W) must be specified. Because of the potentially high sensitivity of the WBF to the choice of W, we propose several new BF approximations with  logOR ∼N, but allow W to take a probability distribution rather than a fixed value. We provide several prior distributions for W which lead to BFs that can be calculated easily in freely available software packages. These priors allow a wide range of densities for W and provide considerable flexibility. We examine some properties of the priors and BFs and show how to determine the most appropriate prior based on elicited quantiles of the prior odds ratio (OR). We show by simulation that our novel BFs have superior true‐positive rates at low false‐positive rates compared to those from both P‐value and WBF analyses across a range of sample sizes and ORs. We give an example of utilizing our BFs to fine‐map the CASP8 region using genotype data on approximately 46,000 breast cancer case and 43,000 healthy control samples from the Collaborative Oncological Gene‐environment Study (COGS) Consortium, and compare the single‐nucleotide polymorphism ranks to those obtained using WBFs and P‐values from univariate logistic regression.Bayes factors (BFs) are becoming increasingly important tools in genetic association studies, partly because they provide a natural framework for including prior information. The Wakefield BF (WBF) approximation is easy to calculate and assumes a normal prior on the log odds ratio (logOR) with a mean of zero. However, the prior variance ( Recently, several methods have been published for analyzing genotype data at the fine‐mapping level  showed that V∝1/n approximately. Studies with total sample sizes of 2,000 will yield most values of V in the region 0.005≤V≤0.2 for SNPs with MAF greater than 0.005. As sample size decreases occasional large values of V may be seen resulting from rare alleles (with MAFs less than 0.005) having very low counts in the case and/or control groups. These SNPs will have broad likelihoods and so are unlikely to have high BFs regardless of the prior. So although very rare alleles may have priors that are different from most of the SNPs, these rare SNPs are unlikely to be retained after filtering.We next consider SNPs with extreme values of W varied with V. If the hypothesis is true then rarer alleles should have larger effect sizes, hence larger values of W. Rarer alleles also have larger values of V. So if our priors possess this property, we should observe that E(W) increases with V. For the power, hybrid, and reciprocal priors, the expected value of W can either be found directly using integration by parts or by finding E(V+W) by integration and then using the relationship E(W)=E(V+W)−V. Those expectations that depend on V are plotted in Figure V likely to occur. E(W) is independent of V for the exponential prior. For the three expectations which depend on V, we were unable to verify algebraically that dE(W)/dV>0∀V>0,b>a>0andd,k within the specified limits (where relevant), but Figure E(W) against V for a range of values of the hyperparameters. Panels (b) and (f) show this appears to be the case for the power and reciprocal prior. However, we can see from panel (d) of Figure E(W) could decrease with V for the hybrid prior. Researchers who believe that rare SNPs will a priori have larger effects should constrain d to be close to zero. Alternatively a generalization of the Savage‐Dickey density ratio  and so lower values of W, which put more weight at small ORs, are expected to perform better. The exception to this is at the lowest FPR where W=0.1 generally yields higher TPRs. It is at the very low (and arguably most relevant) FPRs that the PPBF and HPBF methods generally outperform all the other methods considered. If we focus on the smallest FPR considered at each level of sample size and OR, we see that either the HPBF or the PPBF have the highest TPR of any of the methods considered in eight out of the nine scenarios. It is only when the sample size is 20,000 and the OR is 1.1 that one of the other methods (WBF with W=0.003) is superior at the lowest FPR.Table P‐value method and the methods using BFs, For example, with a sample size of 20,000, an OR of 1.10 and an FPR of 5%, the TPRs for the P‐value and WBF with W=0.003 are 0.497 and 0.579, respectively. The TPR for the WBF method is nearly 17% bigger than the TPR for the P‐value method. Making the same comparison when the FPR is 1% the WBF method is over 14% bigger than the TPR for the P‐value (0.231 compared to 0.202).What is somewhat surprising is that even when the sample size is as high as 20,000, there are substantial differences in the performances of the W=0.003 and W=0.1 and also the results of filtering using a power prior and a hybrid prior with support 0.003≤W≤0.1. The two new priors were chosen so that the power prior put a lot of mass at W=0.003 and hybrid prior out a lot of mass around W=0.1. Consequently, using the power prior produces an ROC curve similar to that using WBFs with W=0.01 and using the hybrid prior produces an ROC curve similar to WBF with W=0.01. There is clearly a lot of variation in the effectiveness of the WBF filter as W changes. Putting too much prior weight on large effect sizes clearly leads to poor performance of the WBF when the actual causal effect size is small. The new BFs can be thought of as a weighted average of the BFs over the support of W and so do not suffer to the same degree as using WBF with a value of W that puts a lot of mass at values of β that have a low likelihood.Figure CASP8 region on chromosome two. We initially asked our expert to give an interval of possible ORs at a single percentile. They thought that the 80th percentile of the OR was between 1.05 and 1.3, yielding a=0.003 and b=0.1. We then asked them to choose three further PIs that they were comfortable thinking in terms of. They provided the 0.95, 0.75, and 0.50 PIs giving =. For these probabilities, we asked for their best estimate of the upper limit of the OR and the expert provided  PI u=. From these we get =. The median value of V for the SNPs in the iCOGS data was 0.00017. We used all these values to carry out a search over −10≤k<−0.5 at intervals of 0.01 for the PPBF. Evaluating Equation k, we found that for the PPBF, the minimum of the sum of squared differences occurs at k=−1.66. If we use the same method to find other prior forms which fit the elicited values we get an exponential prior with c=145 and a hybrid prior with d=0.001 and k=−1.69. Of these three priors, the power prior has the closest fit to the elicited values and is a better fit than the reciprocal prior, so we used this to calculate the BF for the iCOGS data.Before analyzing the iCOGS data [Michailidou et al., P‐value from univariate logistic regression and by WBF with the two values of W that are the extremities of the support used for W with the PPBF. We can see that while all these methods rank SNPs similarly, with the same SNPs always ranked in the top three, the rankings do differ somewhat.Table W=0.003 . Of the top 20 SNPs selected by WBF with W=0.003, 10 SNPs are not in the top 50 for PPBF and three are not in the top 75 for PPBF. There is more agreement between WBF with W=0.1 and PPBF but WBF still selects two SNPs in its top 20 that are not in the top 20 for PPBF.With such a large sample size, the majority of the information comes from the likelihood, rather than the prior, which is why the rankings using the different methods are so similar when considering the full iCOGS data. Many association studies will have smaller sample sizes than those used here and in these studies the prior will have more influence on the BFs obtained and so we would expect much more variation in the ranks across the different BFs. We investigated this using a stratified random subset of the iCOGS subjects, with 2,721 cases and 2,517 controls . The same analyses were carried out on this subset of the data and the results are given in Table W, where we assume that the prior distribution on the logOR of an SNP is N. This allows for the calculation of BFs where a normal distribution is believed to be an appropriate form for the prior, but where there is uncertainty in W. Most of the priors we suggest for W put most of the weight of W at the lower end of the support but the exponential prior allows for an almost uniform prior which is useful when an expert believes a range of values of W are equally likely a priori. The hybrid prior can be specified so that the mode is anywhere in the given range. This might be useful when an expert has a strong prior belief in a particular value of W but wants to allow for some uncertainty in it.We have developed several new forms of approximate BF. These include three parametric families and one fixed form, each relating to a different prior distribution on W appears universally to be an increasing function of V only for the power and reciprocal priors. This means that these priors are consistent with the hypothesis that rarer alleles have have larger effects. Depending on the values of the hyperparameters, E(W) may be a decreasing function of W when the hybrid prior is employed. Such a prior would be inappropriate to use if it was believed that rare causal SNPs do indeed have larger effect sizes. In this case, we suggest a Savage‐Dickey density ratio approach [Verdinelli and Wasserman, Three of these prior forms also depend upon the genotype data. The expectation of All the novel BFs described here are univariate. The solution we have employed in the univariate case relies on recognizing the BF integrands as standard probability densities. We did consider extending these BFs to the multivariate case and while specifying the prior in the multivariate case is straightforward, the resulting integrals appear to be intractable and would probably need to be solved analytically so that easily calculated closed form expressions for the BFs may not be easily available.R (we give code in supplementary data), although the EPBF is computationally intensive and cannot produce results for SNPs which have very small MLEs of the logOR. The computation for all of the other forms is simple and efficient and we therefore recommend using the PPBF or HPBF instead of the EPBF where possible. In most cases, hyperparameters can be found which result in power or hybrid priors very similar to the desired exponential prior. In these situations, we recommend that the EPBF only be used if the investigator does not want to include any information from the data in the prior.All novel BF approximations can be calculated in W has been determined based on the quantiles elicited from the expert, it is important to relay back to them what this means about other quantiles not elicited to check that these are acceptable. A web‐based tool, MATCH, which may help with this purpose is now available [Morris et al., We described how elicitation may be employed with an expert to determine the most appropriate values for the hyperparameters, although such experts may find this task difficult. The importance of feedback in the elicitation process is worth emphasizing. Once a distribution for encode database [Encode Project Consortium, Filtering using BFs has already been used in fine‐mapping [Maller et al., Disclaimer: Supplementary materials have been peer‐reviewed but not copyedited.Supplementary material.Click here for additional data file.


