Recognition of simple shapes and numerosity estimation for small quantities are often studied independently of each other, but we know that these processes are both rapid and accurate, suggesting that they may be mediated by common neural mechanisms. Here we address this issue by examining how spatial configuration, shape complexity, and luminance polarity of elements affect numerosity estimation. We directly compared the Event Related Potential (ERP) time-course for numerosity estimation under shape and random configurations and found a larger N2 component for shape over lateral-occipital electrodes (250–400 ms), which also increased with higher numbers. We identified a Left Mid Frontal  component over left-lateralised medial frontal sites that specifically separated low and high numbers of elements, irrespective of their spatial configuration. Different luminance-polarities increased N2 amplitude only, suggesting that shape but not numerosity is selective to polarity. Functional microstates confined numerosity to a strict topographic distribution occurring within the LMF time-window, while a microstate responding only to shape-configuration was evidenced earlier, in the N2 time-window. We conclude that shape-coding precedes numerosity estimation, which can be improved when the number of elements and shape vertices are matched. Thus, numerosity estimation around the subitizing range is facilitated by a shape-template matching process. For larger numbers, numerosity perception is subserved by mechanisms activated by different ranges of numerosity and/or density3. Numerosity estimation is fast and accurate when the number of items is less than four which has led to the concept of subitizing7, with larger numbers between 5 and 7 being estimated rapidly but with larger errors8 (but see15 for a subitizing limit of up to 7 elements). Numerosity estimation may also be linked to geometric cues in the formation of an object’s shape as demonstrated with dice dot-patterns17, which suggests that processing of small quantities might be linked to high-level pattern recognition . Similarly, shape detection studies have found that simple shapes  are detected faster and more accurately than complex shapes containing more vertices18.Numbers rule our lives: we possess a strong visual sense of number that is used to rank, estimate, and quantify everything we see in the environment . We can estimate numerosities over a broad range, between one to hundreds or thousands of items, without involving cognitive processes such as counting which are ineffective for short viewing times19. For example, numerous studies have examined shape processing using radial frequency (RF) patterns, which are created by sinusoidally modulating the radius of a circle, with the number of full cycles of modulation per 2π radians being the RF number 24. Global RF shape processing is thought to depend upon pooling together information about the points of maximum curvature, i.e., the peaks and troughs (vertices/corners) of the RF pattern30. Loffler et al.18 examined the contribution of local  and global processing, by exploring the amount of global information pooling in a RF shape discrimination task and showed that global pooling only extends up to about RF5 shape (pentagon), while higher frequency RF shapes rely mainly on local processing. Given that global shape processing is limited by the number of shape vertices18, which overlaps with proposed subitizing limits10, it raises the question as to whether numerosity perception and shape processing are intrinsically linked, by sharing a common neural mechanism.To encode the shape of an object, the visual system must spatially integrate information about the relationship between different parts along the shape outline32, long straight lines33 and curvature34. For example, RF detection thresholds increase and shape aftereffects are reduced when the number of consecutive elements of the same polarity decrease31. These findings suggest that changes in luminance polarity negatively affects shape processing mechanisms, and it distorts the perception of shape itself32. Since we asked whether shape processing and numerosity estimation share a common neural mechanism, it is likely that any shared neural mechanism would also be affected by changes in luminance polarity. In this communication, we address these issues by using behavioural measures (reaction time and accuracy), Event Related Potentials (ERPs) and functional microstates to map the time-course of the interplay between shape configuration and luminance polarity on numerosity estimation within and nearby the subitizing range.In addition, previous studies have shown that luminance polarity alternation  disrupts the processing of RF patterns38. Some studies have shown the N1 amplitude plateaus for 3 elements40, yet others suggest a higher quantity35. Nan et al.9 showed that the amplitude of the N1 component is maximal for high numerosities, asymptotes when the number of elements is above 8, and indexes all elements in the stimuli irrespective of these being targets or distractors. As for specifically estimating number, two ERP components have been identified—the N2pc and CDA, or Contralateral Delayed Activity—for a review see41. First, the N2pc is a difference wave reflecting inter-hemispheric differences observed between 170 and 300 ms for target stimuli presented in the left and right visual hemi-fields43. The N2pc effect is reportedly maximal for stimuli containing 3–4 elements, individuating low numeric quantities44. Second, the CDA, a lateralized effect like N2pc, while also modulated by target quantity, ostensibly reflects the encoding and maintenance of items in working memory46. Importantly, these studies used visual features of the elements  to segregate targets from distractors for numerosity estimation. This implies that the generation of N2pc and CDA are explicitly linked to feature-based attention mechanisms. Therefore, in our experiments we also examine the effect of stimulus features, such as spatial configuration and luminance polarity (black vs. white) on numerosity estimation, without the demand from feature and location-based attention.ERP studies of subitizing have highlighted the early N1 component as a possible candidate for numerosity estimation, with N1 amplitudes increasing with numerical quantities47. Our virtual shape configurations by their very nature resemble a collection of illusory lines that are integrated into shapes . Hence, we expected to find higher N1 and N2 amplitudes for shape versus random configurations, but note that the mechanisms of illusory shape perception and shape coding in our experiments, which use Gaussian elements, are different.Here, using behavioural measures (reaction times and accuracy), ERPs, and functional microstates, we examined whether spatial configuration and its complexity  facilitate numerosity estimation. Stimuli consisted of a small number of elements  positioned either on the vertices of simple shapes  which highlight N1, N2pc and CDA components, we could predict an N1, N2 and a late component that all respond preferentially to low numeric quantities (3 or 4 elements). Furthermore, microstate segmentation would allow us to separate the functionality of these three ERP components in relation to shape and numerosity coding. Specifically, we expected to find distinct microstates overlapping the time-course of our predicted ERP components . In addition, microstate analysis allows us to establish whether these ERPs result from distinct neural source generators that encode numerosity, and their time-course, or utilise the same neural source generators for numerosity and shape processing.In Experiment 2, we dissociated shape complexity from numerosity by utilizing the least complex shape (a triangle) sampled by the full range of elements, in comparison to random configurations. Triangular stimuli were sampled by either 3, 4, 5 or 6 elements, with three elements always positioned on the vertices and the remaining elements placed randomly anywhere within and including the virtual contour-path Fig. c. Triang33, we examined whether numerosity mechanisms are selective to elements’ luminance polarity (Experiment 3) by comparing stimuli in which all elements have either the same  or different luminance polarity (white and black). We predicted better performance in the same compared to different polarity conditions, suggesting that luminance polarity consistency can facilitate numerosity estimation in a similar fashion to shape configurations. We expected to find that early ERP components reflecting low-level visual feature processing would be sensitive to different polarities, which may in turn disrupt numerosity estimation.Finally, given that previous studies of shape processing have shown that luminance polarity distorts shape perceptionFigures p < 0.0001, η2 = 0.813 for accuracy; F = 27.289, p < 0.0001, η2 = 0.603 for RTs) with faster RT and higher accuracy for shape compared to random configuration. The effect of number of elements  = 49.351, p < 0.0001, η2 = 0.733 for accuracy; F = 23.608, p < 0.0001, η2 = 0.567 for RTs) was also significant. In addition, a significant interaction effect for RTs  = 23.019, p < 0.0001, η2 = 0.561) but not for accuracy  = 2.023, p = 0.122, η2 = 0.101) was found. Bonferroni-corrected post-hoc analysis on the RTs data was carried out to examine the significant interaction effect. This revealed that all pairwise comparisons were statistically significant (p < 0.0001) except comparisons between 3 and 4 elements and between 5 and 6 elements, irrespective of their spatial configuration. Thus, RT data was shown to separate low from high numeric quantities  irrespective of their spatial configuration. Overall, these results suggest that shape configurations facilitate number estimation.A two-way repeated measures ANOVA with factors spatial configuration (shape vs. random) and number of elements  revealed a significant main effect of spatial configuration  = 78.332, The ERPs averages across number of elements for the shape and random configurations are shown in Fig. N1 component (160–220 ms) revealed that mean N1 amplitude increased with shape-like configurations in comparison to random  = 8.869, p = 0.008, η2 = 0.33) and with the number of elements  = 4.833, p = 0.01, η2 = 0.212). There was also a significant main effect of hemisphere  = 12.907, p = 0.002, η2 = 0.418), showing that N1 mean amplitude was largest over right occipital/parietal locations. This location difference appears to be driven in part by the larger amplitudes for shape configuration in the right hemisphere  = 4.872, p = 0.041, η2 = 0.213). Finally, there was a significant interaction between the configuration and number of elements  = 4.638, p = 0.014, η2 = 0.205). Bonferroni-corrected post-hoc analysis revealed that this interaction effect was driven predominantly by the large increase in N1 amplitude for three elements placed in a triangle compared to random configuration (p < 0.0001), with no other multiple-comparisons being statistically significant (p > 0.05). In sum, the N1 amplitude, which was maximal in the right hemisphere, was increased by shape configurations, and also by the least number of elements in the stimulus.A repeated-measures ANOVA model with factors spatial configuration (shape vs. random), number of elements , hemisphere (left vs. right electrode locations) and electrode  carried out on the N2 component (250–400 ms), a significant main effect of configuration was found  = 105.655, p < 0.001, η2 = 0.854) indicating that shape configuration elicits increased N2 amplitudes in comparison to random configurations —see Fig. p = 0.128, η2 = 0.107). Like the N1 component, N2 amplitude was also maximal in the right hemisphere  = 7.218, p = 0.015, η2 = 0.286). This hemisphere effect interacted with spatial configuration suggesting a larger increase in amplitude for shape configurations in the right compared to the left hemisphere electrodes  = 5.554, p = 0.03, η2 = 0.226). Finally, we found a borderline three-way interaction between configuration, number of elements and hemisphere  = 2.828, p = 0.049, η2 = 0.136), which appears to be driven by a statistically significant effect of the randomly distributed elements in the left hemisphere sites only (p = 0.011).For the later LMF component (400–650 ms) consisted of the factors configuration (shape vs. random), elements  and electrode . Again, shape-like configurations produced larger amplitudes than random  = 15.599, p = 0.0001, η2 = 0.464), and the LMF appeared to separate the number of elements in the stimulus  = 23.03, p < 0.001, η2 = 0.561), with greater LMF amplitude observed for stimuli containing a low number in comparison to high number of elements  except, interestingly, between stimuli with 5 or 6 elements (p > 0.05) which is consistent with the RT data. No other significant effects were found.The ANOVA model for the In order to map the time course of ERP differences that remain stable over time, we conducted paired TANOVA comparisons between all shape and random configurations, averaged across the number of elements  dominated the segmentation. GEV for N11a was subjected to a repeated measures ANOVA of spatial configuration (shape vs. random) and number of elements . The N11a map best fitted conditions with a low number of elements  = 4.166, p = 0.019, η2 = 0.188), yet post-hoc analysis of the number of elements revealed no significant differences between pairs of elements . Thus, the N11a map was not related to changes in shape configuration or the number of elements, implying that processes in the N1 time window are generic for all conditions of the experiment. Considering that TANOVA fails to find topographic differences in the N1 range, it is of no surprise that the only observable difference in N1 topography is due to electric field strength rather than differences in topographic distribution.To further clarify the observed TANOVA differences and their relation to shape configuration and number of elements, we ran a microstate segmentation analysis across the grand-averaged ERPs for all eight conditions of Experiment 1 Fig. e,f. In t1a map and two other maps which we label N21a and LMF1a respectively . There was a main effect of configuration such that each map explained more variance for shape configurations compared to random  = 21.634, p < 0.001, η2 = 0.546). Importantly, a significant map by shape interaction was found  = 12.213, p < 0.001, η2 = 0.404), and further investigation revealed that the shape versus random configuration effect is driven by the N11a map predominantly  = 23.807, p < 0.0001, η2 = 0.569), with this map explaining more variance for shape compared to random, while the N21a map was insensitive to spatial configuration  = 0.672, p = 0.423, η2 = 0.036). Hence the N11a map responds preferentially to shape configurations in the N2 time window. Note that the presence of the N11a map in the N2 time window is not unusual, but reflects the labelling of each microstate by the first point in time when they occur in relation to ERP time windows . The LMF1a map however, dissociated between the number of elements in the stimulus, thus explaining more variance in individual participant data for conditions with a low number of elements compared to high  = 3.472, p = 0.026, η2 = 0.162).Over the N2 time window, three maps were clearly observable: the N1ows see “”. FinallIn sum, the microstate segmentation findings show that microstates N11a in the N2 time window (250–400 ms) indexed the strength of the shape versus random configuration effect, while the LMF1a map dissociates between the number of elements in the stimuli.We further investigated the effect of the number of shape vertices (or shape complexity) independent of the number of elements by utilizing the least complex shape (triangle) sampled by the full range of elements. Accuracy Fig. a and reap < 0.0001, η2 = 0.613 for accuracy; F = 34.627, p < 0.0001, η2 = 0.658 for RTs), suggesting that increasing the number of elements leads to overall reduced accuracy, and longer RTs. The effect of spatial configuration was not significant  = 4.398, p = 0.050, η2 = 0.196 for accuracy; F = 0.023, p = 0.88, η2 = 0.001 for RTs). However, the interaction effect was significant for both accuracy  = 24.809, p < 0.0001, η2 = 0.580) and RTs  = 19.362, p < 0.0001, η2 = 0.518). Bonferroni-corrected post-hoc analysis on both accuracy and RTs data revealed that all pairwise comparisons were statistically significant (p < 0.0001), except comparisons between 4 and 5 elements, irrespective of their spatial configuration.A two-way repeated-measures ANOVA with factors spatial configuration (triangle vs. random) and number of elements  revealed a significant main effect of number of elements  = 28.455, The average ERPs obtained with triangle and random configurations are shown in Fig. N1 component was increased in amplitude for triangle than random configurations  = 54.686, p < 0.001, η2 = 0.752), and also increased with the number of elements in the stimulus  = 4.677, p = 0.014, η2 = 0.206). Similar to Experiment 1, the largest N1 amplitudes were observed in the right hemisphere electrode sites  = 8.822, p = 0.008, η2 = 0.329). No interaction effects were found in the N1 range.The N2 range, triangle configurations elicited a larger negativity than random configurations  = 50.049, p < 0.001, η2 = 0.735). Like the N1 component, N2 amplitude was largest in right hemisphere electrodes  = 6.193, p = 0.023, η2 = 0.256). There was no significant main effect of the number of elements, however, there was an interaction between spatial configuration and number of elements  = 4.875, p = 0.009, η2 = 0.213), such that the largest N2 amplitudes were observed for triangle configurations with a low number of elements , and critically, the LMF also separated the number of elements in the stimulus  = 28.042, p < 0.001, η2 = 0.609), with an inverse relationship between LMF amplitude and number of elements , except for 5 and 6 elements (p = 0.918). No other significant effects were found in the LMF range.To investigate the LMF component (400–650 ms), we ran an ANOVA model with factors of spatial configuration (triangle vs. random), number of elements 3, 4, 5, 6) and electrode . The analysis revealed larger LMF mean amplitude for triangle configurations in comparison with the random conditions  = 11.013, , 4, 5, 6TANOVA analysis revealed topographic differences between triangle and random configurations, averaged across the number of elements, during the N2 time window . This N12a map explained no other differences in individual participant data.Microstate segmentation analysis Fig. e reveale2a Fig. f. This m2a, N22a, and N22b  which revealed that the N22a map explained the least variance in comparison to N12a and N22b maps . No differences were found between N12a and N22b (p = 0.114). Although there was no significant main effect for triangle configurations, we found a significant interaction between map and configuration  = 6.768, p = 0.012, η2 = 0.273). Further analysis of this interaction showed that map N12a explains more variance in the data for triangle configurations compared to random conditions  = 9.471, p = 0.006, η2 = 0.345), whereas no significant effect of configuration for N22a and N22b could be found within the N2 time-window.Over the N2 time-window, the presence of three maps was observed in the segmentation which were labelled as N122b Fig. e,f. We f2a and LMF2a maps , we found no differences in explanatory power of the two maps. In fact, we found a main effect of spatial configuration  = 7.497, p = 0.014, η2 = 0.294), suggesting that both maps better explain triangle than random configurations. There was no significant interaction effect between map type and spatial configuration. However, an interaction between map type and number of elements was found  = 7.076, p = 0.009, η2 = 0.282). Subsequent analysis revealed that the segmentation was dominated by the N22a map which extends from N2 time-window, and which explained most variance in individuals for triangle configurations  = 4.872, p = 0.041, η2 = 0.213). The LMF2a map best fit conditions of 3 elements only  in comparison to 4, 5 or 6 elements, irrespective of configuration  = 11.66, p = 0.001, ήp2 = 0.393). Thus, both N22a and LMF2a maps better fit triangle than random configurations. No other significant effects were found in the segmentation analysis.Segmentation analysis revealed two microstates present in the time window for analysis of the LMF, which we have termed the N2aps Fig. f. Interep = 0.035, η2 = 0.237 for accuracy; F = 6.248, p = 0.023, η2 = 0.269 for RTs) and number of elements  = 14.154, p < 0.0001, η2 = 0.454 for accuracy; F = 24.157, p < 0.0001, η2 = 0.587 for RTs). There was no significant interaction effect  = 0.356, p = 0.785, η2 = 0.021 for accuracy; F = 2.669, p = 0.057, η2 = 0.136 for RTs). Further investigation into luminance polarity effects by comparing same and different polarity conditions for equal number of elements revealed that none of the multiple comparisons were significant . In sum, we found small but significant mean differences between combined same versus different polarity conditions for accuracy  and RTs , most likely driven by the small variance around these combined mean differences, which becomes larger when split into individual conditions .Accuracy Fig. a and reaThe ERPs obtained with same and different polarity configurations are shown in Fig. p = 0.01, η2 = 0.334), with different luminance polarity stimuli eliciting an increased negativity , number of elements 3, 4, 5, 6), hemisphere (left vs. right electrode locations) and electrode  for the N1 and the later N2 component. The analysis revealed no significant differences in the N1 range. In the N2 time-window of 250–400 ms, we found a significant effect of luminance polarity  = 8.52, , 5, 6, hp = 0.105, η2 = 0.147), but similar to Experiments 1 and 2, the LMF was modulated by the number of elements in the stimulus  = 26.948, p < 0.001, η2 = 0.613)—see Fig. p = 0.037, η2 = 0.167). However, this interaction effect between number of elements and polarity appears to be caused mainly by 5 and 6 elements conditions. This effect cannot be generalised to luminance polarity changes, as it was not present in any other numerosity conditions. In sum, LMF amplitude was always largest for stimuli made of 3, then 4 elements, with 5 and 6 elements being almost equivalent , number of elements 3, 4, 5, 6) and electrode  revealed no effect of luminance polarity  = 2.93, , 5, 6 anPaired TANOVA comparisons between all same and different luminance polarity conditions, averaged across the number of elements, are shown in Fig. 3a, in the N1 time range, yet subsequent ANOVA analysis revealed that this map was not specific to luminance polarity changes or the number of elements within the stimulus . Post-hoc analysis of this interaction found no significant differences between same and different polarity conditions, or between number of elements in the stimulus. Hence, we disregard this effect since the variance explained by map N13a differs based upon luminance polarity and the number of elements within the stimulus yet any differences between individual conditions are too small to be meaningful.Microstate segmentation revealed the presence of only one map, termed N1lus Fig. e,f. Howe3a, N23b, and LMF3a . Specifically, the N23a map fits the data in the N2 range better than the LMF3a map (p = 0.04), but there were no differences between N23a and N23b (p = 0.184) or N23b and LMF3a (p = 0.9999). The N23a map was the only map to fit the data better for different than same luminance-polarity conditions  = 4.69, p = 0.045, η2 = 0.216). Further, a main effect of the number of elements was found  = 3.018, p = 0.043, η2 = 0.151), however, no pairwise comparisons showed significant differences in the fit of the three maps according to the number of elements in the stimulus . This result indicates no graded pattern of response for any microstate in the N2 range based on numerosity.Over the N2 time-window, three maps were observed in the segmentation, which we labelled as N2F3a Fig. f. Analys3a and LMF3a  but no main effect of luminance polarity (p > 0.05). However, post-hoc analysis suggested that neither map could suitably explain the ordering of the number of elements in the LMF time window , since no map fitted a graded pattern of response from low to higher numeric quantities.Finally, two microstates were evident in the time window for analysis of the LMF, which we term the N2F3a Fig. f. Theoreshape configuration on both RTs and accuracy (with faster RTs and higher accuracy for shape rather than random configurations), an effect of number of elements , and an effect of luminance polarity .We aimed to systematically investigate the effect of shape configurations and luminance polarity of elements on numerosity estimation within and nearby the subitizing range using multiple analysis strategies. Across three experiments, our behavioural data show an effect of 42. This implies that the N1 component elicited with our stimuli, is not selective to numerosity per se . In Experiment 3, only the N2 component