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Sevastyan Antonov
Sevastyan Antonov

Divergent (2014)2014

Veronica Roth's novel Divergent was adapted for the big screen in 2014. It is a dystopian science fiction film on one hand but is also an action thriller, too. It is the first in the Divergent Series and is sent in a post-apocalyptic Chicago, where people are housed in factions according to their specific qualities and human virtues. On Choosing Day, 16-year-olds are forced to decide which faction they will join for the remainder of their lives. While most people have an aptitude for a single faction, the rare citizens who have aptitudes for more than one faction are called Divergents. The movie's protagonist, Beatrice Prior, played by Shailene Woodley, receives a warning following her aptitude test that she is divergent and must not tell anyone, not even her family. Which faction will she choose? Who can she trust? And how will she keep herself safe with this new, secret bounty placed on her head?

Divergent (2014)2014

The GDP implicit price deflator is reported by the BEA in NIPA Table 1.1.4. The default GDP price deflator is benchmarked to 2009 chained dollars. We re-index the deflator to 2014. We calculate the implicit price deflator for NDP by taking the nominal NDP as a share of real NDP for each year. Again, this deflator is indexed to 2009, so we index it to 2014.

Data on the average hourly earnings of production/nonsupervisory workers are then converted to real (2014) dollars by deflating them by the CPI-U-RS. Finally, we multiply the real average hourly earnings by the real compensation-to-wage ratio to obtain the real average hourly compensation of production/nonsupervisory workers.

The time since divergence of the ancestral Indica and Japonica gene pools is estimated at 0.44 million years, based on sequence comparisons between cv Nipponbare (Japonica) and cv 93-11 (Indica) [8]. This time estimate pre-dates the domestication of O. sativa by several hundred thousand years, suggesting that rice cultivation proceeded from multiple, pre-differentiated ancestral pools [1],[9]-[13]. This is consistent with genome-wide estimates of divergence based on gene content [14], transcript levels [15], single nucleotide polymorphisms (SNPs) [3],[16], and transposable elements [17]. This is also consistent with evidence from the cloning of dozens of genes underlying diverse quantitative trait loci (QTLs) [2],[10],[18]-[21]. Despite ongoing debate about the precise moment and location of the first domestication 'event' in rice, these studies all demonstrate that natural variation in the rice genome is deeply partitioned and that divergent haplotypes can be readily associated with major varietal groups and subpopulations. The course of domestication, as rice transitioned from its ancestral state as a tropical, outcrossing, aquatic, perennial species to a predominantly inbreeding, annual species adapted to a wide range of ecologies, was punctuated by persistent episodes of intermating among the different subpopulations. This resulted in both natural and human-directed gene flow between the different gene pools, but the essential differentiation that distinguishes the Indica and Japonica genomes was maintained and reinforced over time as a result of numerous partial sterility barriers scattered throughout the genome [22]-[25].

Recently, the resequencing of hundreds of wild and cultivated rice genomes using next generation sequencing (NGS) and various complexity-reduction and genotype-by-sequencing strategies have enriched the pool of sequence information available for rice [30],[34],[35]. However, the vast majority of resequenced genomes are aligned to and compared with the Nipponbare reference rather than being assembled de novo, including in our own previous work [35] and in the current 3,000 rice genomes project [36]. This introduces a potential bias due to significant differences in genome size [37],[38] and structure [14],[17],[29],[39] that characterize the different subpopulations and varieties of rice. Alignment to a single reference is particularly problematic when NGS data from indica, aus or divergent wild species genomes from the center of diversity of Oryza are aligned to the genetically and geographically divergent Nipponbare (temperate japonica) reference because of the potential for misalignment, and for elimination of critical sequences that cannot be aligned with confidence.

In this study we use these advances to de novo assemble three divergent rice genomes representing the indica (IR64), aus (DJ123) and temperate japonica (Nipponbare) subpopulations and to determine the extent and distribution of structural variation among them. These varieties were chosen for both biological interest and to facilitate evaluation of assemblies. On the biological side, different subpopulations of rice are adapted to different ecologies and geographies, and harbor different alleles and traits of interest for plant improvement [3],[19],[20],[40]-[43]. The aus subpopulation is of particular interest because it is the source of important alleles conferring disease resistance [44], tolerance to submergence [33], deep water [45], low-phosphorus soils [41], and drought [46]. Indica rice harbors the greatest amount of genetic variation [1],[30] and accounts for the largest contribution to rice production globally. Our choice to sequence Nipponbare was due to the fact that it provided a high quality BAC-by-BAC sequence assembly [27] that served as a solid benchmark for assessing the quality of our three NGS assemblies and provided a context for understanding the impact of varying data sets and parameters used in the assemblies.

Methane (CH4) emissions in cattle are an undesirable end product of rumen methanogenic fermentative activity as they are associated not only with negative environmental impacts but also with reduced host feed efficiency. The aim of this study was to quantify total and specific rumen microbial methanogenic populations in beef cattle divergently selected for residual feed intake (RFI) while offered (i) a low energy high forage (HF) diet followed by (ii) a high energy low forage (LF) diet. Ruminal fluid was collected from 14 high (H) and 14 low (L) RFI animals across both dietary periods. Quantitative real time PCR (qRT-PCR) analysis was conducted to quantify the abundance of total and specific rumen methanogenic microbes. Spearman correlation analysis was used to investigate the association between the relative abundance of methanogens and animal performance, rumen fermentation variables and diet digestibility.

Enteric CH4 emissions are not only influenced by the quantity of feed consumed by ruminants, but also by its chemical composition [8]. For example forage based diets such as grass are composed of structural carbohydrates such as cellulose and hemicellulose, which produce predominantly acetate and butyrate as fermentation end-products compared to the propionate dominant fermentation patterns from a cereal based diets. Recently, using clone-sequencing and next generation methanogen-specific tag-encoded pyrosequencing, we showed that specific species of archaea, Methanobrevibacter spp. are the dominant methanogens in the rumen across contrasting diets, with Methanobrevibacter smithii being the most abundant species followed by Methanobrevibacter ruminantium and Methanosphaera stadtmanae[9]. However, particularly when species are present in low abundance, these technologies are not sufficiently accurate to reliably quantify specific methanogens. Quantitative real-time PCR (qRT-PCR) has become a popular method for estimation of methanogen abundance in the ruminant digestive tract [10]. Therefore the objectives of the present study were to quantify the relative abundance of total methanogens and key species viz Methanobrevibacter smithii, M. ruminantium and Methanosphaera stadtmanae in the ruminal fluid of cattle divergent for RFI offered two contrasting diets: a low energy, high forage diet (HF) and a high energy, low forage diet (LF) respectively. Additionally, correlation analysis was used to assess the association between methanogen abundance and animal performance, diet digestibility and rumen fermentation variables.

While work from our own group has shown moderate within animal repeatability of RFI while maintained on a constant diet type [13] other recent work has shown that the relative ranking of animals for this trait may change when moved from a low to a high energy diet [39]. This highlights the necessity to investigate both RFI and the rumen microbial community of animals divergent for RFI across different diet types. Previous research has shown that the chemical composition of the diet can have a great effect on the overall rumen bacterial composition in animals divergent for RFI [12]. Despite this, previous reports have suggested that no change in total methanogen abundance of either feed efficient or inefficient cattle divergent for RFI occurs when the diet is changed from a low to high energy diet [27]. Furthermore, it has been suggested that diet has a greater effect on the diversity of the methanogen community rather than total methanogen abundance [17, 27]. However, in the current study the abundance of total methanogens was found to be affected by the change in dietary substrate offered. While it was surprising that the relative total methanogen abundance was greater when animals were offered the LF compared to the HF diet, this may have arisen due to the nature of the LF diet (30:70 maize silage:concentrate). With the inclusion of 30% forage in this diet hydrogen fermenting bacteria would still have the ability to proliferate, albeit to a lesser extent, thus providing hydrogen for methanogen growth. In addition, on the LF diet, intakes were higher and there was more easily digestible substrate available to support CH4 emissions and the growth of methanogens. Indeed, overall the CH4 emissions were higher from animals on the LF diet [23]. Furthermore, decreased rumen pH is often associated with high concentrate diets due to a lowering of the acetate:propionate ratio [40] Decreased ruminal pH can indirectly affect CH4 synthesis due to its influence on VFA production [5] and directly through inhibition of methanogen activity [41]. However, data from our own laboratory generated from the same pool of animals as the current study showed that although mean ruminal pH for both high and low RFI phenotypes was lower on the LF diet, the pH did not drop below the optimum range for rumen methanogen growth (pH 6.0-7.5) [23]. Therefore, it is hypothesized that methanogen proliferation would not have been inhibited while animals were offered the LF diet in our study. Differences in our findings to that of others could also have arisen due to differences in qRT-PCR quantification methods. Our qRT-PCR results represent the relative abundance of total methanogens to the total bacterial population (relative quantification) while DNA copy number was used to quantify the methanogen population (absolute quantification) in the study by Zhou et al., [27]. While both these methods have been extensively utilised throughout the literature for the quantification of rumen microbiota, it is important to acknowledge that neither method is without its drawbacks [15]. In the current study, quantification using the relative method was favoured due to the implications of quantification of the target in comparison to a standard curve while using absolute quantification [15]. Additionally, host breed genotype has also been shown to influence methanogen diversity in the rumen and therefore could be a contributing factor to the variation across studies [29]. 041b061a72


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