Organo-mineral complexation alters carbon and nitrogen cycling in stream microbial assemblages

Inland waters are of global biogeochemical importance receiving carbon inputs of ~ 4.8 Pg C y^-1. Of this 12 % is buried, 18 % transported to the oceans, and 70 % supports aquatic secondary production. However, the mechanisms that determine the fate of organic matter (OM) in these systems are poorly defined. One important aspect is the formation of organo-mineral complexes in aquatic systems and their potential as a route for OM transport and burial vs. their use potential as organic carbon (C) and nitrogen (N) sources. Organo-mineral particles form by sorption of dissolved OM to freshly eroded mineral surfaces and may contribute to ecosystem-scale particulate OM fluxes. We tested the availability of mineral-sorbed OM as a C & N source for streamwater microbial assemblages and streambed biofilms. Organo-mineral particles were constructed in vitro by sorption of ¹³C:¹⁵N-labelled amino acids to hydrated kaolin particles, and microbial degradation of these particles compared with equivalent doses of ¹³C:¹⁵N-labelled free amino acids. Experiments were conducted in 120 ml mesocosms over 7 days using biofilms and streamwater sampled from the Oberer Seebach stream (Austria), tracing assimilation and mineralization of ¹³C and ¹⁵N labels from mineral-sorbed and dissolved amino acids.Here we present data on the effects of organo-mineral sorption upon amino acid mineralization and its C:N stoichiometry. Organo-mineral sorption had a significant effect upon microbial activity, restricting C and N mineralization by both the biofilm and streamwater treatments. Distinct differences in community response were observed, with both dissolved and mineral-stabilized amino acids playing an enhanced role in the metabolism of the streamwater microbial community. Mineral-sorption of amino acids differentially affected C & N mineralization and reduced the C:N ratio of the dissolved amino acid pool. The present study demonstrates that organo-mineral complexes restrict microbial degradation of OM and may, consequently, alter the carbon and nitrogen cycling dynamics within aquatic ecosystems.

Using microbiological tracers to assess the impact of winter land use restrictions on the quality of stream headwaters in a small catchment.

Diverse land use activities can elevate risk of microbiological contamination entering stream headwaters. Spatially distributed water quality monitoring carried out across a 17km(2) agricultural catchment aimed to characterize microbiological contamination reaching surface water and investigate whether winter agricultural land use restrictions proved effective in addressing water quality degradation. Combined flow and concentration data revealed no significant difference in fecal indicator organism (FIO) fluxes in base flow samples collected during the open and prohibited periods for spreading organic fertilizer, while relative concentrations of Escherichia coli, fecal streptococci and sulfite reducing bacteria indicated consistently fresh fecal pollution reached aquatic receptors during both periods. Microbial source tracking, employing Bacteroides 16S rRNA gene markers, demonstrated a dominance of bovine fecal waste in river water samples upstream of a wastewater treatment plant discharge during open periods. This contrasted with responses during prohibited periods where human-derived signatures dominated. Differences in microbiological signature, when viewed with hydrological data, suggested that increasing groundwater levels restricted vertical infiltration of effluent from on-site wastewater treatment systems and diverted it to drains and surface water. Study results reflect seasonality of contaminant inputs, while suggesting winter land use restrictions can be effective in limiting impacts of agricultural wastes to base flow water quality.

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as – weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases – i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

Anomaly Detection for Data with Spatial Attributes

The problem of detecting spatially-coherent groups of data that exhibit anomalous behavior has started to attract attention due to applications across areas such as epidemic analysis and weather forecasting. Earlier efforts from the data mining community have largely focused on finding outliers, individual data objects that display deviant behavior. Such point-based methods are not easy to extend to find groups of data that exhibit anomalous behavior. Scan Statistics are methods from the statistics community that have considered the problem of identifying regions where data objects exhibit a behavior that is atypical of the general dataset. The spatial scan statistic and methods that build upon it mostly adopt the framework of defining a character for regions (e.g., circular or elliptical) of objects and repeatedly sampling regions of such character followed by applying a statistical test for anomaly detection. In the past decade, there have been efforts from the statistics community to enhance efficiency of scan statstics as well as to enable discovery of arbitrarily shaped anomalous regions. On the other hand, the data mining community has started to look at determining anomalous regions that have behavior divergent from their neighborhood.In this chapter,we survey the space of techniques for detecting anomalous regions on spatial data from across the data mining and statistics communities while outlining connections to well-studied problems in clustering and image segmentation. We analyze the techniques systematically by categorizing them appropriately to provide a structured birds eye view of the work on anomalous region detection;we hope that this would encourage better cross-pollination of ideas across communities to help advance the frontier in anomaly detection.

Soft-core Stream Processor for Sliding Window Applications

Software-programmable `soft' processors have shown tremendous potential for efficient realisation of high performance signal processing operations on Field Programmable Gate Array (FPGA), whilst lowering the design burden by avoiding the need to design fine-grained custom circuit archi-tectures. However, the complex data access patterns, high memory bandwidth and computational requirements of sliding window applications, such as Motion Estimation (ME) and Matrix Multiplication (MM), lead to low performance, inefficient soft processor realisations. This paper resolves this issue, showing how by adding support for block data addressing and accelerators for high performance loop execution, performance and resource efficiency over four times better than current best-in-class metrics can be achieved. In addition, it demonstrates the first recorded real-time soft ME estimation realisation for H.263 systems.

Fast Rule Mining over Multi-dimensional Windows

Association rule mining is an indispensable tool for discovering
insights from large databases and data warehouses.
The data in a warehouse being multi-dimensional, it is often
useful to mine rules over subsets of data defined by selections
over the dimensions. Such interactive rule mining
over multi-dimensional query windows is difficult since rule
mining is computationally expensive. Current methods using
pre-computation of frequent itemsets require counting
of some itemsets by revisiting the transaction database at
query time, which is very expensive. We develop a method
(RMW) that identifies the minimal set of itemsets to compute
and store for each cell, so that rule mining over any
query window may be performed without going back to the
transaction database. We give formal proofs that the set of
itemsets chosen by RMW is sufficient to answer any query
and also prove that it is the optimal set to be computed
for 1 dimensional queries. We demonstrate through an extensive
empirical evaluation that RMW achieves extremely
fast query response time compared to existing methods, with
only moderate overhead in pre-computation and storage

A primer for the Environmental Impact Assessment of mining at seafloor massive sulfide deposits

Seafloor massive sulfides (SMS) contain commercially viable quantities of high grade ores, making them attractive prospect sites for marine mining. SMS deposits may also contain hydrothermal vent ecosystems populated by high conservation value vent-endemic species. Responsible environmental management of these resources is best achieved by the adoption of a precautionary approach. Part of this precautionary approach involves the Environmental Impact Assessment (EIA) of exploration and exploitative activities at SMS deposits. The VentBase 2012 workshop provided a forum for stakeholders and scientists to discuss issues surrounding SMS exploration and exploitation. This forum recognised the requirement for a primer which would relate concepts underpinning EIA at SMS deposits. The purpose of this primer is to inform policy makers about EIA at SMS deposits in order to aid management decisions. The primer offers a basic introduction to SMS deposits and their associated ecology, and the basic requirements for EIA at SMS deposits; including initial data and information scoping, environmental survey, and ecological risk assessment. © 2013 Elsevier Ltd.

VentBase:Developing a consensus among stakeholders in the deep-sea regarding environmental impact assessment for deep-sea mining-A workshop report

Mining seafloor massive sulfides for metals is an emergent industry faced with environmental management challenges. These revolve largely around limits to our current understanding of biological variability in marine systems, a challenge common to all marine environmental management. VentBase was established as a forum where academic, commercial, governmental, and non-governmental stakeholders can develop a consensus regarding the management of exploitative activities in the deep-sea. Participants advocate a precautionary approach with the incorporation of lessons learned from coastal studies. This workshop report from VentBase encourages the standardization of sampling methodologies for deep-sea environmental impact assessment. VentBase stresses the need for the collation of spatial data and importance of datasets amenable to robust statistical analyses. VentBase supports the identification of set-asides to prevent the local extirpation of vent-endemic communities and for the post-extraction recolonization of mine sites. © 2013.