OCA2-HERC2 locus is responsible for the greatest proportion of eye color variation in humans. Numerous studies extensively described both functional SNPs and associated patterns of variation over this region. The goal of our study is to examine how these haplotype structures and allelic associations vary when highly variable markers such as microsatellites are used. Eleven microsatellites spanning 357 Kb of OCA2-HERC2 genes are analyzed in 3029 individuals from worldwide populations. We found that several markers display large differences in allele frequency (10% to 35% difference) among Europeans, East Asians and Africans. In Europe, the alleles showing increased frequency can also discriminate individuals with (IrisPlex) predicted blue and brown eyes. Distinct haplotypes are identified around the variants C and T of the functional SNP rs12913832 (associated to blue eyes), with linkage disequilibrium r2 values significant up to 237 Kb. The haplotype carrying the allele rs12913832 C has high frequency (76%) in blue eye predicted individuals , while the haplotype associated to the allele rs12913832 T is restricted to brown eye predicted individuals. Finally, homozygosity values reach levels of 91% near rs12913832. Odds ratios show values of 4.2, 7.4 and 10.4 for four markers around rs12913832 and 7.1 for their core haplotype. Hence, this study provides an example on the informativeness of multiallelic markers that, despite their current limited potential contribution to forensic eye color prediction, supports the use of microsatellites for identifying causing variants showing similar genetic features and history.The  Genome-wide association studies in people of European decent3 have instead indicated eye color as a polygenic trait4 yet characterized by a limited number of major genes. The OCA2-HERC2 genes explain most of the blue and brown eye color inheritance5. Different polymorphisms in the regulatory and coding region of OCA2 are primarily associated with different eye, hair and skin pigmentation phenotypes9. These findings increased our understanding of the genetic basis of human pigmentation, and drew attention to their potential applications, such as forensic investigations11, historical and anthropological researches12.The human eye color trait is the most variable in European populations and it was for a long time considered a simple Mendelian trait with a brown eye color dominant allele and a blue eye color recessive alleleHERC2, is responsible for the greatest proportion of eye color predictability, this SNP together with five SNPs located in other genes have been brought together in the IrisPlex eye color prediction panel11. The accuracy rate of correctly predicting an individual's eye color as being blue or brown is on average 94% in Europe13. Additional variation has yet to be identified to account for the poor success rate for intermediate eye color predictions (73% accuracy) and in admixed populations15.One SNP in particular, rs12913832 in HERC2 gene, 21 kb upstream of the promoter of OCA2. The rs12913832 C-derived allele is highly associated with European blue eye color as a recessive trait9. The ability of this conserved element to act as an enhancer of OCA2 transcription has been confirmed in experiments using melanocyte cultures carrying either rs12913832 T/T or rs12913832 C/C genotypes17. The P protein, produced by OCA2, is thought to be a mature melanosomal membrane protein, with a potential role in trafficking other proteins to melanosomes19.The SNP rs12913832 is located in a (11 bp) conserved region of intron 86 of the OCA2-HERC2 region associated with blue eye color in Europeans has been strong20. This region encompass the third longest haplotype spam of diminished heterozygosity in the genome of modern Europeans21 which implies intense selection at this locus in ancestral European populations. Multiple factors possibly played a role such as sexual22, the ability to overcome seasonal affective disorder25 and associated light skin increased risk for developing melanoma and nonmelanoma skin cancer26. Several lines of research indicate that selective pressure for light pigmentation acted independently in Europeans and East Asians, yet with some genes in common. The brown-eyed associated SNPs frequent in Europeans are different from that of Asians, suggesting a population specific history of the genetic component of pigmentation27.Interestingly, the selection pressure on the 17. During the great agriculture migration to the northern part of Europe, the mutations spread out from the Black Sea region. Newer studies have also indicated that some OCA2 missense mutations give rise to blue eye color, when the genotype rs12913832 C/T predicts brown eye color and that these are only found in the Scandinavian population and not in individuals of Southern-European descent28. The OCA2-HERC2 is therefore a region of large interest due to its functional role and population genetics. Global SNP variation has been comprehensively described, yet more data are being available from ongoing projects of whole genome sequencing which indicate that multiallelic sites represent as much as 10% of the genomic variants.Evidences from haplotype analysis comparing Dutch and Mediterranean population samples suggest that blue eye color has only arisen once during the Neolithic period, past 6–10,000 years ago, as a founder mutation shared by diverse European populationsOCA2-HERC2 by using microsatellite markers (STRs) as an example of multiallelic variants. We are interested in exploring how the use of polymorphisms with a different mutational mechanism and higher rate, change the pattern of haplotype structures and allelic associations around functional alleles. Conveniently, the recent history of selection of OCA2 should enhance the degree of correlations of neutral variants even when highly variable polymorphisms are used. Our interest in these data is twofold: on the one hand, to provide an empirical example of the utility of STRs in studies of association mapping of similar traits, on the other hand, to search for rs12913832 proxy STR markers for a presumptive eye color DNA test to be included in conventional forensic STR multiplexes.In this study, we aim at further investigating the genetic variability of 31. Yet, several results suggest that association studies using not only SNPs but also multiallelic STRs within or near candidate loci would be useful to search for a disease susceptibility gene, especially in populations with unknown LD structure. The rationale behind this is that in non-African samples highly significant LD between microsatellite alleles and stable markers is preserved across relatively large genetic distances (about 100 Kb range)29 compared to the shorter range (about 30 Kb) for SNPs32. The average length of LD for microsatellites is ~ 100 Kb, which is considerably higher than that of SNPs34. Therefore, a single microsatellite captures a larger genomic region than does a single SNP35. One STR can harbor both types of alleles: those showing complete association with a SNP and alleles that show little or no association. STR outside LD blocks may still show LD with SNPs inside the LD block. In that case, for association studies we would not need an STR in each haplotype block as for SNPs. At the same time, microsatellites show a smaller interpolation variability37 and a single-step expansion or contraction of the tandem repeat on the background of ancestral SNP haplotypes can break up common haplotypes, leading to greater haplotype diversity within the linkage disequilibrium block of interest. The relative performance of STR and SNP in association mapping will also depend on the frequency of disease variants. Markers alleles achieve maximal power for detecting associations when disease alleles are at similar frequencies38. As a result, STR have the potential to find rare disease variants that common SNPs will miss40. Several genome wide association studies used tens of thousands of STRs to investigate the genetic basis of hypertension, narcolepsy, anorexia nervosa, mandibular prognathism, rheumatoid arthritis, type 2 diabetes and prostate cancer47.The known susceptibility gene for rheumatoid arthritis HLA-DRB1 was successfully confirmed and two new candidates (TNXB and NOTCH4)43 were identified through a combination of STR and SNP analysis. Microsatellites have starred in association studies leading to widely replicated discoveries of type 2 diabetes (TCF7L2)46 and prostate cancer genes (the 8q region)45. Moreover, the ability of microsatellite markers to capture information on coding SNPs was successfully tested for the human major histocompatibility complex to predict HLA functional SNPs49.The density of genetic markers required for successful association mapping of complex diseases depends on linkage disequilibrium between non-functional markers and functional variants. There are few reports about the pattern or extent of linkage disequilibrium (LD) between SNPs and STRs genomewide50. To increase the accuracy of power studies in STR based disease mapping it is therefore important to provide empirical data for genomic regions of large interest and well characterized as the OCA2-HERC2 locus.However, patterns of LD between SNPs and microsatellites markers may vary considerably between lociOCA2-HERC2 region and their correlation with the functional SNP rs12913832 and the eye color trait as predicted by the IrisPlex assay. We also examine degrees of LD, related microsatellites’ haplotype structures and homozygosity levels.Here we present our results on the global variation of eleven microsatellites spanning 357 Kb in the OCA2 to intron 5 of HERC2. Seven STRs are dinucleotide repeats, three are tetranucleotide and one is a pentanucleotide repeat sequence. Markers’ distances