From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

European wildcat populations are subdivided into five main biogeographic groups: consequences of Pleistocene climate changes or recent anthropogenic fragmentation?

Extant populations of the European wildcat are fragmented across the continent, the likely consequence of recent extirpations due to habitat loss and over-hunting. However, their underlying phylogeographic history has never been reconstructed. For testing the hypothesis that the European wildcat survived the Ice Age fragmented in Mediterranean refuges, we assayed the genetic variation at 31 microsatellites in 668 presumptive European wildcats sampled in 15 European countries. Moreover, to evaluate the extent of subspecies/population divergence and identify eventual wild × domestic cat hybrids, we genotyped 26 African wildcats from Sardinia and North Africa and 294 random-bred domestic cats. Results of multivariate analyses and Bayesian clustering confirmed that the European wild and the domestic cats (plus the African wildcats) belong to two well-differentiated clusters (average Ф ST = 0.159, r st = 0.392, P > 0.001; Analysis of molecular variance [AMOVA]). We identified from c. 5% to 10% cryptic hybrids in southern and central European populations. In contrast, wild-living cats in Hungary and Scotland showed deep signatures of genetic admixture and introgression with domestic cats. The European wildcats are subdivided into five main genetic clusters (average Ф ST = 0.103, r st = 0.143, P > 0.001; AMOVA) corresponding to five biogeographic groups, respectively, distributed in the Iberian Peninsula, central Europe, central Germany, Italian Peninsula and the island of Sicily, and in north-eastern Italy and northern Balkan regions (Dinaric Alps). Approximate Bayesian Computation simulations supported late Pleistocene-early Holocene population splittings (from c. 60 k to 10 k years ago), contemporary to the last Ice Age climatic changes. These results provide evidences for wildcat Mediterranean refuges in southwestern Europe, but the evolution history of eastern wildcat populations remains to be clarified. Historical genetic subdivisions suggest conservation strategies aimed at enhancing gene flow through the restoration of ecological corridors within each biogeographic units. Concomitantly, the risk of hybridization with free-ranging domestic cats along corridor edges should be carefully monitored

Fate of the Aortic Arch Following Surgery on Aortic Root and Ascending Aorta in Bicuspid Aortic Valve.

BACKGROUND: Recent guidelines support more aggressive surgery for aneurysms of the ascending aorta and root in patients with bicuspid aortic valve. However, the fate of the arch after surgery of the root and ascending aorta is unknown. We set out to assess outcomes following root and ascending aortic surgery and subsequent growth of the arch. METHODS: Between 2005 and 2016, 536 consecutive patients underwent surgery for aneurysm of the root and ascending aorta. 168 had bicuspid aortic valve. Patients with dissection were excluded. Arch diameter was measured before and after surgery, at six months and then annually. RESULTS: Of 168 patients, 127 (75.6%) had aortic root replacement and 41 (24.4%) had ascending replacement. Mean age was 57±12.8 years, 82.7% were males and five operations were performed during pregnancy. There was one (0.6%) hospital death. One (0.6%) patient had a stroke and one (0.6%) had re-sternotomy for bleeding. Median ICU and hospital stays were 1 and 6 days respectively. Follow-up was complete for 94% at a median of 5.9 years (1-139 months). Aortic arch diameter was 2.9 cm preoperatively and 3.0 cm at follow-up. There was 97% freedom from reoperation and none of the patients required surgery on the arch. CONCLUSIONS: Prophylactic arch replacement during aortic root and ascending aortic surgery in patients with bicuspid aortic valve is not supported. Our data does not support long term surveillance of the rest of the aorta in this population

Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.Comment: 25 page

Women and Illegal Activities: Gender Differences and Women's Willingness to Comply Over Time

