Novel visualization methods for protein data

Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very high-dimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

First demonstration of 2μm data transmission in a low-loss hollow core photonic Bandgap fiber

The first demonstration of a hollow core photonic bandgap fiber suitable for high-rate data transmission at 2µm is presented. Using a custom built Thulium doped fiber amplifier, error-free 8Gbit/s transmission in an optically amplified data channel at 2008nm is reported for the first time.

Demonstration of amplified data transmission at 2 µm in a low-loss wide bandwidth hollow core photonic bandgap fiber

The first demonstration of a hollow core photonic bandgap fiber (HC-PBGF) suitable for high-rate data transmission in the 2 μm waveband is presented. The fiber has a record low loss for this wavelength region (4.5 dB/km at 1980 nm) and a >150 nm wide surface-mode-free transmission window at the center of the bandgap. Detailed analysis of the optical modes and their propagation along the fiber, carried out using a time-of-flight technique in conjunction with spatially and spectrally resolved (S) imaging, provides clear evidence that the HC-PBGF can be operated as quasi-single mode even though it supports up to four mode groups. Through the use of a custom built Thulium doped fiber amplifier with gain bandwidth closely matched to the fiber's low loss window, error-free 8 Gbit/s transmission in an optically amplified data channel at 2008 nm over 290 m of 19 cell HC-PBGF is reported. © 2013 Optical Society of America.

The role of apoptotic cell clearance in a high-lipid environment:links to ageing

Individuals within the aged population show an increased susceptibility to infection, implying a decline in immune function, a phenomenon known as immunosenescence. Paradoxically, an increase in autoimmune disease, such as rheumatoid arthritis, is also associated with ageing, therefore some aspects of the immune system appear to be inappropriately active in the elderly. The above evidence suggests inappropriate control of the immune system as we age. Macrophages, and their precursors monocytes, play a key role in control of the immune system. They play an important role in host defence in the form of phagocytosis, and also link the innate and adaptive immune system via antigen presentation. Macrophages also have a reparative role, as professional phagocytes of dead and dying cells. Clearance of apoptotic cells by macrophages has also been shown to directly influence immune responses in an anti-inflammatory manner. Inappropriate control of macrophage function with regards to dead cell clearance may contribute to pathology as we age. The aims of this study were to assess the impact of lipid treatment, as a model of the aged environment, on the ability of macrophages to interact with, and respond to, apoptotic cells. Using a series of in vitro cell models, responses of macrophages (normal and lipid-loaded) to apoptotic macrophages (normal and lipid-loaded) were investigated. Monocyte recruitment to apoptotic cells, a key process in resolving inflammation, was assessed in addition to cytokine responses. Data here shows, for the first time, that apoptotic macrophages (normal and lipid-loaded) induce inflammation in human monocyte-derived macrophages, a response that could drive inflammation in age-associated pathology e.g. atherosclerosis. Monoclonal antibody inhibition studies suggest the classical chemokine CX3CL1 may be involved in monocyte recruitment to apoptotic macrophages, but not apoptotic foam cells, therefore differential clearance strategies may be employed following lipid-loading. CD14, an important apoptotic cell tethering receptor, was not found to have a prominent role in this process, whilst the role for ICAM-3 remains unclear. Additionally, a small pilot study using macrophages from young (<25) and mid-life (>40) donors was undertaken. Preliminary data was gathered to assess the ability of primary human monocyte-derived macrophages, from young and mid-life donors, to interact with, and respond to, apoptotic cells. MØ from mid-life individuals showed no significant differences in their ability to respond to immune modulation by apoptotic cells compared to MØ from young donors. Larger cohorts would be required to investigate whether immune modulation of MØ by apoptotic cells contribute to inflammatory pathology throughout ageing.

Visualizing uncertainty in multi-spectral remotely sensed imagery

Error and uncertainty in remotely sensed data come from several sources, and can be increased or mitigated by the processing to which that data is subjected (e.g. resampling, atmospheric correction). Historically the effects of such uncertainty have only been considered overall and evaluated in a confusion matrix which becomes high-level meta-data, and so is commonly ignored. However, some of the sources of uncertainty can be explicity identified and modelled, and their effects (which often vary across space and time) visualized. Others can be considered overall, but their spatial effects can still be visualized. This process of visualization is of particular value for users who need to assess the importance of data uncertainty for their own practical applications. This paper describes a Java-based toolkit, which uses interactive and linked views to enable visualization of data uncertainty by a variety of means. This allows users to consider error and uncertainty as integral elements of image data, to be viewed and explored, rather than as labels or indices attached to the data. © 2002 Elsevier Science Ltd. All rights reserved.

Understanding data collection behaviour of mental health practitioners

Failure to detect patients at risk of attempting suicide can result in tragic consequences. Identifying risks earlier and more accurately helps prevent serious incidents occurring and is the objective of the GRiST clinical decision support system (CDSS). One of the problems it faces is high variability in the type and quantity of data submitted for patients, who are assessed in multiple contexts along the care pathway. Although GRiST identifies up to 138 patient cues to collect, only about half of them are relevant for any one patient and their roles may not be for risk evaluation but more for risk management. This paper explores the data collection behaviour of clinicians using GRiST to see whether it can elucidate which variables are important for risk evaluations and when. The GRiST CDSS is based on a cognitive model of human expertise manifested by a sophisticated hierarchical knowledge structure or tree. This structure is used by the GRiST interface to provide top-down controlled access to the patient data. Our research explores relationships between the answers given to these higher-level 'branch' questions to see whether they can help direct assessors to the most important data, depending on the patient profile and assessment context. The outcome is a model for dynamic data collection driven by the knowledge hierarchy. It has potential for improving other clinical decision support systems operating in domains with high dimensional data that are only partially collected and in a variety of combinations.

Improved data visualisation through multiple dissimilarity modelling

Popular dimension reduction and visualisation algorithms rely on the assumption that input dissimilarities are typically Euclidean, for instance Metric Multidimensional Scaling, t-distributed Stochastic Neighbour Embedding and the Gaussian Process Latent Variable Model. It is well known that this assumption does not hold for most datasets and often high-dimensional data sits upon a manifold of unknown global geometry. We present a method for improving the manifold charting process, coupled with Elastic MDS, such that we no longer assume that the manifold is Euclidean, or of any particular structure. We draw on the benefits of different dissimilarity measures allowing for the relative responsibilities, under a linear combination, to drive the visualisation process.

High spatial and temporal resolution interrogation of fully distributed chirped fiber Bragg grating sensors

A novel interrogation technique for fully distributed linearly chirped fiber Bragg grating (LCFBG) strain sensors with simultaneous high temporal and spatial resolution based on optical time-stretch frequency-domain reflectometry (OTS-FDR) is proposed and experimentally demonstrated. LCFBGs is a promising candidate for fully distributed sensors thanks to its longer grating length and broader reflection bandwidth compared to normal uniform FBGs. In the proposed system, two identical LCFBGs are employed in a Michelson interferometer setup with one grating serving as the reference grating whereas the other serving as the sensing element. Broadband spectral interferogram is formed and the strain information is encoded into the wavelength-dependent free spectral range (FSR). Ultrafast interrogation is achieved based on dispersion-induced time stretch such that the target spectral interferogram is mapped to a temporal interference waveform that can be captured in real-Time using a single-pixel photodector. The distributed strain along the sensing grating can be reconstructed from the instantaneous RF frequency of the captured waveform. High-spatial resolution is also obtained due to high-speed data acquisition. In a proof-of-concept experiment, ultrafast real-Time interrogation of fully-distributed grating sensors with various strain distributions is experimentally demonstrated. An ultrarapid measurement speed of 50 MHz with a high spatial resolution of 31.5 μm over a gauge length of 25 mm and a strain resolution of 9.1 μϵ have been achieved.