A concept-based retrieval method for entity-oriented search

Entity-oriented retrieval aims to return a list of relevant entities rather than documents to provide exact answers for user queries. The nature of entity-oriented retrieval requires identifying the semantic intent of user queries, i.e., understanding the semantic role of query terms and determining the semantic categories which indicate the class of target entities. Existing methods are not able to exploit the semantic intent by capturing the semantic relationship between terms in a query and in a document that contains entity related information. To improve the understanding of the semantic intent of user queries, we propose concept-based retrieval method that not only automatically identifies the semantic intent of user queries, i.e., Intent Type and Intent Modifier but introduces concepts represented by Wikipedia articles to user queries. We evaluate our proposed method on entity profile documents annotated by concepts from Wikipedia category and list structure. Empirical analysis reveals that the proposed method outperforms several state-of-the-art approaches.

Epstein-Barr virus-positive diffuse large B-cell lymphoma of the elderly expresses EBNA3A with conserved CD8+ T-cell epitopes

Post-transplantation lymphoproliferative disorders (PTLD) arise in the immunosuppressed and are frequently Epstein-Barr virus (EBV) associated. The most common PTLD histological sub-type is diffuse large B-cell lymphoma (EBV+DLBCL-PTLD). Restoration of EBV-specific T-cell immunity can induce EBV+DLBCL-PTLD regression. The most frequent B-cell lymphoma in the immunocompetent is also DLBCL. ‘EBV-positive DLBCL of the elderly’ (EBV+DLBCL) is a rare but well-recognized DLBCL entity that occurs in the overtly immunocompetent, that has an adverse outcome relative to EBV-negative DLBCL. Unlike PTLD (which is classified as viral latency III), literature suggests EBV+DLBCL is typically latency II, i.e. expression is limited to the immuno-subdominant EBNA1, LMP1 and LMP2 EBV-proteins. If correct, this would be a major impediment for T-cell immunotherapeutic strategies. Unexpectedly we observed EBV+DLBCL-PTLD and EBV+DLBCL both shared features consistent with type III EBV-latency, including expression of the immuno-dominant EBNA3A protein. Extensive analysis showed frequent polymorphisms in EBNA1 and LMP1 functionally defined CD8+ T-cell epitope encoding regions, whereas EBNA3A polymorphisms were very rare making this an attractive immunotherapy target. As with EBV+DLBCL-PTLD, the antigen presenting machinery within lymphomatous nodes was intact. EBV+DLBCL express EBNA3A suggesting it is amenable to immunotherapeutic strategies.

Effectiveness of 7-valent pneumococcal conjugate vaccine against radiologically diagnosed pneumonia in indigenous infants in Australia

Objective To evaluate the effectiveness of the 7-valent pneumococcal conjugate vaccine (PCV7) in preventing pneumonia, diagnosed radiologically according to World Health Organization (WHO) criteria, among indigenous infants in the Northern Territory of Australia. Methods We conducted a historical cohort study of consecutive indigenous birth cohorts between 1 April 1998 and 28 February 2005. Children were followed up to 18 months of age. The PCV7 programme commenced on 1 June 2001. All chest X-rays taken within 3 days of any hospitalization were assessed. The primary endpoint was a first episode of WHO-defined pneumonia requiring hospitalization. Cox proportional hazards models were used to compare disease incidence. Findings There were 526 pneumonia events among 10 600 children - an incidence of 3.3 per 1000 child-months; 183 episodes (34.8%) occurred before 5 months of age and 247 (47.0%) by 7 months. Of the children studied, 27% had received 3 doses of vaccine by 7 months of age. Hazard ratios for endpoint pneumonia were 1.01 for 1 versus 0 doses; 1.03 for 2 versus 0 doses; and 0.84 for 3 versus 0 doses. Conclusion There was limited evidence that PCV7 reduced the incidence of radiologically confirmed pneumonia among Northern Territory indigenous infants, although there was a non-significant trend towards an effect after receipt of the third dose. These findings might be explained by lack of timely vaccination and/or occurrence of disease at an early age. Additionally, the relative contribution of vaccine-type pneumococcus to severe pneumonia in a setting where multiple other pathogens are prevalent may differ with respect to other settings where vaccine efficacy has been clearly established.

Validation of the interRAI Cognitive Performance Scale against independent clinical diagnosis and the Mini-Mental State Examination in older hospitalized patients

Objective To compare the diagnostic accuracy of the interRAI Acute Care (AC) Cognitive Performance Scale (CPS2) and the Mini-Mental State Examination (MMSE), against independent clinical diagnosis for detecting dementia in older hospitalized patients. Design, Setting, and Participants The study was part of a prospective observational cohort study of patients aged ≥70 years admitted to four acute hospitals in Queensland, Australia, between 2008 and 2010. Recruitment was consecutive and patients expected to remain in hospital for ≥48 hours were eligible to participate. Data for 462 patients were available for this study. Measurements Trained research nurses completed comprehensive geriatric assessments and administered the interRAI AC and MMSE to patients. Two physicians independently reviewed patients’ medical records and assessments to establish the diagnosis of dementia. Indicators of diagnostic accuracy included sensitivity, specificity, predictive values, likelihood ratios and areas under receiver (AUC) operating characteristic curves. Results 85 patients (18.4%) were considered to have dementia according to independent clinical diagnosis. The sensitivity of the CPS2 [0.68 (95%CI: 0.58–0.77)] was not statistically different to the MMSE [0.75 (0.64–0.83)] in predicting physician diagnosed dementia. The AUCs for the 2 instruments were also not statistically different: CPS2 AUC = 0.83 (95%CI: 0.78–0.89) and MMSE AUC = 0.87 (95%CI: 0.83–0.91), while the CPS2 demonstrated higher specificity [0.92 95%CI: 0.89–0.95)] than the MMSE [0.82 (0.77–0.85)]. Agreement between the CPS2 and clinical diagnosis was substantial (87.4%; κ=0.61). Conclusion The CPS2 appears to be a reliable screening tool for assessing cognitive impairment in acutely unwell older hospitalized patients. These findings add to the growing body of evidence supporting the utility of the interRAI AC, within which the CPS2 is embedded. The interRAI AC offers the advantage of being able to accurately screen for both dementia and delirium without the need to use additional assessments, thus increasing assessment efficiency.

Mobile silencing in plants: what is the signal and what defines the target

RNA-mediated silencing in plants can spread from cell to cell and over a long distance, and such mobile silencing has been extensively studied in the past decade. However, major questions remain as to what is the exact nature of the mobile silencing signals, how the components of the RNA-directed DNA methylation pathway are involved, and why systemic spread of silencing has only been observed for transgenes but not endogenous genes. In this review, we provide an overview of the current knowledge on mobile gene silencing in plants and present a model where systemic silencing involves long nuclear RNA transcripts that serve as a template to amplify primary siRNA signals.

Application of gene silencing in plants

Recent studies of gene silencing in plants have revealed two RNA-mediated epigenetic processes, RNA-directed RNA degradation and RNA-directed DNA methylation. These natural processes have provided new avenues for developing high-efficiency, high-throughput technology for gene suppression in plants.

Application of gene silencing in plants

Recent studies of gene silencing in plants have revealed two RNA-mediated epigenetic processes, RNA-directed RNA degradation and RNA-directed DNA methylation. These natural processes have provided new avenues for developing high-efficiency, high-throughput technology for gene suppression in plants.

Ontology-based product's reputation model

Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Hot spot identification (HSID) aims to identify potential sites—roadway segments, intersections, crosswalks, interchanges, ramps, etc.—with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.

Development and validation of anthropometric prediction equations for estimation of body fat in Indonesian men

Body composition of 292 males aged between 18 and 65 years was measured using the deuterium oxide dilution technique. Participants were divided into development (n=146) and cross-validation (n=146) groups. Stature, body weight, skinfold thickness at eight sites, girth at five sites, and bone breadth at four sites were measured and body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-stature ratio (WSR) calculated. Equations were developed using multiple regression analyses with skinfolds, breadth and girth measures, BMI, and other indices as independent variables and percentage body fat (%BF) determined from deuterium dilution technique as the reference. All equations were then tested in the cross-validation group. Results from the reference method were also compared with existing prediction equations by Durnin and Womersley (1974), Davidson et al (2011), and Gurrici et al (1998). The proposed prediction equations were valid in our cross-validation samples with r=0.77- 0.86, bias 0.2-0.5%, and pure error 2.8-3.6%. The strongest was generated from skinfolds with r=0.83, SEE 3.7%, and AIC 377.2. The Durnin and Womersley (1974) and Davidson et al (2011) equations significantly (p<0.001) underestimated %BF by 1.0 and 6.9% respectively, whereas the Gurrici et al (1998) equation significantly (p<0.001) overestimated %BF by 3.3% in our cross-validation samples compared to the reference. Results suggest that the proposed prediction equations are useful in the estimation of %BF in Indonesian men.

Climate Change and the Resilience of Commodity Food Production in Australia

The current view of Australian state and national governments about the effects of climate change on agriculture is that farmers – through the adoption of mitigation and adaptation strategies – will remain resilient, and agricultural production will continue to expand. The assumption is that neoliberalism will provide the best ‘free market’ options for climate change mitigation and adaptation in farming. In contrast, we argue that neoliberalism will increase the move towards productivis (‘high-tech’) agriculture – the very system that has caused major environmental damage to the Australian continent. High-tech farming is highly dependent upon access to water and fossil fuels, both of which would appear to be the main limits to production in future decades. Productivist agriculture is a system highly reliant upon fertilizers and fuels that are derived from the petrochemical industry, and are currently increasing in cost as the price of oil increases.

A novel process of group-oriented question reduction for rule-based recommendation websites

Several websites utilise a rule-base recommendation system, which generates choices based on a series of questionnaires, for recommending products to users. This approach has a high risk of customer attrition and the bottleneck is the questionnaire set. If the questioning process is too long, complex or tedious; users are most likely to quit the questionnaire before a product is recommended to them. If the questioning process is short; the user intensions cannot be gathered. The commonly used feature selection methods do not provide a satisfactory solution. We propose a novel process combining clustering, decisions tree and association rule mining for a group-oriented question reduction process. The question set is reduced according to common properties that are shared by a specific group of users. When applied on a real-world website, the proposed combined method outperforms the methods where the reduction of question is done only by using association rule mining or only by observing distribution within the group.

Managing and analysing big audio data for environmental monitoring

Environmental monitoring is becoming critical as human activity and climate change place greater pressures on biodiversity, leading to an increasing need for data to make informed decisions. Acoustic sensors can help collect data across large areas for extended periods making them attractive in environmental monitoring. However, managing and analysing large volumes of environmental acoustic data is a great challenge and is consequently hindering the effective utilization of the big dataset collected. This paper presents an overview of our current techniques for collecting, storing and analysing large volumes of acoustic data efficiently, accurately, and cost-effectively.