V.A. Traag

 

Articles in high-impact journals are by definition more highly cited on average. But are they cited more often because the articles are somehow "better"? Or are they cited more often simply because they appeared in a high-impact journal? Although some evidence suggests the latter the causal relationship is not clear. We here compare citations of published journal articles to citations of their preprint versions to uncover the causal mechanism. We build on an earlier model to infer the causal effect of journals on citations. We find evidence for both effects. We show that high-impact journals seem to select articles that tend to attract more citations. At the same time, we find that high-impact journals augment the citation rate of published articles. Our results yield a deeper understanding of the role of journals in the research system. The use of journal metrics in research evaluation has been increasingly criticised in recent years and article-level citations are sometimes suggested as an alternative. Our results show that removing impact factors from evaluation does not negate the influence of journals. This insight has important implications for changing practices of research evaluation.

Source: arxiv.org

Jose C. Valverde, Henning S. Mortveit, Carlos Gershenson, and Yongtang Shi

Complexity
Volume 2020, Article ID 6183798

 

In recent decades, Boolean networks (BN) have emerged as an effective mathematical tool to model not only computational processes, but also several phenomena in science and engineering. For this reason, the development of the theory of such models has become a compelling need that has attracted the interest of many research groups in recent years. Dynamics of BN are traditionally associated with complexity, since they are composed of many elemental units whose behavior is relatively simple in comparison with the behavior of the entire system.

BN are a generalization of other relevant mathematical models, which appeared previously as cellular automata (CA), inspired by von Neumann and studied by Wolfram and others to explore the computational universe, or Kauffman networks (KN), proposed by Kauffman in 1969 for modeling gene regulatory networks. This gives an idea of the versatility of this new paradigm in applications to several branches of science (mathematics, physics chemistry, biology, ecology, etc.) and engineering (computing, artificial intelligence, electronics, circuits, etc.).

The aim of this special issue was to collect cutting-edge research on the different models of BN (deterministic and nondeterministic, synchronous and asynchronous, homogenous and non-homogenous, directed and undirected, regular and non-regular, etc.). Thus, several research groups in this field submitted their recent developments and future research directions concerning new models. In addition, original research articles showing some applications of BN in science and engineering were received.

 

Source: www.hindawi.com

Mirna Ponce-Flores, Juan Frausto-Solís, Guillermo Santamaría-Bonfil, Joaquín Pérez-Ortega, Juan J. González-Barbosa

Entropy 2020, 22, 89

 

Entropy is a key concept in the characterization of uncertainty for any given signal, and its extensions such as Spectral Entropy and Permutation Entropy. They have been used to measure the complexity of time series. However, these measures are subject to the discretization employed to study the states of the system, and identifying the relationship between complexity measures and the expected performance of the four selected forecasting methods that participate in the M4 Competition. This relationship allows the decision, in advance, of which algorithm is adequate. Therefore, in this paper, we found the relationships between entropy-based complexity framework and the forecasting error of four selected methods (Smyl, Theta, ARIMA, and ETS). Moreover, we present a framework extension based on the Emergence, Self-Organization, and Complexity paradigm. The experimentation with both synthetic and M4 Competition time series show that the feature space induced by complexities, visually constrains the forecasting method performance to specific regions; where the logarithm of its metric error is poorer, the Complexity based on the emergence and self-organization is maximal.

Source: www.mdpi.com

David Fajardo Ortiz, Annie Shattuck, Stefan Hornbostel

 

In the present investigation, we set out to determine and compare the evolution of the research on viral vectors, RNAi and genomic editing platforms as well as determine the profile of the main research institutions and funding agencies. A search of papers on viral vectors RNAi, CRISPR/Cas, TALENs, ZFNs and meganucleases was carried out in the Web of Science. A citation network of 16,746 papers was constructed. An analysis of network clustering combined with text mining was performed. In the case of viral vectors a long term process of incremental innovation in which the clusters of papers are organized around specific improvements of clinical relevance was identified. The most influential investigations on viral vectors were conducted in the United States and the European Union where the main funders were government agencies. The trajectory of RNAi research included clusters related to the study of RNAi as a biological phenomenon and its use in functional genomics, biomedicine and pest control. A British philanthropic organization and a US pharmaceutical company played a key role in the development of basic RNAi research and clinical application respectively, in addition to government agencies and academic institutions. In the case of CRISPR/Cas research, basic science discoveries led to the development of technical improvements, and these two in turn provided the information required for the development of biomedical, agricultural, livestock and industrial applications. The trajectory of CRISPR/Cas research exhibits a geopolitical division of the investigation efforts between the US, as the main producer of basic research and technical improvements, and China increasingly leading the applied research. A set of philanthropic foundations played a key role in specific stages of the CRISPR/Cas research. Our results reflect a change in the model in the financing of science and the emergence of China as a scientific superpower, with implications for the trajectory of development for applications of genomic technologies.

Source: www.biorxiv.org

Klaus Jaffe

 

For the first time, empirical evidence allowed to construct the frequency distribution of an index related to the degree of genetic relatedness between the parents of about 0.5 million humans living in the UK. The results show that a large proportion of the population is not the product of parents choosing a mate randomly. Assortative mating leading to offspring, that occurs between genetic related individuals, is very common. High degrees of genetic relatedness, i.e. extreme inbreeding, is rare. The evidence shows that assortative mating is highly prevalent in this large population sample. This novel empirical result suggests that assuming random mating, as widely done in population genetic studies, is not an appropriate approximation to reality.

Source: www.biorxiv.org