Shear bond strength between different materials bonded with two resin cements

doi: 10.1111/j.1741‐2358.2011.00565.x
Shear bond strength between different materials bonded with two resin cements Background: The aim of this study was to compare the shear bond strength between Ni–Cr alloy specimens bonded to air‐abraded Ni–Cr, bur‐abraded Ni–Cr, etched ceramic and etched enamel substrates using the resin cements RelyX ARC or Enforce. Materials and methods: Ni–Cr specimens were made and sandblasted with Al₂O₃ airborne‐particles. Disc‐shaped patterns were made for each of the four experimental substrates: Ni–Cr treated with Al₂O₃ airborne‐particles, Ni–Cr treated with diamond bur abrasion, etched enamel and etched ceramic. Results: Significant differences in shear bond strength were found between the different materials and luting agents evaluated. The Ni–Cr alloy cylinders bonded to Ni–Cr surfaces sandblasted with 50 μm Al₂O₃ particles and bonded with Enforce achieved the highest bond strength when compared with other substrates (28.9 MPa, p < 0.05). Bur‐abraded metal discs had lowest values, regardless the cement used (2.9 and 6.9 MPa for RelyX and Enforce, respectively). Etched enamel and etched ceramic had similar shear bond strengths within cement groups and performed better when RelyX was used. Conclusions: Bonding Ni–Cr to Ni–Cr and ceramic may result in similar and higher bond strength when compared to Ni–Cr/enamel bonding. For metal/metal bonding, higher shear bond strength was achieved with resin cement Enforce, and for metal/ceramic and metal/enamel bonding, RelyX had higher results.     

Support Vector Machines for Protein Fold Class Prediction

Knowledge of the three‐dimensional structure of a protein is essential for describing and understanding its function. Today, a large number of known protein sequences faces a small number of identified structures. Thus, the need arises to predict structure from sequence without using time‐consuming experimental identification. In this paper the performance of Support Vector Machines (SVMs) is compared to Neural Networks and to standard statistical classification methods as Discriminant Analysis and Nearest Neighbor Classification. We show that SVMs can beat the competing methods on a dataset of 268 protein sequences to be classified into a set of 42 fold classes. We discuss misclassification with respect to biological function and similarity. In a second step we examine the performance of SVMs if the embedding is varied from frequencies of single amino acids to frequencies of tripletts of amino acids. This work shows that SVMs provide a promising alternative to standard statistical classification and prediction methods in functional genomics.     

Prediction of complex super-secondary structure βαβ motifs based on combined features

Prediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting βαβ motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthew’s correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting βαβ motifs and can be used for structure and function studies of proteins.     

RNA-MethylPred: A high-accuracy predictor to identify N6-methyladenosine in RNA

N6-methyladenosine (m⁶A) is present ubiquitously in the RNA of living organisms from Escherichia coli to humans. Nonetheless, the exact molecular mechanism of this modification remains unclear. The experimental identification of m⁶A modification is time-consuming and expensive; therefore, bioinformatics tools with high accuracy represent desirable alternatives for the large-scale, rapid identification of N6-methyladenosine sites. In this study, RNA-MethylPred, a new bioinformatics model, was developed by incorporating bi-profile Bayes, dinucleotide composition, and k nearest neighbor (KNN) scores for three feature extractions. RNA-MethylPred yielded a Matthew's correlation coefficient (MCC) of 0.53 in a jackknife test, which was 0.24 higher than that of iRNA-Methyl and 0.13 higher than that of pRNAm-PC. The obvious improvements demonstrated that RNA-MethylPred might be a powerful and complementary tool for further experimental investigation of N6-methyladenosine modification.     

A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells

Biological trajectories can be characterized by transient patterns that may provide insight into the interactions of the moving object with its immediate environment. The accurate and automated identification of trajectory motifs is important for the understanding of the underlying mechanisms. In this work, we develop a novel trajectory segmentation algorithm based on supervised support vector classification. The algorithm is validated on synthetic data and applied to the identification of trajectory fingerprints of fluorescently tagged human adenovirus particles in live cells. In virus trajectories on the cell surface, periods of confined motion, slow drift, and fast drift are efficiently detected. Additionally, directed motion is found for viruses in the cytoplasm. The algorithm enables the linking of microscopic observations to molecular phenomena that are critical in many biological processes, including infectious pathogen entry and signal transduction.     

Di-codon usage for classification of genes

Genes are often classified into biologically related groups so that inferences on their functions can be made. This paper demonstrates that the di-codon usage is a useful feature for gene classification and gives better classification accuracy than the codon usage. Our experiments with different classifiers show that support vector machines performs better than other classifiers in classifying genes by using di-codon usage as features. The method is illustrated on 1841 HLA sequences which are classified into two major classes, HLA-I and HLA-II, and further classified into the subclasses of major classes. By using both codon and di-codon features, we show near perfect accuracies in the classification of HLA molecules into major classes and their sub-classes.     

带噪生物实验数据的不确定支持向量数据描述

在对候选基因进行排序时,支持向量数据描述（SVDD）可以用来描述各种异构的数据源,如序列数据、学术文献数据、各种生物实验数据等。由于生物实验数据带有噪声,在用SVDD对其描述时,会遇到噪声的影响。本研究通过公式推导扩展了原始的SVDD,提出不确定支持向量数据描述（USVDD）,用来降低噪声的影响。利用酵母基因表达数据进行实验,结果表明该方法比标准的SVDD对带噪声的数据具有更好的描述能力。     

使用图像特征构建快速有效的蛋白质折叠识别方法

蛋白质结构自动分类是探索蛋白质结构- 功能关系的一种重要研究手段。首先将蛋白质折叠子三维空间结构映射成为二维距离矩阵,并将距离矩阵视作灰度图像。然后基于灰度直方图和灰度共生矩阵提出了一种计算简单的折叠子结构特征提取方法,得到了低维且能够反映折叠结构特点的特征,并进一步阐明了直方图中零灰度孤峰形成原因,深入分析了共生矩阵特征中灰度分布、不同角度和像素距离对应的结构意义。最后应用于27类折叠子分类,对独立集测试的精度达到了71.95 %,对所有数据进行10 交叉验证的精度为78.94 %。与多个基于序列和结构的折叠识别方法的对比结果表明,此方法不仅具有低维和简洁的特征,而且无需复杂的分类系统,能够有效和高效地实现多类折叠子识别。     

Comparison of viable cell concentration estimation methods for a mammalian cell cultivation process

Various mechanistic and black-box models were applied for on-line estimations of viable cell concentrations in fed-batch cultivation processes for CHO cells. Data from six fed-batch cultivation experiments were used to identify the underlying models and further six independent data sets were used to determine the performance of the estimators. The performances were quantified by means of the root mean square error (RMSE) between the estimates and the corresponding off-line measured validation data sets. It is shown that even simple techniques based on empirical and linear model approaches provide a fairly good on-line estimation performance. Best results with respect to the validation data sets were obtained with hybrid models, multivariate linear regression technique and support vector regression. Hybrid models provide additional important information about the specific cellular growth rates during the cultivation.     

Using LogitBoost classifier to predict protein structural classes

Prediction of protein classification is an important topic in molecular biology. This is because it is able to not only provide useful information from the viewpoint of structure itself, but also greatly stimulate the characterization of many other features of proteins that may be closely correlated with their biological functions. In this paper, the LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes. It performs classification using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data. It was demonstrated that the LogitBoost outperformed the support vector machines in predicting the structural classes for a given dataset, indicating that the new classifier is very promising. It is anticipated that the power in predicting protein structural classes as well as many other bio-macromolecular attributes will be further strengthened if the LogitBoost and some other existing algorithms can be effectively complemented with each other.