and number of alleles are indicated in Fig. A total of 11 microsatellites polymorphisms are the focus on this study. They span the region from intron 18 of Figure Results of Fig. 11. Based on these data we selected individuals of Europe with a probability of blue or brown eye color higher or equal to 0.8, this arbitrary cut-off was used to reduce the error rate associated to such indirect determination of the phenotype, together with eliminating all individuals with predicted “intermediate eye color”. This analysis determined two groups, 42 predicted blue eyes and 62 predicted brown eyes individuals. In the HGDP-CEPH collection, outside Europe, only two individuals of Central-South Asia and three of Middle-East had a value of blue eye color prediction higher or equal to 0.8. To avoid a population structure bias we limited our analysis to Europeans.The HGDP-CEPH European group includes 158 individuals. Prediction of eye color is possible using the IrisPlex marker set available for all DNA samplesWhen STRs’ allele frequencies are compared between predicted brown-eyed versus blue-eyed Europeans Fig.  several P values ranging from 10−3 to < 10−6 including the haplotype of STR 6–8–9–10 P value 10−6 (data not shown). Smaller P values ranging from 10−5 to < 10−6 are obtained for the same STRs in addition to STR A for the larger dataset when rs12913832 C and T containing haplotypes are compared.The statistical significance of the differences in allele frequency distribution of the data reported in Fig. 2 values for Europeans are shown in Fig. Significant linkage disequilibrium r2 values  and (-4-1-6-4) Fig. b that arHomozygosity values of STR markers in predicted blue eye Europeans are higher than in predicted brown eye individuals and increase for markers located around rs12913832, with values reaching 75% to 91%  polymorphisms, previously elucidated functional variants and patterns of allelic associations including signatures of natural selection in Europe. Here we further investigate this region with a set of microsatellites annotated from whole genome sequencing data. With respect to previous studies based on SNP data, this type of polymorphism (STR) should provide non-redundant genetic information due to the different mutational mechanisms and its effect on patterns of allelic associations and haplotype structure. Here we show for the first time the correlation between multiallelic markers and functional variants of the eye color phenotype.This study focuses on the most important genomic region for eye color variation in humans, which is OCA2-HERC2 locus were genotyped in 1,064 individuals from 52 populations around the world (HGDP–CEPH panel). Data available from whole genome sequencing projects (gnomAD) were retrieved for the same STR when possible, this allowed us to work with larger sample collections from Europe (n = 876) East Asia (n = 801) and Africa (n = 896). Haplotypes were reconstructed integrating the two SNPs of the IrisPlex assay that are located on this chromosome, rs1800407 and rs12913832 and the eye color phenotype was predicted using the 6 SNP panel of IrisPlex.Eleven microsatellites spanning 357 Kb across the The results showed that nine STRs were polymorphic across major population groups. Allele frequency distributions showed as expected, Africa as the most variable region. Europe, Middle-East and Central-South Asia form an undistinguishable homogeneous Eurasian group different from East Asians, Oceanians and Amerindians.51. Although, only samples showing a prediction value for blue or brown higher or equal to 0.8 were included, the phenotyping procedure may have an error rate estimated to be 3–4%28. Because of this limitation, the subsequent analyses of allele imbalance and LD using the larger population data from gnomAD rather focused on the correlation between STRs and the functional variant rs12913832.Interestingly, large differences in allele frequency distributions (up to 36%) were found between predicted blue and brown eyed individuals of Europe with often one allele showing increased frequency in predicted blue eye color. It should be noted here that the actual phenotype data of the individuals was not available and the IrisPlex method was reported to have lower prediction accuracy for intermediate eye colors (non-blue and non-brown) and heavily relies on rs1291383252 to 54%9 including four SNPs in a region from STR 4 (17 Kb upstream) to STR 9 (22.6 Kb downstream) and to rs12913832, respectively.Differences in frequency values go up to 48% when considering two groups of haplotypes containing the variants C or T of rs12913832 in 876 Europeans, with markers STR 5, 6, 8, 9, 10 showing statistical significance. LD analysis indicated some degree of allelic associations among SNPs and STRs, these are more abundant in Europe, where the associated alleles are those that showed skewed allele frequencies between predicted blue and brown eye individuals. Previous studies also showed large difference in SNP haplotypes frequencies between blue and brown eye color individuals going from 362 values are significant up to 295 Kb from rs1800407 to STR 10 and values range from 0.1 to 0.5. Note that r2 values higher than 0.1 should allow identifying a disease susceptibility variation in an association study53. This block includes the functional variant rs12913832, three different haplotypes are associated to the allele C and T of rs12913832, one is most frequent in predicted blue eye individuals 76% (30% in blue) and two others exclusive to predicted brown eye individuals with 7.3% and 4% frequency. Finally, homozygosity values reach levels of 75% to 91% around rs12913832 in predicted blue eye individuals, which are consistent with the recessive mode of inheritance of the blue eye color. In addition, elevated odds ratios values up to 8 and 10.4 were obtained around rs12913832 and for the core haplotype RRRR of STR 6–8–9–10.Two haplotype blocks were identified from STR A to rs1800407 and from STR 6 to STR 10. Linkage disequilibrium r52. These blocks are also detected by this STR based study with some allelic associations going beyond originally defined blocks LD2 and LD3 up to 237 Kb. Eiberg and colleagues17 described a 175 kb long haplotype of 13 SNPs, 97% frequent in Danish individuals with blue eye color. The corresponding position of this block with respect to our data is 27 Kb upstream rs12913832 to 22.5 Kb downstream STR 9.Previous studies using SNPs genotyped in a large population of Netherland indicated three LD blocks going from the position corresponding to STR 2 to rs1800407 for 27 kb (LD1), around STR 6 for 13 Kb (LD2) and around STR 9 for 70.5 kb (LD3)34. This is because the higher mutation rates of STRs underlie a stronger statistical significance of LD. Therefore the importance of using STR also for the first screen of association study to reduce the number of initial association tests or when LD blocks are not known. Although many studies support these predictions theoretically, few have attempted to provide empirical data on the extent of LD detectable at STR and SNP sites at defined distance flanking unknown functional variants of significant effect. In the context of alcohol dependence syndrome studies, the persistence of allele association and differences in allele frequencies of a range of STR markers was investigated around the functional locus aldehyde dehydrogenase ALDH2 known to be under selective constraint in Japanese alcoholic populations33. This study showed the persistence of LD over distances up to 400 Kb for pairs of loci including at least one STR. It follows that the comparison of allele frequency differences for the STR markers in the case  and control populations would have detected the ALDH2 marker as a putative susceptibility locus. It should be noted that the recent origin of the ALDH2 functional markers is very likely to be a major factor determining the strength of the association observed. With regard to the observation of the allele frequency pattern difference between the cases and controls, population demography, allele frequencies and degree of natural selection are additional important factors in determining the success of gene hunting using this type of approach. Similarly, the genetic influence of IL10 SNP allele on HIV-1 infection and AIDS progression was first deduced by observations of epidemiology associations of two STR loci within 4 Kb of the IL10 gene54. In the same way, the data presented here show that considering the two extremes of brown and blue predicted eye color (most robust prediction by IrisPlex) and OCA2-HERC2 as candidate genes, simple parameters of skewed allele frequencies of STRs and levels of homozygosity would allow us to point out the most relevant genetic region carrying the functional variant rs12913832, even with a relatively small sample size and a degree (3–4%) of mislabeled phenotype due to the indirect ascertainment.This study designed around known causal variants confirms that allelic associations with functional SNPs can be detected over greater distance with STRs than with SNPsIn addition, these data confirm the different genetic history of eye color in Asians that showed no allelic associations around rs12913832 and one frequent haplotype that is rare in Europe. The patterns of variability we observed also support previous data indicating strong natural selection in this genomic region in Europe.OCA2-HERC2, markers mutating more rapidly than SNPs also capture the effect of demographic history and selective pressure by showing marked genetic differences between predicted blue and brown eyed individuals within the same population. These data support the hypothesis that with STR markers, besides SNPs, it is possible to investigate the genetics of eye color and effectively use this type of variation as marker for mapping causing variants of similar traits, appeared recently, with large penetrance and under positive selection.This study further describe novel SNP-STR correlations spanning a region of large interest for human genetics, population genetics and forensic science. These results show that in this region of Models for detecting variants responsible of genetic traits have been poorly constrained by available data leading to large uncertainties in model predictions. Studies like our go toward the effort of providing a portion of such empirical results. We hope that these data can help validating novel statistical methods aiming at using multiallelic markers for detecting functional variants.HERC2 previously showed OR of about 3055, due to the difference in ORs and therefore effects sizes of markers, the prediction using STR markers would be lower than using functional SNPs. Conversely, STRs alleles around rs12913832, are not as randomly distributed as one could expect from highly variable loci. In particular, for individuals found to be homozygous RRRR/RRRR around rs12913832 C/C the blue eye color would be predicted in 90% of the cases. Similarly, individuals that lack the haplotype RRRR around rs12913832 T/T are predicted brown eyed color in 89% of the times. Unfortunately, no indications of the phenotype can be obtained from all the other combinations of heterozygous haplotypes. Finally, there is great potential in investigating STRs in the HERC2-OCA2 region to explain the eye color that is wrongly predicted by rs11913832. These data may provide the basis of such future studies that should rely on precise and direct eye color phenotype determination.Finally, the idea of possibility replacing functional SNP genotyping with linked STRs for roughly predicting the eye color phenotype in Europe, is poorly supported by the data. A small set of STRs could be easily added to current forensic DNA profiling multiplex, yet none of the STRs analyzed is strongly associated to rs12913832 to work as proxy. This is not surprising since the highest OR observed with these STR data is about 10 while a single SNP in All methods were performed in accordance with the relevant guidelines and regulations of the journal.56. For all data analyses purposes we considered only 952 individuals (H952 subset) after exclusion of duplicates, first- and second-degree relatives57. Populations were combined into continental-based groups which have been previously established58 with the following composite populations, sample sizes and labels: 6 African (n = 105 AFR), 8 European (n = 158 EUR), 4 North African/Middle Eastern (n = 162 ME), 9 Central-South Asian (n = 202 CSA), 17 East Asian (n = 230 EAS), 2 Oceanian (n = 28 OCE) and 5 Native American (n = 64 NAM). A second population sample included 84 unrelated European individuals with Caucasian appearance and Swiss parents since three generations. DNA was extracted using the QIAamp DNA Mini kit (Qiagen AG Switzerland) according to the manufacturer’s guidelines and quantified using the Quantifiler Human DNA Quantification Kit (Life Technologies).The CEPH Human Genome Diversity panel (HGDP-CEPH) contains 1,064 individuals from African, European, North African/Middle Eastern, Central-South Asian, East Asian, Native American and Oceanian populationsCommission cantonale d'éthique de la recherche sur l'être humain (CER-VD), and were labelled with an anonymous code number linked only to demographic information and sex.For all subjects, blood cell samples were obtained according to protocols and informed-consent procedures approved by the institutional review board https://gnomad.broadinstitute.org/downloads) 59. The gnomAD v3.1.1 track shows variants from 143,150 unrelated individuals sequenced as part of various population-genetic and disease-specific studies, some markers have only frequency values while others show single individual genotypes. All variants are mapped to the GRCh38/hg38 reference sequence. From this database, markers STR 2 and STR B showed low quality genotypes with more than 50% of missing data. The population samples with individual genotypes from gnomAD used in this study include 634 Europeans (Finnish ‘fin’ and Non-Finnish European ‘nfe’), 791 Africans and 571 East-Asians. In summary, the largest sample collections considered in this study include 876 individuals from Europe, 896 form Africa and 801 from East-Asia.The genotypes of additional individuals were obtained from the Genome Aggregation Database , 125 μM dNTP (Thermo Fisher), 1.2 U AmpliTaq Gold DNA Polymerase (Thermo Fisher) and 0.5 ng DNA. Primers’ sequences, quantities and multiplexes are indicated in Supplementary Table STR markers were identified by searching the PCR fragments were separated by capillary electrophoresis after adding 1 μl PCR amplicon to 8.5 μl deionized formamide HI-DI (Thermo Fisher) and to 0.5 μl 600 LIZ size standard (Thermo Fisher). Capillary electrophoresis was performed using an ABI PRISM 3130xl Genetic Analyzer (Thermo Fisher) according to the manufacturer's instruction and analyzed using the GeneMapper® ID v3.2.1 software (Thermo Fisher), with a minimum peak height threshold of 50 RFU. The commercial DNA CEPH 1347-02 (Thermo Fisher) was added to two empty positions in each PCR plate as positive control of amplification and internal standard for allele calls, at least one empty well per plate was used a negative control of amplification.Markers were grouped in three multiplexes as indicated in the table. For allele names, ‘R’ indicates the number of repeats of the reference genome and additional repeats are indicated by ‘+ n’ or’-n’ consistent with gnomAD. Considering all populations, 723 HGDP-CEPH samples genotypes are also reported by gnomAD. These data were used for confirmation and harmonization of allele calls. All the genotypes produced here corresponded to the data available in gnomAD except for STR 9 where we could distinguish the alleles -8 and R, while these two are the same allele R in gnomAD. The allele − 8 was observed only in the HGDP-CEPH Eurasian populations. This is probably due to a neighboring deletion linked to the allele R coamplified by the PCR primers used in this study. It is also possible that, because of the complexity of the tandem repeat sequence, that includes both variable CA repeats and variable TA repeats, two different neighboring sites instead of one are indicated in gnomAD.11 and by using the model from the online tool (https://hirisplex.erasmusmc.nl/). The IrisPlex consists of 6 SNPs, rs12913832 (HERC2), rs1800407 (OCA2), rs12896399 (SLC24A4), rs16891982 (SLC45A2/MATP), rs1393350 (TYR) and rs12203592 (IRF4).The eye color prediction for the 84 Swiss Europeans samples was done by IrisPlex typing according to the protocol described in Walsh et al. 20112, 200 µM of each dNTP and uses adjusted thermocycling conditions for increased specificity: (1) 95 °C for 10 min, (2) 33 cycles of 95 °C for 30 s and 61 °C for 30 s, (3) 5 min at 61 °C. Each primers concentration used was 0.208 µM except for rs1800407 used at 0.104 µM. This was followed by product purification (2.5 µl of PCR product) using an Exo I / SAP treatment (NEB) in CutSmart 10 × Buffer for an incubation time of 90 min at 37 °C and inactivation step of 15 min at 80 °C. Further multiplex single base extension (SBE) reaction using the ABI Prism1 SNaPshot kit (Applied Biosystems) was performed, 1 µl of purified product, SNaPShot reaction mix and Sequencing Primer mix were used in a final volume of 5 µl. Thermocycling conditions used were: (1) 96 °C for 2 min, (2) 25 cycles of 96 °C for 10 s and 50 °C for 5 s, (3) 30 s at 60 °C. The SNaPShot PCR product was then treated with SAP (NEB) for 90 min at 37 °C and inactivation for 15 min at 80 °C. One microliter of cleaned products was analyzed on the ABI 3130xl Genetic Analyser (Applied Biosystems) with POP-4 on a 36 cm capillary length array. Run parameters were optimized to increase sensitivity, with an injection voltage of 6 kV for 11 s, and run time of 1000 s at 60 °C.As previously published, the protocol consists of a single multiplex two step PCR using 1 µl genomic DNA extract (concentration of 0.5 ng/µl) and primers in a 12 µl reaction which includes 1 × PCR buffer, 2.7 mM MgCl61, which implements a Monte Carlo approach by performing repeated simulations.Allele frequencies were estimated by gene counting. The statistical significance of the difference in allele frequency between predicted blue and brown eye color individuals was assessed with the CLUMP program62 as well as pairwise linkage disequilibrium r2 values. Odds ratios (OR) 95% confidence interval (CI) and P values of Table The EM algorithm was used to estimate maximum likelihood haplotype frequencies by using Arlequin version 3.5.1.2The current study was approved by the Centre Hospitalier Universitaire Vaudois and Université de Lausanne institutional review board. Genomic DNA samples fully-consenting individuals were collected by the Human Genome Diversity Project (HGDP), in a collaboration with the Centre Etude Polymorphism Humain (CEPH) in Paris. For all subjects, blood cell samples were obtained according to protocols and informed-consent procedures approved by institutional review boards, and were labelled with an anonymous code number linked only to demographic information and sex. Besides the HGDP-CEPH diversity panel human cell line samples, all other samples involved in the study are long lasting anonymized DNA extracts previously obtained with informed written consent from healthy individuals for research purposes.Each blood sample used was freely donated under conditions of informed consent to participate.Each blood sample used was freely donated under conditions of informed consent to publish.Supplementary Figure 1.Supplementary Table S1.


