RESUMO
Current practice in agriculture applies genomic prediction to assist crop breeding in the analysis of genetic marker data. Genomic selection methods typically use linear mixed models, but using machine-learning may provide further potential for improved selection accuracy, or may provide additional information. Here we describe SelectML, an automated pipeline for testing and comparing the performance of a range of linear mixed model and machine-learning-based genomic selection methods. We demonstrate the use of SelectML on an in silico-generated marker dataset which simulated a randomly-sampled (mixed) and an unevenly-sampled (unbalanced) population, comparing the relative performance of various methods included in SelectML on the two datasets. Although machine-learning based methods performed similarly overall to linear mixed models, they performed worse on the mixed dataset and marginally better on the unbalanced dataset, being more affected than linear mixed models by the imposed sampling bias. SelectML can assist in the training, comparison, and selection of genomic selection models, and is available from https://github.com/darcyabjones/selectml.
RESUMO
Two nanofiltration membranes, a Dow NF 270 polyamide thin film and a TriSep TS 80 polyamide thin film, were investigated for their retention of ionic species when filtering mine influenced water streams at a range of acidic pH values. The functional iso-electric point of the membranes, characterized by changes in retention over a small pH range, were examined by filtering solutions of sodium sulphate. Both membranes showed changes in retention at pH 3, suggesting a zero net charge on the membranes at this pH. Copper mine drainage and synthetic solutions of mine influenced water were filtered using the same membranes. These solutions were characterized by pH values within 2 and 5, thus crossing the iso-electric point of both membranes. Retention of cations was maximized when the feed solution pH was less than the iso-electric point of the membrane. In these conditions, the membrane has a net positive charge, reducing the transmission rate of cations. From the recoveries of a range of cations, the suitability of nanofiltration was discussed relative to the compliance with mine water discharge criteria and the recovery of valuable commodity metals. The nanofiltration process was demonstrated to offer advantages in metal recovery from mine waste streams, concomitantly enabling discharge criteria for the filtrate disposal to be met.
RESUMO
The effect of water transfers between two reservoirs on the water quality of the receiving reservoir was investigated over a 9-year period (2000-2008). Different management strategies were implemented in term of the magnitude and timing of water transfers, i.e. the amount of transferred volume and the frequency at which transfers occurred. These different operational modes were analysed to determine changes in nutrient and metal concentrations, chlorophyll a, algal genera and biovolume. During high water transfers, chlorophyll a and total algal biovolume increased, with larger diatoms preferentially selected due to the high silica content of the pumped inflow and a significant shift in cyanobacteria genera occurring from Microcystis to nitrogen-fixing genera. The magnitude and timing of water transfers exerted a strong control on phytoplankton competition and disturbed the typical seasonal succession during low pumping years of a spring diatom bloom followed by summer cyanobacteria dominance: intensive and frequent water transfers resulted in dominance by diatoms for the whole year and effectively limited cyanobacteria summer growth. From this analysis, we identified iron concentration and diatom biovolume as the key water quality indicators to be included in any optimal management, able to control the transfer regime from both a water quantity and water quality prospective.
