426 research outputs found

    Nutrient utilisation, growth and chemical body composition of pre-weaned lambs reared artificially : effects of feeding milk replacer and pellets : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at Massey University, Palmerston North, Manawatū, New Zealand

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    Listed in 2016 Dean's List of Exceptional ThesesUnderstanding how nutrient supply controls lamb growth is important in improving the efficiency of nutrient utilisation. Estimation of metabolisable energy (ME) requirements for lamb maintenance and growth pre-weaning has been limited to milk-only fed lambs. This is due, at least in part, to the difficulty of measuring pasture intake in pre-weaned lambs, which restricts the determination of nutrient balances and nutrient use efficiencies. The aims of this thesis were to: 1) evaluate the effect of various milk and pellets combinations on lamb growth, organ development, body composition and utilisation of energy for maintenance and growth, 2) derive equations for predicting feed intake, and 3) develop a growth simulation model for use as a tool to develop feeding strategies for lambs. Lambs were offered various diet combinations from age one day until slaughter at 18 kg live weight (LW). Addition of solid feed to the milk diet of pre-weaned lambs improved their growth rates, efficiency of gain and enhanced rumen development. Increasing daily ME intake from 1.5 times maintenance to ad libitum at a constant protein to energy ratio did not alter the total chemical body composition of the lambs fed to a fixed LW. Increasing the crude protein content of milk replacer, and therefore the corresponding protein to energy ratio, increased average daily gain and efficiency of gain in lambs. Further, the protein content in the empty bodies of lambs increased whilst fat content decreased. Growth and body composition of lambs were unaffected by altered pellet protein content. The study also showed that lambs fed in excess of their protein and energy requirements reached maximum potential protein deposition rates. Based on a model developed, overestimating the maintenance energy requirements of milk-only fed lambs underestimated their daily fat deposition rates and underestimating the maintenance requirements of lamb offered milk and ad libitum access to pellets over estimated their daily fat deposition. A greater percentage increase in fat deposited in gain increased the energy requirements for gain in the lambs. This study has contributed to the knowledge on rearing lambs artificially with various combinations of milk and pellets. The findings will provide a useful platform for future studies aiming to develop feeding strategies to improve pre-weaning lamb growth

    Structured Dependence Modelling:A Bayesian Framework for Data-Driven Validation of Psychometric Measurement Instruments

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    This thesis presents Structured Dependence Modelling (SDM), a novel Bayesian matrix-based framework developed for the data-driven validation of psychometric instruments. SDM implements a dependence-based representation of the theorized latent variable models that underlie psychometric measurement instruments. Unlike traditional methods such as Item Response Theory (IRT) and Structural Equation Modelling (SEM), the latent variables are not directly modelled. Instead, their anticipated effect on the statistical dependence among exam items, response times, and other psychometric data is specified through a structured variance-covariance matrix. The proposed framework addresses the computational inefficiencies often encountered with current Bayesian matrix-based approaches in psychometric applications, particularly as the latent variable structures grow in complexity. In SDM this issue is tackled with a truncation function for the prior distribution of matrix parameters, dynamically enforcing boundaries to maintain the positive-definiteness of the structured matrix in the multidimensional parameter space. Gibbs-samplers and a gradient-based Hamilton Monte Carlo (HMC) algorithm are developed that scale to complex psychometric models and high-dimensional data. For inference-making with SDMs, objective Bayes factors and posterior credible intervals for matrix parameters are derived. In simulation studies, SDMs outperformed IRT- and SEM-based methods at testing the dimensionality of categorical response and response time data. The results were obtained with sample sizes that are common in data-driven validation studies for the psychometric properties of measurement instruments. Empirical examples demonstrate the applicability of the proposed framework to real-world, hierarchically nested response and process data from digital assessments. The thesis concludes with a reflection on the objectives of my PhD research, assessing the benefits and limitations of the proposed framework and suggesting potential future directions for its development and application

    Magnetically Induced Metallic Phase in Superconducting Tantalum Films

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    We have studied the electronic transport properties of homogeneously disordered superconducting tantalum thin films in magnetic fields. The films exhibit three distinct transport regimes in the zero temperature limit which we identify as superconducting, metallic, and insulating phases. The metallic phase is unexpected. The transport characteristics of this metallic phase are found to be similar to those of MoGe films and high mobility dilute two-dimensional electrons or holes confined in semiconductor interface or transistor geometry.Comment: four pages, four figure

    Attention Shaping: a Reward-Based Learning Method to Enhance Skills Training Outcomes in Schizophrenia

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    Disturbances in sustained attention commonly interfere with the ability of persons with schizophrenia to benefit from evidence-based psychosocial treatments. Cognitive remediation interventions have thus far demonstrated minimal effects on attention, as have medications. There is thus a gap between the existence of effective psychosocial treatments and patients’ ability to effectively engage in and benefit from them. We report on the results of a multisite study of attention shaping (AS), a behavioral intervention for improving attentiveness and learning of social skills among highly distractible schizophrenia patients. Patients with chronic schizophrenia who were refractory to skills training were assigned to receive either the UCLA Basic Conversation Skills Module (BCSM) augmented with AS (n = 47) or in the standard format (n = 35). AS, a reward-based learning procedure, was employed to facilitate patients’ meeting clearly defined and individualized attentiveness and participation goals during each session of a social skills training group. Primary outcome measures were observational ratings of attentiveness in each session and pre- and post-BCSM ratings of social skill and symptoms. Patients receiving social skills training augmented with AS demonstrated significantly more attentiveness in group sessions and higher levels of skill acquisition; moreover, significant relationships were found between changes in attentiveness and amount of skills acquired. Changes in attentiveness were unrelated to level or change in antipsychotic medication dose. AS is an effective example of supported cognition, in that cognitive abilities are improved within the environmental context where the patient is experiencing difficulty, leading to gains in both attention and functional outcome

    Organizational Twitter Use: A Qualitative Analysis of Tweets During Breast Cancer Awareness Month

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    One in eight women will develop breast cancer in her lifetime. The best-known awareness event to fight the health issue is Breast Cancer Awareness Month (BCAM). Twitter is a growing source of health information amongst users; however, little research exists into understanding how various organizations use their Twitter accounts to communicate about breast cancer during BCAM, as well as implications of this use for the health information consumers. In this context, there is also a dearth of research about if, and how organizations use behavioral change theories to tailor their social media content or not. The paper explored through qualitative content analysis how four different health related organizations- Susan G. Komen, US News Health, Woman’s Hospital and Breast Cancer Social Media use their Twitter accounts to talk about breast cancer during the Breast Cancer Awareness Month (BCAM). In this study, all the tweets by these organizations were analyzed through the framework of behavioral change theory- Health Belief Model (HBM). The main purpose of this research study was to examine the tweets of the varied organizations for the presence or absence of theoretical constructs of Health Belief Model such as perceived threat, perceived benefits, perceived barriers and cues to action, which inform about the potential for users to take protective action against breast cancer. A content analysis based on theoretical lens of Health Belief Model (HBM) of 2916 tweets revealed that majority of the tweets posted by these organizations did not reflect the theoretical constructs of Health Belief Model. Out of all the tweets that represented the theoretical constructs, it was observed that “perceived barrier” (n= 781, 26.37%), was in the maximum number. This was followed by “cues to action” (n= 711, 24.01%), “perceived benefits” (n=397, 13.40%) and “perceived threat” (n=230, 7.76%). Overall the study demonstrated that different organizations shared valuable breast cancer related content on Twitter and each Twitter outlet took a different approach to its use of Twitter, evident through focus on different types of breast cancer related content, use of elements like hashtags and videos etc

    Breast Cancer Awareness Month social media toolkit

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    This toolkit is designed to help stakeholders implement evidence-based practices when communicating about breast cancer. It can also help you plan, implement and evaluate your social media strategy and make the case for why it\u2019s important.Public health professionals, cancer control professionals, cancer centers, coalitions, community-based organizations and other stakeholders can use this toolkit and adapt its messaging for their unique audiences and areas of expertise.Breast Cancer Awareness Month is an annual observance held throughout the month of October. It is intended to raise awareness of breast cancer, the most common cancer in women of all races and ethnicities, and to focus on research into its cause, prevention, diagnosis, treatment, survivorship and cure (Centers for Disease Control and Prevention [CDC], 2019). In 2016, over 245,000 women and over 2,100 men were diagnosed with breast cancer (CDC, 2019). Breast Cancer Awareness Month begins on October 1st and ends on October 31st.This work was supported by Cooperative Agreement #NU58DP006461-01 from the Centers for Disease Control and Prevention (CDC). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC.https://smhs.gwu.edu/cancercontroltap/sites/cancercontroltap/files/Breast%20Cancer%20Awareness%20Month%20Social%20Media%20Toolkit%202019.pdf?deliveryName=USCDC_9_13-DM11056-22019Cooperative Agreement #NU58DP006461-01676

    Breast Cancer Risk and Breast-Cancer-Specific Mortality following Risk-Reducing Salpingo-Oophorectomy in BRCA Carriers : A Systematic Review and Meta-Analysis

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    Funding This research was funded by Rosetrees Trust, grant number CF1\100001, and Barts Charity, grant number ECMG1C3R. The funders had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.Peer reviewedPublisher PD