The transferof PFAS from the lake to the riparian zone, via deposition of emergentaquatic insects, was 280 ng ∑24PFAS m–2 d–1 in 2017 and only 23 ng ∑24PFAS m–2 d–1 in 2018. Becauseof higher production of emergent aquatic insects, the lake had higherPFAS transfer and higher concentrations in terrestrial consumers comparedto the stream, despite the stream having higher PFAS concentrationin water and aquatic insect larvae. Our results indicate that biodriventransfer of PFAS from the aquatic systems and subsequent uptake interrestrial food webs depend more on emergence amounts, i.e., aquaticprey availability, rather than on PFAS concentrations in water andaquatic prey.Emergent aquaticinsects are important food subsidies to riparianfood webs but can also transfer waterborne contaminants to the terrestrialenvironment. This study aimed to quantitatively assess this biodriventransfer for per- and polyfluoroalkyl substances (PFAS). Aquatic insectlarvae, emergent aquatic insects, terrestrial consumers, sediment,and water were collected from a contaminated lake and stream and anuncontaminated pond, and analyzed for PFAS and stable isotopes ofcarbon and nitrogen. Top predators in this study were spiders, whichshowed the highest average ∑ PFAS are transferred from contaminatedaquatic to the terrestrialecosystems via insect emergence and the quantity can be substantialcompared to other local depositions of PFAS. PFAAare ubiquitously found in the environment, and >95% of their emissionshave been estimated to be released into aquatic environments.3 Once in the aquatic ecosystem, many PFAS can easily distribute andbioaccumulate in the aquatic food webs.5Per-and polyfluoroalkyl substances (PFAS) are contaminants ofemerging concern and are widely used in the industry and in consumerproducts because of their unique physicochemical properties . PFAS can be very resistant to degradation, can causea range of adverse health effects, and can be highly bioaccumulativedepending on their fluorocarbon chain length and functional group.6 These transfers areimportant ecosystem functions. For example, emergent aquatic insectsare essential food subsidies to bats, reptiles, amphibians, spiders,and birds at the riparian zone. Aquatic insect emergence varies seasonallyand geographically, and can range from hundreds to 150,000 individualsper square meter per year (as reviewed by Jackson and Fisher7). Emergence from central Swedish wetlands isknown to range from 1200 to 4300 individuals per square meter peryear,8 or in terms of biomass (dry weight)0.5–2.5 grams per square meter per year from Finnish lenticsystems.9 Differences in insect emergenceamong aquatic ecosystems are primarily driven by variation in secondaryproduction, aquatic invertebrate community composition, and ecosystemgeometry.10 Once emerged, most insects deposit on landdirectly adjacent to the aquatic origin , thoughsome taxa  can disperse over long distances.11 The response of riparian consumers, such asspiders, to high aquatic insect emergence can be substantial in termsof their density and relative proportion of aquatic prey in theirdiet.13Aquaticecosystems interact with the surrounding terrestrial environmentvia transfer of energy and matter, primarily via the movement of emergentaquatic organisms.14 heavy metals,17 algal toxins,18 pharmaceuticals,19 and lipophilic persistent organic pollutantssuch as polybrominated diphenyl ethers (PBDEs),20 polychlorinated biphenyls (PCBs),25 and 2,3,7,8-tetrachlorodibenzofuran.26 Spiders as well as mayflies have been used as biomonitoringspecies for movements of contaminants,27 but environmentalconsequences of such movements have rarely been investigated, althoughthey may influence community structure and health indicators of terrestrialinsectivores.29Aquatic prey subsidies are not necessarilyonly beneficial to terrestrialconsumers, as they also can contain contaminants that the aquaticinsects are exposed to in water and sediment before emerging as adults.Hence, contaminants that bioaccumulate internally can be moved fromwater to land at emergence. Such biodriven transfer has been describedfor nitrogen,16 Basedon this, we identified four possible scenarios of biodriven contaminanttransfer  is when the contaminantconcentration has reached a toxic threshold, reducing the emergenceof aquatic insects and, thus, the uptake by terrestrial consumers.28 This is independent of the potential productivity of the aquaticsystem. Lastly, in a fourth scenario (D), the aquatic system is simplynot productive enough  to produce sufficientamounts emergent aquatic prey that results in a noticeable uptakein terrestrial consumers. This is independent of the internal concentrationof each aquatic prey. As such, it should be important to identifywhich factors drive the contaminant transfer and uptake in terrestrialfood webs, to obtain a mechanistic understanding which can enablemore efficient remediation actions.There are several factorsthat can influence biodriven transferof contaminants, such as PFAS, from water to land: (1) the concentrationof the contaminant in the surface water/sediment and its bioavailabilityand toxicity, (2) level of uptake in aquatic insects , (3) quantity of emergent aquatic insect biomassmoving from water to land ,and (4) diet of terrestrial consumers .transfer 1:(A) a 32 In a previous study, we investigated a pond contaminatedby aqueousfilm forming foam (AFFF), and found high concentrations of 24 PFASin aquatic invertebrates, including emergent aquatic insects, suchas adult dragonflies.33 Hence, terrestrialconsumers feeding on emergent aquatic insects from PFAS-contaminatedsystems could be exposed to PFAS via predation.33 However, the biomass of emergent aquatic insects collectedin that study was not sufficient for quantification of PFAS transferredfrom water to land.33 Here, we extend thisconcept by providing quantitative data on PFAS and explore biodriventransport based on different scenarios for three other sites of varyingsize, productivity, and contamination extent. To do this, we collecteda comprehensive set of samples in 2017 and in 2018, in a PFAS-affectedarea in Sweden. Samples included aquatic insect larvae, emergent aquaticinsects, terrestrial consumers, sediment, and water, at two sitesnear a PFAS point source, as well as at a reference site. Chemicalanalysis included 26 target PFAS and branched isomers of PFOS andPFHxS, as well stable isotope analysis of carbon and nitrogen to elucidatefood web structure and nutrient sources. The aim was to provide quantitativeestimates of PFAS transfer from water to land  and to measure their uptakein terrestrial consumers. To obtain an understanding of which factorsregulate the transfer, we conceptualize and discuss the results inrelation to the different scenarios presented in Aquatic organisms can take up and bioaccumulatesome PFAS.–1 wet weight (ww).34 The lake has an areaof ca. 29 ha and receives about 4.7 million m3 of waterper year from the stream (Ulfstorpsbäcken) in the Kvarntorparea.35 Sources of PFAS at Kvarntorp aremost likely complex due to the different industrial activities. Moreinformation about the industrial history and PFAS concentration measuredpreviously can be found under “Study site” in the Supporting Information (SI).Kvarntorp is an industrial area which ispart of the municipality of Kumla, located in central Sweden 2. The arFor this study, samples were collected from the stream Ulfstorpsbäckenand two sites at Söderhavet : one located close to the inflowand one close to the outflow of the lake 2. SampleTable S1 in Supporting Information (SI)) at the Department of Soiland Environment at the Swedish University of Agricultural Science.Several sediment cores with a maximum depth of 20 cm were sampledwith a core borer (diameter 5 cm) in October 2017 and were combinedas composite samples resulting in one sample per site. During thesoil sampling, earthworms were collected from the surface soil (<20cm depth), rinsed with Milli-Q-water, and kept in a plastic containeron wet filter  for 48 h to clean their guts, before being frozen.All worms were combined to a composite sample per site sampled.Various matrices were collectedfrom all three sites in 2017 and 2018 (May 2 to July 3). Grab samples of surface water were takenin spring and fall 2017 as well as in spring 2018. Polypropylene (PP)bottles were rinsed three times with surface water and then filledfrom >10 cm below the surface and >1 m away from the shore.Waterchemistry parameters  were determinedduring water sampling, and water samples collected in fall 2017 wereanalyzed for 19 water chemical parameters (3 squared plasticboxes), to collect aquatic emergent insects, were deployed for a periodof 16 days in spring 2017 (May 31 to June 15).36 Sets of three deposition traps were placed about 5 m from each otherwithin 5 m from the shore at three locations per sampling site, resultingin nine traps per site (double at the lake since two sampling locationswere there). In 2018, deposition traps were deployed during the entireearly spring emergence (May 2 to July 3). During that period, depositiontraps were collected and refilled four times; after the first 13 days(T1), after another 13 days (T2), thereafter after 16 days (T3), andafter 21 days (T4). In 2018, sets of two deposition traps were placedat three locations per sampling site, resulting in six traps per site.Terrestrial consumers were collected in pitfall traps during spring2017. The pitfall traps were 250 mL PPcups buried with the rim ofthe cup at ground level. Each pitfall trap was placed a few centimetersfrom a deposition trap, resulting in nine traps per site. In all depositionand pitfall traps, a mixture of 50% propylene glycol, 50% Milli-Qwater, and a few drops of detergent to break the surface tension wasused as a preservative. Glycol from a few traps was extracted to testfor PFAS contamination, and the test revealed that no PFAS were abovethe detection limits. Samples from all the traps were stored at 4°C until they were sorted into selected taxonomic groups, pooled,weighed, and then frozen. Before chemical analysis, each sample wasfreeze-dried and the dry weight determined.Deposition traps . Additionally, aquatic invertebrateswere collected with a kick net  in fall 2017. In brief, the net was movedwith fast turns for 1 min at the shoreline to stir up bottom sedimentand to collect the released suspended material and aquatic invertebrates.Kick-net samples were collected at the shoreline close to where thedeposition traps were placed during spring. Therefore, three locationswere sampled three times, resulting in nine samples per site. Theinsects from these samples were most likely to emerge in spring 2018and, thus, potentially those ending up as prey in the terrestrialfood web. All samples from the sweep net and kick-net sampling werefrozen until they were sorted and pooled in the lab for chemical analysis.Flying emerginginsects were captured with a sweep net (up to 10m from the shore) during sampling periods T1 and T2 in 2018, and thesewere analyzed for both PFAS and stable isotopes. The reason only sweepnet samples were used for isotope analysis was because insects fromdeposition traps were soaked in glycol, and potential influence fromcarbons in glycol on the isotope ratios could not be ruled out . The group aquatic insectlarvae consisted of all insect larvae that were caught, mainly dragonfly,damselfly, and alderfly larvae. Emergent aquatic insects representthe adult emergent insects captured by deposition traps and sweepnets and were midges, dragonflies, and damselflies. Terrestrial consumersin this study included a variety of riparian invertebrates, and weremainly spiders, beetles, and ants. For pitfall and deposition trapcollections, all nine replicates per site were pooled to obtain sufficientbiomass for later analysis. In cases where more than sufficient biomasswas collected, duplicates or triplicates for a sample group were used.Detailed information about each sample can be seen in –2 d–1) multiplied by ∑24PFAS.To estimate PFAS transfer for the whole riparian zone of Söderhavet,the estimated area of the 3-m-wide riparian zone surrounding the lake(the circular ring area: 5800 m2) was multiplied by thenanograms of PFAS determined for the specific sampling periods in2017 (16 days) and 2018 (63 days). For the stream site, the samplingsection of 50 m length and the riparian zone width of 2 × 3 m2 (to cover for both sides of the stream) were used to estimatetransfer.Depositionof emergent aquatic insects was collected within 3 mfrom the lake/stream shore and this distance was therefore definedas the riparian zone surrounding the aquatic systems in this area.Estimation of emergence, number of insects, and dry biomass, as wellas PFAS transfer, was based on emergent aquatic insects collectedin the deposition traps at the riparian zone. Emergence was calculatedby the sum of number of insects or weight of dry biomass from thewhole period divided by the number of traps used to collect theseand corrected by the trap area to get square meters. Finally, thevalue was divided by the number of days to get the mean number/biomassof insects deposited onto land for each of the sites. PFAS transferwas estimated by the value of emerged biomass ,PFSA with perfluorocarbon chain length of C4–C10 and C12 , fluorotelomer sulfonic acids , and perfluorooctane sulfonamide (FOSA). More details aboutthe target PFAS and addressed isotopically labeled PFAS for quantificationcan be found in Table S3 in Supporting Information. For the analysis of branched PFOS (br-PFOS) and PFHxS (br-PFHxS)isomers, two reference standards were used. The first one containedlinear PFOS, 1m-PFOS, 6/2m-PFOS, 3/4/5m-PFOS, and 4.4/4.5/5.5-m2-PFOS(brPFOSK0113) and the second one contained linear PFHxS, 1m-PFHxS,2/4m-PFHxS, 3m-PFHxS, and 3–3-m2-PFHxS (brPFHxSK0612). Allreference PFAS were purchased from Wellington Laboratories .A total of 26 target PFAS were studied;PFCA with perfluorocarbon chain length of CSI. For isotope analysis, subsamplesfrom the pooled invertebrate samples were used, if those had sufficientbiomass for both PFAS and isotope analyses.Description of water and solid sample extractions,chemical analysis of PFAS, and stable isotope analysis can be foundin the Table S4 in Supporting Information). Procedural blanks were in general<5% of the detected concentrations, and thus, the PFAS concentrationswere not blank subtracted. Method detection limits (MDLs) were calculatedfor each batch as the average concentration of the solvent blanksplus three times the standard deviation . Method quantification limits(MQLs) were calculated by MDL × 3.3 (Table S5 in Supporting Information). For compounds where no peakwas detected in the solvent blanks, the lowest point of calibration(20 pg) was used to calculate the detection limits. For calculationsand graphs, values that were below the MQL but above the MDL wereused without modification, whereas values below MDL were replacedby MDL/2. A detailed description of PFAS quantification can be foundin the “Data analysis” section in the Supporting Information. Results were reported as concentrationson a dry weight basis (Tables S7 and S8 in Supporting Information). However, for comparison with other studies, concentrationswere also converted to wet weight, using water contents determinedfor all invertebrate groups (Table S2 in Supporting Information).During each batch extraction, two solventblanks as well as one spiked (1 ng native standard) quality control(QC) sample were processed. The QC samples were Milli-Q-water forwater and commercially purchased aquatic bloodworms (Chironomidaetetans) for the biota extractions (n ≤2) were excluded, as they cannot be statistically compared to theother groups. For pairwise comparison of PFAS concentration betweeninvertebrate groups , the nonparametric Kruskal–Walliswas used with Dunn’s post hoc test, performed in R (version3.6.0). Here, different taxa were pooled to obtain sufficient amountsfor analysis, but we did not account for potential differences inthe relative abundance of different taxa among samples and betweenyears. Regression analysis was used to investigate the relationshipbetween PFOS concentrations and δ15N in emergentaquatic insects and terrestrial invertebrate consumers.During statistical comparisons, a PFASwas excluded when more than 50% of the samples showed values belowMDL and groups with low number of samples .PFAS were detected in all samples. Among the 26 PFAS, four compounds were below MDL in allsamples. Furthermore, PFBA and PFPeA were excluded from this study,because their quantification could not be confirmed in most samplesdue to matrix effects.Thissection discusses PFAS concentration in surface water, sediment, aquaticinsects, emergent aquatic insects, and terrestrial consumers, as wellas stable isotope ratio of carbon and nitrogen from all three samplingsites . Sample informationcan be found in 24PFAS in the stream were 700 ng L–1 in spring 2017,2400 ng L–1 in fall 2017, and 1600 ng L–1 in spring 2018 , whereas lake PFAS concentrationswere about one order of magnitude lower at 370, 890, and 180 ng L–1 for the same sampling occasions, respectively, mostlikely due to dilution effect in the lake. Although three measurementsover time are too few to draw any general conclusions, the observedtemporal variation was likely due to seasonal differences in waterdischarge, i.e., the amount of water diluting the PFAS, and between-yearvariation in precipitation and temperature. PFAS concentration inthe reference pond was <3 ng L–1, which is inaccordance with environmental background concentrations for Swedishsurface water.37 PFAS profiles in all watersamples showed that PFOS was the most prominent PFAS  followed by PFHxS and PFBS . PFAS profile patterns were similarfor samples from the stream and the lake, indicating that the streamwas the major point source of PFAS to Lake Söderhavet. In general,PFAS concentrations in the study area were elevated compared to meansurface water concentrations measured across Sweden (n = 289) of 110 ng L–1 ∑24PFAS(median 3.9 ng L–1).37 Furthermore, water concentrations in both the stream and the lakegreatly exceeded the annual average (AA)-EQS of the EU Water FrameworkDirective of 0.65 ng L–1 for total PFOS, indicatingthat both sites were PFAS contaminated (maximum ∑PFOS 1040ng L–1 at the stream (W4) and 230 ng L–1 at the lake (W1)).Concentrations of∑24PFASmeasured in sediment were 220 ng g–1 dw in the stream,280 ng g–1 dw in the lake, and 2.4 ng g–1 dw in the reference pond. PFAS profiles were similar to those ofwater, generally with high contribution of PFOS to the total PFASlevels in the stream and the lake sediments . Sediment concentrations were comparableto concentrations reported from a lake in Northern Sweden, which wasimpacted by PFAS.38Concentrations of ∑–1 dw (n = 5) for dragonfly larvae, 720 ng g–1 dw (n = 1) for damselfly larvae, 750 ng g–1 dw (n = 1) for midges, and 660 ng g–1 dw (n = 1) for caddisfly larvae. Aquatic insectlarvae that were sampled in fall 2017 had high concentration of ∑24PFAS in the stream and lake, with means of 2000 ± 610ng g–1 dw (n = 4) and 520 ±160 ng g–1 dw (n = 8), respectively,and the reference pond had 12 ± 7.1 ng g–1 dw(n = 4) (Tables S7 and S8 in Supporting Information). Differences among sites in internalPFAS concentrations correspond to differences in water and sedimentconcentrations. In general, PFAS congener profiles showed that short-and medium-chain PFAS were more prominent in water, whereas PFOS andother long-chain PFAS were more prominent in biota . Dragonfly concentrations were higher in this study than previouslyreported for dragonflies caught at six sites  in South Africa (0.32 and 9.3 ng g–1 ww ∑8PFAS).39 The isotope ratios δ13C and δ15N  frompooled samples of each invertebrate group per sampling site at the stream and −30 ± 1.2 ‰ at the lake (n = 10), and for δ15N 17 ± 2.1 ‰at the stream (n = 7) and 13 ± 1.3 ‰at the lake (n = 10) .In the lake, mean concentrations forthe different groups in the lake were 400 ± 66 ng gsite see  were, fo–1 dw ∑24PFAS was determined for all samples fromdeposition traps, where midges made up all the captured insects. Dragonfliesand damselflies caught by sweep net at the stream had concentrationsof 230 ± 4.8 ng g–1 dw (n =2) and 1200 ng g–1 dw ∑24PFAS(n = 1), respectively (Tables S7 and S8). At the lake, mean concentration for some insectgroups from 2017 and 2018 were 160 ± 180 ng g–1 dw in midges (n = 11), 1040 ± 480 ng g–1 dw in alderflies (n = 5), and 170± 60 ng g–1 dw ∑24PFAS (n = 5) in damselflies. Mean concentrations of ∑24PFAS in emergent aquatic insects from deposition traps were820 ± 590 ng g–1 dw (n = 7)in 2017 and 230 ± 260 ng g–1 dw in 2018 (n = 16). The sample with the highest PFAS concentration,1900 ng g–1 dw, was alderflies from the lake in2017 (D24 in Table S8 in Supporting Information). Compared to PFAS concentrations in aquatic larvae, concentrationsin adult emerged insects were lower, e.g., four times lower in damselflies.Dragonflies that were caught in sweep nets had mean ∑24PFAS concentration of 130 ± 48 ng g–1 dw (n = 2) at the lake. The mean concentration of adult dragonfliesfrom lake Söderhavet was similar to concentrations found inadult dragonflies at a pond in southern Sweden where AFFF had contaminatedthe water (∑24PFAS 270 ± 190 ng g–1 dw).33 However, in the current study,damselfly concentrations were lower than at the AFFF affected sitewhich had average ∑24PFAS of 1600 ± 1300 ngg–1 dw.33 Body burdenin individuals from the lake, determined by multiplying the mean ∑24PFAS concentration with the dry weight of the pooled sampleand dividing by the number of individuals of each pooled sample, wason average 5 ng for adult damselfies and 16 ng for adult dragonfliesin this study. Emergent insects from the reference pond showed a meanconcentration of 10 ± 4.7 ng g–1 dw (n = 9). The mean δ13C in emergent aquaticinsects was around 30 ‰ , similar to those of aquatic larvae.Emergent aquatic insects showedlower, but still relatively high, PFAS concentrations than aquaticinsect larvae. At the stream, a mean of 700 ng g24PFASconcentrations (ng g–1 dw ±1 SD) of all terrestrialconsumers were 404 ± 270 (n = 8) at the stream,708 ± 770 (n = 21) at the lake, and 16 ±11 (n = 6) at the reference pond (15N) andproportion of aquatic diet of predators (δ13C). Spiders,a typical invertebrate top-consumer, had highest values for δ15N and highest ∑24PFAS concentration with630 ng g–1 dw at the stream and a mean of 1400 ±82 ng g–1 dw at the lake site (S7 and S8 in Supporting Information) among the collected terrestrialinvertebrates in this study. When comparing isotope ratios of terrestrialconsumers to aquatic insect larvae and emergent aquatic insects, δ13C was on average higher  and δ15N was on average lower , indicating a clear difference between the aquaticand terrestrial ecosystem.Mean ∑nce pond 1. PFAS l24PFAS in aquatic insect larvae weresomewhat higher than in emergent aquatic insects and significantlyhigher to terrestrial consumer (Dunn’s test p = 0.02)  . Furthermore, branched PFOS isomerswere enriched in the surface water , whereas lower proportions were found in all biota(0–20%) indicating faster elimination rate of branched isomersin biota, as mentioned in previous studies .40Atthe stream, ∑ = 0.02) 3. At the= 0.003) 3. PFAS p13C and δ15N weresignificantly different between aquatic insect larvae and terrestrialinvertebrate consumers at both the stream and the lake site , indicating differences in dietand trophic positions.41 Aquatic insects were more depletedin δ13C and enriched in δ15N thanterrestrial consumers, whereas δ13C of emergent aquaticinsects were located in between, suggesting a mixed diet . This can be expected since adult dragonflies, making up a largeportion of the samples, prey on both emerged aquatic and terrestrialprey . The more depleted δ13C for mostterrestrial invertebrate consumers indicates predominately terrestrialfood sources, but two web-building spider samples showed similar ratiosas the aquatic samples, indicating high consumption of aquatic prey.Furthermore, ratios of δ15N have increasingly beenused to quantify the biomagnification of contaminants, since thereis a positive relationship between concentration of contaminants  and trophic position.25 In this study,near-significant positive relationships between δ15N and ∑PFOS for emergent aquatic insects  and terrestrial consumers  from thelake  indicate a higher nitrogen ratio in terrestrial consumers feedingon aquatic prey, and/or higher PFAS uptake at higher trophic levels,as found in our previous study.33On average, δ–2 d–1 in 2017 and 17 per m–2 d–1 in 2018 (–2 d–1 in 2017 and 6.4 mg dw m–2 d–1 in 2018. Based on the study period (63 days),annual biomass was estimated to be 3.2 and 0.4 g dw m–2 y–1 in 2017 and 2018, respectively, which waswithin the range of what was found previously in Finland.42 At the stream site (K1), emergence was generally low in spring 2017,and biomass captured was therefore not sufficient for chemical analysis.In 2018, there was sufficient biomass for PFAS analysis of one pooledsample of midges  and reference pond (9.2 mg dw m–2 d–1) for 2018. At the reference pond, aquaticinsect deposition was similar to that of the lake 44 mg dw m–2 d–1 in 2017 and 9.2 mg dw m–2 d–1 in 2018, which indicates similar productivity.At lake Söderhavet, themean number of deposited emergent insects was 25 individuals per m in 2018 1. There 24PFAS m–2 d–1 in 2017 and 2018,respectively. The total amount of PFAS deposited on the whole lakeriparian zone (3 m in width) was estimated to be 101 mg ∑24PFAS for the sampling period (May 31 to June 15) in 2017and 8 mg ∑24PFAS from May 2 to July 3 in 2018, showingsubstantial seasonal (during early summer peak emergence) transferto the riparian zone, but also high between-year variation in thistransfer. Little or no emergence of aquatic insects can be expectedfor the rest of the year (October–April).PFAS transfer from the lake via emergentaquatic insects was estimated to be 280 and 23 ng ∑24PFAS transfer  occurred during T2, whereasthe highest number of individuals emerged during T3 (Table S9 in Supporting Information). In other words, mid-Mayto mid-June (Table S9 in Supporting Information and –2 d–1) were midges, which was expected based on results from previousstudies.8 Across insect groups, the daily biomasstransfer was 3–14 times lower in 2018 than in 2017. This couldbe explained by spring 2018 being exceptionally warm, especially inMay when our sampling occurred, as local weather conditions couldplay an important role for insect emergence. Since the PFAS transferwas highest during T2 when the biomass of the alderflies was substantial,alderflies could be important for biodriven PFAS transfer in thisarea. When comparing PFAS transfer per insect group (in ng ∑24PFAS m–2 d–1), the rankorder was as follows: alderflies (140) > midges (113) > stoneflies(17) > damselflies (6) in 2017 and alderflies (21) > damselflies(3.8)> midges (3.5) > caddisflies (1) in 2018 ,and differences between time periods at the lake can be investigated.Here, the highest biomass and ∑24PFAS from the stream to the riparianzone was estimated to be on average 2.1 ng m–2 d–1 and for the whole study period (63 days) 5 mg ∑24PFAS in 2018. The transfer estimate at the stream was lowerthan at the lake, despite higher concentration in the surface water(1550 ng L–1 for ∑24PFAS in spring2018). Therefore, the low emergence from the stream limited transferof PFAS at that site , although negativeeffects on aquatic insects might have occurred to some extent, asthe estimated no-effects value used by Environment Canada (491 ngL–1 in water) for aquatic organisms was exceeded.44 Hence, rather than acute effects, potentiallynegative effects on emergence could be caused by chronic exposureof relatively low PFAS concentration over time.Transfer of∑24PFAS m–2 d–1 in 2017 and 2018,respectively. The fact that there was a measurable PFAS transfer atthe reference site, which represents PFAS background levels in theenvironment, confirms that PFAS are ubiquitously detectable, possiblydue to atmospheric deposition or other diffuse sources.At the referencepond, the PFAS transfer was substantially lowercompared to the other sites, with 0.4 and 0.1 ng ∑24PFAS concentrations most likely attributable to high preyavailability. Hence, as for the lake and the reference pond, PFASuptake in the terrestrial food web was magnified by high aquatic insectemergence  and 2018 (19 ng m–2 d–1) was within the range of rain deposition rates (10–510ng PFOS m–2 d–1) found in 28 citiesin China,51 and higher than rain depositionrates (0.1–12 ng m–2 d–1) found in a semirural area in Germany52 and PFOS wet deposition at various sites around Sweden (0.057–7.4ng m–2 d–1)54 (Table S10). This indicates that biodriventransfer via emergent aquatic insects can be similar to or even higherthan rain deposition at a local scale. Moreover, because biodriventransfer to a large part consists of prey, this PFAS transfer is likelymore readily incorporated into terrestrial consumer food webs.In a broader context, biodriven transfer around contaminatedsurface waters might be comparable to atmospheric wet deposition ofPFAS, and therefore play an important role at a local and regionalscale, given the large number of PFAS-contaminated sites.


The physiological benefits associated with corporately sponsored weight loss programs are increasingly well documented. However, less is known about how these programs affect employees’ quality of life (QoL). The purpose of the present analysis was to examine the association between weight loss, change in physical activity, and change in QoL following a corporately sponsored, online weight loss program.We examined the relationship between weight loss, self-reported change in physical activity, and change in several QoL indices in 26,658 participants (79% women) after the initial 10 weeks of the online weight loss program. The trend in changes in each QoL index with increasing weight loss and change in physical activity was examined using logistic regression analysis.p-for-trend, < 0.001) as well as with progressive increases in physical activity . The combination of increasing weight loss and increases in physical activity were associated with the greatest improvements in each QoL index (additive effect). The percentage of employees reporting improvements in QoL (“improved” or “very much improved”) was 64% for energy, 63% for mood, 33% for sleep, 65% for self-confidence, 68% for indigestion, and 39% for musculoskeletal pain.We observed greater improvements in each QoL index with increasing weight loss (Among people, who engage with a commercial weight loss program, greater weight loss during the program was associated with greater improvements in QoL, and increases in physical activity further enhanced the QoL-related benefits. Obesity continues to be a highly prevalent disease that directly affects health, health care costs to the individual and employers, overall psychological health, and quality of life (QoL) . It is nN = 70,000), a higher body mass index (BMI) is associated with lower QoL and reduced work productivity ) and categorical data as N (%). Weight change (in kg and percent) is presented with corresponding 95% confidence intervals (CI).Using logistic regression (proc logistic), change in QoL (binary) was regressed against percent weight loss (continuous) and change in PA category with adjustment for age and gender. To create the binary QoL variable, the four QoL categories were collapsed into the two categories “improved”, composed of “very much improved” and “improved”, and “not improved” composed of “no change” and “worsened”. Weight loss was categorized using the clinical cut-points < 3.0%, 3.0–4.9, and > 5.0%. For the change in PA category, due to the very small number of participants in the “slightly less” (1.4%) and “much less” (0.3%) PA categories, we collapsed these groups into the “no change” category, resulting in the three PA change categories “quite a bit more”, “slightly more”, and “no change”. Using a generalized linear model adjusted for age and gender, we examined the percentage of participants indicating improvements in the binary QoL category (“very much improved” or “improved”) for all nine combinations of weight loss category and change in PA category.p < 0.05.All analyses were performed with SAS version 9.4 , and significance was accepted at ). Participants (79% women) were 53.8 (SD 9.8) years old, attended 9.9 (SD 0.3) of the total of ten classes, and lost a significant amount of their starting weight . Participants who attended ≥8 classes but did not complete the QoL survey  were excluded from the analyses presented herein.In total, 388,600 employees started the corporately sponsored weight loss program and 174,821 participants (45%) attended ≥8 classes. Of those who attended ≥8 classes, 26,658 completed the QoL survey and they are included in the present analyses , 63% percent reported an improvement in mood, (55% “improved” and 8% “very much improved”), 33% reported improved sleep quality (30% “improved” and 3% “very much improved”), and 65% reported an improvement in self-confidence (55% “improved” and 10% “very much improved”). Further, of the 6932 participants who reported indigestion at baseline, 68% showed improvements (52% “improved” and 16% “very much improved”), and, of the 8498 participants who reported having musculoskeletal pain at baseline, 39% reported improvements, (36% “improved” and 3% “very much improved”). Specific gender characteristics of these findings are reported in Table The majority of participants also reported increases in PA during the program. Fifty-four percent of the entire cohort reported “slightly more” PA and 16% reported “quite a bit more” PA. Approximately 30% reported no change and less than 2% reported decreases in PA throughout the program (Table p < 0.001) and progressing increases in PA . Each 1% weight loss was associated with an 18% probability of improved energy, 16% probability of improved mood, 4% for sleep, 23% for self-confidence, and 7% for both indigestion and musculoskeletal pain. Similarly, each category increase in PA was associated with a 148% increased probability of improved energy, 106% probability of improved mood, 89% for sleep, 94% for self-confidence, 72% for indigestion, and 95% for musculoskeletal pain. The combination of increasing weight loss and increases in physical activity were associated with the greatest improvements in each QoL index (additive effect). Specifically, the stratum of participants who lost the least amount of weight (< 3.0%) and reported no change in PA had the smallest percentage of participants with improvements in each QoL index. Conversely, the stratum with participants who lost the most amount of weight (≥5.0%) and reported having increased their PA “quite a bit more”, had the greatest percentage of participants with improvements in each QoL index  and that increases in PA during the program further enhance these benefits.


There is evidence for the positive impact of mindfulness in children. However, little is known about the techniques through which mindfulness practice results in differential outcomes. Therefore, this study intended to systematically review the available evidence about the efficacy of meditation techniques used by mindfulness-based programs on cognitive, socio-emotional, and academic skills of children from 6 to 12 years of age. The review was registered on the PROSPERO database, and the literature search was conducted according to PICO criteria and PRISMA guidelines. The EBSCO databases were searched, and 29 studies were eligible: nine randomized controlled trials and 20 quasi-experimental studies. All the included randomized controlled trials were rated as having a high risk of bias. Overall, the evidence for mindfulness techniques improving cognitive and socio-emotional skills was reasonably strong. Specifically, for cognitive skills, results showed that all the interventions used “body-centered meditations” and “mindful observations.” Regarding socio-emotional skills, although all the studies applied “body-centered meditations” and “mindful observations,” “affect-centered meditations” were also frequent. For academic skills, just one quasi-experimental trial found improvements, thus making it difficult to draw conclusions. Further research is crucial to evaluate the unique effects of different meditation techniques on the cognitive, social-emotional, and academic skills of children.Systematic Review Registration: Identifier: RD42019126767. Some ofResearch has repeatedly shown that mindfulness training improves the performance of children on tasks that assess cognitive functioning such as attention or executive functions  AND (Intervention* OR Training* OR Program* OR Exercise* OR Techniques*) NOT . Filters for source types , age (6–12 years), and language (English) were applied. An additional search through other sources was conducted.A systematic literature search was carried out using MEDLINE and EBSCO  databases from the year 2009 to March 2019. An update was conducted from the year 2019 to March 2021. The review was conducted according to PRISMA guidelines  framework, inclusion and exclusion criteria were based on the following research question: In typical school-age children, what meditation techniques used by mindfulness-based programs, compared with other types of intervention and/or placebo conditions, are more effective in developing cognitive, social-emotional, and academic outcomes? .In order to be included, studies had to: (i) include typically developing children aged 6–12 years; (ii) clearly describe the mindfulness techniques employed; (iii) include an active or an inactive/passive control condition to provide a comparable condition to test the effects of techniques; (iv) include measures of cognitive, socio-emotional, or academic skills as outcomes; (v) provide quantitative measures; and (vi) be published in English. Reviews, meta-analyses, editorials, opinion papers, and dissertations were excluded.To assess the risk of bias in randomized trials, we used the Cochrane Collaboration's tool, namely, the RoB 2.0 . The types of techniques used within each intervention were coded according to the seven main clusters suggested by Matko and Sedlmeier . Thus, 32 papers were included, and their full-text analyzed, of which 29 met inclusion criteria. This trial flow is presented in a PRISMA flow diagram in n = 2), Israel (n = 2), United Kingdom (n = 2), Spain (n = 2), Australia (n = 1), Brazil (n = 1), Germany (n = 1), Korea (n = 1), Italy (n = 1), Netherlands (n = 1), New Zealand (n = 1), Philippines (n = 1), and Portugal (n = 1) (Twelve of the 29 studies included in this review were conducted in the United States. The other studies were conducted in Canada ( (n = 1) . Four st (n = 1) .Age varied across studies. Four studies included 6-year-olds, nine studies recruited 7-year-olds, 13 studies recruited 8-year-olds, 19 studies recruited children with 9 years old, 16 studies included children with 10 years old, and 15 studies included children with 11 years old . Sample Nine randomized controlled trials (RCT) and 20 quasi-experimental design  were included in the review . RegardiSeveral authors fail to report design characteristics  to conduct an accurate assessment of the risk of bias. There was almost perfect agreement between the two judges that conduct the assessment (Cohen's κ = 0.89). Overall, there was a high-risk of bias across the 9 RCTs included in the review, with variation in high-risk areas across studies see Duration, intensity, and dosage of mindful interventions varied across the 29 included studies. For studies reporting adequate information, a wide range of daily doses of formal mindfulness practice were reported, from 4 to 90 min per session. Interventions ranged from 2 weeks to 9 months. Studies also varied in terms of how frequently children met to receive the intervention, from one time per week to daily interventions. The most frequent option was sessions of 45 min once a week for 8 weeks .As can be seen in Overall, as shown in viz. executive functions, attention, and self-control) and social-emotional  skills , and academic skills , social-emotional abilities , “mantra meditations” (55%), “meditations with movement” (52%), and “visual concentration” (48%) . Patternn = 6), “mindful observations” (n = 6), “affect-centered meditations” (n = 4), “meditations with movement” (n = 3), “mantra meditations” (n = 3), and “visual concentration” (n = 2). The 10 QEDS that showed benefits in cognitive outcomes employed “body-centered meditations” (n = 10), “mindful observations” (n = 10), “meditations with movement” (n = 7), “mantra meditations” (n = 5), “visual concentration” (n = 5), and “affect-centered meditations” (n = 4) (As detailed in  (n = 4) .n = 3), “mindful observations” (n = 3), “affected-centered meditations” (n = 3), “meditations with movement” (n = 2), “mantra meditation” (n = 1), and “visual concentration” (n = 1). Sixteen QEDs also showed benefits in socio-emotional skills and used “body-centered meditations” (n = 16), “mindful observations” (n = 16), “mantra meditations” (n = 10), “visual concentration” (n = 10), “affect-centered meditations” (n = 10), and “meditations with movements” (n = 10)  .Just one QED found improvements in academic skills. This study used “body-centered meditations,” “mindful observations,” “mantra meditations,” “visual concentration,” and “affect-centered meditations” .To the best of our knowledge, this is the first review of the efficacy of specific meditation techniques used by mindfulness-based programs on the cognitive, social-emotional, and academic skills of children. A total of 29 studies (nine RCTs and 20 QED studies) met the selection criteria. Overall, results provided support for the use of mindfulness interventions to improve cognitive and social-emotional outcomes but found no support for the use of these interventions to enhance academic skills. Regarding meditation mindfulness techniques, all the interventions used “body-centered meditations” and “mindful observations.” In addition, “affect-centered meditations” were also frequently applied to improve socio-emotional outcomes. Less frequent techniques were “meditations with movements,” “mantra meditation,” and “visual concentration.” Thus, the effective techniques used in mindfulness-based programs differ in terms of activation and amount of body orientation, dimensions that highlight the role of embodied cognition in meditation. The most frequent techniques for improving cognitive and socio-emotional outcomes were active practices with a lower amount of body orientation. For socio-emotional outcomes, effective practices also included a higher abstract and conceptual focus and a neutral amount of body orientation. However, given the high risk of bias across the included studies in several domains, caution is needed in interpreting the results.The findings of the present review support the favorable impacts of mindfulness interventions on cognitive outcomes, as expected  although we reported study characteristics that are indicators of study quality, the risk of bias assessment was conducted only for randomized studies; (ii) the heterogeneity of the studies is considerable, and due to the vast array of practices, this review only examined clusters of techniques; and (iii) the frequent lack of blinded raters, randomization, active comparison groups, and small samples of the included studies mitigate the impact of our findings. Future studies must address these issues to support empirical evidence about the effect of mindfulness techniques on the development of children. A well-designed intervention should have the following key features: (a) randomization of participants into the experimental and control groups; (b) control for participants and expectations of an informant through blinding and assessment of expectations before the beginning of the intervention to control for possible placebo effects; and (c) comparison of the performance of the experimental group to both active and passive control conditions  and what works for whom. Indeed, studies have found that baseline characteristics predict intervention outcomes  and was conducted within the M2S Project funded through the Operational Programme for Competitiveness and Internationalization, supported by FEDER and national funds allocated to FCT (NORTE-01-0145-FEDER-028404).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Following publication of the original article , the autThe affected sentence originally read:P < 0.001] , the average number of procedures was reduced , the differences were statistically significant.Compared with the silicone stents group, the average operative duration and the hospital length of stay in the bioabsorbable steroid-eluting stents group were decreased  , the average number of procedures was reduced , the differences were statistically significant.Compared with the silicone stents group, the average operative duration and the hospital length of stay in the bioabsorbable steroid-eluting stents group were decreased [(97.46 ± 15.37) min vs (83.49 ± 19.16) min t = 13.733, The original article  has been


One hundred fifty patients were recruited and categorized into two distinct populations (“A” and “B”), according to the indications given by the World Health Organization. Patients belonging the population “A” presented with mild disease not requiring oxygen support, whereas population “B” presented with a severe disease needing oxygen support. The AUC curve of PaO2/FiO2 in the discovery cohort was 0.838 (95% CI 0.771–0.908). The optimal cut-off value for distinguishing population “A” from the “B” one, calculated by Youden’s index, with sensitivity of 71.79% and specificity 85.25%, LR+4.866, LR−0.339, was < 274 mmHg. The AUC in the validation cohort of 170 patients overlapped the previous one, i.e., 0.826 (95% CI 0.760–0.891). PaO2/FiO2 ratio < 274 mmHg was a good predictive index test to forecast the development of a severe respiratory failure in SARS-CoV-2-infected patients. Moreover, our work highlights that PaO2/FiO2 ratio, compared to inflammatory scores  indicated to be useful in clinical managements, results to be the most reliable parameter to identify patients who require closer respiratory monitoring and more aggressive supportive therapies. Clinical trial registration: Prognostic Score in COVID-19, prot. NCT04780373 https://clinicaltrials.gov/ct2/show/NCT04780373 (retrospectively registered).We studied the predictive value of the PaOThe online version contains supplementary material available at 10.1007/s11739-021-02840-7. The natural history of SARS-CoV-2 infection is extremely variable, ranging from asymptomatic or mild infection to a severe acute distress respiratory syndrome (ARDS), characterized by a typical hyperinflammatory response associated with a microangiopathy and a widespread thrombosis –4.In the face of the rapidly spreading disease and the large number of infected people, there is an urgent need to cluster patients in risk categories by identifying reliable parameters related to COVID-19 disease progression to stratify high-risk patients, who will suffer rapid disease progression to severe complications and death.2) to fraction of inspired oxygen (FiO2) ratio (PaO2/FiO2) .The population “B” was characterized by the presence of older patients respect to the population “A” , population A and population B length stay were 8.5 (6.00–13.00) and 18 (11.25–26.00) days, respectively (Table Most severe cases (population “B”) exhibited higher rate of co-morbidities (58.93 vs 38.30%) without reaching significance. The rate of patients worsening to ARDS was 38.6% and mortality was 9.3%.P < 0.001, P < 0.01, P < 0.05, P < 0.001, P < 0.01, P < 0.001, respectively), whereas the liver functional parameters were not significantly different between the two populations.Procalcitonin, ferritin, fibrinogen, D‐dimer, international normalized ratio (INR) and alkaline phosphatase (ALP) were significantly higher in the population “B” respect to “A” ], neutrophil [4.32 (2.83–6.56)] and platelet [196.0 (154.5–254.0)] were collected in whole patients and significant differences in neutrophil and platelet count were observed in the population “B” respect to “A” [median 227 (146.5–294.8) and 261 (194–309), 2/FiO2 ratio (P < 0.001).Logistic regression analysis revealed a significant negative prediction of the clinical outcome at day 7 by PaOP < 0.001, P < 0.001, P = 0.016, respectively) (Table S1).Similarly, LDH, hs-CRP, and PLR were positively correlated with a poor outcome   at the time of hospitalization with the clinical outcome at T7. Our analyses showed that the most representative variables were LDH and hs-CRP .Remarkably, when we analyzed age, at univariate analysis, as a single predictor, it resulted very significant . The PaO2/FiO2 appeared as the most reliable prognostic biomarker compared to hs-CRP, NLR, PLR and LDH , see Fig. Dealing with the diagnostic accuracy of an index test, we generate the AUC curves of all the studied parameters. AUC curve of PaO2/FiO2 ratio cut-off value to separate population “A” from “B” was < 274 mmHg, with sensitivity and specificity of 71.79% and 85.25% , respectively.The optimum PaO2/FiO2 ratio in the validation cohort, including retrospectively 170 patients, 100 were female, with a median age of 68.00 years (Table S2). The AUC of the PaO2/FiO2 ratio resulted to be 0.826 (95% CI 0.760–0.891), Fig. To establish its reproducibility, we tested the reliability of the PaO2/FIO2 ratio could be considered a reliable prognostic biomarker for patients’ management of COVID-19 pneumonia, as demonstrated by the AUC curve that indicate as cut-off the value < 274 mmHg. The performance of this biomarker was more than satisfactory with a good accuracy based on AUCs, in both the discovery and validation cohorts, of 0.838 and 0.826, respectively.Summarizing the key results, our research highlights that PaO2/FiO2 ratio. Presenting the importance of the study, we think that one of the strengths of our work is that we tracked levels of biomarkers with the aim to establish trends for each patient towards the successive phases of the disease.It is known that COVID-19 prognosis is worse in older patients, but in an interesting way, age did not modify the prediction by PaO2/FiO2 ratio, hs-CRP, NLR, PLR and LDH), PaO2/FiO2 ratio was the best independent prognostic biomarker for forecasting pneumonia progression toward ARDS in COVID-19 patients.The likely contribution of this work to literature consists in evidencing that in our cohort of patients, among the variables analyzed , found a reduced median of 160 mmHg IQR 114–220) . Accordi . AccordOur study covered a time period during which advances and changes in treatment approaches may have differently reduced the levels of inflammatory biomarkers among patients hospitalized with COVID-19, but this aspect does not affect the importance of our index parameter, on the contrary it reinforces its reliability.Could our results fill existing gaps in the field that had not been previously exposed? We aimed to give to clinicians a way to discriminate SARS-CoV-2-positive patients with different fate. We intentionally categorized our cohort in “low” (population “A”) or “high” (population “B”)-risk patients in such a way that clinicians could make decisions quickly based on local or regional conditions and opportune therapeutic approaches. In fact, we consider of great importance the access to good clinical and supportive care for providing a more or less aggressive care whereby the patient’s outcomes might be optimized.2/FiO2 ratio as a prognostic biomarker representative of a general population.Possible limitations exist in this paper. The study was conducted with a not so large sample size and on data deriving only from the central Italy, which could limit the value of PaO2/FiO2 ratio is simple and easily available, it has a suboptimal power in categorizing patients with ARDS [Moreover, although the PaOith ARDS .As work in progress, we will follow up these cohorts of patients. Nowadays, everlasting effects of COVID-19 are still largely unknown. Long-term pulmonary and systemic complications have been reported in some patients after recovery, with residual fatigue or muscle weakness, chest CT abnormalities, cardiovascular and neurological complications , 23.Supplementary file1 (DOCX 15 KB)Supplementary file2 (DOCX 14 KB)Supplementary file3 (DOCX 16 KB)Below is the link to the electronic supplementary material.


