How domestication bottlenecks and artificial selection shaped the amount and distribution of genetic variation in the genomes of modern crops is poorly understood. We analyzed diversity at 462 simple sequence repeats (SSRs) or microsatellites spread throughout the maize genome and compared the diversity observed at these SSRs in maize to that observed in its wild progenitor, teosinte. The results reveal a modest genome-wide deficit of diversity in maize relative to teosinte. The relative deficit of diversity is less for SSRs with dinucleotide repeat motifs than for SSRs with repeat motifs of more than two nucleotides, suggesting that the former with their higher mutation rate have partially recovered from the domestication bottleneck. We analyzed the relationship between SSR diversity and proximity to QTL for domestication traits and observed no relationship between these factors. However, we did observe a weak, although significant, spatial correlation for diversity statistics among SSRs within 2 cM of one another, suggesting that SSR diversity is weakly patterned across the genome. Twenty-four of 462 SSRs (5%) show some evidence of positive selection in maize under multiple tests. Overall, the pattern of genetic diversity at maize SSRs can be explained largely by a bottleneck effect with a smaller effect from selection.
BETWEEN 5000 and 10,000 years ago, humans domesticated virtually all major crop species used by modern agricultural societies (Smith 2001). This feat was accomplished through artificial selection for traits that improved agronomic qualities. As a result of this process, favorable alleles at loci controlling agronomic traits were brought to fixation in the population during the domestication period. After the initial domestication, the continued practice of selective breeding allowed additional favorable alleles to sweep through the crop species, while diversifying selection in response to the different environments encountered during the geographic expansion of the crop caused regional fixation of distinct favorable alleles. As a consequence of this complex history of selection, only a limited portion of the population contributed to each subsequent generation. Some anticipated consequences are a genome-wide loss of diversity at unselected genes because of the genetic bottleneck effect, a severe reduction in diversity at genes under directional selection during domestication, and artificially high diversity at genes under diversifying selection.
These two processes—selection targeted on agronomic genes and drift due to the domestication bottleneck affecting the entire genome—are the principal factors that influence the amount and distribution of genetic variation in crop genomes as compared to their wild progenitors. Studies on isozymes and gene sequences revealed a general reduction of genetic variation in crops as a result of the domestication bottleneck (Doebley et al. 1984; Eyre-Walker et al. 1998; Hilton and Gaut 1998); however, these exploratory studies involved relatively few loci and thus the generality of their results needs confirmation. Our knowledge of the impact of selection on diversity in crops is more restricted since very few agronomic genes have been identified and characterized for their level of genetic diversity (Wang et al. 1999; Whitt et al. 2002; Tenaillon et al. 2004). Thus, our present picture of how drift and selection have sculpted the diversity landscape of crop genomes is fragmentary.
To begin to better define genetic diversity in the maize (Zea mays ssp. mays) genome and to identify the forces that have shaped it, we have constructed a diversity map of the maize genome using microsatellites or simple sequence repeats (SSRs). We scanned the genomes of maize and its close wild relatives, annual teosinte (Z. mays ssp. huehuetenangensis, ssp. mexicana, and ssp. parviglumis), using 462 SSRs. The phylogenetic relationships of these taxa are well known (Doebley 1990; Buckler and Holtsford 1996) and ssp. parviglumis has been shown to be the progenitor of maize (Wang et al. 1999; Matsuoka et al. 2002b). Because of this well-characterized phylogeny, maize and annual teosinte provide a good model for the analysis of the genetic consequences of domestication. The goals of this study are (1) to provide a general picture of genetic diversity for SSRs in maize and teosinte, (2) to determine if there is heterogeneity in diversity among genomic regions, (3) to measure the relative impact of selection vs. drift on the observed pattern of diversity, and (4) to assess the degree to which mutation has allowed SSRs to recover diversity lost from the effects of domestication.
We sampled individual maize plants from a set of 45 landraces covering the entire pre-Columbian range of maize. We also sampled 45 annual teosinte plants representing three wild taxa: Z. mays ssp. huehuetenangensis (1 plant), ssp. mexicana (23 plants) and ssp. parviglumis (21 plants). Passport data for the plants are available at www.genetics.org/supplemental (Table S1).
We used 462 SSRs, representing a variety of repeat types from dinucleotide to hexanucleotide motifs, distributed throughout the genome. These SSRs were divided in two groups, dinucleotide and "other" repeat SSRs, because the mutation rate for dinucleotide SSRs is higher than that for other SSR types (Vigouroux et al. 2002a). Detailed information on the SSRs used in this study including their genetic map position is available at www.genetics.org/supplemental (Table S2). The source of SSRs, whether from expressed sequence tags, known genes, or SSR-enriched genomic libraries, is available at www.maizgdb.org (see also Sharopova et al. 2002). SSR genotyping was done on automated sequencers at Cornell University (Ithaca, NY), Pioneer Hi-Bred International (Johnston, IA), and Celera AgGen (Davis, CA), following procedures that have been published elsewhere (Matsuoka et al. 2002b).
Gene diversity or heterozygosity (H), the number of alleles (N), and Fst between maize and teosinte were calculated using the software program Fstat (Goudet 2001). The significance of Fst was assessed by 10,000 resamplings of the genotypic data. To measure the relative deficit of gene diversity (GD) in maize vs. teosinte, we have defined a parameter ΔGD = 1 − (HM/HT), where HM and HT are genetic diversity in maize and teosinte, respectively. If HM is higher than HT, then we calculated this parameter as ΔGD = (HT/HM) − 1. The relative deficit of the number of alleles is Δallele = 1 − (NM/NT), where NM and NT are the number of alleles in maize and teosinte, respectively. If NM is higher than NT, then we calculated this parameter as Δallele = (NT/NM) − 1. These statistics vary between −1 and 1, positive when diversity is higher in teosinte and negative otherwise. The Wilcoxon signed-rank test (W), Kruskal-Wallis test (KW), and Mann-Whitney test (MW) were performed using SYSTAT (SPSS, Chicago).
The relationship between s, r, and diversity statistics (Δallele, ΔGD, or Fst) is complicated and there is no known function to describe it. Therefore, we took an ad hoc approach. Two different functions (f) were investigated: a linear monotonic decrease f(r) = 50 − r and an exponential decrease f(r) = e−λr. For the latter, we used two different values (1 and 5) for λ. The QTL effect is almost zero for λ = 1 after 10 cM and for λ = 5 after 2 cM. For each particular function, if r > 50 cM, the QTL effect was considered to be zero. Spearman correlation coefficients between QE and diversity statistics (Δallele, ΔGD, or Fst) over all SSRs were calculated. Only SSRs placed on the IBM v3 map were tested (www.maizgdb.org).
where xi and xj are the chromosomal map positions of two SSRs, Z(xi) and Z(xj) are the values of their diversity statistics, and N(h) is the number of pairs of SSRs separated by a distance h or less (Armstrong 1998). Three different values of h were investigated: 1, 2, and 5 cM.
follows a t-distribution with n − 1 d.f. There is no objective criterion for the sample size n, so we chose the five points that were the closest to the location x. Outliers were excluded at the 95% significance level.
To test if a particular value of the semivariance is significantly different from a random effect, we used permutation tests in which the diversity statistics for the SSRs were randomized with respect to chromosomal position. One thousand permuted data sets were generated and the probability of finding a value higher than the observed value for a distance class was then calculated using the distribution of the permuted data.
The Ewens-Watterson test of neutrality enables one to detect deviations from a neutral-equilibrium model as either a deficit or an excess of genetic diversity relative to the number of alleles at a locus (Ewens 1972; Watterson 1978). This test was performed using the program Arlequin (Schneider et al. 2000). The probability that an SSR fits the neutral expectation under this test was assessed using both the homozygosity test (PH) and Slatkin's (1994, 1996) exact test (PE).
The degree of differentiation between populations at a locus as measured by Fst can be used to assess whether SSRs show more differentiation than expected under a purely neutral (drift) model (Bowcock et al. 1991; Beaumont and Nichols 1996). We tested whether Fst between maize and teosinte at SSRs is greater than expected by the domestication bottleneck effect (drift) alone. To do this, Fst was conditioned on the total number of alleles in maize and teosinte to control more effectively for the variable mutation rate among maize SSRs (Vigouroux et al. 2002a). Three different mutation models were investigated (see below). We set the 95% confidence limits for this one-tailed test using coalescence simulations that incorporate genetic drift due to the domestication bottleneck (see below). We refer to this as the Fst test.
Both selection and drift during domestication are expected to reduce gene diversity in maize relative to teosinte. To ask whether SSRs have less variation in maize relative to teosinte than that expected from drift alone, we compared gene diversity in maize vs. teosinte for our observed data with the 95% confidence limits for these parameters established by simulations as a two-tailed test (see below). We refer to this as the GD test.
The Fst and GD tests ask whether divergence between maize and teosinte or gene diversity in maize relative to teosinte deviates from a neutral model that incorporates the domestication bottleneck. To establish 95% confidence limits for these tests, we performed coalescence simulations (Hudson 1990; see also Vigouroux et al. 2002b). The model for the simulations involves a crop (maize) that split at some time in the past from its progenitor (teosinte). The maize population undergoes a "bottleneck" during the domestication period and then expands rapidly to a large size while the progenitor population remains at equilibrium from the time of divergence until the present (Eyre-Walker et al. 1998; Hilton and Gaut 1998). A sample size equivalent to our experimental samples of maize and ssp. parviglumis was used. Separate topologies for maize and ssp. parviglumis were simulated first and then the coalescence times for each node in these topologies were added. The bottleneck in the maize topology was taken into account by rescaling the coalescent times during the bottleneck by the ratio of the effective population size of maize during the bottleneck (Nb) divided by the size after expansion (Nm). The nodes of these two topologies at the time of the split between maize and ssp. parviglumis were then treated as a new sample for another simulation to create a single topology combining maize and teosinte.
of 3.2 determined from a mutation-accumulation study for maize SSRs (Vigouroux et al. 2002a; see also Pritchard et al. 1999).
