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Event-by-event fluctuations of the mean transverse momentum of charged particles produced in pp collisions at s√ = 0.9, 2.76 and 7 TeV, and Pb-Pb collisions at sNN−−−−√ = 2.76 TeV are studied as a function of the charged-particle multiplicity using the ALICE detector at the LHC. Dynamical fluctuations indicative of correlated particle emission are observed in all systems. The results in pp collisions show little dependence on collision energy. The Monte Carlo event generators PYTHIA and PHOJET are in qualitative agreement with the data. Peripheral Pb-Pb data exhibit a similar multiplicity dependence as that observed in pp. In central Pb-Pb, the results deviate from this trend, featuring a significant reduction of the fluctuation strength. The results in Pb--Pb are in qualitative agreement with previous measurements in Au-Au at lower collision energies and with expectations from models that incorporate collective phenomena.
We report on the production of inclusive Υ(1S) and Υ(2S) in p-Pb collisions at sNN−−−√=5.02 TeV at the LHC. The measurement is performed with the ALICE detector at backward (−4.46<ycms<−2.96) and forward (2.03<ycms<3.53) rapidity down to zero transverse momentum. The production cross sections of the Υ(1S) and Υ(2S) are presented, as well as the nuclear modification factor and the ratio of the forward to backward yields of Υ(1S). A suppression of the inclusive Υ(1S) yield in p-Pb collisions with respect to the yield from pp collisions scaled by the number of binary nucleon-nucleon collisions is observed at forward rapidity but not at backward rapidity. The results are compared to theoretical model calculations including nuclear shadowing or partonic energy loss effects.
We report on the production of inclusive Υ(1S) and Υ(2S) in p-Pb collisions at sNN−−−√=5.02 TeV at the LHC. The measurement is performed with the ALICE detector at backward (−4.46<ycms<−2.96) and forward (2.03<ycms<3.53) rapidity down to zero transverse momentum. The production cross sections of the Υ(1S) and Υ(2S) are presented, as well as the nuclear modification factor and the ratio of the forward to backward yields of Υ(1S). A suppression of the inclusive Υ(1S) yield in p-Pb collisions with respect to the yield from pp collisions scaled by the number of binary nucleon-nucleon collisions is observed at forward rapidity but not at backward rapidity. The results are compared to theoretical model calculations including nuclear shadowing or partonic energy loss effects.
The pT-differential production cross section of electrons from semileptonic decays of heavy-flavor hadrons has been measured at mid-rapidity in proton-proton collisions at s√=2.76 TeV in the transverse momentum range 0.5 < pT < 12 GeV/c with the ALICE detector at the LHC. The analysis was performed using minimum bias events and events triggered by the electromagnetic calorimeter. Predictions from perturbative QCD calculations agree with the data within the theoretical and experimental uncertainties.
The differential charged jet cross sections, jet fragmentation distributions, and jet shapes are measured in minimum bias proton-proton collisions at centre-of-mass energy s√=7 TeV using the ALICE detector at the LHC. Jets are reconstructed from charged particle momenta in the mid-rapidity region using the sequential recombination kT and anti-kT as well as the SISCone jet finding algorithms with several resolution parameters in the range R=0.2 to 0.6. Differential jet production cross sections measured with the three jet finders are in agreement in the transverse momentum (pT) interval 20<pjet,chT<100 GeV/c. They are also consistent with prior measurements carried out at the LHC by the ATLAS collaboration. The jet charged particle multiplicity rises monotonically with increasing jet pT, in qualitative agreement with prior observations at lower energies. The transverse profiles of leading jets are investigated using radial momentum density distributions as well as distributions of the average radius containing 80% (⟨R80⟩) of the reconstructed jet pT. The fragmentation of leading jets with R=0.4 using scaled pT spectra of the jet constituents is studied. The measurements are compared to model calculations from event generators (PYTHIA, PHOJET, HERWIG). The measured radial density distributions and ⟨R80⟩ distributions are well described by the PYTHIA model (tune Perugia-2011). The fragmentation distributions are better described by HERWIG.
