Immunization strategies for epidemic processes in time-varying contact networks

Spreading processes represent a very efficient tool to investigate the structural properties of networks and the relative importance of their constituents, and have been widely used to this aim in static networks. Here we consider simple disease spreading processes on empirical time-varying networks of contacts between individuals, and compare the effect of several immunization strategies on these processes. An immunization strategy is defined as the choice of a set of nodes (individuals) who cannot catch nor transmit the disease. This choice is performed according to a certain ranking of the nodes of the contact network. We consider various ranking strategies, focusing in particular on the role of the training window during which the nodes' properties are measured in the time-varying network: longer training windows correspond to a larger amount of information collected and could be expected to result in better performances of the immunization strategies. We find instead an unexpected saturation in the efficiency of strategies based on nodes' characteristics when the length of the training window is increased, showing that a limited amount of information on the contact patterns is sufficient to design efficient immunization strategies. This finding is balanced by the large variations of the contact patterns, which strongly alter the importance of nodes from one period to the next and therefore significantly limit the efficiency of any strategy based on an importance ranking of nodes. We also observe that the efficiency of strategies that include an element of randomness and are based on temporally local information do not perform as well but are largely independent on the amount of information available

An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices

The integration of empirical data in computational frameworks to model the spread of infectious diseases poses challenges that are becoming pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios and designing containment strategies. However, the integration of detailed data sources yields models that are less transparent and general. Hence, given a specific disease model, it is crucial to assess which representations of the raw data strike the best balance between simplicity and detail. We consider high-resolution data on the face-to-face interactions of individuals in a hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the contact patterns. We show that a contact matrix that only contains average contact durations fails to reproduce the size of the epidemic obtained with the high-resolution contact data and also to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data. Our results mark a step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management

Late Onset Myasthenia Gravis Is Associated with HLA DRB1*15:01 in the Norwegian Population

BACKGROUND: Acquired myasthenia gravis (MG) is a rare antibody-mediated autoimmune disease caused by impaired neuromuscular transmission, leading to abnormal muscle fatigability. The aetiology is complex, including genetic risk factors of the human leukocyte antigen (HLA) complex and unknown environmental factors. Although associations between the HLA complex and MG are well established, not all involved components of the HLA predisposition to this heterogeneous disease have been revealed. Well-powered and comprehensive HLA analyses of subgroups in MG are warranted, especially in late onset MG. METHODOLOGY/PRINCIPAL FINDINGS: This case-control association study is of a large population-based Norwegian cohort of 369 MG patients and 651 healthy controls. We performed comprehensive genotyping of four classical HLA loci (HLA-A, -B, -C and -DRB1) and showed that the DRB1*15:01 allele conferred the strongest risk in late onset MG (LOMG; onset ≥ 60 years) (OR 2.38, p(c)7.4 × 10(-5)). DRB1*13:01 was found to be a protective allele for both early onset MG (EOMG) and LOMG (OR 0.31, p(c) 4.71 × 10(-4)), a finding not previously described. No significant association was found to the DRB1*07:01 allele (p(nc) = 0.18) in a subset of nonthymomatous anti-titin antibody positive LOMG as reported by others. HLA-B*08 was mapped to give the strongest contribution to EOMG, supporting previous studies. CONCLUSION: The results from this study provide important new information concerning the susceptibility of HLA alleles in Caucasian MG, with highlights on DRB1*15:01 as being a major risk allele in LOMG

Processus Epidémiques sur Réseaux Dynamiques

In this thesis, we investigate how various properties of temporal networks influence epidemic processes on networks. We are in particular interested in the role the data representation plays in this context and in how much detail of the data is necessary in order to obtain sufficiently accurate spreading results and decide on immunization strategies.Dans cette thèse, nous étudions l’influence des propriétés diverses des réseaux dynamiques et statiques sur la propagation des épidémies. Nous utilisons des données récentes de contacts en face à face de haute résolution. Pour les simulations de la propagation des épidémies, les nœuds du réseau sont divisés en compartiments de nœuds susceptibles (S), infectés (I), exposés (E) et guéris (R). Outre le modèle SEIR, des modèles avec moins de compartiments, comme SIR ou SI, seront utilisés aussi

