Solidago canadensis L.) remains elusive. In the present study, we explored the sources and the number of introduction events of this species from its native areas into China. Using the genotyping-by-sequencing approach, we identified 34,035 selectively neutral single-nucleotide polymorphism (SNP) markers to infer the evolutionary trajectories of 77 S. canadensis individuals. Both the principal component analysis and the ADMIXTURE analysis revealed two genetic groups that are sympatric to each other in China and suggested the absence of genetic admixtures. The phylogenetic analysis indicated three feasible introduction routes and multiple introduction events of Canada goldenrod into China. Specifically, the one from the USA directly into China, the other from the USA into China through Japan, and the third from the USA into China through Europe. Based on the site frequency spectrum of these identified SNPs, we inferred strong bottleneck events for both genetic groups, and that the multiple introductions did not rescue the decline of genetic diversity. To conclude, multiple introduction events, genetic bottlenecks, and potential human-mediated spread characterize the introduction history of Canada goldenrod in China. The present study harnesses the power of SNP data in deciphering the evolutionary trajectory of invasive plants and paves the way for future studies concerning the invasion mechanism of Canada goldenrod.Despite numerous studies reported in the context of ecology, the introduction history of the infamous invasive plant Canada goldenrod ( As a distinguishing feature of the Anthropocene, biological invasions pose severe threats to local communities and ecosystems ,2,3. ForHowever, there is a genetic paradox that falls out of the scope of direct interactions between invasive and native species. How do alien species persist and subsequently become invasive when they suffer from strong founder effects (bottlenecks) in the meantime ,11,12,13Solidago canadensis L.) is now a notorious invasive weed globally .,42.S. canadensis . Long-ranadensis  43]. It. ItS. cae nature . Recentlerstated ,46. Nevenadensis . Some donadensis ,48. SpecS. canadensis on introduction to different areas. Alexander et al. .,33.S. caogically  and rescequently . The typcrossing ,51,52. Geriments ,28,43. At et al. .Reynoutria japonica Houtt., the Japanese knotweed; Taraxacum officinale G. H. Weber ex Wiggers, the dandelion), the low genetic diversity together with the phenotypic plasticity of S. canadensis in China strongly implies that epigenetic variations could play critical roles in its invasion success [Sporobolus alterniflorus (Loisel.) P.M.Peterson and Saarela) were related to non-coding RNAs’ activities [S. canadensis [S. canadensis. Therefore, we strongly recommend in-depth explorations of epigenetic variations and their underlying molecular mechanisms in future works that aim at decoding the invasion mechanisms of Canada goldenrod in China.Similar to many poster-child invasive plants  to explore the invasion routes of this species in China, we collected one European  and two Japanese  samples as representative individuals . Later, the DNA libraries were pooled, size-selected (300–400 bp) on 1% agarose gels, column-cleaned using PCR purification kits, and amplified for 12 cycles using Phusion™ DNA polymerase . The average fragment size was estimated on a 2100 Bioanalyzer Instrument , and the library quantification was performed using the PicoGreen™ kit . Finally, the pooled libraries were sequenced with the NovaSeq™ 6000 platform to generate paired-end reads of 150 bp . All the sequencing data have been deposited at GenBank under the accession number PRJNA937432.The GBS procedure was conducted following the original protocol ,65. Firsde novo assembly of clean reads and the clustering of tags were performed using the Stacks (version 1.43) pipeline [We conducted the SNP calling with the following steps. First, the raw sequencing data were processed using fastp (version 0.18.0)  to removpipeline . Basicalpipeline . Next, wpipeline . Later, pipeline .S. canadensis individuals with PCA using PLINK (version 1.9) [K) with 5-fold cross-validations.Prior to the subsequent analyses, we tested whether these retained SNPs were selectively neutral using BayeScan (version 2.1) with the default settings . Based oion 1.9) . Meanwhiion 1.9) . Five boS. canadensis individuals with different geographical origins, we reconstructed the individual phylogeny based on the putatively neutral SNPs with both the NJ method and the ML method [To explore the evolutionary affinities among L method . The conL method ,76. The L method ,78. As iL method . Both thS. canadensis (see Results) as follows. First, we obtained the folded site frequency spectrum (SFS) using the SNPs identified above and easySFS “https://github.com/isaacovercast/easySFS” (accessed on 4 September 2022). Then, the effective population size (eN) of the two genetic groups over time was simulated using Stairway Plot 2 (version 2.1.1) [S. canadensis was assumed to be one year [−9 base substitutions per site per generation following Arabidopsis thaliana [eN dynamics were inferred with 100 estimates.We inferred the demographic history of the two distinct genetic groups of n 2.1.1) . The genone year . The mutthaliana . For eacS. canadensis.By exploring genomic SNPs of both the native and invasive Canada goldenrod individuals, our results indicate that multiple introduction events, genetic bottlenecks, potential human-mediated dispersal events, and clonal reproduction all play roles in the invasion history of Canada goldenrod into China. The present study highlights the power of genome-wide SNPs in inferring the evolutionary history of invasive plants and paves the way for future studies regarding the invasion mechanism of


Neither the induction of extrinsic synaptic plasticity nor the blockage of presynaptic activity degrades the lognormal-like distribution but changes its mean, variance and skewness. The skewed distribution develops early in the life of the neuron. Our findings and their computational modelling support the idea that intrinsic synaptic plasticity is sufficient for the generation, while a combination of intrinsic and extrinsic synaptic plasticity maintains lognormal-like distribution of spines.Dendritic spines are crucial for excitatory synaptic transmission as the size of a spine head correlates with the strength of its synapse. The distribution of spine head sizes follows a lognormal-like distribution with more small spines than large ones. We analysed the impact of synaptic activity and plasticity on the spine size distribution in adult-born hippocampal granule cells from rats with induced homo- and heterosynaptic long-term plasticity  The spine head contains the postsynaptic density (PSD) with AMPA and NMDA glutamate receptors . The sizin vivo homo- and heterosynaptic long-term plasticity. In addition, we studied spine size distribution in Munc13 double-knockout (DKO) mouse brain circuits with completely blocked presynaptic activity. We found that homosynaptic long-term potentiation (LTP), with associated spine growth, and heterosynaptic long-term depression (LTD), with associated spine shrinkage, do not disrupt the lognormal-like spine size distribution but rather modulate its parameters. Moreover, we report that the lognormal-like distribution of spine sizes emerges even with entirely blocked synaptic activity.Given the overwhelming evidence for activity-dependent, extrinsic spine dynamics, the conventional view would be to expect spine size distributions to depend heavily on synaptic activity and associated synaptic plasticity  . Those effects were restricted to the stimulated ipsilateral hemisphere and therefore the unstimulated contralateral site served as control. Here, we fitted a lognormal function to the raw data  to test whether it provides a good fit for the size distribution of mushroom spines. In the first round of analyses, this was done collectively for all of the cells of one condition  together  of three different cell ages. These are characterized by gradual onset and development of homo- and heterosynaptic plasticity  , soon aftogether .Figure r2) values of 0.95–0.99 calculated over all cells and animals in each condition. As expected, changes in the shape (peak and width) of the distribution reflected the overall homosynaptic spine enlargement in the ipsilateral MML with respect to the contralateral MML as well as the overall heterosynaptic spine shrinkage in the ipsilateral OML and IML with respect to the contralateral OML and IML. This confirms that after plasticity induction, the number of large spines increased and the number of small spines decreased in the stimulated layer while opposite changes occurred in the adjacent unstimulated layers [In all conditions—in ipsi- and contralateral dentate gyrus (DG), at all cell ages and in all three layers—the lognormal-like distribution matched the data exceptionally well with very high goodness of fit  dentate abGCs showed highly rightward skewed distributions at all cell ages and in all layers with a variety in shapes, peaks and widths, and a lognormal-like spine size distribution was observed in all individual cells.r2)  were available for analysis compared to more mature cells (up to 105 spines), and one outlier was as low as −0.5 . The generally high r2 values indicate a lognormality of the data at the individual cell level, independent of cell age, cell layer or stimulation (hemisphere). Thus, the rightward skewness of the spine size distribution is a robust and synaptic plasticity-independent phenomenon that is already present at an early neuronal age.To quantify the comparison of spine size distributions between ipsilateral (stimulated) DG with induced synaptic plasticity and the contralateral (control) side, we calculated the cell individual goodness of fit (r2)  and skewp < 0.05) in the goodness of fit between the two hemispheres; however, layer and time comparisons show some significant differences , which is an indicator of the width of the distribution and in this case the range of the spine sizes, some significant differences (p < 0.05) were observed . The sigma value for the stimulated ipsilateral MML at 28 dpi significantly increased compared to the contralateral side. This indicates that the shape widened and that there was an increase in bigger spines due to the induction of homosynaptic LTP. There was a significant decrease in the ipsilateral spine sizes in the IML at 21 dpi and the OML at 35 dpi compared to the contralateral side, indicating that the shape narrowed and the number of smaller spines increased due to heterosynaptic LTD. There were also significant differences when comparing layers and time points. Sigma increased significantly ipsilaterally in the MML compared to the IML and OML at both 28 and 35 dpi, mirroring the skewness findings. The induction of plasticity appeared to broaden the spine size distribution, thus increasing sigma, by increasing the number of medium and large spines, which in turn also decreased skewness. Sigma also increased significantly over time in both the MML and OML, again mirroring the previous parameter comparisons.There were no significant differences . Our analyses and comparisons revealed that of the three distributions tested , the lognormal distribution had an advantage over the other two, indicating that it was the best fit for the data .. 2.2in vivo rat abGCs showed a strong independence of the lognormal-like spine size distribution from homosynaptic and heterosynaptic plasticity. This raises the question as to whether synaptic activity in general affects spine size distributions. To assess this, we analysed spines in nerve cells with blocked presynaptic transmitter release.Viewed together, the data from in vitro, div) and two further groups, one where synaptic activity (presynaptic transmitter release) was blocked (DKO group 0) and the corresponding control group (group 1). CA1 PCs possess three different spine types: 22.85 ± 6.01% mushroom spines (mean ± s.d.), 23.73 ± 4.83% thin spines and 51.16 ± 6.62% stubby spines. About 2.26 ± 2.53% were defined as ‘other’ and not included in further analyses.We used a dataset of CA1 pyramidal cell (CA1 PC) spines from organotypic hippocampal cultures obtained from Munc13-1/Munc13-2 DKOs . In thesin vitro (div) and group. In the first step, all cells and spine types were analysed together in each condition. In the second step, spine sizes were analysed at the single-cell level, for all spine types together. Finally, the three different spine types were analysed separately, first for all cells in one condition, then at the individual cell level as well.The data were analysed by different conditions, separated by time r2) showed that the lognormal fit described the spine size distribution very well, in all conditions and for all spines . There were differences in the shape and width of the distribution, but the rightward skewness was preserved even at the individual cell level.Again, like with the abGC data, at the individual cell level, spine sizes in every CA1 PC in both groups followed a lognormal distribution, at each cell culture age (div) that we studied a. There r2 between the groups and different time points (div), for all spines together (b). There were no significant differences between the two groups, only a trend in the blocked activity group towards a slightly reduced r2. Comparing the time points, there was no significant difference (p < 0.05) within the blocked activity group. In the control group there was a significant increase (p < 0.01) in the goodness of fit from day 7 to day 21 in vitro, indicating that the lognormal distribution described the data better for more mature slice cultures. A similar trend was seen in the blocked activity group, but without reaching statistical significance. This shows that there is a lognormal-like distribution of spine sizes irrespective of whether the presynaptic transmitter release is blocked or not.We compared the cell individual goodness of fit parameter together b. There A closer analysis of the spine size data revealed that the skewness values were typically above 0 , confirming that the spine sizes were not symmetrically distributed. Comparing the different conditions revealed no significant differences between different time points (cell culture age in div) or between the groups . Within each group, the skewness increased slightly but not significantly over time. The sigma comparison revealed no significant differences in the width of the distribution and the range of spine sizes . A trend was seen at 21 div, where the blocked presynaptic transmitter release group has a slightly increased sigma compared to the control group, indicating that the range of spine sizes increases.c). The thin spine population showed lower r2 values than the mushroom and stubby spine population. In line with this, thin spines also showed the lowest score for skewness . At the individual cell level, mushroom spines in each cell followed a lognormal distribution (a). The group with blocked presynaptic transmitter release showed a similar goodness of fit as the control group. There was a significant increase of r2 over time (p < 0.05) in the control group (b). Mushroom spines had a slightly higher skewness in the control group, but the difference was not significant , and they showed the lowest sigma value in comparison to the other two spine types, indicating a smaller range of sizes . Analyses of thin and stubby spines at the individual cell level are shown in electronic supplementary material, figures S9 and S10.Next, we tested whether a deeper analysis of spine type subgroups  would show inter- or intra-group differences c. The thribution a. The grol group b. MushroAs with the abGC dataset, we conducted the AIC analysis and comparison for mushroom spines, to check whether the lognormal distribution was the best fit out of three skewed distributions. The lognormal distribution had an advantage over the other two in both experimental groups and at all cell ages . These findings indicate that a lognormal-like spine size distribution is preserved even when synaptic activity is blocked. Intriguingly, the sizes of thin spines showed a less good fit to a lognormal distribution.a), at all cell ages and in both experimental groups. For mushroom spines, there was a clear cutoff to the left (b), whereas for thin spines, there was a cutoff to the right of the peak (c). This was due to the method by which spines were categorized by size into thin or mushroom spines. Stubby spines showed the best Gaussian fit, indicating that the stubby spines were distributed strictly lognormally. The bias to the left might be an artefact of the method used to detect and measure the spines. Overall, the findings indicate, similar to the abGC dataset, that spines were lognormal-like distributed independently of synaptic activity.Again, as with the abGC spine dataset, a final analysis of spine data from Munc13 DKOs and control littermates focused on the lognormal-like distributions of spine sizes in more detail by employing the normal (Gaussian) fits of logarithmically transformed data. The logarithm of lognormal-like spine size data should lead to a normal-like distribution. Taking the logarithm of the data and fitting a Gaussian distribution to the transformed data revealed for all spine types that the distribution had a bias towards the left side of the peak, meaning there was an overabundance of small spines in the samples a, at allthe left b, whereathe peak c. This w. 2.3Many computational models of synaptic dynamics presume that the distribution of synaptic weights arises predominantly due to activity-dependent (extrinsic) synaptic plasticity –41. Theret al. [a). The distribution became lognormal over time, showing that multiplicative noise is indeed sufficient to generate lognormal distributions [Lognormal distributions are typically preserved when applying multiplicative stochastic operations. Combined intrinsic and extrinsic synaptic plasticity might represent a biological implementation of such multiplicative changes of synaptic weights. Thus, to investigate the influence of intrinsic and extrinsic plasticity on the lognormal distribution of spine sizes, we developed a minimal computational model, that was able to account for the experimental data. Extrinsic synaptic plasticity was modelled as Hebbian activity-dependent spike-timing-dependent plasticity (STDP) consisting of additive LTP and multiplicative LTD. Intrinsic synaptic plasticity was based on activity-independent fluctuations modelled as a combination of additive  and multet al. . The synibutions .Figure in silico how different modelling parameters could contribute to the range of different lognormal-like distributions produced by different experimental protocols. To do this, we combined our model with simulation-based inference [et al. [Figure 8b plots the marginal single and pairwise distributions of parameters associated with intrinsic (top right) and extrinsic (bottom left) synaptic plasticity. Interestingly, the parameters did not typically show strong pairwise dependencies. There were two exceptions to this: the proportion of spines receiving external stimulation was positively correlated with the strength of multiplicative noise and the strengths of potentiation and depression under STDP were negatively correlated. Running simulations with the maximum a posteriori parameters produced lognormal-like distributions of spine sizes that closely matched the experimental data (dashed versus solid lines in figure 8c).Next, we explored nference ,45, whic [et al. ) datasetet al. [As shown by Rossum et al. , additivIn sum, and in agreement with the abGC spine data, combined extrinsic and intrinsic plasticity can maintain the skewed distributions in the presence of correlated LTP-inducing synaptic activation. Furthermore, in line with Munc13 DKO spine size data, our modelling shows that extrinsic plasticity is not necessary for the generation of skewed spine size distributions and that intrinsic plasticity alone is sufficient.. 3in vivo and in organotypically cultured CA1 PCs. In dentate abGCs, a lognormal-like distribution of spine sizes was present at all studied cell ages, irrespective of homo- or heterosynaptic long-term plasticity induction. Most strikingly, in CA1 PCs, spine size distributions were skewed and lognormal-like even in Munc13 DKOs, in which presynaptic transmitter release is entirely blocked. These data show that the lognormal-like distribution of spine sizes is activity and plasticity independent. The skewness of spine size distributions develops early in cell age without extrinsic influences related to presynaptic transmitter release and therefore seems to be determined intrinsically. However, we cannot exclude potential extrinsic influences that are not related to presynaptic transmitter release, such as trophic factors or adhesion proteins.Excitatory postsynaptic potential sizes and spine head sizes have lognormal-like distributions –7,46,47.. 3.1Intriguingly, we detected robust lognormal-like distributions of spine sizes in young newborn GCs that had experienced homo- and heterosynaptic plasticity. This is in agreement with previous studies showing unchanged spine size, spine type distribution and spine numbers at 30 min and 2 h after homosynaptic LTP in dentate GCs and CA1 PCs, respectively ,48. Togeet al. [A recent study on cultured primary cortical neurons  providedet al.  who obseet al. [2+ channels in synapses in cultured hippocampal neurons did not impair synapse structure [in vivo-like synapse diversity emerges in neurons in chronically silenced organotypic cultures [An important finding of Yasumatsu et al.  was thatet al. ,55,56. Tet al. . Howeveret al. ,36,58,59et al. . In lineet al. . Consistet al. , althouget al. ,36. Eventructure . All thecultures –65 (but cultures ).. 3.2The finding that synaptic activity is not necessary for the skewed spine size and synapse weight distribution is unexpected in the context of several prominent theoretical models. Many computational models of synaptic weight dynamics assume that realistic weight distributions emerge due to a combination of Hebbian and non-Hebbian activity-dependent synaptic plasticity. For example, spiking network simulations led to the suggestion that a highly skewed distribution of synaptic weights appears due to network self-organization , by the Similarly, other computational studies  used an Indeed, our plasticity model, using a Kesten process as multiplicative noise for implementing intrinsic synaptic fluctuations , generatActivity-independent computational models based on stochastic multiplicative shrinkage and additive growth of synapses  successfully account for the emergence of lognormal-like synaptic strength distributions  . In thiet al. . ConcurrThe dataset comprised spine data for individual cells in (i) the three different layers , (ii) at the three different cell ages  and (iii) from both the contra- and ipsilateral hemisphere. The contralateral side without the induction of synaptic plasticity  was incl. 4.2in vitro, div). The dataset was split into apical and basal dendrites, and in three spine subgroups .The blocked presynaptic activity dataset contained spine data from CA1 PCs in hippocampal organotypic slices from Munc13 DKO mice . In thes. 4.3The spine head area was used to analyse the distribution of spine sizes. All analyses were done with Matlab software using a custom-written script. We analysed cells individually as well as collectively by combining and averaging all cells for one condition.µ) and standard deviation (σ) of the spine sizes' natural logarithms were calculated. They functioned as a starting point for the algorithm implemented to fit the lognormal distribution over the spine data. Because the data spans multiple scales, the raw size data was normalized. For the normalization, the integral of the spine size distribution was calculated, and the absolute number of spines in each size bin was divided by that integral.From the raw data, the mean  as the dependent and x as the independent variable. The lognormal distribution was then fitted to the normalized data. With the fit function, plots and respective goodness of fit statistics for each of the fits were generated. The goodness-of-fit statistics give an indication of how well the respective fit or model fitted the data. The r-square (r2) value was used in all further analysis.The next step in the analysis was to build the lognormal function that would be fitted to the normalized data. For this, a customized fitting procedure had to be derived for which the probability density function of the lognormal distribution was used:a, b and c are the parameters, f(x) the dependent and x the independent variable. With the fit function fitting a Gaussian distribution to the logarithm of the data, new plots were generated that compared the logarithmic data with the fit.The key characteristic of a lognormal distribution is that the logarithm of the random variable will be normally distributed. Thus, taking the logarithm of the spine data is another good method to check if the data is distributed lognormally like. A similar fitting procedure as above was applied. The data was first transformed by taking the logarithm of the spine sizes, then a Gaussian distribution was fitted to the logarithmic data:r2 for each condition was compared, using statistical non-parametric tests. r2, or the coefficient of determination, is used to determine how well the variation in f(x) (the dependent variable) can be explained by x (the independent variable(s)). Essentially, it provides a measure of how well the observed outcomes can be replicated by a model. In our case, how well the applied fits describe the spine size data. The value is less than or equal to 1, with 1 being a perfect fit of the model. The coefficient of determination is calculated as follows:To determine differences between the different layers, cell ages or experimental and control groups, the given To support the findings of the goodness of fit comparisons, we also looked at the skewness (asymmetry around the mean) of the data and the width of the distribution . More information about these comparisons can be found in the electronic supplementary material, methods. Additionally, we conducted a model fit comparison for which we fit two additional skewed distributions (gamma and Weibull) to the data and then used the AIC to compare all three distribution fits. This was done to see whether or not the lognormal distribution was the best fit for the data. More information about the AIC calculations and comparisons can be found in the electronic supplementary material, Methods.. 4.4r2 for a lognormal and the skewness between the different conditions in both datasets. The distribution analysis showed a lognormal distribution in the spine data, so only non-parametric tests were applied.Several statistical tests were applied to test for statistical differences of U-test or rank-sum test. To compare between the three different dentate layers (IML/MML/OML), we used Friedman's test. Since all three layer-samples in one cell come from the same cell, it was a repeated measurement of multiple variables. The Kruskal–Wallis test was used for the comparison between different cell ages or cell culture ages. If significant differences (p < 0.05) were found in one sample, both for the time comparison and the layer comparison, post hoc paired rank sum tests were conducted. A Bonferroni-Holm correction for multiple tests was applied to test for specific significant differences in the sample.For the hemisphere  comparison in the rat dentate abGC spine data and the group comparison (Munc13 DKO group with blocked presynaptic release versus control group) in the mouse CA1 PC spine data, we used a Mann–Whitney . 4.5et al. [t. Negative values of Δt, where the presynaptic event precedes the postsynaptic event, lead to potentiation w → pw and positive values lead to depression w → dw.w is the synaptic weight, pc is the weight of potentiation (pc = 0.007 pS), dc is the weight of depression (dc = 0.003) and To further investigate the influence of plasticity on the lognormal-like distribution of synaptic weights, we developed a simple model based on van Rossum et al. . The modThe model consists of a population of 1000 neurons, and the synapses that receive inputs are stimulated either in a Poisson manner or with periodic spiking, at different input frequencies depending on the simulation condition.To see whether or not multiplicative noise (i.e. intrinsic mechanisms) is enough to generate a lognormal distribution, a uniform distribution was fed into the model as an initial distribution and the synaptic weights were measured throughout the simulation. The model was then used to replicate the three experimental datasets. First, the HFS that induced LTP in the stimulated spines was recreated with the model, using periodic spiking as input at a 200 Hz frequency. This was compared with a control simulation that received 10 Hz Poisson input. Finally, a silent simulation without inputs was considered. A lognormal distribution was fitted to the synaptic weight data in the same way as previously described. Additionally, the logarithm was taken of the data and a Gaussian distribution was fitted to the transformed data.. 4.6Simulation-based inference with sequential neural posterior estimation ,79 was u


Erysiphe necator, the fungus causing grapevine powdery mildew infect grapevine in Europe, yet the processes sustaining stable genetic differences between those groups are less understood. Genotyping of over 2000 field samples from six wine regions in Hungary collected between 2017 and 2019 was conducted to reveal E. necator genotypes and their possible differentiation. The demethylase inhibitor (DMI) fungicide resistance marker A495T was detected in all wine regions, in 16% of the samples. Its occurrence differed significantly among wine regions and grape cultivars, and sampling years, but it did not differ between DMI-treated and untreated fields. Multilocus sequence analyses of field samples and 59 in vitro maintained isolates revealed significant genetic differences among populations from distinct wine regions. We identified 14 E. necator genotypes, of which eight were previously unknown. In contrast to the previous concept of A and B groups, European E. necator populations should be considered genetically more complex. Isolation by geographic distance, growing season, and host variety influence the genetic structuring of E. necator, which should be considered both during diagnoses and when effective treatments are planned.Genetically distinct groups of  Co-infection, that is, infection of a host by different pathogens5 or by different genotypes of the same pathogenic species7 can also influence pathogen diversity5. Different species of powdery mildew (PM) fungi, belonging to the Erysiphaceae family of the ascomycetes, may co-infect the same host individuals11, and different genotypes of the same PM species may co-infect the same host12. An example is Erysiphe necator, the causal agent of grapevine powdery mildew (GPM), as its distinct genotypes co-infect grapevines and cause economically significant epidemics worldwide17.Genetic diversity of plant pathogen populations is affected by several factors, such as reproductionErysiphe necator was hypothesized to originate in North America18, however, the Asian origin of some genotypes of the species was also assumed19. The fungus is introduced to Europe, where two distinct genetic groups, designated as A and B, were identified first based on DNA-fingerprint techniques21. Afterwards single nucleotide polymorphisms (SNPs) were assigned to those genotypes, in sequences of the β-tubulin (TUB2)22, translation elongation factor 1-a (EF1-a)24 and eburicol 14α-demethylase (CYP51) genes21, and also in the sequences of the nrDNA internal transcribed spacer (ITS)21 and intergenic spacer (IGS) regions18.E. necator groups in European vineyards, studies concluded that these two overwinter in different ways26. Group A was reported to overwinter as dormant mycelium within buds27. Such infected buds may develop characteristic ‘flag shoots’ to restart the asexual cycle of the fungus in spring28. It was also concluded that during winter, group B persists as sexual spores, i.e., ascospores, in chasmothecia, the sexual fruiting bodies of E. necator29. The proposed association of genetic groups with distinct symptoms, however, was queried by studies reporting that both groups may cause flag shoots30.The biological differences of groups A and B are not well understood. Soon after the discovery of the two 31. However, samplings from different populations showed that sexual reproduction could happen in group A32, and, mating-type assays revealed that group A produced chasmothecia and viable ascospores in laboratory conditions32. DNA markers of group A in field-collected chasmothecia were also detected, suggesting that sexual reproduction is also possible in natural conditions16.Some studies found that the reproduction of group A is mainly clonal33. In Hungary, however, group B was detected throughout the vegetation period, and group A was also reported from samples collected later in the growing season, and it was mostly present together with group B17. Thus, these results did not support the hypothesis of temporal separation of groups A and B.Some studies revealed that group A spread mainly in spring, at the beginning of the season, while group B caused epidemics later in the seasonE. necator, may affect the niche partitioning of GPM groups as hypothesized34. DMI fungicides  inhibit CYP51, a key enzyme of the fungal sterol biosynthetic pathway, which catalyzes the biosynthesis of ergosterol, a fundamental membrane component of many fungi35. The intensive use of DMIs may lead to the spread of fungicide resistance in GPM populations36. A marker for DMI resistance is an A to T nucleotide substitution in position 495 (A495T) in the CYP51 gene of E. necator37. This mutation results in an amino acid substitution at position 136 (known as Y136F in E. necator)15. Several studies have shown that the presence of the mutation correlated with high levels of DMI resistance38. Significant correlation of an other nucleotide substitution, A1119C in CYP51 with overexpression of the CYP51 enzyme and azole resistance was found in GPM samples from United States and Chile36. The correlation of this latter substitution with genetic groups A and B has not been investigated.The demethylation inhibitor (DMI) fungicides, commonly applied against 19. The IL group was present from spring to late autumn in the sampled vineyards. Groups A, B40 and IL19 differed from each other in terms of latency period, the size of lesions caused, and spore production based on laboratory experiments.In addition to the A and B groups, in Israel a third well-defined group, IL, was found based on microsatellite markers and multi-locus sequencingE. necator in Hungarian vineyards, (ii) determine whether there is any genetic differentiation in E. necator populations according to season, wine regions, and grape cultivars, (iii) determine the frequency of the DMI-resistance marker A495T, in relation to fungicide treatment, GPM genetic groups and sampling sites; and (iv) investigate the co-infection on single leaves.It is not understood if temporal succession, differences in fungicide resistance, variability in infection behavior, and other factors such as grapevine cultivars, or co-infection, are responsible for genetic differences between GPM genetic groups. We carried out an intensive sampling to (i) assess the genetic diversity of TUB2 nucleotide positions 79 and 368; EF1-α positions 33, 336 and 420 and CYP51 position 495. All ITS and IGS sequences gained from chasmothecia were identical except for three samples differing in ITS position 48 and IGS position 108 , 485 (which include 59 isolates and 426 field samples) had none, while 36 field samples had one ambiguous nucleotide position. We identified altogether 14 genotypes , and these were detected only at the end of the seasons. However, considering CYP51 sequences, all isolates except for one (from Budapest) belonged to genotype B  of the 14 genotypes were found in the conidial isolates  did not deviate significantly from zero (p = 0.69). The analysis of the population structure of GPM with STRUCTURE yielded well-defined clusters at K values up to 5  genetic differences among the populations which were distinguished based on results of STRUCTURE  were in linkage equilibrium.According to genetic differentiation analyzes, there were significant .The genetic diversity of populations was in the range from 0.565 to 0.683, with the lowest being in Neszmély and the highest in Badacsony (Supplementary Table p = 0.001) from those of collected in 2018 and 2019, with the measure of differentiation (PhiPT) being ≤ 0.014. Samples from 2018 and 2019 did not show significant population differentiation (p = 0.29). Genetic composition differed significantly between summer and autumn samples (p = 0.001) with a PhiPT of 0.129, and also between cultivars Kékfrankos and Chardonnay (p = 0.001), with a PhiPT of 0.024.The genetic composition of samples collected in 2017 differed significantly , and only group A was present in 82 (~ 7.5%) chasmothecia. Group A and group B were present together in 136 chasmothecia (~ 12.5%). Thus, group A was mostly present together with group B in chasmothecia: in 62% of chasmothecia (136 out of 218) in which group A was detected, group B was also present. Among 311 mycelial samples, group B was present in 282 and group A in 53 samples, including 24 in which both groups were found. The Chi-squared test did not show a significant correlation of the chasmothecial origin of a given sample and its assignation to either genetic group (p = 0.338).Based on nucleotide C or T present in position 79 in TUB2 genotypes and seasons . Samples that had been treated in the previous year but not in the sampling year did not differ significantly in the probability of A495T occurrence from either treated or untreated samples , except for a single isolate, which had a G-to-A change in position 1170  carried more than two genotypes. The minimum number of genotypes present on a single leaf varied generally between 2 and 6. The highest number of genotypes found on one leaf sample was 10 . The proportion of chasmothecia originating from the same leaf and carrying the marker varied from 0 to 87.5%  could be unequivocally categorized as group B genotype. Out of the remaining nine genotypes we found, seven can be rated as group B genotypes according to SNPs in ITS and IGS (H5-H11), five according to SNPs in TUB2 , and eight according to SNPs in EF1-α . However, according to the SNPs in CYP51, all but one genotypes belong to group B. Based on this, our data strengthen the recommendation24 that discrimination of E. necator genetic groups should be based on several genes.In general, majority of the 41. Alternatively, or additionally, some of the genotypes present in Hungary may be introduced to the country from unknown provenances.As some genotypes contain SNPs characteristics to both groups A and B, we suppose that some of our newly described genotypes may be recombinants. These either result from local sexual crosses or parasexual recombinationE. necator populations40, we frequently (~ 20%) detected genotypes belonging to group A based on position 79 of TUB2 in chasmothecia, the sexual fruiting bodies. Neither group A nor B was detected more frequently in chasmothecia than in mycelial samples, suggesting lack of correlation between sexual reproduction and A and B genetic groups. The fact that we did not detect linkage disequilibrium in our dataset means that sexual reproduction of E. necator takes place regularly. Our data indicate that group A is also reproducing sexually, similarly to other results32.Although sexual recombination was found to be less common in group A in some E. necator groups A and B mate and reproduce sexually. We assumed this previously16 based on a limited number of field samples, considering that the two groups were found to be interfertile, as found in laboratory experiments33.Based on our data on the presence of group A genotypes in chasmothecia, the revealed diversity in Hungary, and the new genotypes, some of which we consider recombinants, we deduce that E. necator in Hungary is exceptionally high in comparison to other regions of the world, surpassed only by the diversity in the USA, the proposed source of origin of E. necator42. It is possible that the previously unknown haplotypes detected from Hungary during the current study simply have not been detected in the USA. However, genetic differentiation between samples from Hungary and USA could be detected. The high number of genotypes detected in Hungary but not elsewhere, and because of the genetic differentiation between populations in Hungary and the USA, our results might support the hypothesis of a possible non-North American (presumably Asian) origin of some E. necator genotypes, following the reasoning of Gur et al.19.The revealed genetic diversity of E. necator among wine regions separated by a few hundred kilometers, and the genetic distance was higher with geographical distance. In addition, population structure analysis also showed genetic composition characteristic to the sampled wine regions. These results point to the isolation by geographical distance. However, no genetic differences based on geography were detected in Israel in E. necator populations located within a few hundred kilometers from each other19. On the other hand, results obtained on a smaller sampling scale (approximately 30–100 m) in North American vineyards43 were similar to our findings.We identified genetic structure in the Hungarian populations of 19. Furthermore, we detected higher differentiation between sampling seasons than between cultivars, similar to what was detected in Israel19. Thus, even if the temporal isolation hypothesis33 was refuted by more recent samplings17, our data indicate that the genetic composition of E. necator populations of Hungarian wine regions mainly depends on the growing season, and to a lesser extent also on grapevine variety.We found significant, although low genetic differentiation between cultivars, Kékfrankos and Chardonnay. Similarly, group IL was predominant on the wild and traditional vines, and on the table grapes, while group B was dominant on wine grapes in Israel, showing significant genetic differentiation according to host typesE. necator isolates belonging to group A were shown to be more sensitive to triadimenol, a DMI-type fungicide34. Thus, if resistance in those experiments was conferred by A495T, group B should possess the resistance marker more frequently. Contrarily, we did not detect significant difference in the occurrence of A495T between groups A and B. However, different DMI fungicides do not show complete cross-resistance38. In addition, the A495T mutation is not the solely possible mechanism of the DMI resistance, there are alternative mechanisms36, which could also explain the seemingly contrasting results.Mutation A1119C was detected neither in conidial isolates, nor in field samples (data not shown), therefore, the study focused on the A495T marker. We investigated if the occurrence of A495T mutation is associated with group B. In a small scale study 44. In our study we found no significant effect of the fungicide treatments on the frequency of A495T resistance marker. This apparent contradiction may be explained by the use of fungicide mixes by growers, potentially lowering the selection pressure on the resistance to DMI-type fungicides45. Alternatively, or additionally, other resistance mechanisms of DMI resistance could explain the apparent lack of treatment effect on A495T marker frequency.Fungicide treatments impose selection pressure on the populations, in which the resistance levels may increaseE. necator is airborne, the spread of resistant genotypes containing A495T from the surrounding areas can explain the presence of the marker in untreated plots, as the migration of such genotypes into sensitive populations is possible46. For example, some strains E. necator47 and those of Podosphaera xanthii48, the fungus causing cucurbit PM, collected from untreated plants were shown to be resistant. Alternatively, if fungicide treatments were halted not long before our samplings, the strains carrying the marker can also be the descendants of such strains from earlier years.As E. necator in France49, and to succinate dehydrogenase inhibitors in the USA50 were also proven. These patterns likely reflect the local differences in disease pressures due to geographic location and climate51.CWRC, however, did influence the presence of A495T. This finding is in line with the results of the population genetic analyses on grapevine variety and geographic distance effecting genetic composition of the pathogen populations. Similarly, regional differences of the marker frequencies of resistance to DMI and to quinone outside inhibitors of E. necator has a bipolar-heterothallic mating system20, the presence of chasmothecia is in itself a proof for the co-infection by two different genotypes of the pathogen on the sample. In this respect, most of our samples were co-infected. This result, however, is not specific to chasmothecial sample type, as when only mycelia of E. necator were sampled, a similarly high level of mixed infection was detected17.As 15, as well as by co-infection, CYP51 sequences we not considered in the co-infection analysis. Because of this strict assumption, the number of co-infecting genotypes is possibly underestimated. Altogether, co-infection by E. necator is common, as it is in Podosphaera plantaginis populations causing PM on Plantago lanceolata52 and in E. alphitoides infecting oak trees53.During our survey we found that most single-leaf samples were co-infected by at least three, and up to ten different GPM genotypes. Remarkably, in some cases, the A495T marker was present in some, but not in all chasmothecia originating from the same leaf. As the presence of A495T marker together with the wild type allele in the sample can be caused by the presence of both alleles in a single isolateP. plantaginis form co-infections more likely than less aggressive strains54. The aggressiveness of the detected E. necator genotypes is unknown, but we assume that the more widespread genotypes or strains may be more aggressive and/or more successful in spreading and colonization, as it was found for certain E. necator genotypes39. However, genotypes characterized by differences in their aggressivity may be maintained by co-infection5. We cannot exclude that the high-level of co-infection in E. necator populations helps in maintaining genetic diversity of the pathogen.A phenotyping study revealed that more aggressive strains of 17 we conclude that several genotypes of E. necator can be differentiated in Europe, rather than solely groups A and B. Thus, the binary genetic group concept should be abandoned. The fundamental role of the locality in the genetic structure of E. necator populations and on the distribution of the resistance-associated marker stress the importance of the local monitoring to understand the evolution and spread of this important pathogen.Taking together our data and recent resultsGPM samples were collected from 2017 to 2019 in six wine regions  of Hungary, located approximately 70–320 km from each other. Sampling sites, 15 vineyards altogether, were chosen to represent different grapevine cultivars, training systems, and conventional and organic farming . SamplesAll actions, including sampling and experiments during this study complied with all institutional, national, and international guidelines and legislation. Sampling was always done with the knowledge, consent and permission of the owners and/or growers of vineyards.E. necator strains. Young shoots were grown from grape cuttings (cv. ‘Chardonnay’ and ‘Kékfrankos’) in the laboratory under daily illumination. Grapevine stems, 5–10 cm long, were cut and surface sterilized in 1% calcium hypochlorite for 15 min, rinsed three times with sterile deionized water, and air-dried in a sterile laminar flow hood. Then stems were placed into plastic containers with Murashige and Skoog (MS) medium , solidified with 6.5 g/l Phyto agar (Duchefa Biochemie); supplemented with 500 µg/l (2.46 µM) indol-3-butyric acid  to enhance the formation of roots. After two months, leaves, at least two cm diameter, were cut from the in vitro plantlets and placed in Petri dishes containing MS media without IBA. Plantlet stems were cut into two or three node fragments and were placed in new containers for a continuous supply of plant material.Plant tissue culture technique was used to produce in vitro grape plants with aseptic and susceptible grape leaves for isolation of Erysiphe necator was isolated from field-collected PM colonies. To obtain isolates, and later, during the passages, a single PM conidium or conidial pseudochain was picked using a glass needle under a dissecting microscope in a laminar flow hood, and placed on the upper surface of an in vitro grapevine leaf in a Petri dish. Inoculated leaves were incubated for 14 days at 22 °C under 12-h/day illumination. Colonies were treated as single-conidial isolates after two passages. We obtained 59 isolates  as described previously16. For DNA extraction from samples without chasmothecia , fungal material was collected by touching an ~ 1 cm2 piece of office cellotape (Henkel Pritt) to the surface of the infected leaves and berries. The tape was boiled in 100 µl of TE buffer for 10 min in a 1.5 ml centrifuge tube, then 1 μl of the solution was used as the target in PCR amplifications.Chasmothecia are considered the smallest discrete units of PM fungi that can be easily handled, and provide sufficient amount of DNA for molecular biology analysesTo study the possible co-occurrence of different genotypes on single leaves, additional chasmothecia were collected from distinct regions of the leaf surfaces. These 321 leaves were selected based on carrying abundant chasmothecia widespread on the leaf surface. From these leaves, additional chasmothecia were sampled and used for DNA extraction, in addition to the single chasmothecium sampled first (see above). From 266 leaves, one additional; from 25 leaves, two; and from eight leaves, 3–5 additional chasmothecia were sampled, and from 22 leaves, almost fully covered with powdery mildew, seven or more additional chasmothecia were collected. This sampling resulted in altogether 590 further single chasmothecial DNA samples.TUB2 were carried out as described previously16. EF1-618 and EF1-5alt (GATCGCAACAATGAGCTGCTT) primers were used for PCR-amplification and sequencing for EF1-α. EF1-5alt was designed by aligning the sequence of EF1-518 to EF1-α reference sequences downloaded from whole genome sequence data23 using MEGA755, and adjusting the primer sequence to avoid potential mismatches. TUB2 and EF1-α loci were PCR-amplified and sequenced from 1963 and 1835 samples, respectively. ITS and IGS regions were sequenced from a subset of samples  as these loci showed low variability (see below). For detection of the A495T nucleotide substitution in the CYP51 gene, direct sequencing of the 5’ region of the gene encompassing position 495 was applied for non-chasmothecial samples, and real-time PCR assay was used for genotyping chasmothecial samples; both as described16. The occurrence of the A495T marker was investigated in 2065 samples in total. The 3’ region of CYP51 was sequenced from 138 single chasmothecial samples and all 59 isolates  using the same PCR protocol16 with primers EnCYP1055F and EnCYP1752R36.Multiple loci were amplified and sequenced from the extracted DNA. PCR-amplifications and sequencing of ITS, IGS and 19 were checked for SNPs. Possible variations of CYP51 were also investigated, especially at positions 143, 608, 1170 and 1620, as these sites are characteristic to the genetic groups A and B21, and positions 495 and 1119, which were reported as markers of DMI resistance36. GenBank accessions GQ255473 (ITS), GQ255476 (IGS), GQ255475 (TUB2), GQ255471 (EF1-α), and U83840 (CYP51) were used as references. Nucleotide positions showing double peaks on chromatograms were considered to contain both alleles in the DNA samples57. Representative sequences obtained in the present study were deposited in NCBI GenBank under accession numbers OQ709801-OQ709802, OQ709882 and OQ723652-OQ723677.Electrophoregrams were processed and individually checked using the CodonCode Aligner 8.0.2 . The four sequenced lociCYP51 sequence results were manually converted into presence-absence data, based on the nucleotide position 495 of CYP51 on the chromatograms. The final complete dataset  are needed for chasmothecia to form, even if they show no differences in the sequenced loci. Thus, altogether 983 entries (including 924 data from 462 field samples and 59 from isolates (single entries each)) were considered for defining genotypes and for assigning samples to the defined genotypes. Sequence data from isolates were only included in Dataset B.The real-time PCR assay resulted in presence-absence data. TUB2 and EF1-α could be fully sequenced (irrespective of ITS and IGS), and in which ambiguous nucleotide positions were also treated as above, was used for STRUCTURE analysis (see below) and for visualizing the geographical and temporal distribution of samples.A third dataset with 2108 entries from 1054 samples (Dataset C), which contained field samples of which TUB and TEF sequence data were also omitted. CYP51 was also omitted as it is under selection pressure by fungicide treatments23. In the samples where one ambiguous nucleotide position was found, genotype was resolved as above. Data of samples not showing any ambiguous positions were duplicated, as above. The final Dataset D contained 1694 samples (3388 entries).To create the dataset for other population genetic analyses (Dataset D), Bükk wine region, represented with small sample size, from only one small vineyard from unknown grapevine varieties, was excluded. Samples without any CYP51 was omitted as it is known that it may be present with more alleles in a single genome23.Data from leaves with more than one chasmothecium sampled, were used to analyze the co-infection level on single leaves, by comparing sequences obtained from different chasmothecia from the same leaves. For this, samples representing double peaks in more than one SNP positions were also included to calculate the minimal number of genotypes present on single leaves. In these analyses, TUB2 and EF1-α. Network was reconstructed with PopART v. 1.759 using the TCS method60. In the first analysis genotype frequency data from Hungary were included. In a second analysis, presence-absence data in Hungary and other locations of the world 24 were included.To infer and visualize the genetic similarities of the genotypes detected, a multilocus haplotype network was created using 37 variable nucleotide positions of ITS, IGS, 61 was used. Association indexes  were estimated from the complete dataset. Genotypic diversity (a measure of the probability that two randomly selected samples are of different genotype), the number of different genotypes detected, and the frequency of the most frequent genotype were also calculated. “Fix missing data during randomizations” option of the software was in effect. To test statistical significance, 1000 random permutations were run and actual data were compared to the randomized dataset to determine p-values.To assess deviations from random mating in the whole sampled population, Multilocus v1.3bAs population structure was identified in the dataset (see below), the detected populations were also characterized separately. For this, five individual populations were introduced and population indexes were calculated using Multilocus v1.3b for each population as above.62 was used to determine population structure. Five independent analyzes were carried out, from K (number of clusters) = 2–7, with admixture models and 250,000 Markov Chain-Monte Carlo (MCMC) iterations, after a burn-in of 100,000 steps. Population structure was then displayed graphically with DISTRUCT v1.163. We used the Evanno method64, via the STRUCTURE Harvester website (http://taylor0.biology.ucla.edu/structureHarvester/)65, to identify the K value corresponding to the most supported structure.The Bayesian clustering program STRUCTURE 2.3.466 with GenAlEx v6.567. PhiPT (ФPT) value, the measure of genetic differentiation was determined (i) among distinct subpopulations shown by STRUCTURE, (ii) among samples collected in three sampling years, and (iii) between samples collected in two sampling seasons, late spring–summer, and autumn. We also analyzed (iv) the possible effects of grapevine cultivar on the genetic composition. For this, data from samples from cultivars Kékfrankos (n = 906) and Chardonnay (n = 180), originating from Eger and Szekszárd were involved; these two cultivars were thoroughly sampled in these two regions. The sample sizes in this analysis were: 84 for Chardonnay samples originating from Eger and 96 from Szekszárd; 488 for Kékfrankos samples from Eger and 418 from Szekszárd.To analyze the partition of genetic variation within and among populations, we used Analysis of Molecular Variance (AMOVA)Significant genetic differentiation was determined using comparisons to 999 random permutations of data with GenAlEx v6.5. Distance Calculation was set to “Haploid”; missing data were coded as zeros and “Interpolate Missing” function was in effect, except for analysis (iii) where ITS sequence data were insufficient for interpolation. Other options were as defaults.E. necator samples, Mantel test was applied43 on a geographical distance (in kilometers) matrix compiled from Google Maps data and a genetic similarity matrix. The latter was created using MEGA755 by calculating mean pairwise nucleotide differences between populations in each combination. For gaps and missing data, pairwise deletion was in effect. The observed data were compared to 999 randomly permuted datasets for frequency distribution and determination of p-value in the Mantel test.To test correlation between genetic and physical distances of the collected TUB2  and the sample type (chasmothecium or mycelium). The calculation was conducted in R v4.0.368.To infer if groups A and B significantly differ in the frequency of forming chasmothecia, a Chi-squared test was conducted with the data on the sample belonging to group A or B, based on TUB2, traditionally associated with genetic groups A and B, with the presence of A495T marker was investigated. For this, all samples diagnosed positive with the real-time PCR method, and all samples found to carry the A495T mutation based on sequencing were included. Treatment history was recorded as a three-category factor: untreated; treated in the previous year(s) and in the year of sampling; and treated in the previous year(s), but not in the year of sampling.A495T marker presence-absence data were used to analyze the effects of (i) wine region, cultivar and DMI fungicide treatment, (ii) season and (iii) year of collection on the probability of the presence of A495T mutation. Furthermore, (iv) the possible correlation of SNP present at nucleotide position 79 of 2 tests: p < 0.001), we combined these three variables  into a single categorical factor to avoid multi-collinearity among the explanatory variables in the model. We included this combined categorical factor, season (summer or autumn), the two-way interaction of the latter two variables, TUB2 genotype , and year as explanatory variables (fixed factors). In the model we did not include sample type (i.e. whether the sample originated from mycelia or chasmothecia), because including it would have resulted in multi-collinearity (variance inflation factor > 9). However, it is unlikely that ignoring sample type would have biased the results, because the relative frequency of TUB2 genotypes was similar between the two sample types (see Results). To handle separation in the binomial model (i.e. lack of variance in A495T occurrence in certain explanatory categories), we ran the analysis using the median-bias reducing score adjustments69, as implemented in the brglm2 package of R v4.0.368. To test the effects of the explanatory variables, we calculated linear contrasts from the model’s estimates, using the emmeans package of R, as follows. First, we tested whether the effect of treatment differed between seasons. To this end, we estimated the treatment effects (i.e. pairwise differences among the three treatment categories) separately for each season, and we compared the 84% confidence interval (CI) of each treatment effect between summer and autumn. The lack of overlap between two 84% CIs indicates a significant difference, i.e. that the 95% CI of the difference does not include zero70. Second, we tested the effects of treatment (regardless of season) in two ways. In the first approach, we compared the average occurrence of resistance among the three treatment categories as described above, but not separating the data by season. In the second approach, we restricted the same calculation to only those combinations of wine region and cultivar  for which we had data from both treated and untreated samples. Finally, we estimated the differences in A495T occurrence between TUB2 genotypes and among years by pairwise linear contrasts, and we assessed the differences in A495T occurrence between CWRCs by the overlap of their 84% CIs.We analyzed the probability of A495T occurrence with a generalized linear model with binomial error and logit link, using independent samples . Because fungicide treatment was not statistically independent of the wine region and cultivar (χSupplementary Information 1.Supplementary Information 2.


Traveling salesman problem (TSP) is a decision-making problem that is essential for a number of practical applications. Today, this problem is solved on digital computers exploiting Boolean-type architecture by checking one by one a number of possible routes. In this work, we describe a special type of hardware for the TSP solution. It is a magnonic combinatorial device comprising magnetic and electric parts connected in the active ring circuit. There is a number of possible propagation routes in the magnetic mesh made of phase shifters, frequency filters, and attenuators. The phase shifters mimic cities in TSP while the distance between the cities is encoded in the signal attenuation. The set of frequency filters makes the waves on different frequencies propagate through the different routes. The principle of operation is based on the classical wave superposition. There is a number of waves coming in all possible routes in parallel accumulating different phase shifts and amplitude damping. However, only the wave(s) that accumulates the certain phase shift will be amplified by the electric part. The amplification comes first to the waves that possess the minimum propagation losses. It makes this type of device suitable for TSP solution, where waves are similar to the salesmen traveling in all possible routes at a time. We present the results of numerical modeling illustrating the TSP solutions for four and six cities. Also, we present experimental data for the TSP solution with four cities. The prototype device is built of commercially available components including magnetic phase shifters/filters, coaxial cables, splitters, attenuators, and a broadband amplifier. There are three examples of finding the shortest route between the cities for three different sets of city-to-city distances. The proposed approach is scalable to TSP with a larger number of cities. Physical limits and challenges are also discussed. It can be stated as follows: "Given a list of cities and the distances between each pair of cities, find the shortest possible route that visits each city exactly once and returns to the origin city." It is a non-deterministic polynomial-time hardness (NP-hard) problem which means that there is no guarantee to get the optimal route and no exact algorithm to solve it in polynomial time. TSP is important in a variety of practical applications including transportation2, scheduling3, and genomics4. Mathematically, it can be defined as given a set of TSP is one of the most well-known combinatorial optimization problems6. For instance, 6-city TSP was solved using a Kohonen-type optical network6. However, none of these approaches resulted in a practically viable device. Recently, a variety of optimization techniques used in artificial intelligence (AI) have been applied to TSP7. It may significantly speed up the TSP solution but cannot provide a fundamental advantage being implemented on conventional hardware.There were several attempts to build special hardware for TSP solution8. The main idea is to exploit the advantage of classical wave superposition, where a number of waves can propagate through different routes at the same time. It may be possible to amplify only those signals that propagate through the selected sites in the mesh and accumulate a certain phase shift. Spin wave (magnonic) devices are most suitable for this purpose due to the prominent phase shifts that can be achieved in micrometer-length waveguides. The rest of the work is organized as follows. In “Here, we consider a special type of combinatorial device in which an act of computation is associated with finding a propagation route of the wave through the selected nodes in the mesh. This approach was first described in Ref.The schematics of the combinatorial device for TSP with four cities are shown in Fig. entclass1pt{minima8. There is a number of possible routes for signals to propagate in the mesh. The different propagation paths are associated with different phase shift -th route. There is a superposition of waves propagating through the different paths in the circuit. However, only some of the frequencies will be amplified to enable stable auto-oscillations. There are two conditions for auto-oscillations to occur in an active ring circuit9:9.There is a broadband amplifier mentclass2pt{minimThe computational procedure for TSP is the following. The external phase shifter 8. All other signal propagating on other paths  won’t be amplified as the phase condition  will be amplified as the total phase shift through the ring is entclass1pt{minimahe system starts with a superposition of all possible frequencies propagating through all possible paths. We can amplify only the signals coming through the selected nodes/cities using the external phase shifter to satisfy the phase condition  using the external attenuator entclass1pt{minimaentclass1pt{minimaTo illustrate the solution procedure described above, we present the results of numerical modeling for the four-cities TSP. In Fig. There are 27 possible routes between the four cities . In Fig.  to Eqs.  and 4)..\documenThe above-described algorithm can be extended to a larger number of cities. For example, there are shown the schematics of a device for TSP with six cities in Fig. Only routes coming through all cities  will be amplified. The results of numerical modeling in Fig. It should be noted that in the presented above examples TSP was solved using a general type computer. All possible routes were calculated one by one. Routes with a phase shift satisfying Eq.  were sel10. Active ring circuits with spin wave delay lines are widely used as tunable microwave sources11. Radiofrequency spin waves propagate in magnetic waveguides much slower compared to electromagnetic waves in coaxial cables. For instance, the group velocity of magnetostatic spin waves in a single-crystal yttrium iron garnet Y3Fe2(FeO4)3 (YIG) film of thickness 7 µm is about 104 m/s12. It makes it possible to obtain large  phase shifts for the propagating signal in millimeter-length waveguides. At the same time, spin wave dispersion strongly depends on waveguide magnetization. Spin waves propagating along the direction of magnetization  and spin waves propagating perpendicular to the direction of magnetization  possess different dispersion13. Thus, both the phase shift and the frequency window for spin wave propagation can be controlled by the locally applied magnetic field. It may be just a micro-scale magnet placed on the top of the YIG waveguide. The first proof-of-the-concept experiment on signal re-direction in the two-path combinatorial logic device was accomplished using a YIG-based active ring circuit with BVMSW and MSSW8.In this part, we present experimental data showing TSP solution for four cities. There is a variety of possible approaches to the practical implementation of the device shown in Fig. In this work, the mesh is built using commercially available YIG-based frequency filters produced by Micro Lambda Wireless, Inc, model MLFD-40540. The experimental data on the filter transmission and phase delay can be found in the entclass1pt{minimaIn the next three examples, we present experimental data demonstrating the shortest route finding depending on the set of attenuators for different paths. It should be noted that not the bandpass frequencies nor connectivity nor the position of the external phase shifter are changed during the experiments.Experiment 1: In Fig. Experiment 2: Next, the set of attenuators was changed. In Fig. Experiment 3: In Fig. There are several observations we would like to make based on the obtained experimental data. The prototype magnonic combinatorial device does show the shortest route of propagation through the selected sites in the mesh made of frequency filters, phase shifters, and attenuators. It is critically important that the change in the signal propagation route demonstrated in the examples is only due to the change in the path attenuation. Thus, the device can be utilized for all possible combinations of path attenuation (distances) between the nodes (cities).happens naturally in the prototype device due to the non-linear phenomena. This effect is known as mode suppression15 which is observed in mechanical, acoustic, and optical resonators. It explains the energy re-distribution of power between the modes, where the mode with minimum attenuation receives the most of the amplification. It is one rare case when the experimental prototype works more efficiently than the theoretical device for numerical modeling.There is a prominent difference in the power flow through the different routes which exceeds 30 dBm at room temperature. The active ring circuit device selectively amplifies only the routes which satisfy resonant conditions  and 2)..2). The It should be noted the difference between the general-type device shown in Fig. The ability to exploit classical wave superposition provides a fundamental advantage over conventional digital computers in functional throughput. Functional throughput is a commonly accepted metric for logic devices evaluation16, which can be defined as follows:There is a variety of approaches to practical implementation of combinatorial devices. Magnonic approach has certain advantages  and disadvantages . It may be possible to build optical combinatorial devices with low damping and fast signal propagation if the prominent phase delay can be achieved. 17. However, these Boolean-type devices can accomplish only one operation at a time. In contrast, combinatorial logic devices are efficient in parallel search. This search through multiple routes is equivalent to the number of subsequent operations for the digital computer. The number of possible routes in TSP scales proportional to Conventional logic devices based on Complementary Metal-Oxide Semiconductor (CMOS) technology possess deep sub-micrometer characteristic size  and perform one operation in a very short time 2 which is above the limits of the existing supercomputers combined.Taking the length of the waveguide to be 10 mm and the group velocity to be 8. In the future, it may be possible to build a universal combinatorial device capable of solving a variety of NP-hard and NP problems.Surprisingly, there is a lot of similarity in solving different problems such as the Bridges of Konigsberg, TSP, and prime factorization. The use of prime numbers for marking unique mesh nodes  looks very efficient and guarantees the uniqueness of the phase accumulated through the different routes. In turn, the same device can be used for prime factorization. The external phase shifter can be set to combinatorial filters transmitting signals comprising waves with a certain combination of frequencies  for building a device for TSP with a large number of cities. In Table entclass1pt{minimaIt should be noted that every new problem requires circuit adjustment or reconfiguration. For instance, the circuit for four cities presented in this work can be used for any other TSP with four cities. One needs to adjust the attenuation according to the distances between the cities. It can be done using voltage-tunable attenuators. The increase in the number of cities requires an increase in the number of phase shifters in the circuit. However, TSP circuits can be used for a smaller number of cities without removing/adding new components. For example, TSP with eight cities can be used for solving problems for eight, seven, six, five, and four cities.18. Less accuracy is required for the power detectors. A prominent difference in the power flow  can be achieved regardless of the number of cities. There are certain pros and cons of using magnonic circuits for TSP. On the one hand, spin waves provide prominent phase shifts on millimeter-length waveguides. On the other hand, spin wave dispersion significantly complicates circuit engineering. Besides, spin waves possess much stronger interaction compared to optical waves. For instance, spin waves propagating in the orthogonal directions through the junction may completely reflect each other19. It constitutes an additional problem for spin wave interconnects. This and many other questions deserve special consideration.Another practical challenge is associated with the accuracy of the parts with respect to the number of cities. The difference in the distances between the cities may significantly decrease for TSPs with a large number of cities. There are physical limits to the accuracy of attenuator control. Also, the increase of possible propagation routes inevitably decreases the difference in the accumulated phase for different routes. In turn, it will lead to the problem with the shortest route finding for a large number of cities. The increase in the precision of measurements/calculations is a common problem for TSP with a large number of cities that may be addressed with heuristic algorithmsWe described an approach to TSP solution using combinatorial devices. The principle of operation is based on the classical wave superposition, where each wave corresponds to a traveling salesman. The waves propagate through a mesh of waveguides, where phase shifters represent the cities in TSP and the distance between the cities is encoded in the signal attenuation. Only waves coming through the selected cities and accumulating the specific phase shift are amplified. The most amplified is the wave traveling through the route with minimum attenuation. TSP solution is illustrated by numerical modeling and experimentally demonstrated. The first prototype is a multi-path magnonic active ring circuit comprising magnetic phase shifters, frequency filters, and attenuators. The signal propagation route is detected via the power detector. We presented thee examples for three different sets of attenuators/city-to-city distances. The device literally finds the shortest propagating route through the cities. The operation is robust as the power difference between the active and passive routes exceeds 30 dBm. All experiments are accomplished at room temperature. This work is a step toward combinatorial logic devices for special task data processing. Potentially, combinatorial devices may provide a fundamental advantage in functional throughput compared to conventional digital devices. The ability to use classical superposition of waves is the key advantage and the most appealing property of the presented approach.Supplementary Information.