displayed an increased amplitude for different-polarity stimuli. Importantly, in all three experiments, the LMF increased in amplitude for stimuli containing a low number of elements , but did not distinguish between higher numbers of elements in the stimuli (5 and 6 elements), nor was the LMF affected by luminance polarity. Note that our LMF responses cannot be driven simply by motor responses from participants, since participants made a response on every trial, and we included all trials in our analysis , as suggested by other studiessis see “” section, 4, 5, 6By showing temporally distinct ERP and microstate components that respond predominantly to shape configurations and number estimation, our data demonstrates that shape coding occurs before number estimation. Theoretically, this may imply that shape processing and subitizing are mediated by two separable independent mechanisms. However, we found that shape-configuration conditions also lead to an increase in amplitude of the LMF component in Experiment 1, and also in Experiment 2 only when the number of elements match the number of shape vertices. This implies that the processes supporting shape recognition and numerosity estimation interact with one another. Moreover, our behavioural data, indicating higher accuracy and faster RT for shape configurations, highlight the facilitation of number estimation mechanisms by shape template matching. In sum, facilitation of numerosity estimation by shape template matching, evident in our behavioural data, is reinforced by our ERP and microstate data.48, a limited number of studies have argued against this idea51. Gebuis et al.51 showed that when visual cues were equated across stimulus displays, only N1 and P2 effects were present but no number-related effects occurred, thus, suggesting that number is estimated by weighting the different visual features/cues present in a stimulus. Previous ERP studies of subitizing have used stimuli presented in the periphery in a variety of behavioural tasks , and have identified two lateralized components specifically related to subitizing—the N2pc and CDA—for a review see41. The N2pc component is modulated by the number of perceived items, despite variations in stimulus properties, such as luminance change, size/area, total number of items in the stimulus55. In contrast, we found an N2 component that responded preferentially to spatial configuration, and also to luminance polarity changes, but did not consistently separate numerosity. Specifically, we found that the N2 was largest when the number of elements matched the number of shape vertices (Experiment 2), i.e. 3 elements on a triangle but not 4, 5, or 6 elements on a triangle. It may be tempting to interpret this finding as evidence for numerosity estimation and shape processing unfolding in parallel in the N2 range, however, we did not find direct evidence for this in our data. The ordering by number found in Experiment 2 occurred only when elements were presented in a shape configuration and not random, suggesting that N2 is sensitive to shape, and is agnostic to ordering of the number of elements. Given that we found the N2 to be modulated by visual features such as luminance polarity (Experiment 3) irrespective of feature-based attention, it is likely a perceptually-driven effect.Our conclusions are also supported by findings from other behavioural and ERP studies. While it has been suggested that number is processed independently of its visual propertiesp’s > 0.111). In Experiment 2, only an equilateral triangle was used as the shape configuration, which was sampled by an increasing number of elements  of the virtual triangle  that reportedly indexes symmetry perception and/or structure. SPN was found to be sensitive to luminance-polarity mismatch across the symmetry axis57, with early SPN differences driven not by symmetry sensitive mechanisms per se, but by pattern perception enhanced by the form/gestalt of the pattern . In contrast to the symmetry literature, our N2 component is not as prolonged in duration (250–400 ms), yet this likely reflects subsequent numerosity estimation observed in the LMF time window. Similar to the symmetry literature, our N2 component was found to be sensitive to the luminance-polarity of the elements, with different luminance-polarity stimuli eliciting an increased negativity , our findings also show an improvement in performance as indicated by faster RTs and higher accuracy, in respect to different polarity conditions.Moreover, our data suggests that the N2 component, might also reflect a general processing of form or structure-like, as commonly observed in studies of symmetry perceptiontion 250–00 ms, yesustained posterior negativity occurring later (~ 300–400 ms after stimulus onset and lasting until the end of epoch) contralateral to the attended items, was found to increase monotonically with the number of items, and ostensibly represents visual working memory during periods of sustained attention55 (but note that no previous studies examining CDA used a working-memory task). In contrast, the LMF component amplitude is largest in response to low numeric quantities, supported by the presence of specific microstates within this range (see Experiments 1 and 2), whereas earlier microstates within N2 range restrict processing of the N2 ERP component explicitly to spatial configuration. Different from our N2 and LMF components, the previously defined N2pc and CDA are attention-mediated components generated by isolating targets from distractors via visual features (e.g. colour) that are elicited only under lateralized stimulus presentations. Our data extends upon these designs and clarifies the neural time course of numerosity estimation without interference from object individuation processes via feature-based attention.Our findings suggest that LMF overlaps the time course of the CDA found in previous studies. In feature-based attention studies of numerosity, the CDA component, which is a 6, instead these processes may be maximally activated by low numeric quantities. In addition, one may be tempted to think that the processes indexed by the LMF are the same as those reflected in RT responses, i.e., that the LMF represents some form of post-perceptual processing, such as response monitoring. However, one should be cautious in inferring such a relationship given that response latency is only one of many possible consequences of stimulus presentation and it is not the end point of information processing60. It is highly likely that RTs and ERP component latencies are served by different subsets of processes invoked by stimulus presentation60. This is supported by numerous ERP studies showing that ERPs do not map directly with the behavioural task .The LMF component identified in the three experiments appears to separate numeric quantities and was not modulated by low-level visual features . Do these LMF results, supported by behavioural data, imply that two separate processes for numerosity estimation are occurring for low  and high  numeric quantities, i.e., subitising (up to 4) compared to higher  numerosity estimation? If two separable brain processes were activated for low versus high quantities, this would be evidenced by two distinct microstates, one corresponding only to low numeric quantities, and another for high. However, we found only a single microstate for all numerosities across the three experiments, suggesting that the same neural source generator is involved in processing all numeric quantities up to 6. That is, the processes involved in estimating low  and high  numeric quantities employ the same neural mechanisms, and they are not separated by a strictly-defined subitising limit of 4 proposed by some studies67 . These suggested stages overlap roughly with those proposed by Mazza and Caramazza41. However, not all these stages are necessary for estimating number in the context of our study (low numerosity within the subitizing range), given that our elements were not embedded in a background and/or it was not required to segment a subset of elements from a background by virtue of feature-based attention . Although it is difficult to ascribe with certainty the contribution of each of these processes to a specific ERP modulation, our results demonstrate that the processes involved in shape coding occur before those responsible for quantifying number. Considering that we manipulated spatial configuration, while other studies have used only randomly distributed items38, it is highly likely that different processing steps were engaged when coding numerosity and spatial relationships  versus visual features , and these might take precedence over numerosity in this context.What brain processes underlie the ERPs in response to numerosity estimation? A number of possible processes may be indexed by any given signal. Several studies have suggested that in the context of higher numerosity, these processes may be an early unsegmented representation of the stimuli, a later refined/segmented representation, an abstract representation emerging in high-level areas, a decision process based on numerosity, and a working memory storage of such a representationIn sum, our data reveals the neural time-course of numerosity estimation, which we replicated across three different participant samples: the LMF component, and its corresponding microstate, separated low numerical quantities up to 4 elements, but showed comparable responses for 5 or 6 elements. Furthermore, we show that number estimation can be facilitated when the number of elements in the stimulus matches the number of shape vertices, which was consistently observed in both behavioural data and ERPs (as indicated by the increase in amplitude of the LMF for shape configurations). It is interesting to note that luminance polarity increased N2 amplitude only, suggesting that shape but not numerosity estimation is selective to luminance polarity. Across all three experiments, the quicker RTs, higher accuracy, and increased LMF amplitude demonstrate that shape coding precedes number estimation, implying that subitizing can be facilitated by a shape-template matching process which considers the relationship between vertices.A total of seventy-two observers (24 participants in each experiment), who were naive with regard to the experimental aims participated in this study. All observers had normal or corrected-to-normal visual acuity. Observers gave their written informed consent prior to participating and were treated in accordance with the Declaration of Helsinki . All research procedures were approved by the University of Stirling Ethics Committee.2. Viewing distance was 100 cm.The stimuli were created using Matlab and presented on a Sony Trinitron monitor with a 1,024 × 768 spatial resolution and a refresh rate of 120 Hz. The R (red), G (green) and B (blue) outputs of the monitor were gamma-corrected after calibration with an Optical OP200E photometer. All stimuli were presented in the center of the monitor on a mid-gray background with mean luminance of 65.5 cd/mThe stimuli consisted of a small number of elements, either 3, 4, 5 or 6 elements presented in the center of the monitor within a circular area of 8 deg diameter on the mean-luminance background Fig. . The eleIn Experiment 2, we dissociated between the number of elements and shape vertices by using only the simplest shape configuration—an equilateral triangle, which was sampled by either 3, 4, 5 or 6 elements, with three elements always positioned on the vertices and the remaining elements placed randomly anywhere within and including the virtual contour-path Fig. c. These Finally, to examine the effect of luminance polarity (Experiment 3) we used stimuli made of either same . We asked participants to use both their hands while responding in order to balance differences in motor responses within and between participants. No feedback was given to participants after responding. For each stimulus condition and observer, we measured accuracy (proportion correct answers) and reaction times (RTs) from the point of stimulus onset. We then calculated the average across-participants for each of these measures and the standard error across participants.68. Signals exceeding ± 75 μV in any given epoch were automatically discarded. EEG recordings were cut into epochs ranging from − 100 ms to 1,000 ms after stimulus onset and averaged for each individual according to the experimental conditions. Grand-averages for each experiment were calculated after re-referencing individual participant ERPs to the common average reference. Participants whose