The whole and its parts : why and how to disentangle plant communities and synusiae in vegetation classification

Most plant communities consist of different structural and ecological subsets, ranging from cryptogams to different tree layers. The completeness and approach with which these subsets are sampled have implications for vegetation classification. Non‐vascular plants are often omitted or sometimes treated separately, referring to their assemblages as “synusiae” (e.g. epiphytes on bark, saxicolous species on rocks). The distinction of complete plant communities (phytocoenoses or holocoenoses) from their parts (synusiae or merocoenoses) is crucial to avoid logical problems and inconsistencies of the resulting classification systems. We here describe theoretical differences between the phytocoenosis as a whole and its parts, and outline consequences of this distinction for practise and terminology in vegetation classification. To implement a clearer separation, we call for modifications of the International Code of Phytosociological Nomenclature and the EuroVegChecklist. We believe that these steps will make vegetation classification systems better applicable and raise the recognition of the importance of non‐vascular plants in the vegetation as well as their interplay with vascular plants

Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure

<p>Abstract</p> <p>Background</p> <p>Amino acid sequence probability distributions, or profiles, have been used successfully to predict secondary structure and local structure in proteins. Profile models assume the statistical independence of each position in the sequence, but the energetics of protein folding is better captured in a scoring function that is based on pairwise interactions, like a force field.</p> <p>Results</p> <p>I-sites motifs are short sequence/structure motifs that populate the protein structure database due to energy-driven convergent evolution. Here we show that a pairwise covariant sequence model does not predict alpha helix or beta strand significantly better overall than a profile-based model, but it does improve the prediction of certain loop motifs. The finding is best explained by considering secondary structure profiles as multivariant, all-or-none models, which subsume covariant models. Pairwise covariance is nonetheless present and energetically rational. Examples of negative design are present, where the covariances disfavor non-native structures.</p> <p>Conclusion</p> <p>Measured pairwise covariances are shown to be statistically robust in cross-validation tests, as long as the amino acid alphabet is reduced to nine classes. An updated I-sites local structure motif library that provides sequence covariance information for all types of local structure in globular proteins and a web server for local structure prediction are available at <url>http://www.bioinfo.rpi.edu/bystrc/hmmstr/server.php</url>.</p

A series of PDB related databases for everyday needs

The Protein Data Bank (PDB) is the world-wide repository of macromolecular structure information. We present a series of databases that run parallel to the PDB. Each database holds one entry, if possible, for each PDB entry. DSSP holds the secondary structure of the proteins. PDBREPORT holds reports on the structure quality and lists errors. HSSP holds a multiple sequence alignment for all proteins. The PDBFINDER holds easy to parse summaries of the PDB file content, augmented with essentials from the other systems. PDB_REDO holds re-refined, and often improved, copies of all structures solved by X-ray. WHY_NOT summarizes why certain files could not be produced. All these systems are updated weekly. The data sets can be used for the analysis of properties of protein structures in areas ranging from structural genomics, to cancer biology and protein design

Cardiopulmonary exercise testing during follow-up after acute pulmonary embolism

BACKGROUND Cardiopulmonary exercise testing (CPET) may provide prognostically valuable information during follow-up after pulmonary embolism (PE). Our objective was to investigate the association of patterns and degree of exercise limitation, as assessed by CPET, with clinical, echocardiographic and laboratory abnormalities and quality of life (QoL) after PE. METHODS In a prospective cohort study of unselected consecutive all-comers with PE, survivors of the index acute event underwent 3- and 12-month follow-ups, including CPET. We defined cardiopulmonary limitation as ventilatory inefficiency or insufficient cardiocirculatory reserve. Deconditioning was defined as peak O$_{2}$ uptake (V'$_{O_{2}}$ ) <80% with no other abnormality. RESULTS Overall, 396 patients were included. At 3 months, prevalence of cardiopulmonary limitation and deconditioning was 50.1% (34.7% mild/moderate; 15.4% severe) and 12.1%, respectively; at 12 months, it was 44.8% (29.1% mild/moderate; 15.7% severe) and 14.9%, respectively. Cardiopulmonary limitation and its severity were associated with age (OR per decade 2.05, 95% CI 1.65-2.55), history of chronic lung disease (OR 2.72, 95% CI 1.06-6.97), smoking (OR 5.87, 95% CI 2.44-14.15) and intermediate- or high-risk acute PE (OR 4.36, 95% CI 1.92-9.94). Severe cardiopulmonary limitation at 3 months was associated with the prospectively defined, combined clinical-haemodynamic end-point of "post-PE impairment" (OR 6.40, 95% CI 2.35-18.45) and with poor disease-specific and generic health-related QoL. CONCLUSIONS Abnormal exercise capacity of cardiopulmonary origin is frequent after PE, being associated with clinical and haemodynamic impairment as well as long-term QoL reduction. CPET can be considered for selected patients with persisting symptoms after acute PE to identify candidates for closer follow-up and possible therapeutic interventions

ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction

We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28°/46°, which is ∼10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value <1.0×10−300 (or <1.0×10−148) by Wilcoxon signed rank test. For some residues (ILE, LEU, PRO and VAL) and especially the residues in helix and buried regions, the MAE of phi angles is much smaller (10–20°) than that in other environments. Thus, although the average accuracy of the ANGLOR prediction is still low, the portion of the accurately predicted dihedral angles may be useful in assisting protein fold recognition and ab initio 3D structure modeling

STAR: predicting recombination sites from amino acid sequence

BACKGROUND: Designing novel proteins with site-directed recombination has enormous prospects. By locating effective recombination sites for swapping sequence parts, the probability that hybrid sequences have the desired properties is increased dramatically. The prohibitive requirements for applying current tools led us to investigate machine learning to assist in finding useful recombination sites from amino acid sequence alone. RESULTS: We present STAR, Site Targeted Amino acid Recombination predictor, which produces a score indicating the structural disruption caused by recombination, for each position in an amino acid sequence. Example predictions contrasted with those of alternative tools, illustrate STAR'S utility to assist in determining useful recombination sites. Overall, the correlation coefficient between the output of the experimentally validated protein design algorithm SCHEMA and the prediction of STAR is very high (0.89). CONCLUSION: STAR allows the user to explore useful recombination sites in amino acid sequences with unknown structure and unknown evolutionary origin. The predictor service is available from

Chronic thromboembolic pulmonary hypertension and impairment after pulmonary embolism: the FOCUS study

AIMS: To systematically assess late outcomes of acute pulmonary embolism (PE) and to investigate the clinical implications of post-PE impairment (PPEI) fulfilling prospectively defined criteria. METHODS AND RESULTS: A prospective multicentre observational cohort study was conducted in 17 large-volume centres across Germany. Adult consecutive patients with confirmed acute symptomatic PE were followed with a standardized assessment plan and pre-defined visits at 3, 12, and 24 months. The co-primary outcomes were (i) diagnosis of chronic thromboembolic pulmonary hypertension (CTEPH), and (ii) PPEI, a combination of persistent or worsening clinical, functional, biochemical, and imaging parameters during follow-up. A total of 1017 patients (45% women, median age 64 years) were included in the primary analysis. They were followed for a median duration of 732 days after PE diagnosis. The CTEPH was diagnosed in 16 (1.6%) patients, after a median of 129 days; the estimated 2-year cumulative incidence was 2.3% (1.2-4.4%). Overall, 880 patients were evaluable for PPEI; the 2-year cumulative incidence was 16.0% (95% confidence interval 12.8-20.8%). The PPEI helped to identify 15 of the 16 patients diagnosed with CTEPH during follow-up (hazard ratio for CTEPH vs. no CTEPH 393; 95% confidence interval 73-2119). Patients with PPEI had a higher risk of re-hospitalization and death as well as worse quality of life compared with those without PPEI. CONCLUSION: In this prospective study, the cumulative 2-year incidence of CTEPH was 2.3%, but PPEI diagnosed by standardized criteria was frequent. Our findings support systematic follow-up of patients after acute PE and may help to optimize guideline recommendations and algorithms for post-PE care

Orientation-dependent backbone-only residue pair scoring functions for fixed backbone protein design

