Experimental demonstration of a hyper-entangled ten-qubit Schr\"odinger cat state

Coherent manipulation of an increasing number of qubits for the generation of entangled states has been an important goal and benchmark in the emerging field of quantum information science. The multiparticle entangled states serve as physical resources for measurement-based quantum computing and high-precision quantum metrology. However, their experimental preparation has proved extremely challenging. To date, entangled states up to six, eight atoms, or six photonic qubits have been demonstrated. Here, by exploiting both the photons' polarization and momentum degrees of freedom, we report the creation of hyper-entangled six-, eight-, and ten-qubit Schr\"odinger cat states. We characterize the cat states by evaluating their fidelities and detecting the presence of genuine multi-partite entanglement. Small modifications of the experimental setup will allow the generation of various graph states up to ten qubits. Our method provides a shortcut to expand the effective Hilbert space, opening up interesting applications such as quantum-enhanced super-resolving phase measurement, graph-state generation for anyonic simulation and topological error correction, and novel tests of nonlocality with hyper-entanglement.Comment: 11 pages, 5 figures, comments welcom

Observation of eight-photon entanglement

Using ultra-bright sources of pure-state entangled photons from parametric down conversion, an eight-photon interferometer and post-selection detection, we demonstrate the ability to experimentally manipulate eight individual photons and report the creation of an eight-photon Schr\"odinger cat state with an observed fidelity of $0.708 \pm 0.016$.Comment: 6 pages, 4 figure

Self-supervised Outdoor Scene Relighting

Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo. Current techniques are completely supervised, requiring high quality synthetic renderings to train a solution. Such renderings are synthesized using priors learned from limited data. In contrast, we propose a self-supervised approach for relighting. Our approach is trained only on corpora of images collected from the internet without any user-supervision. This virtually endless source of training data allows training a general relighting solution. Our approach first decomposes an image into its albedo, geometry and illumination. A novel relighting is then produced by modifying the illumination parameters. Our solution capture shadow using a dedicated shadow prediction map, and does not rely on accurate geometry estimation. We evaluate our technique subjectively and objectively using a new dataset with ground-truth relighting. Results show the ability of our technique to produce photo-realistic and physically plausible results, that generalizes to unseen scenes.Comment: Published in ECCV '20, http://gvv.mpi-inf.mpg.de/projects/SelfRelight

Ratio-Based Analysis of Differential mRNA Processing and Expression of a Polyadenylation Factor Mutant pcfs4 Using Arabidopsis Tiling Microarray

US National Institutes of Health [1R15GM07719201A1]; US National Science Foundation [IOS-0817818]; Ohio Plant Biotech Consortium; National Natural Science Foundation of China [60774033]; Specialized Research Fund for the Doctoral Program of Higher EducatiBackground: Alternative polyadenylation as a mechanism in gene expression regulation has been widely recognized in recent years. Arabidopsis polyadenylation factor PCFS4 was shown to function in leaf development and in flowering time control. The function of PCFS4 in controlling flowering time was correlated with the alternative polyadenylation of FCA, a flowering time regulator. However, genetic evidence suggested additional targets of PCFS4 that may mediate its function in both flowering time and leaf development. Methodology/Principal Findings: To identify further targets, we investigated the whole transcriptome of a PCFS4 mutant using Affymetrix Arabidopsis genomic tiling 1.0R array and developed a data analysis pipeline, termed RADPRE (Ratio-based Analysis of Differential mRNA Processing and Expression). In RADPRE, ratios of normalized probe intensities between wild type Columbia and a pcfs4 mutant were first generated. By doing so, one of the major problems of tiling array data-variations caused by differential probe affinity-was significantly alleviated. With the probe ratios as inputs, a hierarchy of statistical tests was carried out to identify differentially processed genes (DPG) and differentially expressed genes (DEG). The false discovery rate (FDR) of this analysis was estimated by using the balanced random combinations of Col/pcfs4 and pcfs4/Col ratios as inputs. Gene Ontology (GO) analysis of the DPGs and DEGs revealed potential new roles of PCFS4 in stress responses besides flowering time regulation. Conclusion/Significance: We identified 68 DPGs and 114 DEGs with FDR at 1% and 2%, respectively. Most of the 68 DPGs were subjected to alternative polyadenylation, splicing or transcription initiation. Quantitative PCR analysis of a set of DPGs confirmed that most of these genes were truly differentially processed in pcfs4 mutant plants. The enriched GO term "regulation of flower development'' among PCFS4 targets further indicated the efficacy of the RADPRE pipeline. This simple but effective program is available upon request

Alveolar macrophages regulate neutrophil recruitment in endotoxin-induced lung injury

BACKGROUND: Alveolar macrophages play an important role during the development of acute inflammatory lung injury. In the present study, in vivo alveolar macrophage depletion was performed by intratracheal application of dichloromethylene diphosphonate-liposomes in order to study the role of these effector cells in the early endotoxin-induced lung injury. METHODS: Lipopolysaccharide was applied intratracheally and the inflammatory reaction was assessed 4 hours later. Neutrophil accumulation and expression of inflammatory mediators were determined. To further analyze in vivo observations, in vitro experiments with alveolar epithelial cells and alveolar macrophages were performed. RESULTS: A 320% increase of polymorphonuclear leukocytes in bronchoalveolar lavage fluid was observed in macrophage-depleted compared to macrophage-competent lipopolysaccharide-animals. This neutrophil recruitment was also confirmed in the interstitial space. Monocyte chemoattractant protein-1 concentration in bronchoalveolar lavage fluid was significantly increased in the absence of alveolar macrophages. This phenomenon was underlined by in vitro experiments with alveolar epithelial cells and alveolar macrophages. Neutralizing monocyte chemoattractant protein-1 in the airways diminished neutrophil accumulation. CONCLUSION: These data suggest that alveolar macorphages play an important role in early endotoxin-induced lung injury. They prevent neutrophil influx by controlling monocyte chemoattractant protein-1 production through alveolar epithelial cells. Alveolar macrophages might therefore possess robust anti-inflammatory effects

O-GlcNAc Modification of NFκB p65 Inhibits TNF-α-Induced Inflammatory Mediator Expression in Rat Aortic Smooth Muscle Cells

BACKGROUND: We have shown that glucosamine (GlcN) or O-(2-acetamido-2-deoxy-D-glucopyranosylidene)amino-N-phenylcarbamate (PUGNAc) treatment augments O-linked-N-acetylglucosamine (O-GlcNAc) protein modification and attenuates inflammatory mediator expression, leukocyte infiltration and neointima formation in balloon injured rat carotid arteries and have identified the arterial smooth muscle cell (SMC) as the target cell in the injury response. NFκB signaling has been shown to mediate the expression of inflammatory genes and neointima formation in injured arteries. Phosphorylation of the p65 subunit of NFκB is required for the transcriptional activation of NFκB. This study tested the hypothesis that GlcN or PUGNAc treatment protects vascular SMCs against tumor necrosis factor (TNF)-α induced inflammatory stress by enhancing O-GlcNAcylation and inhibiting TNF-α induced phosphorylation of NFκB p65, thus inhibiting NFκB signaling. METHODOLOGY/PRINCIPAL FINDINGS: Quiescent rat aortic SMCs were pretreated with GlcN (5 mM), PUGNAc (10(-4) M) or vehicle and then stimulated with TNF-α (10 ng/ml). Both treatments inhibited TNF-α-induced expression of chemokines [cytokine-induced neutrophil chemoattractant (CINC)-2β and monocyte chemotactic protein (MCP)-1] and adhesion molecules [vascular cell adhesion molecule (VCAM)-1 and P-Selectin]. Both treatments inhibited TNF-α induced NFκB p65 activation and promoter activity, increased NFκB p65 O-GlcNAcylation and inhibited NFκB p65 phosphorylation at Serine 536, thus promoting IκBα binding to NFκB p65. CONCLUSIONS: There is a reciprocal relationship between O-GlcNAcylation and phosphorylation of NFκB p65, such that increased NFκB p65 O-GlcNAc modification inhibits TNF-α-Induced expression of inflammatory mediators through inhibition of NFκB p65 signaling. These findings provide a mechanistic basis for our previous observations that GlcN and PUGNAc treatments inhibit inflammation and remodeling induced by acute endoluminal arterial injury

Anatomy of the Epidemiological Literature on the 2003 SARS Outbreaks in Hong Kong and Toronto: A Time-Stratified Review

Weijia Xing and colleagues reviewed the published epidemiological literature on SARS and show that less than a quarter of papers were published during the epidemic itself, suggesting that the research published lagged substantially behind the need for it

FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data

BACKGROUND: Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process. RESULTS: The clustering algorithm is named Fuzzy clustering by Local Approximation of MEmbership (FLAME). Distinctive elements of FLAME are: (i) definition of the neighborhood of each object (gene or sample) and identification of objects with "archetypal" features named Cluster Supporting Objects, around which to construct the clusters; (ii) assignment to each object of a fuzzy membership vector approximated from the memberships of its neighboring objects, by an iterative converging process in which membership spreads from the Cluster Supporting Objects through their neighbors. Comparative analysis with K-means, hierarchical, fuzzy C-means and fuzzy self-organizing maps (SOM) showed that data partitions generated by FLAME are not superimposable to those of other methods and, although different types of datasets are better partitioned by different algorithms, FLAME displays the best overall performance. FLAME is implemented, together with all the above-mentioned algorithms, in a C++ software with graphical interface for Linux and Windows, capable of handling very large datasets, named Gene Expression Data Analysis Studio (GEDAS), freely available under GNU General Public License. CONCLUSION: The FLAME algorithm has intrinsic advantages, such as the ability to capture non-linear relationships and non-globular clusters, the automated definition of the number of clusters, and the identification of cluster outliers, i.e. genes that are not assigned to any cluster. As a result, clusters are more internally homogeneous and more diverse from each other, and provide better partitioning of biological functions. The clustering algorithm can be easily extended to applications different from gene expression analysis

Tissue Microenvironments Define and Get Reinforced by Macrophage Phenotypes in Homeostasis or during Inflammation, Repair and Fibrosis

Current macrophage phenotype classifications are based on distinct in vitro culture conditions that do not adequately mirror complex tissue environments. In vivo monocyte progenitors populate all tissues for immune surveillance which supports the maintenance of homeostasis as well as regaining homeostasis after injury. Here we propose to classify macrophage phenotypes according to prototypical tissue environments, e.g. as they occur during homeostasis as well as during the different phases of (dermal) wound healing. In tissue necrosis and/or infection, damage- and/or pathogen-associated molecular patterns induce proinflammatory macrophages by Toll-like receptors or inflammasomes. Such classically activated macrophages contribute to further tissue inflammation and damage. Apoptotic cells and antiinflammatory cytokines dominate in postinflammatory tissues which induce macrophages to produce more antiinflammatory mediators. Similarly, tumor-associated macrophages also confer immunosuppression in tumor stroma. Insufficient parenchymal healing despite abundant growth factors pushes macrophages to gain a profibrotic phenotype and promote fibrocyte recruitment which both enforce tissue scarring. Ischemic scars are largely devoid of cytokines and growth factors so that fibrolytic macrophages that predominantly secrete proteases digest the excess extracellular matrix. Together, macrophages stabilize their surrounding tissue microenvironments by adapting different phenotypes as feed-forward mechanisms to maintain tissue homeostasis or regain it following injury. Furthermore, macrophage heterogeneity in healthy or injured tissues mirrors spatial and temporal differences in microenvironments during the various stages of tissue injury and repair. Copyright (C) 2012 S. Karger AG, Base