Haplotype networks were generated via statistical parsimony implemented in TCS 1.21. With the 95% parsimony criterion (default setting) applied to the single-marker alignments of the mitochondrial datasets, TCS generated 17 networks on COI and 19 unconnected haplotype networks based on 16S rRNA (Figure 2H, I). Statistical parsimony was in agreement with our PSH described above and recovered all the identified MOTUs as unconnected networks. Additionally, 16S rRNA analysis split populations identified above as P. milaschewitchii and P. verrucosa into unconnected haplotypes (Figure 2I). In COI analyses P. milaschewitchii formed one entity but populations of P. verrucosa showed unconnected networks (Figure 2H). COI results also showed MOTU II and XII (P. brasilensis) each including multiple unconnected networks and the ambiguous MOTU VIII (recovered as two or three putative species in GMYC) resulted in two (COI), or four (16S) unconnected haplotypes under statistical parsimony. The nuclear 28S rRNA haplotype network resulted in connected haplotype networks for representatives of two different (morphologically well-supported) outgroup genera (Microhedyle and Paraganitus). We thus considered this approach problematic for species delineation in Pontohedyle and excluded it from our consensus approach.
The Three Body Problem Epub 245
Our SSH is based on a minimum consensus approach (see Figure 2N, Material and Methods and detailed discussion below) across species delineation approaches. It was identical to our PSH and suggested at least 12 mainly cryptic candidate species, three of which correspond to existing names in nomenclature. Pontohedyle sp. 6 (corresponding to MOTU VIII), however, remains problematic, since nearly all molecular species delineation approaches split this unit into a minimum of two independent lineages (with high support, see e.g., Figure 3B); only the ABGD analysis based on 16S rRNA did not support this split.
Puillandre et al. [55] proposed a workflow for large-scale species delineation in hyperdiverse groups, starting with a COI barcoding dataset analyzed with ABGD and GMYC which leads to the primary species hypothesis (PSH). Independent information (from other molecular markers, morphology and ecological traits) is subsequently added to lead to the secondary species hypothesis (SSH) [55]. This formalized strategy [55] is linear, starting with pre-selecting samples according to a PSH that depends on a single mitochondrial marker, before further information is added that might expand or contradict the PSH. What is an efficient workflow for large-datasets with dense sampling coverage and thus high-quality COI barcoding output, may be inapplicable for datasets in little known and putatively under-sampled taxa. The latter would benefit from full consideration of all information already available for a PSH, and a parallel, combined approach of multiple markers and multiple delimitation methods. Especially when it is unfeasible to sample multiple specimens, multiple loci lead to more reliable results [22]. Multi-marker barcoding provides an a posteriori double-check for contamination, sequencing errors or mitochondria-specific pitfalls (e.g., the presence of numts or mitochondrial introgression), and the idiosyncrasies of individual markers [16, 56]. Our study shows that COI analyses perform well on our dataset but due to amplification problems applying universal COI barcoding primers, three candidate species would have remained unconsidered. Multi-marker barcoding makes better use of rare specimens.
Arantza Jency A, Sharma RK, Singh G. (2020). Stationary solutions, critical mass, Tadpole orbits in the circular restricted three-body problem with the more massive primary as an oblate spheroid. Indian Journal of Science and Technology.13(39):4168-4188.
In contrast, previous studies either did not observe any impact of the CYP1A2 gene in caffeine-exercise studies [273, 274], or reported benefits only in slow metabolizers [75]. There are several reasons that may explain discrepancies in study outcomes. These include smaller samples sizes with few and/or no subjects in one genotype [75, 273, 274], as well as shorter distances or different types of performance test (power versus endurance) [75] compared to the aforementioned trials, which reported improved endurance after caffeine ingestion in those with the CYP1A2 AA genotype [208, 263]. The effects of genotype on performance might be the most prominent during training or competition of longer duration or an accumulation of fatigue (aerobic or muscular endurance) [149], where caffeine appears to provide its greatest benefits, and where the adverse effects to slow metabolizers are more likely to manifest [195, 260]. Indeed, in a study of performance in elite basketball players [240], only in those with the AA genotype caffeine improved repeated jumps which requires maintaining velocity at take-off repeatedly as an athlete fatigues throughout a game (muscular endurance) - even though there was no caffeine-genotype interaction effect for this outcome. However, caffeine similarly improved performance in those with the both AA and C-genotypes during a simulated basketball game [240]. In a cross-over design of 30 resistance-trained men, caffeine ingestion resulted in a higher number of repetitions in repeated sets of three different exercises, and for total repetitions in all resistance exercises combined, which resulted in a greater volume of work compared to placebo conditions, but only in those with the CYP1A2 AA genotype [262]. Although more research is warranted, there is a growing body of evidence to support the role of CYP1A2 in modifying the effects of caffeine ingestion on aerobic or muscular endurance-type exercise, which helps to determine which athletes are most likely to benefit from caffeine.
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