Waters draining into a lake carry with them much of the suspended sediment that is transported by rivers and streams from the local drainage basin. The organic matter processing in the sediments is executed by heterotrophic microbial communities, whose activities may vary spatially and temporally. Thus, to capture and evaluate some of these variabilities in the sediments, we sampled six sites: three from the St. Clair River and three from Lake St. Clair in spring, summer, fall, and winter of 2016. At all sites and dates, we investigated the spatial and temporal variations in 19 extracellular enzyme activities using API ZYM. Our results indicated that a broad range of enzymes were found to be active in the sediments. Phosphatases, lipases, and esterases were synthesized most intensively by the sediment microbial communities. No consistent difference was found between the lake and sediment samples. Differences were more obvious between sites and seasons. Sites with the highest metabolic (enzyme) diversity reflected the capacity of the sediment microbial communities to breakdown a broader range of substrates and may be linked to differences in river and lake water quality. The seasonal variability of the enzymes activities was governed by the variations of environmental factors caused by anthropogenic and terrestrial inputs, and provides information for a better understanding of the dynamics of sediment organic matter of the river and lake ecosystems. The experimental results suggest that API ZYM is a simple and rapid enzyme assay procedure to evaluate natural processes in ecosystems and their changes.

Information on the biodegradation potential of lake and river microbial communities is essential for watershed management. The water draining into the lake ecosystems often carries a significant amount of suspended sediments, which are transported by rivers and streams from the local drainage basin. The organic carbon processing in the sediments is executed by heterotrophic microbial communities, whose activities may vary spatially and temporally. Thus, to capture and apprehend some of these variabilities in the sediments, we sampled six sites: three from the Saint Clair River (SC1, SC2, and SC3) and three from Lake Saint Clair in the spring, summer, fall, and winter of 2016. Here, we investigated the shifts in metabolic profiles of sediment microbial communities, along Saint Clair River and Lake Saint Clair using Biolog EcoPlates, which test for the oxidation of 31 carbon sources. The number of utilized substrates was generally higher in the river sediments (upstream) than in the lake sediments (downstream), suggesting a shift in metabolic activities among microbial assemblages. Seasonal and site-specific differences were also found in the numbers of utilized substrates, which were similar in the summer and fall, and spring and winter. The sediment microbial communities in the summer and fall showed more versatile substrate utilization patterns than spring and winter communities. The functional fingerprint analyses clearly distinguish the sediment microbial communities from the lake sites (downstream more polluted sites), which showed a potential capacity to use more complex carbon substrates such as polymers. This study establishes a close linkage between physical and chemical properties (temperature and organic matter content) of lake and river sediments and associated microbial functional activities.

Lead is an extensive contaminant. Pb-resistant bacterial strains were isolated from Saint Clair River sediments on two enrichment media with increasing concentrations of Pb (NO). Bacterial strains that grew at 1.25 or 1.5 g L of Pb (NO) L) were purified and selected for further study. Ninety-seven Pb-resistant strains were screened for the ability to produce bioflocculants. The majority of the Pb-resistant strains demonstrated moderate to high flocculation activity. Metal removal assays demonstrated that the higher is the flocculation activity, the higher is the efficiency of metal removal. In the multi-metal solutions, the bacterial strain with the highest flocculation activity (R19) had the highest metal removing capability (six out of eight metals) and the highest metal removal efficiency. The highly selective affinity towards Pb observed for strain R19 suggests its use for the recovery of Pb from multiple metal solutions. Because they are well adapted to unfavorable conditions due to their resistance to metals (e.g., Pb) and antibiotics, these characteristics may help in developing an effective process for wastewater treatment using these strains.

Purpose The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course. Design/methodology/approach The paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines. Findings The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable. Originality/value This paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

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