The politics of smart expectations: Interrogating the knowledge claims of smart mobility

This paper studies the performativity of smart mobility expectations in envisioning urban futures. Smart mobility, or ICT-enabled transport services, are increasingly considered a necessary ingredient for sustainability transitions in cities. Expectations of smart mobility’s contribution to such a transition are constituted by a strong belief in the transformative potential of data collection and use. These knowledge claims embedded in smart mobility expectations tend to be unchallenged, yet contribute to a particular future vision of urban mobility. Our empirical analysis, which draws on two empirical smart cycling case studies in Utrecht, the Netherlands, and Bordeaux, France, underlines the politics of such smart knowledge claims in two smart cycling projects and identifies distinct processes as to how such claims may shape and structure mobility futures. We observe intimate entanglements between what is being developed in terms of technologies and services; and the societal needs that the projects’ expectations promise to fulfil. At the same time, we witness a disentanglement of these interconnected knowledge claims when projects unfold, leaving the promise of (un)achieved societal benefits out of view. Indeed, smart knowledge claims carried strong inherent legitimacy in the cases studied, thereby risking to exclude non-smart alternatives

Protein Kinase C θ Is Critical for the Development of In Vivo T Helper (Th)2 Cell But Not Th1 Cell Responses

The serine/threonine-specific protein kinase C (PKC)-θ is predominantly expressed in T cells and localizes to the center of the immunological synapse upon T cell receptor (TCR) and CD28 signaling. T cells deficient in PKC-θ exhibit reduced interleukin (IL)-2 production and proliferative responses in vitro, however, its significance in vivo remains unclear. We found that pkc-θ−/− mice were protected from pulmonary allergic hypersensitivity responses such as airway hyperresponsiveness, eosinophilia, and immunoglobulin E production to inhaled allergen. Furthermore, T helper (Th)2 cell immune responses against Nippostrongylus brasiliensis were severely impaired in pkc-θ−/− mice. In striking contrast, pkc-θ−/− mice on both the C57BL/6 background and the normally susceptible BALB/c background mounted protective Th1 immune responses and were resistant against infection with Leishmania major. Using in vitro TCR transgenic T cell–dendritic cell coculture systems and antigen concentration-dependent Th polarization, PKC-θ–deficient T cells were found to differentiate into Th1 cells after activation with high concentrations of specific peptide, but to have compromised Th2 development at low antigen concentration. The addition of IL-2 partially reconstituted Th2 development in pkc-θ−/− T cells, consistent with an important role for this cytokine in Th2 polarization. Taken together, our results reveal a central role for PKC-θ signaling during Th2 responses

Smart Eco-CityDevelopment in Europe and China: Opportunities, Drivers and Challenges

The policy pointers presented in this report are the result of a three-year (2015-18) research project led by Federico Caprotti at the University of Exeter. The project, Smart Eco-Cities for a Green Economy: A Comparative Analysis of Europe and China, was delivered by a research consortium comprising scholars and researchers in the UK, China, the Netherlands, France, and Germany. The aim of the project was to investigate the way in which smart city and eco-city strategies are used to enable a transition towards digital and green economies. While previous work has considered smart cities and eco-cities as separate urban development models, the project considers them together for the first time. We use the term ‘the smart eco-city’ to focus on how green targets are now included in smart city development policies and strategies. This report presents a summary of policy pointers, or ‘lessons’, learned through our work on the cities we studied in the UK, China, the Netherlands, France and Germany. Specifically, we studied, in depth, the cities of Manchester, Amsterdam, Hamburg, Bordeaux, Shanghai, Shenzhen, Ningbo and Wuhan. This work included interviews with policymakers, urban municipal authorities, tech firm executives, and grassroots and community representatives and stakeholders. Our work also included intensive and in-depth qualitative analysis of documentary sources including policy and corporate reports and other materials.The research undertaken to produce this report was supported by funding from: the Economic and Social Research Council (ESRC) through research grant ES/ L015978/1; the National Natural Science Foundation of China, project number 71461137005; the Netherlands Organisation for Scientific Research (NWO) through research grant 467-14-153 and the Dutch Academy of Sciences (KNAW) through research grant 530-6CD108; the French National Research Agency (ANR) through research grant ANR-14-02; and the German Research Foundation DFG through research grant SP 1545/1-1

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Hereditary angioedema in children and adolescents - A consensus update on therapeutic strategies for German-speaking countries.

BACKGROUND/METHODS At a consensus meeting in August 2018, pediatricians and dermatologists from German-speaking countries discussed the therapeutic strategy for the treatment of pediatric patients with types I and II hereditary angioedema due to C1 inhibitor deficiency (HAE-C1-INH) for Germany, Austria, and Switzerland, taking into account the current marketing approval status. HAE-C1-INH is a rare disease that usually presents during childhood or adolescence with intermittent episodes of potentially life-threatening angioedema. Diagnosis as early as possible and an optimal management of the disease are important to avoid ineffective therapies and to properly treat swelling attacks. This article provides recommendations for developing appropriate treatment strategies in the management of HAE-C1-INH in pediatric patients in German-speaking countries. An overview of available drugs in this age group is provided, together with their approval status, and study results obtained in adults and pediatric patients. RESULTS/CONCLUSION Currently, plasma-derived C1 inhibitor concentrates have the broadest approval status and are considered the best available option for on-demand treatment of HAE-C1-INH attacks and for short- and long-term prophylaxis across all pediatric age groups in German-speaking countries. For on-demand treatment of children over 2 years of age, bradykinin-receptor icatibant is an alternative. For long-term prophylaxis in adolescents, the parenteral kallikrein inhibitor lanadelumab has recently been approved and can be recommended due to proven efficacy and safety

Representing complex data using localized principal components with application to astronomical data

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex''. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of localized versions of PCA, focusing on local principal curves and local partitioning algorithms. Furthermore we discuss projections other than the local principal components. When performing local dimension reduction for regression or classification problems it is important to focus not only on the manifold structure of the covariates, but also on the response variable(s). Local principal components only achieve the former, whereas localized regression approaches concentrate on the latter. Local projection directions derived from the partial least squares (PLS) algorithm offer an interesting trade-off between these two objectives. We apply these methods to several real data sets. In particular, we consider simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds), Lecture Notes in Computational Science and Engineering, Springer, 2007, pp. 180--204, http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-

Long-term follow-up of high-dose chemotherapy with autologous stem-cell transplantation and response-adapted whole-brain radiotherapy for newly diagnosed primary CNS lymphoma: results of the multicenter Ostdeutsche Studiengruppe Hämatologie und Onkologie OSHO-53 phase II study

Background We previously reported the results of a phase II study for patients with newly diagnosed primary central nervous system lymphoma treated with autologous peripheral blood stem-cell transplantation (aPBSCT) and response-adapted whole-brain radiotherapy (WBRT). Now, we update the initial results. Patients and methods From 1999 to 2004, 23 patients received high-dose methotrexate. In case of at least partial remission, high-dose busulfan/thiotepa (HD-BuTT) followed by aPBSCT was carried out. Patients refractory to induction or without complete remission after HD-BuTT received WBRT. Eight patients still alive in 2011 were contacted and Mini-Mental State Examination (MMSE) and the European Organisation for Research and Treatment of Cancer quality-of-life questionnaire (QLQ)-C30 were carried out. Results Of eight patients still alive, median follow-up is 116.9 months. Only one of nine irradiated patients is still alive with a severe neurologic deficit. In seven of eight patients treated with HD-BuTT, health condition and quality of life are excellent. MMSE and QLQ-C30 showed remarkably good results in patients who did not receive WBRT. All of them have a Karnofsky score of 90%-100%. Conclusions Follow-up shows an overall survival of 35%. In six of seven patients where WBRT could be avoided, no long-term neurotoxicity has been observed and all patients have an excellent quality of lif

A simulated annealing methodology for clusterwise linear regression

In many regression applications, users are often faced with difficulties due to nonlinear relationships, heterogeneous subjects, or time series which are best represented by splines. In such applications, two or more regression functions are often necessary to best summarize the underlying structure of the data. Unfortunately, in most cases, it is not known a priori which subset of observations should be approximated with which specific regression function. This paper presents a methodology which simultaneously clusters observations into a preset number of groups and estimates the corresponding regression functions' coefficients, all to optimize a common objective function. We describe the problem and discuss related procedures. A new simulated annealing-based methodology is described as well as program options to accommodate overlapping or nonoverlapping clustering, replications per subject, univariate or multivariate dependent variables, and constraints imposed on cluster membership. Extensive Monte Carlo analyses are reported which investigate the overall performance of the methodology. A consumer psychology application is provided concerning a conjoint analysis investigation of consumer satisfaction determinants. Finally, other applications and extensions of the methodology are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45745/1/11336_2005_Article_BF02296405.pd