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Effect-directed analysis (EDA) aims at the detection of bioactive chemicals of emerging concern (CECs) by combining toxicity testing and high-resolution mass spectrometry (HRMS). However, consolidation of toxicological and chemical analysis techniques to identify bioactive CECs remains challenging and laborious. In this study, we incorporate state-of-the-art identification approaches in EDA and propose a robust workflow for the high-throughput screening of CECs in environmental and human samples. Three different sample types were extracted and chemically analyzed using a single high-performance liquid chromatography HRMS method. Chemical features were annotated by suspect screening with several reference databases. Annotation quality was assessed using an automated scoring system. In parallel, the extracts were fractionated into 80 micro-fractions each covering a couple of seconds from the chromatogram run and tested for bioactivity in two bioassays. The EDA workflow prioritized and identified chemical features related to bioactive fractions with varying levels of confidence. Confidence levels were improved with the in silico software tools MetFrag and the retention time indices platform. The toxicological and chemical data quality was comparable between the use of single and multiple technical replicates. The proposed workflow incorporating EDA for feature prioritization in suspect and nontarget screening paves the way for the routine identification of CECs in a high-throughput manner., A comprehensive workflow was developed that incorporates effect-directed analysis in suspect and nontarget screening for feature prioritization, allowing for the high-throughput identification of bioactive chemicals of emerging concern.

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

Background: The NORMAN Association (https://www.norman-.network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-.network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.Results: The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https:// zenodo.org/communities/norman-.sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox. epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).Conclusions: The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-.network.com/nds/SLE/).

In Black-Box Checking (BBC) incremental hypotheses of a system are learned in the form of finite automata. On these automata LTL formulae are verified, or their counterexamples validated on the actual system. We extend the LearnLib’s system-under-learning API for sound BBC, by means of state equivalence, that contrasts the original proposal where an upper-bound on the number of states in the system is assumed. We will show how LearnLib’s new BBC algorithms can be used in practice, as well as how one could experiment with different model checkers and BBC algorithms. Using the RERS 2017 challenge we provide experimental results on the performance of all LearnLib’s active learning algorithms when applied in a BBC setting. The performance of learning algorithms was unknown for this setting. We will show that the novel incremental algorithms TTT, and ADT perform the best.

International audience; Created in 2011, the Model Checking Contest (MCC) is an annual competition dedicated to provide a fair evaluation of software tools that verify concurrent systems using state-space exploration techniques and model checking. This article presents the principles and results of the 2017 edition of the MCC, which took place along with the Petri Net and ACSD joint conferences in Zaragoza, Spain.

We investigate the use of bandwidth and wavefront reduction algorithms to determine a static BDD variable ordering. The aim is to reduce the size of BDDs arising in symbolic reachability. Previous work showed that minimizing the (weighted) event span of the variable dependency graph yields small BDDs. The bandwidth and wavefront of symmetric matrices are well studied metrics, used in sparse matrix solvers, and many bandwidth and wavefront reduction algorithms are readily available in libraries like Boost and ViennaCL. In this paper, we transform the dependency matrix to a symmetric matrix and apply various bandwidth and wavefront reduction algorithms, measuring their influence on the (weighted) event span. We show that Sloan’s algorithm, executed on the total graph of the dependency matrix, yields a variable order with minimal event span. We demonstrate this on a large benchmark of Petri nets, Dve, Promela, B, and mcrl2 models. As a result, good static variable orders can now be determined in milliseconds by using standard sparse matrix solvers.

We demonstrate the applicability of bandwidth and wavefront reduction algorithms to static variable ordering. In symbolic model checking event locality plays a major role in time and memory usage. For example, in Petri nets event locality can be captured by dependency matrices, where nonzero entries indicate whether a transition modifies a place. The quality of event locality has been expressed as a metric called (weighted) event span. The bandwidth of a matrix is a metric indicating the distance of nonzero elements to the diagonal. Wavefront is a metric indicating the degree of nonzeros on one end of the diagonal of the matrix. Bandwidth and wavefront are well studied metrics used in sparse matrix solvers. In this work we prove that span is limited by twice the bandwidth of a matrix. This observation makes bandwidth reduction algorithms useful for obtaining good variable orders. One major issue we address is that the reduction algorithms can only be applied on symmetric matrices, while the dependency matrices are asymmetric. We show that the Sloan algorithm executed on the total graph of the adjacency graph gives the best variable orders. Practically, we demonstrate that our work allows to call standard sparse matrix operations in Boost and ViennaCL, computing very good static variable orders in milliseconds. Future work is promising, because a whole new spectrum of more off-the-shelf algorithms, including metaheuristic ones, become available for variable ordering., preprint