Phylogenetic relationships within Maldanidae (Capitellida,Annelida), based on morphological characters

The Maldanidae annelid worms are reviewed and the phylogenetic relationships of their subgroups provided, based on Hennigian principles and maximum parsimony. Characters were coded as binary and multistate (transformation series). We used 33 terminal taxa (species), and 50 characters. Characters were treated as unordered and of equal weight, and analysis was run in TNT. Three equally most-parsimonious trees were obtained with heuristic searches, with lengths of 64 steps; CI = 0.95, and RI = 0.98. The monophyly of Maldanidae was supported with 100% of bootstrap and jackknife values. As a result of our analysis, Arenicolidae remains the sister-group of Maldanidae, and both should be referred to Maldanomorpha. Maldanidae was supported by the following synapomorphies: dorsal prostomium; prostomium keel-shaped and fused to peristomium; torus globose behind median chaetigers; median chaetigers greatly elongated; number of pre-anal segments reduced. The subfamily Bogueinae was not monophyletic; Boguea and Boguella were included within Rhodininae. Clymenura, previously included in the Clymenurinae, was included within Euclymeninae. The taxa Notoproctinae, Maldaninae, Nicomachinae and Euclymeninae were grouped in the Maldanoplaca, a new taxon. Eight further new clades have been found, but were not named.     

Outlier detection methods are still effective even using virtual species created with the probabilistic approach

Liu et al. (Journal of Biogeography, 2018, 45 :164–176) presented an approach to detect outliers in species distribution data by developing virtual species created using the threshold approach. Meynard et al. (Journal of biogeography, 2019, 46 :2141–2144) raised concerns about this approach stating that ‘using a probabilistic approach … may significantly change results’. Here we provide a new series of simulations using the two approaches and demonstrate that the outlier detection approach based on pseudo species distribution models was still effective when using the probabilistic approach, although the detection rate was lower than when using the threshold approach.     

SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data

Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom .     

Non-linear molecular pattern classification using molecular beacons with multiple targets

In vitro pattern classification has been highlighted as an important future application of DNA computing. Previous work has demonstrated the feasibility of linear classifiers using DNA-based molecular computing. However, complex tasks require non-linear classification capability. Here we design a molecular beacon that can interact with multiple targets and experimentally shows that its fluorescent signals form a complex radial-basis function, enabling it to be used as a building block for non-linear molecular classification in vitro. The proposed method was successfully applied to solving artificial and real-world classification problems: XOR and microRNA expression patterns.     

Are there pollination syndromes in the Australian epacrids (Ericaceae: Styphelioideae)? A novel statistical method to identify key floral traits per syndrome

Background and Aims

Convergent floral traits hypothesized as attracting particular pollinators are known as pollination syndromes. Floral diversity suggests that the Australian epacrid flora may be adapted to pollinator type. Currently there are empirical data on the pollination systems for 87 species (approx. 15 % of Australian epacrids). This provides an opportunity to test for pollination syndromes and their important morphological traits in an iconic element of the Australian flora.

Methods

Data on epacrid–pollinator relationships were obtained from published literature and field observation. A multivariate approach was used to test whether epacrid floral attributes related to pollinator profiles. Statistical classification was then used to rank floral attributes according to their predictive value. Data sets excluding mixed pollination systems were used to test the predictive power of statistical classification to identify pollination models.

Key Results

Floral attributes are correlated with bird, fly and bee pollination. Using floral attributes identified as correlating with pollinator type, bird pollination is classified with 86 % accuracy, red flowers being the most important predictor. Fly and bee pollination are classified with 78 and 69 % accuracy, but have a lack of individually important floral predictors. Excluding mixed pollination systems improved the accuracy of the prediction of both bee and fly pollination systems.

Conclusions

Although most epacrids have generalized pollination systems, a correlation between bird pollination and red, long-tubed epacrids is found. Statistical classification highlights the relative importance of each floral attribute in relation to pollinator type and proves useful in classifying epacrids to bird, fly and bee pollination systems.     

Races of Xanthomonas citri subsp. malvacearum,the causal organism of bacterial blight of cotton in northern Nigeria

Xanthomonas citri subsp. malvacearum (Xcm) causes severe qualitative and quantitative losses to farmers in cotton-growing areas of the world. Isolates of Xcm were extracted from cotton seeds obtained from five ginneries located in Funtua, Malumfashi, Gusau and Zaria and standardised to 10^?5?cfu/ml. One isolate per location was used to inoculate three sets of 10 cotton differential lines known to differentiate races of Xcm through possession of B-genes for resistance to bacterial blight. Each cotton differential line was inoculated with the isolates at the six-leaf stage and SDW was inoculated as control. One hundred and sixty pots used were arranged in completely randomised design on the screen house bench. Four different pathogenic races were identified namely race 1, race 12, race 13 and race 16. This confirms the existence of an evolution of the species across northern Nigeria.     

Identification of a novel IVD mutation in a consanguineous family with isovaleric acidemia

Isovaleric acidemia (IVA) is a rare autosomal recessive disorder caused by a deficiency of isovaleryl-CoA dehydrogenase encoded by IVD gene. In this case study we report the first Saudi IVA patients from a consanguineous family with a novel transversion (p.G362V) and briefly discuss likely phenotype–genotype correlation of the disease in the Saudi population. We explored the functional consequences of the mutation by using various bioinformatics prediction algorithms and discussed the likely mechanism of the disease caused by the mutation.     

A probabilistic model for secondary structure prediction from protein chemical shifts

Protein chemical shifts encode detailed structural information that is difficult and computationally costly to describe at a fundamental level. Statistical and machine learning approaches have been used to infer correlations between chemical shifts and secondary structure from experimental chemical shifts. These methods range from simple statistics such as the chemical shift index to complex methods using neural networks. Notwithstanding their higher accuracy, more complex approaches tend to obscure the relationship between secondary structure and chemical shift and often involve many parameters that need to be trained. We present hidden Markov models (HMMs) with Gaussian emission probabilities to model the dependence between protein chemical shifts and secondary structure. The continuous emission probabilities are modeled as conditional probabilities for a given amino acid and secondary structure type. Using these distributions as outputs of first‐ and second‐order HMMs, we achieve a prediction accuracy of 82.3%, which is competitive with existing methods for predicting secondary structure from protein chemical shifts. Incorporation of sequence‐based secondary structure prediction into our HMM improves the prediction accuracy to 84.0%. Our findings suggest that an HMM with correlated Gaussian distributions conditioned on the secondary structure provides an adequate generative model of chemical shifts. Proteins 2013; © 2012 Wiley Periodicals, Inc.