Epidemic Processes on Dynamic Networks

Dans cette thèse nous contribuons à répondre aux questions sur les processus dynamiques sur réseaux temporels. En particulier, nous etudions l'influence des représentations de données sur les simulations des processus épidémiques, le niveau de détail nécessaire pour la représentation des données et sa dépendance des paramètres de la propagation de l'épidémie. Avec l'introduction de la matrice de distributions du temps de contacts nous espérons pouvoir améliorer dans le futur la précision des prédictions des épidémies et des stratégies d'immunisation en intégrant cette représentation des données aux modèles d'épidémies multi-échelles. De plus nous montrons comment les processus épidémiques dynamiques sont influencés par les propriétés temporelles des données.In this thesis we contribute to provide insights into questions concerning dynamic epidemic processes on data-driven, temporal networks. In particular, we investigate the influence of data representations on the outcome of epidemic processes, shedding some light on the question how much detail is necessary for the data representation and its dependence on the spreading parameters. By introducing an improvement to the contact matrix representation we provide a data representation that could in the future be integrated into multi-scale epidemic models in order to improve the accuracy of predictions and corresponding immunization strategies. We also point out some of the ways dynamic processes are influenced by temporal properties of the data

Plant X-tender

Cloning multiple DNA fragments for delivery of several genes of interest into the plant genome is one of the main technological challenges in plant synthetic biology. Despite several modular assembly methods developed in recent years, the plant biotechnology community has not widely adopted them yet, probably due to the lack of appropriate vectors and software tools. Here we present Plant X-tender, an extension of the highly efficient, scar-free and sequence-independent multigene assembly strategy AssemblX, based on overlap-depended cloning methods and rare-cutting restriction enzymes. Plant X-tender consists of a set of plant expression vectors and the protocols for most efficient cloning into the novel vector set needed for plant expression and thus introduces advantages of AssemblX into plant synthetic biology. The novel vector set covers different backbones and selection markers to allow full design flexibility. We have included ccdB counterselection, thereby allowing the transfer of multigene constructs into the novel vector set in a straightforward and highly efficient way. Vectors are available as empty backbones and are fully flexible regarding the orientation of expression cassettes and addition of linkers between them, if required. We optimised the assembly and subcloning protocol by testing different scar-less assembly approaches: the noncommercial SLiCE and TAR methods and the commercial Gibson assembly and NEBuilder HiFi DNA assembly kits. Plant X-tender was applicable even in combination with low efficient homemade chemically competent or electrocompetent Escherichia coli. We have further validated the developed procedure for plant protein expression by cloning two cassettes into the newly developed vectors and subsequently transferred them to Nicotiana benthamiana in a transient expression setup. Thereby we show that multigene constructs can be delivered into plant cells in a streamlined and highly efficient way. Our results will support faster introduction of synthetic biology into plant science

Magnetism on a Mesoscopic Scale: Molecular Nanomagnets Bridging Quantum and Classical Physics

In recent years polynuclear transition metal molecules have been synthesized and proposed for example as magnetic storage units or qubits in quantum computers. They are known as molecular nanomagnets and belong in the class of mesoscopic systems, which are large enough to display many-body effects but small enough to be away from the finite-size scaling regime. It is a challenge for physicists to understand their magnetic properties, and for synthetic chemists to efficiently tailor them by assembling fundamental units. They are complementary to artificially engineered spin systems for surface deposition, as they support a wider variety of complex states in their low energy spectrum. Here a few characteristic examples of molecular nanomagnets showcasing unusual many-body effects are presented. Antiferromagnetic wheels and chains can be described in classical terms for small sizes and large spins to a great extent, even though their wavefunctions do not significantly overlap with semiclassical configurations. Hence, surprisingly, for them the transition from the classical to the quantum regime is blurred. A specific example is the Fe18 wheel, which displays quantum phase interference by allowing Néel vector tunneling in a magnetic field. Finally, the Co5Cl single-molecule magnet is shown to have an unusual anisotropic response to a magnetic field

Immunization strategies for epidemic processes in time-varying contact networks

preading processes represent a very efficient tool to investigate the structural properties of networks and the relative importance of their constituents, and have been widely used to this aim in static networks. Here we consider simple disease spreading processes on empirical time-varying networks of contacts between individuals, and compare the effect of several immunization strategies on these processes. An immunization strategy is defined as the choice of a set of nodes (individuals) who cannot catch nor transmit the disease. This choice is performed according to a certain ranking of the nodes of the contact network