Ultra-fast processing of gigapixel Tissue MicroArray images using high performance computing.

BACKGROUND:
tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.
METHODS:
a High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.
RESULTS:
the automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.
CONCLUSIONS:
the methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research.

Does the Child Health Computing System adequately identify children with cerebral palsy?

Background This paper assesses the usefulness of the Child Health Computing System as a source of information about children with cerebral palsy.

Methods A comparative survey of information held on the Child Health Computing System (CHCS) and the Northern Ireland Cerebral Palsy Register (NICPR) in one Health and Social Services Board in Northern Ireland was carried out. The sample comprised children with cerebral palsy aged 5–9 years.

Results Of the 135 cases recorded on the NICPR, 47 per cent were not found on the CHCS; the majority of these children had no computer record of any medical diagnosis. Of the 82 cases recorded on the CHCS, 10(12 per cent) were not found on the NICPR; five of these cases (6 per cent) were found on follow–up not to have CP.

Conclusions Unless improvements are made in case ascertainment, case validation and recording activities, the evidence suggests that the CHCS will not be able to provide the same quality of information for needs assessment and surveillance of very low birthweight infants in relation to cerebral palsy as is provided by a specialist case register.

Restoration of adipose function in obese, glucose-tolerant men following pioglitazone treatment is associated with CCAAT enhancer-binding protein β upregulation

Obese AT (adipose tissue) exhibits increased macrophage number. Pro-inflammatory CD16+ peripheral monocyte numbers are also reported to increase with obesity. The present study was undertaken to simultaneously investigate obesity-associated changes in CD16+ monocytes and ATMs (AT macrophages). In addition, a pilot randomized placebo controlled trial using the PPAR (peroxisome-proliferator-activated receptor) agonists, pioglitazone and fenofibrate was performed to determine their effects on CD14+/CD16+ monocytes, ATM and cardiometabolic and adipose dysfunction indices. Obese glucose-tolerant men (n=28) were randomized to placebo, pioglitazone (30 mg/day) and fenofibrate (160 mg/day) for 12 weeks. A blood sample was taken to assess levels of serum inflammatory markers and circulating CD14+/CD16+ monocyte levels via flow cytometry. A subcutaneous AT biopsy was performed to determine adipocyte cell surface and ATM number, the latter was determined via assessment of CD68 expression by IHC (immunohistochemistry) and real-time PCR. Subcutaneous AT mRNA expression of CEBPß (CCAAT enhancer-binding protein ß), SREBP1c (sterol-regulatory-element-binding protein 1c), PPAR?2, IRS-1 (insulin receptor substrate-1), GLUT4 (glucose transporter type 4) and TNFa (tumour necrosis factor a) were also assessed. Comparisons were made between obese and lean controls (n=16) at baseline, and pre- and post-PPAR agonist treatment. Obese individuals had significantly increased adipocyte cell surface, percentage CD14+/CD16+ monocyte numbers and ATM number (all P=0.0001). Additionally, serum TNF-a levels were significantly elevated (P=0.017) and adiponectin levels reduced (total: P=0.0001; high: P=0.022) with obesity. ATM number and percentage of CD14+/CD16+ monocytes correlated significantly (P=0.05). Pioglitazone improved adiponectin levels significantly (P=0.0001), and resulted in the further significant enlargement of adipocytes (P=0.05), without effect on the percentage CD14+/CD16+ or ATM number. Pioglitazone treatment also significantly increased subcutaneous AT expression of CEBPß mRNA. The finding that improvements in obesity-associated insulin resistance following pioglitazone were associated with increased adipocyte cell surface and systemic adiponectin levels, supports the centrality of AT to the cardiometabolic derangement underlying the development of T2D (Type 2 diabetes) and CVD (cardiovascular disease).

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.