Prognostic hallmarks in AML

A data-clustering method that incorporates prior knowledge of biological context reveals prognostic signatures of proteomic expression in patients with acute myeloid leukaemia

Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field

A Neural Attention Model for Categorizing Patient Safety Events

Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care. Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.Comment: ECIR 201

Balancing Act - the Tightrope of Corporate Governance Reform

Over the last five years, corporate governance has proved an insistent issue in the boardrooms of Australia. In March 2003, the Australian Stock Exchange (ASX) Corporate Governance Council launched its Principles of Good Corporate Governance and Best Practice Recommendations (the guidelines). The following year amendments to the Corporations Act 2001 came into force, designed to improve corporate accountability and auditing practices. In annual reports for 2004-2005, Australian corporations were asked to disclose more information about their corporate governance practices than ever before. This prompted a review of existing governance structures and procedures against those recommended or required by the new regulation

Structure of the clean Ta(100) surface

The clean Ta(100) surface and some aspects of hydrogen adsorption have been studied by LEED and AES. The thorough examination of LEED patterns did not provide any evidence for an atomic reconstruction of the clean surface over the entire temperature range investigated, 150–600 K. The r-factor analysis used for comparison between measured and calculated I–V spectra yields a contraction of the topmost layer spacing of about 11% and an expansion of the second layer spacing of about 1% compared to the bulk value. The hydrogen adsorption does not induce any superstructures, but small hydrogen exposures lass then 1 L influence I–V spectra substantially

A study of physico-chemical characteristics of Ugborikoko/Okere stream as an index of pollution

A total of 8 samples were collected from strategic points along the Ugborikoko/Okere creek. The levels of the physico-chemical parameters and heavy metal pollutants were determined. Results of bothphysico-chemical characteristics and heavy metals revealed some degree of pollution in the creek. The mean values of pH, temperature, turbidity, conductivity, total dissolved solids, total suspended solids and chloride were 6.8, 30.3°C, 106.53 NTU, 471.45 ohms/cm, 354.56 mg/l, 123.56 mg/l and 78.11 mg/l respectively. The average value obtained for oil and grease was 14.13 ppm. The average value for the heavy metals were 2.258 ppm for iron, 0.0729 ppm for cadmium, 0.4225 ppm for zinc, 0.0997 ppm formanganese, 0.0236 ppm for copper, 0.2117 ppm for nickel. The results obtained indicated a significant level of pollution of the Ugborikoko/Okere creek. It was observed that the levels of iron, lead, chromium, cadmium, manganese and nickel in the samples were not in compliance with recommended standards set by world health organization for inland (fresh) and potable water

Geometry, kinematics and rates of deformation in a normal fault segment boundary, central Greece

The geometry, kinematics and rates of deformation within a fault segment boundary between the ends of two major active normal fault segments have been investigated through examination of a faulted 126 ka marine terrace. Slip‐vector azimuths defined by striations on the faults indicate N‐S extension on c. E‐W faults, sub‐parallel to those from earthquake focal mechanisms, together with significant and contemporaneous E‐W extension on c. N‐S faults. Summed rates of E‐W extension along a c. 550 m transect (0.17 mm/yr) are comparable with those for N‐S extension (0.20 mm/yr) along a c. 350 m transect. Our observations show that distributed non‐plane strain extension occurs in fault segment boundaries and this should be noted when studying fault‐tip fracture toughness and regional deformation rates

Structural features of adenovirus 2 virus-associated RNA required for binding to the protein kinase DAI

The double-stranded RNA activated protein kinase DAI contains an RNA binding domain consisting of two copies of a double-stranded RNA binding motif. We have investigated the role of RNA structure in the interaction between DAI and the structured single-stranded RNA, adenovirus VA RNAI, which inhibits DAI activation. Mutations in the apical stem, terminal stem, and central domain of the RNA were tested to assess the contribution of these elements to DAI binding in vitro. The data demonstrate that over half a turn of intact apical stem is required for the interaction and that there is a correlation between the binding of apical stem mutants and their ability to function both in vivo and in vitro. There was also evidence of preference for GC-rich sequence in the proximal region of the apical stem. In the central domain the correlation between binding and function of mutant RNAs was poor, suggesting that at least some of this region plays no direct role in binding to DAI, despite its functional importance. Exceptionally, central domain mutations that encroached on the phylogenetically conserved stem 4 of VA RNA disrupted binding, and complementary mutations in this sequence partially restored binding. Measurement of the binding of wild-type VA RNAI to DAI and p20, a truncated form of the protein containing the RNA binding domains alone, under various ionic conditions imply that the major interactions are electrostatic and occur via the protein's RNA binding domain. However, differences between full-length DAI and p20 in their binding to mutants in the conserved stem suggest that regions outside the RNA binding domain also participate in the binding. The additional interactions are likely to be non-ionic, and may be important for preventing DAI activation during virus infection

Geography of Diet in the UK Women’s Cohort Study: A Cross-Sectional Analysis

Diet can influence health outcomes and chronic disease risk, therefore a better understanding of factors influencing diet is important in promotion of healthier dietary choices. Many factors influence food choice, including the environment in which we live. This study aims to explore differences in dietary pattern consumption by two spatial measures: Government Office Region (a large regional unit of geography) and Output Area Classification (a small area geography combined with demographic characteristics). A cross-sectional analysis using data from the UK Women’s Cohort Study was carried out. This cohort included ~35000 middle aged women recruited between 1995 and 1999. Dietary patterns were derived using a k-means cluster analysis from diet data collected using a validated 217 item Food Frequency Questionnaire. Multinomial logit regression was used to test whether the area in which the women live, predicts their dietary pattern consumption. Results show that dietary patterns vary significantly by both spatial measures. The Government Office Region, the North West of England has the highest proportion of individuals consuming the least healthy, monotonous diets, while Greater London has the highest proportion of vegetarian diets. Individuals living in Supergroups ‘Countryside’ and ‘Prospering Suburbs’ consume healthier, more diverse diets. Those in ‘Constrained by Circumstance’ and ‘Blue Collar Communities’ consume monotonous, less healthy diets. Using a combination of spatial scales such as Government Office Region and Output Area Classification Supergroup could have a beneficial impact on targeting of public health dietary interventions and subsequent health

On the determination of a cloud condensation nuclei from satellite : Challenges and possibilities

We use aerosol size distributions measured in the size range from 0.01 to 10+ μm during Transport and Chemical Evolution over the Pacific (TRACE-P) and Aerosol Characterization Experiment-Asia (ACE-Asia), results of chemical analysis, measured/modeled humidity growth, and stratification by air mass types to explore correlations between aerosol optical parameters and aerosol number concentration. Size distributions allow us to integrate aerosol number over any size range expected to be effective cloud condensation nuclei (CCN) and to provide definition of a proxy for CCN (CCNproxy). Because of the internally mixed nature of most accumulation mode aerosol and the relationship between their measured volatility and solubility, this CCNproxy can be linked to the optical properties of these size distributions at ambient conditions. This allows examination of the relationship between CCNproxy and the aerosol spectral radiances detected by satellites. Relative increases in coarse aerosol (e.g., dust) generally add only a few particles to effective CCN but significantly increase the scattering detected by satellite and drive the Angstrom exponent (α) toward zero. This has prompted the use of a so-called aerosol index (AI) on the basis of the product of the aerosol optical depth and the nondimensional α, both of which can be inferred from satellite observations. This approach biases the AI to be closer to scattering values generated by particles in the accumulation mode that dominate particle number and is therefore dominated by sizes commonly effective as CCN. Our measurements demonstrate that AI does not generally relate well to a measured proxy for CCN unless the data are suitably stratified. Multiple layers, complex humidity profiles, dust with very low α mixed with pollution, and size distribution differences in pollution and biomass emissions appear to contribute most to method limitations. However, we demonstrate that these characteristic differences result in predictable influences on AI. These results suggest that inference of CCN from satellites will be challenging, but new satellite and model capabilities could possibly be integrated to improve this retrieval