Rapid bacterial and fungal successional dynamics in first year after chaparral wildfire.

The rise in wildfire frequency and severity across the globe has increased interest in secondary succession. However, despite the role of soil microbial communities in controlling biogeochemical cycling and their role in the regeneration of post-fire vegetation, the lack of measurements immediately post-fire and at high temporal resolution has limited understanding of microbial secondary succession. To fill this knowledge gap, we sampled soils at 17, 25, 34, 67, 95, 131, 187, 286, and 376 days after a southern California wildfire in fire-adapted chaparral shrublands. We assessed bacterial and fungal biomass with qPCR of 16S and 18S and richness and composition with Illumina MiSeq sequencing of 16S and ITS2 amplicons. Fire severely reduced bacterial biomass by 47%, bacterial richness by 46%, fungal biomass by 86%, and fungal richness by 68%. The burned bacterial and fungal communities experienced rapid succession, with 5-6 compositional turnover periods. Analogous to plants, turnover was driven by "fire-loving" pyrophilous microbes, many of which have been previously found in forests worldwide and changed markedly in abundance over time. Fungal secondary succession was initiated by the Basidiomycete yeast Geminibasidium, which traded off against the filamentous Ascomycetes Pyronema, Aspergillus, and Penicillium. For bacteria, the Proteobacteria Massilia dominated all year, but the Firmicute Bacillus and Proteobacteria Noviherbaspirillum increased in abundance over time. Our high-resolution temporal sampling allowed us to capture post-fire microbial secondary successional dynamics and suggest that putative tradeoffs in thermotolerance, colonization, and competition among dominant pyrophilous microbes control microbial succession with possible implications for ecosystem function.

Cerebral cryptococcoma mimicking glioblastoma.

Cryptococcus neoformans and C. gattii cause invasive fungal disease, with meningitis being the most common manifestation of central nervous system (CNS) disease. Encapsulated cryptococcomas occur rarely, predominantly in immunocompetent hosts, usually related to C. gattii Our patient was an immunocompetent man who presented with headache and a large cystic CNS lesion thought to be glioblastoma. Biopsy of a concomitant lung lesion confirmed cryptococcoma and empiric antifungal therapy was started for presumed CNS cryptococcoma. Antifungal therapy failed to shrink the CNS lesion, and surgical excision confirmed C. gattii CNS cryptococcoma. Following surgery he had complete resolution of symptoms. This case highlights that cryptococcoma cannot be distinguished from tumour on clinical or imaging findings. A combined medical and surgical approach is optimal for the management of large or surgically accessible cryptococcomas, as antifungal therapy alone is unlikely to penetrate large lesions sufficiently to lead to a cure.

Mitochondrial DNA COI characterization of Helicoverpa armigera (Lepidoptera: Noctuidae) from Paraguay and Uruguay.

Since its detection in Brazil in 2013, the Old World cotton bollworm Helicoverpa armigera has been reported in Argentina, Paraguay, and Bolivia. Here we present evidence extending the South American range of H. armigera to Uruguay, using polymerase chain reaction and sequencing of the partial mitochondrial DNA (mtDNA) cytochrome oxidase I region. Molecular characterization of this gene region from individuals from Paraguay also supports previous morphological identification of H. armigera in Paraguay. Shared mtDNA haplotypes in H. armigera from Brazil, Uruguay, and Paraguay were identified. Additional surveying of populations in this region will be imperative to better monitor and understand factors that are underpinning its presence and successful adaptation in these South American regions. We discuss our findings with respect to the development of resistance pest management strategies of this invasive insect pest in a predominantly monoculture soybean crop landscape in the Southern Cone region.

Quantifying variances in comparative RNA secondary structure prediction.

BACKGROUND: With the advancement of next-generation sequencing and transcriptomics technologies, regulatory effects involving RNA, in particular RNA structural changes are being detected. These results often rely on RNA secondary structure predictions. However, current approaches to RNA secondary structure modelling produce predictions with a high variance in predictive accuracy, and we have little quantifiable knowledge about the reasons for these variances. RESULTS: In this paper we explore a number of factors which can contribute to poor RNA secondary structure prediction quality. We establish a quantified relationship between alignment quality and loss of accuracy. Furthermore, we define two new measures to quantify uncertainty in alignment-based structure predictions. One of the measures improves on the "reliability score" reported by PPfold, and considers alignment uncertainty as well as base-pair probabilities. The other measure considers the information entropy for SCFGs over a space of input alignments. CONCLUSIONS: Our predictive accuracy improves on the PPfold reliability score. We can successfully characterize many of the underlying reasons for and variances in poor prediction. However, there is still variability unaccounted for, which we therefore suggest comes from the RNA secondary structure predictive model itself.

Oxfold: kinetic folding of RNA using stochastic context-free grammars and evolutionary information.

MOTIVATION: Many computational methods for RNA secondary structure prediction, and, in particular, for the prediction of a consensus structure of an alignment of RNA sequences, have been developed. Most methods, however, ignore biophysical factors, such as the kinetics of RNA folding; no current implementation considers both evolutionary information and folding kinetics, thus losing information that, when considered, might lead to better predictions. RESULTS: We present an iterative algorithm, Oxfold, in the framework of stochastic context-free grammars, that emulates the kinetics of RNA folding in a simplified way, in combination with a molecular evolution model. This method improves considerably on existing grammatical models that do not consider folding kinetics. Additionally, the model compares favourably to non-kinetic thermodynamic models.

Characterising RNA secondary structure space using information entropy.

Comparative methods for RNA secondary structure prediction use evolutionary information from RNA alignments to increase prediction accuracy. The model is often described in terms of stochastic context-free grammars (SCFGs), which generate a probability distribution over secondary structures. It is, however, unclear how this probability distribution changes as a function of the input alignment. As prediction programs typically only return a single secondary structure, better characterisation of the underlying probability space of RNA secondary structures is of great interest. In this work, we show how to efficiently compute the information entropy of the probability distribution over RNA secondary structures produced for RNA alignments by a phylo-SCFG, and implement it for the PPfold model. We also discuss interpretations and applications of this quantity, including how it can clarify reasons for low prediction reliability scores. PPfold and its source code are available from http://birc.au.dk/software/ppfold/.

StatAlign 2.0: combining statistical alignment with RNA secondary structure prediction.

MOTIVATION: Comparative modeling of RNA is known to be important for making accurate secondary structure predictions. RNA structure prediction tools such as PPfold or RNAalifold use an aligned set of sequences in predictions. Obtaining a multiple alignment from a set of sequences is quite a challenging problem itself, and the quality of the alignment can affect the quality of a prediction. By implementing RNA secondary structure prediction in a statistical alignment framework, and predicting structures from multiple alignment samples instead of a single fixed alignment, it may be possible to improve predictions. RESULTS: We have extended the program StatAlign to make use of RNA-specific features, which include RNA secondary structure prediction from multiple alignments using either a thermodynamic approach (RNAalifold) or a Stochastic Context-Free Grammars (SCFGs) approach (PPfold). We also provide the user with scores relating to the quality of a secondary structure prediction, such as information entropy values for the combined space of secondary structures and sampled alignments, and a reliability score that predicts the expected number of correctly predicted base pairs. Finally, we have created RNA secondary structure visualization plugins and automated the process of setting up Markov Chain Monte Carlo runs for RNA alignments in StatAlign. AVAILABILITY AND IMPLEMENTATION: The software is available from http://statalign.github.com/statalign/.

Frnakenstein: multiple target inverse RNA folding.

BACKGROUND: RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard. RESULTS: In this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets. CONCLUSIONS: Our method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein.

Quantitative measurements of CO2 and CH4 using a multipass Raman capillary cell.

Raman measurements of two common gases are made using a simple multipass capillary Raman cell (MCC) coupled to an unfiltered 18 around 1 fiber-optic Raman probe. The MCC, which is fabricated by chemical deposition of silver on the inner walls of a 2 mm inner diameter glass capillary tube, gives up to 20-fold signal enhancements for nonabsorbing gases. The device is relatively small and suitable for remote and in situ Raman measurements with optical fibers. The optical behavior of the MCC is similar to previously described liquid-core waveguides and hollow metal-coated waveguides used for laser transmission, but unlike the former devices, the MCC is generally applicable to a very wide range of nonabsorbing gases.

The adhesion of glow--discharge polymers, Silastic and Parylene to implantable platinum electrodes: results of tensile pull tests after exposure to isotonic sodium chloride.

Thin organic coatings commonly are used for insulating microelectrodes and electronic packages designed for implant applications. The adherence of these coatings to the underlying substrates is a key parameter in their selection for various devices. Instron pull tests were performed on glow-discharge polymerized monomers, Parylene-N, medical-grade Silastic and various epoxies. The application of a thin coating of glow-discharge polymerized methane under a thicker Parylene-N coating improved the adhesion of the latter to the underlying substrate in isotonic sodium chloride solution and during accelerated testing conditions done by boiling.