Optimizing surveillance for livestock disease spreading through animal movements

The spatial propagation of many livestock infectious diseases critically depends on the animal movements among premises; so the knowledge of movement data may help us to detect, manage and control an outbreak. The identification of robust spreading features of the system is however hampered by the temporal dimension characterizing population interactions through movements. Traditional centrality measures do not provide relevant information as results strongly fluctuate in time and outbreak properties heavily depend on geotemporal initial conditions. By focusing on the case study of cattle displacements in Italy, we aim at characterizing livestock epidemics in terms of robust features useful for planning and control, to deal with temporal fluctuations, sensitivity to initial conditions and missing information during an outbreak. Through spatial disease simulations, we detect spreading paths that are stable across different initial conditions, allowing the clustering of the seeds and reducing the epidemic variability. Paths also allow us to identify premises, called sentinels, having a large probability of being infected and providing critical information on the outbreak origin, as encoded in the clusters. This novel procedure provides a general framework that can be applied to specific diseases, for aiding risk assessment analysis and informing the design of optimal surveillance systems.Comment: Supplementary Information at https://sites.google.com/site/paolobajardi/Home/archive/optimizing_surveillance_ESM_l.pdf?attredirects=

Predicting epidemic risk from past temporal contact data

Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system's pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node's loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node's epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.Comment: 24 pages, 5 figures + SI (18 pages, 15 figures

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Organoiridium complexes : anticancer agents and catalysts

Iridium is a relatively rare precious heavy metal, only slightly less dense than osmium. Researchers have long recognized the catalytic properties of square-planar Ir(I) complexes, such as Crabtree's hydrogenation catalyst, an organometallic complex with cyclooctadiene, phosphane, and pyridine ligands. More recently, chemists have developed half-sandwich pseudo-octahedral pentamethylcyclopentadienyl Ir(III) complexes containing diamine ligands that efficiently catalyze transfer hydrogenation reactions of ketones and aldehydes in water using H2 or formate as the hydrogen source. Although sometimes assumed to be chemically inert, the reactivity of low-spin 5d(6) Ir(III) centers is highly dependent on the set of ligands. Cp* complexes with strong σ-donor C^C-chelating ligands can even stabilize Ir(IV) and catalyze the oxidation of water. In comparison with well developed Ir catalysts, Ir-based pharmaceuticals are still in their infancy. In this Account, we review recent developments in organoiridium complexes as both catalysts and anticancer agents. Initial studies of anticancer activity with organoiridium complexes focused on square-planar Ir(I) complexes because of their structural and electronic similarity to Pt(II) anticancer complexes such as cisplatin. Recently, researchers have studied half-sandwich Ir(III) anticancer complexes. These complexes with the formula [(Cp(x))Ir(L^L')Z](0/n+) (with Cp* or extended Cp* and L^L' = chelated C^N or N^N ligands) have a much greater potency (nanomolar) toward a range of cancer cells (especially leukemia, colon cancer, breast cancer, prostate cancer, and melanoma) than cisplatin. Their mechanism of action may involve both an attack on DNA and a perturbation of the redox status of cells. Some of these complexes can form Ir(III)-hydride complexes using coenzyme NAD(P)H as a source of hydride to catalyze the generation of H2 or the reduction of quinones to semiquinones. Intriguingly, relatively unreactive organoiridium complexes containing an imine as a monodentate ligand have prooxidant activity, which appears to involve catalytic hydride transfer to oxygen and the generation of hydrogen peroxide in cells. In addition, researchers have designed inert Ir(III) complexes as potent kinase inhibitors. Octahedral cyclometalated Ir(III) complexes not only serve as cell imaging agents, but can also inhibit tumor necrosis factor α, promote DNA oxidation, generate singlet oxygen when photoactivated, and exhibit good anticancer activity. Although relatively unexplored, organoiridium chemistry offers unique features that researchers can exploit to generate novel diagnostic agents and drugs with new mechanisms of action

Associazione tra variabili climatiche e incursione di BTV-1 in Italia centrale nel 2014

In 2012, six years after the previous epidemic, Bluetongue virus serotype 1 (BTV-1) re-emerged in Sardinia causing a limited number of outbreaks. Due to impossibility of implementing a vaccination campaign, the BTV-1 then spread all over the island in 2013 with about 7,000 outbreaks and, in September 2013, the virus reached Central Italy, with a limited number of outbreaks located along the Tyrrhenian coast. The surveillance system in place in Italy detected viral circulation during the following winter, when a few seroconversions were notified. Starting from mid July 2014, a huge number of outbreaks were reported and the disease spread toward inland territories, affecting Umbria, Abruzzo and Marche. In 2014, BTV-1 affected areas where Culicoides species belonging to the Obsoletus and Pulicaris complexes were identified as main vectors. The analysis of temperature and rainfall in Central Italy revealed a significant warmer winter (2013-2014) and a cooler and rainy summer season (2014). These climatic aspects might have certainly favored the overwintering of the virus in local vector or host populations in the Tyrrhenian coast, and, then, the spread of the virus to the rest of Central Italy. However, the heavy circulation of BTV-1 and the severity of clinical outbreaks recorded leave a number of ‘open questions’ that are currently under investigations

Enabling planetary science across light-years. Ariel Definition Study Report

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution

Global Roadkill Data: a dataset on terrestrial vertebrate mortality caused by collision with vehicles

Roadkill is widely recognized as one of the primary negative effects of roads on many wildlife species and also has socioeconomic impacts when they result in accidents. A comprehensive dataset of roadkill locations is essential to evaluate the factors contributing to roadkill risk and to enhance our comprehension of its impact on wildlife populations and socioeconomic dimensions. We undertook a compilation of roadkill records, encompassing both published and unpublished data gathered from road surveys or opportunistic sources. GLOBAL ROADKILL DATA includes 208,570 roadkill records of terrestrial vertebrates from 54 countries across six continents, encompassing data collected between 1971 and 2024. This dataset serves to minimise the collection of redundant data and acts as a valuable resource for local and macro scale analysis regarding rates of roadkill, road- and landscape-related features associated with risk of roadkill, vulnerability of species to road traffic, and populations at risk of local extinction. The objective of this dataset is to promote scientific progress in infrastructure ecology and terrestrial vertebrate conservation while limiting the socio-economic costs

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely