Abstract Political participation (POP), social participation (SOP), and political interest (PI) are important indicators of social status and social inequality. Previous studies on related trait differences yielded genetic and environmental contributions. However, focusing on adult samples, classical twin designs, and convenience samples often restricts parameter estimation and generalizability, and limits the understanding of age differences. We investigated sources of variance in POP, SOP, and PI in late adolescence and early adulthood with an extended twin family design (ETFD). We analyzed data from over 2,000 representative German twin families. Individual environments not shared by family members reflected the major source of variance for all variables, but genetic influences were also pronounced. Genetic effects were mostly higher for young adults, whereas effects of twins’ shared environment were significant in adolescence. Our study deepens the understanding of the interplay between genetic and environmental factors in shaping differences in young persons’ integration in society.

Citation: Kornadt AE, Hufer A, Kandler C, Riemann R (2018) On the genetic and environmental sources of social and political participation in adolescence and early adulthood. PLoS ONE 13(8): e0202518. https://doi.org/10.1371/journal.pone.0202518 Editor: Huiping Zhang, Boston University, UNITED STATES Received: February 8, 2018; Accepted: August 3, 2018; Published: August 24, 2018 Copyright: © 2018 Kornadt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The full TwinLife dataset and materials can be downloaded by the scientific community at http://dx.doi.org/10.4232/1.12665. Funding: The TwinLife study is supported by a grant from the German Research Foundation (www.dfg.de) awarded to Rainer Riemann (RI 595/8-1), Martin Diewald (DI 759/11-1) and Frank M. Spinath (SP 610/6-1). Maritin Diewald and Frank Spinath are Co-PIs on this award but are not authors on the present study. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Individuals’ participation in the political decision process (political participation, POP) and participating in societal institutions like volunteer organizations (social participation, SOP) are indispensable features and pillar stones of modern democratic societies [1]. By voting for their preferred party or candidate or by participating in rallies and protests, people attempt to influence the direction of policies in their countries or municipalities [2]. More individually, POP for example fosters perceptions of political efficacy [3, 4]. Furthermore, the integration into social groups increases social networks and support. SOP is also instrumental in gaining opportunities, skills, and knowledge for further civic engagement, and promotes a sense of integration in the community [5–8]. So even though POP and SOP focus on somewhat different behaviors and outcomes, they both indicate whether people are included, can partake and participate in or profit from social and political processes. Therefore, it is highly relevant to understand the underlying factors of individual differences in POP and SOP, as well as political interest (PI), which represents the intrinsic motivation and a prerequisite to POP [2, 9, 10]. Since people differ strongly in interests and engagement in politics or social groups, a large body of research has tried to identify what sources determine individual differences in SOP, POP, and PI. Environmental factors such as parental and peer socialization or socioeconomic resources have been considered, since parents and their children show pronounced similarities in those characteristics [11, 12]. This seems like a strong argument for the existence of variation in parental socialization of SOP, POP, and PI. However, focusing exclusively on environmental explanations of individual differences does not take into account that children are not only subjected to their parents as role models and the environments provided by the parents, but also inherit their parents’ genes. Thus, individual differences between families and similarity between parents and children within families might be due to both socialization and (or) genetic transmission. Moreover, genetic and environmental influences are not independent, but show patterns of correlation and interaction: For example, the parents’ genetic makeup is related to the environments they provide for their offspring (passive gene-environment correlation, e.g. [13]). Genetic and environmental explanations of individual differences can thus be seen not as mutually exclusive but rather intertwined and integrative [11, 14–17]. Considering the role of genes and their interplay with the environment is necessary to fully understand the etiology of individual differences in POP, SOP, and PI. In support of this claim, previous studies that investigated genetic and environmental variance components have found evidence for both influences (for overviews, see e.g., [18, 19]). Heritability estimates (i.e., the degree to which individual differences are due to genetic variance) for POP typically range between .30 and .60 [14, 16, 17, 20] in adult samples. Regarding the environmental contribution, environmental factors not shared by family members acting to decrease the similarity of family members (i.e., non-shared environmental effects) were strongest, whereas shared environmental effects (i.e., factors making people from the same family more similar) are considerably smaller or even negligible. Similar results were found for individual differences in acts of civic engagement, volunteering, charitable giving, and social activities [15, 21]. However, we are not aware of behavior genetic studies that have looked at the genetic and environmental contributions to the variance in SOP operationalized as integration in a variety of social groups. This concept of SOP taps into a special form of social participation aimed at social integration per se and not necessarily at civic duty or social responsibility compared to previous studies interested in civic engagement and volunteerism in a narrower sense [15, 21]. Individual differences in PI also seem to be primarily due to genetic differences and variation in individual environmental experiences, with only small effects of the shared environment [14, 22], leading Klemmensen et al. [9] to the conclusion that PI might be a dispositional trait and part of a person’s political personality. Since gene expression and societal circumstances of an individual’s SOP and POP develop and change over the life span, considering age differences is crucial when investigating genetic and social transmission within families [23]. This is especially true for adolescence (roughly the age from about 12 to 18 years [24]) and young adulthood (roughly the age from 18 years until the late 20ies [25]), since they can be considered as “developmental periods of substantial flux in gene expression and emerging environmental opportunities” ([26], p. 424). During adolescence, children most commonly live together with their parents and siblings, and therefore, are more likely directly influenced by environments shared by family members, such as the living environment, parental expectations and norms, or the household’s socioeconomic status. Thus, individual differences in adolescent political activities and interests might be more strongly influenced by parents and siblings. During emerging adulthood, children have more freedom in their life conduct, and thus shared environmental influences on individual differences may decrease [19]. This may be accompanied by an increased expression of young adults’ individuality including their genetic predispositions, and thus empirically, an increase in population estimates of genetic variance can be observed, since young adults are attracted to specific environments that match their heritable traits (active gene-environment correlation; [13]). Indeed, the meta-analysis by Bergen et al. [26] shows that for most behavioral phenotypes, genetic variance increases from adolescence to young adulthood. These age differences might be especially relevant for our variables of interest, since adolescence and young adulthood are critical periods in the formation of political and civic behavior [27]. Furthermore, emerging adulthood also marks a period when people come of age and thus have the legal opportunities to participate in more and different political and social activities (for social activities such as sports participation, see also [28]). Previous behavior genetic studies on traits related to SOP, POP, and PI that take age or developmental differences into account indeed found that genetic and environmental contributions to variance depend on participants’ age: Eaves and colleagues [29] investigated a large sample of twins aged 9 to 75 years and showed that the shared environment contributed to a large degree to the variance in conservatism under the age of 20, whereas genetic effects were most important in adulthood. Hatemi and colleagues [23] reported that whereas genetic influences on individual differences in political attitudes (liberalism-conservatism) were negligible in childhood (around age 10), and environmental effects (shared and non-shared) most pronounced in adolescence (around age 17), genetic effects became substantial starting in young adulthood (around age 21). Investigating social attitudes (religiousness and conservatism) in an adoption study of adolescents aged 12 to 15 years, Abrahamson and colleagues [30] also found strong influences of the shared environment and fewer genetic influences during this age period. Besides these findings concerned with more attitudinal aspects of political and social traits, age group comparisons for SOP, POP, and PI are absent. The goal of our study was to use a sophisticated behavior genetic design and a large sample to illuminate the genetic and environmental sources of individual differences in SOP, POP, and PI in two cohorts of individuals in critical developmental periods and compare the resulting estimates. Previous genetically informative studies on SOP, POP, and PI have mostly relied on classical twin designs (CTD). Including only the twins in the analyses means that there is not enough information in the data to estimate genetic and environmental transmission between generations, specific age-dependent environmental sources of siblings’ similarity, and gene-environment interplay. Including siblings and parents of the twins allows for the estimation of these effects and thus helps us to better understand refined genetic and environmental sources and their interplay [31]. Our study thus applies this method to study the sources of individual differences in SOP, POP, and PI. It allows us to disentangle genetic from environmental effects due to direct parental transmission, different aspects of siblings’ environments, and environments specific for different family members, as well as effects due to passive gene-environment correlation. Drawing on previous, established literature, we assume strong genetic contributions to variance in POP and PI. Furthermore, we include a measure of SOP for which behavior genetic results have to our knowledge not been reported. The inclusion of these three distinct but related variables broadens the focus of previous research towards a more comprehensive picture on political and social integration and participation. This is especially relevant, since we can base our analyses on a large population-based sample from Germany representing the whole spectrum of socioeconomic status in the country and considerably less biased in terms of over-representing highly educated, high-income families [32]. In addition, we were also interested in age-group differences in genetic and environmental influences and hypothesize that genetic variance will be larger for young adults compared to adolescents, indicating a more important role of active gene-environment correlation in adulthood.

Materials and methods Sample We used data from the TwinLife study, a study of twins and their families in Germany. TwinLife is designed to understand the development of social inequalities over the life course, and captures a variety of constructs related to life chances in childhood, adolescence and early adulthood. It combines a prospective multi-cohort cross-sequential design with an extended twin family design (see analyses section), and the sample is based on a national probability sample. Since there is no central twin register in Germany, same-sex twin pairs from four birth cohorts (5-, 11-, 17-, and 23-year old at the first measurement point) were identified via local registry offices. The sophisticated sampling and recruitment procedure is described in detail in [33]. The goal was to obtain a large sample from each birth cohort within a broad range of the population, and about equal proportions of MZ and DZ twins. Thus, a two-step national probability-based sampling procedure was implemented. First, a sample of communities within Germany was drawn to generate the addresses. Then, for each of the interesting cohorts, individuals with the same sex born at the same day and living at the same address were identified (a slightly different procedure was used for the oldest cohort, due to the low probability that they still live at the same household, for details, see [33]). From these samples of addresses, subsamples for each cohort were drawn and the twins and their families were contacted by a social survey research institute. If they agreed to participate, they were visited in their homes by a trained interviewer, who distributed the research instruments. Due to the sampling method and design, the TwinLife sample is representative of German families that have multiple children, for example with respect to region of residence and the size of the communities where the household is located. The sample is also representative in terms of German citizenship status on the household level, highest educational and occupational status of parents in the household and monthly net equivalent household income in euros, and therefore covers the whole range of socioeconomic structure in Germany [33] Further details regarding the TwinLife rationale, the study design, recruitment strategies, data collection, and zygosity determination can be found in [32, 33]. The TwinLife data set is open source and can be obtained for research projects (http://dx.doi.org/10.4232/1.12665, see also http://www.twin-life.de/en). The TwinLife study was approved by the Ethics Committee of the German Association of Psychology. For the current analyses, we included cross-sectional data from the oldest two cohorts of twins, born 1990–1993 (Cohort 23, C23) and 1997/1998 (Cohort 17, C17) respectively, and if available, their parents and siblings. This selection was made since detailed data on SOP and POP was only available for participants aged 13 and older (Since PI was assessed already from age 10, the sample includes some younger siblings for this variable). Our sample consisted of N = 2042 full twin pairs, this sample size provides sufficient power for our intended analyses [34]. We also included biological mothers and fathers, as well as one full sibling into our design if available, thus the maximum of included participants per family was five. A detailed sample description of the subsample is presented in Table 1. PPT PowerPoint slide

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larger image TIFF original image Download: Table 1. Sample description. https://doi.org/10.1371/journal.pone.0202518.t001 Measures Political participation. POP was assessed with three items that capture different activities indicative of political participation: “Which of the following activities did you take part in within the last 12 months?” 1) Taken part in a political meeting/a discussion event/a demonstration; 2) Taken part in an online-petition/a signature collection; 3) Boycotted a company or products for political or ethical reasons or on environmental grounds, e.g. did not buy products or avoided them (cf. [20]). All items were answered with yes or no and combined into an additive index, with higher values indicating higher POP [15]. Cronbach’s alpha across all family members was .584, which is consistent with results for similar scales in the previous literature [11] and acceptable for such a brief scale summing rather heterogeneous behaviors subsumed under the multifaceted construct of POP [1]. Political interest. PI was assessed with one item “Generally speaking, how interested are you in politics”. Participants rated this question on a four-point scale (1 –not interested at all, 2 –not strongly interested, 3 –strongly interested, 4—extremely interested). Social participation. To measure participants SOP, we assessed the amount of activity in different groups or clubs; items were adapted from the German Youth Survey [35]. “In the following you see a number of groups one can be active in. Please indicate to what extent you are active in these respective groups. 1) Sports association or club; 2) Choir/music or theatre group or similar; 3) Church or religious group; 4) Trade union/professional association/student council; 5) Voluntary fire and rescue services/technical relief association THW/DLRG German Lifesaving Association etc.; 6) Local history association/Citizens association/Shooting club ("Schuetzenverein"); 7) Other associations/federation”. One item concerning membership in a political organization was not included in the analyses, in order not to confound measures of SOP and POP. For each group, participants had to indicate how often they were active by answering on a four-point scale with 1 –every week, 2 –every month, 3 –less than once a month, 4 –never. Answers were combined to a composite score, with lower values representing more frequent activities. Extended Twin Family Model analyses Since we had data not only for the twins, but also their parents and a sibling, we were able to derive our estimates from an Extended Twin Family Design (also Nuclear Twin Family Design; [36]). The ETFD has several advantages over the CTD (see [31], for a detailed overview) and allows a refined and more precise estimation of different genetic and environmental components. The full models for MZ and DZ twins are displayed in Fig 1. The ETFD separates additive genetic influences (a)―that is, effects due to different segregating genes given from parents to their offspring (50% from the father and 50% from the mother), which “add up” to affect phenotypic differences―from non-additive genetic influences, such as effects due to multiple interactions among different gene variants across different gene loci (epistasis, i). Whereas additive genetic effects are shared between relatives and act to increase their similarity as a function of their genetic relatedness, non-additive influences due to epistasis are completely shared only by MZ twins but not by other family relatives. They thus act to make MZ twins more similar but all other relatives less similar to each other. Both effects can be estimated in the presence of direct maternal (m) and paternal influences (f) from parents to offspring (i.e., the vertical transmission/socialization from parents to offspring that is shared by siblings and thus acts to make the children more similar) and non-shared environmental influences (i.e., environmental effects that are not shared by family members and thus act to make the children less similar, e, including error of measurement). This goes beyond the possibilities of the CTD and ameliorates the fact that, if certain effects are present but not taken into account in the model, estimates of variance components will be biased [31]. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 1. Extended Twin Family Model for monozygotic twins (upper part) and dizygotic twins (lower part). a = additive genetic effects; i = non-additive genetic effects due to epistasis; e = non-shared environmental effects including error of measurement; m = mother-specific environmental effects; f = father-specific environmental effects; c s = sibling-specific shared environmental effects; c t = twin-specific shared environmental effects; μ = assortative mating. https://doi.org/10.1371/journal.pone.0202518.g001 The inclusion of twins’ parents further enables the estimation of the contribution of assortative mating―the similarity of parents regarding the trait of interest (μ)―to the offspring’s similarity. Spousal similarity due to active assortment has been found to be especially pronounced for example in political attitudes (e.g., [37, 38]) and might also be true for POP and PI [39]. If assortative mating is present and not accounted for, shared environmental effects will be overestimated in a CTD, whereas additive genetic effects will be underestimated [31]. The model of twins and their parents thus enables to estimate the total variance component due to environmental transmission from both parents to offspring (m2 + f2 + 2mfμ). A further advantage of the inclusion of the twins’ parents is that it allows for the estimation of the contribution of passive gene-environment covariation [40] to individual differences. Passive gene-environment covariation is the covariance between parents’ genetic makeup and the parental environment shared by the offspring, which means that the environment parents create for their offspring is correlated with the genetic makeup the children also inherit from their parents. For example, politically interested parents might discuss politics at home and encourage their children’s political activities, creating an environment that further fosters high political interest in their children. Passive gene-environment covariation can be estimated as the covariance between additive genetic effects and parental environment taking assortative mating into account: a2m(1 + μ) + a2f(1 + μ). Besides adding the parents to the design, the additional inclusion of a full non-twin sibling of twins also increases the power to detect common environmental effects [41–43] and in addition makes it possible to disentangle environmental effects shared by non-twin siblings within an offspring generation (c s ) from twin-specific, and thus age-related shared environmental influences (c t ). For more advantages of the ETFD see [42]; for a detailed description of the parameters see [31]. Correlation analyses were based on scores corrected for linear age effects and sex differences and computed with SPSS 23. For the ETFD analyses, the unstandardized residual scores corrected for age and sex differences were entered into the models [44]. Four-group (2 zygosity groups × 2 age groups) structural equation models were conducted with AMOS 24 [45]. We identified the best fitting model via χ2-difference tests and the most parsimonious model by dropping the non-significant effects that did not lead to a decrease in model fit. Parameters are derived from the respective best-fitting, most parsimonious model. Differences between the cohorts were tested by setting the respective parameters equal and reviewing subsequent changes in model fit. Estimates were derived on the basis of full information maximum likelihood (FIML) procedures to analyze all available data and handle missing values due to a missing family member within specific families [46].

Results Bivariate and family correlations Bivariate correlations between the three variables ranged from rather low to high, the mean correlations (averaged over all family members) for SOP and POP were r = -.18, for SOP and PI r = -.13, and for POP and PI r = .45 (all single correlations were significant at p < .001). The univariate correlations between family members for the variables of interest are presented in Table 2 (as additional information, Twin-Cotwin correlations for male and female participants are presented in S1 Table). Since the degree of genetic relatedness between family members is known, comparing phenotypic correlations for different family members already gives a first indication regarding the effect of genes and the environment on individual differences in SOP, POP, and PI. In general, almost all correlations were significant. MZ twin correlations were highest, compared to all other correlations between family members that share on average only 50% of segregating genes (i.e. DZ twins, siblings, parents). Since MZ twins are 100% genetically related, the highest similarity of MZ twins compared to other family dyads indicates the presence of genetic effects. However, MZ correlations were below 1, which indicates non-shared environmental contributions to the variance. Furthermore, in the younger cohort, the DZ correlations tended to be more than half of the MZ correlations, which indicates the influence of shared environments. Indeed, and more specifically, in this cohort, the DZ correlations were on average higher than parent–child and sibling–twin correlations, which speaks for a twin-specific environmental influence that act to increase the similarity of same-aged siblings. Correlations between parents were also significant, indicating a medium high degree of assortative mating, except for a smaller correlation for PI. PPT PowerPoint slide

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larger image TIFF original image Download: Table 2. Family correlations for all dyads. https://doi.org/10.1371/journal.pone.0202518.t002 Extended Twin Family Model estimates In order to obtain stable parameter estimates, we identified the structural equation model (SEM) that fit the correlations between family members for each construct best while at the same time being the most parsimonious with regard to the number of parameters that had to be estimated. Therefore, we started with two baseline models in which either epistasis or sibling-specific shared environmental effects was set to zero (i = 0 or c s = 0), because i and c s cannot be estimated simultaneously in the present model. These non-nested models were descriptively compared by using the Comparative Fit Index (CFI) and Akaike’s Information Criterion (AIC). Larger CFIs and smaller AICs indicated a better model fit [47]. We then stepwise dropped non-significant parameters and checked for significant decrease in model fit as indicated by χ2-difference tests. A detailed account of model comparisons is presented in Tables A-C in S1 File. In the best fitting, most parsimonious model for SOP, sibling-specific shared environmental effects were set to zero (baseline model, χ2 (45) = 113.133, p < .001; CFI = .935; RMSEA = .027). For POP, the model in which epistasis was set to zero in addition to sibling-specific effects fit the data best (χ2 (47) = 43.82, p = .61, CFI >.999, RMSEA < .001; Δχ2 = 2.603, Δdf = 2, p = .272). For PI, epistasis, sibling-specific, paternal and twin-specific shared environmental effects were dropped from the full model (χ2 (51) = 72.282, p = .03, CFI = 0.966, RMSEA = 0.014; Δχ2 = 7.961, Δdf = 6, p = .241). Standardized path estimates for model parameters are presented in Table 3, the resulting variance components in Table 4. Unstandardized path estimates and their respective confidence intervals are presented in S2 Table. PPT PowerPoint slide