For the simulations, we must estimate the time of divergence of maize and its progenitor, the effective population size of the wild progenitor, the effective population size of maize during the bottleneck and after its expansion, the duration of the bottleneck, and the mutation rate for SSRs. The time of divergence was set at 7500 years (Iltis 1983). The ssp. parviglumis effective size was fixed to 40,000 (Vigouroux et al. 2002a). The duration of the bottleneck and the effective sizes of maize during and after the bottleneck are unknown, but these parameters are not independent from each other. For estimating the relationship between these parameters, we developed a mathematical model for maize domestication using the GSM (see appendix). Fixing the effective population size of the expanded population of maize to 1 million, we simulated bottlenecks of lengths 100, 200, 500, 1000, and 2500 years and determined their corresponding effective population sizes to be 107, 220, 553, 1117, and 2875. We have used these values for the simulation.
The mutation rate for maize SSRs is variable among loci and the mutation rate for any individual SSR is unknown (Vigouroux et al. 2002a). Therefore, we have chosen for each simulation a value of this parameter by the following approach. First, a value for gene diversity (or number of alleles) was picked at random from between 0 and 1 (or between 1 and 51 for number of alleles). Second, the mutation rate that gives this gene diversity (or number of alleles) at equilibrium in ssp. parviglumis was calculated and used for simulations. Third, we constrained the mutation rate to be >5 × 10−7 in accordance with empirical data (Vigouroux et al. 2002a).
Fst (as described in Weir 1996, pp. 181–182), gene diversity, and the total number of alleles for both maize and ssp. parviglumis were calculated from the results of 500,000 simulations for each mutation model. This information was then used to estimate the median values and the 95% confidence intervals. As gene diversity is a continuous variable, the expected value of the parameter was calculated using a sliding window of ±0.0125. To analyze how well the simulated results fit our actual data, we took two approaches. First, we constructed decile curves with the simulated data and calculated the number of actual SSRs lying between two decile curves for the Fst by the number of alleles' distribution (Bowcock et al. 1991). If the model fits the data perfectly, the number of SSRs lying between two deciles curves should be one-tenth of the total number of SSRs studied. Second, we calculated the mean Fst on the basis of the simulation results for a given number of alleles. Then, we used these mean values to calculate an overall expected mean Fst for a set of SSRs with the same numbers of alleles as observed in the actual data. We then compared this mean Fst for the simulated data with that for the actual data. The same two procedures were used to compare the fit between the actual and simulated data for gene diversity except that the mean expected gene diversity in maize was conditioned on observed gene diversity in ssp. parviglumis.
Maize possesses less variation at SSRs than does teosinte, whether measured as the number of alleles or as gene diversity (Table 1). Over all SSRs, the average number of alleles is significantly lower in maize landraces (9.0) than in teosinte (11.8; W test, P < 0.001). The relative deficit in allele number or Δallele is 0.24, meaning that maize has 24% fewer alleles than teosinte. Gene diversity is also significantly lower in maize (0.64) as compared to teosinte (0.74; W test, P < 0.001) with a ΔGD of 0.12 or a 12% deficit in maize relative to teosinte. The deficit in the number of alleles (24%) is significantly greater than the deficit in gene diversity (12%; W test, P < 0.001).
Our prior work on mutation rates for maize SSRs indicated that SSRs with dinucleotide repeat motifs have a much higher mutation rate than SSRs with trinucleotide or larger motifs (here called "other repeat SSRs"; Vigouroux et al. 2002a). This difference in mutation rates is reflected in the diversity statistics (Table 1). Dinucleotide SSRs have more alleles than other repeat SSRs both in maize (MW test, P < 0.001) and in teosinte (MW test, P < 0.001). They also have a higher gene diversity in both maize (MW test, P < 0.001) and teosinte (MW test, P < 0.001). Therefore, in addition to analyses using all the markers, we performed separate analyses for dinucleotide and other repeat SSRs.
For both dinucleotide and other repeat SSRs, the average number of alleles is higher in teosinte than in maize (W test, P < 0.001 and P < 0.001, respectively); however, the relative deficit in the number of alleles (Δallele) is greater for other repeat SSRs than for dinucleotide SSRs (MW, P < 0.001) (Table 1). Maize shows a relative deficit of 28% for the number of alleles at other repeat SSRs, but a deficit of only 19% for dinucleotide SSRs. Gene diversity exhibits the same trends with a higher diversity in teosinte than in maize for both dinucleotide (W test, P < 0.001) and other repeat SSRs (W test, P < 0.001), but with ΔGD being greater for other repeat than for dinucleotide SSRs (MW test, P < 0.001).
Fst between maize and teosinte is low with an average value of 0.071 ± 0.004. Overall, the differentiation between maize and teosinte is highly significant (P ≪ 0.001). Out of the 462 SSRs, 368 exhibit an Fst that is significantly >0 at a noncorrected P-value of 0.05. Mean Fst is higher (MW, P < 0.001) for other repeat SSRs (0.087 ± 0.005) as compared to dinucleotide SSRs (0.044 ± 0.004). There is no difference between dinucleotide and other repeat SSRs in the proportion showing a significant Fst (G-test = 0.63, P = 0.43). Fis is 0.38 ± 0.010 for maize and 0.43 ± 0.009 for teosinte. Fis is similar for dinucleotide and other repeat SSRs for both maize (MW, P = 0.62) and teosinte (MW, P = 0.13).
The QTL for plant and inflorescence architecture that differentiate maize and teosinte are mostly found on chromosomes 1–5 (Figure 1; Doebley and Stec 1993). Therefore, if selection on these QTL during domestication caused a severe loss of diversity, one might expect some chromosomal effect on diversity. When all the SSRs are considered, we found no chromosome effect for the parameters ΔGD (KW, P = 0.38) and Fst (KW, P = 0.22), but a significant effect for Δallele (KW, P = 0.006). If we considered dinucleotide SSRs (Δallele, KW, P = 0.11; ΔGD, KW, P = 0.12; Fst, KW, P = 0.83) and other repeat SSRs (Δallele, KW, P = 0.08; ΔGD, KW, P = 0.40; Fst, KW, P = 0.37) separately, there are no significant associations. However, if we combined the two probabilities for Δallele for dinucleotide and other repeat SSRs using Fisher's method for combining probabilities (Sokal and Rohlf 1995), we observe a significant chromosome effect (P = 0.049). This result suggests that the chromosome effect is driven by both kinds of repeats. Chromosome 4 has the highest value for Δallele followed by chromosomes 6, 10, 7, 8, 5, 9, 1, 3, and 2 in descending order.
Plot of Fst and ΔGD along the genetic map of maize. Fst and ΔGD are plotted as a function of the distance in centimorgans along the 10 chromosomes of maize. A representation of domestication QTL effect is shown as a shaded area (see text for details).
We can also test if selection on domestication QTL has affected genetic diversity in windows surrounding the individual QTL. If one visually examines the relationship between ΔGD or Fst and QTL effect, there is no obvious correlation (Figure 1). At the large-effect QTL region on chromosome 1, neither ΔGD nor Fst is particularly large. The same is true for the large-effect QTL regions on chromosomes 2, 3, 4, and 5. Indeed, SSRs with exceptionally large values of Fst or ΔGD appear randomly dispersed along the chromosomes.
For a more definitive analysis of the relationship between QTL and SSR diversity, we calculated the correlation between QTL effect and the diversity statistics for the SSRs. If all SSRs are considered together, we observe 1 significant correlation out of 12 between SSR diversity statistics and QTL effect (Table 2). If dinucleotide and other repeat SSRs are analyzed separately, there is also only 1 significant result among 24 tests. We conclude that there is no convincing evidence for a relationship between diversity statistics and QTL effect since single significant tests can readily result by chance alone when doing 24 tests.
In addition to domestication QTL, other spatial factors, such as distance from the centromere, could influence the distribution of diversity. To detect if neighboring SSRs exhibit a similar pattern of diversity, we calculated the semivariance of each of the diversity statistics: Δallele, ΔGD, and Fst. If diversity is spatially correlated along the chromosomes, then γ(h) for the actual data should be lower than that for a data set obtained by permuting SSRs. Using all SSRs and values of 1, 2, and 5 cM for h, we observed significant (P > 0.95) values for γ(h) for all of the diversity statistics (Table 3). The analysis using only other repeat SSRs gives a similar result. For dinucleotide SSRs, only Δallele and ΔGD show significance, perhaps because of the smaller number of dinucleotide SSRs and corresponding reduced statistical power. Thus, there is evidence that diversity at neighboring SSRs is correlated within recombination distances ranging from 1 to 5 cM. We note that significant spatial correlations are observed only when outlier SSRs were removed from the analysis. Outlier SSRs may result from the variability in mutation rate among SSRs or misplacement of SSRs on the genetic map.
To examine further whether the significant correlations in Table 3 are strictly dependent on the exclusion of outliers, we also calculated γ(h) using the P-values from the Fst test and the GD test for SMM (see below). The use of P-values reduces the noise introduced by differences in mutation rates among SSRs. For this analysis, we calculated the odds ratio of the P-value as ln(p/(1 − p)). Using all the SSRs, we found significant (P > 0.95) variogram P-values (with outliers, without outliers) from the GD test at 1 cM (P = 0.981, P = 0.969), 2 cM (P = 0.997, P = 0.995), but not at 5 cM (P = 0.93, P = 0.804). For the P-values from the Fst distribution, we observed significant or near significant associations at 1 cM (P = 0.944, P = 0.904) and 2 cM (P = 0.954, P = 0.949), but not at 5 cM (P = 0.72, P = 0.66). Thus, the exclusion of outliers appears not have biased the observed significant spatial correlation for diversity statistics. Overall, these analyses indicate a significant spatial correlation among SSRs within 2 cM of each other.
The Ewens-Watterson test enables one to detect deviations from a neutral-equilibrium model as either a deficit of gene diversity relative to the number of alleles at a locus (below the curve in Figure 2) or an excess of gene diversity (above the curve in Figure 2; Ewens 1972; Watterson 1978). In maize, the number of SSRs showing excess in gene diversity compared to the number of alleles (P < 0.025) is 36, and the number showing a deficit in gene diversity (P > 0.975) is 12 (supplementary Table S2 at http://www.genetics.org/supplemental/). In teosinte, the number of SSRs showing excess in gene diversity compared to the number of alleles (P < 0.025) is 34, and the number showing a deficit in gene diversity (P > 0.975) is 5. Maize shows more SSRs with a deficit in gene diversity as expected under selection or a bottleneck.
Graphical representation of the Ewens-Watterson test. The 97.5 and 2.5% percentile curves are represented by the solid lines and the expected median value by the dashed line. Circles are dinucleotide and triangles are other repeat SSRs.
