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We summarize a study of optical, UV and X-ray light curves of the nearby changing look active galactic nucleus in the galaxy NGC 1566 obtained with the Neil Gehrels Swift Observatory and the MASTER Global Robotic Network over the period 2007-2019. We also report on optical spectroscopy using the South African Astronomical Observatory 1.9-m telescope between Aug. 2018 and Mar. 2019. A substantial increase in X-ray flux by 1.5 orders of mag- nitude was observed following the brightening in the UV and optical bands during the first half of 2018 year. After a maximum was reached at the beginning of July 2018, the fluxes in all bands decreased with some fluctuations. The most remarkable re-brightenings in of the light curve following the decline from the bright phase were observed at MJD range 58440-58494 and 58603-58654. The amplitude of the flux variability is strongest in the X-ray band and decreases with increasing wavelength. Low-resolution spectra (Aug 2018) reveal a dramatic strengthening of the broad emission as well as high-ionization [FeX]6374 lines. These lines were not detected so strongly in the past published spectra. The change in the type of the opti- cal spectrum was accompanied by a significant change in the X-ray spectrum. For the last optical spectra (31 Nov. 2018 – 28 Mar. 2019) we see dramatic changes compared to 2 Aug 2018, accompanied by the fading of broad emission lines and high-ionization [FeX]6374 line. Effectively, two changing look (CL) cases were observed for this object: changing to Sy1.2 type and then returning to the low state as Sy 1.8–Sy1.9 type. Some possible explanations of the observed dramatic changes are discussed.

The quality of corporate governance service in the parastatal (also called public entity) and listed industrial company sectors of the South African economy was assessed. The assessment was done using newly developed service quality assessment instruments. The reliability of the findings on the quality of corporate governance service implied a 99.5% probability that the sample mean did not differ from the population mean by more than 0.2 on a seven-point scale. In both sectors the actual corporate governance service was assessed against two levels of expectation, namely the desired service level and the lowest acceptable service level. The instruments were the same except for a few adaptations to cater for the less stringently legislated listed company sector and to provide for insights gained from the first assessment. Both assessments resulted in the same four dimensions, namely 'directing and monitoring', 'board capacity', 'assurance' and 'responsiveness and reliability'. One fundamental difference was reported, namely that the listed company directors' corporate governance was in total, in all four dimensions and on all criteria assessed as between the lowest acceptable and the desired service levels. The public entity directors' corporate governance service was in total, in all four dimensions and on all criteria assessed as below the desired as well as the lowest acceptable service levels. The standard deviations as reported were such that it has to be concluded that acceptable and unacceptable corporate governance service levels are found in public entities as well as in listed companies. The assessment results are reported below graphically. Three criteria appeared on both assessments' lists often worst-assessed criteria. They were directors 'being always properly prepared for meetings', 'doing their homework thoroughly' and 'displaying impeccable integrity and honesty, for example with their own claims'.

We investigate the effect of varying the ion temperature gradient (ITG) and toroidal equilibrium scale sheared flow on ion-scale turbulence in the outer core of MAST by means of local gyrokinetic simulations. We show that nonlinear simulations reproduce the experimental ion heat flux and that the experimentally measured values of the ITG and the flow shear lie close to the turbulence threshold. We demonstrate that the system is subcritical in the presence of flow shear, i.e., the system is formally stable to small perturbations, but transitions to a turbulent state given a large enough initial perturbation. We propose that the transition to subcritical turbulence occurs via an intermediate state dominated by low number of coherent long-lived structures, close to threshold, which increase in number as the system is taken away from the threshold into the more strongly turbulent regime, until they fill the domain and a more conventional turbulence emerges. We show that the properties of turbulence are effectively functions of the distance to threshold, as quantified by the ion heat flux. We make quantitative comparisons of correlation lengths, times, and amplitudes between our simulations and experimental measurements using the MAST BES diagnostic. We find reasonable agreement of the correlation properties, most notably of the correlation time, for which significant discrepancies were found in previous numerical studies of MAST turbulence. [ABSTRACT FROM AUTHOR]

