Uncertainties in projecting climate-change impacts in marine ecosystems

Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet theyare inevitablyassociated withuncertainty.Identifying,quantifying,andcommunicatingthisuncertaintyis keytobothevaluatingtherisk associated with a projection and building confidence in its robustness. Wereview howuncertainties in such projections are handled in marine science. We employan approach developedin climatemodelling by breaking uncertainty down into(i) structural (model) uncertainty,(ii) initialization and internalvariabilityuncertainty,(iii)parametricuncertainty,and(iv)scenariouncertainty.Foreachuncertaintytype,wethenexaminethecurrent state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gainsontheseasonal-to-decadaltime-scale.Weconcludethatallpartsofmarinesciencecouldbenefitfromagreaterexchangeofideas,particularly concerningsuchauniversalproblemsuchasthetreatmentofuncertainty.Finally,marinescienceshouldstrivetoreachthepointwherescenario uncertainty is the dominant uncertainty in our projections

Fibrosis in the kidney: is a problem shared a problem halved?

Fibrotic disorders are commonplace, take many forms and can be life-threatening. No better example of this exists than the progressive fibrosis that accompanies all chronic renal disease. Renal fibrosis is a direct consequence of the kidney's limited capacity to regenerate after injury. Renal scarring results in a progressive loss of renal function, ultimately leading to end-stage renal failure and a requirement for dialysis or kidney transplantation

A software pipeline for processing and identification of fungal ITS sequences

<p>Abstract</p> <p>Background</p> <p>Fungi from environmental samples are typically identified to species level through DNA sequencing of the nuclear ribosomal internal transcribed spacer (<it>ITS</it>) region for use in BLAST-based similarity searches in the International Nucleotide Sequence Databases. These searches are time-consuming and regularly require a significant amount of manual intervention and complementary analyses. We here present software – in the form of an identification pipeline for large sets of fungal <it>ITS </it>sequences – developed to automate the BLAST process and several additional analysis steps. The performance of the pipeline was evaluated on a dataset of 350 <it>ITS </it>sequences from fungi growing as epiphytes on building material.</p> <p>Results</p> <p>The pipeline was written in Perl and uses a local installation of NCBI-BLAST for the similarity searches of the query sequences. The variable subregion <it>ITS2 </it>of the <it>ITS </it>region is extracted from the sequences and used for additional searches of higher sensitivity. Multiple alignments of each query sequence and its closest matches are computed, and query sequences sharing at least 50% of their best matches are clustered to facilitate the evaluation of hypothetically conspecific groups. The pipeline proved to speed up the processing, as well as enhance the resolution, of the evaluation dataset considerably, and the fungi were found to belong chiefly to the <it>Ascomycota</it>, with <it>Penicillium </it>and <it>Aspergillus </it>as the two most common genera. The <it>ITS2 </it>was found to indicate a different taxonomic affiliation than did the complete <it>ITS </it>region for 10% of the query sequences, though this figure is likely to vary with the taxonomic scope of the query sequences.</p> <p>Conclusion</p> <p>The present software readily assigns large sets of fungal query sequences to their respective best matches in the international sequence databases and places them in a larger biological context. The output is highly structured to be easy to process, although it still needs to be inspected and possibly corrected for the impact of the incomplete and sometimes erroneously annotated fungal entries in these databases. The open source pipeline is available for UNIX-type platforms, and updated releases of the target database are made available biweekly. The pipeline is easily modified to operate on other molecular regions and organism groups.</p

The p53HMM algorithm: using profile hidden markov models to detect p53-responsive genes

<p>Abstract</p> <p>Background</p> <p>A computational method (called p53HMM) is presented that utilizes Profile Hidden Markov Models (PHMMs) to estimate the relative binding affinities of putative p53 response elements (REs), both p53 single-sites and cluster-sites. These models incorporate a novel "Corresponded Baum-Welch" training algorithm that provides increased predictive power by exploiting the redundancy of information found in the repeated, palindromic p53-binding motif. The predictive accuracy of these new models are compared against other predictive models, including position specific score matrices (PSSMs, or weight matrices). We also present a new dynamic acceptance threshold, dependent upon a putative binding site's distance from the Transcription Start Site (TSS) and its estimated binding affinity. This new criteria for classifying putative p53-binding sites increases predictive accuracy by reducing the false positive rate.</p> <p>Results</p> <p>Training a Profile Hidden Markov Model with corresponding positions matching a combined-palindromic p53-binding motif creates the best p53-RE predictive model. The p53HMM algorithm is available on-line: <url>http://tools.csb.ias.edu</url></p> <p>Conclusion</p> <p>Using Profile Hidden Markov Models with training methods that exploit the redundant information of the homotetramer p53 binding site provides better predictive models than weight matrices (PSSMs). These methods may also boost performance when applied to other transcription factor binding sites.</p

Identification and Characterization of an Unusual Class I Myosin Involved in Vesicle Traffic in Trypanosoma brucei

Myosins are a multimember family of motor proteins with diverse functions in eukaryotic cells. African trypanosomes possess only two candidate myosins and thus represent a useful system for functional analysis of these motors. One of these candidates is an unusual class I myosin (TbMyo1) that is expressed at similar levels but organized differently during the life cycle of Trypanosoma brucei. This myosin localizes to the polarized endocytic pathway in bloodstream forms of the parasite. This organization is actin dependent. Knock down of TbMyo1 results in a significant reduction in endocytic activity, a cessation in cell division and eventually cell death. A striking morphological feature in these cells is an enlargement of the flagellar pocket, which is consistent with an imbalance in traffic to and from the surface. In contrast TbMyo1 is distributed throughout procyclic forms of the tsetse vector and a loss of ∼90% of the protein has no obvious effects on growth or morphology. These results reveal a life cycle stage specific requirement for this myosin in essential endocytic traffic and represent the first description of the involvement of a motor protein in vesicle traffic in these parasites

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

<p>Abstract</p> <p>Background</p> <p>Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiment, there is a desire to predict phosphorylation sites using computational methods. However, previous studies regarding <it>in silico </it>prediction of plant phosphorylation sites lack the consideration of kinase-specific phosphorylation data. Thus, we are motivated to propose a new method that investigates different substrate specificities in plant phosphorylation sites.</p> <p>Results</p> <p>Experimentally verified phosphorylation data were extracted from TAIR9-a protein database containing 3006 phosphorylation data from the plant species <it>Arabidopsis thaliana</it>. In an attempt to investigate the various substrate motifs in plant phosphorylation, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. Profile hidden Markov model (HMM) is then applied to learn a predictive model for each subgroup. Cross-validation evaluation on the MDD-clustered HMMs yields an average accuracy of 82.4% for serine, 78.6% for threonine, and 89.0% for tyrosine models. Moreover, independent test results using <it>Arabidopsis thaliana </it>phosphorylation data from UniProtKB/Swiss-Prot show that the proposed models are able to correctly predict 81.4% phosphoserine, 77.1% phosphothreonine, and 83.7% phosphotyrosine sites. Interestingly, several MDD-clustered subgroups are observed to have similar amino acid conservation with the substrate motifs of well-known kinases from Phospho.ELM-a database containing kinase-specific phosphorylation data from multiple organisms.</p> <p>Conclusions</p> <p>This work presents a novel method for identifying plant phosphorylation sites with various substrate motifs. Based on cross-validation and independent testing, results show that the MDD-clustered models outperform models trained without using MDD. The proposed method has been implemented as a web-based plant phosphorylation prediction tool, PlantPhos <url>http://csb.cse.yzu.edu.tw/PlantPhos/</url>. Additionally, two case studies have been demonstrated to further evaluate the effectiveness of PlantPhos.</p