The interaction map of yeast: terra incognita?

A systematic curation of the literature on Saccharomyces cerevisiae has yielded a comprehensive collection of experimentally observed interactions. This new resource augments current views of the topological structure of yeast's physical and genetic networks, but also reveals that existing studies cover only a fraction of the cell

Neural Architecture Search without Training

The time and effort involved in hand-designing deep neural networks is immense. This has prompted the development of Neural Architecture Search (NAS) techniques to automate this design. However, NAS algorithms tend to be slow and expensive; they need to train vast numbers of candidate networks to inform the search process. This could be alleviated if we could partially predict a network's trained accuracy from its initial state. In this work, we examine the overlap of activations between datapoints in untrained networks and motivate how this can give a measure which is usefully indicative of a network's trained performance. We incorporate this measure into a simple algorithm that allows us to search for powerful networks without any training in a matter of seconds on a single GPU, and verify its effectiveness on NAS-Bench-101, NAS-Bench-201, NATS-Bench, and Network Design Spaces. Our approach can be readily combined with more expensive search methods; we examine a simple adaptation of regularised evolutionary search. Code for reproducing our experiments is available at https://github.com/BayesWatch/nas-without-training.Comment: Accepted at ICML 2021 for a long presentatio

VisANT: data-integrating visual framework for biological networks and modules

VisANT is a web-based software framework for visualizing and analyzing many types of networks of biological interactions and associations. Networks are a useful computational tool for representing many types of biological data, such as biomolecular interactions, cellular pathways and functional modules. Given user-defined sets of interactions or groupings between genes or proteins, VisANT provides: (i) a visual interface for combining and annotating network data, (ii) supporting function and annotation data for different genomes from the Gene Ontology and KEGG databases and (iii) the statistical and analytical tools needed for extracting topological properties of the user-defined networks. Users can customize, modify, save and share network views with other users, and import basic network data representations from their own data sources, and from standard exchange formats such as PSI-MI and BioPAX. The software framework we employ also supports the development of more sophisticated visualization and analysis functions through its open API for Java-based plug-ins. VisANT is distributed freely via the web at and can also be downloaded for individual use

VisANT: data-integrating visual framework for biological networks and modules

VisANT is a web-based software framework for visualizing and analyzing many types of networks of biological interactions and associations. Networks are a useful computational tool for representing many types of biological data, such as biomolecular interactions, cellular pathways and functional modules. Given user-defined sets of interactions or groupings between genes or proteins, VisANT provides: (i) a visual interface for combining and annotating network data, (ii) supporting function and annotation data for different genomes from the Gene Ontology and KEGG databases and (iii) the statistical and analytical tools needed for extracting topological properties of the user-defined networks. Users can customize, modify, save and share network views with other users, and import basic network data representations from their own data sources, and from standard exchange formats such as PSI-MI and BioPAX. The software framework we employ also supports the development of more sophisticated visualization and analysis functions through its open API for Java-based plug-ins. VisANT is distributed freely via the web at and can also be downloaded for individual use

Cyclic-AMP Increases Nuclear Actin Monomer Which Promotes Proteasomal Degradation of RelA/p65 Leading to Anti-Inflammatory Effects

The second messenger, cAMP has potent immunosuppressive and anti-inflammatory actions. These have been attributed, in part, to the ability of cAMP-induced signals to interfere with the function of the proinflammatory transcription factor Nuclear Factor-kappa B (NF-κB). However, the mechanisms underlying the modulation of NF-κB activity by cAMP remain unclear. Here we demonstrate an important role for cAMP-mediated increase in nuclear actin monomer levels in inhibiting NF-κB activity. Elevated cAMP or forced expression of a nuclear localised polymerisation defective actin mutant (NLS-Actin(R62D)) inhibited basal and TNFα induced mRNA levels of NF-κB-dependent genes and NF-κB-dependent reporter gene activity. Elevated cAMP or NLS-Actin(R62D) did not affect NF-κB nuclear translocation but did reduce total cellular and nuclear RelA/p65 levels. Preventing the cAMP-induced increase in nuclear actin monomer, either by expressing a nuclear localised active mutant of the actin polymerising protein mDIA, silencing components of the nuclear actin import complex IPO9 and CFL1 or overexpressing the nuclear export complex XPO6, rescued RelA/p65 levels and NF-κB reporter gene activity in forskolin-stimulated cells. Elevated cAMP or NLS-Actin(R62D) reduced the half-life of RelA/p65, which was reversed by the proteasome inhibitor MG132. Accordingly, forskolin stimulated association of RelA/p65 with ubiquitin affinity beads, indicating increased ubiquitination of RelA/p65 or associated proteins. Taken together, our data demonstrate a novel mechanism underlying the anti-inflammatory effects of cAMP and highlight the important role played by nuclear actin in the regulation of inflammation