data showed irretrievable noise contamination or a significant loss of channels, leading to no discernible ERP signal, were removed from the analysis. This resulted in a total of 19 participants in both Experiments 1 and 2, and 18 in Experiment 3 being used in the final analysis.For all three experiments, raw EEG signals were recorded from the scalp at a 1 kHz sampling rate from 64 Ag/AgCl electrodes distributed according to the extended 10–20 system and using CZ as the online reference. All electrode recording impedances were kept below 5 KΩ. The electroencephalogram was filtered on-line between 0.01 and 200 Hz and off-line with a band-pass zero phase shift digital filter between 0.1 and 30 Hz . Eye blink artefacts were mathematically corrected using a model blink artefact computed for each individual based upon the method of Gratton, Coles and Donchin2) is also reported. It is important to note that effects of numerosity, i.e., number of elements, found in ANOVA models may not necessarily mean that a given ERP component displays gradual changes in amplitude in line with increasing (or decreasing) numerosity. We consider an ERP component to be selective to number when a rank ordering of the number of elements is present in the statistical analysis, rather than randomly ordered differences.ERPs across all three experiments displayed a typical P1–N1–P2–N2 complex. The N1 component was identified as the first negative peak observable in waveform data and was maximal over posterior occipital electrode locations , defined by topographic differences in the N1 range. Mean amplitude analysis of the N1 component was conducted between 160 and 220 ms. In addition, we analysed the negative component N2, occurring after the N1 in the time window 250 to 400 ms using the same electrode locations. Finally, we also identified a later component, which we termed Left-Mid-Frontal effect (or LMF), appearing to be modulated by the number of elements, and being maximal in the left hemisphere frontal/central electrode locations  between 400 and 650 ms. These three components  were observed across all three experiments and, for consistency, the same analysis approach  was applied across all three data sets. All ERP data were subjected to repeated-measures ANOVAs, with each model explained fully in the results section for each experiment. Greenhouse–Geisser corrections were used where applicable. To demonstrate the magnitude of effects, partial eta-square . We use this method to highlight global trends in the ERP data. However, TANOVA can only identify when in time those differences arise, and not how those differences are generated. To highlight differences in topographic periods of stability not covered by TANOVA, we ran a microstate segmentation analysis77.We conducted paired topographic ANOVA (or TANOVA) comparisons for differences between conditions to assess changes in global dissimilarity (DISS). DISS is an index of configuration divergence between two electric fields over time, independent of their strength79, to determine the microstates that explain the greatest amount of variance in the ERP map series. Next, we assessed the statistical validity of our segmentation by determining the amount of variance explained in each map in the ERPs of each observer by condition for the time windows used in analysis of the three components: N1, N2, and LMF. For clarity, the naming of each microstate derived from the segmentation procedure relates to the overlap between each microstate and these ERP components, and is subscripted with the experiment number  and the order of microstate occurrence , e.g., N11a, N11b, N12a etc. Repeated-measures ANOVAs were carried out on the Global Explained Variance (GEV) of each microstate fitted to individual participant data, in order to compare how each map could explain each condition. This procedure was completed for each of the three experiments.Using a hierarchical cluster analysis technique, we used grand averaged ERP data to produce a series of microstates in the form of topographic maps. The optimal number of microstates was found using a cross-validation criterionSupplementary information


We aimed to investigate the correlation of factors involved in the change of Mini-Mental State Examination (MMSE) and type 2 diabetes in the elderly.This study was a secondary analysis of a prospective cohort study. Type 2 diabetes patients aged > 55 years were recruited and assigned into three groups based on their glycated hemoglobin (HbA1c) levels: HbA1c < 7, 7% ≤ HbA1c < 8% and HbA1c ≥8%. MMSE decline was considered the endpoint. Factors related to MMSE decline were identified by univariate and multivariate regression analyses.P = 0.029) and the occurrence of cognitive decline (odds ratio (OR) = 1.55, 95%CI:1.13–2.12, P = 0.007).Altogether, 1519 subjects were included, 883 in the Low group, 333 in the Median group, and 303 in the High group. Age ≥ 75 years, education below elementary school level, not participating in seminars or consultation on healthcare, physical activity less than 30 min/day, cerebrovascular disease history, MMSE score at baseline, and HBA1c ≥8% were associated with cognitive decline by univariate and multivariate analysis. When the other factors were adjusted for, HBA1c ≥8% was independently associated with the severity of cognitive decline (β = 0.58, 95%CI:0.06–1.11, In elderly patients with type 2 diabetes, HbA1c ≥8% is an independent factor for cognitive decline and is also associated with the severity of the cognitive decline. The number of people with diabetes mellitus is increasing rapidly and has more than doubled globally in thirty years, making it a major public health challenge for all nations . Around Individuals with T2DM score less well on cognitive function tests such as the Mini-Mental State Examination (MMSE) than those without diabetes , and a cHemoglobin A1C (HbA1c) or glycated hemoglobin represents a measure of the blood glucose levels over the previous three months. HbA1c can evaluate glucose control in individuals with diagnosed diabetes and can also be used in diagnosing diabetes and people at risk of developing diabetes . HbA1c iIf HbA1c is an important indicator of cognitive decline, this suggests that improving glycemic control in elderly patients is vital to decrease the risk of cognitive decline. However, understanding other factors that are also involved would help target the patients most at risk of cognitive decline. Therefore, the aim of this study was to investigate the association of HbA1c levels with MMSE decline in elderly patients with T2DM and find the correlative factors involved.This was a retrospective analysis of a community-based cohort study (BLSA II) , which iThe study was approved by the research ethics committee of Xuanwu Hospital of Capital Medical University before study initiation. Written informed consent was obtained from all participants for the original data collection.The baseline information of the subjects was collected, including sex, age, smoking, alcohol consumption, education, whether they lived alone, income, physical exercise, dietary habits, intake of fruit and vegetables, body mass index (BMI), history of cerebrovascular disease (CED), history of cardiovascular disease (CVD) and peripheral vascular disease, history of hypertension, participation in community-based healthcare seminars and consultations, alanine aminotransferase (ALT), triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), glucose (GLU), urinary albumin (UA), creatinine (Cr), apolipoprotein E (ApoE), and HbA1c. The biochemical analysis was performed by a Hitachi 7600 automatic biochemical analyzer . The HbA1c level was determined by a Bio-Rad VARIANT™ II TURBO Hemoglobin Testing System for HbA1c when baseline information was collected.The patient’s cognitive function was evaluated by the Chinese edition of the Mini-Mental State Examination (MMSE) . The MMSThe change of MMSE was defined as the difference between baseline and follow-up values after 3 years.The last follow up occurred between November 2013 and April 2014 in the form of a questionnaire in the community hospitals.www.R-project.org). The continuous variables that followed normal distribution were represented as the mean ± standard deviation (SD). The continuous variables that did not follow normal distribution were represented by median (range or interquartile range (IQR)). The categorical variables were represented as n (%). The continuous variables that follow normal distribution were examined by t-test or analysis of variance (ANOVA). The continuous variables that did not follow normal distribution were tested by the Mann-Whitney U or Kruskal-Wallis test. The categorical variables were tested by the χ2 test or Fisher exact test. The association analysis was performed by univariate or multivariate regression. A P < 0.05 value was considered statistically significant.The data analysis was performed using R software , HbA1c (P < 0.001), TGP = 0.01), and UA (P = 0.005). The number of patients educated below elementary school level (P = 0.003), BMI (P = 0.001), and MMSE variation (P = 0.022) but not the MMSE scores at baseline or follow-up were also different between the groups.The study included 1519 subjects. Their mean age was 72 ± 6.3 years old. Among them, 580 (38.2%) were male, and 939 (61.8%) were female. The numbers of subjects in the three groups were 883 in the Low group, 333 in the Median group, and 303 in the High group. The baseline information collected from the patients is shown in Table P = 0.001), education below elementary school level , not participating in seminars or consultation on healthcare , physical activity more than 30 min/day , CED history , MMSE score at baseline , and HBA1c ≥8% .Univariate analysis of factors related to a decline in MMSE score Table , identifP < 0.1) were then included in multivariate analysis for independent factors related to MMSE decline (Table P < 0.001), education to elementary school and below , physical activity for more than 30 min/day , a CED history , MMSE score at baseline , and HbA1c ≥8%  all remained significant factors.Factors that had a level at or close to significance (ne Table . This shP < 0.001), physical activity than 30 min/day , MMSE score at baseline , LDL  and HBA1c ≥8% .When logistic regression was used for analysis of factors related to a decline in MMSE score Table , the uniP < 0.001), education to elementary school and below , a CED history , MMSE score at baseline , and HbA1c ≥8%  were all significant factors independently associated with decline in MMSE score.The multivariate analysis is shown in Table Figure The aim of this study was to investigate the association between HbA1c levels and MMSE decline in elderly patients with T2DM. The results show that age ≥ 75 years, education below elementary school level, not participating in seminars or consultation on healthcare, physical activity less than 30 min/day, cerebrovascular disease history, MMSE score at baseline, and HBA1c ≥8% were all associated with MMSE decline by univariate and multivariate analysis. After adjustment for the other factors, HbA1c ≥8% was independently associated with MMSE decline and its severity.There has been some controversy over whether HbA1c levels as an indicator of glycemic control in patients with T2DM can be used to predict cognitive decline. There is strong evidence that HbA1c is associated with cognitive decline . Most stAge is clearly related to the degree of decline in MMSE scores both in populations with and without T2DM , 26. So,This study has some limitations. The baseline measurement of HbA1c was undertaken at one time point and may not have been a true reflection of the typical value. As a retrospective study, no cause and effect can be deduced from the results. Cognitive function was based on MMSE alone, and other measures of cognitive function may not support the results. We did not have the full data regarding the medication use for all patients, some drugs such as benzodiazepines are known to impair cognitive function, so there may have been some differences in their use between groups that may have influenced the results of the study. The 3-year follow-up might be too short to study a decrease in cognitive functions. But the follow up has continued, and long-term changes of cognitive functions will be analyzed in the future.In elderly patients with type 2 diabetes, a number of factors were related to MMSE decline. Nevertheless, the analysis suggested that HbA1c ≥8% was an independent factor for cognitive decline and was also associated with the severity of the cognitive decline.