The Fst test asks if the degree of differentiation at an SSR exceeds neutral expectations. Figure 3 provides a graphical representation of the Fst test, showing the medians and upper 95% confidence limits for the SMM, GSM, and IAM established by simulation. The three mutation models give similar results for SSRs with five or fewer alleles; however, for SSRs with more than five alleles, the SMM and GSM have a lower median and 95% confidence limit. To analyze the fit between the simulated model and the observed data set, we calculated the mean of the expected Fst for each individual locus given the number of observed alleles. For dinucleotide SSRs, this average is 0.045 (SMM), 0.070 (GSM), and 0.16 (IAM) compared to the observed mean of 0.054. For the other repeats, this average is 0.107 (SMM), 0.138 (GSM), and 0.163 (IAM) compared to the observed mean of 0.097. We also calculated the number of SSRs lying between consecutive decile curves for each mutation model for both dinucleotide and other repeat SSRs. The IAM does not fit the dinucleotide SSR data because of an excess of SSRs with low Fst values (χ2 = 275.4, P ≪ 0.001); the GSM and the SMM models are also rejected, but less markedly (χ2 = 29.3, P < 0.001 and χ2 = 21.2, P < 0.02). For the other repeat SSRs, the SMM (χ2 = 7.18, P = 0.62) is not rejected, but the GSM (χ2 = 27.8, P < 0.001) and IAM (χ2 = 63.2, P < 0.001) are rejected. Thus, our actual data best fit the SMM although the fit is not perfect.
Plot of the Fst by the number of alleles. Curves correspond to the 50 and 95% percentiles based on simulated data for three mutation models: an infinite allele model (solid line), the generalized stepwise model (long-dashed line) and a stepwise model (short-dashed line). The plot is presented for dinucleotide SSRs (circles) and other repeat SSRs (triangles).
With 462 SSRs, the Bonferroni correction threshold would be 0.99989 for the Fst test. To test that a locus shows a departure at this P-value with good precision would require an inordinate number of simulations. So for practical reasons we report here SSRs that exhibit a probability of <0.995 and not the Bonferroni-corrected threshold. Eleven SSRs exhibit higher Fst values than expected for the SMM model and zero for both the GSM and IAM at the P = 0.995 level. At the P = 0.95 level, 46 SSRs are significant for the SMM, 12 for the GSM, and none for the IAM. So with the SMM 10% of the SSRs exhibit a significant value as compared to the 5% expected under a completely neutral distribution.
The GD test asks if there has been a greater than expected loss of gene diversity in maize relative to ssp. parviglumis given the model for the domestication bottleneck used in the simulations. For all models (IAM, GSM, and SSM), if gene diversity at an SSR in ssp. parviglumis is <0.5, then gene diversity in maize can be zero due to loss from the domestication bottleneck alone (Figure 4). To analyze the fit between the simulated model and the observed data, we calculated the mean of the expected gene diversity in maize given the observed gene diversity in teosinte. For dinucleotide SSRs, this average is 0.785 (SMM), 0.768 (GSM), and 0.705 (IAM) compared to the observed 0.787. For the other repeats, this average is 0.541 (SMM), 0.524 (GSM), and 0.495 (IAM) compared to the observed 0.546. We also calculated the number of SSRs lying between consecutive decile curves for each mutation model for both dinucleotide and other repeat SSRs. For dinucleotide SSR data, the IAM (χ2 = 88.7, P < 0.001) is rejected but not the SMM (χ2 = 12.8, P = 0.17) and the GSM (χ2 = 12.7, P = 0.18). For the other repeat SSRs, the SMM (χ2 = 10.1, P = 0.35) and GSM (χ2 = 9.7, P = 0.37) are not rejected, but the IAM is rejected (χ2 = 41.8, P < 0.001). Thus, our data best fit the GSM and SMM, although the fit is not perfect.
Plot of the gene diversity in maize as compared to the gene diversity in ssp. parviglumis. The curves correspond to the 2.5, 50, and 97.5% percentiles based on simulations for three mutation models: an infinite allele model (solid line), the generalized stepwise model (long-dashed line) and a stepwise model (short-dashed line). The plot is presented for dinucleotide SSRs (circles) and other repeat SSRs (triangles).
For the SMM, 25 SSRs exhibit a significant deficit in diversity in maize relative to teosinte (P < 0.025). This represents ∼5.4% of the SSRs where only 2.5% (12 SSRs) would be expected by chance. Thus, if the model and parameters used in the simulations are correct, we are likely detecting some SSRs that have reduced diversity because of positive selection during maize domestication or improvement. Fifteen SSRs (3.2%) show a significant excess of diversity in maize (P > 0.975) under the SMM where ∼12 SSRs would be expected by chance. The expected (12) and observed (15) values are fairly close so there is no compelling evidence for SSRs that are under balancing or diversifying selection in maize.
We summarized the SSRs where two different tests in maize indicate a significant (P = 0.05) deviation from neutrality (Table 4). Twenty-nine SSRs in maize show a significant result for multiple tests, 6% of the total number of SSRs. Of these, 24 SSRs or 5% of the 462 show reduced diversity as expected under positive selection. There are similar numbers of dinucleotide and other repeat SSRs with significant tests (Table 4), and these numbers are not significantly different (G = 3.27, P = 0.07) from a random expectation based on the number in each class of markers in our sample.
Genetic diversity in maize as in other crops has been reduced during domestication as previously shown (Doebley et al. 1984; Hilton and Gaut 1998) and further illustrated in this study. For SSRs, maize has 88% of the gene diversity found in teosinte and 76% of the number of alleles. If we divide the SSR data according to the length of the repeat motif, we observe that maize has 91% of gene diversity of teosinte at dinucleotide SSRs and 85% of that at other repeat SSRs. For number of alleles, these values are 81% at dinucleotide SSRs and 72% of that at other repeat SSRs. This deficit of diversity is less than what has been found at the DNA level for adh1, 75% (Eyre-Walker et al. 1998), or glb1, 60% (Hilton and Gaut 1998), as expected since the higher mutation rate for SSRs relative to that for nucleotide substitutions allows SSRs to recover more rapidly from the bottleneck effect (Vigouroux et al. 2002a).
We observed a relatively low, although significant, level of differentiation between maize and teosinte (Fst = 0.07). Since differentiation is driven mostly by drift and both maize and teosinte have large population sizes, the low level of differentiation is not unexpected. Dinucleotide SSRs show a significantly smaller Fst value than other repeat SSRs; however, these two types of SSRs exhibit a similar proportion of Fst values that are significantly greater than zero. The smaller Fst for dinucleotide SSRs occurs because of their higher mutation rate (Vigouroux et al. 2002a) and the statistical properties of Fst. Fst is the function of two probabilities, the probability of identity of two alleles within a population and the probability of identity of two alleles between populations. As the mutation rate increases, the probability of identity within a population decreases and so does the Fst value (Weir 1996). This smaller Fst value does not mean that the populations are not differentiated, but just illustrates the effect of the mutation rate on Fst. The same phenomenon has been observed elsewhere with empirical and simulated data (Balloux et al. 2000).
Fis is moderately high in both maize (0.38) and teosinte (0.43), but this is likely a function of our sampling strategy. We attempted to maximize the breadth of genetic diversity in our maize and teosinte samples by selecting accessions from maximally divergent geographical locations. This sampling strategy will increase the probability of observing SSRs that have become fixed for alternate alleles in different populations. When multiple plants from single populations are sampled in maize, Fis values are much smaller (Labate et al. 2003).
A study of the inheritance of domestication traits in maize reported a concentration of QTL on chromosomes 1–5 (Doebley and Stec 1993). This suggests that these chromosomes might have experienced a stronger selective force than chromosomes 6–10 and that there may be heterogeneity among chromosomes in genetic diversity. Nevertheless, no chromosomal effect was detected for either the relative deficit in gene diversity or Fst, suggesting a somewhat homogenous genome-wide loss of diversity during domestication (Figure 1, Table 2). The relative deficit of alleles shows some evidence of heterogeneity among chromosomes. Why this effect is observed only for the number of alleles (Δalleles) is unclear. If this effect is due to selection during domestication, it is unlikely that this selection was targeted at the genes (QTL) controlling the differences in plant and inflorescence architecture studied by Doebley and Stec (1993) since the chromosomes that show the most modest losses of alleles (5, 9, 1, 3, and 2) include four of the five chromosomes identified as possessing the largest numbers of QTL.
We asked whether there is a correlation between the location of domestication QTL and genomic regions of lower genetic diversity as expected if selection during domestication had caused regional losses in diversity. Addressing this question is not straightforward since multiple QTL can be linked in a single region and maize has a complex history. Thus, although the interaction of linkage, selection, and gene diversity has been extensively studied (Maynard Smith and Haigh 1974; Ohta and Kimura 1975; Wiehe and Stephan 1993; Kim and Stephan 2000), no clear models can be applied to maize domestication. For these reasons, we have taken an ad hoc approach involving several assumptions: (1) the effect of each domestication QTL on SSR diversity is a decreasing function of the recombination distance to the SSR; (2) the QTL were positively selected; (3) each QTL contributed to the loss of diversity in proportion to the amount of variance it explains (i.e., that selection was stronger for the QTL explaining a higher percentage of the phenotypic variance); and (4) QTL contributed additively to the diversity loss.
Using this approach, we did not observe a significant correlation between QTL effect and loss in the number of alleles (Δallele), gene diversity (ΔGD), or Fst (Figure 1). This result can be explained several ways. First, the method we used may not be sensitive enough given the uncertainty of marker positions on the map. Second, we considered here only QTL for morphological traits and not all the potential traits that differentiate teosinte from maize (e.g., seed quality). Third, forces other than directional selection (drift, mutation, diversifying selection) may have created sufficient noise to obscure much of the signal from directional selection. Fourth, none of the SSRs may be sufficiently close to the QTL to have been affected by selection on the QTL. Finally, SSRs used in this study were developed in maize after screening to eliminate invariant SSRs, giving an ascertainment bias since invariant SSRs, which are the most likely candidates for selected SSRs, were excluded from our sample (see Vigouroux et al. 2002b).
Selective sweeps or background selection can reduce diversity throughout a chromosomal region (Maynard Smith and Haigh 1974; Charlesworth et al. 1993). Therefore, we tested whether linked SSRs are more similar in diversity and we observed multiple significant tests for pairs of SSRs within distances of 2 cM from one another (Table 3).
What mechanisms could produced this correlation? One interpretation is that we are detecting regional variation in the strength of selection during domestication. Where selection was strongest, maize is less diverse (or more differentiated from teosinte) relative to regions that experienced weaker selection. This interpretation, if correct, would appear to contradict prior evidence that the effects of selection on diversity in maize are very narrow (Wang et al. 1999) and that that linkage disequilibrium between loci decreases rapidly (Remington et al. 2001; Tenaillon et al. 2001). Another interpretation may be that there is some bias in the data (or in the parameters) that creates a correlation among neighboring SSRs. For example, if there are regions of high vs. low recombination and if recombination is correlated with SSR mutation rate (see Tenaillon et al. 2001), then a statistic like Fst that is influenced by the mutation rate could show a spatial correlation in the absence of any effect from selection during domestication.