Reflection ion microscopy (RIM) is a technique that uses a low angle of incidence and scattered ions to form an image of the specimen surface. This paper reports on the development of the instrumentation and the analysis of the capabilities and limitations of the scanning RIM in a helium ion microscope (HIM). The reflected ions were detected by their “conversion” to secondary electrons on a platinum surface. An angle of incidence in the range 5–10° was used in the experimental setup. It was shown that the RIM image contrast was determined mostly by surface morphology but not by the atomic composition. A simple geometrical analysis of the reflection process was performed together with a Monte Carlo simulation of the angular dependence of the reflected ion yield. An interpretation of the RIM image formation and a quantification of the height of the surface steps were performed. The minimum detectable step height was found to be approximately 5 nm. RIM imaging of an insulator surface without the need for charge compensation was successfully demonstrated. Reflection ion microscopy (RIM) is a technique that uses low-angle, scattered ions to form an image of the specimen surface. This technique is similar to reflection electron microscopy (REM) proposed by Ruska in 1933 –4 was deThe helium ion microscope is a new scanning microscope with a single atom ion source that was developed in 2006 –15. One 16In this paper we report on the development of the instrumentation and the analysis of the capabilities and limitations of scanning reflection ion microscopy (RIM) in a helium ion microscope. In the experimental part of our work we describe a configuration of a sample holder developed for RIM and demonstrate first results obtained for standard test samples. In the discussion part a simple theoretical treatment of the image contrast formation in RIM is provided, which is necessary to obtain quantitative information from RIM images.All investigations were performed in a Zeiss Orion scanning helium ion microscope. The detection of reflected ions (RIs) requires an additional detector installed below the specimen. One of the easiest ways to detect the reflected ions is by their “conversion” to secondary electrons (SEs). Previously, such a RI-to-SE conversion was used for the detection of backscattered electrons in SEM  and for The sample (1) was mounted on a stage at a grazing angle relative to the focused helium ion beam. The reflected helium ions were directed to the Pt-coated plate (2) which served as the main RI-to-SE converter. Platinum was chosen because of its high SE yield under He-ion excitation . The SE The angular aperture of detected reflected ions was limited by the slit (6) installed under specular reflection conditions. The slit, with the width of about 1 mm, was mounted parallel to the tilt axis of the sample stage at a distance of about 14 mm, which corresponds to an RI detection angular aperture of 4°. Backscattered ions (BSI) were detected by the microchannel plate detector (7). The width of the scanned area was less than tens of micrometers, corresponding to the variation of the detection angle of less than 0.1°. The incidence angle of the RIs was also practically unchanged during the scan over the small sample area, which assured the proportionality between the number of SEs excited from the Pt plate and the number of RIs.−7 Torr. The energy of the He ions was 35 keV with a beam current of 0.5 pA. The compensation of the sample charge by the electron flood gun was not possible due to the configuration of the sample holder.All measurements were performed in the chamber with a base pressure in the range of 102 square bars of 22 nm thickness on a silicon substrate, and (3) a mica (phlogopite) sample prepared by mechanical cleavage.The following model test samples were investigated: (1) a Au on carbon SEM test sample from Agar scientific, (2) an AFM standard TGQ-1 from NT-MDT: a grid of SiOIn 2 bars on silicon obtained by the detection of SEs .The number of reflected ions per incident ion is called the reflection coefficient or reflected ion yield. In the simple geometrical model, the reflection coefficient,η, depends on the angle of incidence and the reflection only, that is, η = η = η. This assumption is valid for amorphous materials and in the absence of ion channeling in crystalline materials. Ion channeling can cause the reflection coefficient to become strongly dependent on the angle in the vicinity of the channeling critical angles [Firstly, we assume that the reflection coefficient is independent of the polar incident angle, φl angles ,30 and cl angles .NI, and the proportionality constant is the reflection coefficient, η. In fact, only those reflected ions that are transmitted through the slit aperture can reach the Pt-coated surface and excite the SEs. The portion of the RIs that reach the Pt converter is a function of the ion reflection angles f. The number of SEs excited from th Pt surface is proportional to the number of ions reflected within the aperture, and the proportionality constant is the secondary electron yield for the Pt-coated surface, γ0. The SE yield depends on the angle of incidence and is determined by Θ2, thus, γ0 = γ0(Θ2). Thus, the detected RI signal should be calculated as the product of the number of reflected ions and the efficiency of SE detection, while integrating over all angles as shown in The RI signal is the number of SEs per second measured by the ET detector as the flux of SEs from the Pt RI–SE converter (see (2) in 2, one can assume that the portion of RIs that reaches the Pt converter is independent of the polar angle, that is, f = f(Θ2). This assumption gives rise to the explicit form of the f(Θ2) dependence: f(Θ2) = 1 if Θ0−ΔΘ/2 < Θ2 < Θ0+ΔΘ/2, otherwise f(Θ2) = 0.In the case of the slit-like aperture installed at a fixed angle, Θ0/cosβ. The typical ion grazing angle of incidence in our work was 5° and the slit angular aperture was 4°. In this case, β can vary between 6° and 14°. The cosine function varies only within 2% for this angular range and we can neglect dependence of the SE yield on the ion reflection angle, thus, γ0(Θ2) = γ0. We factor out the integral sign for the number of primary ions and SE yield and integrate 1 and φ2 to arrive at the expression:The SE yield from the Pt surface depends on the ion angle of incidence with this surface, β, and can be approximated by the inverse cosine law : γ(β) = According to this expression, the RI signal is determined by the ion reflection coefficient and its angular dependence, the angular distribution of the reflected ions and the angular aperture of the slit diaphragm. We will now consider the effect of all these factors separately.The dependence of the reflection coefficient for singly charged He ions on the grazing angle as obtained by a Monte Carlo simulation with SRIM software  for diff1 approaches zero, the reflection coefficient tends towards unity, implying that for reflection at very low angles nearly all ions will pass over the specimen surface. The highest relative difference in the reflection coefficient for the different materials  is observed at normal incidence, whereas this difference reduces for low grazing angles and the material contrast vanishes. At the same time, the slope of angular dependence on the reflection coefficient is high at low grazing angles and low for normal incidence. This explains why the morphology contrast in the RI images is determined mostly by the surface morphology but not by the surface composition in our experiments.As it can be seen in 1−Θ0| = α < ΔΘ. Under these conditions the expression for the RI signal given by In fact, the RI contrast is determined by the angular dependence of the reflection coefficient only when all of the reflected ions are detected. In turn, this assumption is valid when the halfwidth of the angular aperture is larger than the halfwidth of the RI angular distribution, that is, ΔΘ > δΘ, and the sample surface is sufficiently smooth so that |Θ0(Θ1) is the RI reflection coefficient as a function of the grazing angle.where η1,Θ2) has a maximum at an angle of Θ20 that corresponds to specular reflection: Θ20 = Θ20 + 2α and the shape of the angular distribution of reflected ions is independent of the Θ1 angle.As indicated above, the signal of the reflected ions is determined by the angle between surface and primary ion beam if all reflected ions are detected. In practice, some part of the reflected ions may be stopped by the diaphragm or by the elements of the specimen surface (shadowing effect). Firstly, we suppose that the angular distribution of the reflected ions η = η0(Θ1) g(Θ2−Θ20), where η(Θ1) is described by g(Θ2–Θ20) is the normalized angular distribution of the reflected ions, centered at Θ20. Substituting this reflection coefficient into The reflection coefficient can be presented as two multiplicands: η is required for the calculation of the RI signal profile with the help of g(t), that is, all ions are stopped by the diaphragm. Hence, such regions of the sample will appear in dark contrast in the RI image. Additionally, double (or multiple) reflections can take place giving rise to a “shadow effect”. This so-called shadow effect means that after the first reflection from the surface with low α, the ions are iteratively reflected from some pronounced details of the specimen and do not contribute to the signal. The simplest example of the shadow effect is the rectangular upward step that was investigated for the SiO2 sample bars on silicon  and the step sidewall is perpendicular to the surface . The area at the step can be then divided into three regions, as shown in S(α) = 0. In this case, a dark area of width d1 = hcosΘ0 appears in the image, where h is the step height.In region 1 in x, then the ions can pass through aperture when: Θ2 = arctan(h/x). In this case, the detected signal is described as follows:In region 2, all ions reflected from a plane bottom sample surface will be scattered by the sidewall, and an additional dark area (shadow) appears in the image see . If we dd2 = h∙sinΘ0/tan(Θ0 + ΔΘ).The width of the shadowed region 2, as calculated from simple geometry, is given by In region 3 a smooth transition (penumbra) from zero RI signal to a constant RI signal from the substrate takes place. The form of the transition curve is determined by the RI angular distribution according to:The total width of the dark area in the image of the step will then be a sum of the widths of regions 1 and 2:Thus, the step height can be calculated from the dark contrast width in the RI image and the halfwidth of the slit aperture as follows:0 = 10°, ΔΘ = 2°, d = 40 ± 10 nm), where a value of h = 22 ± 6 nm was obtained, in good agreement with the AFM results.Using In conclusion, the shadow effect and a finite aperture size result in dark areas in the image. These dark areas appears in the regions of a sample where α > δΘ/2 + ΔΘ/4. If a surface feature can be described with the rectangular step model, then The contrast formation mechanisms presented above describe the RI signal from the parts of the sample surface facing the incident ion beam. On the opposite side, the primary beam does not reach the surface and, accordingly, no RI signal can be obtained. In the vicinity of a sharp edge of a feature the ion transmission may contribute to the image contrast formation. As was demonstrated in *. The asterisk is used to emphasize that the reflection coefficient near the step edge differs from the reflection coefficient of the bulk sample surface when the distance to the step edge is comparable with the ion