Graphene-assisted microfiber for optical-power-based temperature sensor

Combined the large evanescent field of microfiber with the high thermal conductivity of graphene, a sensitive all-fiber temperature sensor based on graphene-assisted micro fiber is proposed and experimentally demonstrated. Microfiber can be easily attached with graphene due to the electrostatic 6 force, resulting in an effective interaction between graphene and the evanescent field of microfiber. The change of the ambient temperature has a great influence on the conductivity of graphene, leading to the variation of the effective refractive index of microfiber. Consequently, the optical power transmission will be changed. The temperature sensitivity of 0.1018 dB/°C in the heating process and 0.1052 dB/°C in the cooling process as well as a high resolution of 0.0098 °C is obtained in the experiment. The scheme may have great potential in sensing fields owing to the advantages of high sensitivity, compact size, and low cost.

A comparative molecular similarity indices (CoMSIA) study of peptide binding to the HLA-A3 superfamily

Epitope identification is the basis of modern vaccine design. The present paper studied the supermotif of the HLA-A3 superfamily, using comparative molecular similarity indices analysis (CoMSIA). Four alleles with high phenotype frequencies were used: A*1101, A*0301, A*3101 and A*6801. Five physicochemical properties—steric bulk, electrostatic potential, local hydro-phobicity, hydrogen-bond donor and acceptor abilities—were considered and ‘all fields’ models were produced for each of the alleles. The models have a moderate level of predictivity and there is a good correlation between the data. A revised HLA-A3 supermotif was defined based on the comparison of favoured and disfavoured properties for each position of the MHC bound peptide. The present study demonstrated that CoMSIA is an effective tool for studying peptide–MHC interactions.

Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

Efficient 1,4-addition of enones and boronic acids catalyzed by a Ni-Zn hydroxyl double salt-intercalated anionic rhodium(III) complex

Intercalation of an in situ prepared [Rh(OH)6]3- complex into an anion exchangeable Ni-Zn layered hydroxy double salt (Rh/NiZn) was demonstrated. The resulting Rh/NiZn effectively catalyzed the 1,4-addition of diverse enones and phenylboronic acids to their corresponding β-substituted carbonyl compounds. In the case of 2-cyclohexen-1-one and phenylboronic acid, a turnover frequency (TOF) of 920 h-1 based on Rh was achieved. The [Rh(OH)6]3- complex maintained its original monomeric trivalent state within the NiZn interlayer following catalysis, attributable to a strong electrostatic interaction between the NiZn host and anionic Rh(III) complex.

Hydrophenylation of internal alkynes with boronic acids catalysed by a Ni–Zn hydroxy double salt-intercalated anionic rhodium(III) complex

[Rh(OH)6]3− intercalated Ni–Zn mixed basic salt (Rh/NiZn) acts as an efficient catalyst for the hydrophenylation of internal alkynes with arylboronic acids under mild conditions. The turnover number per Rh site approached 740 in the reaction between 4-octyne and phenylboronic acid. The catalytic monomeric Rh(III) complex is stabilised within the NiZn interlayers, attributable to a strong electrostatic interaction, promoting its re-use.

CO-stabilisation mechanisms of nanoparticles and surfactants in Pickering emulsions produced by membrane emulsification

Two different membrane emulsification methods were used to study mechanisms for co-stabilisation of emulsions, by either electrostatic or steric stabilised nanoparticles with anionic, cationic or non-ionic surfactants. The experimental results demonstrated the existence of two distinct co-stabilisation mechanisms that arise from interactions of the nanoparticles and surfactant molecules. When significant interaction is not involved, independent competitive adsorption of nanoparticles and surfactant molecules occurs spontaneously to stabilise droplets in formation. The adsorption/desorption equilibrium between surfactant molecules determines the longevity of the droplet stability. When the surfactant molecule reacts with the nanoparticle surface, the resultant surface modification appears to generate faster wetting kinetics for nanoparticles at the oil/water interface and yields enhanced stabilisation. The paper discusses the implications of controlling these interactions for emulsion production membrane systems.

Effect of spatial inhomogeneity of charge carrier mobility on current-voltage characteristics in organic field-effect transistors

A semi-quantitative model is put forward elucidating the role of spatial inhomogeneity of charge carrier mobility in organic field-effect transistors. The model, based on electrostatic arguments, allows estimating the effective thickness of the conducting channel and its changes in function of source-drain and gate voltages. Local mobility gradients in the direction perpendicular to the insulator/semiconductor interface translate into voltage dependences of the average carrier mobility in the channel, resulting in positive or negative deviations of current-voltage characteristics from their expected shapes. The proposed effect supplements those described in the literature, i.e., density-dependent mobility of charge carriers, short-channel effects, and contribution of contact resistance.

Receptor activity-modifying proteins 2 and 3 generate adrenomedullin receptor subtypes with distinct molecular properties

Adrenomedullin (AM) is a peptide hormone with numerous effects in the vascular systems. AM signals through the AM1 and AM2 receptors formed by the obligate heterodimerization of a G protein-coupled receptor, the calcitonin receptor-like receptor (CLR), and receptor activity-modifying proteins (RAMP) 2 and 3, respectively. These different CLR-RAMP interactions yield discrete receptor pharmacology and physiological effects. The effective design of therapeutics that target the individual AM receptors is dependent on understanding the molecular details of the effects of RAMPs on CLR. To understand the role of RAMPs 2 and 3 on the activation and conformation of the CLR subunit of AM receptors we mutated 68 individual amino acids in the juxtamembrane region of CLR, a key region for activation of AM receptors and determined the effects on cAMP signalling. Sixteen CLR mutations had differential effects between the AM1 and AM2 receptors. Accompanying this, independent molecular modelling of the full-length AM-bound AM1 and AM2 receptors predicted differences in the binding pocket, and differences in the electrostatic potential of the two AM receptors. Druggability analysis indicated unique features that could be used to develop selective small molecule ligands for each receptor. The interaction of RAMP2 or RAMP3 with CLR induces conformational variation in the juxtamembrane region, yielding distinct binding pockets, probably via an allosteric mechanism. These subtype-specific differences have implications for the design of therapeutics aimed at specific AM receptors and for understanding the mechanisms by which accessory proteins affect G protein-coupled receptor function.