Sepsis and Macrophage Activation Syndrome: Two Successive Complications of Staphylococcus aureus Infection

The macrophage activation syndrome (MAS) and the sepsis are both a condition of hyperinflammation. The differential diagnosis can be difficult. The two pathologies can succeed with each other. The purpose of this literature review is to illustrate these diagnostic and therapeutic difficulties through a reported case of a macrophage activation syndrome secondary to infection (MASI). It was a 42- year- old diabetic man, hospitalized for an acute myositis due to an infection of Staphylococcus aureus. These germs caused a septic shock requiring antibiotic therapy. After an initial clinical improvement, the diagnosis of MASI post-Staphylococcus aureus was retained with a persistence of the inflammatory syndrome and appearance of fever with splenomegaly associated with hepatic cytolysis a 30% decrease in prothrombin time, a mild regenerative anemia without hemophagocytosis. An inflammatory syndrome may have several completely opposite etiologies and pathophysiology. Our case fulfilled both of the criteria of sepsis and MAS. Several signs were common for the two pathologies, but a detailed analysis of the clinical and biological elements lead to the diagnostic orientation

Effect of Ductile Damage Evolution in Sheet Metal Forming: Experimental and Numerical Investigations

The numerical simulation based on the Finite Element Method (FEM) is widely used in academic institutes and in the industry. It is a useful tool to predict many phenomena present in the classical manufacturing forming processes such as necking, fracture, springback, buckling and wrinkling. But, the results of such numerical model depend strongly on the parameters of the constitutive behavior model. In the first part of this work, we focus on the traditional identification of the constitutive law using oriented tensile tests (0°, 45°, and 90° with respect to the rolling direction). A Digital Image Correlation (DIC) method is used in order to measure the displacements on the surface of the specimen and to analyze the necking evolution and the instability along the shear band. Therefore, bulge tests involving a number of die shapes (circular and elliptic) were developed. In a second step, a mixed numerical–experimental method is used for the identification of the plastic behavior of the stainless steel metal sheet. The initial parameters of the inverse identification were extracted from a uniaxial tensile test. The optimization procedure uses a combination of a Monte-Carlo and a Levenberg-Marquardt algorithm. In the second part of this work, according to some results obtained by SEM (Scaning Electron Microscopy) of the crack zones on the tensile specimens, a Gurson Tvergaard Needleman (GTN) ductile model of damage has been selected for the numerical simulations. This model was introduced in order to give informations concerning crack initiations during hydroforming. At the end of the paper, experimental and numerical comparisons of sheet metal forming applications are presented and validate the proposed approach

Discretization of Continuous Attributes

7 pagesIn the data mining field, many learning methods -like association rules, Bayesian networks, induction rules (Grzymala-Busse & Stefanowski, 2001)- can handle only discrete attributes. Therefore, before the machine learning process, it is necessary to re-encode each continuous attribute in a discrete attribute constituted by a set of intervals, for example the age attribute can be transformed in two discrete values representing two intervals: less than 18 (a minor) and 18 and more (of age). This process, known as discretization, is an essential task of the data preprocessing, not only because some learning methods do not handle continuous attributes, but also for other important reasons: the data transformed in a set of intervals are more cognitively relevant for a human interpretation (Liu, Hussain, Tan & Dash, 2002); the computation process goes faster with a reduced level of data, particularly when some attributes are suppressed from the representation space of the learning problem if it is impossible to find a relevant cut (Mittal & Cheong, 2002); the discretization can provide non-linear relations -e.g., the infants and the elderly people are more sensitive to illness