penetration depth. Near the step edge some part of the incident beam penetrates through it with the probability ρ and hits the substrate at an angle Θ3, which we assume to be close to the angle of incidence, that is, Θ3 ≈ Θ1. The transmitted ions are reflected from the substrate with the reflection coefficient η  only the side facing the beam is suited for investigation.In this work, RIM was demonstrated to obtain sharp images of an insulator surface without the need for charge compensation or a conductive coating. A positive surface charge produced by ions attracts SEs but does not significantly change the RI trajectory and their detection. The changes in trajectory caused by the electric field of the charged surface can reduce the ion incident and reflection angles, but our calculation shows that their influence on the step height determination is negligible and can be monitored by changing the imaging magnification. The alternative method of the imaging of an insulating surface in HIM is charge compensation by an electron flood gun. Imaging with a flood gun requires accurate adjustment of the flood gun parameters and ion beam parameters to neutralize the surface charge. The scan speed decreases when line-by-line charge compensation is used. The reflected ions are less sensitive to surface charge than SEs, thus RIM of insulating surfaces is more straightforward. At the same time, RIM requires a special sample holder. One of the main disadvantages of RIM is the inability to image the whole surface of a sample. Part of a sample is always hidden by sample features. Thus, the potential advantage of RIM over the use of a flood gun is determined by the sample surface details. Unfortunately, the construction of the RIM specimen holder does not allow for charge compensation, and a direct comparison of these methods is impossible.Beside the factors mentioned above, the sensitivity and the precision of the RIM measurements are obviously defined by the signal-to-noise ratio that increases with the image acquisition duration. There are two factors that limit measurement time in RIM: the ion beam induced deposition (IBID) of hydrocarbons and the sputtering of the surface material.The first problem exists both in a case of RIM and in conventional HIM. Fortunately, IBID that is mainly stimulated by secondary electrons  can be e14 cm−2, and the largest ion dose used for the acquisition of RI images of Au on carbon was about 1015 cm−2. In this case we expect the sputtering of a single atomic layer of the sample. The resolution of the obtained images was insufficient to observe such a sputtering effect, but it can be a limiting factor for the observation of the atomic steps. Thus, the further enhancement of the detection system is desirable to reduce the measurement time and to avoid sputtering of a sample.Ion beam-induced sputtering always takes place and, unfortunately, the sputtering yield increases at low angles, and even for ions as light as He this can exceed one atom per ion. Thus, this effect is the main factor limiting the acquisition time. Most of the images were obtained with a dose of about 10In conclusion, reflection helium ion microscopy is a promising tool for the investigation of surface morphology and is especially useful when applied to non-conductive materials. Further development of this technique would require a special, more sensitive RI detector, higher vacuum conditions, and a polar angle limiting aperture.


How do we learn from errors during complex movement tasks with redundancy? A new study shows that ambiguous mistakes in bimanual movements are corrected by the non-dominant hand, and responsibility for the error is assumed to fall to the effector with a recent history of poor performance. In recent years, there have been a number of important theoretical developments which have revised the way we think about the control of human movement. Imagine a task such as playing a game of tennis. The challenges are daunting — the modern game is extraordinarily fast, and each action must be made at the limits of human reaction times, so that tennis strokes are planned and executed well before the ball arrives at the racquet. For a powerful player like Andy Roddick, the ball is served so fast — 155 mph — that it reaches the opponent's baseline in about 350 milliseconds. Estimating the ball's trajectory accurately enough to reach and return it requires the combination of incomplete sensory information from the visual system with prior knowledge of the ball's likely distribution of positions — Roddick tends to get the ball inside the tramlines more often than not. Kording and Wolpert Consider next the challenge for Roddick's opponent in returning the serve. He plans the swing of the racquet to reach the approaching ball, but again must integrate his motor plan with sensory feedback about its execution. The evolving act must be refined and modified as the latest sensory information is processed, specifying both where the ball is bouncing and how his race along the baseline is progressing. Todorov and Jordan Now, take things a step further. Imagine when someone like me attempts to play tennis. I might be ambitious and try a two-handed backhand stroke. But it goes wrong  and the racquet misses the ball A. Is it Current Biology, White and Diedrichsen As they report in this issue of White and Diedrichsen Finally, White and Diedrichsen vice versa.White and Diedrichsen This new paper


A system has been developed to measure the vertical position and angle of the electron beam at a single location from a synchrotron source. The system uses a monochromator tuned to the absorption edge of a contrast material and has a sensitivity comparable with other beam position monitors. The stability of the photon beam position on synchrotron beamlines is critical for most if not all synchrotron radiation experiments. The position of the beam at the experiment or optical element location is set by the position and angle of the electron beam source as it traverses the magnetic field of the bend-magnet or insertion device. Thus an ideal photon beam monitor would be able to simultaneously measure the photon beam’s position and angle, and thus infer the electron beam’s position in phase space. X-ray diffraction is commonly used to prepare monochromatic beams on X-ray beamlines usually in the form of a double-crystal monochromator. Diffraction couples the photon wavelength or energy to the incident angle on the lattice planes within the crystal. The beam from such a monochromator will contain a spread of energies due to the vertical divergence of the photon beam from the source. This range of energies can easily cover the absorption edge of a filter element such as iodine at 33.17 keV. A vertical profile measurement of the photon beam footprint with and without the filter can be used to determine the vertical centroid position and angle of the photon beam. In the measurements described here an imaging detector is used to measure these vertical profiles with an iodine filter that horizontally covers part of the monochromatic beam. The goal was to investigate the use of a combined monochromator, filter and detector as a phase-space beam position monitor. The system was tested for sensitivity to position and angle under a number of synchrotron operating conditions, such as normal operations and special operating modes where the photon beam is intentionally altered in position and angle at the source point. The results are comparable with other methods of beam position measurement and indicate that such a system is feasible in situations where part of the synchrotron beam can be used for the phase-space measurement. From knowledge of this beam’s position and angle or its position in phase space, it is possible to transform back to the source and determine the source coordinates in phase space if the optics in the intervening locations and their effects are known.This paper describes a method to simultaneously measure the photon beam position and angle at one location. The system was developed at the Canadian Light Source (CLS) on the Biomedical Imaging and Therapy (BMIT) bend-magnet beamline 05B1-1 (BM).x = 18 nm rad, ∊y = 0.10 nm rad). Beam instability, be it electron or photon beam, can be a concern especially to the third-generation facilities. It has adverse effects on the required low electron beam emittance, effective brilliance of the synchrotron radiation, and experiments performed at the experimental stations  beamline and the X-ray Synchrotron Radiation (XSR) beamline . The method relies on the energy-dispersive properties of flat crystals and makes use of the absorption edge of a filter in the photon beam path to determine a specific energy or angle of the photon beam. This, coupled with a measurement of the beam in the absence of the filter, allows beam position and angle to be determined.We present a method for measuring the position and angle of a photon beam simultaneously, 1.1.et al., 2009et al., 2009The single electron vertical photon emission distribution is properly described by a modified Bessel function of the second kind  is the spacing between the  lattice planes.Diffraction of X-rays in crystals can be thought of as arising from constructive interference from reflections at lattice planes as described by Bragg’s law,et al., 1981For an X-ray synchrotron beamline, usually a pair of parallel crystals is used, one to monochromatize the incident beam and one to diffract that beam back parallel to the incident beam. With a coordinated motion of the two crystals, a range of energies can be chosen while keeping the monochromatic beam from the second crystal in the same location which is very useful for much of the research being done. Additionally, the near unit reflectivity of the perfect crystals often used means there is little intensity loss from the pair. This arrangement is commonly called a double-crystal monochromator (DCM)  reflection. We can estimate the range of wavelengths using the derivative of Bragg’s law with respect to angle,−6. Thus the intrinsic wavelength spread is 2.12 × 10−6 nm and the intrinsic energy spread is 1.88 eV. This wavelength and energy spread is also shown in Fig. 3There is also an energy or wavelength spread due to the finite reflectivity width of the dispersion curve. For diffraction, the energy or wavelength bandwidth is a fixed quantity away from absorption edges of the crystal. For silicon  the bandwidth is 56.6 × 10c)K-edge then the top of the beam will be absorbed more than the bottom creating an asymmetric beam profile shown on the right side of the figure. A calculation of the beam shape including the DuMond dispersion effects is shown in Fig. 4Schematically, the effect of an iodine filter on the transmitted beam of a DCM set at 33.17 keV is shown in Fig. 2(2.2.1.a)–5(d)y is the vertical position of the electron beam source, D is the distance from the source to the detector.When a photon beam moves at the source location the monochromatic beam after the DCM is sensitive to the motion. If the source point moves up then the beam measured at the detector location will also move up by the same amount. If the beam moves in vertical angle then the beam at the detector position will move by the product of the angle times the distance from the source to the detector. This effect is shown schematically in Figs. 5–5(d)K-edge measured at the detector is then simplyyc is the measured vertical K-edge location and y is the vertical position of the electron beam source. Changes in the vertical source angle do not change the location of the K-edge at