The injury epidemiology of cyclists based on a road trauma registry

<p>Abstract</p> <p>Background</p> <p>Bicycle use has increased in some of France's major cities, mainly as a means of transport. Bicycle crashes need to be studied, preferably by type of cycling. Here we conduct a descriptive analysis.</p> <p>Method</p> <p>A road trauma registry has been in use in France since 1996, in a large county around Lyon (the Rhône, population 1.6 million). It covers outpatients, inpatients and fatalities. All injuries are coded using the Abbreviated Injury Scale (AIS). Proxies were used to identify three types of cycling: learning = children (0-10 years old); sports cycling = teenagers and adults injured outside towns; cycling as means of transport = teenagers and adults injured in towns. The study is based on 13,684 cyclist casualties (1996-2008).</p> <p>Results</p> <p>The percentage of cyclists injured in a collision with a motor vehicle was 8% among children, 17% among teenagers and adults injured outside towns, and 31% among those injured in towns. The percentage of serious casualties (MAIS 3+) was 4.5% among children, 10.9% among adults injured outside towns and 7.2% among those injured in towns. Collisions with motor-vehicles lead to more internal injuries than bicycle-only crashes.</p> <p>Conclusion</p> <p>The description indicates that cyclist type is associated with different crash and injury patterns. In particular, cyclists injured in towns (where cycling is increasing) are generally less severely injured than those injured outside towns for both types of crash (bicycle-only crashes and collisions with a motor vehicle). This is probably due to lower speeds in towns, for both cyclists and motor vehicles.</p

Accuracy of Protein-Protein Binding Sites in High-Throughput Template-Based Modeling

The accuracy of protein structures, particularly their binding sites, is essential for the success of modeling protein complexes. Computationally inexpensive methodology is required for genome-wide modeling of such structures. For systematic evaluation of potential accuracy in high-throughput modeling of binding sites, a statistical analysis of target-template sequence alignments was performed for a representative set of protein complexes. For most of the complexes, alignments containing all residues of the interface were found. The full interface alignments were obtained even in the case of poor alignments where a relatively small part of the target sequence (as low as 40%) aligned to the template sequence, with a low overall alignment identity (<30%). Although such poor overall alignments might be considered inadequate for modeling of whole proteins, the alignment of the interfaces was strong enough for docking. In the set of homology models built on these alignments, one third of those ranked 1 by a simple sequence identity criteria had RMSD<5 Å, the accuracy suitable for low-resolution template free docking. Such models corresponded to multi-domain target proteins, whereas for single-domain proteins the best models had 5 Å<RMSD<10 Å, the accuracy suitable for less sensitive structure-alignment methods. Overall, ∼50% of complexes with the interfaces modeled by high-throughput techniques had accuracy suitable for meaningful docking experiments. This percentage will grow with the increasing availability of co-crystallized protein-protein complexes

Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites

Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice-Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice-phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice-Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice-Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC-Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice-phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.</p> <p>Results</p> <p>We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.</p> <p>Conclusions</p> <p>The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.</p> <p>Availability</p> <p>Datasets and software are available at <url>http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm</url>.</p

Integrated genomic analyses of ovarian carcinoma

SummaryThe Cancer Genome Atlas (TCGA) project has analyzed mRNA expression, miRNA expression, promoter methylation, and DNA copy number in 489 high-grade serous ovarian adenocarcinomas (HGS-OvCa) and the DNA sequences of exons from coding genes in 316 of these tumors. These results show that HGS-OvCa is characterized by TP53 mutations in almost all tumors (96%); low prevalence but statistically recurrent somatic mutations in 9 additional genes including NF1, BRCA1, BRCA2, RB1, and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three miRNA subtypes, four promoter methylation subtypes, a transcriptional signature associated with survival duration and shed new light on the impact on survival of tumors with BRCA1/2 and CCNE1 aberrations. Pathway analyses suggested that homologous recombination is defective in about half of tumors, and that Notch and FOXM1 signaling are involved in serous ovarian cancer pathophysiology

Amyloidogenic Regions and Interaction Surfaces Overlap in Globular Proteins Related to Conformational Diseases

Protein aggregation underlies a wide range of human disorders. The polypeptides involved in these pathologies might be intrinsically unstructured or display a defined 3D-structure. Little is known about how globular proteins aggregate into toxic assemblies under physiological conditions, where they display an initially folded conformation. Protein aggregation is, however, always initiated by the establishment of anomalous protein-protein interactions. Therefore, in the present work, we have explored the extent to which protein interaction surfaces and aggregation-prone regions overlap in globular proteins associated with conformational diseases. Computational analysis of the native complexes formed by these proteins shows that aggregation-prone regions do frequently overlap with protein interfaces. The spatial coincidence of interaction sites and aggregating regions suggests that the formation of functional complexes and the aggregation of their individual subunits might compete in the cell. Accordingly, single mutations affecting complex interface or stability usually result in the formation of toxic aggregates. It is suggested that the stabilization of existing interfaces in multimeric proteins or the formation of new complexes in monomeric polypeptides might become effective strategies to prevent disease-linked aggregation of globular proteins

<i>ABCB1</i> (MDR1) induction defines a common resistance mechanism in paclitaxel- and olaparib-resistant ovarian cancer cells

BACKGROUND: Clinical response to chemotherapy for ovarian cancer is frequently compromised by the development of drug-resistant disease. The underlying molecular mechanisms and implications for prescription of routinely prescribed chemotherapy drugs are poorly understood. METHODS: We created novel A2780-derived ovarian cancer cell lines resistant to paclitaxel and olaparib following continuous incremental drug selection. MTT assays were used to assess chemosensitivity to paclitaxel and olaparib in drug-sensitive and drug-resistant cells±the ABCB1 inhibitors verapamil and elacridar and cross-resistance to cisplatin, carboplatin, doxorubicin, rucaparib, veliparib and AZD2461. ABCB1 expression was assessed by qRT-PCR, copy number, western blotting and immunohistochemical analysis and ABCB1 activity assessed by the Vybrant and P-glycoprotein-Glo assays. RESULTS: Paclitaxel-resistant cells were cross-resistant to olaparib, doxorubicin and rucaparib but not to veliparib or AZD2461. Resistance correlated with increased ABCB1 expression and was reversible following treatment with the ABCB1 inhibitors verapamil and elacridar. Active efflux of paclitaxel, olaparib, doxorubicin and rucaparib was confirmed in drug-resistant cells and in ABCB1-expressing bacterial membranes. CONCLUSIONS: We describe a common ABCB1-mediated mechanism of paclitaxel and olaparib resistance in ovarian cancer cells. Optimal choice of PARP inhibitor may therefore limit the progression of drug-resistant disease, while routine prescription of first-line paclitaxel may significantly limit subsequent chemotherapy options in ovarian cancer patients