Gut immune dysfunction through impaired innate pattern recognition receptor expression and gut microbiota dysbiosis in chronic SIV infection.

HIV targets the gut mucosa early in infection, causing immune and epithelial barrier dysfunction and disease progression. However, gut mucosal sensing and innate immune signaling through mucosal pattern recognition receptors (PRRs) during HIV infection and disease progression are not well defined. Using the simian immunodeficiency virus (SIV)-infected rhesus macaque model of AIDS, we found a robust increase in PRRs and inflammatory cytokine gene expression during the acute SIV infection in both peripheral blood and gut mucosa, coinciding with viral replication. PRR expression remained elevated in peripheral blood following the transition to chronic SIV infection. In contrast, massive dampening of PRR expression was detected in the gut mucosa, despite the presence of detectable viral loads. Exceptionally, expression of Toll-like receptor 4 (TLR4) and TLR8 was downmodulated and diverged from expression patterns for most other TLRs in the gut. Decreased mucosal PRR expression was associated with increased abundance of several pathogenic bacterial taxa, including Pasteurellaceae members, Aggregatibacter and Actinobacillus, and Mycoplasmataceae family. Early antiretroviral therapy led to viral suppression but only partial maintenance of gut PRRs and cytokine gene expression. In summary, SIV infection dampens mucosal innate immunity through PRR dysregulation and may promote immune activation, gut microbiota changes, and ineffective viral clearance

Transmission fiber chromatic dispersion dependence on temperature: implications on 40 Gb/s performance

In this letter we will evaluate the performance degradation of a 40 km high-speed (40 Gb/s) optical System, induced by optical fiber variations of the chromatic dispersion induced by temperature changes. The chromatic dispersion temperature sensitivity will be estimated based on the signal quality parameters

Dengue disease surveillance: an updated systematic literature review

Objectives To review the evidence for the application of tools for dengue outbreak prediction/detection and trend monitoring in passive and active disease surveillance systems in order to develop recommendations for endemic countries and identify important research needs. Methods This systematic literature review followed the protocol of a review from 2008, extending the systematic search from January 2007 to February 2013 on PubMed, EMBASE, CDSR, WHOLIS and Lilacs. Data reporting followed the PRISMA statement. The eligibility criteria comprised (i) population at risk of dengue, (ii) dengue disease surveillance, (iii) outcome of surveillance described and (iv) empirical data evaluated. The analysis classified studies based on the purpose of the surveillance programme. The main limitation of the review was expected publication bias. Results A total of 1116 papers were identified of which 36 articles were included in the review. Four cohort-based prospective studies calculated expansion factors demonstrating remarkable levels of underreporting in the surveillance systems. Several studies demonstrated that enhancement methods such as laboratory support, sentinel-based reporting and staff motivation contributed to improvements in dengue reporting. Additional improvements for passive surveillance systems are possible by incorporating simple data forms/entry/electronic-based reporting; defining clear system objectives; performing data analysis at the lowest possible level (e.g. district); seeking regular data feedback. Six studies showed that serotype changes were positively correlated with the number of reported cases or with dengue incidence, with lag times of up to 6 months. Three studies found that data on internet searches and event-based surveillance correlated well with the epidemic curve derived from surveillance data. Conclusions Passive surveillance providing the baseline for outbreak alert should be strengthened and appropriate threshold levels for outbreak alerts investigated. Additional enhancement tools such as syndromic surveillance, laboratory support and motivation strategies can be added. Appropriate alert signals need to be identified and integrated into a risk assessment tool. Shifts in dengue serotypes/genotype or electronic event-based surveillance have also considerable potential as indicator in dengue surveillance. Further research on evidence-based response strategies and cost-effectiveness is needed

Discovering universal statistical laws of complex networks

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their generalisation power, which we identify with large structural variability and absence of constraints imposed by the construction scheme. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This allows, for instance, to infer global features from local ones using regression models trained on networks with high generalisation power. Our results confirm and extend previous findings regarding the synchronisation properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks with good approximation. Finally, we demonstrate on three different data sets (C. elegans' neuronal network, R. prowazekii's metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models

Detection of diploid males in a natural colony of the cleptobiotic bee Lestrimelitta sp (Hymenoptera, Apidae)

When working at quantifying the genome size of stingless bees, it was observed that males of Lestrimelitta sp possessed the same amount of nuclear DNA as the females. Thus, we used flow cytometry (FCM) and cytogenetic analysis to confirm the ploidy of these individuals. The males analyzed proved to be diploid, since, through cytometric analysis, it was demonstrated that the mean genome size of both males and females was the same (C = 0.463 pg), and, furthermore, cytogenetic analysis demonstrated that both had 2n = 28 chromosomes