<p>Abstract</p> <p>Background</p> <p>Empirical scoring functions have proven useful in protein structure modeling. Most such scoring functions depend on protein side chain conformations. However, backbone-only scoring functions do not require computationally intensive structure optimization and so are well suited to protein design, which requires fast score evaluation. Furthermore, scoring functions that account for the distinctive relative position and orientation preferences of residue pairs are expected to be more accurate than those that depend only on the separation distance.</p> <p>Results</p> <p>Residue pair scoring functions for fixed backbone protein design were derived using only backbone geometry. Unlike previous studies that used spherical harmonics to fit 2D angular distributions, Gaussian Mixture Models were used to fit the full 3D (position only) and 6D (position and orientation) distributions of residue pairs. The performance of the 1D (residue separation only), 3D, and 6D scoring functions were compared by their ability to identify correct threading solutions for a non-redundant benchmark set of protein backbone structures. The threading accuracy was found to steadily increase with increasing dimension, with the 6D scoring function achieving the highest accuracy. Furthermore, the 3D and 6D scoring functions were shown to outperform side chain-dependent empirical potentials from three other studies. Next, two computational methods that take advantage of the speed and pairwise form of these new backbone-only scoring functions were investigated. The first is a procedure that exploits available sequence data by averaging scores over threading solutions for homologs. This was evaluated by applying it to the challenging problem of identifying interacting transmembrane alpha-helices and found to further improve prediction accuracy. The second is a protein design method for determining the optimal sequence for a backbone structure by applying Belief Propagation optimization using the 6D scoring functions. The sensitivity of this method to backbone structure perturbations was compared with that of fixed-backbone all-atom modeling by determining the similarities between optimal sequences for two different backbone structures within the same protein family. The results showed that the design method using 6D scoring functions was more robust to small variations in backbone structure than the all-atom design method.</p> <p>Conclusions</p> <p>Backbone-only residue pair scoring functions that account for all six relative degrees of freedom are the most accurate and including the scores of homologs further improves the accuracy in threading applications. The 6D scoring function outperformed several side chain-dependent potentials while avoiding time-consuming and error prone side chain structure prediction. These scoring functions are particularly useful as an initial filter in protein design problems before applying all-atom modeling.</p

Joint Effects of Febrile Acute Infection and an Interferon-γ Polymorphism on Breast Cancer Risk

BACKGROUND: There is an inverse relationship between febrile infection and the risk of malignancies. Interferon gamma (IFN-γ) plays an important role in fever induction and its expression increases with incubation at fever-range temperatures. Therefore, the genetic polymorphism of IFN-γ may modify the association of febrile infection with breast cancer risk. METHODOLOGY AND PRINCIPAL FINDINGS: Information on potential breast cancer risk factors, history of fever during the last 10 years, and blood specimens were collected from 839 incident breast cancer cases and 863 age-matched controls between October 2008 and June 2010 in Guangzhou, China. IFN-γ (rs2069705) was genotyped using a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry platform. Odds ratios (OR) and 95% confidence intervals (CIs) were calculated using multivariate logistic regression. We found that women who had experienced ≥1 fever per year had a decreased risk of breast cancer [ORs and 95% CI: 0.77 (0.61-0.99)] compared to those with less than one fever a year. This association only occurred in women with CT/TT genotypes [0.54 (0.37-0.77)] but not in those with the CC genotype [1.09 (0.77-1.55)]. The association of IFN-γ rs2069705 with the risk of breast cancer was not significant among all participants, while the CT/TT genotypes were significantly related to an elevated risk of breast cancer [1.32 (1.03-1.70)] among the women with <1 fever per year and to a reduced risk of breast cancer [0.63 (0.40-0.99)] among women with ≥1 fever per year compared to the CC genotype. A marked interaction between fever frequencies and the IFN-γ genotypes was observed (P for multiplicative and additive interactions were 0.005 and 0.058, respectively). CONCLUSIONS: Our findings indicate a possible link between febrile acute infection and a decreased risk of breast cancer, and this association was modified by IFN-γ rs2069705

Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan

CD4 positive T helper cells control many aspects of specific immunity. These cells are specific for peptides derived from protein antigens and presented by molecules of the extremely polymorphic major histocompatibility complex (MHC) class II system. The identification of peptides that bind to MHC class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent. Validation of the method includes identification of endogenously derived HLA class II ligands, cross-validation, leave-one-molecule-out, and binding motif identification for hitherto uncharacterized HLA-DR molecules. The validation shows that the method can successfully predict binding for HLA-DR molecules-even in the absence of specific data for the particular molecule in question. Moreover, when compared to TEPITOPE, currently the only other publicly available prediction method aiming at providing broad HLA-DR allelic coverage, NetMHCIIpan performs equivalently for alleles included in the training of TEPITOPE while outperforming TEPITOPE on novel alleles. We propose that the method can be used to identify those hitherto uncharacterized alleles, which should be addressed experimentally in future updates of the method to cover the polymorphism of HLA-DR most efficiently. We thus conclude that the presented method meets the challenge of keeping up with the MHC polymorphism discovery rate and that it can be used to sample the MHC "space," enabling a highly efficient iterative process for improving MHC class II binding predictions