An Adult`s Vitiligo in Estonia: Study of 155 Patients

Background: Vitiligo is a common depigmentary disorder characterized by white patches of the skin, hair and mucous membranes due to selective destruction of melanocytes. Objective: The objective of this study was to analyze the clinical characteristics, coexisting diseases, presence of autoantibodies and autoimmune polyglandular syndrome (APS) in Estonian adult vitiligo patients. Methods:Adult patients with vitiligo were called to participate in the study at the Dermatology Department of Tartu University from January 2005 to July 2008. One hundred fifty five subjects were examined in 141 of those the level of thyroid peroxidase antibodies (TPO-Ab), gastric parietal cell antibodies (PCA), antinuclear antibodies (ANA), antiadrenal cortex antibodies (AAA) and rheumatoid factor (RF) in blood were measured. Results: Study group (mean age 44.9 years, mean age of vitiligo onset 28.5 years, mean duration of vitiligo 16.9 years) consisted of 44 males and 111 females. Vitiligo vulgaris was the most common clinical type (81.3%), followed by acrofacial, focal, segmental and universal vitiligo. Two-thirds of subjects reported a coexisting disease and 36.7% had one or more disease of autoimmune origin. The presence of autoantibodies was established in 49.6%. TPO-Ab was found in 36.9%, PCA in 14.2%, ANA and AAA both in 2.8% and positive RF in 7.8% cases. 17 subjects had APS 3, 35 had subclinical APS 3 and two subjects had APS 4. Conclusions: Vitiligo vulgaris was the most frequent clinical type. Vitiligo was associated with other autoimmune diseases, the presence of autoantibodies in the blood was frequent (especially TPO-Ab) and many subjects had APS

Gene expression analysis of the corticotrophin-releasing Hormone-proopiomelanocortin system in psoriasis skin biopsies

The corticotrophin-releasing hormone-proopiomelano-cortin (CRH-POMC) system in the skin coordinates pigmentation and the immune response. The aim of this study was to evaluate the regulatory role of the neuroendocrine system in the pathogenesis of psoriasis. Using quantitative real-time-PCR, mRNA expression levels of 15 genes related to the CRH-POMC system were measured in punch biopsies from lesional and non-lesional skin of patients with psoriasis and from skin of healthy control subjects. Statistically significant up-regulation of POMC, CRH receptor type 1, melanin-concentrating hormone receptor (MCHR1) and melanocortin receptors 2, 3 and 4 mRNA expression in lesional and in non-lesional skin compared with healthy control samples were established. Tyrosinase (TYR), T(Y)RP-1 and ASIP genes were statistically significantly down-regulated in lesional and non-lesional skin of psoriasis samples compared with healthy subjects. The up-regulation of POMC, melanocortin receptors, CRH receptor type 1 and MCHR1 in the lesional and non-lesional skin of psoriasis patients supports the importance of the local CRH-POMC system in the pathogenesis of psoriasis

The PRO2268 Gene as a Novel Susceptibility Locus for Vitiligo

Letter to the edito

Polymorphisms in the ATG16L1 Gene are associated with psoriasis vulgaris

Letter to the Edito

Polymorphisms in Toll-like receptor genes are associated with vitiligo

Background: The members of Toll-like receptor (TLR) family are responsible for recognizing various molecular patterns associated with pathogens. Their expression is not confined to immune cells and have been detected in skin cells such as keratinocytes and melanocytes. As part of a generated response to pathogens, TLRs are involved in inducing inflammatory mediators to combat these threats. It is therefore not surprising that TLRs have been implicated in inflammatory skin diseases, including atopic dermatitis and psoriasis. Likewise, as key players in autoimmunity, they have been associated with a number of autoimmune diseases. Based on this, the role of TLRs in vitiligo could be suspected, but is yet to be clearly established. Methods: In order to conduct a genetic association analysis, 30 SNPs were selected from TLR1-TLR8 and TLR10 regions to be genotyped in Estonian case-control cohort consisting of 139 vitiligo patients and 307 healthy control individuals. The patients were further analyzed in subgroups based on sex, age of onset, occurrence of vitiligo among relatives, extent of depigmented areas, vitiligo progression activity, appearance of Köbner's phenomenon, existence of halo naevi, and incidence of spontaneous repigmentation. Results: The most notable finding came with SNP rs179020 situated in TLR7 gene, that was associated in entire vitiligo (Padj = 0.0065) and also several subgroup analyses. Other single marker and haplotype analyses pointed to TLR3, TLR4, and TLR10 genes. Conclusions: This study investigated the genetic regions of nine TLR genes in relation to vitiligo susceptibility. The main results were the associations of TLR7 SNPs with vitiligo, while several other associations were obtained from the remaining TLR gene regions. This suggests that in addition to other inflammatory skin diseases, TLRs affect the development of vitiligo, thus making them interesting targets for future research

Large scale extreme risk assessment using copulas: an application to drought events under climate change for Austria

Droughts pose a significant challenge to farmers, insurers as well as governments around the world and the situation is expected to worsen in the future due to climate change. We present a large scale drought risk assessment approach that can be used for current and future risk management purposes. Our suggested methodology is a combination of a large scale agricultural computational modelling -, extreme value-, as well as copula approach to upscale local crop yield risks to the national scale. We show that combining regional probabilistic estimates will significantly underestimate losses if the dependencies between regions during drought events are not taken explicitly into account. Among the many ways to use these results it is shown how it enables the assessment of current and future costs of subsidized drought insurance in Austria

Expression of Class II Cytokine Genes in Children’s Skin

Immune regulation of the skin plays an important role in susceptibility and development of illnesses. The aim of our study was to localise the interleukin (IL)-10 family of cytokines, in children’s skin and to determine possible age-related differences in the expression level. The mRNA expression level of IL10, IL19, IL20, IL22, IL24, IL26, IL28B, IL29 and their receptors IL10RA, IL10RB, IL20RA, IL20RB, IL22RA1, IL22RA2, IL28RA was compared in skin biopsies of children and adults and in childrens’ skin cells by quantitative real-time PCR (qRT-PCR). Immunohistochemistry was performed to confirm the qRT-PCR findings. We found age-related differences in the expression of IL10RB, IL20, IL20RA, IL22RA1, IL22RA2, IL26 and IL28RA genes. Cell type-dependent expression of IL10 family cytokines was apparent in the skin. In addition to previously known differences in systemic immunological response of adults and children, the present results reveal differences in immune profile of adult and juvenile skin

Promoter polymorphism -119C/G in MYG1 (C12orf10) gene is related to vitiligo susceptibility and Arg4Gln affects mitochondrial entrance of Myg1

<p>Abstract</p> <p>Background</p> <p><it>MYG1 </it>(<it>Melanocyte proliferating gene 1</it>, also C12orf10 in human) is a ubiquitous nucleo-mitochondrial protein, involved in early developmental processes and in adult stress/illness conditions. We recently showed that <it>MYG1 </it>mRNA expression is elevated in the skin of vitiligo patients. Our aim was to examine nine known polymorphisms in the <it>MYG1 </it>gene, to investigate their functionality, and to study their association with vitiligo susceptibility.</p> <p>Methods</p> <p>Nine single nucleotide polymorphisms (SNPs) in the <it>MYG1 </it>locus were investigated by SNPlex assay and/or sequencing in vitiligo patients (n = 124) and controls (n = 325). <it>MYG1 </it>expression in skin biopsies was detected by quantitative-real time PCR (Q-RT-PCR) and polymorphisms were further analysed using luciferase and YFP reporters in the cell culture.</p> <p>Results</p> <p>Control subjects with -119G promoter allele (rs1465073) exhibited significantly higher <it>MYG1 </it>mRNA levels than controls with -119C allele (<it>P </it>= 0.01). Higher activity of -119G promoter was confirmed by luciferase assay. Single marker association analysis showed that the -119G allele was more frequent in vitiligo patients (47.1%) compared to controls (39.3%, <it>P </it>< 0.05, OR 1.37, 95%CI 1.02-1.85). Analysis based on the stage of progression of the vitiligo revealed that the increased frequency of -119G allele occurred prevalently in the group of patients with active vitiligo (n = 86) compared to the control group (48.2% <it>versus </it>39.3%, <it>P </it>< 0.05; OR 1.44, 95%CI 1.02-2.03). Additionally, we showed that glutamine in the fourth position (in Arg4Gln polymorphism) completely eliminated mitochondrial entrance of YFP-tagged Myg1 protein in cell culture. The analysis of available EST, cDNA and genomic DNA sequences revealed that Myg1 4Gln allele is remarkably present in human populations but is never detected in homozygous state according to the HapMap database.</p> <p>Conclusions</p> <p>Our study demonstrated that both <it>MYG1 </it>promoter polymorphism -119C/G and Arg4Gln polymorphism in the mitochondrial signal of Myg1 have a functional impact on the regulation of the <it>MYG1 </it>gene and promoter polymorphism (-119C/G) is related with suspectibility for actively progressing vitiligo.</p

An ecological time-series study of heat-related mortality in three European cities

BACKGROUND: Europe has experienced warmer summers in the past two decades and there is a need to describe the determinants of heat-related mortality to better inform public health activities during hot weather. We investigated the effect of high temperatures on daily mortality in three cities in Europe (Budapest, London, and Milan), using a standard approach. METHODS: An ecological time-series study of daily mortality was conducted in three cities using Poisson generalized linear models allowing for over-dispersion. Secular trends in mortality and seasonal confounding factors were controlled for using cubic smoothing splines of time. Heat exposure was modelled using average values of the temperature measure on the same day as death (lag 0) and the day before (lag 1). The heat effect was quantified assuming a linear increase in risk above a cut-point for each city. Socio-economic status indicators and census data were linked with mortality data for stratified analyses. RESULTS: The risk of heat-related death increased with age, and females had a greater risk than males in age groups > or =65 years in London and Milan. The relative risks of mortality (per degrees C) above the heat cut-point by gender and age were: (i) Male 1.10 (95%CI: 1.07-1.12) and Female 1.07 (1.05-1.10) for 75-84 years, (ii) M 1.10 (1.06-1.14) and F 1.08 (1.06-1.11) for > or = or =85 years in Budapest (> or =24 degrees C); (i) M 1.03 (1.01-1.04) and F 1.07 (1.05-1.09), (ii) M 1.05 (1.03-1.07) and F 1.08 (1.07-1.10) in London (> or =20 degrees C); and (i) M 1.08 (1.03-1.14) and F 1.20 (1.15-1.26), (ii) M 1.18 (1.11-1.26) and F 1.19 (1.15-1.24) in Milan (> or =26 degrees C). Mortality from external causes increases at higher temperatures as well as that from respiratory and cardiovascular disease. There was no clear evidence of effect modification by socio-economic status in either Budapest or London, but there was a seemingly higher risk for affluent non-elderly adults in Milan. CONCLUSION: We found broadly consistent determinants (age, gender, and cause of death) of heat related mortality in three European cities using a standard approach. Our results are consistent with previous evidence for individual determinants, and also confirm the lack of a strong socio-economic gradient in heat health effects currently in Europe

Exploring the potential of phone call data to characterize the relationship between social network and travel behavior

[EN] Social network contacts have significant influence on individual travel behavior. However, transport models rarely consider social interaction. One of the reasons is the difficulty to properly model social influence based on the limited data available. Non-conventional, passively collected data sources, such as Twitter, Facebook or mobile phones, provide large amounts of data containing both social interaction and spatiotemporal information. The analysis of such data opens an opportunity to better understand the influence of social networks on travel behavior. The main objective of this paper is to examine the relationship between travel behavior and social networks using mobile phone data. A huge dataset containing billions of registers has been used for this study. The paper analyzes the nature of co-location events and frequent locations shared by social network contacts, aiming not only to provide understanding on why users share certain locations, but also to quantify the degree in which the different types of locations are shared. Locations have been classified as frequent (home, work and other) and non-frequent. A novel approach to identify co-location events based on the intersection of users' mobility models has been proposed. Results show that other locations different from home and work are frequently associated to social interaction. Additionally, the importance of non-frequent locations in co-location events is shown. Finally, the potential application of the data analysis results to improve activity-based transport models and assess transport policies is discussed.The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no 318367 (EUNOIA project) and no 611307 (INSIGHT project). The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educacion, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013-2017.Picornell Tronch, M.; Ruiz Sánchez, T.; Lenormand, M.; Ramasco, JJ.; Dubernet, T.; Frías-Martínez, E. (2015). Exploring the potential of phone call data to characterize the relationship between social network and travel behavior. Transportation. 42(4):647-668. https://doi.org/10.1007/s11116-015-9594-1S647668424Ahas, R., Aasa, A., Silm, S., Tiru, M.: Daily rhythms of suburban commuters’ movements in the tallinn metropolitan area: case study with mobile positioning data. Transp. Res. Part C 18, 45–54 (2010)Arentze, T.,Timmermans, H. J.: social networks, social interactions and activity-travel behavior: a framework for micro-simulation. Paper presented at the 85th annual meeting of the Transportation Research Board, Washington, D. C., Jan 2006 (2006)Arentze, T., Timmermans, H.: Social networks, social interactions, and activity-travel behavior: a framework for microsimulation. Environ. Plan. 35, 1012–1027 (2008)Axhausen, K.W.: Social networks and travel: some hypotheses. In: Donaghy, K.P., Poppelreuter, S., Rudinger, G. (eds.) Social Aspects of Sustainable Transport: Transatlantic Perspectives, pp. 90–108. Ashgate, Aldershot (2005)Bagrow, J.P., Lin, Y.-R.: Mesoscopic structure and social aspects of human mobility. PLoS One 7(5), 1–11 (2012)Bar-Gera, H.: Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: a case study from israel. Transp. Res. Part C 15(2007), 380–391 (2007)Becker, R.A., Cáceres, R., Hanson, K., Loh, J.M., Urbanek, S., Varshavsky, A., Volinsky, C.: A tale of one city: using cellular network data for urban planning. Pervasive Comput. IEEE 10(4), 18–26 (2011)Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439, 462 (2006)Caceres, N., Wideberg, J.P., Benitez, F.G.: Deriving origin–destination data from a mobile phone network. IET Intell. Transp. Syst. 1(1), 5–26 (2007)Caceres, N., Wideberg, J.P., Benitez, F.G.: Review of traffic data estimations extracted from cellular networks. IET Intell. Transp. Syst. 2(3), 179–192 (2008)Caceres, N., Romero, L.M., Benitez, F.G., Castillo, J.M.D.: Traffic flow estimation models using cellular phone data. IEEE Trans. Intell. Transp. Syst. 13(3), 1430–1441 (2012)Calabrese, F., Pereira, F. C., Lorenzo, G. D., Liu, L., Ratti, C.: The geography of taste: analyzing cell-phone mobility and social events. In: Proceedings of IEEE International Conference on Pervasive Computing (2010)Calabrese, F., Smoreda, Z., Blondel, V.D., Ratti, C.: Interplay between telecommunications and face-to-face interactions: a study using mobile phone data. PLoS One 6(7), e20814 (2011a). doi: 10.1371/journal.pone.0020814Calabrese, F., Lorenzo, G.D., Liu, L., Ratti, C.: Estimating origin-destination flows using mobile phone location data. Pervasive Comput. IEEE 10(4), 36–44 (2011b)Carrasco, J.A., Miller, E.J.: Exploring the propensity to perform social activities: social networks approach. Transportation 33, 463–480 (2006)Carrasco, J.A., Hogan, B., Wellman, B., Miller, E.J.: Collecting social network data to study social activity-travel behaviour: an egocentric approach. Environ. Plan. B 35(6), 961–980 (2008a)Carrasco, J.A., Hogan B., Wellman B., Miller E. J.: Agency in social activity and ICT interactions: The role of social networks in time and space, Tijdschrift voor Economische en Sociale Geografie (J. Eco. Soc. Geogr.), 99(5), 562–583 (2008b)Carrasco, J.A., Miller, E.J., Wellman, B.: How far and with whom do people socialize? Empirical evidence about the distance between social network members. Transp. Res. Rec. 2076, 114–122 (2008b)Carrasco, J.A., Miller, E.J.: The social dimension in action: a multilevel, personal networks model of social activity frequency. Transp. Res. Part A 43(1), 90–104 (2009)Chen, C., Mei, Y.: Does distance still matter in facilitating social ties? The roles of mobility patterns and the built environment. Presented at 93rd TRB annual meeting (2014)Cho E., Myers S.A., Leskovek J.: Friendship and mobility: user movement in location-based social networks. In: KDD ‘11 Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1082–1090 (2011)Clifton, K.J.: The social context of travel behavior. In: Zmud, J., et al. (eds.) Transport Survey Methods: Best Practice for Decision Making, pp. 441–448. Emerald Press, London (2013)Do T., Gatica-Perez D.: Contextual conditional models for smartphone-based human mobility prediction. In: Proceedings ACM International Conference on Ubiquitous Computing, Pittsburgh, Sept (2012)Doyle, J., Hung, P., Kelly, D., Mcloone, S., Farrell, R.: Utilising mobile phone billing records for travel mode discovery. ISSC 2011, Trinity College Dublin, June (2011)Dubernet, T., Axhausen K. W.: Solution concepts for the simulation of household-level joint descision making in multi-agent travel simulation tools, paper presented at the 14th Swiss Transport Research Conference (STRC), Ascona (2014)Dugundji, E., Walker, J.: Discrete choice with social and spatial network interdependencies: an empirical example using mixed GEV models with field and “panel” effects. Transp. Res. Rec. 1921, 70–78 (2005)Eagle, N., Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. Proc. Natl. Acad. Sci. (PNAS) 106(36), 15274–15278 (2009)González, M.C., Hidalgo, C.A., Barabási, A.-L.: Understanding individual human mobility patterns. Nature 453(2008), 779–782 (2008)Gould, J.: Cell phone enabled travel surveys: the medium moves the message. In: Zmud, J., et al. (eds.) Transport Survey Methods: Best Practice for Decision Making, pp. 51–70. Emerald Press, Bingley (2013)Habib, K.N., Carrasco, J.A.: Investigating the role of social networks in start time and duration of activities: a trivariate simultaneous econometric model. Transportation Research Record: Journal of the Transportation Research Board 2230, 1–8 (2011)Hackney, Jeremy K., Kay W. Axhausen: An agent model of social network and travel behavior interdependence. Paper presented at the 11th international conference on Travel Behaviour Research, Kyoto, Aug (2006)Hackney, J., Marchal, F.: A model for coupling multi-agent social interactions and traffic simulation, in: TRB 2009 annual meeting (2009)Hackney, J., Marchal, F.: A coupled multi-agent microsimulation of social interactions and transportation behavior. Transp. Res. Part A 45, 296–309 (2011)Horni, A.: Destination choice modeling of discretionary activities in transport microsimulations, Ph.D. Thesis, ETH Zurich, Zurich (2013)Isaacman, S.,Becker, R., Caceres, R., Kobourov, S., Martonosi, M., Rowland, J., Varshavsky, A.: Identifying important places in people’s lives from cellular network data. In: Procedings International Conference on Pervasive Computing, San Francisco, June (2011)Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. Commun. Mag. IEEE 48(9), 140–150 (2010)Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., Van Alstyne, M.: Computational Social Science. Science 323, 721 (2009)Ma, H., Ronald, N., Arentze, T.A., Timmermans, H.J.P.: New credit mechanism for semicooperative agent-mediated joint activity-travel scheduling. Transp. Res. Rec. 2230, 104–110 (2011)Ma, H., Arentze, T. A., Timmermans, H. J. P.: Incorporating selfishness and altruism into dynamic joint activity-travel scheduling. Paper presented at the 13th international conference on Travel Behaviour Research (IATBR), Toronto, July (2012)Marchal, F., Nagel, K.: Allowed cooperative agents in a microsimulation to share information with each other about activity locations and about other agents, in order to optimize trip chains (2006)Molin, E.J.E., Arentze, T.A., Timmermans, H.J.P.: Social activities and travel demands : a model-based analysis of social-network data. Transp. Res. Rec. 2082, 168–175 (2007)Moore, J., Carrasco, J.A., Tudela, A.: Exploring the links between personal networks, time use, and the spatial distribution of social contacts. Transportation 40(4), 773–788 (2013)Onnela, J.