Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments – data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures – offer hope for the future.

When is an image a health claim? A false-recollection method to detect implicit inferences about products' health benefits

Objective: Images on food and dietary supplement packaging might lead people to infer (appropriately or inappropriately) certain health benefits of those products. Research on this issue largely involves direct questions, which could (a) elicit inferences that would not be made unprompted, and (b) fail to capture inferences made implicitly. Using a novel memory-based method, in the present research, we explored whether packaging imagery elicits health inferences without prompting, and the extent to which these inferences are made implicitly. Method: In 3 experiments, participants saw fictional product packages accompanied by written claims. Some packages contained an image that implied a health-related function (e.g., a brain), and some contained no image. Participants studied these packages and claims, and subsequently their memory for seen and unseen claims were tested. Results: When a health image was featured on a package, participants often subsequently recognized health claims that—despite being implied by the image—were not truly presented. In Experiment 2, these recognition errors persisted despite an explicit warning against treating the images as informative. In Experiment 3, these findings were replicated in a large consumer sample from 5 European countries, and with a cued-recall test. Conclusion: These findings confirm that images can act as health claims, by leading people to infer health benefits without prompting. These inferences appear often to be implicit, and could therefore be highly pervasive. The data underscore the importance of regulating imagery on product packaging; memory-based methods represent innovative ways to measure how leading (or misleading) specific images can be. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Overcoming bottlenecks in the membrane protein structural biology pipeline

Membrane proteins account for a third of the eukaryotic proteome, but are greatly under-represented in the Protein Data Bank. Unfortunately, recent technological advances in X-ray crystallography and EM cannot account for the poor solubility and stability of membrane protein samples. A limitation of conventional detergent-based methods is that detergent molecules destabilize membrane proteins, leading to their aggregation. The use of orthologues, mutants and fusion tags has helped improve protein stability, but at the expense of not working with the sequence of interest. Novel detergents such as glucose neopentyl glycol (GNG), maltose neopentyl glycol (MNG) and calixarene-based detergents can improve protein stability without compromising their solubilizing properties. Styrene maleic acid lipid particles (SMALPs) focus on retaining the native lipid bilayer of a membrane protein during purification and biophysical analysis. Overcoming bottlenecks in the membrane protein structural biology pipeline, primarily by maintaining protein stability, will facilitate the elucidation of many more membrane protein structures in the near future.

Representation of people of South Asian origin in cardiovascular outcome trials of glucose-lowering therapies in Type 2 diabetes

Aims : Our aim was to investigate the proportional representation of people of South Asian origin in cardiovascular outcome trials of glucose-lowering drugs or strategies in Type 2 diabetes, noting that these are among the most significant pieces of evidence used to formulate the guidelines on which clinical practice is largely based. Methods : We searched for cardiovascular outcome trials in Type 2 diabetes published before January 2015, and extracted data on the ethnicity of participants. These were compared against expected values for proportional representation of South Asian individuals, based on population data from the USA, from the UK, and globally. Results : Twelve studies met our inclusion criteria and, of these, eight presented a sufficiently detailed breakdown of participant ethnicity to permit numerical analysis. In general, people of South Asian origin were found to be under-represented in trials compared with UK and global expectations and over-represented compared with US expectations. Among the eight trials for which South Asian representation could be reliably estimated, seven under-represented this group relative to the 11.2% of the UK diabetes population estimated to be South Asian, with the representation in these trials ranging from 0.0% to 10.0%. Conclusions : Clinicians should exercise caution when generalizing the results of trials to their own practice, with regard to the ethnicity of individuals. Efforts should be made to improve reporting of ethnicity and improve diversity in trial recruitment, although we acknowledge that there are challenges that must be overcome to make this a reality.

The use of modelling in purchasing/supply management research

The purpose of this research to explore the use of modelling in the field of Purchasing and Supply Management (P/SM). We are particularly interested in identifying the specific areas of P/SM where there are opportunities for the use of modelling based methods. The paper starts with an overview of main types of modelling and also provides a categorisation of the main P/SM research themes. Our research shows that there are many opportunities for using descriptive, predictive and prescriptive modelling approaches in all areas of P/SM research from the ones with a focus on the actual function from a purely operational and execution perspective (e.g. purchasing processes and behaviour) to the ones with a focus on the organisational level from a more strategic perspective (e.g. strategy and policy). We conclude that future P/SM research needs to explore the value of modelling not just at the functional or operational level, but also at the organisation and strategic level respectively. We also acknowledge that while using empirical results to inform and improve models has advantages, there are also drawbacks, which relate to the value, the practical relevance and the generalisability of the modelling based approaches.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.

Writing and characterisation methods for UV-photosensitivity based devices and their applications

The underlying work to this thesis focused on the exploitation and investigation of photosensitivity mechanisms in optical fibres and planar waveguides for the fabrication of advanced integrated optical devices for telecoms and sensing applications. One major scope is the improvement of grating fabrication specifications by introducing new writing techniques and the use of advanced characterisation methods for grating testing. For the first time the polarisation control method for advanced grating fabrication has successfully been converted to apodised planar waveguide fabrication and the development of a holographic method for the inscription of chirped gratings at arbitrary wavelength is presented. The latter resulted in the fabrication of gratings for pulse-width suppression and wavelength selection in diode lasers. In co-operation with research partners a number of samples were tested using optical frequency domain and optical low coherence reflectometry for a better insight into the limitations of grating writing techniques. Using a variety of different fabrication methods, custom apodised and chirped fibre Bragg gratings were written for the use as filter elements for multiplexer-demultiplexer devices, as well as for short pulse generation and wavelength selection in telecommunication transmission systems. Long period grating based devices in standard, speciality and tapered fibres are presented, showing great potential for multi-parameter sensing. One particular scope is the development of vectorial curvature and refractive index sensors with potential for medical, chemical and biological sensing. In addition the design of an optically tunable Mach-Zehnder based multiwavelength filter is introduced. The discovery of a Type IA grating type through overexposure of hydrogen loaded standard and Boron-Germanium co-doped fibres strengthened the assumption of UV-photosensitivity being a highly non-linear process. Gratings of this type show a significantly lower thermal sensitivity compared to standard gratings, which makes them useful for sensing applications. An Oxford Lasers copper-vapour laser operating at 255 nm in pulsed mode was used for their inscription, in contrast to previous work using CW-Argon-Ion lasers and contributing to differences in the processes of the photorefractive index change

Reactive processing methods for functionalisation of polymers and in-situ compatibilisation of poly(ethylene terephthalate)-based blends

One of the main objectives of this study was to functionalise various rubbers (i.e. ethylene propylene copolymer (EP), ethylene propylene diene terpolymer (EPDM), and natural rubber (NR)) using functional monomers, maleic anhydride (MA) and glycidyl methacrylate (GMA), via reactive processing routes. The functionalisation of the rubber was carried out via different reactive processing methods in an internal mixer. GMA was free-radically grafted onto EP and EPDM in the melt state in the absence and presence of a comonomer, trimethylolpropane triacrylate (TRlS). To optinuse the grafting conditions and the compositions, the effects of various paranleters on the grafting yields and the extent of side reactions were investigated. Precipitation method and Soxhlet extraction method was established to purifY the GMA modified rubbers and the grafting degree was determined by FTIR and titration. It was found that without TRlS the grafting degree of GMA increased with increasing peroxide concentration. However, grafting was low and the homopolymerisation of GMA and crosslinking of the polymers were identified as the main side reactions competing with the desired grafting reaction for EP and EPDM, respectively. The use of the tri-functional comonomer, TRlS, was shown to greatly enhance the GMA grafting and reduce the side reactions in terms of the higher GMA grafting degree, less alteration of the rheological properties of the polymer substrates and very little formation of polyGMA. The grafting mechanisms were investigated. MA was grafted onto NR using both thermal initiation and peroxide initiation. The results showed clearly that the reaction of MA with NR could be thermally initiated above 140°C in the absence of peroxide. At a preferable temperature of 200°C, the grafting degree was increased with increasing MA concentration. The grafting reaction could also be initiated with peroxide. It was found that 2,5-dimethyl-2,5-bis(ter-butylproxy) hexane (TIOI) was a suitable peroxide to initiate the reaction efficiently above I50°C. The second objective of the work was to utilize the functionalised rubbers in a second step to achieve an in-situ compatibilisation of blends based on poly(ethylene terephthalate) (PET), in particular, with GMA-grafted-EP and -EPDM and the reactive blending was carried out in an internal mixer. The effects of GMA grafting degree, viscosities of GMAgrafted- EP and -EPDM and the presence of polyGMA in the rubber samples on the compatibilisation of PET blends in terms of morphology, dynamical mechanical properties and tensile properties were investigated. It was found that the GMA modified rubbers were very efficient in compatibilising the PET blends and this was supported by the much finer morphology and the better tensile properties. The evidence obtained from the analysis of the PET blends strongly supports the existence of the copolymers through the interfacial reactions between the grafted epoxy group in the GMA modified rubber and the terminal groups of PET in the blends.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system - for example protein, DNA or RNA - the isoelectric point - commonly referred to as the pI - can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge - and thus the electrophoretic mobility - of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.