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

Analysis Of The Required Infrastructure For Electrified Heavy-Duty Commercial Vehicles

The decarbonization of the mobility sector is considered a crucial milestone to reach climate goals set by the Paris Climate Agreement and implemented into national law by the German Federal Government. When it comes down to decarbonization strategies for the road traffic, topic of political and scientific discussions so far was mainly the electrification of passenger vehicles. But with regards to the fact, that heavy duty vehicles account for just 1 % of the entire vehicle fleet but cause 25 % of the CO2 emissions of the road traffic, it has to be reevaluated whether this focus is purposeful. Due to their large payload and their high annual mileage, heavy-duty vehicles offer a large lever to reduce CO2 emissions in road traffic with a relatively small number of vehicles. Due to different applications, the requirements towards the drivetrain vary to a large degree in this vehicle segment, which makes it difficult to apply one decarbonization technology to the entire market. Other than for passenger vehicles, battery electric drivetrains are expected to account for a share of decarbonized heavy-duty vehicles, but not entirely. Fuel cell or pantograph electric drivetrains as well as synthetic fuels based internal combustion engines or electrified trailer drivetrains can be a part of achieving the 2030 emission targets defined by the Federal Government. In order to point out a possible path to decarbonizing the heavy-duty commercial vehicle segment, different powertrain configurations for in particular semitrailer trucks are analyzed and optimized in terms of their investment and operating costs. Belonging requirements for the necessary infrastructure are derived based on common driving profiles

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