Art, Attention, and Consciousness: An Experiment in Experiential Painting

A “transformation of perception” is investigated by looking both at the interrelationship among art, attention, and consciousness and by looking into their common origin. The role attention plays in consciousness is considered. A new model of consciousness is summarized that claims that attention is the primary factor in creating consciousness, and posits a prereflective self prior to all perceptual experience. This model is compared to states of pure consciousness described by Eastern sages, and the role attention plays in achieving those states is examined. Our experiment in experiential painting is described, and we then attempt to tie together the three main topics

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Synthesis Paper: Targeted Livestock Grazing: Prescription for Healthy Rangelands

Targeted livestock grazing is a proven tool for manipulating rangeland vegetation, and current knowledge about targeted livestock grazing is extensive and expanding rapidly. Targeted grazing prescriptions optimize the timing, frequency, intensity, and selectivity of grazing (or browsing) in combinations that purposely exert grazing/browsing pressure on specific plant species or portions of the landscape. Targeted grazing differs from traditional grazing management in that the goal of targeted grazing is to apply defoliation or trampling to achieve specific vegetation management objectives, whereas the goal of traditional livestock grazing management is generally the production of livestock commodities. A shared aim of targeted livestock grazing and traditional grazing management is to sustain healthy soils, flora, fauna, and water resources that, in turn, can sustain natural ecological processes (e.g., nutrient cycle, water cycle, energy flow). Targeted grazing prescriptions integrate knowledge of plant ecology, livestock nutrition, and livestock foraging behavior. Livestock can be focused on target areas through fencing, herding, or supplement placement. Although practices can be developed to minimize the impact of toxins contained in target plants, the welfare of the animals used in targeted grazing must be a priority. Monitoring is needed to determine if targeted grazing is successful and to refine techniques to improve efficacy and efficiency. Examples of previous research studies and approaches are presented to highlight the ecological benefits that can be achieved when targeted grazing is applied properly. These cases include ways to suppress invasive plants and ways to enhance wildlife habitat and biodiversity. Future research should address the potential to select more adapted and effective livestock for targeted grazing and the associated animal welfare concerns with this practice. Targeted livestock grazing provides land managers a viable alternative to mechanical, chemical, and prescribed fire treatments to manipulate rangeland vegetation

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