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larger image TIFF original image Download: Table 3. Standardized path estimates for model parameters derived from the best fitting, most parsimonious ETFD model. https://doi.org/10.1371/journal.pone.0202518.t003 PPT PowerPoint slide

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larger image TIFF original image Download: Table 4. Variance components derived from the best fitting, most parsimonious ETFD model. https://doi.org/10.1371/journal.pone.0202518.t004 The best fitting models for SOP, POP, and PI yielded significant additive genetic effects (a). Besides this general pattern, cohort differences in the size of the genetic coefficients emerged (see Tables A-C in S2 File for detailed model comparisons). For SOP, non-additive genetic effects were larger for C23 than for C17 (explaining 16 vs. 3% of variance, respectively), even though this difference was not significant. A similar and significant age trend was visible for POP, where additive genetic effects were responsible for 25 vs. 46% of variance in C17 and C23 respectively, whereas for PI, additive genetic effects were similar for the two cohorts (44% and 48% of explained variance, respectively). In addition to the additive genetic effects, environmental effects were significant in all models. They manifested most strongly in non-shared environmental effects (e), which make participants growing up in the same family less similar, but also include measurement error. These non-shared environmental effects explained between 28 and 52% of variance in SOP, POP, and PI, respectively. The effects tended to be larger in the older cohort for POP, whereas no significant cohort effects were detected for the other variables. Influence of the shared environment, i.e. those environmental effects that make participants from the same family more similar to each other, was smaller than for the non-shared environment. We found evidence for effects of the environment that specifically act to increase the similarity of twins (c t ) in C17 but not in C23 for SOP and POP, indicating that this environment might decrease in importance as the twins get older. Environmental transmission effects of the parents, i.e. parental socialization effects (m, f) were mostly small or non-significant in both age groups and for all variables, except for SOP in both cohorts and, interestingly, for POP and PI in C23. Assortative mating (μ) was also present for all cohorts and variables providing evidence for the fact that with regard to their SOP, POP, and PI, partners were more similar than expected by chance. Coefficients for passive gene-environment covariation were positive and explained 14% of variance in C17 and 9% in C23 for SOP, 7% of variance in C23 for PI and 6% of variance for C17 in POP. This indicates that children in these age groups with a genetic predisposition to develop higher SOP, POP or PI, respectively, also grow up in environments that positively contribute to these traits.

Discussion The goal of our study was to disentangle genetic and environmental contributions to variance in SOP, POP, and PI with the help of a twin family design that yields information beyond the possibilities of the CTD and also allows for more confident estimates. Furthermore, our results go beyond previous studies by including a large twin sample from two age groups in crucial developmental periods that is representative of families with multiple children in Germany. Our results support previous studies showing that individual differences in POP and PI seem to be partly due to genetic differences [19], with up to almost 50% of phenotypic variation explained by genetic variation. Genetic effects were also found for SOP, albeit to a slightly smaller degree. This finding is especially notable since behavior genetic studies on social integration are rare. Genetic influences on the variance of these variables might be mediated by individual differences in a genetically affected inherent motive to affiliate with others in an organized way and to exert influence in a community. However, since it is likely that genetic differences exert their influences on individual differences in attitudes and behaviors in a rather complex way, other possible variables mediating the genetic effects, such as personality traits [48, 49] are promising candidates and should be investigated in future research. As would be expected from the postulates of lifespan theories of gene expression and previous empirical findings [26], genetic effects were larger in the older cohorts for SOP (a2 plus i2) and POP (a2). For PI, however, estimates of genetic and environmental effects remained virtually the same across the two cohorts. This might be due to the fact that already in older adolescence, people have the freedom to show or not show interest in political affairs. It would thus be necessary to include even younger participants in order to investigate the onset of the genetic unfolding within the opportunities provided by individual environments (i.e., active gene-environment correlation). Furthermore, the results might also indicate that PI is a more trait-like prerequisite for continuing participation, developing earlier in life and thus paving the way for more intensive activities later on. However, since our analyses are solely based on cross-sectional data, we cannot make any claims regarding developmental processes. The speculation described above as well as all further discussions on developmental aspects need to be addressed by future research, preferably with longitudinal data and multiple measurement points that allow for the investigation of developmental and age-related processes. As in previous studies on various other political traits [19], we found that individual experiences and exposure to environments not shared by family members are major contributors to individual differences in all three variables of interest, explaining a substantial part of variance in the respective variables (including variance due to error of measurement). Thus, individual factors such as peers, individual media exposure but also idiosyncratic experiences are candidates that might shape people’s participation and make them different from their family members. Our study design allows for a refined and differentiated look at the contribution of environments shared by family members, so we could go beyond previous findings showing effects of the environment shared by twin siblings (but not by other family members) on individual differences in political attitudes in adolescence [23]. We found that the twin-specific shared environment but not the environment shared by the twins and the non-twin siblings plays a role for the variation of SOP and POP in younger participants. This might be shared friends, similar school and club contexts, and shared social networks. In addition, this includes environmental events of shorter duration that differentially impact children depending on their age. It might be worthwhile for future research to investigate what exactly these environments are made of and which developmental processes might play a role. Including habitational and emotional closeness of twins might also provide further insight. A limitation, however, is our relatively small sibling sample with a wide age range, thus the twin-specific effects warrant replication in future studies. What is eye-catching in this matter is the fact that in almost all cases the impact of variation in the shared environment of any kind is smaller for variation in PI than for the two participation variables. This again might suggest that PI is developed earlier in life and the unfolding of variation in PI is less dependent on within-familial shared social influences in the investigated age. In line with other behavior genetic studies, parental environments had virtually no effects, speaking against the idea of parental socialization as one important source of individual differences in participation in politics and social life beyond genetic transmission. This finding, however, stands in contrast to adoption studies investigating the sources of individual differences in voting turnout and political candidacy, that find effects of pre-birth (i.e., genetic) and post-birth (i.e., environmental) differences to be both important and also similar in size [12, 50]. These studies, however, differ from ours in several aspects that might lead to the divergent findings. Different sample ages, contexts, variable operationalization (e.g., general mass participation vs. elite political behavior), analytical frameworks, and design assumptions complicate direct comparisons. This calls for a multi-method, multi-design approach to understand what exactly drives participation across the life span [50]. In addition, our study explicitly models passive gene-environment correlation (see below). In studies where this is not accounted for, these effects will instead lead to an overestimation of shared environmental effects [31]. For all variables, we found some effects for passive gene-environment correlation: The genetic makeup of the parents covaries with the environments they provide for their children which in turn might influence between-family differences in their children’s traits. Furthermore, since our model only tests shared parental effects affecting all siblings in the same way, the possibility that parental effects might affect siblings differently is not accounted for. Children might perceive or react to the same parental influences in a different way, this would manifest not as environmental effect shared by family members but rather as non-shared environmental effect [40]. Our results thus do not imply that parents do not play a role in their children’s social and political development, but rather suggest that direct parental influences, if at all, only marginally affect an increase in the similarity of offspring regarding POP, SOP, and PI. Some limitations have to be noted when interpreting the presented findings which at the same time point to important directions for future research. Our study does not provide any information on the factors that mediate between genetic differences and the phenotypic expression in SOP, POP, and PI. Genetically influenced individual characteristics such as personality characteristics and cognitive abilities might be interesting candidates that have for example been shown to account for genetic variance in political attitudes [38, 51]. Including these variables goes beyond the scope of our study but should be addressed in future research to understand the genetic sources of variation in POP, SOP, and PI. Another issue is the operationalization we chose for our variables of interest. The POP index showed relatively low reliability, which might increase estimates of non-shared environmental influences and decrease estimates of other sources of variance due to lower correlations between family members. However, the unidimensionality and content validity (being designed to assess a rather hetereogeneous conglomerate of behaviors [1]) of the scale support its use in our study [14, 52]. Besides, our measure of SOP mixes different behaviors of social participation aimed at social integration in general irrespective of their underlying motivation aimed at social contact vs. social responsibility whereas previous studies have focused on civic duty or volunteerism in a narrower sense [14, 21]. Future studies should include other operationalizations of the construct that allow to disentangle indicators of SOP with regard to their motivation (social integration vs. responsibility for the community). Despite these limitations, our study provides confident estimates for genetic and environmental effects on individual differences in three variables related to political and social integration. Our findings attest to the role of both genetic and environmental factors in shaping people’s investment in their political and social surroundings and also gives first ideas about the role of gene-environment interplay. As such, it provides the basis for a better understanding of SOP, POP, and PI at different ages and thus a starting point for developmental investigations.

Supporting information S1 Table. Twin-cotwin correlations for all cohorts and constructs divided by participant sex. Note. MZ = monozygotic twins; DZ = dizygotic twins; C17 = younger cohort; C23 = older cohort; Values in bold print differ between the sexes at p ≤ .05. https://doi.org/10.1371/journal.pone.0202518.s001 (DOCX) S2 Table. Unstandardized path estimates and 95% confidence intervals for model parameters derived from the best fitting, most parsimonious ETFD model. Note. p-values in parentheses. 95% confidence intervals in square brackets. SOP = Social Participation; POP = Political Participation; PI = Political Interest; C17 = younger cohort; C23 = older cohort; a = additive genetic effects; i = non-additive genetic effects (epistasis); e = non-shared environmental effects including error of measurement; m = mother-specific environmental effects; f = father-specific environmental effects; c s = sibling-specific shared environmental effects; c t = twin-specific shared environmental effects; μ = assortative mating. https://doi.org/10.1371/journal.pone.0202518.s002 (DOCX) S1 File. (Tables A-C). Model fitting results. Note. Best fitting, most parsimonious model is indicated by bold print. i = non-additive-genetic effects; f = father-specific environmental effects; m = mother-specific environmental effects; c s = sibling-specific shared environmental effects; c t = twin-specific environmental effects. https://doi.org/10.1371/journal.pone.0202518.s003 (DOCX) S2 File. (Tables A-C). χ2-difference tests for cohort model comparisons. Note. For cohort comparisons, the baseline model c s = 0 was used. a = additive genetic effects; i = non-additive epistasis; e = non-shared environmental effects including error of measurement; m = mother-specific environmental effects; f = father-specific environmental effects; c s = sibling-specific shared environmental effects; c t = twin-specific shared environmental effects; μ = assortative mating. https://doi.org/10.1371/journal.pone.0202518.s004 (DOCX)


Afghan Kilim cushion (2015 Afghanistan)

Dimensions

0.46m wide, 0.46m high

46.00cm wide, 46.00cm high

1.51 feet wide, 1.51 feet high

18.11 inches wide, 18.11 inches high

Description

Description

The kilim cushion cover shows traditional designs. The central design assumes a very distinctive shape. The other figures are organised accordingly. The main colours are green, red, orange and brown. This cover is absolutely unique since the energy of the colours. The cover derives from an authentic hand woven Kilim rug. The Kilim Cushion cover has a strong cotton reverse with zip.