To test whether an SSR exhibits a nonneutral pattern of variation, one needs to know the neutral distribution against which the observed data can be compared. To compute such a distribution, we have used coalescent simulations that incorporate the domestication bottleneck. These simulations were performed using three different models for microsatellite evolution: IAM, SMM, and GSM. The simulations are also based on estimates of the current effective population size of maize, the duration of the bottleneck, and the population size of maize during the bottleneck (Eyre-Walker et al. 1998; Vigouroux et al. 2002a). Error in these estimates could bias the results. Nevertheless, this approach has the advantage of clearly specifying the model used and takes into account some aspects of maize history, although it does not include more complex features like population structure.
We examined the fit between our actual data and the simulated data and found that the mean gene diversity and Fst values from the simulated data were closest to the actual data when the simulations were based on the SMM as opposed to the IAM and GSM. Similarly, the distributions of the gene diversity and Fst values for our actual data were closest to the simulated distributions when the simulations were based on the SMM. Overall, the SMM fit the actual data in three of the four tests performed. Nevertheless, the fit is not exact and the results of the simulations differ from expectations based on our prior empirical work. Notably, our prior work on SSR mutation rates (Vigouroux et al. 2002a) indicates that dinucleotide SSRs should best fit the GSM, while a study of sequence diversity at other repeat SSRs (Matsuoka et al. 2002a) suggests that the IAM might provide the best model for this class of SSR. Other factors not incorporated into the simulations such as population structure or directional evolution (Vigouroux et al. 2003) could be responsible for the imperfect fit between the actual and simulated data. Therefore, caution is advised in interpreting the simulation results and the tests of neutrality based upon them.
We performed two tests of nonneutral evolution for which the expected distribution of the test statistic was determined using coalescent simulations. For the Fst test, 46 SSRs or 10% of the 462 SSRs exhibited a higher Fst value between maize and teosinte than expected under the SMM at the P = 0.05 significance level or twice the expected number (23) under purely neutral evolution (Table S4 at http://www.genetics.org/supplemental/). For the GD test, 25 SSRs or 5.4% of the 462 SSRs exhibit a deficit in diversity relative to teosinte under the SMM at the P = 0.025 significance level or twice the expected number (12) under purely neutral evolution (Table S4). This excess of loci with significant Fst or ΔGD values suggests that some of these SSRs (or sites closely linked to them) may have been under selection during maize domestication. These loci merit further investigation by DNA sequence analysis to better assess whether they have indeed experienced past selection.
We have also investigated the influence of selection on diversity by analyzing individual SSRs for evidence of nonneutral evolution using the Ewens-Watterson test. A large number of SSRs (34 in teosinte and 36 in maize) exhibit excess gene diversity relative to the number of alleles (Figure 2, Table S4). This result may indicate balancing (diversifying) selection or population subdivision (Kreitman 2000). For teosinte, population subdivision is a likely explanation because our sample includes three different clusters, ssp. parviglumis, ssp. mexicana, and ssp. huehuetenangensis, which are highly structured (Matsuoka et al. 2002b). Similarly, our maize sample was chosen to maximize the geographic regions represented and does not represent a single Hardy-Weinberg population, an assumption of the Ewens-Watterson test.
In maize 12 SSRs (2.6%) exhibit a deficit in gene diversity relative to the number of alleles as expected under positive selection or a bottleneck (Figure 2, Table S4). This is about the number of significant tests expected by chance alone given the significance threshold of P = 0.975 for the two-tailed Ewens-Watterson test. Thus, this test did not enable us to identify any likely targets of selection during maize domestication. In a previous article, we identified 7 of 39 maize SSRs with a deficit in gene diversity relative to the number of alleles using the Ewens-Watterson test (Vigouroux et al. 2002b). However, in this prior work, we biased our choice of SSRs to enrich the sample for ones that were likely targets of selection. The failure to identify nonneutral SSRs with the Ewens-Watterson test in the present analysis could also be influenced by ascertainment bias. Since we studied only SSRs that were polymorphic in maize and could thus be placed on the maize genetic map, we systematically excluded low-diversity (invariant) SSRs that are the most likely targets of selection.
Our results enable us to make some tentative interpretations concerning the forces that have sculpted SSR diversity across the maize genome. First, we infer that mutation has allowed dinucleotide SSRs with their high mutation rates (10−3–10−4) to partially recover from the loss of diversity during maize domestication. We make this inference since ΔGD for these SSRs is only 9% as compared to 15% for other repeat SSRs, which have a lower mutation rate (∼10−5) (Vigouroux et al. 2002a). Similarly, we infer that other repeat SSRs have also made a partial, although weaker, recovery since ΔGD for these loci is still smaller than the ΔGD of 33% for nucleotide substitutions that have even a lower mutation rate (∼10−9; White and Doebley 1999). Nevertheless, since SSR gene diversity remains lower in maize than in teosinte at both dinucleotide and other repeat SSRs, we conclude that new mutation over the ∼5000 years since the end of the bottleneck has not produced a complete recovery. Thus, SSR diversity can provide some insights into the relative roles of drift and selection as well.
Given that SSRs show reduced diversity in maize relative to teosinte, we can ask what were the relative roles of drift and selection in producing this reduction. Our data do not allow an unequivocal answer to this question, but they can be used to suggest that drift was the dominant force. First, the results of our coalescent simulations indicate that diversity at the vast majority of SSRs can be explained by a simple model that incorporates the domestication bottleneck (drift), thereby obviating the need to infer selection. Similarly, we observed no correlation between the chromosomal position of domestication QTL and diversity as expected if selection coupled with hitchhiking had caused strong regional reductions in diversity.
Even if drift during the domestication bottleneck is the major factor influencing SSR diversity in maize, we ask whether some SSRs have reduced diversity because of selection during maize domestication. A conservative approach for identifying SSRs that were likely targets of selection is to consider only SSRs that exhibit significant results with two different neutrality tests in maize. Taking this approach we identified 29 of the 462 SSRs that we consider the best candidates for selected SSRs (Table 4). Under a complete independence of the three tests, the probability to observe at least two tests significant at a 0.05 level is 0.00725. So, under neutrality, the expected number of SSRs with two significant tests for 462 SSRs analyzed is 3.3 compared to 29 actually observed. However, the tests are not completely independent from each other, so the number 29 is somewhat of an upper limit of the number of selected SSRs under the neutral models considered.
Of the 29 SSRs with two or three significant tests, 24 show evidence for positive selection during maize domestication as either a higher than expected ΔGD value or a Ewens-Watterson test indicating a deficit of gene diversity as compared to the number of alleles. Thus, an average value of 5% of the total number of SSRs may have experienced positive selection during maize domestication. This value of 5% "selected" loci may underestimate the actual number of genes that have experienced selection for several reasons. For example, we analyzed only loci that were known to be polymorphic in maize and thereby excluded invariant SSRs, the class most likely to have experienced past selection. Similarly, some SSRs that experienced selection during maize domestication some 9000 years ago may have recovered their loss diversity because of the high mutation rate for SSRs and thereby give nonsignificant neutrality test results. Nevertheless, this 5% value provides a rough first estimate of the portion of the maize genome that was under positive selection during maize domestication.
A5Assuming that the ancestral population was at equilibrium and had the same effective size as ssp. parviglumis today, we can use σ2parvi as an estimate for σ20. Knowing σ2m, Nmaize, T, and Tb, we can estimate the effective population size of maize during the bottleneck (Nb).
over 33 dinucleotide SSRs was estimated using the data from Matsuoka et al. (2002b) as 23.5 and 26.8, respectively. The effective population size of the expanded maize population after the bottleneck in a range from 105 to 109 has only a small effect on the estimated size during the bottleneck (Eyre-Walker et al. 1998 and data not shown), so we have considered only a large effective population size of 1 million for maize after the expansion.
With these values for the parameters, we can estimate Nb for different values of Tb using Equation A5. Archaeological information indicates that the domestication bottleneck was probably within the range of a few hundred to 2000 years (Smith 2001). Therefore, we calculated the effective size for bottlenecks of 100, 200, 500, 1000, and 2500 years in duration and obtained values for Nb of 107, 220, 553, 1117, and 2875, respectively. These values are in good agreement with previous independent estimates using DNA sequence polymorphism (Hilton and Gaut 1998).
We thank Montgomery Slatkin and Jody Hey for advice on the mathematical and simulation models. We thank Marit Haug for technical assistance and Major Goodman and Jesus Sanchez for help in obtaining seeds. This work is supported by National Science Foundation grant DBI-0096033.
↵ 1 These authors contributed equally to this work.
↵ 2 Present address: Diversité et Génomes des Plantes Cultivées, UMR141, Institut de Recherche Pour le Développement, Montpellier, 34394, France.
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Looking back two years, the first drawing (seen below) stretched from the Post Office to the Methodist Church.
You can just see the southern side of 'The Square' in the middle.
A year later and Cheshire Street appeared. This shows the ribbon of buildings lining the side of the A529 from the edge of St James's Church up to No 17.
Cheshire St (work in progress) seen below the first street drawing.
Now I can share the latest drawing which although relatively short, contains the third side of the Square.
My thanks again to Judy of 'Williams of Audlem' who is stocking signed limited edition prints of the drawing. I'm only doing a very small print run of 20 from this drawing, available to order in one size 500mm x 200mm. Unframed prints are £54 each.
There are two framed prints in stock at Williams, one in matt black and the other in mahogany, for £125. If you are in Audlem for the festival over the Bank Holiday, call in and have a look – all prints can be seen together as a set. If you can't get to Williams and would like to buy any of the Audlem series, drop me an email (RonnieCruwys@drawingthstreet.co.uk).
Have a good week and thanks for reading.


