Elaeis guineensis is the world’s leading source of vegetable oil, and the demand is still increasing. Oil palm breeding would benefit from marker-assisted selection but genetic studies are scarce and inconclusive. This study aims to identify genetic bases of oil palm production using a pedigree-based approach that is innovative in plant genetics.A quantitative trait locus (QTL) mapping approach involving two-step variance component analysis was employed using phenotypic data on 30852 palms from crosses between more than 300 genotyped parents of two heterotic groups. Genome scans were performed at parental level by modeling QTL effects as random terms in linear mixed models with identity-by-descent (IBD) kinship matrices. Eighteen QTL regions controlling production traits were identified among a large genetically diversified sample from breeding program. QTL patterns depended on the genetic origin, with only one region shared between heterotic groups. Contrasting effects of QTLs on bunch number and weights reflected the close negative correlation between the two traits.The pedigree-based approach using data from ongoing breeding programs is a powerful, relevant and economic approach to map QTLs. Genetic determinisms contributing to heterotic effects have been identified and provide valuable information for orienting oil palm breeding strategies.The online version of this article (doi:10.1186/s12864-015-1985-3) contains supplementary material, which is available to authorized users. Elaeis guineensis Jacq.) is a perennial allogamous species originating from Africa. It is the world’s leading crop for vegetable oil production . A dynamic breeding sector has contributed to enhancing oil yields by 60 % in the last 50 years [Oil palm  breeding program of PalmElit, a CIRAD subsidiary and leading oil palm breeding company . This breeding program is shared and conducted with partners, i.e. PT Socfin Indonesia (Indonesia) and INRAB (Benin). The 30852 palms with phenotypic evaluations belong to 478 crosses between 146 and 156 parents from heterotic groups A (GA) and B (GB), respectively. Most of the parents were planted in Pobè , where the crosses were carried out. AxB crosses were planted in Aek Loba estate (PT Socfin Indonesia). Heterotic groups were based on the complementarity of yield components, bunch number and average weight, with GA palms having a low number of big bunches and GB palms having a high number of small bunches [All palms analyzed belonged to families of the commercial oil palm  between 1995 and 2000 in a study lasting 11 years. The experimental designs of the trials involved randomized complete block designs (RCBD) with five or six blocks and balanced lattices of ranks four or five. The data used in this study was from oil palms which had reached a mature stage 6 to 10 years after planting. Mature bunch harvests were performed every 10 days, and the bunch numbers and weights were recorded. Based on these data, three production traits were analyzed, i.e. fresh fruit bunch yield , bunch number  and average bunch weight , which is the ratio between FFB and BN estimated annually.Y is a n × 1 observation vector of production traits (n = 30852) averaged over 4 years in the A × B population, X is a n × m design matrix relating observations to trial fixed effects β with β being a m × 1 vector (m = 26), Z1 is a n × b design matrix relating observations to block random effects u ~ N with u being a b × 1 vector (b = 677), Z2 is a n × c design matrix relating observations to SCA random effects s ~ N with s being a c × 1 vector (c = 478), ZA and ZB are n × qA and n × qB design matrices relating observations to GCA random effects for heterotic groups A and B, gA ~ N and gB ~ N respectively, with gA and gB being qA × 1 and qB × 1 vectors, respectively (qA = 146 and qB = 156), and eis the n × 1 vector of residual effects ~ N. I is an identity matrix and AA and AB are the pedigree-based kinship matrices of heterotic groups A and B, respectively.A linear mixed model was designed to account for non-genetic  and genetic  and specific combining ability (SCA)) effects and adjusted to the data:2) of each trait were obtained as the ratio of the variance of general combining abilities, for both groups A and B, to the total phenotypic variance of crosses according to the formula:Calculation of heritabilities and genetic correlations were based on variances estimated by the linear mixed model . Narrow-rg) between traits T1 and T2 in each heterotic group were calculated using covariances and variances estimated in a multivariate linear mixed model, based on model  and CovB are additive genetic covariances.GCA were structured as:Cove is residual covariance.Residual effects were structured as:rg) were calculated according to the formula:Genetic correlations (rp) were estimated in the A × B population via the Pearson correlation coefficient.Phenotypic correlations . The PCR conditions were as follows: initial denaturation at 94 °C for 5 min, followed by 35 cycles alternating 30 s of denaturation, 1 min 15 s of hybridation at annealing temperature, 1 min 30 s of extension, and ending with 30 min of final elongation at 72 °C. PCR products were pooled. We used 2 μl of the pooled PCR product with 10 μl of size standard GeneScan ™-600 LIZ ® at 0.0012 % in Hi-Di™ formamide for migration on a 3500xL Genetic Analyzer .Total genomic DNA was extracted from freeze-dried leaf samples of each progeny and parent using the commercial kit NucleoSpin 96 plants II  in accordance with the manufacturer’s protocol. Genomic DNA concentrations were estimated with a spectrometer . Microsatellite fragment amplification was performed in a 384-well plate with 25 ng of DNA in a 10 μl final volume containing 1 U of Taq DNA polymerase, 1 μl of 10x buffer (10 mM Tris (pH 8.3), 50 mM KCl, 1.5 mM MgCl2, 0.001 % (w/v) glycerol), 2 mM dNTP, 0.6 mM MgClGeneMapper© V4.1  software was used to determine allele sizes.A consensus linkage map  relationship matrices as variance-covariance matrices for testing QTL effects at each genetic position. An IBD analysis using SimWalk2 software  was condvA,k and vB,k are qA × 1 and qB × 1 vectors of QTL random effects at the kth genetic position in heterotic group A and B respectively (qA = 146 and qB = 156), with vA,k ~ N and vB,k ~ N. AA,k and AB,k are the IBD kinship matrices at the kth genetic position, calculated in groups A and B, respectively.The model  Phenoty. A genomThe log-likelihood ratio test (LRT) was performed to compare models /4) and  and 4) aAccording to a multiple QTL mapping strategy  to betteCA and CB are subsets of genetic positions where putative QTLs were identified in the first genome scan for groups A and B, respectively. To test the QTL presence at genetic positions within a 20 cM window centered on CA and CB positions, the random putative QTL effect at the considered positions was removed in the null and test models (Where t models  and 6)//CA and Ct models , respectAs for the first genome scan model, /7) and  and 7) aAll linear mixed effect models were estimated by REML using the R-ASReml package  for R .AA,k and AB,k in the scan model , were randomly shuffled before each replicate of genome scan, whereas all other components were unchanged comparing to the null model. 1000 and 500 replicates were obtained to determine each threshold for the first and second genome scans of QTL mapping process respectively, for each trait and heterotic group  and underlying components bunch number (BN) and average bunch weight (ABW) were normally distributed within the hybrid population  derived from crosses between palms of heterotic groups A and B, and ranged from 6 to 411 kg/palm/year, 2 to 34 bunches/year and 1.2 to 31.4 kg, respectively Fig. . GeneticHeritability based on estimation of variance components with only pedigree information ranged from 0.15 for FFB in heterotic group A to 0.36 for ABW in heterotic group B Fig. . HeritabFigure The final QTL models consisted of eight QTLs for FFB, ten for BN and nine for ABW, jointly accounting for 50, 70 and 75 % of the phenotypic variance of the hybrid population, respectively Table . The resA major QTL was found for group B on the bottom of linkage group 4, accounting for 21 and 44 % of the phenotypic variance of BN and ABW, respectively . Figure Figure To assess the complementarity of QTL patterns as a possible cause of the heterosis observed in the hybrid population, the findings of a log-likelihood ratio test (LRT) for the presence of additive QTLs in each heterotic group at all genomic evaluation points were plotted for group A versus group B Fig. . For FFBin silico mapping, i.e. based on data from current breeding programs, has many combined advantages. No extra costs are required to develop and phenotype specific mapping populations and this approach enables screening a high number of individuals and alleles, increasing relevance for the breeding process. Linear mixed model approaches used to map QTLs in such genetic material are flexible and powerful [et al. [QTL mapping is interesting when assessing perennial species , but biopowerful . In the  [et al. , and to In a two-step variance component approach, QTL mapping is influenced by the precision of IBD kinship matrices that highlight allele identities between individuals and recombination events between genetic positions. Marker and pedigree information were combined in multipoint analyses using Simwalk2 Bayesian software to estimate these matrices , which aFew other studies have aimed at mapping QTLs for the same production traits, despite frequent limitations for collecting data for a number of individuals that would be optimal for QTL mapping. Rance et al.  first atet al. [CIRAD microsatellite marker resources have been widely used in oil palm genetics since the publication of a reference genetic map  that facet al.  identifiThe implementation of the large genetic trial used in this study follows the RRS with progeny testing of parents, motivated by the observations of increased FFB in A × B palms compared to the maximal value of intra-group crosses . This heOur QTL mapping approach based on data from current breeding programs overcame usual ineffective analysis problems by pooling a sufficient amount of data, and the identified QTLs are highly relevant for integrating information for breeding. Moreover, the incomplete factorial design allowed us to obtain estimates of QTL allele effects from a wide range of genetic backgrounds, thus enhancing the transferability to breeding programs. However, as mentioned by Würschum , a validFor the QTL validation or marker-assisted selection process, target QTL genotypes of pedigree members must be known in order to be able to design efficient crosses that test and combine segregating QTLs. The variance component approach modeled QTL effects as a random term, and estimates of the additive value of each individual at a given QTL were obtained with BLUPs. Despite the normal distribution of BLUPs for QTL effects, the segregation in full-sib families and alleles carried by pedigree members could be inferred qualitatively, e.g. by looking at the pedigree with projected BLUP values (Additional file Such robust information about QTLs and their segregation in genetic backgrounds used in a breeding scheme could be integrated in the RRS to favor complementation of QTLs for production traits and thus to enhance the heterotic effects. As shown in Additional file 18 QTL regions controlling production traits in oil palm were identified using data from current breeding programs analyzed with an efficient pedigree-based approach. Pleiotropic QTL regions with distinct patterns between and within oil palm heterotic groups were identified and provide valuable information for orienting oil palm breeding strategies.


