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Heterogeneous Selection at Specific Loci in Natural Environments in Arabidopsis thaliana
Cynthia Weinig*,1,
Lisa A. Dorn*,
Nolan C. Kane*,
Zachary M. German*,
Solveig S. Halldorsdottir†,
Mark C. Ungerer†,
Yuko Toyonaga*,
Trudy F. C. Mackay†,
Michael D. Purugganan† and
Johanna Schmitt*
* Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 02912
† Department of Genetics, North Carolina State University, Raleigh, North Carolina 27695
1 Corresponding author: Department of Plant Biology, University of Minnesota, 230 Biosciences Center, 1445 Gortner Ave., St. Paul, MN 55108. E-mail: cweinig{at}umn.edu
Genetic variation for quantitative traits is often greater than that expected to be maintained by mutation in the face of purifying natural selection. One possible explanation for this observed variation is the action of heterogeneous natural selection in the wild. Here we report that selection on quantitative trait loci (QTL) for fitness traits in the model plant species Arabidopsis thaliana differs among natural ecological settings and genetic backgrounds. At one QTL, the allele that enhanced the viability of fall-germinating seedlings in North Carolina reduced the fecundity of spring-germinating seedlings in Rhode Island. Several other QTL experienced strong directional selection, but only in one site and seasonal cohort. Thus, different loci were exposed to selection in different natural environments. Selection on allelic variation also depended upon the genetic background. The allelic fitness effects of two QTL reversed direction depending on the genotype at the other locus. Moreover, alternative alleles at each of these loci caused reversals in the allelic fitness effects of a QTL closely linked to TFL1, a candidate developmental gene displaying nucleotide sequence polymorphism consistent with balancing selection. Thus, both environmental heterogeneity and epistatic selection may maintain genetic variation for fitness in wild plant species.
EXPLAINING the maintenance of quantitative genetic variation in the wild is an important problem in evolutionary biology. The level of standing genetic variation is thought to depend upon the balance between purifying selection, which erodes genetic variation in fitness-related traits, and the introduction of new alleles through mutation (Mousseau and Roff 1987; Barton and Turelli 1989). However, estimates of selection and mutation rates in some species suggest that observed levels of genetic variation are higher than can be explained solely by mutation-selection balance (Houle 1992; Mackayet al. 1995; Nuzhdinet al. 1995; Houleet al. 1996). Possible additional mechanisms for maintaining genetic variation include several forms of heterogeneous natural selection. For example, selection may favor different genotypes in different environments, depending in part on the frequency with which alternative environments are encountered and dominance relations (Levene 1953; Felsenstein 1976; Gillespie and Turelli 1989; Prout and Savolainen 1996). Similarly, epistatic interactions among loci may maintain genetic variation if the fitness effects of an allele at one locus reverse depending on the allelic genotype at another locus (Leips and Mackay 2000). However, the importance of such gene × environment and gene × gene interactions in natural plant populations remains to be determined.
The role of heterogeneous selection is of particular interest for the colonizing annual plant Arabidopsis thaliana (L.) Heynh. (Brassicaceae). This genetic model species exhibits variation in quantitative traits both within and between natural populations (Kuittinenet al. 1997; Nordborg and Bergelson 1999; Dornet al. 2000; Pigliucci and Marlow 2001). In addition, studies of nucleotide sequence variation at specific loci reveal an excess of low-frequency within-species polymorphism in this species relative to the neutral expectation (Purugganan and Suddith 1998, 1999). Such excess polymorphism may result from the inefficient elimination of mildly deleterious mutations in small inbreeding populations (Bustamanteet al. 2002), but may also be maintained by active balancing selection (Levene 1953; Stahlet al. 1999). Several studies have detected intermediate-frequency nucleotide polymorphism, which is also consistent with heterogeneous selection at specific loci in Arabidopsis (Purugganan and Suddith 1998, 1999; Aguade 2001; Olsenet al. 2002), but reports of present-day selection at these loci under field conditions are lacking. Moreover, genome-wide patterns of natural selection on allelic variation have not been measured directly in natural environments.
The genetic tools and ecological information available for A. thaliana make this species ideal for tests of locus-specific selection in heterogeneous natural environments (Mitchell-Olds 2001). A. thaliana is a largely although not exclusively self-fertilizing colonizing annual species (Jones 1971a; Abbott and Gomes 1989), naturalized over a broad latitudinal and climatic range. Consequently, the genetic structure of this species is likely to be shaped by rare outcrossing events among colonists, followed by generations of self-fertilization to produce recombinant inbred lineages (Jones 1971b). The recombinant inbred lines (RILs) recently developed for QTL mapping and gene discovery in A. thaliana (Lister and Dean 1993; A. Alonso-Blancoet al. 1998; Wilsonet al. 2001) are therefore a realistic approximation to the natural genetic structure of wild populations. RILs are ideal for field studies of selection because they express a wide range of phenotypic variation and because siblings within a line are genetically identical and can be replicated across environments (Mackay 2001).
The importance of heterogeneous selection in the wild is also an especially interesting question for A. thaliana because natural populations or "ecotypes" of this widely distributed species experience very different climatic and seasonal environments (Nordborg and Bergelson 1999; Pigliucci and Marlow 2001). In particular, A. thaliana ecotypes differ in germination timing (Napp-Zinn 1985; Muniret al. 2001). Most North American populations have been characterized as winter annuals (Baskin and Baskin 1983; Nordborg and Bergelson 1999), in which all seedlings germinate in fall, overwinter as rosettes, and flower and set seed in the spring. Where winters are mild and summers harsh (i.e., sites at southern latitudes), winter annual life histories are selectively advantageous because plants attain a large size at reproduction. However, some populations at northern latitudes in North America flower and set seed in the fall or germinate, flower, and set seed in the spring (L. A. Dorn, personal observation). These life histories presumably evolve as a result of selection to minimize over-winter mortality. It is therefore important to ask whether the strength or direction of natural selection differs among geographic regions or between spring and fall germination cohorts within populations. In addition, the increasing evidence for epistatic interactions among genes in developmental pathways in A. thaliana (Simpson and Dean 2002) suggests the importance of testing whether selection on natural allelic variation depends upon the genetic background.
Terminal flower 1 (TFL1), a critical gene in the developmental pathway to flowering in A. thaliana, is a strong a priori candidate for heterogeneous selection under field conditions because it displays striking evidence of balancing selection at the nucleotide level. Hudson-Kreitman-Aguadé tests of selection demonstrate that the TFL1 promoter/5′ untranslated region (UTR) and coding regions have been subject to differing selective forces (Olsenet al. 2002). The polymorphisms in the TFL1 promoter/5′ UTR define two distinct classes of alleles (haplogroups), distinguished by 20 nucleotide and 10 insertion/deletion polymorphisms, and present at equal frequency in the 14 sampled A. thaliana ecotypes (Olsenet al. 2002). The presence of these two haplogroups results in a distribution of segregating sites that differ significantly from expectations of the neutral model of molecular evolution with low recombination (Olsenet al. 2002). Moreover, the allelic polymorphism at TFL1 is unique in that it is restricted solely to the promoter/5′ UTR sequence. To the 5′ side of this region, there is no evidence for significant haplogroup differentiation at the proximal rps28 gene (M. D. Purugganan, unpublished data). To the 3′ side, the haplogroup dimorphism does not extend to the adjacent coding region, which has very low levels of nucleotide variation apparently associated with a recent selective sweep (M. D. Purugganan, unpublished data). The localization of allelic polymorphism to the TFL1 promoter region is consistent with the action of balancing selection in this region.
Here we report evidence for heterogeneous selection on quantitative trait loci (QTL) in A. thaliana under natural field conditions. We show that selection acts on different loci in different natural environments and that alternative alleles are favored in different ecological settings and genetic backgrounds. Moreover, although there is no evidence for a significant main effect of TFL1 on fitness, the fitness effects of the marker locus nearest TFL1 reverse, depending on variation elsewhere in the genome. This result suggests the interesting hypothesis that variation at TFL1 is maintained by epistatic selection.
Mapping population and experimental field settings: To map quantitative trait loci for fitness components, we used recombinant inbred lines of Arabidopsis thaliana (L.) Heynh. (Brassicaceae) developed from a cross between the Landsberg erecta (Ler) × Columbia (Col) accessions and advanced through single-seed descent to the F8 (Lister and Dean 1993). Because siblings within a line are genetically uniform, we were able to compare QTL expression in the same set of genotypes across experimental field plantings.
Seedlings of each RIL were planted in field sites in fall 1999 and spring 2000 in both Rhode Island and North Carolina to simulate natural fall and spring germination cohorts. For all plantings, seeds of the 98 RILs were initially sown into randomly assigned cells in each of 30 98-celled flats and cold stratified for 4 days (fall plantings) or 14 days (spring plantings). Following cold stratification, seeds were germinated in the greenhouses of Brown University and North Carolina State University (NCSU). Germination began within a few days of placing germination trays in the greenhouse, and the stratification treatment effectively stimulated synchronous germination (i.e., seedlings germinated within 3–4 days of one another). Slight variation was observed among RILs in the number of emerging seedlings, but all RILs had >20 replicate plants. After 2–3 weeks in the greenhouse, seedlings were transplanted in the order of sowing into 30 randomized blocks (each corresponding to a 98-cell flat) in prepared field plots at Brown's Haffenreffer grant, Bristol, Rhode Island (latitude 40°N 41′W) and NCSU's Clayton agricultural test field, Clayton, North Carolina (latitude 35°N 46′W). For the Rhode Island fall germination cohort, seeds were sown in flats October 13–15, 1999 and transplanted November 4–6 into plowed field blocks at Haffenreffer. Seedlings were spaced at 10-cm intervals to prevent competitive interactions. The spring germination cohort was sown between March 3 and 5, 2000 and transplanted into the field between April 5 and 7. In North Carolina, the fall germination cohort was sown between October 27 and 29 and transplanted to the field between November 16 and 18. The spring germination cohort was sown between February 8 and 10 and transplanted between March 7 and 9. Blocks for the fall and spring plantings were arranged in a checkerboard array in each site, such that neighboring blocks alternated cohorts. All plants were harvested at senescence and scored for fruit number. A subset of blocks was used in North Carolina due to the large number of fruits to be counted on plants in the fall germination cohort and the high mortality resulting from transplant shock of plants in some blocks of the spring cohort; totals of 14 and 16 blocks were used for the fall and spring germination cohorts, respectively.
Estimates of selection: We estimated three fitness components: over-winter survivorship, spring survivorship, and spring fruit production. Both fall and spring survivorship were estimated as the proportion of replicates within a genotype that survived to reproduction (i.e., differentiation of a flowering inflorescence). In A. thaliana, fruit production is highly correlated with seed production and is thus a good estimate of fecundity in this largely self-fertilizing species (Westerman and Lawrence 1970; Mauricioet al. 1997). These three selection episodes determine the number of seeds produced by a genotype and therefore its genetic representation in successive generations. We could thus screen genome-wide for loci experiencing selection in different natural environments. Moreover, Columbia and Landsberg ecotypes differ in their TFL1 promoter haplotype (Olsenet al. 2002), providing the opportunity to test for evidence of heterogeneous selection at the adjacent marker, pAtT80, segregating in the Col × Ler RILs.
