Low voltage water electrolysis: Decoupling hydrogen production using bioelectrochemical system

Decoupling water electrolysis using mediator is an interesting way to produce pure hydrogen. The present work validates the proof of concept of decoupled electrolyser associated with a bioelectrochemical system (MFC-DES) through a redox flow mediator (potassium hexacyanoferrate (KHCF)). Low voltage (1 V) hydrogen production was achieved with a current density up to 25 mA cm^?2. Regeneration of the mediator was performed by glucose fed microbial fuel cells. The oxidation rate of KHCF in the electrolyser is, at least, an order of magnitude higher than the reduction rate in MFC cascade fed system. MFC-DES is thus a promising set up as it desynchronizes limited microbial rate and hydrogen production, generate value from wastewater and reduce energetic cost of water electrolysis.     

The Thyroid Hormone Transporter Mct8 Restricts Cathepsin-Mediated Thyroglobulin Processing in Male Mice through Thyroid Auto-Regulatory Mechanisms That Encompass Autophagy

The thyroid gland is both a thyroid hormone (TH) generating as well as a TH responsive organ. It is hence crucial that cathepsin-mediated proteolytic cleavage of the precursor thyroglobulin is regulated and integrated with the subsequent export of TH into the blood circulation, which is enabled by TH transporters such as monocarboxylate transporters Mct8 and Mct10. Previously, we showed that cathepsin K-deficient mice exhibit the phenomenon of functional compensation through cathepsin L upregulation, which is independent of the canonical hypothalamus-pituitary-thyroid axis, thus, due to auto-regulation. Since these animals also feature enhanced Mct8 expression, we aimed to understand if TH transporters are part of the thyroid auto-regulatory mechanisms. Therefore, we analyzed phenotypic differences in thyroid function arising from combined cathepsin K and TH transporter deficiencies, i.e., in Ctsk^-/-/Mct10^-/-, Ctsk^-/-/Mct8^-/y, and Ctsk^-/-/Mct8^-/y/Mct10^-/-. Despite the impaired TH export, thyroglobulin degradation was enhanced in the mice lacking Mct8, particularly in the triple-deficient genotype, due to increased cathepsin amounts and enhanced cysteine peptidase activities, leading to ongoing thyroglobulin proteolysis for TH liberation, eventually causing self-thyrotoxic thyroid states. The increased cathepsin amounts were a consequence of autophagy-mediated lysosomal biogenesis that is possibly triggered due to the stress accompanying intrathyroidal TH accumulation, in particular in the Ctsk^-/-/Mct8^-/y/Mct10^-/- animals. Collectively, our data points to the notion that the absence of cathepsin K and Mct8 leads to excessive thyroglobulin degradation and TH liberation in a non-classical pathway of thyroid auto-regulation.     

Biosynthesis of Rubellins in Ramularia collo-cygni—Genetic Basis and Pathway Proposition

The important disease Ramularia leaf spot of barley is caused by the fungus Ramularia collo-cygni. The disease causes yield and quality losses as a result of a decrease in photosynthesis efficiency due to the appearance of necrotic spots on the leaf surface. The development of these typical Ramularia leaf spot symptoms is thought to be linked with the release of phytotoxic secondary metabolites called rubellins in the host. However, to date, neither the biosynthetic pathways leading to the production of these metabolites nor their exact role in disease development are known. Using a combined in silico genetic and biochemistry approach, we interrogated the genome of R. collo-cygni to identify a putative rubellin biosynthetic gene cluster. Here we report the identification of a gene cluster containing homologues of genes involved in the biosynthesis of related anthraquinone metabolites in closely related fungi. A putative pathway to rubellin biosynthesis involving the genes located on the candidate cluster is also proposed.     

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools. Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describe Milepost GCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the MILEPOST technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC. We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor. We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilation time and code size by 12% and 7% respectively on Intel Xeon processor.