We use a latent difference score (LDS) model to examine changes in young children’s number-line (NL) error signatures (errors marking numbers on a NL) over 18 months. A LDS model (1) overcomes some of the inference limitations of analytic models used in previous research, and in particular (2) provides a more reliable test of hypotheses about the meaning and significance of changes in NL error signatures over time and task. The NL error signatures of 217 6-year-olds’ (on test occasion one) were assessed three times over 18 months, along with their math ability on two occasions. On the first occasion (T1) children completed a 0–100 NL task; on the second (T2) a 0–100 NL and a 0–1000 NL task; on the third (T3) occasion a 0–1000 NL task. On the third and fourth occasions (T3 and T4), children completed mental calculation tasks. Although NL error signatures changed over time, these were predictable from other NL task error signatures, and predicted calculation accuracy at T3, as well as changes in calculation between T3 and T4. Multiple indirect effects (change parameters) showed that associations between initial NL error signatures (0–100 NL) and later mental calculation ability were mediated by error signatures on the 0–1000 NL task. The pattern of findings from the LDS model highlight the value of identifying direct and indirect effects in characterizing changing relationships in cognitive representations over task and time. Substantively, they support the claim that children’s NL error signatures generalize over task and time and thus can be used to predict math ability. The relationship between age-related changes in number-line (NL) error signatures  and math ability have led some to claim that NL signatures are markers of math competence . Others Determining the stability (or otherwise) of NL error signatures across time and task is more methodologically challenging than it might at first seem. We identified four studies that investigated stability and/or change in NL error signatures over time , each ofDecreases in the magnitude of NL errors are correlated with improvements in age-related math abilities . At leasWe could only locate four studies that investigated stability and/or change in NL error signatures over time . Among oIn One issue with Landerl’s approach is standard regression models limit inferences that can be drawn about changes to NL error signatures. When linear regression slopes are fit to these data, change is conceptualized as differences in estimation accuracy over time at a constant rate. It is possible that error signatures differ initially and change at different rates; insofar as this is correct it would suggest it is important to take into account the magnitude of initial and subsequent error signatures as well as the rates of change across time.Although CLPM potentially provide information about the direction of mutual influence, they do not provide information about change/growth per se. In particular, autoregressive parameters allow an examination of the relative stability of relevant parameters . It is important to focus on change as well as relative stability in NL error signatures over time, and the relationship between them and math problem solving.Although The different analytic models allow different plausible inferences to be made about NL error signatures. Compared to linear regression analyses, CLPM, Nevertheless, CLPMs do not take into account the effects of prior changes on performance (since the focus is on stability or otherwise of measures over time). There are two difficulties associated with an inability to specify change per se as a parameter in CLPM. First, predictions may over- or under-estimate “direct”  and “indirect”  effects and their interrelationships are unlikely to be static across time. Second, the interpretation of change itself is not straightforward in CLPMs, given that measures of current performance are likely to be conflated with the accumulated effects of prior changes, both within and across test measures. The aim of the present study is to overcome these limitations using a latent different score model (LDS).Three issues currently limit the value of NL estimation as an index of later math difficulties: (1) 0–100 NL and 0–1000 NL tasks are rarely assessed together to determine whether children show similar learning across these different number ranges, (2) NL estimation abilities and math problem solving are often assessed at a single time point, limiting conclusions regarding their relationship over time, and (3) even when NL abilities have been examined longitudinally, change in NL error signatures time has been inferred from statistical outcomes, rather than evaluated as part of a developmental model. The present study is designed to overcome these three limitations by examining NL error signatures on different NL tasks over time and by testing the viability of a LDS change model .In this study we use a LDS model to assess change and/or stability in NL error signatures over time . Specifically, “change” in LDS models is defined as an explicit model parameter , which is defined as the difference in scores between adjacent time points. When specified in this way, variance associated with the previous test occasion is removed from the change parameter. A benefit of this procedure is that “changes” between test occasions can be interpreted as independent of the accumulated changes from the initial start point .1.We examine children’s error signatures on 0–100 NL and 0–1000 NL tasks three times over an 18 month period. The change parameters in LDS models can be used to represent indices of cognitive change. If the change score derived from one NL task  predicts improvement in estimation on a different NL task , the statistical change parameters could be interpreted as capturing changes in an underlying  representational systemThe linking of error signatures across NL tasks would suggest a set of common psychological indices independent of a particular number range. Conversely, if change in 0–100 NL scores predict less precise estimation on the 0–1000 NL task at T2, the change measure could be interpreted as reflecting experience or learning effects affecting a particular number range  and not a change in the underlying representation. The failure to find a relationship in error signatures between a previously familiar number range (0–100 NL) and a less familiar number range (0–1000 NL) could be interpreted as suggesting an absence in an underlying representation.If change in 0–100 NL error signatures across time predicts change in 0–1000 NL signatures across time , it would suggest that the change processes are similar across task . On the other hand, an absence of a relationship would suggest that the rates of change differ. The latter effect would be unsurprising since improvements in 0–1000 NL abilities are likely to be still occurring.In general, if a relationship between changes in NL error signatures and mental calculation were observed, it would suggest that improvement in NL estimation ability over time is related to math ability. It is possible that different change effects would be observed for the 0–100 NL and the 0–1000 NL, as well as the relationship between these effects and mental calculation ability (or any change in abilities). The latter information is important in determining whether NL error signatures per se are diagnostically useful in predicting math abilities.The data were collected in sessions on four different occasions at approximately 6-monthly intervals over a 2 year period (hereafter referred as T1–T4). On the first occasion the mean age was 6.30 years (SD = 4.4 months). Participants comprised 217 children  and attended one of seven schools in middle-class suburbs of a large Australian city. All children spoke English, had normal or corrected to normal vision, and, according to school personnel, had no known learning disabilities. The study was conducted with the agreement of, and in compliance with, the requirements of the authors’ University’s Human Ethics Committee.Children completed Number Line Estimation tasks on three test occasions and they also completed a Mental Addition task on two occasions. A graphical representation of test sequences across occasions is reported in Figure A “number—position” task e.g.,  was usedTest trials immediately followed practice trials, prior to which task instructions were repeated. Children received no feedback on the accuracy of their responses. The 0–100 NL task comprised numbers “11, 29, 43, 61, 73, 89,” and the 0–1000 NL task numbers “103, 307, 401, 599, 701, 887.” Numbers were presented in a random order across individuals and sessions. We selected prime numbers as targets to limit the use of estimation strategies based on factorization. Children’s responses were analyzed using the average of absolute deviations from target numbers.Analyses of NL error signatures often involve fitting algebraic functions to the magnitude of estimation errors of target numbers on a NL, which tends to show an age-related shift from a logarithmic to a linear fit function. Nevertheless, the shape of error functions should not be confused with the magnitude (fuzziness) of errors: NL error signatures can be linear but reflect relatively large imprecise (fuzzy) numerical representation . Thus itThe mental calculation tasks comprised 24 arithmetic word problems, presented via the audio system of a laptop computer. The problems comprised eight joining problems , eight separating problems , and eight separating-joining problems . The word problems are age appropriate and have been widely used previously to identify difference in math problem solving abilities . Prior tWe use a LDS model  to invesn = 1), missing T2–T3–T4 (n = 16), missing T3–T4 (n = 1), and missing T4 only (n = 13). Our model results and conclusions are not affected by whether these children are included in our analyses or not, and all missing data occurred for arbitrary reasons . Full-information maximum likelihood estimation was used as a robust index of missing data. All model parameters were derived using robust maximum likelihood estimation in Mplus version 7 , with data from at least one occasion available for the remainder: missing T2 only  and T2 . For 0–1000 NL, average absolute deviations also decreased between T2  and T3 . The proportion of mental calculation problem solved correctly improved over time . Paired sample t-tests confirmed that differences on the 0–100 NL , 0–1000 NL  and mental calculation  tasks were all significant.As expected, precision of NL errors improved for both the 0–100 NL and 0–1000 NL tasks over time. For 0–100 NL, average absolute deviations decreased between T1  replicates previous cross-sectional and longitudinal research.Greater improvement in 0–100 NL estimation precision between T1 and T2 predicted, [6] relatively poorer estimation on the 0–1000 NL task at T2, and [7] less change in mental calculation accuracy between T3 and T4. This finding is unsurprising since it likely shows that greater improvement (from T1 to T2 on the 0–100 NL) is associated with initially poorer 0–1000 NL estimation errors. Improvement on the 0–100 NL task between T1 and T2 was [8] unrelated to similar improvements on the 0–1000 NL task between T2 and T3  or [9] mental calculation accuracy at T3. The finding of unrelated changes across NL tasks suggests that such improvements reflect differences in change processes per se.Error signatures on the 0–1000 NL task showed a similar pattern of predictive relationships to the 0–100 NL task. Poorer estimation on the 0–1000 NL task at T2 predicted, [10] less change on the 0–1000 NL task between T2 and T3, [11] poorer mental calculation accuracy at T3, and [12] less change in mental calculation accuracy between T3 and T4. Moreover, greater change on the 0–1000 NL task between T2 and T3 was related to [13] less change in mental calculation accuracy between T3 and T4, but did not predict [14] mental calculation accuracy at T3. The similarity of effects across both NL tasks strengthens the argument of persistence in NL error signatures over time, and ipso facto a similar underlying representation.Lastly, mental calculation performance was relatively stable over time. Greater mental calculation accuracy at T3 predicted [15] less change in mental calculation accuracy between T3 and T4. Changes in accuracy were relatively small (0.76–0.81 problem correctly solved).The eight indirect effects of the model, with associated non-symmetric 95% confidence intervals, are shown in Table All indirect paths with 0–1000 NL estimation at T2 as a mediator were significant. Conversely, all indirect pathways involving change in 0–1000 NL estimation between T2 and T3 as a mediator were non-significant. We interpret this to suggest that increases in proficiency 0–1000 NL may be still occurring and the change does not sufficiently mediate the relationship between the early NL error signatures and later mental calculation ability. The significant indirect effects are reported below . Greater change in 0–100 NL estimation between T1 and T2 predicted poorer estimation for 0–1000 NL at T2, leading to lower mental calculation accuracy at T3 (indirect effect 3). The fact that change in 0–100 NL is not related to better initial performance on the 0–1000 NL task suggests that this change reflects poorer initial 0–100 NL estimation ability.A similar pattern of indirect effects were found when change in mental calculation accuracy was the predicted outcome. Poorer estimation for 0–100 NL at T1 predicted poorer estimation for 0–1000 NL at T2, leading to less change in mental calculation accuracy between T3 and T4 (indirect effect 5). Greater change in 0–100 NL estimation between T1 and T2 predicted poorer estimation for 0–1000 NL at T2, leading to less change in mental calculation accuracy between T3 and T4 (indirect effect 7).A LDS model was employed to examine stability and changes parameters in NL error signatures over a 2 year period, and to examine the degree to which these changes are linked to mental calculation abilities. Four major findings are worth noting. First, the magnitudes of NL estimation error signatures for the 0–100 NL and the 0–1000 NL both declined over time and initial performance on both tasks were related; nevertheless, the rates of decline differed . In particular, greater change occurred over the 0–100 range than the 0–1000 range over the same period of time but on different occasions. Also, change in 0–100 NL did not predict change in 0–1000 NL  could be predicted by NL error signatures, as well as changes in NL error signatures over time. In particular, less precise initial 0–100 NL and 0–1000 NL estimation predicted poorer mental calculation accuracy at T3 and less change in calculation accuracy between T3 and T4. It is also evident that mental calculation ability was relatively stable over time, with small incremental, correlated changes, found. And fuzzy NL error signatures were associated with poorer mental calculation abilities–a similar finding to that found in previous cross-sectional and longitudinal research . The facFourth, the indirect pathway effects in the LDS model also reveal interesting effects. Of particular note, estimation error signatures on the 0–1000 NL at T2 mediate the 0–100 NL at T1 in predicting mental calculation at T3 . This set of relationships is replicated for the T1 to T2 change in the 0–100 NL. Nevertheless, it should be noted that the T2 to T3 change in the 0–1000 NL did not similarly mediate this prediction. This finding suggests either that (1) insufficient change has occurred in the 0–1000 NL between T2 and T3, or (2) change in the 0–1000 NL is not associated with calculation ability per se. We suggest that the former interpretation is more likely to be the case.However, we acknowledge that children may only need to improve their precision on the 0–100 NL task for a sufficient shift in numerical understanding to occur that is relevant for mental calculation. Improvements to 0–1000 NL estimation may be irrelevant in this case. Relatedly, the NL task may not be the most sensitive measure of mental magnitude representation for larger number ranges. Nevertheless, we believe these measurement issues are inherent in all studies designed to assess cognitive phenomena that change over time. Moreover, our findings correspond with both longitudinal  and expeOverall, the findings show a relative consistency in NL representations over time and task, as indexed by the NL estimation error signatures. Moreover, specific changes in NL representations per se are important predictors of concurrent and future arithmetic problem solving. It is evident that the LDS model framework provides information about change not revealed by other longitudinal analytic approaches . In our view, LDS models overcome some of the limitations of earlier longitudinal research which have used different analytic models. Although our findings are consistent with earlier longitudinal NL findings, they provide a more solid basis for drawing inferences about the relationships between changes in NL representations and calculation abilities.Here we note three points. First, our research approach goes beyond comparing differences in changes in NL error signatures  to makinThe 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.


X-ray repair cross-complementing group 1 (XRCC1) gene Arg194Trp polymorphism has been reported to be associated with risk of lung cancer in many published studies. Nevertheless, the research results were inconclusive and conflicting. To reach conclusive results, several meta-analysis studies were conducted by combining results from literature reports through pooling analysis. However, these previous meta-analysis studies were still not consistent. Hence, we used an updated and cumulative meta-analysis to get a more comprehensive and precise result from 25 case–control studies searching through the PubMed database up to September 1, 2013. The meta-analysis was carried out by the Comprehensive Meta-Analysis software and the odds ratio (OR) with 95 % confidence interval (CI) was used to estimate the pooled effect. The result involving 8,876 lung cancer patients and 11,210 controls revealed that XRCC1 Arg194Trp polymorphism was not associated with lung cancer risk . The cumulative meta-analysis showed that the results maintained the same, while the ORs with 95 % CI were more stable with the accumulation of case–control studies. The sensitivity and subgroups analyses showed that the results were robust and not affected by any single study with no publication bias. Relevant studies might not be needed for supporting these results. X-ray repair cross-complementing group 1 (XRCC1) is involved in base excision repair protein that located on chromosome 19q13.2–13.3 with a length of 33 kb . The polIn order to obtain more comprehensive and precise results, we conducted cumulative meta-analysis , 35 to eA study met all of the following inclusion criteria was included: (1) to evaluate the association between XRCC1 Arg194Trp polymorphism and risk of lung cancer; (2) cohort or case–control design and the patients were diagnosed by histology or pathology; (3) the number of genotype distribution in both case and control group were directly reported or calculated from the reported data; and (4) the published language was English or Chinese.The search terms [(“XRCC1” or “X-ray repair cross-complementing group 1”) and “polymorphism” and (“lung cancer” or “lung carcinoma”)] were used to search the PubMed database up to September 1, 2013. The reference list of the included articles and relevant meta-analyses were manually searched.Two authors independently chose 25 case–control studies, which were illustrated in Fig. I2 statistics (I2 ≤ 40 %)  were used to calculate the pooled odds ratio (OR) and its 95 % confidence interval (CI) to present the strength of associations between XRCC1 Arg194Trp polymorphism and risk of lung cancer. The fixed-effects model was used firstly, if heterogeneity among included studies was detected by  ≤ 40 %) , we shifThe influence of sample size on the overall risk estimation was carried out by cumulative meta-analysis , and theThe electronic searching yielded 128 studies, and the hand searching yielded 15 studies initially; finally, 23 articles involving 25 case–control studies  containeI2 ≤ 40 %), and meta-analysis based on fixed-effects model showed that there was no association of XRCC1 Arg194Trp polymorphism with risk of lung cancer .Figure Meta-analysis is a statistical method of combining results across studies from literatures to resolve discrepancy in genetic association studies . The metCompared to previously meta-analyses –33, the Obviously, there were potential to moderate level heterogeneity. From the subgroups analysis, we found that ethnicity and source of control might not be the source of heterogeneity Table . When weThere were some limitations of our meta-analysis. First, there was heterogeneity among included studies. Although the heterogeneity was probably from the study reported by Buch et al. , we coulIn conclusion, this meta-analysis suggests that XRCC1 Arg194Trp polymorphism is not associated with lung cancer risk, either in Asians or Caucasians, either the controls were sourced with or without HWE. These results were not influenced by any single study, and relevant studies are not needed for supporting this result. Due to the limitations of this meta-analysis, current results should be viewed with caution and future studies should be conducted in gene-gene and gene-environment interactions.


Agrobacterium tumefaciens transfers DNA and proteins to a plant cell through a type IV secretion apparatus assembled by the VirB proteins. All VirB proteins localized to a cell pole, although these conclusions are in dispute. To study subcellular location of the VirB proteins and to identify determinants of their subcellular location, we tagged two proteins, VirB4 and VirB11, with the visual marker green fluorescent protein (GFP) and studied localization of the fusion proteins by epifluorescence microscopy. Both GFP-VirB4 and GFP-VirB11 fusions localized to a single cell pole. GFP-VirB11 was also functional in DNA transfer. To identify the polar localization domains (PLDs) of VirB4 and VirB11, we analyzed fusions of GFP with smaller segments of the two proteins. Two noncontiguous regions in VirB4, residues 236–470 and 592–789, contain PLDs. The VirB11 PLD mapped to a 69 amino acid segment, residues 149–217, in the central region of the protein. These domains are probably involved in interactions that target the two proteins to a cell pole. Agrobacterium tumefaciens, a soil bacterium pathogenic to most plants, is the causative agent of the cancerous “Crown Gall” tumor disease  region of the tumor inducing (Ti-) plasmid. The vir region encompasses ∼30 kilobase (kb) of DNA and encodes more than 20 proteins. Eighteen of the Vir proteins are essential for DNA transfer. These proteins process the T-DNA and assemble a passageway at the bacterial membranes for the transfer of the T-strand DNA intermediate to the plant cell. VirD4 and 11 VirB proteins participate in the function and assembly of a type IV secretion apparatus and a T-pilus used for DNA transfer , peptic ulcer and cancer (Helicobacter pylori), Legionnaires’ disease (Legionella sp.), and brucellosis (Brucella sp.)  or no recognized hydrophobic region (VirB11). We used deletion methods and cloning of defined gene segments to identify the regions required for the targeting of the two proteins to their appropriate subcellular location.Green fluorescent protein (GFP) is widely used to study subcellular location of a protein  was isolated as a SacI fragment from plasmid pAD1885 . Plasmid pADI16 was introduced into the virB11 deletion mutant Agrobacterium A348ΔB11 (Berger and Christie The virB4 gene in plasmid pADI15 contains two in-frame EcoRI and two in-frame BamHI sites. Two deletions within virB4 were made by deleting the 1.4 kb EcoRI fragment (pADI15ΔR) or the 0.7 kb BamHI fragment (pADI15ΔB). All other derivatives of gfp-virB4 and all derivatives of gfp-virB11 were constructed by cloning the desired fragment obtained by PCR amplification. Primers used for PCR amplification are listed in Table S1. The PCR amplified fragments were cloned as a BamHI-Acc65I fragment into plasmid vector pAD2005. All plasmids were confirmed by DNA sequence analysis.The μg/mL kanamycin). To induce expression of a fusion gene, 5 mL AB induction medium  was inoculated with 0.35 mL of the overnight culture and grown for 16–18 h at room temperature . virD promoter on a wide host range RK2-derived plasmid. Plasmids pADI15 and pADI16 containing the gfp-virB4 and gfp-virB11 fusions were introduced into Agrobacterium A348ΔB4 and A348ΔB11, respectively. Analysis of subcellular location by epifluorescence microscopy showed that bacteria expressing GFP-VirB4 had localized foci of fluorescence  mapped to a single cell pole while a few cells had fluorescence foci at both poles .A GFP tag was introduced at the N-terminus of VirB4 and VirB11 to facilitate analysis of subcellular location of the two proteins. The fusion genes are expressed from the inducible n = 191) of GFP-VirB4 foci and 76% (n = 154) of GFP-VirB11 foci localized to a cell pole . Almost all foci mapped to a cell pole (right panel) indicating that GFP-VirB11, like GFP-VirB4, localized to a cell pole. Quantitative analysis showed that 99% , and a BamHI fragment that deleted virB4 codons 1–235 (pADI15ΔB).VirB4 is a large protein containing 789 residues. We used deletion methods and cloning of defined segments to identify the minimum sequences required for polar localization of VirB4. We introduced two deletions in the Analysis by epifluorescence microscopy showed that bacteria harboring plasmid pADI15ΔR had fluorescence throughout the cell while bacteria with pADI15ΔB formed polar foci Figs.A and B. gfp fusions containing virB4 codons 236–470, 413–614, and 562–789 , however, led to a large increase in polar targeting  and R2 residues 592–789), could independently target a GFP fusion to a cell pole. Whether both regions are required for polar localization in the native protein is not known. For efficient polar targeting, region R1 is required in its entirety as small deletions at either end of region R1 led to either a significant reduction in or a complete loss of polar targeting  contain one or more MSD. None of these fusions, however, localized to a cell pole indicating that the mere presence of a MSD is not sufficient for polar targeting of VirB4 Fig.. At leasE. coli are sometime targeted inappropriately to the cell poles , however, had no effect on polar localization of GFP-VirD4 . The C. crescentus TipN protein marks the new pole and is required for proper localization of proteins and organelles (Huitema et al. Bacillus subtilis SpoVM directly recognizes the curvature of a membrane to localize to the forespore membrane, and not to the cytoplasmic membrane (Ramamurthi et al. Bacillus subtilis DivIVA protein is believed to contain a polar targeting sequence that recognizes some physical aspect of the pole, for example, curvature, to take it to the cell pole (Lenarcic et al. Several mechanisms for the targeting of a protein to a cell pole have been identified Dworkin . The E.


Filaggrin is a multifunctional protein and plays a key role in skin barrier formation. Loss-of-function mutations in the gene encoding filaggrin We sought to interrogate tissue-specific variations in the expressed genome in the skin of children with AD and to investigate underlying pathomechanisms in atopic skin.FLG genotype to stratify the data set.We applied single-molecule direct RNA sequencing to analyze the whole transcriptome using minimal tissue samples. Uninvolved skin biopsy specimens from 26 pediatric patients with AD were compared with site-matched samples from 10 nonatopic teenage control subjects. Cases and control subjects were screened for P < .05) were identified, of which 211 were significantly upregulated and 490 downregulated by greater than 2-fold. Gene ontology terms for “extracellular space” and “defense response” were enriched, whereas “lipid metabolic processes” were downregulated. The subset of FLG wild-type cases showed dysregulation of genes involved with lipid metabolism, whereas filaggrin haploinsufficiency affected global gene expression and was characterized by a type 1 interferon–mediated stress response.Two thousand four hundred thirty differentially expressed genes (false discovery rate, FLG genotype, whereas an aberrant defense response is seen in subjects with FLG mutations. Genotype stratification of the large data set has facilitated functional interpretation and might guide future therapy development.These analyses demonstrate the importance of extracellular space and lipid metabolism in atopic skin pathology independent of  Atopic dermatitis  is an itchy inflammatory skin disease with a spectrum of clinical skin phenotypes.Previous studies have investigated the transcriptome in atopic and eczematous skin by using microarray technologies and quantitative PCR, identifying a variety of pathomechanisms, including abnormalities in epidermal differentiation,(FLG) are strongly and significantly associated with AD riskH2 cytokinesFLG intragenic copy number variation influences AD risk in a dose-dependent fashion, even in the absence of FLG null mutations.The finding that loss-of-function mutations in the skin barrier gene encoding filaggrin FLG genotype was used to define subphenotypes for the stratification of this large data set. Uninvolved skin was chosen to study the underlying pathomechanisms of AD without the confounding effects of secondary inflammation or infection. Our strategy for analysis was stepwise, as follows: (1) all cases compared with all control subjects, (2) case-control comparison stratified by FLG genotype, and (3) case-case comparison stratified by FLG genotype . Pediatric patients with AD of Irish ethnicity attending the dermatology department were invited to participate. Twenty-six children aged 6 to 16 years and their parents/guardians provided written informed consent; 19 were male, and 7 were female . Each chThe collection of samples from healthy volunteers was reviewed and approved by the East of Scotland Research Ethics Service, United Kingdom (LR/11/ES/0043). Nonatopic teenage volunteers with 4 grandparents of Irish or Scottish descent were recruited. Skin biopsy specimens were collected in the same way as for the patients with pediatric AD, and 5 mL of venous blood was collected for DNA extraction.A260/280 ratio of 1.9 to 2.1.The protocol for extraction of total RNA of greater than 200 nucleotides in length from tissue by using the Qiagen RNeasy Mini Kit  was modified and optimized, as follows. Working on dry ice, subcutaneous fat was trimmed from the skin biopsy specimen, and the remaining sample was placed in a 2-mL sample tube RB  with 600 μL of Buffer RLT  and one 7-mm stainless steel bead . The sample was disrupted and homogenized at 4°C in a Qiagen TissueLyser LT for 5 minutes at 50 oscillations per second. RNA extraction then proceeded according to a standard protocol with 2 final elution steps each using 30 μL of RNase-free water and centrifuged at 12,000 rpm for 90 seconds. Spectrophotometry  and a microfluidics platform for size analysis and quantification  were used for quality control. This protocol yielded 2.5 to 10.8 μg per sample of RNA with an FLG null mutations with TaqMan allelic discrimination assays , as previously described.Genomic DNA samples extracted from blood were initially screened for DRS was performed on a HeliScope Sequencer  with 500 ng of extracted RNA, as previously described.www.jacionline.org. DRS resulted in 480 million reads across all 36 samples, providing gene expression data for 11,259 genes. The raw data are deposited at the European Genome-Phenome Archive (EGAS00001000823/EGAC00001000200); processed data are available at polyAdb (http://www.compbio.dundee.ac.uk/polyAdb) and can be viewed in the Integrated Genome Browser or as data tracks at www.ensembl.org. Scripts for performing analysis and generating the figures that accompany this article are available at polyAdb (http://www.compbio.dundee.ac.uk/polyAdb).Details of sequence alignment and analysis are presented in the EdgeR (version 2.6.12 in R version 2.15.1) analysis was performed with generalized linear models to control for sex bias between comparisons. Unless otherwise stated, genes were called as significantly differentially expressed if the Benjamini-Hochberg corrected false discovery rate (FDR) was less than 0.05.http://amigo.geneontology.org/cgi-bin/amigo/search.cgi?action=advanced_query). Functional protein association networks were investigated in silico by using STRING9.05 (http://string-db.org/).Gene ontology (GO) has been developed to provide a controlled vocabulary of terms to describe the characteristics of genes and gene products with standardization across species and between databases. This allows the bioinformatic analysis of GO terms in large data sets for thematic classification. GO analyses were performed with AmiGO gene ontology version 1.8 ((GAPDH).RNA (1.4 ng) was converted to cDNA by using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems), according to a standard protocol. Aliquots of RNA extracted from atopic skin samples were analyzed in triplicate by using real-time quantitative PCR performed according to standard protocols with the TaqMan 7900HT Fast (Applied Biosystems) with normalization to glyceraldehyde-3-phosphate dehydrogenase FLG null mutations revealed a high proportion of mutation carriers , and comparison of the control samples with AD cases revealed no obvious clustering by phenotype or FLG genotype (see www.jacionline.org). This highlights the subtlety of changes in gene expression in uninvolved atopic skin. The samples do not cluster according to age, providing some assurance that the use of samples from older subjects in the control group does not result in bias to account for the observed differences in transcriptional profile. However, in contrast, correlation with FLG gene expression showed a striking global change between samples of different FLG genotypes , the “extracellular region” or “extracellular space”  and “receptor binding” . The most highly significant terms in the less than 0.5-fold downregulated genes were “lipid metabolic process” , “small molecule metabolic process” , and “organic acid metabolic process” . The full list is shown in www.jacionline.org.Comparing all 26 cases with all 10 control subjects showed 2430 differentially expressed genes with an FDR of less than 0.05, including 211 with a fold change of greater than 2.0 and 490 genes with a fold change of less than 0.5. The full list is shown in www.jacionline.org). Significantly downregulated genes show predicted functional networks within “lipid metabolic processes,” “small molecule metabolism,” and “organic acid metabolic processes” (see www.jacionline.org).Analysis of the significantly upregulated genes identified predicted protein interaction networks classified as “defense response,” “extracellular region,” and “receptor binding” . Significant GO terms in the transcripts upregulated more than 2.0-fold included “extracellular region”  and “lipid particle” . Significantly downregulated transcripts (fold change <0.5) were classified with the GO terms “cellular lipid metabolic process”  and “lipid metabolic process” . These included 137 with a fold change of greater than 2.0 and 266 with a fold change of less than 0.5 (the full list can be found in www.jacionline.org). Significant GO terms in the upregulated transcripts included “defense response”  and “response to biotic stimulus” , “chemokine activity” , and “chemokine receptor binding” . The most significant GO term in the transcripts downregulated less than 0.5-fold was “steroid metabolic process”  in which 104 are significantly upregulated  and 313 are significantly downregulated . The upregulated transcripts showed enrichment for GO terms in the “defense response”  forming a predicted protein network analogous to that seen in the wild-type control subject versus compound heterozygous case comparison, with the addition of GRIN2B, GRIK2, and MNDA (see B). Genes classified for the ontology terms “receptor binding” , “cytokine activity” , and “extracellular region”  were also significantly upregulated. The most highly significant GO term in the transcripts downregulated less than 0.5-fold were “small molecule metabolic process” , “carboxylic acid metabolic process,” and “oxoacid metabolic process” , but there is significantly greater mRNA expression in wild-type AD cases than wild-type control subjects (P = 3.0 × 10−3), suggesting that there might be a compensatory upregulation of FLG mRNA in atopic skin of FLG wild-type subjects. with AD . This inutations . Comparicis-eQTLs) were identified on the basis of a transcript proximity of less than 250 kb between the single nucleotide polymorphism site and the 5′ end of the transcript (see www.jacionline.org).cis-eQTLs correspond to previously reported AD candidate genes , whereas 13 represent novel candidates : 87 genes were differentially expressed in the FLG wild-type cases versus compound heterozygous cases, and 127 were differentially expressed in the wild-type versus heterozygote comparison (see www.jacionline.org). Forty-one genes showed a fold change of greater than 2.0 or less than 0.5. After controlling for multiple testing, 2 genes showed a statistically significant difference in expression: FLG  and cartilage intermediate layer protein . The differential expression of FLG, CILP, and selected other transcripts was validated by using quantitative PCR with aliquots of the previously extracted RNA samples (see www.jacionline.org).A comparison of the 26 cases stratified by FLG wild-type and compound heterozygous cases showed the highest number of genes to be associated with “extracellular region” , “carbohydrate binding” , and “calcium ion binding” .GO analysis of the 127 transcripts showing differential expression between FLG expression was used to investigate filaggrin-related mechanisms and pathways in atopic skin. Twenty genes show strong correlation with FLG expression (each r > 0.98 and P < .05), including 7 classified within the extracellular region: CA2 (carbonic anhydrase 2), COL12A1 , MUCL1 (mucin-like 1), PIP (prolactin-induced protein), PRELP (proline/arginine-rich and leucine-rich repeat protein), SCGB1D2 , and ZG16B .Correlation of gene expression with FLG expression , “cellular response to type I interferon,” “response to type I interferon,” and “type I interferon–mediated signaling pathway” . Combining these 6 anticorrelated and upregulated genes with the 17 defense response genes that are upregulated in the FLG genotype–stratified case-control analyses  as a strong and significant risk factor for AD22 represented a fundamental breakthrough in understanding pathogenesis.The identification of genes involved in the pathogenesis of AD represents a significant challenge because of the clinical heterogeneity and complexity of multiple interrelated genetic and environmental mechanisms in patients with this disease. The identification of null mutations within the gene encoding filaggrin This study used DRS to quantify the whole transcriptome of atopic skin in a unique collection of pediatric AD skin biopsy specimens; it represents the largest collection of AD skin transcriptomes reported to date. Skin offers the advantage of sampling the tissue of interest, maximizing power to detect expression traits correlating with clinical phenotype.FLG expression  and in the FLG wild-type case-control comparison , but we have shown a significant difference between FLG expression in wild-type control subjects compared with wild-type cases  might contribute to the propensity to keratinocyte apoptosis that some authors consider to be a key pathomechanism in atopic spongiosis.Intercellular edema (spongiosis) is a characteristic feature in patients with AD, but the underlying mechanisms are unclear. The differential expression of genes encoding proapoptotic and antiapoptotic proteins , immune response , transcriptional regulation , mitochondrial (TST), and lipid biosynthetic (AGPAT1).The majority of AD risk loci identified by using genome-wide association studies are located within intergenic regions of unknown function. Our analysis has offered insight into possible genetic mechanisms associated with 14 of the previously reported AD risk loci. We propose FLG genotype. The strength of using a functional genotype for phenotype stratification is apparent, and this approach might prove useful for other tissue-specific inflammatory disorders and personalized medicine in the future.•Atopic skin shows differential gene expression in pathways classified in the extracellular space, lipid metabolism, and stress response.•FLG genotype reveals a type 1 interferon–mediated stress response in filaggrin-deficient skin.Substratification of the whole transcriptome data set according to •These findings offer insight into the underlying abnormalities in uninflamed atopic skin and might guide future therapy development.The use of DRS has allowed detailed study of tissue-specific gene expression data from small amounts of tissue. This transcriptomic analysis has provided new insight into the mechanistic pathways in atopic skin, which are both dependent and independent of


Our mechanistic investigations suggest that the phenomenon stems from spatial confinement of energy migration in nanosized structures. We show that confining energy migration constitutes a general and versatile strategy to manipulating multiphoton upconversion, demonstrating an efficient five-photon upconversion emission of Tm3+ in a stoichiometric Yb lattice without suffering from concentration quenching. The high emission intensity is unambiguously substantiated by realizing room-temperature lasing emission at around 311 nm after 980-nm pumping, recording an optical gain two orders of magnitude larger than that of a conventional Yb/Tm-based system operating at 650 nm. Our findings thus highlight the viability of realizing diode-pumped lasing in deep ultraviolet regime for various practical applications.Manipulating particle size is a powerful means of creating unprecedented optical properties in metals and semiconductors. Here we report an insulator system composed of NaYbF A general approach to maximize upconversion luminescence in stoichiometric lanthanide lattices is lacking. Here, Chen et al. report a NaYbF4:Tm lattice and demonstrate fine-tuning of energy migration by controlling dimensions of the crystal lattice, highlighting their potential for deep ultraviolet lasing. The construction of functional materials with designable optical properties is fundamentally important for scientific research and technological applications in diverse fields encompassing energy, environment and biomedicine123456789101213141516171820Photon absorption and emission in upconversion nanoparticles are due to the lanthanide dopants localized on the lattice sites2224252624274 lattice. We demonstrate fine tuning of energy migration through controlling the dimensions of the crystal lattice. Our mechanistic investigation reveals a spatial confinement of energy migration that prevents energy loss to the crystal lattice and increases the local density of excitation energy. Through the use of Tm3+ ion as an energy accumulator, the excitation energy can be maximally amassed to generate intense ultraviolet emissions on near-infrared excitation. We show that the technological advancement may revolutionize the fabrication of cost effective and compact diode-pumped solid-state deep ultraviolet lasers that are useful for environmental, life science and industrial applicationsIn this work, we describe an investigation of energy migration in a nanosized NaYbF3+ ions in the inner shell layer of a hexagonal phase NaYF4@NaYbF4:Tm@NaYF4 host  because a NaYF4@NaYbF4:Tm core–shell structure yields luminescence that is substantially weak due to surface quenching  laser diode at a power density of 20 W cm−2. The spectrum consists of characteristic emission peaks that can be assigned to 1I6→3H6 and 3F4 (290 and 350 nm), 1D2→3H6 and 3F4 (360 and 450 nm), 1G4→3H6 and 3F4 (475 and 650 nm) and 3H4→3H6 (800 nm) transitions of Tm3+, respectively. Both the violet and overall emissions surpass that of the NaYF4@NaYbF4:Tm/Y@NaYF4 counterparts comprising lower Yb3+ contents . The absence of long-distance energy migration is likely to suppress energy loss to the crystal lattice accounting for luminescence quenching. Furthermore, the localization of excitation energy raises the rate of energy transfer to a nearby Tm3+ activator, which facilitates the multiphoton upconversion process.We attribute the observations to confined migration of excitation energy within the nanoshells, which prevents the excitation energy from travelling a long distance at a high Yb4@NaYbF4:Tm@NaYF4 nanoparticles of varying inner shell thickness from 1 to 17 nm verified the spatial confinement of energy migration @NaYF4 core–shell nanoparticles  (−1) in Gd3+ favours the preservation of the excitation energy as supported by time decay studies  . Importa studies . The lonnversion , which i30 mol%; , which p30 mol%; .To realize lasing emission, we constructed a five-pulse pumping scheme to excite the upconversion process . The pulDm) of the resonator  under excitation of varying powers at room temperature. The light input–output curve shown in Pa=∼78 mJ cm−2 and Pth=∼86 mJ cm−2). This S-like spectrum clearly indicates a transition from a spontaneous emission to an amplified spontaneous emission and to a lasing emission. Pa), a relatively broad spontaneous band is observed. As the excitation power increases slightly above Pa, a sharp peak ascends from the emission spectrum. Through further increases in the excitation power above Pth, well-defined sharp peaks with a linewidth <0.11 nm emerge from the spectrum. The measured mode spacing (0.25−0.27 nm) is in good agreement with the theoretical value (∼0.26 nm), confirming that lasing emissions have been achieved. Notably, single-mode lasing was also obtained by using a thinner microresonator  by further refining the upconversion process. Hence, our study will lead to the development of near-infrared diode-pumped deep ultraviolet lasers which can avoid the difficulty of shifting the operating wavelength of GaN-based laser diodes below 300 nm ref. , evade uWe synthesized the core–shell–shell nanoparticles using the method described in L, respectively in the orientation perpendicular to the length of the cuvette. Photoluminescence intensity emitted from the shorter side of the cuvette, Itot(λ), was recorded by the monochromator set-up. The net optical gain, G(λ), was deduced by fitting Itot =Isp(λ) [exp(G(λ)L)−1]/G(λ) with the measured values of Itot(λ), where λ is the wavelength and Isp(λ) is the spontaneous emission intensity.Net optical gain of the nanoparticle colloid was measured using variable stripe length methodQ-switched Nd:YAG laser and a continuum Panther EX optical parametric oscillator. A 980-nm laser pulse  with a diameter of ∼10 mm was generated from the optical parametric oscillator system under the excitation of the Nd:YAG laser. By splitting the 980-nm pulse into five equal-power pulses through the use of four beam splitters , we can obtain a five-pulse (time delay between adjacent pulses is 10 ns) laser beam. This is possible because the pulses are forced to travel in five different distances to obtain a time delay of 10 ns between the adjacent pulses. These five pulses are then combined through two polarization-dependent beam splitters and two λ/2 waveplates to form three laser beams. All the laser beams, which are spatially overlapped, are focussed onto a sample through three cylindrical lenses to form a pump stripe of width equal to ∼30 μm. Photoluminescence emission from the sample was collected and analysed by an optical fibre (core diameter of 400 μm) coupled to an Oriel MS257 monochromator attached with a photomultiplier tube. The spectral resolution of the monchromator is about 0.1 nm.The optical set-up consists of a Powerlite DLS 9010 For the fabrication of the bottle-like microresonator, a bared standard optical fibre was coated with a tiny drop of nanoparticles and silica resin mixture. The prolate surface-tention-induced microresonator was then solidified in an arid and clean atmosphere. The surrounding temperature of the sample was kept at 23 °C to avoid deformation due to the influence of thermal effects. Whispering gallery modes can be excited by optically pumping the equatorial zone of the microresonator. Notably, the pump stripe is oriented perpendicular to the length of the fibre. Light emitted from the surface of the microresonator can be collected through an optical fibre.How to cite this article: Chen, X. et al. Confining energy migration in upconversion nanoparticles towards deep ultraviolet lasing. Nat. Commun. 7:10304 doi: 10.1038/ncomms10304 (2016).Supplementary Figures 1-14 and Supplementary Notes 1-2