Analysis of variance for components of fitness: Random-effects ANOVA was used to partition variance for a given fitness component into sources originating from line (L) and error according to the model, y =μ + L + error, where μ is the overall mean. The mixed model, y =μ+ L + Se + Si + L × Se + L × Si + Se × Si + L × Se × Si + error, was used to account for variation attributable to environment, where Se is the seasonal growth environment (fall or spring), Si is the geographic site (Rhode Island or North Carolina), and both Se and Si are treated as fixed effects. The significance of these variance components was tested using Proc Catmod and GLM (SAS 1999) for survivorship and fruit number, respectively. Proc Catmod is more suited than Proc GLM to analyses of binomial data and was used to test the significance of the line × season and line × site interactions for survivorship in two separate models. However, Catmod could not calculate the chi-square test statistic for the two- and three-way interactions in analyses including the Rhode Island spring cohort, presumably due to the absence of line variation (see results below) in this cohort and resulting low cell counts. Because ANOVA is typically robust to the assumption of normality, Proc GLM was used to determine the significance of the three-way interaction for survivorship. The GLM results were entirely consistent with the results of the two-way analyses performed by Proc Catmod; GLM results are presented so that the same statistical tests are used in one-way and higher-order ANOVA.
We calculated across-season correlations (rGE) from the components of variance as cov12∕(V1.1×V1.2) , where cov12 represents the covariance of fecundity or survivorship across the two seasonal environments and VL1 and VL2 are the among-line variance components within each of the two environments (Robertson 1959). Significant correlations across the fall and spring seasonal environments within each geographic region are of particular relevance, because genetic similarity of fitness components across seasonal cohorts could constrain adaptive evolution within each environment. Results from the two-way ANOVA including line and season as main effects are used to determine whether estimates of rGE differ significantly from zero or unity: across-season genetic correlations are significantly different from 1 if the L × Se interaction terms are significant and are significantly different from 0 if the among-line variance is significant in the same analysis.
Linkage map construction: Although a large number of polymorphic marker loci have been identified in the Ler × Col RI lines, not all RILs have been genotyped at all loci. A subset of marker loci (n = 225) was therefore used to construct the linkage map. Markers genotyped in 80% or more of the lines were selected to provide even coverage of the genome. Genotypic data for these lines are publicly available from the Nottingham Arabidopsis Stock Centre and were downloaded from the website, http:/nasc.nott.ac.uk/. Maps were constructed using Mapmaker/Exp 3.0 (Landeret al. 1997); exact methods are described in greater detail elsewhere (Ungereret al. 2002).
QTL mapping of fitness components: RIL means for fruit production were nonnormally distributed in all experimental cohorts. We therefore square-root transformed the phenotypic values for individual replicates and mapped the RIL means of the transformed data. We used composite interval mapping (Zeng 1994) as implemented by QTL Cartographer software (Basten et al. 1994, 1999) to test for the presence of significant QTL. This procedure tests for a QTL between two adjacent markers while statistically controlling for the effects of additional QTL segregating outside the test interval. Controls for variation at other QTL (i.e., cofactors) were initially selected using forward-backward stepwise regression. The likelihood-ratio (LR) test statistic evaluates the hypothesis of a significant QTL in the test interval. Within each experimental environment, the significance threshold of the LR for a QTL was determined through permutation analyses (Doerge and Churchill 1996). Additive effects of a given QTL were estimated as the difference between the two homozygous classes divided by two. QTL-by-environment interactions were tested using analysis of variance (Fryet al. 1998). The ANOVA model included season and marker loci nearest all QTL that were significant in the genome-wide screen. When all possible two-way interactions are tested, ANOVA effectively corrects for potential type I error resulting from multiple tests. Maximum-likelihood techniques are also available to test for QTL-by-environment interactions, but these analyses were inappropriate for our data, due to the large number of cofactors present within each environment for a given fitness component. We also used ANOVA to test for epistatic selection, including the marker loci nearest all significant fitness QTL from the composite interval mapping (CIM) analyses as main effects and all pairwise interactions among those markers. In the same model, we included pAtT80, the marker closest to TFL1, in the ANOVA model as an a priori candidate for epistatic selection on the basis of the strong molecular signature of balancing selection at this locus.
Change in allele frequency resulting from selection at a given QTL: Selection coefficients were calculated from the relative fitness of different allelic classes at a QTL, such that w22 = 1 – s, where w22 is the ratio of the allelic class with lower relative to higher fitness (Hedrick 1985; Hartl and Clark 1989). The change in allele frequency across generations was then calculated from the haploid model, –sq(1 – q)/(1 – sq), where q is the frequency of the allele with lower relative fitness and s is the selection coefficient (Hedrick 1985).
Restricted maximum-likelihood estimates of the among-line variance component, VLINE, for each fitness estimate in each environment and the associated P value
Genetic variation in the expression of fitness components across natural seasonal environments: We examined variation in the expression of fitness (i.e., survival to reproduction and fruit production) in 98 RILs grown in two seasonal environments (fall and spring) within each of two geographic regions (Rhode Island and North Carolina). RILs within cohorts differed significantly in over-winter mortality, which ranged from 8 to 48% in Rhode Island on average within an RIL and from 7 to 57% in North Carolina (apparently due to an atypical December snowstorm; Table 1). RILs also differed significantly in prereproductive mortality of the spring cohort in North Carolina, which ranged from 5 to 70% of seedlings per RIL due to hot spring temperatures that reached 27°–28° shortly after planting. In contrast, prereproductive mortality was only 3% and did not differ among lines in the Rhode Island spring seasonal cohort (Table 1). In both Rhode Island and North Carolina, plants in the fall cohort that survived the winter flowered and senesced earlier in the spring season than did those in the spring cohort, suggesting that physiological factors rather than environmental severity imposed a limit on lifespan. Fruit production varied significantly among RILs in all cohorts, with genotypic means ranging from 11 to 115 fruit in Rhode Island fall, 76 to 242 fruit in Rhode Island spring, 75 to 1428 fruit in North Carolina fall, and 3 to 63 fruit in North Carolina spring cohorts (Table 1). Thus, strong natural selection acted on the genetic variation present in these lines for all three fitness components. Patterns of natural selection differed substantially between sites and seasonal cohorts. The relative fitness of individual RILs differed significantly across sites and seasons, as indicated by significant line × site × season interactions for both survivorship and fecundity (Table 2). Across-season genetic correlations were fairly small, as follows: rGE = 0.46 (line P = 0.02, line × season P = 0.03 from two-way ANOVA) for fruit number in Rhode Island and rGE = 0.37 (line P = 0.31, line × season P < 0.0036) in North Carolina. For survivorship, rGE = 0.27 (line P = 0.18, line × season P = 0.03) in Rhode Island and rGE = 0.20 (line P = 0.02, Line × season P = 0.05) in North Carolina. Genetic correlations across seasons were significantly <1 in both sites and not significantly different from zero for fruit number in North Carolina and survivorship in Rhode Island.
Three-way ANOVA for survivorship and fecundity
Significant QTL for fecundity and survivorship
—QTL for fitness components (survival to reproduction and fecundity) in natural seasonal environments and geographic regions experienced by A. thaliana. All QTL displayed significant QTL × environment interactions (P < 0.05), with the exception of the North Carolina QTL at CAT-TS039 and KG31 for spring survivorship and the Rhode Island fall QTL for over-winter survivorship (P > 0.10). Because these traits determine genetic representation in successive generations, season- and site-specific effects indicate that selection on individual QTL varies across environments. Circles on linkage groups denote the position of markers. Symbols beside linkage groups indicate the position of QTL peaks in centimorgans, while lines to either side of the QTL symbols show the 2-LOD support limits.
Heterogeneous natural selection across seasons and geographic regions: CIM analyses detected one to six QTL with significant effects on fitness for every selection episode except for fruit production by the North Carolina fall cohort (Figure 1, Table 3). PAtT80, the marker closest to the TFL1 promoter polymorphism, had no main effect on fitness in any environment, in contrast with the molecular evidence for balancing selection at this locus. Thus, there was no evidence that geographical or seasonal variation in selection could maintain the observed allelic variation at TFL1. However, consistent with the small across-season genetic correlations and the significant two- and three-way interactions, we observed substantial environmental variation in selection at other QTL. Nearly all of the observed QTL were site and season specific, exhibiting significant QTL × environment interactions (Figure 1, Table 3). Thus, natural selection acted at different loci in different environments. Selection coefficients were on the order of 0.01–0.06, and the estimated change in allele frequency in one generation resulting from variation among genotypes in survivorship and fecundity ranged from 0.003 to 0.02 at individual QTL (Table 3). Thus, strong directional selection acted at specific loci in each site and season.
Heterogeneous natural selection can maintain quantitative genetic variation in the wild if it favors different alleles in different environments. We observed such heterogeneous selection for one QTL associated with marker g2368 at the bottom of chromosome 5. At this QTL, the Columbia allele reduces over-winter survivorship in the North Carolina fall cohort, but enhances fecundity in the Rhode Island spring cohort (Table 3). This trade-off between fitness components in different geographic regions may select for locally adapted populations and maintain allelic variation among northern vs. southern ecotypes. Either pleiotropy or physical linkage may account for the antagonistic fitness effects of this QTL. However, either of these genetic mechanisms is sufficient to impose an evolutionary constraint and a performance trade-off in the short term. This geographically heterogeneous selection would maintain variation among ecotypes, the level at which molecular polymorphism is usually assessed in A. thaliana (Purugganan and Suddith 1999; Aguade 2001; Olsenet al. 2002). However, additional mechanisms are needed to explain the maintenance of the quantitative genetic variation observed within natural populations (Jones 1971b; Dornet al. 2000).
—Epistatic interactions for fruit production in the Rhode Island spring cohort between marker loci (A) mi61 and h2a1, (B) g2368 and g4564a, (C) pAtT80 and mi61, and (D) pAtT80 and h2a1 (the marker locus nearest the candidate gene, TFL1). Least-squares means ± 1 standard error are shown. See Table 4 for significance tests of the interactions.
Seasonal heterogeneity in selection within sites may be one contributing mechanism because temporal variation can slow the rate at which genetic variation is eroded, even if it cannot permanently protect polymorphisms in the haploid case (Felsenstein 1976). Allelic effects did not reverse across seasonal environments for fitness QTL detected within the geographic regions examined here, but most QTL had environment-specific fitness effects. Thus, different loci were exposed to selection in different ecological settings. Such conditional neutrality for fitness can slow the erosion of genetic variation within a population if the population experiences selection in both environments, resulting in higher levels of variation maintained by mutation-heterogeneity on the maintenance of genetic variation assume a diploid, randomly mating population. Bearing in mind that the population genetics of A. thaliana violate model assumptions, natural populations of Arabidopsis with two seasonal germination cohorts do meet the condition of temporally varying selective environments encountered in potentially similar frequency. If seasonally heterogeneous selection contributes to maintenance of quantitative genetic variation, we should expect to find higher levels of standing variation in such populations than in natural populations with a fixed winter annual life history.
Epistatic selection and the maintenance of genetic variation within populations: Genetic variation may also be maintained within natural Arabidopsis populations by epistatic interactions among loci. Just as selection at specific loci may vary with environment (Fryet al. 1998; Leips and Mackay 2000) or between sexes (Vieiraet al. 2000; Mazer 2002), fitness effects of specific alleles may vary with genetic background. When genetic backgrounds are mixed by migration among populations, epistasis for fitness can constrain fixation of allelic variants at specific loci, resulting in higher levels of standing variation (Wade 2001).
