Expression profile of genes involved in hydrogen sulphide liberation by _Saccharomyces cerevisiae_ grown under different nitrogen concentrations

The present work aims to elucidate molecular mechanisms underlying hydrogen sulphide production in _S. cerevisiae_ associated to nitrogen deficiency. To assess, at a genome-wide level, how the yeast strain adapted to the progressive nitrogen depletion and to nitrogen re-feeding, gene expression profiles were evaluated during fermentation at different nitrogen concentrations, using the DNA array technology. The results showed that most MET genes displayed higher expression values at the beginning of both control and N-limiting fermentation, just before the time at which the release of sulphide was observed. MET genes were downregulated when yeast stopped growing which could associate MET gene expression levels with cell growth. The over expression of MET genes after nitrogen addition was confirmed by a new release of H2S during the new set of fermentation experiments. In addition, to confirm gene expression profiles observed from macroarray results, real time RT-PCR was performed on 6 genes using additional sets of biological replicates. These genes were selected based on the assumption that differences in sulphide production observed among strains are due to genetic variations of the expression of genes involved in the Sulphate Reduction Pathway. An integration of expression data of genes involved in sulphur assimilation and sulphur amino acid biosynthesis with hydrogen sulphide production is presented

Impact Evaluation of Wet-Weather Events on Influent Flow and Loadings of a Water Resource Recovery Facility

Since the introduction of environmental legislation anddirectives in Europe, the impact of combined sewer overflows (CSO) on receiving waterbodies has become a priority concern in water and wastewater treatment industry. Timeconsumingand expensive local sampling and monitoring campaigns have been carried outto estimate the characteristic flow and pollutant concentrations of CSO water. This studyfocused on estimating the frequency and duration of wet-weather events and their impactson influent flow and wastewater characteristics of the largest Italian water resource recoveryfacility (WRRF) in Castiglione Torinese. Eight years (viz. 2009-2016) of routinely collectedinfluent data in addition to the arithmetic mean daily precipitation rates (PI) of the plantcatchment area, were elaborated. Relationships between PI and volumetric influent flow rate(Qin), chemical oxygen demand (COD), ammonium concentration (N-NH4) and totalsuspended solids (TSS) are investigated. Time series data mining (TSDM) method isimplemented for segmentation of time series by use of sliding window algorithm to partitionthe available records associated with wet and dry weather events based on the dailyvariation of PI time series. Appling the methodology in conjunction with results obtained fromdata reduction techniques, a wet-weather definition is proposed for the plant. The resultsconfirm that applied methodology on routinely collected plant data can be considered as agood substitute for time-consuming and expensive sampling campaigns and plantmonitoring programs usually conducted for accurate emergency response and long-termpreparedness for extreme climate conditions