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Multilevel Selection 2: Estimating the Genetic Parameters Determining Inheritance and Response to Selection
Piter Bijma, William M. Muir, Esther D. Ellen, Jason B. Wolf and Johan A. M. Van Arendonk
Genetics January 1, 2007 vol. 175 no. 1 289-299; https://doi.org/10.1534/genetics.106.062729
Piter Bijma
William M. Muir
Esther D. Ellen
Jason B. Wolf
Johan A. M. Van Arendonk
Interactions among individuals are universal, both in animals and in plants and in natural as well as domestic populations. Understanding the consequences of these interactions for the evolution of populations by either natural or artificial selection requires knowledge of the heritable components underlying them. Here we present statistical methodology to estimate the genetic parameters determining response to multilevel selection of traits affected by interactions among individuals in general populations. We apply these methods to obtain estimates of genetic parameters for survival days in a population of layer chickens with high mortality due to pecking behavior. We find that heritable variation is threefold greater than that obtained from classical analyses, meaning that two-thirds of the full heritable variation is hidden to classical analysis due to social interactions. As a consequence, predicted responses to multilevel selection applied to this population are threefold greater than classical predictions. This work, combined with the quantitative genetic theory for response to multilevel selection presented in an accompanying article in this issue, enables the design of selection programs to effectively reduce competitive interactions in livestock and plants and the prediction of the effects of social interactions on evolution in natural populations undergoing multilevel selection.
INTERACTIONS among individuals are universal, both in animals and in plants and in natural populations as well as in domestic populations. These interactions severely affect the direction and magnitude of responses to either natural or artificial selection (Dawkins 1982) and thus have important implications both for domestic breeding (Griffing 1967; Muir 1996) and for the outcome of evolution (Griffing 1981a; Ridley 1995; Goodnight and Stevens 1997; Keller 1999; Clutton-Brock 2002).
With respect to agriculture, understanding how to reduce competitive interactions through artificial breeding is critical for global food security (Denison et al. 2003) and required to improve animal well-being in species currently used in agriculture, such as swine and poultry (Muir 2003). Animal and plant breeders have been concerned about interactions among individuals, but have lacked direction as to methods to address these issues, and have been unsure as to the magnitude of the problem or even whether it exists at all. Griffing (1976a) observed that group selection could be used to ensure positive response to selection, but also noted that group selection was not very efficient. Determining the impact of interactions for artificial selection programs requires first of all knowledge of the genetic parameters underlying the interactions. Such knowledge would enable optimization of breeding programs to maximize total genetic improvement of complex traits, but is currently lacking.
With respect to the study of evolution of interactions, one can distinguish two approaches: the adaptionist approach (e.g., Hamilton 1964a; Williams 1966; Maynard-Smith 1974; Wilson and Sober 1989; West et al. 2002) and the genetic approach (Williams and Williams 1957; Griffing 1967, 1981a; Wright 1968, 1977; Wade 1979; Moore and Boake 1994; Goodnight and Stevens 1997; Roff 1997; Goodnight 2005). The adaptionist approach focuses on explaining existing phenotypic adaptations, whereas the genetic approach focuses on modeling ongoing evolution by natural selection. Adaptionists treat the current population as an end point and seek to explain current phenotypes by considering whether they are evolutionary stable (Maynard-Smith 1974). Hence, they implicitly assume that current trait values are optimal, meaning that traits have no heritable covariance with fitness. Geneticists, in contrast, take the opposite approach by assuming that all traits are heritable. With respect to behavioral traits, the adaptionist approach has so far had the most impact (Moore and Boake 1994; Goodnight 2005). In many cases, however, it is unknown whether or not traits in natural populations have a heritable covariance with fitness, which is particularly true for complex traits related to behavior.
In general, response to selection depends on the heritable variation in the traits of interest. Classical quantitative genetic theory states that response to selection equals the product of heritability and the selection differential, R = h2S, a result known as the "breeder's equation" (Lynch and Walsh 1998). Griffing (1967, 1968a,b, 1969, 1976a,b, 1981a,b) and later developments of others (Moore et al. 1997; Wolf et al. 1998), however, showed that interactions among individuals involve additional heritable components. The accompanying article (Bijma et al. 2007, this issue) shows that interactions among individuals may create substantial additional heritable variation. Selection among individuals does not capture this additional variation, but selection acting on higher levels of organization, such as a group, captures the full heritable variation. Thus, knowledge of the amount of heritable variation hidden due to social interactions is critical to understand and predict consequences of multilevel selection.
At present, there is very little information on the amount of heritable variation hidden due to social interactions. The large responses observed in selection experiments applying selection among groups, however, provide indirect evidence for substantial hidden variation (Craig and Muir 1996; Muir 1996; see Goodnight and Stevens 1997 for a review). It is clear from those experiments (see Table 1 in Goodnight and Stevens 1997), and from results presented herein, that multilevel selection could result in the next major advance in both domestic plant and animal breeding. Full utilization of heritable interactions and multilevel selection in artificial breeding, and a better understanding of the impacts of interactions for evolution by natural selection, however, requires methodology to estimate the relevant genetic parameters in general populations.
Here we present statistical methodology to estimate the genetic parameters of traits affected by interactions, for interactions among any number of individuals in general nondesigned populations with any degree of relatedness among individuals. Next, we present estimates for the amount and composition of the total heritable variation for survival days in a population of layer chickens (Gallus gallus) and investigate the prospects of using multilevel selection and relatedness to improve survival in that population, by utilizing the total heritable variation.
This section briefly summarizes the quantitative genetic theory of multilevel selection as presented in Bijma et al. (2007).
We consider a population in which interactions occur within "groups" of n individuals each and affect phenotypic values of individuals. In classical quantitative genetic theory, the phenotypic trait value of an individual, P, is the sum of a heritable component, A, referred to as "breeding value" and a residual component, E, referred to as "environment": P = A + E. With interactions among n individuals, however, the observed phenotypic trait value of an individual is due to two unobserved phenotypic effects: a direct effect originating from the focal individual and the sum of the associative effects originating from of each of its n − 1 group members (Griffing 1967). Associative effects may be interpreted as heritable environmental effects provided by associates to the focal individual (Wolf 2003). Both phenotypic direct and associative effects are the sum of a heritable component (A) and a nonheritable component (E), so that the observed phenotypic trait value of an individual is given by
(1)(Griffing 1967), in which i denotes the focal individual and j one of its associates (i.e., group members). The first two terms in Equation 1 represent effects due to the focal individual; the last two terms represent the effects due to its n − 1 associates. The effect
is the direct breeding value (DBV) and ED,i is the direct nonheritable value of individual i, whereas
is the associative breeding value (SBV) and ES,j the associative nonheritable value of associate j. The DBV and the SBV represent the heritable components underlying the observed phenotypes.
Note that nonheritable associative effects, indicated by
in Equation 1, may contribute to the observed phenotype. This is because the full associative effect of an individual is a phenotypic effect, which may contain both heritable and nonheritable components. The genes of an individual do not directly affect the associates; the associates experience a phenotype. We emphasize those nonheritable associative effects, because they modify the statistical model required to obtain unbiased estimates of genetic parameters (see below).
Heritable variation:
Because each individual interacts with n − 1 associates, the total heritable impact of an individual on the population mean, referred to as its total breeding value (TBV), equals the sum of its DBV and n − 1 times its SBV: TBVi = DBVi + (n − 1)SBVi. Response to selection equals the change per generation of the TBV. Hence, the TBV is a generalization of the classical breeding value, to account for interactions. The total heritable variation available for response to selection, therefore, equals the variance of the TBV,
(2)(Bijma et al. 2007), in which
is the variance of DBV,
is the variance of SBV, and
is the covariance between DBV and SBV. The sign of the covariance between DBV and SBV is a measure of competition vs. cooperation. Negative
may be interpreted as "heritable competition," in the sense that individuals with positive breeding values for their own phenotype (DBV) have on average a negative heritable impact on the phenotypes of their associates (SBV). Conversely, positive
may be interpreted as "heritable cooperation."
Classical quantitative genetic methodology to estimate additive genetic variances of traits rests on similarity of phenotypes' relatives. Associative breeding values of individuals, however, are not expressed in their own phenotypes, but in the phenotypes of their associates. Thus only the variance of DBV surfaces in classical analyses; the remaining part of the full heritable variance,
, is hidden. Depending on group size and the heritability of interactions, this hidden heritable variance may be substantially larger than the variance of DBVs. In those cases, classical methods will substantially underestimate the total heritable variation. With specific family structures, the SBV may also be confounded with the DBV (see discussion and Wolf 2003), which would make the result of classical analyses an inaccurate estimate of even the direct genetic variance. The total heritable variation (Equation 2) does not depend on relatedness among associates. This is because all components of the TBV of an individual refer to its own genes. (See Bijma et al. 2007 for the effect of population structure.)
Response to selection:
In classical quantitative genetic theory, response to selection is in the same direction as selection pressure; heritability is merely a scaling factor affecting the magnitude of response, not its direction. With interactions among individuals, however, not only the magnitude, but also the direction of response depend on the genetic parameters (Griffing 1967; Kirkpatrick and Lande 1989; Moore et al. 1997). To illustrate this phenomenon, we consider response to selection among unrelated individuals, which follows from substituting r = g = 0 into Equation 5 of Bijma et al. (2007), giving
, in which
is the selection gradient. (See also Griffing 1976a.) This result shows that the direction of response depends on the covariance between DBV and SBV (
). Strong competition, i.e., large and negative
, will yield response in the direction opposite to the direction of selection. With interactions, therefore, the genetic parameters play a key role in the outcome of selection; they are no longer merely a scaling factor.
Powerful methods to estimate genetic parameters have been developed in the field of animal breeding (Henderson 1950; Lynch and Walsh 1998). Those methods have become known as "animal models" and have recently been introduced into other fields in biology (Kruuk et al. 2003). In the following, we extend the animal model to account for interactions. Particular emphasis is given on the proper inclusion of nonheritable associative effects.
In its basic form, i.e., in the absence of interactions, an animal model is given by
(3)in which y is a vector of observed phenotypes; b is a vector of so-called "fixed" effects, with incidence matrix X linking observations on fixed effects; a is a vector of breeding values, with incidence matrix Z linking phenotypic values of individuals to their breeding values; and e is a vector of residuals. Fixed effects in b account for systematic nongenetic differences among observations, such as age or treatment effects. Covariance structures of model terms are:
, in which A is a matrix of coefficients of relatedness between individuals and
the variance of breeding values (which is to be estimated), and
, in which I is an identity matrix (Ijj = 1 and Ijk = 0) and
the residual variance. The Za term in the model, together with its variance structure, accounts for the genetic covariances between all observations.
To account for interactions in the animal model, we investigated the structure of the covariances between observations that arise from Equation 1 and formulated a statistical model that yields the same covariance structure. (The derivation is in the next section.) The resulting statistical model is summarized as
(4)in which aD is a vector of DBVs, with incidence matrix ZD linking phenotypic values of individuals to their DBV; aS is a vector of SBVs, with incidence matrix ZS linking phenotypic values of individuals to the SBVs of their associates; and e is a vector of residuals. The covariance structure of genetic terms is
, where
indicates the Kronecker product of matrices. (Note that the element yi of y in Equation 4 corresponds to Pi in Equation 1.) This fully describes the genetic components of the covariance between observations.
The covariance structure of the residual term of the model, e, can be derived using Equation 1 and is given by
(5)(see next section for derivation), in which
, which is the correlation between residuals of group members. Together, Equations 4 and 5 fully describe the covariance structure that arises from Equation 1.
Derivations:
This section describes the derivations of Equations 4 and 5. Consider two individuals, i with group members denoted by j and k with group members denoted by l. The covariance between phenotypes of i and k follows from substituting Equation 1 for both Pi and Pk, giving
, which consists of a genetic and an environmental component. Analogous to the basic animal model, we account for the genetic components by including direct and associative breeding values in the model, together with their covariance structure. This yields Equation 4. For example, in Equation 4, the ith element of y corresponds to Pi, the ith element of ZDaD corresponds to AD,i, the ith element of ZSaS corresponds to
, and the ith element of e corresponds to
. As usual in an animal model, we account for covariances between breeding values in aD and aS by incorporating the relatedness matrix A in the statistical analyses. (See below and the mixed-model equations in Muir 2005.)
What remains to be done is the covariance structure of the vector of residuals e. The covariance between residuals in e originates from the environmental component of the covariance between observations and can be partitioned into
. As usual in quantitative genetics, we assume that E-terms of distinct individuals are independent. (Environmental effects common to multiple individuals are accounted for by the fixed effects Xb.) Thus a nonzero residual covariance occurs only when it involves the same individual, which happens either when i and k are the same individual or when i and k are group members. When i and k are the same individual,
, which is the residual variance. The first term is due to the direct effect of the individual itself, and the second term is due to its n − 1 group members. When i and k are group members, their residuals are correlated for two reasons. First, for groups larger than two, i and k will have n − 2 group members in common, and thus they have n − 2 identical associative effects in their phenotype, giving
. Second, the direct effect of i is expressed in its own phenotype, whereas the associative effect of i is expressed in the phenotype of k, and vice versa for k. As with any two traits of a single individual, the direct and associative effect originating from a single individual may be correlated Walsh (Lynch and Walsh 1998), giving
. Thus the full covariance between residuals of group members equals
represents the nonheritable relationship between the direct and associative effect of an individual and is a nonheritable measure of competition vs. cooperation (analogous to
, see above). A negative
may, for example, arise from variation in the juvenile environment experienced by individuals. Individuals having experienced good (bad) environments may be more (less) competitive, in which case they will have higher (lower) phenotypic values at the expense (to the advantage) of their associates. In Equation 5, we accounted for the nongenetic covariance between group members by fitting the correlation ρ between records of group members.
Experimental design for application:
The use of Equations 4 and 5 does not require a specific experimental design; groups may be composed of any combination of related and/or unrelated individuals and may vary in size. To calculate the relationship matrix A, information on at least one generation of family relationships will be required, so that one can distinguish full sibs, half sibs, and unrelated individuals. Information on more generations of pedigree, which provides information on more distant relationships, will add to the accuracy of results, but is not required for application. With groups of equal size, there will be no information in the data to separate
, because different combinations of those two parameters may yield an identical residual correlation. The inability to estimate
with equal group size is no problem, because the residual correlation itself, which included these components as part of a ratio, is estimable and sufficient in the analysis to avoid bias in the estimates of the genetic parameters. There are population structures from which the parameters of interest cannot be estimated. (See discussion on the work of Wolf 2003).
We used Monte Carlo simulations to validate our statistical methodology (appendix). Table 1 shows that estimated genetic parameters obtained from simulated data are unbiased; true values of genetic parameters are within ±3 standard errors of mean estimated values in all cases. These results confirm that Equations 4 and 5 correspond to the covariance structure that arises from Equation 1 and that there is no need for a specific structure of the family relationships within and between groups.
Validation of statistical methodology
The methodology presented above was applied to analyze mortality in a commercial population of layer chickens. Field data on this population were provided by Hendrix Genetics and consisted of observations on survival days of a single generation of 3800 hens bred from 36 sires and 287 dams, which had been mated at random. Each sire had been mated to ∼8 dams, and each dam contributed on average 13.2 female offspring. Five generations of pedigree were included in the calculation of the matrix of relatedness (A).
Mortality in commercial chicken populations is largely due to cannibalistic pecking behavior (e.g., Craig and Muir 1996). The direct effect of an individual is, therefore, related to its ability to survive by avoiding being pecked, whereas its associative effect measures its effect on survival of its cage members, which is closely related to its own pecking behavior. In commercial egg production, individuals are frequently beak trimmed, to reduce mortality due to pecking behavior. In this data set, however, individuals had not been beak trimmed, which will be obligatory in the European Union from 2011 onward.
Data consisted of four age groups of approximately equal numbers of hens, differing by 2 weeks of age each. At an age of 20 weeks, individuals were allocated randomly to 950 standard commercial battery cages, four individuals per cage. Due to chance, some cages contained full or half sibs, but most cages contained unrelated individuals only. Facilities consisted of eight rows of cages. Each row was 40 cages long and consisted of 3 cages on top of each other, giving 120 cages per row. The eight rows were grouped into four double rows; both rows of a double row were put with the backs against each other. Each age group was allocated to a single double row. Back fences of cages consisted of latticework allowing limited contact between "backdoor neighbors," i.e., between the individuals of two corresponding cages, each in one of the two rows of a double row. Adjacent cages in the same row were separated by a closed fence fully preventing contact. Individuals were kept 60 weeks and culled at an age of 80 weeks. Survival (0/1) was recorded daily on all individuals. For each individual, survival days were defined as age at death in days. Individuals surviving until 80 weeks of age had 80 × 7 = 560 survival days. Fifty-four percent of individuals survived until 80 weeks of age. Mean survival time was 454 days, with a standard deviation of 122 days. Inspection of dead hens showed that the vast majority of chickens had died due to being pecked. This observation was supported by previous data on beak-trimmed individuals of the same line kept in the same facilities, which showed substantially lower mortality (<10%). Thus the absence of beak trimming substantially amplified the consequences of social interactions.
Data were analyzed with Equations 4 and 5 as a model, using restricted maximum likelihood (REML) (Patterson and Thompson 1971) as implemented in the ASReml software package (Gilmour et al. 2002). The model included a fixed effect for each combination of row (1 through 8) and level (top, middle, and bottom) of the cage and for the number of surviving birds in the back cage, to account for a possible associative effect due to the backdoor neighbors. As usual with an animal model, this analysis directly yields estimates of the parameters of interest and utilizes the full data.
When using a conventional model without associative effects, estimated heritability for survival days in the chicken line was 6.7%, which is in the range common for survival traits in livestock (Table 2; Beaumont et al. 1997; Carlen et al. 2005; Gitterle et al. 2005). The total heritable variance estimated from the full model (Equations 2, 4, and 5), however, was 2742 days2, which corresponds to 20% of the phenotypic variance. This result indicates a "heritability" threefold greater than that obtained using classical methods, i.e., 20% vs. 6.7%. Two-thirds of the heritable variation, therefore, is due to social interactions among individuals and is hidden from classical analyses.
Estimated genetic parameters in a population of laying hens
At first glance, one might expect direct and associative effects for mortality due to pecking behavior to be negatively correlated, because dominant individuals may kill others and survive themselves. However, we found a small but nonsignificant positive correlation of +0.28 (P = 0.016), suggesting that individuals benefit from not harming others. In this population, there seems to be mutual benefit of behaving peacefully and a mutual cost of behaving aggressively. A positive covariance between DBV and SBV increases the total heritable variation (Equation 2) and therefore increases the potential of the population to respond to selection.
Figure 1 shows predicted responses to multilevel selection applied to the chicken population, using equations presented in Bijma et al. (2007). (Details and an example of calculation are in the appendix.) In Figure 1, the strength of multilevel selection is indicated by a factor g, where g = 0 represents selection among individuals, g = 1 represents selection among groups, and values of g between 0 and 1 represent a mix of selection among individuals and groups (Bijma et al. 2007). Classical theory suggests a response of only 7.8 days of survival. Predictions accounting for heritable interactions, however, yield substantially higher responses. Selection among individuals applied to a population composed of groups of unrelated individuals yields an expected response of ∼11 days of survival (g = 0, r = 0). Mild multilevel selection applied to a population composed of groups of full sibs more than doubles the predicted response (g ≥ 0.3,
). The maximum response that can be obtained equals 23 days (g = 0.6–0.8 and
), which is nearly threefold greater than suggested by classical theory. (Clones do not occur in reality.) The lines in Figure 1 are curved, which indicates that a degree of between-group selection (g) that maximizes response exists, which was also observed by Griffing. This value of g may be applied in artificial breeding programs, where the aim is to maximize response to selection. Note that results in Figure 1 are specific to the chicken population and to the environment in which it was kept, such as the density of housing; other populations may have other genetic parameters and other relationships among relatedness, multilevel selection, and response. Results in Figure 1 should, therefore, not be taken to conclude that relatedness has a bigger impact than multilevel selection in general.
Predicted responses to selection for the trait survival days in the chicken population, with groups consisting of one of four types of relatives. Predictions are obtained using results in Table 2 and a selection intensity of 1 unit
. (Methods are in the appendix). The g denotes the strength of multilevel selection. The prediction from classical theory is
. Results for clones, which do not occur in chicken, are included to enable generalization to other species.
We have presented methodology to estimate the genetic parameters of traits affected by interactions among individuals of any degree of relatedness in general populations. The Monte Carlo simulations confirm that our methods yield unbiased results. In an accompanying article (Bijma et al. 2007, this issue), we provide the quantitative genetic theory for the consequences of multilevel selection. That article shows that, in theory, heritable interactions may have substantial consequences, such as the existence of considerable yet hidden heritable variation. This article provides the methods to estimate the genetic parameters required for applying the theory. Analysis of the chicken population revealed a threefold greater heritable variation and potential response to selection than estimated by classical methods. These findings demonstrate the relevance of our theoretical work for real populations.
Together, both articles provide testable predictions of response to multilevel selection in general populations. These predictions may be used to optimize artificial breeding programs for heritable traits that have nevertheless been refractory to selection among individuals (Teichertcoddington and Smitherman 1988; Vangen 1993; Kruuk et al. 2001; Muir 2005). Even for traits not refractory to selection, responses obtained in current selection programs are most likely less than what can be achieved by accounting for interactions. For example, results for the chicken population show that optimal multilevel selection will yield a response twofold greater than that of individual selection (23 vs. 11 days, Figure 1). The additional response to selection is expected to be even greater for situations where the covariance between direct and associative effects is negative, e.g., in situations with competition for limited resources. In the field of evolutionary biology, our methods may be used to quantify the potential of social traits to evolve in response to natural selection acting on multiple levels and also to investigate whether social traits in current populations show a heritable covariance with fitness, in which case they would respond to natural selection, indicating that current trait values deviate from evolutionary stable state trait values.
In stark contrast to classical theory, the total heritable variance (
) can in principle exceed the observed phenotypic variance. Consider, for example, groups consisting of unrelated individuals. In that case, the variance among observed phenotypes equals
(Equation 1; appendix), whereas the total heritable variance equals
(Equation 2). Comparing both expressions shows that
is most likely to exceed the phenotypic variance when groups are large, associative effects are largely heritable, and the covariance between DBV and SBV is positive. For example, with n = 10,
, heritable variances of direct and associative variances equal to 50% of the corresponding phenotypic variances, and
, the phenotypic variance equals 1 + 11 × 0.2 = 3.2, whereas the heritable variance equals 0.5 + 92 × 0.1 = 8.6. This example shows that interactions can in principle cause the heritable variance to exceed the phenotypic variance. Whether this occurs in real populations has not yet been investigated to our knowledge.
Previous work on the genetic background and evolution of social effects (Hamilton 1963, 1964a,b; Wolf et al. 1998; Wolf 2003) has focused predominantly on pairs of individuals, whereas the current approach applies to groups of any number of individuals. For certain types of interactions, such as maternal effects, it is obvious to consider only two individuals. Results for two individuals, however, do not generalize to larger groups. In larger groups, the variance of SBVs becomes the dominant term, both for response to selection and for the total heritable variation (Equation 2) (Bijma et al. 2007). The total heritable variation contains a term
(Equation 2), which increases rapidly with group size, indicating that populations consisting of large groups may contain substantially more heritable variation than populations consisting of small groups.
The impact of group size on the heritable variation and on response to selection depends critically on the relationship between the variance of SBVs and group size. Depending on the nature of the trait, the variance of SBVs may or may not decrease with group size, and consequently the total heritable variance may either increase rapidly with group size or maintain a stable value (Bijma et al. 2007). Knowledge of the relationship between group size and the heritable variance would provide insight into the contribution of group augmentation to the evolution of social behaviors (Clutton-Brock 2002). For populations consisting of groups of different sizes, the methodology presented here can be extended to examine that relationship (not shown).
In comparison to other methods, Wolf (2003) presented an experimental design and a statistical method to estimate direct and associative genetic (co)variances in populations consisting of groups of two individuals and presented results for body length in Drosophila melanogaster. Results indicated an associative genetic variance of approximately half the direct genetic variance and a strong negative genetic correlation of −0.85 between direct and associative effects. In the experimental setup, flies were kept in pairs of individuals that were full sibs, half sibs, or unrelated. With unrelated individuals, the same two half-sib families were always combined in a pair. Dams were nested within sires; each sire was mated to exactly two dams and each dam to a single sire only. Data were analyzed using a sire and dam-within-sire model (referred to as "sire–dam model" in the following), and residuals were treated as independent, both within and between tubes (i.e., groups). Direct and associative genetic (co)variances were estimated using an ANOVA method, in which estimated sire and dam variances were equated to their expectations. Further details are in Wolf (2003). We investigated this experimental design using Monte Carlo simulation of the population structure of Wolf (2003). Simulated populations were analyzed using either Equations 4 and 5 or a sire–dam model. To our surprise, results from Equations 4 and 5 indicated that the associative genetic variance could not be estimated with this experimental design, whereas estimates from a sire–dam model were severely biased. To determine for certain whether or not the associative genetic variance can be estimated in this experimental design, we examined in detail the expectations of the sire and dam variances presented in Wolf (2003), which revealed that they are incomplete (appendix). Investigation of the complete expectations confirmed that the associative genetic variance cannot be estimated using the experimental design proposed by Wolf (2003). Moreover, it revealed that a sire–dam model is unsuitable for estimating direct and associative genetic (co)variances, because the true expectations of sire and dam "variances" can take negative values (because they are, in reality, covariances that, unlike variances, are not restricted to nonnegative values). Indeed, the expected sire variance would take a negative value when estimates presented in Wolf (2003) would be the true values (appendix). Results presented in Wolf (2003), therefore, may need to be reconsidered.
Furthermore, the assumption of independent residuals within tube may have caused overestimation of the genetic variances presented in Wolf (2003). To illustrate the potential consequences of assuming independent residuals, we reanalyzed the data on the chicken line by fitting both direct and associative genetic effects according to Equation 4, but replacing Equation 5 by
. A likelihood-ratio test (Wald 1943; Lynch and Walsh 1998) strongly favored correlated residuals (P < 0.001). Results (not shown) indicated severely affected estimates of the genetic parameters, which increased by 37% for the variance of DBV, by 175% for the variance of SBV, and by 360% for the covariance between DBV and SBV, yielding a total heritable variation 2.6-fold greater than the original result (7294 instead of the 2742 days2 in Table 2). These results demonstrate that even a very small residual correlation (
, Table 2) may substantially bias the estimated genetic parameters and illustrate the need to allow for nonheritable associative effects in the statistical analysis.
Muir (2005) presented similar methodology to estimate direct and associative genetic (co)variances in general populations. With respect to genetic components, our model (Equation 4) is the same as that of Muir (2005). However, Muir (2005) assumed that
. This would imply that
, which is necessarily positive. That assumption is likely to be violated, because an environmental effect that increases its own direct effect will most likely have a negative impact on others (except for altruistic effects). Here we presented the general solution, which obeys the basic quantitative genetic assumptions that (i) phenotypic values are the sum of both genetic and environmental values and (ii) phenotypic covariances are the sum of both genetic and environmental covariances.
The above considerations show that the choice of proper models and statistical methods is critical to the outcome and biological interpretation of results. Furthermore, the analysis of the experimental design of Wolf (2003) shows that for certain population structures the genetic parameters of interest cannot be estimated. That result is not specific to our statistical methodology, but holds in general. The experimental design used in Wolf (2003) yields a covariance structure between observations that is independent of the relative magnitudes of direct and associative genetic variances (appendix). To estimate genetic parameters of traits affected by interactions, we suggest the use of animal models and random composition of groups. Animal models directly include the animal component, instead of underlying components as sire and dam, and are a powerful tool for data analyses in both natural and domestic populations (Kruuk 2004). Application of animal models does not require designed populations and avoids the complexity of deriving expectations of sire and dam variances and the risk of obtaining estimates outside the parameter space, which may occur in ANOVA-based methods.
Monte Carlo simulation used for Table 1:
A population of five discrete generations was simulated. First, a base generation of 250 sires and 500 dams was generated. DBVs of base individuals were sampled from
and nongenetic direct effects from
. Associative effects of individuals were simulated conditional on their direct effects; the SBV of the ith individual of the base generation was sampled as
, in which rA is the correlation between DBV and SBV, and z is a random variable taken from a standard normal distribution,
. Nongenetic indirect effects were sampled as
, in which rE is the nongenetic correlation between direct and associative effects. Phenotypes were created according to Equation 1 and parents for the next generation were taken at random from the base generation.
Second, four additional generations were generated. DBVs of individuals in those generations were sampled as
and SBVs as
, in which MS denotes Mendelian sampling components of breeding values, which were sampled as
. Nongenetic effects were sampled in the same way as in the base generation. Phenotypic values from the fifth generation were used to estimate variance components, and all five generations were included in the calculation of the relatedness matrix (A). Fifty replicates were simulated for each set of genetic parameters and estimates were averaged over replicates.
Example of calculation of response to selection for the chicken line:
Estimated genetic parameters for the chicken line were
, and ρ = 0.09. In the data, groups consisted of four unrelated individuals, so that the phenotypic variance equals
(Table 2).
In the following, we give an example of calculation of response to selection for a population consisting of groups composed of four full sibs (n = 4, r =
), with substantial selection pressure given to group performance (g = 0.8), and for an intensity of selection of 1 unit (ι = 1). The general expression for response to selection is
, in which C denotes the selection criterion,
, and g the relative emphasis given to group performance in the selection criterion (Bijma et al. 2007). Thus, calculation of response to selection requires calculating the variance of the total breeding values,
, the covariance between phenotypic values and TBVs of individuals,
, and the selection gradient,
. We start with the selection gradient and subsequently calculate
The denominator of the selection gradient, the standard deviation of the selection criterion,
, depends on the phenotypic variance,
, and on the covariance between phenotypes of group members,
. The general expression for the phenotypic variance follows from Equation 1, giving
. The covariance between phenotypes of associates also follows from Equation 1,
. Next, it follows from
. Thus the selection gradient equals
The variance of the total breeding value equals
(Equation 2). The covariance between phenotypic values and TBVs of individuals equals
(Bijma et al. 2007). Finally, response to selection follows from
Expected sire and dam covariances in the design of Wolf (2003):
In the experimental setup of Wolf (2003), flies were kept in pairs of two individuals that were full sibs, half sibs, or unrelated. With unrelated individuals, the same two half-sib families were always combined in a pair. Dams were nested within sires; each sire was mated to exactly two dams and each dam to a single sire only.
Investigation of the expectations of sire and dam variances presented in Wolf (2003) shows that they are incomplete. Consider, for example, the case where tube members are half sibs. In this case, the sire covariance between two individuals in different groups (tubes) equals
, instead of
as listed in Table 1 of Wolf (2003), i.e., a difference of
. This extra
originates from the full-sib relationship that exists between a focal individual and the tube member of a half sib of the focal individual, which is not accounted for in Wolf (2003). When each sire is mated to exactly two dams, the group member of a half sib of the focal individual must be a full sib of the focal individual (in the case where tube members are half sibs). Note that
is not a variance, but a covariance. For example, if estimates presented in Wolf (2003) would have been the true values, so that
, then the expected sire variance would equal −2666, i.e., a negative value. Hence, a sire plus dam-within-sire model is inappropriate, because the expected sire variance may be negative for combinations of direct and indirect genetic variances that are biologically possible.
To investigate whether direct and associative genetic variances can be estimated from the data structure used in Wolf (2003), we derive general expressions for expectations of sire and dam covariances. We distinguish between (i) covariances between individuals in the same group and (ii) covariances between individuals in different groups. The expected covariance between two individuals, i and j, in the same group is
(A1)and in different groups is
(A2)in which
is relatedness between i and j,
is relatedness between i and the group member of j,
is relatedness between j and the group member of i, and
is relatedness between the group members of i and j. Table A1 shows expected covariances between full sibs, half sibs, and unrelated individuals for each of the three tube compositions used in Wolf (2003). The expectations of sire and dam covariances given in Table 3 differ from those presented in Wolf (2003), primarily because covariances due to group members of full and half sibs are incomplete in Wolf (2003).
TABLE A1
Expectations of full- and half-sib covariances in the data structure used in Wolf (2003)
It follows from Equations A1 and A2 that
can be separated only when
. Thus the information in the data to separate
depends on whether and how frequently
occurs. It follows from Table A1 that
occurs only in the situation where groups consist of two unrelated individuals and only when relatedness between the two group members of both individuals differs from that between both individuals considered. (see Table A1 footnote). Whether or not that situation occurs in the data depends on details of the experimental implementation that are not described in Wolf (2003). Thus, the data structure used in Wolf (2003) provides either very little or no information at all to separate the direct and the associative heritable variance.
To validate this theoretical result we performed Monte Carlo simulation of the data structure used in Wolf (2003), where, in the case of groups consisting of unrelated individuals, group members of full sibs were full sibs as well, and group members of half sibs were half sibs as well. Hence, theoretically this data structure should not contain any information to separate
. Statistical analyses of the simulated data using Equations 4 and 5 as a model showed that the matrix of second derivatives of the likelihood (Fisher's information matrix) was singular, indicating that there are an infinite number of combinations of parameters that yield the identical maximum likelihood. Indeed identical maximum likelihoods were observed for multiple combinations of
, which confirms the theoretical result derived above.
We thank Henk Bovenhuis, Bart Ducro, Charles Goodnight, Han Mulder, Jeroen Visscher, Patrick Wissink, and Addie Vereijken for stimulating discussions during the development of this work and Hendrix Genetics for providing the data on laying hens. Part of this work was funded by Senter Novem, The Netherlands.
Communicating editor: J. B. Walsh
Received June 27, 2006.
Copyright © 2007 by the Genetics Society of America
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Gene Knock-Ins in Drosophila Using Homology-Independent Insertion of Universal Donor Plasmids
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Lysias Conference Dauphine Mooting and Public Speaking Competitions: an exceptional evening with Dauphine law students, a prestigious jury and an audience of peers.
The room was cloaked in a near-religious silence, the tension palpable. Time seemed suspended in Université Paris-Dauphine's Raymond Aron auditorium where the audience was the only sign that this was a public event. The competitors, waiting two by two were ready, totally concentrated, and you could tell from their faces just how important the Lysias Dauphine Mooting finals are to them.
The 80 contestants, all 1st and 2nd year Dauphine Bachelor's students, had been preparing for months, competing in successive qualifying rounds before making it to the final face-off.
Their goal? Win the Dauphine Lysias ooting competition and go on to represent Paris-Dauphine in the Lysias Conference's National Championship, France's premier mooting competition.
Over 200 spectators turned out in support of the contestants – motivated by the quality of the competitors, by the suspense of the competition itself, and by the exceptionally distinguished jury. Frank Berton, a prominent member of the Paris Bar and criminal lawyer who has defended Florence Cassez (a French citizen wrongly condemned and held in Mexico on kidnapping charges) and Salah Abdeslam( currently being held on terrorist charges), presided over the Mooting Jury. Other members included lawyers Charles Cazals of Cazals & Associés, Stéphane Baller of EY Société d'Avocats, and Nelson Dijoux Coquillas of White and Case LLP, as well as political figure Nicole Guedj, lawyer, humanitarian activist, and former French secretary of state (junior minister) for victims' rights.
The evening was all the more spectacular thanks to Dauphine's Public Speaking competition for 3rd year Bachelor's and Master's students, held at the same time as the Mooting final. Students debated, incisive and perspicacious, sometimes witty, sometimes caustic, but always vivacious, in front of a benevolent but uncompromising jury.
Kudos to Milo Fontaine and Stanislas Kulaga, winners of the Mooting Final in the 1st and 2nd year Bachelor's categories. And hearty congratulations to Kamel Derouiche – a 2nd year Master's student in Advanced Corporate Law (122) winner of the Public Speaking Competition 2017!
"The Dauphine Law Association is honored to have hosted the annual Dauphine Lysias Mooting and Public Speaking competitions on March 3rd 2017 in Paris-Dauphine's Raymond Aron amphitheater. We would like to congratulate all of the finalists on their excellent performance, thank all of the law professionals and Mrs. Nicole Guedj who generously accepted to take part in the competition's jury, the public for having turned out in number to encourage the contestants, Université Paris-Dauphine for making the Raymond Aron auditorium available to the competition, and all of the partners who made this event possible: Paris Sciences et Lettres (PSL), EY Société d'Avocats, Oxynomia Associés, Simon Associés, Capavocat, Dalloz, Lexis Nexis, Editions Francis Lefebvre, Lydia, and Dauphine Travel. Last but not least, we would like to extend our warmest thanks to Lysias National Conference and to its president, Théotime Aubert, former president of the Dauphine Law Association."


text:
Q: Why does mutation make a react component non-reusable on it's own? So I'm reading the react doc on HOC's (found here: https://reactjs.org/docs/higher-order-components.html) and there's this section:
"Resist the temptation to modify a component's prototype (or otherwise mutate it) inside a HOC."
function logProps(InputComponent) {
  InputComponent.prototype.componentWillReceiveProps = function(nextProps) {
    console.log('Current props: ', this.props);
    console.log('Next props: ', nextProps);
  };
  // The fact that we're returning the original input is a hint that it has
  // been mutated.
  return InputComponent;
}

// EnhancedComponent will log whenever props are received
const EnhancedComponent = logProps(InputComponent);

"There are a few problems with this. One is that the input component cannot be reused separately from the enhanced component."
Why is the mutated input component not considered reusable while the wrapped component is?
Edit: If you think this section of the docs is just nonsense, I'd accept that as an answer if there's somewhat of an explanation.

A: Modifying InputComponent's prototype changes the definition of what an InputComponent is, and if affects every instance of InputComponent.
If you modify the prototype in a private, encapsulated way -- i.e., in a way that nobody knows about -- then nobody knows what an InputComponent is anymore, so they can't reliably use it.

redpajama_set_name: RedPajamaStackExchange


Good afternoon Muncie! Today will be another warmer than average day. The average high for today is 41 degrees and we will be at least 15 plus degrees above that today. Clouds will be here to stay throughout the afternoon with a pop up shower possible. Winds will be slow down as the afternoon progresses.


008 The Write Idea Workshop - Mega Holiday Hooplah!
This, the 8th episode of THE WRITE IDEA WORKSHOP is a special, super crazy double-jointed holiday ep without all the seasonal thingamajigs. Instead, just lots of talk about writing, comic books, poetry and heaps of other stuff!
This episode features chats with Jac Kincaid (http://www.underless.com) and Sean Corbin (http://antilachia.wordpress.com/ and http://electricbluegrass.tumblr.com/).
Give it a listen and have a happy holiday!
This episode welcomes back Matt Wexler to the podcast, and for the first time in this podcasts' very short history, three speaking voices are featured through-out! Gasp and wail!
In this episode, the fellows discuss at legnth Point of View/Narration, Fan Fiction, and the significance/ reliance on dues es machina in modern story telling.
This episode features harsh language, some pretty good readings of original work, and a challenge most challenging!
006 The Write Idea Workshop - The World Ends Soon!
In this episode, Justin and Ryan discuss the complexities of being a vampire, give a few well-deserving shout outs, talk about more than enough writerly things to fill an hour, and throw in a little mouth music while they're at it.
This is the Write Idea Workshop!


We thought the snow had left us after it was 60 degrees, but it sure showed us that it isn't over. King is obsessed with shoveling…like watches out the window all day to go shovel and tells me all day that he needs his coat on, boots, and to go outside. I love that he is such a little helper. However, I may have told him that mom's don't shovel ha and only dads do because I don't want to be outside all day every day ha. He would stay outside and shovel nana's house, the sidewalks, neighbors, everyones house all day if I would let him.
Teaching King a good work ethic is something so important to me, I want him to learn to serve others, and that helping others is something so important. I try so hard to lead by example, but how is the best way to teach your kids service, work ethic, and everything else? I truly believe that each of us at mama's are doing the very best we can with our littles, that we want them to be good people with good values when they grow up. I feel like it takes a village to raise our little ones and you guys are my village, the people who I look to for support and love. Help a mama out pretty please?
Well, until then I will just keep doing my best with the things I know, leading by example, and praying that I don't screw up this sweet little boy of mine. Being a mom is so hard sometimes, but I truly do my best and love this sweet boy more than he will ever, ever know.
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It was a cold wet February evening in 2003 and I was out for a quick 20 miles after work as part of my preparation for a ride with a few friends from Montpellier over the Ventoux through Paris and back to home in the UK to was to celebrate us all hitting 50 (years old that is, not miles per hour).
Anyway, there I was, screaming down a hill at @ 30mph when a cat ran in front. I hit the cat, I went over the top, the cat squealed, the cat ran away. I rolled in a heap. Darn it I though, I've scratched my handlebars, bent my wheel and my arm hurts.
Actually my arm hurt a lot so off to casualty I went.
They looked at me, held my hand and said I was a silly boy for cycling so fast especially in the dark and wet then added that they suspected I had dislocated my shoulder and it was going to hurt when they it back in place. That was just what I wanted to hear (not).
Naturally I was all wrapped up for the cold weather so getting stuff off was rather painful so they used scissors on best cold wet weather top. It was then that I mentioned that my left knee felt wet, so they used scissors on best cold wet weather bottoms. they came and there in all its glory was my entire left knee cap: The knee has split from one side to the other. X-Rays confirmed that while there was no structural damage to the knee I had indeed dislocated my left shoulder and broke my left arm. Bugger, I thought, looks like I'm not going home tonight. Looking on the bright side though, they gave me a general anesthetic while they fixed me so I didn't have the pain of the shoulder being put back.
Anyway, I went for a ride on a hospital trolley, loads of people in gowns and masks bore down on me and I went to sleep. By the time I woke up, magic had happened and after three days in hospital and six weeks in slings and things, I got better and I got back on my bike (April) and restarted (gingerly at first) my training and in June that same year, I and a few others, flew to Montpellier, were reunited with our trusty steeds and cycled home via Mont Ventoux (see 'The Fondue Ride') in 10-days.
The Ventoux was a struggle but I did it.


