A framework for a European network for a systematic environmental impact assessment of genetically modified organisms (GMO)

The assessment of the impacts of growing genetically modified (GM) crops remains a major political and scientific challenge in Europe. Concerns have been raised by the evidence of adverse and unexpected environmental effects and differing opinions on the outcomes of environmental risk assessments (ERA). The current regulatory system is hampered by insufficiently developed methods for GM crop safety testing and introduction studies. Improvement to the regulatory system needs to address the lack of well designed GM crop monitoring frameworks, professional and financial conflicts of interest within the ERA research and testing community, weaknesses in consideration of stakeholder interests and specific regional conditions, and the lack of comprehensive assessments that address the environmental and socio economic risk assessment interface. To address these challenges, we propose a European Network for systematic GMO impact assessment (ENSyGMO) with the aim directly to enhance ERA and post-market environmental monitoring (PMEM) of GM crops, to harmonize and ultimately secure the long-term socio-political impact of the ERA process and the PMEM in the EU. These goals would be achieved with a multi-dimensional and multi-sector approach to GM crop impact assessment, targeting the variability and complexity of the EU agro-environment and the relationship with relevant socio-economic factors. Specifically, we propose to develop and apply methodologies for both indicator and field site selection for GM crop ERA and PMEM, embedded in an EU-wide typology of agro-environments. These methodologies should be applied in a pan-European field testing network using GM crops. The design of the field experiments and the sampling methodology at these field sites should follow specific hypotheses on GM crop effects and use state-of-the art sampling, statistics and modelling approaches. To address public concerns and create confidence in the ENSyGMO results, actors with relevant specialist knowledge from various sectors should be involved

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Madrigals, Book I, by George Crumb : an explanation of the harmonics and rhythmic complexities for the double bass performer

There is no abstract available for this creative project.School of MusicTape includes selections from Georg Philipp Telemann, et al.Thesis (M.M.