Catálogo de publicaciones - revistas

<jats:title>Machine-learning fluid flow</jats:title> <jats:p> Quantifying fluid flow is relevant to disciplines ranging from geophysics to medicine. Flow can be experimentally visualized using, for example, smoke or contrast agents, but extracting velocity and pressure fields from this information is tricky. Raissi <jats:italic>et al.</jats:italic> developed a machine-learning approach to tackle this problem. Their method exploits the knowledge of Navier-Stokes equations, which govern the dynamics of fluid flow in many scientifically relevant situations. The authors illustrate their approach using examples such as blood flow in an aneurysm. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6481" page="1026" related-article-type="in-this-issue" vol="367" xlink:href="10.1126/science.aaw4741">1026</jats:related-article> </jats:p>

<jats:title>Metastable pathways allow high rates</jats:title> <jats:p> In batteries that allow for fast charging and discharging, lithium usually forms a solid solution with the anode so that the only limiting factor is the ionic diffusion. However, for a lithium titanate (Li <jats:sub>4</jats:sub> Ti <jats:sub>5</jats:sub> O <jats:sub>12</jats:sub> ) anode, the lithium ions interact with two phases and the diffusion is slow in both, but it still shows high-rate capabilities. Zhang <jats:italic>et al.</jats:italic> used electron energy-loss spectroscopy combined with density functional theory calculations to probe the anomalous behavior. They found that a diffuse interface forms between the starting and ending compositions, Li <jats:sub>4</jats:sub> Ti <jats:sub>5</jats:sub> O <jats:sub>12</jats:sub> and Li <jats:sub>7</jats:sub> Ti <jats:sub>5</jats:sub> O <jats:sub>12</jats:sub> , and this is what allows the lithium ions to travel quickly. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6481" page="1030" related-article-type="in-this-issue" vol="367" xlink:href="10.1126/science.aax3520">1030</jats:related-article> </jats:p>

<jats:title>Resisting extinction</jats:title> <jats:p> Prevailing evolutionary wisdom tells us that ecological differentiation leads to speciation. Whether this pattern can be seen over paleontological time, however, has been difficult to test. Knope <jats:italic>et al.</jats:italic> looked at a dataset of thousands of modern and extinct marine groups and found the relationship to be more complex than expected. Ecological diversification is associated with lower rates of origination, and the taxonomical richness seemingly associated with these groups is due to resistance to extinction. Furthermore, the researchers found that the strong association between ecological differentiation and taxonomic diversity is a recent development shaped by extinction events over time. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6481" page="1035" related-article-type="in-this-issue" vol="367" xlink:href="10.1126/science.aax6398">1035</jats:related-article> </jats:p>

<jats:title>Polymerization regulates hexokinase activity</jats:title> <jats:p> The yeast hexokinase Glk1 is an actin-fold protein that forms polymers in response to binding its substrates and products. Stoddard <jats:italic>et al.</jats:italic> now show that Glk1 polymers are structurally distinct from actin filaments and suggest that polymerization of Glk1 evolved independently of the polymerization of other actin-like polymers. Glk1 polymerization inhibits its hexokinase activity, and the monomerpolymer equilibrium appears to set a maximum rate for the entire enzyme pool rather than a maximum rate per enzyme. This inhibition was found to be important for cell viability in the context of nutrient shifts, allowing yeast cells to modulate their metabolism rapidly in response to stochastic changes in the environment. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6481" page="1039" related-article-type="in-this-issue" vol="367" xlink:href="10.1126/science.aay5359">1039</jats:related-article> </jats:p>

<jats:title>Speech versus music in the brain</jats:title> <jats:p> To what extent does the perception of speech and music depend on different mechanisms in the human brain? What is the anatomical basis underlying this specialization? Albouy <jats:italic>et al.</jats:italic> created a corpus of a cappella songs that contain both speech (semantic) and music (melodic) information and degraded each stimulus selectively in either the temporal or spectral domain. Degradation of temporal information impaired speech recognition but not melody recognition, whereas degradation of spectral information impaired melody recognition but not speech recognition. Brain scanning revealed a right-left asymmetry for speech and music. Classification of speech content occurred exclusively in the left auditory cortex, whereas classification of melodic content occurred only in the right auditory cortex. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6481" page="1043" related-article-type="in-this-issue" vol="367" xlink:href="10.1126/science.aaz3468">1043</jats:related-article> </jats:p>