Evaluation of various cheese whey treatment scenarios in single-chamber microbial electrolysis cells for improved biohydrogen production.

In this study single-chamber microbial electrolysis cells (MECs) were applied to treat cheese whey (CW), an industrial by-product, and recover H2 gas. Firstly, this substrate was fed directly to the MEC to get the initial feedback about its H2 generation potential. The results indicated that the direct application of CW requires an adequate pH control to realize bioelectrohydrogenesis and avoid operational failure due to the loss of bioanode activity. In the second part of the study, the effluents of anaerobic (methanogenic) digester and hydrogenogenic (dark fermentative H2-producing) reactor utilizing the CW were tested in the MEC process (representing the concept of a two-stage technology). It turned out that the residue of the methanogenic reactor - with its relatively lower carbohydrate- and higher volatile fatty acid contents - was more suitable to produce hydrogen bioelectrochemically. The MEC operated with the dark fermentation effluent, containing a high portion of carbohydrates and low amount of organic acids, produced significant amount of undesired methane simultaneously with H2. Overall, the best MEC behavior was attained using the effluent of the methanogenic reactor and therefore, considering a two-stage system, methanogenesis is an advisable pretreatment step for the acidic CW to enhance the H2 formation in complementary microbial electrohydrogenesis.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.