Cherenkov detectors enable a valuable tool to identify high-energy particles. However, their sensitivity and momentum coverage are limited by the refractive index of host materials. Especially, identifying particles with energy above multiple gigaelectronvolts requires host materials with a near-unity refractive index, which are limited to bulky gas chambers. Overcoming this fundamental material limit is important for future particle detectors yet remains a long-standing challenge. Here, we propose a different paradigm for Cherenkov detectors that utilizes the broadband angular filter made from stacks of variable one-dimensional photonic crystals. Owing to the Brewster effect, the angular filter is transparent only to Cherenkov photons from a precise incident angle. Particle identification is achieved by mapping each Cherenkov angle to the peak-intensity position of transmitted photons in the detection plane. Such angular filtering effect, although decreases the photon number collected in the detection plane, enables the realization of a non-dispersive pseudo refractive index over the entire visible spectrum. Moreover, the pseudo refractive index can be flexibly designed to different values close to unity. Our angular-selective Brewster paradigm offers a feasible solution to implement compact and highly sensitive Cherenkov detectors especially in beam lines with a small angular divergence using regular dielectrics. Cherenkov detectors are used to detect high energy particles and their performance capabilities depend heavily on the material used. Here, the authors propose use of a Brewster-optics-based angular filter for a detector with increased sensitivity and particle identification capability. M. Frank and I. Tamm4. Remarkably, Cherenkov radiation8 has enabled the invention of Cherenkov detectors14 for identifying particles over a large momentum range in high-energy physics and astrophysics. The Cherenkov detector has played an essential role in the discovery of many elementary particles, including anti-protons15, J/ψ particles17, neutrino oscillations18, etc.A charged particle traveling in a transparent host medium emits photons when it travels faster than the phase velocity of the photons in that medium. This phenomenon is known as Cherenkov radiation, which was observed experimentally by P. A. Cherenkov (under the guidance of S. Vavilov)19, the particle velocity v can be determined by measuring the light emission angle θCR n is the refractive index of the host medium, and c is the speed of light in a vacuum. Although larger refractive indices give rise to lower Cherenkov thresholds and higher photon yield, they are not always desirable in Cherenkov detectors. The reason is that a large refractive index decreases the sensitivity of the Cherenkov angle to small changes in the velocity. In general, the highest detection sensitivity is obtained at θCR→0 or n→c/v (detection sensitivity is defined as θCR→0). In other words, to identify high-energy particles , transparent dielectrics with a near-unity refractive index are often required. Such a constraint limits the host materials used in many Cherenkov radiators to a low index. This is for example the case in ring imaging Cherenkov (RICH) detectors that use gases for the identification of particles with momenta larger than 10 GeV/c21.According to Frank and Tamm’s theory of Cherenkov radiationv→c/n), the photon yield approaches zero. Consequently, traditional gas radiators generally require bulky gas chambers to produce sufficient photons for detection14. Regular dielectrics offer a possible route to increase the photon emission efficiency, but their large refractive indices  cause different types of high-energy particles to have nearly identical Cherenkov angles, namely θCR→cos−1(1/n), which then makes particle identification impossible. As an example, quartz has a refractive index around 1.4, and its corresponding momentum coverage for the identification of pions and kaons is currently limited to be below 6 GeV/c23. All high-energy particles with momenta above this value emit light at θCR ∈ , independent of the particle velocity and particle type. Under this scenario, measuring θCR cannot lead to the identification of the corresponding particle type.Another limitation in state-of-the-art Cherenkov detectors is related to the photon emission efficiency: the use of low-index materials inevitably leads to low efficiency, since the photon yield is proportional to 25. One attempt proposes metal-based anisotropic metamaterials with one component of the effective refractive index close to unity24. Another study makes use of all-dielectric one-dimensional (1D) photonic crystals, where the constructive interference of resonance transition radiation is adopted to control the effective Cherenkov angle25. These nanophotonic Cherenkov detectors can achieve an enhanced sensitivity for any desired momentum range, however only at a specific working frequency. In fact, the working frequency is the major drawback of all nanophotonics-based Cherenkov detectors, i.e., they all have a narrow working bandwidth, resulting from the inherent chromatic dispersion of the constitutive materials  and the resonant nature of periodic structures . There are many more recent advances and ongoing efforts on the Cherenkov effect in nanophotonic settings and in emerging material platforms37. Nevertheless, the design of a broadband Cherenkov detector using regular transparent dielectrics and at the same time with enhanced performance has remained a long-standing scientific challenge.Recently, several theoretical attempts have been made to relax the material limitations in Cherenkov detectors by using modern concepts from nanophotonics and metamaterialsp-polarized  light incident at the Brewster angle, while the light with other polarizations or incident at other angles is totally reflected. Moreover, we can readily tailor the band gaps of these 1D photonic crystals to cover a broad spectral range, thus also making the angular selectivity broadband, spanning the entire visible spectrum41. After transmission through the broadband angular filter, the Cherenkov radiation in the detection plane features a pseudo Brewster-Cherenkov angle θBCR, namely the angle between the particle velocity and the tangential wavevector parallel to the detection plane  is completely different from the effective refractive index obtained using the standard homogenization theory. Interestingly, nBCR is related to the pseudo Brewster-Cherenkov angle θBCR [see inset of Fig. We emphasize that the broadband angular filter is treated as a realistic multilayered structure, instead of an effectively homogeneous material, and hence Equation  generaliz-axis [Fig. c [Supplementary Figs. θBCR [Fig. θCR [Fig. To facilitate the conceptual demonstration, Fig. xis Fig. . The numxis Fig. –8. For eBCR Fig.  is much θCR Fig. . This renBCR in the generalized Frank–Tamm formula can be made approximately nondispersive using regular transparent dielectrics that have a negligible dispersion in the visible range44. To illustrate this point, we plot nBCR as a function of the Brewster angle. As shown in Fig. nBCR can be flexibly designed to values close to unity by a suitable choice of εr1 and εr2 for the two constituent materials of the broadband angular filter. For example, according to Eq. εr1 = 2.18  and εr2 = 3.07 (Al2O3)44 give rise to a pseudo refractive index nBCR = 1.13, which is much smaller (and hence more close to unity) than the lowest refractive index found in natural solid materials 47. A pseudo refractive index even closer to unity can be achieved using other material combinations. For example, nBCR = 1.0026, if taking polymers with εr1 = 1.8578 ) and εr2 = 2.1904 (poly(methyl methacrylate), namely PMMA)51.We emphasize that such sensitivity improvement occurs over a broadband frequency range [Supplementary Fig. nBCR determines their sensitivity and momentum range. Fig. θBCR and particle momenta for four elementary particles, namely electron, pion, kaon, and proton. In this exemplary case, we take nBCR = 1.13 and a particle momentum fixed at 2 GeV/c. The resulting values of θBCR are 27.8° for electron, 27.5° for pion, 24.3° for kaon, and 12.2° for proton [Fig. θBCR indicates that nBCR = 1.13 is suitable for the identification of particles (such as pion (or electron), kaon, and proton) with a momentum less than 10 GeV/c. In comparison, nBCR = 1.0026 gives rise to a Brewster-Cherenkov detector capable of identifying particles with a momentum larger than 10 GeV/c. More interestingly, when nBCR = 1.000001, the corresponding Brewster-Cherenkov detector can even identify particles with ultra-high momenta in the TeV/c range. These results clearly demonstrate that the proposed Brewster-Cherenkov detectors can achieve high sensitivity within the desired momentum coverage through a proper choice of the pseudo refractive index. Covering a range of higher momenta requires further refinement of the detector design, for example using a larger number of layers for better angular resolution.A key feature of Brewster-Cherenkov detectors is that the pseudo refractive index ton Fig. . Such a xBCR of Cherenkov radiation in the detection plane [Fig. y0) is the