Individuals with autism spectrum disorder (ASD) reportedly possess preserved or superior music-processing skills compared to their typically developing counterparts. We examined auditory imagery and earworms (tunes that get “stuck” in the head) in adults with ASD and controls. Both groups completed a short earworm questionnaire together with the Bucknell Auditory Imagery Scale. Results showed poorer auditory imagery in the ASD group for all types of auditory imagery. However, the ASD group did not report fewer earworms than matched controls. These data suggest a possible basis in poor auditory imagery for poor prosody in ASD, but also highlight a separability between auditory imagery and control of musical memories. The separability is present in the ASD group but not in typically developing individuals. Musical ability is dependent upon a group of auditory perceptual and cognitive skills which may be affected by autism in ways that are poorly understood. The relationship between language and music is itself complex. In 1871, Darwin speculated that they might share a common evolutionary origin Darwin . Both arEarly studies suggested that the processing of music may be preserved in autism even when language is impaired  studies of pitch processing in ASD. A recent meta-analysis of results using these techniques suggests that infants and children (but not adults) with ASD have impaired auditory brainstem response to sound  predicts vocal pitch imitation accuracy  subscale predicts performance on musical imagery related tasks  processing and control, an area which has been hitherto neglected in studies of musicality and ASD.The study was approved by the University of Reading ethics committee and performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Participants were recruited through a variety of ways, such as word-of-mouth, contacting local organisations associated with autism, and online advertising.t-tests confirmed that the two groups did not differ in age, self-reported years of musical training, non-verbal IQ as measured by Raven’s progressive matrices, or receptive vocabulary as measured by the fourth edition of the Receptive One Word Picture Vocabulary Test , (t (32) = 5.1, p < .001, Cohen’s d = 1.75; BF10 = 1115.205, median = − 1.59, 95% CI − 2.42, − .77) and Systemizing Quotient (SQ) (t(32) = 4.7, p < .001, Cohen’s d = 1.61, BF10 = 400.729, median = 1.46, 95% CI .66, 2.25).The sample consisted of 34 participants: 17 ASD participants , aged 16–56, with an average of 36.6 years old matched with 17 controls on gender. All ASD participants had an official clinical diagnosis, which is also confirmed by their AQ scores. Bayesian https://osf.io/q7k9n/. Three ASD participants answered all questions with only 1 (one participant) or 7 (two participants), and these participants (and their matched controls) were excluded from subsequent analyses on the presumption that they had failed to understand the task. Importantly, including these individuals does not affect the results except on the one occasion noted in the text.The full data-set, including all participants, are available via the Open Science Framework at The materials used were two online questionnaires, which were provided through the JISC (formerly BOS) survey platform. First, the Bucknell Auditory Imagery Scale (BAIS) was given to participants online. The second questionnaire used was an earworms questionnaire, initially used by Beaman and Williams  and showLinks to both questionnaires were sent by email, for participants to complete in their own time. The AQ was sent first, then the BAIS and finally the earworms questionnaire. Time pressure was removed for participants as there was no time limit given and no supervision was given to ensure that they necessarily completed the questionnaires in this order. As part of a general recruitment procedure all individuals also subsequently completed a number of other tests, as indicated in Table Bayesian analyses were run on JASP software using default priors because there were no strong a priori commitments to any particular effect size  = 8.13, p = .008, eta squared = .238), a main effect of type of vividness  = 207.04, p < .001, partial eta squared = .89), and an interaction between group and vividness type  = 3.31, p = .04, partial eta squared = .11), as shown in Fig. p value of .052 for the interaction, but the results are otherwise unaffected. Bayesian model comparisons lead to similar conclusions: Using JASP to calculate BFinclusion values to indicate the extent to which Bayes the data support inclusion of the factor of interest, a Bayesian model with an effect of group was revealed to be more likely than the model of no group differences (BF10 = 9.106). A model with an effect of soundtype was also more likely than the null model of no difference in soundtype . Finally, a model in which these factors interact was more likely than a model with no interaction (BF10 = 4.95).1The three factors identified by factor analysis Halpern  as contrF,26 = 8.1t-tests and a Holm’s sequential Bonferroni correction (Holm t(26) = 3.55, p = .001, Cohen’s d = 1.34, BF10 = 22.37, median = − 1.1.35, 95% CI − 1.976, − .328) for environmental sounds (t(26) = 2.86, p = .008, Cohen’s d = 1.08, BF10 = 5.93, median = − .878, 95% CI − 1.693, − .137) and for music (t(26) = 1.69, p = .104, Cohen’s d = .64, BF10 = .997, median = − .48, 95% CI − 1.204, .178). Thus, the evidence favours the hypothesis that ASD individuals differ from controls in vividness of auditory imagery in two cases , in the third case (music) the observed effect size was smaller and the results inconclusive.Follow-up analyses (2-tailed) using Bayesian ion Holm  for the F = 11.16, p = .003, eta squared = .3), a main effect of type of control  = 4.74, p < .001, partial eta squared = .82) and a group by control type interaction  = 118.3, p = .013, partial eta squared = .15), as shown in Fig. inclusion values revealed that a model with an effect of group was more likely than the null model, (BF10 = 34.34), one with an effect of soundtype was also more likely than the null model of no effect (BF10 = 6.005 × 1015), and a model in which both effects interact was more likely than a model with no interaction (BF10 = 11.58).A similar pattern was apparent with Control scores of the BAIS where once again there was strong evidence for an effect of group  = 3.99, p < .001, Cohen’s d = 1.51, and t(26) = 3.34, p < .001, p = .003, Cohen’s d = 1.26, t(26) = 2.36, p = .026, Cohen’s d = .89, respectively) and in all cases a difference between groups is more likely than a null difference . Note however that the evidence in support of the alternate hypothesis is much weaker in the case of verbal/environmental sounds than in the other instances and also that the frequentist t test would not have survived a stricter Bonferroni correction.For BAIS-C, the three factors contributing to control scores were identifiable as arising from questions concerning verbal/environmental sounds, music, and verbal sounds Halpern . Follow-fewer earworms in the ASD group relative to the control group but this hypothesis is decisively rejected by a 1-tailed Bayesian t-test . Figure 10 = 1.48, median = .462, 95% CI .037, 1.17 when including only the individuals who successfully completed the BAIS scale; BF10 = 2.67, median = .551, 95% CI .06, 1.239 when all individual are included).Given that BAIS scores were lower amongst the ASD group than in controls, the positive correlation between BAIS scores and earworms in typically developing individuals reported by Beaman  would sumore prevalent in an ASD population—the current data-set is insufficient to settle this point. It is clear, however, that any hypothesis about earworms predicated upon superior auditory imagery is not supported. It was also plausible that either or both of the vividness and control subscales would show differential effects of ASD upon the types of auditory imagery such that, for example, musical imagery might be preserved or even enhanced whereas vocal or verbal imagery might not be. Analyses of variance showed interaction effects, but Figs. As far as we are aware, these results represent the first attempt to examine the phenomenology of auditory imagery in an ASD population and directly compare this to a matched control group. The results of this study show that both self-reported vividness and control of auditory imagery are reduced in our ASD group relative to the typically developing control group. Contrary to predictions, however, this did not result in fewer earworms and it remains possible that Ockelford’s  speculatSignificantly, the Bucknell Auditory Imagery Scale was previously validated in a nonclinical population and shown to possess some interesting properties with respect to that population. Most notably, BAIS-V not only predicted individual differences in gray matter density in auditory and sensory-motor regions of the brain regions (Lima et al. The interpretation outlined above raises the question of why, if auditory imagery is deficient the ASD group examined here did not also show fewer earworms. Indeed, the data are roughly equally consistent with the ASD participants being equivalent to the matched controls or showing more earworms as speculated by Ockelford . The mosA clear limitation of the current study is the relatively small sample size, which requires us to be cautious not to over-interpret these results until such time they may be replicated with a larger sample and, perhaps, a subgroup analyses of ASD individuals. Notably, the sample size was too small for meaningful correlation coefficients to be calculated so that, for example, the correlations between BAIS scores and self-reports of earworms previously observed Beaman  were notImportantly, the groups were matched on a number of cognitive and demographic variables; including verbal and non-verbal ability, and age and musical training. It is not clear what results might be obtained with a less cognitively capable ASD group, but the data are clear in indicating that the differences in auditory imagery reported here cannot be explained in terms of poorer overall cognitive capability or even just a simple failure to understand the task. This is important because the literature on musical ability in ASD is mixed, with some suggestions that preserved or superior musical capability is the provenance of a subgroup of ASD individuals who show little or no cognitive impairment (Bonnel et al. There are also strengths and weaknesses associated with the auditory imagery scales employed within the current study. The vividness of a mental image, being the property of introspection, is intrinsically subjective and can only be directly assessed by self-evaluation. The advantages and disadvantages attendant to a questionnaire design are discussed by Hubbard . StrengtAuditory imagery scores are shown to be lower in an ASD group than in a matched control group in terms of both the self-rated vividness and mental control experienced over imagery. This contrasts with the case of visual imagery, in which poorer mental imagery in ASD is only associated with “unreal” or impossible objects. Contrary to predictions, this poorer auditory imagery was not associated with fewer earworms amongst individuals with ASD. There was limited evidence that individuals with ASD might in fact experience more earworms than the control group, consistent with clinical observations by Ockelford .


