Learning to read with a critical eye: cultivating discerning readers of media reports with a science component.

It is important for young people to be able to read science-related media reports with discernment. ‘Getting Newswise’ was a research project designed to enable science and English teachers, working collaboratively, to equip pupils through the curriculum with critical reading skills appropriate for science news. Phase one of the study found that science and English teachers respond differently to science news articles and eight categories of critical response were identified. These findings informed phase two, in which classroom activities were devised whereby pupils examined, evaluated and responded to science-related news reports. Science-English collaboration had positive outcomes for pupil understanding

Genetic analyses reveal high levels of seed and pollen flow in hawthorn (Crataegus monogyna Jacq.), a key component of hedgerows

Hedgerows represent important components of agri-environment landscapes that are increasingly coming under threat from climate change, emergent diseases, invasive species and land use change. Given that population genetic data can be used to inform best-practice management strategies for woodland and hedgerow tree species, we carried out a study on hawthorn (Crataegus monogyna Jacq.), a key component of hedgerows, on a regional basis using a combination of nuclear and chloroplast microsatellite markers. We found that levels of genetic diversity were high and comparable to, or slightly higher than, other tree species from the same region. Levels of population differentiation for both sets of markers, however, were extremely low, suggesting extensive gene flow via both seed and pollen. These findings suggest that a holistic approach to woodland management, one which does not necessarily rely on the concept of “seed zones” previously suggested, but which also takes into account populations with high and/or rare chloroplast (i.e. seed-specific) genetic variation, might be the best approach to restocking and replanting.

Incidence and clinicobiologic characteristics of leukemic B-cell chronic lymphoproliferative disorders with more than one B-cell clone.

Leukemic B-chronic lymphoproliferative disorders (B-CLPDs) are generally believed to derive from a monoclonal B cell; biclonality has only occasionally been reported. In this study, we have explored the incidence of B-CLPD cases with 2 or more B-cell clones and established both the phenotypic differences between the coexisting clones and the clinicobiologic features of these patients. In total, 53 B-CLPD cases with 2 or more B-cell clones were studied. Presence of 2 or more B-cell clones was suspected by immunophenotype and confirmed by molecular/genetic techniques in leukemic samples (n = 42) and purified B-cell subpopulations (n = 10). Overall, 4.8% of 477 consecutive B-CLPDs had 2 or more B-cell clones, their incidence being especially higher among hairy cell leukemia (3 of 13), large cell lymphoma (2 of 10), and atypical chronic lymphocytic leukemia (CLL) (4 of 29). In most cases the 2 B-cell subsets displayed either different surface immunoglobulin (sIg) light chain (n = 37 of 53) or different levels of the same sIg (n = 9 of 53), usually associated with other phenotypic differences. Compared with monoclonal cases, B-CLL patients with 2 or more clones had lower white blood cell (WBC) and lymphocyte counts, more frequently displayed splenomegaly, and required early treatment. Among these, the cases in which a CLL clone coexisted with a non-CLL clone were older and more often displayed B symptoms, a monoclonal component, and diffuse infiltration of bone marrow and required early treatment more frequently than cases with monoclonal CLL or 2 CLL clones.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.