We have approved an Ethics Code that establishes standards of professional activity and ethics for all our employees, including members of the Board of Directors, Management Board and Internal Audit Commission.
Members of our Board of Directors are elected at our annual general shareholders' meeting by cumulative voting. Each director is elected for a term that expires at the next annual general shareholders, meeting and may be reelected for an unlimited number of periods. Our Board of Directors currently consists of eleven members, including five independent directors. According to our charter the Board of Directors has the authority to make the principal management decisions for TMK, except in respect of those matters reserved for the shareholders.


Passionate about data analytics, including machine learning and high performance computing, Janet Morss works in product marketing. Her favorite: Sharing the amazing impact our customers have on people's lives using technology. Prior to joining Dell EMC, Janet worked in HPE Server Strategy, Planning and Operations with a focus on SMB and Enterprise solutions. With multiple degrees and a love of learning, Janet is a start-up style marketer from Colorado who loves to snowboard.
Ladies and Gentlemen, Start Your Engines: The Race to SC18 is On!


A man needed hospital treatment after being assaulted in Glossop last weekend.
Sometime between midnight and 1am on Sunday (September 4), a man was walking along Smithy Fold with his friends when he spoke to another man.
He was then punched in the face, sustaining a broken jaw and requiring treatment in hospital.
Anyone with information is asked to contact Detective Constable Laura Weston on 101, quoting reference number 16000271119, or call Crimestoppers on 0800 555 111.


And my monthly Thank you to my sponsors!
Thank you West Seattle Fabric Shop! They are very excited about their rag quilt kits! I love the pirate one! Which one is your favorite?
Sewing Parts Online! Have you found a great goodie from them yet?
My mother and wonderful Mary Kay director, Jenny Mullins for Mary Kay! I haven't tried the Translucent Powder yet, but I want to!
The awesome Kids Clothes pattern maker, Dear My Kids! How cute is this little tunic?
And last but not least! Michele from Michele Made Me! Check out her shop for great ideas to do with your recycling!
Man, I keep saying I'm going to sew clothes for me and the kiddo, but what keeps emerging from the sewing machine are quilts!
Here's another one! This one a friend asked me to make for her and her co-workers to give to their boss as a baby present. Her requests were an under-the-sea creature fabric, blues and greens and the baby's name on it.
As soon as she said she wanted the name on it, I knew I wanted do to the name just like this! I have never quilted words before and Was excited to give it a try! And once I knew the fabric we were using I made sure to fuzzy cut the anilamls into the letters. I am very excited about how the name turned out.
I was a bit worried about finding the fabric, honestly. Everything I kept finding just wasn't exactly what we wanted. I didn't want to order online, because she needed it NOW. And I wanted it to be something I liked too, since I was going to be sewing it. Online I kept finding things I liked, but they weren't the right colors or the creatures just weren't right. Or I would find cheesy. Um, nope! And then I found this! Local. We both loved it!
The sea creatures inspired the quilting. The design I came up with had some large blocks that I knew would need some quilting on the inside of the blocks. I quilted around the animals for the bit of quilting needed inside the large blocks.
I debated and debated about the color of the letters. My husband didn't think they stood out enough. But I didn't want them to stand out any more than they do. So I was content, until I started second guessing myself. In the end I kept it the way it was and perfectly please with the decision!
And my friend was in love! Whenever I make something for someone and go off in my own direction, I always worry if they will like it or not. So, needless to say, seeing her excitement put me at ease!
I'd for the title to be shopping, but it's just looking! As you know, we recently took our yearly trip to my home in Alabama and our family beach house.
See that screened in porch?? That's where I LOVE to sew and where I was telling you about in this post.
But we didn't start off our visit at the beach. We started in Montgomery visiting my Dad and Stepmom. Where we had our yearly trip to the zoo and Max had his first bowling adventure with the funny shoes!
There was a day or two when the grandparents had to work, so the kiddo and I went and checked out some flea markets! The kiddo got lots of vintage books and I got to look at some great quilts! That blue hexi one was my favorite! I'm still in love with it and I still want it. I even negotiated the price a little, but they only came down from $175 to $150… It's totally worth $150, but I didn't have it to be spending on a quilt top this year! The other quilts were fantastic too. And, Ok, I couldn't help but through in this AMAZING vintage dress too!
I didn't get the hexi quilt, but I did find these hexi flowers that were way more in line with my budget, so I brought those home! And I'm pretty sure they are leftovers from the blue hexi quilt top!
Then the rest of trip looked more like this everyday!
We did leave the beach one day and headed inland to do some shopping. We were supposed to be going to a fabric shop, but somehow we missed it and they closed before we could get there….. I guess that's what happens when your the only girl shopping with 2 boys! Instead we found a few more antiquey flea markets and these were some of the quilts we found! These were more my price range, but I wasn't in love like I am with the blue one up top.
Then we finished all the trip like this again! Check out that amazing storm cloud! We watched it roll down the beach. We were going to wait it out, but it also brought wind and freaked out the little guy. As we were headed to the car we realized it was a HAIL cloud! Once we made it to the car, packed the car and got in, it was over! I'd never been hailed on at the beach!
I'll have to keep ya posted on what I do with those vintage hexi flowers I found! Do you have any summer plans?
The Winner of the Best Father Ever Goes To…..
Happy Father's Day to You….
Awesome Best Dad Ever Printable via Yesterday on Tuesday.
And the winner is…. You decide! And when you do, be sure to make him this super simple Happy Father's Day Award!
Be sure to go to Go To Sew to see how I made it! Seriously, it probably takes longer to gather materials than it does to make the award. Every great Dad deserves one!
And I know one kiddo who believes that the Best Dad Ever is the one who lives in our house! Thank you Daddy!
I'm back! Vacation was wonderful! As usual! Now we're just trying to get back in the swing of things and as I started unpacking I realized I had to clean my closet before I could put anything back in it. That has led to a huge purge. Of the whole house. And the spear heading of a neighborhood garage sale! I'll let you know how it goes!
But in the mean time I want to share our latest quilt from our do. Good Stitches Nurture Group! The March quilt was totally new to me!
The block is much more traditional than I typically do! It is also an applique block. Another thing I haven't done. The tutorial for the block is here at Tweety-Bee Stitches blog!
And it ends up we have a whole little section of just these fabrics at Stash, the shop where I work!
So a huge thank you to Debi, aka Tweety-Bee Stitches, for introduicing me to this new fabric collection and quilt block. And more importantly, for designing and quilt a wonderful quilt for our bee! Thank you!
OH, and I have some news I almost forgot to share! While I was on vacation I got emails from Melissa, our group host, and Rachel, the host of the bee itself, asking if I could be the new group host! Melissa had to step down due to some super fun family obligations. I was honored that they thought of me and of course I said yes! Yea!


Decom Offshore is a customer focused consultancy offering impartial advice and support when planning decommissioning, decontamination, demolition and land remediation projects.
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Environmental remediation removal of pollution or contaminants through independently developed solutions. Decom Offshore's impartial advice demonstrating (BAT) Best Available Techniques have saved their clients many millions of pounds.
With the ever increasing cost of sending waste to landfill, Decom Offshore's experience has allowed many millions of tonnes of material to be diverted from landfill and recycled. Waste Management & Managing waste is a critical part to reduce cost.
Decom Offshore have managed the divestment of large production sites including maximising revenue for their clients from the sale of redundant assets and plant, recovery of metals and recovering value in land and buildings.
Providing clients with demolition consultants who manage project health and safety risk and project management. From the early part of planning to provision of site based demolition engineers to manage complex projects to completion.


In 2011, the Oregon legislature passed the Farm Direct Marketing Law (FDML), which took effect in 2012. The law clarified licensing and food safety requirements for direct-to-consumer sales at farmers markets, farm stands, and similar venues. It also included a "cottage food" provision allowing farms to make and sell certain low-risk, value-added products from farm-grown ingredients, direct to consumer, without a food processor's license. Advocates predicted enhanced small farm viability through new products and revenue streams, market season extension, reduced processing costs, test marketing opportunities, and other avenues. Detractors warned the deregulation would cause outbreaks of foodborne illness. In 2016, the law's fifth year, we explored these predictions and early outcomes. We conducted a focus group with stakeholders and semistructured interviews with two key informants, 18 farmers, and 24 farmers market managers around Oregon. We found farmers making and selling a variety of value-added products under the FDML. We found no foodborne illness linked to FDML products. Interviewees described multiple benefits resulting from the law, many corresponding to predicted benefits. They also described unanticipated benefits at the community level. Interviewees identified barriers and recommended changes related to the law and related education. We discuss the feasibility of these recommendations as well as the long-term potential of the cottage food provision. We end by reflecting on the FDML as a whole, as it supports Oregon's direct market farming sector.


text:
Mark Twain: The Adventures of Tom Sawyer
26. CHAPTER XXVI
ABOUT noon the next day the boys arrived at the dead tree; they had come for their tools. Tom was impatient to go to the haunted house; Huck was measurably so, also -- but suddenly said:
"Lookyhere, Tom, do you know what day it is?"
Tom mentally ran over the days of the week, and then quickly lifted his eyes with a startled look in them --
"My! I never once thought of it, Huck!"
"Well, I didn't neither, but all at once it popped onto me that it was Friday."
"Blame it, a body can't be too careful, Huck. We might 'a' got into an awful scrape, tackling such a thing on a Friday."
"MIGHT! Better say we WOULD! There's some lucky days, maybe, but Friday ain't."
"Any fool knows that. I don't reckon YOU was the first that found it out, Huck."
"Well, I never said I was, did I? And Friday ain't all, neither. I had a rotten bad dream last night -- dreampt about rats."
"No! Sure sign of trouble. Did they fight?"
"Well, that's good, Huck. When they don't fight it's only a sign that there's trouble around, you know. All we got to do is to look mighty sharp and keep out of it. We'll drop this thing for to-day, and play. Do you know Robin Hood, Huck?"
"No. Who's Robin Hood?"
"Why, he was one of the greatest men that was ever in England -- and the best. He was a robber."
"Cracky, I wisht I was. Who did he rob?"
Buy a copy of The Adventures of Tom Sawyer at Amazon.com

redpajama_set_name: RedPajamaCommonCrawl


text:
CURC is a coalition that operates by consensus. We offer different types of general membership levels and associated dues for organizations that have different business structures – such as labor groups and academic institutions. Steering Committee members, as described below, function much like a Board of Directors for the organization. CURC also offers an Associate membership, which provides access to a limited set of activities and information that CURC generates at a lower cost. CURC offers membership at a reduced rate for startup companies and small businesses, as well as for international observers that are simply looking to understand technology initiatives in the U.S. and participate in CURC activities.
The CURC Steering Committee sets organizational priorities and activities each year, after which the majority of work is conducted through two Subcommittees – the Technical Subcommittee and the Government Affairs Subcommittee. Both Steering Committee and General Members are invited to participate in Subcommittee activities.
The CURC Steering Committee functions as the Board of Directors, developing organizational policies and positions on pending legislation, regulations, and other policy initiatives, as well as determining the annual budget and activities. Steering Committee members may serve in leadership positions within CURC, in addition to having the full suite of Subcommittee participation and information access available to General Members.
Technical Subcommittee
CURC's Technical Subcommittee is made up of technical experts representing CURC member organizations. Participation on the Subcommittee is open to all non-Associate members of CURC. The Subcommittee is responsible for assessing technology proposals and policies that impact the use and capability of technology in response to proposed regulations and in support CURC's mission. Much of the work of this Subcommittee is communicated to the Steering Committee, the General Membership, and the CURC Government Affairs Subcommittee to inform the agenda for those Committees and the overall direction of the organization.
The Technical Subcommittee is responsible for identifying a set of cost and performance goals, and outlining specific technology research, development, and deployment needs to ready a suite advanced fossil energy technologies that can collectively meet those targets. Members of the Subcommittee evaluate and assess each technology, including the annual development requirements and cost estimates for each stage of technology development. The Subcommittee uses this analysis to develop a set of recommendations that are embodied in the CURC-EPRI Advanced Fossil Energy Technology Roadmap, which serves as the basis for CURC's funding and program recommendations to the U.S. Department of Energy (DOE) and Congress. CURC's Roadmap recommendations have been adopted in several DOE programs, including those funding large-scale carbon capture pilot projects, Front-End Engineering and Design (FEED) studies for carbon capture projects, and the development of geologic storage sites for carbon dioxide derived from industrial processes.
The cost and performance projections included in the 2018 CURC-EPRI Roadmap also served as the basis for a study projecting the macroeconomic benefits of carbon capture and storage, entitled "Making Carbon a Commodity: The Potential of Carbon Capture RD&D."
Government Affairs Subcommittee
CURC's Government Affairs Subcommittee consists of CURC members' federal affairs representatives. The Subcommittee is responsible for helping to craft advocacy and lobbying strategies for the policies designed and recommended by the CURC membership. Subcommittee membership is open to all Non-Associate members of CURC. Notable activities of the CURC Government Affairs Subcommittee include:
Advocacy before Members of Congress, federal agencies, and the White House. The CURC Government Affairs Subcommittee is responsible for conveying CURC's policy priorities to lawmakers. Members assist with the development of policy and communications-related materials, and are used to advocate before Congress and the federal agencies in support of those policy goals.
Educating Policymakers and Stakeholders. The Subcommittee is the primary driver behind CURC's educational efforts on Capitol Hill. CURC routinely hosts briefings on Capitol Hill for Congressional staff, holds a semi-annual Technology Showcase, and deploys members to collaborate with industry and environmental stakeholders in support of technology development.
Participate in CURC's annual Capitol Hill Day. CURC's Capitol Hill Day is the flagship event for the CURC Government Affairs Subcommittee, during which all CURC members fly in to Washington, D.C. to meet with Members of Congress and their staff. CURC members routinely meet with over 50 Congressional offices during this event, touching on all issues relevant to technology development and deployment.
To download a copy of our membership materials, click here. Please contact CURC Associate Director Mike Weiner if you have any questions.
Please tell us about your interest in joining CURC


Elizabeth Arthur was born in New York City to an Irish-American father who wrote mysteries and fantasies and a Hungarian-American mother who wrote poetry and novels. She attended the University of Michigan with the intention of majoring in philosophy, but after two years dropped out of college and became an instructor at the National Outdoor Leadership School in Lander, Wyoming, instead. She met her first husband at NOLS and became a writer only after the two of them had emigrated to Canada, bought a tiny wilderness island in a lake in northern British Columbia, and built a house on it. Her first book, Island Sojourn, was a memoir published in 1980, and in the novels and second memoir that followed she has consistently focused her attention on literal or metamorphic islands.
William Sloane Fellowship in Prose, the Bread Loaf Writers' Conference, Middlebury, VT. 1980; Writing Fellowship, the Ossabaw Island Project, Savannah, GA, 1981; Grant-in-Aid, the Vermont Council on the Arts, Montpelier, VT, 1981-1982; Fellowship in Prose, the National Endowment for the Arts, Washington, D.C.,1982-1983; Master Artist Fellowship, the Indiana Arts Commission, Indianapolis, IN, 1988; Fellowship in Fiction, the National Endowment for the Arts, Washington, D.C., 1989-1990; Antarctic Artists and Writers Operational Support Grant, the National Science Foundation, Washington, D.C., 1990. In 1995 Antarctic Navigation was chosen by the New York Times as a Notable Book, received a Critics Choice Award from the San Francisco Review of Books and Today's First Edition and was chosen as a Best Book of 1995 by A Common Reader. In 1996 it received the Ohioana Book Award For Fiction from the Oioana Library Association.


text:
Book > Academic Books > Urban Geography
Killer Cities
Nigel Thrift - Tsinghua University, China
Published in association with Theory, Culture & Society
February 2021 | 264 pages | SAGE Publications Ltd
Killer Cities uses a combination of social theory, polemic and close attention to empirical detail to tell the story of how and why cities cause mass animal death and, in the process, hasten the destruction of the planet. This book is not just a lament, however. It is an attempt to navigate out of this mess of planned and unplanned violence towards a world in which cities no longer act as killers but become aligned with the lives of other beings. It offers pragmatic ways of diminishing the death toll and changing mindsets without ever minimizing the dilemmas that inevitably will have to be faced. Killer cities can be rehabilitated so that they offer brighter paths towards the future - for animals, for human beings, and for the planet. A new urban geography could be within our grasp. Indeed, it has to be, for all of our sakes.
Chapter 1: An uncommon humanity
Part I: Cities
Chapter 2: The Urban World
Part II: Life
Chapter 3: Thinking Animals
Chapter 4: Animals Thinking
Part III: Death
Chapter 5: The Animal City
Chapter 6: The City of Surplus Death
Chapter 7: Not Meat but Still Dead
Chapter 8: But Some Animals Do Adapt to the City
Part IV: A New Settlement
Chapter 9: Dreaming More Human Cities 1
Chapter 10: Dreaming More Human Cities 2
Chapter 11: There is Another World but It Is This One
You can't change the world without first seeing it through new lenses. Killer Cities shines a light on the ecocidal underbelly of urban life in a capitalist world. Using animals as a focus, Nigel Thrift advances concepts, arguments and evidence that might inspire us to make a very different urban future. The book is creative and hopeful in the face of formidable forces of mental and practical inertia.
Noel Castree
University of Manchester (England) and University of Wollongong (Australia)
This epic compendium on the ravages of planetary urbanism from one of geography's most generative thinkers is above all, as the title suggests, a provocation. Whether it inspires or infuriates, it cannot fail to force thought.
Sarah Whatmore
Professor of Environment and Public Policy, University of Oxford
People love cities. New York, Paris, Barcelona, London: These are the places where modern life has thrived. So much so that, by 2050, the United Nations predicts that almost 70 percent of the global population will live in cities. In the process, cities have become selfish places for humans to think only of themselves. In Killer Cities, Nigel Thrift invites us to include a broader menagerie into cities—many of which are already there anyway, but pinned under the boot of humanity. The result is liberatory—for people and creatures of all kinds, but also for cities themselves.
Ian Bogost
Killer Cities documents the long histories of violence done by cities, the killings, displacements and neglect. It also holds out the hope that cities can be reimagined as unfolding sites of experimental cohabitation. Thrift draws from a vast range of sources, celebrating those who recognize the multiple knots of obligation that human beings have to each other, to other-than-human beings and to the planet itself. It is a book for our time, as pandemics, climate change and the anthropocene increasingly unsettle the category of the human.
Penny Harvey
Professor Social Anthropology, University of Manchester
The breath-taking thesis of this book is that the re-cognition of cities and the future of humanity requires not only thinking about but with animals. To use a term the book itself employs, this is social theory as enjambment. In Killer Cities, thinking runs on and over - from species to species - without terminal punctuation.
Celia Lury
Director, Centre for Interdisciplinary Methodologies, University of Warwick
PaperbackHardcoverElectronic Version
VitalSource
Key Thinkers on Cities
Key Concepts in Urban Studies
Urban Studies Inside/Out
Circulation and Urbanization
Doing Global Urban Research
Urban Analytics
The SAGE Handbook of New Urban Studies


There are two main subjects on this week's podcast, united by the common theme or respect and what it really means.
Firstly there's the fallout from that Messi/Suarez penalty and why so many people got hot under the collar about it. A moment of genius or a slap in the face? Can something be both things?
More controversially, for fans of English football at least, are the supposed plans to shake up the FA Cup — plans that include moving to a more continental style tournament with midweek fixtures and the end of replays. For many this shows a complete lack of respect for the oldest tournament in the world. Are they right or should the competition move with the times?
Graham, Terry and Jan add their own opinions to these polarised debates, including an earth shattering proposal for the future of the much-maligned League Cup.


text:
City Lights at Night: Astronaut's Amazing View from Space
With hardware from the Earth-orbiting International Space Station appearing in the near foreground, a night time European panorama reveals city lights from Belgium and the Netherlands at bottom center. The picture was taken by members of the space station's Expedition 30 crew on Jan. 22, 2012.
(Image: © NASA)
A remarkable nighttime panorama taken from the International Space Station captured a dazzling cobweb of city lights as the orbiting complex flew roughly 240 miles (386 kilometers) overhead.
The captivating picture of Earth from space was taken on Jan. 22. The image shows the space station in the foreground, with the lights of Belgium and the Netherlands shining at the bottom center. The British Isles are slightly blocked by the station's solar array panels on the left, according to NASA officials.
The North Sea appears at the left center, and Scandinavia is at the right center, beneath the end of the space station's robotic arm.
The station is currently home to a truly international crew: two American astronauts, three Russian cosmonauts and a Dutch astronaut.
Commander Dan Burbank and Don Pettit of NASA, Russian cosmonauts Anton Shkaplerov, Anatoly Ivanishin and Oleg Kononenko, and European Space Agency astronaut Andre Kuipers make up the station's Expedition 30 crew.
In addition to amazing photos, the space station's crew has also recorded some stunning videos of Earth from space, including this one of comet Lovejoy over the planet at night:
Follow SPACE.com for the latest in space science and exploration news on Twitter @Spacedotcom and on Facebook.


text:
The San Remo Oil Agreement was an agreement between Britain and France  signed at the San Remo conference on 24 April 1920. As a result of the agreement, the French Compagnie Française de Petroles (CFP)
acquired a 25% share in the  Turkish Petroleum Company (TPC). The other shareholders were the Anglo-Persian Oil Company (APOC) with 47.5%, the Anglo Saxon Petroleum Co 22.5% and the remaining 5% Calouste Gulbenkian.

Background
On 19 March 1914, the British and German governments had signed an agreement whereby the interest of National Bank of Turkey in TPC was transferred to APOC. The newly reconstituted TPC then applied for a concession for Mesopotamian oil which was granted subject to various conditions at which point World War I intervened. In December 1918, the British expropriated the 25% share of Deutsche Bank in TPC.

It was this latter share that was ultimately to be given to the French under the San Remo oil agreement. There were prior abortive attempts at an agreement, preliminary and then final version of the Long-Bérenger Agreement, then the Greenwood-Bérenger Agreement before the final San Remo version. All versions can be seen at.

The agreement delimited the oil interests in Russia and Romania, British (British Mandate of Mesopotamia) and French colonies. The initial agreement takes the names of the British petroleum minister, Sir Walter Long, and the French petroleum minister, Henri Bérenger, who negotiated the agreement.

See also
Partitioning of the Ottoman Empire

Notes

References

France–United Kingdom treaties
1919 in France
1919 in the United Kingdom
Aftermath of World War I in France
Treaties concluded in 1919
Treaties of the United Kingdom (1801–1922)
Treaties of the French Third Republic
Energy treaties
Petroleum politics
Aftermath of World War I in the United Kingdom
April 1919 events

redpajama_set_name: RedPajamaWikipedia


Full Name: ERTICO – ITS Europe – European Road Transport Telematics Implementation Coordination SCRL.
ERTICO – ITS Europe is a multi-sector, public/private partnership pursuing the development and deployment of Intelligent Transport Systems and Services (ITS). ERTICO connects public authorities, industry players, infrastructure operators, users, national ITS associations and other organisations to work together to bring "Intelligence into Mobility". The ERTICO work programme focuses on initiatives to improve transport safety, security and network efficiency whilst taking into account measures to reduce environmental impact. Our vision is of a future transport system working towards zero accidents, zero delays and fully informed people, where services are affordable and seamless, the environment is protected, privacy is respected and security is provided.


The good news about Catalina St. is the unrestricted free parking. The bad news is the high crime rates. I parked my car here when I attended classes at the University of Southern California. I saw dozens of cars brokem into over the years. Fortunately, my car was never one of them.
The residents here most likely have minimum wage jobs. There are mostly large apartment buildings suitable for students and low income families.
Travelling to South Catalina St?