Medium

Medium

wool and cotton

Condition

Condition

new

Price

Price

£45.00

Change Currency

Please note: This is a guide conversion price only as the currency table is only updated every six hours and may vary slightly from the rate used by your card provider. All transactions through this site are in GB£.


/*-
 * <<
 * UAVStack
 * ==
 * Copyright (C) 2016 - 2017 UAVStack
 * ==
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * >>
 */

package com.creditease.uav.feature.runtimenotify.test;

import java.util.Map;

import com.creditease.agent.helpers.JSONHelper;
import com.creditease.agent.log.SystemLogger;
import com.creditease.uav.cache.api.CacheManager;
import com.creditease.uav.feature.runtimenotify.scheduler.RuntimeNotifyStrategyMgr;

public class DoTestLoadRuntimeStrategy {

    public static void main(String[] args) {

        SystemLogger.init("DEBUG", true, 5);

        // CacheManager.build("localhost:6379", 5, 5, 5);

        CacheManager.build("127.0.0.1:6379", 5, 5, 5);

        loadRuntimeStrategy(CacheManager.instance());
    }

    private static void loadRuntimeStrategy(CacheManager cm) {

        Map<String, String> strategyMap = cm.getHashAll(RuntimeNotifyStrategyMgr.UAV_CACHE_REGION,
                RuntimeNotifyStrategyMgr.RT_STRATEGY_KEY);

        System.out.println(JSONHelper.toString(strategyMap));
    }

    @SuppressWarnings("unused")
    private static void cleanAllNCInfo(CacheManager cm) {

        cm.del(RuntimeNotifyStrategyMgr.UAV_CACHE_REGION, RuntimeNotifyStrategyMgr.RT_STRATEGY_KEY);

        loadRuntimeStrategy(cm);
    }
}


Yeah, looks like, this was the wrong solution. Note sure, why it seemed
to work for me (now I can't make it work any longer). How about, this
patch, which should redraw the old window, if the cursor position
changed (thus it should redraw for all your problems mentioned) just
before switching windows.

Yeah, looks like, this was the wrong solution. Note sure, why it seemed
to work for me (now I can't make it work any longer). How about, this
patch, which should redraw the old window, if the cursor position
changed (thus it should redraw for all your problems mentioned) just
before switching windows.


“We were self-quarantining in an empty house owned by Maeve’s mother Kathleen on the Chesapeake Bay, hoping to give our kids more space than we have at home in D.C. to run around,” he wrote. “Gideon and Maeve were playing kickball by the small, shallow cove behind the house, and one of them kicked the ball into the water.”


' Copyright (c) 2015 ZZZ Projects. All rights reserved
' Licensed under MIT License (MIT) (https://github.com/zzzprojects/Z.ExtensionMethods)
' Website: http://www.zzzprojects.com/
' Feedback / Feature Requests / Issues : http://zzzprojects.uservoice.com/forums/283927
' All ZZZ Projects products: Entity Framework Extensions / Bulk Operations / Extension Methods /Icon Library

Imports System.IO
Imports System.Linq

Public Module Extensions_759

	''' <summary>
	'''     A DirectoryInfo extension method that deletes the files where.
	''' </summary>
	''' <param name="obj">The obj to act on.</param>
	''' <param name="predicate">The predicate.</param>
	<System.Runtime.CompilerServices.Extension> _
	Public Sub DeleteFilesWhere(obj As DirectoryInfo, predicate As Func(Of FileInfo, Boolean))
        obj.GetFiles().Where(predicate).ForEach(Sub(x) x.Delete())
	End Sub

	''' <summary>
	'''     A DirectoryInfo extension method that deletes the files where.
	''' </summary>
	''' <param name="obj">The obj to act on.</param>
	''' <param name="searchOption">The search option.</param>
	''' <param name="predicate">The predicate.</param>
	<System.Runtime.CompilerServices.Extension> _
	Public Sub DeleteFilesWhere(obj As DirectoryInfo, searchOption As SearchOption, predicate As Func(Of FileInfo, Boolean))
        obj.GetFiles("*.*", searchOption).Where(predicate).ForEach(Sub(x) x.Delete())
	End Sub
End Module


The favorable prognostic impact of tissue inhibitor of matrix metalloproteinases-1 protein overexpression in breast cancer cells.
The tissue inhibitor of metalloproteinases-1 (TIMP1) inhibits tumor cell invasion and metastasis in experimental models; in addition, TIMP1 is supposed to possess another important function, cell growth promotion. The potential prognostic significance of TIMP1 in breast cancer remains unclear. We evaluated the significance of the immunohistochemical expression of TIMP1 in a well-documented series of 133 infiltrating breast carcinomas by examining any possible statistical association between this expression and numerous clinicopathological parameters as well as patients' disease-free interval. TIMP1 was generally expressed in both stromal and cancer cells in our specimens. TIMP1 was overexpressed in cancer cells of 60.15% of all cases. Tumors of high histological and nuclear grade were found to overexpress TIMP1 less frequently than the rest (p=0.003 and p=0.057, respectively). Interestingly, TIMP1 overexpression was inversely associated with cell proliferation, the latter being evidenced by Ki67 immunoreactivity (p=0.028). TIMP1 immunostaining was in parallel with metalloproteinase-2 (MMP2) immunoexpression in both cancer and stromal cells. Multivariate analysis disclosed that TIMP1 overexpression in cancer cells was an independent determining factor for prognosis (p=0.006); TIMP1 overexpression in malignant cells appeared to correlate with favorable outcome, particularly in patients with lack of nodal metastases and in patients with MMP2-negative immunophenotype (p=0.0252). The upregulation of TIMP1 cancer cell expression in breast cancer may suggest that this marker has a multifunctional role apart from that of metalloproteinase inhibitor since it was found to be related to malignant cells' differentiation and proliferation. TIMP1 overexpression in cancer cells appears for the first time to be a promising indicator of favorable prognosis in breast cancer.


<?xml version="1.0" encoding="utf-8"?><topic xmlns="http://wsdev.schemas.microsoft.com/authoring/2008/2">
<whitepaper_page><metadata id="fa4989ea-bf89-4ea7-bfdb-9c7ed4811701" build_format="rfc" section_number="1.288"><title>Attribute rangeLower</title>
<tech value="protocol" />
<index /></metadata>

<section_contents>
This attribute specifies the minimum value or length of an attribute.

<example><snippet type="syntax">cn: Range-Lower
ldapDisplayName: rangeLower
attributeId: 1.2.840.113556.1.2.34
attributeSyntax: 2.5.5.9
omSyntax: 2
isSingleValued: TRUE
schemaIdGuid: bf967a0c-0de6-11d0-a285-00aa003049e2
systemOnly: FALSE
searchFlags: 0
isMemberOfPartialAttributeSet: TRUE
systemFlags: FLAG_SCHEMA_BASE_OBJECT</snippet></example>

Version-Specific Behavior: Implemented on <auto_text>adam</auto_text>, <auto_text>windows_server_2008</auto_text>, <auto_text>ad_lds_windows_vista</auto_text>, <auto_text>windows_server_2008_r2</auto_text>, <auto_text>ad_lds_windows_7</auto_text>, <auto_text>windows_server_8</auto_text>, <auto_text>ad_lds_windows_8</auto_text>, <auto_text>winblue_server_1</auto_text>, and <auto_text>ad_lds_winblue</auto_text>.
</section_contents>
</whitepaper_page>
</topic><?PROP fmtid="{D5CDD505-2E9C-101B-9397-08002B2CF9AE}" pid="2" name="Server" value="10.185.184.7"?><?PROP fmtid="{D5CDD505-2E9C-101B-9397-08002B2CF9AE}" pid="4" name="Project" value="MS-ADLS"?><?PROP fmtid="{D5CDD505-2E9C-101B-9397-08002B2CF9AE}" pid="6" name="TopicTitle" value="Attribute rangeLower [_rfc_ms-adls1_288]"?><?PROP fmtid="{D5CDD505-2E9C-101B-9397-08002B2CF9AE}" pid="8" name="Schema" value="Protocol"?><?PROP fmtid="{D5CDD505-2E9C-101B-9397-08002B2CF9AE}" pid="10" name="CustomField2" value=""?><?PROP fmtid="{D5CDD505-2E9C-101B-9397-08002B2CF9AE}" pid="12" name="DocumentVersion" value="2.0"?>


Drew Barrymore: Her Allure Photo Shoot

The actress posed for her second Allure cover just weeks after giving birth to daughter Olive. Check out these exclusive photos from the shoot, photographed by Carter Smith at the Chateau Marmont in West Hollywood, California.

Barrymore's new husband, art consultant Will Kopelman, has a background that's more traditional than hers. But she has found a relationship that makes perfect sense. "We have opposite experiences, but we're not opposites," she said. "We're both very particular and detail oriented. We love art and music and adventures, and we laugh our asses off together. He's my home now.... It makes me almost want to cry. He's the father of my child, and I'm the mother of his child. I'm finally a part of a family, and it's a miracle." Coat by Rochas.

