Confronting the conspiracy of silence and denial of difference for late discovery adoptive persons and donor conceived people

This article outlines the impact that a conspiracy of silence and denial of difference has had on some adopted and donor conceived persons who have been lied to or misled about their origins. Factors discussed include deceit - expressed as a central secret which undermines the fabric of a family and through distortion mystifies communication processes; the shock of discovery - often revealed accidentally and the associated sense of betrayal when this occurs; and a series of losses, for example, kinship, medical history, culture and agency which result in having to rebuild personal identity. By providing those affected with a voice, validation and vindication healing can begin. Any feelings of disregard, of betrayal of trust, of anger, frustration, sorrow or loss, need to be regarded as real, expected, and above all, a valid reaction to what has occurred. The author is a 'late discoverer' of her adoption and draws on the information from her doctoral research on the same topic which was completed in 2012.

Exploring the ethical implications of the late discovery of adoptive and donor-insemination offspring status

Some children adopted under the now discredited period of closed adoption were never told of their adoptive status until it was revealed to them in adulthood. Yet to date, this ‘late-discovery’ experience has received little research attention. Now a new generation of ‘late discoverers’ is emerging as a result of (heterosexual couple) donor insemination (DI) practices. This study of 25 late-discovery participants of either adoptive or (heterosexual couple) DI offspring status reveals ethical concerns particular to the lateness of discovery. Most of the participants were Australian, with the remainder from the UK, USA and Canada. All were asked to give an ‘open’ account of their experience, with four themes or suggestions provided on request. These accounts were added to those available in relevant publications. The analysis employed a hermeneutic phenomenological methodology and all accounts were analysed using an ethical perspective developed by Walker (2006, 2007). The main themes that emerged were: disrupted personal autonomy, betrayal of deep levels of trust and feelings of injustice and diminished self-worth. The lack of recognition of concerns particular to late discovery has resulted in late discoverers (i) feeling unable to regain a sense of personal control, (ii) significantly disrupted relationships with those closest to them and others, including community and institutions, and (iii) feelings of diminished value and self-worth.

An evaluation framework for cross-lingual link discovery

Cross-Lingual Link Discovery (CLLD) is a new problem in Information Retrieval. The aim is to automatically identify meaningful and relevant hypertext links between documents in different languages. This is particularly helpful in knowledge discovery if a multi-lingual knowledge base is sparse in one language or another, or the topical coverage in each language is different; such is the case with Wikipedia. Techniques for identifying new and topically relevant cross-lingual links are a current topic of interest at NTCIR where the CrossLink task has been running since the 2011 NTCIR-9. This paper presents the evaluation framework for benchmarking algorithms for cross-lingual link discovery evaluated in the context of NTCIR-9. This framework includes topics, document collections, assessments, metrics, and a toolkit for pooling, assessment, and evaluation. The assessments are further divided into two separate sets: manual assessments performed by human assessors; and automatic assessments based on links extracted from Wikipedia itself. Using this framework we show that manual assessment is more robust than automatic assessment in the context of cross-lingual link discovery.

Overview of the NTCIR-10 cross-lingual link discovery task

This paper presents an overview of NTCIR-10 Cross-lingual Link Discovery (CrossLink-2) task. For the task, we continued using the evaluation framework developed for the NTCIR-9 CrossLink-1 task. Overall, recommended links were evaluated at two levels (file-to-file and anchor-to-file); and system performance was evaluated with metrics: LMAP, R-Prec and P@N.

Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies

To enhance the therapeutic efficacy and reduce the adverse effects of traditional Chinese medicine, practitioners often prescribe combinations of plant species and/or minerals, called formulae. Unfortunately, the working mechanisms of most of these compounds are difficult to determine and thus remain unknown. In an attempt to address the benefits of formulae based on current biomedical approaches, we analyzed the components of Yinchenhao Tang, a classical formula that has been shown to be clinically effective for treating hepatic injury syndrome. The three principal components of Yinchenhao Tang are Artemisia annua L., Gardenia jasminoids Ellis, and Rheum Palmatum L., whose major active ingredients are 6,7-dimethylesculetin (D), geniposide (G), and rhein (R), respectively. To determine the mechanisms underlying the efficacy of this formula, we conducted a systematic analysis of the therapeutic effects of the DGR compound using immunohistochemistry, biochemistry, metabolomics, and proteomics. Here, we report that the DGR combination exerts a more robust therapeutic effect than any one or two of the three individual compounds by hitting multiple targets in a rat model of hepatic injury. Thus, DGR synergistically causes intensified dynamic changes in metabolic biomarkers, regulates molecular networks through target proteins, has a synergistic/additive effect, and activates both intrinsic and extrinsic pathways.

Urinary biomolecular indicators of exercise-induced over exertion injury

Poor health and injury represent major obstacles to the future economic security of Australia. The national economic cost of work-related injury is estimated at $57.5 billion p/a. Since exposure to high physical demands is a major risk factor for musculoskeletal injury, monitoring and managing such physical activity levels in workers is a potentially important injury prevention strategy. Current injury monitoring practices are inadequate for the provision of clinically valuable information about the tissue specific responses to physical exertion. Injury of various soft tissue structures can manifest over time through accumulation of micro-trauma. Such micro-trauma has a propensity to increase the risk of acute injuries to soft-tissue structures such as muscle or tendon. As such, the capacity to monitor biomarkers that result from the disruption of these tissues offers a means of assisting the pre-emptive management of subclinical injury prior to acute failure or for evaluation of recovery processes. Here we have adopted an in-vivo exercise induced muscle damage model allowing the application of laboratory controlled conditions to assist in uncovering biochemical indicators associated with soft-tissue trauma and recovery. Importantly, urine was utilised as the diagnostic medium since it is non-invasive to collect, more acceptable to workers and less costly to employers. Moreover, it is our hypothesis that exercise induced tissue degradation products enter the circulation and are subsequently filtered by the kidney and pass through to the urine. To test this hypothesis a range of metabolomic and proteomic discovery-phase techniques were used, along with targeted approaches. Several small molecules relating to tissue damage were identified along with a series of skeletal muscle-specific protein fragments resulting from exercise induced soft-tissue damage. Each of the potential biomolecular markers appeared to be temporally present within urine. Moreover, the regulation of abundance seemed to be associated with functional recovery following the injury. This discovery may have important clinical applications for monitoring of a variety of inflammatory myopathies as well as novel applications in monitoring of the musculoskeletal health status of workers, professional athletes and/or military personnel to reduce the onset of potentially debilitating musculoskeletal injuries within these professions.

The association between time spent outdoors and myopia using a novel biomarker of outdoor light exposure

PURPOSE: We sought to determine whether conjunctival ultraviolet autofluorescence (UVAF), a biomarker of outdoor light exposure, is associated with myopia. METHODS: We performed a cross-sectional study on Norfolk Island and recruited individuals aged ≥ 15 years. Participants completed a sun-exposure questionnaire and underwent non-cycloplegic autorefraction. Conjunctival UVAF used a specially adapted electronic flash system fitted with UV-transmission filters (transmittance range 300-400 nm, peak 365 nm) as the excitation source. Temporal and nasal conjunctival UVAF was measured in both eyes using computerized photographic analysis with the sum referred to as "total UVAF." RESULTS: In 636 participants, prevalence of myopia decreased with an increasing quartile of total UVAF (P(trend) = 0.002). Median total UVAF was lower in subjects with myopia (spherical equivalent [SE] ≤ -1.0 diopter [D]) than participants without myopia: 16.6 mm(2) versus 28.6 mm(2), P = 0.001. In the multivariable model that adjusted for age, sex, smoking, cataract, height and weight, UVAF was independently associated with myopia (SE ≤ -1.0 D): odds ratio (OR) for total UVAF (per 10 mm(2)) was 0.81, 95% confidence interval (CI) 0.69 to 0.94, P = 0.007. UVAF was also significantly associated with myopia when analysis was restricted to subjects <50 years, and in moderate-severe myopia (SE ≤ -3.0 D). Prevalence of myopia decreased with increasing time outdoors (P(trend) = 0.03), but time outdoors was not associated with myopia on multivariable analysis. CONCLUSIONS: Study authors identified a protective association between increasing UVAF and myopia. The protective association of higher UVAF against myopia was stronger than that of increased levels of time spent outdoors as measured by this study's questionnaire. Future studies should investigate the association between UVAF and incident myopia, and its relationship to myopic progression.

A data mining framework for relevance feature discovery

This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

Culture in global business transformation projects : the discovery of a grounded theory

Presently organisations engage in what is termed as Global Business Transformation Projects [GBTPs], for consolidating, innovating, transforming and restructuring their processes and business strategies while undergoing fundamental change. Culture plays an important role in global business transformation projects as these involve people of different cultural backgrounds and span across countries, industries and disciplinary boundaries. Nevertheless, there is scant empirical research on how culture is conceptualised beyond national and organisational cultures but also on how culture is to be taken into account and dealt with within global business transformation projects. This research is situated in a business context and discovers a theory that aids in describing and dealing with culture. It draws on the lived experiences of thirty-two senior management practitioners, reporting on more than sixty-one global business transformation projects in which they were actively involved. The research method used is a qualitative and interpretive one and applies a grounded theory approach, with rich data generated through interviews. In addition, vignettes were developed to illustrate the derived theoretical models. The findings from this study contribute to knowledge in multiple ways. First, it provides a holistic account of global business transformation projects that describe the construct of culture by the elements of culture types, cultural differences and cultural diversity. A typology of culture types has been developed which enlarges the view of culture beyond national and organisational culture including an industry culture, professional service firm culture and 'theme' culture. The amalgamation of the culture types instantiated in a global business transformation project compromises its project culture. Second, the empirically grounded process for managing culture in global business transformation projects integrates the stages of recognition, understanding and management as well as the enablement providing a roadmap for dealing with culture in global business transformation projects. Third, this study identified contextual variables to global business transformation projects, which provide the means of describing the environment global business transformation projects are situated, influence the construct of culture and inform the process for managing culture. Fourth, the contribution to the research method is the positioning of interview research as a strategy for data generation and the detailed documentation applying grounded theory to discover theory.

The efficiency of corpus-based distributional models for literature-based discovery on large data sets

This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.

MS-based metabolomics facilitates the discovery of in vivo functional small molecules with a diversity of biological contexts

In vivo small molecules as necessary intermediates are involved in numerous critical metabolic pathways and biological processes associated with many essential biological functions and events. There is growing evidence that MS-based metabolomics is emerging as a powerful tool to facilitate the discovery of functional small molecules that can better our understanding of development, infection, nutrition, disease, toxicity, drug therapeutics, gene modifications and host-pathogen interaction from metabolic perspectives. However, further progress must still be made in MS-based metabolomics because of the shortcomings in the current technologies and knowledge. This technique-driven review aims to explore the discovery of in vivo functional small molecules facilitated by MS-based metabolomics and to highlight the analytic capabilities and promising applications of this discovery strategy. Moreover, the biological significance of the discovery of in vivo functional small molecules with different biological contexts is also interrogated at a metabolic perspective.

Dynamic contrast-enhanced magnetic resonance imaging as a biomarker for the pharmacological response of PTK787/ZK 222584, an inhibitor of the vascular endothelial growth factor receptor tyrosine kinases, in patients with advanced colorectal cancer and liver metastases: Results from two phase I studies

Purpose: PTK787/ZK 222584 (PTK/ZK), an orally active inhibitor of vascular endothelial growth factor (VEGF) receptor tyrosine kinases, inhibits VEGF-mediated angiogenesis. The pharmacodynamic effects of PTK/ZK were evaluated by assessing changes in contrast-enhancement parameters of metastatic liver lesions using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced colorectal cancer treated in two ongoing, dose-escalating phase I studies. Patients and Methods: Twenty-six patients had DCE-MRI performed at baseline, day 2, and at the end of each 28-day cycle. Doses of oral PTK/ZK ranged from 50 to 2000 mg once daily. Tumor permeability and vascularity were assessed by calculating the bidirectional transfer constant (Ki). The percentage of baseline Ki (% of baseline Ki) at each time point was compared with pharmacokinetic and clinical end points. Results: A significant negative correlation exists between the % of baseline Ki and increase in PTK/ZK oral dose and plasma levels (P = .01 for oral dose; P = .0001 for area under the plasma concentration curve at day 2). Patients with a best response of stable disease had a significantly greater reduction in Ki at both day 2 and at the end of cycle 1 compared with progressors (mean difference in % of baseline Ki, 47%, P = .004%; and 51%, P = .006; respectively). The difference in % of baseline Ki remained statistically significant after adjusting for baseline WHO performance status. Conclusion: These findings should help to define a biologically active dose of PTK/ZK. These results suggest that DCE-MRI may be a useful biomarker for defining the pharmacological response and dose of angiogenesis inhibitiors, such as PTK/ZK, for further clinical development. © 2003 by American Society of Clinical Oncology.

Angiogenesis as a biomarker and target in cancer chemoprevention

Angiogenesis, the formation of new blood vessels from existing vasculature, is essential to the late stages of carcinogenesis, allowing tumours to grow beyond 1-2 mm in diameter, invade surrounding tissue, and metastasise. However, more than two decades ago, angiogenesis that preceded neoplastic transformation was seen. Indeed, it can be detected in inflammatory and infectious diseases that increase the risk of developing cancer. Recent advances in fluorescence endoscopy and histological assessment suggest that, for certain cancers, the degree of new blood-vessel formation may differ between the early and late stages of carcinogenic progression. The association between angiogenesis and cancer occurrence, and ease of detection of this process in accessible tissues early in carcinogenesis, mean that angiogenesis fulfils the criteria for a biomarker of the effectiveness of chemopreventive intervention. There is also some evidence that biochemical assays of angiogenic growth factors may after similar potential as surrogate biomarkers. Many natural and synthetic chemopreventive agents in development or in clinical use inhibit new vessel formation in vivo. Validation of angiogenesis as a biomarker for the effectiveness of chemoprevention should further the advancement of some chemopreventive agents.

Enhanced n-gram extraction using relevance feature discovery

Guaranteeing the quality of extracted features that describe relevant knowledge to users or topics is a challenge because of the large number of extracted features. Most popular existing term-based feature selection methods suffer from noisy feature extraction, which is irrelevant to the user needs (noisy). One popular method is to extract phrases or n-grams to describe the relevant knowledge. However, extracted n-grams and phrases usually contain a lot of noise. This paper proposes a method for reducing the noise in n-grams. The method first extracts more specific features (terms) to remove noisy features. The method then uses an extended random set to accurately weight n-grams based on their distribution in the documents and their terms distribution in n-grams. The proposed approach not only reduces the number of extracted n-grams but also improves the performance. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms the state-of-art methods underpinned by Okapi BM25, tf*idf and Rocchio.