Comparative and international education: policy transfer, context sensitivity and professional development

This paper examines the intellectual and professional contribution of comparative and international studies to the field of education. It explores the nature of the challenges that are currently being faced, and assesses its potential for the advancement of future teaching, research and professional development. Attention is paid to the place of comparative and international education (CIE)-past and present-in teacher education, in postgraduate studies, and in the realms of policy and practice, theory and research. Consideration is first given to the nature and history of CIE, to its initial contributions to the field of education in the UK, and to its chief mechanisms and sites of production. Influential methodological and theoretical developments are examined, followed by an exploration of emergent questions, controversies and dilemmas that could benefit from sustained comparative analysis in the future. Conclusions consider implications for the place of CIE in the future of educational studies as a whole; for relations between and beyond the 'disciplines of education'; and for the development of sustainable research capacity in this field.

Antioxidant and tyrosinase inhibitory activity of mango seed kernel by product

The antioxidant and tyrosinase inhibitory properties of extracts of mango seed kernel (Mangifera indica L.), which is normally discarded when the fruit is processed, were studied. Extracts contained phenolic components by a high antioxidant activity, which was assessed in homogeneous solution by the 2,2-diphenyt-1-picrylhydrazyl radical and 2,2'-azinobis (3-ethylbenzothialozinesulfonic acid) radical cation-scavenging assays and in an emulsion with the ferric thiocyanate test. The extracts also possessed tyrosinase inhibitory activity. Drying conditions and extraction solvent were varied, and optimum conditions for preparation of mango seed kernel extract were found to be sun-drying with ethanol extraction at room temperature. Refluxing in acidified ethanol gave an increase in yield and the obtained extract had the highest content of total phenolics, and also was the most effective antioxidant with the highest radical-scavenging, metal-chelating and tyrosinase inhibitory activity. The extracts did not cause acute irritation of rabbit skins. Our study for the first time reveals the high total phenol content, radical-scavenging, metal-chelating and tyrosinase inhibitory activities of the extract from mango seed kernel. This extract may be suitable for use in food, cosmetic, nutraceutical and pharmaceutical applications. (C) 2009 Elsevier Ltd. All rights reserved.

Acute effects of meal fatty acids on postprandial NEFA, glucose and apo E response: implications for insulin sensitivity and lipoprotein regulation?

Our aim was to determine whether meal fatty acids influence insulin and glucose responses to mixed meals and whether these effects can be explained by variations in postprandial NEFA and Apo, which regulate the metabolism of triacylglycerol-rich lipoproteins (Apo C and E). A single-blind crossover study examined the effects of single meals enriched in saturated fatty acids SFA), n-6 PUFA and MUFA on plasma metabolite and insulin responses. The triacylglycerol response following the PUFA meal showed a lower net incremental area under the curve than following the SFA and MUFA meals (P < 0.007). Compared with the SFA meal, the PUFA meal showed a lower net incremental area under the curve for the NEFA response from initial suppression to the end of the postprandial period (180-480 min; P < 0.02), and both PUFA and MUFA showed a lower net incremental glucose response (P < 0.02), although insulin concentrations were similar between meals. The pattern of the Apo E response was also different following the SFA meal (P < 0.02). There was a significant association between the net incremental NEFA (180-480 min) and glucose response (r(s)=0.409, P=0.025), and in multiple regression analysis the NEFA response accounted for 24 % of the variation in glucose response. Meal SFA have adverse effects on the postprandial glucose response that may be due to greater elevations in NEFA arising from differences in the metabolism of SFA- v. PUFA- and MUFA-rich lipoproteins. Elevated Apo E responses to high-SFA meals may have important implications for the hepatic metabolism of triacylglycerol-rich lipoproteins.

Increased n-6 polyunsaturated fatty acids do not attenuate the effects of long-chain n-3 polyunsaturated fatty acids on insulin sensitivity or triacylglycerol reduction in Indian Asians

Background: Indian Asians in Western countries have a higher rate of coronary artery disease than do the indigenous white populations, and this higher rate may be influenced by a dietary imbalance of n-6 and n-3 polyunsaturated fatty acids (PUFAs). Objective: The objective of the study was to test the hypothesis that a high background dietary intake of n-6 PUFA attenuates the effects of fish-oil supplementation on insulin sensitivity and associated blood lipids of the metabolic syndrome. Design: Twenty-nine Indian Asian men were recruited to participate in a 12-wk dietary intervention trial. Volunteers were randomly assigned to receive either a moderate or a high n-6 PUFA diet featuring modified oils and spreads over a 6-wk period. After this 6-wk period, both groups were supplemented with 4.0 g fish oil/d (2.5 g eicosapentaenoic acid + docosahexaenoic acid) for an additional 6 wk in combination with the dietary treatment. Volunteers participated in a postprandial study and an insulin sensitivity test after the 6-wk dietary intervention and again after the fish-oil supplementation period. Results: There was no significant time X treatment interaction for blood lipids or insulin action after dietary intervention with the moderate or high n-6 PUFA diets in combination with fish oil. After the 6-wk period of fish oil supplementation, fasting and postprandial plasma triacylglycerol concentrations decreased significantly. Conclusion: The background dietary n-6 PUFA concentration did not modulate the effect of fish-oil supplementation on blood lipids or measures of insulin sensitivity in this ethnic group.

Revised QUICKI provides a strong surrogate estimate of insulin sensitivity when compared with the minimal model

OBJECTIVE: To compare insulin sensitivity (Si) from a frequently sampled intravenous glucose tolerance test (FSIGT) and subsequent minimal model analyses with surrogate measures of insulin sensitivity and resistance and to compare features of the metabolic syndrome between Caucasians and Indian Asians living in the UK. SUBJECTS: In all, 27 healthy male volunteers (14 UK Caucasians and 13 UK Indian Asians), with a mean age of 51.2 +/- 1.5 y, BMI of 25.8 +/- 0.6 kg/m(2) and Si of 2.85 +/- 0.37. MEASUREMENTS: Si was determined from an FSIGT with subsequent minimal model analysis. The concentrations of insulin, glucose and nonesterified fatty acids (NEFA) were analysed in fasting plasma and used to calculate surrogate measure of insulin sensitivity (quantitative insulin sensitivity check index (QUICKI), revised QUICKI) and resistance (homeostasis for insulin resistance (HOMA IR), fasting insulin resistance index (FIRI), Bennetts index, fasting insulin, insulin-to-glucose ratio). Plasma concentrations of triacylglycerol (TAG), total cholesterol, high density cholesterol, (HDL-C) and low density cholesterol, (LDL-C) were also measured in the fasted state. Anthropometric measurements were conducted to determine body-fat distribution. RESULTS: Correlation analysis identified the strongest relationship between Si and the revised QUICKI (r = 0.67; P = 0.000). Significant associations were also observed between Si and QUICKI (r = 0.51; P = 0.007), HOMA IR (r = -0.50; P = 0.009), FIRI and fasting insulin. The Indian Asian group had lower HDL-C (P = 0.001), a higher waist-hip ratio (P = 0.01) and were significantly less insulin sensitive (Si) than the Caucasian group (P = 0.02). CONCLUSION: The revised QUICKI demonstrated a statistically strong relationship with the minimal model. However, it was unable to differentiate between insulin-sensitive and -resistant groups in this study. Future larger studies in population groups with varying degrees of insulin sensitivity are recommended to investigate the general applicability of the revised QUICKI surrogate technique.

Impact of menstrual cycle phase on insulin sensitivity measures and fasting lipids

There is evidence to suggest that insulin sensitivity may vary in response to changes in sex hormone levels. However, the results Of human studies designed to investigate changes in insulin sensitivity through the menstrual cycle have proved inconclusive. The aims of this Study were to 1) evaluate the impact of menstrual cycle phase on insulin sensitivity measures and 2) determine the variability Of insulin sensitivity measures within the same menstrual cycle phase. A controlled observational study of 13 healthy premenopausal women, not taking any hormone preparation and having regular menstrual cycles, was conducted. Insulin sensitivity (Si) and glucose effectiveness (Sg) were measured using an intravenous glucose tolerance test (IVGTT) with minimal model analysis. Additional Surrogate measures Of insulin sensitivity were calculated (homoeostasis model for insulin resistance [HOMA IR], quantitative insulin-to-glucose check index [QUICKI] and revised QUICKI [rQUICKI]), as well as plasma lipids. Each woman was tested in the luteal and follicular phases of her Menstrual cycle, and duplicate measures were taken in one phase of the cycle. No significant differences in insulin sensitivity (measured by the IVGTT or Surrogate markers) or plasma lipids were reported between the two phases of the menstrual cycle or between duplicate measures within the same phase. It was Concluded that variability in measures of insulin sensitivity were similar within and between menstrual phases.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

Sparse kernel modelling: a unified approach

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Kernel density construction using orthogonal forward regression

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.