Public papers of Roswell P. Flower, Governor, 1892-[1894]

Mode of access: Internet.

Public papers of David B. Hill : governor /

[v.1] 1885 -- [v.2] 1886 -- [v.3] 1887 -- [v.4] 1888 -- [v.5] 1889 -- [v.6] 1890 -- [v.7] 1891.

Stochastic models for mainland-island metapopulations in static and dynamic landscapes

This paper has three primary aims: to establish an effective means for modelling mainland-island metapopulations inhabiting a dynamic landscape: to investigate the effect of immigration and dynamic changes in habitat on metapopulation patch occupancy dynamics; and to illustrate the implications of our results for decision-making and population management. We first extend the mainland-island metapopulation model of Alonso and McKane [Bull. Math. Biol. 64:913-958,2002] to incorporate a dynamic landscape. It is shown, for both the static and the dynamic landscape models, that a suitably scaled version of the process converges to a unique deterministic model as the size of the system becomes large. We also establish that. under quite general conditions, the density of occupied patches, and the densities of suitable and occupied patches, for the respective models, have approximate normal distributions. Our results not only provide us with estimates for the means and variances that are valid at all stages in the evolution of the population, but also provide a tool for fitting the models to real metapopulations. We discuss the effect of immigration and habitat dynamics on metapopulations, showing that mainland-like patches heavily influence metapopulation persistence, and we argue for adopting measures to increase connectivity between this large patch and the other island-like patches. We illustrate our results with specific reference to examples of populations of butterfly and the grasshopper Bryodema tuberculata.

Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102:a bioinformatic approach to the prediction of new epitopes

Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Empirical prediction of peptide octanol-water partition coefficients

Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient P (commonly expressed in logarithm form: logP), is useful for screening out unsuitable molecules and also for the development of predictive Quantitative Structure-Activity Relationships (QSARs). In this paper we develop a new approach to the prediction of LogP values for peptides based on an empirical relationship between global molecular properties and measured physical properties. Our method was successful in terms of peptide prediction (total r2 = 0.641). The final model consisted of 5 physicochemical descriptors (molecular weight, number of single bonds, 2D-VDW volume, 2D-VSA hydrophobic and 2D-VSA polar). The approach is peptide specific and its predictive accuracy was high. Overall, 67% of the peptides were able to be predicted within +/-0.5 log units from the experimental values. Our method thus represents a novel prediction method with proven predictive ability.

On the hydrophobicity of peptides:comparing empirical predictions of peptide log P values

Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient logP, is useful for the development of predictive Quantitative Structure-Activity Relationships (QSARs). We have investigated the accuracy of available programs for the prediction of logP values for peptides with known experimental values obtained from the literature. Eight prediction programs were tested, of which seven programs were fragment-based methods: XLogP, LogKow, PLogP, ACDLogP, AlogP, Interactive Analysis's LogP and MlogP; and one program used a whole molecule approach: QikProp. The predictive accuracy of the programs was assessed using r(2) values, with ALogP being the most effective (r( 2) = 0.822) and MLogP the least (r(2) = 0.090). We also examined three distinct types of peptide structure: blocked, unblocked, and cyclic. For each study (all peptides, blocked, unblocked and cyclic peptides) the performance of programs rated from best to worse is as follows: all peptides - ALogP, QikProp, PLogP, XLogP, IALogP, LogKow, ACDLogP, and MlogP; blocked peptides - PLogP, XLogP, ACDLogP, IALogP, LogKow, QikProp, ALogP, and MLogP; unblocked peptides - QikProp, IALogP, ALogP, ACDLogP, MLogP, XLogP, LogKow and PLogP; cyclic peptides - LogKow, ALogP, XLogP, MLogP, QikProp, ACDLogP, IALogP. In summary, all programs gave better predictions for blocked peptides, while, in general, logP values for cyclic peptides were under-predicted and those of unblocked peptides were over-predicted.

Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Background - MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results - A large dataset comprising MHC-peptide structural complexes was created by re-modelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion - The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).

Quantitative online prediction of peptide binding to the major histocompatibility complex

With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501.

Africa's World Cup:critical reflections on play, patriotism, spectatorship, and space

Africas World Cup: Critical Reflections on Play, Patriotism, Spectatorship, and Space focuses on a remarkable month in the modern history of Africa and in the global history of football. Peter Alegi and Chris Bolsmann are well-known experts on South African football, and they have assembled an impressive team of local and international journalists, academics, and football experts to reflect on the 2010 World Cup and its broader significance, its meanings, complexities, and contradictions. The World Cups sounds, sights, and aesthetics are explored, along with questions of patriotism, nationalism, and spectatorship in Africa and around the world. Experts on urban design and communities write on how the presence of the World Cup worked to refashion urban spaces and negotiate the local struggles in the hosting cities. The volume is richly illustrated by authors photographs, and the essays in this volume feature chronicles of match day experiences; travelogues; ethnographies of fan cultures; analyses of print, broadcast, and electronic media coverage of the tournament; reflections on the World Cups private and public spaces; football exhibits in South African museums; and critiques of the World Cups processes of inclusion and exclusion, as well as its political and economic legacies. The volume concludes with a forum on the World Cup, including Thabo Dladla, Director of Soccer at the University of KwaZulu-Natal, Mohlomi Kekeletso Maubane, a well-known Soweto-based writer and a soccer researcher, and Rodney Reiners, former professional footballer and current chief soccer writer for the Cape Argus newspaper in Cape Town. This collection will appeal to students, scholars, journalists, and fans. Cover illustration: South African fan blowing his vuvuzela at South Africa vs. France, Free State Stadium, Bloemfontein, June 22, 2010. Photo by Chris Bolsmann.

Primary data sets of the hydrographic atlas of the Southern Ocean

The general knowledge of the hydrographic structure of the Southern Ocean is still rather incomplete since observations particularly in the ice covered regions are cumbersome to be carried out. But we know from the available information that thermohaline processes have large amplitudes and cover a wide range of scales in this part of the world ocean. The modification of water masses around Antarctica have indeed a worldwide impact, these processes ultimately determine the cold state of the present climate in the world ocean. We have converted efforts of the German and Russian polar research institutions to collect and validate the presently available temperature, salinity and oxygen data of the ocean south of 30°S latitude. We have carried out this work in spite of the fact that the hydrographic programme of the World Ocean Circulation Experiment (WOCE) will provide more new information in due time, but its contribution to the high latitudes of the Southern Ocean is quite sparse. The modified picture of the hydrographic structure of the Southern Ocean presented in this atlas may serve the oceanographic community in many ways and help to unravel the role of this ocean in the global climate system. This atlas could only be prepared with the altruistic assistance of many colleagues from various institutions worldwide who have provided us with their data and their advice. Their generous help is gratefully acknowledged. During two years scientists from the Arctic and Antarctic Research Institute in St. Petersburg and the Alfred Wegener Institute for Polar and Marine Research in Bremerhaven have cooperated in a fruitful way to establish the atlas and the archive of about 38749 validated hydrographic stations. We hope that both sources of information will be widely applied for future ocean studies and will serve as a reference state for global change considerations.

Seawater carbonate chemistry and cell numbers during experiments with dinoflagellates, 2007

The effects of dissolved inorganic carbon (DIC) on the growth of 3 red-tide dinoflagellates (Ceratium lineatum, Heterocapsa triquetra and Prorocentrum minimum) were studied at pH 8.0 and at higher pH levels, depending upon the pH tolerance of the individual species. The higher pH levels chosen for experiments were 8.55 for C. lineatum and 9.2 for the other 2 species. At pH 8.0, which approximates the pH found in the open sea, the maximum growth in all species was maintained until the total DIC concentration was reduced below ~0.4 and 0.2 mM for C. lineatum and the other 2 species, respectively. Growth compensation points (concentration of inorganic carbon needed for maintenance of cells) were reached at ~0.18 and 0.05 mM DIC for C. lineatum and the other 2 species, respectively. At higher pH levels, maximum growth rates were lower compared to growth at pH 8, even at very high DIC concentrations, indicating a direct pH effect on growth. Moreover, the concentration of bio-available inorganic carbon (CO2 + HCO3-) required for maintenance as well as the half-saturation constants were increased considerably at high pH compared to pH 8.0. Experiments with pH-drift were carried out at initial concentrations of 2.4 and 1.2 mM DIC to test whether pH or DIC was the main limiting factor at a natural range of DIC. Independent of the initial DIC concentrations, growth rates were similar in both incubations until pH had increased considerably. The results of this study demonstrated that growth of the 3 species was mainly limited by pH, while inorganic carbon limitation played a minor role only at very high pH levels and low initial DIC concentrations.