¿Existe una clase de trabajadores que vive en la pobreza en los Países Bajos?

This contribution deals with the question of the e xistence of "working poor" in the Netherlands. The rest of the world tends to see the Netherlands as a success story. It is against this background that we investigate whether there are people in the Netherlands that are in paid employment, but are nevertheless confronted with problems of poverty. The statistical data available at the macro-level give clear indications of the e xistence of "working poor". In the light of this fact the issue of the "working poor" should be given a more prominent place on the political and trade union agenda. The trade unions, in particular, should play a far more active role. They should make more detailed studies of the problem, taking as their starting point the day-to-day experience of those affected, and should design measures that are commensurate with the interests of these people.Esta contribución trata sobre la el problema de la e xistencia de "trabajadores pobres" en Holanda. El resto del mundo tiende a considerar a Holanda como un éxito económico. Es en este conte xto en el que el artículo investiga sobre la e xistencia de trabajadores en Holanda que teniendo un trabajo remunerado se ven inmersos en los problemas de la pobreza. Los datos estadísticos disponibles a nivel macroeconómico dan indicaciones claras de la e xistencia de "trabajadores pobres". A la luz de estos datos, la cuestión de los "trabajadores pobres" debería tener un lugar más preeminente entre los puntos de la agenda de los sindicatos y los políticos. En particular, los sindicatos deberían jugar un papel más activo profundizando en el problema a través de estudios más detallados, que tomaran como punto de partida la experiencia cotidiana de las personas afectadas, y deberían desarrollar medidas que dieran respuesta a los intereses de dichas personas

Convolutional Neural Networks for Diabetic Retinopathy

The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a difficult and time consuming task. In this paper, we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify the intricate features involved in the classification task such as micro-aneurysms, exudate and haemorrhages on the retina and consequently provide a diagnosis automatically and without user input. We train this network using a high-end graphics processor unit (GPU) on the publicly available Kaggle dataset and demonstrate impressive results, particularly for a high-level classification task. On the dat

Effect of smoke-free legislation on the incidence of sudden circulatory arrest in the Netherlands

Objective To investigate whether smoke-free legislation in the Netherlands led to a decreased incidence of out-of-hospital sudden circulatory arrest (SCA). Smoke-free legislation was implemented in two phases: a workplace ban in 2004 and an extension of this ban to the hospitality sector on 1 July 2008. Design Weekly incidence data on SCA were obtained from the ambulance registry of South Limburg, the Netherlands. Three time periods were distinguished: the pre-ban period (1 January 2002-1 January 2004), the first post-ban period (1 January 2004-1 July 2008) and the second post-ban period (1 July 2008-1 May 2010). Trends in absolute SCA incidence were analysed using Poisson regression, adjusted for population size, ambient temperature, air pollution and influenza rates. Results A total of 2305 SCA cases were observed (mean weekly incidence 5.3 +/- 2.3 SD). The adjusted Poisson regression model showed a small but significant increase in SCA incidence during the pre-ban period (+0.20% cases per week, p = 0.044). This trend changed significantly after implementation of the first ban (with -0.24% cases per week, p = 0.043), translating into a 6.8% (22 cases) reduction in the number of SCA cases after 1 year of smoke-free legislation. No further decrease was seen after the second smoking ban. Conclusions After introduction of a nationwide workplace smoking ban in 2004, a significant decrease in the incidence of out-of-hospital SCA was seen in South Limburg. Poor enforcement of the 2008 hospitality sector ban may account for the fact that no further decrease in the incidence of SCA was seen at this time

Update on Dendritic Cell-Induced Immunological and Clinical Tolerance.

Dendritic cells (DCs) as highly efficient antigen-presenting cells are at the interface of innate and adaptive immunity. As such, they are key mediators of immunity and antigen-specific immune tolerance. Due to their functional specialization, research efforts have focused on the characterization of DCs subsets involved in the initiation of immunogenic responses and in the maintenance of tissue homeostasis. Tolerogenic DCs (tolDCs)-based therapies have been designed as promising strategies to prevent and control autoimmune diseases as well as allograft rejection after solid organ transplantation (SOT). Despite successful experimental studies and ongoing phase I/II clinical trials using autologous tolDCs in patients with type 1 diabetes, rheumatoid arthritis, multiple sclerosis, and in SOT recipients, additional basic research will be required to determine the optimal DC subset(s) and conditioning regimens for tolDCs-based treatments in vivo. In this review, we discuss the characteristics of human DCs and recent advances in their classification, as well as the role of DCs in immune regulation and their susceptibility to in vitro or in vivo manipulation for the development of tolerogenic therapies, with a focus on the potential of tolDCs for the treatment of autoimmune diseases and the prevention of allograft rejection after SOT

¿Existe una clase de trabajadores que vive en la pobreza en los Países Bajos?

Esta contribución trata sobre la el problema de la existencia de "trabajadores pobres" en Holanda. El resto del mundo tiende a con-siderar a Holanda como un éxito económico. Es en este contexto en el que el artículo investiga sobre la existencia de trabajadores en Holanda que teniendo un trabajo remunerado se ven inmersos en los problemas de la pobreza. Los datos estadísticos disponibles a nivel macroeconómico dan indicaciones claras de la existencia de "traba-jadores pobres". A la luz de estos datos, la cuestión de los "trabaja-dores pobres" debería tener un lugar más preeminente entre los puntos de la agenda de los sindicatos y los políticos. En particular, los sindicatos deberían jugar un papel más activo profundizando en el problema a través de estudios más detallados, que tomaran como punto de partida la experiencia cotidiana de las personas afectadas, y deberían desarrollar medidas que dieran respuesta a los intereses de dichas personas

FCNN:Fourier Convolutional Neural Networks

The Fourier domain is used in computer vision and machine learning as image analysis tasks in the Fourier domain are analogous to spatial domain methods but are achieved using different operations. Convolutional Neural Networks (CNNs) use machine learning to achieve state-of-the-art results with respect to many computer vision tasks. One of the main limiting aspects of CNNs is the computational cost of updating a large number of convolution parameters. Further, in the spatial domain, larger images take exponentially longer than smaller image to train on CNNs due to the operations involved in convolution methods. Consequently, CNNs are often not a viable solution for large image computer vision tasks. In this paper a Fourier Convolution Neural Network (FCNN) is proposed whereby training is conducted entirely within the Fourier domain. The advantage offered is that there is a significant speed up in training time without loss of effectiveness. Using the proposed approach larger images can therefore be processed within viable computation time. The FCNN is fully described and evaluated. The evaluation was conducted using the benchmark Cifar10 and MNIST datasets, and a bespoke fundus retina image dataset. The results demonstrate that convolution in the Fourier domain gives a significant speed up without adversely affecting accuracy. For simplicity the proposed FCNN concept is presented in the context of a basic CNN architecture, however, the FCNN concept has the potential to improve the speed of any neural network system involving convolution

Automatic detection and identification of retinal vessel junctions in colour fundus photography

The quantitative analysis of retinal blood vessels is important for the management of vascular disease and tackling problems such as locating blood clots. Such tasks are hampered by the inability to accurately trace back problems along vessels to the source. This is due to the unresolved challenge of distinguishing automatically between vessel branchings and vessel crossings. In this paper, we present a new technique for tackling this challenging problem by developing a convolutional neural network approach for first locating vessel junctions and then classifying them as either branchings or crossings. We achieve a high accuracy of 94% for junction detection and 88% for classification. Combined with work in segmentation, this method has the potential to facilitate automated localisation of blood clots and other disease symptoms leading to improved management of eye disease through aiding or replacing a clinicians diagnosis

Automatic detection and distinction of retinal vessel bifurcations and crossings in colour fundus photography

The analysis of retinal blood vessels present in fundus images, and the addressing of problems such as blood clot location, is important to undertake accurate and appropriate treatment of the vessels. Such tasks are hampered by the challenge of accurately tracing back problems along vessels to their source. This is due to the unresolved issue of distinguishing automatically between vessel bifurcations and vessel crossings in colour fundus photographs. In this paper, we present a new technique for addressing this problem using a convolutional neural network approach to firstly locate vessel bifurcations and crossings and then to classifying them as either bifurcations or crossings. Our method achieves high accuracies for junction detection and classification on the DRIVE dataset and we show further validation on an unseen dataset from which no data has been used for training. Combined with work in automated segmentation, this method has the potential to facilitate: reconstruction of vessel topography, classification of veins and arteries and automated localisation of blood clots and other disease symptoms leading to improved management of eye disease