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Relying on the existing estimates for the production cross sections of mini black holes in models with large extra dimensions, we review strategies for identifying those objects at collider experiments. We further consider a possible stable final state of such black holes and discuss their characteristic signatures. Keywords: Black holes
The elliptic, v2, triangular, v3, and quadrangular, v4, azimuthal anisotropic flow coefficients are measured for unidentified charged particles, pions, and (anti-)protons in Pb–Pb collisions at √sNN=2.76 TeV with the ALICE detector at the Large Hadron Collider. Results obtained with the event plane and four-particle cumulant methods are reported for the pseudo-rapidity range |η|<0.8 at different collision centralities and as a function of transverse momentum, pT, out to pT=20 GeV/c. The observed non-zero elliptic and triangular flow depends only weakly on transverse momentum for pT>8 GeV/c. The small pT dependence of the difference between elliptic flow results obtained from the event plane and four-particle cumulant methods suggests a common origin of flow fluctuations up to pT=8 GeV/c. The magnitude of the (anti-)proton elliptic and triangular flow is larger than that of pions out to at least pT=8 GeV/c indicating that the particle type dependence persists out to high pT.
Abstract: The hallmarks of Alzheimer’s disease (AD) are characterized by cognitive decline and behavioral changes. The most prominent brain region affected by the progression of AD is the hippocampal formation. The pathogenesis involves a successive loss of hippocampal neurons accompanied by a decline in learning and memory consolidation mainly attributed to an accumulation of senile plaques. The amyloid precursor protein (APP) has been identified as precursor of Aβ-peptides, the main constituents of senile plaques. Until now, little is known about the physiological function of APP within the central nervous system. The allocation of APP to the proteome of the highly dynamic presynaptic active zone (PAZ) highlights APP as a yet unknown player in neuronal communication and signaling. In this study, we analyze the impact of APP deletion on the hippocampal PAZ proteome. The native hippocampal PAZ derived from APP mouse mutants (APP-KOs and NexCreAPP/APLP2-cDKOs) was isolated by subcellular fractionation and immunopurification. Subsequently, an isobaric labeling was performed using TMT6 for protein identification and quantification by high-resolution mass spectrometry. We combine bioinformatics tools and biochemical approaches to address the proteomics dataset and to understand the role of individual proteins. The impact of APP deletion on the hippocampal PAZ proteome was visualized by creating protein-protein interaction (PPI) networks that incorporated APP into the synaptic vesicle cycle, cytoskeletal organization, and calcium-homeostasis. The combination of subcellular fractionation, immunopurification, proteomic analysis, and bioinformatics allowed us to identify APP as structural and functional regulator in a context-sensitive manner within the hippocampal active zone network.
Author Summary: More than 20 years ago, the amyloid precursor protein (APP) was identified as the precursor protein of the Aβ peptide, the main component of senile plaques in brains affected by Alzheimer’s disease. However, little is known about the physiological function of amyloid precursor protein. Allocating APP to the proteome of the structurally and functionally dynamic presynaptic active zone highlights APP as a hitherto unknown player within the presynaptic network. The hippocampus is the most prominent brain region for learning and memory consolidation, and a vulnerable target for neurodegenerative disease, e. g. Alzheimer’s disease. Therefore, our experimental design is focused on the hippocampal neurotransmitter release site. Currently, the underlying mechanism of how APP acts within presynaptic networks is still elusive. Within the scope of this research article, we constructed a network of APP within the presynaptic active zone and how deletion of APP affects these individual networks. We combine bioinformatics tools and biochemical approaches to address the dataset provided by proteomics. Furthermore, we could unravel that APP executes regulatory functions within the synaptic vesicle cycle, cytoskeletal rearrangements and Ca2+-homeostasis. Taken together, our findings offer a new perspective on the physiological function of APP in the central nervous system and may provide a molecular link to the pathogenesis of Alzheimer’s disease.
Within the scenario of large extra dimensions, the Planck scale is lowered to values soon accessible. Among the predicted effects, the production of TeV mass black holes at the LHC is one of the most exciting possibilities. Though the final phases of the black hole’s evaporation are still unknown, the formation of a black hole remnant is a theoretically well motivated expectation. We analyze the observables emerging from a black hole evaporation with a remnant instead of a final decay. We show that the formation of a black hole remnant yields a signature which differs substantially from a final decay. We find the total transverse momentum of the black hole event to be significantly dominated by the presence of a remnant mass providing a strong experimental signature for black hole remnant formation.
The inclusive transverse momentum (pT) distributions of primary charged particles are measured in the pseudo-rapidity range |η|<0.8 as a function of event centrality in Pb–Pb collisions at √sNN=2.76 TeV with ALICE at the LHC. The data are presented in the pT range 0.15<pT<50 GeV/c for nine centrality intervals from 70–80% to 0–5%. The results in Pb–Pb are presented in terms of the nuclear modification factor RAA using a pp reference spectrum measured at the same collision energy. We observe that the suppression of high-pT particles strongly depends on event centrality. The yield is most suppressed in central collisions (0–5%) with RAA≈0.13 at pT=6–7 GeV/c. Above pT=7 GeV/c, there is a significant rise in the nuclear modification factor, which reaches RAA≈0.4 for pT>30 GeV/c. In peripheral collisions (70–80%), only moderate suppression (RAA=0.6–0.7) and a weak pT dependence is observed. The measured nuclear modification factors are compared to other measurements and model calculations.
Cell-free therapy using extracellular vesicles (EVs) from adipose-derived mesenchymal stromal/stem cells (ASCs) seems to be a safe and effective therapeutic option to support tissue and organ regeneration. The application of EVs requires particles with a maximum regenerative capability and hypoxic culture conditions as an in vitro preconditioning regimen has been shown to alter the molecular composition of released EVs. Nevertheless, the EV cargo after hypoxic preconditioning has not yet been comprehensively examined. The aim of the present study was the characterization of EVs from hypoxic preconditioned ASCs. We investigated the EV proteome and their effects on renal tubular epithelial cells in vitro. While no effect of hypoxia was observed on the number of released EVs and their protein content, the cargo of the proteins was altered. Proteomic analysis showed 41 increased or decreased proteins, 11 in a statistically significant manner. Furthermore, the uptake of EVs in epithelial cells and a positive effect on oxidative stress in vitro were observed. In conclusion, culture of ASCs under hypoxic conditions was demonstrated to be a promising in vitro preconditioning regimen, which alters the protein cargo and increases the anti-oxidative potential of EVs. These properties may provide new potential therapeutic options for regenerative medicine.
Purpose: To evaluate the efficacy of the virtual reality training simulator Eyesi to prepare surgeons for performing pars plana vitrectomies and its potential to predict the surgeons’ performance.
Methods: In a preparation phase, four participating vitreoretinal surgeons performed repeated simulator training with predefined tasks. If a surgeon was assigned to perform a vitrectomy for the management of complex retinal detachment after a surgical break of at least 60 hours it was randomly decided whether a warmup training on the simulator was required (n = 9) or not (n = 12). Performance at the simulator was measured using the built-in scoring metrics. The surgical performance was determined by two blinded observers who analyzed the video-recorded interventions. One of them repeated the analysis to check for intra-observer consistency. The surgical performance of the interventions with and without simulator training was compared. In addition, for the surgeries with simulator training, the simulator performance was compared to the performance in the operating room.
Results: Comparing each surgeon’s performance with and without warmup trainingshowed a significant effect of warmup training onto the final outcome in the operating room. For the surgeries that were preceeded by the warmup procedure, the performance at the simulator was compared with the operating room performance. We found that there is a significant relation. The governing factor of low scores in the simulator were iatrogenic retinal holes, bleedings and lens damage. Surgeons who caused minor damage in the simulation also performed well in the operating room.
Conclusions: Despite the large variation of conditions, the effect of a warmup training as well as a relation between the performance at the simulator and in the operating room was found with statistical significance. Simulator training is able to serve as a warmup to increase the average performance.
The ALICE Collaboration has made the first measurement at the LHC of J/ψ photoproduction in ultra-peripheral Pb–Pb collisions at sNN=2.76 TeV. The J/ψ is identified via its dimuon decay in the forward rapidity region with the muon spectrometer for events where the hadronic activity is required to be minimal. The analysis is based on an event sample corresponding to an integrated luminosity of about 55 μb−1. The cross section for coherent J/ψ production in the rapidity interval −3.6<y<−2.6 is measured to be dσJ/ψcoh/dy=1.00±0.18(stat)−0.26+0.24(syst) mb. The result is compared to theoretical models for coherent J/ψ production and found to be in good agreement with those models which include nuclear gluon shadowing.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.