Simulating Brownian suspensions with fluctuating hydrodynamics

Fluctuating hydrodynamics has been successfully combined with several computational methods to rapidly compute the correlated random velocities of Brownian particles. In the overdamped limit where both particle and fluid inertia are ignored, one must also account for a Brownian drift term in order to successfully update the particle positions. In this paper, we present an efficient computational method for the dynamic simulation of Brownian suspensions with fluctuating hydrodynamics that handles both computations and provides a similar approximation as Stokesian Dynamics for dilute and semidilute suspensions. This advancement relies on combining the fluctuating force-coupling method (FCM) with a new midpoint time-integration scheme we refer to as the drifter-corrector (DC). The DC resolves the drift term for fluctuating hydrodynamics-based methods at a minimal computational cost when constraints are imposed on the fluid flow to obtain the stresslet corrections to the particle hydrodynamic interactions. With the DC, this constraint need only be imposed once per time step, reducing the simulation cost to nearly that of a completely deterministic simulation. By performing a series of simulations, we show that the DC with fluctuating FCM is an effective and versatile approach as it reproduces both the equilibrium distribution and the evolution of particulate suspensions in periodic as well as bounded domains. In addition, we demonstrate that fluctuating FCM coupled with the DC provides an efficient and accurate method for large-scale dynamic simulation of colloidal dispersions and the study of processes such as colloidal gelation

Breast density classification with deep convolutional neural networks

Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explore the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We use this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we find that our model can perform this task comparably to a human expert

Regulation strength and technology creep play key roles in global long-term projections of wild capture fisheries

Unidad de excelencia María de Maeztu CEX2019-000940-MIdentificadors digitals: Digital object identifier for the 'European Research Council' (http://dx.doi.org/10.13039/501100000781) Digital object identifier for 'Horizon 2020' (http://dx.doi.org/10.13039/501100007601) - BIGSEA projectMany studies have shown that the global fish catch can only be sustained with effective regulation that restrains overfishing. However, the persistence of weak or ineffective regulation in many parts of the world, coupled with changing technologies and additional stressors like climate change, renders the future of global catches uncertain. Here, we use a spatially resolved, bio-economic size-spectrum model to shed light on the interactive impacts of three globally important drivers over multidecadal timescales: imperfect regulation, technology-driven catchability increase, and climate change. We implement regulation as the adjustment of fishing towards a target level with some degree of effectiveness and project a range of possible trajectories for global fisheries. We find that if technological progress continues apace, increasingly effective regulation is required to prevent overfishing, akin to a Red Queen race. Climate change reduces the possible upper bound for global catches, but its economic impacts can be offset by strong regulation. Ominously, technological progress under weak regulation masks a progressive erosion of fish biomass by boosting profits and generating a temporary stabilization of global catches. Our study illustrates the large degree to which the long-term outlook of global fisheries can be improved by continually strengthening fisheries regulation, despite the negative impacts of climate change

Profiling of oligolignols reveals monolignol coupling conditions in lignifying poplar xylem

Lignin is an aromatic heteropolymer, abundantly present in the walls of secondary thickened cells. Although much research has been devoted to the structure and composition of the polymer to obtain insight into lignin polymerization, the low-molecular weight oligolignol fraction has escaped a detailed characterization. This fraction, in contrast to the rather inaccessible polymer, is a simple and accessible model that reveals details about the coupling of monolignols, an issue that has raised considerable controversy over the past years. We have profiled the methanol-soluble oligolignol fraction of poplar (Populus spp.) xylem, a tissue with extensive lignification. Using liquid chromatography-mass spectrometry, chemical synthesis, and nuclear magnetic resonance, we have elucidated the structures of 38 compounds, most of which were dimers, trimers, and tetramers derived from coniferyl alcohol, sinapyl alcohol, their aldehyde analogs, or vanillin. All structures support the recently challenged random chemical coupling hypothesis for lignin polymerization. Importantly, the structures of two oligomers, each containing a γ-p-hydroxybenzoylated syringyl unit, strongly suggest that sinapyl p-hydroxybenzoate is an authentic precursor for lignin polymerization in poplar