Phase transition of the susceptible-infected-susceptible dynamics on time-varying configuration model networks

We present a degree-based theoretical framework to study the susceptible-infected-susceptible (SIS) dynamics on time-varying (rewired) configuration model networks. Using this framework, we provide a detailed analysis of the stationary state that covers, for a given structure, every dynamic regimes easily tuned by the rewiring rate. This analysis is suitable for the characterization of the phase transition and leads to three main contributions. (i) We obtain a self-consistent expression for the absorbing-state threshold, able to capture both collective and hub activation. (ii) We recover the predictions of a number of existing approaches as limiting cases of our analysis, providing thereby a unifying point of view for the SIS dynamics on random networks. (iii) We reinterpret the concept of hub-dominated phase transition. Within our framework, it appears as a heterogeneous critical phenomenon : observables for different degree classes have a different scaling with the infection rate. This leads to the successive activation of the degree classes beyond the epidemic threshold.Comment: 14 pages, 11 figure

Geometric evolution of complex networks with degree correlations

We present a general class of geometric network growth mechanisms by homogeneous attachment in which the links created at a given time t are distributed homogeneously between a new node and the existing nodes selected uniformly. This is achieved by creating links between nodes uniformly distributed in a homogeneous metric space according to a Fermi-Dirac connection probability with inverse temperature β and general time-dependent chemical potential μ(t). The chemical potential limits the spatial extent of newly created links. Using a hidden variable framework, we obtain an analytical expression for the degree sequence and show that μ(t) can be fixed to yield any given degree distributions, including a scale-free degree distribution. Additionally, we find that depending on the order in which nodes appear in the network—its history—the degree-degree correlations can be tuned to be assortative or disassortative. The effect of the geometry on the structure is investigated through the average clustering coefficient ⟨c⟩. In the thermodynamic limit, we identify a phase transition between a random regime where ⟨c⟩→ 0 when β 0 when β>βc

Spinocerebellar ataxia types 1, 2, 3, and 6: disease severity and nonataxia symptoms.

OBJECTIVE: To identify factors that determine disease severity and clinical phenotype of the most common spinocerebellar ataxias (SCAs), we studied 526 patients with SCA1, SCA2, SCA3. or SCA6. METHODS: To measure the severity of ataxia we used the Scale for the Assessment and Rating of Ataxia (SARA). In addition, nonataxia symptoms were assessed with the Inventory of Non-Ataxia Symptoms (INAS). The INAS count denotes the number of nonataxia symptoms in each patient. RESULTS: An analysis of covariance with SARA score as dependent variable and repeat lengths of the expanded and normal allele, age at onset, and disease duration as independent variables led to multivariate models that explained 60.4% of the SARA score variance in SCA1, 45.4% in SCA2, 46.8% in SCA3, and 33.7% in SCA6. In SCA1, SCA2, and SCA3, SARA was mainly determined by repeat length of the expanded allele, age at onset, and disease duration. The only factors determining the SARA score in SCA6 were age at onset and disease duration. The INAS count was 5.0 +/- 2.3 in SCA1, 4.6 +/- 2.2 in SCA2, 5.2 +/- 2.5 in SCA3, and 2.0 +/- 1.7 in SCA6. In SCA1, SCA2, and SCA3, SARA score and disease duration were the strongest predictors of the INAS count. In SCA6, only age at onset and disease duration had an effect on the INAS count. CONCLUSIONS: Our study suggests that spinocerebellar ataxia (SCA) 1, SCA2, and SCA3 share a number of common biologic properties, whereas SCA6 is distinct in that its phenotype is more determined by age than by disease-related factors

Smoking in asthma is associated with elevated levels of corticosteroid resistant sputum cytokines—an exploratory study

<p>Background: Current cigarette smoking is associated with reduced acute responses to corticosteroids and worse clinical outcomes in stable chronic asthma. The mechanism by which current smoking promotes this altered behavior is currently unclear. Whilst cytokines can induce corticosteroid insensitivity in-vitro, how current and former smoking affects airway cytokine concentrations and their responses to oral corticosteroids in stable chronic asthma is unclear.</p> <p>Objectives: To examine blood and sputum cytokine concentrations in never, ex and current smokers with asthma before and after oral corticosteroids.</p> <p>Methods: Exploratory study utilizing two weeks of oral dexamethasone (equivalent to 40 mg/day prednisolone) in 22 current, 21 never and 10 ex-smokers with asthma. Induced sputum supernatant and plasma was obtained before and after oral dexamethasone. 25 cytokines were measured by multiplex microbead system (Invitrogen, UK) on a Luminex platform.</p> <p>Results: Smokers with asthma had elevated sputum cytokine interleukin (IL) -6, -7, and -12 concentrations compared to never smokers with asthma. Few sputum cytokine concentrations changed in response to dexamethasone IL-17 and IFNα increased in smokers, CCL4 increased in never smokers and CCL5 and CXCL10 reduced in ex-smokers with asthma. Ex-smokers with asthma appeared to have evidence of an ongoing corticosteroid resistant elevation of cytokines despite smoking cessation. Several plasma cytokines were lower in smokers wi</p> <p>Conclusion: Cigarette smoking in asthma is associated with a corticosteroid insensitive increase in multiple airway cytokines. Distinct airway cytokine profiles are present in current smokers and never smokers with asthma and could provide an explanatory mechanism for the altered clinical behavior observed in smokers with asthma.</p&gt

Multiple factors interact to produce responses resembling spectrum of human disease in Campylobacter jejuni infected C57BL/6 IL-10-/- mice

<p>Abstract</p> <p>Background</p> <p><it>Campylobacter jejuni </it>infection produces a spectrum of clinical presentations in humans – including asymptomatic carriage, watery diarrhea, and bloody diarrhea – and has been epidemiologically associated with subsequent autoimmune neuropathies. This microorganism is genetically variable and possesses genetic mechanisms that may contribute to variability in nature. However, relationships between genetic variation in the pathogen and variation in disease manifestation in the host are not understood. We took a comparative experimental approach to explore differences among different <it>C. jejuni </it>strains and studied the effect of diet on disease manifestation in an interleukin-10 deficient mouse model.</p> <p>Results</p> <p>In the comparative study, C57BL/6 interleukin-10^-/-mice were infected with seven genetically distinct <it>C. jejuni </it>strains. Four strains colonized the mice and caused disease; one colonized with no disease; two did not colonize. A DNA:DNA microarray comparison of the strain that colonized mice without disease to <it>C. jejuni </it>11168 that caused disease revealed that putative virulence determinants, including loci encoding surface structures known to be involved in <it>C. jejuni </it>pathogenesis, differed from or were absent in the strain that did not cause disease. In the experimental study, the five colonizing strains were passaged four times in mice. For three strains, serial passage produced increased incidence and degree of pathology and decreased time to develop pathology; disease shifted from watery to bloody diarrhea. Mice kept on an ~6% fat diet or switched from an ~12% fat diet to an ~6% fat diet just before infection with a non-adapted strain also exhibited increased incidence and severity of disease and decreased time to develop disease, although the effects of diet were only statistically significant in one experiment.</p> <p>Conclusion</p> <p><it>C. jejuni </it>strain genetic background and adaptation of the strain to the host by serial passage contribute to differences in disease manifestations of <it>C. jejuni </it>infection in C57BL/6 IL-10^-/-mice; differences in environmental factors such as diet may also affect disease manifestation. These results in mice reflect the spectrum of clinical presentations of <it>C. jejuni </it>gastroenteritis in humans and contribute to usefulness of the model in studying human disease.</p

Recognition in Terra Incognita

It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems. (The dataset is available at https://beerys.github.io/CaltechCameraTraps/

Issuance of the CERCLA rod for an operable unit remedial action at the Weldon Spring Site: Lessons learned

Remedial action needs of the Weldon Springs Site are briefly discussed

Recognition in Terra Incognita

It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems. (The dataset is available at https://beerys.github.io/CaltechCameraTraps/