Replica-mean-field limits of fragmentation-interaction-aggregation processes

Network dynamics with point-process-based interactions are of paramount modeling interest. Unfortunately, most relevant dynamics involve complex graphs of interactions for which an exact computational treatment is impossible. To circumvent this difficulty, the replica-mean-field approach focuses on randomly interacting replicas of the networks of interest. In the limit of an infinite number of replicas, these networks become analytically tractable under the so-called ‘Poisson hypothesis’. However, in most applications this hypothesis is only conjectured. In this paper we establish the Poisson hypothesis for a general class of discrete-time, point-process-based dynamics that we propose to call fragmentation-interaction-aggregation processes, and which are introduced here. These processes feature a network of nodes, each endowed with a state governing their random activation. Each activation triggers the fragmentation of the activated node state and the transmission of interaction signals to downstream nodes. In turn, the signals received by nodes are aggregated to their state. Our main contribution is a proof of the Poisson hypothesis for the replica-mean-field version of any network in this class. The proof is obtained by establishing the propagation of asymptotic independence for state variables in the limit of an infinite number of replicas. Discrete-time Galves–Löcherbach neural networks are used as a basic instance and illustration of our analysis.

Monitoring dynamic pre-crystallization aggregation processes in solution by VT-DOSY NMR spectroscopy

Equimolar mixtures of pyridine (Py) with para-halotetrafluoro-pyridine (BrTFP and ITFP) were investigated by VT-diffusion NMR experiments. The formation of a halogen-bond-stabilized ITFP·Py complex was de¬tected upon cooling a solution in...

Dynamics of Large, Wet Volcanic Clouds : The 25.4 ka Oruanui Eruption of Taupo Volcano, New Zealand

<p>This work investigates the dynamics of large-scale, ‘wet’ volcanic eruption clouds generated by the interaction of silicic magma with external water. The primary case study draws from a detailed record of non-welded pyroclastic deposits from the ~25.4 ka Oruanui eruption of Taupo volcano, New Zealand, one of the largest phreatomagmatic eruptions documented worldwide. This research uses a three-pronged approach, integrating results from (i) field observations and textural data, (ii) mesoscale numerical modeling of volcanic plumes, and (iii) analogue laboratory experiments of volcanic ash aggregation. This interdisciplinary approach provides a new understanding of dynamic and microphysical interactions between collapsing and buoyant columns, and how this behavior controls the large-and small-scale nature of phreatoplinian eruption clouds. Stratigraphic field studies examine the styles of dispersal and emplacement of deposits from several phases of the Oruanui eruption (primarily phases 2, 3, 5, 6, 7 and 8). Detailed stratigraphic observations and laser diffraction particle size analysis of ash aggregates in these deposits clarify the evolution of aggregation mechanisms with time through the relevant eruption phase, and with distance from vent. Deposits of the wettest phase (3) show the key role of turbulent lofting induced by pyroclastic density currents in forming aggregates, particularly those with ultrafine ash rims (30-40 vol.% finer than 10 μm) which are uniquely formed in the ultrafine ash-dominated clouds above the currents. Drier deposits of phases 2 and 5, which also saw lower proportions of material emplaced by pyroclastic density currents, contain fewer aggregates that are related to low water contents in the medial to distal plume. Discovery and documentation of high concentrations of diatom flora in the Oruanui deposits indicates efficient fragmentation and incorporation of paleo-lake Taupo sediments during the eruption. This highlights the potential for incidental contamination of volcanic deposits with broader implications for correlation of distal tephras and possible contamination of paleoenvironmental records due to incorporation of diachronous populations of volcanically-dispersed diatoms. The impact of extensive surface water interaction on large-scale volcanic eruptions (>108 kg s-1 magma) is examined by employing the first 2-D large-eddy simulations of ‘wet’ volcanic plumes that incorporate the effects of microphysics. The cloud-resolving numerical model ATHAM was initialized with field-derived characteristics of the Oruanui case study. Surface water contents were varied from 0-40 wt.% for eruptions with equivalent magma eruption rates of c. 1.3 x108 and 1.1 x109 kg s-1. Results confirm that increased surface water has a pronounced impact on column stability, leading to unstable column behavior and hybrid clouds resulting from simultaneous ascent of material from stable columns and pyroclastic density currents (PDCs). Contrary to the suggestion of previous studies, however, abundant surface water does not systematically lower the spreading level or maximum height of volcanic clouds, owing to vigorous microphysics-assisted lofting of PDCs. Key processes influencing the aggregation of volcanic ash and hydrometeors (airborne water phases) are examined with a simple and reproducible experimental method employing vibratory pan agglomeration. Aggregation processes in the presence of hail and graupel, liquid water (<30 wt.%), and mixed water phases are investigated at temperatures from 18 to -20 °C. Observations from impregnated thin sections, SEM images and x-ray computed microtomography of these experimental aggregates closely match natural examples from phreatomagmatic phases of the ~25.4 ka Oruanui and Eyjafjallajökull (May 2010) eruptions. These experiments demonstrate that the formation of concentric, ultrafine rims comprising the outer layers of rim-type accretionary lapilli requires recycled exposure of moist, preexisting pellets to regions of volcanic clouds that are relatively dry and dominated by ultrafine (<31 μm) ash. This work presents the first experimentally-derived aggregation coefficients that account for changing liquid water contents and sub-zero temperatures, and indicates that dry conditions (<10 wt.% liquid) promote the strongly size-selective collection of sub-31 μm particles into aggregates (given by aggregation coefficients >1). These quantitative relationships may be used to predict the timescales and characteristics of aggregation, such as aggregate size spectra, densities and constituent particle size characteristics, when the initial size distribution and hydrometeor content of a volcanic cloud are known. The integration of numerical modeling, laboratory experimentation and field data lead to several key conclusions. (1) The importance of the microphysics of ash-water interactions in governing the eruption cloud structure, boosting the dispersal power of the cloud and controlling aggregate formation in response to differing water contents and eruption rates. (2) Recognition of the contrasting roles of differential aggregation versus cloud grain size in controlling the formation and nature of aggregate particles, notably those with characteristic ultrafine outer rims. (3) The importance of pyroclastic density currents as triggers for convection and aggregation processes in the eruption cloud.</p>

Dynamics of Large, Wet Volcanic Clouds : The 25.4 ka Oruanui Eruption of Taupo Volcano, New Zealand

<p>This work investigates the dynamics of large-scale, ‘wet’ volcanic eruption clouds generated by the interaction of silicic magma with external water. The primary case study draws from a detailed record of non-welded pyroclastic deposits from the ~25.4 ka Oruanui eruption of Taupo volcano, New Zealand, one of the largest phreatomagmatic eruptions documented worldwide. This research uses a three-pronged approach, integrating results from (i) field observations and textural data, (ii) mesoscale numerical modeling of volcanic plumes, and (iii) analogue laboratory experiments of volcanic ash aggregation. This interdisciplinary approach provides a new understanding of dynamic and microphysical interactions between collapsing and buoyant columns, and how this behavior controls the large-and small-scale nature of phreatoplinian eruption clouds. Stratigraphic field studies examine the styles of dispersal and emplacement of deposits from several phases of the Oruanui eruption (primarily phases 2, 3, 5, 6, 7 and 8). Detailed stratigraphic observations and laser diffraction particle size analysis of ash aggregates in these deposits clarify the evolution of aggregation mechanisms with time through the relevant eruption phase, and with distance from vent. Deposits of the wettest phase (3) show the key role of turbulent lofting induced by pyroclastic density currents in forming aggregates, particularly those with ultrafine ash rims (30-40 vol.% finer than 10 μm) which are uniquely formed in the ultrafine ash-dominated clouds above the currents. Drier deposits of phases 2 and 5, which also saw lower proportions of material emplaced by pyroclastic density currents, contain fewer aggregates that are related to low water contents in the medial to distal plume. Discovery and documentation of high concentrations of diatom flora in the Oruanui deposits indicates efficient fragmentation and incorporation of paleo-lake Taupo sediments during the eruption. This highlights the potential for incidental contamination of volcanic deposits with broader implications for correlation of distal tephras and possible contamination of paleoenvironmental records due to incorporation of diachronous populations of volcanically-dispersed diatoms. The impact of extensive surface water interaction on large-scale volcanic eruptions (>108 kg s-1 magma) is examined by employing the first 2-D large-eddy simulations of ‘wet’ volcanic plumes that incorporate the effects of microphysics. The cloud-resolving numerical model ATHAM was initialized with field-derived characteristics of the Oruanui case study. Surface water contents were varied from 0-40 wt.% for eruptions with equivalent magma eruption rates of c. 1.3 x108 and 1.1 x109 kg s-1. Results confirm that increased surface water has a pronounced impact on column stability, leading to unstable column behavior and hybrid clouds resulting from simultaneous ascent of material from stable columns and pyroclastic density currents (PDCs). Contrary to the suggestion of previous studies, however, abundant surface water does not systematically lower the spreading level or maximum height of volcanic clouds, owing to vigorous microphysics-assisted lofting of PDCs. Key processes influencing the aggregation of volcanic ash and hydrometeors (airborne water phases) are examined with a simple and reproducible experimental method employing vibratory pan agglomeration. Aggregation processes in the presence of hail and graupel, liquid water (<30 wt.%), and mixed water phases are investigated at temperatures from 18 to -20 °C. Observations from impregnated thin sections, SEM images and x-ray computed microtomography of these experimental aggregates closely match natural examples from phreatomagmatic phases of the ~25.4 ka Oruanui and Eyjafjallajökull (May 2010) eruptions. These experiments demonstrate that the formation of concentric, ultrafine rims comprising the outer layers of rim-type accretionary lapilli requires recycled exposure of moist, preexisting pellets to regions of volcanic clouds that are relatively dry and dominated by ultrafine (<31 μm) ash. This work presents the first experimentally-derived aggregation coefficients that account for changing liquid water contents and sub-zero temperatures, and indicates that dry conditions (<10 wt.% liquid) promote the strongly size-selective collection of sub-31 μm particles into aggregates (given by aggregation coefficients >1). These quantitative relationships may be used to predict the timescales and characteristics of aggregation, such as aggregate size spectra, densities and constituent particle size characteristics, when the initial size distribution and hydrometeor content of a volcanic cloud are known. The integration of numerical modeling, laboratory experimentation and field data lead to several key conclusions. (1) The importance of the microphysics of ash-water interactions in governing the eruption cloud structure, boosting the dispersal power of the cloud and controlling aggregate formation in response to differing water contents and eruption rates. (2) Recognition of the contrasting roles of differential aggregation versus cloud grain size in controlling the formation and nature of aggregate particles, notably those with characteristic ultrafine outer rims. (3) The importance of pyroclastic density currents as triggers for convection and aggregation processes in the eruption cloud.</p>

Metals in ALS TDP-43 Pathology

Amyotrophic lateral sclerosis (ALS), Alzheimer’s disease, Parkinson’s disease and similar neurodegenerative disorders take their toll on patients, caregivers and society. A common denominator for these disorders is the accumulation of aggregated proteins in nerve cells, yet the triggers for these aggregation processes are currently unknown. In ALS, protein aggregation has been described for the SOD1, C9orf72, FUS and TDP-43 proteins. The latter is a nuclear protein normally binding to both DNA and RNA, contributing to gene expression and mRNA life cycle regulation. TDP-43 seems to have a specific role in ALS pathogenesis, and ubiquitinated and hyperphosphorylated cytoplasmic inclusions of aggregated TDP-43 are present in nerve cells in almost all sporadic ALS cases. ALS pathology appears to include metal imbalances, and environmental metal exposure is a known risk factor in ALS. However, studies on metal-to-TDP-43 interactions are scarce, even though this protein seems to have the capacity to bind to metals. This review discusses the possible role of metals in TDP-43 aggregation, with respect to ALS pathology.

A stochastic model, simulation, and application to aggregation of cadmium sulfide nanocrystals upon evaporation of the Langmuir–Blodgett matrix

A stochastic model of nanocrystals clusters formation is developed and applied to simulate an aggregation of cadmium sulfide nanocrystals upon evaporation of the Langmuir–Blodgett matrix. Simulations are compared with our experimental results. The stochastic model suggested governs mobilities both of individual nanocrystals and its clusters (arrays). We give a comprehensive analysis of the patterns simulated by the model, and study an influence of the surrounding medium (solvent) on the aggregation processes. In our model, monomers have a finite probability of separation from the cluster which depends on the temperature and binding energy between nanocrystals, and can also be redistributed in the composition of the cluster, leading to its compaction. The simulation results obtained in this work are compared with the experimental data on the aggregation of CdS nanocrystals upon evaporation of the Langmuir–Blodgett matrix. This system is a typical example from real life and is noteworthy in that the morphology of nanocrystals after evaporation of the matrix cannot be described exactly by a model based only on the motion of individual nanocrystals or by a cluster-cluster aggregation model.

Oceanographic Drivers of Cuvier’s (Ziphius cavirostris) and Sowerby’s (Mesoplodon bidens) Beaked Whales Acoustic Occurrence along the Irish Shelf Edge

Cuvier’s and Sowerby’s beaked whales occur year-round in western Irish waters, yet remain some of the most poorly understood cetaceans in the area. Considering the importance of the area for anthropogenic activities and the sensitivity of beaked whales to noise, understanding their ecology is essential to minimise potential overlaps. To this end, fixed bottom-mounted autonomous acoustic recorders were deployed at 10 stations over four recording periods spanning from May 2015 to November 2016. Acoustic data were collected over 1934 cumulative days, for a total of 7942 h of recordings. To model the probability of presence of Cuvier’s and Sowerby’s beaked whales in the area as a function of oceanographic predictors, we used Generalised Additive Models, fitted with Generalised Estimating Equations to deal with temporal autocorrelation. To reflect prey availability, oceanographic variables acting as proxies of primary productivity and prey aggregation processes such as upwelling events and thermal fronts were selected. Our results demonstrated that oceanographic variables significantly contributed to the occurrence of Cuvier’s and Sowerby’s beaked whales (p-values between <0.001 and <0.05). The species showed similar preferences, with the exception of sdSST. The inclusion of a parameter accounting for the recorders location confirmed the existence of a latitudinal partitioning for those species in the area. This study provides a point of comparison for future research and represents an important step towards a better understanding of those elusive species.

Synthesis and Structure of Nanocomposites based on Linear Polymers and Nanoparticles of Titanium Dioxide

Nanocomposites based on titanium dioxide and epoxy polymer nanoparticles have been obtained and investigated by the in situ method at the stage of curing with preliminary ultrasonic dispersion and evacuation. The composition and structure of the obtained TiO2 nanocomposites have been studied by IR spectroscopy and scanning electron microscopy. It is shown that with an increase in the content of nanoparticles, their average size increases to 88 nm at a TiO2 concentration of 1% as a result of secondary aggregation processes

Direct Observation of Anisotropy-Driven Formation of Amyloid Protein Core-Shell Structures in Real-time by Super-resolution Microscopy

Proteins misfolding and aggregation in the form of fibrils or amyloid containing spherulite-like structures, are involved in a spectrum of degenerative diseases. Current understanding of protein aggregation mechanism primarily relies on conventional spectrometric methods reporting the average growth rates and microscopy readouts of final structures, consequently masking the morphological and growth heterogeneity of the aggregates. Here we developed REal-time kinetics via binding and Photobleaching LOcalization Microscopy (REPLOM) super resolution method to observe directly and quantify the existence and abundance of diverse aggregation morphologies as well as the heterogeneous growth kinetics of each of them. Our results surprisingly revealed insulin aggregation is not exclusively isotropic, but it may also occur anisotropically. Combined with Machine learning we associated growth rates to specific morphological transitions and provided energy barriers and the energy landscape for each aggregation morphology. Our unifying framework of detection and analysis of spherulite growth can be extended to other protein systems and reveal their aggregation processes at single molecule level.