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme

Background/AimsNational guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to referable DR beyond DR grading, and the potential impact on assigned screening intervals, within the Scottish screening programme.MethodsWe consider 21346 and 247233 people with T1DM and T2DM respectively each contributing on average 4.8 and 4.4 screening intervals of which 1339 and 4675 intervals concluded with a referable screening episode. Information extracted from fundus images using DL were used to predict referable status at the end of interval and its predictive value in comparison to screening-assigned DR grade was assessed.ResultsThe DL predictor increased the AUC in comparison to a predictor using current DR grades from 0.809 to 0.87 for T1DM and from 0.825 to 0.87 for T2DM. Expected sojourn time – the time from becoming referable to being rescreened - was found to be 3.4 (T1DM) and 2.7 (T2DM) weeks less for a DL-derived policy compared to the current recall policy.ConclusionsWe showed that, compared to using the current retinopathy grade, DL of fundus images significantly improves the prediction of incident referable retinopathy before the next screening episode. This can impact screening recall interval policy positively, for example, by reducing the expected time with referable disease for a fixed workload - which we show as an exemplar. Additionally, it could be used to optimise workload for a fixed sojourn time

Role of Systemic Factors in Improving the Prognosis of Diabetic Retinal Disease and Predicting Response to Diabetic Retinopathy Treatment

TOPIC: To review clinical evidence on systemic factors that might be relevant to update diabetic retinal disease (DRD) staging systems, including prediction of DRD onset, progression, and response to treatment.CLINICAL RELEVANCE: Systemic factors may improve new staging systems for DRD to better assess risk of disease worsening and predict response to therapy.METHODS: The Systemic Health Working Group of the Mary Tyler Moore Vision Initiative reviewed systemic factors individually and in multivariate models for prediction of DRD onset or progression (i.e., prognosis) or response to treatments (prediction).RESULTS: There was consistent evidence for associations of longer diabetes duration, higher glycosylated hemoglobin (HbA1c), and male sex with DRD onset and progression. There is strong trial evidence for the effect of reducing HbA1c and reducing DRD progression. There is strong evidence that higher blood pressure (BP) is a risk factor for DRD incidence and for progression. Pregnancy has been consistently reported to be associated with worsening of DRD but recent studies reflecting modern care standards are lacking. In studies examining multivariate prognostic models of DRD onset, HbA1c and diabetes duration were consistently retained as significant predictors of DRD onset. There was evidence of associations of BP and sex with DRD onset. In multivariate prognostic models examining DRD progression, retinal measures were consistently found to be a significant predictor of DRD with little evidence of any useful marginal increment in prognostic information with the inclusion of systemic risk factor data apart from retinal image data in multivariate models. For predicting the impact of treatment, although there are small studies that quantify prognostic information based on imaging data alone or systemic factors alone, there are currently no large studies that quantify marginal prognostic information within a multivariate model, including both imaging and systemic factors.CONCLUSION: With standard imaging techniques and ways of processing images rapidly evolving, an international network of centers is needed to routinely capture systemic health factors simultaneously to retinal images so that gains in prediction increment may be precisely quantified to determine the usefulness of various health factors in the prognosis of DRD and prediction of response to treatment.FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p

VisANT 3.0: new modules for pathway visualization, editing, prediction and construction

With the integration of the KEGG and Predictome databases as well as two search engines for coexpressed genes/proteins using data sets obtained from the Stanford Microarray Database (SMD) and Gene Expression Omnibus (GEO) database, VisANT 3.0 supports exploratory pathway analysis, which includes multi-scale visualization of multiple pathways, editing and annotating pathways using a KEGG compatible visual notation and visualization of expression data in the context of pathways. Expression levels are represented either by color intensity or by nodes with an embedded expression profile. Multiple experiments can be navigated or animated. Known KEGG pathways can be enriched by querying either coexpressed components of known pathway members or proteins with known physical interactions. Predicted pathways for genes/proteins with unknown functions can be inferred from coexpression or physical interaction data. Pathways produced in VisANT can be saved as computer-readable XML format (VisML), graphic images or high-resolution Scalable Vector Graphics (SVG). Pathways in the format of VisML can be securely shared within an interested group or published online using a simple Web link. VisANT is freely available at http://visant.bu.edu