could be used to address this challenge, as voltage could drastically be reduced. The use of a source not able to provide sufficient current to cause possible harm could also be considered.Another aspect that should be considered is the prolonged use of ACB in contact with the human skin. Future studies should investigate the use of a layer of anti-allergic, breathable fabric laid at the interface between ACB and skin, which wicks away the skin sweat and moisture. Such fabrics are currently used in available compression garments . The useFuture studies should also consider the effect of the stress relaxation behavior of DEAs and its implications in the use of active compression garments, such as the one investigated in this work. It should in fact be noted that the stress relaxation in the ACB’s elastomer may potentially present a considerable reduction in the mechanical compression of the ACB over time. The amount of relaxation depends on the material of the elastomer and is a function of time, temperature and stress/strain level. Typically, the level of relaxation is very large and considerable in acrylic elastomers, however it is small particularly in silicone elastomers . TypicalFor the prototype investigated in this work, Fig. In this paper the behavior of the active compression bandage (ACB) made out of dielectric elastomer actuator (DEA) was studied on a simulated human leg. ACBs could potentially be used in the treatment of different types of disorders associated with venous pooling in lower extremities such as orthostatic hypotension and edema. External compression on the lower leg helps balance the capillary filtration and venous return which in turn results in the reduction of venous pooling and normalizing the blood return. The literature suggests that a compression of 35–40 mmHg (4.67–5.33 kPa) at the ankle is required to prevent edema in patients with varicose veins. Also a graded compression of 18 mmHg (2.40 kPa) at the ankle, 14 mmHg (1.87 kPa) at mid-calf and 8 mmHg (1.07 kPa) at knee optimizes the blood flow in lower leg.Since the human calf is flexible and compressible, the size and shape of the calf changes upon application of external compression. The literature was reviewed to obtain a relation between external pressure and change in volume of the calf (i.e. compliance). Different methods were discussed in this regard and finally a model for the relation between external compression and radial strain of the calf was developed based on . This moIn this paper it was shown that calf compressibility caused variations in the amount of compression exerted by elastic bandages. Specifically, the actual amount of compression by elastic bandages is different from the expected value. In the case of using the ACB, consideration of ankle flexibility resulted in approximately 9.7 % variations in the total pressure of the ACB for a case when the initial mechanical pressure of the ACB was 50 mmHg (6.70 kPa). However, results suggest that the effect of calf compressibility on the total pressure of the ACB was less than 5 % for initial mechanical pressures of less than 23 mmHg (3.07 kPa), 24.5 mmHg (3.27 kPa) and 27.5 mmHg (3.67 kPa) at the knee, mid-calf and ankle regions. Generally, the ACB with a specific value of stretch ratio, exerted higher mechanical pressure and resulted in higher actuation pressure for calf regions which had smaller sizes. The study in this work suggested that, the size and the compressibility of the calf should be taken into account in order to create a sufficiently accurate design of the elastic bandages that are supposed to exert a specific range of compressions.


OU) model. We show that the OU model is frequently incorrectly favoured over simpler models when using Likelihood ratio tests, and that many studies fitting this model use datasets that are small and prone to this problem. We also show that very small amounts of error in datasets can have profound effects on the inferences derived from OU models. Our results suggest that simulating fitted models and comparing with empirical results is critical when fitting OU and other extensions of the Brownian model. We conclude by making recommendations for best practice in fitting OU models in phylogenetic comparative analyses, and for interpreting the parameters of the OU model.Phylogenetic comparative methods are increasingly used to give new insights into the dynamics of trait evolution in deep time. For continuous traits the core of these methods is a suite of models that attempt to capture evolutionary patterns by extending the Brownian constant variance model. However, the properties of these models are often poorly understood, which can lead to the misinterpretation of results. Here we focus on one of these models – the Ornstein Uhlenbeck ( The OU model was introduced to population genetics by Lande  to modelIn this paper we present an introduction to the OU model, its general properties and some issues with its use in ecology, evolution and palaeontology. We use simulations to demonstrate the inherent bias in estimating the core parameter of the OU model, α, that describes the strength of pull towards a central value . We discuss the intricacies of interpreting OU models biologically, and provide advice for appropriate use of OU models in phylogenetic comparative analyses. We also show that very small amounts of intraspecific trait variation (including measurement error) can profoundly affect the performance of models. These findings will be applicable to other models of evolution, but we focus on the OU model because of its widespread use and because of the ambiguity in the link between pattern and process when interpreting estimates of the α parameter. We are not the first to describe some of these problems  models incorporate information about the relationships among species into the error term of a generalized least squares model. This error term generally consists of a variance–covariance matrix of the phylogeny, but various transformations are used  is drawn at random from a normal distribution with mean 0 and variance σ2. The model assumes that there is no overall drift in the direction of evolution (hence the expectation of W(t) is zero) and that the rate of evolution is constant. Because the direction of change in trait values at each step is random, Brownian motion is often described as a ‘random walk’. The model assumes the correlation structure among trait values is proportional to the extent of shared ancestry for pairs of species. This means that close relatives will be more similar in their trait values than more distant relatives. It also means that variance in the trait will increase (linearly) in proportion to time. The model has two parameters, the Brownian rate parameter, σ2, and the state of the root at time zero, X(0).According to the Brownian model . This is referred to as a ‘single stationary peak’ or SSP model   ≠ μ this is referred to as a single peak OU process. However, estimating X(0) can sometimes lead to nonsensical values of μ, i.e. far outside the range of values for the trait, so results should be interpreted with caution. X(0) can also be defined a priori, but this is only appropriate when fossil data, or other independent evidence, allow confident estimates of the root state.In many implementations of the OU model  ≠ μ, when μ is zero, evolution approximates Brownian motion with a trend; Hansen, et al., t12 (see ‘Recommendations for interpreting α’ below).When α is close to zero, evolution is approximately Brownian  or temporally varying speciation rate  models. We simulated 1000 phylogenies for each combination of tips and models resulting in 56 000 simulated phylogenies in total. Trees were simulated using the R package TESS Hohna, . We thenhttps://github.com/ghthomas/motmot). This mirrors the common situation where researchers fit Brownian and OU models and then use Likelihood ratio tests to select the ‘best’ model. We then estimated the rejection rate of the null (Brownian) model for each set of simulations. We refer to this measure as the Type I error rate for the OU model.To determine whether α is biased under conditions where it should be zero, and whether Likelihood ratio tests are appropriate for use with the OU model, we estimated α for each of our simulated phylogenies and data, and compared the fit of a Brownian model with that of an OU model using a Likelihood ratio test with 1 degree of freedom with the transformPhylo.ML function in MOTMOT  1%, (2) 5% or (3) 10% of the tree height to the tips of the simulated trees. We then simulated data under a Brownian model on each tree. All our simulated trees and data are available on GitHub: We compared the fit of a Brownian model with that of an OU model using Likelihood ratio tests as described above, using the original trees without the addition of extra branch length to the tip edges. Our expectation is that the OU model should fit the data better than the Brownian model because the α parameter should account for much of the error. In this case, rejection of the Brownian model does not represent Type I error because it would be quite correct to reject the Brownian model. However, the reason for the better fit would be entirely unrelated to any macroevolutionary process.Table Although it is possible to create and implement new models for comparative data that encompass a range of processes, we have to be aware that such models are statistically complex and may behave in unexpected ways. Transformations of the variance–covariance matrix in the Brownian model  χ2‐distributed. In the OU model α is bounded  and has a non‐linear effect on the expected variances, so the assumptions of the Likelihood ratio test are not likely to be upheld for small samples. Our simulations indicate that Likelihood ratio tests should not be relied upon for analyses with small sample sizes, and that for robust inference and testing, alternatives, such as simulation and Monte Carlo Markov chains (MCMC), should be considered [see Measurement error increases Type I error rates. Our results show that a simple Brownian process can be mistaken for an OU process when a small amount of error is added to the data. The effects of measurement error become more severe with increasing tree size. These limitations mean that when evidence for the OU model is found, the results should be interpreted with caution, particularly where there is likely to be intraspecific variation or measurement error in the data.In the case of the OU model, there are several limitations worth highlighting:We have highlighted several issues with the OU model above. These can be broadly divided into two classes: (1) fitting the model and (2) interpreting model parameters. These issues can be at least partially addressed with additional or alternative analytical approaches. Below we make recommendations for best practice in fitting the model and approaches to interpret model parameters. We highlight areas of future research that may be important in alleviating outstanding issues.Simulate under the null model. OU models should not be applied to small trees. Our simulations indicate that trees with > 200 tips are necessary to obtain acceptable Type I error rates. However, we are cautious about recommending a minimum tree size because the performance of the OU model may vary among datasets for reasons other than tree size. We note also that large trees are particularly susceptible to issues arising from unaccounted measurement error in the data. Instead, we suggest using simulations to assess the model fit. Simulating data under the Brownian model will generate null distributions  an exponential distribution with mean = 1, (2) an exponential distribution with mean = 10 and (3) a uniform distribution bounded at 0 and 20. For all analyses we used a uniform −100 to 100 prior for μ and uniform 0–100 prior for σ2.We explored Bayesian methods as a possible remedy to the limitations of fitting OU models in a Likelihood framework. We repeated our simulations in a Bayesian framework implemented in BayesTraits  of a trait, i.e. the time it takes for a species entering a new niche to evolve halfway toward its new expected optimum , and thus should not be interpreted as evidence of any kind of process. As a further note of caution, it is important to recognize that biases in the estimation of α would lead to similar biases in t12.If possible, include ancillary data. If data on fossils are available then these could be incorporated into the analysis , Docker (www.docker.com) or packrat  gave a talk on Open Science and reproducibility. We have therefore tried to make this paper as open and reproducible as possible. All simulated phylogenies, data and R code are available on GitHub: Table S1. Details of the papers used in our literature review.Click here for additional data file.


The aim of this study is to extend the methodology developed in the EUFRAT project to quantify the transfusion transmission (TT) risk from travelling donors.The EUFRAT (European Up-Front Risk Assessment Tool) was developed as an online risk assessment tool  would have been required to prevent only 41 % of all expected transmissions at that time. For Q fever, in which the transmission of chronic Q fever is considered, even at the end of the third annual outbreak’s peak 47 % of all (chronic) Q fever transmissions could still be prevented.The updated model allows estimation of the infection transmission risk from travelling donors. In combination with the distinction between past and future transmissions, these estimates provide valuable information to support decisions concerning communication with the public and/or the implementation of safety interventions.The online version of this article (doi:10.1186/s12879-016-1452-z) contains supplementary material, which is available to authorized users. Trends in globalisation have brought increased blood safety concerns as population members  have become highly mobile. This presents challenges for blood safety when travellers go to areas in which there is ongoing transmission of infectious diseases (either endemic or epidemic) and import infections to their home countries. Blood safety concerns for emerging infectious diseases (EID) are identified and documented extensively in the literature –4. CurreSeveral blood establishments have travel risk policies for donors who report having visited EID risk areas , 6, 8–10The aim of this study was to extend EUFRAT to characterise and model the risk of disease transmission from travelling donors to blood safety. We propose generic formulas for calculating the risk of transmission taking into account transfusions that have already occurred by the time of observation (past transmissions), and the projected (future) transmissions from travelling donors. The method was applied to the chikungunya outbreak in Italy , 10 and For the quantification of the risk of infected donations from travellers who visited an EID risk area, a number of definitions are required. These relate to (1) the transmission in the EID risk area , (2) the infection , and (3) the travelling donor population . Next, based on these definitions, formulas for the expected number of transmissions by blood transfusion from travellers were derived. A distinction is made between transmissions that have already occurred by the end of the observed outbreak (past transmissions) and for transmissions yet to occur (future transmissions).The model of a travelling donor entering a risk area, his exposure, infection and his infectious period is illustrated in Fig. te, where he remains for a time period Dv after which he returns to his home country. If he becomes infected he will remain infectious for a period Di, and in case he donates within this time period upon returning home, he is expected to transmit the infection acquired. Thus, the exposure of the traveller begins upon entering the risk area at te, and lasts for a period Dv. Because we are interested in the donations made by the traveller after the end of his visit Dv, the time being infectious while able to donate is defined as the time interval from te + Dv to ti + Di. Let us call this interval Dx(=ti + Di − te − Dv), which is relevant for transfusion transmission. All time intervals are presumed to be non-negative.We consider the situation in which a traveller enters an area with ongoing transmission of an infectious disease (which we refer to as “risk area”) at time I is the number of infections notified in the time interval Do corrected for underreporting, N is the size of the population at risk, and Do is the period of transmissions observed. For a transmission where the incidence rate varies over time, the risk can be estimated for smaller Do intervals with varying incidence rates. These varying incidence rates can be modelled as a series of individual small outbreaks of which the results can be subsequently aggregated. We next assume that the incidence rate can be interpreted as a proxy for the force of infection to which a susceptible traveller is exposed, that a donor will be infected only once during his visit, that the infection risk is proportional to the duration of stay in the risk area, that τ is the number of donors per unit time travelling to the risk area, that φ is the donation rate , and that each donation made by each infected donor upon returning home results in one infected recipient. The total number of transmissions from travelling donors (NV) is (in case Di ≥ Dv) equal to:We assume that transmission in the area during the risk assessment can be characterised by a constant incidence rate φ) and the duration of the infectious period (Di). For travelling donors however, half the duration of the visiting time is subtracted from the infectious period. This makes sense as donors will not be able to donate during their visit to the risk area and therefore the infectious period of a visiting donor will on average be reduced by half the visiting time.The number of transmissions per infected donor would generally be proportional to the donation rate , and transmissions after tobs (=Do) (future):For those cases where Nvf) expected at tobs for te + Dv < tobs can be calculated as:Thus, the number of future transmissions from travelling donors (Di ≥ Dv), the number of future transmissions (so transmissions that will occur after tobs) is equal to:For situations in which the infectious period is longer than the duration of stay  the distinction between past and future transmissions can be informative. This allows a decision maker to assess the impact of safety interventions and the necessity for communication of risks to transfusion recipients even at the end or (long) after an outbreak has occurred.Di < Dv and the infection occurs at the beginning of the visit, so within Dv < Di after entering the risk area, the infectious period has already passed before the donor returns home and no transmission will occur. This means that the duration of exposure with a risk of transmission had length Di instead of Dv, and the risk of transmission has to be corrected for the likelihood of an early infection. The risk of the donor getting infected in this first period of his visit is equal to λ(Dv – Di). Hence the number of (future) infected donors can be calculated using Eqs. Di) and noting that the risk is overestimated by a  factor 1/(1 − λ(Dv − Di)).In situations where The estimated number of infected donations might have to be adjusted according to the proportion of under-reporting of notified cases, according to the time that infected donors will actually donate during their infectious period before symptom onset , or adapted for the infectivity of the specific transfused product . In addiThe travellers’ risk model was applied to the chikungunya outbreak in Italy in 2007 . We illuAs a second example the Q fever outbreak, which lasted for over a 3-year period in the Netherlands, was assessed for the transmission risk of chronic Q fever in non-Dutch travellers to the Netherlands . The monAll formulas were derived using Mathematica . The formulas were implemented in Microsoft Excel  for analysis and graphing.τ = 10) visited the risk area, remaining in the area for 7 days each (Dv = 7). For this scenario, the total cumulative (15 weeks) travellers’ transmission risk is 4.45 per million blood donations from travellers to Italy. At the end of outbreak week 7, the total number of expected transmissions is 0.85 per million and the proportion of future transmissions –given that 19 % of all infections were observed by the end of week 7– is 41 %. Implementation of interventions like stopping blood collection at this early stage only would substantially mitigate future transmissions from donors that travelled to the risk area.Weekly chikungunya infections reported during the 15-week outbreak in 2007 in Italy were used to estimate the total number of transmissions by blood transfusion from travellers to Italy, as well as the expected proportion of future transmissions. The number of chikungunya infections was estimated by adjusting the notified cases for the proportion of asymptomatic cases (15 %); we used the adjusted value to allow for a comparison with earlier results. The expected future transmissions are shown in Table First, the duration of visit was varied. For duration of the travellers’ visit of 2, 4, and 8 days, the total transmission risk is equal to 2, 3.4 and 4.5 per million, respectively. Given that the infectious period is 8 days, the duration of the visit from 8 days onwards will result in a similar transmission risk for this setting. What can be observed is the decreasing relative increase in the number of transmissions with increasing visit length as a result of the negative quadratic term in visit length in Eq. Next, the duration of visit was assessed for an infectious period of 1 year (365 days). Now the observed transmission risk becomes 100, 200, 410 and 710 per million for respective visit lengths of 2, 4, 8 and 14 days. Here it can be noted that the risk from travelling donors increases almost linear with the duration of visit (14/2 × 100 = 700 ≈ 710). The proportion of future transmissions at any given time during and after the outbreak period will be closely similar for each of these scenarios, as most transmissions (approximately 87 %) will occur after the end of the observed outbreak.Using the disease parameters from a previous Q fever study and visit characteristics as provided in Table It is clear that incorporating uncertainty in all model parameters would allow construction of credible intervals (CIs) around the estimated number of transmissions. However, the figures are provided for illustration of the application of the methodology rather than for estimating the number of transmissions, hence CIs are not provided.Our study shows that it is feasible to estimate the number of transmissions from travelling donors by applying the travellers’ risk model described in this paper. The model developed can support decision-making concerning safety interventions and communication related to donors travelling to EID risk areas. The model allows –using only a few basic parameters related to the outbreak, the infectious disease, and the travelling donor population– estimation of the transmission risk from travelling donors.For the chikungunya outbreak we noted that the estimated traveller’s risk for a 1 week visit is 0.1 per million . Liumbruno and colleagues reported that during this outbreak a 21-day deferral policy was implemented nationally for all donors who had visited the risk area even for a few hours, although their acceptable cut-off risk is one in 380,000, which is much higher than our model’s estimate . Had theDc, the risk from such an infection can be calculated by simply calculating the number of infections that would have been transmitted if infections would have been transmitted over the full infectious period Di and subtracting the infections transmitted from the time of infection up until the incubation time Dc.One of the limitations of our model is that it assumes that travelling donors only visit the risk area once. In some cases, travellers might make recurring visits and such repeated exposures might further increase their susceptibility to infection. This was also noted by Massad and colleagues . For asyWe noted that the travellers’ risk model is generally applicable, and its application is illustrated for two real outbreaks. Our model, with its distinction between past and future transmissions, can be considered a useful extension to currently available models for estimating transfusion transmission risks. Application of the travellers’ risk model may assist blood establishments in harmonising risks posed by travelling donors . SeveralThe example applications in this paper assumed 100 % transmissibility of the infection during the infectious period that will provide conservative risk estimates. The web based EUFRAT model however allows specification of the probability of transmission per type of transfusion product . It should be noted that transfusion transmission for chikungunya has not yet been demonstrated , 17. NevWe have developed a model that allows estimation of transfusion transmission risk from travelling donors. The limited number of generic model parameters required permits the model to be applied in very diverse settings. This not only empowers public health decision-makers with an appropriate tool to objectively quantify the risk from traveling donors, but also provides a sound proactive basis for enhanced management and response to outbreak situations.


The purpose of this study was to clarify rotational relationships between the anatomical landmarks of the glenohumeral joint in maximum elevation.Twenty-five healthy volunteers  held the arm in maximum elevation in an open MRI system. In each three-dimensionally computer-generated image, elevation angle of the humerus in the plane of elevation was measured, based on the glenoid and the scapular planes. Using the equator set on the head surface by the plane parallel to the humeral axis, involving the head center and the bicipital groove, glenoid location and rotational relationships were investigated.The elevation angle was 102° ± 9° in the plane 7° ± 8° anterior to the scapular plane, and axial rotation was fixed with the glenoidal long axis parallel to the equator (within 2°). Each glenoid center located on antero-superior portion of the humeral head, and the direction from the top of the head to its location was the same as that of the shaft tilting, indicating the glenoid only translated without rotation after reaching the top of the head on the equator.Before reaching maximum elevation, the glenohumeral joint would be locked in axial rotation. The position when the glenoid is on the top of the humeral head with the humeral shaft perpendicular to the glenoid is considered to be essentially the final position of elevation, above which the glenohumeral joint only translates without axial rotation even if the humerus is more elevated.The online version of this article (doi:10.1007/s00276-014-1257-y) contains supplementary material, which is available to authorized users. Arm elevation is one of the most important functions of the shoulder in our daily lives , 12, 13.Twenty-five volunteers  without symptoms or history of shoulder disease were enrolled in the study. Mean age was 29 21–35) years. All participants provided informed consent. The maximum elevation of the shoulder was defined as the position in which the tips of fingers could reach highest in a standing position. A surgical mattress  made of micro-balls of styrofoam, which could be stabilized by vacuum, was adopted around his or her upper extremity and body to reproduce shoulder position  and a 3D image of the glenohumeral joint including the proximal part of the humerus was generated using computer software . Providing instant access to 3D information, the same software allowed anatomies to be viewed from any angle.Anatomical landmarks such as the glenoidal long axis, glenoid center, humeral head center, and humeral shaft axis Fig.  were defTwo cross-sections of the humerus were obtained at 7.5 and 15 cm from the proximal end. The center for these cross-sections of the cortical bone was determined by fitting a circle, and the humeral axis was defined as the line that passed through the center of these circles. Using the data of Iannotti et al. , which sThe slope of the humeral long axis on the glenoid was determined by measuring the angle relative to the glenoidal long axis and analyzing their orientation in the transverse plane, relative to the scapular plane, in which the humerus was elevated. Rotation of the glenohumeral joint was visualized on the computer screen as follows. The equator was set on the head surface by the plane parallel to the humeral long axis, including the head center and the bicipital groove. Parallel lines to the equator were analogous to latitudes. Rotation was referenced to latitude by rotating the globe to align the longitude, including the midpoint of the glenoidal long axis, with the vertical. The angle when the glenoidal long axis became parallel to latitude was defined as 0° and values in external rotation were defined as positive.To investigate location of the glenoid center, the surface of the humeral head was divided into four segments , using the equator and the circle of longitude crossing the top of the head. Meanwhile, the shaft axis and the center of the humerus in each subject were projected orthogonally to the glenoid plane to confirm the relation between the anatomical landmarks.Ten different glenoids were analyzed measuring length values of the long and the transverse axes by two independent investigators to determine the inter-observer variability. To measure the intra-observer variability, those values in 10 glenoids were measured twice by the same person. Angle values of the plane including the shaft axis and the head center to the equator on the head surface were also analyzed to determine these variabilities for the humerus. All data are expressed as mean ± SD. To assess these variabilities, the interclass and intraclass correlation coefficients were used (Wilcoxon Signed Ranks test).Length values of the long and the transverse axes of the glenoid, and angle values of the plane including the shaft axis and the head center to the equator on the head surface were 36.0 ± 3.0 mm, 22.5 ± 2.1 mm, 10.0° ± 8.8°. Those values in ten glenoid and humeral bones showed the variability or the reproducibility was high with a correlation coefficient ranging from 0.73 to 0.98 (Wilcoxon Signed Ranks test) .Figure The humeral head centers and shaft axes projected on the glenoidal plane are shown in Fig. The final position was proved to be the same in all subjects, suggesting this position is unique and may be reached whatever the course of the humerus.The glenohumeral joint was considered to reach a general position at the end of raising the arm whatever the passway followed by the humerus , 11, 13.For investigating rotational alignment of the joint, we set the equator and its parallel lines on the surface of the humeral head. The equator was determined to include the bicipital groove because several authors , 12, 13 Ranges of rotation would be restricted with arm elevation because insertion of the short rotators approaches the glenoid surface and the capsule surrounding the head also becomes tight. Thus, irrespective of the plane of elevation, the shoulder joint would finally converge to the same, i.e., the preferential position. Gagey et al.  describeFigure Certain shortcomings of this study must be acknowledged. A surgical mattress was adopted around elevated arm and body in a standing position and the angle of the upper extremity to the trunk was reproduced in an open MRI system. However, each participant had to lie in a supine position in the system and it was possible his or her position of the joint was changed. Systems with upright coils should be used for directly investigating the difference in relationship in standing or sitting positions and that would enable determining the effect of gravity on the glenohumeral relationship. We could not clarify which part of anatomy was essential for determining the positioning in maximum elevation in this study. Information about cases with rotator cuff tear or without the long head of the biceps might be helpful for clarifying which part of the joint is essential to determine the positioning. Experiments like ex vivo studies simulating rotator cuff tears , 18 mighEven though those limitations should be taken into consideration, the current study might be sufficient to describe the static position of the glenohumeral joint in maximum elevation. The result of the study suggested (a) the position in which the glenoid is on the top of the humeral head with the humeral shaft perpendicular to the glenoid is essentially the final position of elevation, and (b) the humeral axis is slightly anterior to the plane of the scapula and no axial rotation is available for the humerus after that final position. Rehabilitation programs aiming at reaching this position when the joint is impaired after injury or operation would be reasonable from this biomechanical point of view.Supplementary material 1 (WMV 26711 kb)Below is the link to the electronic supplementary material.


Thermotoga maritima belongs to a domain of unknown function protein family. It was predicted that Tm1631 binds with the DNA and that the Tm1631–DNA complex is an endonuclease repair system with a DNA repair function : e1003341, 2+ ions to the reaction center and extension of classical MD by 50 ns , the DNA ligand stayed bolted to the protein. Results from density functional theory quantum mechanics/molecular mechanics (QM/MM) calculations suggest that the reaction is analogous to known endonuclease mechanisms: an enzyme reaction mechanism with two Mg2+ ions in the reaction center and a pentacovalent intermediate. The minimum energy pathway profile shows that the phosphodiester bond cleavage step of the reaction is kinetically controlled and not thermodynamically because of a lack of any energy barrier above the accuracy of the energy profile calculation. The role of ions is shown by comparing the results with the reaction mechanisms in the absence of the Mg2+ ions where there is a significantly higher reaction barrier than in the presence of the Mg2+ ions.A protein, Tm1631 from the hyperthermophilic organism The online version of this article (doi:10.1186/s13065-016-0188-6) contains supplementary material, which is available to authorized users. Tm1631 is a member of the domain of unknown function 72 (DUF) family in the Protein family (Pfam) database, a protein domain that has no characterized function [2+ ions act in this DNA binding site  endonuclease enzyme presented by Mol et al. [2+ ions. In the proposed catalytic mechanism  and the DNA chains from another crystal structure (PDB: 2NQJ).The structures used in this work were previously equilibrated by classical MD simulation. Since there is no crystal structure of Tm1631 with DNA available in the Protein Data Bank (PDB), we used as the starting structure for this study the predicted Tm1631–DNA complex after 90 ns of classical MD, which was performed in our previous study . To vali2+ ion and water molecules positions. In this search we required that Mg2+ initially coordinates with six oxygen atoms , and with the distant environment atoms in the same positions for both, the reactant and product conformations. The final energy minimization procedures were performed without any constraints or restraints and we were able to obtain suitable starting positions for the reaction mechanism studies with the above mentioned properties.In order to obtain good reactant and product structures we probed different Mg2+ ions and then minimized the system. A minimized structure with Mg2+ ions was further optimized at quantum mechanics/molecular mechanics (QM/MM) level using the CHARMM software package. The CHARMM force field parameters were used to describe the molecular mechanics (MM) part, while the quantum mechanics (QM) region  and P (3DR7) which remained 3.5 Å apart. On both bivalent metal ions, the Mg2+ harmonical restraint (the “cons harm” command in CHARMM) was used to preserve the coordination of ions. At this stage 100 steps of ABNR minimization were performed. Then we removed CONS HARM restraint from the minimization run of 300 steps with only RESD restraints for bonds left. Subsequently we removed all the restraints and ran 1500 steps of minimization.A reactant was constructed for which the QM/MM minimization procedure was initiated by using RESD  restrain− (water 1863) and P (3DR7) 1.6 Å using restraints. We also restrained the distance between O5′ (3DR7) and P (3DR7) at 1.6 Å. After 300 steps with RESD, we removed all the restraints and ran 1500 steps of a geometry minimization procedure.From the structure of the reactant we made a product, breaking the bond between P (3DR7) and O3′(CYT6) and created a bond between OHWe replicated the whole system 16 times and produced initial replica conformations by linear interpolation of the coordinates between the reactant and the product. The first and last replica represented reactant and product conformations, respectively, and were fixed throughout the pathway minimizations. With this setup a 3000 steps ABNR minimization was performed.The following steps define the procedure for the reaction with two Mg2+ ions in the binding pocket:2+ ions, then removed them from the structure and held the distance between OH− (water 1863) and P (3DR7) at 4 Å for 300 steps. Afterwards we removed all RESD restraints and ran 3000 steps of ABNR minimization.We started with the minimized reactant from the simulation with two Mg2+ ions and removed them; subsequently running 3000 steps of an ABNR minimization procedure.We took the product from the previous simulation with two MgWe performed an RPATh calculation, with 3000 steps of ABNR minimization procedure using identical number of replicas as in the calculations with ions present in the system.Procedure steps for reaction without MgTo check the influence of the protein environment on the lowering of the energy barrier, we performed pure QM calculations with the GAMESS program as well as the reaction path calculations with the RPATh method. B3LYP/6–31G* level of theory was also used for all calculations in the pure QM case.2+ ions and pure QM calculations:2+ ions. Subsequently we removed the whole protein, DNA structure and water molecules that were not in the QM region. Finally we ran 3000 steps of ABNR geometry minimization with restrains, holding all three protons attached to the Lys73.First we made the reactant state from the already QM/MM minimised structure without Mg2+ ions in the binding pocket. Then we ran 3000 steps of ABNR minimization without restrains.We made a product from the QM/MM calculations without MgRPATh calculation was finally performed with 3000 steps of ABNR minimization, using 16 replicas.Procedure steps for reaction without MgT. maritima has a similar binding site to that of DNA repair proteins, as established earlier [2+ ions catalysis that we propose is most likely the so-called Steitz’s mechanism [The Tm1631 protein with yet unknown function from the organism  earlier . By expl earlier  stronglyechanism , 37.2+ ions in the binding pocket . Many similar systems with two Mg2+ ions in the binding pocket have been studied [N2-like nucleophilic attack on the scissile phosphate performed by an hydroxide ion, which is formed upon water activation [2+ ions is lowering the pKa of its ligands and also for the presence of second metal ion (which coordinates nucleophilic water or hydroxide in the binding pocket). This leads to a mechanism with early proton transfer [2+ ions have an essential contribution for the specific catalytic reactions by lowering the pKa of the leaving group and can impose specific geometry for the triphosphate chain—pentacovalent transition state and products [Magnesium ions coordination is essential for most phosphoryl transfer enzymes . The com studied . Distanctivation –26, 45. transfer , 47, preproducts .2+ ions shown in Fig. 2+ ions effectively stabilize the energy of pentacovalent intermediate to make it a stable minimum. In order to check this structure and its energy we performed a separate calculation. We minimized the geometry of the single number five replica structure. After 10,000 steps of ABNR minimization the geometry had no visible changes to the one in the chain of replicated structures and the energy was lowered by 2.5 kcal/mol due to the removal of the restraint which slightly distorts the distances and makes the energy higher. The effect of the distortion could be made smaller by increasing the number of replicas, however this would not change the overall properties of the reaction mechanism. At the current level of accuracy one can estimate that energy noise level in Fig. − was found to be −18.1 kcal/mol. The distances to important amino acids are reported in Additional file We observe six critical points in the energy profile for two Mg2+ ions (Fig. 2+ ions is to transform this configuration into a stable and low energy pentacovalent intermediate.There is a high 17 kcal/mol barrier in the middle of the reaction path between structures five and nine. This structure has features analogous to those of structure six from Fig. 2+ ions are not present the conformational space of the entering species is larger than the one with the two ions present. This makes the reaction energetically less favourable because the system may explore more pathways which are of higher energy than the ones with the ions present. This works in addition to the fact that the environment atoms in the enzyme systems usually lower the energy barriers of any transition state structure. At this point we can add that a possible solution to choose the most favourable reaction channel would be the use of QM/MM molecular dynamics. But there is a sampling problem to be emphasized which, to be resolved, would require tens of nanoseconds of simulation time what translates into tens of millions steps of complete QM/MM calculations. In the present studies less than 100 thousands steps of QM/MM calculations were performed and still it took a few months of CPU time using between 32 and 128 processors depending on the task.Many chemical reactions have multiple reaction channels which depend mostly on the positions of the species entering the reaction. In the case where the two MgNext, we present the results of QM/MM calculations without Mgons Fig. , which s2+ ions. The vacuum calculation was set up keeping just the QM region in the reaction path calculations. From the Fig. To show the impact of the protein, DNA, solvent and ion environment on the studied reaction, we also studied the system in vacuum without MgIn order to verify the stability of the initial QM/MM setup we performed two additional classical MD 50 ns simulations starting with the reactant and product structures that we used for the minimum energy pathway calculations. The ions in these simulations kept the hexacoordinated structures throughout the simulations. In the case of reactant simulations none of the waters were exchanged and all 12 atoms around the two ions were identical. This means that the atom positions of reactant are in the stable and favorable positions to enter the reaction.T. maritima was predicted to be an endonuclease-like DNA binding protein, and consequently we investigated its function focusing specifically on the role of Mg2+ ions in its binding pocket. We performed a QM/MM study of Tm1631 in a complex with damaged DNA. We found that Mg2+ ions are required in the binding pocket in order that the reaction occurs. This allows us to conclude that Tm1631 is indeed an endonuclease binding protein with a reaction mechanism similar to that of other endonucleases. Some reconciliation is still needed regarding the number of metal ions, e.g. it is possible that only one ion suffices for the reaction to take place. The present paper is one of the few theoretical insights in the available literature to study a series of the reactions that play a role in the complex of the endonuclease repair process. Future work should be aimed at determination of the precise number and type of ions that are needed for the reaction to occur. Another interesting study would be to explain the formation of the hydroxide ion in connection with the protonation of Lys73 and the role of ions in such mechanisms. It would also be interesting to compare the present results with the results obtained by similar studies in different proteins (Additional file Protein Tm1631 from the organism