Towards an ecosystem model of infectious disease

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics

Publisher Correction: Towards an ecosystem model of infectious disease

Correction to: Nature Ecology & Evolution https://doi.org/10.1038/s41559-021-01454-8, published online 17 May 2021

The oceanic biogeochemistry of nickel and its isotopes: New data from the South Atlantic and the Southern Ocean biogeochemical divide

12 months embarg

Iron biogeochemistry across marine systems progress from the past decade

Based on an international workshop (Gothenburg, 14–16 May 2008), this review article aims to combine interdisciplinary knowledge from coastal and open ocean research on iron biogeochemistry. The major scientific findings of the past decade are structured into sections on natural and artificial iron fertilization, iron inputs into coastal and estuarine systems, colloidal iron and organic matter, and biological processes. Potential effects of global climate change, particularly ocean acidification, on iron biogeochemistry are discussed. The findings are synthesized into recommendations for future research areas

Parasitic Dinoflagellate \u3ci\u3eHematodinium perezi\u3c/i\u3e Prevalence in Larval and Juvenile Blue Crabs \u3ci\u3eCallinectes sapidus\u3c/i\u3e from Coastal Bays of Virginia

The parasitic dinoflagellate Hematodinium perezi infects the American blue crab Callinectes sapidus and other decapods along the Eastern seaboard and Gulf of Mexico coast of the USA. Large juvenile and adult blue crabs experience high mortality during seasonal outbreaks of H. perezi, but less is known about its presence in the early life history stages of this host. We determined the prevalence of H. perezi in megalopae and early benthic juvenile crabs from multiple locations along the Virginia portion of the Delmarva Peninsula. The DNA of H. perezi was not detected in any megalopae collected from several locations within the oceanic coastal bay complex in which H. perezi is found at high prevalence levels. However, prevalence levels were high in early benthic juveniles from 2 oceanic coastal embayments: South Bay and Cobb Bay. Prevalence levels were lower at locations within Chesapeake Bay, including Cherrystone Creek, Hungars Creek, and Pungoteague Creek. Sampling over different seasons and several consecutive years indicates that disease transmission occurs rapidly after megalopae settle in high-salinity bays along the Delmarva Peninsula during the late summer and fall. Infected juvenile crabs can overwinter with the parasite and, when subjected to increasing water temperatures in spring, infections progress rapidly, culminating in transmission to other crabs in late spring and early summer. In high-salinity embayments, H. perezi can reach high prevalence levels and may significantly affect recruitment of juvenile blue crabs into the adult fishery

The unaccounted dissolved iron (II) sink: Insights from dFe(II) concentrations in the deep Atlantic Ocean

Hydrothermal vent sites found along mid-ocean ridges are sources of numerous reduced chemical species and trace elements. To establish dissolved iron (II) (dFe(II)) variability along the Mid Atlantic Ridge (between 39.5°N and 26°N), dFe(II) concentrations were measured above six hydrothermal vent sites, as well as at stations with no active hydrothermal activity. The dFe(II) concentrations ranged from 0.00 to 0.12 nmol L−1 (detection limit = 0.02 ± 0.02 nmol L−1) in non-hydrothermally affected regions to values as high as 12.8 nmol L−1 within hydrothermal plumes. Iron (II) in seawater is oxidised over a period of minutes to hours, which is on average two times faster than the time required to collect the sample from the deep ocean and its analysis in the onboard laboratory. A multiparametric equation was used to estimate the original dFe(II) concentration in the deep ocean. The in-situ temperature, pH, salinity and delay between sample collection and its analysis were considered. The results showed that dFe(II) plays a more significant role in the iron pool than previously accounted for, constituting a fraction >20 % of the dissolved iron pool, in contrast to <10 % of the iron pool formerly reported. This discrepancy is caused by Fe(II) loss during sampling when between 35 and 90 % of the dFe(II) gets oxidised. In-situ dFe(II) concentrations are therefore significantly higher than values reported in sedimentary and hydrothermal settings where Fe is added to the ocean in its reduced form. Consequently, the high dynamism of dFe(II) in hydrothermal environments masks the magnitude of dFe(II) sourced within the deep ocean

Iron Distribution in the Subtropical North Atlantic: The Pivotal Role of Colloidal Iron

The low availability of the essential micronutrient iron (Fe) in the ocean impacts the efficiency of the biological carbon pump, and hence, it is vital to elucidate its sources, sinks, and internal cycling. We present size‐fractionated dissolved Fe (dFe, <0.2 μm) measurements from 130 surface samples and 7 full‐depth profiles from the subtropical North Atlantic during summer 2017 and demonstrate the pivotal role of colloidal (cFe, 0.02 to 0.2 μm) over soluble (sFe, <0.02 μm) Fe in controlling the dFe distribution. In the surface (<5 m), a strong west‐to‐east decrease in dFe (1.53 to 0.26 nM) was driven by a dust gradient, which retained dFe predominantly as cFe (61% to 85% of dFe), while sFe remained largely constant at 0.19 ± 0.05 nM. In the euphotic zone, the attenuation of dFe resulted from the depletion of cFe (0% to 30% of dFe), with scavenging as an important driver. In the mesopelagic, cFe was released from sinking biogenic and lithogenic particles, creating a zone of elevated dFe (0.7 to 1.0 nM) between 400 to 1100 m depth. While the ocean interior, below the mesopelagic and above the seafloor boundary, exhibited a narrow range of cFe (40% to 60% of dFe), the abyssal cFe fraction varied in range from 26% to 76% due to interactions with seafloor sediments and a hydrothermal source with almost 100% cFe. Overall, our results produced an hourglass shape for the vertical cFe‐to‐dFe fraction and highlight the primary control of cFe on the dFe distribution

Enhanced topical delivery of dexamethasone by β-cyclodextrin decorated thermoresponsive nanogels

Highly hydrophilic, responsive nanogels are attractive as potential systems for the topical delivery of bioactives encapsulated in their three-dimensional polymeric scaffold. Yet, these drug carrier systems suffer from drawbacks for efficient delivery of hydrophobic drugs. Addressing this, β-cyclodextrin (βCD) could be successfully introduced into the drug carrier systems by exploiting its unique affinity toward dexamethasone (DXM) as well as its role as topical penetration enhancer. The properties of βCD could be combined with those of thermoresponsive nanogels (tNGs) based on dendritic polyglycerol (dPG) as a crosslinker and linear thermoresponsive polyglycerol (tPG) inducing responsiveness to temperature changes. Electron paramagnetic resonance (EPR) studies localized the drug within the hydrophobic cavity of βCD by differences in its mobility and environmental polarity. In fact, the fabricated carriers combining a particulate delivery system with a conventional penetration enhancer, resulted in an efficient delivery of DXM to the epidermis and the dermis of human skin ex vivo (enhancement compared to commercial DXM cream: ∼2.5 fold in epidermis, ∼30 fold in dermis). Furthermore, DXM encapsulated in βCD tNGs applied to skin equivalents downregulated the expression of proinflammatory thymic stromal lymphopoietin (TSLP) and outperformed a commercially available DXM cream

Microwave, infrared and Raman spectra, r0 structural parameters, ab initio calculations and vibrational assignment of 1-fluoro-1-silacyclopentanea)

The microwave spectrum (6500–18 500 MHz) of 1-fluoro-1-silacyclopentane, c-C4H8SiHF has been recorded and 87 transitions for the 28Si, 29Si, 30Si, and 13C isotopomers have been assigned for a single conformer. Infrared spectra (3050-350 cm−1) of the gas and solid and Raman spectrum (3100-40 cm−1) of the liquid have also been recorded. The vibrational data indicate the presence of a single conformer with no symmetry which is consistent with the twist form. Ab initio calculations with a variety of basis sets up to MP2(full)/aug-cc-pVTZ predict the envelope-axial and envelope-equatorial conformers to be saddle points with nearly the same energies but much lower energy than the planar conformer. By utilizing the microwaverotational constants for seven isotopomers (28Si, 29Si, 30Si, and four 13C) combined with the structural parameters predicted from the MP2(full)/6–311+G(d,p) calculations, adjusted r0 structural parameters have been obtained for the twist conformer. The heavy atom distances in Å are: r0(SiC2) = 1.875(3); r0(SiC3) = 1.872(3); r0(C2C4) = 1.549(3); r0(C3C5) = 1.547(3); r0(C4C5) = 1.542(3); r0(SiF) = 1.598(3) and the angles in degrees are: ∠CSiC = 96.7(5); ∠SiC2C4 = 103.6(5); ∠SiC3C5 = 102.9(5); ∠C2C4C5 = 108.4(5); ∠C3C5C4 = 108.1(5); ∠F6Si1C2 = 110.7(5); ∠F6Si1C3 = 111.6(5). The heavy atom ring parameters are compared to the corresponding rs parameters. Normal coordinate calculations with scaled force constants from MP2(full)/6–31G(d) calculations were carried out to predict the fundamental vibrational frequencies, infrared intensities, Raman activities, depolarization values, and infrared band contours. These experimental and theoretical results are compared to the corresponding quantities of some other five-membered rings

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.publishedVersionPeer reviewe