Antagonistic Structural Patterns in Complex Networks

Identifying and explaining the structure of complex networks at different scales has become an important problem across disciplines. At the mesoscale, modular architecture has attracted most of the attention. At the macroscale, other arrangements --e.g. nestedness or core-periphery-- have been studied in parallel, but to a much lesser extent. However, empirical evidence increasingly suggests that characterizing a network with a unique pattern typology may be too simplistic, since a system can integrate properties from distinct organizations at different scales. Here, we explore the relationship between some of those organizational patterns: two at the mesoscale (modularity and in-block nestedness); and one at the macroscale (nestedness). We analytically show that nestedness can be used to provide approximate bounds for modularity, with exact results in an idealized scenario. Specifically, we show that nestedness and modularity are antagonistic. Furthermore, we evince that in-block nestedness provides a parsimonious transition between nested and modular networks, taking properties of both. Far from a mere theoretical exercise, understanding the boundaries that discriminate each architecture is fundamental, to the extent modularity and nestedness are known to place heavy constraints on the stability of several dynamical processes, specially in ecology.Comment: 7 pages, 4 figures and 1 supplemental information fil

Modeling of High Nanoparticle Exposure in an Indoor Industrial Scenario with a One-Box Model

Mass balance models have proved to be effective tools for exposure prediction in occupational settings. However, they are still not extensively tested in real-world scenarios, or for particle number concentrations. An industrial scenario characterized by high emissions of unintentionally-generated nanoparticles (NP) was selected to assess the performance of a one-box model. Worker exposure to NPs due to thermal spraying was monitored, and two methods were used to calculate emission rates: the convolution theorem, and the cyclic steady state equation. Monitored concentrations ranged between 4.2 × 104–2.5 × 105 cm−3. Estimated emission rates were comparable with both methods: 1.4 × 1011–1.2 × 1013 min−1 (convolution) and 1.3 × 1012–1.4 × 1013 min−1 (cyclic steady state). Modeled concentrations were 1.4-6 × 104 cm−3 (convolution) and 1.7–7.1 × 104 cm−3 (cyclic steady state). Results indicated a clear underestimation of measured particle concentrations, with ratios modeled/measured between 0.2–0.7. While both model parametrizations provided similar results on average, using convolution emission rates improved performance on a case-by-case basis. Thus, using cyclic steady state emission rates would be advisable for preliminary risk assessment, while for more precise results, the convolution theorem would be a better option. Results show that one-box models may be useful tools for preliminary risk assessment in occupational settings when room air is well mixed

Revealing in-block nestedness: Detection and benchmarking

As new instances of nested organization—beyond ecological networks—are discovered, scholars are debating the coexistence of two apparently incompatible macroscale architectures: nestedness and modularity. The discussion is far from being solved, mainly for two reasons. First, nestedness and modularity appear to emerge from two contradictory dynamics, cooperation and competition. Second, existing methods to assess the presence of nestedness and modularity are flawed when it comes to the evaluation of concurrently nested and modular structures. In this work, we tackle the latter problem, presenting the concept of in-block nestedness, a structural property determining to what extent a network is composed of blocks whose internal connectivity exhibits nestedness. We then put forward a set of optimization methods that allow us to identify such organization successfully, in synthetic and in a large number of real networks. These findings challenge our understanding of the topology of ecological and social systems, calling for new models to explain how such patterns emerge

Effects of a hyperlipidic diet on experimental carcinogenesis of the breast: content and type of tumors

En el presente trabajo se han estudiado los efectos de una dieta hiperlipídica (20 % aceite de maíz) sobre las principales etapas de la carcinogénesis. Dicho estudio se ha realizado utilizando como soporte experimental el modelo del cáncer de mama inducido en la rata mediante dimetilbenz(a) antraceno. Los tumores mamarios malignos representan la patología más abundante aparecida en los animales. Tal resultado es atribuido específicamente a la acción del carcinógeno. Por otra parte, al comparar el número de tales tumores entre los diferentes grupos experimentales y el grupo control, se observa que la dieta hiperlipídica administrada tiene efectos promotores de la carcinogénesis mamaria en la rata. Este efecto no se obtiene si la dieta hiperlipídica se administra a los 157 días de la inducción carcinogénica. Además, los resultados obtenidos también indican que dicha dieta no tiene acción sobre la iniciación. Sin embargo, tal probabilidad no puede afirmarse dado que cabe la posibilidad de que la dieta hiperlipídica haya inducido cambios en los animales que hayan modificado la eficacia de la carcinogénesis

