Mental imagery is the ability to generate images in the mind in the absence of sensory input. Both perceptual visual processing and internally generated imagery engage large, overlapping networks of brain regions. However, it is unclear whether they are characterized by similar temporal dynamics. Recent magnetoencephalography work has shown that object category information was decodable from brain activity during mental imagery, but the timing was delayed relative to perception. The current study builds on these findings, using electroencephalography to investigate the dynamics of mental imagery. Sixteen participants viewed two images of the Sydney Harbour Bridge and two images of Santa Claus. On each trial, they viewed a sequence of the four images and were asked to imagine one of them, which was cued retroactively by its temporal location in the sequence. Time-resolved multivariate pattern analysis was used to decode the viewed and imagined stimuli. Although category and exemplar information was decodable for viewed stimuli, there were no informative patterns of activity during mental imagery. The current findings suggest stimulus complexity, task design and individual differences may influence the ability to successfully decode imagined images. We discuss the implications of these results in the context of prior findings of mental imagery. Does the Mona Lisa face left or right? A common method of solving this problem is to form an image of the Da Vinci painting in your ‘mind’s eye’. Our ability to imagine scenes and objects can help us solve everyday problems and accomplish day-to-day tasks, such as retracing our steps to find a lost item or navigating from a memorised map. These mentally-generated images are formed in the absence of visual information and are instead based on short- or long-term memories ,2. ImageRecent work has revealed overlapping neural substrates for visual perception and imagery. Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have revealed similar patterns of brain activity during perception and imagery, suggesting computational overlap in the neural systems responsible for each process ,5,6,7. TDespite overlapping neural activation for vision and imagery, the neural processes are not identical. For example, there is more overlap in higher, anterior regions , comparTo date, research has focused on understanding the brain networks recruited by a variety of imagery tasks ,23, yet Multi-Variate Pattern Analysis (MVPA) applied to neuroimaging data can elucidate the information represented in different brain regions (fMRI) and at particular points in time (M/EEG). MVPA offers an advantage in analysing data from mental imagery, as analyses are conducted at an individual-subject level, and mental imagery ability is understood to vary significantly between people . MVPA iHere, we examined how the neural representation of mental images develops and changes over time. Participants imagined one of four previously learned pictures: two faces and two places. Each image was visually dissimilar to the other within the category, while maintaining clear category divisions. Neural responses were measured using EEG while participants viewed the experimental images, imagined the images, and viewed fast streams of semantically related images . We expected that category information would be decodable from the EEG data during mental imagery, that it would be broadly generalisable across the imagery period, and delayed relative to vision . We alsoAt the start of the session, participants completed the Vividness of Visual Imagery Questionnaire (VVIQ) . They weWe recruited 16 right-handed subjects , of mean age 23 , with normal or corrected-to-normal vision and no history of psychiatric or neurological disorders. The experiment was approved by the Human Ethics Committee of the University of Sydney . Written, informed consent was obtained from all participants.No image at all, you only ‘know’ that you are thinking of an object”) to 5 . All questions were completed twice, once with open eyes and once with closed eyes. A final summed score between 32 and 160 was calculated for each subject; higher scores indicate greater vividness.To measure individual variation in vividness, we administered a modified VVIQ  prior toFour stimuli were used in this experiment: two images of Santa and two images of the Sydney Harbour Bridge A. The inAll stimuli were displayed on a 1920 × 1080-pixel Asus monitor on a grey background. Participants viewed stimuli at approximately 57 cm, such that all stimuli subtended approximately 4.1 degrees of visual angle (including a 0.15-degree black border). Responses were made using a mouse with the right hand. A grey fixation cross was superimposed on all stimuli, with horizontal and vertical arms subtending approximately 0.6 degrees of visual angle. Experimental presentations were coded in MATLAB using extensions from the PsychoPhysics Toolbox ,34,35.The experimental structure is shown in The fourth stimulus was followed by a 1000 ms fixation cross, then a numerical cue appeared (1–4). This cue referred to the target’s position in the stream; for example, ‘3′ indicated the target was the third image in the stream. Participants were instructed to click the mouse once they had identified the target and were mentally “projecting an image into the square”. Upon clicking, the number was replaced with a dark grey fixation cross, and the frame was filled light grey. This ‘imagery’ screen was displayed for 3000 ms before automatically advancing to a response screen. On the response screen, participants were shown the four stimuli and horizontal mirror images of these stimuli. They used a mouse to select which of these images they were imagining. Mirror images were used as distractors because they are semantically identical but visually different, to determine if participants were using a semantic strategy rather than an imagery-based strategy. Horizontal positioning changed across blocks (stimulus identity), and vertical positioning was randomised every trial (mirror images/stimulus) such that for some trials the mirror image was in the top row, and some in the bottom row. This randomisation aimed to reduce predictability in responses.Participants completed a block of 24 practice trials of the imagery sequence before EEG recording. We expected these training trials to give participants the opportunity to learn task structure and observe more details about the images to facilitate vivid imagery. Training trials were similar to experimental trials C. The fiWe also included a Pattern Estimator at the beginning of each block to investigate the degree of generalisation across semantic category. Images presented during the pattern estimator were semantically similar to the critical experimental stimuli. Participants passively viewed a rapid stream containing the four stimuli from the imagery sequence, as well as horizontally flipped, inverted and blurred versions of these images B, left. EEG data were continuously recorded at 1000 Hz using a 64-channel Brain Products  ActiCAP system with active electrodes. Electrode locations corresponded to the modified 10–10 international system for electrode placement , with thEEG pre-processing was completed offline using EEGLAB  and ERPLAll EEG analyses were performed using time-resolved decoding methods, custom-written using CoSMoMVPA functions in MATLAB . For allFor category decoding, a classifier was trained to distinguish images of Santa from images of the Sydney Harbour Bridge for recordings from the same type . To determine if exemplars were also uniquely represented, a classifier was trained to distinguish between the two exemplars within each category . Classifiers were trained and tested for each time point using a 12 ms sliding time window (three time points).To analyse data from the Pattern Estimator and Vision epochs, each presentation sequence was treated as independent. We used a leave-one-trial-out cross-validation approach, where Vision trials were composed of the four stimuli in each imagery sequence and Pattern Estimator trials were composed of a single sequence containing all 56 semantically relevant images. Imagined stimuli were analysed using a leave-two-out cross-validation approach, which took each imagery epoch as independent and left one exemplar of each category (one Santa and one Sydney Harbour Bridge) in the test set. Cross-decoding analyses were conducted using a leave-one-out cross-validation for imagery and vision cross-overs to avoid auto-correlations in the signal if the epochs from the same trial are used to train and test the classifier. All other cross-decoding used a split-half method, where a classifier was trained on one trial type and tested on another trial type . To investigate the possibility of similar processes occurring in vision and imagery at different times, we used temporal generalisation methods , in whicp-values were thresholded at pcorrected < 0.05 for significance.To compute statistical probability for all within-type, cross-decoding and time generalisation analyses, we used the Monte Carlo Cluster Statistics function in the CoSMoMVPA toolbox ,42,43. TIn this experiment, participants viewed rapid streams of images (Pattern Estimator) and series of imagery trials. In imagery trials, participants were presented with a sequence of four images (Vision) and then were cued to imagine one of the images (Imagery). We trained and tested multivariate classifiers to decode exemplar and category of the object in all three conditions, as well as tested the generalisation performance of classifiers between vision and imagery trials.SD = 15.93, range 82–150), similar to previously reported means [M = 56.44, SD = 8.54) were very similar to eyes closed . The distribution of overall scores is shown in The VVIQ was scored out of 160, a sum of responses to each of the 16 questions on a five-point scale. The VVIQ was given to participants both with eyes open and closed . The aveed means ,45,46. RSD = 4.40). Of the trials which were errors, most participants chose one of the four original images (67% of errors). Approximately a third of incorrect responses were to the flipped version of the target. This suggests participants successfully learned the basic characteristics of the target images and were not simply relying on a mnemonic strategy to complete the task. The mean response time from cue to imagery was 3.21 s (SD = 1.86), and the most frequent response time was between 1.5 and 2 s  after 88 ms . Category decoding was continuously above chance . All images were labelled according to super-ordinate categories of ‘face’ or ‘place’. To assess the model’s utility, we cross-validated it on the Pattern Estimator trials. There was sustained above-chance category decoding from 124 ms after stimulus onset until approximately 535 ms after stimulus onset . The clap-value was calculated for each time point, based on the number of permutations with a greater decoding accuracy than the correct label decoding. We used the False Discovery Rate to correct for multiple comparisons. This test was conducted on both Response- and Cue-Locked epochs, and we found decoding was not significantly above chance for any individual at any time point for either Cue- or Response-Locked data (ps > 0.05).To determine if category or exemplar information was decodable from imagined data, we trained and tested a classifier on the Cue- and Response-Locked Imagined epochs . Brain aps > 0.05).To test whether there was any representational overlap in imagery and vision, we ran a cross-decoding analysis. We ran all pairwise combinations of vision and imagery; a classifier was trained to distinguish Santas from Harbour Bridges in the viewed stimuli (Pattern Estimator or Vision epochs) and was tested on imagery periods (Cue-Locked or Response-Locked). There were no significant periods of overlap for any cross-decoding involving imagined trials (ps > 0.05), indicating there was no point where the patterns of brain activity during perceptually processed stimuli were present during imagery.It could be that the processes in vision and imagery engage overlapping representations but at different times. To test this, we conducted a time generalisation analysis . A classAnother possibility is that people with greater capacity for imagery have more decodable imagery representations. To investigate the effects of subjective imagery vividness on decoding accuracy, we grouped the participants as ‘high’ or ‘low’ imagery vividness based on a median split of their ‘eyes-open’ scores in the VVIQ. Two participants had the median score and were excluded from further analysis. We used the eyes-open score because it was the most relevant for the task at hand and makes our results comparable to prior MEG research , where oThe current study used time-series decoding to capture the precise temporal fluctuations underlying mental imagery. Based on prior MEG evidence showing the category and identity of imagined objects can be decoded, we expected successful category and exemplar decoding from imagery. However, contrary to our predictions, we were unable to detect any systematic representations of category or exemplar information during imagery. Based on previous evidence that imagery recruits similar neural networks to vision , we alsoTo compare the overlap between imagery and visual processing, we first defined the temporal dynamics of visual processing for the images in this experiment. For stimuli presented as part of the imagery sequence (Vision), image category was predictable from approximately 100 ms after stimulus presentation until offset 1400 ms later. Exemplar decoding was also significant from 100 ms, albeit for less continuous time than category decoding, reflecting well-established evidence that both categories and exemplars evoke distinct patterns of brain activity . For theIn both visual conditions, exemplar decoding peaked earlier than category decoding. This reflects well-established evidence of increasing abstraction along the ventral visual pathway ,50. It aWhen the classifier trained on the visual stimuli was tested on imagery, there were no time points where the signal was sufficiently similar to accurately predict image category or identity. To investigate the possibility that the processes were not temporally aligned, we conducted a temporal generalisation analysis. There were no regular patterns of activity at the group level, indicating there was no overlap in representations at any point in the imagery period. Based on evidence that areas of activation during imagery vary across people , we exaSeveral factors may have impacted our capacity to decode imagined mental representations, including decreased power compared to previous experiments and other methodological concerns. For example, it is possible that some aspects of the experimental design, such as the mouse-click, and the working memory requirements of four images, decreased the similarities between vision and imagery. However, a recent experiment used similar design choices and found that animacy information was available before information about visual