Faraday-Talbot Effect from a Circular Array of Pillars

When an oil bath is vertically oscillating with an acceleration above some critical value, known as the Faraday threshold, the bath surface becomes unstable and nonlinear standing wave patterns emerge. One phenomenon that has been observed above the Faraday threshold is the formation of Faraday-Talbot carpets, resulting from near-field diffraction. The optical Talbot effect occurs when a monochromatic wave passes through a diffraction grating. In the near-field, the formation of self- images is observed at integer multiples of what is known as the Talbot length. These two-dimensional patterns have various applications including X-ray imaging and atom and particle trapping. Two- dimensional Faraday-Talbot wave patterns have been observed in an oil bath oscillating above the Faraday threshold containing a row of evenly spaced, protruding pillars. These pillars generate sloshing waves which serve as active sources of monochromatic Faraday waves, the interference of which generates the Faraday-Talbot wave patterns. These patterns were observed to trap bouncing and walking droplets at the location of the pillar images. As an extension of the two-dimensional linear Faraday-Talbot effect, we present novel stable and transient Faraday-Talbot carpets created from a circular array of evenly spaced pillars. An understanding of the formation of stable Faraday- Talbot carpets can act as an analogy to atom and particle trapping and may provide insights into particle trapping mechanisms

Laboratory study of magnetic reconnection in lunar-relevant mini-magnetospheres

Mini-magnetospheres are small ion-scale structures that are well-suited to studying kinetic-scale physics of collisionless space plasmas. Such ion-scale magnetospheres can be found on local regions of the Moon, associated with the lunar crustal magnetic field. In this paper, we report on the laboratory experimental study of magnetic reconnection in laser-driven, lunar-like ion-scale magnetospheres on the Large Plasma Device (LAPD) at the University of California - Los Angeles. In the experiment, a high-repetition rate (1 Hz), nanosecond laser is used to drive a fast moving, collisionless plasma that expands into the field generated by a pulsed magnetic dipole embedded into a background plasma and magnetic field. The high-repetition rate enables the acquisition of time-resolved volumetric data of the magnetic and electric fields to characterize magnetic reconnection and calculate the reconnection rate. We notably observe the formation of Hall fields associated with reconnection. Particle-in-cell simulations reproducing the experimental results were performed to study the micro-physics of the interaction. By analyzing the generalized Ohm's law terms, we find that the electron-only reconnection is driven by kinetic effects, through the electron pressure anisotropy. These results are compared to recent satellite measurements that found evidence of magnetic reconnection near the lunar surface

High repetition rate exploration of Biermann battery generated magnetic fields in vacuum and hydrodynamic shock waves

Magnetic fields are ubiquitous throughout the universe, but the origin of cosmic and galactic magnetic fields is still under investigation. One theorized source of cosmic magnetic seed fields, which also occurs in many astrophysical and laboratory plasma environments, is the Biermann battery effect. This is a thermoelectric effect that spontaneously generates magnetic fields due to non-parallel electron temperature and density gradients in a plasma. In this dissertation, we present high repetition rate, three-dimensional investigations of the Biermann battery effect in laser-generated plasmas and laser-driven hydrodynamic shock waves. Magnetic field measurements revealed azimuthally symmetric magnetic fields reaching values up to 60 G in vacuum and up to 350~G in the presence of laser-driven Sedov-Taylor shock waves. Two-dimensional Thomson scattering measurements of electron temperature and density in laser-driven shock waves revealed electron temperatures up to 25~eV and electron densities up to $2\times 10^{16}$~$cm^{-3}$. 2D Thomson scattering measurements were used to obtain the novel measurements of electron temperature and density gradients within a plasma. The gradients were used to calculate a theoretical value of the Biermann fields due to the laser-driven shock waves, which was in general agreement with the experimental measurements, confirming that magnetic fields are generated by shock waves. Preliminary uncalibrated 3D FLASH simulations were generally in agreement with the experiments, from which we conclude that the majority of the fields measured in the shock waves are due to magnetic field generation by the Biermann battery effect. Dimensionless parameters were used to compare the laboratory experiments with astrophysical systems. The Reynolds and magnetic Reynolds numbers in the shock fronts were found to be much larger than unity, as in many astrophysical systems. A comparison of the experimental $R_m$ and $R_e$ with several astrophysical systems revealed that our experiments may be relevant to supernova remnant shocks, stellar atmospheres, and protogalactic and primordial magnetic field generation

All-cause mortality and disease progression in SARS-CoV-2-infected patients with or without antibiotic therapy: an analysis of the LEOSS cohort

Purpose!#!Reported antibiotic use in coronavirus disease 2019 (COVID-19) is far higher than the actual rate of reported bacterial co- and superinfection. A better understanding of antibiotic therapy in COVID-19 is necessary.!##!Methods!#!6457 SARS-CoV-2-infected cases, documented from March 18, 2020, until February 16, 2021, in the LEOSS cohort were analyzed. As primary endpoint, the correlation between any antibiotic treatment and all-cause mortality/progression to the next more advanced phase of disease was calculated for adult patients in the complicated phase of disease and procalcitonin (PCT) ≤ 0.5 ng/ml. The analysis took the confounders gender, age, and comorbidities into account.!##!Results!#!Three thousand, six hundred twenty-seven cases matched all inclusion criteria for analyses. For the primary endpoint, antibiotic treatment was not correlated with lower all-cause mortality or progression to the next more advanced (critical) phase (n = 996) (both p > 0.05). For the secondary endpoints, patients in the uncomplicated phase (n = 1195), regardless of PCT level, had no lower all-cause mortality and did not progress less to the next more advanced (complicated) phase when treated with antibiotics (p > 0.05). Patients in the complicated phase with PCT > 0.5 ng/ml and antibiotic treatment (n = 286) had a significantly increased all-cause mortality (p = 0.029) but no significantly different probability of progression to the critical phase (p > 0.05).!##!Conclusion!#!In this cohort, antibiotics in SARS-CoV-2-infected patients were not associated with positive effects on all-cause mortality or disease progression. Additional studies are needed. Advice of local antibiotic stewardship- (ABS-) teams and local educational campaigns should be sought to improve rational antibiotic use in COVID-19 patients

Clinical course and predictive risk factors for fatal outcome of SARS-CoV-2 infection in patients with chronic kidney disease

Purpose!#!The ongoing pandemic caused by the novel severe acute respiratory coronavirus 2 (SARS-CoV-2) has stressed health systems worldwide. Patients with chronic kidney disease (CKD) seem to be more prone to a severe course of coronavirus disease (COVID-19) due to comorbidities and an altered immune system. The study's aim was to identify factors predicting mortality among SARS-CoV-2-infected patients with CKD.!##!Methods!#!We analyzed 2817 SARS-CoV-2-infected patients enrolled in the Lean European Open Survey on SARS-CoV-2-infected patients and identified 426 patients with pre-existing CKD. Group comparisons were performed via Chi-squared test. Using univariate and multivariable logistic regression, predictive factors for mortality were identified.!##!Results!#!Comparative analyses to patients without CKD revealed a higher mortality (140/426, 32.9% versus 354/2391, 14.8%). Higher age could be confirmed as a demographic predictor for mortality in CKD patients (> 85 years compared to 15-65 years, adjusted odds ratio (aOR) 6.49, 95% CI 1.27-33.20, p = 0.025). We further identified markedly elevated lactate dehydrogenase (> 2 × upper limit of normal, aOR 23.21, 95% CI 3.66-147.11, p < 0.001), thrombocytopenia (< 120,000/µl, aOR 11.66, 95% CI 2.49-54.70, p = 0.002), anemia (Hb < 10 g/dl, aOR 3.21, 95% CI 1.17-8.82, p = 0.024), and C-reactive protein (≥ 30 mg/l, aOR 3.44, 95% CI 1.13-10.45, p = 0.029) as predictors, while renal replacement therapy was not related to mortality (aOR 1.15, 95% CI 0.68-1.93, p = 0.611).!##!Conclusion!#!The identified predictors include routinely measured and universally available parameters. Their assessment might facilitate risk stratification in this highly vulnerable cohort as early as at initial medical evaluation for SARS-CoV-2

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

Purpose!#!While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.!##!Methods!#!We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).!##!Results!#!The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.!##!Conclusion!#!We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19