When Barrymore posed naked for Playboy in 1995, Steven Spielberg sent her a copy of the magazine. "His art department put clothes on me," she recalled. He also gave her a quilt with a note attached: "Cover up." "I was like, 'Yeah, I know, I'm sorry.'" She laughed wryly. "It's embarrassing to go through any rebellious stage in front of people that you love and respect, and yet I'm glad I did. I'm glad I lived such a full life before I settled down into a family, because I got to enjoy it and get it out of my system." Coat by Gucci.

"We wanted the focus of this look to be Drew's beautiful ivory skin," said makeup artist Debra Ferullo, who has been working with Barrymore since 2006 and collaborated with her on Flower, her cosmetics line that launches this month. Ferullo swiped a shimmery copper cream shadow on Barrymore's lids. She skipped eyeliner and applied black mascara, then smoothed primer all over Barrymore's face but added foundation only under her eyes and around her nose and lips to even out her skin tone. On the apples of the actress's cheeks, she applied pink blush. The finishing touch: a dab of rose lipstick.

Hairstylist Renato Campora added ombré extensions to Barrymore's dark hair. "That touch of color is very Drew," he said. He worked a palmful of Fekkai Full Blown Volume Styling Whip through her hair before blowing it out, then added bend to strands around Barrymore's face with a one-and-a-half-inch curling iron. Silk top by Olatz.

When she takes on a project, "I will always commit," said Barrymore. "It's like [my new] makeup line: I can tell you every single detail about how and why we got here. I want to be that kind of mother. I'm that kind of friend. I'm that kind of producer. I do everything in this life-or-death way."

"I don't think I've ever had or will ever have that kind of body where it's like, 'Watch me now! I'm in a bikini!'" said Barrymore. But she can live with that. "I cannot live a life where I'm deprived. I'd much rather be five, ten pounds heavier. With my luck, I'll get myself to that perfect goal weight, and I'll get hit by a bus. Then I'll be like, Shit. I'll be looking at myself from some afterlife, going, 'You idiot. You could have had the agnolotti, dummy.'" Here, she previews cover images with Allure's Paul Cavaco and photographer Carter Smith.

Barrymore's ex-boyfriend, Strokes drummer Fabrizio Moretti, is currently dating her good friend and Saturday Night Live impersonator, Kristen Wiig. "She's got me down," said Barrymore, who sometimes texts lines back and forth with Wiig before a sketch. Barrymore acknowledged the irony of her SNL double being involved with her ex. "It all seems so wacky and incestuous, but that's kind of how life works," she said. "It seems fitting that they would find each other. I was like, 'Right, of course, that makes perfect sense.'"


Post navigation

Above is a youtube video that was filmed at Orchard Hill Farm last weekend by our son, Grayden, and his friend Ivan. Grayden and our apprentices were brain storming earlier in the season on promoting farming as a good way to get fit and dreamed up a competition day with various events…somehow it actually managed to come to pass. T-shirts were designed for the occasion with the “Farm Fit” logo. It was a fun weekend, but I think more fun for the city slickers who came than for our tired farm team here! Grayden says that next time he will try and schedule it in the off season so that more farmers will actually attend!

Bill with Mater and Buttons Cultivating before Planting the Cover Crop for Next Year's Hoop House

We have been busy planting cover crops for the 2013 garden areas. Our garlic has been drying for a month and today we took it down and bagged it for future use. We are also reorganizing the middle barn to make room for the lumber that Ken is finally having time to saw. The motor for the new saw mill is now fixed and the sawing has begun! Yesterday, Michelle and I bent over the tops of our onions to get them ready for pulling and curing next week. The crop looks good. We were also very pleased to see that the squash and pumpkins are producing a good crop. This year we tried a new method of preparation and planted directly into a rye cover crop that had been cut off just as the heads were coming out instead of ploughing in the rye…as a result I was watching them very closely and getting worried that they weren’t setting enough fruit…I decided that I should just leave them alone to grow and was pleasantly surprised to see that left on their own they did fine!

CSA member Bette Conners with her Bouquet

Our cut-your-own flowers have been a big hit and I wish I had pictures of all the lovely bouquets that CSA members have taken home with them. I’ve planted several succession plantings of zinnias, snap dragons and calendula to keep the flowers coming longer and an pleased with the results. Over the years I have tried to select flowers that grow well here and produce good cut flowers for our CSA members. Gradually I feel like I am succeeding in growing a better “cut flower garden.”

Post navigation

2 thoughts on “Farm Fit Competition”

Grayden, why wasnt I invited to Farm Fit!?! It looks like it was so much fun! I love the logo!! I NEED one of those shirts, man! Here’s a fist pump to the revolution of reviving this ancient form of fitness that has been cultivated by farm families for generations!! :^D

Grayden, why wasnt I invited to Farm Fit!?! It looks like it was so much fun! I LOVE the logo!! I NEED one of those shirts, man! Here’s a fist pump to the revolution of reviving this ancient form of zen fitness that has been cultivated by farm families for generations!! :^D


Bradley McStravick

Bradley Steven McStravick (born 25 May 1956) is a British athlete. He competed in the men's decathlon at the 1980 Summer Olympics and the 1984 Summer Olympics. McStravick represented Scotland at the 1982 and 1986 Commonwealth Games.

References

Category:1956 births
Category:Living people
Category:Athletes (track and field) at the 1980 Summer Olympics
Category:Athletes (track and field) at the 1984 Summer Olympics
Category:Scottish male athletes
Category:British decathletes
Category:Olympic athletes of Great Britain
Category:Athletes (track and field) at the 1982 Commonwealth Games
Category:Athletes (track and field) at the 1986 Commonwealth Games
Category:Commonwealth Games competitors for Scotland
Category:Sportspeople from Plymouth


'Uncoordinated' codeine use caused Ipswich woman's death Published duration 11 November 2019

image caption Debbie Headspeath bought codeine from online pharmacies on top of prescriptions from her local GP

A coroner questioned the regulation of online pharmacies after a woman died as a result of her addiction to the painkiller codeine.

Debbie Headspeath, 41, collapsed at home in Ipswich in 2017.

The inquest heard she had been prescribed the opiate for back pain by her GP in 2008 and had later bought more online without his knowledge.

The Suffolk Coroner said he would ask the government to look at closing "regulatory gaps" in the system.

The inquest found Mrs Headspeath died from pneumonitis caused by acute pancreatitis which in turn was caused by chronic codeine use.

An investigation by the coroner's office found she had been prescribed codeine from 16 online companies spending more than £10,000 - on top of her prescriptions from her local NHS surgery.

Family 'let down'

Her GP, Dr Peter Burn of the Two Rivers practice, told the inquest he felt she had been honest about her dependence, but had been reluctant to fully engage with drug addiction agencies.

He said he was not aware of her use of online chemists, but he had kept prescribing codeine to stop her taking over-the-counter ibuprofen and she had had 120 appointments with them between 2014 and 2017 as they worked towards weaning her off the drug.

Coroner Nigel Parsley said Mrs Headspeath had been able to "manipulate" the system and he delivered a narrative conclusion that she died as a result of the "uncoordinated availability of codeine from multiple suppliers".

Mrs Headspeath's mother Elaine Gardiner said the family felt "let down" and it was "disgraceful" there was no system where online firms were legally obliged to check with a patient's local GP.

The coroner said he would prepare a full prevention of future deaths report for the family and Department of Health.

Related Topics Ipswich

Painkillers

NHS

Inquests


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  Volume 4

QuickChick: Property-Based Testing in Coq

              Leonidas Lampropoulos

              Benjamin C. Pierce

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Version 1.0 (09 Oct 2018, Coq 8.8.1)


Leaders across a wide range of industries have joined the disruptive tech community in grasping blockchain's transformative potential. Currently, thousands of fledgling projects, joined by a growing number of giants (e.g. IBM, Microsoft, and Amazon), are actively working to demonstrate blockchain’s potential in major industries like finance, healthcare, and supply chain.

Given the fervor, one question resounds: why isn’t there widespread blockchain adoption?

The short answer is because blockchain adoption is challenging. In this post, we’ll explore the details which include issues in scalability, talent, cost, regulation, and interoperability.

What is a blockchain?

Before we begin, let’s first establish the definition of a blockchain. A blockchain is a distributed ledger that links a growing list of blocks, or a set of ordered data.

A blockchain’s distinguishing traits, compared to a normal database, is its resistance to tampering after data has been added to the network. The data itself is consistent across nodes, agreed upon by network participants.

Scalability

One of the biggest barriers to blockchain adoption is scalability challenges. Centralized transaction networks can process thousands of transactions per second (TPS). For example, Visa handles roughly 1700 (TPS) and has the capability to increase that number to 24.000. Meanwhile, Bitcoin and Ethereum, crypto’s leading blockchain networks, can handle three to seven TPS and approximately 20 TPS respectively. This drastic difference between centralized and decentralized scalability virtually bars mainstream services.

Potential solutions are in the works, IDEX 2.0’s own O2 Rollup highlights several viable use cases here.

Talent Shortage

For many organizations, blockchain adoption requires either a complete restructuring of their legacy system or configuring an approach to integrating both. Both processes require drawing from a talent pool that can’t quite yet meet the demands.

Blockchain development is still a nascent field. Like any other technological emergence, developer communities need time to test, learn, and grow. Colleges and other various educational institutions have only recently begun accommodation for blockchain-related courses, meaning talent pools still have some time before students turn into seasoned developers.

Enlisting the help of third-party developers can temporarily alleviate the shortage, but high costs (driven by the same talent scarcity) combined with the fear of data loss and third-party breaches are enough to dissuade businesses from taking the leap.

Cost

While this factor varies depending on implementation, the cost of maintaining a blockchain can be a nonstarter for some organizations. Traditional databases require data to be written only once. In comparison, the costs of maintaining a blockchain where data needs to be written, checked, and transmitted thousands of times are drastically higher.

Regulation

Part of blockchain’s appeal is its ability to introduce concepts like private keys and smart contracts to transform the way an organization runs. Ironically, because current regulations don’t address these features, they’ve become a source of friction in various applications. For example, healthcare services looking to utilize blockchain for direct and secure patient record sharing must also abide by HIPAA rules which exist in a lot of ways at odds with blockchain fundamentals.

We’re starting to see pieces of legislation acknowledge components of blockchain technology, but the major hurdles remain a work in progress.

Interoperability

While blockchain’s rapid growth through new projects and ideas is exciting, it’s also created an unstandardized and chaotic state filled with different networks and solutions that can’t communicate with each other. This state represents the space’s newness and the freedom it’s afforded many projects eager to disrupt. However, it’s nothing short of a nightmare for dev teams that need multiple platforms to communicate.

Several projects have begun work on enabling interoperability between blockchains and their progress is eagerly awaited by many.

The topic of blockchain is on an ascending trend with major industries expressing real interest in its adoption but, in short, blockchain adoption is challenging. Multiple issues in scalability, talent, cost, regulation, and interoperability remain. However, multiple teams are also hard at work to make progress against these challenges, bringing blockchain closer to its breakout moment.


The development of new efficient processes to construct *N*-heterocycles continues to motivate synthetic groups because of the ubiquitous nature of these structural motifs in bioactive and electronic molecules.^[@ref1],[@ref2]^ Our group believes that these important compounds could be efficiently synthesized through transition-metal-catalyzed C--H bond amination, which would create the ArN--C bond from aryl azides. While we have successfully developed a series of C--H bond amination processes,^[@ref3]−[@ref5]^ the number of steps often required to access the aryl azide substrates diminished the overall efficiency of our *N*-heterocycle synthesis. In our intramolecular sp^2^-C--H bond amination studies,^[@cit5b]^ seven linear steps were required to introduce the fully substituted *o*-alkyl substituent present in the aryl azide (e.g., **1**) (Scheme [1](#sch1){ref-type="scheme"}). This study underscored the need to streamline our substrate construction,^[@ref6]^ and we anticipated that a modular synthesis could be achieved if the aryl azide moiety was installed through an α-arylation of a carbonyl compound.^[@ref7]−[@ref12]^ We were surprised, however, to find that no examples of this reaction existed with aryl azides.^[@ref13]^ Herein, we report the first α-arylation of β-ketoesters and γ-lactams with aryl azides and leverage this reaction to efficiently synthesize 3*H*-indoles.

![Current Challenges To Synthesizing 2-Substituted Aryl Azides](ol-2014-010615_0002){#sch1}

While there are many catalyzed and noncatalyzed α-arylation reactions of carbonyl compounds,^[@ref7]−[@ref13]^ our survey of popular methods and environmentally benign arylating reagents found them to be incompatible with the *o*-azide moiety.^[@ref14]^ The failure of these methods prompted us to examine the α-arylation of β-ketoesters using an aryllead as the electrophilic reagent. Although the use of these complexes is well-established,^[@ref11]^ there are no examples of using an aryl azide substituent (much less an *o*-azide) in the α- arylation processes. The requisite 2-azidoaryllead acetate was readily prepared from either the 2-azidoarylboronic acid pinacolate ester (**8**)^[@ref15]^ or analogous stannane using the conditions reported by Pinhey and co-workers without any decomposition of the azido group.^[@cit11b],[@cit11c]^ With **4a** in hand, a variety of conditions were screened to find the optimal conditions for the α-arylation of β-ketoester **9a** (Table [1](#tbl1){ref-type="other"}). For the initial screen, an excess of **9a** was used (entries 1--3). We found that the equivalents of **9a** could be reduced to three without attenuating the yield of **6a**. A significant reduction in conversion was observed, however, when a slight excess of the β-ketoester was used (entry 3). To improve the conversion, several amine bases were screened (entries 4--6).^[@cit11d]^ While the addition of dabco, phenanthroline, or pyridine did improve the yield, it plateaued at 78%. Using pyridine, a further improvement was realized by increasing the temperature of the reaction to 50 °C (entry 7). Next, we examined if the stoichiometry of our α-arylation could be reversed in order to enable the use of the 2-azidoaryllead acetate as a reagent for the α-arylation of more valuable β-ketoesters (entries 8 and 9). To our delight, we found that yield diminished only slightly, and if 3 equiv of **4a** was used, the yield recovered to 88%. Finally, we attempted the α-arylation in one-flask directly from 2-azidophenylboronic pinacolate ester **8a** without isolation the 2-azidoaryllead acetate to afford **6a** in 45% (entry 10).

###### Determination of the Optimal Conditions for α-Arylation

![](ol-2014-010615_0003){#fx1}

  entry   base (equiv)         **4a** (equiv)   **9a** (equiv)   yield[a](#t1fn1){ref-type="table-fn"} (%)
  ------- -------------------- ---------------- ---------------- -------------------------------------------
  1       none                 1                5                90
  2       none                 1                3                89
  3       none                 1                1.05             59
  4       dabco (3)            1                1.05             44
  5       phenanthraline (3)   1                1.05             62
  6       pyridine (3)         1                1.05             78
  7       pyridine (3)         1                1.05             87[b](#t1fn2){ref-type="table-fn"}
  8       pyridine (3)         1.05             1                83[b](#t1fn2){ref-type="table-fn"}
  9       pyridine (3)         3                1                88[b](#t1fn2){ref-type="table-fn"}
  10      pyridine (3)         1                1.05             45[c](#t1fn3){ref-type="table-fn"}

As determined using ^1^H NMR spectroscopy using CH~2~Br~2~ as an internal standard.

Reaction performed at 50 °C.

Two-step yield from **8a**.

###### Effect of Changing the Identity of the β-Ketoester

![](ol-2014-010615_0004){#fx2}

![](ol-2014-010615_0005){#fx3}

Reaction performed using 1 equiv of **4a**, 1.05 equiv of **9**, and 3 equiv of pyridine in CHCl~3~ at 50 °C.

Isolated yield of **6** after silica gel chromatography; only product obtained.

Using these optimal conditions, a series of β-ketoesters were examined to determine the scope and limitations of our α-arylation reaction (Table [2](#tbl2){ref-type="other"}). We found that the ring size of the β-ketoester could be modified without affecting the yield of our α-arylation reaction to provide functionalized aryl azides **6b**--**d** (entries 1--3). Acyclic β-ketoesters, such as **4e**, could even be smoothly converted to product without much attenuation of the yield (entry 4). Next, the effect of the composition of the β-ketoester on the yield of the arylation was examined (entries 5--7): indanone **9f**, 4-tetrahydropyranone **9g**, and 4-aminocyclohexanone **9h** produced aryl azides **6f**--**h** in good yields. To our surprise, while the α-arylation of amides has generated considerable excitement,^[@ref16],[@ref17]^ γ-lactams have never been used in these processes despite the synthetic utility of these molecules. We found that they could be efficiently arylated to produce **6i** in good yield (entry 8). Next, the stereoselectivity of our α-arylation reaction was investigated. Exposure of 4-*tert*-butyl-substituted β-ketoester **9j** to our reaction conditions furnished **6j** with 10:1 diastereoselectivity (entry 9). To our delight, the selectivity was not affected by the size of the 4-substituent: the diastereoselectivity remained 10:1 when the 4-*tert*-butyl group was replaced with a smaller 4-phenyl group (entry 10). Finally, we examined the effect of changing the nature of the carboxylate group (entries 11--13). We found that an ester was necessary for the α-arylation reaction. While the methyl ester could replaced with either an allyl or menthol (albeit with no diastereoselectivity observed), β-ketoamides proved to be unreactive in our process.

###### Determination of the Optimal Conditions for α-Arylation

![](ol-2014-010615_0006){#fx4}

![](ol-2014-010615_0007){#fx5}

Reaction performed using 1 equiv of **4**, 1.05 equiv of **9**, 3 equiv of pyridine in CHCl~3~ at 50 °C.

Isolated yield of **6** after silica gel chromatography; only product obtained.

3 equiv of **4** used.

The effect of adding substituents to the 2-azidoaryllead acetate on the α-arylation of β-ketoester **9a** or γ-lactam **9i** was examined next (Table [3](#tbl3){ref-type="other"}). For β-ketoester **9a**, we found that the arylation reaction tolerated halide, alkyl, or ether substituents (entries 1--6). The yield, however, did depend on the electronic nature of **4** with the highest yields observed for the electron-rich or electron-neutral arylleads bearing methoxy- or methyl groups (entries 4 and 5). Further, the azide group could be placed at the 4-position without much diminishment of the yield of the arylation reaction (entry 7). To determine the generality of our reaction, we next examined the α-arylation of γ-lactam **9i**, a substrate never reported as nucleophile in this process (entries 8--12).^[@ref17]^ To our delight, we found that a range of different 2-azidoaryllead acetates reacted with γ-lactam **9i**. Its reactivity, however, was diminished in comparison to β-ketoester **9a**. To obtain comparable yields, it was often necessary to increase the amount of the 2-azidoaryllead to 3 equiv (entries 10--12).

###### Conversion of Aryl Azides to 3*H*-Indoles

![](ol-2014-010615_0008){#fx6}

![](ol-2014-010615_0009){#fx7}

Isolated yield of **7** after silica gel chromatography.

As determined using ^1^H NMR spectroscopy; 3*H*-indole **7b** rapidly decomposed upon exposure to air.

The synthetic utility of our α-arylation reaction was demonstrated next by exposing aryl azides **6** to a Staudinger reduction (Table [4](#tbl4){ref-type="other"}).^[@ref18]^ We found that exposure of aryl azides **6** to triphenylphospine produced 3*H*-indoles **7** in nearly quantitative yield. Although the ring size of the β-ketoester could be modified in between the 5- and 7-carbons without affecting the Staudinger reaction, 3*H*-indole **7b** readily decomposed when exposed to air (entries 1--3). The reduction tolerated heteroatoms in the β-ketoester to enable access to important *N*-heterocyclic structural motifs,^[@ref19]^ such as γ-carboline **7h** (entries 4 and 5). The Staudinger reaction could even be extended to γ-lactams to efficiently access 3*H*-pyrroloindole **7i** in nearly quantitative yield (entry 6), whose structure is ubiquitous in bioactive alkaloids.^[@ref20]^ Finally, submission of aryl azide **6k** to the reduction conditions furnished 3*H*-indole **7k** in good yield without loss of any diastereoselectivity (entry 7). Together these results illustrate that when paired with a Staudinger reduction our α-arylation reaction to diastereoselectively access a range of 3*H*-indoles.

