PREDBALB/c: a system for the prediction of peptide binding to H2(d) molecules, a haplotype of the BALB/c mouse

PREDBALB/c is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2(d)) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2(d) class I ( H2-K-d, H2-L-d and H2-D-d) and class II (I-E-d and I-A(d)) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PREDBALB/c is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2(d) haplotype). PREDBALB/c is available at http://antigen.i2r.a-star.edu.sg/predBalbc/.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.

The Learning Tourism Destination: The potential of a learning organisation approach for improving the sustainability of tourism destinations

Globalisation, increasing complexity, and the need to address triple-bottom line sustainability has seen the proliferation of Learning Organisations (LO) who, by definition, have the capacity to anticipate environmental changes and economic opportunities and adapt accordingly. Such organisations use system dynamics modelling (SDM) for both strategic planning and the promotion of organisational learning. Although SDM has been applied in the context of tourism destination management for predictive reasons, the current literature does not analyse or recognise how this could be used as a foundation for an LO. This study introduces the concept of the Learning Tourism Destinations (LTD) and discusses, on the basis of a review of 6 case studies, the potential of SDM as a tool for the implementation and enhancement of collective learning processes. The results reveal that SDM is capable of promoting communication between stakeholders and stimulating organisational learning. It is suggested that the LTD approach be further utilised and explored.

Vascular endothelial growth factor-B-deficient mice show impaired development of hypoxic pulmonary hypertension

To test the hypothesis that Vegf-B contributes to the pulmonary vascular remodelling, and the associated pulmonary hypertension, induced by exposure of mice to chronic hypoxia. Methods: Right ventricular systolic pressure, the ratio of right ventricle/[left ventricle+septum] (RV/[LV+S]) and the thickness of the media (relative to vessel diameter) of intralobar pulmonary arteries (o.d. 50-150 and 151-420 mum) were determined in Vegfb knockout mice (Vegfb(-/-); n=17) and corresponding wild-type mice (Vegfb(+/+); n=17) exposed to chronic hypoxia (10% oxygen) or housed in room air (normoxia) for 4 weeks. Results: In Vegfb(+/+) mice hypoxia caused (i) pulmonary hypertension (a 70% increase in right ventricular systolic pressure compared with normoxic Vegfb(+/+) mice; P

The role of the Eph-ephrin signalling system in the regulation of developmental patterning

The Eph and ephrin system, consisting of fourteen Eph receptor tyrosine kinase proteins and nine ephrin membrane proteins in vertebrates, has been implicated in the regulation of many critical events during development. Binding of cell surface Eph and ephrin proteins results in bi-directional signals, which regulate the cytoskeletal, adhesive and motile properties of the interacting cells. Through these signals Eph and ephrin proteins are involved in early embryonic cell movements, which establish the germ layers, cell movements involved in formation of tissue boundaries and the pathfinding of axons. This review focuses on two vertebrate models, the zebrafish and mouse, in which experimental perturbation of Eph and/or ephrin expression in vivo have provided important insights into the role and functioning of the Eph/ephrin system.

Structure of circulin B and implications for antimicrobial activity of the cyclotides

The solution structure of one of the first members of the cyclotide family of macrocyclic peptides to be discovered, circulin B has been determined and compared with that of circulin A and related cyclotides. Cyclotides are mini-proteins derived from plants that have the characteristic features of a head-to-tail cyclised peptide backbone and a knotted arrangement of their three disulfide bonds. First discovered because of their uterotonic or anti-HIV activity, they have also been reported to have activity against a range of Gram positive and Gram negative bacteria as well as fungi. The aim of the current study was to develop structure-activity relationships to rationalise this antimicrobial activity. Comparison of cyclotide structures and activities suggests that the presence and location of cationic residues may be a requirement for activity against Gram negative bacteria. Understanding the topological differences associated with the antimicrobial activity of the cyclotides is of significant interest and potentially may be harnessed for pharmaceutical applications.

NF-gamma B - a target in the inflammation of bone destruction

NF-kappaB activation is associatied with the inflammation of bone destruction and certain cancers. The NEMO (NF-kB essential modulator)-binding domain (NBD) protein inhibits the activation of NF-kappaB. Cellular studies have shown that the NBD protein inhibits osteoclastogenesis. Mimicking infection with a lipopolysaccharide injection in mice resulted in activated osteoclasts and reduced bone mineral density. These responses are inhibited with the NBD peptide. In a mouse model of rheumatoid arthritis, collagen-induced arthritis, treatment with the NBD protein delayed the onset, lowered the incidence and decreased the severity of the arthritis. NF-kappaB is a target in the inflammation associated with bone destruction. A key issue is whether or not this important transcription factor can be inhibited without causing excessive adverse effects and/or toxicity.

Prediction of protein B-factor profiles

The polypeptide backbones and side chains of proteins are constantly moving due to thermal motion and the kinetic energy of the atoms. The B-factors of protein crystal structures reflect the fluctuation of atoms about their average positions and provide important information about protein dynamics. Computational approaches to predict thermal motion are useful for analyzing the dynamic properties of proteins with unknown structures. In this article, we utilize a novel support vector regression (SVR) approach to predict the B-factor distribution (B-factor profile) of a protein from its sequence. We explore schemes for encoding sequences and various settings for the parameters used in SVR. Based on a large dataset of high-resolution proteins, our method predicts the B-factor distribution with a Pearson correlation coefficient (CC) of 0.53. In addition, our method predicts the B-factor profile with a CC of at least 0.56 for more than half of the proteins. Our method also performs well for classifying residues (rigid vs. flexible). For almost all predicted B-factor thresholds, prediction accuracies (percent of correctly predicted residues) are greater than 70%. These results exceed the best results of other sequence-based prediction methods. (C) 2005 Wiley-Liss, Inc.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD