Inertia and the Learning Journey:Choice, Resources and the Student Experience

Aim: Students’ ability to make informed choices can be influenced by those resources which are available to them. (Schwartz, 2005) The interaction between student and university is enriched by deeper comprehension of choice processes and how resources can be positioned effectively. (Foskett and Hemsley-Brown, 2001) Three contrasting degree programmes at the University of Manchester participated in HEARing Student Voices through focus groups and individual interviews and have begun action research concerning: resources available to inform students’ choices, feedback as resources for learning, support students need to understand their university experience.(Ertl et al., 2008, Johnston and Watson, 2009) (Hattie and Timperley, 2007)We aim to understand how best to create resources that answers the needs of students and staff, can be flexible enough to be useful for a variety of learning styles and will help trace learning development throughout the student’s time at university. Methodology: Themes were formulated after analysis of data from focus groups and interviews. (Blake, 2010) Three sets of focus groups were run: specific degree only, mixed-degree, and specific degree only staff; themes were then drawn using grounded theory. (Kitzinger, 1995) From the three distinct themes emerging from the initial qualitative study, specific interventions were developed and will be tested using action research methodology. Results: Findings suggest resources must be mapped and reliable, feedback focus on improvement and students given structured and purposeful support as they develop as learners. The themes found in the study fit into the idea of fixed versus group mindset in learning and suggest different ways to approach this issue.(Dweck, 2007) They suggest an optimal place for resources and support to sit is within learning environments of the individual degree programmes. Resources could benefit from being designed with time and place for reflection as a purposeful addition, especially as students and staff both express a need for opportunities to go “beyond the mark” in discussions and planning. Relevance: The student experience is a complex issue, but a key part of that experience is their ability to use and understand resources available to them, the place and purpose of the feedback received,(Hattie and Timperley, 2007) and their ability to understand where they are in terms of personal development and placement in their programme of study. (Harvey, 2006) This study indicates a clear and structured programme must be in place which allows students to understand the value of the resources available and enable them to discern what resources are most appropriate for a task. It also touches on the differences found in contrasting curricula and learning environments and how this impacted perceived support. Feedback must be part of a pedagogical plan that allows students to move forward from the mark rather than react to it, able to power the journey through the degree.(Elliott and Dweck, 1988) Finally, there must be places in the programme for reflection and assimilation, somewhere for students to pull together what they are learning about their subjects and themselves

hTERT protein expression is independent of clinicopathological parameters and c-Myc protein expression in human breast cancer

Background Telomerase is a ribonucleoprotein enzyme that synthesises telomeres after cell division and maintains chromosomal stability leading to cellular immortalization. Telomerase has been associated with negative prognostic indicators in some studies. The present study aims to detect any association between telomerase sub-units: hTERT and hTR and the prognostic indicators including tumour's size and grade, nodal status and patient's age. Methods Tumour samples from 46 patients with primary invasive breast cancer and 3 patients with benign tumours were collected. RT-PCR analysis was used for the detection of hTR, hTERT, and PGM1 (as a housekeeping) genes expression. Results The expression of hTR and hTERT was found in 31(67.4%) and 38 (82.6%) samples respectively. We observed a significant association between hTR gene expression and younger age at diagnosis (p = 0.019) when comparing patients ≤ 40 years with those who are older than 40 years. None of the benign tumours expressed hTR gene. However, the expression of hTERT gene was revealed in 2 samples. No significant association between hTR and hTERT expression and tumour's grade, stage and nodal status was seen. Conclusion The expression of hTR and hTERT seems to be independent of tumour's stage. hTR expression probably plays a greater role in mammary tumourogenesis in younger women (≤ 40 years) and this may have therapeutic implications in the context of hTR targeting strategies

Self Portraits and Perpetual Motion:The Student Experience of Informed Choice and Feedback

Abstract:With the goal of universal higher education and the growth in multiple careers for many there has been significant expansion in the skills and associated strategies that Universities must communicate to students, especially around the employability agenda (Cochrane & Straker, 2005, Ertl et al., 2008) Resources exist to facilitate informed choice but organization and presentation of resources is essential. Universities must understand which resources are most useful for students to avoid the “paralysis [that] is a consequence of having too many choices.” (Schwartz, 2005). This study uses focus groups and interviews with students in three disciplines with contrasting curricula structures and career paths to discover how students make informed choices, which resources they use and the utility of feedback and resources currently offered. Initial findings show that available resources need to be valid, verified and limited. The interaction between student and university can only be enriched by a deeper comprehension of choice processes and how resources and support can be positioned most effectively. (Foskett & Hemsley-Brown, 2001) This study will highlight resources that students feel contribute to informed choices and to discover what role students and university play in designing and validating resources.One resource available to students is academic feedback. Feedback can be used by students and faculty to inform choice and provide guidance and as an opportunity for students to develop a sense of their strengths and weaknesses (Hounsell, McCune, Hounsell, & Litjens, 2008). If students are to form portraits of themselves as learners then self-awareness and the ability to recognize aspects of their abilities are essential. Through integrating effective feedback the portrait they develop becomes more accurate. This paper will report on initial findings with regard to the student experience of feedback structures, and its impact on their learning. This research has implications for student learning styles, resource creation, and the use of feedback as a tool students can use to become an active contributor to the learning process