Our experiment revealed epistatic selection as a significant interaction between several QTL at markers h2a1, mi61, g4564a, and g2368 (Figure 2, A and B, Table 4) for fruit production in the Rhode Island spring cohort. Of particular interest, the Landsberg allele at marker mi61 reversed in effect with genetic background, conferring higher fitness than the Columbia allele in the Columbia h2a1 background, but lower fitness than the Columbia allele in the Landsberg h2a1 background (Figure 2A). The advantage of the repulsion phase distinguishes these loci as independently significant QTL, despite the overlap of their confidence intervals. We also observed significant epistatic selection in the vicinity of TFL1. The closest marker, pAtT80, displayed significant epistatic interactions with both mi61 and h2a1 for spring fruit production in Rhode Island (Table 4), reversing in allelic effect depending upon the genotype at each of these loci (Figure 2, C and D). This fitness reversal with genetic background explains our inability to detect a QTL for fitness in the region of pAtT80 in our initial analysis using exclusively additive genetic models and indicates that strong selection is nevertheless acting near this marker. The observation that the direction of selection at a QTL closely linked to TFL1 reverses with genetic background suggests the interesting hypothesis that epistatic selection may help to maintain the observed nonneutral polymorphism in the promoter of this gene. This hypothesis deserves further investigation for TFL1 as well as other important developmental genes.
Evolutionary and molecular genetics of A. thaliana: This study illustrates the power of genetic models such as A. thaliana for examining evolutionary mechanisms from the ecological to the molecular level. The observation that selection acts at different QTL across sites and seasons suggests that different natural populations of this colonizing species with similar initial genetic compositions will follow different evolutionary trajectories in response to natural selection. Such heterogeneous selection also suggests that individual traits, not surprisingly, differ in their fitness effects across natural environments; the fitness effects of QTL underlying the expression of life-history and architectural traits in the Rhode Island and North Carolina seasonal environments will be presented elsewhere (C. Weinig and J. Schmitt, unpublished results). Heterogeneous selection may also favor different alleles at the same locus in different sites, thus actively maintaining genetic variation among populations.
ANOVA for QTL × QTL epistasis of fruit production in the Rhode Island spring cohort
The observation that alternative alleles are favored in different genetic backgrounds under natural conditions suggests that epistasis may be an additional mechanism for maintenance of genetic variation both among and within populations. Epistatic interactions among QTL for fitness components (Shook and Johnson 1999; Leips and Mackay 2000) and other important quantitative traits (C. Alonso-Blanco et al. 1998, 1999; Borevitzet al. 2002; Ungereret al. 2002) have now been observed within several model species, suggesting that they may be relatively common. Epistasis between natural allelic variants at the developmental loci, FRI and FLC, have also been detected (Koornneefet al. 1994; Leeet al. 1994). Such epistatic interactions may have particular evolutionary impact in largely self-fertilizing species such as A. thaliana, in which novel variants may be produced by recombination after rare outcrossing events, and subsequent lineage selection among the inbred descendants of these variants may act upon both additive and epistatic variation (Wright 1931; Goodnight 1995; Wade and Goodnight 1998). The genetic resources and molecular tools available for A. thaliana provide an opportunity to test such hypotheses about evolutionary mechanisms.
The authors thank Josh Banta, Anthony Giunta, Marah Gotschik, Kelly Gravuer, and Mason Israel for extensive field help and fruit processing, as well as Marc Tater and David Rand for comments on early manuscript drafts. Comments from two anonymous reviewers significantly improved the clarity and content of this article. The Central Crops Research Station contributed to the maintenance of the field site in Clayton, North Carolina, and we especially appreciate the help of Kathy Haring at the research station in North Carolina and Fred Jackson at the Brown University greenhouse. This research was supported by National Science Foundation grant DEB-9976997 to T.F.C.M., M.D.P., and J.S.
Communicating editor: O. Savolainen
Received July 24, 2002.
Accepted April 2, 2003.
Copyright © 2003 by the Genetics Society of America
Abbott R. J.,
Gomes M. F.
, 1989 Population genetic structure and outcrossing rate of Arabidopsis thaliana (L.) Heynh. Heredity 62: 411–418.
Aguade M.
, 2001 Nucleotide sequence variation at two genes in the phenylpropanoid pathway, the FAH1 and F3H genes, in Arabidopsis. Mol. Biol. Evol. 18: 1–9.
Alonso-Blanco A.,
Peeters A. J. M.,
Koornneef M.,
Lister C.,
Dean C.
, 1998 Development of an AFLP based linkage map of Ler, Col, and Cvi Arabidopsis thaliana ecotypes and construction of a Ler/Cvi recombinant inbred line population. Plant Journal 14: 259–271.
Alonso-Blanco C.,
El-Assal S. E.-D.,
Coupland G.,
Koornneef M.
, 1998 Analysis of natural allelic variation at flowering time loci in the Landsberg erecta and Cape Verde Island ecotypes of Arabidopsis thaliana. Genetics 149: 749–764.
Vries B. D.,
Hanhart C. J.,
, 1999 Natural allelic variation at seed size loci in relation to other life history traits of Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA 96: 4710–4717.
Barton N. H.,
Turelli M.
, 1989 Evolutionary quantitative genetics: How little do we know? Annu. Rev. Genet. 23: 337–370.
Baskin J. M.,
Baskin C. C.
, 1983 Seasonal changes in the germination responses of buried seeds of Arabidopsis thaliana and ecological interpretation. Bot. Gaz. 144: 540–543.
Smith C.,
Gavora J. S.,
Benkel B.,
Chesnais J.,
Fairfull W.
Basten C. J.,
Weir B. S.,
Zeng Z-B.
, 1994 Zmap—a QTL cartographer, pp. 65–66 in Proceedings of the 5th World Congress on Genetics Applied to Livestock Production: Computing Strategies and Software, edited by Smith C., Gavora J. S., Benkel B., Chesnais J., Fairfull W.. Organizing Committee, 5th World Congress on Applied Genetics Applied to Livestock Production, Guelph, Ontario, Canada.
, 1999 QTL Cartographer, Version 1.13. Department of Statistics, North Carolina State University, Raleigh, NC.
Borevitz J. O.,
Maloof J. N.,
Lutes J.,
Dabi T.,
Redfern J. L.
et al., 2002 Quantitative trait loci controlling light and hormone response in two accessions of Arabidopsis thaliana. Genetics 160: 683–696.
Bustamante C.,
Nielsen R.,
Sawyer S.,
Olsen K. M.,
Purugganan M. D.
et al., 2002 The cost of inbreeding in Arabidopsis. Nature 416: 531–534.
Doerge R. W.,
Churchill G. A.
, 1996 Permutation tests for multiple loci affecting a quantitative character. Genetics 142: 285–294.
Dorn L. A.,
Hammond Pyle E.,
Schmitt J.
, 2000 Plasticity to cues and resources in Arabidopsis thaliana: testing for adaptive value and costs. Evolution 54: 1982–1994.
Felsenstein J.
, 1976 The theoretical population genetics of variable selection and migration. Annu. Rev. Genet. 10: 253–280.
Fry J. D.,
Heinsohn S. L.,
Mackay T. F. C.
, 1996 The contribution of new mutations to genotype-environment interactions in Drosophila melanogaster. Evolution 50: 2316–2327.
Nuzhdin S. V.,
Pasuykova E.,
, 1998 QTL mapping of genotype-environment interaction for fitness in Drosophila melanogaster. Genet. Res. 71: 133–141.
Gillespie J. H.,
, 1989 Genotype-environment interactions and the maintenance of polygenic variation. Genetics 121: 129–138.
Goodnight C. J.
, 1995 Epistasis and the increase in additive genetic variance: implications for phase I of Wright's shifting balance process. Evolution 49: 502–511.
Hartl D. L.,
Clark A. G.
, 1989 Principle of Population Genetics. Sinauer, Sunderland, MA.
Hedrick P. W.
, 1985 Genetics of Populations. Jones & Bartlett Publishers, Boston.
Houle D.
, 1992 Comparing evolvability and variability of quantitative traits. Genetics 130: 535–566.
Houle D.,
Morikawa B.,
Lynch M.
, 1996 Comparing mutational variabilities. Genetics 143: 1467–1483.
Jones M. E.
, 1971a The population genetics of Arabidopsis thaliana. I. The breeding system. Heredity 27: 39–50.
, 1971b The population genetics of Arabidopsis thaliana. II. Population structure. Heredity 27: 51–58.
Vries H. B.-D.,
Hanhart C.,
Soppe W.,
Peeters T.
, 1994 The phenotype of some late-flowering mutants is enhanced by a locus on chromosome 5 that is not effective in the Landsberg erecta phenotype. Plant J. 6: 911–919.
Kuittinen H.,
Mattila A.,
Savolainen O.
, 1997 Genetic variation at marker loci and in quantitative traits in natural populations of Arabidopsis thaliana. Heredity 79: 144–152.
Lander E. S.,
Green P.,
Abrahamson J.,
Barlow A.,
Daly M. J.
et al., 1997 Mapmaker: an interactive computer package for constructing primary genetic linakage maps of experimental and natural populations. Genomics 1: 174–181.
Lee I.,
Michaels S. D.,
Masshardt A. S.,
Amasino R. M.
, 1994 The late-flowering phenotype of FRIGIDA and mutations in LUMINIDEPENDENS is suppressed in the Landsberg erecta strain of Arabidopsis. Plant J. 6: 903–909.
Leips J.,
, 2000 Quantitative trait loci for life span in Drosophila melanogaster: interactions with genetic background and larval density. Genetics 155: 1773–1788.
Levene H.
, 1953 Genetic equilibrium when more than one ecologial niche is available. Am. Nat. 87: 331–333.
, 1993 Recombinant inbred lines for mapping RFLP and phenotypic markers in Arabidopsis thaliana. Plant J. 105: 1203–1208.
, 2001 Quantitative trait loci in Drosophila. Nat. Genet. 2: 11–20.
Mackay T. F. C.,
Lyman R. F.,
Hill W. G.
, 1995 Polygenic mutation in Drosophila melanogaster: non-linear divergence among unselected strains. Genetics 139: 849–859.
Mauricio R.,
Rausher M. D.,
Burdick D. S.
, 1997 Variation in the defense strategies of plants: Are resistance and tolerance mutually exclusive? Ecology 78: 1301–1311.
Mazer S.
, 2002 Trade-offs between male and female reproduction associated with allozyme variation in phosphoglucoisomerase in an annual plant (Clarkia unguiculata): (Onagraceae). Evolution 55: 2421–2428.
Mitchell-Olds T.
, 2001 Arabidopsis thaliana and its wild relatives: a model system for ecology and evolution. Trends Ecol. Evol. 16: 693–700.
Mousseau T. A.,
Roff D. A.
, 1987 Natural selection and the heritability of fitness components. Genet. Res. 59: 181–198.
Munir J.,
Donohue K. A.,
, 2001 The effect of maternal photoperiod on seasonal dormancy in Arabidopsis thaliana (Brassicaceae). Am. J. Bot. 88: 1240–1249.
Halevy A. H.
Napp-Zinn K.
, 1985 Arabidopsis thaliana, pp. 492–503 in CRC Handbook of Flowering, edited by Halevy A. H.. CRC Press, Boca Raton, FL.
Nordborg M.,
Bergelson J.
, 1999 The effect of seed and rosette cold treatment on germination and flowering time in some Arabidopsis thaliana (Brassicaceae) ecotypes. Am. J. Bot. 86: 470–475.
, 1995 Polygenic mutation in Drosophila melanogaster: the causal relationship to bristle number and fitness. Genetics 139: 861–872.
Womack A.,
Garret A. R.,
Suddith J. I.,
, 2002 Contrasting evolutionary forces in the Arabidopsis floral developmental pathway. Genetics 160: 1641–1650.
Pigliucci M.,
Marlow E. T.
, 2001 Differentiation for flowering time and phenotypic integration in Arabidopsis thaliana in response to season length and vernalization. Oecologia 127: 501–508.
Prout T.,
, 1996 Genotype-by-environment interaction is not sufficient to maintain variation: Levene and the leafhopper. Am. Nat. 148: 930–936.
Purugganan M.,
Suddith J. I.
, 1998 Molecular population genetics of the Arabidopsis CAULIFLOWER regulatory gene: nonneutral evolution and naturally occurring variation floral homeotic function. Proc. Natl. Acad. Sci. USA 95: 8130–8134.
Purugganan M. D.,
, 1999 Molecular population genetics of floral homeotic loci: departures from the equilibrium-neutral model at the APETALA3 and PISTILLATA genes of Arabidopsis thaliana. Genetics 151: 839–848.
Robertson A.
, 1959 The sampling variance of genetic correlations. Biometrics 15: 469–485.
SAS, 1999 SAS/STAT User's Guide. SAS Institute, Cary, NC.
Shook D. R.,
Johnson T. E.
, 1999 Quantitative trait loci affecting survival and fertility-related traits in Caenorhabditis elegans show genotype-environment interactions, pleiotropy and epistasis. Genetics 153: 1233–1243.
Simpson G. G.,
, 2002 Arabidopsis, the rosetta stone of flowering time? Science 296: 285–289.
Stahl E. A.,
Dwyer G.,
Kreitman M.,
, 1999 Dynamics of disease resistance polymorphism at the Rpm1 locus of Arabidopsis. Nature 400: 667–671.
Ungerer M. C.,
Halldorsdottir S. S.,
Modliszewski J. L.,
, 2002 Quantitative trait loci for inflorescence development in Arabidopsis thaliana. Genetics 160: 1133–1151.
Vieira C.,
Pasyukova E. G.,
Zeng Z-B.,
Hackett J. K. B.,
Lyman R. F.
et al., 2000 Genotype-environment interaction for quantitative trait loci affecting lifespan in Drosophila melanogaster. Genetics 154: 213–227.
Wade M. J.
, 2001 Epistasis, complex traits, and mapping genes. Genetica 112–113: 59–69.
Wade M. J.,
, 1998 Perspective: the theories of Fisher and Wright in the context of metapopulations: when nature does many small experiments. Evolution 52: 1537–1553.
Westerman J. M.,
Lawrence M. J.
, 1970 Genotype-environment interaction and developmental regulation in Arabidopsis thaliana. Heredity 25: 609–627.
Wilson I. W.,
Schiff C. L.,
Hughes D. E.,
Somerville S. C.
, 2001 Quantitative trait loci analysis of powdery mildew disease resistance in the Arabidopsis thaliana accession kashmir-1. Genetics 158: 1301–1309.
Wright S.
, 1931 Evolution in Mendelian populations. Genetics 16: 97–159.
, 1994 Precision mapping of quantitative trait loci. Genetics 136: 1457–1468.
The Genetics Society of America (GSA), founded in 1931, is the professional membership organization for scientific researchers and educators in the field of genetics. Our members work to advance knowledge in the basic mechanisms of inheritance, from the molecular to the population level.


text:
Borovnica (lat. Vaccinium), biljni je rod iz porodice vrjesovki. To su listopadni ili zimzeleni polugrmovi, grmovi i drveće, a najpoznatiji predstavnici ovog roda su obična borovnica (V. myrtillus), američka borovnica ili kultivirana borovnica (V. corymbosum), močvarna borovnica (Vaccinium uliginosum) i brusnica (V. vitis-idaea). Sve četiri navedene vrste rastu i po Hrvatskoj.

Brusnica, Vaccinium vitis-idaea L. je zimzeleni, niski grm s tankim, okruglastim i poleglim grančicama koja raste najčešće s borovnicom, a raširena je po u sjevernoj Europi, Aziji i Sjevernoj Americi i često se koristi u tradicionalnoj medicini, te za izradu sokova, kompota i pekmeza.

Borovnica se ne smije brkati s rodom borovica (Juniperus).

Nekadašnji rod Oxycoccus uklopljen je u Vaccinium.

Vrste

 Vaccinium absconditum J.J.Sm.
 Vaccinium acrobracteatum K.Schum.
 Vaccinium acutissimum F.Muell.
 Vaccinium adenochaetum Sleumer
 Vaccinium adenopodum Sleumer
 Vaccinium adenotrichum Sleumer
 Vaccinium agusanense Elmer
 Vaccinium aitapense Sleumer
 Vaccinium alainii Acuña & Roíg
 Vaccinium albicans Sleumer
 Vaccinium albidens H.Lév. & Vaniot
 Vaccinium almedae Wilbur & Luteyn
 Vaccinium altiterrae Veldkamp
 Vaccinium alvarezii Merr.
 Vaccinium amakhangium S.Panda & Sanjappa
 Vaccinium amazonicum Pedraza & Luteyn
 Vaccinium ambivalens Sleumer
 Vaccinium ambyandrum F.Muell.
 Vaccinium amphoterum Sleumer
 Vaccinium amplexicaule J.J.Sm.
 Vaccinium ampullaceum Sleumer
 Vaccinium andersonii Sleumer
 Vaccinium angiense Kaneh. & Hatus.
 Vaccinium angustifolium Aiton
 Vaccinium apiculatum Sleumer
 Vaccinium apophysatum Sleumer
 Vaccinium appendiculatum Schltr.
 Vaccinium arboreum Marshall
 Vaccinium arbutoides C.B.Clarke
 Vaccinium arctostaphylos L.
 Vaccinium ardisioides Hook.f. ex C.B.Clarke
 Vaccinium aristatum Luteyn & Pedraza
 Vaccinium artum J.J.Sm.
 Vaccinium × atlanticum E.P.Bicknell
 Vaccinium aucupis Sleumer
 Vaccinium auriculifolium Sleumer
 Vaccinium bancanum Miq.
 Vaccinium banksii Merr.
 Vaccinium barandanum S.Vidal
 Vaccinium barbatum J.J.Sm.
 Vaccinium bartlettii Merr.
 Vaccinium benguetense S.Vidal
 Vaccinium besagiense J.J.Sm.
 Vaccinium bissei R.Berazaín
 Vaccinium blepharocalyx Schltr.
 Vaccinium bocatorense Wilbur
 Vaccinium bodenii Wernham
 Vaccinium boninense Nakai
 Vaccinium boreale I.V.Hall & Aalders
 Vaccinium brachyandrum C.Y.Wu & R.C.Fang
 Vaccinium brachybotrys (Franch.) Hand.-Mazz.
 Vaccinium brachycladum Sleumer
 Vaccinium brachygyne J.J.Sm.
 Vaccinium brachytrichum Sleumer
 Vaccinium bracteatum Thunb.
 Vaccinium brassii Sleumer
 Vaccinium breedlovei L.O.Williams
 Vaccinium brevipedicellatum C.Y.Wu
 Vaccinium brevipedunculatum J.J.Sm.
 Vaccinium bullatum (Dop) Sleumer
 Vaccinium bulleyanum (Diels) Sleumer
 Vaccinium caesariense Mack.
 Vaccinium calycinum Sm.
 Vaccinium camiguinense Merr.
 Vaccinium campanense Wilbur & Luteyn
 Vaccinium candidum Veldkamp
 Vaccinium capillatum Sleumer
 Vaccinium cardiophorum Sleumer
 Vaccinium carlesii Dunn
 Vaccinium carneolum Sleumer
 Vaccinium carolinianum Ashe
 Vaccinium caudatum Warb.
 Vaccinium cavendishioides Sleumer
 Vaccinium cavinerve C.Y.Wu
 Vaccinium cebuense Salares & Pelser
 Vaccinium centrocelebicum Sleumer
 Vaccinium ceramense Sleumer
 Vaccinium cercidifolium J.J.Sm.
 Vaccinium cereum (L.f.) G.Forst.
 Vaccinium cespitosum Michx.
 Vaccinium chaetothrix Sleumer
 Vaccinium chamaebuxus C.Y.Wu
 Vaccinium chengiae W.P.Fang
 Vaccinium chihuahuense Wilbur & Luteyn
 Vaccinium chimantense Maguire, Steyerm. & Luteyn
 Vaccinium chunii Merr. ex Sleumer
 Vaccinium ciliatum Thunb.
 Vaccinium claoxylon J.J.Sm.
 Vaccinium clementis Merr.
 Vaccinium coelorum Wernham
 Vaccinium commutatum Mabb. & Sleumer
 Vaccinium conchophyllum Rehder
 Vaccinium confertum Kunth
 Vaccinium consanguineum Klotzsch
 Vaccinium continuum Luteyn & Pedraza
 Vaccinium contractum Sleumer
 Vaccinium convallariiflorum J.J.Sm.
 Vaccinium convexifolium J.J.Sm.
 Vaccinium cordifolium (M.Martens & Galeotti) Hemsl.
 Vaccinium coriaceum Hook.f.
 Vaccinium cornigerum Sleumer
 Vaccinium corymbodendron Dunal
 Vaccinium corymbosum L.
 Vaccinium costaricense Wilbur & Luteyn
 Vaccinium costerifolium Sleumer
 Vaccinium craspedotum Sleumer
 Vaccinium crassiflorum J.J.Sm.
 Vaccinium crassifolium Andrews
 Vaccinium crassistylum Sleumer
 Vaccinium crassivenium Sleumer
 Vaccinium crenatifolium Sleumer
 Vaccinium crenatum (G.Don) Sleumer
 Vaccinium crinigerum Kloet
 Vaccinium cruentum Sleumer
 Vaccinium cubense Griseb.
 Vaccinium culminicola Wernham
 Vaccinium cumingianum S.Vidal
 Vaccinium cuneifolium (Blume) Miq.
 Vaccinium cuspidifolium C.Y.Wu & R.C.Fang
 Vaccinium cyclopense J.J.Sm.
 Vaccinium cylindraceum Sm.
 Vaccinium damingshanense Y.H.Tong & N.H.Xia
 Vaccinium daphniphyllum Schltr.
 Vaccinium darrowii Camp
 Vaccinium debilescens Sleumer
 Vaccinium decumbens J.J.Sm.
 Vaccinium delavayi Franch.
 Vaccinium deliciosum Piper
 Vaccinium dendrocharis Hand.-Mazz.
 Vaccinium densifolium J.J.Sm.
 Vaccinium dentatum Sm.
 Vaccinium dependens (G.Don) Sleumer
 Vaccinium dialypetalum J.J.Sm.
 Vaccinium dictyoneuron Sleumer
 Vaccinium didymanthum Dunal
 Vaccinium distichum Luteyn
 Vaccinium dobbinii Burnham
 Vaccinium dominans Sleumer
 Vaccinium dubiosum J.J.Sm.
 Vaccinium duclouxii (H.Lév.) Hand.-Mazz.
 Vaccinium dunalianum Wight
 Vaccinium dunnianum Sleumer
 Vaccinium ekmanii R.Berazaín
 Vaccinium elegans Elmer
 Vaccinium elliptifolium Merr.
 Vaccinium elvirae Luteyn
 Vaccinium emarginatum Hayata
 Vaccinium endertii J.J.Sm.
 Vaccinium epiphyticum Merr.
 Vaccinium erythrocarpum Michx.
 Vaccinium euryanthum A.C.Sm.
 Vaccinium evanidinervium Sleumer
 Vaccinium exaristatum Kurz
 Vaccinium exul Bolus
 Vaccinium eymae Sleumer
 Vaccinium filipes Schltr.
 Vaccinium fimbribracteatum C.Y.Wu
 Vaccinium fimbricalyx Chun & W.P.Fang
 Vaccinium finisterrae Schltr.
 Vaccinium floccosum (L.O.Williams) Wilbur & Luteyn
 Vaccinium floribundum Kunth
 Vaccinium foetidissiumum H.Lév. & Vaniot
 Vaccinium fragile Franch.
 Vaccinium fraternum Sleumer
 Vaccinium furfuraceum Wilbur & Luteyn
 Vaccinium gaultheriifolium (Griff.) Hook.f. ex C.B.Clarke
 Vaccinium geminiflorum Kunth
 Vaccinium gitingense Elmer
 Vaccinium gjellerupii J.J.Sm.
 Vaccinium glabrescens King & Gamble
 Vaccinium glandellatum Sleumer
 Vaccinium glaucoalbum Hook.f. ex C.B.Clarke
 Vaccinium glaucophyllum C.Y.Wu & R.C.Fang
 Vaccinium globosum J.J.Sm.
 Vaccinium goodenoughii Sleumer
 Vaccinium gracile J.J.Sm.
 Vaccinium gracilipes Sleumer
 Vaccinium gracillimum J.J.Sm.
 Vaccinium grandibracteatum Schltr.
 Vaccinium griffithianum Wight
 Vaccinium guangdongense W.P.Fang & Z.H.Pan
 Vaccinium habbemae Koord.
 Vaccinium haematinum Standl. & Steyerm.
 Vaccinium haematochroum Sleumer
 Vaccinium × hagerupii (Á.Löve & D.Löve) Rothm.
 Vaccinium hainanense Sleumer
 Vaccinium haitangense Sleumer
 Vaccinium halconense Merr.
 Vaccinium hatamense Becc.
 Vaccinium hellwigianum Sleumer
 Vaccinium henrici Sleumer
 Vaccinium henryi Hemsl.
 Vaccinium hiepii Kloet
 Vaccinium hirsutum Buckley
 Vaccinium hirtum Thunb.
 Vaccinium hispidulissimum Wernham
 Vaccinium hooglandii Sleumer
 Vaccinium horizontale Sleumer
 Vaccinium × hybridum (Avrorin) Czerep.
 Vaccinium igneum J.J.Sm.
 Vaccinium imbricans J.J.Sm.
 Vaccinium impressinerve C.Y.Wu
 Vaccinium inconspicuum J.J.Sm.
 Vaccinium indutum S.Vidal
 Vaccinium insigne (Koord.) J.J.Sm.
 Vaccinium × intermedium Ruthe
 Vaccinium iragaense Merr.
 Vaccinium iteophyllum Hance
 Vaccinium jacobeanum P.F.Stevens
 Vaccinium jagorii Warb.
 Vaccinium japonicum Miq.
 Vaccinium jefense Luteyn & Wilbur
 Vaccinium jevidalianum Smitinand & P.H.Hô
 Vaccinium kachinense Brandis
 Vaccinium kingdon-wardii Sleumer
 Vaccinium kjellhergii J.J.Sm.
 Vaccinium korinchense Ridl.
 Vaccinium korthalsii Miq.
 Vaccinium kostermansii Sleumer
