Prediction of Clinical Deep Brain Stimulation Target for Essential Tremor From 1.5 Tesla MRI Anatomical Landmarks

International audienceBackground: Deep brain stimulation is an efficacious treatment for refractory essential tremor, though targeting the intra-thalamic nuclei remains challenging. Objectives: We sought to develop an inverse approach to retrieve the position of the leads in a cohort of patients operated on with optimal clinical outcomes from anatomical landmarks identifiable by 1.5 Tesla magnetic resonance imaging. Methods: The learning database included clinical outcomes and post-operative imaging from which the coordinates of the active contacts and those of anatomical landmarks were extracted. We used machine learning regression methods to build three different prediction models. External validation was performed according to a leave-one-out cross-validation. Results: Fifteen patients (29 leads) were included, with a median tremor improvement of 72% on the Fahn-Tolosa-Marin scale. Kernel ridge regression, deep neural networks, and support vector regression (SVR) were used. SVR gave the best results with a mean error of 1.33 ± 1.64 mm between the predicted target and the active contact position. Conclusion: We report an original method for the targeting in deep brain stimulation for essential tremor based on patients' radio-anatomical features. This approach will be tested in a prospective clinical trial

Influence of root and leaf traits on the uptake of nutrients in cover crops

Aims: Cover crops play an important role in soil fertility as they can accumulate large amounts of nutrients. This study aimed at understanding the nutrient uptake capacity of a wide range of cover crops and at assessing the relevance of acquisition strategies. Methods: A field experiment was conducted to characterize 20 species in terms of leaf and root traits. Plant traits were related to nutrient concentration and shoot biomass production with a redundancy analysis. Acquisition strategies were identified using a cluster analysis. Results: Root systems varied greatly among cover crop species. Five nutrient acquisition strategies were delineated. Significant amounts of nutrients (about 120 kg ha−1 of nitrogen, 30 kg ha−1 of phosphorus and 190 kg ha−1 of potassium) were accumulated by the species in a short period. Nutrient acquisition strategies related to high accumulations of nutrients consisted in either high shoot biomass and root mass and dense tissues, or high nutrient concentrations and root length densities. Species with high root length densities showed lower C/N ratios. Conclusions: The same amounts of nutrients were accumulated by groups with different acquisition strategies. However, their nutrient concentrations offer different perspectives in terms of nutrient release for the subsequent crop and nutrient cycling improvement

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

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

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Library fee-based information services: financial considerations

Fee-based services in libraries offer research and document delivery services to non-primary clientele on a cost-recovery basis. Highlights services at the University of Colorado, Boulder, Rice University and Purdue University. Explores the major financial considerations involved in starting a new fee-based service, including planning, staffing, pricing and marketing. Relates several special opportunities to which the libraries could not have responded without having had an existing fee-based service with experienced staff in place. Also examines Internet opportunities