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Glyphosate is the active ingredient of some of the most highly produced and used herbicides worldwide. The intensive applications of glyphosate-based herbicides and its half-life in water lead to its presence in many aquatic ecosystems. Whereas recent studies have reported neurotoxic effects of glyphosate including autism-related effects, most of them used extremely high (mg/L to g/L) concentrations, so it is still unclear if chronic, low environmentally relevant concentrations of this compound (ng/L to μg/L) can induce neurotoxicity. In this study we analyzed the neurotoxicity of glyphosate in adult zebrafish after waterborne exposure to environmentally relevant concentrations (0.3 and 3 μg/L) for two weeks. Our data showed that exposed fish presented a significant impairment of exploratory and social behaviors consistent with increased anxiety. The anterior brain of the exposed fish presented a significant increase in dopamine and serotonin levels, as well as in the DOPAC/dopamine and homovanillic acid/dopamine turnover ratios. Moreover, the expression of genes involved in the dopaminergic system, as th1, th2, comtb, and scl6a3 was downregulated. Finally, the brain of exposed fish presented a significant increase in the catalase and superoxide dismutase activities, with a concomitant decrease of glutathione stores. These changes in the antioxidant defense system are consistent with the observed increase in oxidative stress, reflected by the increase in the levels of lipid peroxidation in the brain. The presented results show that current glyphosate concentrations commonly found in many aquatic ecosystems may have detrimental consequences on fish survival by decreasing exploration of the environment or altering social interactions. Furthermore, as zebrafish is also a vertebrate model widely used in human neurobehavioral studies, these results are relevant not only for environmental risk assessment, but also for understanding the risk of chronic low-dose exposures on human health. ; This work was supported by the Spanish Government with FEDER Funds (CTM2017-83242-R; D.R.) and the network of recognized research groups by the Catalan Government (2017 SGR_902). J.B. was supported by a Spanish fellowship PRE2018-083513. Mention of specific products or trade names does not indicate endorsement by the US federal government. ; Peer reviewed

Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant. ; Andreas Holzinger acknowledges funding support from the Austrian Science Fund (FWF), Project: P-32554 explainable Artificial Intelligenceand from the European Union's Horizon 2020 research and innovationprogram under grant agreement 826078 (Feature Cloud). This publi-cation reflects only the authors' view and the European Commissionis not responsible for any use that may be made of the informationit contains; Natalia Díaz-Rodríguez is supported by the Spanish Gov-ernment Juan de la Cierva Incorporación contract (IJC2019-039152-I); Isabelle Augenstein's research is partially funded by a DFF Sapere Auderesearch leader grant; Javier Del Ser acknowledges funding supportfrom the Basque Government through the ELKARTEK program (3KIAproject, KK-2020/00049) and the consolidated research group MATH-MODE (ref. T1294-19); Wojciech Samek acknowledges funding Support from the European Union's Horizon 2020 research and innovationprogram under grant agreement No. 965221 (iToBoS), and the German Federal Ministry of Education and Research (ref. 01IS18025 A, ref. 01IS18037I and ref. 0310L0207C); Igor Jurisica acknowledges funding support from Ontario Research Fund (RDI 34876), Natural Sciences Research Council (NSERC 203475), CIHR Research Grant (93579),Canada Foundation for Innovation (CFI 29272, 225404, 33536), IBM, Ian Lawson van Toch Fund, the Schroeder Arthritis Institute via theToronto General and Western Hospital Foundation.