8 research outputs found
Composing Elements and Kinematics Simulation of Three Gear-Plates Planet Drive with Small Teeth Difference Used in Robot
No full textA Comparative Study of Four Parametric Hysteresis Models for Magnetorheological Dampers
No full textThe dynamics of the magnetorheological damper is complex, including the inherent hysteresis characteristics and nonlinear creep behavior in the low-velocity region. Mathematical models for these complex dynamics are very important to the function of the damper. In this paper, a comparative study of the four parametric dynamic models, which are the hysteresis bi–viscous model, viscoelastic-plastic model, Bouc–Wen model, and improved Bouc–Wen model, is performed. The study includes the building of a common test apparatus and the parameter identification for the four models. The comparison of the four models concludes that (1) all four models are comparative and that (2) the improved Bouc–Wen model has the highest accuracy
Comparison of MgO-GGBS, CaO-GGBS, and cement for construction of two-phase TRD cut-off walls in sand: workability, strength, and permeability
No full textCement–bentonite–soil cut-off walls installed using the trench remixing and deep (TRD) wall method have been widely used to control seepage. The TRD construction is usually executed in two-phase cut-off walls; however, previous studies mainly focus on the properties of the one-phase cut-off walls. Besides, chemical interactions between bentonite and cement will induce a reduction in the swelling potential and water retention capacity of bentonite, leading to a weakness in its seepage barrier performance. Thus, it is of great significance to develop alternative cementitious materials to replace cement for the construction of cement–bentonite–soil cut-off walls. Ground granulated blastfurnace slag (GGBS) has been increasingly used for partial or full replacement of cement in geotechnical engineering. In this study, the performance of cut-off walls constructed using the two-phase TRD method was fully evaluated based on the use of GGBS with the activators of magnesia (MgO) and quicklime (CaO) to replace cement. A series of laboratory tests were first conducted to evaluate the workability (i.e., flowability and bleeding) of fresh bentonite–sand mixtures with four types of binder slurry (i.e., cement, GGBS, MgO-GGBS, and CaO-GGBS). Subsequently, binder–bentonite–sand mixtures that achieved the required workability were selected for unconfined compressive strength (UCS) and permeability tests. The results indicated that both the flowability and the bleeding water of fresh mixtures were remarkably increased by the addition of cement or CaO-GGBS compared to that of GGBS or MgO-GGBS, easily exceeding the acceptable range. It may be attributed to the cation exchange of Ca2+ released from cement or CaO hydration with Na+ in bentonite; however, this interaction might be concealed for the one-phase cut-off walls. More importantly, MgO-GGBS showed much better performance in the strength and permeability than cement; for example, the average 28-day UCS could reach up to 1.12 MPa for the binder type of MgO/GGBS = 1:9 at the binder content of 10% and bentonite content of 3%, four times that of cement. On the other hand, it is revealed that the addition of a large amount of bentonite does not always make a significant improvement on the permeability of two-phase cut-off walls. Overall, the results promote the application of MgO-GGBS as a potential substitute for cement in cut-off walls using the two-phase TRD method.Ministry of Education (MOE)Submitted/Accepted versionThis study is financially supported by the Ministry of Education, Singapore, under its Academic Research Fund Tier 2 (MOE-T2EP50220-0004) and the National Natural Science Foundation of China (51938005 and 52090082). The first author acknowledges the International Post doctoral Fellowship Program from the Office of China Postdoc Council (20190043)
