Simplicial complexes: higher-order spectral dimension and dynamics

Simplicial complexes constitute the underlying topology of interacting complex systems including among the others brain and social interaction networks. They are generalized network structures that allow to go beyond the framework of pairwise interactions and to capture the many-body interactions between two or more nodes strongly affecting dynamical processes. In fact, the simplicial complexes topology allows to assign a dynamical variable not only to the nodes of the interacting complex systems but also to links, triangles, and so on. Here we show evidence that the dynamics defined on simplices of different dimensions can be significantly different even if we compare dynamics of simplices belonging to the same simplicial complex. By investigating the spectral properties of the simplicial complex model called ``Network Geometry with Flavor'' (NGF) we provide evidence that the up and down higher-order Laplacians can have a finite spectral dimension whose value depends on the order of the Laplacian. Finally we discuss the implications of this result for higher-order diffusion defined on simplicial complexes showing that the $n$-order diffusion dynamics have a return type distribution that can depends on $n$ as it is observed in NGFs

Explosive higher-order Kuramoto dynamics on simplicial complexes

The higher-order interactions of complex systems, such as the brain, are captured by their simplicial complex structure and have a significant effect on dynamics. However the existing dynamical models defined on simplicial complexes make the strong assumption that the dynamics resides exclusively on the nodes. Here we formulate the higher-order Kuramoto model which describes the interactions between oscillators placed not only on nodes but also on links, triangles, and so on. We show that higher-order Kuramoto dynamics can lead to explosive synchronization transition by using an adaptive coupling dependent on the solenoidal and the irrotational component of the dynamics

Predictable hydrodynamic conditions explain temporal variations in the density of benthic foraging seabirds in a tidal stream environment

Tidal stream turbines could have several direct impacts upon pursuit-diving seabirds foraging within tidal stream environments (mean horizontal current speeds > 2 ms−1), including collisions and displacement. Understanding how foraging seabirds respond to temporally variable but predictable hydrodynamic conditions immediately around devices could identify when interactions between seabirds and devices are most likely to occur; information which would quantify the magnitude of potential impacts, and also facilitate the development of suitable mitigation measures. This study uses shore-based observational surveys and Finite Volume Community Ocean Model outputs to test whether temporally predictable hydrodynamic conditions (horizontal current speeds, water elevation, turbulence) influenced the density of foraging black guillemots Cepphus grylle and European shags Phalacrocorax aristotelis in a tidal stream environment in Orkney, United Kingdom, during the breeding season. These species are particularly vulnerable to interactions with devices due to their tendency to exploit benthic and epi-benthic prey on or near the seabed. The density of both species decreased as a function of horizontal current speeds, whereas the density of black guillemots also decreased as a function of water elevation. These relationships could be linked to higher energetic costs of dives in particularly fast horizontal current speeds (>3 ms−1) and deeper water. Therefore, interactions between these species and moving components seem unlikely at particularly high horizontal current speeds. Combining this information, with that on the rotation rates of moving components at lower horizontal current speeds, could be used to assess collision risk in this site during breeding seasons. It is also likely that moderating any device operation during both lowest water elevation and lowest horizontal current speeds could reduce the risk of collisions for these species in this site during this season. The approaches used in this study could have useful applications within Environmental Impact Assessments, and should be considered when assessing and mitigating negative impacts from specific devices within development sites

Increased Feeding and Nutrient Excretion of Adult Antarctic Krill, Euphausia superba, Exposed to Enhanced Carbon Dioxide (CO2)

Ocean acidification has a wide-ranging potential for impacting the physiology and metabolism of zooplankton. Sufficiently elevated CO2 concentrations can alter internal acid-base balance, compromising homeostatic regulation and disrupting internal systems ranging from oxygen transport to ion balance. We assessed feeding and nutrient excretion rates in natural populations of the keystone species Euphausia superba (Antarctic krill) by conducting a CO2 perturbation experiment at ambient and elevated atmospheric CO2 levels in January 2011 along the West Antarctic Peninsula (WAP). Under elevated CO2 conditions (similar to 672 ppm), ingestion rates of krill averaged 78 mu g C individual(-1) d(-1) and were 3.5 times higher than krill ingestion rates at ambient, present day CO2 concentrations. Additionally, rates of ammonium, phosphate, and dissolved organic carbon (DOC) excretion by krill were 1.5, 1.5, and 3.0 times higher, respectively, in the high CO2 treatment than at ambient CO2 concentrations. Excretion of urea, however, was similar to 17% lower in the high CO2 treatment, suggesting differences in catabolic processes of krill between treatments. Activities of key metabolic enzymes, malate dehydrogenase (MDH) and lactate dehydrogenase (LDH), were consistently higher in the high CO2 treatment. The observed shifts in metabolism are consistent with increased physiological costs associated with regulating internal acid-base equilibria. This represents an additional stress that may hamper growth and reproduction, which would negatively impact an already declining krill population along the WAP

Brain Performance versus Phase Transitions

We here illustrate how a well-founded study of the brain may originate in assuming analogies with phase-transition phenomena. Analyzing to what extent a weak signal endures in noisy environments, we identify the underlying mechanisms, and it results a description of how the excitability associated to (non-equilibrium) phase changes and criticality optimizes the processing of the signal. Our setting is a network of integrate-and-fire nodes in which connections are heterogeneous with rapid time-varying intensities mimicking fatigue and potentiation. Emergence then becomes quite robust against wiring topology modification—in fact, we considered from a fully connected network to the Homo sapiens connectome—showing the essential role of synaptic flickering on computations. We also suggest how to experimentally disclose significant changes during actual brain operation.The authors acknowledge support from the Spanish Ministry of Economy and Competitiveness under the project FIS2013-43201-P

Synchronization in network geometries with finite spectral dimension

Recently there is a surge of interest in network geometry and topology. Here we show that the spectral dimension plays a fundamental role in establishing a clear relation between the topological and geometrical properties of a network and its dynamics. Specifically we explore the role of the spectral dimension in determining the synchronization properties of the Kuramoto model. We show that the synchronized phase can only be thermodynamically stable for spectral dimensions above four and that phase entrainment of the oscillators can only be found for spectral dimensions greater than two. We numerically test our analytical predictions on the recently introduced model of network geometry called Complex Network Manifolds which displays a tunable spectral dimension.Comment: (15 pages, 5 figures

Fluoxetine: a case history of its discovery and preclinical development

Introduction: Depression is a multifactorial mood disorder with a high prevalence worldwide. Until now, treatments for depression have focused on the inhibition of monoaminergic reuptake sites, which augment the bioavailability of monoamines in the CNS. Advances in drug discovery have widened the therapeutic options with the synthesis of so-called selective serotonin reuptake inhibitors (SSRIs), such as fluoxetine. Areas covered: The aim of this case history is to describe and discuss the pharmacokinetic and pharmacodynamic profiles of fluoxetine, including its acute effects and the adaptive changes induced after long-term treatment. Furthermore, the authors review the effect of fluoxetine on neuroplasticity and adult neurogenesis. In addition, the article summarises the preclinical behavioural data available on fluoxetine’s effects on depressive-like behaviour, anxiety and cognition as well as its effects on other diseases. Finally, the article describes the seminal studies validating the antidepressant effects of fluoxetine. Expert opinion: Fluoxetine is the first selective SSRI that has a recognised clinical efficacy and safety profile. Since its discovery, other molecules that mimic its mechanism of action have been developed, commencing a new age in the treatment of depression. Fluoxetine has also demonstrated utility in the treatment of other disorders for which its prescription has now been approved

Integrating pressure sensor control into semi-solid extrusion 3D printing to optimize medicine manufacturing

Semi-solid extrusion (SSE) is a three-dimensional printing (3DP) process that involves the extrusion of a gel or paste-like material via a syringe-based printhead to create the desired object. In pharmaceuticals, SSE 3DP has already been used to manufacture formulations for human clinical studies. To further support its clinical adoption, the use of a pressure sensor may provide information on the printability of the feedstock material in situ and under the exact printing conditions for quality control purposes. This study aimed to integrate a pressure sensor in an SSE pharmaceutical 3D printer for both material characterization and as a process analytical technology (PAT) to monitor the printing process. In this study, three materials of different consistency were tested (soft vaseline, gel-like mass and paste-like mass) under 12 different conditions, by changing flow rate, temperature, or nozzle diameter. The use of a pressure sensor allowed, for the first time, the characterization of rheological properties of the inks, which exhibited temperature-dependent, plastic and viscoelastic behaviours. Controlling critical material attributes and 3D printing process parameters may allow a quality by design (QbD) approach to facilitate a high-fidelity 3D printing process critical for the future of personalized medicine