Chrysanthemummorifolium Ramat. ‘Daboju’ is a C. morifolium cultivar with important ornamental and medicinal values, and is often used in the treatment of colds, blurred vision, dizziness, and itchy skin. As the morphological characteristics of C. morifolium ‘Daboju’ are very similar to those of other C. morifolium cultivars, they are often confused in practice. However, the medicinal value and practical use of C. morifolium depends on using the correct rapid and accurate identification of C. morifolium ‘Daboju’ and its differentiation from other, morphologically similar C. × morifolium cultivars. Twenty-one polymorphic start codon-targeted (SCoT) primers were amplified in 21 distinct C. morifolium cultivars. One cultivar-specific DNA marker was developed with the aim of the rapid and accurate identification of C. morifolium ‘Daboju’ and its differentiation from other, similar C. morifolium cultivars. Twenty-one polymorphic start codon-targeted (SCoT) primers were amplified in 21 distinct C. morifolium cultivars. One cultivar-specific 385-bp amplicon (named SCoT36-385), amplified only in C. morifolium ‘Daboju’ , was identified, cloned, and sequenced. Subsequently, a sequence-characterized amplified region (SCAR) marker (named DBJF/DBJR), generating a 360-bp amplicon, was developed from SCoT36-385 and tested for amplification in all 21 C. morifolium cultivars, ten C. morifolium ‘Daboju’ populations, and different simulated adulterations of ‘Daboju’ with other cultivars. The primers amplified the specific 360-bp-long DNA fragment in all the tested C. morifolium ‘Daboju’ samples but failed in the absence of ‘Daboju’. The detection limit of the SCAR primer pair (DBJF/DBJR) was 100 pg of DNA extracted from C. morifolium ‘Daboju’. Hence, this SCAR marker has a very high detection sensitivity, and can be used for accurate and rapid identification of C. morifolium ‘Daboju’. It can play an important role in ensuring the quality of medicinal preparations and protecting C. morifolium ‘Daboju’ germplasm resources in breeding programs and in identifying lines generated from this cultivar. Chrysanthemum morifolium Ramat., belongs to the family Asteraceae, and it has a long history of cultivation and use as a popular ornamental and herbal medicine use and cultivation in China, South Korea, Thailand, and Japan [C. morifolium flowers have significant pharmacological effects for treating headaches, dizziness, sore carbuncles, swelling, poison, and cold or wind heat [C. morifolium cultivars classified as ‘Boju,’ ‘Chuju,’ ‘Gongju,’ ‘Hangju,’ or ‘Huaiju’ types, based on origin and processing methods, are legal Chinese medicinal materials, according to the 2015 edition of the Pharmacopoeia of the People’s Republic of China [Chrysanthemum, nd Japan ,2. As a ind heat . As growof China . C. morifolium preparations are slightly cold in effect, with a sweet and bitter taste, with effects on wind, clearing heat, liver problems (ping mingmu tea), and for detoxification issues. Many studies have shown that the main active ingredients of chrysanthemum medicinal preparations include flavonoids, phenylpropanoids, and volatile oils, which have antiviral, anti-tumor, and anti-inflammatory effects [ effects ,5,6. Fam effects .However, despite the wide variety of chrysanthemum cultivars, the morphological characteristics of cultivars used in Traditional Chinese Medicine (TCM) are relatively similar; with the mutual introduction and cultivation of the same cultivars in different regions, the classification of chrysanthemum cultivars has become quite confused at present, and the problem of ’synonyms’ and ’homonyms’ is very serious ,7. StudiC. morifolium ‘Daboju,’ as an important cultivar of the ‘Boju’ type, has attracted extensive interest due to its unique phenolic and volatile components, and its anti-inflammatory and anti-allergenic biological activities [In recent years, tivities ,11. Howetivities . TherefoInitially, morphological ,15, cellIn the present study, we developed a useful SCAR marker for ‘Daboju’ identification via the use of start codon-targeted (SCoT) primers, which are PCR-based gene-targeted markers, in which the SCoT primers are based on conserved regions flanking the translation start codon of genes . At the After the initial primer screening, a total of 21 SCoT primers, each generating clear and repeatable polymorphic patterns, were selected for further study . The 21 The 385-bp amplicon specific to ‘Daboju,’ named SCoT36-385, was cloned, sequenced, and deposited in GenBank (GenBank accession number: OL456174). The specific nucleotide sequence of SCoT36-385 consisted of 63.12% A + T and 36.88% G + C, as shown in The designed SCAR primer pair DBJF/DBJR was then used to amplify genomic DNA from the 21 chrysanthemum cultivars at the optimum annealing temperature of 62 °C to evaluate the specificity of amplification. The amplification profile of the primer pair DBJF/DBJR is shown in The sensitivity of the SCAR marker DBJF/DBJR was evaluated using a dilution series of ‘Daboju’ genomic DNA samples. The results suggested that the sensitivity limit of the primers DBJF/DBJR was 100 pg of genomic DNA in the 20 μL PCR reaction mixture . FurtherAccurate identification of plant cultivars is an important part of plant product quality control, especially in situations where suppliers may be intentionally providing material from the wrong cultivars . ‘DabojuUp to now, many types of molecular markers, including RAPD, AFLP, SSR, ISSR, SRAP, and SCoT, have been used for genetic diversity evaluation, genetic relationship analysis, genetic map construction, and trait correlation analysis of chrysanthemum germplasm resources ,26,40,41Ocimum tenuiflorum [Trapa natans [Lycium chinense [Dendrobium officinale [Crocus sativus [Aconitum heterophyllum and Cyperus rotundus [Punica granatum [Panax ginseng [To overcome these shortcomings, the SCAR marker, a more specific type of marker, was developed. SCAR markers have been widely used in the identification of cultivars, species, substitutes, and adulterants because they are specific to single loci and their PCR amplification is relatively insensitive to reaction conditions. Traditional DNA molecular markers, such as RAPD, ISSR, AFLP, SRAP, and SSR, can be transformed into SCAR markers based on sequence data, a process that improves the reproducibility of the PCR products of SCAR markers. In the present study, a specific SCAR marker was developed for the authentication of ‘Daboju,’ based on SCoT analysis. In recent years, SCAR markers have increasingly been used for the authentication of medicinal plants, such as uiflorum , Trapa na natans , Lycium chinense , and Denficinale . In addi sativus , Aconiturotundus , Punica granatum , and Pan ginseng .As a molecular marker technique, generating high levels of polymorphism and capable of high stability, the recent introduction of SCoT markers has been widely welcomed in recent years . Some SCThere are many cultivars of medicinal chrysanthemum, and the specific uses and medicinal values of different cultivars also vary. In order to ensure the clinical safety and effectiveness of preparations of chrysanthemum ‘Daboju,’ genuine raw material of ‘Daboju’ must be used. Therefore, it is necessary to establish a rapid, inexpensive, efficient, and accurate method by which to identify chrysanthemum ‘Daboju’ and distinguish it from other cultivars or from adulterated preparations. In recent years, DNA barcoding has gained popularity in species identification ,49,50,51Twenty-one chrysanthemum cultivars, including ‘Jinju 1,’ ‘Jinju 2,’ ‘Jinju 3,’ ‘Xiaoyangju,’ ‘Zaoxiaoyangju,’ ‘Dayangju,’ ‘Yizhongdabaiju,’ ‘Xiaohuangju,’ ‘Huangyaoju,’ ‘Jiuyueju,’ ‘Zhaohua 1,’ ‘Jinsihuangju,’ ‘Daboju,’ ‘Zaogongju,’ ‘Wuyuanhuangju,’ ‘Xiaobaiju,’ ‘Machengju,’ ‘Dahuangju,’ ‘Hongxinju,‘Changbanju,’ and ‘Dabaiju,’ were collected from their main cultivation areas  for SCoTw/v) agarose gel, and the concentration of DNA was determined using a UV spectrophotometer.Total genomic DNA preparations were extracted after pooling fresh leaves from six or more different individuals of each cultivar, using the Plant Genomic DNA Extraction Kit . The integrity and quality of the DNA preparations were evaluated by electrophoresis on 1.0% , 1 μL of genomic DNA template (50 ng), 1 μL of each primer (10 μM), and 8 μL of ddH2O. The PCR reaction was performed as follows: 94 °C for 5 min, 35 cycles of 94 °C for 1 min, 50–60 °C for 50 s , and 72 °C for 1.5 min, followed by extension at 72 °C for 10 min. The PCR products were analyzed by electrophoresis on 1.5% (w/v) agarose gels. The amplified DNA bands were stained with GelStain  and the images photographed on a UV transilluminator. To check for reproducibility of results, PCR reactions were repeated at least three times.Thirty-six PCR primers for SCoT analysis were selected from the study of Collard and MacKill . SCoT-PCIn order to count the number of primer-amplified loci and to evaluate the degree of polymorphism of primer-amplified DNA bands, the number of amplified bands was calculated by Quantity One software  with manual correction. The bands that were reproducible and unambiguous were marked as ‘1’ and the absence of a band at the same locus was scored as ‘0.’ Any DNA band present in a specific cultivar and absent in all the other cultivars at the same locus was defined as a cultivar-specific marker.TM19-T vector  according to the manufacturer’s protocol, transformed into DH5α Escherichia coli competent cells, and cultured at 37 °C overnight. The recombinant plasmids were selected by colony PCR, and clones with correct-sized inserts were sequenced bidirectionally, using M13 universal primers by Shanghai Sunny Biotechnology Co. Ltd. .A selected cultivar-specific band was extracted and purified from an agarose gel using a SanPrep Column DNA Gel Extraction Kit . The purified DNA fragment was cloned into the pMDhttps://www.ncbi.nlm.nih.gov/tools/vecscreen/, accessed on 28 December 2021) was used to remove vector sequences from the obtained sequence, and the nucleotide database of the BLASTN program  was used to identify the similarities between sequences. Ultimately, the sequence obtained was submitted to the GenBank database (GenBank accession number: OL456174) [The online tool VecScreen (L456174) . The SCAL456174)  , 1 μL of genomic DNA template (50 ng), 1 μL of forward primer (10 μM), 1 μL of reverse primer (10 μM), and 7 μL of ddH2O. PCR reaction was performed under the following conditions: 94 °C for 5 min, 35 cycles of 94 °C for 1 min, 62 °C for 50 s, and 72 °C for 1.5 min, followed by 72 °C extension for 10 min. The conditions of agarose gel  electrophoresis were the same as described above.First, the specificity of the SCAR marker was evaluated by PCR amplification of the DNA of the 21 medicinal chrysanthemum cultivars . FurtherThe sensitivity of the SCAR marker (DBJF/DBJR) was tested with a dilution series of purified ‘Daboju’ genomic DNA: 50 ng, 40 ng, 30 ng, 20 ng, 10 ng, 100 pg, and 10 pg in 20 μL of PCR reaction mixture. To further test the applicability of the SCAR marker primer pairs (DBJF/DBJR) in practice, genomic DNA of ‘Xiaoyangju,’ ‘Zaoxiaoyangju,’ ‘Dayangju,’ ‘Machengju,’ and ‘Hongxinju,’ the flowers of which are similar to those of ‘Daboju’ , were utChrysanthemum cultivars. Thus, the development of these efficient SCAR markers for ‘Daboju’ will greatly contribute to the conservation and rational utilization of this important Chrysanthemum plant resource.A ‘Daboju’-specific DNA fragment, SCoT36-385, was obtained, and a reliable and reproducible SCAR marker primer pair (DBJF/DBJR) was subsequently developed from this fragment for the identification of genuine samples of ‘Daboju’. The SCAR marker DBJF/DBJR could specifically identify genetic material of ‘Daboju’ and distinguish it from those of other


Using a simulation example, we showed that the proposed method can rigorously quantify the expected underdetermination  of relative contributions. Moreover, the application to the published field data highlighted two problematic aspects of the underdetermination: 1) ordinary SIMMs was difficult to quantify underdetermination of each source, and 2) the marginal posterior median was not necessarily consistent with the joint posterior peak in the case of underdetermination. Our study theoretically and numerically confirmed that β-dependent SIMMs provide a useful diagnostic tool for the underdetermined mixing problem. In addition to ordinary SIMMs, we recommend reporting the results of β-dependent SIMMs to obtain a biologically feasible and sound interpretation from stable isotope data.Stable isotope mixing models (SIMMs) provide a powerful methodology for quantifying relative contributions of several sources to a mixture. They are widely used in the fields of ecology, geology, and archaeology. Although SIMMs have been rapidly evolved in the Bayesian framework, the underdetermination of mixing space remains problematic, i.e., the estimated relative contributions are incompletely identifiable. Here we propose a statistical method to quantitatively diagnose underdetermination in Bayesian SIMMs, and demonstrate the applications of our method (named  In animal ecology, the development of a methodology for quantifying trophic interactions between consumers and their dietary sources has a long history , 2. Stabθ and 1−θ . For sound mixing spaces, the unique relative contributions of these sources have the maximal posterior probability, e.g., P(θ = 0.5)>P(θ = 1.0)≥P(θ = 0.0). Statistically, such relative contributions are referred to as being identifiable (or estimable). On the other hands, the underdetermined mixing problem has two or more relative contributions with the maximal posterior probability, e.g., P(θ = 0.5) = P(θ = 1.0)≥P(θ = 0.0). Importantly, this problem may result in the inappropriate interpretation and wrong secondary use of SIMM results; in the case of underdetermination, SIMM results cannot be summarized only by a representative value  of the marginal posterior distribution.However, the considerable advances in SIMMs have made it tempting to overlook a fundamental issue in mixing models, the underdetermined mixing problem , 16–19 . As the Branta bernicla) [β-dependent posterior probability), and thus achieves accurate diagnosability and broad applicability to all Bayesian SIMMs.The underdetermination is a general statistical problem but particularly notorious for SIMMs, in which increasing the number of elements, rather than the number of isotope samples, is needed to improve the identifiability of relative contributions. Previous studies proposed several methods to diagnose this problem  , 20 , is derived from a prior, π(θ), and a data probability, p, as follows:θ is a vector of estimated quantities , φ is a vector of user-given quantities, and X is a vector of isotopic data. As an example of Eq (β-dependent posterior probability:β-dependent posterior probability with β = 1 reduces to the ordinary posterior probability. For large β (→ ∞), the posterior probability of estimates other than the join posterior peak become zeros . Importantly, the model complexity of SIMMs does not influence diagnosability of our method. In this paper, we used the ΔBDP  as a quantitative diagnostic for underdetermination. The ΔBDP will be near zero when the underdetermined problem does not exist in the focal mixing space. Note that β has little influence on ΔBDP if the β is sufficiently large .Since SIMM is one of Bayesian statistical models, its posterior probability, φ)π(θ)dθ,where θ Our method is non-Bayesian despite adoption of the full Bayesian setup for standard Markov chain Monte Carlo calculations. Because it works just as the device to numerically obtain the joint posterior peaks , simultaneous calculations of the ordinary posterior are needed to interpret the whole results of Bayesian SIMMs . Note thWe provide two motivated examples. The first example is a two-isotope simulation in which the consumer signature is at the centroid of four source signatures .β-dependent SIAR . In the latter, we set β = 1,000 because sufficiently large β qualitatively unchanged the results (see S3 Appendix in https://github.com/yutakaos/archives/tree/master/simm/siarbeta). We assigned uninformative or vague priors for all parameters according to previous studies [In these examples, we estimated relative contributions of sources to the mixture using ordinary SIAR and mination , which wmination  . The BDP of Zostera and Ulva lactuca were intermediate . Interestingly, all the marginal posterior medians fell outside the BDP , but the inverse relationship was observed for ΔBDP (0.8% vs 5.3%). The multi-modality cannot even be detected for our examples. Checking correlation and multi-modality should be recognized as a preliminary tool for diagnosing serious underdetermined problems.The serious underdetermined mixing problem results in high correlation and multi-modality for estimated relative contributions . HoweverOur method exponentiates not only data probability but also prior probability because our interest is to diagnose underdetermination of posterior probability. Therefore, our method inherits the benefits and limitations of Bayesian methods. Specifically, we can utilize the information from other investigations  as informative priors, while we can obtain trivial results when data has little information. For Bayesian users, it is important to understand that underdetermined mixing problems are resolved either by improving data probability  or by using informative priors. Future works should aim at determining appropriate priors for underdetermined mixing problems, although it is beyond the scope of this study.Enteromorpha spp. involved high uncertainty due to underdetermination probably because the isotopic signatures were on the inside of the polygon composed of the other sources. Such problematic isotopic geometries frequently occur in isotopic studies [β-dependent SIMMs.SIMMs are widely used in stable isotope studies to improve the biological interpretation of isotope data. However, misinterpretation may result from underdetermined mixing problems, even in sophisticated field studies. In the case of the Brent geese data, the relative contribution of  studies –29, requ studies , 31. We  studies . The allRecently, there have been the remarkable developments of biological tracer analysis . These S1 File(DOCX)Click here for additional data file.S1 Scripts(ZIP)Click here for additional data file.


Communications Biology 10.1038/s42003-019-0693-y, published online 3 December 2019.Correction to: In the original version of the article, authors found that there had been an overestimation of overnight slow wave energy for a subset of individuals for which the data are presented in Fig. 3In the original version of the article, Fig. 3 appeared as:All three panels have been replaced so in the new version of the article Fig. 3 appears as:In the “Methods” section on page 8, under the heading of “Sleep assessment and spectral power analysis,” the text:“The night was divided into 30-min periods, from sleep onset until lights on. For each 30-min period, SWE was computed as the sum of generated power in the delta band, both for the lower range (0.75–1 Hz) and higher range (1.25–4 Hz), during all the NREM 2 and NREM 3 epochs of the given period, after adjusting for the number of NREM 2 and 3 epochs to account for artefacted data.”has been replaced with:“The night was divided into 30-min periods, from sleep onset until lights on. For each 30-min period, SWE was computed as the sum of generated power in the delta band, both for the lower range (0.5–1 Hz) and higher range (1.25–4 Hz), during all the NREM 2 and NREM 3 epochs of the given period, after adjusting for the number of NREM 2 and 3 epochs to account for artefacted data.”In the “Results” section on page 3, under the heading “CEP and slow waves generation” the text:26. We found that frontal CEP was significantly related to slow wave energy (SWE), a cumulative measure of slow waves generated during NREM sleep, both in the slower range  and in the higher range , such that young-like frontal CEP was associated with increased, and presumably preserved SWE. We further found that SWE in the lower 0.75–1 Hz range was expectedly associated with GM volume , as well as with whole-brain Aβ burden , confirming previous reports11,27, but was not linked to whole-brain tau burden . In contrast, CEP was not associated with any of the brain structural integrity measures . Frontal CEP relates therefore to a gold standard measure of sleep homeostasis known to decline in aging13,15 and to be associated with Aβ burden11, but is not significantly linked to the hallmarks of AD neuropathology.”“We then confronted the validity of frontal CEP as a measure of sleep–wake regulation by testing its association with slow waves generation during sleephas been replaced with:26. We found that frontal CEP was significantly related to slow wave energy (SWE), a cumulative measure of slow waves generated during NREM sleep, both in the slower range  and in the higher range , such that young-like frontal CEP was associated with increased, and presumably preserved SWE. We further found that SWE in the lower 0.5–1 Hz range was not significantly associated with GM volume , nor with whole-brain Aβ burden , contrasting with previous reports11,27, and was not linked to whole-brain tau burden . Likewise, CEP was not associated with any of the brain structural integrity measures . Frontal CEP relates therefore to a gold standard measure of sleep homeostasis known to decline in aging13,15, but is not significantly linked to the hallmarks of AD neuropathology.”“We then confronted the validity of frontal CEP as a measure of sleep–wake regulation by testing its association with slow waves generation during sleepIn the “Discussion” section of page 4, the text:N = 60) younger sample, slow waves generation during sleep is not associated with tau burden nor with cognitive measures including memory, attention, and executive functions. We demonstrate instead that, at around 60 years, wake-dependent variations of basic cortical function is associated with cognitive fitness, independently of Aβ and tau burden and GM volume.”“Here, we confirm that brain activity during sleep is associated with Aβ burden also in healthy late middle-aged individuals (59.6 ± 5.5 years). Yet, in this relatively large (has been replaced with:N = 60) younger sample, slow waves generation during sleep is not associated with tau burden nor with cognitive measures, including memory, attention, and executive functions. We demonstrate instead that, at around 60 years, wake-dependent variations of basic cortical function are associated with cognitive fitness, independently of Aβ and tau burden and GM volume.”“Here, we did not find a significant association between brain activity during sleep and Aβ burden in our sample of healthy late middle-aged individuals (59.6 ± 5.5 years). In addition, in this relatively large  during habitual sleep . b Positive association between CEP and cumulated frontal NREM SWE in the higher range (1.25–4 Hz) during habitual sleep . c Negative association between NREM SWE (0.75–1 Hz range) and whole-brain amyloid-beta burden . Simple regressions were used only for a visual display and do not substitute the GLMM outputs. Dotted lines represent 95% confidence interval of these simple regressions.”“Cortical excitability, slow wave energy, and brain structural integrity. has been replaced with:a Positive association between CEP and cumulated frontal NREM SWE in the lower range (0.5–1 Hz) during habitual sleep . b Positive association between CEP and cumulated frontal NREM SWE in the higher range (1.25–4 Hz) during habitual sleep . c No significant association was found between NREM SWE (0.5–1 Hz range) and whole-brain amyloid-beta burden . c is presented to allow comparison with the previous published version of the manuscript. Simple regressions were used only for a visual display and do not substitute the GLMM outputs. Dotted lines represent 95% confidence interval of these simple regressions.”“Fig. 3 Cortical excitability, slow wave energy, and brain structural integrity. As a result, some text in the manuscript has also been replaced.The errors have been corrected in the HTML and PDF versions of the Article.


APOE gene. These studies may have missed the APOE genotype-specific predictability of PRS for disease progression to AD.The combinatorial effect of multiple genetic factors calculated as a polygenic risk score (PRS) has been studied to predict disease progression to Alzheimer’s disease (AD) from mild cognitive impairment (MCI). Previous studies have investigated the performance of PRS in the prediction of disease progression to AD by including and excluding single nucleotide polymorphisms within the region surrounding the n = 270) and remained stable as MCI (n = 462). The predictability of PRS including and excluding the APOE region (PRSAPOE+ and PRSAPOE−) on the conversion to AD and its interaction with the APOE ε4 carrier status were assessed using Cox regression analyses.We analyzed 732 MCI from the Alzheimer’s Disease Neuroimaging Initiative cohort, including those who progressed to AD within 5 years post-baseline  and PRSAPOE−  were both associated with a significantly increased risk of MCI progression to dementia. The interaction between PRSAPOE+ and APOE ε4 carrier status was significant with a P-value of 0.0378. The association of PRSs with the progression risk was stronger in APOE ε4 non-carriers  than in APOE ε4 carriers .PRSAPOE ε4 non-carriers than APOE ε4 carriers. This indicates PRS as a potential genetic predictor particularly for MCI with no APOE ε4 alleles.PRS could predict the conversion of MCI to dementia with a stronger association in The online version contains supplementary material available at 10.1186/s40035-021-00259-w. APOE ε4 allele  florbetapir PET scans [T1-weighted brain MRI scan was processed with FreeSurfer version 5.1 to measure hippocampal and intracranial volumes . For assET scans  and calcET scans . In our ET scans .P-value threshold to select AD-associated SNPs, we iterated P-value thresholds of 1 × 10–6, 1 × 10–5, 1 × 10–4, 1 × 10–3, 1 × 10–2, 0.05, and 0.5. PRSs were separately calculated including and excluding SNPs within the 1 Mb-region surrounding the APOE gene, which was defined as a region from 1 Mb before rs429358 to 1 Mb after rs7412. The numbers of SNPs used for PRS calculation depending on the various P-value thresholds are shown in Additional file n = 138).PRSs were calculated using the software PRSice v2.3.1.e . The GWAwww.R-project.org). Comparisons of demographics between stable MCI, who remained stable at MCI during 5 years after baseline, and progressive MCI, who progressed to dementia within 5 years, were made with Mann–Whitney U test and Chi-squared test as appropriate.All statistical analyses were made using the R v4.0.2 software  of PRS in z-scores was obtained with Cox regression analysis. PRSs including and excluding SNPs within the 1 Mb-region surrounding the r2 ≥ 0.6 with one of the independent significant SNPs used for PRS calculation, were also included for gene mapping using FUMA. Gene mapping was conducted by three methods, including positional mapping, expression quantitative trait loci (eQTL) mapping, and chromatin interaction mapping. In the positional mapping, SNPs were mapped to genes with a maximum distance of 10 kb. eQTL data from the eQTL catalogue (BrainSeq brain) [–6 and promotor region window of 250 bp up- and 500 bp down-stream of the transcription start site. The possible biological processes (BP), molecular functions (MF), and involved cellular components (CC) of mapped genes were explored by gene set enrichment analysis using data from the gene ontology (GO) [P-value < 0.05.Functional mapping and annotation of SNPs used for PRS calculation was performed with the FUMA v1.3.6a software . The GWAq brain) , the Bloq brain) , Zhernakq brain) ), the BRq brain) , and theq brain)  datasetsq brain)  with FDRogy (GO) , 33. StaAPOE ε4 allele-carriers, lower MMSE scores and higher clinical dementia rating sum of boxes (CDR-SB) scores at baseline  were converted to dementia within 5 years (“progressive MCI”) and 462 MCI patients remained at MCI (“stable MCI”). There was no significant difference in age, sex, or education level at baseline between the two groups. The progressive MCI group had a significantly higher proportion of ne Table .Table 1BP-value thresholds to find the optimal threshold for selection of AD-associated SNPs, and the model with threshold P < 1 × 10–5 showed the best predictability and was used for further analysis  and PRSAPOE−  both presented a significant association with the risk of disease progression  than MCI with APOE ε4 alleles   than in MCI with APOE ε4 alleles . However, the interaction between PRSAPOE− and APOE ε4 carrier status was not significant, with a coefficient of − 0.174. HRs of PRSAPOE+ and PRSAPOE− in MCI with and without APOE ε4 alleles are presented with Kaplan Meier curves  compared to the MCI not carrying the APOE ε4 allele. The hippocampal volume on MRI was related to a decreased risk of disease progression , and the amyloid PET positivity predicted disease progression with HR of 7.449 (95% CI 4.199–13.215)  that had r2 ≥ 0.6 with one of the independent SNPs  used to calculate PRS at a selection threshold of P < 1 × 10–5, were mapped on 424 genes. Among the 4967 SNPs, 3551 SNPs were used for positional mapping of 316 genes, 2898 SNPs for eQTL mapping of 264 genes, and 73 SNPs for chromatin interaction mapping of 19 genes. Among a total of 424 mapped genes, 133 genes were mapped by multiple independent significant SNPs. The mapped genes are listed in Additional file For gene mapping, all SNPs , PRS without APOE could not predict the progression with HR of 1.03 (95% CI 0.79–1.34) [APOE and additionally confirmed different predictability between APOE ε4 carriers and non-carriers of the MCI participants.A previous study showed that the higher PRS, excluding chromosome 19 SNPs to avoid the  of 0.43 . In anot79–1.34) . In the APOE− showed less prominent association with disease progression risk than PRSAPOE+, which suggests that APOE is the strongest known risk gene for LOAD and affects the predictability of PRS. The APOE ε4 carriers show a  three- to four-fold increase of risk of AD development compared to the APOE ε4 non-carriers [APOE alone, other AD-associated genes can interact with APOE to affect the disease course. A previous study has revealed that the SNP variants at FYN and RNF219 loci are associated with decreased LOAD age-of-onset in APOE ε4 non-carriers but not in APOE ε4 carriers [RNF219 variant is also related with the beta-amyloid (Aß) load in APOE ε4 non-carriers, but not in APOE ε4 carriers [CETP gene could alter AD risk in an APOE-dependent manner [CLU is associated with AD only in APOE ε4 non-carriers and PICALM only in APOE ε4 carriers [CLU and PICALM, and the integrated effect of interaction between genes containing SNPs of PRS and APOE could manifest as a stronger progression risk in APOE ε4 non-carriers of our MCI cohort.Here, PRScarriers . In addicarriers . The RNFcarriers . Allelest manner . In a mecarriers . The SNPAPOE ε4 allele for amyloid clearance and aggregation and its association with AD-associated genes remain unclear [APOE ε4 allele is associated with Aß elevation and accumulation on PET, whereas PRS is more associated with faster cognitive decline in amyloid-positive status [APOE ε4 contributes to the initiation of amyloidopathy in an early stage, while other genetic variants are involved in disease progression. In addition, pathway analysis has shown that a large proportion of the AD-related pathways is associated with the APOE region. Furthermore, pathways such as the protein-lipid complex subunit organization and protein-lipid complex assembly have also been found to be involved in the AD risk independent of the APOE region [APOE variants, and immune response (45.5%) when the analysis excluded APOE variants [How the AD-associated genes impact AD pathology is not completely elusive, although the exact mechanism of the well-known  unclear . Recent e status , 40. ThiE region , 39. Agavariants .APOE and PRS cannot be distinguished clearly, and a complex interaction of Aß, tau metabolism, cholesterol/lipid metabolism [APOE, CLU, ABCA7), tau toxicity , immune response , and endocytosis  [However, the pathways related to tabolism , 43, endtabolism , neurointabolism , 46, certabolism , 48, andtabolism  should b, SORL1) .APOE+ and PRSAPOE− had relatively lower predictability for the conversion of MCI to AD, compared to MRI and amyloid PET biomarkers, which implies a limitation of PRS use in disease stages when abnormalities on MRI and amyloid PET biomarkers have fully developed. However, PRS as a non-modifiable risk factor given at birth could offer early prediction, independent of the disease stage.Another finding in our study was that PRSThere are a few limitations in the current study. The sample size was small, so replication in an independent cohort would be desirable. With a longer follow-up period, some stable MCI participants might convert to AD. There are several challenges for PRS utility in clinical practice. Although the predictive utility of PRS has been validated in clinically well-defined and biomarker-confirmed cohorts, the PRS risk estimation in clinical populations from the community could be affected by variables such as ethnicity, environmental factors, and mixed pathologies. In addition, PRS as an additive indicator should be interpreted with caution to avoid ethical issues such as genetic determinism , 52. FurAPOE ε4 [APOE ε4, but also analyzed, for the first time, the interaction between PRS and APOE ε4. In addition, we present, for the first time, a comparison of the predictability of PRS for disease progression, with conventional imaging AD biomarkers including PET-based amyloid deposition and MRI-based brain atrophy.PRS is also an emerging tool for genetic risk estimation in AD. The predictability of PRS for disease progression has been reported with inconsistency depending on the inclusion or exclusion of APOE ε4 carrier status, particularly stronger association in APOE ε4 non-carriers than in carriers. These results support that PRS can predict disease progression in MCI patients with no APOE ε4 allele, which has so far been the primary genetic risk factor used for clinical assessment. Furthermore, individualized risk estimation with PRS may allow timely interventions such as life style modification for disease prevention, which may reduce the dementia risk despite high genetic risk [In summary, we demonstrated that PRS could predict conversion of MCI to dementia, showing a significant interaction between PRS and tic risk .Additional file 1: Fig. S1. The multidimensional scaling analysis for study population selection. Table S1. Number of SNPs for PRS calculation depending on GWAS association P-value thresholds. Table S2. Predictability of PRS for progression from mild cognitive impairment to dementia depending on GWAS P-value thresholds.Additional file 2: Table S3. List of mapped genes.