As a result of the COVID‐19 outbreak, hygiene regulations have been revised and hand sanitation has been intensified.To investigate the onset of hand eczema during the COVID‐19 pandemic in healthcare workers (HCWs) directly involved in intensive care of COVID‐19 patients and HCWs without direct contact with COVID‐19 patients. Hereby, we aim at increasing awareness about occupational hand eczema and preventive measures that can be adopted.A survey was distributed amongst 114 HCWs at a single surgical centre and at a COVID‐19 intensive care unit of the university hospital Ludwig Maximilian University Munich, Germany. Participants were questioned about the daily frequency of hand hygiene prior to and during the pandemic. Participants self‐reported the onset of hand eczema and associated symptoms.P‐value <.001), regardless of having direct contact with COVID‐19 patients. A high prevalence of symptoms associated with acute hand dermatitis of 90.4% was found across all HCWs, whereas hand eczema itself was underreported (14.9%).Our study revealed a significant increase in hand washing, disinfection, and use of hand cream across all participants (The increase in hand sanitation during the COVID‐19 pandemic impairs the skin of the hands across all HCWs, independent of direct intensive care of affected patients. The World Health Organization recommends an increase of personal hygiene in order to “flatten the curve” of infections and to prevent healthcare facilities from collapsing.For example, excessive sanitizing of hands using soap or alcohol‐based products leads to a disruption of the skin flora and the natural protective skin barrier.The present study evaluated whether the recommendations for increased hand sanitation affected HCW’s hand washing and disinfection frequency at a single surgical centre. Additionally, we investigated whether this correlated with an onset of hand eczema and associated symptoms. Moreover, our results were compared to HCWs involved in direct intensive care of COVID‐19 patients.22.1Basic information regarding gender, age, occupation (physician or nurse), smoking behaviour, and clinical history of type IV hypersensitivity and atopic diathesis  were obtained. The present condition of the hands during the pandemic was self‐evaluated with regard to possible onset of hand eczema and associated symptoms such as erythema, itching, scaling, pain, burning, fissures, dryness, and other symptoms. Participants were questioned about the daily frequency of hand washing and hand disinfection and their responses were rated as follows: <5x =1, 5–10x =2, 10–20x =3, 20–30x=4, >30x=5, and for hand cream application: 0x=1, 1x=2, 1‐2x=3, 2‐5x=4, 5–10x=5, >10x=6, prior to, and during, the COVID‐19 pandemic.2.2Ethical approval was obtained by the ethics committee of the medical faculty of the Ludwig Maximilian University (LMU), Munich, Germany (Ref.‐No. 20‐330). Patient information and identification were kept confidential at all times and data analysis was performed anonymously. The survey was distributed to 114 HCWs in the first 2 weeks of April 2020. The study population included surgeons and nurses from a single surgical centre and physicians and nurses from an intensive‐care unit (ICU) for COVID‐19 patients. Therefore, HCWs were divided into two groups: a non‐COVID (Non‐C) and COVID (C) group.2.3P‐value of .05 was set as the level of significance for all statistical tests. Highly significant results were defined as P‐value <.01. Descriptive statistics  for demographic data were calculated. Non‐parametric tests such as Mann‐Whitney U test, Wilcoxon test, chi‐square test, and Fisher's exact test were applied as appropriate.A professional statistician performed all data analysis using SPSS software version 26 . A 33.1P = .007). Both groups were comparable regarding the distribution of physicians and nurses . Smokers were distributed evenly between both groups (P = .33).The study population included 114 HCWs  with a mean (±SD) age of 35.23 ± 10.78 years. The demographics of both groups (Non‐C and C) are presented in Table 3.2P > .05).  and atopic diathesis , but differences were all non‐significant , followed by erythema (38.6%), itching (28.9%), burning (21.1%), scaling 18.4%), fissures (9.6%), and pain . (Table P = .70)  Table .P < .001) (Table P = .09)  Table . Detaile9) Table .3.5P < .001) (Table P = .074)  Table . HCWs in4) Table .4During the ongoing COVID‐19 pandemic, first scientific reports have identified skin injuries in “front line” HCWs related to PPE use and intensified hand hygiene regulations.P = .09), which could be related to the frequent change of PPE with hand disinfection involved in every step, .The present study revealed a highly significant increase in hand washing and disinfection frequency across all HCWs after the start of the COVID‐19 outbreak, indicating that the proposed hygiene regulations have been implemented across all investigated disciplines . We found no significant difference with regard to the frequency of hand sanitation when comparing the Non‐C and C group, further stressing that the increase of hygienic safety precautions affects HCWs in general. In terms of hand disinfection, our data revealed slightly higher rates in the C group (P = .19). For instance, dryness of hands was reported in >80% of HCWs and ~ 40% suffered from erythema. Symptoms characteristic of a chronic state such as fissures or pain were reported only in 9.6% and 4.4% of all cases, respectively. Thus, our findings add to the current data of Lan et al, who found an increasing onset of skin damage of the hands in 74.5% of 526 front‐line COVID‐19 HCWs, especially in those who washed their hands more than 10 times per day.As hypothesized, our data show a high prevalence of characteristic symptoms for acute hand eczemaInterestingly, only 15% of our study population stated they had hand eczema, although a majority reported symptoms associated with the disease. Similarly, Ibler et al previously found the notification rate of OSD to be as low as 12% in professions with clearly documented work‐related irritant exposures to the skin.Currently, studies are focusing on proposing measures to prevent hand eczema during the pandemic. For instance, the use of sanitizers with ethanol as the main component is recommended, and application of moisturizers after hand washing.In line with this, we found a higher frequency of hand cream application after COVID‐19 outbreak for all HCWs. However, although frequency of skin care increased from 1x to 1‐2x daily, rates are still rather low. Diepgen et al recommend the frequent application of lipid rich moisturizers during the working day and especially after work and at night.Skincare measures should be taken very seriously to prevent a potential chronic and relapsing state of hand eczema,The sample investigated in this study reflects the overall distribution amongst HCWs with regard to gender  and profession . The two groups (Non‐C and C) showed no significant differences regarding type IV hypersensitivity, atopic diathesis, or smoking status, and were therefore comparable. Interestingly, we observed a significant age difference between both groups. HCWs working in intensive care of COVID‐19 patients were significantly younger compared to the Non‐C group. This might point towards a preferred allocation of relatively younger staff to infectious ICU areas, to protect older personnel as a potential risk group.To the best of our knowledge, this is the first study comparing the onset of hand eczema and associated symptoms during the COVID‐19 pandemic between HCWs directly involved in intensive care of COVID‐19 patients and HCWs without direct contact to this patient cohort in a single surgical clinic. This study is limited by the relatively small sample size. In the future, prospective studies should collect pooled data of large centers to further elucidate the onset of occupational hand eczema regarding different medical wards, working hours, shifts, and environmental factors. Furthermore, future studies should explore whether symptoms can be related to certain hand sanitizing products, in order to determine whether one product is preferable to another.The authors declare no conflicts of interest.Anne Guertler: Conceptualization; data curation; formal analysis; investigation; methodology; visualization; writing‐original draft; writing‐review and editing. Nicholas Moellhoff: Conceptualization; data curation; formal analysis; investigation; methodology; visualization; writing‐original draft; writing‐review and editing. Thilo Schenck: Conceptualization; data curation; methodology; supervision; validation. Christine Hagen: Data curation; investigation. Benjamin Kendziora: Formal analysis; methodology. Riccardo Giunta: Conceptualization; supervision; validation; writing‐review and editing. Lars French: Conceptualization; supervision; validation; writing‐original draft; writing‐review and editing. Markus Reinholz: Conceptualization; methodology; supervision; validation; writing‐original draft; writing‐review and editing.


N‐acetylaspartate levels in the cingulate cortex (CC) containing anterior (ACC) and posterior (PCC) regions of 64  participants using MEscher–GArwood‐Point‐RESolved spectroscopy sequence. Result indicated, tissue corrected GSH depletion in PCC among MCI (p = .001) and AD (p = .028) and in ACC among MCI (p = .194) and AD (p = .025) as compared to NC. Effects of the group, region, and group × region on GSH with age and gender as covariates were analyzed using a generalized linear model with Bonferroni correction for multiple comparisons. A significant effect of group with GSH depletion in AD and MCI was observed as compared to NC. Receiver operator characteristic (ROC) analysis of GSH level in CC differentiated between MCI and NC groups with an accuracy of 82.8% and 73.5% between AD and NC groups. Multivariate ROC analysis for the combined effect of the GSH alteration in both ACC and PCC regions provided improved diagnostic accuracy of 86.6% for NC to MCI conversion and 76.4% for NC to AD conversion. We conclude that only closed GSH conformer depletion in the ACC and PCC regions is critical and constitute a potential biomarker for AD.Oxidative stress (OS) plays an important role in Alzheimer's disease (AD) and glutathione (GSH) mitigates this effect by maintaining redox‐imbalance and free‐radical neutralization. Quantified brain GSH concentration provides distinct information about OS among age‐matched normal control (NC), mild cognitive impairment (MCI) and AD patients. We report alterations of in vivo GSH conformers, along with the choline, creatine, and  The transition from a normal healthy person to one afflicted with AD is routed through an intermediate state called mild cognitive impairment (MCI)  was observed in negligible amount (0.10–0.20 μM)  and 0.24 ± 0.10 μM in AD   with high amplitude creatine (Cr) peak  and extended (GSHex) , and both can be detected with specific experimental settings using MEGA‐PRESS sequence  detected using MEGA‐PRESS sequence was quantified for HP and FC regions in AD and MCI patients. It was shown that the amount of GSH varies over different brain anatomical regions in healthy control (HC) subjects and the estimated mean GSH concentration was found to be 1.02 ± 0.17 mM in HP and 1.12 ± 0.18 mM in FC  pulse sequence (such as Point‐RESolved spectroscopy [PRESS]) in the brain is challenging and ambiguous due to spectral proximity of CHp < .05) of neuronal density with the memory capacity in advanced old age participants with better episodic memory function (SuperAgers) than the average elderly and MCI  has also reported a significant positive correlation in apathy and neurofibrillary tangles in ACC brain region . However, these findings were neither concurrent with glutamate–glutamine (Glx) and gamma‐aminobutyric acid plus macromolecule (GABA+) concentrations in PCC, nor with occipital metabolite concentrations  and posterior cingulate cortex (PCC) , MCI, and AD using MEGA‐PRESS pulse sequence. Our study probes for the role of GSH conformer in the CC as a potential biomarker for distinguishing MCI and AD from NC. Furthermore, the data processing scheme in this study also incorporates measurement of GSH with a concurrent assessment of other metabolites that is, choline (Cho), creatine (Cr), and NAA from the same MEGA‐PRESS outcome data. Moreover, the quantified absolute GSH levels, as well as tissue corrected GSH levels using partial volume correction (PVC), is also investigated among the three groups. This novel scheme of multiple metabolite detection and quantitation from single noninvasive MRS data acquisition broadens the use of MEGA‐PRESS pulse sequence in clinical setting.22.1A total of 64  participants including both male (M) and female (F) were recruited for this cross‐sectional study Table  and the 2.21H) MRI/MRS data on human and phantom were acquired at NBRC with a 3 T MR scanner  equipped with a dual tuned (1H/31P) transmit/receive volume head coil . Scout images were obtained in axial, sagittal, as well as coronal planes. In order to aid the anatomical localization, T2‐weighted MRI images with turbo spin echo were acquired in all the three planes with the following acquisition parameters: repetition time (TR) = 3,000 ms; echo time (TE) = 80 ms; flip angle = 90°; and turbo factor = 15. The MRI images were used as a visual reference for voxel placement on the ACC and the PCC regions  was selected in the center of the phantom for in vitro and over the ACC and the PCC regions for in vivo data collection  at 2.80 ppm, the editing pulse was applied to GSH spins (Hα–Cys) at 4.40 ppm (referred to as “ON”)  gives the average of all the MEGA‐ON (averaged‐ON) and the MEGA‐OFF (averaged‐OFF) spectra separately. The difference between the averaged‐ON and averaged‐OFF, that is, edited difference, was used for the GSH peak area estimation and quantitation. Spectral apodization was performed over the time domain data using mixed Gaussian and exponential window functions, and subsequently, Fourier transformation was applied on the apodized time‐domain data. In addition, manual zero‐order phase correction was applied on the edited difference and the averaged‐OFF spectrums to ensure the correct GSH and other metabolite peak areas estimation, respectively. Selective peak suppression of the residual water  and lipid  was performed using Hankel–Lanczos singular value decomposition filtering method  based, MRS signal processing and analysis toolbox, KALPANA , and Cho, Cr, and NAA (using averaged‐OFF) in ACC and PCC regions of a single NC participant is presented in Figure cl peak among, NC, MCI, and AD groups were assessed in the frequency domain in terms of full‐width‐at‐half maximum (FWHM), SNR, and Cramer Rao Lower Bound (CRLB) , SNR (3.48 ± 1.86), and CRLB (10.84 ± 3.80) for GSHcl peak was measured for both the ACC and the PCC regions in the three study groups. Individual data having CRLB value of less than 20% for fitted GSHcl peak was rejected in this study. The GSHcl peak from both ACC  in mM was estimated by applying T1 and T2 relaxation time correction factors on the estimated GSH peak area (AreaGSH) from edited‐difference spectra. The conversion of signal peak area to concentration was performed using the slope (m) and intercept (k) of the fitted line equation of phantom calibration curve as given in Figure The absolute quantitation of in vivo GSH was performed with external calibrated referencing using GSH phantom, where GSH (Sigma Aldrich) solution was kept in a spherical phantom (diameter = 17 cm) containing 1–5 mM/L. The GSH solution in a phosphate buffer at neutral pH 7.2 was used to overcome the loading factors and similar receiver gain was maintained in phantom and in vivo studies. The in vitro GSH signal area was found to be linearly dependent on actual GSH concentration values, with a coefficient of determination 4 Figure . The absT1 and T2) of GSH for both phantom and in vivo were inferred from published literature for 3 T system as follows: T1GSH (397)   (from Andreas Hock email communication to P.K.M.) . Mean values of mentioned T1 and T2 were used to calculate the respective correction factors as appropriate. TE = 120 ms and TR = 2,500 ms, while m and k of linearly fitted calibration curve were taken as 0.00245 and −0.00091, respectively.The relaxation times (2.5GSH_conc]). The method used here considers the partial volume effect of only CSF , and the GSH concentration results were corrected for CSF contamination in the MRS voxel for both ACC and PCC regions. Binary MRS voxel masks were coregistered over segmented tissue maps to give the relative proportion of GM, WM, and CSF within each voxel. The segmentation of the MRI and calculation of PVC correction factors were performed using our in‐house package KALPANA  were calculated using Equation GSH_conc] is the relaxation time corrected absolute GSH concentration (in mM) obtained using KALPANA package  contained within the MRS voxel was obtained by applying binary voxel mask coregistration over segmented tissue and CSF planes generated from SPM 12.In AD pathology, brain CSF increases significantly within the lateral ventricle and nearby regions , p‐value]) or Kruskal–Wallis ANOVA (H(2), p‐value) for continuous variables as appropriate. Normality and homoscedasticity for all metabolite levels were assessed using the Shapiro–Wilk test and Levene's test (W(2), p‐value), respectively. The effect of clinical status on brain metabolite levels was assessed by the use of one‐way ANOVA, followed by Tukey test for pairwise post hoc comparisons. The association between GSH level and factors  was assessed using a generalized linear model (GLM) including age and gender as covariates. Adjustment for multiple comparisons was performed using Bonferroni correction. Significance levels for all the statistical analyses were set at p < .05. Statistical analyses were performed using IBM SPSS Statistics V25.0.Differences in demographic characteristic variables  and metabolic measurements  among participant groups  were assessed using cl concentration levels from both ACC and PCC regions together. Optimal cut‐off values for ROC curves were determined using the Youden index criteria. Corresponding area under curves (AUCs), sensitivity, specificity, 95% confidence intervals (CI), and accuracy were estimated and presented with χ2 statistics to determine the significance of the model. The effect of clinical status on GSH level was assessed in the individual ACC, PCC, and CINGULATE regions (considering ACC and PCC regions together from the same participant). The GSHcl peak area, absolute relaxation time corrected GSH levels and the tissue corrected GSH concentration levels were considered for all three regions  as a within‐subject fixed factor, and between‐subject fixed factor for the three groups . Age and gender were considered as two covariates to analyze the main effect of cingulate GSH level on the three groups.To evaluate, compare, and differentiate among the three groups  in accordance with GSH level in specific brain region, receiver operator characteristic (ROC) curve analyses were performed using DeLong criteria  was 69.81 ± 7.61 years with 42 males and 22 females. There was no significant difference in age (p = .477), and gender (p = .122) among the three groups  and MCI (p < .001) as compared to NC, whereas no significant difference (p = .999) between MCI and AD was observed. The results suggest the involvement of the CC region in early pathogenesis from NC to MCI conversion. Independent GSHcl peak area level using one‐way ANOVA followed by post hoc analysis showed a significant change in the PCC and the ACC region between NC‐MCI , and NC‐AD  groups; however, no significant change was observed between MCI‐AD groups . The post hoc comparison signifies the higher susceptibility of OS over PCC region than the ACC region at an early stage of the disease. Figure cl peak areas of NC, MCI and, AD participants from the PCC and the ACC regions, respectively. Apart from this, peak area levels of GSHex estimated from phase corrected edited MEGA‐PRESS spectra do not reveal any significant variation among the three groups  was indistinct and needs to be further investigated. The existence of the two conformers using both the TEs  and the combination of ON pulses at 4.40 ppm as well as 4.56 ppm in different brain regions have already been established in our recently conducted multicenter in vivo MR studies  with the progressive disease severity in MCI and AD pathology, the mean ± ns Table . Pairwiss Figure b. The GS1H‐MRS metabolite peak areas for Cho, Cr, and NAA obtained from averaged‐OFF spectra were also evaluated using ANOVA for between‐group changes. Post hoc analysis result revealed significant decrease in all these three metabolites between NC‐MCI  and NC‐AD  groups. However, no significant changes were observed for disease progression from MCI to AD  concentration levels revealed a significant difference among NC, MCI, and AD groups  and AD  groups as compared to NC  was observed. Post hoc analysis of the PVC–GSH concentration levels obtained using Equation p = .001) and AD (p = .028) groups as compared to NC, while ACC region showed such changes only for AD (p = .025) but not for MCI (p = .194) as compared to NC group  and AD (p = .008) groups as compared to NC. None of the regions showed significant difference in tissue corrected GSH levels between MCI and AD groups .One‐way ANOVA of both n Figure a. HoweveD p = .02 but not 3.4p < .001), region (p < .001) and their interaction, that is, group × region (p = .004), whereas the PVC–GSH levels showed significant effect due to group (p < .001) and region (p < .001) but not in group × region (p = .431). Pairwise comparisons between the groups showed significant decrease in absolute GSH levels in both MCI  and AD  as compared to NC, where, MD is a mean‐difference value (Table p < .001) and PCC  region as compared to CINGULATE region (Table p < .001) and AD , as compared to NC (Table p < .001) and PCC  region as compared to CINGULATE region , region , and group × region interaction on absolute GSH levels Table  as well n p = .00, whereas3.5ROC curve analysis was performed to assess the diagnostic accuracy of absolute GSH Figure  as well 3.6p < .001) and AD (p < .001) groups from NC with improved accuracy of 90.3% and 83.4%, respectively, while the classification accuracy of MCI from AD (p = .045) was obtained as 69.1% (Table p < .001) and AD (p = .004) groups from NC with an accuracy of 86.6% and 76.4%, respectively, while the classification accuracy of MCI from AD (p = .017) was obtained as 78.5%  and the medial temporal lobe  have been studied as the two prime regions for new memory formation  examined gender‐specific patterns of age‐related volume decline for cingulate GM and showed significant gray volume loss with age as well as gender differences in structure and function  demonstrated significant correlations between the relative size of the ACC and PCC regions with overall performance and the amount of different types of errors in neuropsychological memory tests  volume was observed with significantly smaller 18F‐FDG uptake in the ACC (p < .05) as well as PCC (p < .001) regions among MCI and AD groups than HC. No significant difference was observed for hypometabolism in ACC but found in PCC (p < .05) between AD and MCI patients  and AD (N = 45) compared to age‐matched controls (N = 28)  and FC (N = 31) has also been reported  were reported to be 1.18 ± 0.09 and 0.89 ± 0.03 mM, respectively, from the WM and the GM of parietal cortex regions  with the decline in cognitive functions  has also been reported; however, no significant result in HP was observed among the three study groups  and PCC (p < .001) regions of MCI and ACC (p = .008) and PCC (p < .001) regions of AD as compared to NC. However, the tissue corrected GSH levels resulted in significant change only in PCC (p = .001) region but not in ACC (p = .194) among MCI group as compared to NC. Whereas in AD group as compared to NC, the tissue corrected GSH levels significantly differ in both ACC (p = .025) and PCC (p = .028) regions. Our results also support the literature on autopsy and hypometabolism studies discussed earlier /Cr in the ACC region have been reported in depressive AD patients comparative to nondepressed AD patients  in the ACC but not in PCC and also a significant change in the Cr (p = .01) in the PCC region of aMCI. Changes in these neurometabolites with healthy aging have also been reported with significant positive correlation of Cho , Cr , and NAA  in the ACC; and Cho , Cr , and NAA  in the PCC; and NAA  in the LH, with age , mI (p < .001) in ACC and GABA (p < .009), Glu (p < .024), and mI (p < .010) in PCC regions and no significant change in GSH was observed in either of these regions , Cr (p < .001), and NAA (p < .001) in both the ACC and PCC regions for MCI as compared to NC  as well as tissue‐based in vitro studies. Also, inconsistencies in GSH levels using PRESS sequence were found with either increased  can be simultaneously detected with optimized and specific parameter selection in MEGA‐PRESS sequence  , the GSHex peak was indistinct and appeared out of phase with GSHcl peak  Detection and quantitation of in vivo GSH‐closed form (GSHcl) and GSH‐extended form (GSHex) from the fitted peak of respective phase corrected edited‐difference spectra of MEGA‐PRESS from PCC region of age‐matched control (NC). (B) Changes in estimated GSHex peak areas in the NC, MCI and AD participants in the ACC, PCC and CINGULATE regions.Click here for additional data file.Figure S2 Comparison of 1H‐MRS peaks for Cho, Cr and NAA detected using PRESS sequence and averaged‐OFF spectra of MEGA‐PRESS from the PCC region of healthy young control.Click here for additional data file.Figure S3 Comparison of GSH‐closed form (GSHcl) peak areas .Click here for additional data file.