the detector.The 3.K-edge filter in place (edge side); using equations (3)yc. Also, changing the detector’s vertical position will alter the yd value. Both have a direct impact on the calculated electron beam position and angle, y and y′. Thus the system measures relative values of position and angle and must be calibrated to obtain absolute values.We are now in a situation to be able to independently determine the vertical electron beam position and angle by measuring the beam location through a DCM without a filter (beam side) and the edge location with a yd, and the K-edge, yc, are integral to the success of this method. A fitting procedure will be used to determine the location of each. To be properly fit there needs to be sufficient intensity, detector resolution and vertical size to encompass the profile along with any vertical motion that may occur. The vertical beam size at the detector is a relatively weak function of the energy selected by the monochromator. However, the reflection chosen in the monochromator may have a strong effect on the intensity since the reflection sets the monochromator bandwidth.The ability to determine the location of the beam centroid, yc will also depend on the width of the K-edge and the thickness and density of the contrast filter. A rough estimate of the optimal projected iodine filter density was found to be ∼70 mg cm−2 by numerical simulation. This estimate was based on a contrast to noise model using Poisson statistics.In addition, the ability to determine K-edge with the DCM will depend on the intrinsic K-edge width for the contrast element  at 33.17 keV], and will also be dispersed vertically, z, across the detector, approximately asD is the source-to-detector distance and E is the K-edge energy. For silicon  at the iodine K-edge this spatial dispersion is 74 µm eV−1. Therefore, the 15 eV of energy spread will correspond to a spatial width of 1.1 mm.The width of the transmitted −1 and the 15 eV energy spread will correspond to a width of ∼2.2 mm which indicates increased spatial dispersion sensitivity. But this sensitivity will come with a loss of intensity of over a factor of six due to the decreased  bandwidth (9.1 × 10−6) compared with the  bandwidth (56.6 × 10−6) and therefore, for the bulk of the measurements, we chose silicon .When compared with silicon  under similar conditions the spatial dispersion at 33.17 keV will change to 150 µm eV4.K-edge at 33.17 keV. The vertical dispersion of the monochromator allows an energy range that covers the K-edge of iodine. Figs. 2(a)–2(c)−2 iodine filter and the other side with no filter. A Hamamatsu flat-panel detector with 0.1 mm pixel size was used to collect data. Measurements were made in the POE-2 hutch, which was ∼25 m away from the source.The experiments for this project were performed at the CLS BMIT bend-magnet beamline 05B1-1. A silicon  and  double-crystal monochromator was tuned to the iodine Two types of measurements were performed. One type was made during the normal operational mode to assess the beam stability. A second type was made during special shifts where the synchrotron beam was intentionally moved at the source location with specific vertical, horizontal and angular offsets. This second type was used to assess, in part, the sensitivity of the system and to independently measure the motions made to the electron beam source in the ring.Data were in the form of images of the split beam with the iodine filter on one side and no filter on the other. Sets of 400 data images were saved into individual directories and a measurement set might range from a few to several hundred directories. It took around 12 s to collect 400 images for each directory and roughly 1 min dead-time to save the data to the disk. For each set of measurements, ten dark images (the detector response without beam) and ten flat images (no contrast agent in the beam) were also collected for data normalization. These ten images were averaged to form single ‘dark’ and ‘flat’ images.5.K-edge side were selected from the data and each side was corrected for dark response. An example of the regions chosen from the images is shown in Fig. 6To analyze the data, several procedures were written in IDL . Regions for the unfiltered beam side and the yd which was found by fitting the horizontally averaged vertical beam profile using a Gaussian function. An example of this fitting was shown in Fig. 1z, was measured in terms of detector pixels that can be easily converted into micrometers using the pixel size. The vertical center of the detector was the origin used in this part of the analysis.The beam side data were used to determine K-edge side profile was normalized by the matching region from the flat to form an I/I0 profile. However, the flat was corrected for vertical beam motion by using the beam side of the data to vertically move the flat prior to normalization. The low-intensity regions of this corrected flat were ignored using a threshold value and not used in the analysis. An example of this is shown in Fig. 7The horizontally averaged K-edge in the filtered beam profile. In general, three types of functions were investigated to fit the edge: Gaussian, Lorentzian and Voigt. These functions were chosen because they are mathematically simple and make physical sense. The functions were used directly to fit the derivative of the K-edge profile, and integrated versions of each were used to fit the measured edge profile directly. Finally the K-edge could be fit to the I/I0 values or the negative logarithm of the I/I0 values. Of the 12 fit types the Gaussian fit to the derivative of the negative logarithm of the profile was chosen due to its simplicity and the robustness of the fit. For this analysis the main parameter of interest in the fit was the transition center location. Other parameters determined in the fit were the amplitude, width and the background values. Fig. 8A number of fitting algorithms were used to find the location of the 6.A number of measurements have been taken with the system. In total, eight shifts (1 shift = 8 h) were used for the beam motion measurements. These shifts were during normal operations and by special request when the ring parameters could be altered. During the special machine study shifts, the electron beam was moved and measurements were made to assess the response of the system.6.1.The electron beam position and angle were varied in the machine study shifts. To move the electron beam in the BMIT sector, electron BPM (eBPM) target values were changed. This change in target value moves the electron beam orbit using dipole steering magnets. The eBPMs numbered 17 and 18 are located on the upstream and downstream sides of the bend magnet which is our photon beam source , +100 µm  and −100 µm ; and purple, the overall beam position, y + Dy′.For vertical motion, the eBPM17 and eBPM18 target values were changed by the same amount. The beam was moved vertically between +100 and −100 µm in 0, ±1, ±2, ±5, ±10, ±20 and ±100 µm increments. Examples are shown where the beam is at the zero location  were made as the beam was moved. For each beam location, between four and five slices were taken. In reviewing Table 1From Fig. 116.1.2.Similar measurements were made when the eBPM values were changed asymmetrically to create electron beam angle at the beamline. Tests were made with eBPM offsets between +20 and −20 µm in 0, ±5, ±10, ±15 and ±20 increments.y, Dy′ and y + Dy′. The dashed horizontal line in the figures identifies the measured zero location where the electron beam is at the zero location in eBPM units. The red line in Fig. 13Plots of the electron beam position and angle as a function of eBPM values are shown in Figs. 13The measured and calculated beam responses are summarized in the lower half of Table 26.2.Measurements with the ps-BPM system were made during a number of normal operation shifts between December 2013 and August 2014. During the December 2013 to early 2014 period the CLS storage ring was experiencing beam instabilities from a storage ring dipole magnet power supply.y, the bottom line is the vertical displacement due to angle, Dy′, and the middle line is the sum of the two, y + Dy′. Each of the three measurement periods are shown side by side in the figure. The offsets for the three position types have been removed so that their average is zero to make the comparison easier.Fig. 15It is clear that the ring stability improved dramatically during that time period and that the instability measured arose primarily from the angle displacements. Fig. 15The DCM could be responsible for some of the measured beam motion due to vibrations and thermal motion. Internal vibrations within the monochromator are somewhat minimized as we used a gravity flow water system for cooling both crystals in the DCM. Also the use of a copper and aluminium filter dropped the incident power on the first crystal to below 2 W for the size of the beam and ring current used for the experiments. Finally, based on our measurements, as the beam is moved in the machine the error bar on the beam displacement was typically less than 10 µm and the angle less than 0.5 µrad, indicating that the combination of the monochromator and electron beam motions must be less than these values.7.K-edge side measurements. A prototype Laue-type system is being designed.All of the proof-of-principle measurements required the use of the entire beamline and as such would be completely impractical as a monitor. Probably the most challenging aspect of implementing such a monitor would be the dedicated use of the DCM. The complexity of the DCM in which the two crystals must be maintained in sub-microradian alignment in the Bragg geometry could be mitigated by the use of a single-crystal Laue or transmission-type monochromator. The Laue-type monochromator is not so susceptible to crystal heating effects as there is no thermal bump on the crystal’s surface. Much of the power can be transmitted through where it can be absorbed elsewhere. The width of the beam being used need not be very wide as two line detectors can be used for the beam and 8.A unique method for simultaneously measuring the vertical position and angle (phase space) of synchrotron photon beams using the combination of X-ray diffraction and absorption edges has been developed and tested at the BMIT beamline at the Canadian Light Source. This system allows the measurement of the photon beam centroid in phase space, and thus the electron beam source position and angle, at a single location along the beamline. Temporal stability and beam motion system response measurements have been performed with good results.The sensitivity of this system is comparable with other photon beam position monitors with detectable position errors on the scale of 10 µm and angle errors of 0.5 µrad. These errors are based on the noise level in a 12 s time measurement with 400 time points.K-edge filter and two line detectors can be built and implemented to make a practical device that uses a small piece of the horizontal width of a bend-magnet or wiggler white beam.We are optimistic that a compact dedicated system employing a single transmission or Laue monochromator coupled with a Such a phase-space system provides a more complete view of beam motion in a synchrotron source and may be used to improve the synchrotron source position and angle stability. The ps-BPM also provides a better diagnostic should instabilities or beam drift occur, and could be used to correct experimental data for beam motion and to actively control the trajectory of the photon beam in the beamline.


This article provides the dataset of operating conditions of a hybrid organic Rankine plant generated by the optimization procedure employed in the research article “A genetic optimization of a hybrid organic Rankine plant for solar and low-grade energy sources”   Specifications TableValue of the data•This data provides several working conditions of a hybrid power plant by considering 24 working fluids and several design parameters.•The reader can choice among the different solutions by evaluating their thermodynamic and economic performances.•The reader can compare these solutions with other layouts fed by the same sources and by employing the same working fluids.•These solutions can be compared with other solutions that employ working fluids not considered in this study.•The readers can exclude any particular solution under consideration by checking it on the unfeasible solution dataset.1This article provides the feasible and unfeasible operating conditions of a hybrid organic Rankine plant, given in Ref. The unfeasible solutions are characterized by a set of working conditions that give an unphysical behavior of the plant. These solutions are reported in a distinct dataset.2In the following, the configuration of the power plant is firstly described, then the design variables are given, the solution procedure and the model used to assess the plant cost are described, and finally the three performance indicators are discussed.2.1The layout of the power plant is shown in The preheater is fed by a mass flow rate of water, equal to 1 kg/s, at a temperature of 90 °C. The pressure drops in the heat exchangers are neglected. The solar field is made up of evacuated tube collectors, where the heat transfer fluid, assumed to be water in this study, can work at a maximum pressure of 6 bar and a maximum temperature of 150 °C. The ambient air has a temperature of 20 °C and a pressure of 1 bar.2.2Tdes that rules the following relations:Tpinch is the pinch temperature. For a subcritical working condition, Eq. T(pev) the phase change temperature of the working fluid at the pressure pev.The design variables are: i) the working fluid, which is selected among the fluids listed in T3) of the working fluid at the outlet section of the preheater, the temperatures (T7 and T10) at which water is discharged into the environment by the preheater and by the condenser, the minimum temperature (T8) of the collector thermal fluid, the net power (Wnet), the input powers of the heat exchangers (QPH and QEVG), the size of the heat exchangers and of the solar collectors.The dataset provides, for the feasible solutions, the following dependent variables: the temperature  defined as:


The continuous downscaling of the process size for semiconductor devices pushes the junction depths and consequentially the implantation depths to the top few nanometers of the Si substrate. This motivates the need for sensitive methods capable of analyzing dopant distribution, total dose and possible impurities. X-ray techniques utilizing the external reflection of X-rays are very surface sensitive, hence providing a non-destructive tool for process analysis and control.X-ray reflectometry (XRR) is an established technique for the characterization of single- and multi-layered thin film structures with layer thicknesses in the nanometer range. XRR spectra are acquired by varying the incident angle in the grazing incidence regime while measuring the specular reflected X-ray beam. The shape of the resulting angle-dependent curve is correlated to changes of the electron density in the sample, but does not provide direct information on the presence or distribution of chemical elements in the sample.Grazing Incidence XRF (GIXRF) measures the X-ray fluorescence induced by an X-ray beam incident under grazing angles. The resulting angle dependent intensity curves are correlated to the depth distribution and mass density of the elements in the sample. GIXRF provides information on contaminations, total implanted dose and to some extent on the depth of the dopant distribution, but is ambiguous with regard to the exact distribution function.Both techniques use similar measurement procedures and data evaluation strategies, i.e. optimization of a sample model by fitting measured and calculated angle curves. Moreover, the applied sample models can be derived from the same physical properties, like atomic scattering/form factors and elemental concentrations; a simultaneous analysis is therefore a straightforward approach. This combined analysis in turn reduces the uncertainties of the individual techniques, allowing a determination of dose and depth profile of the implanted elements with drastically increased confidence level.Silicon wafers implanted with Arsenic at different implantation energies were measured by XRR and GIXRF using a combined, simultaneous measurement and data evaluation procedure. The data were processed using a self-developed software package (JGIXA), designed for simultaneous fitting of GIXRF and XRR data. The results were compared with depth profiles obtained by Secondary Ion Mass Spectrometry (SIMS). •The parameter optimization by curve fitting uses differential evolution .•Implantation profiles are modeled by using the Pearson distribution system.•The implant distribution profile is discretized to allow calculation similar to a layered sample.•Total implanted dose, implantation depth and profile shape can be determined nondestructively. The grazing incidence angular dependence of the X-ray fluorescence signal provides information on the depth distribution and total concentration per unit area of the elements in the near surface region. At glancing incidence the predominant part of the incident radiation is reflected and forms – within the limits of coherence – standing waves above the surface while the other part of the field intensity penetrates into the refracting medium as an evanescent wave. In 1954 Parratt A more general procedure is presented in detail in this work, respecting the physical relevant parameters and avoiding possibly time-consuming numerical integration.1.2Measuring the angle dependent fluorescence signal in grazing incidence on an optical flat results in distinct shapes of the recorded angle curve depending on whether an element is present in the bulk material, a thin layer or implant near the surface or a residue on the surface 2As mentioned in the introduction GIXRF alone is not able to provide an unambiguous solution for concentration profiles in a substrate material. In fact the determination of the concentration requires the solution of an ill-posed inverse problem.To demonstrate this ambiguity, However, when introducing the XRR measurement and simulations  based onHere it has to be noted that using only the XRR data is also not sufficient to determine the depth profile because also the fit to the collected XRR data has no unique solution 3The simulation and fitting of depth profiles is a rather complex task, due to the large number of parameters involved. We therefore provide in the following only a brief, general overview over the approach as implemented in the JGIXA software, further details will be published elsewhere.3.1To allow a successful calculation of XRF and XRR following the approach of de Boer When the peak concentrations of the depth profiles approach elemental concentrations beyond the dilute regime, that is when the index of refraction starts differing significantly from the value of the substrate, the profile itself starts to severely alter the field intensities in the material. Any data evaluation therefore has to include these modifications of the electrical field using a layer model of the depth profile fitting the key parameters defining each layer.The values to be evaluated for each layer are the complex refractive index derived from the atomic scattering factors Henke , the refλwavelength of the incident radiationhPlanck' s constantcvelocity of light in vacuumCajmass fraction of element a in layer jρjdensity of layer jτaλλphotoelectric absorption coefficient for element a at wavelength μjλlinear absorption coefficient of the incident radiation in layer jJaabsorption jump factor for the considered shellawfluorescence yieldagrelative emission rateμnalinear absorption coefficient of the considered fluorescence of element a in layer nndthickness of layer nSirradiated detected sample areaψddetector anglePjzz component of the Poynting vectorμjalinear absorption coefficient of the considered fluorescence of element a in layer jThe following equation shows the fluorescence intensity of element a in layer j:In addition to the parameters depending on the experimental geometry, the effect of the angular beam divergence has to be taken into account. To model the effect of angular beam divergence the calculated curve is convoluted with a Gaussian point spread function.As commonly used in XRR data fitting, the XRR curves are fitted starting from slightly below the critical angle to reduce the influence of the direct beam. Furthermore, the XRR data are multiplied by the incident angle to the power of 4 xmax is the maximum calculated value of each curve. This normalization of the chi-squares allows for the combined evaluation of the measurement data, which have a varying number of measurement points and different orders of magnitude.Finally, the goodness of the fits is described by a sum of the reduced chi-square of the individual curves  with i datapoints:3.2For the optimization procedure two sets of parameters, the experimental setup parameters and the sample parameters have to be defined as initial values. Then the simulated GIXRF and XRR data are calculated for each parameter set and the chi-square between measured and fitted data is determined. If one of the stopping criteria is reached, the best parameter set is reported and no new parameter set is created. A flowchart of the optimization procedure is provided in The setup parameters like beam divergence, inspected area, beam size and shape and the relative system sensitivity for fluorescence lines are determined by the measurement and evaluation of reference samples with known composition and are fixed for the evaluation of unknown samples.The sample parameters consist of shape, depth and total dose of the implantation profile as well as the surface roughness. Because a fit with independent dopant concentrations for each layer would not only be very time consuming but also unrealistic, we limit the number of variable parameters by using the Pearson distribution system Iterative fitting, using random parameter values as starting points for the fit, performs the optimization. Because the hyper-surface in parameter space for XRR fitting can contain local minima 44.1The measurements have been performed using the GIXRF vacuum spectrometer at the Atominstitut, as described in The primary X-ray source was a Mo 3 kW LFF tube operated at 50 kV/40 mA and a W/C multilayer monochromatized the beam. All spectra were measured for 100 s livetime per angle step, summing to a total measurement time of 3–4 h for each sample.4.275As + was implanted in the Czochralski (100) Si wafers (300 mm in diameter) at 0° tilt and twist angles with an Applied Materials Quantum X implanter to produce two sets of samples: one with a constant nominal fluence of 1 × 1015 atoms/cm2 and implantation energies of 0.5, 2, and 3 keV and one with a constant implantation energy of 2 keV and varying nominal fluence of 1 × 1014, 5 × 1014 and 1 × 1015, , but different implantation dose  are shown in In This discrepancy between the results and the SIMS profiles motivated an investigation of the sensitivity of the fits to changes in implantation depth. As a final remark it can be concluded that by using the same layer model to simultaneously calculate the theoretical GIXRF and XRR signals and performing a combined fitting of simulated signals to measured GIXRF and XRR data it becomes possible to find a global minimum in parameter space.6The combined evaluation of GIXRF and XRR measurements resulted in an improved agreement on profile shape between SIMS and X-ray techniques as well as resolving the ambiguity of fitting only GIXRF data. For deeper implanted samples agreement on depth profile to SIMS data is very good (As4), for very shallow implantation (As1) in a depth of 3–4 nm slight deviations of 0.5–1 nm were observed and have to be investigated further, given that results from SIMS and MEIS also showed minor discrepancies


Further improvement in the accuracy of any neutron lifetime experiment by means of ultracold neutrons (UCN) in material bottles is limited due to unavoidable systematic effects when the UCN are reflected from the walls. However, such effects can be excluded in principle if magnetic trapping of UCN is used. The storage of UCN in a small magnetic trap made of permanent magnets was demonstrated for the first time ever. The measured storage time in this feasibility study was (882 ± 16) s. At this level of accuracy no depolarization was observed. In recent years the accuracy of τn experiments has been considerably improved by the use of ultracold neutrons (UCN) stored in traps. At the moment the value adopted by the Particle Data Group is τn = (885.7 ± 0.8) s [The precise measurement of the mean lifetime ± 0.8) s . Limits The first ideas for the magnetic storage of neutrons came from W. Paul and V. V. Vladimirski . It shouB and one may use a normal solenoid as a magnetic shutter. The main aim of this work is to study magnetic UCN trapping systematically and to start measuring the neutron lifetime in the permanent-magnet trap.The first real magnetic trap for neutrons was tested in the eighties . This trThe proposed magneto-gravitational trap is a vertical cylinder with a conical lower part . In the Br ≥ 1.2 T and Hcm ≥ 1800 kA/m. Such large values of Br and Hcm permit one to obtain a magnetic flux density near the wall of about 1 T and to create a field gradient of about 2 T/cm.The cross-section of the magnetic trap is shown in The experimental scheme is the same as that for material traps. After filling the trap with UCN one waits some time in order to clean the neutron spectrum from its high-energy components. Afterwards one has to determine the number of trapped neutrons as a function of their storage time. Previous experiments with material traps showed that the main systematic effects could be eliminated if one had the possibility to compare analogous results for UCN with different energy spectra. All of these ideas are implemented into the experimental scheme proposed here.The trap walls consist of a periodic structure with a characteristic period of ~1 cm. The magnetic field decreases quite fast (gradient ≈2 T/cm). Due to the concentration of the field in a small volume, the required magnetic material is minimized and the effective trap volume may be increased.The UCN are transferred to the trap through a neutron guide inside the solenoid at the bottom. After loading the trap this entrance is closed by switching on the current in the solenoid. To facilitate fast operation, we use a normal-conducting solenoid with iron core and permanent magnets.UCN with energies exceeding the solenoid magnetic barrier will penetrate the barrier and disappear. Thus changing the current in the solenoid easily modifies the spectrum of trapped UCN.Moreover, by applying a magnetic barrier at the entrance during trap loading, the spectrum may also be cut from the low-energy side. This flexibility in the choice of the UCN spectrum is very useful for eliminating systematic errors in the neutron lifetime measurement.To avoid UCN depolarization at the points of zero magnetic fields we use the field generated by the lower solenoid, which is orthogonal to the magnetic field from the permanent magnets. For this purpose an iron yoke guides the magnetic field from the solenoid to the top of the trap.To investigate the depolarization of UCN we intend to cover the inner trap walls with Fomblin oil to reflect depolarized UCN. In this case the depolarized UCN penetrate the magnetic barrier inside the solenoid and are measured by the UCN detector installed below the solenoid. Hence this detector may be used as monitor for depolarization losses during neutron storage.The most important features of our design  are the γn = 1.83 · 108 s−1 T−1. The neutron magnetic moment will follow the magnetic field direction if the adiabatic condition is fulfilled:ν is the neutron velocity. Thus for B = 1 T, ∇B = 1 T/mm and ν = 3.4 m/s, one obtains that the adiabatic condition is well fulfilled, 1.83 · 108 ≫ 3.4 · 103. Obviously this condition becomes invalid only in weak magnetic fields (about 10−4 T).As we mentioned above, neutron losses in the magnetic trap take place predominantly due to their depolarization . Let us try to estimate the probability of neutron depolarization in a region with a strong magnetic field. The precession of the magnetic moment is described byzH = const, and another one—xH will change its direction. In [ω = z/ħμH is the precession frequency of the neutron magnetic moment in Hz, and τ= z/ḢH·is an effective rotation time for the magnetic field. If the neutron passes such points of fast magnetic field rotation N times, one has to have wN ≪ 1 in order to fulfill the adiabatic condition. Thus one obtains for the minimum value of the magnetic fieldTo estimate the probability of depolarization in the weak magnetic field we will follow . Let us tion. In  it was szH that depolarization may be neglected. In our trap the lower solenoid and an additional small solenoid at the upper flange create a component zH that is orthogonal to the magnetic field from the permanent magnets. If the characteristic dimension of field rotation is equal to 3 mm, then the minimum effective time is equal to 10−3 s. Hence it is enough to create zH = 796 A/m (10 Oe) to fulfill the adiabatic condition for 105 passes near a rotating field of ~ 80 A/m (1 Oe). This corresponds to a depolarization probability of ~ 10−4/s for the characteristic dimensions of our trap.Therefore for each value of the effective time of magnetic field rotation one may choose such a value of In order to check the proposed ideas the more complicated lower part of the trap and the magnetic shutter were manufactured. The height of this part of the trap is 16 cm. The magnetic field created by the shutter in the lowest point of the trap (at a height of 10 mm from the edge of the neutron guide) is equal to 1.4 T. The diameter of the neutron guide was equal to 20 mm.3. The measured storage time in the trap was equal to (882 ± 16) s. The statistical error quoted was obtained in 245 runs lasting 143 h. By searching for an increase of the background counts in the detector during storage we were able to detect depolarized neutrons. At this level of accuracy no depolarization was observed.The operation of this small trap was investigated at the UCN beam of the Institut Laue-Langevin (ILL). To cut the spectrum of stored neutrons a polyethylene absorber was placed inside the trap at a height of 15 cm above its bottom. This resulted in an effective volume of the trap of 3.6 L. The trap was filled for 170 s. The density of stored neutrons reached 0.017 n/cm3. It isn’t a record value of neutron density and it can be increased by more careful assembling of the neutron guide.To estimate the number of UCN that may be stored inside a trap of 55 cm height, an aluminum cylinder with the diameter of the trap simulated the missing upper part. It was found that the density of neutrons in this extended set-up was equal to 0.11 n/cmOur experiment showed that magnetic trapping is a viable method for neutron lifetime measurements. Now we have increased the height of the trap to 55 cm.As a next stage we are planning to increase the diameter of the trap by a factor of two. This possibility is based on the similarity relation for magnetic systems. To use the existing magnets and poles in a new setup, one must change the quantity of poles of the trap from twenty to forty. The diameter of the neutron guide will be increased as well. So in this case we’ll repeat the manufacture of a second set of magnets and poles and after that we’ll construct a trap of larger volume from these two sets (new and previous one). The increase in the trap volume will permit us to improve the experimental sensitivity in the storage time measurement to ±1.6 s in a 6 d run. Estimated data for such a trap is shown in the last column of The main cost of the trap consists of the cost of the permanent magnets. The main parameters are shown in