The physics of spreading processes in multilayer networks

The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of complex systems, which often include different types of relationships (i.e., "multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and be a major obstacle towards attempts to understand complex systems. The recent "multilayer" approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remain hidden when using ordinary graphs, the traditional network representation. Here we survey progress towards attaining a deeper understanding of spreading processes on multilayer networks, and we highlight some of the physical phenomena related to spreading processes that emerge from multilayer structure.Comment: 25 pages, 4 figure

Characterizing the Chemical Profile of Incidental Ultrafine Particles for Toxicity Assessment Using an Aerosol Concentrator

Incidental ultrafine particles (UFPs) constitute a key pollutant in industrial workplaces. However, characterizing their chemical properties for exposure and toxicity assessments still remains a challenge. In this work, the performance of an aerosol concentrator (Versatile Aerosol Concentration Enrichment System, VACES) was assessed to simultaneously sample UFPs on filter substrates (for chemical analysis) and as liquid suspensions (for toxicity assessment), in a high UFP concentration scenario. An industrial case study was selected where metal-containing UFPs were emitted during thermal spraying of ceramic coatings. Results evidenced the comparability of the VACES system with online monitors in terms of UFP particle mass (for concentrations up to 95 µg UFP/m3 ) and between filters and liquid suspensions, in terms of particle composition (for concentrations up to 1000 µg/ m3). This supports the applicability of this tool for UFP collection in view of chemical and toxicological characterization for incidental UFPs. In the industrial setting evaluated, results showed that the spraying temperature was a driver of fractionation of metals between UF (<0.2 µm) and fine (0.2– 2.5 µm) particles. Potentially health hazardous metals (Ni, Cr) were enriched in UFPs and depleted in the fine particle fraction. Metals vaporized at high temperatures and concentrated in the UF fraction through nucleation processes. Results evidenced the need to understand incidental particle formation mechanisms due to their direct implications on particle composition and, thus, exposure. It is advisable that personal exposure and subsequent risk assessments in occupational settings should include dedicated metrics to monitor UFPs (especially, incidental).What’s important about this paper: Our work addresses the challenge of characterizing the bulk chemical composition of ultrafine particles in occupational settings, for exposure and toxicity assessments. We tested the performance of an aerosol concentrator (VACES) to simultaneously sample ultrafine particles (UFPs) on filter substrates and as liquid suspensions, in a high UFP concentration scenario. An industrial case study was selected where metal-bearing UFPs were emitted. We report the chemical exposures characterized in the industrial facility, and evidence the comparability of the VACES system with online monitors for UFP particle mass (up to 95 µg UFP/m3) as well as between UFP chemical composition on filters and in suspension. This supports the applicability of this tool for UFP collection in view of chemical and toxicological characterization of exposures to incidental UFPs in workplace settings.Highlights: - The VACES system is a useful tool for UFP sampling in high-concentration settings; - UFP collected simultaneously on filters and in suspension showed good comparability; - UFP chemical profiles were characterized; - Health-hazardous metals Ni and Cr accumulated in UFPs; - Understanding emission mechanisms is key to identifying exposure sources.This work was funded by SIINN ERA-NET (project id: 16), the Spanish MINECO (PCIN-2015-173-C02-01) and the French agency (Region Hauts de France). The Spanish Ministry of Science and Innovation (Project CEX2018-000794-S; Severo Ochoa) and the Generalitat de Catalunya (project number: AGAUR 2017 SGR41) provided support for the indirect costs for the Institute of Environmental Assessment and Water Research (IDAEA-CSIC). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).info:eu-repo/semantics/publishedVersio