 Vaccinium kunthianum Klotzsch
 Vaccinium lageniforme J.J.Sm.
 Vaccinium lamellatum P.F.Stevens
 Vaccinium lamprophyllum C.Y.Wu & R.C.Fang
 Vaccinium lanigerum Sleumer
 Vaccinium latifolium (Griseb.) Benth. & Hook.f. ex B.D.Jacks.
 Vaccinium latissimum J.J.Sm.
 Vaccinium laurifolium (Blume) Miq.
 Vaccinium leonis Acuña & Roíg
 Vaccinium leptocladum Sleumer
 Vaccinium leptomorphum Sleumer
 Vaccinium leptospermoides J.J.Sm.
 Vaccinium leucanthum Schltdl.
 Vaccinium ligustrifolium J.J.Sm.
 Vaccinium linearifolium Kloet
 Vaccinium littoreum Miq.
 Vaccinium longepedicellatum Sleumer
 Vaccinium longicaudatum Chun ex W.P.Fang & Z.H.Pan
 Vaccinium longiporum Schltr.
 Vaccinium longisepalum J.J.Sm.
 Vaccinium loranthifolium Ridl.
 Vaccinium lorentzii Koord.
 Vaccinium lucidum (Blume) Miq.
 Vaccinium lundellianum L.O.Williams
 Vaccinium luteynii Wilbur
 Vaccinium luzoniense S.Vidal
 Vaccinium macgillivrayi Seem.
 Vaccinium macrocarpon Aiton
 Vaccinium madagascariense (Thouars ex Poir.) Sleumer
 Vaccinium malacothrix Sleumer
 Vaccinium mandarinorum Diels
 Vaccinium manipurense (Watt ex Brandis) Sleumer
 Vaccinium marianum P.Watson
 Vaccinium mathewsii Sleumer
 Vaccinium megalophyes Sleumer
 Vaccinium membranaceum Douglas ex Torr.
 Vaccinium meridionale Sw.
 Vaccinium microcarpum (Turcz. ex Rupr.) Schmalh.
 Vaccinium minimiflorum Sleumer
 Vaccinium minusculum Sleumer
 Vaccinium minuticalcaratum J.J.Sm.
 Vaccinium miquelii Boerl.
 Vaccinium mjoebergii J.J.Sm.
 Vaccinium modestum W.W.Sm.
 Vaccinium molle J.J.Sm.
 Vaccinium monanthum Ridl.
 Vaccinium monetarium Sleumer
 Vaccinium monteverdense Wilbur & Luteyn
 Vaccinium montis-ericae Sleumer
 Vaccinium morobense Kloet
 Vaccinium moupinense Franch.
 Vaccinium muriculatum J.J.Sm.
 Vaccinium myodianum S.Panda & Sanjappa
 Vaccinium myrsinites Lam.
 Vaccinium myrsinoides Schltr.
 Vaccinium myrtilloides Michx.
 Vaccinium myrtillus L.
 Vaccinium myrtoides (Blume) Miq.
 Vaccinium nagamasu Argent
 Vaccinium neilgherrense Wight
 Vaccinium nhatrangense Dop
 Vaccinium nitens Sleumer
 Vaccinium nubigenum Fernald
 Vaccinium nummularia Hook.f. & Thomson ex C.B.Clarke
 Vaccinium nuttallii (C.B.Clarke) Sleumer
 Vaccinium obatapaquiniorum Takeuchi
 Vaccinium obedii Veldkamp
 Vaccinium oldhamii Miq.
 Vaccinium omeiense W.P.Fang
 Vaccinium oranjense J.J.Sm.
 Vaccinium oreites Sleumer
 Vaccinium oreogenes W.W.Sm.
 Vaccinium oreomyrtus Sleumer
 Vaccinium orosiense Wilbur & Luteyn
 Vaccinium ortizii Luteyn & Pedraza
 Vaccinium oscarlopezianum Co
 Vaccinium otophyllum Sleumer
 Vaccinium ovalifolium Sm.
 Vaccinium ovatum Pursh
 Vaccinium oxycoccos L.
 Vaccinium pachydermum Stapf
 Vaccinium padifolium Sm.
 Vaccinium palawanense Merr.
 Vaccinium pallidum Aiton
 Vaccinium papillatum P.F.Stevens
 Vaccinium papulosum C.Y.Wu & R.C.Fang
 Vaccinium paradisearum Becc.
 Vaccinium parvifolium Sm.
 Vaccinium parvulifolium F.Muell.
 Vaccinium paucicrenatum Sleumer
 Vaccinium perrigidum Elmer
 Vaccinium petelotii Merr.
 Vaccinium philippinense Warb.
 Vaccinium phillyreoides Sleumer
 Vaccinium piceifolium Wernham
 Vaccinium piliferum (Hook.f. ex C.B.Clarke) Sleumer
 Vaccinium pilosilobum J.J.Sm.
 Vaccinium piperifolium Sleumer
 Vaccinium pipolyi Luteyn
 Vaccinium platyphyllum Merr.
 Vaccinium poasanum Donn.Sm.
 Vaccinium podocarpoideum W.P.Fang & Z.H.Pan
 Vaccinium praeces P.F.Stevens
 Vaccinium praestans Lamb.
 Vaccinium pratense P.C.Tam ex C.Y.Wu & R.C.Fang
 Vaccinium prostratum Sleumer
 Vaccinium psammogenes Sleumer
 Vaccinium pseudobullatum W.P.Fang & Z.H.Pan
 Vaccinium pseudocaracasanum Sleumer
 Vaccinium pseudocaudatum Sleumer
 Vaccinium pseudodialypetalum Ng
 Vaccinium pseudorobustum Sleumer
 Vaccinium pseudospadiceum Dop
 Vaccinium pseudotonkinense Sleumer
 Vaccinium pterocalyx Luteyn
 Vaccinium puberulum Klotzsch ex Meisn.
 Vaccinium pubicalyx Franch.
 Vaccinium pugionifolium Sleumer
 Vaccinium pullei J.J.Sm.
 Vaccinium pumilum Kurz
 Vaccinium quinquefidum J.J.Sm.
 Vaccinium racemosum (Vahl) Wilbur & Luteyn
 Vaccinium randaiense Hayata
 Vaccinium rapae Skottsb.
 Vaccinium reticulatovenosum Sleumer
 Vaccinium reticulatum Sm.
 Vaccinium retivenium Sleumer
 Vaccinium retusifolium J.J.Sm.
 Vaccinium retusum (Griff.) Hook.f. ex C.B.Clarke
 Vaccinium rigidifolium Sleumer
 Vaccinium roraimense N.E.Br.
 Vaccinium roseiflorum J.J.Sm.
 Vaccinium rubescens R.C.Fang
 Vaccinium rubroviolaceum Sleumer
 Vaccinium sanguineum Schltr.
 Vaccinium santafeense Wilbur & Luteyn
 Vaccinium sarawakense Merr.
 Vaccinium saxatile Luteyn & Pedraza
 Vaccinium saxicola Chun
 Vaccinium scandens Schltr.
 Vaccinium schlechterianum Sleumer
 Vaccinium schoddei Sleumer
 Vaccinium schultzei Schltr.
 Vaccinium sciaphilum C.Y.Wu
 Vaccinium sclerophyllum Sleumer
 Vaccinium scoparium Leiberg ex Coville
 Vaccinium scopulorum W.W.Sm.
 Vaccinium scortechinii King & Gamble
 Vaccinium scyphocalyx Sleumer
 Vaccinium secundum Klotzsch
 Vaccinium selerianum (Loes.) Sleumer
 Vaccinium serrulatum W.P.Fang & Z.H.Pan
 Vaccinium setipes Airy Shaw
 Vaccinium shaferi Acuña & Roíg
 Vaccinium shastense J.K.Nelson & Lindstrand
 Vaccinium shikokianum Nakai
 Vaccinium sieboldii Miq.
 Vaccinium sikkimense C.B.Clarke
 Vaccinium simulans Sleumer
 Vaccinium singularis N.R.Salinas & Betancur
 Vaccinium sinicum Sleumer
 Vaccinium smallii A.Gray
 Vaccinium sororium J.J.Sm.
 Vaccinium spaniotrichum Sleumer
 Vaccinium sparsicapillum Sleumer
 Vaccinium sparsum Sleumer
 Vaccinium sphyrospennoides Sleumer
 Vaccinium spiculatum C.Y.Wu & R.C.Fang
 Vaccinium sprengelii (G.Don) Sleumer ex Rehder
 Vaccinium stamineum L.
 Vaccinium stanleyi Schweinf.
 Vaccinium stapfianum Sleumer
 Vaccinium steinii Sleumer
 Vaccinium stellae-montis Sleumer
 Vaccinium stenanthum Sleumer
 Vaccinium stenolobum Schltr.
 Vaccinium stenophyllum Steud.
 Vaccinium steyermarkii Luteyn
 Vaccinium striicaule Sleumer
 Vaccinium subdissitifolium P.F.Stevens
 Vaccinium suberosum Kloet
 Vaccinium subfalcatum Merr. ex Sleumer
 Vaccinium subulisepalum J.J.Sm.
 Vaccinium sumatranum Jack
 Vaccinium summifaucis Sleumer
 Vaccinium supracostatum Hand.-Mazz.
 Vaccinium sylvaticum Elmer
 Vaccinium symplocifolium (D.Don ex G.Don) Alston
 Vaccinium talamancense (Wilbur & Luteyn) Luteyn
 Vaccinium taxifolium Sleumer
 Vaccinium tectiflorum Danet
 Vaccinium tenax Argent
 Vaccinium tenellum Aiton
 Vaccinium tenerellum Sleumer
 Vaccinium tentaculatum J.J.Sm.
 Vaccinium tenuiflorum R.C.Fang
 Vaccinium tenuipes Merr.
 Vaccinium thibaudifolium Wernham
 Vaccinium timonioides Wernham
 Vaccinium timorense Fawc.
 Vaccinium tomicipes J.J.Sm.
 Vaccinium tonkinense Dop
 Vaccinium trichocarpum Sleumer
 Vaccinium trichocladum Merr. & F.P.Metcalf
 Vaccinium triflorum Rehder
 Vaccinium truncatocalyx Chun
 Vaccinium tubiflorum J.J.Sm.
 Vaccinium turfosum Sleumer
 Vaccinium uliginosum L.
 Vaccinium urceolatum Hemsl.
 Vaccinium uroglossum Sleumer
 Vaccinium urquiolae Berazaín
 Vaccinium urubambense Luteyn & Pedraza
 Vaccinium utteridgei Argent
 Vaccinium vacciniaceum (Roxb.) Sleumer
 Vaccinium varingiifolium (Blume) Miq.
 Vaccinium venosum Wight
 Vaccinium versteegii Koord.
 Vaccinium vidalii Merr. & Rolfe
 Vaccinium vietnamense Smitinand & P.H.Hô
 Vaccinium villosiflorum J.J.Sm.
 Vaccinium viridiflorum J.J.Sm.
 Vaccinium viscifolium King & Gamble
 Vaccinium vitis-idaea L.
 Vaccinium vonroemeri Koord.
 Vaccinium warburgii Sleumer
 Vaccinium whiteanum Sleumer
 Vaccinium whitfordii Merr.
 Vaccinium whitmeei F.Muell.
 Vaccinium whitmorei Ng
 Vaccinium wilburii Almeda & Breedlove
 Vaccinium wisselianum Sleumer
 Vaccinium wollastonii Wernham
 Vaccinium wondiwoiense J.J.Sm.
 Vaccinium woodianum H.F.Copel.
 Vaccinium wrightii A.Gray
 Vaccinium xerampelinum Sleumer
 Vaccinium yakushimense Makino
 Vaccinium yaoshanicum Sleumer
 Vaccinium yatabei Makino
 Vaccinium youngii Pedraza & Luteyn

Izvori

Vaccinioideae

redpajama_set_name: RedPajamaWikipedia


text:
Team Crowned Indoor Cricket Champions
A combined age group team travelled to Nuffield indoor sports centre to take part in the Hull and East Riding School's tournament for indoor cricket. The boys had assembled a good squad with a range of batting, bowling and the all-important fielding skills when indoors. They played a round robin tournament versus South Hunsley, Malet Lambert, Headlands and St Mary's. Mr Duke, Head of PE, said "The closest games were against St Mary's and Headlands, where the games went down to the finals overs, but calm bowling and fielding led to an unbeaten record and the title of area champions - well done boys"!

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Split list python list of number into multiple lists I have the following python list:
x = ["56843", "84631", "13831"]

And I want to get the following list:
x = [ ["5","6","8","4","3"] , ["8","4","6","3","1"] ]

How can I achieve this?
I have tried with split, but I need a separator that I don't know which is

A: Just this:
x = [list(y) for y in x]

list(y) which y is a string will convert the string into a list of characters.

A: x = [[ch for ch in s] for s in x]

A: x = ["56843","84631","13831"]

c = []

for i in x:
    c.append(list(i))

print(c)

redpajama_set_name: RedPajamaStackExchange


You Must File Real Tax Returns, Not "Indian Treaty Disclosure Documents" To Discharge Income Taxes in Bankruptcy | Law Office of J. Thomas Black, P.C.
"Ain't America great?" Instead of just filing normal tax returns, this taxpayer filed Indian "treaty-based position disclosure documents" (described by the Tax Court as "disclosure documents").
According to the U.S. Bankruptcy Court opinion, the Plaintiff Green claimed authority to file these disclosure documents pursuant to Section 6114 of the Internal Revenue Code. Plaintiff Green claimed in Tax Court that due to his status as a member of the Potawaomi Citizens Tribe of Oklahoma, his treaty-based "disclosure documents" constituted "tax returns" that triggered the running of the statute of limitations.
Well, the Bankruptcy Court ruled against him. And since there had been no tax returns that were filed, the taxes were not dischargeable. The Court said: "Therefore, Plaintiff's MSJ (motion for summary judgment) must be denied and Defendant IRS is entitled to summary judgment as the Taxes are excepted from discharge under §523(a)(1)(B)(i), or alternatively, §523(a)(1)(B)(ii) of the Bankruptcy Code."
In short, if you expect to discharge your taxes in bankruptcy, you must have filed your tax returns. You cannot "hide behind a log" and then expect your taxes to be dischargeable. There are a couple of very narrow exceptions as described in the opinion, but for most people, timely filing your tax returns is the way to go.


Of course, all this comes with a price. A cold, cold price. The story also sparks one of the most heated debates in Arkham Sessions history! Dr. Letamendi believes that Nora represents Victor's grief, symbolizing his inability to move on. In her view, by refusing to go with Batman and Robin at the end of the episode, Victor denies himself a chance to return to the world to begin healing from his trauma. Had he chosen a restorative life, instead of sinking to the bottom of the ocean with Nora, Victor would have shown his ability to move forward and begin his psychological recovery process.
Brian believes Nora represents…well…Nora. And that leaving her behind, at the bottom of the ocean, would not only be out of character for Victor, it could potentially be devastating to him, as he spends every day of the rest of his life, aware that his wife is alive, in cryogenic statis, as the bottom of the ocean. By remaining with Nora, Brian believes we've seen Victor come back to life, now having been tasked with the mission of completing his work, curing Nora of her illness and thawing her so that the two of them could be together again.


is lacking (right), the cells are round.
Like metastasizing cancer cells, embryonal cells are capable of migrating and penetrating into other tissues. Based on studies of frog embryos, KIT scientists have now proved for the first time that a known compound, cadherin-11, is decisive for cell migration. This glycoprotein is also responsible for the erroneous cell behavior in case of prostate cancer and arthritis. The results were published in the latest edition of Genes & Development (issue 23 (12), June 15, 2009, DOI No. 10.1101/gad.519409).
Normally, cadherins have the task to link cells. "In principle, they are working like a zipper and link cells to form a cell composite or tis-sue," explains Professor Doris Wedlich, Head of the KIT Zoological Institute II. The molecules prevent the cells from detaching and emi-grating from the cell composite. Cadherin-11, one of the about 300 cadherins known in vertebrates, however, behaves exactly the other way around in tumor cells in particular: It initiates migration. "Its presence causes the formation of cell tails or "fingertips" that explore with their sense of touch the appropriate migration path", says Wedlich. KIT scientists studied the question why it is this cadherin that stimulates the tumor cells to migrate. For this purpose, they investigated the functioning of cadherin-11 in neural crest cells, i.e. early embryonal cells that migrate towards the face field among others in order to form face bones. "In early embryonal develop-ment, surprising parallels to tumors can be found", explains the de-velopmental biologist Wedlich. Embryonal cells represent an inter-esting model system to study tumor formation, as their behavior is controlled by the same principles: A tumor is characterized by un-controlled growth, which means unhindered cell division. Its cells do no longer fulfill their original task and change their shape. They re-assume old properties, for instance, the tendency to migrate and penetrate into foreign environment.
At the moment, the KIT young investigator group headed by Jubin Kashef focuses on the structural features of the bonding sites of trio and cadherin-11 and on the factors activated by the coupling of both proteins. "If we succeed in answering these questions, we might interrupt the signal path and, hence, prevent the migration of the cells in the long term", outlines Doris Wedlich.
The Karlsruhe Institute of Technology (KIT) is the merger of the Forschungszentrum Karlsruhe, member of the Helmholtz Association, and the Universität Karlsruhe. This merger will give rise to an institution of internationally excellent research and teaching in natural and engineering sciences. In total, the KIT has 8000 em-ployees and an annual budget of 700 million Euros. The KIT focuses on the knowledge triangle of research – teaching – innovation.
The Karlsruhe institution is a leading European energy research center and plays a visible role in nanosciences worldwide. KIT sets new standards in teaching and promotion of young scientists and attracts top scientists from all over the world. More-over, KIT is a leading innovation partner of industry.
presseFte7∂kit edu or phone: +49 721 608-47414.


Millions of dollars are spent each year on hair products and treatments. Often, damage results from over-treating or roughly treating the hair. It is a myth that shampooing increases hair loss; gentle washing even on a daily basis will not damage the hair or lead to hair loss. Hair type, whether physically active and even your occupation, should determine the frequency of shampooing. Most experts recommend at least once a week washing to prevent dandruff from clogging the scalp. If you have oily scalp, frequent shampooing will keep the hair from lying flat, weighted down by the fats in sebum, an oily substance secreted by the skin's sebaceous glands that coats the hair shaft.
Coloring, perming, combing, teasing, and harsh shampooing can damage the hair. Conditioning products may provide a protective film that smoothes the hair and reduces snarls and tangles. Conditioners that give "extra body" may contain waxes that, when dry, make it look fuller. Some contain oil/fats (e.g., lanolin, mineral oil) to smooth hair, and a few have humectants that help to hold in water content.
Hair requires gentle handling especially when wet. Towel dry gently after washing and avoid curling irons and blow dryers whenever possible. Brushing or combing hair when wet instead of damp or dry will result in more breakage at the root and hair shaft. Instead, untangle long hair gently with the fingers and use a wide tooth-comb or natural bristle brush to avoid pulling, stretching, or breaking the hair. Because natural oils do not normally reach the hair ends, it is important to brush regularly, especially for long hair.
The quality and health of your hair is partly reflected in having a well-balanced diet containing protein, amino acids, and vitamins. Iron deficiency, due to a lack of meat products in the diet or heavy menstrual bleeding in women, could affect the health of the hair. Crash diets and eating disorders can damage hair dramatically.
Life Extension Shampoo is a deep cleansing shampoo, yet a gentle formula perfect for everyday use.
Deionized water, sodium laureth sulfate, disodium cocoamphodiacelate, sodium C14-16 olefin sulfonate, cocamidopropyl betaine, lauramide DEA, polysorbate 80, sodium PCA, niacin, biotin, methylparaben, propylparaben, diazolidinyl urea, citric acid, FD&C blue #1, FD&C yellow #5, fragrance.
Individuals with a full head of hair should use Life Extension Shampoo as they would any other shampoo.
Those with thinning hair or partial baldness should leave the shampoo lather on for five minutes before rinsing the scalp.


The immunologists, honored with the 2018 award in Physiology or Medicine, pioneered immunotherapy, which harnesses the body's immune system to fight cancer.
According to citation statistics, researchers behind programmed cell death pathways and CRISPR/Cas9 are among those in line for Nobel Prizes this year.
DNA-damage response and cancer immunotherapy discoveries are among those recognized by the Albert and Mary Lasker Foundation this year.


text:
<?php

class ProjectHistoryController extends AdminController {

	/**
	 * @param HTTPRequest $request The input HTTP request
	 * @return HTTPResponse The output HTTP response
	 * @see HTTPController::run()
	 */
	public function run(HTTPRequest $request) {

		/* @var $USER User */
// 		global $USER;

		/* @var $projectUser ProjectUser */
		/* @var $project Project */
		/* @var $workingDay ProjectWorkingDay */

		$projectDomain	= Project::getDomain();
		$projectWorkingDayDomain	= ProjectWorkingDay::getDomain();

		$projectID	= $request->getPathValue('projectID');
		$project		= Project::load($projectID, false);
		$projectUser	= ProjectUser::getOne(User::getLoggedUser(), $project);
// 		$workingToday	= $projectUser->getWorkingToday();

		try {
// 			debug('Post', $request->getAllData());
			if( $request->hasDataKey('submitAddDayWorkDone', $workingDayID) ) {
				$workingDay = ProjectWorkingDay::load($workingDayID);
				if( $workingDay->projectuser_id != $projectUser->id() ) {
					ProjectUser::throwException('forbiddenOperation');
				}
// 				debug('submitAddWorkingToday - work_done - before => '.$workingToday->work_done);
				$workingDay->addSeconds($request->getData('add_min', 0)*60);
// 				debug('submitAddWorkingToday - work_done - after => '.$workingToday->work_done);
				reportSuccess('successAddWorkingToday', $projectWorkingDayDomain);

			}
		} catch( UserException $e ) {
			reportError($e);
		}
		endReportStream();

		return $this->renderHTML('app/user_project_history', array(
			'project'		=> $project,
			'projectUser'	=> $projectUser,
			'projectWorkingDays'	=> $projectUser->listWorkingDays(),
// 			'workingToday'	=> $workingToday
		));
	}

}

redpajama_set_name: RedPajamaGithub


PA Realty Advisors is a Philadelphia based real estate advisory firm providing a wide range of real estate services. Like you, we strive to work smarter, make the complex simple and are driven by our entrepreneurial spirit. Bound by innovation, not tradition, we continue to redefine the way real estate services to residential buyers are offered. If you are looking for commercial or invest properties, call PA Realty Advisors today.
Attorney Stuart Udis of PA Realty Advisors provides comprehensive real estate legal services throughout all of Philadelphia, Montgomery County, Delaware County and Bucks County, Philadelphia. Call him today for help with your real estate matter.


All Hands on Deck for Our Pinot Bottling!
The bottling of our 2016 Pinot Noirs marks our first bottling at our new Carneros Winery.
We are happy to report that we have received temporary occupancy from Sonoma County to permanently occupy our winemaking areas of our new winery.
Delivery of our 25 stainless steel wine tanks arrived today at our new winery just in time to welcome the arrival of our 2017 Vintage in the next two weeks.
Our first customer event was held this past Saturday, July 15th at our soon-to-be-finished winery. The Roche family hosted 72 guests at our annual Winemaker Dinner.
Roche Winery Platinum Club Members were invited to celebrate the raising of the last structural steel member of the new Roche Winery construction, topping off its new barrel room and tasting room.
Our social media team asked our winemaker, Michael Carr, to share some of his thoughts about the construction and design of the new Roche winery.
The most important delivery required for the construction of our new winery has arrived today on schedule. Today our buildings have arrived!
After having recently received the new Roche Winery Site grading permit, the Roche Family and Lunny Engineering have broken ground on the first phase of the new Roche Winery!


"Make plans for your bright future"
As a license holder in the province, Ontario P.Engs are part of a community of more than 80,000 professionals committed to enhancing the quality of life, safety and well-being of all Ontarians.
Applicants are not required to be a Canadian citizen or landed immigrant, making it easier for those from outside of Canada to apply for and obtain a license.
In addition, newcomers to Canada and Canadian engineering graduates may be eligible to apply for their Ontario engineering license at no cost through PEO's Financial Credit Program.
If you already hold a P.Eng. Licensefrom another Canadian engineering association/order, a national between all the provinces and territories allows you to move between provinces and territories without having to repeat the entire licensing process. PEO applicants who are licensed as professional engineers in good standing in another Canadian engineering association/order are not required to submit transcripts or write PEO's Professional Practice Examination.
For more information on how to become a licensed professional engineer in Ontario, please check out this PowerPoint presentation and PEO's Licensing Guide and Application.


text:
Goengineer, Inc
PPP Loan Data — Goengineer, Inc, Salt Lake City, UT
Industry: Software and Other Prerecorded Compact Disc, Tape, and Record Reproducing
Goengineer, Inc is a subchapter s corporation located at 1787 E Fort Union Blvd Ste 100 in Salt Lake City, Utah that received a Coronavirus-related PPP loan from the SBA of $4,047,300.00 in April, 2020.
The company has reported itself as a White male owned business, and employed at least 231 people during the applicable loan loan period.
Goengineer, Inc in Salt Lake City, UT received a Paycheck Protection Loan of $4.05M through Comerica Bank, which was approved in April, 2020.
This loan's status is reported by the SBA as "Paid in Full", which includes both loans repaid and those fully forgiven from repayment under PPP guidelines. The loan's status was last updated by the SBA in November, 2021.
For Goengineer, Inc, the calculation used to estimate payroll costs is shown below:
($4.05M / 2.5) * 12 = $19.43M
Based on the standard PPP eligibility formula, it may be possible to estimate the payroll expenses represented by a company on their PPP application (see details above). In order to qualify for the PPP loan amount received, Goengineer, Inc's 2019 payroll expenses are estimated to be at least $19.43M.
Based on their reported 231 jobs retained, this equals an estimated average yearly compensation of $84,100 per employee1
On the PPP application, Goengineer, Inc reported intending to use the proceeds of their PPP loan for the following expenses:
Payroll: $3,237,909
Mortgage Interest: $809,391
Because the loan received by Goengineer, Inc is at least $2M dollars, it is subject to a full review by the SBA to ensure eligibility and compliance with PPP program requirements.
Business Information - Goengineer, Inc in Salt Lake City, UT
1787 E Fort Union Blvd Ste 100
Software and Other Prerecorded Compact Disc, Tape, and Record Reproducing
Gender: Male Owned
Veteran Status: Non-Veteran
Salt Lake County, UT — SBA Office 0883 — Congressional District: UT-03
Nationwide, 1,481 businesses in the "Software and Other Prerecorded Compact Disc, Tape, and Record Reproducing" industry received a total of $158.77M in PPP loans. This industry in total received less than 1% of the total PPP funding distributed.
PPP recipients in this industry report an average of 8 employees, 97% lower than Goengineer, Inc's reported 231 employees, and received an average PPP loan of $107,208, 97% lower than this company's loan of $4.05M.
Source: www.federalpay.org/paycheck-protection-program/goengineer-inc-salt-lake-city-ut


text:
Dainik Statesman
Home » Bengal » Linked scams: Wife of middleman arrested in WBSSC scam summoned by CBI in cattle case
Linked scams: Wife of middleman arrested in WBSSC scam summoned by CBI in cattle case
CBI is in the process of establishing links between two much-talked-about financial embezzlement scams in West Bengal.
IANS | Kolkata | October 14, 2022 5:47 pm
The Central Bureau of Investigation (CBI) is in the process of establishing links between two much-talked-about financial embezzlement scams in West Bengal — the multi-crore West Bengal School Service Commission (WBSSC) recruitment irregularities and the cattle smuggling case.
The CBI officials on Friday summoned for questioning one Kajal Roy and one Rohit Singh in connection with the cattle smuggling scam. Incidentally, the CBI sources said that while Kajal Roy is the wife of Prasanna Roy, the middleman arrested by the central agency sleuths in the WBSSC scam, Rohit Singh is one of his cousins. Kajal Roy also happens to be the niece of former West Bengal education minister and Trinamool Congress secretary general, Partha Chatterjee, who is currently in judicial custody for his alleged involvement in the WBSSC scam.
To recall, the CBI sleuths, on August 27 this year, arrested Prasanna Roy. CBI sources said that during the course of investigation in the cattle smuggling scam, their officers have come across a number of shell companies, which were floated with the purpose of diverting scam proceeds to different channels. "After thoroughly examining different documents and papers of these companies, the involvement of Kajal Roy and Rohit Singh in these entities evolved," a CBI official said.
After his arrest in August, the CBI sleuths were surprised by the meteoric northward graph of Roy's financial fortune between 2014 and 2018, the time slot when the teachers' recruitment scam took place.
He started as a small-time house painting contractor and slowly extended his business in other spheres like holiday resorts and even tea gardens in north Bengal. However, the sources of the funds through which Roy funded his businesses were highly mysterious. The CBI sleuths then procured documents relating to his ownership of a number of flats in and around Kolkata.
Roy also used to run a car-rental business and his major clients on this count were a number of top officials of WBSSC, some of whom are currently in judicial custody for their alleged involvement in the teacher's recruitment scam.
He also used to pose as a representative of the United Nations Educational, Scientific and Cultural Organization (UNESCO as well as the "International Peoples' Alliance of the World" in the social media.
Besides, Roy claimed to be the chief financial advisor to World Hindu Struggle Committee, a self-styled organisation claiming to be a dedicated international team fighting to raise its voice against fundamentalism, terrorism, and inhuman groups.
cattle case
Partha Chatterjee
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Earlier on Thursday, President El-Sisi attended India's 74th Republic Day parade as the chief guest. He is the first Egyptian premier to have been invited to the Republic Day parade.
Modi to address annual 'NCC PM' rally on January 28
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Aam Aadmi Party moves SC demanding immediate MCD mayor election
PEOPLE'S PARLIAMENT, ALWAYS IN SESSION
Copyright © 2022 The Statesman Limited. All rights reserved.