A procedure is presented to map from the spatial correlation parameters of a turbulent density field (the radial and binormal correlation lengths and wavenumbers, and the fluctuation amplitude) to correlation parameters that would be measured by a beam emission spectroscopy (BES) diagnostic. The inverse mapping is also derived, which results in resolution criteria for recovering correct correlation parameters, depending on the spatial response of the instrument quantified in terms of point-spread functions (PSFs). Thus, a procedure is presented that allows for a systematic comparison between theoretical predictions and experimental observations. This procedure is illustrated using the Mega-Ampere Spherical Tokamak BES system and the validity of the underlying assumptions is tested on fluctuating density fields generated by direct numerical simulations using the gyrokinetic code GS2. The measurement of the correlation time, by means of the cross-correlation time-delay method, is also investigated and is shown to be sensitive to the fluctuating radial component of velocity, as well as to small variations in the spatial properties of the PSFs. [ABSTRACT FROM AUTHOR]

The flow shear associated with the differential toroidal rotation of tokamak plasmas breaks an underlying symmetry of the turbulent fluctuations imposed by the up–down symmetry of the magnetic equilibrium. Using experimental beam-emission-spectroscopy measurements and gyrokinetic simulations, this symmetry breaking in ion-scale turbulence in MAST is shown to manifest itself as a tilt of the spatial correlation function and a finite skew in the distribution of the fluctuating density field. The tilt is a statistical expression of the ‘shearing’ of the turbulent structures by the mean flow. The skewness of the distribution is related to the emergence of long-lived density structures in sheared, near-marginal plasma turbulence. The extent to which these effects are pronounced is argued (with the aid of the simulations) to depend on the distance from the nonlinear stability threshold. Away from the threshold, the symmetry is effectively restored. [ABSTRACT FROM AUTHOR]

Background: There is currently limited evidence regarding the potential complications of sphenopalatine artery ligation. The post-operative outcomes at two secondary care centres over a 10-year period were reviewed., Methods: A retrospective review was undertaken of patients undergoing emergency and elective sphenopalatine artery ligation between January 2011 and January 2021. Their demographics, peri-operative care and post-operative outcomes were recorded. The median follow-up time was 54 days (range, 0-2657 days)., Results: Ninety-one patients were included. Four patients (4.4 per cent) had a septal perforation at post-operative review. Nineteen patients (20.9 per cent) had post-operative bleeding that extended their in-patient stay, with five patients (5.5 per cent) requiring revision surgery. Pre-operative non-dissolvable nasal packing was used a median of 1 time (range, 0-8 times)., Conclusion: Further research on outcomes of sphenopalatine artery ligation is needed. Pre-operative non-dissolvable nasal packing, concurrent septal surgical procedures, surgical techniques, and co-morbidities such as hypertension represent potential confounding factors that could not be further assessed in this small, retrospective study.

Background: Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minimal set of streaming physiological data in real time., Methods and Findings: A total of 29,552 adult patients were admitted to the intensive care unit across five regional hospitals in Memphis, Tenn, over 18 months from January 2017 to July 2018. From these, 5,958 patients were selected after filtering for continuous (minute-by-minute) physiological data availability. A total of 617 (10.4%) patients were identified as sepsis cases, using the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria. Physiomarkers, a set of signal processing features, were derived from five physiological data streams including heart rate, respiratory rate, and blood pressure (systolic, diastolic, and mean), captured every minute from the bedside monitors. A support vector machine classifier was used for classification. The model accurately predicted sepsis up to a mean and 95% confidence interval of 17.4 ± 0.22 h before sepsis onset, with an average test accuracy of 83.0% (average sensitivity, specificity, and area under the receiver operating characteristics curve of 0.757, 0.902, and 0.781, respectively)., Conclusions: This study demonstrates that salient physiomarkers derived from continuous bedside monitoring are temporally and differentially expressed in septic patients. Using this information, minimalistic artificial intelligence models can be developed to predict sepsis earlier in critically ill patients., Competing Interests: The authors report no conflicts of interest., (Copyright © 2020 by the Shock Society.)