^ a b c d e f "GETRAG Corporate Group - corporate history". Getrag Corporate Group. Getrag.de. Retrieved 3 November 2009.
^ "GETRAG Corporate Group - Sites". Getrag Corporate Group. Getrag.de. Retrieved 9 January 2013.
^ "GETRAG Corporate Group - Global Presence". Getrag Corporate Group. Getrag.de. Retrieved 9 January 2013.
^ a b c =Getrag Corporate Group "Facts and figures of the GETRAG Corporate Group" (PDF). Getrag.de. Retrieved 9 January 2013.
^ "FAQ 320is". BMW M Registry. Retrieved 2011-09-23.
^ "Varianten des Doppelkupplungsgetriebes". Retrieved 18 October 2012.
^ "Start of Production of the Dry Getrag Powershift Transmission 6DCT250". Retrieved 24 April 2010.
^ "Getrag Produces 1 Million Dual Clutch Transmissions 6DCT250" (PDF). Getrag Corporate Group. Getrag.de. Retrieved 17 October 2012.


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text:
Slack's AI boss explains its secret weapons in the coming chat wars with Microsoft, Facebook, and Google
Apr. 7, 2017, 6:26 PM
Slack head of Search, Learning, and Intelligence Noah Weiss
Three years ago, a startup named Slack came out of nowhere to become what's believed to be the fastest-growing business software company ever, hitting a $1 billion valuation within the first 8 months of its existence.
Today, Slack is a $3.8 billion business with over 800 employees, and a growing roster of paying customers that include IBM, Walmart's Jet.com, and, yes, Business Insider. For many companies, chat is the way people are getting things done. Slack wasn't the first company to do chat at work, but it's certainly the most visible.
And so, tech titans like Microsoft, Google, and Facebook have all launched their own work chat apps, trying to crush Slack before it's too late. Under that pressure, Slack is doubling down on its biggest advantage: Generally speaking, people actually enjoy using it, which isn't often the case with software meant for use in the office.
Enter Noah Weiss, former Googler and Senior VP of Product Management at Foursquare, who now leads Slack's Search, Learning and Intelligence team out of the company's spiffy new New York City offices. It's his team that's going to be making much of the "magic" that keeps users glued to Slack, even as the alternatives proliferate.
"Slack is a very bizarre enterprise software company," says Weiss. "And I mean 'bizarre' in the best possible way."
'Connective tissue'
Because people are using Slack all workday, every workday, there's an astonishing amount of information that's already flowing through the app. Not just text, but the photos and links and documents that your coworkers are sharing with each other.
"All of that data exhaust is something that we think we can tap into to make Slack itself smarter," says Weiss.
Slack already integrates some of that, with smarter search suggestions. And users can opt-in to receive regular suggestions for chat rooms within their company — whether that room is for a special sales project populated by the people they talk to every day, or for "Game of Thrones" fans.
The data that's feeding the beast is largely coming from outside apps, says Weiss. For example, he says, teams at Slack itself use Google Docs, Microsoft Word, Dropbox Paper, and Quip, just for the creation and sharing of documents. Regardless which app employees use to create the file, they use Slack to share it with coworkers.
Slack CEO Stewart Butterfield
Craig Barritt/Getty Images
"Slack ends up being pretty good connective tissue for this increasingly diverse set of tools," says Weiss.
Over time, Weiss says, Slack can really start to "learn" things about users based on the things they send around. If you share a lot of spreadsheets with "expenses" in the title, you probably work in accounting. If you post a lot of links about Elon Musk, you're probably into space. And so on.
All of which comes back to helping people love Slack more, explains Weiss. All of this data will help its users sift through the noise that comes with everyday conversation, and find the answers they're looking for, more quickly. Once Slack "understands" the relationships between people and files, it can subtly point you towards the right answers, without requiring you to change the way you work.
"There is tremendous collective knowledge building up in Slack," says Weiss.
Versus Microsoft and Google
Microsoft, in particular, is hard at work on similar principles. In 2016, the company introduced the Microsoft Graph, which, as you may guess from the name, traces the relationships between documents and coworkers the same way that Facebook's famed social graph does for your friends.
Still, Weiss believes that Slack has a few advantages. First and foremost, it's been doing it longer, and claims a leading 5 million daily active users. That means lots more data flowing in, which means it can release new, smarter, time-saving features faster.
Slack's channel recommendations are an early version of how the company wants to use existing data to make it much smarter.
"We kind of created this category; we have the most usage in this category," says Weiss.
Furthermore, Weiss says, Slack is way more focused than Facebook, Google or Microsoft, all of whom support a plethora of messaging apps beyond whatever it is they offer to office workers. For Slack, messaging is literally all they do. Weiss' team has one mission, and it's not powering a search engine or whatnot — it's building a chat app that helps people be more productive.
"When you think about how Slack itself is wired, we are here on this Earth to do this one thing," Weiss says.
SEE ALSO: A former Microsoft intern got his Yale classmates to apply for jobs at his hilariously fake startup
NOW WATCH: Stewart Butterfield, co-founder of Slack and Flickr, says 2 beliefs have brought him the greatest success in life
More: Slack Microsoft Facebook Google

redpajama_set_name: RedPajamaCommonCrawl


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In Australia in conjunction with Milton Park country house hotel and spa, Westpac Private Bank and Trivett Bespoke New Zealand Secret were part of an exclusive Drive Day.
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The buzzing group of motor fanatics set out for a private lunch at the eloquent Milton Park, New South Wales, Australia, more than 100 kilometres from the starting point allowing the sleek beasts to run their engines.
At several points of the enviable drive, 10 guests were able to change between the driver's seats of the Bentley GT Continental, McLaren 570S, Rolls-Royce Wraith, Aston Martin DB11 and Aston Martin Vantage, soaring through the back of the southern highlands south of Sydney.
A private dining room nestled off Milton Park's exclusive library, donned with candelabras, crystal glassware and a crackling fireplace, was brimming with delectable meals to tantalise taste buds after the thrilling two-hour drive.
Over a three-course, custom-designed menu boasting French classics which, of course, was topped off with espressos and petit-fours, the guests had a chance to discuss their passion towards the luxe supercars.
The guests, who all received gifts from New Zealand Secret Skincare, basked in the glory of getting behind the wheel of some of the finest supercars, picturing the prestigious reality of ownership.


text:
Performance Management: Practice Makes Perfect
In this three-part blog series, Namely's VP of Product, Brian Crofts, and Namely's HR Advisor, Sneh Kadakia, are stepping back to look at both sides of the performance management spectrum. By uniting product expertise and HR experience, we hope to offer guidance for those tasked with driving a successful performance management strategy.
(Miss part one? Check it out here).
Practice Comes First
Driving your employee experience comes down to the type of performance management culture you put in place. In part one, we posed the question: What comes first when deciding on the right approach? Is it the practice (i.e. what to do), or the tool (i.e. how to do it)?
Today, we'll start with the practice. Whether you're developing a performance management strategy from the ground up or simply ready for a refresh, let's dive into the common questions HR professionals must consider.
How should feedback be delivered?
We all know that it's important to provide feedback—both informal and formal—in a proactive and timely manner.
Informal feedback is guidance that people managers provide to employees daily, weekly, or "in the moment." Usually, this type of feedback isn't formally documented, but these conversations can help align both parties on the current state of an employee's performance and provide direction. This is a must for any organization, especially as individual and team goals quickly evolve.
Beyond informal feedback, many HR teams rely on formal performance reviews. These reviews typically consist of a performance assessment: one form filled out by the people manager about the direct report's performance, and one form filled out by the employee about his or her own performance, which is then reviewed jointly and filed in the employee's folder.
These reviews are often the basis for annual decisions such as merit increases, bonus payouts or promotions. Recently though, they have been questioned for accuracy and effectiveness. Some companies are doing away completely with annualized reviews towards a more frequent check-in model.
So, which elements of formalized review are still critical to maintain? It's vital to document performance feedback from people managers and self-assessments from employees, and acknowledgement that the feedback has been delivered to the employee. These records provide a foundation for making and justify important talent decisions, from promotions to separation—and ultimately, it ensures a more fair, unbiased, and transparent practice for everyone involved.
Who should be involved in the process?
Performance feedback is often delivered to employees from one person, top-down. But as cross-functional collaboration, increased teamwork, and project-based roles become more prevalent, new practices allow for a more holistic view of an individual's performance.
Start by identifying sources of feedback that would be helpful to an employee, given his or her role. Bringing in a variety of perspectives will reduce the burden on the direct manager and also increase the chances of getting more meaningful insight.
Some companies have adopted 360 degree review programs, to formally collect insights from managers, peers, clients, co-workers and direct reports. These are the people that have day-to-day visibility into the employee's work. While these can help elucidate an individual's overall performance and work style, these reviews can be time-consuming. Consider the cadence and employee's level in the organization when deciding on whether to adopt this approach.
One way to start with the idea of 360 degree reviews is to regularly collect feedback from coworkers and project managers after a project is completed. Why? It makes the feedback actionable, reliable and timely. Plus, encouraging peer-to-peer feedback gives employees a more team-based perspective, which can help them enhance their performance.
Regardless of the approach, think about about how you can bring the appropriate perspectives to the table. Solely relying on a direct manager's review is not necessarily the best practice anymore.
What questions should be asked?
As you consider what to ask in your feedback outreach, start by asking yourself:
1. What are you hoping to take away from performance feedback?
2. What insights are you trying to gather about your talent?
3. What employee and business decisions are you going to make with those insights?
Keep in mind, the answers may be different for people managers, HR and leadership. Work backwards from here to create the questions that will be used in assessments or check-in conversations between managers and employees.
If you're launching a formal feedback assessment, think carefully about the number of questions you're including. Are you asking things for the sake of asking, or because the responses will be used to make decisions?
Further, what you want to learn about your employees may not be best captured in a performance review or assessment. There are other ways to gather insights about the workforce—through talent analytics and employee data, for example.
How should feedback be given?
The practice of performance management is an art. While people managers typically shoulder the heaviest burden of delivering feedback, many of them have little or no training on how to do just that. If feedback is not given in a constructive way, it can have a negative and demotivating impact on the workforce.
Ensure that you provide guidance to your people managers about how performance can be enhanced in your organization. Provide formal training on delivering feedback, coach them on how to manage difficult conversations, and offer resources, such as tips and frameworks. Providing this support to first-time managers can especially go a long way to make them effective in their roles.
Further, consider educating all employees on performance management practices to encourage heightened self-awareness, better adoption of feedback, and more opportunities for peer-to-peer feedback.
Remember, developing that right performance management practice takes trial and error. Don't be afraid to challenge existing methods and continuously refine approaches. Practice makes perfect.
HR Experts Share Their Performance Management Tips
6 Tips for Conducting 360 Performance Reviews
Simki Dutta
How to Train Managers to Give Great Performance Reviews


text:
Q: Unable to use encrypt option on s3cmd I have two simple scripts to upload and download some code to and from S3 on Amazon web services:
#####Upload Script
zip -r file.zip www
s3cmd del s3://mybucket/file.zip --encrypt
s3cmd put file.zip s3://mybucket --encrypt
rm -rf file.zip

#####Download Script
rm -rf /home/ubuntu/www/*
s3cmd sync s3://mybucket/file.zip /home/ubuntu/ --encrypt
unzip -u /home/ubuntu/file.zip -d /home/ubuntu/
rm -rf /home/ubuntu/file.zip

However these scripts only work when I don't use the --encrypt option. The resulting file has 11MB without encrypt and it unzips just fine, and 9MB using encrypt but gives me an error when I try to unzip it. How do I decrypt files from S3 since there is no --decrypt option and using --encrypt on the download also didn't worked?
I configured s3cmd correctly with encryption password, and it passed the config test. Here goes the error before anybody asks:

Archive:  /home/ubuntu/file2.zip
  End-of-central-directory signature not found.  Either this file is not
  a zipfile, or it constitutes one disk of a multi-part archive.  In the
  latter case the central directory and zipfile comment will be found on
  the last disk(s) of this archive.
note:  /home/ubuntu/file2.zip may be a plain executable, not an archive
unzip:  cannot find zipfile directory in one of /home/ubuntu/file2.zip or
        /home/ubuntu/file2.zip.zip, and cannot find /home/ubuntu/file2.zip.ZIP, period.

A: I had this issue when I tried downloading the backups directly from S3 rather than using s3cmd get to retrieve the file. When I used s3cmd to retrieve it, the file unzipped without any problem. You do not need to run it with any flags for it to decrypt. It will do it intuitively.

A: Encryption appears to be broken in current upstream master branch HEAD 81e3842f7afb.  You are best off encrypting, and the putting the encrypted file, directly.  Certainly --encrypt with the [sync] command does not work.

redpajama_set_name: RedPajamaStackExchange


The upcoming Municipal Election to elect a Reeve, Deputy Reeve, Municipal Councillors and School Board Trustees will be held on Monday, October 22, 2018.
A candidate for an office on Municipal Council and his or her spouse shall not make contributions to the candidate's own election campaign that, combined, for the office of Reeve exceed an amount equal to $7500 plus 20 cents for each elector entitled to vote for the office or for the office of Councillor exceed an amount equal to $5000 plus 20 cents per elector entitled to vote for the office.
The Province has not completed the 2018 forms at this time. They will be available in April 2018. Please check back.


text:
Jan Maciej Malkiewicz (ur. 18 grudnia 1971) – polski urzędnik, od 2020 Konsul Generalny RP w Monachium.

Życiorys
Jan Malkiewicz uzyskał tytuł magistra nauk politycznych w specjalności stosunki polsko-niemieckie na Uniwersytecie Jagiellońskim. W latach 1994–1995 stypendysta programu "Tempus" na Uniwersytecie Ruhry w Bochum. W 1999 został zastępcą dyrektora sekretariatu Prezesa Rady Ministrów, a następnie dyrektorem sekretariatu Prezesa Urzędu Regulacji Telekomunikacji i Poczty. W latach 2002–2003 stypendysta Fundacji Roberta Boscha. Odbywał staże zawodowe w Urzędzie Regulacji Telekomunikacji i Poczty w Bonn. Później pracował w sektorze prywatnym w obszarze ICT i technologii medycznych. W październiku 2020 objął stanowisko konsula generalnego w Monachium.

Był pomysłodawcą i inicjatorem ustanowienia Międzynarodowej Nagrody im. Stefana Banacha. Dwukrotnie nagrodzony Red Dot Design Award oraz iF Design Award.

Przypisy

Absolwenci Uniwersytetu Jagiellońskiego
Konsulowie III Rzeczypospolitej
Ludzie związani z Monachium
Urodzeni w 1971
Urzędnicy Kancelarii Prezesa Rady Ministrów i Urzędu Rady Ministrów (III Rzeczpospolita)

redpajama_set_name: RedPajamaWikipedia


At Sift Science, APIs are incredibly important. We spend a lot of time improving upon our existing APIs and thinking about how to design even better ones. A couple of weeks ago at the San Francisco API Craft Meetup, I gave a talk on how we built the API that powers our new Sift console.
In my talk, I discussed how we migrated our API and console, as well as some of the lessons we learned along the way.
If you missed the talk, you can check it out below! Questions about Sift? Feel free to drop us a line any time.


text:
Matayoshi Shinkō (; * 1888; † 1947) war ein Okinawanischer Experte des Kobudō und des Karate sowie Schöpfer des Matayoshi-Kobudō, eines Kampfkunststils, der durch das Wirken seines Sohns Matayoshi Shinpō weltweit bekannt wurde.

