Exploratory modelling of grinding pressure within a compressed particle bed

With increasing industry interest in high pressure roll grinding (HPGR) technology, there is a strong incentive for improved understanding of the nature of grinding pressure that exists in the interior of a compressed particle bed. This corresponds to the crushing region of the HPGR. The relationship between applied pressure (stress) to the particle bed and induced pressure (stress) within particles and at contact points between particles is of particular interest. A detailed parametric investigation is beyond the scope of this exploratory paper. However, this exploratory investigation does suggest some interesting behaviour. The compressed particle bed within an 80 turn diameter piston has been modelled using Particle Flow Code for three dimensions. PFC3D is a discrete element code. The total number of simulated particles was 1225 and 2450 for two beds of different thickness. Particle diameters were uniformly distributed between 4 and 4.5 mm. The results of the simulations show that stress intensity within the simulated particle beds and within the observed particles increased with increase of the applied stress. The intensity of the average vertical stress in the selected particles tended to be comparable with the intensity of the pressure applied to the surface of particle bed and was only occasionally higher. However, the stress at contact points between particles could be several times higher. In a real crusher, such high stress amplification at contacts will quickly decrease due to local crushing and a resultant increase the size of the contact area. Therefore, its significance is likely to be relatively small in an industrial context. The modelling results also suggest that failure within the particle bed will progress from the crushing surface towards the depth of the bed. (c) 2006 Elsevier Ltd. All rights reserved.

London-van der Waals adhesiveness of rough particles

Van der Waals forces often dominate interactions and adhesion between fine particles and, in turn, decisively influence the bulk behaviour of powders. However, so far there is no effective means to characterize the adhesive behaviour of such particles. A complication is that most powder particles have rough surfaces, and it is the asperities on the surfaces that touch, confounding the actual surface that is in contact. Conventional approaches using surface energy provide limited information regarding adhesion, and pull-off forces measured through atomic force microscope (AFM) are highly variable and difficult to interpret. In this paper we develop a model which combines the Rumpf-Rabinovich and the JKR-DMT theories to account simultaneously for the effects of surface roughness and deformation on adhesion. This is applied to a 'characteristic asperity' which may be easily obtained from AFM measurements. The concept of adhesiveness, a material property reflecting the influences of elastic deformability, surface roughness, and interfacial surface energy, is introduced as an efficient and quantitative measure of the adhering tendency of a powder. Furthermore, a novel concept of specific adhesiveness is proposed as a convenient tool for characterizing and benchmarking solid materials. This paper provides an example to illustrate the use of the proposed theories. (c) 2005 Elsevier B.V. All rights reserved.

Sulfadoxine resistance in Plasmodium vivax is associated with a specific amino acid in dihydropteroate synthase at the putative sulfadoxine-binding site

Sulfadoxine is predominantly used in combination with pyrimethamine, commonly known as Fansidar, for the treatment of Plasmodium falciparum. This combination is usually less effective against Plasmodium vivax, probably due to the innate refractoriness of parasites to the sulfadoxine component. To investigate this mechanism of resistance by P. vivax to sulfadoxine, we cloned and sequenced the P. vivax dhps (pvdhps) gene. The protein sequence was determined, and three-dimensional homology models of dihydropteroate synthase (DHPS) from P. vivax as well as P. falciparum were created. The docking of sulfadoxine to the two DHPS models allowed us to compare contact residues in the putative sulfadoxine-binding site in both species. The predicted sulfadoxine-binding sites between the species differ by one residue, V585 in P. vivax, equivalent to A613 in P. falciparum. V585 in P. vivax is predicted by energy minimization to cause a reduction in binding of sulfadoxine to DHPS in P. vivax compared to P. falciparum. Sequencing dhps genes from a limited set of geographically different P. vivax isolates revealed that V585 was present in all of the samples, suggesting that V585 may be responsible for innate resistance of P. vivax to sulfadoxine. Additionally, amino acid mutations were observed in some P. vivax isolates in positions known to cause resistance in P. falciparum, suggesting that, as in P. falciparum, these mutations are responsible for acquired increases in resistance of P. vivax to sulfadoxine.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.

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.

The onset and effectiveness of Adult Plant Resistance (APR) in Tallon barley

Field observations of net blotch epidemics indicated that Tallon barley was quite resistant to infection during later stages of growth despite being susceptible as a seedling. A glasshouse experiment was conducted to determine the effectiveness of this resistance and when it became operative. Three cultivars – Gilbert (very susceptible), Patty (resistant) and Tallon – were inoculated at various stages of growth with conidia of Pyrenophora teres f. teres and the infection response and leaf area diseased, recorded 13 days later. The response of Tallon clearly changed from susceptible to moderately susceptible at growth stage 33. Plants sown two weeks earlier were susceptible and plants sown two weeks later were moderately resistant. The response of the other two cultivars at similar growth stages paralleled their seedling responses. The resistance of Tallon appeared to increase with maturity so that, at its most resistant growth stage, the leaf area diseased was just 10% that of the susceptible, Gilbert. While this resistance appears pathotype specific, this experiment demonstrated very effective APR to net blotch. As most losses to this disease occur during the later stages of plant development, APR offers a valuable source of resistance.