We found equipotential rings around the NWs perimeter that we attribute to azimuthal 1D modes. Along the NW axis, these modes are altered, forming potential ripples in the local density of states, due to intrinsic disturbances. Potential mapping of electrically biased NWs enabled us to accurately determine their conductivity which was found to increase with the decrease of NW diameter, consistent with surface dominated transport. Our results demonstrate that TI NWs can pave the way to both exotic quantum states and novel electronic devices.Topological insulators (TI) nanowires (NW) are an emerging class of structures, promising both novel quantum effects and potential applications in low-power electronics, thermoelectrics and spintronics. However, investigating the electronic states of TI NWs is complicated, due to their small lateral size, especially at room temperature. Here, we perform scanning probe based nanoscale imaging to resolve the local surface potential landscapes of Bi While no modulation of the surface potential around the NWs’ perimeter is observed, however, the KPM measurements reveal spatial potential ripples along the NWs’ axis that can not be clearly associated to the surface topology of the nanowire. Our results show that the azimuthal 1D modes around TI NWs’ surface are relatively robust against defects and diameter variations, but their spatial distribution along the NWs axis can be significantly altered.Here we use Kelvin probe microscopy (KPM)282Te3 NWs are grown via an electrodeposition technique30via focused ion beam. Tungsten serves as the contact282Te3 nanowires does not affect the KPM detection of the TI surface states. Contrary to non-topological surface states, TI surface states have recently been shown to be immune to surface imperfectionsThe Bi2 surfaces in the same sample, confirming that these oscillations are an intrinsic property of the NWs instead of the contact materials or electrical noise. The average separation and amplitude of these potential ripples show no observable difference for the parts of the NW on top of Au electrodes and the freestanding parts. Moreover, these potential ripples were observed also in a nanowire on the gold layer without electrodes at its ends.The NWs are  oriented, and have smooth surfaces with small random diameter variations as revealed by the scanning electron microscopy (SEM) and KPM topography images . Transmi2Te3 NWs no correlation was observed between SEM images and KPM topography images with the KPM potential images, and it is not possible to directly correlate the TEM images with the potential images since different NWs were imaged. In 3D topological surface states none of the structure/charge disorder effects is expected to induce modulations in the local chemical potential in a specific spatial direction, but rather non-directional random fluctuations across the whole 2D planeSuch spatial surface potential fluctuations are one of the most significant types of disorder in the 2D surface states of 3D topological insulators when their Fermi level is close to the Dirac point9102Te3 nanowires with similar preparation methods (electrodeposition) and similar sizes as ours. Their magnetoconductance results confirms the presence of Dirac surface states, and therefore the Fermi level of their sample must lie near the Dirac point. We expect the same for our nanowires due to the similarity in composition, preparation method, size, and even device fabrication methods. The picture drawn from our experiments is that the azimuthal 1D surface states of TI NWs are relatively stable against a certain amount of distortion, but respond similarly to spatial disturbances in the local chemical potential along the NW axis as their 2D counterparts.Since the surface potential images correspond to the projections of the curved NW surfaces onto the substrate plane, we conclude the actual NW surface potential consists of equipotential rings around the nanowire perimeter, attributed to azimuthal 1D surface modes on the TI NWs surface as expected from theory22I–V measurements an error up to 50% can occur, due to the contact resistance. Two terminal I–V measurements on this NW give a total resistance of 99 σ of (2.93 ± 0.17) × 104 S/m. Applying this method to the 75 nm and 250 nm NWs we obtain contact resistances of 18 σ obtained from both methods  for all the NWs are shown in σ, which was accurately determined by KPM. It increases with decreasing NW diameter, as theoretically expecteds/v). Fitting the actual electrical conductivity vs. s/v (σ = σb + σs(s/v), where the subscripts b and s refer to the bulk and surface, gives a bulk electrical conductivity of (0.51 ± 0.20) × 104 S/m, which agrees well with literature values. Consequently, the surface contribution to total electrical conductivity of the nanowires is 43%–77%, increasing with decreasing diameter, demonstrating the significance of the TI surface states in room temperature NW devices. This size-dependent conductivity is otherwise obscure from simple I–V measurements. Despite the presence of potential ripples, surface state transport is still significant, likely due to the topological protection which makes it immune to backscattering. Our observation is consistent with previous magnetotransport measurements of Aharonov-Bohm (AB) oscillations and Subnikov-de Hass (SdH) oscillations of Bi2Te3 NWs, which revealed surface state dominated transport at low temperatureNow the question arises whether these surface potential ripples have an influence on the total electrical conductivity of the TI nanowires. From the discussion above it becomes clear that determining the actual electrical conductivity of a TI NW is not straightforward. The actual conduction pathways depends critically on the chemical potential and thus on the amplitude of the individual potential ripplese of 99  . From th vs. s/v  to a sim2Te3 NWs of 45–250 nm diameter, rippled along the NW axis with peak-to-peak amplitudes of 12–100 mV and average separations of 90–215 nm. The azimuthal equipotential rings are assigned to 1D modes as expected for TI NWs from theory7372 nanobeams2Te3 NWs is up to 77%, increasing with decreasing diameter. Thus the surface states in TI NWs can not only dominate at low temperatures, but also play a significant role in their electrical transport at room temperature. Combining this high surface contribution with control over the structural and/or chemical disorder to modulate the NWs’ local potential energy landscape might pave the way to practical TI NW devices at room temperature for thermoelectric, spintronic, or quantum computing application3We have observed azimuthal equipotential rings on the surface of TI Bi2Te3 nanowires with different diameters were fabricated via pulsed electrodeposition inside of porous alumina templates with three different porous diameter2Te3 nanowires is described in ref. BiThe composition and crystalline orientation of the nanowires was carried out with a transmission electron microscopy (TEM) in the Molecular Foundry at the Lawrence Berkeley National Laboratory (LBNL) . X-Ray dx by plasma-enhanced chemical vapor deposition (PECVD) was used to obtain an insulating substrate. Afterwards, we deposited 5 nm of chromium (Cr) and 200 nm of gold (Au). The focused ion beam (FIB) was used to draw a 1 μm gap coil/line that divides the deposited gold in two parts, each serving as an electrode. Gallium ions were used at an aperture of 60 μm, an intensity of 120 pA and a dose of 25.5 mC/cm2. In −6 mbar, a reservoir temperature for the tungsten of 60 °C, an aperture of 30 μm, current of 18 pA, a refreshing time of 0.015 ms and a dose of 400 mC/cm2.A Si wafer (1–10 Ω·cm) on which we deposited 300 nm of SiOTo resolve the surface potential  of the NWs and measure the electrical conductivity of the nanowires, we used a home-built KPM setup, which measures topography and surface potential distribution simultaneously, in a single-pass, frequency modulation mode.How to cite this article: Muñoz Rojo, M. et al. Spatial potential ripples of azimuthal surface modes in topological insulator Bi2Te3 nanowires. Sci. Rep.6, 19014; doi: 10.1038/srep19014 (2016).


It is often desirable to estimate the concentration needed to achieve a particular effect, or potency, for each chemical tested in an assay. Potency estimates can be used to directly compare chemical profiles and prioritize compounds for confirmation studies, or employed as input data for prediction modeling and association mapping. The concentration for half-maximal activity derived from the Hill equation model  is the most common potency measure applied in pharmacological research and toxicity testing. However, the AC50 parameter is subject to large uncertainty for many concentration-response relationships. In this study we introduce a new measure of potency based on a weighted Shannon entropy measure termed the weighted entropy score (WES). Our potency estimator  is defined as the concentration producing the maximum rate of change in weighted entropy along a concentration-response profile. This approach provides a new tool for potency estimation that does not depend on the assumption of monotonicity or any other pre-specified concentration-response relationship. PODWES estimates potency with greater precision and less bias compared to the conventional AC50 assessed across a range of simulated conditions.High-throughput  AUC) of concentration-response curvesAUCWES)Quantitative high-throughput screening (qHTS) assaysAC50 parameter in the Hill Equation modelAC50 parameter estimates the concentration at which a chemical produces the half-maximal response along a sigmoidal curveAC50 estimates for sigmoidal curvesAC50 parameter estimation from the Hill model very unreliable, even with increased sample sizes10AC50 estimates to be accompanied by large standard errors even when one or both asymptotes can be definedThe most common approach used to approximate chemical potency in chemical genomics and large-scale toxicity testing is the In vitro POD estimates have been calculated based on linear interpolation between the two concentrations that lie on either side of the assay detection threshold164A point of departure (POD) represents a concentration derived from observed concentration-response data that is associated with a defined effect. H) describes the average information content in a probability distributionH is computed from the probability distribution obtained from the observed responses and naturally accommodates any concentration-response pattern, not just monotonic trends such as the sigmoidal shape of the Hill equation model.We propose a nonparametric approach based on information theory to improve the precision of compound potency estimation in qHTS studies. Information theoretic concepts were originally developed for communication technology19WES)WES weights responses found within the noise region so that profiles with larger WES scores have greater probability mass  in the detectable region of the assay. Accordingly, the point of departure is found at the concentration where the rate of change in weighted entropy is maximized along the tested concentration range. This new potency estimator is termed PODWES. Unlike the AC50 value, PODWES does not rely on the shape of the profile far removed from the point of departure. Observed concentration-response profiles that lie entirely within the assay noise region are assigned the outcome “undefined”. Profiles which have detectable responses and for which the maximum rate of change in weighted entropy is located at the lowest observed concentration C1, where PODWES must be less than C1 but cannot be estimated from the given data, are assigned the outcome “less than C1”.We define compound potency as the concentration producing the maximal rate of change in entropy. This potency is calculated by finding the maximum first derivative of the entropy measure across the concentration range. However, Shannon entropy does not take into account the uncertainty in response measurements when responses are within the noise region, i.e., measurements that are less than the assay detection limit. We therefore employ a weighted version of Shannon entropy , WES computed at each concentration level (row 2), the first derivative of WES at each concentration level (row 3) and the second derivative of WES at each concentration level (row 4). The concentration at which the first derivative of WES is maximized is indicated by an open square.Chemical-1 is the most potent of the three chemicals shown in AC50 value equal to 1.00 × 10−3 μM, which corresponds to an PODWES of 4.19 × 10−4 μM. Chemical-2 has two clearly defined asymptotes with an AC50 value of 0.1 μM and a calculated PODWES of 0.07 μM. One data point, indicated by an open triangle, was extrapolated in order to find PODWES for Chemical-3, which had an AC50 value of 10 μM and a calculated PODWES of 3.73 μM. In this case, a single extrapolated data point was used in order to calculate the deviation of the estimate of WES at the kth concentration level becomes smaller as the AC50 of a profile increases, but the potency measure PODWES is located at the concentration for which the the rate of change in WES is increasing most rapidly. PODWES calculated from curves generated with the “gain-loss” model given in R0 = 0% and h = 1 for profiles having [1] only an upper asymptote (AC50 = 0.001 μM), [2] both asymptotes well defined (AC50 = 0.1 μM) and [3] only a lower asymptote (AC50 = 10 μM). In the simulations, |RMAX| values were selected as weak (|RMAX| = 25%), moderate (|RMAX| = 50%), and strong (|RMAX| = 100%) activity. A total of 10,000 profiles were generated for each of these nine combinations of AC50 and |RMAX| and the residual errors were modeled as ERROR ~ N  where σi = 5% or σi = 10%. In i = 5%. PODWES estimates were generally more repeatable with confidence interval widths (CIWs) ranging from 1.03 to 1.53 orders of magnitude (OM). AC50 at the same error level ranged from 0.27 to 13.80 OM. The precision of PODWES at σi = 10% was comparable to the levels achieved at σi = 5% for curves simulated under conditions in which “true” maximum response is greater than the detection limit of the assay. By contrast, precision of the AC50 estimator was noticeably lower for σi = 10% compared to σi = 5%.To explore precision and bias of the potency estimates derived from sigmoidal models, we generated 15-point concentration-response profiles from i = 5%, the bias in PODWES estimates was less than about 2.0-fold on the natural scale, ranging from log10Bias = −0.03 (<1.1-fold less than expectation) to log10Bias = −0.27 (<1.9-fold less than expectation). For the same data sets, the bias in AC50 estimates ranged from log10Bias = −0.0002 (<1.1-fold less than expectation) to log10Bias = 1.38 (24.0-fold greater than expected). The estimation bias of PODWES at σi = 10% was similar to the values found at σi = 5%. By contrast, the estimation bias of AC50 was about 10-fold greater for σi = 10% compared to σi = 5% in some instances.As shown in PODWES was less than 1.5 OM at σi = 5% and σi = 10%. By contrast, the AC50 measure could be extremely imprecise for these curve shapes, even reaching 19.78 OM in one case. Similar to the evaluation of Hill model curves, the log10Bias of PODWES did not exceed −0.42 (<2.7-fold less than expectation). The bias of the AC50 metric was often greater than 2.0 OM (or 100-fold).In addition to investigating the precision and bias of potency estimators based on the sigmoidal Hill model, we also investigated the precision and bias of estimators using the “gain-loss” model from in vitro BG1 estrogen receptor alpha (ERα) assay from phase II of Tox21 was used to screen for agonist activity in an ER reporter gene cell line with an endogenous full length ERα. Approximately 10,000 compounds were assayed in three different experimental runs and activity measurements for 15-point concentration-response curves were obtained as luciferase activity readings from the BG1 ERα cell lineWES) and PODWES values were calculated as described here. Ranking profiles based on WES is not based on any pre-specified concentration response model form or direction of responsePreviously, the WES. The corresponding potency value for this compound (PODWES) is assigned a value of “less than the lowest tested concentration” in each run. Gestrinone is a synthetic hormone that elicits an agonistic response of 0.05 ± 0.03 μM (across runs) in this experiment, and is ranked in the top 250 compounds based on WES in each run shown here. As shown in WES and has a corresponding in vitro potency of 17.7 ± 8.8 μM. Finally, the biocide 2-Phenylphenol does not show detectable activity in the assay in any experimental run and is therefore ranked very low based on the WES score in each case. The potency of this compound is assigned the value of “undefined.”10 potency differences between intra-assay duplicates and inter-assay duplicates interrogated across experimental runs. It is expected that duplicated compounds will have a log10 mean ratio of 0, which corresponds to a mean ratio of 1.00 on the natural scale. All duplicated chemicals that had at least one observed response above the assay detection limit were included in the analysis. A shown in 10 potency differences for intra-array duplicates compared to inter-array duplicates as assessed by the median absolute deviation from zero. The dispersion of log10 potency differences is noticeably greater for the AC50 value compared to PODWES, indicating that AC50 values are less reproducibile potency estimates in this experiment.The reproducibility of the potency estimates in this data set was evaluated by calculating logAC50 value as an estimate for compound potency. However, the uncertainty  of these nonlinear parameter estimates can be extremely large and potentially limit the reproducibility of results obtained from qHTS studiesPODWES) provides more precise estimates of potency than typically obtained by nonlinear parameter estimation from the Hill model.High-throughput screening of compounds for biological activity can play a fundamental role in the advancement of drug discoveryCIW for AC50 estimates extended to greater than 13 orders of magnitude for low efficacy compounds at |RMAX| = 25% (see CIW of PODWES was less than 1.53 orders of magnitude (less than 34-fold) for data simulated from a Hill equation model or a “gain-loss” model. The bias in PODWES estimates was less than 2.7-fold (or |log10Bias| ≤ 0.42), and usually less than 1.5-fold. AC50 estimates showed less bias than PODWES for Hill model curves generated with two clearly defined asymptotes, but bias was much greater when the data was generated under other conditions.Regardless of the level of error used to simulate the concentration-response curves, under most circumstances potency measures examined here were subject to empirical confidence interval widths spanning at least one order of magnitude. However, the  25% see . Even soPODWES may be improved by focusing on robust assays with good agreement between compound measurementsAcross-run comparisons of potency can be more variable than within-run comparisons see . HoweverAC50 estimates can be extremely small for compounds with low efficacy or for situations in which one of the asymptotes cannot be established16The repeatability of PODWES does not rely on a pre-specified concentration-response pattern, can be applied to complex response patterns without respect to the direction of response and naturally accommodates missing data into the estimation framework.Each compound in a qHTS assay can be expected to have a distinctive set of parameters governing its response behavior. However, the approach proposed here to estimate potency using This section describes the procedures used to estimate compound potency based on maximizing the rate of change in weighted entropy. Data sets are simulated based on the Hill equation in order to evaluate the precision and bias of estimated potencies across a range of parameter space characterizing qHTS studies. In addition, the new potency measure is applied to an experimental data set assaying for estrogen receptor agonist activity from phase II of Tox21.Similar to previous studies710Ri is a normalized response representing a percentage of the positive control activity at concentration Ci. RMAX is the maximal response, R0 is the minimal response, AC50 is the concentration of half-maximal response, h affects the shape of the curve and ERROR is residual error of the model. The logarithm in AC50 obtained from log1050AC are restricted to positive valuesCi) are based on equivalent log10 concentration spacing ranging from 0.0001 to 100 μM for fifteen-point concentration-response curves. The values of RMAX and AC50 were set to  and , respectively, for a total of 9 different data sets. The R0 parameter was set to 0 and h was set to 1. Other data sets were simulated using a “gain-loss” model defined as the product of two Hill equation models,where RMAX now represents a shared upper asymptote, both bottom asymptotes equal 0, AC50(G) is the concentration of half-maximal response in the gain direction and AC50(L) is the concentration of half-maximal response in the loss directionRMAX, AC50(G) and AC50(L) were set to ,  and , respectively, for a total of 27 different data sets. However, only 12 of these data sets, for which the maximum response (or Peak Response) exceeded the specified detection limit, were included in the analyses here. Residual errors for ERROR ~ N with σ = 5% or 10%, where σ is related to the percent of negative control responses producing variation levels often seen in Tox21 Phase II assays6RMAX = 25%, 50%, or 100% of positive control activity) were simulated for each data set.where  25, 50, 00%,  contains an observed response Ri for each of N concentrations, where Ri corresponds to the response at the ith concentration, Ci. The relative response at Ci is defined as.The weighted entropy score provides a measure of average relative activity across a concentration-response profilepi ≥ 0 and pi define a probability mass distribution based on the magnitude of Ri, where Ri may be positive or negative for activation or inhibition, respectivelyRi, or surprisal of the ith event, is defined aswhere WES) of a substance across N concentration levels is given by the expressionwhere the units of information are in bits. The weighted average entropy across the response profile takes into account the extent of each response compared to the detection limit of the assay. The weighted entropy score . However, when Ri values are less than the assay detection limit of 3σ the weights wi are defined as the ratio of the surprisal frequency for a relative response within the assay noise region  divided by the uncorrected surprisal frequency , or wi = −pi,noiselog2pi,noise/−pilog2pi = (pi/3σ)log2(pi/3σ)/(pilog2pi). Larger values of WES indicate more detectable responses across concentration levelskth tested concentration (Hk or WESk) is computed by considering only the responses Rk =  that are observed within the full concentration-response profile RN.where WES is greatest. This maximum rate of change defines the potency regardless of the direction of change, i.e., irrespective of whether the chemical is an activator or inhibitor. The rate of change in WES across the profile is computed as the derivative of WES with respect to concentration, or 10 units. In mathematical terms, PODWES is located at the concentration with the maximal value of WES using finite difference calculus, a mathematical procedure based on a Taylor series procedure that provides difference formulas for a grid sampled at discrete data pointsundefined”. However, if potency cannot be estimated within the observed response profile but a detectable response is found within data region, finite difference calculus is used to predict the assay response beyond the tested concentration range. This extrapolation continues until PODWES is quantitatively estimated or designated “less than C1” for profiles that have substantial activity at the lowest observed concentration but no quantitative estimate is obtainable  as the concentration along the response profile at which the magnitude of the rate of change in able see . No dataAC50 estimates were determined according to ShockleyPODWES approach was described above and presented in 10 transformed estimates within a generated data set. Bias was calculated by subtracting the “true” value θ of potency estimator U  from the mean of the estimated values of U according to 10 transformation so that the distributions of potencies better approximate a normal distribution with constant errorHow to cite this article: Shockley, K. R. Estimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon Entropy. Sci. Rep.6, 27897; doi: 10.1038/srep27897 (2016).


N-vinylcarbazole) (PVK), as a donor material, to fac-Re(CO)3Cl, as a catalyst acceptor, for its potential application towards CO2 reduction. Photoluminescence quenching experiments reveal dynamic quenching through resonance energy transfer in solid donor/acceptor mixtures and in solid/liquid systems. The bimolecular reaction rate constant at solution–film interfaces for the elementary reaction of the excited state with the quencher material could be determined as 8.8(±1.4)×1011 L mol−1 s−1 by using Stern–Volmer analysis. This work shows that PVK is an effective and cheap absorber material that can act efficiently as a redox photosensitizer in combination with fac-Re(CO)3Cl as a catalyst acceptor, which might lead to possible applications in photocatalytic CO2 reduction.This work investigates the photoinduced energy transfer from poly( The global energy supply depends on limited fossil-fuel resources that produce substantial amounts of the greenhouse gas carbon dioxide upon combustion. This problem has created significant scientific interest in the capture and utilization of carbon dioxide.–3 An encfac-Re(CO)3Cl [Re(CO)3Cl] is known to be an efficient photocatalyst for the reduction of CO2, producing mainly CO, with the aid of a sacrificial electron donor such as triethanolamine (TEOA). The quantum yield (ΦCO) for photocatalytic CO2 reduction with this compound was reported as 0.14 almost 30 years ago by Lehn et al. in 1984. should give a straight line, which is observed for this system to a good degree . is the quencher concentration. Evaluating Equation ×102 gDonor g−1Quencher. In the case of dynamic quenching, for example, in Förster resonance energy or Dexter-type electron transfer, KSV is the product of the true quenching constant kq and the excited state lifetime τ in the absence of the quencher material, according to Equation In Equation , KSV is Equation  for the τF) has been reported to be 5.34(±0.03) ns. until gas saturation and by dissolving the catalyst material, forming a homogenous solution.. For a schematic drawing of the experimental setup for PL quenching in ACN solution, see 102 cavity and Oxford ESR910 continuous flow He cryostat operating between 4 and 300 K. For solutions containing polar solvents, highly absorbing microwave radiation were soaked into quartz capillaries and placed in ESR tubes, otherwise a small amount of the solutions was added directly into the ESR tube. The samples were measured in the dark and under illumination with a halogen lamp to check for light-induced species. The samples were measured at various power levels from 20 μW to 200 mW with a modulation amplitude of 1 Gauss.ESR spectra were recorded on Bruker EMX X-band spectrometer (9.45 GHz) with a rectangular TE


Most future information processing techniques using electron spins in non-magnetic semiconductors will require both the manipulation and transfer of spins without their coherence being lost. The spin–orbit effective magnetic field induced by drifting electrons enables us to rotate the electron spins in the absence of an external magnetic field. However, the fluctuations in the effective magnetic field originating from the random scattering of electrons also cause undesirable spin decoherence, which limits the length scale of the spin transport. Here we demonstrate the drift transport of electron spins adjusted to a robust spin structure, namely a persistent spin helix. We find that the persistent spin helix enhances the spatial coherence of drifting spins, resulting in maximized spin decay length near the persistent spin helix condition. Within the enhanced distance of the spin transport, the transport path of electron spins can be modulated by employing time-varying in-plane voltages. Spin-orbit effects in non-magnetic semiconductors allow for the manipulation of electronic spins in the absence of an applied magnetic field. Here, the authors exploit a persistent spin helix state in single quantum wells to enhance the coherence length of electronic drift transport. Transporting electron spins in spin–orbit coupled systems is the key to developing future spintronics devices and spin-based information processing12The two-dimensional electron systems in semiconductors are promising for manipulating travelling spins with controllable SOIs. This is not just because of their high mobility, which leads to high-speed spin transfer, but also because of the ability to tune the strength of the Rashba SOI6et al.11Here we demonstrate the drift transport of electron spins in the PSH state, which has so far only been studied without lateral electric fields89k-linear term of the SOI Hamiltonian for this system is written as HSO=(α−β)kyσx−(α+β)kxσy, using a coordinate system with base vectors α and β represent the strengths of Rashba and Dresselhaus SOIs, respectively. In the balanced condition where α=β (α=−β), the effective magnetic fields become unidirectional along y(x) and proportional to kx(ky), respectively. This results in the appearance of a helical spin mode or PSH that is robust against spin-independent scattering. An optical technique using the magneto-optic effect enables us to access the helical mode through the distribution of the z-component of the spins, which forms stripe patterns in a two-dimensional plane9We used two-dimensional electron systems in (001) III–V semiconductor quantum wells (QWs), which provide SOIs suited to the direct observation of a PSH. The dQW thicknesses of 15 and 25 nm) embedded in a high electron mobility transistor structure. The energy band structures calculated by the k·p perturbation theoryα and β, which are close to the PSH condition (α∼β) for the 25-nm-thick QW, whereas they are far from the PSH condition (α<β) for the 15-nm-thick QW. The samples were grown by molecular-beam epitaxy and fabricated into cross-shaped mesa structures with four ohmic contacts (Vx or Vy) creates electric fields to transport electrons in the QW plane, while the top gate voltage (Vg) allows us to tune the Rashba SOI strength simultaneouslyTo investigate the drift transport of helical spin states, we designed two samples, each of which contains a single GaAs/AlGaAs QW = on the QW, and a linearly polarized light probes the magneto-optic Kerr rotation θK, which is proportional to the spin density at the focused position. For the microscopic measurement of spin coherence, the pump and probe lights were focused on the sample with 6 and 3 μm diameter spots, which are smaller than the typical spin precession length resulting from an SOI (∼20 μm) estimated for the present QWs.We employed Kerr microscopy to measure the spin dynamics of drifting electrons. A circularly polarized pump light from a continuous wave (cw) Ti:sapphire laser generates electron spins oriented in the θK along the x-axis measured for different in-plane voltages Vx and a fixed Vg of −4.45 V applied to the 25-nm-thick QW. The symmetrical diffusion of the spins at Vx=0 V shows the spin precession caused by the SOI, as reported previouslyx|≲10 μm. When we increase |Vx|, the signal becomes asymmetric, indicating that spins flow in the electron drift direction. The Kerr signals observed under in-plane electric fields provided real-space imaging of the drifting spins in a steady state, whose dynamics have been previously investigated in QWs only with a Dresselhaus SOI by spin grating spectroscopy11Vx=0 V, thus enabling us to determine the SOI strengths more precisely.The application of an in-plane electric field changes the spin transport regime significantly. Vg=−5.0 V =exp(−d/ls) cos(2πd/LSO), where d is the distance from the origin, ls is the spin decay length caused by the spin diffusion and/or decoherence, and LSO is the spin precession length in which electron spins rotate a full cycle. As shown in For a quantitative analysis of the spin dynamics during drift transport, we extracted two parameters that characterize the two-dimensional distribution of the Kerr rotation signal. We fitted the cross-sectional profile of the data with a function ℏ and m* are Planck's constant and the effective mass of an electron, respectively. The LSO obtained by the fitting and SOI parameters calculated from α is tuned by the gate voltage and has the same value as β at Vg=−4.28 V. We also found that there was a slight variation in β=γ<kz2>, where γ is a material constant, when modifying the gate voltage, which is due to the gate voltage dependence of the wavenumber <kz2> quantized in the z-direction.where The spatial evolution of spins observed in si is the i-th component of the spin vector, Ds is the spin diffusion coefficient, v is the drift velocity, and where qi is the wavenumber of the spin mode in the x and y directions and E is an identical matrix. where q1=2m*(α+β)/ħ2 and q2=2m*(α−β)/ħ2. The spin distribution in real space can be obtained from the inverse Fourier transform of the solution of kF and drift wavenumber kd=m*vd/ħ are estimated to be 107 m−1 or smaller for the condition used in the experiment, thus the cubic term (Ωcubic=γk3/ħ) should be one or two orders smaller than the linear term (Ωlinear=2βk/ħ). The Ds values were estimated from the lateral broadening of the Kerr rotation signal for the spins drifting in the x direction  spin relaxation caused by PSH also appears in the spin transport length. It should be noted that the fact that the spin decay length in the experiment is shorter than in the simulation could be due to spin decoherence caused by the cubic Dresselhaus term although its magnitude is one order smaller than the linear Dresselhaus term.Our experimental and calculated results show that the PSH condition is favourable for long-distance drift spin transport, which agrees with the prediction by random walk theoryf in the y direction and dc voltages x direction. To excite spins at the timing of a certain phase of f or f/2, whereas the Kerr signal was detected with a probe light from a cw laser. When a sinusoidal voltage was applied in the transverse direction of the drift spin transport, a clear spin flow along a winding path was observed  semi-insulating GaAs substrate. The QW was located 120 nm below the surface, whereas three δ-doping layers were embedded 50, 100 and 105 nm below the surface. The epitaxial wafers were fabricated into cross-shaped structures with four ohmic contacts consisting of AuGe (260 nm) and Ni (40 nm) layers. The central part of the structure was covered with a 7-nm-thick Au gate electrode through which we could obtain the optical signal from the QW.The samples were 15- and 25-nm-thick modulation-doped Alz||[001] direction was generated with a circularly polarized pump light (100∼275 μW average power) from a cw Ti:sapphire laser and detected with a linearly polarized probe light (1.0∼6.8 μW). To excite spins at the timing of a certain phase of f of the in-plane electric field, whereas the Kerr signal was detected with a probe light from a cw laser. The wavelengths of both the pump and probe lights were adjusted to maximize the Kerr signals for each QW and gate voltage. The polarization of the pump light was modulated between left- and right-circular polarizations at 50.1 kHz, and the probe light was chopped with an acousto-optic modulator at 52.0 kHz. The difference frequency (1.9 kHz) was used as a reference for lock-in detection. The full width at half maximum spot size of the normally incident probe beam was ∼3 μm, whereas the waist size of the obliquely incident pump beam was 6 μm and its spot on the sample was slightly elongated in the We employed spatially resolved Kerr rotation microscopy in which spin polarization along the How to cite this article: Kunihashi, Y. et al. Drift transport of helical spin coherence with tailored spin–orbit interactions. Nat. Commun. 7:10722 doi: 10.1038/ncomms10722 (2016).