In conclusion, we developed an α-arylation reaction of β-ketoesters using 2-azidoaryllead acetates to afford a range complex, functionalized aryl azides with fully substituted *o*-alkyl substituents. The synthetic utility of our process was showcased using γ-lactam substrates to enable efficient construction of functionalized 3*H*-indoles after Staudinger reduction.

Complete experimental procedures and spectroscopic and analytical data for the products. This material is available free of charge via the Internet at <http://pubs.acs.org>.

Supplementary Material
======================

######

ol5010615_si_001.pdf

The authors declare no competing financial interest.

We are grateful to the National Science Foundation (CHE-126563), National Institutes of Health NIGMS (R01GM084945), and the University of Illinois at Chicago for their generous support. We also thank Mr. Furong Sun for mass spectrometry data.


#!/bin/bash
#
# Original: Eric Gebhart, 2013.02.17
#           https://github.com/EricGebhart/view_attachment
#
# Modifications: Brandon Amos
#                https://github.com/bamos/dotfiles
#   [2014.04.03] Support Linux and OSX.
#
# Purpose: To be called by mutt as indicated by .mailcap to handle
#   mail attachments.

tmpdir="/tmp/mutt_attach"

type=$2
open_with=$3

mkdir -p $tmpdir

# Mutt puts everything in /tmp by default.
# This gets the basic filename from the full pathname.
filename=`basename $1`

# get rid of the extenson and save the name for later.
file=`echo $filename | cut -d"." -f1`

# if the type is empty then try to figure it out.
if [ -z $type ]; then
    type=`file -bi $1 | cut -d"/" -f2`
fi

# if the type is '-' then we don't want to mess with type.
# Otherwise we are rebuilding the name.  Either from the
# type that was passed in or from the type we discerned.
if [ $type = "-" ]; then
    newfile=$filename
else
    newfile=$file.$type
fi

newfile=$tmpdir/$newfile

# Copy the file to our new spot so mutt can't delete it
# before the app has a chance to view it.
cp $1 $newfile

case $(uname) in
  "Linux")
    if [ -z $open_with ]; then
      xdg-open $newfile
    else
      # TODO - handle this case
      $open_width $newfile
    fi
  ;;
  "Darwin")
    if [ -z $open_with ]; then
      open $newfile
    else
      open -a "$open_with" $newfile
    fi
  ;;
esac


Column: Despite What You Read, Nobody Knows How Many Rape Accusations Are False

Rape statistics are a mess.

Rape is at the center of the national debate, and it’s no wonder: the Obama administration’s push to crack down on campus rapes through the Civil Rights Act, the notorious and retracted story alleging a gang rape at UVA, the raft of accusations against Bill Cosby. Naturally, statistics about rape have been an important part of this conversation. Unfortunately, these statistics are hard to agree upon. What you get is a duel between conflicting sets of numbers, which should be resolved by careful analysis but instead leads each side to turn up the volume and wave its favored research harder.

The “careful analysis” option turns out to be grueling. Human sexuality is complicated, and has defied almost every society’s attempt to neatly divide it into two piles labeled “right” and “wrong.” Wherever one draws the line, it’s going to create difficult boundary cases. I’ve been groped in a crowd more than once, and I’d call that “disgusting,” not “sexual assault.” Nor would I think of applying that name to the birthday reveler who grabbed me in a bar one night and kissed me. On the other hand, what if he’d slipped his hands up my shirt, or under my skirt? At some point it progresses to something that everyone in the country can agree was sexual assault — but where is that point? Some activists would say that the kiss was an assault. Others will draw the line someplace very different.

Or take drinking. At exactly how many drinks, at what level of intoxication, does one become unable to consent? Does it matter who bought the drinks? If so, if one person bought drinks for the other in the hope of a spark, is that the behavior of a rapist? Does it matter whether the person buying drinks was drinking as well? Whether the buyer was drinking more or less? None of that is a simple case like the villain slipping roofies into his date’s drink.

There’s a vast array of complications that are hard to adjudicate, even when consent is not muddied by alcohol or drugs. If a teenage boy pushes the boundaries while he and his girlfriend are fooling around, and she tells him to stop and he does, should we call that sexual assault? Surely the context matters; if a stranger walked up to the girl and did the same things to her, and stopped when she told him to, we would be much more likely to think of the act as assault.

So there’s a naming issue. But even if everyone agreed on what terms like “sexual assault” and “rape” mean, gathering data on their incidence would be a challenge. People lie to researchers, especially about behavior that is considered immoral or taboo or very emotional. So should researchers ask women whether they have been sexually assaulted, or ask about a long list of specific behaviors instead? The polling approach has been the topic of some controversy.

The starting point on gathering this data is to manage expectations: Many of the statistics that could inform our national conversation will always be “dark figures.” And not just because words fail us. It’s thorny because we know that people do make false accusations at least occasionally, and there is very rarely a third witness for sexual encounters who could give an opinion on whether they were consensual. Thornier still when both parties are intoxicated. And rape is different from other violent crimes; unlike, say, punching someone or grabbing money from the cash register, sex is often undertaken consensually and joyfully. If I punched you, investigators would probably not need to ask whether you gave affirmative consent to have your nose broken.

It’s certain that we used to undercount rape, too often assuming consent in contexts where none was given. It’s possible that we are moving toward overcounting it, by too often assuming a lack of consent. As all scientists know, there is almost always some tradeoff between these two types of error; generally any steps to reduce the false negatives will generate more false positives.

This reality rarely seems to register with the concerned people marshaling little armies of numbers. They prefer the statistics that support their side, presented with angry certainty. They shun nuance, rarely acknowledging what is unknown and what is unknowable. Nowhere is this more obvious than in discussions of false accusations of rape.

The number of false rape reports is obviously a number we’d like to have. Whether that number is many, or few, alters how vigorously police interrogate victim’s stories, how the media treats accusations of rape, how juries decide tricky cases. Maybe it shouldn’t, you’d argue, but humans are imperfect, and it inevitably does. It’s not surprising that in the wake of the Rolling Stone debacle, we’ve had a lot of feminists claiming that we should draw no wider lessons from this case because statistics show that false positives are rare, and a lot of people on the other side arguing that they can show beyond a reasonable doubt that false reports are epidemic. Both sides should stop, because they are wrong.

I don’t mean that they disagree with me. I don’t mean I think they are wrong. I mean that they are wrong.

Any number of pieces have recently been written suggesting that we actually know — or have a pretty good idea — how many rape reports are false. Deadspin, for example: “There’s no doubt that being falsely accused of rape is a dreadful thing that no one should have to endure. One of the reasons it is such a dreadful thing is that false accusations of rape basically do not happen. Statistically, between 2 percent and 8 percent of reported rapes are found to be false, but only about 40 percent of rapes are reported. Do a little math and that means that, for every false accusation of rape, there are up to 100 actual rapes that take place.” When I pointed out on Twitter that the author did not know the percentage of false rape reports, and therefore could not possibly calculate the ratio of false reports to rapes, he suggested that this was a matter of opinion: Maybe I liked one study better, but he thought his was pretty good. This is not a difference of opinion; it is simply a misunderstanding about the data. He has substituted a number he knows — which is, presented in its absolutely best light, the percentage of reports that can definitely be shown to be false by investigators using stringent criteria — for a number he does not know, which is how many reports of rape are actually false.

Perhaps a parallel will make what I mean more clear. Every year, it’s virtually certain that some number of people get away with killing their spouses. More than occasionally, it happens that investigators think they killed their spouses. They’re maybe even pretty sure that they killed their spouses. But they can’t prove it. In the statistics, this will not show up as “spousal murder” or “intimate partner violence”; it will show up as an unsolved case. But they still killed their spouse. How often does this happen? We have absolutely no idea.

I’ve now spent quite a bit of time reading research on rape prevalence over the last few decades. What you see in the literature on false reports is a general move from methods designed to exclude more false negatives (finding a rape report to be true, when in fact it was false), toward one that is designed to minimize the number of false positives (finding a rape report to be false, when in fact it was true). They use quite stringent criteria, where you basically need a confession, or strong evidence that the attack could not have happened as described, to declare it false.

Proponents of this approach tend to describe it using phrases like “more methodologically rigorous,” but this oversells things a bit. What it is is more conservative, in the accounting sense: it is a nice baseline number that we can be reasonably sure of. It almost certainly excludes some reports that were, in fact, false, but could not be conclusively shown to be so. But while approaches that include more questionable cases — like reports that investigators think were probably false but could have happened the way the accuser says — are going to have a lot more variability, and may well dramatically overstate the problem of false accusations.

Critics point out that the convergence upon this particular methodological choice has a political and ideological component, and of course they’re right, it does. But that doesn’t mean it’s wrong for researchers to use those criteria. It’s very useful to know how many accusations can be demonstrably proven to be false, as a floor, a minimum, that gives us a solid starting point.

The problem comes when this baseline number is abused by journalists and activists who would declare the floor to be a ceiling. This is simply wrong. It does not help to say, well, all the studies tend to cluster together around similar results, so there’s a research “consensus.” It is completely unsurprising that they cluster; if the consensus approach is the conservative method, then we would expect the results of well-done studies to cluster pretty tightly. That’s the beauty of the conservative approach.

But it has its limits. Definitionally, the number of false reports is the number of detected false reports plus the number of undetected false reports. Even assuming you can get an accurate count of the former, the latter is definitionally unknown. So unless you assume that the police have superpowers and no false reports ever go undetected, the sum of the two will always be a dark figure.

These numbers are often further tortured by overgeneralization. For example, I’ve seen people saying that police think false reports are more common than they actually are, when a careful understanding of the limited data would show this statement to be obviously nonsensical. Or they say that the data shows that false reports are more likely to include elements like strangers and violence, which fit the traditional narrative about rape. That may well be. It may also be that false accusations against people you know are harder for the police to conclusively disprove.

The most egregious error, however, consists of applying studies of law enforcement investigations to other contexts where there’s no reason to think that the numbers would be identical:

∎ Campus judiciary processes.

∎ Public accusations of crimes that are well beyond the statute of limitations and are therefore not investigated.

∎ Media stories about sexual assaults that were not reported to the police.

∎ Accusations made in social contexts or support groups.

The true numbers of false reports in any of these situations could be higher or lower than that of police reports. The only certainty is that we should not assume studies from the justice system are relevant.

On the flip side is the way I’ve seen people use the Kanin study, which purported to show that 41 percent of rape accusations were false, based on a police department that made “serious” offers to polygraph both accuser and accused. Polygraphs are not truth machines; they are especially unreliable when the person being polygraphed is under great emotional distress. And dismay at being distrusted could easily cause genuine victims to disengage from a hostile process, even though their disengagement could wrongly land them in the “false accuser” category. It was also conducted in a single city decades ago, with vaguely described methodology. I see people using this study to make a mistake like the one above: treating the 41 percent figure as a floor, when at best it’s probably more like a ceiling. Maybe even a loft ceiling.

Could the number be between 3 and 8 percent? Absolutely. But it could be substantially higher than 8 percent; it could even be that 40 percent of rape accusations or more are false, though I’d bet against that. It’s possible that less than 3 percent of rape accusations are false, though again, I would offer good odds against that. The point is that we don’t know, and the groups that claim to know are wrong together.

Until we get mind reading machines, the only thing we can know about the actual prevalence of false rape reports is that we don’t know it.

Megan McArdle is a Bloomberg View columnist who writes about economics, business and public policy.


Ditz is a little (ruby) gem I ran into today. It allows every developer to maintain their own issue tracking database, saved in human readable YAML files, which can be easily exchanged along with the corresponding code changes through a distributed version control system, such as git.

As the latest version number, 0.4, implies, ditz is still at an early stage, but the idea of distributed issue tracking and managing the issues themselves as part of the codebase sounds very interesting to me.


In 1975, Vezina et al., identified (3S,6R,7E,9R,10R,12R,14S,15E,17E,19E,21S,23S,26R,27R,34aS)-9,10,12,13,14,21,22,23,24,25,26,27,32,33,34,34a-Hexadecahydro-9,27-dihydroxy-3-[(1R)-2-[(1S,3R,4R)-4-hydroxy-3-methoxycyclo hexyl]-1-methylethyl]-10,21-dimethoxy-6,8,12,14,20,26-hexamethyl-23,27-epoxy-3H-pyrido[2,1-c] [1,4]oxaazacyclohentriacontine-1,5,11,28,29(4H,6H,31H)-pentone, also known by the synonyms rapamycin and sirolimus as an antifungal antibiotic harvested from a Streptomyces hygroscopicus culture, which was isolated from an Easter Island soil sample. (J. Antibiot. 28, 721-726 (1975); and U.S. Pat. No. 3,929,992, was issued to Sehgal, et al. Dec. 30, 1975). Martel, R. et al. (1977) described the ability of this compound to inhibit the immune response (Can. J. Physiol. Pharmacol., 55, 48-51). More recently, Calne, R. Y. et al. (1989), has described rapamycin to be immunosuppressive in rats given heterotopic heart allografts. (Lancet vol. 2, p. 227). A compound, 15,19-Epoxy-3H-pyrido[2,1-c][1,4]oxaazacyclotricosine-1, 7,20,21(4H,23H)-tetrone, 5,6,8,11,12,13,14,15,16,17,18,19,24,25,26,26a-hexadecahydro-5,19-dihydroxy-3-[(1E)-2-[(1R,3R,4R)-4-hydroxy-3-methoxycyclohexyl]-1-methyl ethenyl]-14,16-dimethoxy-4,10,12,18-tetramethyl-8-(2-propenyl), (3S,4R,5S,8R,9E,12S,14S,15R,16S,18R,19R,26aS), also known by the synonyms FK506 and Tacrolimus disclosed by EP 184162 and U.S. Pat. No. 4,894,366 is also useful as immunosuppressant. Another compound, 15,19-Epoxy-3H-pyrido[2,1-c][1,4]oxaazacyclotricosine-1,7,20,21 (4H,23H)-tetrone,8-ethyl-5,6,8,11,12,13,14,15,16,17,18,19,24,25,26,26a-hexadecahydro-5,19-dihydroxy-3-[(1E)-2-[(1R,3R,4R)-4-hydroxy-3-methoxycyclohexyl]-l-methylethenyl]-14,16-dimethoxy-4,10,12,18-tetramethyl-, (3S,4R,5S,8R,9E,12S,14S,15R,16S,18R,19R,26aS)-, also known by the synonyms Ascomycin, Immunomycin and FK520, which is 17-ethyl analog of Tacrolimus, disclosed in EPO Publication No. 0184162 is also useful as an immunosuppressant.
Many other derivatives of these compounds as well as structural analogues have immunosuppressant property.
Many other derivatives or analogs of these compounds are known, which have antibiotic and/or immunosuppressant activity. These agents inhibit the proliferative response of lymphocytes to alloantigen stimulation, and a variety of T cell associated immune reaction. The compounds suppress immune responses in vivo as well as in vitro and are highly potent. The immunosuppressive action of the compound is applicable in organ transplantation.
EP 0 184 162 disclosed production of Tacrolimus by submerged fermentation from Streptomyces tsukubaensis.
EP 0 184 162 also discloses production of immunomycin by submerged fermentation from Streptomyces tsukubaensis or Streptomyces hygroscopicus.
Streptomyces hygroscopicus is also known to produce sirolimus.
The present invention discloses a new strain of Streptomyces sp. BICC 7522, its variants or mutants and use of the strain for the production of macrolides.


Biofilm formation and indole-3-acetic acid production by two rhizospheric unicellular cyanobacteria.
Microorganisms that live in the rhizosphere play a pivotal role in the functioning and maintenance of soil ecosystems. The study of rhizospheric cyanobacteria has been hampered by the difficulty to culture and maintain them in the laboratory. The present work investigated the production of the plant hormone indole-3-acetic acid (IAA) and the potential of biofilm formation on the rhizoplane of pea plants by two cyanobacterial strains, isolated from rice rhizosphere. The unicellular cyanobacteria Chroococcidiopsis sp. MMG-5 and Synechocystis sp. MMG-8 that were isolated from a rice rhizosphere, were investigated. Production of IAA by Chroococcidiopsis sp. MMG-5 and Synechocystis sp. MMG-8 was measured under experimental conditions (pH and light). The bioactivity of the cyanobacterial auxin was demonstrated through the alteration of the rooting pattern of Pisum sativum seedlings. The increase in the concentration of L-tryptophan and the time that this amino acid was present in the medium resulted in a significant enhancement of the synthesis of IAA (r > 0.900 at p = 0.01). There was also a significant correlation between the concentration of IAA in the supernatant of the cyanobacteria cultures and the root length and number of the pea seedlings. Observations made by confocal laser scanning microscopy revealed the presence of cyanobacteria on the surface of the roots and also provided evidence for the penetration of the cyanobacteria in the endorhizosphere. We show that the synthesis of IAA by Chroococcidiopsis sp. MMG-5 and Synechocystis sp. MMG-8 occurs under different environmental conditions and that the auxin is important for the development of the seedling roots and for establishing an intimate symbiosis between cyanobacteria and host plants.


Taking a Tumble

A little over a week ago, the morning after a long day in the car following two weeks at the lake, I chose to go for a run. The July weather was unseasonably perfect for a run with low temps and no humidity. Running was not advised as I’m battling plantar fasciitis, but I was tempted and I gave in. Reasoning that it would be better for my foot (and less painful) to run on a soft surface rather than roads, I headed directly for my beloved trails.

Lush with recent rainfall and filled with the music of birds, the wooded path offered me the peace and shade I craved. Reaching the halfway point, I turned onto the path for my route home. I passed a woman walking four dogs, the first person I’d seen that morning. I stumbled over a rock on the path and caught myself. I wondered if the woman saw me and then I wondered why I’m so self-conscious. A mile later, picking up speed on a flat area of the path, I stumbled again, but didn’t catch myself. I went sprawling. A young couple emerged from the curve on the path as I picked myself up.

“Are you okay?” said the young man.

“Yeah, I think so,” I said, looking over my scrapes.

“Here, take some water to wash them out.” He handed me his water bottle.

I squirted water over my elbow and knee.

“That’s how we wake up out here, right?” the young woman said, grinning. “Take a tumble, wake right up.”

The trails where I run are studded with rocks so the surprise is not that I fell and scraped myself up; the surprise is that I’ve never fallen. I’ve stumbled plenty, but I’ve always caught myself. In fact, I’d go as far as to say that I took some pride in my quick reflexes. Now I felt oddly…betrayed. Not by the lovely trail, but by my body.

“I’m so old,” I complained when I arrived home dripping blood from my elbow and my knee. “I’m not as nimble as I was.”

A friend and fellow trail runner said the same when I ran into her – my elbow bandaged up - at a favorite coffee shop a few days later.

“Oh, I fall all the time,” she said cheerfully

“She does,” her husband agreed.

I reasoned that my friend falls while running because she’s a super-fast runner whereas I simply fell because I’m clumsy. I do this a lot. I reason away someone else’s similar mishap and view my own mishap as a personal failing of some sort. It’s an oddly egocentric approach, like I’m holding myself to a higher standard than I hold for others.

Yesterday I returned to the scene of the crime while walking with my dog. The particular part of the path where I fell is no different from any other part of the path where I was running. Dirt and rocks bordered by ferns and trees. Lovely as always. There’s no reason for me to have fallen in that spot versus any other. Just as there’s no reason for countless things that happen – good and bad – to me and those around me. Things happen. Not because we are good or bad. They just happen. Friends get cancer. Family members die. Our kids face challenges. We fall down. We get back up.

My cuts and scrapes are pretty much healed now and I’m hoping this plantar fasciitis resolves soon so I can get back out there. And if I fall, I’ll simply get back up.