126 research outputs found

    Continuous Symmetry Breaking in a Two-dimensional Rydberg Array

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    Spontaneous symmetry breaking underlies much of our classification of phases of matter and their associated transitions. The nature of the underlying symmetry being broken determines many of the qualitative properties of the phase; this is illustrated by the case of discrete versus continuous symmetry breaking. Indeed, in contrast to the discrete case, the breaking of a continuous symmetry leads to the emergence of gapless Goldstone modes controlling, for instance, the thermodynamic stability of the ordered phase. Here, we realize a two-dimensional dipolar XY model -- which exhibits a continuous spin-rotational symmetry -- utilizing a programmable Rydberg quantum simulator. We demonstrate the adiabatic preparation of correlated low-temperature states of both the XY ferromagnet and the XY antiferromagnet. In the ferromagnetic case, we characterize the presence of long-range XY order, a feature prohibited in the absence of long-range dipolar interaction. Our exploration of the many-body physics of XY interactions complements recent works utilizing the Rydberg-blockade mechanism to realize Ising-type interactions exhibiting discrete spin rotation symmetry.Comment: 18 pages, 3 figures in main text, 9 figures in supplemental method

    A Bayesian reanalysis of the Standard versus Accelerated Initiation of Renal-Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial

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    Background Timing of initiation of kidney-replacement therapy (KRT) in critically ill patients remains controversial. The Standard versus Accelerated Initiation of Renal-Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial compared two strategies of KRT initiation (accelerated versus standard) in critically ill patients with acute kidney injury and found neutral results for 90-day all-cause mortality. Probabilistic exploration of the trial endpoints may enable greater understanding of the trial findings. We aimed to perform a reanalysis using a Bayesian framework. Methods We performed a secondary analysis of all 2927 patients randomized in multi-national STARRT-AKI trial, performed at 168 centers in 15 countries. The primary endpoint, 90-day all-cause mortality, was evaluated using hierarchical Bayesian logistic regression. A spectrum of priors includes optimistic, neutral, and pessimistic priors, along with priors informed from earlier clinical trials. Secondary endpoints (KRT-free days and hospital-free days) were assessed using zero–one inflated beta regression. Results The posterior probability of benefit comparing an accelerated versus a standard KRT initiation strategy for the primary endpoint suggested no important difference, regardless of the prior used (absolute difference of 0.13% [95% credible interval [CrI] − 3.30%; 3.40%], − 0.39% [95% CrI − 3.46%; 3.00%], and 0.64% [95% CrI − 2.53%; 3.88%] for neutral, optimistic, and pessimistic priors, respectively). There was a very low probability that the effect size was equal or larger than a consensus-defined minimal clinically important difference. Patients allocated to the accelerated strategy had a lower number of KRT-free days (median absolute difference of − 3.55 days [95% CrI − 6.38; − 0.48]), with a probability that the accelerated strategy was associated with more KRT-free days of 0.008. Hospital-free days were similar between strategies, with the accelerated strategy having a median absolute difference of 0.48 more hospital-free days (95% CrI − 1.87; 2.72) compared with the standard strategy and the probability that the accelerated strategy had more hospital-free days was 0.66. Conclusions In a Bayesian reanalysis of the STARRT-AKI trial, we found very low probability that an accelerated strategy has clinically important benefits compared with the standard strategy. Patients receiving the accelerated strategy probably have fewer days alive and KRT-free. These findings do not support the adoption of an accelerated strategy of KRT initiation

    Multiple centrosomes enhance migration and immune cell effector functions of mature dendritic cells

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    Centrosomes play a crucial role during immune cell interactions and initiation of the immune response. In proliferating cells, centrosome numbers are tightly controlled and generally limited to one in G1 and two prior to mitosis. Defects in regulating centrosome numbers have been associated with cell transformation and tumorigenesis. Here, we report the emergence of extra centrosomes in leukocytes during immune activation. Upon antigen encounter, dendritic cells pass through incomplete mitosis and arrest in the subsequent G1 phase leading to tetraploid cells with accumulated centrosomes. In addition, cell stimulation increases expression of polo-like kinase 2, resulting in diploid cells with two centrosomes in G1-arrested cells. During cell migration, centrosomes tightly cluster and act as functional microtubule-organizing centers allowing for increased persistent locomotion along gradients of chemotactic cues. Moreover, dendritic cells with extra centrosomes display enhanced secretion of inflammatory cytokines and optimized T cell responses. Together, these results demonstrate a previously unappreciated role of extra centrosomes for regular cell and tissue homeostasis

    Changes in the pattern of suicides and suicide attempt admissions in relation to the COVID-19 pandemic

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    The consequences of the current COVID-19 pandemic for mental health remain unclear, especially regarding the effects on suicidal behaviors. To assess changes in the pattern of suicide attempt (SA) admissions and completed suicides (CS) in association with the COVID-19 pandemic. As part of a longitudinal study, SA admissions and CS are systematically documented and analyzed in all psychiatric hospitals in Frankfurt/Main (765.000 inhabitants). Number, sociodemographic factors, diagnoses and methods of SA and CS were compared between the periods of March–December 2019 and March–December 2020. The number of CS did not change, while the number of SA significantly decreased. Age, sex, occupational status, and psychiatric diagnoses did not change in SA, whereas the percentage of patients living alone while attempting suicide increased. The rate and number of intoxications as a SA method increased and more people attempted suicide in their own home, which was not observed in CS. Such a shift from public places to home is supported by the weekday of SA, as the rate of SA on weekends was significantly lower during the pandemic, likely because of lockdown measures. Only admissions to psychiatric hospitals were recorded, but not to other institutions. As it seems unlikely that the number of SA decreased while the number of CS remained unchanged, it is conceivable that the number of unreported SA cases increased during the pandemic. Our data suggest that a higher number of SA remained unnoticed during the pandemic because of their location and the use of methods associated with lower lethality

    Resonant Inelastic Soft X-ray Scattering and X-ray Emission Spectroscopy of Solid Proline and Proline Solutions

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    The amino group of proline is part of a pyrrolidine ring, which makes it unique among the proteinogenic amino acids. To unravel its full electronic structure, proline in solid state and aqueous solution is investigated using X-ray emission spectroscopy and resonant inelastic soft X-ray scattering. By controlling the pH value of the solution, proline is studied in its cationic, zwitterionic, and anionic configurations. The spectra are analyzed within a “building-block principle” by comparing with suitable reference molecules, i.e., acetic acid, cysteine, and pyrrolidine, as well as with spectral calculations based on density functional theory. We find that the electronic structure of the carboxyl group of proline is very similar to that of other amino acids as well as acetic acid. In contrast, the electronic structure of the amino group is significantly different and strongly influenced by the ring structure of proline

    Fever and hypothermia represent two populations of sepsis patients and are associated with outside temperature

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    Background!#!Fever and hypothermia have been observed in septic patients. Their influence on prognosis is subject to ongoing debates.!##!Methods!#!We did a secondary analysis of a large clinical dataset from a quality improvement trial. A binary logistic regression model was calculated to assess the association of the thermal response with outcome and a multinomial regression model to assess factors associated with fever or hypothermia.!##!Results!#!With 6542 analyzable cases we observed a bimodal temperature response characterized by fever or hypothermia, normothermia was rare. Hypothermia and high fever were both associated with higher lactate values. Hypothermia was associated with higher mortality, but this association was reduced after adjustment for other risk factors. Age, community-acquired sepsis, lower BMI and lower outside temperatures were associated with hypothermia while bacteremia and higher procalcitonin values were associated with high fever.!##!Conclusions!#!Septic patients show either a hypothermic or a fever response. Whether hypothermia is a maladaptive response, as indicated by the higher mortality in hypothermic patients, or an adaptive response in patients with limited metabolic reserves under colder environmental conditions, remains an open question. Trial registration The original trial whose dataset was analyzed was registered at ClinicalTrials.gov (NCT01187134) on August 23, 2010, the first patient was included on July 1, 2011

    Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

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    Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd
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