Catálogo de publicaciones - revistas

<jats:sec> <jats:title>Background</jats:title> <jats:p>Economic science has evolved over several decades toward greater emphasis on empirical work. The data revolution of the past decade is likely to have a further and profound effect on economic research. Increasingly, economists make use of newly available large-scale administrative data or private sector data that often are obtained through collaborations with private firms, giving rise to new opportunities and challenges.</jats:p> </jats:sec> <jats:sec> <jats:title>Advances</jats:title> <jats:p>These new data are affecting economic research along several dimensions. Many fields have shifted from a reliance on relatively small-sample government surveys to administrative data with universal or near-universal population coverage. This shift is transformative, as it allows researchers to rigorously examine variation in wages, health, productivity, education, and other measures across different subpopulations; construct consistent long-run statistical indices; generate new quasi-experimental research designs; and track diverse outcomes from natural and controlled experiments.</jats:p> <jats:p>Perhaps even more notable is the expansion of private sector data on economic activity. These data, sometimes available from public sources but other times obtained through data-sharing agreements with private firms, can help to create more granular and real-time measurement of aggregate economic statistics. The data also offer researchers a look inside the “black box” of firms and markets by providing meaningful statistics on economic behavior such as search and information gathering, communication, decision-making, and microlevel transactions. Collaborations with data-oriented firms also create new opportunities to conduct and evaluate randomized experiments.</jats:p> <jats:p>Economic theory plays an important role in the analysis of large data sets with complex structure. It can be difficult to organize and study this type of data (or even to decide which variables to construct) without a simplifying conceptual framework, which is where economic models become useful. Better data also allow for sharper tests of existing models and tests of theories that had previously been difficult to assess.</jats:p> </jats:sec> <jats:sec> <jats:title>Outlook</jats:title> <jats:p>The advent of big data is already allowing for better measurement of economic effects and outcomes and is enabling novel research designs across a range of topics. Over time, these data are likely to affect the types of questions economists pose, by allowing for more focus on population variation and the analysis of a broader range of economic activities and interactions. We also expect economists to increasingly adopt the large-data statistical methods that have been developed in neighboring fields and that often may complement traditional econometric techniques.</jats:p> <jats:p>These data opportunities also raise some important challenges. Perhaps the primary one is developing methods for researchers to access and explore data in ways that respect privacy and confidentiality concerns. This is a major issue in working with both government administrative data and private sector firms. Other challenges include developing the appropriate data management and programming capabilities, as well as designing creative and scalable approaches to summarize, describe, and analyze large-scale and relatively unstructured data sets. These challenges notwithstanding, the next few decades are likely to be a very exciting time for economic research.</jats:p> </jats:sec>

<jats:title>The wheres and whys of protein translation</jats:title> <jats:p> Localized protein synthesis is important for a broad range of biological activities, from specifying the animal body plan to coordinating entry into the secretory pathway. Few tools are available that can investigate translation at specific subcellular sites. Jan <jats:italic>et al.</jats:italic> present a flexible ribosome profiling–based methodology to enable precise characterization of localized protein synthesis (see the Perspective by Shao and Hegde). Proximity-specific ribosome profiling provides a high-precision tool for looking at the mechanism of localized protein targeting and synthesis in living cells. The approach yielded a high-resolution systems-level view of cotranslational translocation at the endoplasmic reticulum. Williams <jats:italic>et al.</jats:italic> applied the technique to look at localized mRNA translation at the mitochondrial outer membrane. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" related-article-type="in-this-issue" xlink:href="10.1126/science.1257521">10.1126/science.1257521</jats:related-article> , p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6210" page="701" related-article-type="in-this-issue" vol="346" xlink:href="10.1126/science.1261602">701</jats:related-article> ; see also p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6210" page="748" related-article-type="in-this-issue" vol="346" xlink:href="10.1126/science.1257522">748</jats:related-article> </jats:p>

<jats:sec> <jats:title>Introduction</jats:title> <jats:p>The ability to manufacture inorganic nanoparticles (NPs) with arbitrarily prescribed three-dimensional (3D) shapes and positional surface modifications is essential to enabling diverse applications (e.g., in nano-optics and biosensing). However, it is challenging to achieve 3D arbitrary user-specified shapes with sub–5-nm resolution. Top-down lithography has limited resolution, particularly for 3D shapes; capping ligands can be used to tune the energy difference of selected crystallographic facets, but typically only for highly symmetric shapes with identical surface facets.</jats:p> </jats:sec> <jats:sec> <jats:title>Rationale</jats:title> <jats:p>We developed a framework to program arbitrary 3D inorganic NPs using DNA, which serves both as an informational “genome” to encode the 3D shape of a NP and as a physical “fabricator” to retrieve the information and execute the instruction to manufacture the NP. Specifically, our method uses a computationally designed, mechanically stiff synthetic DNA nanostructure with a user-specified cavity as a “mold” to cast the target inorganic NP. The mold encloses a small gold (Au) “seed.” Under mild conditions, the Au seed grows into a larger metal NP that fills the entire cavity, thereby replicating its prescribed 3D shape. The remaining DNA mold additionally acts as a spatially programmable functionalization surface.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Using this DNA nanocasting method, we constructed three distinct sub–25-nm 3D cuboid silver (Ag) NPs with three independently tunable dimensions. The shape versatility of DNA-based nanocasting was further demonstrated via the synthesis of Ag NPs with equilateral triangular, right triangular, and circular cross sections. The material versatility was demonstrated via synthesis of a Au cuboid in addition to the Ag NPs. The DNA mold served as an addressable coating for the casted NP and thus enabled the construction of higher-order composite structures, including a Y-shaped Ag NP composite and a quantum dot (QD)–Ag-QD sandwiched structure through one-step casting growth.</jats:p> <jats:p>We investigated the key design parameters for stiff DNA molds through mechanical simulations. Multilayered DNA molds provided higher mechanical stiffness for confining NP growth within the mold than single-layer DNA molds, as confirmed by experimental observation.</jats:p> <jats:p>We additionally characterized plasmonic properties of the designer equilateral Ag triangle and Ag sphere through electron energy loss spectroscopy. Tuning of particle symmetry produced a shape-specific spectrum, which is consistent with the predictions of electromagnetism-based simulations.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>DNA nanocasting represents a new framework for the programmable digital fabrication of 3D inorganic nanostructures with prescribed shapes, dimensions, and surface modifications at sub– 5-nm resolution. The key design strategy is to encode linear sequences of DNA with the sophisticated user-specified 3D spatial and surface information of an inorganic NP, as well as to retrieve and execute the information to physically produce this structure via geometric confinement. Such a method may lead to computationally designed functional materials for the digital manufacture of optical nanocircuits, electronic nanocomputers, and perhaps even sophisticated inorganic nanorobots, each with their blueprints (or “genomes’’) encoded in the DNA molecules that constitute their “nanofabricators.”</jats:p> </jats:sec>

<jats:p>Reproducibility, rigor, transparency, and independent verification are cornerstones of the scientific method. Of course, just because a result is reproducible does not necessarily make it right, and just because it is not reproducible does not necessarily make it wrong. A transparent and rigorous approach, however, can almost always shine a light on issues of reproducibility. This light ensures that science moves forward, through independent verifications as well as the course corrections that come from refutations and the objective examination of the resulting data.</jats:p>

<jats:p>A roundup of weekly science policy and related news.</jats:p>

<jats:p>Continuing degradation threatens reef's World Heritage Site status.</jats:p>