Long-range angular correlations on the near and away side in p–Pb collisions at √sNN=5.02 TeV
(2013)
Angular correlations between charged trigger and associated particles are measured by the ALICE detector in p–Pb collisions at a nucleon–nucleon centre-of-mass energy of 5.02 TeV for transverse momentum ranges within 0.5<pT,assoc<pT,trig<4 GeV/c. The correlations are measured over two units of pseudorapidity and full azimuthal angle in different intervals of event multiplicity, and expressed as associated yield per trigger particle. Two long-range ridge-like structures, one on the near side and one on the away side, are observed when the per-trigger yield obtained in low-multiplicity events is subtracted from the one in high-multiplicity events. The excess on the near-side is qualitatively similar to that recently reported by the CMS Collaboration, while the excess on the away-side is reported for the first time. The two-ridge structure projected onto azimuthal angle is quantified with the second and third Fourier coefficients as well as by near-side and away-side yields and widths. The yields on the near side and on the away side are equal within the uncertainties for all studied event multiplicity and pT bins, and the widths show no significant evolution with event multiplicity or pT. These findings suggest that the near-side ridge is accompanied by an essentially identical away-side ridge.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Challenges of FAIR phase 0
(2018)
After two-year's shutdown, the GSI accelerators plus the latest addition of storage ring CRYRING, will be back into operation in 2018 as the FAIR phase 0 with the goal to fulfill the needs of scientific community and the FAIR accelerators and detector development. Even though GSI has been well known for its operation of a variety of ion beams ranging from proton up to uranium for multi research areas such as nuclear physics, astrophysics, biophysics, material science, the upcoming beam time faces a number of challenges in re-commissioning its existing circular accelerators with brand new control system and upgrade of beam instrumentations, as well as in rising failures of dated components and systems. The cycling synchrotron SIS18 has been undergoing a set of upgrade measures for fulfilling future FAIR operation, among which many measures will also be commissioned during the upcoming beam time. This paper presents the highlights of the challenges such as re-establishing the high intensity heavy ion operation as well as parallel operation mode for serving multi users. The status of preparation including commissioning results will also be reported.
Vögel spielen vor allem aufgrund ihrer Häufigkeit in natürlichen und anthropogenen Habitaten sowie ihrer von anderen Wirbeltieren unübertroffenen Mobilität als natürliche Reservoire von Krankheitserregern eine bedeutende infektionsepidemiologische Rolle. Darüber hinaus können Zugvögel während ihrer Migration Erreger wie Vektoren über hunderte oder tausende von Kilometern transportieren und in ihrer Verbreitung extrem begünstigen. Dass Vögel neben anderen Tiergruppen eine wichtige Wirtsfunktion für Zecken besitzen, ist bekannt. Ebenso wird Vögeln eine Reservoirfunktion für bestimmte Genospezies des Borrelia burgdorferi-Artenkomplexes zugesprochen (GERN et al. 1998; GYLFE et al. 2000). In welchem Maße verschiedene Vogelarten an der geographischen Verbreitung dieser Bakterienarten beteiligt sind, ist jedoch noch weitgehend unklar. In dieser Studie sollte durch die Untersuchung von an Vögeln abgesammelten Zecken (Ixoes ricinus) die Bedeutung von drei Drosselarten (Turdidae) für den Lebenszyklus der Borrelien genauer untersucht werden. Des Weiteren wurde die Reservoirfunktion dieser Vogelarten für vier verschiedene Genospezies des B. burgdorferi-Komplexes geprüft.
Only a few Methyl-[11C]-l-methionine (MET) positron emission tomography (PET) studies have focused on children and young adults with brain neoplasm. Due to radiation exposure, long scan acquisition time, and the need for sedation in young children MET-PET studies should be restricted to this group of patients when a decision for further therapy is not possible from routine diagnostic procedures alone, e.g., structural imaging. We investigated the diagnostic accuracy of MET-PET for the differentiation between tumorous and non-tumorous lesions in this group of patients. Forty eight MET-PET scans from 39 patients aged from 2 to 21 years (mean 15 ± 5.0 years) were analyzed. The MET tumor-uptake relative to a corresponding control region was calculated. A receiver operating characteristic (ROC) was performed to determine the MET-uptake value that best distinguishes tumorous from non-tumorous brain lesions. A differentiation between tumorous (n = 39) and non-tumorous brain lesions (n = 9) was possible at a threshold of 1.48 of relative MET-uptake with a sensitivity of 83% and a specificity of 92%, respectively. A differentiation between high grade malignant lesions (mean MET-uptake = 2.00 ± 0.46) and low grade tumors (mean MET-uptake = 1.84 ± 0.31) was not possible. There was a significant difference in MET-uptake between the histologically homogeneous subgroups of astrocytoma WHO grade II and anaplastic astrocytoma WHO grade III (P = 0.02). MET-PET might be a useful tool to differentiate tumorous from non-tumorous lesions in children and young adults when a decision for further therapy is difficult or impossible from routine structural imaging procedures alone. Keywords Brain tumor - Children - PET - Methionine - Molecular imaging
Observation of enhanced subthreshold K+ production in central collisions between heavy nuclei
(1994)
In the very heavy collision system 197Au+197Au the K+ production process was studied as a function of impact parameter at 1 GeV/nucleon, a beam energy well below the free N-N threshold. The K+ multiplicity increases more than linearly with the number of participant nucleons and the K+/ pi + ratio rises significantly when going from peripheral to central collisions. The measured K+ double differential cross section is enhanced by a factor of 6 compared to microscopic transport calculations if secondary processes (Delta N-->K Lambda N and Delta Delta -->K Lambda N) are ignored.
Quantum Molecular Dynamics (QMD) calculations of central collisions between heavy nuclei are used to study fragment production and the creation of collective flow. It is shown that the final phase space distributions are compatible with the expectations from a thermally equilibrated source, which in addition exhibits a collective transverse expansion. However, the microscopic analyses of the transient states in the intermediate reaction stages show that the event shapes are more complex and that equilibrium is reached only in very special cases but not in event samples which cover a wide range of impact parameters as it is the case in experiments. The basic features of a new molecular dynamics model (UQMD) for heavy ion collisions from the Fermi energy regime up to the highest presently available energies are outlined.
In this paper, the concepts of microscopic transport theory are introduced and the features and shortcomings of the most commonly used ansatzes are discussed. In particular, the Ultrarelativistic Quantum Molecular Dynamics (UrQMD) transport model is described in great detail. Based on the same principles as QMD and RQMD, it incorporates a vastly extended collision term with full baryon-antibaryon symmetry, 55 baryon and 32 meson species. Isospin is explicitly treated for all hadrons. The range of applicability stretches from E lab < 100$ MeV/nucleon up to E lab> 200$ GeV/nucleon, allowing for a consistent calculation of excitation functions from the intermediate energy domain up to ultrarelativistic energies. The main physics topics under discussion are stopping, particle production and collective flow.
The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986). In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO2)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Nino = Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference. It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CMFSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Attribution and detection of anthropogenic climate change using a backpropagation neural network
(2002)
The climate system can be regarded as a dynamic nonlinear system. Thus traditional linear statistical methods are not suited to describe the nonlinearities of this system which renders it necessary to find alternative statistical techniques to model those nonlinear properties. In addition to an earlier paper on this subject (WALTER et al., 1998), the problem of attribution and detection of the observed climate change is addressed here using a nonlinear Backpropagation Neural Network (BPN). In addition to potential anthropogenic influences on climate (CO2-equivalent concentrations, called greenhouse gases, GHG and SO2 emissions) natural influences on surface air temperature (variations of solar activity, volcanism and the El Niño/Southern Oscillation phenomenon) are integrated into the simulations as well. It is shown that the adaptive BPN algorithm captures the dynamics of the climate system, i.e. global and area weighted mean temperature anomalies, to a great extent. However, free parameters of this network architecture have to be optimized in a time consuming trial-and-error process. The simulation quality obtained by the BPN exceeds the results of those from a linear model by far; the simulation quality on the global scale amounts to 84% explained variance. Additionally the results of the nonlinear algorithm are plausible in a physical sense, i.e. amplitude and time structure. Nevertheless they cover a broad range, e.g. the GHG-signal on the global scale ranges from 0.37 K to 1.65 K warming for the time period 1856-1998. However the simulated amplitudes are situated within the discussed range (HOUGHTON et al., 2001). Additionally the combined anthropogenic effect corresponds to the observed increase in temperature for the examined time period. In addition to that, the BPN succeeds with the detection of anthropogenic induced climate change on a high significance level. Therefore the concept of neural networks can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Simulation of global temperature variations and signal detection studies using neural networks
(1998)
The concept of neural network models (NNM) is a statistical strategy which can be used if a superposition of any forcing mechanisms leads to any effects and if a sufficient related observational data base is available. In comparison to multiple regression analysis (MRA), the main advantages are that NNM is an appropriate tool also in the case of non-linear cause-effect relations and that interactions of the forcing mechanisms are allowed. In comparison to more sophisticated methods like general circulation models (GCM), the main advantage is that details of the physical background like feedbacks can be unknown. Neural networks learn from observations which reflect feedbacks implicitly. The disadvantage, of course, is that the physical background is neglected. In addition, the results prove to be sensitively dependent from the network architecture like the number of hidden neurons or the initialisation of learning parameters. We used a supervised backpropagation network (BPN) with three neuron layers, an unsupervised Kohonen network (KHN) and a combination of both called counterpropagation network (CPN). These concepts are tested in respect to their ability to simulate the observed global as well as hemispheric mean surface air temperature annual variations 1874 - 1993 if parameter time series of the following forcing mechanisms are incorporated : equivalent CO2 concentrations, tropospheric sulfate aerosol concentrations (both anthropogenic), volcanism, solar activity, and ENSO (all natural). It arises that in this way up to 83% of the observed temperature variance can be explained, significantly more than by MRA. The implication of the North Atlantic Oscillation does not improve these results. On a global average, the greenhouse gas (GHG) signal so far is assessed to be 0.9 - 1.3 K (warming), the sulfate signal 0.2 - 0.4 K (cooling), results which are in close similarity to the GCM findings published in the recent IPCC Report. The related signals of the natural forcing mechanisms considered cover amplitudes of 0.1 - 0.3 K. Our best NNM estimate of the GHG doubling signal amounts to 2.1K, equilibrium, or 1.7 K, transient, respectively.
Antisynthetase syndrome (ASSD) is a rare clinical condition that is characterized by the occurrence of a classic clinical triad, encompassing myositis, arthritis, and interstitial lung disease (ILD), along with specific autoantibodies that are addressed to different aminoacyl tRNA synthetases (ARS). Until now, it has been unknown whether the presence of a different ARS might affect the clinical presentation, evolution, and outcome of ASSD. In this study, we retrospectively recorded the time of onset, characteristics, clustering of triad findings, and survival of 828 ASSD patients (593 anti-Jo1, 95 anti-PL7, 84 anti-PL12, 38 anti-EJ, and 18 anti-OJ), referring to AENEAS (American and European NEtwork of Antisynthetase Syndrome) collaborative group’s cohort. Comparisons were performed first between all ARS cases and then, in the case of significance, while using anti-Jo1 positive patients as the reference group. The characteristics of triad findings were similar and the onset mainly began with a single triad finding in all groups despite some differences in overall prevalence. The “ex-novo” occurrence of triad findings was only reduced in the anti-PL12-positive cohort, however, it occurred in a clinically relevant percentage of patients (30%). Moreover, survival was not influenced by the underlying anti-aminoacyl tRNA synthetase antibodies’ positivity, which confirmed that antisynthetase syndrome is a heterogeneous condition and that antibody specificity only partially influences the clinical presentation and evolution of this condition.
Damage control resuscitation may lead to postoperative intra-abdominal hypertension or abdominal compartment syndrome. These conditions may result in a vicious, self-perpetuating cycle leading to severe physiologic derangements and multiorgan failure unless interrupted by abdominal (surgical or other) decompression. Further, in some clinical situations, the abdomen cannot be closed due to the visceral edema, the inability to control the compelling source of infection or the necessity to re-explore (as a “planned second-look” laparotomy) or complete previously initiated damage control procedures or in cases of abdominal wall disruption. The open abdomen in trauma and non-trauma patients has been proposed to be effective in preventing or treating deranged physiology in patients with severe injuries or critical illness when no other perceived options exist. Its use, however, remains controversial as it is resource consuming and represents a non-anatomic situation with the potential for severe adverse effects. Its use, therefore, should only be considered in patients who would most benefit from it. Abdominal fascia-to-fascia closure should be done as soon as the patient can physiologically tolerate it. All precautions to minimize complications should be implemented.
A 48 year old patient with dilated cardiomyopathy and chronic acne inversa underwent implantation of a LVAD system (Heartmate II, Thoratec, USA) March 2011. During 2011 and 2012 the patient was repeatedly readmitted for treatment of driveline infection with MRSA. Colonization was controlled with Linezolid and Rifampicin however reoccurred after discontinuation. In August 2012 the LVAD-system was exchanged due to pump dysfunction (HVAD, HeartWare Inc., USA). Postoperatively, the patient presented with ascites which secreted through the driveline exit. Consequently, the abdominal wall was surgically corrected to prevent exit of peritoneal fluid through the driveline, and the patient was discharged with sterile wound swabs. However 6 weeks after discharge the driveline exit wound started secreting pus showing abundant growth of multi resistant staphylococcus aureus (MRSA). With clinical signs of increasing liver failure with regular need for paracentesis, and clinical signs of local infection, a CT scan of the abdomen was performed revealing an enrichment of contrast medium along the driveline and an abscess-like formation on the abdominal wall. Patient was admitted receiving regular dose Daptomycin and Rifampicin. The latter was discontinued after ten days. The abscess, surrounding driveline exit and abdominal wall cavity was excised and vacuum treatment initiated. Total duration of Daptomycin therapy was 3 weeks. While first week skin and wound swabs were still positive for MRSA, all samples were sterile after the second week. Inflammation was monitored by leucocyte count and IL6. The secretion of pus along the driveline ceased, the wound cavity was closed subsequently. After discharge and stop of antibiotics skin and driveline swabs remained negative for MRSA (10 weeks).