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Sustainable management of sites affected by past industrial activities in Wallonia: the stakes and the challenges of risk assessment within the framework of the new policy developments on (local) soil pollutions. Risk-based land management is a concept that stands out more and more frequently in soil policies adopted by different European countries, as well as within the framework of the future European directive on soil protection. This concept intends to act as a lever for sustainable solution development in terms of rehabilitation by ensuring the following principles: (1) reduction of risks (human health, ecosystems) allowing sites to be reused safely (concept of fitness for use); (2) prevention of any pollutant dispersion risk (concept of environmental protection); (3) long-term care while minimising the burden of controlling, maintenance and monitoring. These ideas have been taken as backdrop for the Walloon decree on soil remediation and rehabilitation of disused economic activity sites. Risk control has been taken as a key point for any decision related to historically polluted sites. However, to be effective, this option stands out that reliable and efficient methodological tools are available for land evaluation (risk assessment). Three decision-making tools, specifically developed for the intended applications in the Walloon Region, are presented in the paper: (1) a global risk evaluation method allowing ranking of polluted sites; (2) a method to decide, at the end of the first step of the investigation procedure (preliminary soil investigation), whether further investigations are required or not; (3) a methodological procedure to proceed to a detailed risk assessment at the end of the in-depth investigation step. Perspectives are proposed to improve these first tools. It has also been shown that the challenge of sustainable solutions for the management of polluted sites finally depends on answers that will be progressively developed by the scientific community. These have to take into account all aspects of the Walloon context.

Abstract Objective For patients with mild hepatitis A virus (HAV) infection, this study compared estimates of total costs associated with managing cases under a policy of mandatory hospitalization in the Republic of Kazakhstan and estimates of total costs associated with managing cases in outpatient settings. Costs were estimated both from the perspective of the Ministry of Health and from a broader societal perspective. Methods Data were collected by using a standardized structured questionnaire. For cases of mild HAV infection, medical records were obtained from 200 patients managed by hospitalization and from 251 patients managed in an outpatient setting. Personal interviews were also conducted to collect information on productivity losses and out-of-pocket expenses. Results Nationally, we estimated about 21,600 cases of mild HAV infection annually. The mean annual treatment costs in hospital for mild HAV infection was estimated at US$3.39 million (2001 US$) (95% confidence interval [CI] = [US$3.26 million – US$3.52 million]). The total annual mild HAV infection cost to the society, including direct medical and nonmedical costs and productivity losses due to 721,440 lost work days, was estimated at US$6.26 million (95% CI [US$6.05 million – US$6.47 million]). In sensitivity analyses, the total annual cost of mild HAV infection ranged from US$4.37 million to US$24.66 million. The survey results showed that a relatively minor change in the current policy of mandatory hospitalization could result in an estimated total annual savings of US$4.62 million (2001 US$) in Kazakhstan. Conclusion Adoption of an outpatient management policy for cases of mild HAV infection would generate substantial cost savings to the Ministry of Health and society.

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure-strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure-strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm 2 , respectively. Regarding the pressure-strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure-strain loop of central arteries while observing pressure signals from peripheral arteries.

Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.

The problem of defining a decision rule which takes into account performance constraints and class-selective rejection is formalized in a general framework. In the proposed formulation, the problem is defined using three kinds of criteria. The first is the cost to be minimized, which defines the objective function, the second are the decision options, determined by the admissible assignment classes or subsets of classes, and the third are the performance constraints. The optimal decision rule within the statistical decision theory framework is obtained by solving the stated optimization problem. Two examples are provided to illustrate the formulation and the decision rule is obtained.

Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependent on each class and on each wrong or partially correct decision. It is based on nu-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers.

Previous work has shown that treatment of HT-29 methotrexate (MTX) cells with benzyl-N-acetyl-alpha-D-galactosaminide results in profound changes in mucin oligosaccharide chains. To analyse in depth the effect of this drug, we first determined the structure of mucin oligosaccharide chains synthesized by HT-29 MTX cells and the changes induced by permanent drug exposure. Mucins from untreated cells contained nine monosialylated structures (core types 1, 2, 3 and 4) and four disialylated structures (types 1, 2 and 4). Core 1 structures predominated, in particular NeuAcalpha2-3Galbeta1-3GalNAc-ol. Exposure of HT-29 MTX cells to benzyl-N-acetyl-alpha-D-galactosaminide from days 2-21 resulted in a decrease in intracellular mucins and both their sialic acid and galactose content, and an increased T (Galbeta1-3GalNAcalpha-O-Ser/Thr) and Tn (GalNAcalpha-O-Ser/Thr) antigenicity. A 3-fold increase in both Galbeta1-3GalNAc alpha2, 3-sialyltransferase activity and mRNA expression was detected. At the ultrastructural level, T-antigen was not detectable in mucin droplets in control cells, but was strongly expressed in intracytoplasmic vesicles in treated cells. In these cells, MUC1 and MUC3 transcripts were up-regulated, whereas MUC2, MUC5B and MUC5AC were down-regulated. Furthermore, constitutive and secretagogue-induced MUC5AC secretion was reduced and no mucus layer was detected. In conclusion, benzyl-N-acetyl-alpha-D-galactosaminide induces abnormal O-glycosylation and altered regulation of MUC5AC secretion.