This paper gives an overview of the meaning of the term 'roughness' in the field of fluvial hydraulics, and how it is often formulated as a 'resistance to flow' term in 1-D, 2-D & 3-D numerical models. It looks at how roughness is traditionally characterised in both experimental and numerical fields, and subsequently challenges the definitions that currently exist. In the end, the authors wonder: is roughness well understood and defined at all? Such a question raises a number of concerns in both research and practice; for example, how does one modeller use the roughness value from an experimental piece of work, or how does a practitioner identify the roughness value of a particular river channel? The authors indicate that roughness may not be uniquely defined, that there may be distinct 'experimental' and 'numerical' roughness values, and that in each field nuances exist associated with the context in which these values are used.

The CAMI registry represents a well-supported and the largest national long-term registry-research-education platform for surveillance, research, prevention and care improvement for AMI in China, the world's most populous nation. The broad representation of all provinces and different-level hospitals will allow for the exploration of AMI across diverse geographic regions and economic circumstances.

No abstract

Renal cell carcinoma (RCC) is the second most lethal urinary cancer. RCC is frequently asymptomatic and it is already metastatic at diagnosis. There is an urgent necessity for RCC specific biomarkers selection for diagnostic and prognostic purposes. In present study, we applied liquid chromatography—mass spectrometry (LC-MS) based metabolomics to analyze urine samples of 100 RCC, 34 benign kidney tumors and 129 healthy controls. Differential metabolites were analyzed to investigate if urine metabolites could differentiate RCC from non-RCC. A panel consisting of 9 metabolites showed the best predictive ability for RCC from the health controls with an area under the curve (AUC) values of 0.905 for the training dataset and 0.885 for the validation dataset. Separation was observed between the RCC and benign samples with an AUC of 0.816. RCC clinical stages (T1 and T2 vs. T3 and T4) could be separated using a panel of urine metabolites with an AUC of 0.813. One metabolite, N-formylkynurenine, was discovered to have potential value for RCC diagnosis from non-RCC subjects with an AUC of 0.808. Pathway enrichment analysis indicated that tryptophan metabolism was an important pathway in RCC. Our data concluded that urine metabolomics could be used for RCC diagnosis and would provide candidates for further targeted metabolomics analysis of RCC.