text:
package com.shapesecurity.functional.data;

import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNotEquals;
import static org.junit.Assert.assertTrue;
import static org.junit.Assert.fail;

import com.shapesecurity.functional.TestBase;
import com.shapesecurity.functional.Thunk;

import javax.annotation.Nullable;
import org.junit.Test;

public class MaybeTest extends TestBase {
    final Integer notNull = 3;
    @Nullable
    final Integer nulled = null;

    @Test
    public void testFromNullable() {
        assertEquals(Maybe.of(3), Maybe.fromNullable(notNull));
        assertEquals(Maybe.<Integer>empty(), Maybe.fromNullable(nulled));
    }

    @Test
    public void testToNullable() {
        assertEquals((Integer) 3, Maybe.of(notNull).toNullable());
        assertEquals(null, Maybe.<Integer>empty().toNullable());
    }

    @Test
    public void testIff() {
        assertEquals(Maybe.of(3), Maybe.iff(true, notNull));
        assertEquals(Maybe.<Integer>empty(), Maybe.iff(false, notNull));
    }

    @Test
    public void testCatMaybes() {
        new ImmutableListTest().testWithSpecialLists(this::testCatMaybes);
    }

    private void testCatMaybes(ImmutableList<Integer> list) {
        assertEquals(list, Maybe.catMaybes(list.map(Maybe::of)));
    }

    @Test
    public void testMapMaybe() {
        new ImmutableListTest().testWithSpecialLists(this::testMapMaybe);
    }

    private void testMapMaybe(ImmutableList<Integer> list) {
        assertEquals(list.map(x -> x + 1), Maybe.mapMaybe(x -> x + 1, list.<Maybe<Integer>>map(Maybe::of)));
    }

    @Test
    public void testBind() {
        assertEquals(Maybe.fromNullable(notNull + 1), Maybe.of(notNull).bind(x -> Maybe.of(
                x + 1))); //not fully sure why I am responsible for wrapping A into a Maybe<A>
        assertEquals(Maybe.<Integer>empty(), Maybe.fromNullable(nulled));
    }

    @Test
    public void testForEach() {
        Maybe.fromNullable(nulled).foreach(x -> {
            fail("Maybe.forEach should not execute f on a empty"); //should never call f
        });
        final int[] effect = {0};
        Maybe.fromNullable(notNull).foreach(x -> {
            effect[0] += 1;
        });
        assertEquals(1, effect[0]);//of should be side effected into incrementing once and only once
    }

    @Test
    public void testForEachOverload() {
        final int[] effect = {0};
        Maybe.empty().foreach(() -> {
            effect[0] += 1;
        }, x -> {
            fail("Maybe.forEach(r, f) should not execute f on Nothing");
        });
        assertEquals(1, effect[0]);

        effect[0] = 0;
        Maybe.of(notNull).foreach(() -> {
            fail("Maybe.forEach(r, f) should not execute r on Just");
        }, x -> {
            effect[0] += 1;
        });
        assertEquals(1, effect[0]);
    }

    @Test
    public void testToList() {
        assertEquals(ImmutableList.<Integer>empty(), Maybe.<Integer>empty().toList());
        assertEquals(ImmutableList.of(notNull), Maybe.of(notNull).toList());
    }

    @Test
    public void testEq() {
        assertTrue(Maybe.fromNullable(notNull).eq(Maybe.of(notNull)));
        assertFalse(Maybe.fromNullable(notNull).eq(Maybe.of(notNull + 1)));
        assertFalse(Maybe.fromNullable(notNull).eq(Maybe.<Integer>empty()));
        assertTrue(Maybe.fromNullable(nulled).eq(Maybe.<Integer>empty()));
        assertFalse(Maybe.fromNullable(nulled).eq(Maybe.fromNullable(notNull)));
    }

    @Test
    public void testOrJusts() {
        assertEquals(notNull, Maybe.fromNullable(notNull).orJust(notNull));
        assertEquals(notNull, Maybe.fromNullable(nulled).orJust(notNull));
        assertEquals(notNull, Maybe.fromNullable(notNull).orJustLazy(Thunk.from(() -> notNull)));
        assertEquals(notNull, Maybe.fromNullable(nulled).orJustLazy(Thunk.from(() -> notNull)));
    }

    @Test
    public void testHashCode() {
        assertEquals(Maybe.fromNullable("hash").hashCode(), Maybe.fromNullable("hash").hashCode());
        assertEquals(Maybe.fromNullable(null).hashCode(), Maybe.fromNullable(null).hashCode());
        assertNotEquals(Maybe.fromNullable("hash").hashCode(), Maybe.fromNullable(null).hashCode());
        assertNotEquals(Maybe.fromNullable(null).hashCode(), Maybe.fromNullable("hash").hashCode());
        assertNotEquals(Maybe.fromNullable("hash").hashCode(), Maybe.fromNullable("not hash").hashCode());
    }

    @Test
    public void testFlatMap() {
        assertEquals(Maybe.of(notNull + 1), Maybe.fromNullable(notNull).flatMap(x -> Maybe.fromNullable(x + 1)));
        assertEquals(Maybe.<Integer>empty(), Maybe.fromNullable(nulled).flatMap(x -> Maybe.fromNullable(x + 1)));
    }

    @Test
    public void testIs() {
        assertTrue(Maybe.fromNullable(nulled).isNothing() && !Maybe.fromNullable(nulled).isJust());
        assertTrue(!Maybe.fromNullable(notNull).isNothing() && Maybe.fromNullable(notNull).isJust());
    }

    @Test
    public void testMaybe() {
        assertEquals(notNull, Maybe.fromNullable(notNull).maybe(1, x -> x));
        assertEquals((Integer) 1, Maybe.fromNullable(nulled).maybe(1, x -> x));
    }

    @Test
    public void testJust() {
        assertEquals(notNull, Maybe.fromNullable(notNull).fromJust());
        try {
            Integer i = Maybe.fromNullable(nulled).fromJust();
            fail("Did not throw NullPointerException");
        } catch (NullPointerException e) {
            // do nothing
        }
    }

    @Test
    public void testTry() {
        try {
            Maybe<Integer> m = Maybe._try(() -> 0);
            assertEquals(Maybe.of(0), m);

            m = Maybe._try(() -> {
                throw new Exception("exception");
            });
            assertEquals(Maybe.empty(), m);
        } catch (Exception e) {
            fail("Maybe._try should never allow exceptions to propagate");
        }
    }

    @Test
    public void testJustIterable() {
        int iterationCount = 0;
        for (String item : Maybe.of("test")) {
            assertEquals("test", item);
            ++iterationCount;
        }
        assertEquals(1, iterationCount);
    }

    @Test
    public void testEmptyIterable() {
        for (String item : Maybe.<String>empty()) {
            fail("Maybe.empty() should not iterate");
        }
    }
}


Advanced Modular Range Finder (AMRF) is designed to determine the exact distance between the observer and the target. Range finder can work with any scope, both optical and night vision or thermal. Measurement results are displayed on indicators (displays), which are built into the rangefinder. Using an optional cable rangefinder can also be connected to Night Vision Digital Scope. The results of measurement are displayed on indicators of rangefinder and in FOV of compatible carrying device.
The AMRF is a pulsed laser rangefinder, which makes a distance measurement using a laser beam. Semiconductor laser which generates emission at a wavelength of 905 nm is a source of a laser emission of the unit. The range finder enables distance measurements to the object of interest in the range from 5 to 2000 m.
The mount system is designed to fit any Picatinny MIL-STD-1913 or Weaver weapon rail. Manufactured from aluminum alloy, it is lightweight and robust. An adjustable lever-cam clamping device ensures quick, easy, and reliable mounting and disassembly.


text:
Home > science > Facebook-owner Meta sets out grounds for UK appeal on Giphy
Facebook-owner Meta sets out grounds for UK appeal on Giphy
01/06/2022 mediabest science
Meta, which owns Instagram and WhatsApp as well as Facebook, said the CMA had failed to assess its offer to ensure Giphy could continue to provide services to competitors like Snapchat and TikTok on the same terms.
Facebook-owner Meta has applied to appeal a British competition watchdog ruling that it must sell Giphy on grounds that include challenging the finding that the deal removed a potential competitor in display advertising.
(Sign up to our Technology newsletter, Today's Cache, for insights on emerging themes at the intersection of technology, business and policy. Click here to subscribe for free.)
Meta said last month it had lodged an appeal against the decision by the Competition and Markets Authority (CMA) to block its 2020 acquisition of Giphy.
Also Read | Big moves in 'Big Tech'
It was the first time the British regulator had blocked a major digital acquisition, and it signalled a step change in its scrutiny of "big tech" companies.
The Competition Appeal Tribunal on Wednesday published a summary of Meta's application, outlining its challenge on six grounds.
The U.S company, which owns Instagram and WhatsApp as well as Facebook, said the CMA had failed to assess its offer to ensure Giphy could continue to provide services to competitors like Snapchat and TikTok on the same terms.
Also Read | Facebook fined $70 mln in Britain for breaching order in Giphy deal
The regulator's decision was also procedurally flawed, Meta said.
The CMA ordered Meta to sell Giphy, which it acquired for a reported $400 million in May 2020, in November after it decided the remedies offered by the U.S. company did not answer its concerns.
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The Miku Hatsune 2012 GT Project exclusive mascot character!
The Nendoroid is based on the official 2012 ver. Racing Miku illustration and pose by GAN!
This entry was posted in 2012/10, Preview and tagged Good Smile Company, Good Smile Racing, hatsune miku, Nendoroid, Racing Miku, Vocaloid.


text:
Oberto Grimaldi, en français Aubert Grimaud (vers 1140 – vers 1232), est un amiral de la flotte génoise à Damiette, commissaire de Gênes. Il est le fils de Grimaldo Canella, consul de Gênes, qui s'appellera  par la suite Grimaldo Grimaldi et qui donnera ce nom de Grimaldi à sa descendance

Biographie
Il a épousé Conradine Spinola (Corradina), fille d'Oberto Spinola et de Sibilla della Volta, dont il eut au moins quatre enfants :
Grimaldo II (Grimaud) (vers 1170 – après 1257), membre du Conseil de Gênes, consul de Gênes, grand-père de Lanfranco (vers 1230-1293), viguier de Provence, ambassadeur de Gênes, gouverneur de Nice, qui sera la souche des seigneurs de Monaco,
Ingone (1210-1235),
Oberto II (avant 1233 – après 1258),
Niccolo (???? – avant 1258).

Généalogie

Articles connexes
Famille Grimaldi
Familles subsistantes de la noblesse française
Armorial des Grimaldi

Maison Grimaldi
Personnalité liée à Gênes


It's not about me, yet it is about me. It isn't about us, yet it is about us. "Say what?" That's probably what the disciples where also thinking when Jesus said, "..let them deny themselves and take up their cross and follow me." This sermon, utilizing Mark 8:3-37, explores the difference between being a follower of Jesus and a disciple of Jesus; how we must remove ourselves from the center of our concerns in order to love more extravagantly and to serve others more freely. What does it mean to you to be a Christian?