DNA sequencing (ddRADseq) protocol using DNAs from four genera extracted from both silica‐dried and herbarium tissue.The ability to sequence genome‐scale data from herbarium specimens would allow for the economical development of data sets with broad taxonomic and geographic sampling that would otherwise not be possible. Here, we evaluate the utility of a basic double‐digest restriction site–associated DNAs from Draba, Boechera, Solidago, and Ilex were processed with a ddRADseq protocol. The effects of DNA degradation, taxon, and specimen age were assessed.Although taxon, preservation method, and specimen age affected data recovery, large phylogenetically informative data sets were obtained from the majority of samples.RADseq project designs, and that specimen age can be used as a rapid on‐site guide for sample choice. The detailed protocol we provide will allow users to pursue herbarium‐based ddRADseq projects that minimize the expenses associated with fieldwork and sample evaluation.These results suggest that herbarium samples can be incorporated into dd Genomic tools that best combine data quality, ease, and cost‐effectiveness become standard in the empirical studies that advance our knowledge of diversity and phylogeny. Of the six broad categories of genomic tools outlined by McKain et al. , restricA broader evaluation of the feasibility of RADseq with herbarium DNA is needed. In this study, we assess the relationships between preservation method, specimen age, DNA shearing, and data recovery both within and across four sample sets representing three angiosperm families, four genera, and both silica‐dried and herbarium tissues. A double‐digest RADseq (ddRADseq) protocol was used to process all samples. The success of a single, streamlined protocol incorporating a single restriction enzyme pair would reduce upfront enzyme costs and allow for the simultaneous preparation of ddRADseq libraries from diverse sample sets. This flexibility would allow a researcher to quickly assemble geographically and taxonomically broad sample sets by utilizing herbarium DNAs, thus reducing the need for costly fieldwork and allowing for sampling regimes that would otherwise be unattainable.Draba L. (Brassicaceae) pool comprised 20 silica‐dried and 28 herbarium samples extracted using a standard cetyltrimethylammonium bromide (CTAB) protocol  pool comprised eight silica‐dried and 40 herbarium samples extracted using the 96‐well CTAB protocol outlined in Beck et al. (Solidago L. (Asteraceae) pool comprised 48 herbarium samples extracted using the 96‐well CTAB protocol with the addition of 400 μg of RNase A  during incubation. The 48‐sample, single‐taxon Ilex L. (Aquifoliaceae) pool comprised 48 silica‐dried samples extracted using the standard CTAB protocol. All DNAs that did not undergo a standard RNase treatment during the extraction were incubated (post‐extraction) at 37°C for 1 h with 100–200 μg of RNase A (QIAGEN). All extracts were desalted . Ilex DIN values were not determined for logistical reasons. DIN values range from 1–10, with lower numbers indicating more degradation. Genome sizes estimated from C‐values , 0.39 Gbp (Draba), and 1.1 Gbp (Solidago and Ilex). While the Draba data set included both diploids and polyploids, all Boechera, Solidago, and Ilex individuals were diploid.DNA degradation was quantified as the DNA integrity number (DIN) for the EcoRI/SphI in Draba  suggested that this enzyme pair would also be suitable for the confamilial Boechera. In silico digests of the Daucus carota L. genomic sequence in Geneious version 10.2  at the University of Kansas Genome Sequencing Core. Demultiplexing, clustering, and SNP calling were conducted for each 48‐sample pool using PyRAD version 3.0  were specified, with data type “pairddrad.” Runs were performed on the SAVIO server of the University of California Berkeley Computing Facility , allowing zero barcode mismatches during demultiplexing. Before continuing with PyRAD, PEAR . We first normalized the number of assembled reads  and then log‐transformed it, which improved model fit as estimated by visually assessing Pearson residuals. Silica‐dried samples were excluded from all analyses incorporating age, and herbarium sample age was centered and scaled using the “scale” function.We first explored which variable to use as an assessment of output quality: the number of eventual loci available for tree construction, or the number of assembled reads. Because ddRADseq across divergent taxa is subject to locus dropout, which can have substantial impacts on the number of loci available for tree construction, we used the number of assembled reads as an estimate of ddRADseq success. Regardless, we note that the number of assembled reads is positively correlated with the number of downstream loci . In the second linear model, we asked whether the effects of herbarium sample age varied by taxon, coded as an interaction term. For this herbarium‐only analysis, we removed the Ilex samples , eight silica‐dried Boechera samples, 20 silica‐dried Draba samples, and three outliers . We note that because taxon and sequencing lane are confounded, models accounting for variation due to lane were not estimable. It is likely that some variation attributed to taxon is due to variation in lane. To assess significance, we used the ANOVA function from the car package . For each genus, maximum likelihood (ML) phylogenies and bootstrap searches were performed on concatenated locus alignments resulting from each of four PyRAD filtering thresholds: min_samples_locus = 4, 6, 12, and 24. ML phylogenies were inferred from the unpartitioned data using Garli version 2.0.1019 Zwickl, , under tIt should be noted that this study is intended as a proof of concept of the use of ddRADseq with herbarium material, and our phylogenetic conclusions themselves should be interpreted with caution. Specifically, our ML analyses assume that the sites in an alignment are homologous to each other—that is, that they share a single evolutionary history. Given that the great majority of our ddRADseq markers are from the nucleus, this assumption will be frequently violated in our data sets that have extensive intraspecific sampling, where independent assortment and recombination will result in individual markers with distinct evolutionary histories Moore, . ResearcDraba pool, 20.1–92.9 ng/μL (mean 47.2 ± 17.5 ng/μL) in the Boechera pool, 19.1–84.4 ng/μL (mean 59.0 ± 14.7 ng/μL) in the Solidago pool, and 2–164 ng/μL (mean 33.4 ± 33.8 ng/μL) in the Ilex pool. It should be noted that we did not fully normalize DNA input due to the time needed to add a custom volume for each of the 192 samples during the time‐sensitive double‐digest preparation  are presented in Appendix Draba individuals, 44 of the 48 Boechera individuals, and all 48 Solidago individuals  in the Boechera pool, and 1.0–6.9 (mean 3.4 ± 1.6) in the Solidago pool  in the Boechera pool, 34,516–3,240,725  in the Solidago pool, and 3,716–4,770,722  in the Ilex pool  in the Boechera pool, 178–6527 (mean 2186 ± 1568) in the Solidago pool, and 7–10,117 (mean 2430 ± 2557) in the Ilex pool . Higher DIN values indicate less DNA shearing; thus DNA that was more intact was associated with a higher read number. This was similarly reflected in the herbarium‐only data set . Log‐transformed normalized assembled reads were negatively correlated with herbarium specimen age , with older specimens associated with lower read numbers. Linear modeling of the full 192‐sample data set as a function of taxon and preservation mode revealed that both factors significantly contributed to the variation in the assembled read number  = 6.41, P < 0.0001; Table R2 = 0.17, F = 5.57, P = 1.33e–04; Table In the full 192‐sample data set, log‐transformed normalized assembled reads were positively correlated with DIN . Maximum likelihood trees inferred from these four data sets are shown in Appendices Ilex opaca Aiton data set exhibited the lowest average bootstrap support at (0.513), although a number of highly supported (>90% bootstrap) internal branches suggest that individuals from putatively natural populations of this species are frequently most closely related to individuals from the same natural population as opposed to those from likely planted populations (Appendix Draba data set exhibited the third highest average bootstrap support (0.756) and exhibited highly supported branches, most notably along the backbone , and highly supported clades corresponded to relationships at both shallow (10 of 14 species or subspecies were monophyletic) and deeper levels (Solidago series Odorae (Mack.) Semple and Solidago subsection Triplinerviae (Torr. & A. Gray) G. L. Nesom were monophyletic; Appendix Boechera data set exhibited the highest average bootstrap support (0.957)—only two internal nodes did not receive maximum ML bootstrap support , with older specimens exhibiting lower DIN values (higher degree of shearing). Brewer et al.  and ddRADseq results .Click here for additional data file.APPENDIX S3. Unrooted phylogeny of 29 Ilex opaca samples using a data set filtered at the threshold min_samples_locus = 4.Click here for additional data file.APPENDIX S4. Phylogeny of 43 Draba (or outgroup) samples using a data set filtered at the threshold min_samples_locus = 4.Click here for additional data file.APPENDIX S5. Unrooted phylogeny of 40 Solidago samples using a data set filtered at the threshold min_samples_locus = 4.Click here for additional data file.APPENDIX S6. Phylogeny of 48 Boechera samples using a data set filtered at the threshold min_samples_locus = 4.Click here for additional data file.APPENDIX S7. ddRAD library map, with colors corresponding to Appendix Click here for additional data file.APPENDIX S8. Oligo tube order (see Appendix Click here for additional data file.APPENDIX S9. Oligo plate order (see Appendix Click here for additional data file.