In recent years the topics of illegal activities such as corruption or tax evasion have attracted a great deal of attention. However, there is still a lack of substantial empirical evidence about the determinants of compliance. The aim of this paper is to investigate empirically whether women are more willing to be compliant than men and whether we observe (among women and in general) differences in attitudes among similar age groups in different time periods (cohort effect) or changing attitudes of the same cohorts over time (age effect) using data from eight Western European countries from the World Values Survey and the European Values Survey that span the period from 1981 to 1999. The results reveal higher willingness to comply among women and an age rather than a cohort effect. Working Paper 06-5

Exacerbated inflammatory arthritis in response to hyperactive gp130 signalling is independent of IL-17A

Objective Interleukin (IL)-17A producing CD4 T-cells (TH-17 cells) are implicated in rheumatoid arthritis (RA). IL-6/STAT3 signalling drives TH-17 cell differentiation, and hyperactive gp130/STAT3 signalling in the gp130F/F mouse promotes exacerbated pathology. Conversely, STAT1-activating cytokines (eg, IL-27, IFN-γ) inhibit TH-17 commitment. Here, we evaluate the impact of STAT1 ablation on TH-17 cells during experimental arthritis and relate this to IL-17A-associated pathology. Methods Antigen-induced arthritis (AIA) was established in wild type (WT), gp130F/F mice displaying hyperactive gp130-mediated STAT signalling and the compound mutants gp130F/F:Stat1−/− and gp130F/F: Il17a−/− mice. Joint pathology and associated peripheral TH-17 responses were compared. Results Augmented gp130/STAT3 signalling enhanced TH-17 commitment in vitro and exacerbated joint pathology. Ablation of STAT1 in gp130F/F mice (gp130F/F: Stat1−/− ) promoted the hyperexpansion of TH-17 cells in vitro and in vivo during AIA. Despite this heightened peripheral TH-17 cell response, disease severity and the number of joint-infiltrating T-cells were comparable with that of WT mice. Thus, gp130-mediated STAT1 activity within the inflamed synovium controls T-cell trafficking and retention. To determine the contribution of IL-17A, we generated gp130F/F:IL-17a−/− mice. Here, loss of IL-17A had no impact on arthritis severity. Conclusions Exacerbated gp130/STAT-driven disease in AIA is associated with an increase in joint infiltrating T-cells but synovial pathology is IL-17A independent

Metabolomics and lipidomics reveal perturbation of sphingolipid metabolism by a novel anti-trypanosomal 3-(oxazolo[4,5-b]pyridine-2-yl)anilide

Introduction: Trypanosoma brucei is the causative agent of human African trypanosomiasis, which is responsible for thousands of deaths every year. Current therapies are limited and there is an urgent need to develop new drugs. The anti-trypanosomal compound, 3-(oxazolo[4,5-b]pyridine-2-yl)anilide (OXPA), was initially identified in a phenotypic screen and subsequently optimized by structure–activity directed medicinal chemistry. It has been shown to be non-toxic and to be active against a number of trypanosomatid parasites. However, nothing is known about its mechanism of action. Objective: Here, we have utilized an untargeted metabolomics approach to investigate the biochemical effects and potential mode of action of this compound in T. brucei. Methods: Total metabolite extracts were analysed by HILIC-chromatography coupled to high resolution mass spectrometry. Results: Significant accumulation of ceramides was observed in OXPA-treated T. brucei. To further understand drug-induced changes in lipid metabolism, a lipidomics method was developed which enables the measurement of hundreds of lipids with high throughput and precision. The application of this LC–MS based approach to cultured bloodstream-form T. brucei putatively identified over 500 lipids in the parasite including glycerophospholipids, sphingolipids and fatty acyls, and confirmed the OXPA-induced accumulation of ceramides. Labelling with BODIPY-ceramide further confirmed the ceramide accumulation following drug treatment. Conclusion: These findings clearly demonstrate perturbation of ceramide metabolism by OXPA and indicate that the sphingolipid pathway is a promising drug target in T. brucei.No Full Tex

Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure