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Due to the increase of data volumes expected for the LHC Run 3 and Run 4, the ALICE Collaboration designed and deployed a new, energy efficient, computing model to run Online and Offline O2 data processing within a single software framework. The ALICE O2 Event Processing Nodes (EPN) project performs online data reconstruction using GPUs (Graphic Processing Units) instead of CPUs and applies an efficient, entropy-based, online data compression to cope with Pb–Pb collision data at a 50 kHz hadronic interaction rate. Also, the O2 EPN farm infrastructure features an energy efficient, environmentally friendly, adiabatic cooling system which allows for operational and capital cost savings.

Introduction. In the animal model, preconditioning is a powerful weapon against ischemic damage. The reason why the human heart cannot be protected from ischemic damage by preconditioning remains unclear. There are assumptions that the lack of preconditioning in humans is caused by concomitant diseases such as dyslipoproteinemia and arteriosclerosis. This study investigates whether dyslipoproteinemia and the resulting arteriosclerosis can be a cause of a reduced precondition effect of heart in mice. Methods. LDL receptor-deficient mice were fed a long-term (14-16 weeks) high-fat atherogenic diet to induce arteriosclerosis. Arteriosclerosis was identified by histological examination and vessel contraction tests. LDLR-/- and wild-type mice were randomly assigned to anesthetic-induced, remote ischemic, or no preconditioning. All mice were subjected to 45 minutes of coronary artery occlusion and 180 minutes of reperfusion. The area at risk and infarct size were determined by Evans Blue and triphenyltetrazolium chloride staining. Results. Histopathological examination showed atherosclerosis in high-fat atherogenic fed LDLR-/- mice, and the vessel relaxation capacity was significantly reduced compared to wild-type mice. In the wild type, as expected, infarct size was significantly reduced by preconditioning compared to the control. In LDLR-/- mice, infarct size was significantly reduced by preconditioning compared to the control. Surprisingly, the LDLR-/- control group also had a significantly reduced infarct size compared to the wild-type control group. Conclusion. We were able to demonstrate that a high-fat diet morphologically and functionally triggered atherosclerosis in LDLR-/- mice. Interestingly, LDLR-/- mice with an atherogenic diet had smaller infarct sizes compared to wild-type mice. Moreover, preconditioning additionally reduced myocardial infarct size in LDLR-/- mice. A long-term high-fat atherogenic diet and preconditioning seem to result in additive cardioprotection in LDLR-/- mice.

Introduction. Anticoagulants such as argatroban and heparins (low-molecular-weight and unfractionated) play an immense role in preventing thromboembolic complications in clinical practice. Nevertheless, they can also have a negative effect on the immune system. This study is aimed at investigating the influence of these substances on polymorphonuclear neutrophils (PMNs), whose nonspecific defense mechanisms can promote thrombogenesis. Methods. Blood samples from 30 healthy volunteers were investigated, whereby PMNs were isolated by density gradient centrifugation and incubated with 0.8 μg/mL of argatroban, 1.0 U/mL of low-molecular-weight heparin (LMWH), 1.0 U/mL of unfractionated heparin (UFH), or without drug (control). A collagen-cell mixture was prepared and filled into 3D μ-slide chemotaxis chambers (IBIDI® GmbH, Germany). Stimulation was initiated by using a chemokine gradient of n-formyl-methionine-leucyl-phenylalanine (fMLP), and microscopic observation was conducted for 4.5 hours. The cells’ track length and track straightness, as well as the number of attracted granulocytes, level of ROS (reactive oxygen species) production, and NET (neutrophil extracellular traps) formation, were analyzed and categorized into migration distances and time periods. Results. All three anticoagulants led to significantly reduced PMN track lengths, with UFH having the biggest impact. The number of tracks observed in the UFH group were significantly reduced compared to the control group. Additionally, the UFH group demonstrated a significantly lower track straightness compared to the control. ROS production and NET formation were unaffected. Conclusion. Our data provide evidence that anticoagulants have an inhibitory effect on the extent of PMN migration and chemotactic migration efficiency, thus indicating their potential immune-modulatory and prothrombotic effects.

Introduction It is highly uncertain whether volatile organic compounds (VOCs) in exhaled breath of critically ill intensive care unit patients are formed in the lung locally, in the air compartment or lung tissue, or elsewhere in the body and transported to the lung via the bloodstream. We compared VOC mixtures in exhaled breath and in air coming from extracorporeal support devices in critically ill patients to address this issue. Methods First, we investigated whether it was safe to connect an electronic nose (eNose) or a gas sampling pump to extracorporeal support membranes. Then, breath and air from extracorporeal support devices were collected simultaneously for continuous monitoring of VOC mixtures using an eNose. In addition, samples for gas chromatography/mass spectrometry (GC-MS) analysis were taken daily at the two measurement sites. Results 10 critically ill patients were monitored for a median (interquartile range) duration of 73 (72–113) h; in total, we had 887 h of air sampling. The eNose signals of breath correlated moderately with signals of air from the extracorporeal support devices (R2=0.25–0.44). After GC-MS analysis, 96 VOCs were found both in breath and air from the extracorporeal support devices; of these, 29 (30%) showed a significant correlation (p

Introduction. Recent studies have shown that acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) may serve as important diagnostic and therapeutic targets in sepsis. Since polymorphonuclear neutrophils (PMNs) play a pivotal role in the early phase of sepsis, we evaluated the potential therapeutic effects of cholinesterase inhibitors on PMN functions during cecal ligation and puncture- (CLP-) induced sepsis and investigated the roles of AChE and BChE as inflammatory markers under standardized experimental conditions. Methods. Sham surgery or CLP was performed in male Wistar rats (n=60). Animals were randomized into four groups: physostigmine, 100 μg/kg; neostigmine, 75 μg/kg; 0.9% saline (control group); and sham group, each applied four times over 24 h. The levels of reactive oxygen species (ROS) production and CD11b/CD62l expression were quantified by flow cytometry at t=0, 6, 15, 20, and 24 h. Blood gas analysis as well as AChE and BChE activity levels was measured by validated point-of-care measurements. Clinical scores and survival times were determined. Results. CLP induced a significant increase in ROS production and CD11b upregulation by rat PMNs. Treatment with physostigmine or neostigmine significantly reduced ROS production and CD11b upregulation by PMNs 20 h after CLP induction. In physostigmine-treated animals, survival times were significantly improved compared to the control animals, but not in neostigmine-treated animals. While AChE activity significantly decreased in the control animals at t>6 h, AChE activity did not change in the sham group. BChE activity decreased at t>20 h in the control animals. Conclusion. While AChE activity may serve as an acute inflammatory marker, BChE activity shows a delayed decrease. Administration of centrally acting physostigmine in CLP-induced sepsis in rats has protective effects on PMN functions and improves survival times, which may be of interest in clinical practice.

The volatile anesthetic desflurane (DES) effectively reduces cardiac infarct size following experimental ischemia/reperfusion injury in the mouse heart. We hypothesized that endogenous estrogens play a role as mediators of desflurane-induced preconditioning against myocardial infarction. In this study, we tested the hypothesis that desflurane effects local estrogen synthesis by modulating enzyme aromatase expression and activity in the mouse heart. Aromatase metabolizes testosterone to 17β- estradiol (E2) and thereby significantly contributes to local estrogen synthesis. We tested aromatase effects in acute myocardial infarction model in male mice. The animals were randomized and subjected to four groups which were pre-treated with the selective aromatase inhibitor anastrozole (A group) and DES alone (DES group) or in combination (A+DES group) for 15 minutes prior to surgical intervention whereas the control group received 0.9% NaCl (CON group). All animals were subjected to 45 minutes ischemia following 180 minutes reperfusion. Anastrozole blocked DES induced preconditioning and increased infarct size compared to DES alone (37.94 ± 15.5% vs. 17.1 ± 3.62%) without affecting area at risk and systemic hemodynamic parameters following ischemia/reperfusion. Protein localization studies revealed that aromatase was abundant in the murine cardiovascular system with the highest expression levels in endothelial and smooth muscle cells. Desflurane application at pharmacological concentrations efficiently upregulated aromatase expression in vivo and in vitro. We conclude that desflurane efficiently regulates aromatase expression and activity which might lead to increased local estrogen synthesis and thus preserve cellular integrity and reduce cardiac damage in an acute myocardial infarction model.

Abstract A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.

In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.

A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9% and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10% and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units.

Searches for supersymmetry (SUSY) at the LHC frequently assume the conservation of R-parity in their design, optimization, and interpretation. In the case that R-parity is not conserved, constraints on SUSY particle masses tend to be weakened with respect to R-parity-conserving models. We review the current status of searches for R-parity-violating (RPV) supersymmetry models at the ATLAS and CMS experiments, limited to 8 TeV search results published or submitted for publication as of the end of March 2015. All forms of renormalisable RPV terms leading to prompt signatures have been considered in the set of analyses under review. Discussing results for searches for prompt R-parity-violating SUSY signatures summarizes the main constraints for various RPV models from LHC Run I and also defines the basis for promising signal regions to be optimized for Run II. In addition to identifying highly constrained regions from existing searches, also gaps in the coverage of the parameter space of RPV SUSY are outlined.