844 resultados para Aid to families with dependent children programs


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A brief survey of the history of this most severe pathogen of wheat and our developing understanding of it.

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The communal lands of the Eastern Cape have been regarded as both tools and problems by policy-makers. In particular, communal lands are problematised as environmentally degraded, of suboptimum productivity and constraining economic development. The Eastern Cape Communal Lands Research Project was framed within this policy discourse with the aim of introducing legume-based pasture into ‘abandoned arable lands’. Initial results from community workshops show that the institutional arrangements for these arable lands vary widely and, with them, the capacity to utilise any new technology that may have application to them. Rather than simply draw on social capital, if a participatory research approach is to enhance the agency of the participating communites, it may need to contribute to social capital building and especially to create a dialogical space in which the matters being researched can be discussed meaningfully.

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A straightforward one-step method for the N-methylthiomethylation of benzimidazoles has been developed employing DMSO as a solvent and as a reagent. This methodology has been applied for the synthesis of diverse N-methylthiomethyl derivatives of benzimidazoles. The products can be chemoselectively oxidized to the corresponding sulfoxides with NaBiO3 in acetic acid. Both the N-methylthiomethyl derivatives of benzimidazoles and their corresponding sulfoxides are important medicinal scaffolds.

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We extended 'littleBits' electronic components by attaching them to a larger base that was designed to help make them easier to pick up and handle, and easier to assemble into circuits for people with learning disabilities. A pilot study with a group of students with learning disabilities was very positive. There were fewer difficulties in assembling the components into circuits, and problems such as attempting to connect them the wrong way round or the wrong way up were eliminated completely.

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The quantification of uncertainty is an increasingly popular topic, with clear importance for climate change policy. However, uncertainty assessments are open to a range of interpretations, each of which may lead to a different policy recommendation. In the EQUIP project researchers from the UK climate modelling, statistical modelling, and impacts communities worked together on ‘end-to-end’ uncertainty assessments of climate change and its impacts. Here, we use an experiment in peer review amongst project members to assess variation in the assessment of uncertainties between EQUIP researchers. We find overall agreement on key sources of uncertainty but a large variation in the assessment of the methods used for uncertainty assessment. Results show that communication aimed at specialists makes the methods used harder to assess. There is also evidence of individual bias, which is partially attributable to disciplinary backgrounds. However, varying views on the methods used to quantify uncertainty did not preclude consensus on the consequential results produced using those methods. Based on our analysis, we make recommendations for developing and presenting statements on climate and its impacts. These include the use of a common uncertainty reporting format in order to make assumptions clear; presentation of results in terms of processes and trade-offs rather than only numerical ranges; and reporting multiple assessments of uncertainty in order to elucidate a more complete picture of impacts and their uncertainties. This in turn implies research should be done by teams of people with a range of backgrounds and time for interaction and discussion, with fewer but more comprehensive outputs in which the range of opinions is recorded.

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The “littleBits go LARGE" project extends littleBits electronic modules, an existing product that is aimed at simplifying electronics for a wide range of audiences. In this project we augment the littleBits modules to make them more accessible to people with learning disabilities. We will demonstrate how we have made the modules easier to handle and manipulate physically, and how we are augmenting the design of the modules to make their functions more obvious and understandable.

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Bone tumor incidence in women peaks at age 50-60, coinciding with the menopause. That estrogen (E2) and triiodothyronine (T3) interact in bone metabolism has been well established. However, few data on the action of these hormones are available. Our purpose was to determine the role of E2 and T3 in the expression of bone activity markers, namely alkaline phosphatase (AP) and receptor activator of nuclear factor kappa B ligand (RANKL). Two osteosarcoma cell lines: MG-63 (which has both estrogen (ER) and thyroid hormone (TR) receptors) and SaOs-29 (ER receptors only) were treated with infraphysiological E2 associated with T3 at infraphysiological, physiological, and supraphysiological concentrations. Real-time RT-PCR was used for expression analysis. Our results show that, in MG-63 cells, infraphysiological E2 associated with supraphysiological T3 increases AP expression and decreases RANKL expression, while infraphysiological E2 associated with either physiological or supraphysiological T3 decreases both AP and RANKL expression. On the other hand, in SaOs-2 cells, the same hormone combinations had no significant effect on the markers` expression. Thus, the analysis of hormone receptors was shown to be crucial for the assessment of tumor potential growth in the face of hormonal changes. Special care should be provided to patients with T3 and E2 hormone receptors that may increase tumor growth. Copyright (C) 2007 John Wiley & Sons, Ltd.

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There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

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Research shows that people with diabetes want their lives to proceed as normally as possible, but some patients experience difficulty in reaching their desired goals with treatment. The learning process is a complex phenomenon interwoven into every facet of life. Patients and healthcare providers often have different perspectives in care which gives different expectations on what the patients need to learn and cope with. The aim of this study, therefore, is to describe the experience of learning to live with diabetes. Interviews were conducted with 12 patients afflicted with type 1 or type 2 diabetes. The interviews were then analysed with reference to the reflective lifeworld research approach. The analysis shows that when the afflicted realize that their bodies undergo changes and that blood sugar levels are not always balanced as earlier in life, they can adjust to their new conditions early. The afflicted must take responsibility for balancing their blood sugar levels and incorporating the illness into their lives. Achieving such goals necessitates knowledge. The search for knowledge and sensitivity to changes are constant requirements for people with diabetes. Learning is driven by the tension caused by the need for and dependence on safe blood sugar control, the fear of losing such control, and the fear of future complications. The most important responsibilities for these patients are aspiring to understand their bodies as lived bodies, ensuring safety and security, and acquiring the knowledge essential to making conscious choices.