Cardiovascular disease continues to be the leading cause of death globally. Clinical practice guidelines aimed at improving disease management and positively impacting major cardiac adverse events recommend genetic testing for inherited cardiovascular conditions such as dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), long QT syndrome (LQTS), hereditary amyloidosis, and familial hypercholesterolemia (FH); however, little is known about how consistently practitioners order genetic testing for these conditions in routine clinical practice. This study aimed to assess the adoption of guideline-directed genetic testing for patients diagnosed with DCM, HCM, LQTS, hereditary amyloidosis, or FH.This retrospective cohort study captured real-world evidence of genetic testing from ICD-9-CM and ICD-10-CM codes, procedure codes, and structured text fields of de-identified patient records in the Veradigm Health Insights Ambulatory EHR Research Database linked with insurance claims data. Data analysis was conducted using an automated electronic health record analysis engine. Patient records in the Veradigm database were sourced from more than 250,000 clinicians serving over 170 million patients in outpatient primary care and specialty practice settings in the United States and linked insurance claims data from public and private insurance providers. The primary outcome measure was evidence of genetic testing within six months of condition diagnosis.Between January 1, 2017, and December 31, 2021, 224,641 patients were newly diagnosed with DCM, HCM, LQTS, hereditary amyloidosis, or FH and included in this study. Substantial genetic testing care gaps were identified. Only a small percentage of patients newly diagnosed with DCM , HCM , LQTS , hereditary amyloidosis , or FH  received genetic testing.Genetic testing is underutilized across multiple inherited cardiovascular conditions. This real-world data analysis provides insights into the delivery of genomic healthcare in the United States and suggests genetic testing guidelines are rarely followed in practice. Cardiovascular disease (CVD) is the leading cause of death worldwide, accounting for 32% of deaths in 2019 .For example, cardiomyopathies may have a genetic or acquired etiology, or a combination of both. Inherited cardiomyopathies include hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM), among other less common phenotypes. Estimates of genetic and non-genetic etiologies vary by study, depending on the composition of the cohort . GeneticGenetic testing for heritable genomic variations supports many clinical decision-making activities including diagnostic confirmation, risk stratification, refinement of prognosis, individualized patient management, selection of targeted therapeutics, identification of asymptomatic or pre-symptomatic at-risk relatives, and reproductive planning. Prior studies have shown that genetic testing has a critical role in the diagnosis and management of patients with inherited cardiovascular disorders and their family members –18. For Since the publication in 2011 of the first guideline recommending genetic testing for cardiomyopathies and channelopathies , cardiolAlthough guidelines and statements offer a rationale and evidence to support the utilization of genetic testing for patients suspected of having an inherited cardiac condition, the adoption of guideline-based recommendations in real-world clinical practice has not been thoroughly assessed , 42. EleThe objective of this retrospective cohort study was to understand the current landscape of guideline-directed genetic testing for select inherited cardiovascular conditions by analyzing the EHR and claims data of a large patient population with incident disease.The Veradigm Health Insights Ambulatory EHR Research Database linked with insurance claims data (Veradigm database) was used to perform a retrospective cohort study. The Veradigm database consists of de-identified patient records from more than 250,000 clinicians serving over 170 million patients in outpatient primary care and specialty practice settings across the United States, including internal and family medicine practitioners as well as cardiologists and other related specialties. The linked insurance claims data are sourced from United States public and private insurance providers and include enrollment data from medical and pharmacy claims.The primary cohort for this study was identified from within the Veradigm database using the inclusion and exclusion criteria specified below. Five subcohorts of inherited cardiovascular conditions were defined from within the primary cohort for patients diagnosed with DCM, HCM, LQTS, hereditary amyloidosis, or FH.Other conditions for which clinical genetic testing is available were considered during a feasibility assessment. Brugada syndrome, arrhythmogenic right ventricular cardiomyopathy (ARVC), and catecholaminergic polymorphic ventricular tachycardia (CPVT) presented fewer than 100 cases per diagnosis in the primary cohort and were not included in the study.The main inclusion criterion used to filter records for this study from the Veradigm database was new diagnosis of a subcohort-specific condition between January 1, 2017 and December 31, 2021, including records collected during the COVID-19 pandemic. Diagnoses were based on International Classification of Diseases, Clinical Modification, 9th (ICD-9-CM) and 10th (ICD-10-CM) revisions codes and/or structured EHR text fields . New diaPatients under 18 years of age were excluded. To avoid overestimating the proportion of genetic testing associated with each subcohort, additional filtering was done to exclude patients with a diagnosis of cancer or cystic fibrosis, as well as instances where a new diagnosis coexisted with a pre-existing diagnosis of another subcohort-specific condition. Patients with concurrent pregnancy were also excluded as a proxy of possible prenatal genetic testing. The codes and filters used to identify the study cohort and subcohorts are detailed in Evidence of genetic testing was captured from ICD-9-CM and ICD-10-CM codes, procedure codes, and structured text fields within the Veradigm database. To most accurately capture only genetic testing intended for the subcohort-specific cardiovascular diagnosis, genetic testing associated with non-cardiac procedure codes was explicitly excluded. A one-year window (plus/minus six months) around the index date, i.e., the date when the diagnosis was recorded, was used to infer the indications of genetic testing when not explicitly stated. The use of CPT codes allowed the capture of genetic tests included on an insurance claim in either an inpatient or outpatient setting.Genetic testing guidelines were codified for this analysis using a compilation of clinical practice guidelines and expert scientific statements , 32–34. P values for bivariate analyses were calculated using t-tests or chi-square tests, as appropriate. P values were corrected for multiple testing using the two-stage step-up method of Benjamini, Krieger and Yekutieli  (Demographics and baseline comorbidities at the time of diagnosis (index date) were analyzed to compare individuals who received genetic testing with those who did not. Patients who received genetic testing were significantly younger (cohorts) . HoweverP < 0.001 for DCM and LQTS; P = 0.005 for FH). Except for those with hereditary amyloidosis, patients receiving genetic testing were significantly more likely to have a family history of CVD , and for the HCM and LQTS subcohorts also a family history of cardiac arrest (P < 0.001 for both subcohorts). In terms of clinical presentation, untested individuals were significantly more likely to also be affected by one or more baseline comorbidities . Patients with DCM, LQTS, and FH who received genetic testing were significantly more likely to have documented diagnostic imaging of the heart.To understand the role of genetic testing in the clinical care pathway of patients with suspected inherited cardiovascular conditions, the timing of genetic testing relative to the date of diagnosis was also explored . Most inFurther information about the study cohort, including additional comorbidities, and cardiovascular interventions, medications, and laboratory results is provided in This study quantified the current use of guideline-directed genetic testing for 224,641 patients newly diagnosed with DCM, HCM, LQTS, hereditary amyloidosis, or FH in the United States. The profound underutilization of genetic testing observed in this cohort represents a missed opportunity in the evaluation and care of patients with inherited cardiovascular disorders.N = 2,999) with DCM and HCM, respectively, from the Cardiomyopathy Registry of the EURObservational Research Programme consisting of 69 cardiology centers across 18 European countries (N = 315) with HCM and LQTS, respectively, from six specialty cardiology clinics in large US pediatric healthcare centers (Previous multi-center studies have examined adoption of genetic testing in cardiomyopathies and LQTS, reporting adherence ranging from 18% to 92% , 51. Howountries ; and Kni centers . In contThe proportion of patients newly diagnosed with DCM, HCM, LQTS, and FH who received genetic testing in this study ranged from 0.8% to 1.6%. Patients receiving genetic testing in our study were significantly more likely to have a family history of cardiac arrest or cardiovascular disease. Although the number of patients tested is quite low, it is plausible that a positive family history, which in certain situations may signal a potential genetic etiology, could have influenced the use of genetic testing. A positive family history is considered a deciding factor in some guidelines to offer genetic testing; however, family history may not always be available to the clinician or may not be obvious in pedigrees with recessive or X-linked inheritance pattern. Genetic etiology should not be discounted in individuals with negative family history.TTR variants (Notably, the utilization of genetic testing in 6% of patients diagnosed with hereditary amyloidosis was much higher than for the other subcohorts in this study. While the data do not support specific conclusions, the availability of targeted medications for patients with variants , 33, 36 In the 12 years since publication of the first guideline recommending genetic testing for cardiovascular conditions, evidence and guidance supporting the use of genetic testing in cardiac care has increased , 28–38. TTR, none of whom had a previous diagnosis of hereditary transthyretin amyloidosis. Adopting implementation science frameworks and practices may also facilitate the uptake of guideline-directed genetic testing. Proposed implementation science strategies include leadership engagement, attention to the structure and clarity of guidelines, and the creation of clinical resources to support implementation of recommendations (Incorporating guideline-based recommendations into quality improvement efforts through EHR integration at the point of care may offer one strategy to overcome barriers and drive utilization of genetic testing in cardiovascular care , 58. In ndations –56.The strength of this study lies in the scope of information available from a nationwide EHR database linked with insurance claims, which includes broad, longitudinal data on patients seen across diverse locations and mixed provider specialties and practice settings as well as insights on billed services such as tests, procedures, and prescriptions. Limitations of the current study, on the other hand, include a lack of data on the types of genetic tests ordered, the percentage of positive genetic test results, the pathogenic variants identified, and the number of patients who were offered genetic testing but declined. The study did not include an analysis of trend of genetic testing utilization over time. Additionally, EHR and claims data are not primarily collected for research purposes so some data elements, such as family history of genetic conditions and results from prior genetic testing of relatives, may be under-recorded. Similar observations were also made in other studies , 43, 44.The current study did not address the relationship between insurance type and rates of genetic testing. However, the claims database used in the analysis includes a mix of commercial and Medicaid Managed Care coverage in roughly similar proportions, combined with Medicare Advantage and other types of coverage in a smaller fraction of cases, thus offering a good representation of different types of coverage available in the US.. Further studies are needed to evaluate possible strategies for the implementation of guideline-directed genetic testing and to establish the relationship between genetic testing and patient outcomes.In conclusion, this real-world data analysis using a large, nationwide EHR database linked with insurance claims revealed a substantial underutilization of genetic testing for patients in the United States newly diagnosed with DCM, HCM, LQTS, hereditary amyloidosis, and FH. These care gaps persist despite professional practice guidelines recommending genetic testing and the existence of reimbursement policies from major insurers. The importance of establishing a molecular etiology extends beyond diagnostic confirmation and determining the risk of recurrence, as new targeted therapies become available. Collaboration between professional societies, healthcare systems, healthcare providers, payors, and clinical laboratories will be required to increase the utilization of genetic testing as part of an overall strategy to improve guideline adoption and elevate the standard of care and outcome for patients with heritable cardiovascular disorders