This paper presents a novel method for hierarchical analysis of machine learning algorithms to improve predictions of at risk patients, thus further enabling prompt therapy. Specifically, we develop a multi-layer machine learning approach to analyze continuous, high-frequency data. We illustrate the capabilities of this approach for early identification of patients at risk of sepsis, a potentially life-threatening complication of an infection, using high-frequency (minute-by-minute) physiological data collected from bedside monitors. In our analysis of a cohort of 586 patients, the model obtained from analyzing the output of a previously developed sepsis prediction model resulted in improved outcomes. Specifically, the original model failed to predict 11.76 ± 4.26% of sepsis patients earlier than Systemic Inflammatory Response Syndrome (SIRS) criteria, commonly used to identify patients at risk for rapid physiological deterioration resulting from sepsis. In contrast, the multi-layer model only failed to predict 3.21 ± 3.11% of sepsis patients earlier than SIRS. In addition, sepsis patients were predicted on average 204.87 ± 7.90 minutes earlier than SIRS criteria using the multi-layer model, which can potentially help reduce mortality and morbidity if implemented in the ICU.

Purpose: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage., Methods: A data-set consisting of high-frequency physiological data from 1161 critically ill patients was analyzed. 377 patients had developed sepsis, and had data at least 3 h prior to the onset of sepsis. A random forest classifier was trained to discriminate between sepsis and non-sepsis patients in real-time using a total of 132 features extracted from a moving time-window. The model was trained on 80% of the patients and was tested on the remaining 20% of the patients, for two observational periods of lengths 3 and 6 h prior to onset., Results: The model that used continuous physiological data alone resulted in sensitivity and F1 score of up to 80% and 67% one hour before sepsis onset. On average, these models were able to predict sepsis 294.19 ± 6.50 min (5 h) before the onset., Conclusions: The use of machine learning algorithms on continuous streams of physiological data can allow for early identification of at-risk patients in real-time with high accuracy., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Precision medicine and the continuous analysis of "Big data" promises to improve patient outcomes dramatically in the near future. Very recently, healthcare facilities have started to explore automatic collection of patient-specific physiological data with the aim of reducing nursing workload and decreasing manual data entry errors. In addition to those purposes, continuous physiological data can be used for the early detection and prevention of common, and possibly fatal, diseases. For instance, poor patient outcomes from sepsis, a leading cause of mortality in healthcare facilities and a major driver of hospital costs in the USA, can be mitigated when detected early using screening tools that monitor the changing dynamics of physiological data. However, the potential cost of collecting continuous physiological data remains a barrier to the widespread adoption of automated high-frequency data collection systems. In this paper, we perform cost-benefit analysis (CBA) of machine learning applied to various types of acquisition systems (with different collection intervals) to determine if the benefits of such systems will outweigh their implementation costs. Although such systems can be used in the detection of various complications, in order to showcase the immediate benefits, we focus on the early detection of sepsis, one of the major challenges of hospital systems. We present a general approach to conduct such analysis for a wide range of hospitals and highlight its applicability using a case study for a small hospital with 150 beds and 3000 annual patients where the acquisition system would collect data at 1-min intervals. Lastly, we discuss how the analysis may help guide incentives/policies with regard to adopting automated data acquisition systems., Competing Interests: Conflict of InterestOn behalf of all authors, the corresponding author states that there is no conflict of interest., (© Springer Nature Switzerland AG 2018, corrected publication 2019.)

A 99mTc-DTPA radiorenographic study, supported by excretory urography, blood chemistry and eventually histology was performed on 8 chacma baboons (Papio ursinus) after surgical induction of unilateral renal vein hypertension. Frequent sequential repeat renograms during 6 post-operative months detected fluctuating renal behaviour but did not reveal typical course of the disease. Neither did any one of the radiorenographic parameters (TP, T1/2, FF, relative clearances and perfusion) prove to be more sensitive than the others in detecting abnormalities. Initial microscopic or macroscopic haematuria and proteinuria in general eventually cleared up. Light microscopy demonstrated only mild abnormalities. The effects of venous occlusion of the kidney and the prognosis seemed to be variable as was reflected by radiorenography.