Traversal Query Language For Scala.Meta

With the rise of metaprogramming in Scala, manipulating ASTs has become a daily job. Yet the standard API provides only low-level mechanisms to transform or to collect information on those data structures. Moreover, those mechanisms often force the programmer to manipulate state in order to retrieve information on these ASTs. In this report, we try to solve those problems by introducing TQL, a high-level combinator Scala library to transform and query data structures in a purely functional way. Parser combinators allow to combine several small parsers to build a bigger one in an expressive way. In this report, we argue that we can apply the same concept to data structure manipulation and construct complicated traversers on top of smaller ones. Yet combinators may feel unnatural or too complicated for certain usage. We therefore built a library on top of TQL to manipulate data structures as a collection. We then put TQL in practice to scala.meta ASTs, and describe the challenges we face when traversing a real-word data structure, especially performance-wise

Array of microbial indicators, a promise for a better monitoring of pesticide effects on stream biological quality

National audienceFreshwater contamination by pesticide residues is a major and growing threat to aquatic communities, ecosystem functioning and ultimately human health worldwide. Typical pesticide contamination in agricultural landscapes is characterized by a cocktail of a large number of active compounds and their main transformation products, each of them found at very low and temporally fluctuating concentrations. This makes the quantification of pesticide residues in streams highly challenging and costly by means of grab chemical sampling. Accordingly, it makes also difficult to characterize the chronic exposure of aquatic communities in pesticide-contaminated streams and the resultingecological effects on community structure and functions. During the last decade, the development and implementation of pesticide integrative samplers has allowed to improve the monitoring of the chemical quality through the determination of time-weighted average concentrations over an exposure period, leading to a better representativeness of measurements. However, scientists and regulators are still facing the challenge of going beyond the estimation of pesticide concentrations to take into account the ecological effects on exposed aquatic communities in the evaluation of the ecological status in the particular context of pesticide contamination. To overcome these limitations, microbial communities can be viewed as potential bioindicators that may provide a broad array of structural and functional metrics to diagnose the effects of stream contamination by pesticide residues, and constitute an innovative toolbox to monitor the effect of pesticide residues on stream biological quality. In this study, we evaluated 13 structural and functional metrics on microbial communities from 10 streams that belonged to 3 catchments, and repeated themeasurements at 2 seasons in 4 out of the 10 streams. Streams were selected in different agrosystems to be representative of different scenarios of pesticide contamination gradient, as evidenced by pesticide quantification using composite silicone rubber and POCIS integrative chemical samplers (66 molecules targeted). Our metrics were measured on microbial communities associated with sediment (3), periphyton (2) or benthic particulate organic matter (8) and spanned a broad range of microorganism types (bacteria, fungi, algae). They included microbial processes (organic matter decomposition, degradation of targeted pesticides, fungal reproduction rates and enzymatic activities), their resilience to pesticide exposure (Pollution Induced Community Tolerance), as well as community features (fungal, bacterial and algal community structure, fungal biomass, abundance of pesticide degrading micro-organisms). Based on our results and on literature data, we identify and discuss the respective objectives, advantages, disadvantages and knowledge gaps associated with each indicator in the context of estimating the effect of pesticide contamination on the biological quality of streams. Finally, based on a survey of stream and water resource managers, we conclude on their possible applicability and interest for end-users