First record of recurring reproduction of captive tawny nurse sharks Nebrius ferrugineus

Between 2008 and 2015, a group of tawny nurse sharks Nebrius ferrugineus reproduced successfully in a captive environment on the central Red Sea coast of Saudi Arabia. Births occurred on an annual basis, except for 2013. Of 12 confirmed birthing events, the most recent (2015) was observed and recorded in detail, which further contributes to the limited reproductive knowledge of this monotypic species.     

Range shifts or extinction? Ancient DNA and distribution modelling reveal past and future responses to climate warming in cold‐adapted birds

Global warming is predicted to cause substantial habitat rearrangements, with the most severe effects expected to occur in high‐latitude biomes. However, one major uncertainty is whether species will be able to shift their ranges to keep pace with climate‐driven environmental changes. Many recent studies on mammals have shown that past range contractions have been associated with local extinctions rather than survival by habitat tracking. Here, we have used an interdisciplinary approach that combines ancient DNA techniques, coalescent simulations and species distribution modelling, to investigate how two common cold‐adapted bird species, willow and rock ptarmigan (Lagopus lagopus and Lagopus muta), respond to long‐term climate warming. Contrary to previous findings in mammals, we demonstrate a genetic continuity in Europe over the last 20 millennia. Results from back‐casted species distribution models suggest that this continuity may have been facilitated by uninterrupted habitat availability and potentially also the greater dispersal ability of birds. However, our predictions show that in the near future, some isolated regions will have little suitable habitat left, implying a future decrease in local populations at a scale unprecedented since the last glacial maximum.     

Ecotype Variation of Methyl Eugenol Content in Tea Tree (Melaleuca alternifolia and Melaleuca linariifolia)

Methyl eugenol is a natural phenylpropanoid compound found in a wide range of plants used for food, flavouring, cosmetics, and health‐care. As a suspected rodent carcinogen, methyl eugenol may also be harmful to humans when present in significant concentrations. Consequently, its level has been restricted in some foodstuffs and cosmetics for some markets. In order to assess the potential to breed uniformly low methyl eugenol cultivars for an essential oil crop, tea tree, the source of ‘Oil of Melaleuca, terpinene‐4‐ol type’, we examine levels in individual trees (n = 30) from two geographic regions and six terpene chemotypes. Overall, methyl eugenol levels were low in this species (Mean [SD] 354 [239] ppm, n = 30), much lower than levels predicted to be of toxicological concern. Within each chemotype, there was a lack of evidence for correlations between terpenoid constituents and methyl eugenol levels. Further support for the independence of methyl eugenol and terpene biosynthesis was evident from similar mean levels in selected (Mean [SD] 586 [339] ppm, n = 12) and undomesticated Melaleuca alternifolia trees (Mean [SD] 480 [299] ppm, n = 5) with terpinen‐4‐ol type oils. By contrast, methyl eugenol level varied by geographic origin and chemotype. Trees from the upland region, where there is a prevalence of terpinolene type trees, had lower average methyl eugenol levels than trees from the coastal region, where there is a prevalence of terpinen‐4‐ol and 1,8‐cineole type trees.     

Electrospray mass spectrometry of trans-[Ru(NO)Cl(dpaH)2] (dpaH=2,2′-dipyridylamine) and ‘caged NO’, [RuCl3(NO)(H2O)2]: loss of HCl and NO from positive ions versus NO and Cl from negative ions

The positive ion electrospray mass spectrometry (ESI-MS) of trans-[Ru(NO)Cl)(dpaH)₂]Cl₂ (dpaH=2,2′-dipyridylamine), obtained from the carrier solvent of H₂O–CH₃OH (50:50), revealed 1+ ions of the formulas [Ru^II(NO⁺)Cl(dpaH)(dpa)]⁺ (m/z=508), [Ru^IIICl(dpaH)(dpa⁻)]⁺ (m/z=478), [Ru^II(NO⁺)(dpa)₂]⁺ (m/z=472), [Ru^III(dpa)₂]⁺ (m/z=442), originating from proton dissociation from the parent [Ru^II(NO⁺)Cl(dpaH)₂]²⁺ ion with subsequent loss of NO (17.4% of dissociative events) or loss of HCl (82.6% of dissociative events). Further loss of NO from the m/z=472 fragment yields the m/z=442 fragment. Thus, ionization of the NH moiety of dpaH is a significant factor in controlling the net ionic charge in the gas phase, and allowing preferential dissociation of HCl in the fragmentation processes. With NaCl added, an ion pair, {Na[Ru^II(NO)Cl(dpa)₂]}⁺ (m/z=530; 532), is detectable. All these positive mass peaks that contain Ru carry a signature ‘handprint’ of adjacent m/z peaks due to the isotopic distribution of ¹⁰⁴Ru, ¹⁰²Ru, ¹⁰¹Ru, ⁹⁹Ru, ⁹⁸Ru and ⁹⁶Ru mass centered around ¹⁰¹Ru for each fragment, and have been matched to the theoretical isotopic distribution for each set of peaks centered on the main isotope peak. When the starting complex is allowed to undergo aquation for two weeks in H₂O, loss of the axial Cl⁻ is shown by the approximately 77% attenuation of the [Ru^II(NO⁺)Cl(dpaH)(dpa)]⁺ ion, being replaced by the [Ru^II(NO⁺)(H₂O)(dpa)₂]⁺ (m/z=490) as the most abundant high-mass species. Loss of H₂O is observed to form [Ru^II(NO⁺)(dpa)₂]⁺ (m/z=472). No positive ion mass spectral peaks were observed for RuCl₃(NO)(H₂O)₂, ‘caged NO’. Negative ions were observed by proton dissociation forming [Ru^II(NO)Cl₃(H₂O)(OH)]⁻ in the ionization chamber, detecting the parent 1− ion at m/z=274, followed by the loss of NO as the main dissociative pathway that produces [Ru^IIICl₃(H₂O)(OH)]⁻ (m/z=244). This species undergoes reductive elimination of a chlorine atom, forming [Ru^IICl₂(H₂O)(OH)]⁻ (m/z=208). The ease of the NO dissociation is increased for the negative ions, which should be more able to stabilize a Ru^III product upon NO loss.     

Yeast and rat Coq3 and Escherichia coli UbiG polypeptides catalyze both O-methyltransferase steps in coenzyme Q biosynthesis.

Ubiquinone (coenzyme Q or Q) is a lipid that functions in the electron transport chain in the inner mitochondrial membrane of eukaryotes and the plasma membrane of prokaryotes. Q-deficient mutants of Saccharomyces cerevisiae harbor defects in one of eight COQ genes (coq1-coq8) and are unable to grow on nonfermentable carbon sources. The biosynthesis of Q involves two separate O-methylation steps. In yeast, the first O-methylation utilizes 3, 4-dihydroxy-5-hexaprenylbenzoic acid as a substrate and is thought to be catalyzed by Coq3p, a 32.7-kDa protein that is 40% identical to the Escherichia coli O-methyltransferase, UbiG. In this study, farnesylated analogs corresponding to the second O-methylation step, demethyl-Q(3) and Q(3), have been chemically synthesized and used to study Q biosynthesis in yeast mitochondria in vitro. Both yeast and rat Coq3p recognize the demethyl-Q(3) precursor as a substrate. In addition, E. coli UbiGp was purified and found to catalyze both O-methylation steps. Futhermore, antibodies to yeast Coq3p were used to determine that the Coq3 polypeptide is peripherally associated with the matrix-side of the inner membrane of yeast mitochondria. The results indicate that one O-methyltransferase catalyzes both steps in Q biosynthesis in eukaryotes and prokaryotes and that Q biosynthesis is carried out within the matrix compartment of yeast mitochondria.     

Prediction of the location and type of beta-turns in proteins using neural networks.

A neural network has been used to predict both the location and the type of beta-turns in a set of 300 nonhomologous protein domains. A substantial improvement in prediction accuracy compared with previous methods has been achieved by incorporating secondary structure information in the input data. The total percentage of residues correctly classified as beta-turn or not-beta-turn is around 75% with predicted secondary structure information. More significantly, the method gives a Matthews correlation coefficient (MCC) of around 0.35, compared with a typical MCC of around 0.20 using other beta-turn prediction methods. Our method also distinguishes the two most numerous and well-defined types of beta-turn, types I and II, with a significant level of accuracy (MCCs 0.22 and 0.26, respectively).     

Towards a lightweight generic computational grid framework for biological research

Background  

An increasing number of scientific research projects require access to large-scale computational resources. This is particularly true in the biological field, whether to facilitate the analysis of large high-throughput data sets, or to perform large numbers of complex simulations – a characteristic of the emerging field of systems biology.     

Comparing species richness among assemblages using sample units: why not use extrapolation methods to standardize different sample sizes?

Comparisons of species richness among assemblages using different sample sizes may produce erroneous conclusions due to the strong positive relationship between richness and sample size. A current way of handling the problem is to standardize sample sizes to the size of the smallest sample in the study. A major criticism about this approach is the loss of information contained in the larger samples. A potential way of solving the problem is to apply extrapolation techniques to smaller samples, and produce an estimated species richness expected to occur if sample size were increased to the same size of the largest sample. We evaluated the reliability of 11 potential extrapolation methods over a range of different data sets and magnitudes of extrapolation. The basic approach adopted in the evaluation process was a comparison between the observed richness in a sample and the estimated richness produced by estimators using a sub-sample of the same sample. The Log-Series estimator was the most robust for the range of data sets and sub-sample sizes used, followed closely by Negative Binomial, SO-J1, Logarithmic, Stout and Vandermeer, and Weibull estimators. When applied to a set of independently replicated samples from a species-rich assemblage, 95% confidence intervals of estimates produced by the six best evaluated methods were comparable to those of observed richness in the samples. Performance of estimators tended to be better for species-rich data sets rather than for those which contained few species. Good estimates were found when extrapolating up to 1.8-2.0 times the size of the sample. We suggest that the use of the best evaluated methods within the range of indicated conditions provides a safe solution to the problem of losing information when standardizing different sample sizes to the size of the smallest sample.