-P., Saramaki, J., Hyvonen, J., Szabo, G., Lazer, D., et al.: Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. U.S.A. 104, 7332–7336 (2007)Páez, A., Scott, D.M.: Social influence on travel behavior: a simulation example of the decision to telecommute. Environ. Plan. A 39(3), 647–665 (2007)Phithakkitnukoon, S., Calabrese, F., Smoreda, Z., Ratti, C.: Out of sight out of mind: how our mobile social network changes during migration. Proceedings of the IEEE International Conference on Social Computing, pp. 515–520. Cambridge University Press, Cambridge (2011)Phithakkitnukoon, S., Smoreda, Z., Olivier, P.: Socio-geography of human mobility: a study using longitudinal mobile phone data. PLoS One 7(6), e39253 (2012). doi: 10.1371/journal.pone.0039253Ronald, N.A., Arentze, T.A., Timmermans, H.J.P.: Modeling social interactions between individuals for joint activity scheduling. Transp. Res. Part B 46, 276–290 (2012a)Ronald, N.A., Dignum, V., Jonker, C., Arentze, T.A., Timmermans, H.J.P.: On the engineering of agent-based simulations of social activities with social networks. Inf. Softw. Technol. 54(6), 625–638 (2012b)Rose, G.: Mobile phones as traffic probes: practices, prospects and issues. Transp. Rev. 26(3), 275–291 (2006)Sharmeen, F., Arentze, T., Timmermans, H.: A multilevel path analysis of social network dynamics and the mutual interdependencies between face-to-face and ICT modes of social interaction in the context of life-cycle events. In: Roorda, M.J., Miller, E.J. (eds.) Travel Behaviour Research: Current Foundations, Future Prospects, pp. 411–432. Lulu Press, Toronto (2013)Sharmeen, F., Arentze, T.A., Timmermans, H.J.P.: Dynamics of face-to-face social interaction frequency: role of accessibility, urbanization, changes in geographical distance and path dependence. J. Transp. Geogr. 34, 211–220 (2014)Silm, S., Ahas, R.: The seasonal variability of population in estonian municipalities. Environ. Plan. A 42, 2527–2546 (2010)Silvis, J., Niemeier, D., D’Souza, R.: Social networks and travel behavior: report from an integrated travel diary. Paper presented at the 11th international conference on Travel Behaviour Research, Kyoto, Aug (2006)Sobolevsky, S., Szell, M., Campari, R., Couronné, T., Smoreda, Z., et al.: Delineating geographical regions with networks of human interactions in an extensive set of countries. PLoS One 8(12), e81707 (2013)Sohn, K., Kim, D.: Dynamic origin–destination flow estimation using cellular communication system. IEEE Trans. Veh. Technol. 57(5), 2703–2713 (2008)Song, C., Koren, T., Wang, P., Barabási, A.-L.: Modelling the scaling properties of human mobility. Nat. Phys. 6(2010), 818–823 (2010a)Song, C., Qu, Z., Blumm, N., Barabási, L.-L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010b)Steenbruggen, J., Borzacchiello, M.T., Nijkamp, P., Scholten, H.: Mobile phone data from gsm networks for traffic parameter and urban spatial pattern assessment: A review of applications and opportunities. GeoJournal 78, 223–243 (2011). doi: 10.1007/s10708-011-9413-yVan den Berg, P., Arentze, T., Timmermans, H.J.P.: A path analysis of social networks, telecommunication and social activity–travel patterns. Transp. Res. Part C 26(2013), 256–268 (2013)Wang, H., Calabrese, F., Lorenzo, G. D., Ratti, C.: Transportation mode inference from anonymized and aggregated mobile phone call detail records. In: 13th international IEEE annual conference on intelligent transportation systems, 318–323 (2010)White, J. and Wells, I.: Extracting origin destination information from mobile phone data. Road transport information and Control, 19–21 Mar (2002)Yim, Y.: The state of cellular probes. California PATH Working Paper, UCB-ITS-PRR-2003-25 (2003)Ythier, J., Walker, J.L., Bierlaire, M.: The influence of social contacts and communication use on travel behavior: a smartphone-based study. In: Transportation Research Board annual meeting (2013