Yet, groundwater CH4 inputs and associated drivers are hitherto poorly constrained because their dynamics and spatial variability are largely unknown. Here, we unravel the important role and drivers of groundwater discharge for CH4 emissions from Arctic lakes. Spatial patterns across lakes suggest groundwater inflows are primarily related to lake depth and wetland cover. Groundwater CH4 inputs to lakes are higher in summer than in autumn and are influenced by hydrological (groundwater recharge) and biological drivers (CH4 production). This information on the spatial and temporal patterns on groundwater discharge at high northern latitudes is critical for predicting lake CH4 emissions in the warming Arctic, as rising temperatures, increasing precipitation, and permafrost thawing may further exacerbate groundwater CH4 inputs to lakes.Lateral CH 4 inputs to Arctic lakes via groundwater discharge are an important pathway that links CH4 production in thawing permafrost to emission via lakes. Here the authors unravel the role and drivers of groundwater inflows for CH4 emissions from Arctic lakes.CH CH4 is a potent GHG responsible for one-quarter of the radiative forcing by all long-lived GHGs2. Arctic lakes represent a large and climate-sensitive natural source of CH4 to the atmosphere, with emissions comparable to those from northern high-latitude wetlands3. In the context of climate warming, CH4 emissions from Arctic lakes are expected to increase 2–3 fold by the end of the twenty-first century5, potentially constituting a strong positive climate feedback. Yet, the sensitivity of CH4 emissions from Arctic lakes to climate change is highly uncertain because of a poor understanding of the underlying mechanisms controlling lake CH4 cycling.A major challenge to forecast future climate is constraining and regulating fluxes of greenhouse gases (GHG) such as methane  are abundant, and water flow paths are constrained within the shallow active layer , resulting in the supply of high loads of inorganic and organic carbon (C) to surface waters13. While external inputs of terrestrial C through groundwater discharge have been identified to have a critical influence on lake C cycling15, the significance of groundwater inflows for CH4 emissions from lakes has rarely been addressed.CH4 for lakes in Alaska, suggesting that groundwater CH4 inputs could entirely sustain CH4 evasion rates in summer13. However, since both mire CH4 production and export to receiving waters are highly dependent on environmental factors such as temperature, water table depth, active layer thickness, and topographic features17, snapshot observations in single lakes or seasons may not fully represent the relevance of large-scale or year-round CH4 inputs via groundwater. Extended studies covering multiple lakes and seasonal variability are thus needed to better assess the role of groundwater discharge on lake CH4 emissions under different environmental conditions, not least in the Arctic, which experiences strong seasonality in terrestrial CH4 dynamics, precipitation, and runoff. To fill this gap, we combined measurements of CH4 and radon (222Rn), a natural tracer of groundwater, in 10 lakes and adjacent groundwater in the Arctic region of Sweden  49–210 μM) were more than two orders of magnitude higher than in lake waters  and inlet streams  18, and in the active layer from the continuous permafrost zone in Alaska . The high CH4 concentrations in groundwater suggests that even relatively low groundwater inflows into lakes may disproportionately affect lake CH4 budgets. There was no evidence that groundwater CH4 concentration differed between seasons  . Similar to CH4, 222Rn concentrations in groundwater  were at least an order of magnitude higher than in lake waters  and inlet streams   and autumn  .We found a consistent enrichment of CH222Rn mass balance  and comparable to those found in two other lakes in Alaska using a similar approach (0.6–2.1 cm d−1)13. Groundwater inflows were higher in summer (range of 1.6–6.4 cm d−1) compared to autumn (range of 0.18–3.4 cm d−1).The 4 inputs supplied by groundwater into the study lakes ranged from 28 to 120 mg CH4 m−2 d−1 in summer and 2.0 to 59 mg CH4 m−2 d−1 in autumn, exceeding up to one order of magnitude the CH4 inputs through the inlet streams (range of <0.01–1.3 mg CH4 m−2 d−1) 12. Lower CH4 inputs through groundwater were found in Toolik Lake in summer (1.6–11 mg CH4 m−2 d−1)13, likely due to the lower CH4 concentrations in groundwater from the active layer (8–35 μM) compared to lakes in this study (50–210 μM).CH−1) Fig. . Similar4 cycling, we compared groundwater CH4 inputs with total CH4 emissions from the lakes. Diffusive CH4 fluxes to the atmosphere in summer ranged from 0.70 to 7.6 mg CH4 m−2 d−1. Lower diffusive CH4 fluxes were observed in autumn, ranging from <0.01 to 2.3 mg CH4 m−2 d−1. As ebullition fluxes were not directly measured here, we used the results from 9 years of flux measurements in three lakes within the Stordalen mire, close to the study lakes  across the boreal and Arctic region20. These potential ebullition rates yield maximum total CH4 emissions that ranged from 4.1 to 44 mg CH4 m−2 d−1 in summer and from <0.1 to 13 mg CH4 m−2 d−1 in autumn. Overall, these potential total CH4 emissions were of the same order of magnitude as the CH4 inputs supplied by groundwater  between groundwater discharge rates and total CH4 emissions during summer  − 0.27·log10(slope); df = 20, F = 7.2, p < 0.002). Groundwater inflows were positively related to lake depth, likely reflecting the higher water interception and groundwater connectivity of large and deep lakes compared to shallow systems. Contrary to our expectations, the strongest explanatory variables, wet area, and mire catchment, showed a negative relationship with groundwater inflows. We initially expected that mire cover would positively affect groundwater inflows to lakes as the cover of wet areas would represent the hydrological connectivity of lakes with the catchment. A possible explanation for this result is that increasing mire cover is related to flatter catchments, reducing the hydrological gradient and, consequently, reducing the lateral water transport and increasing the groundwater residence time in peatlands27. Our findings highlight the need to consider how groundwater inflows overlay landscape patterns of CH4 production to better assess lake CH4 emissions, as they may contrast and lead to unexpected responses on terrestrial CH4 export through groundwater. The complexity of hydrological pathways in a catchment and the number of variables involved in groundwater transport 30 prevents any further assessment of the spatial variance of groundwater inflows. Regardless, we show that it is possible to predict groundwater CH4 inputs into lakes based on spatial variables, which opens the door to future inclusions in regional assessments and/or Earth system models.There was a large variation in the magnitude of groundwater inflows among lakes and seasons. The partial least squares regression (PLS) showed that selected catchment  and lake characteristics (mean depth) together with precipitation explained 72% of the variability in groundwater inflows among lakes  in combination with lower CH4 production rates in autumn, which could result in similar levels of accumulated CH4. Overall, the strong correlation between groundwater inflows and atmospheric CH4 evasion likely results from a higher discharge in early summer, although the role of biological processes in CH4 cycling needs further attention39. The seasonal variability in groundwater inflows into lakes observed here emphasizes the need for a better characterization of temporal variations of this external driver for its inclusion in broad-scale estimates of lake CH4 emissions.Furthermore, changes in biological processes may also affect the CH4 emissions from Arctic lakes. The comparison of the magnitudes of groundwater CH4 inputs to lakes with other potential CH4 sources and sinks . Seasonally, groundwater CH4 inputs to lakes are influenced by hydrological (groundwater recharge) and biological drivers (CH4 production rates). This information on the spatial and temporal patterns of groundwater discharge in permafrost environments is fundamental to understanding the dynamics of landscape-level lake CH4 emissions and associated responses to climate warming.This study unraveled the important role and drivers of groundwater discharge for CH4 inputs to lakes and subsequent emissions to the atmosphere. For instance, rising temperatures result in extensive degradation of permafrost41, deepening active layer thickness, and higher amounts of organic C exposed to decomposition, which can fuel methanogenesis in anoxic environments42. At the same time, CH4 production is highly dependent on temperature43 and thus may also increase with current warming, increasing CH4 concentration in groundwater from the active layer. Furthermore, precipitation is expected to increase up to 40% in the Arctic by the end of this century44 which, together with enhanced groundwater recharge due to permafrost thaw and tundra greening45, may reshape the hydrology of the Arctic and increase groundwater inflows into lakes. Taken together, all these long-term changes can potentially increase the magnitude of lake CH4 emissions derived from groundwater inputs. Thus, groundwater CH4 inputs constitute an often-overlooked feedback in the ongoing climate change that needs to be recognized and addressed appropriately to better forecast future CH4 emissions from Arctic lakes.Our results indicate that the multiple facets of climate change in the Arctic may exacerbate the magnitude of groundwater CH46. Mountain permafrost is found approximately above 880 m a.s.l., whereas at lower elevations permafrost is present on north-facing slopes and wind-exposed areas because of the lack of an insulating snow cover during winter47  occurs near the Norwegian border, while the lowest (~300 mm yr−1) is around Abisko.The Torneträsk catchment in Arctic Sweden  across the precipitation gradient , with an average mean depth of 2.9 m and water volumes ranging from 24,000 to 760,000 m3.About 15% of the total catchment area is composed of lakes, concentrated in low elevation areas where this study was performed222Rn) as tracer of groundwater inputs. 222Rn (T1/2 = 3.82 d) is a radioactive isotope produced in the uranium (238U) decay series. Owing to its high enrichment in groundwater and its conservative behavior in waters54, 222Rn is an ideal geochemical tracer to detect and quantify groundwater inflow rates into surface waters56. We estimated groundwater inflow rates to the study lakes using a 222Rn mass-balance approach58. Major model assumptions include steady-state conditions over a relatively short period  and a well-mixed water column.We quantified groundwater inflow rates into the study lakes using the noble gas radon , diffusion of 222Rn from bottom sediments (Fdiff), discharge from the inlet streams (Finlet), and in situ 222Rn production from decaying 226Ra dissolved in the water column (FRa)), and sinks (evasion to the atmosphere (Fatm), losses through the outlet streams (Foutlet), and radioactive decay (Fdecay)). The change in 222Rn content over time [Bq d−1] can thus be described as:Our survey showed that the Qgw [m3 d−1] is the advective groundwater inflow; CRn,gw, CRn,lake, CRn,inlet, and CRn,outlet [Bq m−3] are the 222Rn concentrations in groundwater, lake water, inlet and outlet streams, respectively; Qinlet and Qoutlet are the mean water flow rates [m3 d−1] for the inlet and outlet streams, respectively; CRa,lake [Bq m−3] is the 226Ra concentration [Bq m−3] in lake water; fdiff is the molecular diffusion flux of 222Rn from underlying sediments [Bq m−2 d−1]; fatm is the atmospheric flux of 222Rn to the atmosphere [Bq m−2 d−1], λ is the 222Rn decay constant [d−1]; and A [m2] and V [m3] are the lake surface and volume, respectively. Note that this approach assumes that all lakes are not losing water via groundwater, resulting in estimates of the minimum amount of groundwater discharging into the lakes.This equation can also be expressed as:222Rn concentration in lake water is nearly in steady-state 222Rn in the water column. The 222Rn residence time , respectively. The rest of the parameters are described in Eq. k and h of 0.67 m d−1 and 2.8 m, respectively, and using the average Qoutlet/V measured in the study lakes (0.06 d−1), the average residence time of 222Rn for the study lakes is ∼2–3 days. Steady-state conditions over three days are thus a reasonable assumption considering the relatively stable environmental conditions in the days before the sampling .We assume that the 222Rn supplied by groundwater  into each lake under steady-state conditions. Uncertainties associated with Fgw were deterministically estimated by propagating the uncertainties of the individual terms in Eq. The mass balance in Eq. 222Rn flux (Fgw) and its uncertainty were then used for quantifying groundwater inflows and associated CH4 inputs to each lake based on a Monte Carlo analysis. The analysis consisted of generating 1000 values of Fgw for each lake . Each generated Fgw was then divided by a 222Rn concentration in groundwater  to derive a groundwater flow, and by a 222Rn to CH4 concentration ratio in groundwater , for calculating CH4 inputs. Both CRn,gw and CRn,gw/CCH4,gw were randomly selected from all groundwater samples collected (n = 41), producing a 1000-length list of groundwater flows and groundwater CH4 fluxes for each lake. Final fluxes are reported as the range of the median values for all the lakes. Groundwater inflows and associated CH4 inputs are reported in the manuscript as the median and the 25th and 75th percentiles of the 1000 simulations for each lake and season.The estimated The first survey in 2018 included three lakes  sampled in June (21–25) and September (8–12). In July (27–28) 2018, a short sampling campaign was conducted to collect groundwater samples from the active layer. For the second survey in 2019, nine lakes  were sampled in July (9–13) and September (13–20). Dates were selected to capture summer high flow and autumn base flow conditions.Surface lake water samples were collected from the shore and open-water areas of the lakes using a submersible pump  bottles, minimizing water-air contact to prevent 222Rn degassing. Shortly after collection, 222Rn activities were determined using a Durridge RAD7 electronic radon-in-air monitor coupled to the RAD7 Soda bottle aerator kit accessory. 222Rn measurements were decay-corrected and converted to water concentrations using the air-water partitioning of 222Rn corrected for water salinity and temperature60.For 222Rn supported by 226Ra decay in the lake water column was determined by measurements of the 226Ra concentration in lake water from five of the study lakes. Large volumes (30–50 L) of lake water were collected using a submergible pump and filtered slowly (<1.0 L min−1) through a column loosely filled with MnO2-impregnated acrylic fiber (ca. 25 g dry) to quantitatively extract Ra isotopes62. Fibers were rinsed with Milly-Q water, incinerated , ground, and transferred to hermetically sealed counting vials. Samples were analyzed using a well-type Ge detector (Canberra model GSW120) after storing the samples for a minimum of three weeks to ensure the radioactive equilibrium between 226Ra and its daughters.The concentration of 4 concentrations were determined by analyzing the headspace of gas-tight vials  after addition of 20 μL of 4% HCl to 4 mL sampled water, using a gas chromatograph . A gas mixture with known concentrations of CH4 (10 and 500 ppm) was prepared, stored, and analyzed as standards together with each batch of samples. Triplicate analyses of the standards were within 2% coefficient of variation. In a few samples (n = 4), CH4 concentrations were below atmospheric saturation and outside the detection limits of the instrument. Those values were assumed to be in equilibrium with the atmosphere.Dissolved CHn = 41) were collected from mire areas right at the lake shoreline  groundwater were collected and directly transferred to 20 mL polyethylene vials prefilled with a 10 mL high-efficiency liquid scintillator cocktail63. Concentrations of 222Rn were analyzed using an ultra-low-level liquid scintillation counter (Quantulus 1220) with alpha-beta discrimination counting . Samples for dissolved CH4 were collected simultaneously following the sampling procedure described above.Groundwater (20–40 cm deep) samples , and specific conductivity were measured in situ in lake and stream waters using a calibrated handheld water monitor (Yellow Springs Instrument ProSolo). A depth profile of temperature and DO was measured every 0.5 m from the surface at the deepest point of each lake. Water pH and conductivity were measured using pH and conductivity electrodes in the laboratory.64 (in 2019). Wind-speed, rainfall, air temperature, and air pressure data were acquired from weather stations permanently installed at the shore close to each lake.Discharge estimates from inlet and outlet streams were measured using an electrode magnetic flow meter  (in 2018) and based on salt slug injections66. The incubation experiments were used to constrain the diffusive 222Rn inputs from underlying sediments (fdiff) for the 222Rn mass balance and to obtain an independent estimate of the 222Rn concentration in the groundwater end-member   using a standard sediment corer made from PVC pipes. Three sediment cores were sliced into 1 cm thick sections, weighted, and dried to calculate porosity and dry bulk density. The remaining sediment cores were reserved for laboratory sediment incubation experiments67. Catchment delineations were made from a 2-m digital evaluation model68 using Whitebox GAT69, allowing to burn channels through road culverts70. Catchment slope was calculated using the “slope” function in the “Spatial Analyst” toolset in Arcmap 10.8 (ESRI). Catchment forest and mire cover were calculated by overlying vegetation maps68 to the catchment areas. Catchments for each lake were delimited using a flow direction and flow accumulation model for the landscape, derived from the national digital elevation model (DEM) with a horizontal resolution of 2 m . This analysis was performed using the hydrological toolbox from ArcMap 10.8 . Once catchments were delineated, several catchment properties were extracted from the DEM . Another landscape property used was modeled soil moisture . This machine learning product represents soil wetness in a scale from 0 to 10071, and here we used it as a proxy of surface hydrological connectivity, calculating the average value for each catchment. We characterized the fraction of the catchment above 70% wetness to quantify the catchment hydrological connectivity, which captures water-saturated zone areas , using as layers the global permafrost map72, Swedish land cover, and national orthophotos .Echo sounding was done at transects 10–20 m apart for bathymetric analysis. Lake average depth and surface area were calculated using ReefMaster v2.0 and the add-on volumes and areas222Rn and CH4 to the atmosphere were calculated as:kgas [cm d−1] is the gas transfer velocity for the corresponding gas at the measured temperature, Cgas,lake and Cgas,air [Bq m−3] are the gas concentration measured in the lake and the concentration expected when the lake is in equilibrium with the atmosphere, respectively.The flux of k, we used a wind-based model developed by Klaus and Vachon73 based on empirical k estimates from 46 globally distributed lakes data (see −1) and lake surface area (from 0.018 to 0.11 km2) cover a substantial range of the calibration dataset . To weigh the different influences on 222Rn budgets of degassing events depending on their proximity to the sampling time, we used a weighting factor to the hourly wind-speed data59. To evaluate the uncertainties associated to k parametrization, two other empirical equations for k estimates commonly used in lakes were used75.For data see . This mo4 fluxes  from several studies across the Arctic to evaluate the importance of groundwater discharge in lake CH4 cycling. This compilation includes warm-season (May–October depending on the location) diffusive and ebullition fluxes extracted from the Boreal-Arctic Wetland and Lake Methane Dataset (BAWLD-CH4)20 that is available at the Arctic Data Center (10.18739/A2C824F9X). Sediment production23 and oxidation23 rates were obtained from incubation experiments found in the literature and include results from 46 lakes across continuous, discontinuous, sporadic, and non-permafrost regions. The compiled values and the original sources and further details are found in Supplementary Data We compiled data on CH222Rn and CH4 concentrations were reported as the mean of collected samples (±1 standard deviation). Differences in 222Rn and CH4 concentrations between water sources  and between seasons (summer and autumn) were tested by analysis of variance (ANOVA), followed by Tukey-Kramer HSD post hoc test to identify differences between groups. All statistical tests were considered statistically significant at p < 0.05. We followed a two-step procedure to analyze spatial patterns in groundwater inflows. First, partial least squares regression (PLS) was conducted to detect correlation structures in the dataset and to rank the relative importance of catchment and lake characteristics on groundwater discharge rates. PLS is especially suitable for correlated covarying predictor variables and when there are more predictor variables than observations77. Catchment  and lake  characteristics, and precipitation were used as predictor variables. Cross-validation was used for selecting the optimal number of components that minimize the prediction errors (RMSE). The variable importance in projection (VIP) coefficients was calculated to classify predictors according to their explanatory power of the dependent variable. Then, multiple stepwise linear regression analysis was used to identify relationships among the most relevant predictor variables and groundwater inflows. Models were selected by considering all subsets on adjusted R2 and goodness-of-fit using the Akaike Information Criteria (AIC), such that the most parsimonious model yielded the lowest AIC value. AIC measures both the model fit and complexity and is used in model selection to reduce over-fitting. Predictor variables were evaluated for multicollinearity using Spearman correlation. Correlations between predictor variables with a p-value less than 0.05 were considered multicollinear and removed from the models. Response and predictor variables were visually inspected for normality and log-transformed to improve normality. All data processing, statistics, and figures were done in R (version 4.1.0)78, using the PLS79, and ggplot280 packages.Lake Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


Phylloscopus trochilus from a migratory divide in Sweden, where South-West migrating, and South-East migrating subspecies form a hybrid swarm. We find evidence that migration direction follows a dominant inheritance pattern with epistatic interaction between two loci explaining 74% of variation. Consequently, most hybrids migrate similarly to one of the parental subspecies, and therefore do not suffer from the cost of following an inferior, intermediate route. This has significant implications for understanding the selection processes that maintain narrow migratory divides.Migratory routes and remote wintering quarters in birds are often species and even population specific. It has been known for decades that songbirds mainly migrate solitarily, and that the migration direction is genetically controlled. Yet, the underlying genetic mechanisms remain unknown. To investigate the genetic basis of migration direction, we track genotyped willow warblers  The genetic determinants of long-distance migration in birds are largely unknown. Sokolovskis et al. tracked genotyped hybrid willow warblers from a migratory divide in Sweden and find that autumn migration direction is consistent with a dominant inheritance pattern of two large effect loci that interact through epistasis. Individuals of many songbird species migrate solitarily to specific and remote locations, implying that instructions for precise orientation are inherited3. Previously, seminal work using selective breeding of migratory blackcaps Sylvia atricapilla concluded that migration direction is controlled by one or few strong effect codominant loci4. Since then, identifying the underlying genes has been a major goal in bird migration research3. Recent efforts combining tracking and genotyping of wild migratory birds have revealed putative associations between a handful of genetic markers and specific wintering sites and/or migratory orientation8. Because the identified genetic regions are specific to each study, it remains unknown to what extent migration direction in birds has a common genetic basis.Seasonal migration is a widespread strategy for animals to avoid temporally adverse conditions on breeding sitesPhyllsocopus trochilus subspecies that have drastically different migratory routes. The nominate subspecies P. t. trochilus breeds in western Europe and migrates toward SW to western Africa while the subspecies P. t. acredula breeds in northern and eastern Europe and migrates SE to eastern and southern Africa10. The breeding ranges of the subspecies overlap in central Scandinavia and in eastern Poland, where they form migratory divides consisting of hybrid swarms12. Genetically, trochilus and acredula are nearly identical with a genome wide Fst = 0.00113. However, inversion polymorphisms on chromosomes 1 (InvP-Ch1) and 5 (InvP-Ch5) containing ~140 and ~50 genes, respectively, and presence or absence of a large (≥12 Mb) repeat-rich block characterized by multiple copies of a specific transposable element motif (TE) not yet assigned to a specific chromosome (Migration Associated Repeat Block in acredula (MARB-a), have almost fixed differences between the subspecies15. Allele frequencies at these loci show coinciding geographic clines with the change in migration direction, as inferred from analyses of feather stable isotopes  on the other hand is associated with a dominant effect for south-eastern migratory direction. We also find evidence for an epistatic effect of MARB-a suppressing the effect of InvP-Ch1.Our results suggest that migration direction in willow warblers follows a dominant inheritance pattern. The trochilus (n = 16) from southern and acredula (n = 5) from northern Sweden, followed the expected routes to western and southern Africa, respectively . The MARB-a effect is maintained regardless of genotype combination on InvP-Ch1 and InvP-Ch5  or presence (>7 copies of TE) of MARB-a  or migration direction . This demonstrates that the effect on migration direction is caused by the mere presence of MARB-a rather than copy number per se, supporting the interpretation that MARB-a has a dominant effect on migration direction.During migration, both subspecies breeding in allopatry, i.e., ely Fig. . Migratirts Fig. . The winrts Fig.  and S3. rts Fig. –S3 in re-a Fig. S, those wF48 = 7.6, p = 1.4 × 10−3, R2 = 0.2, Figs. trochilus allele on InvP-Ch1 appears to have a dominant effect that is epistatically suppressed by presence of MARB-a. Individuals that are heterozygotes or homozygotes for the trochilus allele on InvP-Ch1 tend to follow the western route unless they have the MARB-a . The functional roles of InvP-Ch1 and MARB-a in willow warblers remain unknown. Despite the geographic structure of InvP-Ch5 matching the locations of the migratory divides . However, autumn departure date was positively associated with breeding latitude , suggesting that in willow warblers, migration timing is either a polygenic additive trait that cannot be explained by the three markers and/or is to a large degree controlled by environmental factors. From the migratory divide, the great circle distance to the wintering range of allopatric acredula is about 50% longer than to the wintering range of allopatric trochilus. Because the birds wintering in West Africa cannot migrate more south than to the coast of the Gulf of Guinea (4°N), we restricted the analyses of genetic effects on migration distance to birds that wintered east of longitude 15°E. Almost all of these (21 out of 25) had the MARB-a and displayed large variation in migration distance . However, neither invP-Ch1 nor invP-Ch5 had any effect on migration distance .Autumn departure timing from the migratory divide did not associate with any of the three genomic markers (VPS13A) found in comparative genomic analyses of golden-winged Vermivora chrysoptera and blue-winged V. cyanoptera warblers6. Furthermore, a study on Swainson’s thrushes Catharus ustulatus identified a ~30 Mb block on chromosome 4 containing a cluster of SNPs that was associated with migratory orientation in birds tracked with geolocators and in a separate group tested in captivity5. However, neither VPS13A nor any region on chromosome 4 showed as an outlier in genomic comparisons of the willow warbler subspecies13. Additional evidence that the avian chromosome 1 harbors genes of significance for migration has been recently reported from a study on common quails Coturnix coturnix, where an inversion polymorphism on chromosome 1 sets apart sedentary and migratory individuals17.So far, the single most promising locus correlating with different winter longitudes in migratory passerines is the Vacuolar Protein Sorting 13 Homolog A  of the variation in migration direction was explained by a combination of MARB-a and InvP-Ch1, both with a dominant inheritance pattern and MARB-a suppressing InvP-Ch1 through epistasis. A consequence of this inheritance mode is that the migratory direction of most hybrids .We carried out the field work in Sweden during four breeding seasons (2018–2021). Adult male willow warblers were captured in their breeding territories using mist nets and playback of a song. From each bird, we collected the innermost primary feather from the right wing. From the birds that returned with a logger we also collected ~20 μl of blood from the brachial wing vein. The blood was stored in SET buffer  at room temperature until deposited for permanent storage at −20 °C. We deployed Migrate Technology Ltd geolocators  and used a nylon string to mount them on birds with the “leg-loop” harness method as outlined in our previous worktrochilus and three sites were located above the Arctic circle to record migratory routes of allopatric acredula, whereas the remaining (239) loggers were spread over 11 sites located in the migratory divide. Given the observed densities and distribution of hybrids after analyzing returning birds in 2019, we deployed 117 more loggers at one single site  in 2020. We successfully retrieved tracks from 57 birds tagged in 2019 and 16 from birds tagged in 2021. In search for birds with loggers, we checked circa 3000 willow warbler males and covered an area of at least 0.5 km radius around each site the year after tagging.The tagged birds were ringed with a numbered aluminum ring, and two, colored plastic rings for later identification in the field. In total, we tagged 466 males (349 in 2018 and 117 in 2020) at breeding territories. During the first tagging season (2018), birds were trapped at 17 locations  distributed across Sweden 25 was used to extract and analyze locations from raw geolocator data. All twilight events were obtained with light threshold of 3 lux. The most extreme outliers were trimmed with “loessFilter” function and a K value of 3. We used GeoLight’s function “getElevation” for estimating the sun elevation angle for the breeding period: these sets of locations were used to infer the positions for autumn departure direction. In addition, we carried out a “Hill-Ekström” calibration for the longest stationary winter site during the period before the spring equinox. Winter calibration produced location sets that better reflected the winter coordinates of the main winter site in sub-Saharan Africa26. We reduced some of the inherent geolocation “noise” by applying cantered 5-day rolling means to the coordinates. The equinox periods were visually identified by inspecting standard deviations in latitude. Latitudes from equinox periods were omitted (on average autumn equinox obscured data for 45 days (range 25–68). For the main winter site, we used the longest period at which bird stayed stationary and from which in all cases begun the spring migration . Timing of autumn departure was estimated by manual inspection of longitudes and latitudes plotted in time series. To estimate at which longitude the birds crossed the Mediterranean, we extracted the longitude when birds crossed latitude 35 N° (Mediterranean crossing longitude). For 29 birds, it was possible to directly extract the longitude at crossing latitude 35 N°. For the rest of the cases, the birds had not reached latitude 35 N° before the latitude was obscured by the equinox, we calculated the mean longitude of 10 days from the onset of fall equinox as a measure of the Mediterranean crossing. This measurement correlated highly with the winter longitude . To control for the birds relative breeding site longitude, we extracted the departure direction (1°–360°) relative from the tagging site to the location where the birds crossed the Mediterranean (departure direction). The departure data was of circular type (measured in 360°), however the variance did not span more than 180° degrees (range 151°–224°). Therefore, we proceeded with analyses using linear statistics. Geographic distances and departure direction were calculated using R package “geosphere” (version 1.5-10). Complete set of positions of each individual bird with equinoxes excluded is presented in Supplementary Data The R package 16. Genotyping for divergent regions on chromosome 1 (InvP-Ch1) and chromosome 5 (InvP-Ch5) was done using a qPCR SNP assay16, which is based on one informative SNP per region (SNP 65 for chromosome 1 and SNP 285 for chromosome 5). Probes and primers were produced by Thermo Fisher Scientific and were designed using the online Custom TaqMan® Assay Design tool , “Acr” (homozygous for acredula haplotypes) or “Het” (heterozygous). The method that we used to assess the presence of MARB-a is based on a qPCR assay that quantifies the copy number of a novel TE (previously known as AFLP-WW212) that has expanded in acredula. The quantification of repeats by this method has been shown to be highly repeatable (R2 = 0.88) when comparing estimates obtained from DNA in blood and feathers15. We used the forward (5′-CCTTGCATACTTCTATTTCTCCC-3′) and reverse (5′-CATAGGACAGACATTGTTGAGG-3′) primers developed by Caballero-López et al.15 to amplify the TE motif. For reference of a single copy region we used the primers SFRS3F and SFRS3R27. We diluted DNA to 1 ng/μl−1 and used a Bio-Rad CFX96™ Real-time PCR system  with SYBR-green-based detection. Total reaction volume was 25 μl of which 4 μl of DNA, 12.5 μl of SuperMix, 0.1 μl ROX, 1 μl of primer (forward and reverse), and 6.4 μl of double distilled H2O. We ran quantifications of the single copy gene and the TE variant found on MARB-a on separate plates with the following settings: 50 °C for 2 min as initial incubation, 95 °C for 2 min X 43 . Each sample was run in duplicate and together with a two-fold serial standard dilution (2.5–7.8 × 10−2 ng). Allopatric trochilus have 0–6 copies whereas allopatric acredula have 8–45 copies15; a bimodal distribution was also confirmed in this new data set . However, a precise distinction between heterozygotes and homozygotes on MARB-a is still not possible15.We extracted DNA from blood samples following the ammonium acetate protocolol Table . We usedet Fig. S. Moreove28. We initially constructed mixed effect models with timing of departure and tagging year as random factors however, this delivered singular fits due to insufficient sample sizes across categories. Normality of residuals was checked with a Shapiro–Wilk test. For carrying out circular statistics on autumn migration direction we used the R package “circular” (version 0.4-93). Watson’s U2 pairwise comparisons of different groups delivered the same results as linear models , InvP-Ch1 (a factor with three levels) and InvP-Ch5 (a factor with three levels) as explanatory variables. Models were constructed with R base package “stats”. We reported Type II ANOVA for models with more than one explanatory variable and no interactions and type III ANOVA results for models with interaction term by using R package “Car” version 3.0-12).0-1228. Further information on research design is available in the Supplementary_InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1


Our results demonstrate that pure quadrupolar magnetic excitations can be probed without direct interactions with dipolar excitations or anisotropic perturbations. Originating from on-site double spin-flip processes, the quadrupolar magnetic excitations in Y2BaNiO5 show a remarkable dual nature of collective dispersion. While one component propagates as non-interacting entities, the other behaves as a bound quadrupolar magnetic wave. This result highlights the rich and largely unexplored physics of higher-order magnetic excitations.The microscopic origins of emergent behaviours in condensed matter systems are encoded in their excitations. In ordinary magnetic materials, single spin-flips give rise to collective dipolar magnetic excitations called magnons. Likewise, multiple spin-flips can give rise to multipolar magnetic excitations in magnetic materials with spin S ≥ 1. Unfortunately, since most experimental probes are governed by dipolar selection rules, collective multipolar excitations have generally remained elusive. For instance, only dipolar magnetic excitations have been observed in isotropic S = 1 Haldane spin systems. Here, we unveil a hidden quadrupolar constituent of the spin dynamics in antiferromagnetic S = 1 Haldane chain material Y Multipolar magnetic excitations are challenging to measure as most experimental probes are sensitive to dipolar processes. Here, Nag et al. show the existence of dispersing quadrupolar components to the spin excitations in an antiferromagnet, highlighting the importance of higher order magnetic excitations. S = 1 antiferromagnetic Haldane spin chain, where magnetic order is suppressed in favour of a singlet ground state with nonlocal topological order2. Several theoretical and experimental works have established that a single spin-flip from this exotic ground state creates dipolar magnetic excitations that propagate along the chain above an energy gap of ΔH ~ 0.41J, the Haldane gap8.The elementary excitation of a magnetically ordered material is a single dipolar spin-flip of an electron, delocalised coherently across the system in the form of a spin wave. The spin-wave quasiparticle, known as a magnon, carries a spin angular momentum of one unit and has well-defined experimental signatures. Collective dipolar magnetic excitations also appear in low-dimensional magnets that remain disordered to the lowest achievable temperatures because of quantum fluctuations. A paradigmatic example is the d transition metal ions with a d2 or d8 configuration (such as Ni2+ for the latter) often possess strongly interacting spin-1 local magnetic moments with quantised spin projections Sz = −1, 0, 1. In addition to the usual single spin-flip excitations, it is possible to create quadrupolar magnetic excitations by changing the composite spin by two units. Such excitations can be conceived as flipping two of the constituent spin-1/2’s, as shown in Fig. S = 1 ferromagnetic chains as early as the 1970s10 and may play a role in the iron pnictide superconductors11. Since most probes are restricted by dipolar selection rules, however, such quadrupolar excitations have largely evaded detection except in rare situations where they are perturbed by anisotropic interactions, spin-orbit coupling, lattice vibrations, or large magnetic fields17. Quadrupolar magnetic excitations have never been observed in isotropic Haldane spin chains, even though dipolar magnetic excitations have been extensively studied18. It is then natural to wonder whether purely quadrupolar collective magnetic excitations exist in isotropic spin-1 systems.Materials hosting 3isotropicS = 1 Haldane chain system Y2BaNiO5 using high energy-resolution Ni L3-edge resonant inelastic x-ray scattering (RIXS). Previous studies on nickelates have already shown that Ni L3-edge RIXS can probe dipolar magnons20. In addition, ref. 21 recently showed that double spin-flips are allowed at this edge through the combined many-body effect of core-valence exchange and core-hole spin-orbit interactions23 . The feature with the largest spectral weight follows a dispersion relation consistent with inelastic neutron scattering (INS) results for the dipolar ΔS1 excitation24. This feature is also reproduced in our density matrix renormalisation group (DMRG) calculations of the dynamical structure factor S1 for an isotropic Heisenberg model , where the Haldane gap of ~ 8.5 meV exists. But it does probe an equally interesting region close to q∥ = 0. Prior work25 has focused on observing the breakdown of the well-defined ΔS1 quasiparticle into a two-magnon continuum for q∥ ≾ 0.12 and the spectral weight vanishing as S1 excitation in our experiment, we also observe significant inelastic spectral weight near zero energy close to q∥ = 0 . Interestingly, a new dispersing excitation is clearly visible with an energy maximum of ~ 136 meV at q∥ = 0. Of the calculated S0, S1, S2 dynamical structure factors, shown in Fig. S2 has spectral weight in this region. Moreover, the S2 line profile has two components: a broad low-energy continuum and a sharp high-energy peak, as shown in Fig. S2 excitation in this region of momentum space.Figure Sα . While the spectra are dominated by the dipolar ΔS1 excitations at high q∥ values, the spectral weight close to q∥ = 0 can be fitted with only the quadrupolar ΔS2 excitation. Figure S1 and ΔS2 contributions, respectively, along with their fitted profiles from DMRG. This analysis shows that the low-energy RIXS spectral weight is carried by the low-energy continuum component of the ΔS2 excitations below the quasiparticle decay threshold momentum. This component is gapped and has a peak energy of ~19 meV at q∥ ~ 0, reminiscent of the lower boundary of the two-magnon continuum at 2ΔH predicted for Haldane spin chains7. We note that the energy gap at this momentum has not yet been confirmed for any Haldane spin chain by INS due to the small scattering cross-sections. Figure S0, S1, and S2 obtained by fitting the RIXS spectra. The ΔS0 type of two-magnon continuum excitations provide a negligible contribution to the RIXS spectra (see Methods for the contributions from each type of excitation). Undoubtedly, the two-component excitation (the broad low-energy continuum and the sharp high-energy component), and its dispersion in the momentum space  excitations, convoluted with the experimental energy resolution. Since the raw DMRG data for the ΔS2 channel  finite temperature data using two components (see methods). Our results show that only the ΔS2 excitations obtained from DMRG are needed to reproduce the experimental RIXS spectra, even at finite temperatures. As shown in Fig. 5. Conversely, the high-energy peak begins to soften above the Haldane gap temperature of ~ 100 K. The highest energy value of the ΔS1 peak at q∥ = 0.25 also follows this trend. The bandwidth reduction of the ΔS1 triplet dispersion with temperature occurs due to the thermal blocking of propagation lengths and decoherence26. In a simple picture, if one considers a continuum from pairs of non-interacting triplets due to single spin-flips at multiple sites, then bandwidth reduction of each would manifest as the overall raising and lowering of the lower and upper boundaries of the continuum, respectively  or from the formation of a bound state . In Fig. S2 appears above the upper boundary of the continuum, ruling out the van Hove singularity scenario and suggesting the formation of a bound state. The peak energy of the high-energy component is slightly larger than twice the highest energy value of the ΔS1 peak (by ~ 7 meV at T = 11 K) and, surprisingly, remains so up to the highest measured temperature. The small positive energy difference suggests a weak repulsive interaction between the bound triplets formed after a quadrupolar ΔS2 excitation30. Supplementary Note The sharpness of the high-energy component and its rapid decay with temperature, on the other hand, hints that it behaves as a distinct quasiparticle formed from pairs of triplets propagating coherently see Fig. d28,29. IS1 excitations in Haldane spin chains decay exponentially in the presence of a non-local order, which can be viewed to originate from alternating Sz = ±1 sites intervened by Sz = 0 sites. The ΔS2 excitations also have an exponentially decaying correlation length, albeit smaller than ΔS1 excitations. The difference in correlation length is likely due to the fact that the creation of quadrupolar wave excitations costs energetically at least twice as much the single spin-flip magnon-like excitations at any wave-vector. As such, they may provide a means to detect the hidden non-local order that, at present, is only estimated theoretically by considering the Sz = ±1 states in Haldane spin chains32. Overall, we show that the ΔS2 excitations sustain the isotropic properties, the exchange interactions, and the coherence inherent to the system.The correlation length of the Δ33, multispinons34, spin-nematicity15, on-site multiferroicity12, and bound-magnon states 16, in a wide variety of magnetic systems. However, as noted earlier, multipolar excitations are challenging to detect using conventional probes35. It was shown recently that quadrupolar excitations in S = 1 FeI2 appear in INS only due to their hybridisation with dipolar excitations through anisotropic spin-exchanges14. In the presence of a strong anisotropy, bound magnetic excitations from ΔStot = 2 spin-flips at energies higher than the two-magnon continuum have also been observed in S = 1 spin chains using high-field electron spin resonance37. However, our demonstration of pure quadrupolar spin dynamics in an isotropic Haldane system, without invoking anistropic interactions, suggests that simultaneous confinement and propagation of excitations can occur entirely via higher-order quantum correlations38. This work thus illustrates RIXS’s capability of detecting higher-order dispersing excitations, irrespective of the presence of dipolar excitations14, and thus may be the preferable way to study quadrupolar excitations in, for instance, spin-nematic systems, where the dipolar excitations are suppressed40. Also recently, it has been seen that Cu L3-edge RIXS can probe spin-conserved and non-spin-conserved higher-order four-spinons in spin-1/2 Heisenberg antiferromagnetic spin chains41. Our present work further consolidates L-edge RIXS (in comparison to O K-edge)34, as a powerful probe for characterising nonlocal long-range magnetic correlations via the study of higher-order spin-flip excitations, thereby, extending both the energy-momentum phase space and the diversity of the magnetic quantum systems that can be explored. On the other hand, exploring the physics of high-energy excitations and/or eigenstates of a simple nonintegrable spin chain model is in itself of great theoretical interest. We provide a simple physical intuition about the nature of one of the high-energy eigenstates of the one-dimensional Heisenberg model and our findings may have important consequences for many-body correlated states of matter and thermalisation in quantum systems42. Looking forward, it would be interesting to learn how the quadrupolar excitations can be manipulated with intrinsic perturbations like anisotropy or extrinsic ones like a magnetic field. The indications of a propagating quadrupolar bound excitation in a real material can also have important ramifications for realising quantum information transfer in form of qubit pairs44.Magnetic excitations provide vital information about a system’s thermodynamic, magneto-transport, ultrafast magnetic, spintronic, or superconducting properties. Moreover, higher-order multipolar spin and orbital degrees of freedom give rise to exotic non-classical phenomena like the Kitaev spin liquid2BaNiO5 grown by the floating-zone method was used for the RIXS measurements. The momentum transfer along the chain direction q∥ was varied by changing the x-ray incident angle θ while keeping the scattering angle fixed at 154∘. The lattice constant along the chain or the c-axis used for the calculation of momemtum transfer is 3.77 Å. The crystal was cleaved in vacuum and the pressure in the experimental chamber was maintained below ~5 × 10−10 mbar throughout the experiment. High energy-resolution RIXS data (ΔE ≃ 37 meV) at the Ni L3-edge were collected at I21 RIXS beamline, Diamond Light Source, United Kingdom45. The zero-energy position and resolution of the RIXS spectra were determined from subsequent measurements of elastic peaks from an adjacent carbon tape. The polarization vector of the incident x-ray was parallel to the scattering plane (i.e. π polarization). See Supplementary Note A single crystal of YS = 1 Haldane chain system Y2BaNiO5 might present negligibly small single-ion anisotropy ~0.035J, exchange-anisotropy ~0.011J terms in a Heisenberg model description at low energies. We have verified numerically that these small corrections do not change our magnetic spectra qualitatively, and therefore a pure isotropic Heisenberg model has been adopted throughout our study.In the main text, we pointed out that the T = 0 DMRG calculations on 100 site chains with open boundary conditions (OBC) were carried out with the correction-vector method46 using the Krylov decomposition47, as implemented in the DMRG++ code48. This approach requires real-space representation for the dynamical structure factors in the frequency domain, which can be found in the Supplementary Note T > 0 calculations we used the ancilla (or purification) method with a system of 32 physical and 32 ancilla sites, also with OBC. For more details see Supplementary Note m = 2000 DMRG states to maintain a truncation error below 10−7 and 10−6, respectively and introduced a spectral broadening in the correction-vector approach fixed at η = 0.25J = 6 meV.For both the zero temperature and finite temperature calculations we kept up to S0, S1, and S2 giving information about ΔStot = 0, ΔStot = 1, and ΔStot = 2 excitations, respectively. To make the expressions more transparent, we use the Lehmann representation and construct the corresponding excitation operators in momentum space. The relevant correlation functions areWe consider three correlation functions Stot = 0 RIXS scattering operator is trivial (identity operator) in a low-energy description in terms of spin S = 1 sites, the lowest order operator would involve two-sites and, by rotational symmetry, involves a scalar product of neighbouring spin operators [see Eq. ]. ]. Stot =j = c, respectively, and fitting with a exponential decay relationship f(r) = Aer/ξ−.Figure T = 0 DMRG calculations for ΔStot = 0, 1, and 2  after they were convoluted with a Gaussian function capturing the experimental energy resolution. The total model intensity is given byC0, C1 and C2 account for the varying RIXS scattering cross section for each spin excitation with varying θ (q∥). The reader is referred to the Supplementary Note S2 from DMRG for ΔStot = 2 transitions do not capture the additional spectral weight on the high energy side in the RIXS signal. A ‘half’-Lorentzian truncated damped harmonic oscillator (HLDHO) function centered at 0.175 eV was therefore included in the fits to account for this tail. In Fig. Stot = 2 excitations at q∥ = 0.01(2π/c) are decomposed using a DHO function for the sharp high energy peak and a skewed Gaussian function for the broad lower continuum. Both functions are energy-resolution convoluted and weighted by a Bose factor. DHO functions were used similarly for fitting ΔStot = 1 triplet excitations at q∥ = 0.47 (see Supplementary Note RIXS data were normalised to the incident photon flux and corrected for x-ray self-absorption effects prior to fitting. The elastic peak was fit with a Gaussian function with a width set by the energy resolution. The RIXS spin excitations in Fig. Supplementary InformationPeer Review File


TM and EuroForMix resulting in a significant probative gain of information, .When a minor DNA component to a binary mixture is present at a weight ratio of approximately 1:50 or less, the presence of this minor donor is undetectable (or barely detectable) by standard mixture deconvolution approaches. In an attempt to retrieve probative minor donor DNA profile information, multiple quintuple cell subsamples were collected from a 1:50 DNA mixture using direct single cell subsampling (DSCS) paired with probabilistic genotyping (PG), the latter validated for use with single or few cells. DSCS employs a simplified micromanipulation technique paired with an enhanced DNA profiling approach, involving direct cell lysis and a sensitive PCR process, to genotype individual cells. Multiple five-cell subsamples were used to interrogate sufficient cells from the mixture such that some of the created 5-cell “mini-mixture” subsamples contained a cell from the minor donor. The latter mini-mixture subsamples, which now comprised weight ratios of 1:4 as opposed to the bulk mixture 1:50, were analyzed with the PG systems STRmix Standard DNA mixture analysis typically targets 0.5 ng to 1 ng DNA for amplification from a bulk DNA extraction process that basically “homogenizes” the mixture components. This can be problematic in instances in which one donor’s DNA is present in such a large proportional concentration that it effectively masks the presence of a minor donor due to PCR reaction kinetics. In many cases, alternative approaches such as physical separation by way of differential extraction ,3. HowevProbabilistic genotyping (PG), until recently, has primarily been applied to bulk samples extracted from DNA stains, and although highly useful, does not always result in probative DNA information for all donors to a mixture due to the presence of allele overlap, artifacts such as stutter, and in the case of this paper, the extreme minor presence of one of the donors ,5,6,7,8.A previous report has shown that isolation of multiple groups of cells from mixtures results in sub-populations with differing constituent weight ratios  that, inThe purpose of this study was to develop a cell subsampling strategy to enable the detection and analysis of a minor donor to a binary mixture that would otherwise be un-detectable or barely detectable using standard mixture interpretation methods. In the present work, the direct single cell subsampling approach, which utilizes the simplified micromanipulation technique paired with direct cell lysis and a sensitive PCR process to genotype individual cells, was used to collect five-cell subsamples from a complex binary mixture (1:50) in which the minor donor was virtually undetectable by standard methods. This resulted in new mini-mixture subsamples (with weight ratios of 1:4 as opposed to 1:50) that could then be analyzed and interpreted by way of probabilistic genotyping, resulting in a gain of significant probative DNA information from the minor donor.Buccal cell samples were collected from volunteers using procedures approved by the University of Central Florida’s Institutional Review Board. Samples were obtained from each volunteer by swabbing the inside of the mouth and cheek with a sterile cotton swab.−4, and the resulting solutions were centrifuged at 300 RCF for 7 min to create an epithelial cell pellet. The supernatants were discarded, and 300 μL of TE−4 was added to each cell pellet to create a cell suspension. The Countess™ II FL  automated cell counter was used according to manufacturer recommended protocols to determine the cell concentration of each cell suspension. The appropriate volumes of each donor cell suspension were mixed to achieve the desired donor mixture ratio . The mixture cell suspension was stored frozen (4 °C) before use.Freshly collected or dried/frozen buccal swabs from individual donors (“SA79” and “S3”) were agitated in 300 μL of TE® Gel-Film®  was affixed to a clean glass microscope slide via its adhesive backing, and the clear protective covering removed. Once the desired mixture was created, 60 μL of the mixture solution was pipetted onto the Gel-Film® microscope slide and spread out using a sterile swab. The slide was then stained with Trypan Blue for 1–2 min and gently rinsed with nuclease-free water. Slides were air-dried overnight.Gel-PakA 3M™  adhesive slide reservoir was prepared prior to cell collection, in which double-sided tape was used to attach the 3M™ adhesive to a clean glass microscope slide. The 3M™ adhesive backing was removed, and the slide was stored in a desiccator until needed ,17.TM  present in sterile 0.2 mL PCR flat-cap tubes by way of a sterile tungsten needle and water-soluble 3M™ adhesive. The tungsten needle was used to acquire a small ball of 3M™ adhesive, which was then utilized to adhere selected visualized cells from the mixture slide. Still under the microscope, the adhesive-tipped tungsten needle containing adhered cells was inserted into an amplification tube (0.2 mL) containing PunchSolutionTM  until the 3M™ adhesive is observed to solubilize [Cells were visualized using a Leica M205C stereomicroscope (190–240× magnification) and collected directly into 5 μL PunchSolutionlubilize ,17. An olubilize .TM  in 0.2 mL PCR flat-cap tubes. Samples were incubated until the PunchSolutionTM  evaporated using a lysis protocol of 70 °C → 30 min. After lysis, GlobalFilerTM Express reaction mix was added to each mixture consisting of 2 μL PCR mix, 2 μL primer mix, 1 μL 5X AmpSolutionTM . Samples were amplified using a modified protocol of 95 °C → 1 min; 32 cycles: 94 °C → 3 sec, 60 °C → 30 sec; 60 °C → 8 min; 4 °C → hold.Cells were collected directly into 5 μL PunchSolutionExpress™ Forensic DNA Extraction System  using the PrepFiler Express™ Forensic DNA Extraction Kits . An extraction blank was included with each extraction set performed.DNA was extracted from reference buccal swabs and 60 μL of the mixture cell suspension using the AutoMate TM  amplification kit according to the manufacturer’s recommended protocols. One nanogram of input DNA was used with an amplification protocol of 95 °C → 1 min; 29 cycles: 94 °C → 10 sec, 59 °C → 90 sec; 60 °C → 10 min; 4 °C → hold. Positive and negative amplification controls were included in each amplification.Amplification of the donor reference samples and the bulk DNA mixtures was conducted using the GlobalFiler® size standard . Samples were injected on an Applied Biosystems’ 3500 Genetic Analyzer using POP-4™ polymer and Module J6  and analyzed using GeneMapper™ ID-X v1.6 software .One microliter of GlobalFiler™ or GlobalFiler™ Express  amplified product was added to a mixture comprised of 9.5 μL Hi-Di™ formamide  and 0.5 μL GeneScan™ 600 LIZTM, the sub-source log (LR) was reported. The FBI Caucasian database was used for all allele frequencies in all mixture experiments [Probabilistic Genotyping Software STRmix™ v2.8 and EuroForMix v3.1.0 (Quantitative LR MLE based) was previously internally validated for use with standard bulk mixtures and reference samples. Each sample was analyzed according to the known number of donors. With STRmixProbabilistic Genotyping Software STRmix™ v2.8 [An infographic of the quintuple cell DSCS method is provided in p = 0.02. The binomial probability is 0.092, meaning that if one performed 75 subsamplings of 5 cells, then approximately 6–7 of them would result in mini mixtures containing the minor donor. A similar 75 subsamplings for a 1:100 mixture ) would result in an expectation of approximately 3–4 of the 100 subsamplings to be comprised of minor donor containing mini-mixtures.To determine at what mixture ratio a minor donor would become undetectable using our standard STR analysis system, two-person DNA mixtures ranging from tenfold ratios of 1:20 to 1:50 were prepared and analyzed. It was determined that a donor ratio of approximately 1:50 was the point at which a minor donor became virtually undetectable using standard methods. Thus, we proceeded to develop and investigate a DSCS strategy that would be applicable to undetectable/unresolvable mixtures with a minor donor ratio of approximately 1/50 but which would also probably be applicable to mixtures with lower weight ratios of down to approximately 1/100. The objective of the subsampling is primarily to interrogate a sufficiently large number of mixture cells such that some from the minor donor are recovered and able to be genotyped. The simplest approach would be to remove single cells as per the standard DSCS approach ,7, whichBased upon the above subsampling considerations, seventy-five five-cell subsamples were collected from the original 1:50 mixture. In accordance with the binomial sampling expectations, 6 of the 75 collected subsamples consisted of mini-mixtures, which could then be analyzed by the previously validated PG methods for single or few cells . ProbatiPG analysis of the standard bulk 1:50 mixture returned uninformative LRs  for The DSCS approach was applied to mixtures in which a minor donor was virtually undetectable by standard approaches resulting in a probative gain of DNA profile information. Collecting multiple five-cell subsamples permitted the genetic interrogation of a large number of cells (75 × 5 = 375 cells) in an attempt to detect the presence of a minor donor. This subsampling procedure deliberately created mini-mixtures containing cells from the minor donor and resulted, after PG replicate analysis, in highly probative inclusionary minor donor log(LR)s (~11). Although as a proof-of-concept study we took 75 × 5-cell subsamplings, laboratories could choose different subsampling schemes. For example, one could take 35–40 subsamples and evaluate the results to see whether a minor donor was detected (as might be expected if there was a 1:50 barely detectable minor donor mixture) then decide whether it would be necessary or useful to perform another subsampling. In our experiment, we detected the minor donor on the twelfth collected subsample , therefore 40 × 5-cell subsamples may be sufficient to obtain three replicates of the minor donor.The approach described could prove useful to not only improve the quality of results obtained from mixtures with an undetectable minor donor when both donors are male or when the minor donor is female, but also could provide additional inclusionary support for true donors in those mixtures that currently need to rely on Y-STR analysis for the detection of the person of interest. Although this proof-of-concept experiment was applied to a binary mixture, similar subsampling schemes to interrogate more complex mixtures with >2 donors (to recover probative DNA profile information from undetectable/unresolvable minor donors) can be envisioned.


Personalized approach in drug therapy is an essential line of modern psychiatry. Experimental and clinical studies of anxiolytics have shown differences in drug effects in dependence on genetically determined reactions to stress and personal features.To evaluate of the therapeutic effects and effectiveness of bromodihydrochlorophenylbenzodiazepine and fabomotizole in dependence on individually-typological features of patients with anxiety disorders.45 patients  with generalized anxiety disorder (n=22) and panic disorders with agoraphobia (n=23) participated in this open-label study. 13 patients treated with typical anxiolytics bromodihydrochlorophenylbenzodiazepine at dose 2 mg daily and 32 patients treated with atypical fabomotizole at dose 30 mg daily. The duration of treatment was 14 days. Minnesota Multiphasic Personality Inventory, Psychiatric Symptoms Severity Evaluation Questionnaire and CGI-E were administered.2 = 7,8), whereas in fabomotizole-in patients with stenic individual typological features .Asthenic features  were revealed in 26 patients and stenic features  were revealed in 19 patients. Patients with asthenic features had tranquilo-activating effect of bromodihydrochlorophenylbenzodiazepine, whereas patients with stenic features had tranquilo-sedative effect. The tranquilo-activating effect of fabomotizole was revealed in patients with stenic features. High efficacy of bromodihydrochlorophenylbenzodiazepine was observed in patients with asthenic personality traits (χ Patients with stenic and asthenic features had differences in therapeutic effects and the effectiveness of anxiolytics. Personality features determine the sensitivity of patients with anxiety disorders to psychotropic drugs.No significant relationships.


World Report on Vision, the WHO called for an updated approach to monitor eye health as part of universal health coverage (UHC). This project sought to develop a consensus among eye health experts from all world regions to produce a menu of indicators for countries to monitor eye health within UHC.In its recent World Report on Vision. We recruited a panel of 72 global eye health experts (40% women) to participate in a two-round, online prioritisation exercise. Two-hundred indicators were presented in Round 1 and participants prioritised each on a 4-point Likert scale. The highest-ranked 95 were presented in Round 2 and were (1) scored against four criteria  and (2) ranked according to their suitability as a ‘core’ indicator for collection by all countries. The top 30 indicators ranked by these two parameters were then used as the basis for the steering group to develop a final menu.We reviewed the literature to create a long-list of indicators aligned to the conceptual framework for monitoring outlined in WHO’s The menu consists of 22 indicators, including 7 core indicators, that represent important concepts in eye health for 2020 and beyond, and are considered feasible, actionable, reliable and internationally comparable.We believe this list can inform the development of new national eye health monitoring frameworks, monitor progress on key challenges to eye health and be considered in broader UHC monitoring indices at national and international levels. World Report on Vision released in 2019, WHO included the strengthening of health information systems (HIS) among its five global priority areas for action.In its first World Report on Vision also reflects the limited progress made to date. Several lists of indicators have accompanied global eye health initiatives over the past two decades.The priority placed on HIS in the Universal Eye Health: A Global Action Plan 2014–2019‘GAP’) in 2013, WHO emphasised the need for eye care to be integrated into broader health planning. The World Report on Vision went further to state that eye health should be considered an essential component of universal health coverage (UHC).Based on these and other challenges, in its Lancet Global Health Commission on Global Eye Health.Here we report a collaborative prioritisation process to generate a menu of indicators that may be used by governments to monitor and improve eye health and eye health services at the national level, and to support progress towards achieving UHC. This work was undertaken as part of the www.qualtrics.com). All panellists’ responses were de-identified throughout, however, individuals were provided the option to join a study authorship group.A two-round, prioritisation exercise was undertaken between February and April 2020 using an online survey platform (A project steering group (the co-authors) was convened to guide the development of the initial long-list of indicators, nominate panellists from a network of global eye health experts, review indicator scoring and develop the final menu. We aimed to recruit panellists from all Global Burden of Disease (GBD) Super Regions,World Report on Vision   . When pasystems) .10.1136/bjophthalmol-2020-318481.supp1Supplementary datapriority score for each indicator was calculated by summing the products of two dimensions: the Likert scale score (1–4) x the number times each indicator received that score.Panellists scored the indicators based on perceived priority in their context. Priority was scored from 1 to 4 on a Likert scale, with 1 representing the lowest priority ('no need to collect') and 4 the highest priority  . A fifth10.1136/bjophthalmol-2020-318481.supp2Supplementary dataAt the end of Round 1, an initial threshold for continued inclusion was set at or above the median score. Indicators scoring in the top half were merged where there was sufficient overlap in concepts to do so. Indicators not scoring in the top half were reviewed to determine if any concepts deemed essential to score in Round 2 had been omitted and should be included to ensure representation . In totaEach of the 95 indicators were scored against four new criteria. The panel were asked to indicate their agreement on a 4-point Likert scale  as to whether each indicator was feasible, actionable, reliable and internationally comparable . In addicomposite score of all four was calculated, with all criteria weighted equally. Each indicator was assigned a rank position from 1 to 95 for each of the four criterion and the overall composite score. The ranking of indicators 1 to 10 as core indicators was calculated in a similar way: a vote for first place awarded 10 points, second place awarded 9 points and so on. Points were multiplied by the number of times an indicator received that vote position for an overall core score .Box 1Equity statementAll indicators summarising population-based and eye care facility-based data should report metrics disaggregated by key equitydimensions of sex, place of residence (PoR), socioeconomic position (SEP) and disability status, where available. Additional options, suchethnicity or marital status, can be recorded by countries as appropriate.Inputs and processesGovernanceG1 Eye health is integrated into the national health strategy/plan  ► G1.1 National health plan includes human resources for eye care (Y/N) ► G1.2 Eye health is integrated into the plans, policies and budget of other initiatives such as:  – G1.2.1 National essential package of health services (Y/N)  – G1.2.2 Primary healthcare (Y/N)  – G1.2.3 Maternal and child healthcare (Y/N)  – G1.2.4 Diabetes care (Y/N)  – G1.2.5 School health programmes (Y/N)  – G1.2.6 Healthy ageing programmes (Y/N) ► G1.3 National eye health policies, plans and programmes refer to a multisectoral approach/engagement with other sectors (Y/N)If a national eye health strategy/ plan is unavailable or not up-to-date, record as N  – G2 Is the national eye health plan informed by recent evidence (Y/N): ► G2.1 Time since cited population-based data was collected (in months/years) ► G2.2 Time since cited Eye Care Service Assessment Tool (ECSAT) data was collected (in months/years)FinanceF1 Eye health is integrated into the national health budget (Y/N)Requires a working group to develop sub-indicators and metadata – F2 Eye health is included in national health finance pooling mechanism (Y/N)Scaled response based on scoring outcomes of sub-indicators in ‘checklist’ –    If yes, the range/number/list of services addressing leading causes of vision impairment (VI) included: ► F2.1 Outpatient consultation  ► F2.2 Cataract  ► F2.3 Refraction services  ► F2.4 Glaucoma medication/surgery  ► F2.5 Diabetic retinopathy – laser/anti-vascular endothelial growth factor (VEGF) F3 Proportion of population covered via national health finance pooling mechanisms that includes eye care services: ► F3.1 Proportion covered for: Outpatient consultation ► F3.2 Proportion covered for: Cataract ► F3.3 Proportion covered for: Refraction services ► F3.4 Proportion covered for: Glaucoma medication/surgery ► F3.5 Proportion covered for: Diabetic retinopathy – laser/anti-VEGFInfrastructureI1 Eye health facility density and distribution, disaggregated by: ► I1.1 Primary ► I1.2 Secondary ► I1.3 Tertiary ► I1.4 Low vision servicesBy PoR , total numbers (public and private) per million population  – Additional subnational administrative or geographical divisions as relevant to setting  – Additional dimension: Access to primary eye care and cataract surgery via global positioning system data and geospatial modelling – I2 Percentage of neonatal units providing screening for retinopathy of prematurity nationallySupply chainSC1 Pharmaceuticals specifically for eye care on the National Essential Medicines List  – Total number and proportion compared with a normative standard for eye health pharmaceuticals InformationINFO1 Existence of a National Health Information System that includes eye care service data (Y/N)Eye health workforceHR1 Eye health worker density and distribution, disaggregated by: ► HR1.1 Ophthalmologist ► HR1.2 Optometrist ► HR1.3 Ophthalmic nurse ► HR1.4 Other allied ophthalmic personnel (as relevant to country)By PoR , total number per million population, and by age groups and sex  – Additional subnational administrative or geographical divisions as relevant to setting  – Additional dimension: 5-year trends per cadre  – HR2 Is Primary Eye Care integrated into the national Primary Healthcare training (if applicable)? (Y/N)OutputsAccessAC1 Cataract surgical rateTotal number per million population and including variation in rate across urban/rural or districts – Additional dimension: 5-year trend in cataract surgical rate – Additional dimension: Surgical case-mix in terms of preoperative visual acuity – Quality and safetyQ1 Cataract surgical outcome Proportion of eyes with a 'good' outcome (6/18 or better) – Proportion of eyes with a ‘poor’ outcome (worse than 6/60) – Q2 Number of priority eye conditions with quality of care/clinical practice guidelines endorsed by relevant regulatory bodies ► Q2.1 Cataract (Y/N) ► Q2.2 Refractive error (Y/N) ► Q2.3 Glaucoma (Y/N) ► Q2.4 Age-related macular degeneration (Y/N) ► Q2.5 Diabetic retinopathy (Y/N) ► Q2.6 Child eye health (Y/N)Responsiveness/affordabilityAF1 Median (range) of out-of-pocket payment made for cataract surgery as a proportion of median monthly household incomeReport median and mean payment made at point of service  – Disaggregated by provider type  – Additional dimension: proportion reported for poorest vs wealthiest quintiles – OutcomesCoverageC1 Cataract surgical coverage and effective cataract surgical coverageCSC , eCSC (effective CSC), ‘quality gap’ reported, disaggregated by age, sex, SEP, PoR as available – C2 Refractive error coverage and effective refractive error coverageREC (refractive error coverage), eREC (effective REC), ‘quality gap’ reported, disaggregated by age, sex, SEP, PoR as available – C3 Coverage of diabetic retinopathy screening of all people with diabetes Requires a working group to develop complete indicator metadata – Disaggregated by age, sex, SEP, PoR as available – C4 Coverage of school eye health programmes for schools nationallyProportion of schools receiving screening in the past 12 months – Disaggregated by primary and secondary schools – ImpactImproved outcomesP1 Prevalence of VI ► P1.1 Distance VI prevalence, by WHO categories ► P1.2 Near VI prevalence, by WHO definitionFrom population-based surveys, disaggregated by age, sex, SEP, PoR as available  – P2 Cause-specific prevalence of VIPrevalence of vision-impairing priority eye conditions from population-based surveys, disaggregated by age, sex, SEP, PoR as available –  ► P2.1 Avoidable blindness/severe VI/moderate VI/mild VI prevalence disaggregated by age, sex, SEP, PoR as availableAggregated from VI causes assigned in surveys – P3 Prevalence of childhood VI and blindnessBlindness/severe VI/moderate VI/mild VI from population-based or key-informant surveys, disaggregated by age, sex, SEP, PoR as available  – No major edits to key concepts were undertaken. This process is summarised in Twenty-two distinct eye health indicator concepts were identified . In compThe steering group selected seven core indicators for monitoring eye health as part of countries’ progress towards UHC. These are set out in World Report on Vision. We believe the core indicators highlighted here, if collected by all countries, could allow governments, and supranational organisations, to track progress on key challenges within eye health and UHC. Otherwise, the menu is not intended to be prescriptive; countries could select indicators according to priorities based on population need. We recognise that some countries will likely benefit from collecting and reporting fewer, more important eye health indicators as accurately as possible.This process engaged a large panel of global eye health experts representing all GBD Super Regions and developed a quantitative approach to prioritise existing indicators. The steering group refined the highest ranked selections to produce a menu of indicators for governments to monitor and improve eye health and eye health services, aligned with UHC and the Sustainable Development Goal on health and in keeping with WHO’s call for such a menu in the The core indicators include two candidate WHO UHC service coverage indicators: effective cataract surgical coverage (eCSC) and effective refractive error coverage (eREC).The standard UHC financial risk protection indicators (catastrophic and impoverishing expenditure) adapted to eye health scored lowest among the 95 indicators in Round 2. This likely reflects anticipated complexities in data collection and the possibility that, for non-emergency healthcare, they may not be sufficiently discriminatory. Instead, we have proposed two new proxy measures for financial risk protection. These are not intended as direct replacements for catastrophic and impoverishing expenditure indicators, rather what might be achievable within the constraints of eye health data availability. They will require additional work to develop a full metadata description but provide a way to track eye health insurance coverage (for multiple conditions) and out-of-pocket (OOP) payments for treatment of cataract, the most common cause of blindness globally. The WHO has acknowledged that monitoring the intersection between service coverage and OOP expenditure is key to assessing progress towards UHC.Place of residence should not be a barrier to accessing care, however, human resources for eye care are skewed towards urban settings.The final concept represented in the core indicators selection is the impact of countries’ eye health systems on population health in terms of the prevalence of vision impairment. Vision impairment affects individuals’ quality of life but is also a broader development issue affecting education and employment.Beyond the core indicators, key concepts represented in the menu include the integration of eye health into national health planning and financing. We acknowledge that defining and measuring the degree of integration in these areas requires further discussion, particularly for health financing. The integration of primary eye care into primary healthcare planning, financing and training programmes is also included, along with integration of eye health into HIS.The importance of data disaggregation to monitor eye care equity across the menu should not be understated. We have included an equity statement for consideration across the list and believe equity-relevant monitoring is essential to ensure the most gains are made among population groups with the most need.GAP indicator list, including its six key indicators on vision impairment, human resources for eye care and cataract surgical services. New concepts in our list include eye care insurance coverage and affordability, the UHC dimensions of quality and equity, eye health infrastructure and information systems, primary eye care, child eye health, refractive error and diabetic retinopathy. Our proposed indicators are not only UHC-aligned but also address many areas of global eye health prioritised in a recent ‘grand challenges’ global Delphi process.Our menu has substantial overlap with WHO’s World Report on Vision is not represented. We presented 13 ‘responsiveness’ indicators in Round 1, including 7 patient-reported outcome indicators, but the panel prioritised none; this will require further study. Disease-specific indicators for glaucoma and age-related macular degeneration were potentially under-represented in the initial long-list and not prioritised by the panel, despite their prominence as causes of vision loss globally.World Report on Vision and could be included in future as it gains priority in eye health planning. The GAP indicator for evidence of research on the cost-effectiveness of eye health programmes was not prioritised, but more evidence of cost-effectiveness may strengthen the case for resource allocation. Finally, broader health and financing indicators potentially relevant to eye health  were not prioritised but could be obtained from other national reporting mechanisms to support eye care planning as appropriate.There are some notable absences from the final menu. The concept of ‘people-centred’ eye care proposed in the World Report on Vision and the potential inclusion of eCSC and eREC in the next list of WHO UHC indicators provides impetus for action. In addition, countries will benefit from the ongoing refinement of tools such as WHO’s Eye Care Services Assessment ToolWe recognise that generating this list is insufficient in isolation, and several challenges must be addressed for these indicators to be successfully integrated into countries’ HIS and monitoring frameworks. Fortunately, the priority given to HIS in the We propose that new indicators in this menu be field-tested in several contrasting settings, and that the menu be regularly reviewed and updated according to user feedback. Such reviews would ideally assess whether data collection and indicator usage are viable and valuable for both national and subnational planning, as well as for generating global eye health estimates. These steps require ongoing engagement and resourcing to develop and maintain the utility of the menu. This may be encouraged by a centralised eye health data repository.This study has several limitations. First, inherent in a study that recruits experts, the indicators prioritised reflect the preferences of those invited to participate. We aimed to be as geographically representative as possible, however, the North Africa and Middle East, High Income and Central Europe, Eastern Europe and Central Asia Super Regions had few panellists. Further, despite aiming for gender parity, only 40% of the panel were women. A more diverse panel may have generated a different set of indicators. Second, the online exercise was only available in English, however, no nominated panel members were unable to participate due to language constraints. Third, personal interests and familiarity with some concepts over others may have led to confirmation bias in scoring by panel members. The overlap with existing GAP indicators may be a reflection of this, however, the menu does include many new concepts. Finally, detailed explanations of new concepts are required which was beyond the scope of this prioritisation project.World Report on Vision must be supported with investment in HIS that include eye health data collection and data monitoring via internationally acceptable indicators. We believe this list is well-placed to inform the development of new national eye health monitoring frameworks and shows where eye health metrics might be incorporated into broader UHC monitoring indices at national and international levels.This process sought a broad consensus from 72 eye health experts from all world regions to produce a menu of indicators for countries to monitor eye health as part of UHC. From a long-list of 200, the final menu consists of 22 indicators that represent important concepts in eye health for 2020 and beyond, and are relatively feasible, actionable, reliable and internationally comparable. The new direction in global eye health set by the