A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Linear and weakly nonlinear stability analyses of cooperative car-following models

Stability analyses have been widely used to better understand the mechanism of traffic jam formation. In this paper, we consider the impact of cooperative systems (a.k.a. connected vehicles) on traffic dynamics and, more precisely, on flow stability. Cooperative systems are emerging technologies enabling communication between vehicles and/or with the infrastructure. In a distributed communication framework, equipped vehicles are able to send and receive information to/from other equipped vehicles. Here, the effects of cooperative traffic are modeled through a general bilateral multianticipative car-following law that improves cooperative drivers' perception of their surrounding traffic conditions within a given communication range. Linear stability analyses are performed for a broad class of car-following models. They point out different stability conditions in both multianticipative and nonmultianticipative situations. To better understand what happens in unstable conditions, information on the shock wave structure is studied in the weakly nonlinear regime by the mean of the reductive perturbation method. The shock wave equation is obtained for generic car-following models by deriving the Korteweg de Vries equations. We then derive traffic-state-dependent conditions for the sign of the solitary wave (soliton) amplitude. This analytical result is verified through simulations. Simulation results confirm the validity of the speed estimate. The variation of the soliton amplitude as a function of the communication range is provided. The performed linear and weakly nonlinear analyses help justify the potential benefits of vehicle-integrated communication systems and provide new insights supporting the future implementation of cooperative systems.

The literacy curriculum: A critical review

This is a critical review of the scope of the literacy curriculum in the twenty-first century, uncovering the strengths, controversies, and silences that have divided literacy researchers and educators. It conceptualizes the literacy curriculum as a particular set of socially organized symbolic practices that are always selective, and which are inextricably connected to the function and organization of schooling. We trace the political, historical, and ideological antecedents of literacy curriculum and schooling as a form of cultural apparatus of the nation-state, before tracing some of the major interpretive paradigms that have influenced the shape of the literacy curriculum in many parts of the world. These include debates about skills-based approaches, whole language, systemic functional grammar, and critical literacy. It then draws attention to noteworthy advances and shifts in the field over recent decades: debates about the role of orality in the literacy curriculum, home-school community literacy practices, teacher and student knowledge of language and grammar, and the role of curriculum area literacies. It anticipates the future of the literacy curriculum in online textual environments and the changing sensorial and material nature of literacy practices, while acknowledging that curriculum innovation is always limited in complex ways by historically established pedagogic discourses of schooling.

Do motor vehicle crashes arise from single or multiple unique risk processes? An inquiry into crash causes and modelling

Crashes at any particular transport network location consist of a chain of events arising from a multitude of potential causes and/or contributing factors whose nature is likely to reflect geometric characteristics of the road, spatial effects of the surrounding environment, and human behavioural factors. It is postulated that these potential contributing factors do not arise from the same underlying risk process, and thus should be explicitly modelled and understood. The state of the practice in road safety network management applies a safety performance function that represents a single risk process to explain crash variability across network sites. This study aims to elucidate the importance of differentiating among various underlying risk processes contributing to the observed crash count at any particular network location. To demonstrate the principle of this theoretical and corresponding methodological approach, the study explores engineering (e.g. segment length, speed limit) and unobserved spatial factors (e.g. climatic factors, presence of schools) as two explicit sources of crash contributing factors. A Bayesian Latent Class (BLC) analysis is used to explore these two sources and to incorporate prior information about their contribution to crash occurrence. The methodology is applied to the state controlled roads in Queensland, Australia and the results are compared with the traditional Negative Binomial (NB) model. A comparison of goodness of fit measures indicates that the model with a double risk process outperforms the single risk process NB model, and thus indicating the need for further research to capture all the three crash generation processes into the SPFs.

The function of the RNA-binding protein TEL1 in moss reveals ancient regulatory mechanisms of shoot development

The shoot represents the basic body plan in land plants. It consists of a repeated structure composed of stems and leaves. Whereas vascular plants generate a shoot in their diploid phase, non-vascular plants such as mosses form a shoot (called the gametophore) in their haploid generation. The evolution of regulatory mechanisms or genetic networks used in the development of these two kinds of shoots is unclear. TERMINAL EAR1-like genes have been involved in diploid shoot development in vascular plants. Here, we show that disruption of PpTEL1 from the moss Physcomitrella patens, causes reduced protonema growth and gametophore initiation, as well as defects in gametophore development. Leafy shoots formed on ΔTEL1 mutants exhibit shorter stems with more leaves per shoot, suggesting an accelerated leaf initiation (shortened plastochron), a phenotype shared with the Poaceae vascular plants TE1 and PLA2/LHD2 mutants. Moreover, the positive correlation between plastochron length and leaf size observed in ΔTEL1 mutants suggests a conserved compensatory mechanism correlating leaf growth and leaf initiation rate that would minimize overall changes in plant biomass. The RNA-binding protein encoded by PpTEL1 contains two N-terminus RNA-recognition motifs, and a third C-terminus non-canonical RRM, specific to TEL proteins. Removal of the PpTEL1 C-terminus (including this third RRM) or only 16–18 amino acids within it seriously impairs PpTEL1 function, suggesting a critical role for this third RRM. These results show a conserved function of the RNA-binding PpTEL1 protein in the regulation of shoot development, from early ancestors to vascular plants, that depends on the third TEL-specific RRM.

Mosaic PPM1D mutations are associated with predisposition to breast and ovarian cancer

Improved sequencing technologies offer unprecedented opportunities for investigating the role of rare genetic variation in common disease. However, there are considerable challenges with respect to study design, data analysis and replication. Using pooled next-generation sequencing of 507 genes implicated in the repair of DNA in 1,150 samples, an analytical strategy focused on protein-truncating variants (PTVs) and a large-scale sequencing case-control replication experiment in 13,642 individuals, here we show that rare PTVs in the p53-inducible protein phosphatase PPM1D are associated with predisposition to breast cancer and ovarian cancer. PPM1D PTV mutations were present in 25 out of 7,781 cases versus 1 out of 5,861 controls (P = 1.12 × 10-5), including 18 mutations in 6,912 individuals with breast cancer (P = 2.42 × 10-4) and 12 mutations in 1,121 individuals with ovarian cancer (P = 3.10 × 10-9). Notably, all of the identified PPM1D PTVs were mosaic in lymphocyte DNA and clustered within a 370-base-pair region in the final exon of the gene, carboxy-terminal to the phosphatase catalytic domain. Functional studies demonstrate that the mutations result in enhanced suppression of p53 in response to ionizing radiation exposure, suggesting that the mutant alleles encode hyperactive PPM1D isoforms. Thus, although the mutations cause premature protein truncation, they do not result in the simple loss-of-function effect typically associated with this class of variant, but instead probably have a gain-of-function effect. Our results have implications for the detection and management of breast and ovarian cancer risk. More generally, these data provide new insights into the role of rare and of mosaic genetic variants in common conditions, and the use of sequencing in their identification.

Immunopathogenesis of ankylosing spondylitis

Ankylosing spondylitis is a model immunogenetic disease with major common and rare genetic risk factors, likely environmental contributors to its pathogenesis and, to date, no treatment that has been shown to induce disease remission in long-term studies. The discovery of the association of HLA-B27 with the disease in the early 1970s triggered extensive efforts to elucidate the mechanism of this association. However, the precise role of HLA-B27 in ankylosing spondylitis pathogenesis remains unclear. In recent years, rapid progress made in the discovery of non-MHC genes involved in susceptibility to ankylosing spondylitis has combined with increasing ability to investigate the immune system to make rapid progress in unraveling the etiopathogenesis of the condition. © 2013 Future Medicine Ltd.

Common variants in the HLA-DRB1-HLA-DQA1 HLA class II region are associated with susceptibility to visceral leishmaniasis

To identify susceptibility loci for visceral leishmaniasis, we undertook genome-wide association studies in two populations: 989 cases and 1,089 controls from India and 357 cases in 308 Brazilian families (1,970 individuals). The HLA-DRB1-HLA-DQA1 locus was the only region to show strong evidence of association in both populations. Replication at this region was undertaken in a second Indian population comprising 941 cases and 990 controls, and combined analysis across the three cohorts for rs9271858 at this locus showed P combined = 2.76 × 10 -17 and odds ratio (OR) = 1.41, 95% confidence interval (CI) = 1.30-1.52. A conditional analysis provided evidence for multiple associations within the HLA-DRB1-HLA-DQA1 region, and a model in which risk differed between three groups of haplotypes better explained the signal and was significant in the Indian discovery and replication cohorts. In conclusion, the HLA-DRB1-HLA-DQA1 HLA class II region contributes to visceral leishmaniasis susceptibility in India and Brazil, suggesting shared genetic risk factors for visceral leishmaniasis that cross the epidemiological divides of geography and parasite species. © 2013 Nature America, Inc. All rights reserved.

Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci

Ankylosing spondylitis is a common, highly heritable inflammatory arthritis affecting primarily the spine and pelvis. In addition to HLA-B*27 alleles, 12 loci have previously been identified that are associated with ankylosing spondylitis in populations of European ancestry, and 2 associated loci have been identified in Asians. In this study, we used the Illumina Immunochip microarray to perform a case-control association study involving 10,619 individuals with ankylosing spondylitis (cases) and 15,145 controls. We identified 13 new risk loci and 12 additional ankylosing spondylitis-associated haplotypes at 11 loci. Two ankylosing spondylitis-associated regions have now been identified encoding four aminopeptidases that are involved in peptide processing before major histocompatibility complex (MHC) class I presentation. Protective variants at two of these loci are associated both with reduced aminopeptidase function and with MHC class I cell surface expression.

Fine mapping of the MHC Class III region demonstrates association of AIF1 and rheumatoid arthritis

Objective. The heritability of RA has been estimated to be ∼55%, of which the MHC contributes about one-third. HLA-DRB1 alleles are strongly associated with RA, but it is likely that significant non-DRB1 MHC genetic susceptibility factors are involved. Previously, we identified two three-marker haplotypes in a 106-kb region in the MHC class III region immediately centromeric to TNF, which are strongly associated with RA on HLA-DRB1*0404 haplotypes. In the present study, we aimed to refine these associations further using a combination of genotyping and gene expression studies. Methods. Thirty-nine nucleotide polymorphisms (SNPs) were genotyped in 95 DRB1*0404 carrying unrelated RA cases, 125 DRB1*0404 - carrying healthy controls and 87 parent-case trio RA families in which the affected child carried HLA-DRB1*04. Quantitative RT-PCR was used to assess the expression of the positional candidate MHC class III genes APOM, BAT2, BAT3, BAT4, BAT5, AIF1, C6orf47, CSNK2β and LY6G5C, and the housekeeper genes, hypoxanthine-guanine phosphoribosyltransferase (HPRT) and β2-microglobulin (B2M) in 31 RA cases and 21 ethnically, age- and sex-matched healthy controls. Synovial membrane specimens from RA, PsA and OA cases were stained by an indirect immunoperoxidase technique using a mouse-anti-human AIF1 monoclonal antibody. Results. Association was observed between RA and single markers or two marker haplotypes involving AIF1, BAT3 and CSNK. AIF1 was also significantly overexpressed in RA mononuclear cells (1.5- to 1.9-fold difference, P = 0.02 vs HPRT, P = 0.002 vs B2M). AIF1 protein was clearly expressed by synovial macrophages in all the inflammatory synovial samples in contrast to the non-inflammatory OA samples. Conclusions. The results of the genotyping and expression studies presented here suggest a role for AIF1 in both the aetiology and pathogenesis of RA.

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1–3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region4, 5, 6, 7, 8, 9, 10, 11. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods—recursive partitioning and regression...

Cloning and gastrointestinal expression of rat hephaestin: Relationship to other iron transport proteins

The membrane-bound ceruloplasmin homolog hephaestin plays a critical role in intestinal iron absorption. The aims of this study were to clone the rat hephaestin gene and to examine its expression in the gastrointestinal tract in relation to other genes encoding iron transport proteins. The rat hephaestin gene was isolated from intestinal mRNA and was found to encode a protein 96% identical to mouse hephaestin. Analysis by ribonuclease protection assay and Western blotting showed that hephaestin was expressed at high levels throughout the small intestine and colon. Immunofluorescence localized the hephaestin protein to the mature villus enterocytes with little or no expression in the crypts. Variations in iron status had a small but nonsignificant effect on hephaestin expression in the duodenum. The high sequence conservation between rat and mouse hephaestin is consistent with this protein playing a central role in intestinal iron absorption, although its precise function remains to be determined.

In situ investigation of explosive crystallization in a-Ge: Experimental determination of the interface response function using dynamic transmission electron microscopy

The crystallization of amorphous semiconductors is a strongly exothermic process. Once initiated the release of latent heat can be sufficient to drive a self-sustaining crystallization front through the material in a manner that has been described as explosive. Here, we perform a quantitative in situ study of explosive crystallization in amorphous germanium using dynamic transmission electron microscopy. Direct observations of the speed of the explosive crystallization front as it evolves along a laser-imprinted temperature gradient are used to experimentally determine the complete interface response function (i.e., the temperature-dependent front propagation speed) for this process, which reaches a peak of 16 m/s. Fitting to the Frenkel-Wilson kinetic law demonstrates that the diffusivity of the material locally/immediately in advance of the explosive crystallization front is inconsistent with those of a liquid phase. This result suggests a modification to the liquid-mediated mechanism commonly used to describe this process that replaces the phase change at the leading amorphous-liquid interface with a change in bonding character (from covalent to metallic) occurring in the hot amorphous material.