Explore the vast range of titles available.

Countries and cities are likely to enter economic activities that are related to those that are already present in them. Yet, while these path dependencies are universally acknowledged, we lack an understanding of the diversification strategies that can optimally balance the development of related and unrelated activities. Here, we develop algorithms to identify the activities that are optimal to target at each time step. We find that the strategies that minimize the total time needed to diversify an economy target highly connected activities during a narrow and specific time window. We compare the strategies suggested by our model with the strategies followed by countries in the diversification of their exports and research activities, finding that countries follow strategies that are close to the ones suggested by the model. These findings add to our understanding of economic diversification and also to our general understanding of diffusion in networks. The probability that a region will develop a particular research activity increases with the number of similar activities in neighboring regions. Here the authors analyze diffusion strategies and show that it is not only important to know which activities to target but also when to target them.

The Sustainable Development Goals (SDGs) provide a comprehensive framework for societal progress and planetary health. However, it remains unclear whether universal patterns exist in how nations pursue these goals and whether key development areas are being overlooked. Here, we apply the product space methodology, widely used in development economics, to construct an ‘SDG space of nations’. The SDG space models the relative performance and specialization patterns of 166 countries across 96 SDG indicators from 2000 to 2022. Our SDG space reveals a polarized global landscape, characterized by distinct groups of nations, each specializing in specific development indicators. Furthermore, we find that as countries improve their overall SDG scores, they tend to modify their sustainable development trajectories, pursuing different development objectives. Additionally, we identify orphaned SDG indicators — areas where certain country groups remain under-specialized. These patterns, and the SDG space more broadly, provide a high-resolution tool to understand and evaluate the progress and disparities of countries towards achieving the SDGs. Applying economic complexity methods, this study maps global progress toward the SDGs, uncovering distinct development paths and disparities among 166 countries. It highlights overlooked areas in sustainable development, offering insights into how nations can better achieve the SDGs.

Novel protein-coding genes can arise either from pre-existing genes or de novo ; here it is shown that functional genes emerge de novo through transitory proto-genes generated by widespread translational activity in non-genic sequences. New genes from unlikely beginnings De novo gene birth has occurred in many lineages during evolution, but how functional protein-coding genes emerge in non-functional sequences — rather than through gene rearrangement — remains unresolved. These authors observe in the yeast Saccharomyces cerevisiae that hundreds of species-specific non-genic transcripts are differentially regulated upon stress. These previously overlooked translation events seem to act as a reservoir of adaptive potential, in the form of open reading frames that occupy an evolutionary continuum ranging from non-genic sequences to genes. On the basis of their genome-wide observations, the authors suggest that de novo gene birth from the proto-gene reservoir may be more prevalent than sporadic gene duplication. Novel protein-coding genes can arise either through re-organization of pre-existing genes or de novo 1 , 2 . Processes involving re-organization of pre-existing genes, notably after gene duplication, have been extensively described 1 , 2 . In contrast, de novo gene birth remains poorly understood, mainly because translation of sequences devoid of genes, or ‘non-genic’ sequences, is expected to produce insignificant polypeptides rather than proteins with specific biological functions 1 , 3 , 4 , 5 , 6 . Here we formalize an evolutionary model according to which functional genes evolve de novo through transitory proto-genes 4 generated by widespread translational activity in non-genic sequences. Testing this model at the genome scale in Saccharomyces cerevisiae , we detect translation of hundreds of short species-specific open reading frames (ORFs) located in non-genic sequences. These translation events seem to provide adaptive potential 7 , as suggested by their differential regulation upon stress and by signatures of retention by natural selection. In line with our model, we establish that S. cerevisiae ORFs can be placed within an evolutionary continuum ranging from non-genic sequences to genes. We identify ∼1,900 candidate proto-genes among S. cerevisiae ORFs and find that de novo gene birth from such a reservoir may be more prevalent than sporadic gene duplication. Our work illustrates that evolution exploits seemingly dispensable sequences to generate adaptive functional innovation.

Human activities, such as research, innovation and industry, concentrate disproportionately in large cities. The ten most innovative cities in the United States account for 23% of the national population, but for 48% of its patents and 33% of its gross domestic product. But why has human activity become increasingly concentrated? Here we use data on scientific papers, patents, employment and gross domestic product, for 353 metropolitan areas in the United States, to show that the spatial concentration of productive activities increases with their complexity. Complex economic activities, such as biotechnology, neurobiology and semiconductors, concentrate disproportionately in a few large cities compared to less--complex activities, such as apparel or paper manufacturing. We use multiple proxies to measure the complexity of activities, finding that complexity explains from 40% to 80% of the variance in urban concentration of occupations, industries, scientific fields and technologies. Using historical patent data, we show that the spatial concentration of cutting-edge technologies has increased since 1850, suggesting a reinforcing cycle between the increase in the complexity of activities and urbanization. These findings suggest that the growth of spatial inequality may be connected to the increasing complexity of the economy. Balland et al. use data on scientific papers, patents, employment and GDP for 353 metropolitan areas in the United States to show that economic complexity drives the spatial concentration of productive activities in large cities.

Economic complexity methods have become popular tools in economic geography, international development and innovation studies. Here, I review economic complexity theory and applications, with a particular focus on two streams of literature: the literature on relatedness, which focuses on the evolution of specialization patterns, and the literature on metrics of economic complexity, which uses dimensionality reduction techniques to create metrics of economic sophistication that are predictive of variations in income, economic growth, emissions and income inequality.Economic complexity methods predict changes in the geography of economic activities and explain differences in economic growth, inequality, greenhouse emissions and labour market outcomes. This Review summarizes a decade of research on economic complexity and its applications.

Differential regulation of gene expression is essential for cell fate specification in metazoans. Characterizing the transcriptional activity of gene promoters, in time and in space, is therefore a critical step toward understanding complex biological systems. Here we present an in vivo spatiotemporal analysis for ∼900 predicted C. elegans promoters (∼5% of the predicted protein-coding genes), each driving the expression of green fluorescent protein (GFP). Using a flow-cytometer adapted for nematode profiling, we generated 'chronograms', two-dimensional representations of fluorescence intensity along the body axis and throughout development from early larvae to adults. Automated comparison and clustering of the obtained in vivo expression patterns show that genes coexpressed in space and time tend to belong to common functional categories. Moreover, integration of this data set with C. elegans protein-protein interactome data sets enables prediction of anatomical and temporal interaction territories between protein partners.

Which neighborhoods experience physical improvements? In this paper, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements—an observation that is compatible with the economic literature linking human capital and local success. Second, neighborhoods with better initial appearances experience, on average, larger positive improvements—an observation that is consistent with “tipping” theories of urban change. Third, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods—an observation that is consistent with the “invasion” theories of urban sociology. Together, our results provide support for three classical theories of urban change and illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities.

For Adam Smith, wealth was related to the division of labor. As people and firms specialize in different activities, economic efficiency increases, suggesting that development is associated with an increase in the number of individual activities and with the complexity that emerges from the interactions between them. Here we develop a view of economic growth and development that gives a central role to the complexity of a country's economy by interpreting trade data as a bipartite network in which countries are connected to the products they export, and show that it is possible to quantify the complexity of a country's economy by characterizing the structure of this network. Furthermore, we show that the measures of complexity we derive are correlated with a country's level of income, and that deviations from this relationship are predictive of future growth. This suggests that countries tend to converge to the level of income dictated by the complexity of their productive structures, indicating that development efforts should focus on generating the conditions that would allow complexity to emerge to generate sustained growth and prosperity.

The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that–for the SIS model–differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node's degree and susceptibility are positive and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated). These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptibility of connected individuals.

A traveler visiting Rio, Manila or Caracas does not need a report to learn that these cities are unequal; she can see it directly from the taxicab window. This is because in most cities inequality is conspicuous, but also, because cities express different forms of inequality that are evident to casual observers. Cities are highly heterogeneous and often unequal with respect to the income of their residents, but also with respect to the cleanliness of their neighborhoods, the beauty of their architecture, and the liveliness of their streets, among many other evaluative dimensions. Until now, however, our ability to understand the effect of a city's built environment on social and economic outcomes has been limited by the lack of quantitative data on urban perception. Here, we build on the intuition that inequality is partly conspicuous to create quantitative measure of a city's contrasts. Using thousands of geo-tagged images, we measure the perception of safety, class and uniqueness; in the cities of Boston and New York in the United States, and Linz and Salzburg in Austria, finding that the range of perceptions elicited by the images of New York and Boston is larger than the range of perceptions elicited by images from Linz and Salzburg. We interpret this as evidence that the cityscapes of Boston and New York are more contrasting, or unequal, than those of Linz and Salzburg. Finally, we validate our measures by exploring the connection between them and homicides, finding a significant correlation between the perceptions of safety and class and the number of homicides in a NYC zip code, after controlling for the effects of income, population, area and age. Our results show that online images can be used to create reproducible quantitative measures of urban perception and characterize the inequality of different cities.