Sleep benefits the stabilization of newly acquired information – a process known as memory consolidation. Age-related alterations in sleep physiology may affect memory consolidation and account for reduced episodic memory performance in healthy older individuals. The striking parallelism of age-related changes in sleep and episodic memory has provoked a considerable increment in empirical studies investigating the link between age-related changes in sleep and memory. Still, evidence remains inconclusive under which circumstances and by which mechanisms memory consolidation is affected during aging. In this review we provide an exhaustive summary of the status quo of research on episodic memory consolidation during sleep in healthy aging. On this basis, we derive a cohesive explanatory framework to understand age-related changes in consolidation mechanisms during sleep. Consolidation impairments are not solely caused by sleep changes but arise in synergy with age-related alterations in brain structure and neuromodulation. We argue that sleep oscillations during deep non-rapid eye movement (NREM) sleep guide the reactivation, integration, and redistribution of memory traces. Neuromodulators supporting these mechanisms change in old age. In combination with alterations in brain structure, the generation of sleep oscillations during NREM sleep is impaired, their coordination becomes diffuse, and the processes necessary to render stable episodic memories are impaired. OBJECTIVE We aimed to evaluate the impact of the European Medicines Agency (EMA) and Food Drug and Administration (FDA) alerts on the use of effective contraceptive method in women of childbearing age undergoing valproic acid treatment in a long-stay psychiatric center. MATERIAL AND METHODS An interrupted time-series analysis of women of childbearing age admitted in a long-stay psychiatric center (2013-2019), according to the EMA/FDA restrictions dates (October 2014 and February 2018). RESULTS Of the 82 cases included, 50 (61.0%) had an ‘off-label’ prescription. The percentage of cases with a contraceptive method before October 2014 (31.6%) increased to 61.5% after October 2014, p = 0.004. Phlorizin Women with an ‘off-label’ prescription after 2018 were more likely to use a contraceptive method than those before 2014, and there were not statistically significant differences in women with an ‘under indication’ prescription. CONCLUSIONS The recent regulatory restrictions on the use of a contraceptive method had a positive effect, mainly in women with an ‘off-label’ prescription. No effect was seen in women with epilepsy, probably because the intervention had started long before. BACKGROUND This paper includes the voices of people who are members of a peer-led drug user group (SNAP) in Canada who are receiving heroin-assisted treatment (HAT) outside of a clinical trial. Drawing from critical drug studies, we problematize the criteria for severe opioid use disorder (OUD) from the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders, by exploring SNAP members’ experiences in relation to heroin-assisted treatment, and examining how SNAP participants’ narratives challenge conventional notions of what constitutes severe opioid use disorder. METHOD Drawing on critical analysis and research guidelines developed by drug user unions and organizations, and critical methodological frameworks on ethical community-based-and-responsive research for social justice, in this paper we focus on semi-structured interviews conducted with 36 SNAP members at the Vancouver Area Network of Drug Users site in the Downtown Eastside of Vancouver, Canada. We included opened ended questions atical implications that will determine what types of services and programs will be set up. Treating a disorder, or a person with a disorder, requires a much different approach than understanding heroin use as a habit. SNAP, and their allies, are rupturing conventional ideas about heroin and taken for granted assumptions about people who use heroin. While various aspects of classical biological control (CBC) of weeds, including non-target risk assessment, have been continuously improved in the past few decades, post-release monitoring remains neglected and underfunded. Detailed assessments of the population, community and ecosystem outcomes of CBC introductions, including reasons for success/failure and absence or evidence of non-target effects are generally lacking or fragmentary. Here we review recent advances in understanding the demography of biological control agents released into a novel environment, their impact on the target weed and on non-target species, and the consequences for the resident plant and animal communities and ecosystem functioning, including the restoration of ecosystem services. We argue that post-release monitoring of CBC programs offers unique but largely underutilized opportunities to improve our understanding of CBC outcomes and to inform management and decision-makers on when and how CBC should be integrated with other management options to enhance ecosystem restoration. Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases. This paper presents two approaches to extracting rules from a trained neural network consisting of linear threshold functions. The first one leads to an algorithm that extracts rules in the form of Boolean functions. Compared with an existing one, this algorithm outputs much more concise rules if the threshold functions correspond to 1-decision lists, majority functions, or certain combinations of these. The second one extracts probabilistic rules representing relations between some of the input variables and the output using a dynamic programming algorithm. The algorithm runs in pseudo-polynomial time if each hidden layer has a constant number of neurons. We demonstrate the effectiveness of these two approaches by computational experiments.