Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Novel corrosion sensor based on carbon nanotube composites for structural health monitoring

Bridges are important infrastructures of all nations and are required for transportation of goods as well as human. A catastrophic failure can result in loss of lives and enormous financial hardship to the nation. Hence, there is an urgent need to monitor our infrastructures to prolong their life span, at the same time catering for heavier and faster moving traffics. Although various kinds of sensors are now available to monitor the health of the structures due to corrosion, they do not provide permanent and long term measurements. This paper investigates the fabrication of Carbon Nanotube (CNT) based composite sensors for structural health monitoring. The CNTs, a key material in nanotechnology has aroused great interest in the research community due to their remarkable mechanical, electrochemical, piezoresistive and other physical properties. Multi-wall CNT (MWCNT)/Nafion composite sensors were fabricated to evaluate their electrical properties when subjected to chemical solutions, to simulate a chemical reaction due to corrosion and real life corrosion experimental tests. The electrical resistance of the sensor electrode was dramatically changed due to corrosion. The novel sensor is expected to effectively detect corrosion in structures based on the measurement of electrical impedances of the CNT composite.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

An analytical solution for diffusion and nonlinear uptake of oxygen in a spherical cell

We develop a new analytical solution for a reactive transport model that describes the steady-state distribution of oxygen subject to diffusive transport and nonlinear uptake in a sphere. This model was originally reported by Lin (Journal of Theoretical Biology, 1976 v60, pp449–457) to represent the distribution of oxygen inside a cell and has since been studied extensively by both the numerical analysis and formal analysis communities. Here we extend these previous studies by deriving an analytical solution to a generalized reaction-diffusion equation that encompasses Lin’s model as a particular case. We evaluate the solution for the parameter combinations presented by Lin and show that the new solutions are identical to a grid-independent numerical approximation.

Structural Systematics of the Anhydrous 1:1 Proton-Transfer Compounds of 3,5-Dinitrosalicylic Acid with Aniline and Monosubstituted Anilines

The crystal structures of the proton-transfer compounds of 3,5-dinitrosalicylic acid (DNSA) with a series of aniline-type Lewis bases [aniline, 2-hydroxyaniline, 2-methoxyaniline, 3-methoxyaniline, 4-fluoroaniline, 4-chloroaniline and 2-aminoaniline] have been determined and their hydrogen-bonding systems analysed. All are anhydrous 1:1 salts: [(C6H8N)+(C7H3N2O7)-], (1), [(C6H8NO)+(C7H3N2O7)-], (2), [(C7H10NO)+(C7H3N2O7)-], (3), [(C7H10NO)+(C7H3N2O7)-], (4), [(C6H7FN)+(C7H3N2O7)-], (5), [(C6H7ClN)+(C7H3N2O7)-], (6), and [(C6H9N2)+(C7H3N2O7)-], (7) respectively. Crystals of 1 and 6 are triclinic, space group P-1 while the remainder are monoclinic with space group either P21/n (2, 4, 5 and 7) or P21 (3). Unit cell dimensions and contents are: for 1, a = 7.2027(17), b = 7.5699(17), c = 12.9615(16) Å, α = 84.464(14), β = 86.387(15), γ = 75.580(14)o, Z = 2; for 2, a = 7.407(3), b = 6.987(3), c = 27.653(11) Å, β = 94.906(7)o, Z = 4; for 3, a = 8.2816(18), b = 23.151(6), c = 3.9338(10), β = 95.255(19)o, Z = 2; for 4, a = 11.209(2), b = 8.7858(19), c = 15.171(3) Å, β = 93.717(4)o, Z = 4; for 5, a = 26.377(3), b = 10.1602(12), c = 5.1384(10) Å, β = 91.996(13)o, Z = 4; for 6, a = 11.217(3), b = 14.156(5), c = 4.860(3) Å, α = 99.10(4), β = 96.99(4), γ = 76.35(2)o, Z = 2; for 7, a = 12.830(4), b = 8.145(3), c = 14.302(4) Å, β = 102.631(6)o, Z = 4. In all compounds at least one primary linear intermolecular N+-H…O(carboxyl) hydrogen-bonding interaction is present which, together with secondary hydrogen bonding results in the formation of mostly two-dimensional network structures, exceptions being with compounds 4 and 5 (one-dimensional) and compound 6 (three-dimensional). In only two cases [compounds 1 and 4], are weak cation-anion or cation-cation π-π interactions found while weak aromatic C-H…O interactions are insignificant. The study shows that all compounds fit the previously formulated classification scheme for primary and secondary interactive modes for proton-transfer compounds of 3,5-dinitrosalicylic acid but there are some unusual variants.

Structural and thermal performance of cold-formed steel stud wall systems under fire conditions

Cold-formed steel stud walls are a major component of Light Steel Framing (LSF) building systems used in commercial, industrial and residential buildings. In the conventional LSF stud wall systems, thin steel studs are protected from fire by placing one or two layers of plasterboard on both sides with or without cavity insulation. However, there is very limited data about the structural and thermal performance of stud wall systems while past research showed contradicting results, for example, about the benefits of cavity insulation. This research was therefore conducted to improve the knowledge and understanding of the structural and thermal performance of cold-formed steel stud wall systems (both load bearing and non-load bearing) under fire conditions and to develop new improved stud wall systems including reliable and simple methods to predict their fire resistance rating. Full scale fire tests of cold-formed steel stud wall systems formed the basis of this research. This research proposed an innovative LSF stud wall system in which a composite panel made of two plasterboards with insulation between them was used to improve the fire rating. Hence fire tests included both conventional steel stud walls with and without the use of cavity insulation and the new composite panel system. A propane fired gas furnace was specially designed and constructed first. The furnace was designed to deliver heat in accordance with the standard time temperature curve as proposed by AS 1530.4 (SA, 2005). A compression loading frame capable of loading the individual studs of a full scale steel stud wall system was also designed and built for the load-bearing tests. Fire tests included comprehensive time-temperature measurements across the thickness and along the length of all the specimens using K type thermocouples. They also included the measurements of load-deformation characteristics of stud walls until failure. The first phase of fire tests included 15 small scale fire tests of gypsum plasterboards, and composite panels using different types of insulating material of varying thickness and density. Fire performance of single and multiple layers of gypsum plasterboards was assessed including the effect of interfaces between adjacent plasterboards on the thermal performance. Effects of insulations such as glass fibre, rock fibre and cellulose fibre were also determined while the tests provided important data relating to the temperature at which the fall off of external plasterboards occurred. In the second phase, nine small scale non-load bearing wall specimens were tested to investigate the thermal performance of conventional and innovative steel stud wall systems. Effects of single and multiple layers of plasterboards with and without vertical joints were investigated. The new composite panels were seen to offer greater thermal protection to the studs in comparison to the conventional panels. In the third phase of fire tests, nine full scale load bearing wall specimens were tested to study the thermal and structural performance of the load bearing wall assemblies. A full scale test was also conducted at ambient temperature. These tests showed that the use of cavity insulation led to inferior fire performance of walls, and provided good explanations and supporting research data to overcome the incorrect industry assumptions about cavity insulation. They demonstrated that the use of insulation externally in a composite panel enhanced the thermal and structural performance of stud walls and increased their fire resistance rating significantly. Hence this research recommends the use of the new composite panel system for cold-formed LSF walls. This research also included steady state tensile tests at ambient and elevated temperatures to address the lack of reliable mechanical properties for high grade cold-formed steels at elevated temperatures. Suitable predictive equations were developed for calculating the yield strength and elastic modulus at elevated temperatures. In summary, this research has developed comprehensive experimental thermal and structural performance data for both the conventional and the proposed non-load bearing and load bearing stud wall systems under fire conditions. Idealized hot flange temperature profiles have been developed for non-insulated, cavity insulated and externally insulated load bearing wall models along with suitable equations for predicting their failure times. A graphical method has also been proposed to predict the failure times (fire rating) of non-load bearing and load bearing walls under different load ratios. The results from this research are useful to both fire researchers and engineers working in this field. Most importantly, this research has significantly improved the knowledge and understanding of cold-formed LSF walls under fire conditions, and developed an innovative LSF wall system with increased fire rating. It has clearly demonstrated the detrimental effects of using cavity insulation, and has paved the way for Australian building industries to develop new wall panels with increased fire rating for commercial applications worldwide.