In All Likelihood, Deep Belief Is Not Enough

Statistical models of natural stimuli provide an important tool for researchers in the fields of machine learning and computational neuroscience. A canonical way to quantitatively assess and compare the performance of statistical models is given by the likelihood. One class of statistical models which has recently gained increasing popularity and has been applied to a variety of complex data are deep belief networks. Analyses of these models, however, have been typically limited to qualitative analyses based on samples due to the computationally intractable nature of the model likelihood. Motivated by these circumstances, the present article provides a consistent estimator for the likelihood that is both computationally tractable and simple to apply in practice. Using this estimator, a deep belief network which has been suggested for the modeling of natural image patches is quantitatively investigated and compared to other models of natural image patches. Contrary to earlier claims based on qualitative results, the results presented in this article provide evidence that the model under investigation is not a particularly good model for natural image

Simulating Organogenesis in COMSOL: Comparison Of Methods For Simulating Branching Morphogenesis

During organogenesis tissue grows and deforms. The growth processes are controlled by diffusible proteins, so-called morphogens. Many different patterning mechanisms have been proposed. The stereotypic branching program during lung development can be recapitulated by a receptor-ligand based Turing model. Our group has previously used the Arbitrary Lagrangian-Eulerian (ALE) framework for solving the receptor-ligand Turing model on growing lung domains. However, complex mesh deformations which occur during lung growth severely limit the number of branch generations that can be simulated. A new Phase-Field implementation avoids mesh deformations by considering the surface of the modelling domains as interfaces between phases, and by coupling the reaction-diffusion framework to these surfaces. In this paper, we present a rigorous comparison between the Phase-Field approach and the ALE-based simulation

The Impact of Dynamics in Protein Assembly

Predicting the assembly of multiple proteins into specific complexes is critical to understanding their biological function in an organism, and thus the design of drugs to address their malfunction. Consequently, a significant body of research and development focuses on methods for elucidating protein quaternary structure. In silico techniques are used to propose models that decode experimental data, and independently as a structure prediction tool. These computational methods often consider proteins as rigid structures, yet proteins are inherently flexible molecules, with both local side-chain motion and larger conformational dynamics governing their behaviour. This treatment is particularly problematic for any protein docking engine, where even a simple rearrangement of the side-chain and backbone atoms at the interface of binding partners complicates the successful determination of the correct docked pose. Herein, we present a means of representing protein surface, electrostatics and local dynamics within a single volumetric descriptor, before applying it to a series of physical and biophysical problems to validate it as representative of a protein. We leverage this representation in a protein-protein docking context and demonstrate that its application bypasses the need to compensate for, and predict, specific side-chain packing at the interface of binding partners for both water-soluble and lipid-soluble protein complexes. We find little detriment in the quality of returned predictions with increased flexibility, placing our protein docking approach as highly competitive versus comparative methods. We then explore the role of larger, conformational dynamics in protein quaternary structure prediction, by exploiting large-scale Molecular Dynamics simulations of the SARS-CoV-2 spike glycoprotein to elucidate possible high-order spike-ACE2 oligomeric states. Our results indicate a possible novel path to therapeutics following the COVID-19 pandemic. Overall, we find that the structure of a protein alone is inadequate in understanding its function through its possible binding modes. Therefore, we must also consider the impact of dynamics in protein assembly

Maternal Death, Autopsy Studies, and Lessons from Pathology

The author discusses the implications of a new autopsy study of maternal deaths in Mozambique

Sharing the fiscal burden of the crisis - A Pandemic Solidarity Instrument for the EU

EU member states must share the burden of the fiscal costs of the COVID-19 pandemic. The Pandemic Solidarity Instrument delivers such burden sharing: The EU would borrow 440 billion euros in the market and would give it as grants to member states for specific spending in areas such as health care, short-time works schemes or stimulus packages; it would also give guarantees to the European Investment Bank to provide liquidity to European companies

Sharing the fiscal burden of the crisis: A Pandemic Solidarity Instrument for the EU. Bertelsmann Stiftung Policy Paper April 2020.

The debate over how Europe should cope with the fiscal costs of the COVID-19 pan- demic is in full swing. Adversaries and opponents of “Coronabonds” seem suddenly back in the trenches of the euro crisis. Our proposal attempts to build a bridge bet- ween the two camps: We do not propose a full-on Eurobond or any mutualisation of existing debt, as this is not how we should overcome the unique challenges of this crisis. Instead, we propose a Pandemic Solidarity Instrument that is tailored speci- fically to this crisis. The EU does not need another layer of market-access insurance, as the European Central Bank and the European Stability Mechanism are already in place for this. What it needs is an instrument to share the costs of the crisis. The main problem the EU faces now is that some member states have entered this crisis in a much weaker economic position and with higher debt levels than others. At the same time, all countries have a vital interest in all other countries being able to spend as much as necessary to fight the economic fallout of the pandemic. To ensure that this happens, we need a burden sharing of the fiscal costs of this crisis. The Pandemic Solidarity Instrument delivers this burden sharing. It should be set up as an EU instrument: The EU would borrow 440 billion euros in the market, ba- cked by the EU budget and by guarantees of the member states. As this would be EU debt, it would not count as debt of individual member states. The bonds issued by the EU would have long maturities and could be refinanced in the market at the end of their terms; otherwise, they would be repaid once they come due according to the future state of economic strength of member states. The funds would be used for four purposes: • Grants to member states to partially cover health-related costs; • Guarantees to the European Investment Bank to provide liquidity to European companies; • Subsidies to member states so that they can fund short-time work schemes and short-term unemployment benefits; • Co-financing of national stimulus packages once confinement measures have been lifted. The Instrument would be based on Article 122 of the Treaty on the Functioning of the European Union. This article gives the EU wide discretion to act in emergency situations. In our legal analysis, we show how this article allows the EU to bor- row in this specific context and why our proposal does not conflict with the EU’s no-bailout clause