Multivariate discrimination between East African cape hares (Lepus capensis) and savanna hares (L. victoriae) based on occipital bone shape

In East Africa, cape hares (Lepus capensis) and savanna hares (L. victoriae) look much alike where their ranges overlap. Earlier studies suggested discrimination between the two species by several skull traits, but did not present morphometric statisitics. Our present discriminant analysis based on seven metric variables of the occipital bone related to skull length (condylobasal length) resulted in a high (95.1%) overall probability of correct separation of the two species. While all cape hares were classified correctly, correct classification was a bit lower in savanna hares (88.2%). A principal components analysis of the same variables confirmed the shape difference for the two species. Both region and sex-specific variation in the shape of the studied occipital bone complex was found in savanna hares but not in cape hares. The somewhat reduced level of correct identification of savanna hares might be due to a tendency for higher shape variation. Application of our discriminant analysis to other regions in East Africa is discussed.     

Polymorphic microsatellite loci in the Australian tree frog, <Emphasis Type="Italic">Litoria peronii</Emphasis>

Eleven novel polymorphic microsatellite loci developed from a microsatellite enriched genomic library, are presented for the Australian tree frog Litoria peronii. We screened 29 individuals from a single population and detected high levels of polymorphism for all 11 loci with the number of alleles/locus ranging from 9 to 24. Values of expected and observed heterozygosities ranged from 0.789–0.955 and 0.207–1.00, respectively. These microsatellite markers should prove useful in determining levels of genetic diversity, measuring gene flow and migration, assigning individuals to their most likely population of origin, and in the assignment of paternity.     

NMR-based metabolomics at work in phytochemistry

Metabolomics is defined as both the qualitative and quantitative analysis of all metabolites in an organism unraveling correlation with other OMICs data. Many of the technologies used in metabolomics have method-specific advantages and drawbacks in terms of diversity of metabolites detected, sensitivity, or resolution. In this paper, the potential of NMR spectrometry applied to metabolomics is reviewed using examples of Nicotiana tabacum and Catharanthus roseus.     

Vibrational analysis of a solvated green fluorescent protein chromophore

Resonance Raman (RR) spectra of green fluorescent protein (GFP) model chromophores in solution have been simulated with the CASSCF/MM methodology. Although several reports on vibrational analysis of GFP model chromophores have been recently published, the RR spectra were simulated for the first time in explicit solution with the inclusion of the counterion, as these effects are crucial for unambiguously reproducing the vibrational band assignment in the anionic form of the GFP chromophore. This strategy allows for a one-to-one correspondence of the calculated vibrational modes to the observed RR bands, concerning both the location and intensity pattern. In addition, these simulations were complemented with total energy distribution calculations to aid in the unambiguous assignment of the measured spectra. The current study helps to clarify some of the previous RR bands assignments as well as producing some new assignment for the anionic form of GFP chromophore. The explicit solvent simulations and PCM-based calculations are compared to the measured spectra, and these results demonstrate that explicit solvent simulations provide better agreement with experiment, both in terms of vibrational frequencies and intensity distribution. Figure a Correlation of explicit hydration calculations (CASSCF/6-31G*/MM) for the HBI model chromophore and experimental RR data [21]; slope = 0.982, intercept = 27.210 and regression coefficient = 0.997. b Correlation of implicit PCM calculations (CASSCF/6-31G*) for the HBI model chromophore and experimental RR data [21], slope = 1.017, intercept = −48.838 and regression coefficient = 0.984