normalized displacement resulting from the refraction of light through the broadband angular filter. For simplicity but without loss of generality, we choose nBCR = 1.13 and y0 = 2.3 mm, and plot in Fig. c, the ratio The pseudo Brewster-Cherenkov angle and the particle velocity can be determined by directly measuring the peak-intensity position ane Fig. –13. Math18, our Brewster approach does not have any special requirements on the refractive index of the host material where the particle is traveling, since the sensitivity of Brewster-Cherenkov detectors is directly determined by the nBCR of the broadband angular filter. Consequently, high-index transparent solids or gases with low atomic numbers can now be used as the host material. Such a high-index host material can significantly enhance the number of photons penetrating the broadband angular filter and reaching the detection plane, enabling a higher-efficiency Cherenkov detector.As a final remark, we note that our Brewster approach has several unique advantages over the traditional methods for particle identification. First, the Brewster approach eliminates the strict requirement of near-unity-index host materials for the design of Cherenkov radiators. In other words, distinct from traditional Cherenkov detectors such as the RICH detector25 is that the broadband angular filter does not need to be placed in the path of the high-energy particle beam. The charged particles can travel at a large distance away from the surface of the broadband angular filter so that all the Cherenkov photons are produced in the host material in which the particle is traveling. This way, the generation of secondary particles from the broadband angular filter can be effectively reduced. Last but not least, the performance of the proposed particle detector is robust to fabrication imperfections and geometric fluctuations of the broadband angular filter . Therefore, this Brewster approach provides promising options to facilitate the design of advanced Cherenkov detectors with enhanced sensitivity, large bandwidth, miniaturized size, ultralightweight, and wide momentum coverage, all using readily available regular dielectrics. These enhanced capabilities are especially attractive in the identification of high-energy particles in beamlines with a small angular divergence. As illustrated in Supplementary Fig. Another important advantage of our Brewster approach in comparison with all previous nanophotonic approachesz-axis parallel to the moving direction of a charged particle in Fig. The basic structural setup is shown in Fig. mentclass2pt{minimεr1 and region 2 having the relative permittivity of εr2. By enforcing the electromagnetic boundary condition at the interface between regions 1 and 2, we can readily obtain the Brewster angle θBrewster, at which the reflection for p-polarized waves is zero19. That is, according to the Brewster effect, the Brewster angle for p-polarized waves in region 1 has 20. At this Brewster angle, the wavevector component of light parallel to the interface has kBCR = k1sinθBrewster, where kBCR is the same for different regions in the broadband angular filter, when the light transmits through the angular filter. In addition, we highlight that all the calculations in this work treat the broadband angular filter as a realistic layered structure, instead of an effectively homogenized material by using the effective medium theory. These discussions are shown in Supplementary Note For conceptual demonstration, here we set region 1 having the relative permittivity of M stacks) of 1D photonic crystals. All 1D photonic crystals are made of two regular transparent dielectrics, which have a relative permittivity of εr1 and εr2, respectively. The ith 1D photonic crystal has a pitch of Pi = di1 + di2, where di1 and di2 are the thickness of two dielectric slabs in each pitch. All these 1D photonic crystals have a pitch number of N and a thickness ratio of di1/di2 = 3/2. This way, the band gap of each 1D photonic crystal can be flexibly tunable by changing Pi. The light transmission through the broadband angular filter is both angle-dependent and polarization-dependent. For the p-polarized light with arbitrary incident angle , the light is almost fully reflected by judiciously overlapping the band gaps of these 1D photonic crystals [Supplementary Figs. p-polarized light incident at the Brewster angle, it can safely pass through the broadband angular filter with no reflection [Supplementary Figs. s-polarized light, the transmission through the designed broadband angular filter is negligible for arbitrary incident angle [Supplementary Fig. The broadband angular filter [Supplementary Fig. z axis); see the discussion of Cherenkov radiation in the detection plane in Supplementary Note 5 of the supporting information and their dynamics in the detection plane in Supplementary Movie For Cherenkov radiation passing through the broadband angular filter, while the direction of their in-plane wavevector mentclass2pt{minimyb is the total thickness of dielectric regions with εr2 in the designed broadband angular filter. Since y0) → 0 if yb ≪ y0. In other words, if the detection plane is far away from the particle trajectory and if their distance is much larger than the finite thickness of the broadband angular filter, we have y0 is shown in Supplementary Note The normalized peak-intensity position mentclasspt{minimaxz plane (or the detection plane at y = y0). This is because the intensity distribution of the transmitted Cherenkov radiation in the detection plane is symmetric with respect to the projection of the particle trajectory in the xz plane [Fig. α  spread of the transmitted Cherenkov radiation in the detection plane, such as those shown in Supplementary Fig. M and N in the practical implementation [Supplementary Fig. More discussions on the performance of Brewster-Cherenkov detectors are provided in Supplementary Note Supplementary InformationDescription of Additional Supplementary FilesSupplementary Movie 1


This study aimed to explore the potential role of PPARγ–DNMT1 interaction through PPAR-responsive elements (PPREs), which we have found to be enriched with Alu repeats. Apart from protein–protein interactions and co-expression in multiple cancer types, we exclusively described a prognostic role for PPARγ in uveal melanoma (UM).Background: PPARγ (peroxisome proliferator-activated receptor gamma) is involved in the pathology of numerous diseases, including UM and other types of cancer. Emerging evidence suggests that an interaction between PPARγ and DNMTs (DNA methyltransferase) plays a role in cancer that is yet to be defined. Methods: The configuration of the repeating elements was performed with CAP3 and MAFFT, and the structural modelling was conducted with HDOCK. An evolutionary action scores algorithm was used to identify oncogenic variants. A systematic bioinformatic appraisal of PPARγ and DNMT1 was performed across 29 tumor types and UM available in The Cancer Genome Atlas (TCGA). Results: PPAR-responsive elements (PPREs) enriched with Alu repeats are associated with different genomic regions, particularly the promotor region of DNMT1. PPARγ–DNMT1 co-expression is significantly associated with several cancers. C-terminals of PPARγ and DNMT1 appear to be the potential protein–protein interaction sites where disease-specific mutations may directly impair the respective protein functions. Furthermore, PPARγ expression could be identified as an additional prognostic marker for UM. Conclusions: We hypothesize that the function of PPARγ requires an additional contribution of Alu repeats which may directly influence the DNMT1 network. Regarding UM, PPARγ appears to be an additional discriminatory prognostic marker, in particular in disomy 3 tumors. Studies from the cancer genome suggest that cancer is not merely restricted to certain mutations in genes . Instead, a crosstalk between genetic factors and epigenetic modulators orchestrates the heterogeneous and inter-individual disease pattern ,2,3. In In recent years, there has been a growing debate about the common biological mechanisms in metabolic diseases and cancer ,11,12, aHerein, we investigated a yet to be defined role of the PPARγ–DNMT1 interaction through PPREs, which we have found to be enriched with Alu repeats in this particular genomic region. Apart from protein–protein interactions and co-expression in multiple cancer types, we exclusively described a prognostic role for PPARγ in UM.http://seq.cs.iastate.edu/cap3.html, accessed on 29 November 2019) to minimise the number of identical sequences or copies, 2. by multiple alignments of the resulting sequences in each subfamily to extract the consensus sequence using the program MAFFT . The final Alu map was defined by multiple alignments of all consensus sequences in the subfamily and is represented in a sequence logo plot. The phylogenetic analysis was performed to assign the motif to the closest Alu subfamily, and the phylogenetic tree was created using Neighbor Joining method and MEGA software; Version 10.1.8 https://www.megasoftware.net/ (accessed on 27 June 2020). For further analysis, we named this Alu embedded PPRE sequence as PPREs-Alu motif and extended the