Educational drama in the teaching of education for sustainability

In this paper, I describe part of my research project that examines the use of Educational Drama in Education for Sustainability in the upper stages of the primary school (10- and 11-year-olds). Central to the research is a small-scale qualitative research study. Here, I describe the educational focus of the study and outline the methodology. Central to the study was a series of drama lessons (taught by me) based on environmental themes. The lessons link with some of the key aims in Education for Sustainability - to help young people to develop awareness, knowledge and concepts, to encourage positive attitudes and personal lifestyle decisions and to help them to acquire action skills in and for the environment. The locus is within the Scottish education system. A number of key data were generated during the teaching and evaluation of the lessons. These take the form of field notes, children's evaluations of their work and learning, observation schedules, taped interviews with participants and observers and videotapes of the lessons. The analysis of the data is ongoing, but already there is substantial evidence to suggest that the drama was instrumental in helping the children to achieve the learning outcomes set for the lessons. Some of that evidence is presented here. I suggest that the active, participative learning central to drama is particularly useful for allowing children to develop skills in communication, collaboration and expressing ideas and opinions. Also, the immersion in the imagined context and narrative, integral to the 'stories' in the drama, allows the children to feel sympathy for and empathy with people who are affected by environmental issues and problems. In giving the children a context for research and in helping them to plan solutions and to suggest alternatives, the drama allows the participants opportunities to rehearse active citizenship and facilitates learning in Education for Sustainability

Two-stream instability in quasi-one-dimensional Bose-Einstein condensates

We apply a kinetic model to predict the existence of an instability mechanism in elongated Bose-Einstein condensates. Our kinetic description, based on the Wigner formalism, is employed to highlight the existence of unstable Bogoliubov waves that may be excited in the counterpropagation configuration. We identify a dimensionless parameter, the Mach number at T=0, that tunes different regimes of stability. We also estimate the magnitude of the main parameters at which two-stream instability is expected to be observed under typical experimental conditions

The Mr 28,000 gap junction proteins from rat heart and liver are different but related

The sequence of the amino-terminal 32 residues of the rat heart Mr 28,000 gap junction protein presented here allows, for the first time, a sequence comparison of gap junctional proteins from different tissues (heart and liver). Comparison of the rat heart gap junction protein sequence and that available from rat liver reveals 43% sequence identity and conservative changes at an additional 25% of the positions. Both proteins exhibit a hydrophobic domain which could represent a transmembrane span of the junction. This result unequivocally demonstrates the existence of at least two forms of the gap junction protein. As yet, no homology is evident between the gap junctional proteins of either heart or liver and main intrinsic protein from rat eye lens

Surface phase transitions in one-dimensional channels arranged in a triangular cross-sectional structure: Theory and Monte Carlo simulations

Monte Carlo simulations and finite-size scaling analysis have been carried out to study the critical behavior in a submonolayer lattice-gas of interacting monomers adsorbed on one-dimensional channels arranged in a triangular cross-sectional structure. The model mimics a nanoporous environment, where each nanotube or unit cell is represented by a one-dimensional array. Two kinds of lateral interaction energies have been considered: $1)$ $w_L$, interaction energy between nearest-neighbor particles adsorbed along a single channel and $2)$ $w_T$, interaction energy between particles adsorbed across nearest-neighbor channels. For $w_L/w_T=0$ and $w_T > 0$, successive planes are uncorrelated, the system is equivalent to the triangular lattice and the well-known $(\sqrt{3} \times \sqrt{3})$ $[(\sqrt{3} \times \sqrt{3})^*]$ ordered phase is found at low temperatures and a coverage, $\theta$, of 1/3 $[2/3]$. In the more general case ($w_L/w_T \neq 0$ and $w_T > 0$), a competition between interactions along a single channel and a transverse coupling between sites in neighboring channels allows to evolve to a three-dimensional adsorbed layer. Consequently, the $(\sqrt{3} \times \sqrt{3})$ and $(\sqrt{3} \times \sqrt{3})^*$ structures "propagate" along the channels and new ordered phases appear in the adlayer. The Monte Carlo technique was combined with the recently reported Free Energy Minimization Criterion Approach (FEMCA), to predict the critical temperatures of the order-disorder transformation. The excellent qualitative agreement between simulated data and FEMCA results allow us to interpret the physical meaning of the mechanisms underlying the observed transitions.Comment: 24 pages, 6 figure

The preparation and characterisation of monomeric and linked metal carbonyl clusters containing the closo-Si2Co4 pseudo-octahedral core

PhSiH3 reacts with [Co₄(CO)₁₂] at 50 °C in hydrocarbon solvents to give [(µ₄-SiPh)₂Co₄(CO)₁₁], 2c, shown by an X-ray crystal structure determination to have a pseudo-octahedral Si₂Co₄ core. Substituted aryl-silanes behaved similarly. Mixtures of PhSiH₃, H₃SiC₆H₄SiH₃ and [Co₄(CO)₁₂] in a ca. 2 1 2 ratio gave the dimeric cluster [{Co₄(µ₄-SiPh)(CO)₁₁Si}₂C₆H₄], 3a, which has the two Si₂Co₄ cores linked by a C₆H₄ group to give a rigid molecule which an X-ray structure analysis shows to be over 23 Å long. Related dimers linked by –(CH₂)₈– groups were isolated from mixtures of PhSiH₃, α ,ω-(H₃Si)₂(CH₂)₈ and [Co₄(CO)₁₂]. Electrochemical studies show the two cluster units in 3a do not interact electronically

Models of the SL9 Impacts II. Radiative-hydrodynamic Modeling of the Plume Splashback

We model the plume "splashback" phase of the SL9 collisions with Jupiter using the ZEUS-3D hydrodynamic code. We modified the Zeus code to include gray radiative transport, and we present validation tests. We couple the infalling mass and momentum fluxes of SL9 plume material (from paper I) to a jovian atmospheric model. A strong and complex shock structure results. The modeled shock temperatures agree well with observations, and the structure and evolution of the modeled shocks account for the appearance of high excitation molecular line emission after the peak of the continuum light curve. The splashback region cools by radial expansion as well as by radiation. The morphology of our synthetic continuum light curves agree with observations over a broad wavelength range (0.9 to 12 microns). A feature of our ballistic plume is a shell of mass at the highest velocities, which we term the "vanguard". Portions of the vanguard ejected on shallow trajectories produce a lateral shock front, whose initial expansion accounts for the "third precursors" seen in the 2-micron light curves of the larger impacts, and for hot methane emission at early times. Continued propagation of this lateral shock approximately reproduces the radii, propagation speed, and centroid positions of the large rings observed at 3-4 microns by McGregor et al. The portion of the vanguard ejected closer to the vertical falls back with high z-component velocities just after maximum light, producing CO emission and the "flare" seen at 0.9 microns. The model also produces secondary maxima ("bounces") whose amplitudes and periods are in agreement with observations.Comment: 13 pages, 9 figures (figs 3 and 4 in color), accepted for Ap.J. latex, version including full figures at: http://oobleck.tn.cornell.edu/jh/ast/papers/slplume2-20.ps.g

Anti-oestrogen therapy switches off tumour suppressors and proapoptotic genes in breast cancer and reveals a new therapeutic opportunity

Background Previous studies in the Tenovus Centre have demonstrated that the development of antioestrogen resistance in vitro is accompanied by unfavourable changes in the breast cancer phenotype leading to increase tumour cell growth rate. Here evidence is presented to suggest that this is in part due to antihormones causing the epigenetic silencing of oestrogen-induced genes involved in the negative regulation of cell growth. Importantly, we show that reversal of this process using the demethylation agent 5-azacytidine (5AZA) allows oestrogen-induced cell kill by a previously unrecognised mechanism. Methods The breast cancer cell lines used in this study were MCF7, MCF7-derived tamoxifen-resistant variant (TamR) and TamR sublines that had been withdrawn from tamoxifen (TamRwd) for up to 6 months. Cells were challenged by oestradiol (E2), antihormones and 5AZA. Cell growth responses were assessed by anchorage-dependent growth assays and alterations in expression/activity of oestrogen receptor (ER) and ER-regulated genes were analysed by real-time PCR, western blotting and/or immunocytochemistry. Results Compared with the parental MCF7 cells, TamR cells showed a significant upregulated basal rate of growth that was maintained on tamoxifen withdrawal for 6 months. Following the tamoxifen withdrawal, the cells remained ER-positive and showed a slight growth response to E2. In contrast, they showed no growth inhibitory response to tamoxifen. Examination of the methylation status of the promoters of two classically ER-regulated genes switched off in TamR and TamRwd cells, pS2 and progesterone receptor (PR), confirmed their increased methylation and that 5AZA was able to reverse this process, allowing the re-expression of pS2 and PR on E2 treatment. Although pS2 and PR are not thought to play a role in the regulation of cell growth, these data provide proof of principal that gene silencing occurs in TamR cells and that it can be reinstated by 5AZA plus E2. To determine whether tamoxifen was capable of inducing the methylation of ER-regulated genes involved in cell growth, TamRwd cells pretreated with 5AZA were subject to an E2 dose–response challenge. In contrast to TamRwd cells treated with E2, which promoted a growth response, E2 in combination with 5AZA was strongly inhibitory at physiological doses of the steroid (10-9 M), with this action being reversed by tamoxifen. An Affymetrix analysis of the TamR cells has revealed multiple E2-regulated genes that are switched off in the resistant cells whose ontology indicates tumour suppressor/proapoptotic functions. Conclusion Our data suggest that antihormone resistance may be associated with the epigenetic silencing of growth inhibitory genes leading to enhanced growth rates. We propose that reinstatement of the expression of such genes using demethylation agents in combination with E2 may provide a previously unrecognised therapeutic opportunity in breast cancer

Automatic Spectroscopic Data Categorization by Clustering Analysis (ASCLAN): A Data-Driven Approach for Distinguishing Discriminatory Metabolites for Phenotypic Subclasses

We propose a novel data-driven approach aiming to reliably distinguish discriminatory metabolites from nondiscriminatory metabolites for a given spectroscopic data set containing two biological phenotypic subclasses. The automatic spectroscopic data categorization by clustering analysis (ASCLAN) algorithm aims to categorize spectral variables within a data set into three clusters corresponding to noise, nondiscriminatory and discriminatory metabolites regions. This is achieved by clustering each spectral variable based on the r(2) value representing the loading weight of each spectral variable as extracted from a orthogonal partial least-squares discriminant (OPLS-DA) model of the data set. The variables are ranked according to r(2) values and a series of principal component analysis (PCA) models are then built for subsets of these spectral data corresponding to ranges of r(2) values. The Q(2)X value for each PCA model is extracted. K-means clustering is then applied to the Q(2)X values to generate two clusters based on minimum Euclidean distance criterion. The cluster consisting of lower Q(2)X values is deemed devoid of metabolic information (noise), while the cluster consists of higher Q(2)X values is then further subclustered into two groups based on the r(2) values. We considered the cluster with high Q(2)X but low r(2) values as nondiscriminatory, while the cluster with high Q(2)X and r(2) values as discriminatory variables. The boundaries between these three clusters of spectral variables, on the basis of the r(2) values were considered as the cut off values for defining the noise, nondiscriminatory and discriminatory variables. We evaluated the ASCLAN algorithm using six simulated (1)H NMR spectroscopic data sets representing small, medium and large data sets (N = 50, 500, and 1000 samples per group, respectively), each with a reduced and full resolution set of variables (0.005 and 0.0005 ppm, respectively). ASCLAN correctly identified all discriminatory metabolites and showed zero false positive (100% specificity and positive predictive value) irrespective of the spectral resolution or the sample size in all six simulated data sets. This error rate was found to be superior to existing methods for ascertaining feature significance: univariate t test by Bonferroni correction (up to 10% false positive rate), Benjamini-Hochberg correction (up to 35% false positive rate) and metabolome wide significance level (MWSL, up to 0.4% false positive rate), as well as by various OPLS-DA parameters: variable importance to projection, (up to 15% false positive rate), loading coefficients (up to 35% false positive rate), and regression coefficients (up to 39% false positive rate). The application of ASCLAN was further exemplified using a widely investigated renal toxin, mercury II chloride (HgCl2) in rat model. ASCLAN successfully identified many of the known metabolites related to renal toxicity such as increased excretion of urinary creatinine, and different amino acids. The ASCLAN algorithm provides a framework for reliably differentiating discriminatory metabolites from nondiscriminatory metabolites in a biological data set without the need to set an arbitrary cut off value as applied to some of the conventional methods. This offers significant advantages over existing methods and the possibility for automation of high-throughput screening in "omics" data