Economic complexity and fertility: insights from a low fertility country.
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ABSTRACT:
SUBMITTER: Innocenti N
PROVIDER: S-EPMC8300530 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Two network measures known as the economic complexity index (ECI) and product complexity index (PCI) have provided important insights into patterns of economic development. We show that the ECI and PCI are equivalent to a spectral clustering algorithm that partitions a similarity graph into two parts. The measures are also closely related to various dimensionality reduction methods, such as diffusion maps and correspondence analysis. Our results shed new light on the ECI's empirical success in explaining cross-country differences in gross domestic product per capita and economic growth, which is often linked to the diversity of country export baskets. In fact, countries with high (low) ECI tend to specialize in high-PCI (low-PCI) products. We also find that the ECI and PCI uncover specialization patterns across U.S. states and U.K. regions.
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Project description:Sea anemones are venomous animals that rely on their venom for prey capture, defense against predators, and intraspecific competition. Currently, comprehensive molecular and evolutionary analyses of the toxin repertoire for sea anemones are limited by a lack of proteomic data for most species. In this study, proteo-transcriptomic analysis was used to expand our knowledge of the proteinaceous components of sea anemone venom by determining the secreted venom proteome of Calliactis polypus. Electromechanical stimulation was used to obtain the secreted venom of C. polypus. We identified a low complexity proteome that was dominated by toxins with similarity to known neurotoxins, as well as six novel toxin candidates. The novel putative toxin candidates were found to be taxonomically restricted to species from the superfamily Metridioidea. Furthermore, the secreted venom of C. polypus had only three putative toxins in common with the venom of acontia from the same species and little similarity with the secreted venom of closely related species. Overall, this demonstrates that regionalized and lineage-specific variability in toxin abundance is common among sea anemone species. Moreover, the limited complexity of the toxin repertoire found in C. polypus supports the idea that peptide neurotoxins make up the dominant toxin arsenal found in the venom of sea anemones.
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Project description:We present an integrated database suitable for the investigation of the economic development of countries by using the Economic Fitness and Complexity framework. Firstly, we implement machine learning techniques to reconstruct the export flow of services and we combine them to the export flow of the physical goods, generating a complete view of the international market, denoted the Integrated database. Successively, we support the technical quality of the database by computing the main metrics of the Economic Fitness and Complexity framework: (i) we build a statistically validated network of economic activities, where preferred paths of development and clusters of High-Tech industries naturally emerge; (ii) we evaluate the Economic Fitness, an algorithmic assessment of the competitiveness of countries, removing the unexpected misbehaviour of economies under-represented by the sole consideration of the export of the physical goods.
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Project description: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.
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Project description:Every nation prioritizes the inclusive economic growth and development of all regions. However, we observe that economic activities are clustered in space, which results in a disparity in per-capita income among different regions. A complexity-based method was proposed by Hidalgo and Hausmann [PNAS 106, 10570-10575 (2009)] to explain the large gaps in per-capita income across countries. Although there have been extensive studies on countries' economic complexity using international export data, studies on economic complexity at the regional level are relatively less studied. Here, we study the industrial sector complexity of prefectures in Japan based on the basic information of more than one million firms. We aggregate the data as a bipartite network of prefectures and industrial sectors. We decompose the bipartite network as a prefecture-prefecture network and sector-sector network, which reveals the relationships among them. Similarities among the prefectures and among the sectors are measured using a metric. From these similarity matrices, we cluster the prefectures and sectors using the minimal spanning tree technique. The computed economic complexity index from the structure of the bipartite network shows a high correlation with macroeconomic indicators, such as per-capita gross prefectural product and prefectural income per person. We argue that this index reflects the present economic performance and hidden potential of the prefectures for future growth.
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Project description:What will be the growth of the Gross Domestic Product (GDP) or the competitiveness of China, United States, and Vietnam in the next 3, 5 or 10 years? Despite this kind of questions has a large societal impact and an extreme value for economic policy making, providing a scientific basis for economic predictability is still a very challenging problem. Recent results of a new branch--Economic Complexity--have set the basis for a framework to approach such a challenge and to provide new perspectives to cast economic prediction into the conceptual scheme of forecasting the evolution of a dynamical system as in the case of weather dynamics. We argue that a recently introduced non-monetary metrics for country competitiveness (fitness) allows for quantifying the hidden growth potential of countries by the means of the comparison of this measure for intangible assets with monetary figures, such as GDP per capita. This comparison defines the fitness-income plane where we observe that country dynamics presents strongly heterogeneous patterns of evolution. The flow in some zones is found to be laminar while in others a chaotic behavior is instead observed. These two regimes correspond to very different predictability features for the evolution of countries: in the former regime, we find strong predictable pattern while the latter scenario exhibits a very low predictability. In such a framework, regressions, the usual tool used in economics, are no more the appropriate strategy to deal with such a heterogeneous scenario and new concepts, borrowed from dynamical systems theory, are mandatory. We therefore propose a data-driven method--the selective predictability scheme--in which we adopt a strategy similar to the methods of analogues, firstly introduced by Lorenz, to assess future evolution of countries.
| S-EPMC4325004 | biostudies-literature
Project description:Summarising the complexity of a country's economy in a single number is the holy grail for scholars engaging in data-based economics. In a field where the Gross Domestic Product remains the preferred indicator for many, economic complexity measures, aiming at uncovering the productive knowledge of countries, have been stirring the pot in the past few years. The commonly used methodologies to measure economic complexity produce contrasting results, undermining their acceptance and applications. Here we show that these methodologies - apparently conflicting on fundamental aspects - can be reconciled by adopting a neat mathematical perspective based on linear-algebra tools within a bipartite-networks framework. The obtained results shed new light on the potential of economic complexity to trace and forecast countries' innovation potential and to interpret the temporal dynamics of economic growth, possibly paving the way to a micro-foundation of the field.
| S-EPMC7335174 | biostudies-literature
Project description:Background Mixed-phenotype acute leukemia (MPAL) in children is an uncommon subtype of acute leukemia that cannot be definitively assigned to a specific lineage. There is no consensus on the best approach to therapy. Management is more complex in low–middle-income countries (LMICs). Aim To evaluate the clinicopathological characteristics and outcomes of patients with MPAL in a developing country. Patients and Methods A retrospective descriptive study of 42 pediatric patients newly diagnosed with MPAL from July 2007 until December 2017. Results The immunophenotyping was T/Myeloid in 24 patients (57.1%) and B/Myeloid in 16 (38.1%). Three subjects had MLL gene rearrangement, two had Philadelphia-positive chromosomes, and eight had FMS-like tyrosine kinase 3 (FLT3-ITD) internal tandem duplication (FLT3-ITD) with a ratio >0.4. Two subjects died before starting chemotherapy. Ten patients (25%) received acute lymphoblastic leukemia (ALL) induction, and all achieved complete remission (CR) with no induction deaths and no shift of therapy. Thirty patients (75%) started therapy with acute myeloid leukemia (AML) induction: five (16.6%) died during induction, 17 (56.7%) achieved CR, and 10 patients received maintenance ALL therapy after ending AML treatment. Four of the eight patients with induction failure were switched to ALL therapy. The 5-year event-free survival (EFS) and overall survival (OS) rates were 56.7% [standard error (SE): 8.1%] and 61% (SE: 8%), while the cumulative incidence of relapse was 21.7% (SE: 6.7%), with a median follow-up duration of 5.8 years. Patients treated with ALL-directed therapy had a 5-year EFS rate of 111 70% (SE: 14%) and OS rate of 78.8% (SE: 13%). Patients treated with ALL-directed therapy had a 5-year EFS rate of 70% (SE: 14.5%) and OS rate of 78.8% (SE: 13%). FLT3-ITD mutation showed a significantly lower 5-year EFS rate of 28.6% (SE: 17%) vs. 75% (SE: 9%) for the wild type, p = 0.032. Undernourished patients with a body mass index (BMI) z-score ≤-2 at presentation had a significantly lower 5-year EFS rate of 20% (SE: 17%) compared to 61.8% (SE: 8%) for patients with BMI z-score >-2, p = 0.015. Conclusion This study supports ALL-directed therapy for pediatric MPAL in a setting of LMIC. Given the poor outcome of FLT3-ITD, the role of FLT3 inhibitor needs to be explored in this subset of cases.
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Project description:Low complexity regions are fragments of protein sequences composed of only a few types of amino acids. These regions frequently occur in proteins and can play an important role in their functions. However, scientists are mainly focused on regions characterized by high diversity of amino acid composition. Similarity between regions of protein sequences frequently reflect functional similarity between them. In this article, we discuss strengths and weaknesses of the similarity analysis of low complexity regions using BLAST, HHblits and CD-HIT. These methods are considered to be the gold standard in protein similarity analysis and were designed for comparison of high complexity regions. However, we lack specialized methods that could be used to compare the similarity of low complexity regions. Therefore, we investigated the existing methods in order to understand how they can be applied to compare such regions. Our results are supported by exploratory study, discussion of amino acid composition and biological roles of selected examples. We show that existing methods need improvements to efficiently search for similar low complexity regions. We suggest features that have to be re-designed specifically for comparing low complexity regions: scoring matrix, multiple sequence alignment, e-value, local alignment and clustering based on a set of representative sequences. Results of this analysis can either be used to improve existing methods or to create new methods for the similarity analysis of low complexity regions.
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Project description:Trade networks, across which countries distribute their products, are crucial components of the globalized world economy. Their structure affects the mechanism of propagation of shocks from country to country, as observed in a very sharp way in the past decade, characterized by economic uncertainty in many parts of the world. Such trade structures are strongly heterogeneous across products, given the different features of the countries which buy and sell goods. By using a diversified pool of indicators from network science and product complexity theory, we quantitatively demonstrate that, overall, products with higher complexity-i.e., with larger technological content and/or number of components-are traded through more centralized networks-i.e., with a smaller number of countries concentrating most of the export flow. Since centralized networks are known to be more vulnerable, we argue that the current composition of production and trading is associated to high fragility at the level of the most complex-thus strategic-products.
| S-EPMC6264856 | biostudies-literature