protein–protein association analysis by adding DNMT1 interactors and UM-specific driver genes using string tool . The regulatory network based on the density and profile of the transcription factors was modelled by TRANSFAC and JASPAR using PWMs in the web-based enrichment analysis tool Enrichr . Im. Im31]. p-value = 0.43)  independent of the chromosome 3 status. For patients with disomy 3 tumors  but not for monosomy 3 tumors  PPARγ expression was an additional predictor of survival (n = 17) and only in one patient with a low PPARγ expression (n = 1).80 UM cases from the TCGA database were investigated and a survival analysis comparing two main groups was performed. Of the 80 UM cases in this database 31 cases were from monosomy 3 tumors (with a high likelihood for metastasic disease) and 49 from disomy 3 tumors (with a low likelihood for metastastic disease) . We did It is well known that the activities of PPARs and DNMTs are pivotal, and their dysregulation is associated with diseases such as different types of cancer. Pazienza et al. evaluated the expression of PPARγ, DNMT1, and DNMT3B and their correlation with clinical-pathological features in patients with pancreatic cancer . RecentlWe have previously reported that PPARγ is expressed in UM, which is the most frequent primary intraocular tumor in Caucasian adults with abnormal genetic-epigenetic characteristics and metastatic potential ,33,34. RIn this study, we showed that PPARγ may serve as an additional prognostic factor in UM patients in general and in UM patients with disomy 3 tumors (but not monosomy 3 tumors) in particular. However, a low and prognostically unfavorite PPARγ expression in disomy 3 tumors was not related to SF3B1 mutations as could have been expected due to its association with late metastasis. Since in a clinical trial (phase I) with a PETT schedule including pioglitazone (a PPARγ agonist) revealed a relatively long survival in two UM patients with liver metastases , this isTaken together, we showed that PPREs-Alu region has the tendency to interact with the DNMT1 protein, which was confirmed by our protein-DNA interaction model . Direct PPREs enriched with Alu repeats represent discriminative genomic regions and DNMT1 appears to be the main target in this DNA-protein interaction. The structural docking and interface residue interaction predicts the C-terminals of both proteins to be the potential protein–protein interaction sites where disease-specific mutations may directly impair the respective protein functions. PPARγ–DNMT1 co-expression is associated with several cancers while the evolutionary action (EA) score revealed PPARγ variants to be more of an oncogenic type. In the context of UM, PPARγ appears to be an additional discriminatory prognostic marker, in particular in disomy 3 tumors. Considering the pleiotropic nature of PPARγ and DNMT1 there is a need to investigate if their interaction also plays a role in the association between metabolism and cancer.


Here, we investigated structural brain changes in 11 participants following 30-days of head-down tilt bed rest (HDBR) combined with 0.5% ambient CO2 (HDBR + CO2) as a spaceflight analog. We contrasted brain changes observed in the HDBR + CO2 group with those of a previous HDBR sample not exposed to elevated CO2. Both groups exhibited a global upward shift of the brain and concomitant intracranial free water (FW) redistribution. Greater gray matter changes were seen in the HDBR + CO2 group in some regions. The HDBR + CO2 group showed significantly greater FW decrements in the posterior cerebellum and the cerebrum than the HDBR group. In comparison to the HDBR group, the HDBR + CO2 group exhibited greater diffusivity increases. In half of the participants, the HDBR + CO2 intervention resulted in signs of Spaceflight Associated Neuro-ocular Syndrome (SANS), a constellation of ocular structural and functional changes seen in astronauts. We therefore conducted an exploratory comparison compared between subjects that did and did not develop SANS and found asymmetric lateral ventricle enlargement in the SANS group. These results enhance our understanding of the underlying mechanisms of spaceflight-induced brain changes, which is critical for promoting astronaut health and performance.Astronauts are exposed to elevated CO There is accumulating evidence that spaceflight has an impact on human brain structure. We and others have shown that exposure to microgravity results in an upward displacement of the brain within the cranium , intracr2 levels averaging 0.5%, a level that is more than 10 times greater than terrestrial levels (0.04%)  are exposed to elevated ambient CO (0.04%) . HoweverLong-duration, 6° head-down tilt bed rest (HDBR) has been widely used to simulate the physiological impacts of microgravity such as arterial pressure changes, unloading of the lower body, and cephalad fluid shifts  on Earth2 level of 0.5% (HDBR + CO2). We evaluated brain changes occurring with this intervention and their associations with performance changes, focusing on measures in which we have seen significant pre-to-post bed rest changes in the same cohort , and white matter microstructural changes occurring in 11 subjects who underwent 30 days of HDBR in a controlled environment with an atmospheric COe cohort . In addinditions . This is2 intervention  phase. The participants resided at: envihab  for 58 days participating in various experiments included within the VaPER campaign. Closely replicating the average CO2 concentration on board the ISS . The 0.5% enriched CO2 was continuously supplied and regulated through the ventilation system. The pre- and post-HDBR + CO2 data collections were conducted in ambient air (~0.04% CO2). As part of NASA’s standard measures assessments, blood draws were acquired 3 days prior to bed rest and on the first day after bed rest to calculate arterial partial pressure of carbon dioxide (PaCO2).Eleven participants  aged between 25.3 and 50.3 years at the time of admission completed the study. An additional female was enrolled but withdrew from the study on the first day of the intervention. The experiment was part of the VaPER  bed rest campaign funded by NASA. The campaign comprises three experimental phases: a 14-day baseline data collection (BDC) phase, 30 days of strict 6° HDT with elevated CO the ISS  and foll2 subjects. The methods of this HDBR study were previously published , an aerobic and resistance exercise (n = 4), or a flywheel exercise (n = 8) countermeasure group. Given the similar intensity of the two exercise protocols, these groups were pooled for statistical analyses  and institutional review boards at NASA, The University of Florida (HDBR + CO2), and The University of Michigan (HDBR). All subjects provided written informed consent and received monetary compensation for their participation.All HDBR + CO2 groups nor between the subgroups within each campaign.The demographic information of the groups as well as the subgroups is provided in Supplementary Table 1. There were no significant differences in age, height, weight, and BMI between HDBR and HDBR + COAssessments were made at six times during the study: 1) two baseline measurements at 13 and 7 days before bed rest ; 2) two measurements on days 7 and 29 of bed rest ; and 3) two measurements at 5 and 12 days post bed rest . Neuroimaging and behavioral assessments were conducted on the same day. The behavioral tests were administered ~3 h after neuroimaging and the testing time of day was kept as constant as possible within subjects. The functional mobility and balance tests which require the participants to be upright were only performed 2 times before best rest  and 3 times during the recovery phase: on day R + 0 (~3 h after first standing), on day R + 5, and day R + 12.2 subjects at 7 time points: two times pre- , three times during , and 2 times post-  HDBR. Here, we use the neuroimaging data from the HDBR study only for group comparisons with the HDBR + CO2 group. We examined only the T1 structural and dMRI scans from BDC-8, HDT 8, and HDT 50, as these assessment time points fell closest to those of the HDBR + CO2 group.The HDBR subjects completed the same measurements as the HDBR + CO2 through a mask and gas cylinder when being transported between the bed rest module and the scanner during the HDBR + CO2 phase. The participants were transported on an MR compatible stretcher adjusted to maintain the 6° head-down tilt position.The participants were supplied with 0.5% CO2 was worn during the scanning session during the HDBR + CO2 phase, which required a small rightward head rotation to accommodate the ventilation mask tubing through the head coil. The participants wore the ventilation mask and the T-piece connector to breathe in ambient air during the baseline and re-ambulatory phases to keep the testing consistent.A ventilation mask supplying 0.5% COThe 6° HDT was maintained during MRI using a foam wedge that kept the torso and legs elevated upon transfer to the scanner table. However, the head coil and thus the head position remained horizontal.All scans were acquired in supine position in the scanner.All scans were acquired on a dedicated 3-T Siemens Biograph mMR scanner, with a 16-channel head/neck coil.T1-weighted gradient-echo pulse scans  with the following parameters were obtained: 3D T1 sagittal overlay .3. Thirty noncollinear gradient directions with diffusion-weighting of b = 1000 s/mm2 were sampled twice. A volume with no diffusion weighting (b = 0 s/mm2) was acquired at the beginning of each sampling stream.Diffusion-weighted 2D echo-planar imaging scans with the following parameters were obtained: TR = 9800 ms, TE = 91 ms, flip angle = 90°, FOV = 235 × 235 mm, matrix size = 128 × 128, slice thickness = 2.7 mm, 49 axial slices with zero gap, resulting in a voxel size of 1.8 × 1.8 × 2.7 mm2 group; the exact acquisition parameters are presented in the Supplementary Material (HDBR Image Acquisition).All scans were acquired on a 3-Tesla Siemens Magnetom Skyra MRI scanner utilizing a 32-channel head coil. The scanning parameters differed from those of the HDBR + COThe structural MRI data were processed using the Computational Anatomy Toolbox (CAT12 v.1363)  for StatT1-weighted images were processed following the standard CAT12 preprocessing steps  were automatically estimated by CAT12 using the Neuromorphometrics volume-based atlas map where the CSF volume for the ROIs were estimated in native space . AnalysiEstimation of cerebellar volumes was conducted via CERES (CEREbellum Segmentation) , a patchdMRI data were analyzed using FMRIB Software Library (FSL) version 5.0.9, MATLAB R2015b, Advanced normalization tools , axial and radial diffusivity (ADT and RDT) were analyzed after FW correction; these indices are referred to with a subscript T to indicate that they are based on the tissue compartment.The FW images represent the fractional volume of the FW compartment in a voxel, indicating the proportion of water molecules that are not obstructed by surrounding cellular structures . FractioT, ADT, and RDT images were entered into flexible factorial models  in order to determine brain changes across sessions. Utilizing the same approach applied in our previous bed rest studies . We tested for both increases and decreases over time in separate models, as we expected to see both effects with regional variation  is testing for more gradual changes over time. All contrast vectors were scaled such that the weights sum to zero.Preprocessed GM volumes, FW, FA studies , the chaFor GM volume analyses, voxels with GM values below 0.1 were excluded (absolute threshold masking) to avoid possible edge effects around tissue borders. Analyses of diffusion indices were confined to a white matter mask except for the FW measure, for which a whole-brain mask was used. These masks were constructed following identical steps to those described in our previous study . For all2 groups were carried out to determine the additive effect of increased CO2 levels on HDT. In order to account for the different testing timelines between groups, analyses utilized the change occurring from the second BDC measure to the next two BR measures. First, we estimated the linear slope of GM volume, FW, FAT, ADT, and RDT measures over these time points. Then, we divided the slope images by the intercept images (expressed in %), in order to take into account baseline differences as in our prior studies  an enhanced response consistency on the Rod and Frame Test , 2) reduced time to complete the Digit Symbol Substitution Test (a paper-and-pencil test of processing speed), and 3) increased time to complete the Functional Mobility Test . Given the current objective of deciphering the additive effect of elevated CO2, we focused on these behavioral measures that showed differential changes during HDBR + CO2 relative to HDBR. An identical test battery was administered to both groups  were calculated. Similarly, the difference maps of the GM, FW, and diffusivity measures were computed using the BDC-7 and HDT 29 scans. To examine whether HDBR + CO2-related pre-to-post brain changes were associated with behavioral changes, general linear model (GLM) analyses were performed with the difference maps including the behavioral change score as the regressor of interest (change-change correlations). For the FMT measurement, n = 10 subjects were included in the analyses after removing one outlier participant. The analyses were restricted to the brain areas where significant changes were seen as a function of HDBR + CO2.Recently, we reported the impact of HDBR + COformance  in the soups see . For eac2 subjects who developed signs of SANS  and those who did not . For each GM, FW, and diffusivity measure, two-sample t-tests were carried out using 1) the mean image of the two BDC scans and 2) the difference map between BDC-7 and HDT 29 scans in order to examine the SANS versus NoSANS group differences at baseline and in brain changes as a function of HDBR + CO2, respectively. Finally, an examination was conducted to identify brain areas where differential brain-behavior associations might occur between the groups. We focused only on the measures in which we previously observed significant subgroup differences   .2 and HDBR subjects and 2) SANS and NoSANS subjects in the ventricular volumes, lateral ventricle asymmetry index, and cerebellar volumes. As the intervals between test dates and the overall study duration differed for the 2 studies, time was entered as a continuous variable for the group comparison. Restricted maximum likelihood was selected in all linear mixed model analyses for its greater robustness to small sample bias than traditional maximum likelihood  HDBR + COkelihood . Alpha lkelihood  was used2 versus HDBR group comparison analyses. All within HDBR + CO2 group analyses and SANS subgroup analyses were controlled for age and sex.For GM analyses utilizing modulated normalized images, individual differences in brain size were corrected by entering total intracranial volume (TIV) in the model as a covariate. Age, sex, and exercise group status were entered as covariates of no interest in all HDBR + COt-test revealed no significant change in arterial PaCO2 pressure from pre-to-post bed rest (P > 0.05). Flexible factorial model analyses revealed extensive changes in GM volume following our a priori hypotheses. These changes include increased GM volume in the posterior aspect of the vertex and decreased volume at the base of the cerebrum (a). Structure–function analyses revealed that those who showed less decrements in precuneus cortex volumes at HDBR + CO2 day 29 than BDC-7 had more variable RFT response patterns with bed rest . A significant group by time interaction effect was observed for the third ventricle volume; where the HDBR + CO2 group showed significantly greater increments in the third ventricle volume (% baseline measurement) during bed rest in comparison to the HDBR group . There were no significant group by time interaction effects in the lateral ventricle volumes, fourth ventricle volumes, and the lateral ventricle asymmetry changes. The HDBR + CO2 group also showed a significant increase in cerebellar Crus I volume  during bed rest relative to the HDBR group . Post hoc analyses revealed no significant group difference in any of the ventricle measures, lateral ventricle asymmetry index, and total cerebellum volume at baseline .While some metrics and brain regions exhibited significant intercept differences between the two cohorts, there were more effects associated with the intervention. Group comparisons revealed that the HDBR + COBR group a–c. A si2 intervention. There were significant FW increases over time along the base of the frontal and temporal lobes and decreases in the posterior aspect of the cerebral vertex and the dorsal cerebellum (b). There were no significant associations between changes in FW and changes in behavioral performance as a result of the HDBR + CO2 intervention.We observed widespread FW changes with the HDBR + COrebellum . Post ho2 group showed significantly greater FW reductions in the pre- and post-central gyri, angular gyrus, lateral occipital cortex, and the cerebellum  The SANS group showed a right-biased enlargement of the lateral ventricles, and the NoSANS group exhibited a left-biased asymmetrical enlargement as a function of HDBR + CO2. The result was replicated even after removing all data points of this outlying subject.The SANS and NoSANS groups did not exhibit significant differences in any of the brain structural or dMRI measures at baseline. In addition, there were no differential effects of HDBR + CO2. Specifically, in the SANS group, increased frontal pole volume from pre- to post-bed rest was associated with greater slowing of dual task finger tapping RT following the intervention, while the NoSANS group showed the opposite effect  throughout the course of the intervention, with a subsequent reversal toward pre-intervention levels following bed rest completion . It is highly unlikely that scanner effects such as drift, software upgrades, and hardware upgrades would result in this precise pattern of temporal changes, but future studies would certainly benefit from time-matched controls. Although preliminary in nature, the compelling findings presented here are an important first step in understanding the influence of spaceflight factors namely, the combined effect of 0.5% CO2 and headward fluid shifts .


Moreover, the multiple linear relationship of the total PAHs emission factors with the emission factors of four congeners was also established . Interestingly, the ratio of multicomponents coefficient from the multiple linear relationship in sediments to that from the multiple linear relationship in emission sources correlated positively with octanol–water partition coefficient (logKow)  of the four PAHs congeners. Therefore, a novel model was established to predict CΣPAHs in sediments using the emissions and logKow of the four characteristic PAHs congeners. The percent sample deviation between calculated C∑PAHs and their observed values was 54%, suggesting the established model can accurately predict CΣPAHs in sediments.In this study, we observed that four congeners, including naphthalene (Nap), acenaphthylene (Acy), phenanthrene (Phe), and benz(a)anthracene (BaA), are the characteristic congeners for predicting the emission and the sediment concentrations of polycyclic aromatic hydrocarbons (PAHs). A novel multiple relationship of the total PAHs concentrations (C United States Environmental Protection Agency (USEPA) and European Union (EU) have identified sixteen PAHs  are a group of persistent organic contaminants that originate from the incomplete combustion of organic matter  and non-combustion emissions of petrogenic processesHs Table  as priorƩPAHs) and the content of total organic carbon (foc) in sediments were established to predict CƩPAHs31, built on the premise that the distribution of PAHs between sediments and water is largely depended on the partitioning of PAHs into sediment organic matter32. However, our preliminary work indicated that CƩPAHs predicted using foc  and the great percent sample deviation (SDEV = 771%). One possible reason for the insignificant correlation between CƩPAHs and foc when more data was introduced is that the difference in emissions of PAHs in various regions is ignored. Another possible reason is that the dependence of PAHs partitioning in sediment organic matter and their polarity is ignored33. Furthermore, nonlinear sorption of PAHs on sediments organic matter is also ignored35.In recent literatures, significantly positive correlations between the total concentration of PAHs pyrene (CBaP)37, pyrene (CPyr)38 or acenaphthene (CAce)39 was suggested to predict CƩPAHs (Table ƩPAHs with CBaP (Eq. Pyr (Eq. Ace (Eq. 2 of the linear relationship between CƩPAHs and CAce (Eq. 39 to 0.49 (Eq. 39 (Table 39 (Table ƩPAHs), which is 4.39 g t−1 for iron sintering and 4.51 g t−1 for gasoline combustion  has the best linear correlation with the total EFs of this subgroup, showing the maximum R2 of 0.98   correlates best with the total EFs of the eight PAHs in this subgroup with the maximum R2 of 0.98   are well related with EFNap, EFAcy, EFPhe, and EFBaA in a multilinear relationship  and classifications for relative similaritiesrs Table  show thars Table , Fig. 2.rs Table , Fig. 2 rs Table . Acy is rs Table , Fig. 2 rs Table . The thirs Table . In thisPhe) correlates best with the total concentration of the eight congeners with the maximum R2 of 0.83  (Table 2 was found between the concentration of BaA (CBaA) and the total concentration of the eight congeners  , Phe (CPhe), and BaA (CBaA) correlate best with the total concentrations of congeners in corresponding subgroup  correlate best with the total concentrations of congeners in corresponding subgroup ) and the experimental value (CƩPAHs(exp)) are presented in Fig. ƩPAHs and CƩPAHs(exp) in Fig. ƩPAHs can’t be accurately predicted using two  in sediments can be predicted from the concentrations of four characteristic congeners with high R2 but low SDEV value. Figure ∑PAHs are consistent with C∑PAHs(exp) in sediments sampled in China. Scatterplots of unstandardized residuals  and CƩPAHs(exp)) with CNap, CAcy, CPhe or CBaA in sediments distributed regularly on both sides of the horizontal line and no obvious positive or negative trend existed  also agreed well with CƩPAHs(exp) using additional concentration data  in sediments from the upper reach of Huaihe River50 was higher than that in sediments from the lower reach of Huaihe River (foc = 4.1%)51, CΣPAHs in sediments from the lower reach of Huaihe River 50 were higher than that from the upper reach 51. This can be attributed to the higher emission intensity of PAHs in lower reach of Huaihe River region than that in the upper reach region53. Moreover, a significantly positive correlation between mean concentrations of thirteen PAHs  in sediment samples derived from globe (N = 1445) and their mean EFs in fifteen emission divisions was also observed , which are statistically significant. However, significance of intercept in Eq. (p > 0.05), which is statistically insignificant. The significant intercept (p < 0.05) in Eq.  with their emissions (EPAHs) were established   8) were ved Fig. a. Intere, in Eq. a. This cife Fig. . Therefoshed Eq. . Moreoveq. K Eq.  and L  in surface sediments sampled in investigated provinces of China (N = 30) and other countries (N = 21) versus C∑PAHs predicted using EPAHs  and C∑PAHs is 54%, suggesting that C∑PAHs are well consistent with C∑PAHs(exp). Therefore, the established model in this study can be used to predict CΣPAHs in sediments with high accuracy, resulting in decreasing cost of laboratory analysis.In this study, a multilinear relationship of Cshed Eq. . The conKow Eqs. –12. Thers Tables  and S8 aƩPAHs with CBaP37, CPyr38 or CAce39 could be attributed to the PAHs emissions in the investigated region from one emission source or emission sources with similar EFs  and C∑PAHs was 54%, suggesting the established model can accurately predict CΣPAHs in sediments.A multiple linear relationship of CAlthough the relationship established in this study could be used to predict total sixteen PAHs concentration in surface sediments of China and other countries, the application of this method for predicting additional PAHs in sediment, such as alkylated-PAHs, needs further verified.60. Articles were then examined individually to ensure that the duplicates and irrelevant articles were excluded from further analysis. In addition, articles without individual PAHs concentration data and/or articles that did not report QA/QC procedure and limits of detection (LODs) were also excluded from further analysis60. In order to perform the required comparisons in this study, it was assumed that there were no significant differences in the sampling process and analysis among the investigating groups/laboratories61. In total, 22,349 individual PAH concentrations from 1445 sediments samples were collected from 1184 publications and then used for meta-analysis  of sixteen PAHs from above nine emission sources were summarized in Table PAHs, t a−1) were calculated using Eq.  is the emission factor of the PAHs congener i  in source j, from China, were obtained directly from China Statistical Yearbook (2001–2018) and China Energy Statistical Yearbook (2001–2018), edited by National Bureau of Statistics40. The data from other countries were derived from Food and Agriculture Organization of the United Nations, International Yearbook of Industrial Statistics (2004–2018), and International Energy Agency World Energy Statistics and Balances40.EF  were divided into two or three divisions with or without different emission mitigation measuresrves Eq. .14\documƩPAHs and the concentration of selected congeners in sediments (Table 62. Concentration unit of PAHs were set uniformly to micrograms per kilogram (µg kg−1) of dry weight. Prior to statistical analysis, a histogram with normal curve was viewed and a Kolmogorov–Smirnov test were performed to verify the normality of variables38. If the significance (p) is greater than 0.05 (p > 0.05), it can be judged that the variables are normal38. Hierarchical clustering analysis (HCA) and classifications were performed using the SPSS Statistics 19.0 software  according to relative similarities of EFs in emission sources and concentrations in sediments of sixteen PAHs43. Statistical analysis, including linear and multilinear regression, were also performed using the SPSS Statistics 19.0 with a critical significance (p) up to 0.05 to check significance. Provincial or national PAHs emissions combined with their observed C∑PAHs in surface sediments were used to evaluate the established model between C∑PAHs and emissions of the four characteristic congeners. Moreover, C∑PAHs in surface sediments sampled in the same province or nation at the same year were expressed by the geometric mean value63.11,937 individual concentrations data from 754 sediments sampled from China were used to establish the relationship between Cts Table . To valits Table . Concentexp) and the calculated value 64. In addition to the SDEV, significance of F test (p) and correlation coefficient (R2) were used to evaluate the goodness of the fitting and the established correlations by regression analysis64.Percent sample deviation  was calSupplementary Information.