The diffusion kurtosis observed with diffusion magnetic resonance imaging (dMRI) may vary with direction. This directional variation is summarized in the scalar kurtosis fractional anisotropy (KFA). Recent studies suggest that kurtosis anisotropy offers microstructural contrast not contained in other commonly used dMRI markers. We compare KFA to other dMRI contrasts in fixed rat brain and in human brain. We then investigate the observed contrast differences using data obtained in a physical phantom and simulations based on data from the phantom, rat spinal cord, and human brain. Lastly, we assess a strategy for rapid estimation of a computationally modest KFA proxy by evaluating its correlation to true KFA for varying number of sampling directions and signal-to-noise ratio (SNR) levels. We also map this proxy’s b-value dependency. We find that KFA supplements the contrast of other dMRI metrics – particularly fractional anisotropy (FA) which vanishes in near orthogonal fiber arrangements where KFA does not. Simulations and phantom data support this interpretation. KFA therefore supplements FA and could be useful for evaluation of complex tissue arrangements. The KFA proxy is strongly correlated to true KFA when sampling is performed along at least nine directions and SNR is high. We also perform an investigation of the observed contrast differences between FA and KFA using data obtained in a physical phantom and simulations based on data from the phantom, rat spinal cord, and human brain. The findings supplement and are consistent with the report inDiffusion weighted magnetic resonance imaging (dMRI) offers unrivalled sensitivity to tissue microstructure making it an important investigative and diagnostic tool. Traditional dMRI analysis has employed the diffusion tensor model in which diffusion is assumed to be Gaussian; tissue microstructure, however, causes the spin probability distribution to deviate from normal. This deviation is therefore seen as an indirect microstructural marker. The diffusion kurtosis imaging (DKI) framework257913In DKI the logarithm of the diffusion MRI signal i = x, y, z). In our notation, summation over repeated indices is implied so that e.g. where b is the diffusion weighting applied along where λ are the eigenvalues of the diffusion tensor, std denotes the standard deviation, and rms the root-mean-square value. In complete analogy a kurtosis fractional anisotropy (KFA) has been introduced based on the kurtosis tensor W9A||2 = <A, A> signifies the Frobenius norm of the tensor. Drawing inspiration from the rightmost expression in Here, as well as in While conceptually reflecting the same information, the KFA proxy is not directly equivalent to the KFA defined in Examples of This behavior is even more apparent in the data example from human brain shown in In the following, KFAs robustness to such fiber arrangements where FA ceases to provide information is explored. Firstly, we do this using a physical phantom (asparagus) with three orthogonal fiber directions present in equal proportions. For the analysis, we used the map of The analysis based on the physical phantom demonstrates the sensitivity of FA to the presence of multiple fiber orientations and also the relative insensitivity of KFA to these conditions. in vivo human brain with weaker gradients (affecting DWI encoding timings). We note, however, that the overall behavior is the same. tom data  are seentom data . This isFirstly, 199) is seen to decrease more than true KFA for low VI. However, even here the FKA proxy still indicates higher diffusion anisotropy than FA in configurations where diffusion anisotropy is expected. For these reasons we show the maps on different scales to expose similarities and differences. A common way of comparing contrast in images is by way of the root-mean-square (RMS) contrastTo further assess the proxy’s correlation to true KFA the bottom row of 1 and b2) of the two shells required for proxy estimation. 1 and b2 obtained from simulation . We note that b1 = b2 is not a valid combination which explains the estimation behavior in the vicinity of the b1 = b2 line in the map. From the map we see that choosing a large b2-value  produces a proxy with a correlation to true KFA of ~0.93. However, this is well outside the b-value range typically employed in DKI due to the non-negligible influence of higher order effects at b > 3 ms/μm21 = 1 ms/μm2 and b2 = 2.4–2.6 ms/μm2) provides a correlation of ~0.9, meaning that a good KFA estimate can be obtained from the scheme in1 = 1 ms/μm2, b2 = ~5 ms/μm2. For SNR = 60 (at b = 0) these same b-value combinations provide estimates with correlations of 0.80 (nine directions) and 0.85 (25 directions) respectively. To illustrate the method performance at a more standard SNR level 1 = 1 ms/μm2 and b2 = 2.6 ms/μm2 which had SNR at b = 0 just below 40. The data for this was acquired as part of the human acquisition that produced 2. The linear correlation coefficients between KFA and the proxy for the three slices shown are (left to right): 0.55, 0.54, and 0.56 in agreement with the simulations in 1 = 1 ms/μm2 and b2 = 2.6 ms/μm2). The linear correlation between the maps in in vivo. A b-value optimization similar to 1 = 1.6 ms/μm2 and b2 = 5.0 ms/μm2 providing a correlation of 0.83.Having thus established the correlation between KFA and the KFA proxy as well as the proxy’s requirements in terms of SNR and encoding directions we turn to the choice of b-values , where they were observed to offer markedly different contrast. However, if KFA is indeed insensitive to age related effects, it would be very important for the unbiased study of brain pathology in the elderly, where FA is strongly affected by ageAfter its original proposal in9an brain . Findingin vivo. This is not too surprising since fixation is known to alter tissue properties such as diffusivities and membrane permeabilities. Other causes of this behaviour may be differences in the size of structures in fixed rat brain and human brain. These effects all cause the tensors D and W that we probe to be quite different in the two cases. Furthermore, differences in encoding beyond b-value (combinations of gradient strengths and diffusion timings) employed on pre-clinical and clinical systems may contribute also as noted in connection with results in While some of the above mentioned properties of KFA were already demonstrated in previous work, we confirmed them here using an extensive range of model systems. This serves both to supplement earlier work and as a basis for evaluation of the KFA proxy proposed in 2, b2 = 2.4 ms/μm2) used in simulations for in vivo performance evaluation are optimized values, and that the same b-values are then used for ex vivo evaluation. The b-values are used because these are the values that would most likely be employed for 1-9-9 estimation ex vivo but here full estimation of D and W is feasible so the proxy is less valuable in the ex vivo case. While these b-values were also found to be optimal for 1-9-9 estimation of ex vivo where our analysis showed rather high optimal values of b1 = 1.6 ms/μm2 and b2 = 5.0 ms/μm2. These b-values are high but in fixed tissue diffusivities are much lower than in vivo so the b-values are still within the feasible range for DKI We note that the b-values . Human data acquisition was performed in accordance with the Declaration of Helsinki. All human experimental protocols were approved by the local ethics committee for research (De videnskabsetiske Komitéer for Region Midtjylland). Informed consent was obtained from all human subjects (one) prior to scanning. Smoothing and spatial filtering was not applied in any of the data sets. Throughout, SNR was calculated as the average signal in a homogenous region in the object imaged divided by the standard deviation of the signal in a background region, corrected for Rayleigh distribution in a standard manner2 were acquired. At each b-value, data was acquired along 9 gradient directions, so that the gradient directions at non-zero b-values in combination form a 144 point spherical designAn adult male Wistar rat was euthanized and exsanguinated during intra-aortic perfusion fixation with isotonic saline containing heparin (10 IU/mL), followed by 4% paraformaldehyde in phosphate-buffered saline (PBS) (pH 7.4). A section of spinal cord including the cervical enlargement was then dissected out and stored in 4% PFA for weeks prior to imaging. The spinal cord segment was washed in PBS for 24 hours prior to MR scanning to improve signal by removal of excess fixative. For imaging, the tissue was placed in a 5 mm NMR tube. Imaging was performed on a Bruker Biospec 16.4T  spectrometer equipped with microimaging gradients with a strength of 3T/m. Data was acquired using a 5 mm saddle coil. DWI data acquisition was performed using a standard DW spin echo sequence. A total of 17 b-values equally distributed from 0–15 ms/μm2 in steps of 0.2 ms/μm2 were acquired. At each b-value, data was acquired along 33 gradient directions. These directions were obtained by combination of a 3-dimensional 24-point spherical 7-designThis specimen was obtained using the same fixation protocol as above. After perfusion fixation the brain was removed and immersion fixed in fresh 4% paraformaldehyde solution for weeks. Prior to imaging, the brain was washed in PBS for 24 hours to improve signal by removal of excess fixative. Data was acquired using a Bruker Biospec 9.4T  MRI system equipped with a 15 mm quadrature coil. DWI data acquisition was performed using a standard DW spin echo sequence. A total of 15 b-values ranging from 0–3 ms/μm2. The encoding directions were obtained from a 15 point spherical designHere, we wanted to construct a physical phantom with fiber bundles equally distributed along the sign see . The box2, and along 33 directions at b-values from 0.2–3 ms/μm2 in steps of 0.2 ms/μm2. The encoding scheme was constructed as a combination of a 24 point spherical designHuman data was acquired in one normal volunteer using a Siemens Trio 3T equipped with a 32 channel head coil and a double spin echo DW EPI sequence. Motion of the subject’s head during acquisition was avoided by padding inside the coil. DWI data was recorded at b = 0 ms/μmThe data from physical phantom was processed in two different ways. First, ®.All data sets were evaluated visually for quality (artifacts and subject movement). No artifacts (eddy currents or otherwise) were observed. Due to the padding around the subjects head, image registration was found to be unnecessary. All fits were performed using non-linear least squares optimization in Matlab®. From this fit the full tensors D and W were obtained, and from these all relevant metrics were calculated as described above. This analysis was performed in all the data sets described above. When tissue segmentation is used FA ranges were used to segment the tissue into low anisotropy areas (dominantly GM) in the range 0.1 < FA < 0.3 and high anisotropy  as inFor DKI parameter estimation 2 in steps of 0.2 ms/μm2. For the case of the rat spinal cord data only b-values up 5.5 ms/μm2 were employed in the single fiber fit. The synthesized signals  were then mixed into a series of combined signals ranging from the signal from one single fiber direction (α = 0) to signal containing equal amounts (α = 1/3) of signal from the three fiber orientations. The mixed signals were produced in the interval α = 1/3 in steps of 0.03:A voxel of high FA was chosen in both the rat spinal cord data and the human data set  containing the stalks along each of the primary directions. Data from each bundle direction was normalized and then averaged across voxels. The regions/voxels used in the simulations are shown in The DKI model  was thenTo construct a volume fraction index describing the fiber mixture we set up a diagonal tensor V with the volume fractions along the gy to FA  except w1 = 1 ms/μm2, and b2 = 2.5 ms/μm2. The b-values were chosen to follow the recommendations inhttp://www.emmanuelcaruyer.com/q-space-sampling.php. For each sampling scheme data sets were generated with varying levels of added Rician noise using 500 noise realizations for each SNR level. The KFA proxy was then estimated from all generated data sets and the correlations to the ‘true’ KFA from the ground fit set assessed and averaged over noise realizations. Simulations were performed based on both data from normal human brain .


FST ≈ 0.023 − 0.04) as that observed among multiple ethnic groups sampled throughout Ethiopia. Anthropologists have proposed two competing theories to explain the origins of the Ari Blacksmiths as (i) remnants of a population that inhabited Ethiopia prior to the arrival of agriculturists (e.g. Cultivators), or (ii) relatively recently related to the Cultivators but presently marginalized in the community due to their trade. Two recent studies by different groups analysed genome-wide DNA from samples of Ari Blacksmiths and Cultivators and suggested that genetic patterns between the two groups were more consistent with model (i) and subsequent assimilation of the indigenous peoples into the expanding agriculturalist community. We analysed the same samples using approaches designed to attenuate signals of genetic differentiation that are attributable to allelic drift within a population. By doing so, we provide evidence that the genetic differences between Ari Blacksmiths and Cultivators can be entirely explained by bottleneck effects consistent with hypothesis (ii). This finding serves as both a cautionary tale about interpreting results from unsupervised clustering algorithms, and suggests that social constructions are contributing directly to genetic differentiation over a relatively short time period among previously genetically similar groups.The Ari peoples of Ethiopia are comprised of different occupational groups that can be distinguished genetically, with Ari Cultivators and the socially marginalised Ari Blacksmiths recently shown to have a similar level of genetic differentiation between them  the Cultivators reflect migrants who moved into the region occupied by ancestors of the Blacksmiths perhaps many thousands of years ago, versus (ii) the Blacksmiths and Cultivators comprised the same ancestral group before the former was marginalised due solely to their trade. Recent genetic studies showed that Blacksmiths and Cultivators are distinguishable by their DNA, and suggested that overall DNA patterns among the two groups were consistent with (i). However, we demonstrate here that interpreting the results of currently popular algorithms that compare DNA is not always straight-forward. Instead we use a variety of analyses to show that (ii) seems a more likely explanation, perhaps illustrating how social marginalisation can lead to groups becoming genetically distinguishable over a relatively short time period. Different ethnic groups in present-day Ethiopia show a substantial amount of cultural  and geneRemnants model (RN)—Under the Remnants model, originally proposed by Biasutti 1905) , Marginalisation model (MA)—Under the internal specialisation or Marginalisation model , 7, 3, tTwo alternative hypotheses proposed by anthropologists to explain the origin of marginalised groups in Ethiopia, such as are present in the Ari community, imply very different ancestral histories , 3:RemnStudying patterns of DNA variation among Ari occupational groups can help shed light on which of these theories is more likely. Under the MA model, which is currently favoured among anthropologists to explain the existence of caste-like occupational groups in southwest Ethiopia , observeWe note that these models are not mutually exclusive , as evenFST = 0.04) was at a similar level to that observed between multiple ethnic groups sampled across Ethiopia (FST range 0.02–0.06). The authors used ADMIXTURE 11], our FST values matching that of several of our sampled populations -(C) by performing several simulations designed to capture key features of the Marginalisation and Remnants hypotheses, incorporating one-way migration in the latter to be consistent with previous interpretations of ADMIXTURE results , 11. Theulations  Tables:While it is difficult to discern appropriate parameters for these simulations given the uncertainty surrounding the history of groups in this region, we followed values proposed by  as a guiFST  assigned as agriculturalists in , whose aand ARIc , suggestWe further note that one-way migration from the ancestors of the Blacksmiths into those of the Cultivators, as suggested in , 11, wilWhile there are an infinite number of historical scenarios that could be consistent with observed genetic patterns in present-day Blacksmiths and Cultivators, some of which may reflect the RN model, our primary aim is to assess whether the MA hypothesis alone can fit the observations of  and 11]11] as weIf the Blacksmiths experienced a strong bottleneck relative to the Cultivators, then the genetic diversity among Blacksmiths should be lower than that among Cultivators. Consistent with this, the inferred proportion of Identity-by-descent (IBD) sharing among the ARIc is lower than that among the ARIb  and (C) , ADMIXTURE and (C) Figs  Tables. FXY  are smaller than those between any two other Pagani groups. In particular they are smaller than TVDXY and FXY between groups “AFA” and “ORO” , the nd “ORO”  Tables, i groups  and showi groups , S5 Fig.i groups  Tables.FST = 0.015 and FST = 0.20 are both lower than FST = 0.023  = 0.011 − 0.014, FST values between the two real Ari groups  = 0.019 − 0.027, Our 24 “simplified” simulations under the “RN” model  illustra = 0.023 , e.g. th = 0.023  and “OROFor these “simplified” simulations, we performed an analysis mimicking CHROMOPAINTER analysis (B) in the real data, though note that we used only five surrogate groups to infer ancestry, which could decrease power. Using techniques described in the next section, in these “simplified” simulations we were able to distinguish Pop5 and Pop5b when the split time was ≥ 1300 generations, even when the proportion of admixture from Pop5b into Pop5 was 90%, and when the split time was ≥ 1100 generations when the admixture proportion was 50–75% . For splX and Y:FXY should be smaller if X, Y are both ARIb relative to if X, Y are both ARIc.Due to the bottleneck, on average FXY where X is ARIb and Y is ARIc should be similar to FXY when X, Y are both ARIc.In analyses (B) and (C), In addition to having similar genetic profiles under analysis (B)-(C), a recent split between Blacksmiths and Cultivators followed by a bottleneck in the Blacksmiths (i.e. MA hypothesis) predicts that the genetic diversity of the ARIb might fall somewhere along the spectrum of genetic diversity in the ARIc, assuming drift is relatively low in the Cultivators following this split. In particular, after accounting for bottleneck effects in the ARIb, the differences in inferred ancestry between the ARIb and ARIc should not be substantially greater than differences in inferred ancestry among the ARIc. For example, under the MA hypothesis the following should be true for two Ari individuals FXY for all pairings of individuals X, Y such that (i) X, Y are both ARIb, (ii) X, Y are both ARIc, and (iii) X is ARIb and Y is ARIc. Our real data results show the trends expected under point (1) for all three analysis, and for point (2) under analyses (B)-(C). To assess how well point (2) fits the observed data, we calculated the proportion P(ARIc) of ARIc pairs X, Y with FXY greater than the mean FXY across all pairings where X is ARIb and Y is ARIc (P(ARIc) ≈0.2 is higher than the maximum analogous proportions comparing any two other Pagani groups (Point (1) depends primarily on the magnitude of the bottleneck in ARIb relative to ARIc, while point (2) primarily depends on when the ARIb and ARIc split and any subsequent admixture between them. For each of analyses (A)-(C), in  is ARIc . Under ai groups . Comparii groups .P(ARIb), the proportion of ARIb pairs X, Y with FXY greater than the mean FXY across all pairings where X is ARIb and Y is ARIc (P(ARIb) to be less than P(ARIc) due to the bottleneck in the ARIb. However, even in the presence of a bottleneck in our Remnants simulations, P(Pop5b) is often greater than P(Pop5), in both the “full” (P(Pop5b) > P(Pop5). This in turn suggests that historical parameters behind these simulations are less consistent with the real data observation of P(ARIb) < P(ARIc).We can also calculate  is ARIc . Note the “full”  and the e “full” , recallip-value < 0.01) for at least one admixture event in each of the ARIb and ARIc.We also applied GLOBETROTTER  separateIn each of analyses (B) and (C), we infer a simple admixture event at a single time between two sources in both Ari groups, with similar inferred dates, admixture proportions, and sources of ancestry  Figs betGLOBETROTTER results under analysis (A) are more difficult to compare between the two Ari groups, as they do not use the same set of surrogates here as is the case in analyses (B) and (C). Nonetheless, signals in each group are similar and suggest a complex signal where both Ari groups are admixed with a third group, with this admixture dated to a similar time period as that inferred under analysis (B). For example, the ARIc show mixing around 400BCE-330CE between three distinct sources most similar to the ARIb, ORO and ANU, respectively . For theThe complexity of the inferred admixture under GLOBETROTTER analysis (A) makes it difficult to interpret reliably . For exaNonetheless, the GLOBETROTTER results under analysis (A) support three distinct sources intermixing e.g. see , either Therefore we cannot determine whether this additional admixing source inferred under analysis (A) supports an MA model suggesting the same source contributed DNA to the recent shared common ancestor of the two Ari groups, or whether it supports an RN model where the ARIb and ARIc are anciently related and have each intermixed with one another since their initial split. Nonetheless, the inferred date of intermixing is recent (< 3kya) and thus consistent with the Blacksmiths and Cultivators being anciently or relatively recently related. Furthermore, we again note that whether assuming one or two distinct dates of admixture, the inference under each of analyses (A)-(C) is similar between the two Ari groups  Tables aTo further assess whether the Ari share similar genetic origins, we performed an analysis independent of CHROMOPAINTER analyses (A)-(C), based on separating segments inherited from African and “West Eurasian” ancestral source groups. To identify segments from different sources, within each haploid genome of each Ari individual, we fixed the YRI and CEU as surrogates for the two admixing source groups. We then used CHROMOPAINTER to identify all segments containing ≥ 100 contiguous SNPs that we could confidently assign to one of the two surrogates based on new simulations mimicking the presumed recent admixture history of these groups . Due to Analogous to our comparison of inferred ancestry results for CHROMOPAINTER analyses (B) and (C), for both CEU and YRI segments, the distribution of similarity scores between ARIb and ARIc individuals falls on the distribution of similarity scores among ARIc individuals , S33 FigWhile it is difficult with these data to ascertain precisely when the Blacksmiths and Cultivators split , if the introgression is older than the split, then the ratio of genetic similarity in the ARIb among introgressed versus non-introgressed segments should be the same as the analogous ratio in the ARIc. In contrast, if the introgression occurred more recently than the split and bottleneck in the Blacksmiths, the ratio of genetic similarity among ARIb in introgressed versus non-introgressed segments should be less than the analogous ratio in the ARIc see .Using the same segments inferred using CEU and YRI as surrogates for each of the two admixing sources, FST, unsupervised ADMIXTURE, and our CHROMOPAINTER analysis (A). For example, FST is lower than FST  suggest smaller genetic differences between the ARIc and other sampled groups including the ORO than that between ARIc and ARIb  , and TVDand ARIb  Tables. and ARIb  Tables. These difficulties notwithstanding, we believe the MA hypothesis is a more parsimonious explanation given the Blacksmiths’ currently marginalised status. I.e. such marginalisation can plausibly lead to a substantial bottleneck effect in the Blacksmiths, which in turn is consistent with all of our results. In contrast, harmonizing the RN model with the data analysed here requires an additional assumption beyond this bottleneck effect, namely (1) that we have not sampled a group whose ancestors split more recently from either Ari group than the Ari groups’ ancestors split from each other, or (2) that there were substantial levels of intermixing between the ancestors of ARIb and ARIc since the two groups initially were isolated from one another. Assumption (1) is perhaps less likely given our analyses included other Ethiopian groups described as agriculturalists  and grouFST with the ARIc out of all other sampled groups  to discn groups .An artefact leading to our observations of substantial bottleneck effects in the ARIb could arise if at least some of the sampled ARIb individuals were more closely related (i.e. at a family level) to one another relative to the ARIc, perhaps due to sampling artefacts. However, the ARIb and ARIc from  each conCHROMPAINTER analyses (B) and (C) suggest that the ARIb and ARIc are roughly equally related to all other sampled non-Ari groups. There is some ability to tell the two groups’ inferred ancestries apart under these analyses e.g. , though Using more dense genetic data, e.g. from sequencing, might also increase power in a similar manner. Acquiring sequenced individuals from each Ari group would have the additional benefit of allowing inference of the split time between the two groups using pairwise sequentially Markovian coalescent (i.e. PSMC and MSMC) techniques , 36, 37.Our findings serve as a cautionary tale for over-interpreting clustering, e.g. ADMIXTURE plots or results from other unsupervised learning techniques applied to genetic data. In particular the ADMIXTURE plots appear similar in each of the “MA” and “RN” simulation scenarios in this case , though FST = 0.02 − 0.04) between groups without involving any outside introgression and possibly occurring within the last 4,500 years.Importantly, a comparison of approaches here  allows It is straight-forward to apply these models to samples from other geographic regions, and may be particularly helpful in similar cases where different groups might be subjected to strong isolation effects driving genetic differences due to societal divisions, such as in India . Such cawww.1000genomes.org), taken from the file “ALL_1000G_phase1integrated_v3_impute_macGT1.tgz” from https://mathgen.stats.ox.ac.uk/impute/data_download_1000G_phase1_integrated.html, and 28 individuals from 1 population (MKK) from HapMap Phase3  final fineSTRUCTURE clusters . For each cluster c ∈  we then average these proportions across all MCMC samples, and then  re-classify individual i to the c containing the maximum such average proportion across all clusters C. Taking these new re-classifications of all individuals as the new “final fineSTRUCTURE cluster”, we repeat this procedure for 50 iterations. This gave our final classification of C = 87 clusters, though we note that cluster assignments were very similar to those prior to this re-classification procedure. As before we then used fineSTRUCTURE to merge clusters in a greedy step-wise fashion and build the fineSTRUCTURE tree.We next performed the re-classification procedure first described in . BrieflyThese final 87 clusters and corresponding tree are shown in In our additional CHROMOPAINTER analysis that compared the sizes of haplotype segments across groups to assess the relative genetic diversity within each group, we painted each of the 17 world-wide groups using only individuals from their own group as donors. We infered the switch rate separately for each group using 50 steps of E-M algorithm (i.e. “-i 10 -in”) and using the default mutation/emission rate (which was 0.00771). As the expected lengths of segments copied intact from a single donor in the painting can be affected by the number of donor individuals, we used 10 randomly sampled individuals from each group, matching the sample size of our smallest group (“ARIb”). We used all 659,857 SNPs, allowing individuals to copy only from the 9 other individuals of their own group. Our median inferred values across individuals for average haplotype segment size (in cM) and switch rate, plus the 95% empirical quantiles across the 10 individuals, are provided for each group in a priori groups with more individuals will be copied more often when running CHROMOPAINTER, potentially leading to a biased interpretation of results. To cope with this, we use additional linear modeling described in this section to “clean” the raw CHROMOPAINTER inference as in  using CHROMOPAINTER. We use CHROMOPAINTER to calculate analogous painting profiles for each group k ≠ j ∈  as described above. To measure the relative amount of drift (or “self-copying”) in group j, we introduce a K-vector fj* with j using the following linear model:ϵ is a vector of errors, and we seek the estimates ϵ using least-squares. -(C). To measure uncertainty in the k ∈ ) and re-calculates the In ogous to  and calcogous to  approachainty in , who insX copies from donor group k ∈  as inferred by CHROMOPAINTER. Then to compare the copying vectors of two recipients X and Y, we calculate TVDXY, with:To compare differences in copying vectors, we use total variation distance (TVD) as in . In partTVDXY for TVDXY for Note that TVDXY is significantly different from 0, i.e. assessing the evidence  for rejecting the null hypothesis that X and Y are ancestrally related in the same way to other groups, is not straight-forward when trying to negate founder and/or population-specific drift effects. For example, if a small number of individuals leave a relatively large population and form a “new” group, the mean inferred such ancestries between the new and old groups may be significantly different (i.e. TVDXY >> 0) due to the decreased variance in the smaller group. But this significant difference is not important if one is only interested in genetic differences attributable to ancient relatedness. One way to account for this is to consider the differences in ancestry between the two groups scaled by the differences within each group among independent genetic regions . Each such region will relate ancestrally to other groups differently due to independent drift effects, i.e. because drift affects any two unlinked segments of the genome independently, and hence can provide a means to measure variation attributable to drift effects. Here we use each chromosome as separate regions.Assessing formally whether the observed value of X copies from donor group k ∈  across chromosome i as inferred by CHROMOPAINTER. Then we calculate Li the number of SNPs in chromosome i ∈ . Here Similar to the algorithm implemented in fineSTRUCTURE , but witN individuals into C sub-groups, where C is fixed . Let ψj ∈  be the cluster assignment for individual j, with each cluster equally likely a priori. Let γ ≡ {γ1, …, γC}, with k if assigned to cluster c. Note δ controlling the probability distribution of γc. Intuitively, a larger value of δ makes the parameters of the C clusters more similar to one another, hence discouraging sub-group formation.We want to cluster our ψj for j ∈  based on their posterior probabilities conditional on l ≡ {l1, …, lN}. We do so using the following Markov Chain Monte Carlo (MCMC) technique. We start with initial cluster assignments ψ(0) ≡ {ψ1(0), …, ψN(0)} by assigning each of the N individuals randomly to one of the C clusters.We wish to sample the cluster assignments m = 1, …, M:γ(m) using a Gibbs step, withc = 1, …, C, with 1S an indicator for whether S is true.Sample ψ(m) using a Gibbs step, withj = 1, …, N.Sample c = 1, …, C clusters contain 0 individuals, we randomly assign a single distinct individual to each empty cluster. This enables the cluster painting probabilities in step (A) to move away from the prior distribution defined by δ.If any of the Then for e−7 or > (1 − 1e−7) in step (A) were set to 1e−7 and (1 − 1e−7), respectively, with the values in γc subsequently re-scaled to sum to 1.For numerical stability, any M, this algorithm is guaranteed to converge to the true posterior distribution of the ψj’s . In praC = 2 and present our cluster results for analyses (A)-(C) in δ = {50, 100}. When clustering all 206 Pagani individuals (after sample exclusions), we used δ = 100 and considered several fixed number of clusters C = {2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 50}, providing results for a subset of these values in When clustering the Ari individuals only, we set S “surrogate” groups. I.e. S = 16 for analysis (A) since your own group is excluded as a surrogate group, while S = 15 for analysis (B) and S = 10 for analysis (C). GLOBETROTTER then measures the decay of linkage disequilibrium (LD) versus genetic distance among the segments copied from a given pair of surrogate groups. Assuming a single “pulse” of admixture between two or more distinct admixing source groups, theoretical considerations predict that this decay will be exponentially distributed with rate equal to the time (in generations ago) that this admixture occurred  we sum the products across all chunk pairs separated by g for which one chunk is painted by D1 and one by D2. We repeat this for all pairings D1, D2 of the D donor groups, and re-scale these counts by inferred ancestry coefficients for each of the S surrogate groups  separately in the ARIb and ARIc. When doing so, we used 10 painting samples from each haploid of each Ari individual. Note that under analysis (A), following  we repairibed in , subsequS1, S2 = {ANU, ARIb, ARIc, ORO, SOM, TSI} for analysis (A)   -(C), defined as in  sampled populations (where K = 7 here), rescaled so that j ∈ , and then inferred the k ≠ x ∈ .We also conducted an additional analysis, (A-), which took the results from analysis (A) but—separately when analysing each of Pop5 and Pop5b—subtracted out self-copying within that group, to mimic the way GLOBETROTTER analysis (A) infers proportions and source groups. Specifically, separately for model of  after fiS1 TableThe 17 groups used in this analysis, with groups defined using clustering assignments from fineSTRUCTURE . The fou(PDF)Click here for additional data file.S2 TableNumber of sampled individuals per simulated group for both the Marginalisation and Remnants “full”simulations.(PDF)Click here for additional data file.S3 TableFST [Pairwise FST  values a(PDF)Click here for additional data file.S4 TableFST [Pairwise FST  values a(PDF)Click here for additional data file.S5 TableFST [Pairwise FST  values a(PDF)Click here for additional data file.S6 TableFST [Pairwise FST  values a(PDF)Click here for additional data file.S7 TableFST [Pairwise FST  values a(PDF)Click here for additional data file.S8 TableFST [FST values between each population pair across all “simplified” simulations is given.Pairwise FST  values a(PDF)Click here for additional data file.S9 TableMedian and 95% empirical quantiles for the average sizes of haplotype segments (in cM) that match to a single donor individual, across recipient individuals from the ARIc and ARIb under CHROMOPAINTER analyses (A)-(C). Also shown are corresponding values for simulated “Ari” groups Pop5/Pop5b under the Marginalisations (MA) and Remnants (RN) models in the “full” simulations.(PDF)Click here for additional data file.S10 TableInferred proportions of ancestry (i.e. (PDF)Click here for additional data file.S11 TableInferred proportions of ancestry (i.e. (PDF)Click here for additional data file.S12 TableInferred proportions of ancestry (i.e. (PDF)Click here for additional data file.S13 Tabler2 > 0.2) in a sliding 250 SNP window using the command –indep-pairwise 250 10 0.2.Median PI_HAT values across all pairwise combinations of individuals within each group as calculated using PLINK v1.07 , with 95(PDF)Click here for additional data file.S14 TableCHROMOPAINTER’s inferred average haplotype segment sizes (in cM) copied intact from a single donor, and corresponding number of SNPs per segment and switch rate , when allowing 10 randomly-sampled individuals from each group to copy from the other 9 individuals with the same label, using 50 steps of Expectation-Maximisation (E-M). Median and empirical quantile values across the 10 individuals are given for each group. The segment sizes capture (roughly) the relative amount of haplotype diversity in each group. Note the high values for the ARIb relative to all other groups, including the ARIc.(PDF)Click here for additional data file.S15 TableTVDXY scores (see ores see  measurin(PDF)Click here for additional data file.S16 TableFXY scores (see ores see  measurin(PDF)Click here for additional data file.S17 TableFXY scores under analysis (C) between individuals from the same group (rows) that are greater than or equal to the mean FXY across all pairwise combinations of individuals with one from the group given in the row and the other from the group given in the column.The proportion of pairwise (PDF)Click here for additional data file.S18 TableFXY scores under analysis (B) between individuals from the same group  that are greater than or equal to the mean FXY across all pairwise combinations of individuals with one from Pop5b and the other from Pop5. Results from each of 24 “simplified” simulations are given, which vary in the number of generations ago Pop5 and Pop5b split (“split”), the number of generations Pop5b is bottlenecked (“BN”), and the proportion of migrants contributed from Pop5b to Pop5 (“% admixture”).The proportion of pairwise (PDF)Click here for additional data file.S19 Tableδ -(C). The median and range across 10 independent runs of the MCMC chain are shown. Analogous results are given for the “full” simulations: “MA”, “RN”, “RN+BN”, and “RN+BN+80%”. Results are shown for two different choices of prior value δ see .(PDF)Click here for additional data file.S20 Tableg were converted to years y using the formula: y = 1950 − (g + 1) × 28.GLOBETROTTER’s inferred dates , admixing sources Click here for additional data file.S21 Tableg were converted to years y using the formula: y = 1950 − (g + 1) × 28.GLOBETROTTER’s inferred dates , admixing sources Click here for additional data file.S22 TableRE refers to the goodness-of-fit  × 28.GLOBETROTTER’s inferrence when assuming two distinct dates of admixture, with date estimates , admixing sources  giving more precise inference on the haplotype make-up of the source), and proportion (%) of admixture contributed from each source for each admixture event in the Ari groups under analyses (A), (A-sim) and (B). “Props” gives more stably estimated source compositions than the mixing coefficients where multiple-date admixture is inferred (as in ) and areR2; see  for deta(PDF)Click here for additional data file.S23 TableFST [Pairwise FST  between (PDF)Click here for additional data file.S24 TableFXY scores under analysis (B) between individuals from the same group  that are greater than or equal to the mean FXY across all pairwise combinations of individuals with one from Pop5b and the other from Pop5. Each of 6 simulations are given, which vary in the number of generations ago Pop5a and Pop5 split (“split*”) and the number of generations Pop5b is bottlenecked (“BN”).For simulations under the MA model with an unsampled contributing population 5a, the proportion of pairwise (PDF)Click here for additional data file.S25 TableD-statistics testing {ARIc,ARIb;X,Y}, i.e. testing whether {ARIc,ARIb} form a clade relative to {X,Y} using the program qpDstat with default settings, with the corresponding standardized Z statistic showing the strength of evidence for rejecting this tree structure Click here for additional data file.S1 FigCHROMOPAINTER’s inferred proportion of genome-wide DNA that each of 87 fineSTRUCTURE-inferred clusters (columns) copy from these 87 clusters (rows). Clusters containing any of the Pagani individuals removed from analysis are highlighted with translucent grey vertical bars. The tree at top shows fineSTRUCTURE’s inferred hierarchical merging of these 87 clusters, and colors on the axes show which clusters were assigned to the 17 groups in (PDF)Click here for additional data file.S2 FigK = 15 total clusters, which is the level immediately prior to the Blacksmiths (ARIb) separating into two distinct clusters. CHROMOPAINTER’s inferred painting profiles for all individuals in the fineSTRUCTURE analysis, showing the proportion of genome-wide DNA that each individual (column) copies from each of the 17 groups (color) in (PDF)Click here for additional data file.S3 FigCHROMOPAINTER’s inferred proportion of genome-wide DNA that each Ari individual (column) copies from every other Ari individual (row), for all Ari individuals prior to sample exclusions . One of the Ari individuals, labeled a “Blacksmith” (ARIb) and highlighted with the green ellipse, shows clear mixture between Blacksmiths and Cultivators, suggesting that this individual is recently descended from individuals of each of these groups.(PDF)Click here for additional data file.S4 FigK = 87 final inferred clusters for one of these fineSTRUCTURE runs.Proportion of MCMC samples for which each individual  was clustered with each other individual in the fineSTRUCTURE analysis under two independent fineSTRUCTURE runs . Black ticks along each axis denote the midpoints of each of the (PDF)Click here for additional data file.S5 FigK; labels are based on the 17 inferred fineSTRUCTURE groups. The two Ari groups  are highlighted with the blue rectangle.ADMIXTURE applied to individuals from the seven Pagani groups , the MKK(PDF)Click here for additional data file.S6 FigK. Pop5 and Pop5b are highlighted with the blue rectangle. Note that Pop5 looks like a mixture of ancestries related to Pop5b and other populations for various K across all four simulations (e.g. for K = 11).ADMIXTURE results for “full” simulations under the (a) “Marginalisation (MA)”, (b) “Remants (RN)”, (c) “Remnants + Bottleneck (RN+BN)” and (d) “Remnants + Bottleneck + 80% Mixture (RN+BN+80%)” models see , with “A(PDF)Click here for additional data file.S7 FigCHROMOPAINTER’s inferred painting profiles for each Ari individual, showing the proportion of DNA copied from each world-wide donor group (color), under each of analyses (A)-(C). Group labels (ARIb/ARIc) are given on the x-axis, with group means  at far right. Donor groups are colored according to (PDF)Click here for additional data file.S8 FigAverage proportion of DNA that the ARIb (x-axis) and ARIc (y-axis) copy from each donor group under CHROMOPAINTER analyses (A)-(C). Inner 95% empirical quantiles across individuals within each Ari group are shown with the vertical and horizontal lines. Donor groups are colored according to (PDF)Click here for additional data file.S9 FigCHROMOPAINTER’s inferred painting profiles for each simulated “Ari” individual in the four different “full” simulations see , showing(PDF)Click here for additional data file.S10 FigCHROMOPAINTER’s inferred painting profiles for each simulated “Ari” individual in 15 different “simplified” simulations see , showing(PDF)Click here for additional data file.S11 FigProportions of ancestry each Pagani and 1KGP+MKK group (column) shares with other groups, matching (PDF)Click here for additional data file.S12 FigProportions of ancestry, as inferred using CHROMOPAINTER and linear modelling, for simulations under the Marginalisation  and three different Remants  models in the “full” simulations see  for each(PDF)Click here for additional data file.S13 FigProportions of ancestry, as inferred using CHROMOPAINTER and linear modelling, for simulations under the simplified Remnants simulations see . Pop5b i(PDF)Click here for additional data file.S14 FigCHROMOPAINTER’s inferred average haplotype segment sizes (in cM) from (PDF)Click here for additional data file.S15 FigTVDXY between every pair of Ari individuals X, Y, with group labels (ARIb/ARIc) on the axes, under each of analyses (A)-(C).(PDF)Click here for additional data file.S16 FigTVDXY between every pair of individuals X, Y simulated under the Marginalisation  and Remnants  models in the “full” simulations, with group labels (Pop5/Pop5b) on the axes, under each of analyses (A)-(C).(PDF)Click here for additional data file.S17 FigFXY between every pair of Ari individuals X, Y, with group labels (ARIb/ARIc) on the axes, under each of analyses (A)-(C).(PDF)Click here for additional data file.S18 FigFXY between every pair of individuals X, Y simulated under the Marginalisation  and Remnants  models in the “full” simulations, with group labels (Pop5/Pop5b) on the axes, under each of analyses (A)-(C).(PDF)Click here for additional data file.S19 FigP(Pop5b), P(Pop5) giving the proportion of Pop5b and Pop5 pairings, respectively, with a difference greater than or equal to this mean.Differences in inferred ancestry under analysis (B) between all pairings of simulated “ARIb” individuals , all pairings of simulated “ARIc” individuals , and all pairings of one “ARIb” and one “ARIc” individual (cyan), for 15 of the “simplified” simulations. In each plot the black vertical line gives the mean difference across the pairings of one “Pop5b” and one “Pop5”, with (PDF)Click here for additional data file.S20 FigC, using inferred ancestries from the “all-donors” CHROMOPAINTER analysis (A). Ari individuals are enclosed in a green border. Results are averaged over ten different runs of the MCMC chain.Proportion of MCMC samples for which each pair of Pagani dataset individuals were clustered together when fixing various numbers of clusters (PDF)Click here for additional data file.S22 FigC, using inferred ancestries from the “non-Ari-donors” CHROMOPAINTER analysis (B). Ari individuals are enclosed in a green border, and are not separated when C = 50. Results are averaged over ten different runs of the MCMC chain.Proportion of MCMC samples for which each pair of Pagani dataset individuals were clustered together when fixing various numbers of clusters (PDF)Click here for additional data file.S22 FigC, using inferred ancestries from the “non-Pagani-donors” CHROMOPAINTER analyses (C). Ari individuals are enclosed in a green border, and are not separated when C = 50. Results are averaged over ten different runs of the MCMC chain.Proportion of MCMC samples for which each pair of Pagani dataset individuals were clustered together when fixing various numbers of clusters (PDF)Click here for additional data file.S23 FigGLOBETROTTER coancestry curves under all-donors analysis (A) for the ARIb (top row) and the ARIc (rows 2–3). Black lines give the  probability that two DNA segments within the Ari group are inferred as most ancestrally related to the two donor groups given in the title (y-axis) versus the genetic distance between the two segments’ midpoints (x-axis). Green lines give the best fitting exponential distributions to the black lines assuming a single date of admixture, and red lines give the best fit assuming two distinct dates of admixture. Note in the ARIc that the coancestry curves for each pairwise combination of {ARIb,ANU,ORO} increase with increasing genetic distance, suggesting clear evidence of admixture from three distinct source groups around the same time.(PDF)Click here for additional data file.S24 FigGLOBETROTTER coancestry curves under simplified all-donors analysis (A-sim) for the ARIb (top row) and the ARIc (bottom row) using ANU, TSI, ORO and {ARIc,ARIb} as surrogates. Black lines give the  probability that two DNA segments within the Ari group are inferred as most ancestrally related to the two donor groups given in the title (y-axis) versus the genetic distance between the two segments’ midpoints (x-axis). Green lines give the best fitting exponential distributions to the black lines assuming a single date of admixture, and red lines give the best fit assuming two distinct dates of admixture.(PDF)Click here for additional data file.S25 FigGLOBETROTTER coancestry curves under non-Ari-donors analysis (B) for the ARIb (top row) and the ARIc (bottom row). Black lines give the  probability that two DNA segments within the Ari group are inferred as most ancestrally related to the two donor groups given in the title (y-axis) versus the genetic distance between the two segments’ midpoints (x-axis). Green lines give the best fitting exponential distributions to the black lines assuming a single date of admixture, and red lines give the best fit assuming two distinct dates of admixture.(PDF)Click here for additional data file.S26 FigGLOBETROTTER coancestry curves under non-Pagani-donors analysis (C) for the ARIb (top row) and the ARIc (bottom row). Black lines give the  probability that two DNA segments within the Ari group are inferred as most ancestrally related to the two donor groups given in the title (y-axis) versus the genetic distance between the two segments’ midpoints (x-axis). Green lines give the best fitting exponential distributions to the black lines assuming a single date of admixture, and red lines give the best fit assuming two distinct dates of admixture.(PDF)Click here for additional data file.S27 Figt ∈ {300, …, 500}, with a bottleneck in Pop5b occurring {30, 35} generation ago. These parameters were chosen to give FST values similar to those observed between the ARIb, ARIc and ORO in the real data  model with an additional group Pop5a (shown in grey) providing admixture from an unsampled “Ethiopian” group. Orange arrows indicate migration from Pop6 into Pop4 and Pop5, and the grey arrow indicates migration from Pop5a into Pop5. Pop5 and Pop5a split at varying times data see .(PDF)Click here for additional data file.S28 FigCHROMOPAINTER’s inferred painting profiles for each simulated “Ari” individual in 6 different simulations under the MA model incorporating an unsampled contributing population Pop5a see , showing(PDF)Click here for additional data file.S29 FigInferred proportions of ancestry for each simulated “Ari” individual in a simulation scenario under the MA model incorporating an unsampled contributing population 5a see , as infe(PDF)Click here for additional data file.S30 FigP(Pop5b), P(Pop5) giving the proportion of Pop5b and Pop5 pairings, respectively, with a difference greater than or equal to this mean. The simulations vary in the number of generations Pop5a and Pop5 split (“split*”) and the number of generations Pop5b is bottlenecked (“BN”).Differences in inferred ancestry under analyses (B) between all pairings of simulated “ARIb” individuals , all pairings of simulated “ARIc” individuals , and all pairings of one “ARIb” and one “ARIc” individual (cyan), for simulations under an MA model that incorporate an unsampled contributing population 5a see . In each(PDF)Click here for additional data file.S31 FigX contiguous SNPs (x-axis) all inferred as CEU under the CHROMOPAINTER “NNLS” approach. Each color represents a different threshold for confidently calling a SNP as CEU (see legend), which is meant to act as a surrogate for Saudi. Vertical solid lines show the range, dots the median, and crosses the mean of proportions across all segments whose number of contiguous CEU-inferred SNPs falls between a given pair of consecutive dashed lines . The number of segments falling within each SNP number bin are given at top for each threshold value; only chromosome 2 was simulated.The proportion of SNPs whose true simulated local ancestry is Saudi (y-axis) among segments that have (PDF)Click here for additional data file.S32 FigX contiguous SNPs (x-axis) all inferred as CEU under the CHROMOPAINTER “E-M” approach. Each color represents a different threshold for confidently calling a SNP as CEU (see legend), which is meant to act as a surrogate for Saudi. Vertical solid lines show the range, dots the median, and crosses the mean of proportions across all segments whose number of contiguous CEU-inferred SNPs falls between a given pair of consecutive dashed lines . The number of segments falling within each SNP number bin are given at top for each threshold value; only chromosome 2 was simulated.The proportion of SNPs whose true simulated local ancestry is Saudi (y-axis) among segments that have (PDF)Click here for additional data file.S33 FigThe distributions of genetic similarity scores, separately within segments matched to CEU (top row) and YRI (bottom row), across all pairwise comparisons of individuals within the ARIb (pink), within the ARIc (green), and with one ARIb and one ARIc individual (cyan). Results are shown for segments assigned to CEU and YRI using (left) the E-M model with a threshold of 0.94, and (right) the NNLS model with a threshold of 0.66. The solid black vertical line gives the average across all pairwise combinations of one ARIb and one ARIc individual, with dotted black lines giving the 95% empirical quantile. Note that the ARIb have higher similarity scores within each of the CEU and YRI segments, as expected from bottleneck effects. In addition, differences between ARIb and ARIc pairs are very similar to those among ARIc pairs, consistent with identical non-African and African ancestral sources in each Ari group.(PDF)Click here for additional data file.S34 FigI) or ancestral (A) sources using the NNLS model with a threshold of 0.66 and assuming I is the “West Eurasian” source (see The distributions of urce see . As in t(PDF)Click here for additional data file.


To pilottest if Orthopaedic Surgery residents could self-assess their performance usingnewly created milestones, as defined by the Accreditation Council on GraduateMedical Education. In June 2012, an email was sent to Program Directors and administrativecoordinators of the154 accredited Orthopaedic Surgery Programs, asking them tosend their residents a link to an online survey. The survey was adapted fromthe Orthopaedic Surgery Milestone Project. Completed surveys were aggregated inan anonymous, confidential database. SAS 9.3 was used to perform the analyses.Responses from 71 residents were analyzed. First and second yearresidents indicated through self-assessment that they had substantiallyachieved Level 1 and Level 2 milestones. Third year residents reported they hadsubstantially achieved 30/41, and fourth year residents, all Level 3milestones. Fifth year, graduating residents, reported they had substantiallyachieved 17 Level 4 milestones, and were extremely close on another 15. Nomilestone was rated at Level 5, the maximum possible.  Earlier in training, Patient Care and MedicalKnowledge milestones were rated lower than the milestones reflecting the otherfour competencies of Practice Based Learning and Improvement, Systems BasedPractice, Professionalism, and Interpersonal Communication. The gap was closedby the fourth year.Residents were able to successfully self-assess using the 41 OrthopaedicSurgery milestones. Respondents’ rate improved proficiency over time.Graduating residents report they have substantially, or close to substantially,achieved all Level 4 milestones. Milestone self-assessment may be a useful tool as one component of aprogram’s overall performance assessment strategy.  The Accreditation Council for Graduate Medical Education (ACGME) is the accrediting body for the United States Graduate Medical Education system. It encompasses over 115,000 residents and fellows training in over nine thousand programs.,Since 1999, ACGME has championed competency-based education (CBE), identifying six core competencies expected of all physicians regardless of specialty.,The newly created milestones may address the need for more specific targets within Orthopaedic residency training. In 2009, ACGME initiated partnerships with the American Board of Medical Specialties (ABMS), Program Director Associations, and residents to identify and implement specialty specific milestones to further promote outcomes based training.ACGME requires each program to use a clinical competency committee (CCC) to judge resident performance along a defined progression of cognitive, professional, and technical milestones.  Milestones represent the progressive trajectory of a resident’s expected skill development from basic to aspirational. Most specialties have identified between 13 and 41 milestones and have provided narrative descriptions of five performance levels along this trajectory.   As a CBE tool, it is important to note that milestones are not meant to correspond to “time” in training.  For example, it may be tempting to think of Level 1 as representing mastery of the first postgraduate year.  However, some learners may be able to reach or exceed Level 1 much more quickly than others. This may be a consequence of a variety of factors, such as additional experiences in medical school prior to residency, or a difference in learning speed.   ACGME predicts that Level 4 is a reasonable potential resident graduation target in most specialties, but has not instituted a graduation requirement at this time. Because the milestones are new, it is not known if all residents will achieve Level 4 on all, some, or even any of the currently published milestones by program completion. The eventual release of ACGME milestone data on all residents will create trajectories of specialty specific aggregate resident performance.  Such milestone data will allow programs to compare how their own residents’ trajectories compare to the trajectory of all residents nationwide. They will allow programs to more easily and confidently identify outliers on both ends of the performance spectrum, who may benefit from curricular enhancements or earlier remediation.,-,ACGME did not intend for the milestones to be substituted for the program’s current assessment methods.  Regardless, many programs have substituted actual milestone language for their current assessment tools, at least to some extent. For truly accurate assessment of resident proficiency, multiple types of assessment tools and multiple assessors are necessary.  One such assessment tool is “self-assessment”.  ACGME has long recognized resident “self-assessment” as both a tool and as a foundational skill in the competency of Practice Based Learning and Improvement. Because the ability to self-assess has been believed to be essential to physicians’ lifelong learning, ACGME previously required programs to document how they developed their trainees’ abilities’ in self-assessment and reflection. This was a required component of the Common Program Information Form, which was formerly required for program accreditation.-Surgical training may contain many of the elements that might portend “better” self-assessment.  There are often more explicit expectations of performance.,,-The transition of graduate medical education to CBE using milestones is in an exciting phase. There is tremendous work to be done to integrate milestones into curricula and assessment, faculty development, and the work of clinical competency committees.Though not the only tool in determining how resident performance stacks up against milestones, self-assessment will provide an important data element that can be considered in context with others.  It can also help programs provide residents with feedback on their self-assessment abilities, and hopefully help them improve their own self-assessments. Program directors will be able to combine milestone data with their existing assessment tools to benchmark their own residents’ trajectory of skill acquisition.  They will be able to compare the progress of their resident cohorts by training year, and their overall training program to the national experience. As yet, however, ACGME has not released milestone aggregate data for all specialties, so comparisons to national peer groups are not possible.The objective of this study was to demonstrate if residents could self-assess their performance using milestones. The time frame was deliberately selected to be at the conclusion of the academic year just prior to the adoption of milestones. At this point in time, most programs had not yet devoted much curricular attention to the milestones themselves. Milestones were written to reflect each of ACGME’s six core competencies: Patient Care (PC), Medical Knowledge (MK), Practice Based Learning and Improvement (PBLI), Systems Based Practice (SBP), Professionalism (P) and Interpersonal and Communication Skills (IC). Anecdotally, programs seem to be more confident assessing PC and MK, than the perhaps “softer” skills of PBLI, SBP, P and IC.  In this study the following hypotheses were tested:  1) Orthopaedic residents will be able to self-assess using milestones, 2) Self-assessment scores will increase across postgraduate training years one through five, 3) Self-assessment scores for milestones will differ by competency; specifically, patient care (PC) and medical knowledge (MK) milestones will differ compared with the four general competencies: practice based learning and improvement (PBLI), systems based practice (SBP), professionalism (P), and interpersonal and communication skills (IC).This is a cross sectional descriptive study of Orthopaedic residents who completed an online survey in which they self-assessed their competency using narrative descriptions provided by the milestones.  The survey contained forty-five questions. Four questions were demographic variables. Forty-one questions were adapted from the Orthopaedic Surgery Milestone Project, a Joint Initiative of the Accreditation Council for Graduate Medical Education and the American Board of Orthopaedic Surgery, version December 2012.For each milestone, blank boxes below the descriptions of level 1 to level 5 were labeled A through I. Residents were asked to select the milestone “box” which best characterized their current performance level. The letters were subsequently converted to a numeric scale, 1-9.  Choice A, (corresponding to “1”), indicated the resident believed he or she had substantially achieved the Level 1 milestone.  Choice B, (corresponding to “2”), indicated the resident believed he or she had substantially achieved all Level 1 milestones and some Level 2 milestones.  Choice C, (corresponding to “3”), indicated the resident believed he or she had substantially achieved all Level 2 milestones.  Choice D, (corresponding to “4”), indicated the resident believed he or she had substantially achieved all Level 2 milestones and some Level 3 milestones.  Choice E, (corresponding to “5”), indicated the resident believed he or she had substantially achieved all Level 3 milestones.  Choice F, (corresponding to “6”), indicated the resident believed he or she had substantially achieved all Level 3 milestones and some Level 4 milestones.  Choice G, (corresponding to “7”), indicated the resident believed he or she had substantially achieved all Level 4 milestones. Choice H, (corresponding to “8”), indicated the resident believed he or she had substantially achieved all Level 4 milestones and some Level 5 milestones.  Choice I, (corresponding to “9”), indicated the resident believed he or she had substantially achieved all Level 5 milestones. The study addressed all of the ethical concerns in graduate medical education research identified by Keune.Orthopaedic Surgery residents who voluntarily responded to an email survey forwarded to them by their program director or coordinator were included in the study.In June 2013, an email was sent to the Program Directors and coordinators of the 154 ACGME accredited Orthopaedic Surgery Programs in which approximately 3501 residents were training. Email addresses were identified from the ACGME public database or other public institutional directories.  The email explained the study’s purpose and asked them to send an email to their residents containing a link to a voluntary survey constructed in RedCAP.Respondents completed all surveys online. Their responses populated an anonymous, confidential database in REDCap that captured de-identified aggregate information. The data were stored on a secure server.SAS 9.3 was used to perform the analyses. A one-way ANOVA was performed to determine if there were differences in how residents self-assessed their abilities across residency years. Two-sample t tests were used to determine if the average of each milestone group was different.  P-values were adjusted using empirical methods to account for multiple comparisons.  Aggregate scores from all of the milestones within each of the PC and MK competencies were compared with aggregate scores from the milestones of the other 4 competencies by postgraduate training years. Two-sample t-tests were used to determine if the averages differed.  P-values were adjusted to account for multiple comparisons.Seventy-four residents responded to the study. Seventy-one completed the survey in its entirety and indicated their gender and postgraduate year (PGY) 1 to 5, as there are five postgraduate years in ACGME accredited Orthopaedic residency programs. Responses from those 71 are included in this analysis.  There were 14 PGY1s; 10 PGY2s; 17 PGY3s; 16 PGY4s; and 14 PGY5s. There were no women among the PGY1 respondents.  There were two women PGY2s and PGY3s sand three women among the PGY3s and PGY5s. The self-assessment of PGY1s, (first year residents), and PGY2s, (second year residents), indicated they believed they had substantially achieved Level 1 and Level 2, respectively, for all milestones. PGY3s, (third year residents) reported they had substantially achieved 30/41, and PGY4s (fourth year residents) all Level 3 milestones.  PGY5s  reported they had substantially achieved 17 Level 4 milestones and were “extremely close” to substantially achieving another 15. PGY5 residents only rated two milestones at less than the “midpoint” between Level 3 and 4. Interestingly, no resident scored him or herself as a “9,” the highest possible level and corresponding to substantially achieving Level 5, on any milestone see .Of the 16 paired PC and MK milestones, PGY1s self-assessed MK more highly than PC for 11 milestones, and “the same” for two milestones.  Their lowest rated milestone was MK-ankle arthritis and their highest rated milestone was the SBP milestone, “use of technology.”  PGY1s reported some achievement of Level 2 on twenty-three milestones; substantial achievement of level 2 on four milestones; and partial achievement of Level 3 on five milestones.  Eight of the nine most highly rated milestones were in the four general competencies of PBLI, SBP, P and IC.  Mean PGY2 self-assessments were higher than PGY1 self-assessments for forty of forty-one milestones. The average self-assessment of technology skills was essentially the same: 4.62 for PGY1 and 4.60 for PGY2s.  There were statistical differences for 15 of 16 PC milestones, 13 of 16 MK Milestones and 2 of 9 PBLI-SBP-P-IC milestones. Aggregate PC, MK, and PBLI-SBP-P-IC milestones were also statistically different from PGY1 to PGY 2. Milestones from MK and PC competencies were generally rated less highly than the four general competency milestones.  This difference was statistically different only for PGY1s. See We are unaware of any previously published “baseline” self-assessment of residents using milestones prior to their adoption by the first seven specialties in July 2013 and subsequently by the remaining specialties.  A single study of entering Emergency Medicine interns in summer 2012 reported that many of them were unaware of either teaching or assessment on milestones.In our pilot study, we found Orthopaedic residents were able to self-assess themselves using the Orthopaedic milestones. They appeared to discriminate among the milestones and did not assess themselves as equally proficient in all forty-one milestones. At the conclusion of the first postgraduate year, first year residents generally felt they “knew” more than they could “do.”  Self-assessment of MK milestones was generally higher than PC milestones. Self-assessment scores increased from the first to the fifth postgraduate year. In general, earlier in training, residents self-assessed themselves as more capable in the PBLI-SBP-P-IC domains, than the MK and PC domains. This gap essentially closed by the time they were PGY4s. The trajectory of skill acquisition also varied depending on which milestone was assessed.We believe there is still more to be learned about self-assessment, including how to best correlate it with other sources of assessment. One hundred and eighty-one internal medicine resident self-assessments of their religious/spiritual communication competency correlated well with 541 patients’ ratings of their communication.The study was not designed to compare resident assessment with the assessments of their faculty.  We have no comparison faculty or program ratings and therefore we cannot address whether residents overinflated or underinflated their achievement.  However, at least by one measure, residents seemed not to overinflate their proficiency.   No resident indicated achievement of Level 5, which was the highest level possible.  None self-assessed their performance even at a level midway between Level 4 and Level 5 by the time of program completion. This may be in keeping with findings by Gow and others, who indicate more advanced learners, are better at self-assessment and more critical of their own performance than even their attendings.The general competency milestones (PBLI-SBP-P-IC) were rated more highly than PC or MK milestones, though this was statistically significant only at the PGY1 level.  This is consistent with Blanch-Hartigan’s findings that medical students, as a whole, do not over or underestimate their skills, but are more likely to overestimate their performance on communication skills than knowledge.If first year residents overestimated their skills, it may have been because the lowest option possible corresponded to “substantially achieved Level 1.”  Unlike some other specialties’ milestones, there is no “column to the left” of Level 1, indicating the resident “has not yet substantially achieved Level I” (as do the Emergency Medicine milestones) or, has a “critical deficiency” .  Orthopaedic Surgery may wish to consider adding such an option to help better distinguish performance among the more junior residents. A simple change would be to include the option of “not yet achieved Level 1” for PC and MK Milestones, not just the PLBI-SBP-P-IC milestones.We note with interest that the PBLI-SBP-P-IC milestones were rated more highly than PC and MK milestones. By PGY4, the gap between PBLI-P-SBP-P-IC milestones’ and the MK and PC milestones had substantially been erased. But residents also expressed variability in the PBLI-SBP-P-IC milestones’ developmental trajectory. Those also differed by milestone.Interestingly some of the milestones PGY1s rated the highest, such as technology, were less highly rated by PGY2s and PGY3s.  Perhaps they became better aware of their true skill level as they matured, or the nature of their work in those years required awareness of a skill level beyond their original conception of proficiency.  In other words, they learned that there was still more to be learned. It is anticipated that milestones will benefit other types of assessment. Faculty assessment of resident performance may be improved. Milestones use narrative descriptions of performance, and such narrative descriptions have outperformed the use of numeric scales in helping faculty more authentically assess residents.Our pilot study confirmed Level 4 milestones represented a realistic target for this sample of residents. PGY5 respondents, on average, reported they had substantially achieved 17 Level 4 milestones and were extremely “close” to achieving another 15.  They reported they had substantially achieved competency in Level 3 with some competency in Level 4 for another 7 milestones. There were only two milestones on which they “rated” themselves lower than the midpoint between Level 3 and 4. Both of these were MK milestones: ankle arthritis and degenerative spinal conditions.  Interestingly, Orthopaedic residents have expressed their belief in the importance of case logs in demonstrating performance in these two clinical areas.There are number of limitations to our study.  We are well aware that our 71 participants represent only a small proportion of the country’s Orthopaedic residents.  Our study design utilized a single email request to Program Directors and residency coordinators. We were concerned about survey fatigue and aware that June is an extremely busy time in residencies.  However because we wanted to “biopsy” residents at the conclusion of their training year, when presumably their competence would be the highest, and just prior to milestone implementation, June was the most appropriate month.  We suspect the response rate would have been higher if we had emailed residents directly, or followed up on our initial request with additional emails.  We also do not know either how many, or which programs, are represented in our respondents.  The number of respondents confirms that more than a single program is represented in the study, since no single program has over 70 residents. Our results may be biased if our sample resulted in residents who would rate themselves disproportionately to their colleagues. We note we have few women respondents and the self-assessments of women may differ from their male colleagues.  Orthopaedic Surgery is the US specialty with the fewest percentage of women residents.  Our survey appeared to be representative as 14% of our sample were women, compared with 13% of women Orthopaedic residents nationally.As a cross sectional study, we did not follow the same cohort of residents across five years of residency, but rather “biopsied” residents from each training year, at a single point in time, at the conclusion of an academic year. The mean values overall increased from PGY1-5. But we acknowledge we may have missed individual variability and some individuals’ self-assessment of proficiency may have plateaued or even declined. We have no way of knowing how the respondents’ own programs would have rated their milestone acquisition. Nor are we able to ascertain how their self-assessment compared with any other measure of their performance, such as their in-service examination scores, direct observation by faculty, or multisource feedback from nurses and peers.  We believe the anonymity of the survey counteracted some recognized concerns of self-assessments, such as when learners are concerned their own low self-assessment will result in a lower or negative evaluation from their program.The two milestones assessed as the weakest at program completion may not be representative if there were a larger sample of residents reporting.  All Orthopaedic programs submitted their graduating residents’ performance on milestones to ACGME beginning in June 2013. However, ACGME has not yet publicly released those aggregate milestone reports. When released, we will be interested in comparing the milestones that nationally are felt to be relatively stronger and weaker with our findings. Those milestones on which there is relatively weaker performance will provide rich opportunities for curricular development and resident coaching within individual programs.  It will allow the specialty to engage in conversations about realistic targets, training opportunities, and its own scope of practice.The milestones we presented to the residents in this study were those posted on the ACGME web site as of May 2012.  There is currently a posted version that is dated August 2013.Although a limited response, we believe the results from our pilot study support the proof of concept that Orthopaedic Surgery residents can self-assess their performance using milestones. Residents discriminated among the milestones and did not assess they were equally proficient in all.  Respondents report improved abilities across their five training years, but none believed they were at Level 5, the maximum possible. Graduating residents rated themselves substantially achieving, or very close to substantially achieving, Level 4, which ACGME predicts is the likely graduation target.   In aggregate, PBLI-SBP-P-IC milestones were rated more highly than PC or MK milestones. Resident self-assessment using milestones may be a useful assessment tool for programs and CCCs as they assess resident performance. Milestones may facilitate critical conversations between residents and faculty. They may help residents consider other sources of feedback to better calibrate their own self-assessment.  Milestones will provide opportunities for self-directed learning, ultimately supporting habits of life-long, deliberate practice.Source of Funding: This project was funded internally through a faculty research account that paid for survey construction and database in REDCap.  We appreciate the statistical support partially funded by the National Center for Advancing Translational Science of the National Institutes of Health through Grant Number UL1RR024128.The authors declare that they have no conflict of interest.


Musa spp.) is an important staple food as well as cash crop in tropical and subtropical countries. Various bacterial, fungal, and viral diseases and pests such as nematodes are major constraints in its production and are currently destabilizing the banana production in sub-Saharan Africa. Genetic engineering is a complementary option used for incorporating useful traits in banana to bypass the long generation time, polyploidy, and sterility of most of the cultivated varieties. A robust transformation protocol for farmer preferred varieties is crucial for banana genomics and improvement. A robust and reproducible system for genetic transformation of banana using embryogenic cell suspensions (ECS) has been developed in this study. Two different types of explants  were tested for their ability to develop ECS in several varieties of banana locally grown in Africa. ECS of banana varieties “Cavendish Williams” and “Gros Michel” were developed using multiple buds, whereas ECS of “Sukali Ndiizi” was developed using immature male flowers. Regeneration efficiency of ECS was about 20,000–50,000 plantlets per ml of settled cell volume (SCV) depending on variety. ECS of three different varieties were transformed through Agrobacterium-mediated transformation using gusA reporter gene and 20–70 independent transgenic events per ml SCV of ECS were regenerated on selective medium. The presence and integration of gusA gene in transgenic plants was confirmed by PCR, dot blot, and Southern blot analysis and expression by histochemical GUS assays. The robust transformation platform was successfully used to generate hundreds of transgenic lines with disease resistance. Such a platform will facilitate the transfer of technologies to national agricultural research systems (NARS) in Africa.Banana ( Musa spp.) are the eighth most important staple food and cash crops in tropical and subtropical countries  disease caused by Xanthomonas campestris pv. musacearum. BXW disease affects the livelihoods of millions of farmers  cultures , “Cavendish Williams,” “Gros Michel,” (AAA genome), and “Ngombe” (AAA-EAHB) were collected from banana growing fields at Kenya Agricultural and Livestock Research Organization (KALRO), Kenya. Varieties “Cavendish Williams,” “Gros Michel,” and “Mpologoma” (AAA-EAHB) were obtained from the IITA collection as in vitro plantlets of varieties “Cavendish Williams,” “Gros Michel,” and “Mpologoma” were micropropagated as described by Tripathi et al.  by removing bracts under sterile conditions.Multiple buds were isolated on Callus Induction Medium . About 6–9 tiny flowers were incubated per 90-mm petri dish, and a total of 300 explants were cultured for callus induction in three experiments for each variety. The cultures were kept in the dark at 26 ± 2°C until callus was obtained without sub-culturing. The cultures were checked biweekly for development of friable embryogenic calli.Creamish yellow, translucent friable embryogenic calli of each variety were identified under the microscope and transferred into a 25 ml conical flask containing liquid Callus Induction Medium (LCIM1 or LCIM2 depending upon the explants). Initially a 5 ml medium was used in each 25 ml conical flask up to 1 week; gradually the medium was increased to 10 ml in 3 weeks. On the fourth week, fine granular cells were transferred into a new 25 ml conical flask. After 8 weeks of culture, fine cells were transferred into 250 ml conical flasks containing 30–40 ml medium for further proliferation and maturation. These embryogenic cells were washed and replenished with a new medium every 10–14 days  was spread on nylon mesh and cultured on semisolid Embryo Development Medium  gene as the selectable marker and β-glucuronidase (gusA) gene as a reporter was used for all the transformation experiments . The PCR conditions were: initial denaturation at 95°C for 10 min, 35 cycles of denaturation at 94°C for 15 s, annealing at 62°C for 45 s and extension at 72°C for 50 s followed by final extension at 72°C for 7 min and holding at 4°C. The amplified PCR products were separated by electrophoresis on 0.8% (w/v) agarose gel. Both positive (plasmid DNA) and negative (gDNA of non-transgenic control plant) were included in each experiment.Plant genomic DNA was isolated from regenerated putative transgenic fresh leaves using a DNeasy kit . PCR was performed using gusA gene into the genome of banana was analyzed using dot blot and Southern hybridization. Dot blot was performed using gDNA extracted from 24 PCR positive transgenic lines as described by Nyaboga et al.  method developed by Gawel and Jarret . Southern blot analysis was performed as described by Tripathi et al.  was performed and interaction between means were separated by least significant difference (LSD) at Genetic improvement of bananas is crucial to create new varieties possessing traits of agronomic importance, such as high yield, combined with resistance or tolerance to biotic and abiotic stresses. Other desirable features include better fruit quality, early maturity, short stature, photosynthetic efficiency, strong roots, and uniform fruits  was obtained from ECS of “Sukali Ndiizi” and minimum number of lines (20–30) was obtained for “Gros Michel.” About 30–40 lines were developed from 1 ml SCV of embryogenic cells of “Cavendish Williams”  disease, which is the most important biotic constraint to banana production in east and central Africa  with various gene constructs (Table A robust high throughput regeneration and genetic transformation system is required for rapid testing of large numbers of gene constructs in any crop. The main focus of research in our laboratory is to control the deadly banana ts Table . These tAgrobacterium-mediated transformation and regeneration of economically significant banana varieties grown in east African countries and globally. The ECS of three banana varieties—“Cavendish Williams,” “Gros Michel,” and “Sukali Ndiizi”—which are highly prone to fungal as well as bacterial diseases, were successfully transformed using Agrobacterium-mediated system. Transformed ECS have the potential to generate transgenic plants with high efficiency, therefore, only a few milliliters of cells can provide a sufficient number of transformed plants with the desired traits. This high-throughput protocol can be used for functional genomics and improvement of banana varieties. Using this protocol, hundreds of transgenic banana lines with bacterial wilt resistance were generated (Table In conclusion, we developed a high-throughput technique for ed Table . This stThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Metastable ω phase is common in body-centred cubic (bcc) metals and alloys, including high-alloying steels. Recent theoretical calculations also suggest that the ω structure may act as an intermediate phase for face-centred cubic (fcc)-to-bcc transformation. Thus far, the role of the ω phase played in fcc-bcc martensitic transformation in carbon steels has not been reported. In previous investigations on martensitic carbon steels, extra electron diffraction spots were frequently observed by transmission electron microscopy (TEM), and these spots were historically ascribed to the diffraction arising from either internal twins or carbides. In this paper, an intensive TEM investigation revealed that the extra spots are in fact attributed to the metastable ω phase in particle-like morphology with an overall size of several or dozens of nanometres. The strict orientation relationships between the ω phase and the ferrite matrix are in good agreement with those of the hexagonal (P6/mmm) ω phase in other bcc metals and alloys. The identification of the ω phase as well as the extra diffraction spots might provide a clue to help understand the physical mechanism of martensitic transformation in steels. It has long been known that the rapid quenching of carbon steels can lead to the formation of a very hard phase, martensite, and its outstanding hardness has been recognized to arise from the supersaturated carbon in ferrite formed by a diffusionless martensitic transformation in which the carbon—formerly in face-centred cubic (fcc) austenite—remains in the bcc martensite. Because of its vital importance to metallurgical engineering and academic research in solid-state physics, the above fcc-to-body-centred cubic (bcc) transformation has received persistent attention since the 1920s, and voluminous experimental work as well as theoretical considerations can be found in the literature1134bcc, [113]bcc, and [120]bcc zone axes, and these extra diffraction spots were attributed to the double diffraction of the twins in some studies11121415171819The complexity of martensitic transformation in steels has long been recognized to be closely associated with the existence of carbon because carbon can exert a significant influence on the properties of martensite, such as its hardness, morphologies and substructures. The morphology of the martensites in carbon steels is always observed in two distinctly different types depending on the content of carbon: lath martensite in low-carbon steels and lenticular martensite in high-carbon steels. Twins, often observed as a substructure of martensite in high-carbon steels, were confirmed to be of the {112}<111> type and considered to be similar to other bcc metals and alloys678934bcc, 1bcc, and cc, and 1bcc zone et al. noticed that the extra diffraction spots in commercial spring steel with medium carbon content can be indexed by an ω phase222325Very recently, Ping fcc, {112}bcc systems141328In addition to the diffraction spots from the matrix and twin crystals, the extra spots—as indicated by arrows in 1112131415a = 0.2482 nm, b = 9.83 nm, c = 0.4054 nm and a twinning plane in the middle. Such a unit cell consists of a bcc {112}<111>-type twinning structure. During the calculation, the software generated an arbitrary repeat of the unit cell periodically along its a and b axes. In this situation, another twinning plane exists at the end of the cell because the unit cell is under a periodic boundary condition. The electron beam is parallel with the c axis, and a partial projection along the [110]bcc-Fe direction is shown in To clarify the dynamic diffraction pattern in a bcc twin system, xHREM  was employed to perform a dynamic calculation on the bcc {112}<111>-type twinning. twin and 002 can be seen in The calculated dynamic twinning diffraction patterns with several different thicknesses along the [110] zone axis are shown in α zone axis and the corresponding bright-field TEM image of the as-quenched Fe-0.58 mass% C sample, respectively. The pattern shown in α zone axis and the corresponding bright-field TEM image are shown in 17181930bcc// [ε, (011)bcc // (0001)ε323334353637For further investigation of the extra spots appearing in the electron diffraction of martensites, diffraction patterns from various zone axes were recorded. c and d shown in To confirm the universality of the existing specific phase that contributes the extra diffraction spots in martensitic carbon steels, an Fe-0.98 mass% C binary alloy was selected for further TEM observations. bcc and 2/3. Metastable ω phase is a common phase in Group IV metals and their alloys22234750aω = √2 × abcc and cω = √3/2 × abcc, following the orientation relationships of [bcc//[ω, (110)bcc // bcc planes and keeping the neighbouring (111)bcc plane unaltered within the bcc lattice49Many experimental investigations have demonstrated that a metastable phase, designated as ω, typically precipitates from the matrix of bcc metals and alloys when the bcc lattice becomes unstable by doping, high-speed impact or high pressure22424344bcc bcc // of s) are given in 4 and 90% ethanol at 253 K. The microstructural observation was made with a JEM 2000FX TEM operated at 200 kV and a JEM 2100F high-resolution TEM operated at 200 kV. Electron diffraction patterns of the {112}<111>-type twin structure were calculated using the software application xHREM (HREM Research Inc.).Specimens 0.5 mm thick were cut from commercial carbon-steel bars with 0.58% C and 0.98% C (mass). The detailed chemical compositions of the specimens and their calculated martensitic transformation starting temperatures .


Polyomaviridae) in a lymph node of a Salers cow. As the 2 previously published genome sequences of this virus originated from fetal bovine serum and ground beef, respectively, this is the first BPyV1 genome that could be traced back to an individual.We identified a variant of the first bovine polyomavirus (BPyV1; family  Polyomaviridae) was originally isolated from kidney cell cultures originating from stump-tailed macaque (Macaca arctoides). At discovery, it was regarded as a monkey virus and named stump-tailed macaque virus (STMV)  in the VP1 coding sequence (CDS) of the KM496323 genome. All remaining nt differences represent synonymous base exchanges, with the exception of 2 nt exchanges that cause a Ser114 to Asn amino acid exchange in the putative agnoprotein CDS of the #7535 genome and an Asn104 to Thr exchange in the large T antigen CDS of the KM496323 genome. The BoPyV1 #7535 genome originates from a naturally circulating virus and is the first that is traceable to a documented cattle individual.The first bovine polyomavirus (family s (STMV) . Later, s (STMV) –4. Due t (STMV) ––7. In li(STMV) ––. In 1990(STMV) ––. In that(STMV) ––, 11. How(STMV) ––. SequencKU170643.The BPyV1 #7535 genome has been deposited in GenBank under the accession number