PLGA microspheres for the delivery of a novel subunit TB vaccine

Biodegradable poly(dl-lactide-co-glycolide) microspheres were prepared using a modified double emulsion solvent evaporation method for the delivery of the subunit tuberculosis vaccine (Ag85B-ESAT-6), a fusion protein of the immunodominant antigens 6-kDa early secretory antigenic target (ESAT-6) and antigen 85B (Ag85B). Addition of the cationic lipid dimethyl dioctadecylammonium bromide (DDA) and the immunostimulatory trehalose 6,6'-dibehenate (TDB), either separately or in combination, was investigated for the effect on particle size and distribution, antigen entrapment efficiency, in vitro release profiles and in vivo performance. Optimised formulation parameters yielded microspheres within the desired sub-10 mu m range (1.50 +/- 0.13 mu m), whilst exhibiting a high antigen entrapment efficiency (95 +/- 1.2%) and prolonged release profiles. Although the microsphere formulations induced a cell-mediated immune response and raised specific antibodies after immunisation, this was inferior to the levels achieved with liposomes composed of the same adjuvants (DDA-TDB), demonstrating that liposomes are more effective vaccine delivery systems compared with microspheres.

EM optimization of latent-variable density models

There is currently considerable interest in developing general non-linear density models based on latent, or hidden, variables. Such models have the ability to discover the presence of a relatively small number of underlying `causes' which, acting in combination, give rise to the apparent complexity of the observed data set. Unfortunately, to train such models generally requires large computational effort. In this paper we introduce a novel latent variable algorithm which retains the general non-linear capabilities of previous models but which uses a training procedure based on the EM algorithm. We demonstrate the performance of the model on a toy problem and on data from flow diagnostics for a multi-phase oil pipeline.

A hierarchical latent variable model for data visualization

Visualization has proven to be a powerful and widely-applicable tool the analysis and interpretation of data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach first on a toy data set, and then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines and to data in 36 dimensions derived from satellite images.

Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

W Primary Care Trust v TB & Ors [2009] EWHC 1737

The primary issue in this case related to TB’s clear and expressed desire to leave V, in order that she might be admitted to an NHS hospital for treatment of what she believed to be a physical, as opposed to a psychological, condition...

Do changes in transglutaminase activity alter latent transforming growth factor beta activation in experimental diabetic nephropathy?

Background. Diabetic nephropathy is the leading cause of end-stage kidney failure worldwide. It is characterized by excessive extracellular matrix accumulation. Transforming growth factor beta 1 (TGF-ß1) is a fibrogenic cytokine playing a major role in the healing process and scarring by regulating extracellular matrix turnover, cell proliferation and epithelial mesanchymal transdifferentiation. Newly synthesized TGF-ß is released as a latent, biologically inactive complex. The cross-linking of the large latent TGF-ß to the extracellular matrix by transglutaminase 2 (TG2) is one of the key mechanisms of recruitment and activation of this cytokine. TG2 is an enzyme catalyzing an acyl transfer reaction leading to the formation of a stable e(?-glutamyl)-lysine cross-link between peptides.Methods. To investigate if changes in TG activity can modulate TGF-ß1 activation, we used the mink lung cell bioassay to assess TGF-ß activity in the streptozotocin model of diabetic nephropathy treated with TG inhibitor NTU281 and in TG2 overexpressing opossum kidney (OK) proximal tubular epithelial cells.Results. Application of the site-directed TG inhibitor NTU281 caused a 25% reduction in kidney levels of active TGF-ß1. Specific upregulation of TG2 in OK proximal tubular epithelial cells increased latent TGF-ß recruitment and activation by 20.7% and 19.7%, respectively, in co-cultures with latent TGF-ß binding protein producing fibroblasts.Conclusions. Regulation of TG2 directly influences the level of active TGF-ß1, and thus, TG inhibition may exert a renoprotective effect by targeting not only a direct extracellular matrix deposition but also TGF-ß1 activation and recruitment.

73.7 Tb/s (96 x 3 x 256-Gb/s) mode-division-multiplexed DP-16QAM transmission with inline MM-EDFA

Transmission of a 73.7 Tb/s (96x3x256-Gb/s) DP-16QAM mode-division-multiplexed signal over 119km of few-mode fiber transmission line incorporating an inline multi mode EDFA and a phase plate based mode (de-)multiplexer is demonstrated. Data-aided 6x6 MIMO digital signal processing was used to demodulate the signal. The total demonstrated net capacity, taking into account 20% of FEC-overhead and 7.5% additional overhead (Ethernet and training sequences), is 57.6 Tb/s, corresponding to a spectral efficiency of 12 bits/s/Hz.

Impact of Raman amplification on a 2 Tb/s coherent WDM system

The impact of hybrid erbium-doped fiber amplifier (EDFA)/Raman amplification on a spectrally efficient coherent-wavelength-division-multiplexed (CoWDM) optical communication system is experimentally studied and modeled. Simulations suggested that 23-dB Raman gain over an unrepeatered span of 124 km single-mode fiber would allow a decrease of the mean input power of ~6 dB for a fixed bit-error rate (BER). Experimentally we demonstrated 1.2-dB Q-factor improvement for a 2-Tb/s seven-band CoWDM with backward Raman amplification. The system delivered an optical signal-to-noise ratio of 35 dB at the output of the receiver preamplifier providing a worst-case BER of 2 × 10 -6 over 49 subcarriers at 42.8 Gbaud, leaving a system margin (in terms of Q -factor) of ~4 dB from the forward-error correction threshold.

Overcoming network overload and redundancy in inter-organizational networks:the roles of potential and latent ties

This paper builds on Granovetter's distinction between strong and weak ties [Granovetter, M. S. 1973. The strength of weak ties. Amer. J. Sociol. 78(6) 1360–1380] in order to respond to recent calls for a more dynamic and processual understanding of networks. The concepts of potential and latent tie are deductively identified, and their implications for understanding how and why networks emerge, evolve, and change are explored. A longitudinal empirical study conducted with companies operating in the European motorsport industry reveals that firms take strategic actions to search for potential ties and reactivate latent ties in order to solve problems of network redundancy and overload. Examples are given, and their characteristics are examined to provide theoretical elaboration of the relationship between the types of tie and network evolution. These conceptual and empirical insights move understanding of the managerial challenge of building effective networks beyond static structural contingency models of optimal network forms to highlight the processes and capabilities of dynamic relationship building and network development. In so doing, this paper highlights the interrelationship between search and redundancy and the scope for strategic action alongside path dependence and structural influences on network processes.

1-Tb/s DWDM long-haul transmission employing a fiber optical parametric amplifier

In this letter, we report the performance of a fiber optical parametric amplifier (OPA) when used as a source or intermediate node amplifier in a dense wavelength-division-multiplexed (DWDM) long-haul transmission testbed with 26 DWDM channels modulated at 43.7-Gb/s return-to-zero differential phase-shift keying. In both scenarios, we demonstrate similar performance to an erbium-doped fiber amplifier. This shows the OPAs compatibility with high-capacity (>1 Tb/s) long-haul communication systems.

Fiber optical parametric amplifier performance in a 1-Tb/s DWDM communication system

We have reduced signal-signal four-wave mixing crosstalk in a fiber optical parametric amplifier (OPA) by using a short nonlinear fiber for the gain medium and a high-power pump. This allowed us to obtain less than 1 dB penalty for amplification of 26 dense wavelength-division multiplexed (WDM) channels modulated at 43.7Gb/s return to zero-differential phase-shift keying, with the OPA placed between transmitter and receiver. We then used the same OPA in several different roles for a long-haul transmission system. We did not insert the OPA within the loop, but investigated this role indirectly by using equivalent results for small numbers of loop recirculations. We found that standard erbium-doped fiber amplifiers currently hold an advantage over this OPA, which becomes negligible for long distances. This paper shows that at this time OPAs can handle amplification of WDM traffic in excess of 1 Tb/s with little degradation. It also indicates that with further improvements, fiber OPAs could be a contender for wideband amplification in future optical communication networks.

1-Tb/s DWDM amplification in a fiber optical parametric amplifier

We report less than 1-dB cross-talk penalty for 26 DWDM channels modulated at 43.7 Gb/s RZ-DPSK when amplified by a fiber optical parametric amplifier showing compatibility with high-capacity (> 1 Tb/s) communication systems. © 2010 Optical Society of America.

LRD : Latent Relation Discovery for vector space expansion and information retrieval

In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships between them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.

Multi-level visualisation using Gaussian process latent variable models

Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visualise structures and relationships in the dataset. However, a single two-dimensional visualisation may not display all the intrinsic structure. Therefore, hierarchical/multi-level visualisation methods have been used to extract more detailed understanding of the data. Here we propose a multi-level Gaussian process latent variable model (MLGPLVM). MLGPLVM works by segmenting data (with e.g. K-means, Gaussian mixture model or interactive clustering) in the visualisation space and then fitting a visualisation model to each subset. To measure the quality of multi-level visualisation (with respect to parent and child models), metrics such as trustworthiness, continuity, mean relative rank errors, visualisation distance distortion and the negative log-likelihood per point are used. We evaluate the MLGPLVM approach on the ‘Oil Flow’ dataset and a dataset of protein electrostatic potentials for the ‘Major Histocompatibility Complex (MHC) class I’ of humans. In both cases, visual observation and the quantitative quality measures have shown better visualisation at lower levels.

1-Tb/s DWDM amplification in a fiber optical parametric amplifier

We report less than 1-dB cross-talk penalty for 26 DWDM channels modulated at 43.7 Gb/s RZ-DPSK when amplified by a fiber optical parametric amplifier showing compatibility with high-capacity (> 1 Tb/s) communication systems. © 2010 Optical Society of America.