features for perception, but this was in the reverse order for retrieval from memory . ParticiWe designed our experiment to check if participants used non-imagery-based strategies, to rule this out as a reason for significant decoding. We tested if participants were using a non-imagery strategy by including a superordinate category distinction with two exemplars in each category. We obtained response data after every trial with flipped images as distractors to test whether participants were using an imagery-based strategy. If participants were using a purely semantic label-based strategy, we would expect a similar number of responses for flipped and target images. However, only 0.33% of all responses were the flipped version of the target. These response patterns clearly show participants in our experiment were aware of the visual elements of the images rather than solely the semantic label. Due to the fundamentally introspective nature of mental imagery, there is no way to determine if participants are genuinely completing the imagery portion of the task. However, these response patterns point strongly to the use of an imagery-based strategy. Future experiments with similar hierarchical structure and more subtly modified response options  could help determine whether this is a plausible theoretical explanation for our results.Generation of mental imagery requires activation of complex, distributed systems . Higher Most previous experiments using complex visual scenes as imagery targets use an extensive training period prior to the study, relying on long-term memories of targets for imagery . AlthougAs highlighted in recent research , individIn the current experiment, differences in visual working memory capacity, personal decision-making boundary, and memory strategy may have increased variation between participants. For example, individuals who report stronger imagery ability tend to use an imagery-based strategy on visual working memory tasks . FeatureAnother source of variation may be the possibility that we captured a slightly different stage of imagery across individuals, as it is likely each person based the timing of their mouse clicks on a different threshold criterion for the point at which they had begun to imagine. Different strategies for identifying the target may have directed the focus of imagery. When asked informally at the conclusion of the experiment, all participants could explicitly describe their strategy for identifying the target. Most participants assigned a label to each image and mentally repeated these to remember the image order. The majority of strategies relied on structural characteristics, for example, “fat, tall, under, above”. Several participants also reported a direction-based strategy, for example, “top, bottom, centre, side” or “straight, side, face, body”, indicating the orientation of the main object in the image. Though there is no reliable way to compare decoding accuracy based on strategy, different strategies may direct focus on different aspects of the complex images . These differences in strategy present another potential source of variation between subjects.It is clear that capability to decode visual mental imagery is influenced by several factors, including vividness, memory and stimulus complexity. These factors do not affect imagery in isolation; they are inherently related. Better memory for the details of an image is likely to increase vividness. The number of details remembered by an individual is influenced not only by their memory capacity but also by the complexity of the stimulus and the number of details necessary to generate a vivid image. All these factors create variation in the processes used to generate mental imagery across both people and time ,59. The In this study, we investigated how neural representations of mental imagery change over time. Our results suggest successful category decoding in earlier studies may be a result of better signal to noise ratio from a variety of factors, including individual variation. Variety in response times, imagery strategy and ability, in addition to fewer recording sensors may have reduced our power to find systematic patterns of neural activity during imagery. Furthermore, the interactions between stimulus complexity, working memory and imagery vividness may have increased this variation. Our results raise many questions for further investigation and demonstrate both the challenges and advantages associated with time-series decoding for EEG in investigating the introspective processes underlying mental imagery.


Thalassia testudinum) which is an ecologically important species throughout the tropical Atlantic region. Turtle grass has been documented to have long‐distance dispersal via floating fruits and also extreme clonality and longevity. We hypothesize that across its range, Thalassia testudinum will have very limited regional population structure due to these characteristics and under typical models of population structure would expect to detect high levels of genetic connectivity. There are very few studies of range‐wide genetic connectivity documented for seagrasses or other sessile marine species. This study presents a population genetic dataset that represents a geographic area exceeding 14,000 km2. Population genetic diversity was evaluated from 32 Thalassia testudinum populations sampled across the Caribbean and Gulf of Mexico. Genotypes were based on nine microsatellites, and haplotypes were based on chloroplast DNA sequences. Very limited phylogeographic signal from cpDNA reduced the potential comparative analyses possible. Multiple analytical clustering approaches on population genetic data revealed two significant genetic partitions: (a) the Caribbean and (b) the Gulf of Mexico. Genetic diversity was high (HE = 0.641), and isolation by distance was significant; gene flow and migration estimates across the entire range were however modest, we suggest that the frequency of successful recruitment across the range is uncommon. Thalassia testudinum maintains genetic diversity across its entire distribution range. The genetic split may be explained by genetic drift during recolonization from refugia following relatively recent reduction in available habitat such as the last glacial maxima.Many marine species have widespread geographic ranges derived from their evolutionary and ecological history particularly their modes of dispersal. Seagrass (marine angiosperm) species have ranges that are unusually widespread, which is not unexpected following recent reviews of reproductive strategies demonstrating the potential for long‐distance dispersal combined with longevity through clonality. An exemplar of these dual biological features is turtle grass ( Dispersal can most simply be thought of as the movement of an organism from a source (parent) to a settlement site  in a systematic and meaningful way is complex, particularly in marine environments. Effective dispersal distances in the marine environment may be extremely large and usually do not reflect the mean displacements of the propagules  and dispersal distance. The discrepancy these studies describe is partially attributed to the difficulty in comparing FST results when utilizing different types of genetic markers and the scale that these are used at. Additionally, a debate has been taking place on the appropriate application of the various measures of genetic differentiation , which has a wide geographic range residing in shallow tropical and subtropical waters across the Western Atlantic Ocean. Here, it plays an essential role as a foundation species forming extensive meadows and providing numerous ecosystem services , which allow for LDD beyond the range of local populations  sampling sites. Other collections were made under the auspices of permits to The University of Virginia.2.2T. testudinum were collected throughout the species range  and probabilities for obtaining identical genotypes pgen and psex , and unbiased expected heterozygosity (HE′) with their corresponding standard deviations  for each population, also the significance of the global heterozygote excess and deficit was determined using the default parameters. The average number of alleles per locus (A) and the allelic richness (A[n]) for all populations were estimated with Hp‐Rare , 20 independent runs (105 iterations burn‐in and 107 main) were performed. Summary statistics, ∆K as described by Evanno, Regnaut, and Goudet  was performed on the data as an independent approach to detect genetic patterns. The R package adegenet , FST′  ranged between 3.2 and 6.9  also resulted in above average structure, but less markedly  was higher than that between clusters (FCT 0.084). Due to the nature of this analysis, assignment to clusters was determined by highest proportion of membership. The global measure of genetic structuring is FST = 0.161 . Genetic differentiation measures among the sampled populations increased considerably when the variance was standardized to its potential maximum , whereas F’ST is always higher (0.090–0.900). Values of Jost's D fall generally between the two previous measures ranging from 0.068 to 0.789 . Genetic diversity is highest near Cuba and tapers off toward the periphery of the species distribution. This indicates that the current pattern of genetic diversity can be explained by the central–marginal hypothesis  and alleles per locus (A) toward the periphery of the species biogeographic range . This trend is most likely driven by reduced gene flow toward the edges, an increased likelihood of population extinction and recolonization, leading to genetic drift  and high clonality. Here, local genetic diversity was negatively affected by the restricted gene flow as propagules  must cross ~1,300 km of open ocean before reaching suitable habitat. The open ocean barrier between Bermuda and other T. testudinum populations is a strong indicator of the dispersal potential of this species, a characteristic that is likely widespread across all the seagrass groups .Gene flow was the most important microevolutionary force leading to homogeneity in genetic and allelic diversity among populations Hedrick, . ThalassWhen the populations of the Gulf of Mexico and the Caribbean basin are analyzed as separate entities, IBD relationships are very similar. The potential habitat in the Gulf of Mexico is not continuous but is intermitted along the northeast shores. We therefore expected these sites to be less connected, although this is not reflected in the outcomes. Interestingly, many of the Gulf sites (Laguna Madre and Tampa Bay) contained populations with the highest differentiation values to other sites. It is also important to note that geographic distance between populations is the shortest distance over water possible, which will not be the typical path of dispersal of propagules as local surface and subsurface currents are rarely linear , but low FST and D values (<0.1) are also observed at distances above a 1,000 km, suggesting that long‐distance dispersal might occur, although this is not supported by the chloroplast sequence data. Relative migration based on the alternative measure of Nm (number of migrants per generation) that considers both GST and D in its estimate (formula (12) Alcala et al., When we compare the differentiation measures in this study, re Jost, . Leng anre Jost,  recommen4.3T. testudinum based on the distribution of genetic diversity is moderate (FST = 0.161) which is consistent with previous findings for T. testudinum in Mexico . Ordination‐based analysis visualized as a PCA of the two most informative axes confirms the two‐region split  and that the fast‐flowing currents that lead into the Gulf Current can carry viable propagules, 1,300 km away from the nearest population. Fast‐flowing currents are a key environmental factor for T. testudinum's sexual dispersal as the fruits are buoyant. These fruits eventually open and the seeds, which are not buoyant, drop to the sea floor  are found along the boundary of the Gulf and Caribbean Figure . In part5Thalassia testudinum has been able to persist and thrive across the wide range of marine environments of the Western Atlantic through clonality providing a stable base to release propagules which disperse widely, although appear to recruit at long distances infrequently. Genetic structure modeling and analyses of phylogeography using sequence data indicate that there is population structure at the scale of two major regions, the Caribbean and Gulf of Mexico. As expected, isolated populations such as Bermuda and Tampa Bay show higher levels of genetic differentiation. Lower Laguna Madre populations are genetically secluded from the rest of the Western Atlantic populations and appear to be extending their local range via sexual recruitment. Overall genetic differentiation between populations increases with distance and at the extremes of the range populations are almost completely isolated.The findings presented in this study deliver significant insight into the genetic diversity, clonality, connectivity, and overall range‐wide population structure of a important tropical seagrass species. Our results indicate that the marine angiosperm The extant populations of this keystone species exhibit high levels of genetic diversity with an indicative higher level of diversity is around western Cuba, an area where both lineages exchange genes more frequently. Although across the range as a whole, the scale of connectivity is large, the frequency of successful recruitment appears to be only moderate. In addition, although genetically diverse, the slow rate at which microevolutionary processes occur in this long‐lived clonal species does pose a risk under rapid environmental change scenarios such as climate change.Thalassia testudinum.We provide evidence that demonstrates long‐distance migration is widespread and a variable process that brings new recruits  into local populations. Recent migration patterns  indicate more than 90% of recruits come from local sources. The fact that only two clusters are found with both genotypic (relatively fast‐evolving) and phylogeographic (relatively slow evolving) analysis tools indicates two separate recolonization events occurred in the Caribbean and Gulf of Mexico, which we propose aligns with recolonization following the last glacial maximum when range expansion was likely to be rapid and widespread for The authors have no conflict of interests.K.J.v.D is responsible for design, sample collection, data gathering, and analysis and led writing. E.B. is responsible for Florida Bay population collecting and data analysis and editing of the manuscript. B.I.v.T. involved in main supervision of PhD and is responsible for conception of ideas and interpretation of results and editing of the manuscript. M.W. is the main contributor to analytical approach, interpretation of results, and writing of manuscript. All authors approved the manuscript draft.https://doi.org/10.5061/dryad.g663p2b.DNA sequences: GenBank accessions See Supporting Information Table S15 in Appendix  Click here for additional data file. Click here for additional data file.


Scorzonera austriaca Wild by silica gel column chromatography and preparative HPLC. Their structures were identified, using chemical and spectroscopic methods, as 5,7,4′-trihydroxyflavone 6-C-(2''-O-β-d-glucopyranosyl β-d-glucopyranoside) (1), 5,7,3′,4′-tetrahydroxyflavone 6-C-(2''-O-β-d-glucopyranosyl β-d-glucopyranoside) (2), quercetin 3-O-rutinoside (3), 5,7,4′-trihydroxyflavone 6-C-β-d-glucopyranoside (4), 3′-methoxy-5,7,4′-trihydroxyflavone 6-C-β-d-glucopyranoside (5), 5,7,4′-trihydroxyflavone 8-C-(6''-O-trans-caffeoyl β-d-glucopyranoside) (6), and 5,7,3′,4′-tetrahydroxyflavone 8-C-(6''-O-trans-caffeoyl β-d-glucopyranoside) (7). Compounds 6 and 7 are new flavonoid glycoside derivatives, and compounds 1–5 were isolated from the herbs of Scorzonera austriaca for the first time. Compounds 6 and 7 were also assayed for their hepatoprotective activities with rat hepatocytes in vitro.Five flavonoid glycosides and two derivatives were isolated from the herbs of  Scorzonera austriaca Wild, a perennial herb of Compositae, is widely distributed in the northeast and northwest regions in China, especially abundant in Jilin Province, and has been widely used for curing fever, carbuncle, and mastitis as a traditional herbal medicine + ion peak at m/z 595.1437 , indicating the molecular formula C30H26O13. The 1H- and 13C-NMR spectra showed eleven aromatic or unsaturated proton signals, twenty-four unsaturated carbon signals, and six saturated carbons signals, among which a typical three-proton ABX aromatic spin system at δH 6.70 , 6.91 , 6.80 , a trans-disubstituted double bond conjugated with a carbonyl group at δH 7.40 , 6.15 , and nine unsaturated carbon signals at δC 166.7 (C), 148.4 (C), 145.5 (C), 145.4 (CH), 125.3(C), 120.5 (CH) 115.7 (CH), 115.3 (CH), and 113.6 (CH) were deduced to arise from a trans-caffeoyl moiety based on the analysis of 1H-1H COSY, HMQC, and HMBC of 6 and the comparison of 1H- and 13C-NMR spectra of 6 with the reported values + ion peak at m/z 611.1408 , indicating the molecular formula C30H26O14. The 1H- and 13C-NMR spectra of 7 were very similar to those of the reported compound, 5,7,3′,4′-tetrahydroxyflavone 8-C-β-d-glucopyranoside (7′) + ; 1H-NMR  δ: 7.93 , 6.93 , 6.78 , 6.49 , 4.65 , 4.44 , 3.41 , 3.16 , 3.16 , 3.33 , 3.67 , 4.18 , 2.85 , 3.03 , 3.01 , 2.64 , 2.94 , 3.17 ; 13C-NMR  δ: 163.4 (C-2), 102.7 (C-3), 182.0 (C-4), 161.1 (C-5), 107.9 (C-6), 161.1 (C-7), 93.3 (C-8), 156.4 (C-9), 103.3 (C-10), 121.1 (C-1′), 128.4 , 116.0 , 161.1 (C-4′), 71.1 (Glc-1), 81.0 (Glc-2), 78.4 (Glc-3), 70.4 (Glc-4), 81.6 (Glc-5), 61.4 (Glc-6), 105.3 (Glc′-1), 74.7 (Glc′-2), 76.4 (Glc′-3), 69.3 (Glc′-4), 76.4 (Glc′-5), 60.5 (Glc′-6).Compound 2: Yellow amorphous powder, yielded a positive reaction to FeCl3 reagent, mp 205–207 °C; UV (MeOH), λmax 258, 366 nm; HRESIMS m/z 611.1623 [M + H]+ ; 1H-NMR  δ: 7.41 , 7.40 , 6.89 , 6.65 , 6.45 , 4.66 , 4.44 , 3.45 , 3.17 , 3.17 , 3.41 , 3.68 , 4.19 , 2.86 , 3.06 , 3.03 , 2.65 , 2.99 , 3.17 ; 13C-NMR  δ: 163.5 (C-2), 102.7 (C-3), 181.9 (C-4), 160.6 (C-5), 107.9 (C-6), 163.0 (C-7), 93.3 (C-8), 156.4 (C-9), 103.3 (C-10), 121.4 (C-1′), 113.2 (C-2′), 145.7 (C-3′), 149.7 (C-4′), 116.0 (C-5′), 118.9 (C-6′), 71.2 (Glc-1), 80.9 (Glc-2), 78.4 (Glc-3), 70.4 (Glc-4), 81.6 (Glc-5), 61.4 (Glc-6), 105.5 (Glc′-1), 74.7 (Glc′-2), 76.2 (Glc′-3′), 69.3 (Glc′-4), 76.2 (Glc′-5), 60.6 (Glc′-6).Compound 3: Yellow amorphous powder, yielded a positive reaction to FeCl3 reagent, mp 185–187 °C; UV (MeOH), λmax 259, 359 nm; HRESIMS m/z 611.1620 [M + H]+ . 1H-NMR  δ: 7.54 , 7.53 , 6.83 , 6.36 , 6.17 , 5.33 , 4.39 , 0.99 ; 13C-NMR  δ: 156.4 (C-2), 133.2 (C-3), 177.2 (C-4), 161.1 (C-5), 98.8 (C-6), 164.7 (C-7), 93.6 (C-8), 156.4 (C-9), 103.6 (C-10), 121.0 (C-1′), 115.2 (C-2′), 144.8 (C-3′), 148.6 (C-4′), 116.1 (C-5′), 121.5 (C-6′), 101.3 (Glc-1), 74.0 (Glc-2), 76.4 (Glc-3), 69.9 (Glc-4), 75.9 (Glc-5), 66.9 (Glc-6), 100.7 (Rha-1), 70.5 (Rha-2), 70.3 (Rha-3), 71.8 (Rha-4), 68.2 (Rha-5), 17.7 (Rha-6).Compound 4: Yellow amorphous powder, yielded a positive reaction to FeCl3 reagent, mp 238–240 °C; UV (MeOH), λmax 260, 351 nm; HRESIMS m/z 433.1157 [M + H]+ ; 1H-NMR  δ: 7.90 , 6.97 , 6.74 , 6.64 , 4.58 , 4.07 , 3.20 , 3.15 , 3.15 , 3.43, 3.67 ; 13C-NMR  δ: 163.4 (C-2), 102.6 (C-3), 181.8 (C-4), 160.6 (C-5), 108.9 (C-6), 164.0 (C-7), 93.8 (C-8), 156.2 (C-9), 103.1 (C-10), 121.0 (C-1′), 128.3 , 116.1 , 161.4 (C-4′), 73.0 (Glc-1), 70.1 (Glc-2), 79.0 (Glc-3), 70.5 (Glc-4), 81.5 (Glc-5), 61.3 (Glc-6). Compound 5: Yellow amorphous powder, yielded a positive reaction to FeCl3 reagent, mp 208–210 °C; UV (MeOH), λmax 261, 348 nm; HRESIMS m/z 463.1253 [M + H]+ ; 1H-NMR  δ: 7.33 , 7.33 , 6.82 , 6.50 , 6.37 , 4.66 , 3.25 ; 13C-NMR  δ: 164.1 (C-2), 102.3 (C-3), 182.0 (C-4), 160.1 (C-5), 107.3 (C-6), 163.4 (C-7), 93.5 (C-8), 156.8 (C-9), 103.2 (C-10), 121.6 (C-1′), 108.6 (C-2′), 147.6 (C-3′), 150.3 (C-4′), 115.1 (C-5′), 120.0 (C-6′), 73.5 (Glc-1), 69.8 (Glc-2), 78.2 (Glc-3), 70.7 (Glc-4), 80.7 (Glc-5), 61.1 (Glc-6).Compound 6: Yellow amorphous powder, yielded a positive reaction to FeCl3 reagent, mp 208–210 °C; UV (MeOH), λmax 268, 339 nm; HRESIMS m/z 595.1437 [M + H]+ ; 1H-NMR  and 13C-NMR  see Compound 7: Yellow amorphous powder, yielded a positive reaction to FeCl3 reagent, mp 204–206 °C; UV (MeOH), λmax 269, 350 nm; HRESIMS m/z 611.1408 [M + H]+ ; 1H-NMR  and 13C-NMR  see Compound ·mL−1 penicillin and 100 mg·mL−1 streptomycin). The isolated cells were diluted to 8 × 106 / mL using the culture medium, and every 8 × 105 cells (0.1 mL) were seeded into a 48-well plate and incubated at 37 °C in a humidified atmosphere containing 5% CO2. After preincubation for four hours, the medium was replaced with fresh medium containing CCl4 (15 mM) and test specimens at various concentrations, and four hours later, the medium was taken to measure ALT. Enzyme-linked immunosorbent assay kits and DNM-9602 enzyme immunoassay spectrophotometer were used to measure ALT. For the results, see the Isolated rat hepatocytes from female Wistar rats were prepared by the collagenase perfusion technique with minor modifications . Culture6 and 7 are new flavonoid glycoside derivatives. Compounds 1–5 were isolated from S. austriaca Wild for the first time. Compounds 6 and 7 were also assayed for hepatoprotective activities with rat hepatocytes, and the data proved that compounds 6 and 7 exhibited hepatoprotective activities. Therefore, the new flavonoid glycoside derivatives 6 and 7 are considered to be two of the hepatoprotective properties in this plant. Compounds


For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission. A global decline of malaria in recent decades has led to a push for complete national elimination of human malaria parasites in 21 countries by 2020 . In elimDecisions about how to allocate resources for malaria control and elimination are generally based on reports from hospitals, clinics, community health workers (CHWs) and non-governmental organizations (NGOs) around the country, which provide a measure of the incidence of symptomatic cases . In areaPlasmodium falciparum infections are often composed of multiple different parasite clones, which undergo sexual recombination in the mosquito  or 4 (union). Typical of malaria in Southeast Asian countries, much of the transmission is thought to occur in forests and forest fringes where the principal vectors abound  in Bangladesh currently estimates the burden of malaria using symptomatic case counts aggregated at administrative level 3  of malaria positive individuals reported living outside forested areas. Of these individuals, the majority did not report any travel to forested areas , suggesting that either individuals were acquiring infections outside these areas, or that the travel survey did not capture all forest travel. The survey indicated a general separation between the north and south portions of the endemic region, with fewer people traveling between these two regions. Within the regions, however, substantial travel was reported between the high incidence forested areas and the lower incidence ones (−16). Conversely, we found that other genetic diversity metrics, except for drug resistance-related markers, showed limited spatial resolution  were more likely to come from the same residence unions . For parlocation . Using tlocation . Comparilocation , even thlocation , spatial−16). Using the empirical relationship between geographic and genetic distances, we assumed a uniform prior on geographic distance and calculated the probability of geographic distances between patients given different levels of genetic similarity . In brief, we quantified the decline of parasite genetic similarity with geographic distance, uncovering a robust signal; pairs of parasites sampled from unions that are geographically closer were more likely to be genetically similar . In addiGenetic measures based on SNP barcodes were able to identify unions with a high likelihood of importation events, but not the origin of imported infections since sharing genetic similarity did not provide information about the direction of movement. Epidemiological models that describe directional travel patterns may be used to combine reported incidence with data from travel surveys or mobile phone data to reconstruct routes of importation. We used mobility estimates from the survey and from mobile phone data to infer the amount of travel between locations in a simple epidemiological model that predicts the rate of infections moving between unions and mapped the resulting malaria source and sink locations , this combined approach illustrated that, while the eastern forested regions are indeed contributing imported infections to the lower transmission areas in the southwest, substantial local importation and genetic mixing is also occurring in these highly populated areas along the southwestern part of the CHT. This suggests that, while targeting interventions to forested areas is a key strategy for elimination, it might not be successful unless the mixing between low transmission settings in the southwest region is also addressed. We propose that, with appropriate sampling strategies, parasite genetic analysis could provide actionable insights for national control programs and help evaluate the success of elimination programs.Routine surveillance for malaria among control programs is primarily based on prevalence estimates and/or clinical incidence reported by hospitals, clinics, community health workers and non-governmental organizations. The geographic distribution of clinical cases may not reflect patterns of transmission, particularly in areas hosting highly mobile populations, where importation of parasites is common. Identifying the true foci of transmission is particularly important in elimination settings, such as Bangladesh. A number of approaches have been suggested to augment risk maps based on incidence data, including molecular surveillance and epidemiological models parameterized by new data streams, such as mobile phone data. Here, our goal was to assess whether a combination of these approaches could provide actionable insights for control programs. As a complement to a simple map of clinical incidence, or model-based geospatial maps based on incidence and prevalence estimates , followed by P. vivax (N = 332), mixed infections (N = 215), and unknown (N = 3). Trips from an individual’s residence village were quantified for work, frequent, infrequent, forest, and international travel including the destination , number of nights spent on the trip, frequency of travel (number of trips), and timing (when did the trip occur). Travel survey questions are listed in N = 147, 64%), however coverage was limited in the eastern most unions of the CHT.Patients self-presenting to 58 health facilities across hill districts Bandarban, Khagrachari, and Rangamati and adjacent coastal districts Chittagong, and Cox’s Bazar in Bangladesh from January 2015 to September 2016 and tested positive for malaria were recruited . In totaPlasmodium falciparum cases is reported monthly to the NMEP (N = 95) per month from January 2015 to August 2018, and were used to calculate the average monthly malaria incidence using the estimated population sizes from the most recent census (N = 141), monthly incidence data was available for unions and was used in lieu of the upazila level incidence data  the NMEP . These dt census . For a snce data . The pernce data . Geograpnce data .STF. Genotypes were produced using the mass-spectrometry based platform from Agena  or close (c) and genetically similar (s) or not genetically similar (ns). Since it is more likely to observe between individuals who have physically nearby residences, if a sample had a higher-than-expected probability of sharing genetic similarity with samples far away, it is likely that it was imported. We used samples that were ‘not genetically similar’ to control for the effect of sample sizes. This index is expected to increase with the proportion of imported cases and the number of ‘sources’  related to the travel survey data and compared the empirical results with 100 permutations (1T), (2) individuals coming from places with direct travel (denoted by 2T), and (3) individuals coming from places with indirect travel (denoted by 3T). Two unions were considered to have ‘indirect’ travel if they were both connected by travel to another union that had non-zero incidence. Specifically, we calculated the proportion of parasite pairs under these three scenarios, given different SNP thresholds (denoted by S), as follows:We compared genetic data with both population-level and individual-level travel survey data. At the population level, we examined how SNP differences . The results show parasite pairs with smaller SNP differences, not living in the same unions, were more likely to come from unions with higher direct travel, indicating the association between genetic similarity and mobile phone data . Nearly identical barcodes were defined as barcodes with less than a 10% SNP difference, and ‘not nearly identical barcodes’ was defined as barcodes with SNP differences between the 25th and 75th percentiles for all SNP differences. Based on the estimated population allele frequencies, expected pairwise similarity between two random parents is 0.558 and therefore the expected parent-offspring similarity after one outcrossing event is 0.779 (=0.5 + 0.5*0.558). This indicates that the SNP difference thresholds of 10% and 17.5% used here are conservative, falling within one outcrossing event (i.e. lower than 1–0.779 = 22.1%) and represent high similarity and recent ancestor sharing.At the individual level, we calculated the odds ratio of observing nearly identical barcodes with respect to the residence location or travel pattern was calculated as follows:D=  and six SNP difference windows S= . To establish what genetic distance alone tells us about the spatial relationships between isolates, we assumed a uniform prior with geographic distance. The inferred geographic distances (based on genetic data) can then be compared to the true geographic distance between samples collected by the control program, to assess whether isolates are imported or not. For all pairs of unions within a specified geographic distance window d given a SNP difference s,To investigate the relationship between geographic and SNP distance, we considered seven geographic distance windows Here, a uniform prior allows the genetic data alone to speak; depending on the question of interest, more realistic priors can be incorporated by weighting based on the known sampling distances.i to union j for all pairs of unions, ijT, where i.We calculated the number of trips between all pairs of unions from either the travel survey or mobile phone data. For the travel survey data, all individuals self-reported a residence union. We aggregated the trips to all other destinations by individuals for each residence union. For individuals who did not report any travel, we assumed they remained in their residence union. Based on the design of the travel survey, individuals could report travel for four separate reasons from their residence location  . For indijT was calculated by normalizing the number of trips each union made.Among 176 unions which had travel survey data, 104 of them (59%) had at least one mobile phone tower. We calculated the number of trips between unions with mobile phone towers between consecutive days and used the average number of trips over 6 months. Similar to the travel survey, i coming from union j, ijP, was calculated as follows:We estimated the parasite flow based on travel from the residence union scaled by the relative incidence in the destination union versus the residence union (see iiP and (1–iiP) were the estimated proportion of local transmission and importations, respectively. We calculated a population-level measure of parasite flow, the number of cases in union i from union j (ijM), based on the number of clinical cases in each residence union as follows: In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Neil Ferguson as the Senior Editor and Reviewing Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Edward A. Wenger (Reviewer #1); Oliver Brady (Reviewer #3).Thank you for submitting your article "The geography of malaria elimination in Bangladesh: combining data layers to estimate the spatial spread of parasites" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.The authors provide a logical framework for combining multiple data sources relating to travel history and parasite genetics, resulting in a single updated risk map for the Chittagong Hill Tracts region of Bangladesh. This updated map is informative of source-sink dynamics beyond simple prevalence/incidence measures produced by traditional epidemiological approaches and is therefore likely to be useful to NMEPs. Combining data layers in this way is a non-trivial problem due to the idiosyncrasies of different types of travel data and the complex signal in Plasmodium genetic data, and so this represents a significant step forward in terms of demonstrating the applied utility of Plasmodium genetic data over large spatial scales.The manuscript is clear and well-written. Nevertheless, we have some comments that should be addressed:First, this paper would benefit from more context in the presentation and interpretation of genetic signatures:a) How do complex infections or inferred within-sample diversity vary among samples and between geographic regions? Does this tell a complementary story to the parasite pairwise relatedness?b) Does presenting the relatedness information as a graph structure rather than bulk properties of pairwise relatedness provide any additional insights?c) Can the authors give more context for choices of relatedness thresholds – 10% "nearly identical"  and 17.5% similarity . How do these relate to e.g. 1 or 2 outcrossing events with random parents drawn from population-level minor allele frequencies?The big difference in coverage of travel survey and mobile phone data makes the assessment of their comparability / complementariness quite difficult to assess as a reader. While we appreciate there is not much the authors can do to change this, there are changes to the way the data are presented that could improve this, e.g. Figure 4 would be easier to interpret differences between the two data sources if only areas for which both travel survey and mobile phone data were available. The discussion could also elaborate more on the implications of this disparity in coverage.Given the huge amount of work that has gone into this analysis, the final risk map  is disappointingly non-specific. To what extent is this east-west flow of parasites a new result, or is it already known to some degree? If resources were limited, what would be the suggestion about how best to use them? Can top importation routes be predicted, and how much of total importation would they account for?We have some concerns about the new genetic mixing index. This is presented as an odds ratio on samples being in near vs. far spatial unions given their level of genetic similarity, but does this not have the "exposure" and the "outcome" the wrong way around, i.e. what we observe is genetic similarity as a function of distance, not the converse? The symmetry property of odds ratios means that numerically swapping these round ) would result in the same final value, but would seem to make more intuitive sense.There are many ways of constructing a statistic to measure the relationship between geographic and genetic distance, and so some statistical justification is needed for the use of an odds ratio. I'm sure the authors are aware that odds ratios are typically avoided in general due to frequent misinterpretation as risk ratios and their extreme behaviour in some parts of the parameter space . For example, if the "rare disease" assumption is being used to justify this statistic then this should be stated. If no justification can be given then perhaps a risk-ratio would be more appropriate.Some representation of uncertainty in the genetic mixing index should be given, either analytically or by simulation. Extreme uncertainty is another known weakness of odds ratios, hence it needs to be addressed.There is a tendency to swap between talking about genetic similarity as a function of distance (e.g. subsection “Genetic and model-based evidence of transmission in low incidence areas”) and distance as a function of genetic similarity  without appreciating that this involves a transformation. For example, when talking about the probability of geographic distance between parasite pairs  there must have been an application of Bayes' rule with a particular prior on distance, but this is not formally stated. Note that using the raw counts without a prior is equivalent to assuming a uniform prior on distance, which is almost certainly not appropriate here (we would not expect points to be uniformly separated a priori).How sensitive are values of the genetic mixing index to non-uniform sampling of clinical infections or genetic sequences from different transmission regions or subpopulations?Note – we understand and appreciate the intent of the genetic mixing index and feel it could be a valuable addition to the analytical toolbox, but it does need to pass certain basic statistical checks to avoid issues later on.Reviewer #1:This an important body of work, combining different sources of mobility data – travel surveys, mobile-phone records, parasite genetics – to quantify epidemiological and programmatic endpoints.1) This paper would benefit from significantly more context in the presentation and interpretation of genetic signatures.a) How do complex infections or inferred within-sample diversity vary among samples and between geographic regions? Does this tell a complementary story to the parasite pairwise relatedness?b) Does presenting the relatedness information as a graph structure rather than bulk properties of pairwise relatedness provide any additional insights?c) Can the authors give more context for choices of relatedness thresholds – 10% "nearly identical"  and 17.5% similarity . How do these relate to e.g. 1 or 2 outcrossing events with random parents drawn from population-level minor allele frequencies?2) How sensitive are mixing-metric values to non-uniform sampling of clinical infections or genetic sequences from different transmission regions or subpopulations?Reviewer #2:The authors provide a logical framework for combining multiple data sources relating to travel history and parasite genetics, resulting in a single updated risk map for the Chittagong Hill Tracts region of Bangladesh. This updated map is informative of source-sink dynamics beyond simple prevalence/incidence measures produced by traditional epidemiological approaches, and is therefore likely to be useful to NMEPs. Combining data layers in this way is a non-trivial problem due to the idiosyncrasies of different types of travel data, and the complex signal in Plasmodium genetic data which is just starting to be leveraged effectively. This manuscript therefore represents a significant step forward in terms of demonstrating the applied utility of Plasmodium genetic data over large spatial scales.One potential point of weakness in the analysis is the presentation of the new genetic mixing index. This statistic is presented as an odds ratio on samples being in near vs. far spatial unions given their level of genetic similarity. First, does this not have the "exposure" and the "outcome" the wrong way round, i.e. what we observe is genetic similarity as a function of distance, not the converse? The symmetry property of odds ratios means that numerically swapping these round ) would result in the same calculation, but would seem to make more intuitive sense.Second, there are many possible ways of constructing a statistic to measure the relationship between geographic and genetic distance, and so some statistical justification is needed for the use of an odds ratio. I'm sure the authors are aware that odds ratios are typically avoided in general due to frequent misinterpretation as risk ratios and their extreme behaviour in some parts of the parameter space . For example, if the "rare disease" assumption is being used to justify this statistic then this should be stated. If no justification can be given then perhaps a risk-ratio would be more appropriate. Some representation of uncertainty should also be given, as again this is a weakness of odds ratios. Keep in mind that this statistic may well be taken up by the community and applied/interpreted in other scenarios, and therefore it is important to ensure statistical robustness at this stage.On a similar note, there is a tendency to swap between talking about genetic similarity as a function of distance (e.g. subsection “Genetic and model-based evidence of transmission in low incidence areas”) and distance as a function of genetic similarity  without appreciating that this involves a transformation. For example, when talking about the probability of geographic distance between parasite pairs  there must have been an application of Bayes' rule with a particular prior on distance to obtain this. Note that using the raw counts without a prior is equivalent to assuming a uniform prior on distance, which is almost certainly not appropriate here.I could not find a reference or SI detailing the 101 SNP barcode used. Are all these SNPs in linkage equilibrium? Given that other investigators are likely to apply the genetic mixing index on larger data sets, some comment on the effect of LD on this statistic would be welcome.Other than these statistical issues I have no major concerns with the manuscript, which is well-written and makes a significant contribution to the field.Reviewer #3:This manuscript by Chang et al. details an interesting assessment of the combination of travel and genetic data to assess malaria elimination strategy in Bangladesh. It is highly novel, timely and interesting as well as being well described and reproducible. I have only a few comments on what I found an interesting, if a little difficult to follow, analysis.In the first part of the results it is shown that human movement is important for defining the network of parasite similarity. However in the second part, the authors use a genetic similarity index that only uses simple geographic distance. I understand that the authors want to propose a easily calculatable index, but it would be nice to see the best risk map possible for Bangladesh given the data available.The big difference in coverage of travel survey and mobile phone data makes the assessment of their comparability / complementariness quite difficult to assess as a reader. While I appreciate there is not much the authors can do to change this, there are changes to the way the data are presented that could improve this, e.g. Figure 4 – would be easier to interpret differences between the two data sources if only areas for which both travel survey and mobile phone data were available. The discussion could also elaborate more on the implications of this disparity in coverage.Given the huge amount of through work that has gone into this analysis, the final risk map  is disappointingly non-specific. If resources were limited, what would be the suggestion about how best to use them? Can top importation routes be predicted, and how much of total importation would they account for?eLife. Your revised article has been favorably evaluated by Neil Ferguson as the Senior and Reviewing Editor, and three reviewers.Thank you for resubmitting your work entitled "Mapping imported malaria in Bangladesh using parasite genetic and human mobility data" for further consideration at The manuscript has been improved but two reviewers require some remaining issues to be addressed before acceptance, as outlined below.I would also concur with reviewer 2 that the SNP positions need to be included in this paper or a citation given to a published source. A preprint on bioRxiv would suffice. However, if a preprint is not yet published, please include the positions here.Reviewer #1:Thanks to the authors for the welcome additions and responses to reviewer feedback.A reiteration of the substantial concern that publishing SNP positions with genetic sequences is an integral part of the work, especially in a journal dedicated to "improv[ing] research communication through open science and open technology innovation".Reviewer #2:I am happy that the new version does a good job of addressing the initial concerns, and I appreciate the work done by the authors.is not given by Figure 3—figure supplement 1.My only remaining concern is with the application of Bayes rule to obtain Prob (distance | similarity). First, I agree with the choice to swap Figures 3 and Figure 3—figure supplement 1. But the main text reads that there was "no prior" on geographic distance – there was a prior, it was uniform as stated in the Materials and methods section. It is impossible to apply Bayes rule without the use of a prior, and a uniform prior does not represent no information, it actually represents quite specific information. If the intention is to let the genetic data "speak" and not worry about the prior probability of edges being a certain distance then perhaps it would be better to report this as a raw likelihood, i.e. Prob (similarity | distance). Alternatively if the authors really want to stick with the inverted probability then this could be smoothed over with a statement to the effect of "a uniform prior allows the genetic data alone to speak, and more realistic priors can be incorporated by weighting based on the known sampling distances". At the moment it is unfortunately misleading, because the probability of a randomly chosen pair of samples being a given distance apart based on their genetic similarity almost certainly Reviewer #3:The reviewers have sufficiently addressed all my comments in this revision […] The manuscript is clear and well-written. Nevertheless, we have some comments that should be addressed:First, this paper would benefit from more context in the presentation and interpretation of genetic signatures:a) How do complex infections or inferred within-sample diversity vary among samples and between geographic regions? Does this tell a complementary story to the parasite pairwise relatedness?With the number of possible genetic analyses, we could have conducted and the space limit, we decided to constrain the number of genetic results we presented. However, we agree that we should present the COI results, since these were indicative of different transmission settings in other contexts . We have now expanded the Results to show additional genetic results.In particular, we now include a supplementary figure  that shows the variation in the complexity of infection between geographic regions. In general, the complexity of infection was low (we did not observe an average COI greater than 2 in any union) and the differences between locations were small, and therefore did not provide additional insights into malaria transmission.We mention that genetic measures shown in Figure 1—figure supplement 3 provided no or very limited spatial resolution in the Results as follows:“Conversely, we found that other genetic diversity metrics, except for drug resistance-related markers, showed limited spatial resolution .”b) Does presenting the relatedness information as a graph structure rather than bulk properties of pairwise relatedness provide any additional insights?In Figure 1—figure supplement 3E, we showed that average pairwise difference is not clearly associated with geographic distance. To compare with the graph structure shown in Figure 1C, we added one additional supplementary figure  to show the average pairwise difference in the map, which did not show any obvious geographic pattern. These patterns of relatedness across these populations mean that the most informative signals are found among highly related parasites rather than in average pairwise relatedness in our study, and this likely also applies to proximate populations in other low transmission settings as well.c) Can the authors give more context for choices of relatedness thresholds – 10% "nearly identical"  and 17.5% similarity . How do these relate to e.g. 1 or 2 outcrossing events with random parents drawn from population-level minor allele frequencies?The expected similarity based on population-level allele frequencies is 55.8%, and therefore one outcrossing event corresponds to 77.9% parent-offspring similarity (0.779= 0.5+(1-0.5)*0.558), which is equivalent to 22.1% difference. This means the SNP difference thresholds of 10% and 17.5% used in our study were within one outcrossing events and represent high similarity and recent shared ancestors. We added this information to give more context for these thresholds in the Methods and Materials as follows:“Based on the estimated population allele frequencies, expected pairwise similarity between two random parents is 0.558 and therefore the expected parent-offspring similarity after one outcrossing event is 0.779 (= 0.5+0.5*0.558). This indicates that the SNP difference thresholds of 10% and 17.5% used here are conservative, falling within one outcrossing event (i.e. lower than 1–0.779=22.1%) and represent high similarity and recent ancestor sharing.”The big difference in coverage of travel survey and mobile phone data makes the assessment of their comparability / complementariness quite difficult to assess as a reader. While we appreciate there is not much the authors can do to change this, there are changes to the way the data are presented that could improve this, e.g. Figure 4 would be easier to interpret differences between the two data sources if only areas for which both travel survey and mobile phone data were available. The discussion could also elaborate more on the implications of this disparity in coverage.We have added a new figure where only areas with both travel survey and mobile phone data were plotted . We also now discuss the implications of the disparity in coverage in the Discussion as follows:“The contrasting geographic coverage and scale of the two sources of data on human mobility – mobile phone and travel survey data – lead to different estimates of parasite importation. […] Given the difficulties associated with measuring population mobility and the importance of understanding importation in countries aiming for elimination, multiple  estimates of human travel provide the most complete picture of parasite flows.”Given the huge amount of work that has gone into this analysis, the final risk map  is disappointingly non-specific. To what extent is this east-west flow of parasites a new result, or is it already known to some degree? If resources were limited, what would be the suggestion about how best to use them? Can top importation routes be predicted, and how much of total importation would they account for?The dogma in Bangladesh is that nearly all malaria originates in the forest. While it is true that incidence is highest in the forest regions, this analysis highlights the importance of spread to the west, and the high volume of human travel around these regions. Our analysis suggests that the sheer volume of travel around the other lower transmission regions in the western part of Chittagong is likely to be sustaining transmission, and that these areas may be easier to target in any case. In terms of resource allocation, our study suggests that surveillance and control in lower transmission regions in southwestern Chittagong may be worthwhile, but that the importation routes that are most important are separate from north to south – therefore, it should be possible to conduct a staged elimination program focusing on each region separately. We have added the following section to the Results:“This means that resources for elimination should be targeted in these areas, in addition to the forested regions. […] Our results also suggest that importation routes occur separately in the north and south of the Chittagong region, implying that a staged elimination strategy could be effective.”With regards to the top importation routes: these are plotted in Figure 5 ABC and Figure 5—figure supplement 1. The total importation top routes account for is 86.4% and 87.8% for the epidemiological models parameterized by the travel survey and mobile phone data respectively. We have added the amount of total importation that the top importation routes account for in the legends of Figure 5 and Figure 5—figure supplement 1 as follows:Figure 5:“The top routes of parasite flows (the top 25% for travel survey and the top 1% for mobile phone) and their origins and destinations were shown by lines and dots, respectively. The top routes of parasite flows account for 86.4% (travel survey) and 87.8% (mobile phone) of the total importation.”Figure 5—figure supplement 1:“The top 10 routes of parasite flows using epidemiological models parameterized by the (A) travel survey (blue) or (B) mobile phone data (purple). These routes accounted for 39.1% (travel survey) and 40.2% (mobile phone) of the total importation.”We have some concerns about the new genetic mixing index. This is presented as an odds ratio on samples being in near vs. far spatial unions given their level of genetic similarity, but does this not have the "exposure" and the "outcome" the wrong way around, i.e. what we observe is genetic similarity as a function of distance, not the converse? The symmetry property of odds ratios means that numerically swapping these round ) would result in the same final value, but would seem to make more intuitive sense.After reading reviewers comments, we agree, and have decided to define genetic mixing index that is similar to a risk ratio rather than an odds ratio. We have revised the formula, and it is now Prob . Please see the responses to the comments below for more details.There are many ways of constructing a statistic to measure the relationship between geographic and genetic distance, and so some statistical justification is needed for the use of an odds ratio. I'm sure the authors are aware that odds ratios are typically avoided in general due to frequent misinterpretation as risk ratios and their extreme behaviour in some parts of the parameter space . For example, if the "rare disease" assumption is being used to justify this statistic then this should be stated. If no justification can be given then perhaps a risk-ratio would be more appropriate.We did make the “rare disease” assumption because the chance of observing genetically similar pairs is very low. We compared our original genetic mixing index with risk ratios and the values are nearly identical (the largest difference is 0.00133 and the average difference is 0.00019), but we agree with the reviewer that risk-ratio would be more appropriate and therefore modify our definition of genetic mixing index in the Materials and methods as follows:“Genetic mixing indexi=Probgenetically similar | farProbgenetically similar | close=nf,snf,s+nf,nsnc,snc,s+nc,ns.f,s, nf,ns, nc,si: n, and c,nsn represent the number of samples that are far (f) or close (c) and genetically similar (s) or not genetically similar (ns).”[…] The numbers were then aggregated across individuals within each union Some representation of uncertainty in the genetic mixing index should be given, either analytically or by simulation. Extreme uncertainty is another known weakness of odds ratios, hence it needs to be addressed.We agree that uncertainty is an important factor. We have now reported 95% confidence intervals of genetic mixing index in Supplementary file 1 and mentioned this in the Results as follows:“Figure 3B maps this index across the CHT, showing heterogeneous patterns of mixing in the southwestern region, with high mixing regions indicating frequently imported infections, and low genetic mixing suggesting predominantly local transmission. In contrast, we did not observe elevated genetic mixing in the north .”The formula used to obtain confidence intervals was described in the Materials and methods as follows:“The 95% confidence intervals were calculated as follows :Explog(geneticmixingindex) ± 1.961nf,s-1nf,s+nf,ns+1nc,s-1nc,s+nc,ns.”We emphasized that the uncertainty of genetic mixing index needs to be noted in the Discussion as follows:“The reliance of the genetic mixing index and other spatial approaches on nearly-identical parasites – which are rare, but hold the most meaningful genetic signals – means that small sample sizes and large uncertainty are inevitable challenges to their routine use. […] As molecular surveillance becomes more widespread, clearly establishing the strengths and limitations of genetic data will be critical.”There is a tendency to swap between talking about genetic similarity as a function of distance (e.g. subsection “Genetic and model-based evidence of transmission in low incidence areas”) and distance as a function of genetic similarity  without appreciating that this involves a transformation. For example, when talking about the probability of geographic distance between parasite pairs  there must have been an application of Bayes' rule with a particular prior on distance, but this is not formally stated. Note that using the raw counts without a prior is equivalent to assuming a uniform prior on distance, which is almost certainly not appropriate here (we would not expect points to be uniformly separated a priori).We agree with the reviewer that for most readers it is more intuitive to think about the probabilities of genetic similarity given geographic distance, and decided to replace Figure 3A with the original Figure 3—figure supplement 1 and move Figure 3A to a supplementary figure. We further include the following methodological updates below.We chose a uniform prior since we wanted to show what genetic distance alone tells us without knowing where samples are collected, and to explicitly compare this with the “true” geographic distance. In particular, we developed the method to provide information about future samples as they are collected by the control program, who will want to have some idea whether the transmission event was local or imported. When the SNP difference between samples is small, these isolates are likely to be from nearby locations  or, if the true geographic distance is large, it is likely a non-local transmission event. On the other hand, because the probability of a large SNP difference does not vary much with geographic distance , when the observed SNP difference is large (in our case >30%), we cannot infer anything about importation . We have included an explanation in the Materials and methods as follows:“To establish what genetic distance alone tells us about the spatial relationships between isolates, we assume a uniform prior with geographic distance. The inferred geographic distances (based on genetic data) can then be compared to the true geographic distance between samples collected by the control program, to assess whether isolates are imported or not.”We have modified the text to make it clear that we assumed uniform prior as follows:“Using the empirical relationship between geographic and genetic distances, we assumed no prior on geographic distance and calculated the probability of geographic distances between patients given different levels of genetic similarity .”The details on how we calculated the probability of geographic distance using Bayes’ rule was in the Materials and methods as follows:“Quantifying the probability of a geographic distance given a SNP differenceD=  and six SNP difference windows S= . […] We then calculated the probability of a geographic distance d given a SNP difference s,Probds, by applying Bayes' rule and assuming uniform prior as follows:To investigate the relationship between geographic and SNP distance, we considered seven geographic distance windows Probds=ProbProb(s)=ProbsdProb(d)∑alld∈DProbsdProb(d)⇒uniform priorProbsd∑alld∈DProbsd.”How sensitive are values of the genetic mixing index to non-uniform sampling of clinical infections or genetic sequences from different transmission regions or subpopulations?The genetic mixing index is a “relative probability” based on the proportion of genetically similar parasite pairs between places that are geographically close or far. Because the expected value of proportion does not vary with sample size, genetic mixing index is not expected to be sensitive to non-uniform sampling from different transmission settings. However, for subpopulations with smaller sample sizes, the confidence interval of the genetic mixing index is expected to be higher or the index may not be identifiable. We performed subsampling and showed in Figure 3—figure supplement 2that the genetic mixing index remained qualitatively similar.Thank you for resubmitting your work entitled "Mapping imported malaria in Bangladesh using parasite genetic and human mobility data" for further consideration at eLife. Your revised article has been favorably evaluated by Neil Ferguson as the Senior and Reviewing Editor, and three reviewers.The manuscript has been improved but two reviewers require some remaining issues to be addressed before acceptance, as outlined below.I would also concur with reviewer 2 that the SNP positions need to be included in this paper or a citation given to a published source. A preprint on bioRxiv would suffice. However, if a preprint is not yet published, please include the positions here.We have now included the positions in Supplementary file 6 and cited the file in the Materials and methods as follows:“Genotypes were produced using the mass-spectrometry based platform from Agena (Supplementary file 4 and 6).”Reviewer #1:Thanks to the authors for the welcome additions and responses to reviewer feedback.A reiteration of the substantial concern that publishing SNP positions with genetic sequences is an integral part of the work, especially in a journal dedicated to "improv[ing] research communication through open science and open technology innovation".We have now included the positions in Supplementary file 6.Reviewer #2:I am happy that the new version does a good job of addressing the initial concerns, and I appreciate the work done by the authors.My only remaining concern is with the application of Bayes rule to obtain Prob (distance | similarity). First, I agree with the choice to swap Figures 3 and Figure 3—figure supplement 1. But the main text reads that there was "no prior" on geographic distance – there was a prior, it was uniform as stated in the Materials and methods section. It is impossible to apply Bayes rule without the use of a prior, and a uniform prior does not represent no information, it actually represents quite specific information. If the intention is to let the genetic data "speak" and not worry about the prior probability of edges being a certain distance then perhaps it would be better to report this as a raw likelihood, i.e. Prob (similarity | distance). Alternatively if the authors really want to stick with the inverted probability then this could be smoothed over with a statement to the effect of "a uniform prior allows the genetic data alone to speak, and more realistic priors can be incorporated by weighting based on the known sampling distances". At the moment it is unfortunately misleading, because the probability of a randomly chosen pair of samples being a given distance apart based on their genetic similarity almost certainly is not given by Figure 3—figure supplement 1.We have changed “no prior” to “uniform prior” in the main text and added the sentence the reviewer suggested in the Materials and methods to make our statement clear as follows:“Here, a uniform prior allows the genetic data alone to speak; depending on the question of interest, more realistic priors can be incorporated by weighting based on the known sampling distances.”


AML) has been marred by significant hematologic toxicity due to targeting of both hematopoietic stem and progenitor cells (HSPCs). Achieving greater success with therapeutic antibodies requires careful characterization of the potential target molecules on AML. One potential target is CD300f, which is an immunoregulatory molecule expressed predominantly on myeloid lineage cells. To confirm the value of CD300f as a leukemic target, we showed that CD300f antibodies bind to AML from 85% of patient samples. While one CD300f monoclonal antibody (mAb) reportedly did not bind healthy hematopoietic stem cells, transcriptomic analysis found that CD300f transcripts are expressed by healthy HSPC. Several CD300f protein isoforms exist as a result of alternative splicing. Importantly for antibody targeting, the extracellular region of CD300f can be present with or without the exon 4‐encoded sequence. This results in CD300f isoforms that are differentially bound by CD300f‐specific antibodies. Furthermore, binding of one mAb, DCR‐2, to CD300f exposes a structural epitope recognized by a second CD300f mAb, UP‐D2. Detailed analysis of publicly available transcriptomic data indicated that CD34+HSPC expressed fewer CD300f transcripts that lacked exon 4 compared to AML with monocytic differentiation. Analysis of a small cohort of AML cells revealed that the UP‐D2 conformational binding site could be induced in cells from AML patients with monocytic differentiation but not those from other AML or HSPC. This provides the opportunity to develop an antibody‐based strategy to target AMLs with monocytic differentiation but not healthy CD34+HSPCs. This would be a major step forward in developing effective anti‐AML therapeutic antibodies with reduced hematologic toxicity.Antibody‐based therapy in acute myeloid leukemia ( ADCantibody–drug conjugateAMLacute myeloid leukemiaBMbone marrowCHOChinese hamster ovaryECLenhanced chemiluminescenceHSChematopoietic stem cellHSPCshematopoietic stem and progenitor cellsmAbmonoclonal antibodyMFImedian fluorescence intensityPBMCsperipheral blood mononuclear cellsTCGAThe Cancer Genome Atlas1et al., et al., et al., et al., et al., Intensive chemotherapy with or without allogeneic hematopoietic cell transplant cures a proportion of younger patients diagnosed with AML; however, these therapies are too toxic for the majority of patients , gemtuzumab ozogamicin, is the only current antibody‐based therapy for AML, after it was reapproved by the FDA in 2017 , NP_001276011 (Isoform 2), NP_001276012 (Isoform 3), NP_001276013 (Isoform 4), NP_001276014 (Isoform 5), NP_001276015 (Isoform 6), and NP_001276016 (Isoform 7). Studies have examined the binding of CD300f mAbs to the canonical CD300f (Isoform 1) expressed by transfected cells, but their binding to other isoforms is undefined. We have confirmed the validity of CD300f as a target on AML in a cohort of 35 AML patients and demonstrated that different CD300f‐specific mAbs recognize independent extracellular epitopes. Further, we showed expression of CD300f isoforms by cell lines and primary AMLs is complex and the different isoform expressed affects mAb binding. This work has defined key differences in the extracellular region of CD300f that will help design novel AML therapeutic antibodies to specific isoforms and minimize hematologic toxicity.Effective therapeutic antibodies targeting well‐defined epitopes mitigate off‐target toxicity. CD300f isoforms are expressed in leukemic cell lines; immunoprecipitation of CD300f from the histiocytic lymphoma cell line U937 identified 53 and 59 kDa proteins , UP‐D2 , and 234903 . An in‐house mAb, DCR‐2 , was generated from a mouse immunized with CD300f Chinese hamster ovary (CHO) transfectants and boosted with recombinant human CD300f‐Fc protein . The polyclonal antibodies used were rabbit antibody to the peptide representing residues 63–92 of the canonical CD300f sequence  and goat anti‐human LMIR3 . All antibodies detected epitopes on the CD300f Ig‐like domain. CMRF‐81 ].The myeloid‐derived cell lines HL‐60, U937, HEL, and THP‐1  were grown in complete RPMI containing 200 m2.3Venous blood and bone marrow (BM) samples were obtained, with informed consent, from healthy volunteers collected through the Department of Hematology, Concord Repatriation General Hospital. Cord blood was obtained through the Sydney Cord Blood Bank with ethical approval to use samples that failed banking volume criteria. Mononuclear cells were prepared using density gradient centrifugation over Ficoll‐Paque following the manufacturer's recommendations . Mononuclear blasts were prepared from excess AML diagnostic samples following patient consent. All consent forms were written and accompanied by participant information sheets. All patient sample protocols conformed to the guidelines set by the Declaration of Helsinki. Table S1 summarizes the characteristics of AML samples. The Concord Repatriation General Hospital Human Ethics Committee approved all protocols. Samples of AML were categorized as having a monocytic differentiation if they met the 2016 WHO criteria of acute myelomonocytic leukemia as well as acute monoblastic and monocytic leukemia by morphology and immunophenotyping, irrespective of genetic abnormalities.+ monocytes were purified from peripheral blood mononuclear cells (PBMCs) labeled with CD14‐FITC (clone M5E2) by cytometric sorting on a BD Influx or with a CD14‐positive selection kit from Miltenyi Biotec. AML samples were phenotyped with the following mAbs from BD Biosciences : CD45‐V500 (clone HI30), CD34‐PE‐CY7 (clone 581), CD38‐V450 (clone HB7), and CD33‐PE (clone WM53). Results were analyzed with flowjo software . The gating strategy for identifying the blast and leukemic stem cell populations is shown in Fig. S1. SSCloCD45dim blast populations were purified from AML samples labeled with CD45‐V500.CD142.4et al., 2 secondary reagents . Live cells were identified as propidium iodide‐ events. Data were collected on either an Accuri C6, Fortessa LSR, or Influx (BD Biosciences).Standard protocols were used to stain cells with directly conjugated specific, or isotype control, antibodies as described previously  PBMC. AML samples were gated with CD45 and CD34 to exclude nonblasts.To determine whether different antibodies bound similar epitopes, we preincubated target cells with saturating amounts of primary antibody in 0.5% BSA/PBS for 30 min on ice. Cells were washed with 0.5%BSA/PBS before incubation with a subsaturating concentration of the test antibody. Experiments were repeated three times. Percent binding of the test antibody was determined from median fluorescence intensity (MFI) by [MFI Test Antibody‐MFI Primary isotype]/[MFI Primary Antibody‐MFI isotype control] x 100. In cross‐blocking experiments using primary samples, CD342.57 cells were biotinylated with Sulfo‐NHS‐Biotin (Thermo) before lysis in M‐PER mammalian protein extraction reagent (Thermo) containing protease inhibitors . Proteins were immunoprecipitated with antibodies bound to Protein G Dynabeads according to the manufacturer's recommendations (Thermo). Samples were resolved through a 4–12% Bis‐Tris Plus gel (Thermo), with or without antioxidant, and transferred to nitrocellulose using an iBlot system (Thermo). Membranes, blocked with 5% BSA/TTBS, were incubated with primary antibody, followed by HRP‐conjugated species‐specific antibody, detected with enhanced chemiluminescence (ECL) reagent , and analyzed using a Bio‐Rad ChemiDoc imaging system . Biotinylated protein was detected with streptavidin–HRP and ECL.For immunoprecipitation, 2.5 × 102.6® Green Master Mix (Thermo). Duplicate samples of cDNA were amplified using a 7500 Fast Real‐time PCR System (ABI). CT values for splice variant amplification were normalized to the HPRT endogenous gene and presented as fold changes to a CD14+ or U937 cDNA reference sample using the formula: fold change = 2−ΔΔCT . Integrity and quantity of extracted RNA were assessed using an RNA 6000 Nano Bioanalyzer . All RNA used had a RNA integrity number > 8.8. For cDNA, 100 ng of DNase I (Thermo)‐treated RNA was reverse‐transcribed into cDNA using SuperScript III (Thermo). Oligonucleotide primers designed to detect splice variants were checked for specificity by BLAST alignment and are listed in the supplementary material. Gene expression was performed by qPCR on the cDNA using optimized primers and Fast SYBR Pfaffl, . Primer 2.7GSE63569 and GSE69239) from seven individuals and The Cancer Genome Atlas (TCGA) acute myeloid leukemia (LAML) data sets from 151 patients were downloaded. Due to differences in treatment, acute promyelocytic leukemia (M3) was removed from the TCGA analysis. Data sets were aligned with STAR RNA‐seq aligner version 2.4 to GRCh38.d1.vd1 genome  containing an amino‐terminal c‐myc epitope was expressed under the CMV promoter of the pBud vector in CHO cells. Cells expressing high amounts of surface c‐myc were sorted on a BD Influx. Sequences were validated at the Australian Research Genome Facility.2.9The specificity of antibodies for the CD300f Ig‐like domain, and cross‐reactivity with CD300b, was tested by ELISA using recombinant proteins obtained from Sino Biological. Antibodies and appropriate species and isotype controls were incubated with the immobilized recombinant protein, and binding was detected with the relevant HRP‐labeled secondary antibody and OPD.2.10SI4_F (amplifies exon 4 in Isoform 4 or 6): 5′CACGCCTACCTCCACTACGTTT; CD300fC _F : 5′ATTGACCCAGCACCAGTCACC; CD300f‐Ex4_R : 5′GGTGGCCGGTCAGAGTTG.The primer and probe sequences were Fw_hHPRT1: 5′AATTATGGACAGGACTGAACGTCTTGCT; Rv_hHPRT1: 5′TCCAGCAGGTCAGCAAAGAATTTATAGC; CD300f2.11prism . Comparisons between single groups were analyzed with t‐tests. Exon 3 and exon 4 expressions of RNA‐seq data and UP‐D2 binding of AML cell lines and primary samples were analyzed with one‐way ANOVA with multiple comparisons between groups.Statistical analysis was performed using 33.1loCD45dim AML blasts in 85%  precursor population within healthy BM , or HSC between bone marrow and cord blood .We assessed the binding of the CD300f‐specific mAb, UP‐D1, to 34 newly diagnosed AML samples and healthy bone marrow by flow cytometry using the gating strategy outlined in Fig. S1. UP‐D1 bound to SSC3.2All CD300f protein isoforms listed in NCBI protein database share the CD300f Ig‐like domain but differ in their leader sequence, exon 4‐coded sequence, and their cytoplasmic domain . We confirmed binding of the CD300f antibodies to CD300f Isoform 1  or Isoform 6 expressed on transfected CHO cells. Because all CD300 molecules share significant amino acid sequence similarity in the Ig domain, we confirmed the specificity of each CD300f mAb to CD300f and not the other family members by either flow cytometry on transfectants or ELISA. Of the CD300 molecules, CD300f shares the highest amino acid sequence identity with CD300b. The CLM‐1 peptide antibody and the gLMIR3 polyclonal antibody bound the Ig domain of CD300b .+CD300b‐ myeloid‐derived cell lines tested, with the exception of the CLM‐1 peptide antibody, which only bound to THP‐1 . Each mAb showed a different MFI ratio pattern. UP‐D1 had a low MFI ratio binding to HEL but a high ratio to HL‐60, U937, and THP‐1. The 234903 clone and DCR‐2 mAbs were similar with a high MFI ratio to U937, lower ratio binding to HL‐60 and THP‐1, and an even lower ratio binding to HEL. The mouse UP‐D2 clone and gLMIR3 polyclonal antibody showed similar MFI patterns . Testing the binding of each antibody to CD300f Isoform 1 expressed on transfectants in the presence of other CD300f demonstrated that no mAb completely cross‐blocked binding of another . These data indicated the presence of at least four distinct CD300f epitopes recognized by the antibody panel. They are the UP‐D1 epitope, a DCR‐2/243903 epitope, the UP‐D2 epitope, and a CLM‐1 epitope.Each antibody bound to the four CD300f+CD16−  and CD14dimCD16++ (inflammatory) monocytes . The myeloid‐derived cell lines were expressed in the order of 103.3et al., The MW predicted from the CD300f primary sequences ranges from 21.4 kDa to 33.7 kDa. UP‐D1 was originally shown to immunoprecipitate molecules of 53 and 59 kDa . The alternative has a splice insertion of exon 4 after the Ig domain before the transmembrane domain. Exon 4 encodes a Ser‐Thr‐rich sequence and is found in protein Isoforms 4 and 6 and here will be referred to CD300fSI4.Both alternative splicing events and different post‐translational modifications are likely to contribute to the variation in isoform expression. To understand the effect the expression of different isoforms has on antibody targeting of CD300f3.4SI4 and CD300fC transcripts from cDNA prepared from myeloid‐derived cell lines, healthy monocytes, healthy CB CD34+ HSPC, and AML blasts . We observed that CD300fC transcripts were more abundant than CD300f SI4 present in healthy CB CD34+ HSPC. AML blasts expressed both CD300f SI4 and exon CD300fC transcripts  (P < 0.05).The qPCR was used to amplify CD300f+ HSPC with AML RNA‐seq data from TCGA project. Exon 4 and exon 3  sequences were compared across the bone marrow CD34+ HSPC and both monocytic AML and nonmonocytic AML . There were significant differences between monocytic and nonmonocytic AML phenotypes in both exon 4 (P < 0.001) and exon 3 (P < 0.001) expressions.To confirm our qPCR findings on a larger sample set, we compared publicly available RNA‐seq data from healthy bone marrow CD343.5SI4 and CD300fC extracellular region when expressed in CHO cells. Comparing the MFI ratios of UP‐D1 to each isoform revealed that UP‐D1 bound to the CD300f SI4 isoform with a threefold higher ratio compared to the CD300fC. On the other hand, there was less than twofold difference in UP‐D2, 234903, and DCR‐2 binding to CD300f SI4 isoform compared to CD300fC isoform . Finally, in primary cells, DCR‐2 significantly enhanced UP‐D2 binding on monocytes but not CD34+ HSPC from CB. There was no significant difference in UP‐D2 binding with primary staining using PBS or an isotype control (data not shown). This demonstrated that the isoform of CD300f expressed by HSPCs can be distinguished from that expressed on mature monocytes  but absent on other AMLs compared to CB CD34+ HSPC  and the Australian Government National Health Medical Research Council Program Grant (543727). R.E.G received an NHMRC Scholarship APP1055892, and E.A. is a recipient of an NHMRC Scholarship (APP1134202).GJC is a Director of DendroCyte BioTech Pty Ltd. GJC and REG are listed as inventors on patents protecting DCR‐2. Other authors have no conflict of interest to disclose.EA, REG, FK, PAS, T‐HL, PDF, and GJC performed experiments; EA, REG, HJI, PJH, and KB provided samples and intellectual input; EA, REG, PAS, and GJC analyzed results and made figures; PMH and DNJH contributed intellectually to the project design; EA, REG, and GJC contributed to project design, analysis of results, and writing of manuscript.Table S1. Clinical characteristics of AML samples tested. ND Not determined.Click here for additional data file.Fig. S1. Gating strategy to identify AML and HSCs. (A) After initially gating on PI negative viable cells, hematopoietic stem cells were identified as lineage‐CD45dimCD34+CD38−CD45RA‐CD90+. Multipotent progenitors (MPP) were identified as lineage‐CD45dimCD34+CD38−CD45RA‐CD90−. Myeloid progenitors are contained in the CD34+ CD38+ subset. (B) Blasts were identified as CD45dimSSClow. The leukemia stem cell enriched CD34+CD38− fraction was identified from this gate. (C) The relative MFI ratios of total CD34 + cells, myeloid progenitors (CD34+ CD38+ subset), MPP and HSC were compared between bone marrow and cord blood cells.Click here for additional data file.Fig. S2. Sequences of the CD300f isoforms listed in NCBI indicating the alternating exon structure in blue/black type.Click here for additional data file.Fig. S3. Specificity of CD300f antibodies. (A) Binding of CD300f antibodies to CD300f transfected CHO cells. Antibody (unshaded histogram) compared to isotype for each antibody (shaded histogram). CD300f antibodies were tested by ELISA for binding to (B) CD300f‐Ig fusion protein and (C) CD300b‐Ig fusion protein. ELISA was performed n = 2, error bars represent SEM from duplicate wells of representative result. (D) Graphs showing the geometric MFI mean of CD300f antibodies binding to four myeloid derived cell lines. Error bars represent SEM.Click here for additional data file.Fig. S4. Expression of multiple CD300f splice variants in primary AML samples. Graph showing fold difference in expression of CD300fC and CD300fSI4 in cDNA prepared from AML samples with a high blast count, blasts sorted from AML populations, healthy CD14 monocytes HL‐60, HEL and U937. CD300f specific amplicons were normalized to the HPRT endogenous gene transcripts in U937. Error bars represent SEM.Click here for additional data file.Fig. S5. Cross‐blocking studies with CD300f antibodies. CD300f transfected CHO cells were incubated with a saturating amount of each primary antibody, (x axis). Cells were then stained with the test antibodies (A) UP‐D1 (B) 234903 (C) CLM‐1 (D) LMIR3. The binding of the test antibody in the presence of the primary antibody was calculated as by MFI compared to an isotype control, with 0 binding indicating complete overlap of epitopes, and 1 binding indicating no overlap of epitopes (n = 3). Error bars represent SEM. Click here for additional data file.