Optimization of Optical and Mechanical Properties of Real Architecture for 3-Dimensional Tissue Equivalents: Towards Treatment of Limbal Epithelial Stem Cell Deficiency

Limbal epithelial stem cell (LESC) deficiency can cause blindness. Transplantation of cultured human limbal epithelial cells (hLE) on human amniotic membrane (HAM) can restore vision but clinical graft manufacture can be unreliable. We have developed a reliable and robust tissue equivalent (TE) alternative to HAM, Real Architecture for 3D Tissue (RAFT). Here, we aimed to optimize the optical and mechanical properties of RAFT TE for treatment of LESC deficiency in clinical application. The RAFT TE protocol is tunable; varying collagen concentration and volume produces differing RAFT TEs. These were compared with HAM samples taken from locations proximal and distal to the placental disc. Outcomes assessed were transparency, thickness, light transmission, tensile strength, ease of handling, degradation rates and suitability as substrate for hLE culture. Proximal HAM samples were thicker and stronger with poorer optical properties than distal HAM samples. RAFT TEs produced using higher amounts of collagen were thicker and stronger with poorer optical properties than those produced using lower amounts of collagen. The ‘optimal’ RAFT TE was thin, transparent but still handleable and was produced using 0.6 ml of 3 mg/ml collagen. Degradation rates of the ‘optimal’ RAFT TE and HAM were similar. hLE achieved confluency on ‘optimal’ RAFT TEs at comparable rates to HAM and cells expressed high levels of putative stem cell marker p63α. These findings support the use of RAFT TE for hLE transplantation towards treatment of LESC deficiency

PBStoHTCondor system for campus grids

The campus grid architectures currently available are considered to be overly complex. We have focused on High Throughput Condor HTCondor as one of the most popular middlewares among UK universities, and are proposing a new system for unifying campus grid resources. This new system PBStoCondor is capable of interfacing with Linux based system within the campus grids, and automatically determining the best resource for a given job. The system does not require additional efforts from users and administrators of the campus grid resources. We have compared the real usage data and PBStoCondor system simulation data. The results show a close match. The proposed system will enable better utilization of campus grid resources, and will not require modification in users' workflows

Using Hadoop to implement a semantic method of assessing the quality of research medical datasets.

In this paper a system for storing and querying medical RDF data using Hadoop is developed. This approach enables us to create an inherently parallel framework that will scale the workload across a cluster. Unlike existing solutions, our framework uses highly optimised joining strategies to enable the completion of eight separate SPAQL queries, comprised of over eighty distinct joins, in only two Map/Reduce iterations. Results are presented comparing an optimised version of our solution against Jena TDB, demonstrating the superior performance of our system and its viability for assessing the quality of medical data

Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints'

The problem of how to compare empirical graphs is an area of great interest within the field of network science. The ability to accurately but efficiently compare graphs has a significant impact in such areas as temporal graph evolution, anomaly detection and protein comparison. The comparison problem is compounded when working with graphs containing millions of anonymous, i.e. unlabelled, vertices and edges. Comparison of two or more graphs is highly computationally expensive. Thus reducing a graph to a much smaller feature set – called a fingerprint, which accurately captures the essence of the graph would be highly desirable. Such an approach would have potential applications outside of graph comparisons, especially in the area of machine learning. This paper introduces a feature extraction based approach for the efficient comparison of large topologically similar, but order varying, unlabelled graph datasets. The approach acts by producing a ‘Graph Fingerprint’ which represents both vertex level and global level topological features from a graph. The approach is shown to be efficient when comparing graphs which are highly topologically similar but order varying. The approach scales linearly with the size and complexity of the graphs being fingerprinted

Towards an Info-Symbiotic Decision Support System for Disaster Risk Management

This paper outlines a framework for an info-symbiotic modelling system using cyber-physical sensors to assist in decision-making. Using a dynamic data-driven simulation approach, this system can help with the identification of target areas and resource allocation in emergency situations. Using different natural disasters as exemplars, we will show how cyber-physical sensors can enhance ground level intelligence and aid in the creation of dynamic models to capture the state of human casualties. Using a virtual command & control centre communicating with sensors in the field, up-to-date information of the ground realities can be incorporated in a dynamic feedback loop. Using other information (e.g. Weather models) a complex and rich model can be created. The framework adaptively manages the heterogeneous collection of data resources and uses agent-based models to create what-if scenarios in order to determine the best course of action