Mycobacteriumtuberculosis H37Rv based on their flow to resistance genes.In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation.A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism.Mycobacteriumtuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to resistance genes is more likely to disrupt the communication to these genes. Purposely selected literature review of the top 14 proteins showed that many of them in this list were proposed as drug targets of the pathogen.Potential drug targets of The online version of this article (doi:10.1186/s40169-015-0061-6) contains supplementary material, which is available to authorized users. Mycobacterium tuberculosis (Mtb) which is the etiological agent of tuberculosis (TB) remains one of the main health threats of human by being the cause of morbidity and mortality to millions every year [ery year . Accordiery year . One of ery year , 4. MDR-ery year , 6. XDR-ery year . The WHOery year . Variousery year . Even wiery year . Moreoveery year . These tMycobacterium tuberculosis H37Rv. Structure-based drug target identification is one of these approaches [Mycobacterium tuberculosis H37Rv through network centrality analysis on protein-protein interaction network of the pathogen [Various computational methods were used to identify drug targets for the most studied strain of TB, proaches , 12. Itsproaches . For inspathogen . These ppathogen . This ispathogen . Measurepathogen . Even thpathogen , 17. Allpathogen , 18, 19.Mycobacterium tuberculosis H37Rv based on their flow to resistance genes of existing drugs. Since this approach is based on the flow, it is not expected to be affected by biasness towards shortest paths like the common global centrality measures. More importantly, the inhibition of a protein which has a maximum flow to the resistance genes of the existing drugs is expected to disrupt the communication to these genes. So, this further prioritization is believed to be a rational approach to deal with the problem of resistance at the initial stage of drug discovery process.In this analysis, maximum flow approach has been proposed to further prioritize potential drug targets of Mycobacterium tuberculosis H37Rv have been taken as input. The modified version of Melak and Gakkhar [Mycobacterium tuberculosis H37Rv was retrieved from Tuberculosis database [A set of highly reliable potential drug targets P and disease resistance genes set G of  Gakkhar  has beendatabase , 21. BLAdatabase –24. The database . The seadatabase . This cuMycobacterium tuberculosis H37Rv identified from two published literatures [The drug resistance genes list G is a curated list of 82 genes involved in both intrinsic and extrinsic drug resistance mechanisms of eratures , 18. In Subsequently weighted protein-protein interaction network of the pathogen was constructed by using a dataset retrieved from Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. STRING is a dedicated resource of physical and functional protein-protein interactions . It actsIn order to understand the general functional organization of interacting proteins, the statistical properties of the generated proteome network were characterized by different measures such as; degree distribution, characteristic path length and clustering coefficient.ni is its degreeki, defined as the number of edges adjacent to the vertex. The degree distribution p (k) is the measure of the proportion of edges in the network having degree k [One of the most basic properties of a vertex degree k .dij between any two vertices ni and nj in a network is the length of the shortest path between them. It is the minimal number of edges that need to be traversed to travel from vertex ni tonj. The path between two nodes doesn’t necessarily have to be unique since there could be several alternative paths with the same path length. Thus, the characteristic path length is the average shortest path of overall pairs of nodes in the network with n vertices [The distancevertices .ni with ki neighbours of an undirected network, there exist Ci of vertex ni is the ratio of the actual number of edges Ei between the neighbors to the maximal numberEmax;Clustering coefficient is a measure of the probability of two nodes with a common neighbour to be connected . It is aMean clustering coefficient is then computed as the average of clustering coefficients of all vertices.Mycobacterium tuberculosis H37Rv based on their maximum flow to resistance genes. This has been done through identifying the maximum flow between the candidate proteins P and disease genes G. Maximizing the flow from the candidate proteins to the resistance genes seems to be reasonable since understanding the efficient communications in the biological networks can be helpful to design treatment mechanisms for the problem of resistance in a systematic and rational way. The following are the constraints applied to specify the problem into the classical maximum flow problem [The main focus of this analysis is to further prioritize potential drug targets of  problem :The protein-protein interaction is a bi-directional edge.In the protein-protein interaction network, flow direction is one of the most important features. However, almost all the outcomes of current high-throughput techniques for protein-protein interactions mapping are usually supposed to be non-directional. Thus, the protein-protein interaction in this analysis has been considered as bidirectional edge.A dummy sink node is created to connect all resistance genes with the capacity of these edges set to infinity.All candidate proteins are connected to a dummy source node and the capacity of these edges are set to infinity.Since infinite value is not practical, the value greater than the maximum possible flow which is the product of maximum degree and maximum capacity has been assigned.If a resistance gene is also a candidate protein, to avoid unfair advantage, it has only been connected to dummy source node but not to the dummy sink node.s and t be the source and sink nodes respectively. Further, c is a nonnegative capacity of the edges. Then, for a weighted interaction network G = , the size is denoted as n = |V| and m = |E|. A flow f is a real valued function on the edges and the excess value ef (v) is the difference between the incoming and outgoing flows to a node under consideration. A node v is said to be active if v ∈ V − {s, t}, d(v) < n and ef (v) > 0, and we call an edge  admissible ifd(v) = d(w) + 1. The flow f satisfies the following three constraints [Let V be a set of nodes representing proteins, E be a set of edges representing interactions between proteins, and the nodes straints , 33:1\dof on the network satisfying constraints (1–3).The problem here is to find the maximum flow from the dummy source node to dummy sink node. We wish to achieve that by manipulating the preflow The following initializations have been made :f  is set to zero for all  ∊ (V − {s}) × (V − {s}) and f  set to c for allv ∊ V.The initial value of the flow d(v)to the sink node t is n for v = s and zero for allv ∊ (V − {s}).The distance of a node ef for the source node is the sum of capacities of all of its edges and zero for all verticesv ∊ .The excess flowsaturated.Once the initialization is complete, a repeated push and relabel operations have been performed on active nodes starting from the provided source node until there are no more active nodes or the edges are Push  and Relabel (v) operations:The Push .v, w) is admissibleApplicability: v is active and (δ = min(ef(v), uf) units of flow from v to wAction: send Relabel (v).v is active and push  can’t be appliedApplicability: d(v) by min{d(w) + 1| ∈ Efor by n if ∄  ∈ Ef}Action: replace saturated if the flow on it can’t be increased without violating the capacity constraint and residual otherwise [uf of an edge  is the amount by which the flow can be increased. The push and relabel operations modify the preflow f and labelling d. A push from v to w increases f  andef (w) by f  and ef (v) by the same amount. A relabeling of v sets its label to the largest value allowed by the valid labeling constraints. FIFO algorithm has been used to maintain the set of active nodes in which the front node is always selected for discharging and the newly active node is added to the rear of the queue [g < n. Suppose at a certain stage of the algorithm there are no nodes with distance label g but there are nodes v with g < d (v) < n. This implies that the sink is not reachable from any of these nodes. Therefore, the labels of such nodes may be increased to n (note that these nodes will never be active).An edge is said to be therwise . The reshe queue . Gap relhe queue . If we hCytoscape 3.0.2 was used for the generation of statistical properties of the network . It is aMycobacterium tuberculosis H37Rv were further prioritized based on their maximum flow to the resistance genes to identify those targets in which their inhibition would disrupt communications to these resistance genes in the proteome interaction network. The initial input set of potential drug targets of the pathogen, consisting of a list of 137 proteins, is obtained from systematically integrated comparative genome and network centrality analysis. These proteins are believed to be more reliable targets since they are essential for the survival and growth of the pathogen, non homologous to human and found within the close neighbourhood of the centre of gravity of the protein-protein interaction network of the pathogen. There are four types of mechanisms of resistance for Mycobacterium tuberculosis H37Rv: efflux pumps, target-modification, DNA replication and Horizontal Gene Transfer (HGT) [. Then, protein-protein interaction network was constructed based on the retrieved associations from STRING [Discovery of new drugs of TB is one of the solutions to deal with the problem of drug-resistance more systematically . In thiser (HGT) . A curatm STRING . Only inm STRING . The resclustering coefficient and the clustering coefficient of the resulted network is significantly higher than the clustering coefficient of a random graph with the same number of vertices (0.002). The degree distribution of the resulted network has also been shown in Fig. p (k) = kγ−, with the degree exponent γ ~ 1.753. This means there are very small number of highly connected nodes called hubs and a vast majority of nodes with few connections.General statistical properties of the network which is being used to describe essential properties have been shown in Table In order to find the maximum flow from the potential drug targets to the resistance genes and prioritize them accordingly, we have defined the source and sink nodes. Out of the 137 proteins taken as potential drug target set, 131 proteins were found in the resulted protein-protein interaction network and these proteins were connected to the dummy source node based on constraint 3. From the curated list of 82 resistance genes, 78 were found in the constructed protein-protein interaction network. Three of them were also candidate drug targets and they have only been connected to dummy source node based on constraint 4. Then the remaining 75 proteins  validated targets for diseases . It has etablity . From ouetablity . katG (Retablity . DNA poletablity . ftsZ (Retablity .The schematic diagram which depicts the concept about the mechanism of resistance to the existing drugs and the way to tackle the problem with the newly proposed targets has been shown in Fig. Validation is one of the challenges in identifying drug targets of TB using computational methods mainly due to the unavailability of standard dataset. It would also be ideal to have negative dataset in order to assess their performances. However, such data is not available because of the lack of interest of researchers in validating them . Yeh et In this analysis, validated drug targets for currently existing drugs and essential non- homologous proteins were taken to assess the performance of the method. The 8 clinically used drugs for the treatment of tuberculosis interact with 35 different proteins in the proteome network of the pathogen . Out of Mycobacterium tuberculosis H37Rv were taken as an input and maximum flow approach has been used to prioritize these proteins based on the flow value of each protein to resistance genes. The potential drug target proteins taken as an input are essential to the survival and growth of the pathogen, non-homologous to human proteins and found near to the center of gravity of interactome network. Resistance genes are curated list of reported genes which are involved in both intrinsic and extrinsic drug resistance mechanisms of Mycobacterium tuberculosis H37Rv [. Using maximum flow approach as a new method on proteome interactome network of the pathogen has an importance of considering the flow instead of shortest paths like other global network centrality measures. There are many well established criteria for assessing the targetablity of potential drug target proteins of various diseases which include essentiality, non homologous to the host, availability of solved structure and gene expression under disease conditions. However to our knowledge there is no comprehensive effort to incorporate the influence of drug targets on resistance genes of diseases like Mycobacterium tuberculosis H37Rv as criteria of druggablity. This leads to the possibility of including the influences of newly proposed targets on resistance genes as a new concept to assess the druggability of a target. We successfully prioritised potential drug targets based on their flow to resistance genes of the existing drugs of the pathogen. This increases the success rate of the potential drug targets in the rational drug discovery process. A detail literature review of the top 14 drug targets has also showed that many of these proteins have been suggested as highly reliable targets.In this study, highly reliable potential drug targets and resistance genes to the existing drugs of is H37Rv . Using m


The aerobic synthesis involves reduction of the centrally chelated cobalt metal ion of the corrin ring from Co(II) to Co(I) before adenosylation can take place. A corrin reductase (CobR) enzyme has been identified as the likely agent to catalyse this reduction of the metal ion. Herein, we reveal how Brucella melitensis CobR binds its coenzyme FAD (flavin dinucleotide) and we also show that the enzyme can bind a corrin substrate consistent with its role in reduction of the cobalt of the corrin ring. Stopped-flow kinetics and EPR reveal a mechanistic asymmetry in CobR dimer that provides a potential link between the two electron reduction by NADH to the single electron reduction of Co(II) to Co(I).Adenosylcobalamin, the coenzyme form of vitamin B CobR is a corrin reductase involved in the biosynthesis of adenosylcobalamin. Using a range of biophysical techniques, we have obtained molecular details on substrate binding and investigated protein stability. Kinetic experiments have identified half-site reactivity. It functions as a coenzyme or cofactor in a number of important processes, and is particularly associated with isomerization, methylation and dehalogenation reactions NADD or [(4S)-2H)]NADD. The observed rate constant for the first phase (kobs1) against NADH concentration was fitted to a hyperbolic function (K1) of 24.8±2.6 μM and a limiting maximal rate of reduction (kmax) of 41.3±1.1 s−1. This suggests that following the rapid binding of NADH to CobR, a rearrangement or conformational change is required before hydride transfer from the pyridine nucleotide. This conformational change is the rate limiting step in the reduction of the flavin and precedes a faster hydride transfer reaction. The reduction of the first flavin in the dimer, together with the associated change in conformation, greatly affects the rate of reduction at the second site. This implies communication across the dimer interface and half site reactivity.The reaction mechanism of CobR involves the reduction of the bound flavin prosthetic group by NADH. This reductive half reaction was investigated under anaerobic conditions using the stopped-flow apparatus. Absorption transients at 455 nm were recorded following the mixing of CobR (5 μM) with NADH (10–250 μM) at 25°C. These time-dependent data were best fitted to a double exponential function representing two distinct phases of the reaction. The first phase . DSC studies of the flavin reductase from S. meliloti revealed an apparent Tm of 72°C (results not shown), which raises the question of whether enhanced thermostability or structural rigidity is an evolutionary adaption that is required for the function of the CobR enzyme.Stability studies reveal CobR to be a thermostable protein with a melting temperature of 70°C, which is enhanced in the presence of the cofactor FAD to 80°C. Cofactors can aid protein stability, of course, through preferential binding to the native state ,34. AlsoG of denaturation (ΔGurea−ΔGGdnHCl) of 17.4 kJ mol−1 supporting a significant electrostatic contribution to conformational stability.Despite the protein exhibiting thermal stable properties the chemical denaturation study confirmed the protein can be unfolded at relatively low concentrations of denaturant. Unfolding curves show a two-state folding mechanism, once unfolding occurs to the dimer it precedes to an unfolded state. Differences between the chemical stability estimations via urea and GdnHCl are widespread through the literature. For example, Human Placental Cystatin, a thiol proteinase inhibitor, has a midpoint of transition of 1.5 M in GdnHCl and 3 M in urea . A complIt has been suggested that for an enzyme to be designated a CobR there should be evidence not only for reduction of the cobalt but also corrin binding . Herein,Interestingly, CobR seems to provide an axial ligand for the cobalt ion. This is similar to what is observed with the binding of cobalamin to PduS , the cobin vivo is currently unknown. It is possible that CobR transfers electrons to the adenosyltransferase or alternatively the corrin is reduced while bound to CobR and then passed on the adenosyltransferase enzyme. The fact that CobR has been shown to bind and reduce a range of corrins to the Co1+ state even in the absence of an adenosyl transferase [2+-corrin to the adenosyltransferase enzyme appears to induce a 4-coordinate conformation which reduces the thermodynamic barrier for reduction. In this state, a number of flavoproteins and even free reduced flavin have been shown to catalyse the reduction of the corrin [B. melitensis, the rate of reduction was significantly slower than the PduO type [The site of corrin reduction nsferase  perhaps e corrin . Adenosye corrin , and theduO type . This maReduction of the FAD coenzyme by NADH appears to be at least a three-step process, where a conformational change is required before hydride transfer. The protein displays half site reactivity, where reduction of the flavin in one active site affects the rate of reduction at the second active site in the dimer. This observed conformation change associated with the binding of NADH at one active site is similar to the phenomenon that was observed in the EPR studies looking at cobinamide binding, where two different coordination states were detected depending on the whether one or both of the active sites in the dimer were occupied.The functional importance and structural basis for the observed half site reactivity are unclear. The two available CobR structures (with either 1 or 4 molecules of flavin bound per homodimer) are essentially identical in structure. Comparison of the structures of Phea2 and TftC with and without NAD similarly also shows very little structural change. Consequently, any structural or dynamic changes occurring upon substrate binding or flavin reduction are subtle. Clearly, the binding of flavin or substrate to one active centre will affect the electrostatic potential at the other. Additionally, subtle changes in dynamics at the second active centre may accompany binding at the first. The two active sites of the CobR homodimer are connected through a water containing channel and it is possible that communication is mediated through this channel.The half site reactivity will result in one subunit reacting at a time. This may be beneficial to the catalytic process, since the enzyme links the one electron reduction of the corrin with the two electrons obtained from NADH. The fate of the second electron is currently unknown; however, it is known to disproportionate very quickly and this could be intramolecular rather than an intermolecular process. It is also possible that NADH binds to one active site while the corrin substrate docks at the active site on the opposite side of the dimer. Such a process could allow for regulation of the enzyme thereby controlling adenosylcobalamin synthesis. In this respect the functional importance of this half site reactivity requires further investigation.