Een protocollaire rechtshandhaving

De film Minority Report (2002) speelt zich af in het Washington D.C. van 2054. In die stad pakt de politie moordenaars op voordat ze de misdaad hebben gepleegd. Ze maakt hiervoor gebruik van drie ‘pre-cogs’ die over de bijzondere gave beschikken dat ze toekomstige moorden kunnen zien. Cruciaal in dit systeem is de algemene veronderstelling met betrekking tot het gedrag van de toekomstige daders. Die veronderstelling is een uitdrukking van een mechanistische en lineaire opvatting van ons handelen. Minority Report geeft daarmee een perfect beeld van wat we de kern van de huidige veiligheidspolitiek kunnen noemen. We hoeven daarvoor slechts te verwijzen naar bewakingstechnieken, zoals automatische detectie en patroonherkenningcamera’s die tot doel hebben criminaliteit in de openbare ruimte te voorkomen. Geïnstrueerd met protocollen identificeren zij bepaalde personen als mogelijke daders voordat zij een delict hebben gepleegd. De film roept verschillende dilemma’s op. Hoe veroordeel je een persoon voor een daad die hij niet heeft gepleegd? Hoe voorkom je dat er onjuiste voorspellingen worden gedaan? Voor een antwoord op deze vragen moeten we de relatie tussen politiek en leven verbinden met de uitzonderingstoestand, zoals die door de Italiaanse filosoof Agamben wordt gedefinieerd. Dan wordt duidelijk hoe het leven zich niet meer volgens wetten of regels, maar langs protocollen normaliseert

Big Data Policing:The Use of Big Data and Algorithms by the Netherlands Police

In recent years, the rise of big data has revolutionized many domains, including policing. Research is lacking, however, on the various ways in which the police use big data applications. This study provides new insights into the ways the Netherlands Police currently use big data and algorithmic applications. Based on a novel data source—job vacancies in the IT domain for the Netherlands Police—we distinguish three areas in which big data is used: frontline policing, criminal investigations, and intelligence. Our research shows that the use of big data by the Netherlands Police mainly involves relatively simple applications and that—in contrast to police forces in the USA—big data applications with the objective of assessing risks are the least common. The research also shows that big data policing leads to greater discretionary powers for police functions such as software developers and network designers

Big Data Policing:The Use of Big Data and Algorithms by the Netherlands Police

In recent years, the rise of big data has revolutionized many domains, including policing. Research is lacking, however, on the various ways in which the police use big data applications. This study provides new insights into the ways the Netherlands Police currently use big data and algorithmic applications. Based on a novel data source—job vacancies in the IT domain for the Netherlands Police—we distinguish three areas in which big data is used: frontline policing, criminal investigations, and intelligence. Our research shows that the use of big data by the Netherlands Police mainly involves relatively simple applications and that—in contrast to police forces in the USA—big data applications with the objective of assessing risks are the least common. The research also shows that big data policing leads to greater discretionary powers for police functions such as software developers and network designers

Big data in het veiligheidsdomein:Onderzoek naar big data-toepassingen bij de Nederlandse politie en de positieve effecten hiervan voor de politieorganisatie

In recent years, big data technology has revolutionised many domains, including policing. There is a lack of research, however, exploring which applications are used by the police, and the potential benefits of big data analytics for policing. Instead, literature about big data and policing predominantly focuses on predictive policing and its associated risks. The present paper provides new insights into the police’s current use of big data and algorithmic applications. We provide an up-to-date overview of the various applications of big data by the National Police in the Netherlands. We distinguish three areas: uniformed police work, criminal investigation, and intelligence. We then discuss two positive effects of big data and algorithmic applications for the police organization: accelerated learning and the formation of a single police organizatio

IL-21 production by CD4+ effector T cells and frequency of circulating follicular helper T cells are increased in type 1 diabetes patients.

AIMS/HYPOTHESIS: Type 1 diabetes results from the autoimmune destruction of insulin-secreting pancreatic beta cells by T cells. Despite the established role of T cells in the pathogenesis of the disease, to date, with the exception of the identification of islet-specific T effector (Teff) cells, studies have mostly failed to identify reproducible alterations in the frequency or function of T cell subsets in peripheral blood from patients with type 1 diabetes. METHODS: We assessed the production of the proinflammatory cytokines IL-21, IFN-γ and IL-17 in peripheral blood mononuclear cells from 69 patients with type 1 diabetes and 61 healthy donors. In an additional cohort of 30 patients with type 1 diabetes and 32 healthy donors, we assessed the frequency of circulating T follicular helper (Tfh) cells in whole blood. IL-21 and IL-17 production was also measured in peripheral blood mononuclear cells (PBMCs) from a subset of 46 of the 62 donors immunophenotyped for Tfh. RESULTS: We found a 21.9% (95% CI 5.8, 40.2; p = 3.9 × 10(-3)) higher frequency of IL-21(+) CD45RA(-) memory CD4(+) Teffs in patients with type 1 diabetes (geometric mean 5.92% [95% CI 5.44, 6.44]) compared with healthy donors (geometric mean 4.88% [95% CI 4.33, 5.50]). Consistent with this finding, we found a 14.9% increase in circulating Tfh cells in the patients (95% CI 2.9, 26.9; p = 0.016). CONCLUSIONS/INTERPRETATION: These results indicate that increased IL-21 production is likely to be an aetiological factor in the pathogenesis of type 1 diabetes that could be considered as a potential therapeutic target.This work was supported by the JDRF UK Centre for Diabetes - Genes, Autoimmunity and Prevention (D-GAP; 4-2007-1003) in collaboration with M. Peakman and T. Tree at King’s College London, the JDRF, the Wellcome Trust (WT; WT061858/091157 and 083650/Z/07/Z) and the National Institute for Health Research Cambridge Biomedical Research Centre (CBRC). The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). RCF is funded by a JDRF post-doctoral fellowship (3-2011-374). CW is funded by the Wellcome Trust (088998). The funding organisations had no involvement with the design and conduct of the study; collection,management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.This is the final published version. It first appeared at http://link.springer.com/article/10.1007%2Fs00125-015-3509-8

T1DBase: integration and presentation of complex data for type 1 diabetes research

T1DBase () [Smink et al. (2005) Nucleic Acids Res., 33, D544–D549; Burren et al. (2004) Hum. Genomics, 1, 98–109] is a public website and database that supports the type 1 diabetes (T1D) research community. T1DBase provides a consolidated T1D-oriented view of the complex data world that now confronts medical researchers and enables scientists to navigate from information they know to information that is new to them. Overview pages for genes and markers summarize information for these elements. The Gene Dossier summarizes information for a list of genes. GBrowse [Stein et al. (2002) Genome Res., 10, 1599–1610] displays genes and other features in their genomic context, and Cytoscape [Shannon et al. (2003) Genome Res., 13, 2498–2504] shows genes in the context of interacting proteins and genes. The Beta Cell Gene Atlas shows gene expression in β cells, islets, and related cell types and lines, and the Tissue Expression Viewer shows expression across other tissues. The Microarray Viewer shows expression from more than 20 array experiments. The Beta Cell Gene Expression Bank contains manually curated gene and pathway annotations for genes expressed in β cells. T1DMart is a query tool for markers and genotypes. PosterPages are ‘home pages’ about specific topics or datasets. The key challenge, now and in the future, is to provide powerful informatics capabilities to T1D scientists in a form they can use to enhance their research