Association between Australian-Indian mothers' controlling feeding practices and children's appetite traits

This cross-sectional study examined the association between controlling feeding practices and children's appetite traits. The secondary aim studied the relationship between controlling feeding practices and two proxy indicators of diet quality. Participants were 203 Australian-Indian mothers with children aged 1-5 years. Controlling feeding practices (pressure to eat, restriction, monitoring) and children's appetite traits (. food approach traits: food responsiveness, enjoyment of food, desire to drink, emotional overeating; food avoidance traits: satiety responsiveness, slowness in eating, fussiness and emotional undereating) were measured using self-reported, previously validated scales/questionnaires. Children's daily frequency of consumption of core and non-core foods was estimated using a 49-item list of foods eaten (yes/no) in the previous 24 hours as an indicator of diet quality. Higher pressure to eat was associated with higher scores for satiety responsiveness, slowness in eating, fussiness and lower score for enjoyment of food. Higher restriction was related to higher scores for food responsiveness and emotional overeating. Higher monitoring was inversely associated with fussiness, slowness in eating, food responsiveness and emotional overeating and positively associated with enjoyment of food. Pressure to eat and monitoring were related to lower number of core and non-core foods consumed in the previous 24 hours, respectively. All associations remained significant after adjusting for maternal and child covariates (n = 152 due to missing data). In conclusion, pressure to eat was associated with higher food avoidance traits and lower consumption of core foods. Restrictive feeding practices were associated with higher food approach traits. In contrast, monitoring practices were related to lower food avoidance and food approach traits and lower non-core food consumption.

Using decision trees to understand structure in missing data

Objectives Demonstrate the application of decision trees – classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs) – to understand structure in missing data. Setting Data taken from employees at three different industry sites in Australia. Participants 7915 observations were included. Materials and Methods The approach was evaluated using an occupational health dataset comprising results of questionnaires, medical tests, and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results CART and BRT models were effective in highlighting a missingness structure in the data, related to the Type of data (medical or environmental), the site in which it was collected, the number of visits and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured compared to structured missingness. Discussion Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusion Researchers are encouraged to use CART and BRT models to explore and understand missing data.

Main themes and missing elements in psychological research into child maltreatment in Africa

These 18 papers on aspects of violence against children add to a growing body of African literature that tends to focus on four major themes. The first three are generic to research worldwide, and include efforts to uncover the extent of adverse experiences during childhood, describe the effects on health and well-being, and examine professional practices, concepts and myths. The fourth theme is more particular to Africa, and is focused on understanding how to protect children within the context of poverty, some harmful traditional practices and community violence. Despite best efforts by researchers and child rights advocates, there are missing elements in the research to date, including scant focus on the problems of emotional abuse and neglect, and limited attention to evaluation of the effectiveness of prevention and care programs.

Using genome-Wwide complex trait analysis to quantify 'missing heritability' in Parkinson's disease

Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered. © Published by Oxford University Press 2012.

Estimating equations with nonignorably missing response data

Troxel, Lipsitz, and Brennan (1997, Biometrics 53, 857-869) considered parameter estimation from survey data with nonignorable nonresponse and proposed weighted estimating equations to remove the biases in the complete-case analysis that ignores missing observations. This paper suggests two alternative modifications for unbiased estimation of regression parameters when a binary outcome is potentially observed at successive time points. The weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90, 106-121) is also modified to obtain unbiased estimating functions. The suggested estimating functions are unbiased only when the missingness probability is correctly specified, and misspecification of the missingness model will result in biases in the estimates. Simulation studies are carried out to assess the performance of different methods when the covariate is binary or normal. For the simulation models used, the relative efficiency of the two new methods to the weighting methods is about 3.0 for the slope parameter and about 2.0 for the intercept parameter when the covariate is continuous and the missingness probability is correctly specified. All methods produce substantial biases in the estimates when the missingness model is misspecified or underspecified. Analysis of data from a medical survey illustrates the use and possible differences of these estimating functions.

The missing link of crime analysis: A systematic approach to testing competing hypotheses

Crime analysts have traditionally received little guidance from academic researchers in key tasks in the analysis process, specifically the testing of multiple hypotheses and evaluating evidence in a scientific fashion. This article attempts to fill this gap by outlining a method (the Analysis of Competing Hypotheses) of systematically analysing multiple explanations for crime problems. The method is systematic, avoids many cognitive errors common in analysis, and is explicit. It is argued that the implementation of this approach makes analytic products audit-able, the reasoning underpinning them transparent, and provides intelligence managers a rational professional development tool for individual analysts.

Missing pieces in the puzzle of plant microRNAs

Plant microRNAs (miRNAs) are important regulatory switches. Recent advances have revealed many regulatory layers between the two essential processes, miRNA biogenesis and function. However, how these multilayered regulatory processes ultimately control miRNA gene regulation and connects miRNAs and plant responses with the surrounding environment is still largely unknown. In this opinion article, we propose that the miRNA pathway is highly dynamic and plastic. The apparent flexibility of the miRNA pathway in plants appears to be controlled by a number recently identified proteins and poorly characterized signaling cascades. We further propose that altered miRNA accumulation can be a direct consequence of the rewiring of interactions between proteins that function in the miRNA pathway, an avenue that remains largely unexplored.

Testing a framework for the quality of process models: A case study

Process modeling can be regarded as the currently most popular form of conceptual modeling. Research evidence illustrates how process modeling is applied across the different information system life cycle phases for a range of different applications, such as configuration of Enterprise Systems, workflow management, or software development. However, a detailed discussion of critical factors of the quality of process models is still missing. This paper proposes a framework consisting of six quality factors, which is derived from a comprehensive literature review. It then presents in a case study, a utility provider, who had designed various business process models for the selection of an Enterprise System. The paper summarizes potential means of conducting a successful process modeling initiative and evaluates the described modeling approach within the Guidelines of Modeling (GoM) framework. An outlook shows the potential lessons learnt, and concludes with insights to the next phases of this study.

Economic evaluation and catheter-related bloodstream infections

Catheter-related bloodstream infections are a serious problem. Many interventions reduce risk, and some have been evaluated in cost-effectiveness studies. We review the usefulness and quality of these economic studies. Evidence is incomplete, and data required to inform a coherent policy are missing. The cost-effectiveness studies are characterized by a lack of transparency, short time-horizons, and narrow economic perspectives. Data quality is low for some important model parameters. Authors of future economic evaluations should aim to model the complete policy and not just single interventions. They should be rigorous in developing the structure of the economic model, include all relevant economic outcomes, use a systematic approach for selecting data sources for model parameters, and propagate the effect of uncertainty in model parameters on conclusions. This will inform future data collection and improve our understanding of the economics of preventing these infections.

Peer to peer English/Chinese cross-language information retrieval

Peer to peer systems have been widely used in the internet. However, most of the peer to peer information systems are still missing some of the important features, for example cross-language IR (Information Retrieval) and collection selection / fusion features. Cross-language IR is the state-of-art research area in IR research community. It has not been used in any real world IR systems yet. Cross-language IR has the ability to issue a query in one language and receive documents in other languages. In typical peer to peer environment, users are from multiple countries. Their collections are definitely in multiple languages. Cross-language IR can help users to find documents more easily. E.g. many Chinese researchers will search research papers in both Chinese and English. With Cross-language IR, they can do one query in Chinese and get documents in two languages. The Out Of Vocabulary (OOV) problem is one of the key research areas in crosslanguage information retrieval. In recent years, web mining was shown to be one of the effective approaches to solving this problem. However, how to extract Multiword Lexical Units (MLUs) from the web content and how to select the correct translations from the extracted candidate MLUs are still two difficult problems in web mining based automated translation approaches. Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalized-score based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database but do not consider the retrieval performance of the remote search engine. This thesis presents research on building a peer to peer IR system with crosslanguage IR and advance collection profiling technique for fusion features. Particularly, this thesis first presents a new Chinese term measurement and new Chinese MLU extraction process that works well on small corpora. An approach to selection of MLUs in a more accurate manner is also presented. After that, this thesis proposes a collection profiling strategy which can discover not only collection content but also retrieval performance of the remote search engine. Based on collection profiling, a web-based query classification method and two collection fusion approaches are developed and presented in this thesis. Our experiments show that the proposed strategies are effective in merging results in uncooperative peer to peer environments. Here, an uncooperative environment is defined as each peer in the system is autonomous. Peer like to share documents but they do not share collection statistics. This environment is a typical peer to peer IR environment. Finally, all those approaches are grouped together to build up a secure peer to peer multilingual IR system that cooperates through X.509 and email system.

Identification and analysis of business and software services : A consolidated approach

Although the benefits of service orientation are prevalent in literature, a review, analysis, and evaluation of the 30 existing service analysis approaches presented in this paper have shown that a comprehensive approach to the identification and analysis of both business and supporting software services is missing. Based on this evaluation of existing approaches and additional sources, we close this gap by proposing an integrated, consolidated approach to business and software service analysis that combines and extends the strengths of the examined methodologies.

The user query based learning system for lifetime prediction of metallic components

Real-World Data Mining Applications generally do not end up with the creation of the models. The use of the model is the final purpose especially in prediction tasks. The problem arises when the model is built based on much more information than that the user can provide in using the model. As a result, the performance of model reduces drastically due to many missing attributes values. This paper develops a new learning system framework, called as User Query Based Learning System (UQBLS), for building data mining models best suitable for users use. We demonstrate its deployment in a real-world application of the lifetime prediction of metallic components in buildings

Knowledge channelization and innovation

This paper examines knowledge management and innovation in the Australian Construction Industry. A conceptual model is presented, based upon analysis of the literature and a series of preliminary construction industry interviews. Extensive knowledge management (KM) research has focused upon types of knowledge contained within specific organizational settings. However, we argue that a crucial missing link in KM research concerns the interface between flows of knowledge from external sources of innovations and its channelization in and out, and between organizations. This interface, regulating and facilitating knowledge from external sources of innovation into the organisation, operates under the influence of two main forces visualized as “pulling” and “pushing” forces in the model presented in this paper. The premise of the model lies in a hypothesis that as an organization changes itself into a more mature, learning organization (LO) over time, knowledge flows into it through “pull” rather than “push” forces. We conclude that a successful knowledge management initiative installs a learning and knowledge sharing culture, which is easily adaptable to new learning offering little resistance to new knowledge that flows into the organisation. The model bridges the gap between research and its application in construction practice.

Analysing the internationalisation readiness of firms

The internationalisation of firms has gained much research attention since the 1970s. However, the behavioural aspect of firms’ during their pre-internationalisation phase has not been sufficiently explored. This research proposes that a pre-internationalisation focus would not only offer an additional perspective to the study of firm internationalisation but would also address the significant research gap in studies that are theoretically based around the Uppsala Model. This study views the development of a firm from its pre-internationalisation phase into an international firm in accordance with the Uppsala Model’s ‘state’ to ‘change’ aspect transition, with ‘internationalisation readiness’ established as the missing link between these two aspects. During the pre-internationalisation phase, a firm is exposed to a wide variety of internal and external stimuli that triggers an impulse for foreign market expansion. The perception of these stimuli and the attitudinal commitment taken by the firm’s decision-makers, as well as the firm’s resource capabilities and the moderating effect of lateral rigidity, are influential towards an internationalisation decision. This paper argues the significance of adopting the pre-internationalisation phase as a research platform and the importance of analysing firms’ internationalisation readiness. A method is proposed for the development of an Internationalisation Readiness Index that could be used for measuring a firm’s propensity to commence its first export operation.