Leben
Matayoshi Shinkō hatte bereits in der Kindheit den Umgang mit zahlreichen Waffen des Kobudō erlernt. Bei Agena Shokohu erlernte Matayoshi Shinkō die Techniken für Bō, Sai, Kama und Eiku, und von Ire Matsutaro die Handhabung von Nunchaku und Tonfa. Er bereiste im Jahre 1911 China. Zwei Jahre später kehrte er in seine Heimat zurück, nachdem er in China die Reitkunst, das Bogenschießen, Shuriken jutsu und den Umgang mit dem Lasso erlernt hatte. Im Jahr seiner Rückkehr nahm er gemeinsam mit Funakoshi Gichin an einer öffentlichen Vorführung in Tokio teil und im Jahre 1921 gemeinsam mit Miyagi Chōjun an einer Vorführung vor dem kaiserlichen Kronprinzen Hirohito im Schloss von Shuri, Okinawa. Bei einer zweiten Reise nach China, die ihn nach Shanghai führte, erlernte er die Künste des Suruchin, des Timbe, des Nuntebō, sowie die Akupunktur und die Wissenschaft der Heilkräuter. Seine Rückreise führte ihn durch Fuzhou, wo er den Kungfu-Stil des "Weißen Kranichs" erlernte.

1935 kam Shinkō Matayoshi definitiv nach Okinawa zurück und ließ sich in Naha nieder, um weiterhin zu trainieren und seine Verbindungen mit anderen Kampfkunst-Experten zu fördern. In der Kampfkunstwelt wurde Matayoshi Shinkō wegen seiner großen Fähigkeiten im Gebrauch der Kobudo-Waffe Kama no Mateshi, "Matayoshi der Kama", genannt. Im Alter von 59 Jahren verstarb er und vererbte seine Kunst seinen Sohn Matayoshi Shinpō.

Weblinks

 World Oshukai Federation W.O.F.
 Okinawa Kobudo Verband Deutschland

Literatur

 Roland Habersetzer, Gabrielle Habersetzer: Encyclopédie des Art Martiaux de l'Extrême-Orient. 4. Auflage. Editions Amphora, Paris 2004, ISBN 2-85180-660-2.
 Thomas Heinze: Die Meister des Karate und Kobudo. Teil 1: Vor 1900. Books on Demand, Norderstedt 2009, ISBN 978-3-8391-1785-9.

Kampfkunst (Japan)
Karateka (Japan)
Geboren 1888
Gestorben 1947
Mann
Japaner


Right: Sara wears Edun sweater, Stylist's own scarf.
Left: Sara wears bra by Only Hearts, Trademark Trousers, Stylist's own scarf (worn around waist).
Right: Sara wears Simone Rocha dress.
Sara wears Joseph Coat, Only Hearts bra.
Sara wears Protagonist Top, Stylists own scarf (work as top), Trademark pants, Hue socks, Trademark shoes.
Left: Sara wears Protagonist Top, Stylist's own scarf (work as top) Trademark pants, Hue socks, Trademark shoes.
Left: Sara wears Simone Rocha Dress, Stylists own scarf (holding).
Right: Sara wears Erdem Top, Warm pant, Tommy Hilfiger belt.
Sara wears Suno Dress, Georgia NYC Vintage T-Shirt, Stylist's own scarf (tied around waist).
Sies Marjan jacket, Zimmerman swimwear.
Sara wears Paule Ka sweater, Creatures of the Wind skirt, Stylists own scarf (tied around waist), Tommy Hilfiger belt, Hue Socks, Trademark shoes.
Sara wears Paule Ka sweater, Creatures of the Wind skirt, Stylist's own scarf (tied around waist), Tommy Hilfiger belt.


The hard money lenders often consider income-producing properties equivalent to residences, retail or shopping facilities, industrial, workplace buildings, resorts, motels, medical establishments, and restaurants. Try this site http://usfscorp.com/ for more information on hard money loans with a bankruptcy. They also present loans for non-earnings producing activities corresponding to land acquisition, development and construction, bank workouts, foreclosures and bankruptcies.
Choose The Best No Prepayment Penalty Hard Money Loans And Ease Your Stress. Mobile homes are gaining popularity than ever before. It is also called manufactured home. It is hard to differentiate from a traditional home in appearance and high quality. If you want a mortgage for any mobile residence you will need to find a loan company.
Visit this site http://usfscorp.com/ for more information on Commercial Bridge Loans. There are number of ways to generate a cash flow for your business. Hence remember that not all may be right for you. Suppose your business is starting point or small you may not be eligible for a Commercial Bridge Loans.
Visit this site usfscorp.com/ for more information on Commercial Bridge Loans. There are number of ways to generate a cash flow for your business. Hence remember that not all may be right for you. Suppose your business is starting point or small you may not be eligible for a Commercial Bridge Loans.
Commercial bridge loan programs to bridge the gap until a conventional commercial loan is your best option. We also offer short term commercial bridge loan programs also known as hard money bridge loans can be a life saver for an investor or business person in difficult times. This is more true today than ever due to the increased commercial underwriting requirements instituted by the banks post the real estate and financial debacle in 2007 and 2008.


Steve Komphela sensationally quit as Kaizer Chiefs coach on Saturday after violence marred his side's 2-0 loss to Free State Stars in their Nedbank Cup semifinal at Moses Mabhida Stadium in Durban.
The under-fire Chiefs coach called time on his three-year tenure at the club just months before the end of his contract after disgraceful scenes at the stadium where at least four fires were set in the stands, at least one security guard was beaten severely by fans and property of broadcaster SuperSport was damaged.
"We have to cut this culture of ill-discipline and delinquency. I have to be responsible and protect the players and administration and set an example that this must stop," Komphela said at the post-match press conference.
"If it has to stop with me, then it must be so. I am effectively saying to you that I consider my responsibility to protect everybody at stake."
The coach's exit was confirmed by the club's football manager Bobby Motaung. "There has to be a reality check. We are not safe. We must take responsibility and own up and accept the facts. We have always honoured obligations," he said.
"Steve has done a great job for Kaizer Chiefs but unfortunately if it is not meant to be, it is not meant to be.
"The results are not happening. As a coach he knows we are faced with a serious challenge. We have had discussions with the coach and said let's do the honourable thing.
Chiefs will be out of pocket a further R200,000 after they received a suspended sentence for previous crowd misbehaviour earlier this month, and will face new sanctions for the violence in Durban.
The club has now gone three seasons without a trophy, the longest barren period in their history.


Click the arrow below to listen to the Entertainment Law Update Podcast or subscribe in iTunes.
"Happy Birthday" is Public Domain and ongoing challenges for other songs.
DOJ - ASCAP/BMI Consent Decree (Music publishing public performance issues).
Kirtsaeng Supreme Court Ruling on fee shifting.
Led Zeppelin copyright infringement ruling (and lawyer ethics).
Actor age confidentiality and First Amendment.
Prince's death and the value of music icon estates.
VIMEO lawsuit still holding on.
Changes at the U.S. Copyright Office.
Looking forward to the U.S. Supreme Court docket.
Older PostMusic and Podcasts: How Do I Clear The Music Rights For My Podcast?


text:
Saturday Crapshoot: Rockstar!
By Richard Cobbett 2012-12-15T10:00:45.349Z
Every week, Richard Cobbett rolls the dice to bring you an obscure slice of gaming history, from lost gems to weapons grade atrocities. This week, are you ready to rock? I said ARE YOU READY TO ROCK?! Oh. Okay. I'll come back later. Sorry to have disturbed you.
You've got to admire confidence, I suppose. Rockstar, no relation to either the makers of Grand Theft Auto or games with actual music in them, describes itself like this: "THOSE MESMERISING NEW AGE HYPNOTISTS WIZARD GAMES PROUDLY, NO EXTRAVAGANTLY PRESENT FOR YOUR AMUSEMENT AND AMAZEMENT, BEWILDERMENT AND CONFUSION, THEIR LATEST PSYCHADELIC EXTRAVAGANZA: ROCKSTAR!"
Well, one out of four 'aint bad, I guess...
"Welcome to the wonderful world of WIZARD games," continues the title screen. "You have never seen anything like this before! We don't waste your money on fancy packaging and glossy advertising... we won't insult your intelligence with childish graphic challenges.... we just write unique games that you will become addicted to, where nothing is ever the same twice! You are now entering the world of your own imagination, where nothing can be taken for granted..... SO BEWARE!!"
I'm pretty sure my imagination can beat 'be a struggling British rockstar', to be honest. It was only the other week that I wondered aloud to myself whether blood bank employees occasionally get to suit up in protective gear and have the most epic water balloon fights ever. Still, the credits are definitely crazy. Coding was apparently done by a Sleepy Cat Summer, with someone called Lord B Dog on special effects detail. How special can an effect be when it's entirely done in ASCII? You may be surprised to learn that the answer is 'not very special at all, really', even back in 1989.
Still, I'm sure a game advised by such gaming luminaries as Furry Crab, Pink, Celtic Ray, Guitar Bob and the Schmeelie Man can live up to its own hype. Let's fire it up!
Uh, not particularly, no.
Well, that's it for this week, folks! See you next time, and-
Sigh. Fine...
Rockstar is a time management game at heart, one that encourages you to live the anarchic world of a rock and roll legend by very carefully spending the most precious resource of all - life. Also money. In fact, mostly money, because that buys both happiness and occasionally drugs.
Every great artist needs a name. So do you. I opted to go a little post-modern by calling my band "Boring John". Then, worried that I might be underselling things a bit, I spelled it "BORING JOHN" to add a little excitement to things. My rockstar pamphlets will explain that for anyone who's unclear on the concept, along with providing helpful tips on recycling. Every little bit helps, right? I'm basically Sting.
As far as assets go, starting out, I have - as the game puts it - "a few hundred quid". Wonderfully specific there. My musical rivals include a new single from a band called Smoker's Cough called simply "Knickers", The Giant Panda's "Doom and Gloom", Royal Highness' "Makes You Sick" and The Axe Victims' "Cautious". These are all completely plausible sounding hits.
Speaking of hits, I sit down to write some songs, when my best friend offers me some cocaine. Within the context of the game, it would seem rude to refuse. In real life of course, you should never accept this kind of offer. Smack that cheap shit right out of his hand and tell him to go get something worth sticking up your nose. Like Sinex. Always worth keeping around this time of year in case your pipes get bunged. I'll add that to my pamphlets so that everyone can stock up before the shops close.
Every turn in Rockstar lasts a week, which may sound a long time, but is pretty short compared to, say, having a game of Draw Something after the week or so that anybody liked it. Options are simple enough. Laze around. Write songs and practice. Gig. Record a single or album. Have a holiday. Visit the doctor. Consult your analyst. Unwind in a sanitorium. The tactical options are almost endless.
Since I have a bit of cash to hand, I opt to record a single. Despite not having any music, any fans, any money, or being entirely sure which way round a guitar goes, my manager Dodgy Sam also manages to get BORING JOHN a session on a World Network TV rock show. And people say breaking into this industry is hard. Opportunity isn't knocking, it's got me on speed dial. To celebrate, my drummer offers everyone a new type of heroin. I hope she's like Catwoman. Dodgy Sam immediately comes back to say that I'm also going to be in this year's Eurovideo competition, and my percussionist recommends I take marijuanna. Cheered by success, I accept his sage council without even correcting his spelling.
THIS IS YOUR GAME ON DRUGS.
Eurovideo doesn't go so well, but I'm immmediately offered a place on another TV show, and some LSD. This offers the necessary mental fuel to create a single that is described by the game as "unbelievably dreadful", and by Dodgy Sam as "adequate". Asked for a name, I call it "Punching The Pope" in the hope of stirring up a little controversy, and go check my funds. After all this hard work, my "few hundred quid" is now "a few hundred quid." Can't help but hope that's not what my accountant says when I call him.
While Punching The Pope has only just hit the market, it seems like a good idea to capitalise on it before stocks run out of bonfire supplies by making a video. Dodgy Sam suggests two video directors - One Hand Fred, who is "best avoided" and Tricky James, who is "Pretty Dodgy". Figuring that at least "Pretty Dodgy" can work both ways, I give him a shot. It works out. His fussiness prolongs the shoot, but speaks to a certain inner professionalism. "You think Tricky James is encouragingly stimulating," says the game. Maybe this will prove the break he needs to get into film direction, and in a few decades time, he could be at the head of a major James Bond film. I hope so. Unless that movie is destined to be Die Another Day, in which case I hope he falls off a cliff before the turn of the decade.
I check my funds. "You have a few hundred quid."
This is easy. Why isn't everyone a rockstar?
Oh, right. At some point you're expected to play music. I have Dodgy Sam set up some gigs in local pubs, which he has no problem doing. As Punching The Pope hits the shelves, BORING JOHN heads back to the streets to bask in the joy of success. Which goes like this - quoting directly:
Monday's gig: nobody turns up
Tuesday's gig: nobody turns up
Wednesday's gig: nobody turns up
Thursday's gig: nobody turns up. The groupies in your bed last night say they have AIDS!!!
Friday's gig: fairly cold response
...at least I have groupies, right?
Checking my popularity, it turns out that being on more TV shows than David Mitchell hasn't won me any fans. In fact, feedback says that everyone in the local area, the UK and Europe hates me, though I do have some fans in America - possibly ironic ones. Worse, the "few hundred quid" I used to have is now simply "a few quid". That's probably much less! Certainly, it's not enough to do another record.
"You want a regular supply of amphetamines," says the game. "Yeah, well, you want graphics," I tell it, and pout for a while. This seems to work. Somehow, Punching The Pope gets the world's attention and I'm offered a recording contract worth £39000. That's like, MANY hundred quid! Dodgy Sam says I'm worth much more. I politely inform Dodgy Sam that his opinion is as relevant as herpes in a fruit fly.
With this backing, Punching The Pope goes from mild blasphemy to worldwide indignation. The video is officially Bad, but the reviews are Positive, and it goes to number 53 in the charts in the UK and 98 in America. To celebrate, I take some heroin, crash, and am rushed to hospital. "You abhor the hospital," says the game. "You ignore the nurses: they are not sexy enough." Still, the treatment goes well, so I think the lesson is clear - taking drugs is essentially harmless. Hurray for drugs!
Now armed with a full record company, it's time to find out what 'going platinum' means, and then try to make it happen for Punching The Pope. I invest some of my money into making an album, which I decide should be a rock romance opera called My Sofa Has Chlamydia. The sound engineer spends most of the time bitching, before finishing the project with the polite farewell "I will be chuffed to see the back of you f&*%ers," because even in a game that encourages you to take all the drugs , swearing would be fucking disgraceful. Unfortunately, not only does the album sound terrible, the record company refuses to distribute it. I still have "a few THOUSAND quid" though, so it's all good.
And it's all thanks to drugs! Delicious, tasty drugs!
A holiday seems in order, to recharge the old batteries. With my money, I can take a trip to Sunny Britain, Froggieland, The mediterranean, Africa, India or Paradise Island. I opt for two weeks in the latter. "Lots of sex to be had in Paradise Island," says the game. This cheers you up!"
Yeah. But the hotel mini-golf was probably terrible.
Back in Sunny Britain, I set to work on a new album, when this message pops up.
I just love the way it says "orgy". Nothing says "our programmer was a millionth as cool as he - and it is a he - really wants to be" quite like that. It's a similar story if you go to the drugs menu, which lists your current habits and has the option "More drugs please."
Well, why not? More drugs please!
"There is a lot of drugtaking at the party..." warns the game. Or possibly boasts. Honestly, I'm not entirely sure, but it does seem an adorable way to describe a night at an "orgy".
The new album is a complete disaster, possibly down to the fact that we're in Year 1, Week 32, and I've practiced playing music exactly twice. "The final master sounds bloody dreadful," grumbles Rockstar. "Dodgy Sam says it is mediochre," it adds, but I try to take that as a positive thing. After all, he could have called it ultra-vilent. For a name, I decide it's probably a good idea to appeal to the universe's only arbiter of taste, and subsequently call it "I Love You, Random Number Generator!" - or RNG for short.
To raise some funds, it's time to go gigging. The choice is either playing in local clubs for no purpose, or borrowing some money from the record company in the hopes of stepping things up a notch. The record company lends £61,000, to be repaid in 34 weeks. How nice. They also release a track from I Love You RNG! as a single without asking. Bastards! Parasites! I will have vengeance in blood!
Checking my popularity, I see my biggest fan-base is in America, on the grounds that I actually have a couple of fans in America. That's where the tour will be then...
Wednesday's gig: good applause for most numbers
Thursday's gig: nobody turns up
Friday's gig: nobody turns up
Okay. Not great. Overall though, still an improvement on the last time. The amount of money spent makes it clear I'm never, ever going to be able to repay the record company loan, so I decide to spend a ton of it on taking a "Carribean" cruise. This at least helps dampen the frustration of critics hating my album, but loving the single the record company released with their executive meddling bullshit. Yaaaaaargh! I hate those guys! What do they know about music anyway?
On return, I find the message "Dodgy Sam says he can get you the support to The Bottoms on their 5 week tour of large halls in Europe." I accept, and am described by critics as "adequate".
Oh yeah, and arrested for possession of heroin in one seriously busy Saturday.
Turns out that when you're a rockstar, heroin's just a class "Eh" drug. Hurrah! "I LOVE YOU, RNG!" also magically rises to number 49 in the charts, proving that fate can indeed be sucked up to even when you yourself suck. Finally, success beckons, and I'm almost positive I haven't forgotten anything!
Turns out that even before Napster, making music didn't exactly make a fortune. I head back to the record company to get a loan to repay the original £61,000 loan. They offer me £295,000. I graciously accept, and am immediately hospitalised, then sedated after sexually assaulting a nurse, apparently. Still, nobody seems to care, and I subsequently pull exactly the same scam without the downtime.
Seriously, this rockstar business is easy. Finally, I think I've cracked it - sex, drugs and rock and roll. I am the king of the goddamn world , with a record company that reliably offers to give me interest free loans to repay my last interest free loans, only occasionally stepping in to release a single without asking, and refuse to let me make a record with the word 'Arse' in its name. Which is odd when there are bands out there with names like "Dog Piss" and "John The Rapist", and a group called "The Big Bums" is at number 31 in the singles chart with "Chcoolate Surprise."
Just saying, Rockstar. Just saying...
Still, who cares? Let the sticks and arrows of outrageous fortune land where they may - at the risk of sounding arrogant, I am the greatest person who ever lived. Nothing can stop me now!


text:
Q: MySQL Workbench: Start Transaction seems to be committed before rollback I wrote a bunch of delete statements and wrapped them in a transaction:
start transaction;
delete a...
delete b...
delete c...
rollback;

The idea is I would want the deletes to occur inside a transaction which would rollback upon completion. If a step failed along the way, I would want the successful steps to be rolled back as well.
To my chagrin delete a worked, removing a few thousand rows, delete b failed, but when I reran the statements all the records from a appeared to be gone.
Is this because the transaction is still open? I tried doing:
set session transaction isolation level read committed;
select a.*

and got back zero rows so I think that is not the case. Was the successful a delete committed? And if so, how do I prevent that from happening until I can guarantee a full working query?

A: MySQL Workbench enables auto commit by default. In the SQL editor there is a toolbar button that can be used to toggle auto commit at will:

A: Somebody turned me on to, what I think, is a better way to do this:
begin;
<sql transactions>
commit;

A: Try setting the autocommit flag to false:
set @@autocommit = false;
start transaction;
delete a...
delete b...
delete c...
rollback;