Functional Limbal Epithelial Cells Can Be Successfully Isolated From Organ Culture Rims Following Long-Term Storage

PURPOSE. Because of a shortage of fresh corneal tissue for research, it was of interest to investigate the potential of successfully isolating human limbal epithelial cells (hLECs) from organ culture corneal-scleral (OCCS) rims. METHODS. Superficial segments of corneal limbus were dissected and digested using collagenase (0.5 mg/mL, 16 hours at 378C). Cell suspensions were separated into four different growth conditions: corneal epithelial cell medium (CM); CM þ 3T3-Swiss albino cells; stromal stem cell medium (SM); and SM þ 3T3 cells. Colony number, hLEC count, cell density, and colony forming efficiency (CFE) were quantified to assess different growth conditions. The expression profile associated with basal hLECs was assessed by immunofluorescence, and epithelial integrity was measured using our real architecture for 3D tissue (RAFT) corneal tissue equivalent. RESULTS. Human limbal epithelial cells can be successfully isolated from OCCS rims following 4 weeks in storage with an 80.55% success rate with 36 corneal rims. Stromal stem cell medium þ 3T3s provided optimal growth conditions. Colony number, total cell number, and cell density were significantly higher at day 7 in cultures with SM than in CM. There were no significant differences between SM and CM when assessing CFE and the expression profile associated with basal hLECs. Cells maintained in SM were found to produce a higher quality epithelium than that cultured in CM. CONCLUSIONS. Organ culture corneal-scleral rims can be a valuable source for hLEC. Using a combination of collagenase-based isolation and medium designed for stromal stem cell isolation, a high number of good quality hLECs can be cultured from tissue that would have otherwise been ignored

Using Hadoop To Implement a Semantic Method Of Assessing The Quality Of Research Medical Datasets

In this paper a system for storing and querying medical RDF data using Hadoop is developed. This approach enables us to create an inherently parallel framework that will scale the workload across a cluster. Unlike existing solutions, our framework uses highly optimised joining strategies to enable the completion of eight separate SPAQL queries, comprised of over eighty distinct joins, in only two Map/Reduce iterations. Results are presented comparing an optimised version of our solution against Jena TDB, demonstrating the superior performance of our system and its viability for assessing the quality of medical data

Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain

Recent technological advances in modern healthcare have lead to the ability to collect a vast wealth of patient monitoring data. This data can be utilised for patient diagnosis but it also holds the potential for use within medical research. However, these datasets often contain errors which limit their value to medical research, with one study finding error rates ranging from 2.3%???26.9% in a selection of medical databases. Previous methods for automatically assessing data quality normally rely on threshold rules, which are often unable to correctly identify errors, as further complex domain knowledge is required. To combat this, a semantic web based framework has previously been developed to assess the quality of medical data. However, early work, based solely on traditional semantic web technologies, revealed they are either unable or inefficient at scaling to the vast volumes of medical data. In this paper we present a new method for storing and querying medical RDF datasets using Hadoop Map / Reduce. This approach exploits the inherent parallelism found within RDF datasets and queries, allowing us to scale with both dataset and system size. Unlike previous solutions, this framework uses highly optimised (SPARQL) joining strategies, intelligent data caching and the use of a super-query to enable the completion of eight distinct SPARQL lookups, comprising over eighty distinct joins, in only two Map / Reduce iterations. Results are presented comparing both the Jena and a previous Hadoop implementation demonstrating the superior performance of the new methodology. The new method is shown to be five times faster than Jena and twice as fast as the previous approach

Automating and utilising equal-distribution data classification

Data classification, i.e. organising data items in groups (classes), is a general technique widely used in data visualisation and cartography, in particular, for creation of choropleth maps. Conventionally, data are classified by dividing the data range into intervals and assigning the same symbol or colour to all data falling within an interval. For instance, the intervals may be of the same length or may include the same number of data items. We propose a method for defining intervals so that some quantity represented by values of another attribute is equally distributed among the classes. This kind of classification supports exploratory analysis of relationships between the attribute used for the classification and the distribution of the phenomenon whose quantity is represented by the additional attribute. The approach may be especially useful when the distribution of the phenomenon is very unequal, with many data items having zero or low quantities and quite a few items having larger quantities. With such a distribution, standard statistical analysis of the relationships may be problematic. We demonstrate the potential of the approach by analysing data referring to a set of spatially distributed people (patients) in relationship to characteristics of the areas in which the people live