Multi-class subcellular location prediction for bacterial proteins

Two algorithms, based onBayesian Networks (BNs), for bacterial subcellular location prediction, are explored in this paper: one predicts all locations for Gram+ bacteria and the other all locations for Gram- bacteria. Methods were evaluated using different numbers of residues (from the N-terminal 10 residues to the whole sequence) and residue representation (amino acid-composition, percentage amino acid-composition or normalised amino acid-composition). The accuracy of the best resulting BN was compared to PSORTB. The accuracy of this multi-location BN was roughly comparable to PSORTB; the difference in predictions is low, often less than 2%. The BN method thus represents both an important new avenue of methodological development for subcellular location prediction and a potentially value new tool of true utilitarian value for candidate subunit vaccine selection.

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

Applications of the Owa-Srivastava Operator to the Class of K-Uniformly Convex Functions

2000 Mathematics Subject Classification: Primary 30C45, 26A33; Secondary 33C15

MPGN – An Approach for Discovering Class Association Rules

his article presents some of the results of the Ph.D. thesis Class Association Rule Mining Using MultiDimensional Numbered Information Spaces by Iliya Mitov (Institute of Mathematics and Informatics, BAS), successfully defended at Hasselt University, Faculty of Science on 15 November 2011 in Belgium

On the approximation by convolution operators in homogeneous Banach spaces on R^d

AMS Subject Classification 2010: 41A25, 41A35, 41A40, 41A63, 41A65, 42A38, 42A85, 42B10, 42B20

Generalized D-Symmetric Operators I

2000 Mathematics Subject Classification: Primary: 47B47, 47B10; secondary 47A30.

MHC class II binding prediction by molecular docking

Proteins of the Major Histocompatibility Complex (MHC) bind self and nonself peptide antigens or epitopes within the cell and present them at the cell surface for recognition by T cells. All T-cell epitopes are MHC binders but not all MCH binders are T-cell epitopes. The MHC class II proteins are extremely polymorphic. Polymorphic residues cluster in the peptide-binding region and largely determine the MHC's peptide selectivity. The peptide binding site on MHC class II proteins consist of five binding pockets. Using molecular docking, we have modelled the interactions between peptide and MHC class II proteins from locus DRB1. A combinatorial peptide library was generated by mutation of residues at peptide positions which correspond to binding pockets (so called anchor positions). The binding affinities were assessed using different scoring functions. The normalized scoring functions for each amino acid at each anchor position were used to construct quantitative matrices (QM) for MHC class II binding prediction. Models were validated by external test sets comprising 4540 known binders. Eighty percent of the known binders are identified in the best predicted 15% of all overlapping peptides, originating from one protein. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Explaining the effects of class size on faculty and students

This study explored the effects of class size on faculty and students. Specifically, it examined the relationship of class size and students' participation in class, faculty interactive styles, and academic environment and how these behaviors affected student achievement (percentage of students passing). The sample was composed of 629 students in 30 sections of Algebra I at a large, urban community college. A survey was administered to the students to solicit their perceptions on their participation in class, their faculty interaction style, and the academic environment in their classes. Selected classes were observed to triangulate the findings. The relationship of class size to student participation, faculty interactive styles, and academic environment was determined by using hierarchical linear modeling (HLM). A significant difference was found on the participation of students related to class size. Students in smaller classes participated more and were more engaged than students in larger classes. Regression analysis using the same variables in small and large classes showed that faculty interactive styles significantly predicted student achievement. Stepwise regression analyses of student and faculty background variables showed that (a) students' estimate of GPA was significantly related to their achievement (r = .63); (b) older students reported more participation than did younger ones, (c) students in classes taught by female, Hispanic faculty earned higher passing grades, and (d) students' participation was greater with adjunct professors. Class observations corroborated these findings. The analysis and observational data provided sufficient evidence to warrant the conclusion that small classes were not always most effective in promoting achievement. It was found that small classes may be an artifact of ineffectual teaching, actual or by reputation. While students in small classes participate and are more engaged than students in larger classes, the class-size effect is essentially due to what happens in instruction to promote learning. The interaction of the faculty with students significantly predicted students' achievement regardless of class size. Since college students select their own classes, students do not register for classes taught by faculty with poor teaching reputation, thereby leading to small classes. Further studies are suggested to determine reasons why classes differ in size.

Race, Class and the Promise of the Public University

Commencement address by Professor Thomas Breslin at Florida International University dissects in a few words both the promise of a public university system and the threats to that system embedded in racial and class privilege.

Does a mandibular RDP and new maxillary CD improve masticatory efficiency and quality of life in patients with a mandibular Kennedy class I arch?

Objectives To evaluate the change in masticatory efficiency and quality of life of patients treated with mandibular Kennedy class I removable partial dentures (RPDs) and maxillary complete dentures at the Department of Dentistry of the Federal University of Rio Grande do Norte. Materials and methods A total of 33 Kennedy class I patients were rehabilitated with maxillary complete dentures, and mandibular RPDs were selected for this non-randomized prospective intervention study. The patients had a mean age of 59.1 years. Masticatory efficiency was evaluated by colorimetric assay using fuchsin capsules. The measurements were conducted at baseline and 2 and 6 months after prosthesis insertion. Quality of life was evaluated using the Oral Health Impact Profile (OHIP-14) at baseline and 6 months after denture insertion. The Kolmogorov-Smirnov normality test was applied. Masticatory efficiency was evaluated by repeated measures ANOVA. Oral health-related quality of life was compared using the paired t test. Results There was no statistically significant difference in masticatory efficiency after denture insertion (p = 0.101). Significant differences were found (p = 0.010) for oral health-related quality of life. A significant improvement in psychological discomfort (p < 0.01) and psychological disability (p < 0.01) was observed. Mean difference value (95 % confidence interval) was 6.8 (3.8 to 9.7) points, reflecting a low impact of oral health on quality of life, considering the 0–56 range of variation of the OHIP-14 and a Cohen’s d of 1.13. Conclusion According to the results of the present study, rehabilitation with Kennedy class I RPDs and complete dentures did not influence masticatory efficiency but improved oral health-related quality of life. Clinical relevance The association between the patient’s quality of life and the masticatory efficiency is important for treatment predictability.

Anticancer Properties of a Novel Class of Tetrafluorinated Thalidomide Analogues

Date of Acceptance: 28/07/2015 The authors thank Scott McMenemy for carrying out preliminary, early studies looking at effects of Gu compounds upon chicken embryology, as well as Charles D. Crowe, Jeffrey E. Roth, and Adam C. Rolt for critical comments on the article. fli1:EGFP zebrafish were obtained from the Zebrafish International Research Center (27). mpo:GFP zebrafish [also termed Tg(MPO:GFP)114] zebrafish were obtained from Dr. Stephen Renshaw, University of Sheffield (Sheffield, South Yorkshire, UK; ref. 29).

Anticancer Properties of a Novel Class of Tetrafluorinated Thalidomide Analogues

Date of Acceptance: 28/07/2015 The authors thank Scott McMenemy for carrying out preliminary, early studies looking at effects of Gu compounds upon chicken embryology, as well as Charles D. Crowe, Jeffrey E. Roth, and Adam C. Rolt for critical comments on the article. fli1:EGFP zebrafish were obtained from the Zebrafish International Research Center (27). mpo:GFP zebrafish [also termed Tg(MPO:GFP)114] zebrafish were obtained from Dr. Stephen Renshaw, University of Sheffield (Sheffield, South Yorkshire, UK; ref. 29).

Major Histocompatibility Complex (MHC) Class II sequence polymorphism in long-finned pilot whale (Globicephala melas) from the North Atlantic

Funding Silvia S. Monteiro and Marisa Ferreira were supported by a Ph.D. grant from Fundação para a Ciência e Tecnologia (ref SFRH/BD/38735/2007 and SFRH/BD/30240/2006, respectively). Alfredo López was supported by a postdoctoral grant from Fundação para a Ciência e Tecnologia (ref SFRH/BPD/82407/2011). Catarina Eira is supported by CESAM (UID/AMB/50017), from FCT/MEC through national funds and FEDER (PT2020, Compete 2020). The work related with strandings and tissue collection in Portugal was partially supported by the SafeSea Project EEAGrants PT 0039 (supported by Iceland, Liechtenstein and Norway through the EEA Financial Mechanism), by the Project MarPro–Life09 NAT/PT/000038 (funded by the European Union–Program Life+) and by the project CetSenti FCT RECI/AAG-GLO/0470/2012; FCOMP-01-0124-FEDER-027472 (Funded by the Program COMPETE and Fundação para a Ciência e Tecnologia).

Anticancer Properties of a Novel Class of Tetrafluorinated Thalidomide Analogues

Date of Acceptance: 28/07/2015 The authors thank Scott McMenemy for carrying out preliminary, early studies looking at effects of Gu compounds upon chicken embryology, as well as Charles D. Crowe, Jeffrey E. Roth, and Adam C. Rolt for critical comments on the article. fli1:EGFP zebrafish were obtained from the Zebrafish International Research Center (27). mpo:GFP zebrafish [also termed Tg(MPO:GFP)114] zebrafish were obtained from Dr. Stephen Renshaw, University of Sheffield (Sheffield, South Yorkshire, UK; ref. 29).