Temporal variability and the relationship between benthic meiofaunal and microbial populations of a natural coastal petroleum seep

Previous studies of the Isla Vista petroleum seep in the Santa Barbara Channel found much higher abundances of macrofauna and concentrations of adenosine triphosphate (ATP) in sediments near petroleum seepage compared to those from nonseep areas. To further assess the possible effect of petroleum on organisms at the base of benthic food webs, population abundances of meiobenthos and their suspected microbial food (bacteria and diatoms) were measured biweekly for one year at three stations with differing petroleum exposure. Determinations of suspended particulate matter and the abundance and gut contents of juvenile fishes were also made at seep and nonseep stations. Nematodes and bacteria had higher abundances in areas of active petroleum seepage than in areas of moderate seepage (within 20 m) or no seepage (1.4 km away). Bacterial productivity (based on the frequency of dividing cells) was 340% greater in sediments from areas of active seepage compared to those from a nonseep station. Sediments within the seep, but away from active seepage, had rates of bacterial productivity 15 times greater than a nonseep comparison site. Densities of harpacticoid copepods and their probable principal food, diatoms, were not affected by petroleum seepage. Suspended organic matter caught in settling traps was not different between seep and nonseep stations. In addition, there was no evidence that predation pressure by juvenile fish on meiofauna was different between stations. The higher bacterial biomass and productivity in areas of petroleum seepage are consistent with the hypothesis that petroleum carbon is available for assimilation by sediment bacteria. The enhanced level of microbial carbon associated with the petroleum seep is available for consumption by benthic invertebrates and could explain the higher abundances of macrofauna and meiofauna found there

Temporal variability and the relationship between benthic meiofaunal and microbial populations of a natural coastal petroleum seep

Previous studies of the Isla Vista petroleum seep in the Santa Barbara Channel found much higher abundances of macrofauna and concentrations of adenosine triphosphate (ATP) in sediments near petroleum seepage compared to those from nonseep areas. To further assess the possible effect of petroleum on organisms at the base of benthic food webs, population abundances of meiobenthos and their suspected microbial food (bacteria and diatoms) were measured biweekly for one year at three stations with differing petroleum exposure. Determinations of suspended particulate matter and the abundance and gut contents of juvenile fishes were also made at seep and nonseep stations.Nematodes and bacteria had higher abundances in areas of active petroleum seepage than in areas of moderate seepage (within 20 m) or no seepage (1.4 km away). Bacterial productivity (based on the frequency of dividing cells) was 340% greater in sediments from areas of active seepage compared to those from a nonseep station. Sediments within the seep, but away from active seepage, had rates of bacterial productivity 15 times greater than a nonseep comparison site. Densities of harpacticoid copepods and their probable principal food, diatoms, were not affected by petroleum seepage. Suspended organic matter caught in settling traps was not different between seep and nonseep stations. In addition, there was no evidence that predation pressure by juvenile fish on meiofauna was different between stations.The higher bacterial biomass and productivity in areas of petroleum seepage are consistent with the hypothesis that petroleum carbon is available for assimilation by sediment bacteria. The enhanced level of microbial carbon associated with the petroleum seep is available for consumption by benthic invertebrates and could explain the higher abundances of macrofauna and meiofauna found there.Publishe

Extending point-based deep learning approaches for better semantic segmentation in CAD

Geometry understanding is a core concept of computer-aided design and engineering (CAD/CAE). Deep neural networks have increasingly shown success as a method of processing complex inputs to achieve abstract tasks. This work revisits a generic and relatively simple approach to 3D deep learning - a point-based graph neural network - and develops best-practices and modifications to alleviate traditional drawbacks. It is shown that these methods should not be discounted for CAD tasks; with proper implementation, they can be competitive with more specifically designed approaches. Through an additive study, this work investigates how the boundary representation data can be fully utilised by leveraging the flexibility of point-based graph networks. The final configuration significantly improves on the predictive accuracy of a standard PointNet++ network across multiple CAD model segmentation datasets and achieves state-of-the-art performance on the MFCAD++ machining features dataset. The proposed modifications leave the core neural network unchanged and results also suggest that they can be applied to other point-based approaches