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Recently, 3D metal printing technologies are rapidly spreading. The various types of additive manufacturing (powder bed fusion, directed energy deposition, binder jetting, and sheet lamination) use very different approaches to produce materials/geometries. The material properties (e.g. porosity/density, strength, surface roughness, hardness) of additively manufactured materials differ from the traditional ones and strongly depend on the applied 3D printing parameters. Therefore, a crucial research direction is how the parameters affect the material properties. Currently, 3D metal printing technologies have limitations in the achievable geometrical accuracy and surface roughness. Therefore, they can rarely be used for part production without further machining. For high-quality requirements, an additional subtractive machining procedure is usually required. Consequently, another intensively researched topic is the machinability of 3D metal printed materials. The industry is increasingly demanding to integrate different manufacturing processes (additive and subtractive technologies) into one machine. These hybrid machines have the advantage that they can produce high-quality parts in one step.

Micro machining forms an integral part of modern manufacturing technology. In order to satisfy the most important demands, like cost reduction, quality improvement, and production efficiency, this process has to be implemented into the framework of Industry 4.0, too. Defect reduction of products can be realized by beneficial trends of Industry 4.0, such as application of sensors driven AI based predictive models. Surface roughness is one of the main indicators, which can be used for evaluation of the machined surface. The aim of this research is to develop a predictive surface roughness model in the case of micro milling of hard materials. Most important scientific knowledge related to micro milling, and application possibilities of AI based predictive models in the case of chip removal processes were summarised. Micro milling experiments on hot work tool steel (AISI H13) were carried out in order to determine the effect of different cutting parameters on surface roughness. The effects of the cutting parameters were investigated by the analysis of variance (ANOVA). Two-layer feedforward artificial neural network (ANN) based model was trained, validated and tested by means of experimental results. It was found that the trained model has an average MSE, RMSE and R values of 0.00063850, 0.019116835 and 0.9948, while the additional validation test provided 0.00038148, 0.019531513 and 0.8545, respectively, which are statistically significant. The developed model can effectively predict the surface roughness under the investigated circumstances.

Latency protein LMP2A of Epstein–Barr virus (EBV) has been implicated in EBV related tumorigenesis. To understand the host cell dependent expression of the LMP2A gene, it is necessary to analyse the regulatory mechanisms of the LMP2A promoter (LMP2Ap). By transient transfection and in vitrobinding analyses two CBF1 sites have previously been shown to be involved in the regulation of LMP2Ap. However, the promoter structure has not been examined at the nucleotide level in vivo. Therefore we undertook a comprehensive analysis of in vivoprotein binding and of CpG-methylation patterns at LMP2Ap in a panel of B cell lines carrying latent EBV genomes. The presence of characteristic footprints on two CBF1 and further binding-sites, together with overall hypomethylation of CpG dinucleotides correlated well with promoter activity. In contrast, the absence of several genomic footprints, as well as the presence of patches of highly methylated CpG dinucleotides were characteristic of silent LMP2Aps.

AbstractThe viral interleukin-10 promoter (vIL-10p), overlapping the rep* element in the EpsteinBarr virus (EBV) genome, is a promoter element active mostly in the late phase of the lytic cycle and immediately upon infection of B cells. rep* was, through transfection experiments with small plasmids, characterised as a cis element supporting oriP replicative function. In this study, in vivo protein binding and CpG methylation at rep*/vIL-10p were analysed in five cell lines that harbour strictly latent EBV genomes. Contrary to the invariably unmethylated dyad symmetry element (DS) of oriP, rep*/vIL-10p was highly methylated and showed only traces of protein binding in all examined cell lines. This result is in agreement with vIL-10p being an inactive promoter of EBV genomes, and makes it less likely that rep* functions as a replicative element of latent EBV genomes.

Objective:: To examine the characteristics of patients admitted to the Medical Intensive Care Unit (MICU) after intentional drug overdose. Design:: Retrospective chart review, descriptive. Setting:: Midwestern teaching hospital. Subjects:: Convenience sample of 43 patients admitted to the MICU after intentional drug overdose. Outcome measures:: Survival and disposition of those patients who survived and were discharged from the MICU. Results:: Ninety-five percent of the patients survived and were discharged from the MICU: Acute Physiology and Chronic Health Evaluation II scores ranged from 1 to 29 (mean, 8); Glascow Coma Scale scores ranged from 3 to 15 (mean, 12.5); five patients were intubated and two patients had serious electrocardiograph changes requiring pharmacologic intervention for dysrhythmia. Conclusions:: Neurologic findings were the best indicators of serious complications after drug overdose. Therefore, patients with a Glascow Coma Scale score of more than six, and who are not intubated, may not need admission to an intensive care unit.

Micro milling is a continuously spreading, and therefore heavily researched technology. In this subject plenty of publications are highlighting the fact that there are many issues, which still has to be solved. Micro milling realizes thin chip removal process. In this paper 2D finite element simulation was performed to investigate the characteristics of small sized chip formation. Results of micro milling experiments were analysed too. This paper presents the results regarding effects of the cutting parameters (cutting speed, feed per tooth) on cutting temperature and machining forces at 2D finite element. Results based on different material models were compared with each other too.