Building a classifier for identifying sentences pertaining to disease-drug relationships in tardive dyskinesia
No full textZirconium cage-doped fullerenes
No full textSome zirconium containing fullerenes were synthesized by vaporizing a zirconium-containing graphite anode with DC are discharge technique, extracted by CS2 through Low temperature extraction method, and characterized by field desorption mass spectra (FDMS). The results show that these doped fullerenes were zirconium cage-doped fullerenes.Chemistry, PhysicalMaterials Science, MultidisciplinaryPhysics, Atomic, Molecular & ChemicalSCI(E)EI1ARTICLE6483-489
iSyTE 2.0: a database for expression-based gene discovery in the eye
No full text© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (integrated Systems Tool for Eye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches.published_or_final_versio
Prediction of Human Protein-Protein Interactions Using Support Vector Machines
No full text近年來，透過使用高效能產出的酵母菌雙雜交（yeast-two
hybrid）分析方法產生大量的蛋白質交互作用的資料。透過這些資料的取得，以及其他蛋白質特徵值，使得運用計算方法預測人類同源蛋白質交互作用（Interolog）已變得是可行的方法。因此整合異質性資料，並且提高預測人類蛋白質交互作用的準確度，是生物資訊的方法中最需要的。
在以知識為基礎（knowledge-based）的研究當中，我們提出在蛋白質交互網路中尋找最大相似完全圖，來計算物種間相對的保留性，並且使用其他蛋白質特徵值給予計分。這些所預測的人類同源蛋白質交互作用主要透過6個物種，包含有大鼠、小鼠、果蠅、線蟲、阿拉伯芥及酵母菌。使用功能性關鍵詞（functional
keyword）及基因本體（Gene
Ontology）作為評估，結果也顯示出所預測的蛋白質交互作用有較高的可信度。與其他同源蛋白質交互作用為基礎的方法比較中，所提出的方法也有較高的準確度。
本研究考慮了蛋白質交互作用的特徵值，包含有同源蛋白質交互作用，空間特性（細胞胞器位置及組織特異性），時間特性（細胞周期），功能區塊配對組合。透過這6維度特徵值以及組合氨基酸疏水性、帶電性、分子體積大小，構成3組16維度特徵值，建立多個委員制模型（committee
model）的支援向量機（SVM）。最後使用10組不同大小的測試資料，且在5重交互驗證測試中也能獲得90％以上的準確度。並且，分析比較的結果也顯示我們所提出的方法，比其他以支援向量機為基礎的方法，有較高的準確度。The recent increase in the use of high-throughput two-hybrid
analysis has generated a large amount of data on protein
interactions. Specifically, the availability of information about
experimental protein-protein interactions and other protein features
on the Internet enables human protein-protein interactions to be
computationally predicted from co-evolution events (interolog).
Computational methods must be developed to integrate these
heterogeneous biological data to facilitate the maximum accuracy of
the human protein interaction prediction.
In knowledge-based study, we proposes a relative conservation score
by identifying maximal quasi-cliques in protein interaction
networks, and addressing of other interaction features to formulate
a scoring method. The scoring method can be adopted to discover
which protein pairs are the most likely to interact in multiple
protein pairs. The predicted human protein-protein interactions
associated with confidence scores are derived from six eukaryotic
organisms - rat, mouse, fly, worm, thale cress and baker's yeast.
The evaluation of our proposed method using functional keyword and
gene ontology annotations indicates that some confidence is
justified in the accuracy of the predicted interactions. Comparisons
among existing methods also reveal that the proposed method predicts
human protein-protein interactions more accurately than other
interolog-based methods.
This study considers protein interaction features, including
interolog, spatial proximity (sub-cellular localization,
tissue-specificity), temporal synchronicity (the cell-cycle stage),
and domain-domain pair combinations. Using these protein
features, and combination of hydrophobic, charge, and volume amino
acid property as sets of -dimension features to construct
committee models of support vector machines (SVMs). The final
-fold cross validation testing for different size test sets
revealed that the accuracy of test set can be obtained above 90\%.
Moreover, the analytical comparisons also suggested our proposed
method have higher accuracy than other SVM-based methods.Contents
1 Introduction 1
1.1 Motivation - Cells need interactions . . . . . . . . . . . . . . . 1
1.2 Support vector machine in bioinformatics . . . . . . . . . . . . 2
1.3 Significance of protein interactions . . . . . . . . . . . . . . . . 2
1.4 Dissertation organization . . . . . . . . . . . . . . . . . . . . . 4
2 Background 5
2.1 Investigate protein-protein interactions . . . . . . . . . . . . . 6
2.2 Evolutionary methods of prediction of protein interactions . . 7
2.2.1 Phylogenetic profiles . . . . . . . . . . . . . . . . . . . 7
2.2.2 Gene fusion and domain fusion . . . . . . . . . . . . . 7
2.2.3 Gene order . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.4 mRNA expression . . . . . . . . . . . . . . . . . . . . . 9
2.2.5 Interologs . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Analysis of protein-protein interactions . . . . . . . . . . . . . 12
2.3.1 Proteins networks . . . . . . . . . . . . . . . . . . . . . 12
2.3.2 Validation of predicted protein interactions . . . . . . . 12
2.3.3 Presentation and visualization . . . . . . . . . . . . . . 13
3 Knowledge-based Method 14
3.1 Evolutionary conservation . . . . . . . . . . . . . . . . . . . . 15
3.1.1 InParanoid score (IP) . . . . . . . . . . . . . . . . . . 15
3.1.2 Quasi-clique and conservation score (C) . . . . . . . . 15
3.1.3 Interolog score (I) . . . . . . . . . . . . . . . . . . . . 17
3.2 Functional constitution . . . . . . . . . . . . . . . . . . . . . . 19
3.2.1 Domain-domain combination score (D) . . . . . . . . . 19
3.3 Spatial proximity . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3.1 Tissue specificity score (T) . . . . . . . . . . . . . . . . 20
3.3.2 Sub-cellular localization score (L) . . . . . . . . . . . . 21
3.4 Temporal synchronicity . . . . . . . . . . . . . . . . . . . . . . 22
3.4.1 Cell-cycle stage score (P) . . . . . . . . . . . . . . . . 22
3.5 Scoring function . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.5.1 Confidence score (CS) . . . . . . . . . . . . . . . . . . 22
3.6 Predicted human protein interactions . . . . . . . . . . . . . . 23
3.7 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.7.1 Testing for true positives . . . . . . . . . . . . . . . . . 25
3.7.2 Testing scoring method . . . . . . . . . . . . . . . . . . 26
3.7.3 Testing functional annotation . . . . . . . . . . . . . . 27
3.7.4 Testing conservation score (C) and interolog score (I) 28
3.8 Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.8.1 Comparison with cut-off scores . . . . . . . . . . . . . 31
3.8.2 Comparison with BLAST data sets . . . . . . . . . . . 32
3.8.3 Comparison with experimental data sets . . . . . . . . 33
3.8.4 Comparison with interolog-based approach . . . . . . . 34
3.8.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 34
3.9 Biological significance . . . . . . . . . . . . . . . . . . . . . . . 36
4 SVM method for classification 38
4.1 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.1.1 Confidence protein-protein interactions . . . . . . . . . 38
4.1.2 Human interologs . . . . . . . . . . . . . . . . . . . . . 39
4.1.3 Training set and test set . . . . . . . . . . . . . . . . . 39
4.2 SVM feature vectors . . . . . . . . . . . . . . . . . . . . . . . 39
4.2.1 Comparisons with HomoloGene and InParanoid . . . . 39
4.2.2 HomoloGene interolog score (H) . . . . . . . . . . . . . 41
4.2.3 Feature vectors and SVM models . . . . . . . . . . . . 42
4.2.4 SVM kernel function . . . . . . . . . . . . . . . . . . . 43
5 Analytic results of SVM method 45
5.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.1.1 Cross-validation and SVM parameters . . . . . . . . . 45
5.1.2 Performance evaluation . . . . . . . . . . . . . . . . . . 46
5.1.3 Prediction analysis . . . . . . . . . . . . . . . . . . . . 46
5.2 Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.2.1 Comparison with other kernel functions . . . . . . . . . 49
5.2.2 Comparison with different test sets . . . . . . . . . . . 50
5.2.3 Comparison with SVM-based methods . . . . . . . . . 50
6 Conclusions 55
6.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
6.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
6.3 Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Bibliography 71
A Supplementary Tables 72
B List of Publications 78
B.1 Journal papers . . . . . . . . . . . . . . . . . . . . . . . . . . 78
B.2 Conference papers and posters . . . . . . . . . . . . . . . . . . 7
Understanding the functionality of transcript diversity
No full textRecent years have seen a huge increase in the amount of genomic DNA
being sequenced from a wide variety of organisms, giving us an unprecedented
insight into the molecular diversity seen in nature. As a
result a host of methods have been developed, both experimental and
computational, to understand the functional significance of such diversity
and how it relates to organismal and environmental complexity.
In this thesis I use comparative approaches to explore two areas of
molecular biology where there is evidence for large amounts of transcript
diversity. Firstly, I explore the unprecedented view of microbial
sequence diversity offered by metagenomic sequencing projects, using
sequence similarity and adapted genomic context methods to quantify
the amount of functional novelty in these samples. Secondly, I look
at the transcript diversity generated by alternative splicing. I develop
methods to detect and visualise alternative splicing events and apply
these to the detection of conserved alternative splicing events
