Mobile Communication Signatures of Unemployment

The mapping of populations socio-economic well-being is highly constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess; thus the speed of which policies can be designed and evaluated is limited. However, recent studies have shown the value of mobile phone data as an enabling methodology for demographic modeling and measurement. In this work, we investigate whether indicators extracted from mobile phone usage can reveal information about the socio-economical status of microregions such as districts (i.e., average spatial resolution < 2.7km). For this we examine anonymized mobile phone metadata combined with beneficiaries records from unemployment benefit program. We find that aggregated activity, social, and mobility patterns strongly correlate with unemployment. Furthermore, we construct a simple model to produce accurate reconstruction of district level unemployment from their mobile communication patterns alone. Our results suggest that reliable and cost-effective economical indicators could be built based on passively collected and anonymized mobile phone data. With similar data being collected every day by telecommunication services across the world, survey-based methods of measuring community socioeconomic status could potentially be augmented or replaced by such passive sensing methods in the future

High-resolution measurements of face-to-face contact patterns in a primary school

Little quantitative information is available on the mixing patterns of children in school environments. Describing and understanding contacts between children at school would help quantify the transmission opportunities of respiratory infections and identify situations within schools where the risk of transmission is higher. We report on measurements carried out in a French school (6-12 years children), where we collected data on the time-resolved face-to-face proximity of children and teachers using a proximity-sensing infrastructure based on radio frequency identification devices. Data on face-to-face interactions were collected on October 1st and 2nd, 2009. We recorded 77,602 contact events between 242 individuals. Each child has on average 323 contacts per day with 47 other children, leading to an average daily interaction time of 176 minutes. Most contacts are brief, but long contacts are also observed. Contacts occur mostly within each class, and each child spends on average three times more time in contact with classmates than with children of other classes. We describe the temporal evolution of the contact network and the trajectories followed by the children in the school, which constrain the contact patterns. We determine an exposure matrix aimed at informing mathematical models. This matrix exhibits a class and age structure which is very different from the homogeneous mixing hypothesis. The observed properties of the contact patterns between school children are relevant for modeling the propagation of diseases and for evaluating control measures. We discuss public health implications related to the management of schools in case of epidemics and pandemics. Our results can help define a prioritization of control measures based on preventive measures, case isolation, classes and school closures, that could reduce the disruption to education during epidemics

E6-mediated activation of JNK drives EGFR signalling to promote proliferation and viral oncoprotein expression in cervical cancer

Human papillomaviruses (HPV) are a major cause of malignancy worldwide, contributing to ~5% of all human cancers including almost all cases of cervical cancer and a growing number of ano-genital and oral cancers. HPV-induced malignancy is primarily driven by the viral oncogenes, E6 and E7, which manipulate host cellular pathways to increase cell proliferation and enhance cell survival, ultimately predisposing infected cells to malignant transformation. Consequently, a more detailed understanding of viral-host interactions in HPV-associated disease offers the potential to identify novel therapeutic targets. Here, we identify that the c-Jun N-terminal kinase (JNK) signalling pathway is activated in cervical disease and in cervical cancer. The HPV E6 oncogene induces JNK1/2 phosphorylation in a manner that requires the E6 PDZ binding motif. We show that blockade of JNK1/2 signalling using small molecule inhibitors, or knockdown of the canonical JNK substrate c-Jun, reduces cell proliferation and induces apoptosis in cervical cancer cells. We further demonstrate that this phenotype is at least partially driven by JNK-dependent activation of EGFR signalling via increased expression of EGFR and the EGFR ligands EGF and HB-EGF. JNK/c-Jun signalling promoted the invasive potential of cervical cancer cells and was required for the expression of the epithelial to mesenchymal transition (EMT)-associated transcription factor Slug and the mesenchymal marker Vimentin. Furthermore, JNK/c-Jun signalling is required for the constitutive expression of HPV E6 and E7, which are essential for cervical cancer cell growth and survival. Together, these data demonstrate a positive feedback loop between the EGFR signalling pathway and HPV E6/E7 expression, identifying a regulatory mechanism in which HPV drives EGFR signalling to promote proliferation, survival and EMT. Thus, our study has identified a novel therapeutic target that may be beneficial for the treatment of cervical cancer

Big Data and Urban Governance

This chapter examines the ways in which big data is involved in the rise of smart cities. Mobile phones, sensors and online applications produce streams of data which are used to regulate and plan the city, often in real time, but which presents challenges as to how the city’s functions are seen and interpreted. Using a socio-technical approach, we offer a critical evaluation of the types of data being used in urban governance and their advantages and drawbacks in comparison to previous information systems. Using examples from New York and Abidjan, Côte d’Ivoire, we demonstrate how big data can both illuminate and obscure our understanding of urban development. We outline methodological considerations for the use of such data, offering conclusions towards the development of a critical urban data science