Hot-electron thermocouple and the diffusion thermopower of two-dimensional electrons in GaAs

A simple hot-electron thermocouple is realized in a two-dimensional electron system (2DES) and used to measure the diffusion thermopower of the 2DES at zero magnetic field. This hot-electron technique, which requires no micron-scale patterning of the 2DES, is much less sensitive than conventional methods to phonon-drag effects. Our thermopower results are in good agreement with the Mott formula for diffusion thermopower for temperatures up to T~2 K

‘‘There’s so much more to it than what I initially thought’’: Stepping into researchers’ shoes with a class activity in a first year psychology survey course

In psychology, it is widely agreed that research methods, although central to the discipline, are particularly challenging to learn and teach, particularly at introductory level. This pilot study explored the potential of embedding a student-conducted research activity in a one-semester undergraduate Introduction to Psychology survey course, with the aims of (a) engaging students with the topic of research methods; (b) developing students’ comprehension and application of research methods concepts; and (c) building students’ ability to link research with theory. The research activity explored shoe ownership, examining gender differences and relationships with age, and linking to theories of gender difference and of consumer identity. The process of carrying out the research and reflecting on it created a contextualized, active learning environment in which students themselves raised many issues that research methods lectures seek to cover. Students also wrote richer assignments than standard first year mid-term essay

Thermoelectric response of fractional quantized Hall and reentrant insulating states in the N=1 Landau level

Detailed measurements of the longitudinal thermopower of two-dimensional electrons in the first excited Landau level are reported. Clear signatures of numerous fractional quantized Hall states, including those at ν=5/2 and 7/3, are observed in the magnetic field and temperature dependence of the thermopower. An abrupt collapse of the thermopower is observed below about T=40 mK at those filling factors where reentrant insulating electronic states have been observed in conventional resistive transport studies. The thermopower observed at ν=5/2 is discussed in the context of recent theories which incorporate non-Abelian quasiparticle exchange statistics

Thermopower of Two-Dimensional Electrons at ν\nu = 3/2 and 5/2

The longitudinal thermopower of ultra-high mobility two-dimensional electrons has been measured at both zero magnetic field and at high fields in the compressible metallic state at filling factor $\nu = 3/2$ and the incompressible fractional quantized Hall state at $\nu = 5/2$. At zero field our results demonstrate that the thermopower is dominated by electron diffusion for temperatures below about $T = 150$ mK. A diffusion dominated thermopower is also observed at $\nu = 3/2$ and allows us to extract an estimate of the composite fermion effective mass. At $\nu = 5/2$ both the temperature and magnetic field dependence of the observed thermopower clearly signal the presence of the energy gap of this fractional quantized Hall state. We find that the thermopower in the vicinity of $\nu = 5/2$ exceeds that recently predicted under the assumption that the entropy of the 2D system is dominated by non-abelian quasiparticle exchange statistics.Comment: 10 pages, 10 figures

Thermopower of two-dimensional electrons at filling factors ν = 3/2 and 5/2

The longitudinal thermopower of ultrahigh mobility two-dimensional (2D) electrons has been measured at both zero magnetic field and at high fields in the compressible metallic state at filling factor ν=3/2 and the incompressible fractional quantized Hall state at ν=5/2. At zero field our results demonstrate that the thermopower is dominated by electron diffusion for temperatures below about T=150 mK. A diffusion-dominated thermopower is also observed at ν=3/2 and allows us to extract an estimate of the composite fermion effective mass. At ν=5/2 both the temperature and magnetic field dependence of the observed thermopower clearly signal the presence of the energy gap of this fractional quantized Hall state. We find that the thermopower in the vicinity of ν=5/2 exceeds that recently predicted under the assumption that the entropy of the 2D system is dominated by non-Abelian quasiparticle exchange statistics

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

<p>Abstract</p> <p>Background</p> <p>Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.</p> <p>Results</p> <p>We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC) devoted to BN structure learning.</p> <p>Conclusion</p> <p>We studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.</p

Learning from peer feedback on student-generated multiple choice questions: Views of introductory physics students

PeerWise is an online application where students are encouraged to generate a bank of multiple choice questions for their classmates to answer. After answering a question, students can provide feedback to the question author about the quality of the question and the question author can respond to this. Student use of, and attitudes to, this online community within PeerWise was investigated in two large first year undergraduate physics courses, across three academic years, to explore how students interact with the system and the extent to which they believe PeerWise to be useful to their learning. Most students recognized that there is value in engaging with PeerWise, and many students engaged deeply with the system, thinking critically about the quality of their submissions and reflecting on feedback provided to them. Students also valued the breadth of topics and level of difficulty offered by the questions, recognized the revision benefits afforded by the resource, and were often willing to contribute to the community by providing additional explanations and engaging in discussion