A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli

Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alon

A Simple Screen to Identify Promoters Conferring High Levels of Phenotypic Noise

Genetically identical populations of unicellular organisms often show marked variation in some phenotypic traits. To investigate the molecular causes and possible biological functions of this phenotypic noise, it would be useful to have a method to identify genes whose expression varies stochastically on a certain time scale. Here, we developed such a method and used it for identifying genes with high levels of phenotypic noise in Salmonella enterica ssp. I serovar Typhimurium (S. Typhimurium). We created a genomic plasmid library fused to a green fluorescent protein (GFP) reporter and subjected replicate populations harboring this library to fluctuating selection for GFP expression using fluorescent-activated cell sorting (FACS). After seven rounds of fluctuating selection, the populations were strongly enriched for promoters that showed a high amount of noise in gene expression. Our results indicate that the activity of some promoters of S. Typhimurium varies on such a short time scale that these promoters can absorb rapid fluctuations in the direction of selection, as imposed during our experiment. The genomic fragments that conferred the highest levels of phenotypic variation were promoters controlling the synthesis of flagella, which are associated with virulence and host–pathogen interactions. This confirms earlier reports that phenotypic noise may play a role in pathogenesis and indicates that these promoters have among the highest levels of noise in the S. Typhimurium genome. This approach can be applied to many other bacterial and eukaryotic systems as a simple method for identifying genes with noisy expression

Master equation simulation analysis of immunostained Bicoid morphogen gradient

<p>Abstract</p> <p>Background</p> <p>The concentration gradient of Bicoid protein which determines the developmental pathways in early <it>Drosophila </it>embryo is the best characterized morphogen gradient at the molecular level. Because different developmental fates can be elicited by different concentrations of Bicoid, it is important to probe the limits of this specification by analyzing intrinsic fluctuations of the Bicoid gradient arising from small molecular number. Stochastic simulations can be applied to further the understanding of the dynamics of Bicoid morphogen gradient formation at the molecular number level, and determine the source of the nucleus-to-nucleus expression variation (noise) observed in the Bicoid gradient.</p> <p>Results</p> <p>We compared quantitative observations of Bicoid levels in immunostained <it>Drosophila </it>embryos with a spatially extended Master Equation model which represents diffusion, decay, and anterior synthesis. We show that the intrinsic noise of an autonomous reaction-diffusion gradient is Poisson distributed. We demonstrate how experimental noise can be identified in the logarithm domain from single embryo analysis, and then separated from intrinsic noise in the normalized variance domain of an ensemble statistical analysis. We show how measurement sensitivity affects our observations, and how small amounts of rescaling noise can perturb the noise strength (Fano factor) observed. We demonstrate that the biological noise level in data can serve as a physical constraint for restricting the model's parameter space, and for predicting the Bicoid molecular number and variation range. An estimate based on a low variance ensemble of embryos suggests that the steady-state Bicoid molecular number in a nucleus should be larger than 300 in the middle of the embryo, and hence the gradient should extend to the posterior end of the embryo, beyond the previously assumed background limit. We exhibit the predicted molecular number gradient together with measurement effects, and make a comparison between conditions of higher and lower variance respectively.</p> <p>Conclusion</p> <p>Quantitative comparison of Master Equation simulations with immunostained data enabled us to determine narrow ranges for key biophysical parameters, which for this system can be independently validated. Intrinsic noise is clearly detectable as well, although the staining process introduces certain limits in resolution.</p

Precise Regulation of Gene Expression Dynamics Favors Complex Promoter Architectures

Promoters process signals through recruitment of transcription factors and RNA polymerase, and dynamic changes in promoter activity constitute a major noise source in gene expression. However, it is barely understood how complex promoter architectures determine key features of promoter dynamics. Here, we employ prototypical promoters of yeast ribosomal protein genes as well as simplified versions thereof to analyze the relations among promoter design, complexity, and function. These promoters combine the action of a general regulatory factor with that of specific transcription factors, a common motif of many eukaryotic promoters. By comprehensively analyzing stationary and dynamic promoter properties, this model-based approach enables us to pinpoint the structural characteristics underlying the observed behavior. Functional tradeoffs impose constraints on the promoter architecture of ribosomal protein genes. We find that a stable scaffold in the natural design results in low transcriptional noise and strong co-regulation of target genes in the presence of gene silencing. This configuration also exhibits superior shut-off properties, and it can serve as a tunable switch in living cells. Model validation with independent experimental data suggests that the models are sufficiently realistic. When combined, our results offer a mechanistic explanation for why specific factors are associated with low protein noise in vivo. Many of these findings hold for a broad range of model parameters and likely apply to other eukaryotic promoters of similar structure

Noise Reduction by Diffusional Dissipation in a Minimal Quorum Sensing Motif

Cellular interactions are subject to random fluctuations (noise) in quantities of interacting molecules. Noise presents a major challenge for the robust function of natural and engineered cellular networks. Past studies have analyzed how noise is regulated at the intracellular level. Cell–cell communication, however, may provide a complementary strategy to achieve robust gene expression by enabling the coupling of a cell with its environment and other cells. To gain insight into this issue, we have examined noise regulation by quorum sensing (QS), a mechanism by which many bacteria communicate through production and sensing of small diffusible signals. Using a stochastic model, we analyze a minimal QS motif in Gram-negative bacteria. Our analysis shows that diffusion of the QS signal, together with fast turnover of its transcriptional regulator, attenuates low-frequency components of extrinsic noise. We term this unique mechanism “diffusional dissipation” to emphasize the importance of fast signal turnover (or dissipation) by diffusion. We further show that this noise attenuation is a property of a more generic regulatory motif, of which QS is an implementation. Our results suggest that, in a QS system, an unstable transcriptional regulator may be favored for regulating expression of costly proteins that generate public goods

Adjusting Phenotypes by Noise Control

Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks