Polynomial cubic splines with tension properties

In this paper we present a new class of spline functions with tension properties. These splines are composed by polynomial cubic pieces and therefore are conformal to the standard, NURBS based CAD/CAM systems

Microfluidic Evolution-On-A-Chip Reveals New Mutations that Cause Antibiotic Resistance

Microfluidic devices can mimic naturally occurring microenvironments and create microbial population heterogeneities ranging from planktonic cells to biofilm states. The exposure of such populations to spatially organized stress gradients can promote their adaptation into complex phenotypes, which are otherwise difficult to achieve with conventional experimental setups. Here a microfluidic chip that employs precise chemical gradients in consecutive microcompartments to perform microbial adaptive laboratory evolution (ALE), a key tool to study evolution in fundamental and applied contexts is described. In the chip developed here, microbial cells can be exposed to a defined profile of stressors such as antibiotics. By modulating this profile, stress adaptation in the chip through resistance or persistence can be specifically controlled. Importantly, chip‐based ALE leads to the discovery of previously unknown mutations in Escherichia coli that confer resistance to nalidixic acid. The microfluidic device presented here can enhance the occurrence of mutations employing defined micro‐environmental conditions to generate data to better understand the parameters that influence the mechanisms of antibiotic resistance

Hereditary complement factor I deficiency

Summary We describe four cases (from three families) of hereditary factor I deficiency, bringing the total number of cases now reported to 23. In one family there are two affected siblings: one has suffered recurrent pyogenic infections; the other is asymptomatic. In the second family, the patient had recurrent pyogenic infections and a self-limiting vasculitic illness; in the third family, the patient suffered recurrent pyogenic and neisserial infections. All four patients had markedly reduced concentrations of C3 in the serum (family 1 propositus: 28%; family 1 asymptomatic sibling: 15%; family 2: 31%; and family 3: 31 % normal human serum) which was in the form of C3b. Low lgG2 levels may occur in primary C3 deficiency, and reduction in lgG2 concentration to 1.14 g/l (normal: 1.30-5.90 g/l) was found in the patient from family 2. Using radioligand binding assays, we demonstrated increased binding of C3b to erythrocytes in a patient with factor I deficiency. This C3b could not be cleaved by autologous serum but could be cleaved by normal serum or purified factor I. We review and compare the published cases of C3, factor H and factor I deficienc

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

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

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

lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers

We assume data sampled from a mixture of d-dimensional linear subspaces with spherically symmetric distributions within each subspace and an additional outlier component with spherically symmetric distribution within the ambient space (for simplicity we may assume that all distributions are uniform on their corresponding unit spheres). We also assume mixture weights for the different components. We say that one of the underlying subspaces of the model is most significant if its mixture weight is higher than the sum of the mixture weights of all other subspaces. We study the recovery of the most significant subspace by minimizing the lp-averaged distances of data points from d-dimensional subspaces, where p>0. Unlike other lp minimization problems, this minimization is non-convex for all p>0 and thus requires different methods for its analysis. We show that if 0<p<=1, then for any fraction of outliers the most significant subspace can be recovered by lp minimization with overwhelming probability (which depends on the generating distribution and its parameters). We show that when adding small noise around the underlying subspaces the most significant subspace can be nearly recovered by lp minimization for any 0<p<=1 with an error proportional to the noise level. On the other hand, if p>1 and there is more than one underlying subspace, then with overwhelming probability the most significant subspace cannot be recovered or nearly recovered. This last result does not require spherically symmetric outliers.Comment: This is a revised version of the part of 1002.1994 that deals with single subspace recovery. V3: Improved estimates (in particular for Lemma 3.1 and for estimates relying on it), asymptotic dependence of probabilities and constants on D and d and further clarifications; for simplicity it assumes uniform distributions on spheres. V4: minor revision for the published versio

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-

Changes in Electronic Structure and Chemical Bonding upon Crystallization of the Phase Change Material GeSb₂Te₄

High-resolution photoelectron spectroscopy of in situ prepared films of GeSb₂Te₄ reveals significant differences in electronic and chemical structure between the amorphous and the crystalline phase. Evidence for two different chemical environments of Ge and Sb in the amorphous structure is found. This observation can explain the pronounced property contrast between both phases and provides new insight into the formation of the amorphous state

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