9 research outputs found

    Experimentation at the interface of fluvial geomorphology, stream ecology and hydraulic engineering and the development of an effective, interdisciplinary river science

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    One ‘2020 vision’ for fluvial geomorphology is that it sits alongside stream ecology and hydraulic engineering as a key element of an integrated, interdisciplinary river science. A challenge to this vision is that scientists from these three communities may approach problems from different perspectives with different questions and have different methodological outlooks. Refining interdisciplinary methodology is important in this context, but raises a number of issues for geomorphologists, ecologists and engineers alike. In particular, we believe that it is important that there is greater dialogue about the nature of mutually-valued questions and the adoption of mutually-acceptable methods. As a contribution to this dialogue we examine the benefits and challenges of using physical experimentation in flume laboratories to ask interdisciplinary questions. Working in this arena presents the same challenges that experimental geomorphologists and engineers are familiar with (scaling up results, technical difficulties, realism) and some new ones including recognizing the importance of biological processes, identifying hydraulically meaningful biological groups, accommodating the singular behaviour of individuals and species, understanding biological as well as physical stimuli, and the husbandry and welfare of live organisms. These issues are illustrated using two examples from flume experiments designed (1) to understand how the movement behaviours of aquatic insects through the near-bed flow field of gravelly river beds may allow them to survive flood events, and (2) how an understanding of the way in which fish behaviours and swimming capability are affected by flow conditions around artificial structures can lead to the design of effective fish passages. In each case, an interdisciplinary approach has been of substantial mutual benefit and led to greater insights than discipline-specific work would have produced. Looking forward to 2020, several key challenges for experimentalists working on the interface of fluvial geomorphology, stream ecology and hydraulic engineering are identified

    K-means clustering of the differentially expressed genes between the cold and room temperature treatments.

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    <p>The x-axis represent samples of white adipose tissue (WAT), brown adipose tissue (BAT) and liver (LIV) at room temperature (RT) or cold treated (C) and y-axis the normalised signal level. The lines represent the 1895 differentially expressed probe-sets between treatments in all tissues. The black line represents the mean profile of each cluster.</p

    Venn diagram showing the overlap in genes differentially expressed between the cold and room temperature treatments for brown adipose tissue (BAT), white adipose tissue (WAT) and liver (LIV).

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    <p>Animals were maintained at 22°C ±2°C (room temperature; 3 animals) or at 8°C ±2°C for 24 h (cold; 3 animals). Tissue samples were collected, mRNA prepared and analyzed by Illumina microarray as described in the text. Differentially expressed genes (≥2 fold, P<0.05) between cold and room temperature treated values were identified using Genespring GX.</p

    Hierarchical clustering of differentially expressed genes between the cold and room temperature treatments.

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    <p>The columns represent samples of white adipose tissue (WAT), brown adipose tissue (BAT) and liver (LIV) at room temperature (RT) or cold treated (C). The rows represent the 1895 differentially expressed probe-sets between treatments in all tissues, which are coloured based on their normalised signal values (red - high, blue = low).</p

    Expression of (A) UCP1, (B) PGC-1α, (C ) C/EBPβ, (D) PPARα and (E) HNF4α in brown adipose tissue (BAT), white adipose tissue (WAT) and liver of acutely cold stressed mice.

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    <p>Animals were maintained at 22°C ±2°C (room temperature) or at 8°C ±2°C for 24 h (cold). Tissue samples were collected, mRNA prepared and analyzed by qRT-PCR. Values are normalised against either 18S rRNA or 36B4 expression and represent means +/− SEM of four animals in each group.</p
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